diff --git a/.gitignore b/.gitignore index 78974c5e..46792373 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,4 @@ __pycache__ .DS_Store Ch14_Computer_Vision/ssd_outputs/ +Ch00_Playground/ diff --git a/AAA721.md b/AAA721.md new file mode 100644 index 00000000..132d21f6 --- /dev/null +++ b/AAA721.md @@ -0,0 +1,48 @@ +2021 Fall +========= + +| No | Name | Department/Major | Message | +| ------------- |:--------------:| ----------------:|------------------:| +| 1 | Hyunwoo J. Kim | CS | Hello World! | + + +2020 Fall +========= + +| No | Name | Department/Major | Message | +| ------------- |:--------------:| ----------------:|------------------:| +| 1 | Hyunwoo J. Kim | CS | Hello World! | +| 2 | Minkyu Jeon | CS | I'm the TA for AAA721 | +| 3 | Jeonggyu Song | CS | SATOR | +| 4 | Junyeon Lee | CS | Nice to meet you | +| 5 | DOhyeon Ryu | CS | :) | +| 6 | Sungyeong Jang | CS | Nice to meet you! | +| 7 | Changhyeon An | Convergence Security | I work for Samsung SDS | +| 8 | SeungJun Lee | CS | XD | +| 9 | Heemin Lee | CS | Hello everyone | +| 10 | Hyeonjin Park | CS | Hello | +| 11 | Jungho Lee | IME | Hello | +| 12 | Jihyun Kim | CS | Security + AI | +| 13 | Jiwon Kim | CS | Hello~ | +| 14 | Sangwoo Park | CS | Allergy! | +| 15 | Jinyoung Park | CS | Hello | +| 16 | Yujin Kim | CS | Nice to meet you! | +| 17 | Chongkeun Paik | CRE | Hello | +| 18 | Seokhyun Lee | CS | :octocat::speech_balloon: Nice to meet you | +| 19 | Heejeong Choi | IME | Hello | +| 20 | Hodong Kim | CS | isslab.kroea.ac.kr| +| 21 | Seongjun Yun | CS | Nice to meet you~ | +| 22 | Seunghun Lee | CS | ^^b Have a good day | +| 23 | JongHyun Choi | IME | Hello | +| 24 | Dohyun Kim | CS | Hello!~ | +| 25 | Jeong-gi Kwak | EE | Hello | +| 26 | Bokyeung Lee | EE | HI | +| 27 | Kyungdeuk Ko | EE | Nice to meet you | +| 28 | Hanna Lee | CS | Hello | +| 29 | Sungdong Yoo | CS | Hello~! | +| 30 | Guhnoo Yun | CS | :sunny: Buongiorno! | +| 31 | Haeun Park | IME | Nice to meet you :)| +| 32 | ChangSeok Koh | CS | Hello I've lately joined to this class! | +| 33 | YoungSeon Noh | IME | Hello~ | + + diff --git a/COSE474-03.md b/COSE474-03.md new file mode 100644 index 00000000..0d8daf66 --- /dev/null +++ b/COSE474-03.md @@ -0,0 +1,67 @@ +2022 Fall +== +| No | Name | Department/Major | Message | +| ---- | :------------: | ---------------: | -----------: | +| 1 | Hyunwoo J. Kim | CS | Hello World! | + +2021 Fall +== +| No | Name | Department/Major | Message | +| ---- | :------------: | ---------------: | -----------: | +| 1 | Hyunwoo J. Kim | CS | Hello World! | +| 2 | DongHu Kim | CS | Goodbye, Cruel World! | +| 3 | Wooseok Kim | ACE | Hello World! | +| 4 | Seungyun Baek | Cyber Defense | Hello | +| 5 | SeungHeon Kim | CS | Hello World! | +| 6 | Camden Scott | CS | Hi! | +| 7 | Hyunsoo Lim | CS | Hello World! | +| 8 | Jorge F. Gimenez Perez | CS | Hello KU! | +| 9 | Hyelim Kim | STAT | Hello World! | +| 10 | YooHwan Yeon | EEE | Amor Fati! | +| 11 | Dong Hwan Kim | Computer Info/ AI| It's an honor to be with you | +| 12 | Hanjin Choi | EE | Hello World! | +| 13 | Sang Joon Lee | CS | Hello World! | +| 14 | Mingeun Jo | CS | Hello World! | +| 15 | Minjune Choi | physics | Hello World! | +| 16 | Pilsang Kim | STAT | Hello World! | +| 17 | Inyup Lee | CS | Hi, everyone | +| 18 | Jaehu Lee | CS | Hello World! | +| 19 | Simo Ryu | Cyber Defense / Business Administration | I love DL | +| 20 | Jiseok Ryu | Psychology | Hello World! | + + +2020 Fall +== +| No | Name | Department/Major | Message | +| ---- | :------------: | ---------------: | -----------: | +| 1 | Hyunwoo J. Kim | CS | Hello World! | +| 2 | Dohwan Ko | CS | Hello World! | +| 3 | Choi min hyuk | CS | Hello World! | +| 4 | Seung Joon Park| CS | Jal Butakhapnida! | +| 5 | Juno Kim | Computer Software| Hello World! | +| 6 | Chan Gu Kang | CS | Hello World! | +| 7 | Gichan Kim | CS | Can I Alive? | +| 8 | Seonghoon Park | medical engineering| Hello World! | +| 9 | Youngwun Kim | CS | Hello!!! | +| 10 | JunTae Kim | earth and environmental science / AI| IWBB(I will be back) | +| 11 | SeungHee Han | Physics | HonjongModel | +| 12 | Jihwan Park | CS | Hello HOI | +| 13 | Sujong Chae | CS | Hello!! | +| 14 | Hyunmo Kang | CSE / CS | I want to graduate | +| 15 | JiSeok Ryu | CS | Hello World! | +| 16 | Seung Hyun Kong| CSE / CS | Hello | +| 17 | Jaemin Son | CS | Stand there AlphaGo | +| 18 | Wookyoung Kim | CS | Hello, World! | +| 19 | Jin Hongyu | CS | Hello! | +| 20 | Lee Jaewon | Mathematics | hello | +| 21 | Min Ji Su | CS | HELLO | +| 22 | JongGwan Jung | CS | Wello Horld! | +| 23 | SeokHyeon Chin | CS | Hello World! | +| 24 | Nijat Muzaffarli | CS | Hello, I wish I'm gonna graduate this semester. | +| 25 | Doowoong Choi | CS | Hello WOrld! | +| 26 | Dongyoon Hwang | CS | Hello WOrld! | +| 27 | Juyeon Ko | CS | Hello! | +| 28 | JinHyeok Yang | CS | Hell o world | +| 29 | Joel Jang | CS | Wannabe AI master | +| 30 | Geonu Kim | CS | Hello! | +| 31 | Dongmin Kim | CS | Hello World! | diff --git a/Ch04_The_Preliminaries_A_Crashcourse/Automatic_Differentiation.ipynb b/Ch04_The_Preliminaries_A_Crashcourse/Automatic_Differentiation.ipynb index e0bfcc8b..3a438bd5 100644 --- a/Ch04_The_Preliminaries_A_Crashcourse/Automatic_Differentiation.ipynb +++ b/Ch04_The_Preliminaries_A_Crashcourse/Automatic_Differentiation.ipynb @@ -1,347 +1,549 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automatic Differentiation\n", - "\n", - "In machine learning, we *train* models, updating them successively so that they get better and better as they see more and more data. Usually, *getting better* means minimizing a *loss function*, a score that answers the question \"how *bad* is our model?\" With neural networks, we typically choose loss functions that are differentiable with respect to our parameters.\n", - "Put simply, this means that for each of the model's parameters, we can determine how much *increasing* or *decreasing* it might affect the loss. While the calculations for taking these derivatives are straightforward, requiring only some basic calculus, for complex models, working out the updates by hand can be a pain (and often error-prone)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The autograd package expedites this work by automatically calculating derivatives. And while many other libraries require that we compile a symbolic graph to take automatic derivatives, `autograd` allows us to take derivatives while writing ordinary imperative code. Every time we pass data through our model, `autograd` builds a graph on the fly, tracking which data combined through which operations to produce the output. This graph enables `autograd` to subsequently backpropagate gradients on command. Here *backpropagate* simply means to trace through the compute graph, filling in the partial derivatives with respect to each parameter." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "from torch.autograd import Variable" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## A Simple Example\n", - "\n", - "As a toy example, say that we are interested in differentiating the mapping $y = 2\\mathbf{x}^{\\top}\\mathbf{x}$ with respect to the column vector $\\mathbf{x}$. To start, let's create the variable `x` and assign it an initial value." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[0.],\n", - " [1.],\n", - " [2.],\n", - " [3.]], requires_grad=True)\n" - ] + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + }, + "colab": { + "name": "Automatic_Differentiation.ipynb", + "provenance": [] } - ], - "source": [ - "x = Variable(torch.arange(4, dtype=torch.float32).reshape((4, 1)), requires_grad=True)\n", - "print(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once we compute the gradient of ``y`` with respect to ``x``, we will need a place to store it. We can tell a tensor that we plan to store a gradient by the ``requires_grad=True`` keyword." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we are going to compute ``y`` and PyTorch will generate a computation graph on the fly. Autograd is reverse automatic differentiation system. Conceptually, autograd records a graph recording all of the operations that created the data as you execute operations, giving you a directed acyclic graph whose leaves are the input tensors and roots are the output tensors. By tracing this graph from roots to leaves, you can automatically compute the gradients using the chain rule.\n", - "\n", - "Note that building the computation graph requires a nontrivial amount of computation. So PyTorch will *only* build the graph when explicitly told to do so. For a tensor to be “recordable”, it must be wrapped with torch.autograd.Variable. The Variable class provides almost the same API as Tensor, but augments it with the ability to interplay with torch.autograd.Function in order to be differentiated automatically. More precisely, a Variable records the history of operations on a Tensor." - ] }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[28.]], grad_fn=)\n" - ] - } - ], - "source": [ - "y = 2*torch.mm(x.t(),x)\n", - "print(y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the shape of `x` is (4, 1), `y` is a scalar. Next, we can automatically find the gradient by calling the `backward` function. It should be noted that if `y` is not a scalar, PyTorch will first sum the elements in `y` to get the new variable by default, and then find the gradient of the variable with respect to `x`." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "y.backward()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since every Variable except for inputs is the result of an operation, each Variable has an associated grad_fn, which is the torch.autograd.Function that is used to compute the backward step. For inputs it is None:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": { + "id": "pARL5s_sNHv2" + }, + "source": [ + "# Automatic Differentiation\n", + "\n", + "In machine learning, we *train* models, updating them successively so that they get better and better as they see more and more data. Usually, *getting better* means minimizing a *loss function*, a score that answers the question \"how *bad* is our model?\" With neural networks, we typically choose loss functions that are differentiable with respect to our parameters.\n", + "Put simply, this means that for each of the model's parameters, we can determine how much *increasing* or *decreasing* it might affect the loss. While the calculations for taking these derivatives are straightforward, requiring only some basic calculus, for complex models, working out the updates by hand can be a pain (and often error-prone)." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "x.grad: tensor([[ 0.],\n", - " [ 4.],\n", - " [ 8.],\n", - " [12.]])\n", - "x.grad_fn: None\n", - "y.grad_fn: \n" - ] - } - ], - "source": [ - "print(\"x.grad:\", x.grad)\n", - "print(\"x.grad_fn:\", x.grad_fn)\n", - "print(\"y.grad_fn:\", y.grad_fn)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The gradient of the function $y = 2\\mathbf{x}^{\\top}\\mathbf{x}$ with respect to $\\mathbf{x}$ should be $4\\mathbf{x}$. Now let's verify that the gradient produced is correct." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": { + "id": "rpyYGWxGNHv9" + }, + "source": [ + "The autograd package expedites this work by automatically calculating derivatives. And while many other libraries require that we compile a symbolic graph to take automatic derivatives, `autograd` allows us to take derivatives while writing ordinary imperative code. Every time we pass data through our model, `autograd` builds a graph on the fly, tracking which data combined through which operations to produce the output. This graph enables `autograd` to subsequently backpropagate gradients on command. Here *backpropagate* simply means to trace through the compute graph, filling in the partial derivatives with respect to each parameter." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n", - "tensor([[ 0.],\n", - " [ 4.],\n", - " [ 8.],\n", - " [12.]])\n" - ] - } - ], - "source": [ - "print((x.grad - 4*x).norm().item() == 0)\n", - "print(x.grad)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training Mode and Evaluation Mode\n", - "\n", - "`Model` will change the running mode to the evaluation mode on calling `model.eval()` or to the training mode on calling `model.train()`.\n", - "\n", - "In some cases, the same model behaves differently in the training and prediction modes (e.g. when using neural techniques such as dropout and batch normalization). In other cases, some models may store more auxiliary variables to make computing gradients easier. We will cover these differences in detail in later chapters. For now, you do not need to worry about them." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Computing the Gradient of Python Control Flow\n", - "\n", - "One benefit of using automatic differentiation is that even if the computational graph of the function contains Python's control flow (such as conditional and loop control), we may still be able to find the gradient of a variable. Consider the following program: It should be emphasized that the number of iterations of the loop (while loop) and the execution of the conditional judgment (if statement) depend on the value of the input `b`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def f(a):\n", - " b = a * 2\n", - " while b.norm().item() < 1000:\n", - " b = b * 2\n", - " if b.sum().item() > 0:\n", - " c = b\n", - " else:\n", - " c = 100 * b\n", - " return c" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the number of iterations of the while loop and the execution of the conditional statement (if then else) depend on the value of `a`. To compute gradients, we need to `record` the calculation, and then call the `backward` function to calculate the gradient." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "a = torch.randn(size=(1,))\n", - "a.requires_grad=True\n", - "d = f(a)\n", - "d.backward()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's analyze the `f` function defined above. As you can see, it is piecewise linear in its input `a`. In other words, for any `a` there exists some constant such that for a given range `f(a) = g * a`. Consequently `d / a` allows us to verify that the gradient is correct:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "metadata": { + "id": "5XnuKK9iNHv-" + }, + "source": [ + "import torch\n", + "from torch.autograd import Variable" + ], + "execution_count": 1, + "outputs": [] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([1], dtype=torch.uint8)\n" - ] - } - ], - "source": [ - "print(a.grad == (d / a))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Head gradients and the chain rule\n", - "\n", - "*Caution: This part is tricky and not necessary to understanding subsequent sections. That said, it is needed if you want to build new layers from scratch. You can skip this on a first read.*\n", - "\n", - "Sometimes when we call the backward method, e.g. `y.backward()`, where\n", - "`y` is a function of `x` we are just interested in the derivative of\n", - "`y` with respect to `x`. Mathematicians write this as\n", - "$\\frac{dy(x)}{dx}$. At other times, we may be interested in the\n", - "gradient of `z` with respect to `x`, where `z` is a function of `y`,\n", - "which in turn, is a function of `x`. That is, we are interested in\n", - "$\\frac{d}{dx} z(y(x))$. Recall that by the chain rule\n", - "\n", - "$$\\frac{d}{dx} z(y(x)) = \\frac{dz(y)}{dy} \\frac{dy(x)}{dx}.$$\n", - "\n", - "So, when ``y`` is part of a larger function ``z`` and we want ``x.grad`` to store $\\frac{dz}{dx}$, we can pass in the *head gradient* $\\frac{dz}{dy}$ as an input to ``backward()``. The default argument is ``torch.ones_like(y)``. See [Wikipedia](https://en.wikipedia.org/wiki/Chain_rule) for more details." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": true - }, - "outputs": [ + "cell_type": "markdown", + "metadata": { + "id": "iOBsPSCzNHv_" + }, + "source": [ + "## A Simple Example\n", + "\n", + "As a toy example, say that we are interested in differentiating the mapping $y = 2\\mathbf{x}^{\\top}\\mathbf{x}$ with respect to the column vector $\\mathbf{x}$. To start, let's create the variable `x` and assign it an initial value." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "VM0iXJZtNHwA", + "outputId": "d59e329f-5e69-4eb3-c061-51169a69a245" + }, + "source": [ + "x = Variable(torch.arange(4, dtype=torch.float32).reshape((4, 1)), requires_grad=True)\n", + "print(x)" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor([[0.],\n", + " [1.],\n", + " [2.],\n", + " [3.]], requires_grad=True)\n" + ] + } + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([[0.0000],\n", - " [4.0000],\n", - " [0.8000],\n", - " [0.1200]])\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "MODpmt4wNHwB" + }, + "source": [ + "Once we compute the gradient of ``y`` with respect to ``x``, we will need a place to store it. We can tell a tensor that we plan to store a gradient by the ``requires_grad=True`` keyword." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7YeCcrGTNHwC" + }, + "source": [ + "Now we are going to compute ``y`` and PyTorch will generate a computation graph on the fly. Autograd is reverse automatic differentiation system. Conceptually, autograd records a graph recording all of the operations that created the data as you execute operations, giving you a directed acyclic graph whose leaves are the input tensors and roots are the output tensors. By tracing this graph from roots to leaves, you can automatically compute the gradients using the chain rule.\n", + "\n", + "Note that building the computation graph requires a nontrivial amount of computation. So PyTorch will *only* build the graph when explicitly told to do so. For a tensor to be “recordable”, it must be wrapped with torch.autograd.Variable. The Variable class provides almost the same API as Tensor, but augments it with the ability to interplay with torch.autograd.Function in order to be differentiated automatically. More precisely, a Variable records the history of operations on a Tensor." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "qlqJO2ANNHwC", + "outputId": "f8161cf1-9c10-40c7-a894-8cecee6fa75b" + }, + "source": [ + "y = 2*torch.mm(x.t(),x)\n", + "print(y)" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor([[28.]], grad_fn=)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "055sbI9iNHwD" + }, + "source": [ + "Since the shape of `x` is (4, 1), `y` is a scalar. Next, we can automatically find the gradient by calling the `backward` function. It should be noted that if `y` is not a scalar, PyTorch will first sum the elements in `y` to get the new variable by default, and then find the gradient of the variable with respect to `x`." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "N_ehb6MENHwE" + }, + "source": [ + "y.backward()" + ], + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ogzOdu12NHwF" + }, + "source": [ + "Since every Variable except for inputs is the result of an operation, each Variable has an associated grad_fn, which is the torch.autograd.Function that is used to compute the backward step. For inputs it is None:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "YN8SfEMzNHwF", + "outputId": "f7a39e1a-c5fe-4ff3-c662-daf902a5b3d5" + }, + "source": [ + "print(\"x.grad:\", x.grad)\n", + "print(\"x.grad_fn:\", x.grad_fn)\n", + "print(\"y.grad_fn:\", y.grad_fn)" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "x.grad: tensor([[ 0.],\n", + " [ 4.],\n", + " [ 8.],\n", + " [12.]])\n", + "x.grad_fn: None\n", + "y.grad_fn: \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1a_GAR75NHwG" + }, + "source": [ + "The gradient of the function $y = 2\\mathbf{x}^{\\top}\\mathbf{x}$ with respect to $\\mathbf{x}$ should be $4\\mathbf{x}$. Now let's verify that the gradient produced is correct." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "jHvO1YEJNHwG", + "outputId": "d5dcdeab-c21e-4c5f-a6ce-d0109853b2dc" + }, + "source": [ + "print((x.grad - 4*x).norm().item() == 0)\n", + "print(x.grad)" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "True\n", + "tensor([[ 0.],\n", + " [ 4.],\n", + " [ 8.],\n", + " [12.]])\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HTOkervHNHwH" + }, + "source": [ + "## Training Mode and Evaluation Mode\n", + "\n", + "`Model` will change the running mode to the evaluation mode on calling `model.eval()` or to the training mode on calling `model.train()`.\n", + "\n", + "In some cases, the same model behaves differently in the training and prediction modes (e.g. when using neural techniques such as dropout and batch normalization). In other cases, some models may store more auxiliary variables to make computing gradients easier. We will cover these differences in detail in later chapters. For now, you do not need to worry about them." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "C4tYrWGcNHwH" + }, + "source": [ + "## Computing the Gradient of Python Control Flow\n", + "\n", + "One benefit of using automatic differentiation is that even if the computational graph of the function contains Python's control flow (such as conditional and loop control), we may still be able to find the gradient of a variable. Consider the following program: It should be emphasized that the number of iterations of the loop (while loop) and the execution of the conditional judgment (if statement) depend on the value of the input `b`." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "83S26YLWNHwI" + }, + "source": [ + "def f(a):\n", + " b = a * 2\n", + " while b.norm().item() < 1000:\n", + " b = b * 2\n", + " if b.sum().item() > 0:\n", + " c = b\n", + " else:\n", + " c = 100 * b\n", + " return c" + ], + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Az0abh03NHwI" + }, + "source": [ + "Note that the number of iterations of the while loop and the execution of the conditional statement (if then else) depend on the value of `a`. To compute gradients, we need to `record` the calculation, and then call the `backward` function to calculate the gradient." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Q-qRa2EkNHwI" + }, + "source": [ + "a = torch.randn(size=(1,))\n", + "a.requires_grad=True\n", + "d = f(a)\n", + "d.backward()" + ], + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iVamDxunNHwI" + }, + "source": [ + "Let's analyze the `f` function defined above. As you can see, it is piecewise linear in its input `a`. In other words, for any `a` there exists some constant such that for a given range `f(a) = g * a`. Consequently `d / a` allows us to verify that the gradient is correct:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "BwXGi43jNHwJ", + "outputId": "2324c6d5-6709-45c7-9562-a54fa8bcfec0" + }, + "source": [ + "print(a.grad == (d / a))" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor([True])\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AEkeomgkNHwJ" + }, + "source": [ + "## Head gradients and the chain rule\n", + "\n", + "*Caution: This part is tricky and not necessary to understanding subsequent sections. That said, it is needed if you want to build new layers from scratch. You can skip this on a first read.*\n", + "\n", + "Sometimes when we call the backward method, e.g. `y.backward()`, where\n", + "`y` is a function of `x` we are just interested in the derivative of\n", + "`y` with respect to `x`. Mathematicians write this as\n", + "$\\frac{dy(x)}{dx}$. At other times, we may be interested in the\n", + "gradient of `z` with respect to `x`, where `z` is a function of `y`,\n", + "which in turn, is a function of `x`. That is, we are interested in\n", + "$\\frac{d}{dx} z(y(x))$. Recall that by the chain rule\n", + "\n", + "$$\\frac{d}{dx} z(y(x)) = \\frac{dz(y)}{dy} \\frac{dy(x)}{dx}.$$\n", + "\n", + "So, when ``y`` is part of a larger function ``z`` and we want ``x.grad`` to store $\\frac{dz}{dx}$, we can pass in the *head gradient* $\\frac{dz}{dy}$ as an input to ``backward()``. The default argument is ``torch.ones_like(y)``. See [Wikipedia](https://en.wikipedia.org/wiki/Chain_rule) for more details." + ] + }, + { + "cell_type": "code", + "metadata": { + "scrolled": true, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "HZvGe9O-NHwJ", + "outputId": "61e933e6-f4e9-4e74-c18d-1d46d9e396d9" + }, + "source": [ + "x = Variable(torch.tensor([[0.],[1.],[2.],[3.]]), requires_grad=True)\n", + "y = x * 2\n", + "z = y * x\n", + "\n", + "head_gradient = torch.tensor([[10], [1.], [.1], [.01]])\n", + "z.backward(head_gradient)\n", + "print(x.grad)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor([[0.0000],\n", + " [4.0000],\n", + " [0.8000],\n", + " [0.1200]])\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8KD80FSoNHwK" + }, + "source": [ + "## Summary\n", + "\n", + "* PyTorch provides an `autograd` package to automate the derivation process.\n", + "* PyTorch's `autograd` package can be used to derive general imperative programs.\n", + "* The running modes of PyTorch include the training mode and the evaluation mode." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s0vXkwaoNHwK" + }, + "source": [ + "## Exercises\n", + "\n", + "1. In the control flow example where we calculate the derivative of `d` with respect to `a`, what would happen if we changed the variable `a` to a random vector or matrix. At this point, the result of the calculation `f(a)` is no longer a scalar. What happens to the result? How do we analyze this?\n", + "- They result would be vector or matrix. We can analyze it by externally compute gradient.\n", + "\n", + "2. Redesign an example of finding the gradient of the control flow. Run and analyze the result.\n", + "- Check the code below\n", + "\n", + "3. In a second-price auction (such as in eBay or in computational advertising), the winning bidder pays the second-highest price. Compute the gradient of the final price with respect to the winning bidder's bid using `autograd`. What does the result tell you about the mechanism? If you are curious to learn more about second-price auctions, check out this paper by [Edelman, Ostrovski and Schwartz, 2005](https://www.benedelman.org/publications/gsp-060801.pdf).\n", + "-\n", + "\n", + "4. Why is the second derivative much more expensive to compute than the first derivative?\n", + "- Because of the chain rule, we need to compute $N^2$ elements while we need to compute $N$ elements in the first derivative.\n", + "\n", + "5. Derive the head gradient relationship for the chain rule. If you get stuck, use the [\"Chain rule\" article on Wikipedia](https://en.wikipedia.org/wiki/Chain_rule).\n", + "- $\\text{head gradient} = \\frac{dz}{dy} = \\frac{dz}{dx}\\frac{dx}{dy}$\n", + "\n", + "6. Assume $f(x) = \\sin(x)$. Plot $f(x)$ and $\\frac{df(x)}{dx}$ on a graph, where you computed the latter without any symbolic calculations, i.e. without exploiting that $f'(x) = \\cos(x)$.\n", + "- Check the code below\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "aMYeHC8lSD1m" + }, + "source": [ + "def f0(a):\n", + " b = a * a + a\n", + " if b.sum().item() > 0:\n", + " c = b\n", + " else:\n", + " c = 100 * b\n", + " return c\n" + ], + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PMjCEITmOxfJ" + }, + "source": [ + "$d = 2a^2 + a\\\\\\text{a.grad} = 2a + 1$" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LB9ZyUBSMpL4", + "outputId": "17b03c9e-0a38-4e4e-da7c-fac538fab76a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + } + }, + "source": [ + "a = torch.randn(size=(1,))\n", + "a.requires_grad=True\n", + "d = f0(a)\n", + "d.backward()\n", + "print(a)\n", + "print(d)\n", + "print(a.grad)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor([0.3273], requires_grad=True)\n", + "tensor([0.4344], grad_fn=)\n", + "tensor([1.6546])\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bwVLRMdXM0L_", + "outputId": "fecfa090-a0af-4043-ee68-f4f5a88dba86", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 334 + } + }, + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt \n", + "\n", + "x = np.linspace(-np.pi, np.pi, 1000)\n", + "X = torch.tensor(x, requires_grad=True)\n", + "y = torch.sin(X).sum().backward()\n", + "\n", + "plt.plot(X.detach().numpy(), np.sin(x))\n", + "plt.plot(X.detach().numpy(), X.grad)\n", + "plt.axis('equal')\n" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(-3.4557519189487724,\n", + " 3.4557519189487724,\n", + " -1.0999999381914298,\n", + " 1.0999987020200273)" + ] + }, + "metadata": {}, + "execution_count": 13 + }, + { + "output_type": "display_data", + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] } - ], - "source": [ - "x = Variable(torch.tensor([[0.],[1.],[2.],[3.]]), requires_grad=True)\n", - "y = x * 2\n", - "z = y * x\n", - "\n", - "head_gradient = torch.tensor([[10], [1.], [.1], [.01]])\n", - "z.backward(head_gradient)\n", - "print(x.grad)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "* PyTorch provides an `autograd` package to automate the derivation process.\n", - "* PyTorch's `autograd` package can be used to derive general imperative programs.\n", - "* The running modes of PyTorch include the training mode and the evaluation mode." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercises\n", - "\n", - "1. In the control flow example where we calculate the derivative of `d` with respect to `a`, what would happen if we changed the variable `a` to a random vector or matrix. At this point, the result of the calculation `f(a)` is no longer a scalar. What happens to the result? How do we analyze this?\n", - "1. Redesign an example of finding the gradient of the control flow. Run and analyze the result.\n", - "1. In a second-price auction (such as in eBay or in computational advertising), the winning bidder pays the second-highest price. Compute the gradient of the final price with respect to the winning bidder's bid using `autograd`. What does the result tell you about the mechanism? If you are curious to learn more about second-price auctions, check out this paper by [Edelman, Ostrovski and Schwartz, 2005](https://www.benedelman.org/publications/gsp-060801.pdf).\n", - "1. Why is the second derivative much more expensive to compute than the first derivative?\n", - "1. Derive the head gradient relationship for the chain rule. If you get stuck, use the [\"Chain rule\" article on Wikipedia](https://en.wikipedia.org/wiki/Chain_rule).\n", - "1. Assume $f(x) = \\sin(x)$. Plot $f(x)$ and $\\frac{df(x)}{dx}$ on a graph, where you computed the latter without any symbolic calculations, i.e. without exploiting that $f'(x) = \\cos(x)$.\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + ] +} \ No newline at end of file diff --git a/Ch04_The_Preliminaries_A_Crashcourse/Data_Manipulation.ipynb b/Ch04_The_Preliminaries_A_Crashcourse/Data_Manipulation.ipynb index 50d5d0d9..63049de5 100644 --- a/Ch04_The_Preliminaries_A_Crashcourse/Data_Manipulation.ipynb +++ b/Ch04_The_Preliminaries_A_Crashcourse/Data_Manipulation.ipynb @@ -448,7 +448,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that torch.dot() behaves differently to np.dot(). There's been some discussion about what would be desirable here. Specifically, torch.dot() treats both a and b as 1D vectors (irrespective of their original shape) and computes their inner product. " + "Note that torch.dot() behaves differently to np.dot(). There's been some discussion about what would be desirable here. Specifically, torch.dot() requires both a and b be 1D vectors while numpy.dot() accepts higher dimension arrays." ] }, { @@ -904,7 +904,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -918,9 +918,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.7" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch04_The_Preliminaries_A_Crashcourse/Naive_Bayes_Classification.ipynb b/Ch04_The_Preliminaries_A_Crashcourse/Naive_Bayes_Classification.ipynb index 7ac03e69..212dac1a 100644 --- a/Ch04_The_Preliminaries_A_Crashcourse/Naive_Bayes_Classification.ipynb +++ b/Ch04_The_Preliminaries_A_Crashcourse/Naive_Bayes_Classification.ipynb @@ -47,9 +47,115 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f4a9a38ca20140c59dc325780d0ed7a9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "97a9d0da3bdb41eab99ab21b25e2d498", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "92735059d2be4aad91386f30a24e457c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "abeeee9e51974c558dccfb1c47c8140f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n", + "Processing...\n", + "Done!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jeon/anaconda3/envs/ai_safe/lib/python3.6/site-packages/torchvision/datasets/mnist.py:469: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n", + " return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n" + ] + } + ], "source": [ "%matplotlib inline\n", "import tqdm\n", @@ -82,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -2732,9 +2838,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -2746,9 +2852,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/processed/test.pt b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/processed/test.pt new file mode 100644 index 00000000..a82508f7 Binary files /dev/null and b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/processed/test.pt differ diff --git a/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/processed/training.pt b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/processed/training.pt new file mode 100644 index 00000000..b842a0b9 Binary files /dev/null and b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/processed/training.pt differ diff --git a/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/t10k-images-idx3-ubyte b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/t10k-images-idx3-ubyte new file mode 100644 index 00000000..1170b2ca Binary files /dev/null and b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/t10k-images-idx3-ubyte differ diff --git a/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/t10k-images-idx3-ubyte.gz b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/t10k-images-idx3-ubyte.gz new file mode 100644 index 00000000..5ace8ea9 Binary files /dev/null and b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/t10k-images-idx3-ubyte.gz differ diff --git a/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/t10k-labels-idx1-ubyte b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/t10k-labels-idx1-ubyte new file mode 100644 index 00000000..d1c3a970 Binary files /dev/null and b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/t10k-labels-idx1-ubyte differ diff --git a/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/t10k-labels-idx1-ubyte.gz b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/t10k-labels-idx1-ubyte.gz new file mode 100644 index 00000000..a7e14154 Binary files /dev/null and b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/t10k-labels-idx1-ubyte.gz differ diff --git a/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/train-images-idx3-ubyte b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/train-images-idx3-ubyte new file mode 100644 index 00000000..bbce2765 Binary files /dev/null and b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/train-images-idx3-ubyte differ diff --git a/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/train-images-idx3-ubyte.gz b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/train-images-idx3-ubyte.gz new file mode 100644 index 00000000..b50e4b6b Binary files /dev/null and b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/train-images-idx3-ubyte.gz differ diff --git a/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/train-labels-idx1-ubyte b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/train-labels-idx1-ubyte new file mode 100644 index 00000000..d6b4c5db Binary files /dev/null and b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/train-labels-idx1-ubyte differ diff --git a/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/train-labels-idx1-ubyte.gz b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/train-labels-idx1-ubyte.gz new file mode 100644 index 00000000..707a576b Binary files /dev/null and b/Ch04_The_Preliminaries_A_Crashcourse/data/MNIST/raw/train-labels-idx1-ubyte.gz differ diff --git a/Ch05_Linear_Neural_Networks/Concise_Implementation_of_Linear_Regression.ipynb b/Ch05_Linear_Neural_Networks/Concise_Implementation_of_Linear_Regression.ipynb index 86134d47..b163d32f 100644 --- a/Ch05_Linear_Neural_Networks/Concise_Implementation_of_Linear_Regression.ipynb +++ b/Ch05_Linear_Neural_Networks/Concise_Implementation_of_Linear_Regression.ipynb @@ -422,9 +422,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.6" + "version": "3.7.6" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch05_Linear_Neural_Networks/Concise_Implementation_of_Softmax_Regression.ipynb b/Ch05_Linear_Neural_Networks/Concise_Implementation_of_Softmax_Regression.ipynb index 649a8a78..6e2068df 100644 --- a/Ch05_Linear_Neural_Networks/Concise_Implementation_of_Softmax_Regression.ipynb +++ b/Ch05_Linear_Neural_Networks/Concise_Implementation_of_Softmax_Regression.ipynb @@ -34,7 +34,113 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /home/jeon/.pytorch/datasets/fashion-mnist/FashionMNIST/raw/train-images-idx3-ubyte.gz\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a9b1105cdd8e4f5d92693c0673837b80", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /home/jeon/.pytorch/datasets/fashion-mnist/FashionMNIST/raw/train-images-idx3-ubyte.gz to /home/jeon/.pytorch/datasets/fashion-mnist/FashionMNIST/raw\n", + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /home/jeon/.pytorch/datasets/fashion-mnist/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "02aecb0b4ba14f87844ad40db7830919", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /home/jeon/.pytorch/datasets/fashion-mnist/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /home/jeon/.pytorch/datasets/fashion-mnist/FashionMNIST/raw\n", + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /home/jeon/.pytorch/datasets/fashion-mnist/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7635536fcfec410cb67415361c06a65e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /home/jeon/.pytorch/datasets/fashion-mnist/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /home/jeon/.pytorch/datasets/fashion-mnist/FashionMNIST/raw\n", + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /home/jeon/.pytorch/datasets/fashion-mnist/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "97b99abc919b454889884724f378c3bb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /home/jeon/.pytorch/datasets/fashion-mnist/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /home/jeon/.pytorch/datasets/fashion-mnist/FashionMNIST/raw\n", + "Processing...\n", + "Done!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jeon/anaconda3/envs/ai_safe/lib/python3.6/site-packages/torchvision/datasets/mnist.py:469: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n", + " return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n" + ] + } + ], "source": [ "batch_size = 256\n", "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)" @@ -54,6 +160,13 @@ "execution_count": 3, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, { "data": { "text/plain": [ @@ -178,16 +291,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch 1, loss 0.0031, train acc 0.750, test acc 0.786\n", - "epoch 2, loss 0.0022, train acc 0.813, test acc 0.812\n", - "epoch 3, loss 0.0021, train acc 0.826, test acc 0.815\n", - "epoch 4, loss 0.0020, train acc 0.832, test acc 0.823\n", - "epoch 5, loss 0.0019, train acc 0.837, test acc 0.809\n", - "epoch 6, loss 0.0019, train acc 0.840, test acc 0.814\n", - "epoch 7, loss 0.0018, train acc 0.842, test acc 0.820\n", - "epoch 8, loss 0.0018, train acc 0.845, test acc 0.830\n", - "epoch 9, loss 0.0018, train acc 0.847, test acc 0.834\n", - "epoch 10, loss 0.0018, train acc 0.850, test acc 0.832\n" + "epoch 1, loss 0.0031, train acc 0.747, test acc 0.778\n", + "epoch 2, loss 0.0022, train acc 0.814, test acc 0.788\n", + "epoch 3, loss 0.0021, train acc 0.825, test acc 0.813\n", + "epoch 4, loss 0.0020, train acc 0.832, test acc 0.824\n", + "epoch 5, loss 0.0019, train acc 0.837, test acc 0.827\n", + "epoch 6, loss 0.0019, train acc 0.840, test acc 0.829\n", + "epoch 7, loss 0.0018, train acc 0.843, test acc 0.822\n", + "epoch 8, loss 0.0018, train acc 0.846, test acc 0.831\n", + "epoch 9, loss 0.0018, train acc 0.846, test acc 0.827\n", + "epoch 10, loss 0.0018, train acc 0.848, test acc 0.824\n" ] } ], @@ -218,9 +331,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -232,9 +345,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch05_Linear_Neural_Networks/FashionMNIST/processed/test.pt b/Ch05_Linear_Neural_Networks/FashionMNIST/processed/test.pt new file mode 100644 index 00000000..ca839d51 Binary files /dev/null and b/Ch05_Linear_Neural_Networks/FashionMNIST/processed/test.pt differ diff --git a/Ch05_Linear_Neural_Networks/FashionMNIST/processed/training.pt b/Ch05_Linear_Neural_Networks/FashionMNIST/processed/training.pt new file mode 100644 index 00000000..9b0c219f Binary files /dev/null and b/Ch05_Linear_Neural_Networks/FashionMNIST/processed/training.pt differ diff --git a/Ch05_Linear_Neural_Networks/FashionMNIST/raw/t10k-images-idx3-ubyte b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/t10k-images-idx3-ubyte new file mode 100644 index 00000000..37bac79b Binary files /dev/null and b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/t10k-images-idx3-ubyte differ diff --git a/Ch05_Linear_Neural_Networks/FashionMNIST/raw/t10k-images-idx3-ubyte.gz b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/t10k-images-idx3-ubyte.gz new file mode 100644 index 00000000..667844f1 Binary files /dev/null and b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/t10k-images-idx3-ubyte.gz differ diff --git a/Ch05_Linear_Neural_Networks/FashionMNIST/raw/t10k-labels-idx1-ubyte b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/t10k-labels-idx1-ubyte new file mode 100644 index 00000000..2195a4d0 Binary files /dev/null and b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/t10k-labels-idx1-ubyte differ diff --git a/Ch05_Linear_Neural_Networks/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz new file mode 100644 index 00000000..abdddb89 Binary files /dev/null and b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz differ diff --git a/Ch05_Linear_Neural_Networks/FashionMNIST/raw/train-images-idx3-ubyte b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/train-images-idx3-ubyte new file mode 100644 index 00000000..ff2f5a96 Binary files /dev/null and b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/train-images-idx3-ubyte differ diff --git a/Ch05_Linear_Neural_Networks/FashionMNIST/raw/train-images-idx3-ubyte.gz b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/train-images-idx3-ubyte.gz new file mode 100644 index 00000000..e6ee0e37 Binary files /dev/null and b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/train-images-idx3-ubyte.gz differ diff --git a/Ch05_Linear_Neural_Networks/FashionMNIST/raw/train-labels-idx1-ubyte b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/train-labels-idx1-ubyte new file mode 100644 index 00000000..30424ca2 Binary files /dev/null and b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/train-labels-idx1-ubyte differ diff --git a/Ch05_Linear_Neural_Networks/FashionMNIST/raw/train-labels-idx1-ubyte.gz b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/train-labels-idx1-ubyte.gz new file mode 100644 index 00000000..9c4aae27 Binary files /dev/null and b/Ch05_Linear_Neural_Networks/FashionMNIST/raw/train-labels-idx1-ubyte.gz differ diff --git a/Ch05_Linear_Neural_Networks/Image_Classification_Data(Fashion-MNIST).ipynb b/Ch05_Linear_Neural_Networks/Image_Classification_Data(Fashion-MNIST).ipynb index 1627d9b0..f53d46f5 100644 --- a/Ch05_Linear_Neural_Networks/Image_Classification_Data(Fashion-MNIST).ipynb +++ b/Ch05_Linear_Neural_Networks/Image_Classification_Data(Fashion-MNIST).ipynb @@ -1,1226 +1,540 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Image Classification Data (Fashion-MNIST)\n", - "\n", - "Before we implement softmax regression ourselves, let's pick a real dataset to work with. To make things visually compelling, we will pick an image classification dataset. The most commonly used image classification data set is the [MNIST](http://yann.lecun.com/exdb/mnist/) handwritten digit recognition data set, proposed by LeCun, Cortes and Burges in the 1990s. However, even simple models achieve classification accuracy over 95% on MNIST, so it is hard to spot the differences between better models and weaker ones. In order to get a better intuition, we will use the qualitatively similar, but comparatively complex [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset, proposed by [Xiao, Rasul and Vollgraf](https://arxiv.org/abs/1708.07747) in 2017." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Getting the Data\n", - "\n", - "First, import the packages or modules required in this section.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import sys\n", - "sys.path.insert(0, '..')\n", - "import d2l\n", - "\n", - "import torch\n", - "import torchvision\n", - "from torchvision import transforms\n", - "from torch.utils.data import DataLoader\n", - "import time" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Conveniently, PyTorch's `torchvision.datasets` package provides easy access to a number of benchmark vision datasets for testing our models.\n", - "The first time we invoke `data.vision.FashionMNIST(train=True)`\n", - "to collect the training data,\n", - "PyTorch will automatically retrieve the dataset via our Internet connection.\n", - "Subsequently, PyTorch will use the already-downloaded local copy.\n", - "We specify whether we are requesting the training set or the test set\n", - "by setting the value of the parameter `train` to `True` or `False`, respectively.\n", - "Recall that we will only be using the training data for training,\n", - "holding out the test set for a final evaluation of our model." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# By default pytorch torchvision datasets are of type PIL.\n", - "# Define a transform \"trans\" to change the PIL to Tensor format.\n", - "trans = transforms.ToTensor() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `ToTensor` Transform also moves the image channel from the last dimension to the first dimension to facilitate the convolutional neural network calculations introduced later." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "mnist_train = torchvision.datasets.FashionMNIST(root=\"./\", train=True, transform=trans, target_transform=None, download=True)\n", - "mnist_test = torchvision.datasets.FashionMNIST(root=\"./\", train=False, transform=trans, target_transform=None, download=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The number of images for each category in the training set and the testing set is 6,000 and 1,000, respectively. Since there are 10 categories, the numbers of examples in the training set and the test set are 60,000 and 10,000, respectively." - ] + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "colab": { + "name": "Image_Classification_Data(Fashion-MNIST).ipynb", + "provenance": [] + }, + "language_info": { + "name": "python" + } }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "text/plain": [ - "(60000, 10000)" + "cell_type": "markdown", + "metadata": { + "id": "m8nl8kOUe-wB" + }, + "source": [ + "# Image Classification Data (Fashion-MNIST)\n", + "\n", + "Before we implement softmax regression ourselves, let's pick a real dataset to work with. To make things visually compelling, we will pick an image classification dataset. The most commonly used image classification data set is the [MNIST](http://yann.lecun.com/exdb/mnist/) handwritten digit recognition data set, proposed by LeCun, Cortes and Burges in the 1990s. However, even simple models achieve classification accuracy over 95% on MNIST, so it is hard to spot the differences between better models and weaker ones. In order to get a better intuition, we will use the qualitatively similar, but comparatively complex [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset, proposed by [Xiao, Rasul and Vollgraf](https://arxiv.org/abs/1708.07747) in 2017." ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(mnist_train), len(mnist_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can access any example by indexing into the dataset using square brackets `[]`. In the following code, we access the image and label corresponding to the first example." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "feature, label = mnist_train[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our example, stored here in the variable `feature` corresponds to an image with a height and width of 28 pixels. PyTorch automatically scales it into a tensor with each pixel value between 0 and 1. It is stored in a 3D Tensor. Its first dimension is the number of channels. Since the data set is a grayscale image, the number of channels is 1. When we encounter color, images, we'll have 3 channels for red, green, and blue. To keep things simple, we will record the shape of the image with the height and width of $h$ and $w$ pixels, respectively, as $h \\times w$ or `(h, w)`." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "(torch.Size([1, 28, 28]), torch.float32)" + "cell_type": "markdown", + "metadata": { + "id": "I0Q4Ew9ne-wD" + }, + "source": [ + "## Getting the Data\n", + "\n", + "First, import the packages or modules required in this section.\n", + "\n" ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "feature.shape, feature.dtype" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The label of each image is represented as a scalar in PyTorch. Its type is a 64-bit integer." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "(tensor(9), torch.Tensor, torch.int64)" + "cell_type": "code", + "metadata": { + "id": "Yrc5aq4b-oCg" + }, + "source": [ + "!pip install d2l" + ], + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "uHnsGsXae-wE" + }, + "source": [ + "%matplotlib inline\n", + "import sys\n", + "sys.path.insert(0, '..')\n", + "import d2l\n", + "from IPython import display\n", + "from matplotlib import pyplot as plt\n", + "import torch\n", + "import torchvision\n", + "from torchvision import transforms\n", + "from torch.utils.data import DataLoader\n", + "\n", + "import time" + ], + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R8H2S-RSe-wF" + }, + "source": [ + "Conveniently, PyTorch's `torchvision.datasets` package provides easy access to a number of benchmark vision datasets for testing our models.\n", + "The first time we invoke `data.vision.FashionMNIST(train=True)`\n", + "to collect the training data,\n", + "PyTorch will automatically retrieve the dataset via our Internet connection.\n", + "Subsequently, PyTorch will use the already-downloaded local copy.\n", + "We specify whether we are requesting the training set or the test set\n", + "by setting the value of the parameter `train` to `True` or `False`, respectively.\n", + "Recall that we will only be using the training data for training,\n", + "holding out the test set for a final evaluation of our model." ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "label, type(label), label.dtype" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are 10 categories in Fashion-MNIST: t-shirt, trousers, pullover, dress, coat, sandal, shirt, sneaker, bag and ankle boot. The following function can convert a numeric label into a corresponding text label." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# This function has been saved in the d2l package for future use\n", - "def get_fashion_mnist_labels(labels):\n", - " text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n", - " 'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n", - " return [text_labels[int(i)] for i in labels]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following defines a function that can draw multiple images and corresponding labels in a single line." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# This function has been saved in the d2l package for future use\n", - "def show_fashion_mnist(images, labels):\n", - " d2l.use_svg_display()\n", - " # Here _ means that we ignore (not use) variables\n", - " _, figs = d2l.plt.subplots(1, len(images), figsize=(12, 12))\n", - " for f, img, lbl in zip(figs, images, labels):\n", - " f.imshow(img.reshape((28, 28)).numpy())\n", - " f.set_title(lbl)\n", - " f.axes.get_xaxis().set_visible(False)\n", - " f.axes.get_yaxis().set_visible(False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, let's take a look at the image contents and text labels for the first nine examples in the training data set.\n", - "\n", - "Note: PyTorch DataLoader objects don't support regular array slicing. You can instead iterate through." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ + }, { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" + "cell_type": "code", + "metadata": { + "id": "DIP-pcvJe-wF" + }, + "source": [ + "# By default pytorch torchvision datasets are of type PIL.\n", + "# Define a transform \"trans\" to change the PIL to Tensor format.\n", + "trans = transforms.ToTensor() " ], - "text/plain": [ - "
" + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "McDcTejYe-wG" + }, + "source": [ + "The `ToTensor` Transform also moves the image channel from the last dimension to the first dimension to facilitate the convolutional neural network calculations introduced later." ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "X=[]\n", - "y=[]\n", - "for idx, data in enumerate(mnist_train):\n", - " if(idx>=0 and idx<10):\n", - " X.append(data[0])\n", - " y.append(data[1])\n", - " if (idx>=10):\n", - " break\n", - "# X, y = mnist_train[0:9]\n", - "show_fashion_mnist(X, get_fashion_mnist_labels(y))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reading a Minibatch\n", - "\n", - "To make our life easier when reading from the training and test sets we use a `DataLoader` rather than creating one from scratch, as we did in `chapter_linear_scratch`. Recall that a data loader reads a mini-batch of data with an example number of `batch_size` each time.\n", - "\n", - "In practice, reading data can often be a significant performance bottleneck for training, especially when the model is simple or when the computer is fast. A handy feature of PyTorch's `DataLoader` is the ability to use multiple processes to speed up data reading. For instance, we can set aside 4 processes to read the data (via `num_workers`).\n", - "\n", - "We've already applied required transformations before." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "batch_size = 256\n", - "if sys.platform.startswith('win'):\n", - " # set 0 for windows\n", - " # 0 means no additional processes are needed to speed up the reading of data\n", - " num_workers = 0\n", - "else:\n", - " num_workers = 4\n", - "\n", - "train_iter = DataLoader(mnist_train, batch_size, shuffle=True, num_workers=num_workers)\n", - "test_iter = DataLoader(mnist_test, batch_size, shuffle=False, num_workers=num_workers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The logic that we will use to obtain and read the Fashion-MNIST data set is\n", - "encapsulated in the `d2l.load_data_fashion_mnist` function, which we will use in\n", - "later chapters. This function will return two variables, `train_iter` and\n", - "`test_iter`. As the content of this book continues to deepen, we will further\n", - "improve this function.\n", - "\n", - "Let's look at the time it takes to read the training data." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "'1.39 sec'" + "cell_type": "code", + "metadata": { + "id": "Zz0pkE_Pe-wG" + }, + "source": [ + "mnist_train = torchvision.datasets.FashionMNIST(root=\"./\", train=True, transform=trans, target_transform=None, download=True)\n", + "mnist_test = torchvision.datasets.FashionMNIST(root=\"./\", train=False, transform=trans, target_transform=None, download=True)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bsOEIIzfe-wH" + }, + "source": [ + "The number of images for each category in the training set and the testing set is 6,000 and 1,000, respectively. Since there are 10 categories, the numbers of examples in the training set and the test set are 60,000 and 10,000, respectively." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JBOUknvye-wH", + "outputId": "bbd26b10-fb4d-49e8-de77-49880688f980", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "len(mnist_train), len(mnist_test)" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(60000, 10000)" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fAnZ1O1be-wI" + }, + "source": [ + "We can access any example by indexing into the dataset using square brackets `[]`. In the following code, we access the image and label corresponding to the first example." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "rT37CGmQe-wI" + }, + "source": [ + "feature, label = mnist_train[0]" + ], + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "u5_KbsUBe-wI" + }, + "source": [ + "Our example, stored here in the variable `feature` corresponds to an image with a height and width of 28 pixels. PyTorch automatically scales it into a tensor with each pixel value between 0 and 1. It is stored in a 3D Tensor. Its first dimension is the number of channels. Since the data set is a grayscale image, the number of channels is 1. When we encounter color, images, we'll have 3 channels for red, green, and blue. To keep things simple, we will record the shape of the image with the height and width of $h$ and $w$ pixels, respectively, as $h \\times w$ or `(h, w)`." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lMCbmWOEe-wJ", + "outputId": "61ac3a1b-d9a2-497e-b45c-27b1a82b2225", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "feature.shape, feature.dtype" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(torch.Size([1, 28, 28]), torch.float32)" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SxC-gLi9e-wJ" + }, + "source": [ + "The label of each image is represented as a scalar in PyTorch. Its type is a 64-bit integer." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wHWoYIVae-wJ", + "outputId": "c3e1077e-3c26-4327-e590-0d400ea95f75", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "label, type(label)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(9, int)" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hjXRqLE8e-wJ" + }, + "source": [ + "There are 10 categories in Fashion-MNIST: t-shirt, trousers, pullover, dress, coat, sandal, shirt, sneaker, bag and ankle boot. The following function can convert a numeric label into a corresponding text label." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-QrG4ztYe-wK" + }, + "source": [ + "# This function has been saved in the d2l package for future use\n", + "def get_fashion_mnist_labels(labels):\n", + " text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n", + " 'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n", + " return [text_labels[int(i)] for i in labels]" + ], + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xpRaSnLBe-wK" + }, + "source": [ + "The following defines a function that can draw multiple images and corresponding labels in a single line." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "OKRDNxOre-wK" + }, + "source": [ + "# This function has been saved in the d2l package for future use\n", + "def show_fashion_mnist(images, labels):\n", + " display.set_matplotlib_formats('svg')\n", + " # Here _ means that we ignore (not use) variables\n", + " _, figs = plt.subplots(1, len(images), figsize=(12, 12))\n", + " for f, img, lbl in zip(figs, images, labels):\n", + " f.imshow(img.reshape((28, 28)).numpy())\n", + " f.set_title(lbl)\n", + " f.axes.get_xaxis().set_visible(False)\n", + " f.axes.get_yaxis().set_visible(False)" + ], + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V0r_8Zz0e-wK" + }, + "source": [ + "Next, let's take a look at the image contents and text labels for the first nine examples in the training data set.\n", + "\n", + "Note: PyTorch DataLoader objects don't support regular array slicing. You can instead iterate through." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lcrs8wyPe-wK", + "outputId": "e8f6574c-1704-4bd5-bb79-37ddf7391d54", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + } + }, + "source": [ + "X=[]\n", + "y=[]\n", + "for idx, data in enumerate(mnist_train):\n", + " if(idx>=0 and idx<10):\n", + " X.append(data[0])\n", + " y.append(data[1])\n", + " if (idx>=10):\n", + " break\n", + "# X, y = mnist_train[0:9]\n", + "show_fashion_mnist(X, get_fashion_mnist_labels(y))" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iVpiwmFoe-wL" + }, + "source": [ + "## Reading a Minibatch\n", + "\n", + "To make our life easier when reading from the training and test sets we use a `DataLoader` rather than creating one from scratch, as we did in `chapter_linear_scratch`. Recall that a data loader reads a mini-batch of data with an example number of `batch_size` each time.\n", + "\n", + "In practice, reading data can often be a significant performance bottleneck for training, especially when the model is simple or when the computer is fast. A handy feature of PyTorch's `DataLoader` is the ability to use multiple processes to speed up data reading. For instance, we can set aside 4 processes to read the data (via `num_workers`).\n", + "\n", + "We've already applied required transformations before." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ASaTFlP5e-wL", + "outputId": "d1f6236f-4782-4dab-b606-5a77657b9cbe", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "batch_size = 256\n", + "if sys.platform.startswith('win'):\n", + " # set 0 for windows\n", + " # 0 means no additional processes are needed to speed up the reading of data\n", + " num_workers = 0\n", + "else:\n", + " num_workers = 4\n", + "\n", + "train_iter = DataLoader(mnist_train, batch_size, shuffle=True, num_workers=num_workers)\n", + "test_iter = DataLoader(mnist_test, batch_size, shuffle=False, num_workers=num_workers)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", + " cpuset_checked))\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rOsZShjae-wL" + }, + "source": [ + "The logic that we will use to obtain and read the Fashion-MNIST data set is\n", + "encapsulated in the `d2l.load_data_fashion_mnist` function, which we will use in\n", + "later chapters. This function will return two variables, `train_iter` and\n", + "`test_iter`. As the content of this book continues to deepen, we will further\n", + "improve this function.\n", + "\n", + "Let's look at the time it takes to read the training data." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "T_hpAoOMe-wL", + "outputId": "621f0cdc-30cd-47cf-8079-c679c9a5a10a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + } + }, + "source": [ + "start = time.time()\n", + "for X, y in train_iter:\n", + " continue\n", + "'%.2f sec' % (time.time() - start)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", + " cpuset_checked))\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'6.91 sec'" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XDlnwXgce-wM" + }, + "source": [ + "## Summary\n", + "\n", + "* Fashion-MNIST is an apparel classification data set containing 10 categories, which we will use to test the performance of different algorithms in later chapters.\n", + "* We store the shape of image using height and width of $h$ and $w$ pixels, respectively, as $h \\times w$ or `(h, w)`.\n", + "* Data iterators are a key component for efficient performance. Use existing ones if available." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Spvu1DmEe-wM" + }, + "source": [ + "## Exercises\n", + "\n", + "1. Does reducing `batch_size` (for instance, to 1) affect read performance?\n", + "\n", + "- batch_size = 256\n", + " 6.44 sec\n", + "- batch_size = 128\n", + " 7.15 sec\n", + "- batch_size = 256\n", + " 7.99 sec\n", + "- batch_size = 1\n", + " 153.98 sec\n", + "- performance gets worse when reducing batch_size\n", + "\n", + "2. For non-Windows users, try modifying `num_workers` to see how it affects read performance.\n", + "- Check the code below\n", + "\n", + "3. Use the PyTorch documentation to see which other datasets are available in `torchvision.datasets`.\n", + "\n", + "4. Use the PyTorch documentation to see which other transformations are available in `torchvision.transforms`." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JLTIRfS8Hcbu", + "outputId": "d36b59a7-d410-4b4c-9d11-487469d9299e", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "source": [ + "batch_size = 256\n", + "record = []\n", + "\n", + "for workers in [1, 2]:\n", + " train_iter = DataLoader(mnist_train, batch_size, shuffle=True, num_workers=num_workers)\n", + " \n", + " start = time.time()\n", + " for x, y in train_iter: continue\n", + " record.append(time.time()-start)\n", + "\n", + "print(f'workers: 1 time: {record[0]:.3f}\\nworkers: 2 time: {record[1]:.3f}')\n" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", + " cpuset_checked))\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "workers: 1 time: 6.852\n", + "workers: 2 time: 6.570\n" + ] + } ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" } - ], - "source": [ - "start = time.time()\n", - "for X, y in train_iter:\n", - " continue\n", - "'%.2f sec' % (time.time() - start)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "* Fashion-MNIST is an apparel classification data set containing 10 categories, which we will use to test the performance of different algorithms in later chapters.\n", - "* We store the shape of image using height and width of $h$ and $w$ pixels, respectively, as $h \\times w$ or `(h, w)`.\n", - "* Data iterators are a key component for efficient performance. Use existing ones if available." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exercises\n", - "\n", - "1. Does reducing `batch_size` (for instance, to 1) affect read performance?\n", - "1. For non-Windows users, try modifying `num_workers` to see how it affects read performance.\n", - "1. Use the PyTorch documentation to see which other datasets are available in `torchvision.datasets`.\n", - "1. Use the PyTorch documentation to see which other transformations are available in `torchvision.transforms`." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + ] +} \ No newline at end of file diff --git a/Ch05_Linear_Neural_Networks/Implementation_of_Softmax_Regression_from_Scratch.ipynb b/Ch05_Linear_Neural_Networks/Implementation_of_Softmax_Regression_from_Scratch.ipynb index 81cd330d..ed51afa8 100644 --- a/Ch05_Linear_Neural_Networks/Implementation_of_Softmax_Regression_from_Scratch.ipynb +++ b/Ch05_Linear_Neural_Networks/Implementation_of_Softmax_Regression_from_Scratch.ipynb @@ -248,8 +248,8 @@ { "data": { "text/plain": [ - "(tensor([[0.0120, 0.0471, 0.3944, 0.3469, 0.1997],\n", - " [0.1948, 0.3007, 0.1048, 0.2569, 0.1427]]), tensor([1., 1.]))" + "(tensor([[0.1551, 0.4988, 0.1028, 0.1709, 0.0724],\n", + " [0.2764, 0.1615, 0.1347, 0.3473, 0.0801]]), tensor([1.0000, 1.0000]))" ] }, "execution_count": 7, @@ -372,7 +372,7 @@ "\n", "Given the predicted probability distribution `y_hat`,\n", "we typically choose the class with highest predicted probability\n", - "whenever we must output a *hard* prediction. Indeed, many applications require that we make a choice. Gmail must catetegorize an email into Primary, Social, Updates, or Forums. It might estimate probabilities internally, but at the end of the day it has to choose one among the categories.\n", + "whenever we must output a *hard* prediction. Indeed, many applications require that we make a choice. Gmail must categorize an email into Primary, Social, Updates, or Forums. It might estimate probabilities internally, but at the end of the day it has to choose one among the categories.\n", "\n", "When predictions are consistent with the actual category `y`, they are correct. The classification accuracy is the fraction of all predictions that are correct. Although we cannot optimize accuracy directly (it is not differentiable), it's often the performance metric that we care most about, and we will nearly always report it when training classifiers.\n", "\n", @@ -496,7 +496,7 @@ { "data": { "text/plain": [ - "0.1224" + "0.1081" ] }, "execution_count": 14, @@ -540,11 +540,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch 1, loss 0.7872, train acc 0.750, test acc 0.793\n", - "epoch 2, loss 0.5709, train acc 0.813, test acc 0.806\n", - "epoch 3, loss 0.5255, train acc 0.825, test acc 0.813\n", - "epoch 4, loss 0.5023, train acc 0.831, test acc 0.825\n", - "epoch 5, loss 0.4854, train acc 0.836, test acc 0.827\n" + "epoch 1, loss 0.7884, train acc 0.747, test acc 0.794\n", + "epoch 2, loss 0.5722, train acc 0.812, test acc 0.804\n", + "epoch 3, loss 0.5248, train acc 0.825, test acc 0.817\n", + "epoch 4, loss 0.5007, train acc 0.833, test acc 0.826\n", + "epoch 5, loss 0.4853, train acc 0.837, test acc 0.825\n" ] } ], @@ -599,8 +599,8 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -770,9 +770,9 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-115\"/>\n", " \n", + "\" id=\"DejaVuSans-110\"/>\n", " \n", + "\" id=\"DejaVuSans-100\"/>\n", " \n", + "\" id=\"DejaVuSans-108\"/>\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -908,9 +908,9 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-101\"/>\n", " \n", + "\" id=\"DejaVuSans-107\"/>\n", " \n", + "\" id=\"DejaVuSans-114\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1018,9 +1018,9 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1070,9 +1070,9 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1120,9 +1120,9 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-117\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1199,9 +1199,9 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-112\"/>\n", " \n", + "\" id=\"DejaVuSans-118\"/>\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1294,9 +1294,9 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1377,9 +1348,9 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-98\"/>\n", " \n", + "\" id=\"DejaVuSans-103\"/>\n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1485,9 +1456,9 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-45\"/>\n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1579,7 +1580,9 @@ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1611,13 +1614,20 @@ "1. Is it always a good idea to return the most likely label. E.g. would you do this for medical diagnosis?\n", "1. Assume that we want to use softmax regression to predict the next word based on some features. What are some problems that might arise from a large vocabulary?" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.6.13", "language": "python", - "name": "python3" + "name": "dlwp" }, "language_info": { "codemirror_mode": { @@ -1629,9 +1639,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.13" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch05_Linear_Neural_Networks/Linear_Regression.ipynb b/Ch05_Linear_Neural_Networks/Linear_Regression.ipynb index 9dcc6143..7a22c3e5 100644 --- a/Ch05_Linear_Neural_Networks/Linear_Regression.ipynb +++ b/Ch05_Linear_Neural_Networks/Linear_Regression.ipynb @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -251,7 +251,7 @@ "" ] }, - "execution_count": 35, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -384,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -571,7 +571,7 @@ "" ] }, - "execution_count": 36, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -609,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -664,7 +664,7 @@ "" ] }, - "execution_count": 37, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -682,7 +682,7 @@ "\n", "Information $x_i$ arriving from other neurons (or environmental sensors such as the retina) is received in the dendrites. In particular, that information is weighted by *synaptic weights* $w_i$ which determine how to respond to the inputs (e.g. activation or inhibition via $x_i w_i$). All this is aggregated in the nucleus $y = \\sum_i x_i w_i + b$, and this information is then sent for further processing in the axon $y$, typically after some nonlinear processing via $\\sigma(y)$. From there it either reaches its destination (e.g. a muscle) or is fed into another neuron via its dendrites.\n", "\n", - "Brain *structures* vary significantly. Some look (to us) rather arbitrary whereas others have a regular structure. For example, the visual system of many insects is consistent across members of a species. The analysis of such structures has often inspired neuroscientists to propose new architectures, and in some cases, this has been successful. However, much research in artificial neural networks has little to do with any direct inspiration in neuroscience, just as although airplanes are *inspired* by birds, the study of orninthology hasn't been the primary driver of aeronautics innovaton in the last century. Equal amounts of inspiration these days comes from mathematics, statistics, and computer science.\n", + "Brain *structures* vary significantly. Some look (to us) rather arbitrary whereas others have a regular structure. For example, the visual system of many insects is consistent across members of a species. The analysis of such structures has often inspired neuroscientists to propose new architectures, and in some cases, this has been successful. However, much research in artificial neural networks has little to do with any direct inspiration in neuroscience, just as although airplanes are *inspired* by birds, the study of orninthology hasn't been the primary driver of aeronautics innovation in the last century. Equal amounts of inspiration these days comes from mathematics, statistics, and computer science.\n", "\n", "### Vectorization for Speed\n", "\n", @@ -691,7 +691,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 4, "metadata": { "attributes": { "classes": [], @@ -717,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 5, "metadata": { "attributes": { "classes": [], @@ -729,10 +729,10 @@ { "data": { "text/plain": [ - "0.11870288848876953" + "0.07582354545593262" ] }, - "execution_count": 43, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -754,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 6, "metadata": { "attributes": { "classes": [], @@ -766,10 +766,10 @@ { "data": { "text/plain": [ - "0.0002841949462890625" + "0.0005373954772949219" ] }, - "execution_count": 44, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -797,7 +797,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 7, "metadata": { "attributes": { "classes": [], @@ -812,8 +812,8 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", @@ -843,10 +843,10 @@ " \n", " \n", + "\" id=\"m5e4f385450\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -857,7 +857,7 @@ "L 73.1875 27.203125 \n", "L 10.59375 27.203125 \n", "z\n", - "\" id=\"DejaVuSans-2212\"/>\n", + "\" id=\"DejaVuSans-8722\"/>\n", " \n", + "\" id=\"DejaVuSans-54\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -920,18 +920,18 @@ "L 4.890625 17.1875 \n", "L 4.890625 26.703125 \n", "z\n", - "\" id=\"DejaVuSans-34\"/>\n", + "\" id=\"DejaVuSans-52\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -960,18 +960,18 @@ "Q 44.1875 33.984375 37.640625 27.21875 \n", "Q 31.109375 20.453125 19.1875 8.296875 \n", "z\n", - "\" id=\"DejaVuSans-32\"/>\n", + "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -997,49 +997,49 @@ "Q 6.59375 54.828125 13.0625 64.515625 \n", "Q 19.53125 74.21875 31.78125 74.21875 \n", "z\n", - "\" id=\"DejaVuSans-30\"/>\n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1050,10 +1050,10 @@ " \n", " \n", + "\" id=\"mc9980896a7\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1064,20 +1064,20 @@ "L 21 0 \n", "L 10.6875 0 \n", "z\n", - "\" id=\"DejaVuSans-2e\"/>\n", + "\" id=\"DejaVuSans-46\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1106,20 +1106,20 @@ "Q 22.75 39.890625 18.8125 39.015625 \n", "Q 14.890625 38.140625 10.796875 36.28125 \n", "z\n", - "\" id=\"DejaVuSans-35\"/>\n", + "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1137,68 +1137,68 @@ "L 54.390625 0 \n", "L 12.40625 0 \n", "z\n", - "\" id=\"DejaVuSans-31\"/>\n", + "\" id=\"DejaVuSans-49\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1235,384 +1235,384 @@ "Q 53.90625 49.265625 50.4375 45.09375 \n", "Q 46.96875 40.921875 40.578125 39.3125 \n", "z\n", - "\" id=\"DejaVuSans-33\"/>\n", + "\" id=\"DejaVuSans-51\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", @@ -1648,7 +1648,7 @@ "Q 41.65625 56 45.828125 52.96875 \n", "Q 50 49.953125 52 44.1875 \n", "z\n", - "\" id=\"DejaVuSans-6d\"/>\n", + "\" id=\"DejaVuSans-109\"/>\n", " \n", + "\" id=\"DejaVuSans-101\"/>\n", " \n", + "\" id=\"DejaVuSans-97\"/>\n", " \n", - " \n", + "\" id=\"DejaVuSans-110\"/>\n", + " \n", " \n", + "\" id=\"DejaVuSans-44\"/>\n", " \n", + "\" id=\"DejaVuSans-118\"/>\n", " \n", + "\" id=\"DejaVuSans-114\"/>\n", " \n", + "\" id=\"DejaVuSans-105\"/>\n", " \n", + "\" id=\"DejaVuSans-99\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" @@ -1884,7 +1884,9 @@ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1956,13 +1958,20 @@ " * Suggest a stochastic gradient descent algorithm to solve this problem. What could possibly go wrong (hint - what happens near the stationary point as we keep on updating the parameters). Can you fix this?\n", "4. Compare the runtime of the two methods of adding two vectors using other packages (such as NumPy) or other programming languages (such as MATLAB).\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.6.13", "language": "python", - "name": "python3" + "name": "dlwp" }, "language_info": { "codemirror_mode": { @@ -1974,9 +1983,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.13" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch05_Linear_Neural_Networks/Linear_Regression_Implementation_from_Scratch.ipynb b/Ch05_Linear_Neural_Networks/Linear_Regression_Implementation_from_Scratch.ipynb index 5a9dfd6b..1775ed8b 100644 --- a/Ch05_Linear_Neural_Networks/Linear_Regression_Implementation_from_Scratch.ipynb +++ b/Ch05_Linear_Neural_Networks/Linear_Regression_Implementation_from_Scratch.ipynb @@ -120,7 +120,7 @@ { "data": { "text/plain": [ - "(tensor([-0.8439, 1.2269]), tensor(-1.6509))" + "(tensor([-0.6577, -0.2888]), tensor(3.8533))" ] }, "execution_count": 3, @@ -153,1399 +153,1385 @@ { "data": { "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - "\r\n" + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" ], "text/plain": [ "
" @@ -1638,17 +1624,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor([[-0.3725, -1.6199],\n", - " [-0.8225, -0.2839],\n", - " [-2.6906, 1.3957],\n", - " [-0.2195, -1.0764],\n", - " [ 1.1222, 0.8164],\n", - " [ 0.1019, -0.2203],\n", - " [ 1.6402, -1.0310],\n", - " [-0.1835, -2.4342],\n", - " [ 0.4017, 0.0739],\n", - " [-0.8439, 1.2269]]) tensor([ 8.9629, 3.5227, -5.9169, 7.4336, 3.6522, 5.1387, 11.0027, 12.1090,\n", - " 4.7536, -1.6509])\n" + "tensor([[-0.7912, 1.0422],\n", + " [-0.2270, -1.4380],\n", + " [ 0.3475, 0.4276],\n", + " [ 1.2977, 0.0186],\n", + " [-0.7807, -1.0604],\n", + " [-1.0798, -0.8918],\n", + " [-0.2583, -0.0752],\n", + " [ 2.6692, 0.1933],\n", + " [ 1.0112, -0.9083],\n", + " [-0.9813, 0.0336]]) tensor([-0.9118, 8.6427, 3.4704, 6.7263, 6.2441, 5.0800, 3.9327, 8.8959,\n", + " 9.3030, 2.1161])\n" ] } ], @@ -1927,9 +1913,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch 1, loss 8.885523\n", - "epoch 2, loss 5.022894\n", - "epoch 3, loss 2.841459\n" + "epoch 1, loss 9.185371\n", + "epoch 2, loss 4.992885\n", + "epoch 3, loss 2.714041\n" ] } ], @@ -1976,8 +1962,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Error in estimating w tensor([ 0.9384, -1.4121], grad_fn=)\n", - "Error in estimating b tensor([1.7719], grad_fn=)\n" + "Error in estimating w tensor([ 0.8176, -1.3684], grad_fn=)\n", + "Error in estimating b tensor([1.6768], grad_fn=)\n" ] } ], @@ -2026,13 +2012,20 @@ "1. Experiment using different learning rates to find out how fast the loss function value drops.\n", "1. If the number of examples cannot be divided by the batch size, what happens to the `data_iter` function's behavior?" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -2044,9 +2037,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch05_Linear_Neural_Networks/Softmax_Regression.ipynb b/Ch05_Linear_Neural_Networks/Softmax_Regression.ipynb index 80767c11..eed9cb9e 100644 --- a/Ch05_Linear_Neural_Networks/Softmax_Regression.ipynb +++ b/Ch05_Linear_Neural_Networks/Softmax_Regression.ipynb @@ -450,13 +450,34 @@ " * What does the soft-min look like?\n", " * Extend this to more than two numbers." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -468,9 +489,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch06_Multilayer_Perceptrons/Concise_Implementation_of_Multilayer_Perceptron.ipynb b/Ch06_Multilayer_Perceptrons/Concise_Implementation_of_Multilayer_Perceptron.ipynb index 2f472182..fc80433d 100644 --- a/Ch06_Multilayer_Perceptrons/Concise_Implementation_of_Multilayer_Perceptron.ipynb +++ b/Ch06_Multilayer_Perceptrons/Concise_Implementation_of_Multilayer_Perceptron.ipynb @@ -104,16 +104,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch 1, loss 0.0030, train acc 0.718, test acc 0.673\n", - "epoch 2, loss 0.0019, train acc 0.818, test acc 0.834\n", - "epoch 3, loss 0.0016, train acc 0.844, test acc 0.832\n", - "epoch 4, loss 0.0015, train acc 0.857, test acc 0.838\n", - "epoch 5, loss 0.0014, train acc 0.865, test acc 0.786\n", - "epoch 6, loss 0.0014, train acc 0.870, test acc 0.847\n", - "epoch 7, loss 0.0013, train acc 0.878, test acc 0.856\n", - "epoch 8, loss 0.0013, train acc 0.882, test acc 0.859\n", - "epoch 9, loss 0.0012, train acc 0.884, test acc 0.834\n", - "epoch 10, loss 0.0012, train acc 0.889, test acc 0.860\n" + "epoch 1, loss 0.0030, train acc 0.712, test acc 0.792\n", + "epoch 2, loss 0.0019, train acc 0.821, test acc 0.828\n", + "epoch 3, loss 0.0017, train acc 0.843, test acc 0.773\n", + "epoch 4, loss 0.0016, train acc 0.853, test acc 0.843\n", + "epoch 5, loss 0.0014, train acc 0.864, test acc 0.850\n", + "epoch 6, loss 0.0014, train acc 0.871, test acc 0.839\n", + "epoch 7, loss 0.0013, train acc 0.877, test acc 0.843\n", + "epoch 8, loss 0.0013, train acc 0.881, test acc 0.838\n", + "epoch 9, loss 0.0012, train acc 0.885, test acc 0.870\n", + "epoch 10, loss 0.0012, train acc 0.890, test acc 0.864\n" ] } ], @@ -157,9 +157,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -171,9 +171,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch06_Multilayer_Perceptrons/Considering_The_Environment.ipynb b/Ch06_Multilayer_Perceptrons/Considering_The_Environment.ipynb index ed28450c..cbbfdc54 100644 --- a/Ch06_Multilayer_Perceptrons/Considering_The_Environment.ipynb +++ b/Ch06_Multilayer_Perceptrons/Considering_The_Environment.ipynb @@ -121,12 +121,14 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs8AAADACAYAAAD7u45LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsvXmwZMl13vc7mXlvVb2l11m7p2cDMFiInQBkkiIo0jQpUSHuC2SKokSJthQUaYeDVtgMKmyFHKJFh/+wLVFeZIcdsiTS3EWRFm2KMmWJC0AKKwmAwAxmMAtm6Z7ufltV3ZuZx39k5r236tXraVBEd8O4X0RHv6q6S97MvJnf+c7Jk6KqjBgxYsSIESNGjBgx4pVhbncBRowYMWLEiBEjRoz4fMFInkeMGDFixIgRI0aMuEmM5HnEiBEjRowYMWLEiJvESJ5HjBgxYsSIESNGjLhJjOR5xIgRI0aMGDFixIibxEieR4wYMWLEiBEjRoy4SYzkecSIESNGjNgAEflfReS/uN3lGDHis8XYdz+3GMnz/w8hIk+KyFff7nKMGPHZYuy7I0aMGDHiTsdInkeMGDFixIgRI0aMuEmM5PkOh4hcEpGfEZGXROSKiPxtEXmViPxq/nxZRP6BiJzJx/994EHgF0TkQET+6u19ghFfqBj77ojPN4jI20TkX4vIvoj8BDAd/Pa9IvJJEXlZRP6xiFwY/PY1IvJxEbkuIj8mIr8mIn/xtjzEiC9IjH331mIkz3cwRMQC/wR4CngYuAj8OCDAjwAXgNcDl4D/HEBVvwv4NPCnVHVHVX/0lhd8xBc8xr474vMNIlIDPwf8feAc8JPAt+TfvorUb78duJ/Ur388/3YX8FPAfwqcBz4OfOktLv6IL2CMfffWQ1T1dpdhxAkQkS8B/jFwv6r6Gxz3jcB/pqpvy5+fBP6iqv7KLSnoiBFrGPvuiM83iMi7SaTiouaJUUR+HfhVEum4oqp/NX+/A1wFXgO8G/jLqvol+TchGYF/XVX/3i1/kBFfcBj77q2Hu90FGHFDXAKeWicfInIP8N8CXw7skjwIV2998UaMOBFj3x3x+YYLwLO6qig9NfjtX5cvVfVARK6QPCoXgKcHv6mIPHMLyjtiRMHYd28xxrCNOxtPAw+KyLqR8yOAAm9W1VPAnyG5wwtGd8KI242x7474fMNngItZfSt4MP//HPBQ+VJEtklu7mfzeQ8MfpPh5xEjbgHGvnuLMZLnOxvvJXXu/1JEtkVkKiJfRlLsDoBrInIR+I/XznsBePTWFnXEiBWMfXfE5xt+A/DAD4iIE5FvBt6Vf/uHwJ8XkbeKyAT4m8BvqeqTwC8CbxKRb8zG4vcB99364o/4AsbYd28xRvJ8B0NVA/CngFeT4pCeAb4D+OvA24HrpM7/M2un/gjwwyJyTUR+8NaVeMSIhLHvjvh8g6o2wDcDf44USvQd5P6pqv8M+GvAT5OMwlcB78m/XQa+DfhR4ArwBuC3geUtfYARX7AY++6tx7hgcMSIESNGjPhDgogYkrH4nar6z293eUaMuFmMfffmMSrPI0aMGDFixL8BRORrReRMdov/ECmO/zdvc7FGjHhFjH33D4aRPI8YMWLEiBH/ZvgS4HHgMilc6RtVdX57izRixE1h7Lt/AIxhGyNGjBgxYsSIESNG3CRG5XnEiBEjRowYMWLEiJvEHb1JijFG5bMUxg2KCOQ9dvK36SJRwOhxi0FEEFUiwwqRQcJZRfJnyfco50XV9MmAieloK0LIir4px+W/Nd/HACFfz4p035d7REAERMEKGISIYjCoKjY/mxohaARNZexKLHT3KYiD+5c6EknXo3umdM+I4G+QcleP1TGIKAGwlGswaIv+2n09pmPSP8GWxtZ8jXztgObjhyks6X5bLxf5/prvt1Q9fuLnGPeZU1rqejpteewxx8uXhWYpHC6Uw8PULwzS9RFVxQDGetQpJjfezAm1MdSAMxHV1DZBDE1UmjZiTGpb52ogohaiKqJCCErbKOojVBZXg7GKUc+scsQ2YtUiUfAa8QFqqVj4ABIxxhC8oiLEmHqtj1A7gwZQiQSvBBTjLMHEdO+SclQVGw3RSN+PbXo+V6W3qQmRSSXsnhYQT/RKVVUcHiohBKrKMN1SrBU0pn1X2mXN/nXFh/T9ZKJMphBCoPWWxVF6dgArhhgjihBj7Oobcl8URzVVprVy7/0w227BQqMVL7/kObxmaeeWo6MlzjnOnRF2tgLYyDLA/Mhx1CizCUwmMKkFiUqz8NBYFq1hqYYQArYSJk4wNuBqixCxNn1uvcXWgaqyxAi/+buHt7zvPrq7lV+qmJtPcWK6OlNVjDEoMY1P1jL0YBpjMApitHzR9fFhGlpRwNzE46nhJA9pKVO5bilf+U0k9RdjzMo5w79XU+P2Y0gM4DWybFraGLrrlv+NWR2Lh+Up9yufrbXEmN+l6NO7b/o6FS3zRJk35Fh5ynPGMJiZ8vOuj4PD44d1MyzX8LiT6nW9blfum+el4THD8n7y6q3tu9ePmpUHWe8b0NeJaszfn5RiXmAwz78SzHofWrvyOtbLdbPY9DyU58zfxcFjDOfBlesMDzZr7+T6MRuuE9GN717iUrlcZvXvk44t90rz31o/ivk+cvycxIduXIfr48JwHOjGifJVfuCdmbuphrmjyfM61ongSVAtRHm1x4jkDj1oMKvphEhpwEFHyodGEaxq18eiJKLcddjCdklEePj/sOyiiTCXiYRBZxIRbD42qA6Ic7lOPt4IZvBYJipGDH5QI2VwsB3hLgOopmcp90ERjavdT3tibJF0zIbBs9QnlIEo193g7RsS53T+YHha+80ATgXNhkqq0lR2NzBkSlk7Ym+E1UE937cf+24LVPsynD8jvOVh4ffjAdPplE8+YTg4qlLrFOOLPJEP/laNqESim9JG8G2LswZjeqPHSsBUVT4JguRrBiG0IdWhd4gxRBHEJEJQWwtaM/fKlCpN2iYSYsDMHG1osBMhBosPAVwiztZa2hCpjaVtPNYYQtCO+GuIGCMYa3I9KFGV6Ax4CAJODUE8YPBtKrqR1NfbeYutDKqGo4NAu4S6BmcTwfANtG0NEjmaKyGa1A/FI+LwPuBbaBpLDIpINiFN6acWm0mPIGgERBFRrAE3MSybiJtULI8i1697lnPH0UFEvYImQ2JvaQjqELEsWk/whukkMJlC5QQfIst5QILDquAqg0SYqyEGw5LA1FpQZWJrYkjt1iwjE6PYWQR/y3lzRuwIMwCZ6KkqYsCI6SZF66o8dg3GTXN8YuzHoHR9ERnMWhuw8l7LsUl3iG4MNclYLKOEqkKMYAXVnux2t0A6ljM0AMr1IhGJlmBDRxRCCMiAgHpiGq/yu5WIsMnvsua+KWgMiWBpxJk8AmsqaTqyMIJEtEu9rRsDGjPJjpHh3OFYrZ/UVoN+roqIAe0Fh011uV4HQyNzpV3W2nxIbm4XNhLLwf9D3LCcEkFvzilfrqL5PkMSPZyzhmVYN2g2k/vN8+5Jhszq3xuezfTGUCqn6b7v7qtJjDv2jCJozGZd6Rf0/WMdMXfs9N6X/r3hmoO+WX4fPj8b6mX4t0E2GgZDrBv1JxnhmjnXZ4M7mjyvV0oErGhHwkoDmk0sqWNP6X8pVnJn2SciW9TZRG7lWLcTBmp1UVAHk4JoIuAnvYwrlpXQK8aqDO2bYeewWanWwSCnRqiw6MCsLP1fY69+xGLB5R8L0Y6D5y4wg864Yoxobzik506dPJSfZfhscWCjK5ZMYBmS46K89x20KNBQFOnVlz6SFfKVYqVjzICVx0F92DxhhcG93aaB5FbAQNRUZ3vXLC9cd/zxd9W89pF7+D9+8QWef68QNRE/IwYxIfUnAazgnCTLXRwhRmJWJ8UKmgeBibUsIokAAoik+hCDDx4RC15pomJEkVoQURBFI7TZUrIquKhYZxCnmElStxWg9Vip8D5gQoUuQvLSSCLSGmMi0DGCTboyA7UBBKIm4mrS+xU0kbMY831UsZUlaKQSh/cxKeUYVJQQAxNT0baeZhlRLOodIWTSIkXZ88RoaL1luWxBBWMF55LqZ23VEY8VQqLQ+JbJVk3whqPDlsZDswzM55b5QgltIVcGVwmhCRxFQ/SBoIq1HuuE6TT5U5YH0LaWGCwTASTVkzGG1kdUDQs8REcbF1RURANEQ6gizgp2aCXfQjgxiFklT4JirUmKk4IzhoDpFGlIk1MyCIFMVteRJj3XTWSFdB2D9uO7DuohjStxZbwtZERUVsZMEcE6SwiJuGpUnLXd8TFGnHOdd8UkqzT3zeTBEInU0eFNTJ+z9FDIUm0dZfQUBWtsV0ciq+rzSn2epOwaIcSiMPfqqOTyQxrjO1V9SDby9QPajfNDEtyR7kHbdvcdHF+MhPXyppv3CmdPeY7f6ySS8rnEzRD3XnU2A8K0VtYTiPOwHsqc1zGMbj6KXb8/VgfFKCvXUT123eHndc/BsfLQt4lKeYo1Y2hggHXvyVq7d58Hk/awDTWLdl27r523qcxduXIdMeBIpY5Ekuce6e9XbN90TjJGyv37ahyQ6Eygh78Vw/6YMSclKqEoW4NKGowbN4s7mjxvgmoiYvYVjjMYIhFEOwJ5UsUMwxuCJMI57IIigtNe7Ry62ZNiLR3RXO9ExwMG+o7nSNQzauxVkHJPUseRMonEZImJoVMfio4thk6JMFq0jOSCTx20p6HDl2j4EpTnK+qnqhKGysJgQI4bqtEqHbnuB+6ikgyeS1cHnXLuutmSS3HMNRNIFzC5rcj9waHJi0B6IfNPx8bFW4WiAAiR60u4/NIhb/pKRXiZs+c9D91lePLFRPyURG4nTsBFMIK1BpVkQolIIlVRkJiIqkPYCy3O1Who8QYqsUQjJO+JoW1TiIWpUv8xqkid3pwQAupTuNIiK4EVkcnU4qxinFBVhqN5RIPHWUuzjEgFeV4lxtgNtkmBjhibVXGUEJWgmklVIl6l8xi1g8E+EIPSqiALxVjBe0sblMqCqYqCVoFGvBeaALUBUyuqyQ2uGLxX2qVJRkLuN03TUNc13vuufQSTVcQ0MFemollGYiAR5XkkhprFIoXVhBiQKJjaYEzEVBB8JjiqiLMYFA3JOPAxqd9tG2msMHEG8R6hSoquNYDS+mT2ti200WCsZWYti8OGyfT2LElxtid9iWDabnyxSDLuBSrNg2Vx4RLQqCvkwUpR/fvJzIeAM5ImySEh2UASUviXrqqta8d35F0ENYNxPqZ7VlWVwmWsTaFhtoRMpHehqqZMJhPqyQRbOZrFEu89TdPQtm0yYptCJrPhh3ZjlBEQTX2+lHGTAnrS5/IMyXuTxq9I7MSCdE4fypHmn/wvG6hx7doisqK0dd+7ZPCu33sdm8Jcbqgsr319OxTom1W+V4/pBbZXPnbzWSvGQiaEIrKqQuuqB6FcA46HfGwysIbtEU/gMetGy6Z+dqPvhu28blAVwnsScb6ZPt4pxsUjssEwWCfgHbkeXkfowjmOIa56cjfdvxikK2WVm+8/BXc0ed4U/1W+LwXvY29hYMwBYUUtVj0e9iHdeanDW2Bd7CmV2UoiZ8NY5DA8TrV7kaB3Eaw3iGi2/qyhmu1Cu6BZLBIBH3Q4kwmwdmqtEDVgMd3AqZqVoBI5Oyi7alajlS5OOlHofN3YE8w08fWkFFKoyjE1uDc9KVVbvuritYcGgNKVv8RBlzjnIbEdGkMlZAOSSj9UNwTJ6nVnmsKA1Le9Ld7df1Ms4K3Aa98859GHTvOB31zw/IsWv3D89u9N+I1PB2q2WDapDZ1L7e5qpZoYkKTmeQl475lNakRDUt+sY94sERxeQcRxNE9xDyoRbwUxoUzpWGvxrVJVjqAexBBzfDSxgqz+xhioa4epHGIDZuKpa4dIZMdNODxogJaqNrRR0nVj7Fzd6RoRMYoxFhXtBiQnghhD27bY3Moxeqht6hvGpL4YAs2BonVuzRiJIaB1RfAQ2qQ4hmhom8h04hDj8SER4xACMVqCWtQoEgwIxBgwxtB4jxXBOYf3nhgSgSiKo4jifSq395GYw7IIgg+KqiBGsA7sxGBdMm6S4F5RW7DiWTYe7yPLeUUTYip3UKLAxFZYk4iRV3C45CGIjqPFAmsq4tJTVZbDa+CnNwpO+9xhOFEbYzriGWPslObUrkJUn0YfcYTQJoJqbVZt8zgTE0FIRm3Emp7umS6cJvfafO/YubcGpHFDvO66mlY8k+m3dIEYQ4rPXyMEVVWxvbvFzu5pzpw5xe7uLrOtKW3bsnd9n+vXr3PlyhXk4AhoiG1PFMqYmsizXZt8kzejqFxD8rMuXqw/i00PjA5FhiKiDFTPcj/oh/3u+UJEjcF7v0JUUn8eiCA34Akxt4WqrsyTQ4j0Mdq3gyyv40Zl6IllT3t7Ikj33TqGZHGjcUdPZksfXz9vE+Fc/7xudKV3JxlHHamNIOXdUdt7bWS1rMPrrT/H+rO90jHrx5W+vP4uDf/e5Mm40d/l83p9rL/vK8eb1VCQoXJ9Upy3YDpvjjG9B1JM5muFxNwk7mjyXAYN6JXGziVVxgl6S7wcB6uqfKnMY94Zkls/6Oqij8Hls+KVUKayoH2oiOZj3IYGZjhoZRgxnNnZwp2+m3d+1R/jMx97nI+899eTICdhtfBZSRUhx2YOBt08uBqxdEsNb7AuTliNqV55Vk1xqbbrgH0cUseXSYaFZoLauVTRFTJsyYpwue9anfex5f21UwhNf6AdEOjhfdKx2a1YXKP0xk8xBmziPCuhIbca7/iy01zaXbA8jFz+l8JHn7R88vkl02nEOthbGkxliRJSHLMNVE6pjaFlzsxWRGuxdolYIFjUt7hK8A0cRk/rwUhFJEBMJDXGFtQgSYJDCKTY6UKEFDDEENJxBlxdUU8Aaalm+R2yKa7Vt02OBRaCAReFoyODMxCCT9cY9PE2KNaYFB9tItFagvdUxvbhElUOgdCAamrvKMrEOsju8phN0xAiYQ5tY1DxQMAIuCopzlPniCFgnGMxT0S+dpYgISnwMS0UFAxRIt43aDRdbJ+PEWehqixOLU277OJzowiqHmMmeZJqsLVlVkOkJViDOMH7hkmskrofhGYOPlpwivURL5EdI1RVi6mFOkLrFWKk8QEV0w1gqsp8DsvKELW61d0WSO9T27YYkwyw0nZmEKOcPgtGLDIg2N77fuKOijOp3g2CywTWWIuG5GERpIvNhaEaV1QP04eJbSAf6V8WGAYTe4wRNSWcI30fY0RcIveTWc3u2XNcfOA+7r/nPOfvPcvWzjY7OzuIwP71Pa68cI3PfOYlPvXUp3n+M5fRowVp1/lk5IYQQLMXR5MhMCxnCQsJwa+oiMlDCNY6QhZ5RCQtyiUrbUXtk2xcDsh3ETxQOgW8U65FwFpibqtIxFgLJQSkzEn05H5Yn+X/lXnMHCf6hbQdD3Tc3Fa3C+v1XtbngOT2+oPPEMWw6Rb8l/pnjZhK780dKqrd3DpQY08i2iV0If1ukoEV1wMbV9tz/e+V+2dyOiSpN3zWYiBsiBtev8+6cn6jsq0fX7DuNXkl75SWMccMQrcEIK2DSGEtq8/fX6sXe9BXimnocUeT500wArImyBQluP8sQwk6faerGS6gVztP6jpl8Vq+wsDKTt7noQq9cu/ujDTADQcYK/BVX/cn2V+2/NG3fzG7f+JP8Ne+/2Ncvvwi05wRoG/TobVXSPJ6GXO8HWZFjTiGzoLo467LYsAyEZbyRz0p3njtkvRKcRgOBgODp6BfOKApAoViHPXuz+G9kspMV86Qj6hI4SU6uL6u3TsMmv92DeFPfXKPJxth0RrO7grXLxtsHTj3YMVkFtCnA4dXBBMEsiIqEvEozpkU91wJ4Nh2jtY3qDUcLpTWpJg9fEy9MgDG0PqANbZrUyFdV0OEGPE5vCFm1bgNLZUT6glYq9Qzg3WKE0cI2QBxgrYBEyLOCK1GXG1pG9DYLyK01hJiCxIzkbBEdeBjUilFiF6xLoWWlPYusdUSU7hDt/o7Ks452mWTlOFo8KpY55IC3rbYusIT0rvhBfUQvEecdC2/4nImx4SrJrefAWctBvBLT4y9Eh1CQCJIdGhM2UnIZLKeQBtCel+D4BuPmhwn2wbEOJwmku+nMIsON/G42qf2UMVEofFQO8uy9dSVo23SgkbfgqhwsAwbetbnHlGhqmq8b5NXgdW1GkOVSFWTceQcMWeR6H4vE67SLSAVSX4ycWnqKcR4XZkdqqLdmKZmdfI0eS1LVsOLS3g4MYYQuu/IZZhOp5y/5xyPPHKJRx59iHvuv5sz584ym826EI9wf2D/whH3XHyR3bNn2N5+nCc/9QwhtHjfJEJMIWd0oS2pr6bJ2AIyiO8k1+Nqn9SOIBspapoeU96GRMLIUEEz3ZqHsgYhZULRlXNsIUiFTJqTaG+p29X35yTD5SScOA/dAgzV0eOErR8bbgYnuf7Lb8WjWwyXLqRC1zwO6YREsE12LXTjdE8Wh16Jk55FhveBDc94vD7Wf7vRc92oHm6mXTfd75Wue5InZhOBPna/9CJmZ/Tme65kEdHj6nl/jyLHvjLuaPJs87N166G0r4SiXm6KTx7K99ATN4AyPJWQgGF2h2FVDtVVym+SFxdqctHY7rdN4RmlPBbVgCUtXjq1PeX+N7yV+wl80TvfztWr1/nvf/YX+OPv/kre+cVv4YO/81sYTQSxj62mUySOdcgNavNG18km8lte5kzyIREmmye+fjGPpAFbY0eAV65DUvA9fRq+Mtn6XO6Yn8Eh2KxpF0JT2ihIH/+suc5jmYOFjqSXuw9j1Ut7lzlbDX28+G1Au3Q8+3hS2A6vtlA5Lj6sPPYmaJeeMLc0+0JsUxy7tRbnDFKlFG7epzCOrVoQ8ZiFY+EjrhJMBZMArRg8Qh2E6APR5kV2KtiYFxblwVnzoBw0YtUQFerapPqlxdYWVyUysmwjoWmJbZ3CN7JyZkJSbIPAUdsgOLAGJSBiqCrXKclpUklvblAlxEhlLW1MmrIxgojBKTkNWPIqBI0QQBFCE7FapbSJ3mPscNGKEJcKxtKGkpIuLaoc9n1ncgiBS+pbcs0VFa6P5RNJI4LXmFxLeYCRaPNEmO8cI4t5WoDW+oD6tFgxLQiOYGosymSaYrCbdoFzhroStnemLJctbQNtFOrK4Bdp0dlyucSYmhgNqh4rFsVzOxDzyvpC+kwXdiPUVdV5EKxzyRATQTV0xxbvUCK3ihRySyGXMS1+syaR7tCHCUAac4aevLiSAWBIXnrSvE7mjDGoD1TG4jViVFARtrdnXLh0gde+/mEefuQSDzx0gd3TO0ynW7h6mjJdxCWL+ZzJ1ozdc2c4dfYcZ86eYufULh//+Cd48TNXaJqmq6vKJF9ZMVzLAsc+ThxiSAtGixs+9b+8ZqV71jSiGU3vqwpo0G6QKx6RISmTMinRq5OpbtL71RseaS1ElNi7admgMrNxStms+A3OXSc/t1N5Xn2mQpjXDOnu7/L95vlxE6ErKKLNULzpCLT0Mc8mahIickniIOZ8SJaH5VPI2Vv645WsnMfSRqvEs3vmqEQr1GL53Y99mKNr17h2fZ8v/4qvYjKb4r0/1m4rz7RBOb5Rpoo/DKwT7hsZASeS6FKHg/R2RXVeOU97JR5TFuPyWSttdzR5LuizaZTO0rvmh3Vc3GFKH49c8giXWNkS0xxIqZhutkMIfeyXkgiB3dDp11UX1ZCIAmCrmnd9zTfw+ocv0jZHPP/0ExzsXWdy/Tn+yc/9DPvzQ77nPd/CjLQASxi44uiV2/VyMaD+ieTnVaX5He1cEqVOBn+vq/S28AZW63aFjK+7im5Yb8nYKH0zpRCUzmjpjZehCl2u22dW6SZkeoIMq7mkBbpQDtXe23A7cDRvODh0hKVBKkc1jTz62hnbdykHnwnsX1WaeUrrZq0haiJuToobv8U5JRpLM/eIRKS2GAlZyYWqVqw45keephFiFEQNjSrBBAw2LewyDkLOl2wt6gTnkvo0s5Ht7QpMwDcxKcOAypQgkUXTUtc1eE/bBAiG1htELdGkiUFsKk+UiHWCcUCI2GjRKGjwSWURgVCMnNQyaQGoTZN7FGxOQaNRUIRoFMlqdEr6roTQImJTtpw2pZBL76BJsWw5dZ6YpDxasQQfunRLtuTXZTgpCMF7TDYwUkYX17/bacQleEHEgrYoDjUGlxd3ttEmQzS/d8bCrDJMpkLlUvyv95HoLcQWsLgKXLSoq6BVWvUp97XenDv1cwFrkpdNgibXdkixzs4OFg5aoaScK4szDYk8E31Wmm1HpgFEHCjkTGzJoxQCRhzOlPc4iwSDcWB9rlwnzEMCUohGUfSG57iq4vzd53nd61/FG9/8Gu6+7zxnz59jsjXr6tpaS2wrYlgiErEauP/+u6nqGldPcc6yPPoQ16/v07Ztnni1u7e1tutftlOOFckeBxFJ4R7iutjw8r21lqglLC/Fyas9nrfaZMK3nnJuheSth1oUMWnDnHeMLG2o75OwTiw3kfHbhfRMm75b71ub6+Om7gFdRoiOaA4+iySxwWy45kmKcEeeB4bIpkWCm0IjIBmiy2XDN37jN7FYLJlUFbNqwt/6kf+Ke++7i//xf/qfmWxvr5RxUzk+m/p4JeX6DwPr5TxmyJTPRjaSkk3PtyKYfpbl/zwgz2kQWTeHIykvMPTxst2iPcpit/xyFMKsmjNcJDQaqQcqJ7BC3brzSQtYZGVI7wl0gUiO0daiaKfJ2RkDUXnt297KV3/1l1JVwrVrR5w9dw+6pcTlgt976n2864++m7/x3/wdfvg/+D7OiiUYTSpYN0iWEmoXQjIsK7CSxm1lhxld7WjlqxICs6IilPFTYDXvdSHxq9jU5YbpkjSXqSxg7Evdk+WiSheloLTjepz2kISXRYgU4lwm0BPKdCtx16UdxBxw7bJltgPn7l1wsDenbR0vPRt5+SUHwa6we2OzUhsCdV3TzgPBRIJOMLSIC0ynKQvG1sywmDdpoIwNdVXTLAw+8VRmVCw14CxE32Ktw6hBrUGMokaZ1AFrIISWiuQdOLweMM6mfp8V4Ljw1E5wdU0TIpV9+5A2AAAgAElEQVSDpSgSE+MJebJ3dYrzdVEQ61jGBqUCtYjk9rcGiSkURJxNmwxpUqIjkehTuAb4FC4hNhtRQgw+GRvGoCHStgFjXE5XqIhEMKYLH0jWdXp3jVgayR4RFYKGlJpPI0KK1ZWcCtJIMlxjIF8rGQGI4H3OtiA2GcVRUjgMmbwHQYwSFp7JRJidNkzqrIznhZTWClSGWW3wTUvbZKUqWByRyoKGpNTeDhSvkTjTiRFVCbPQnBaQkHLd57CDLgVa3pBkRQ3O7/yQpInkBYcxL2DN9+zyLQ9indnk2StEcm1x4zArSKR3ndfOsn1qh0df/RCve+whLj5wL2fOn2Wys5My1qiiORRCxSfvmUba0OAEzp87BTxMjJ69a3s88cSTHOwf0S4SgR4SpmRYZDHHpDF0GLpRlPtheEkZe60k9drnlDYxxmMhLGWsk8F5QVevZdbSrYkk6dOqEG0SVoz2QsqK+1wG99qgvr4SbpfqfFIfOcn1P1RsN5V5UzjEpuNW4pfXzishkYUMd2tz1q7RzV2Da3XHrhDXVSV1+GzlOWKMfOe3vIfQBiqbvIFtCNhJzcFBw3d867fx7X/62/nT3/Xd3aL+9XJsqteN5PMV1OAb4ZX61frvJxkf6/cvxgdKxyM6czwr0SUXdcfZpF9cfrP4PCDPJ8PnldXrj1vSqg0zaiT1kpXpSDb14u63dPT6pimwugHKitU/yOE2tDStGKbnz/Jnv/s7uXDxAXa2dlksUhok7yOzeosL99yLCUv+nXd/OQ/93C/woz/8Q3zmicdZzhcr9+nLdyNrMEUpduQVyYpDn7uzhIWU+lqvh7JLYglzKZHVw3uvWMnpobuck/0uQKxsVjMcZIboA2eOv0xeetXfwcouhl0sZvYMeI4rVbcDZ+7yvOUd9/HkE/t4jbzj7Q/x+Mev8sRHWz75MU+cu5SKMKbd9zREJKSAlug9i1ZwxrFsI2rnnNqZYeqW7S1DPUuZJkxtaNqWnWhZzD3gEJ9IXyBShaT2CRFMJAShCS3Opvj52jpclUJB4gKIiUy3rRBjS1XVNE2DIcUBq0SMDUwqQU/D/MDQaMAYCC7tCWlzrHL0ETSpba5K8cpGyZkWlKhJbU8LpwSvKRQiiOJjWqyW0jImF6PEVIaUbs4lkhEii+USYxzGZvVOgZBIfYrdl7y7oCJie6U5Tzaxe38NaNJIoyqhTaEDKR1i8TKBBE2ZIDKrFJGUeUFIscoCISiuslgLGkJ26QttE3EyRTUw3VastEwnFYf7hnnjEaPZMADn0qLM2wE1KezHWZcMuWE8syqiqe/46Klsem9t9j4YerW+/G9NJpWm9zi4HPJRubQospBJU7wSQpezviRA74hCdrW6bOR05Y6KQ/NCKpNi4TWFl2zvTHnk0Qd47WMPcPHh+zh/7z1MtnewrqabQSYGCZ7ol2mM9Atic4hUgWm1w113nwJ9hIPrV1i2S6699DLL5TKFcETpFk0WolxPXLdgsKoqrLXM53NUYXt7m6OjozQHtLEjPSXvedDYu+vzc/uymDbGlFedfl4wmp5/ZdOLdTVakqfPopBjpcvPK258+r/X/y/GyJ2iLg+x7gEuf5/0e/E4yRoRWA9peCWCuCI8bSCZXb9lswK9Xs4/KFKf9fx7f+57WDZHHBwcUNc1Ozs7OSzMcPXwMmfP3MM/+oc/xU/+xE/xkz//s1j7yjTwJOJ6Uh3dDDG+mee5mXJt4kLH2z8Z8cNsHN3va8b9zeIOJ8+a8pUPTO+yxXZHwjIj7i2M7sxjG4eso4tL3njvTBfLgEEhwv2LljZVkWJu9teVsqo3r8S1hv/oB/9DtiZTnv70pzl36hxnz9zNcrlkZ/sUloZ6+y6uvXyV5eEBb3rdG/jz3/Pd/I3/5IcJeUeqPnOEdtbS5mYuZsKQsCoqIYeBCJDyBEe0i50aDB2JDKAdgVaho6isPmoHj3aWSV+vQpWvLmK6rcyTRtdfsdS/DMpRAjrK9TpCbkyKIcyTx2oqumJdDr+5PYP8uVM7nN1x+Ae3uXbtCBF47PXnODp8gQvPCy891bDYrzAYkEhdKUYiIg4VYb5ImSamU6jFsZgfcn6nAvGpfWygWXhUKtxEmUmKdY4LpaoMi4XHuAoJAamEGBxel1SuIsRA5QxtaKjqCWHpcxpxm/Ikt0lJC8Yn5T8q8+WC6bRmulMTYoN4TTG+R1USeH2LF4MxgUYM7ULRoClbBgGM5MW6eWOY0r+y68A5m3KUS3HlF3UgvdXiIMY+RViIHlWozTRdI3oQaGPKAJIm+OQWTxKbYiwETeqCFYf6hiSeRzSaFO4h5PCD5I4XzSFQqgQtfReIKYZWkPS9Ly55TYsIEbTyRG/RVmi0ZTlPeaErC7NJhWpKE5jyelu8JAKkKD74lJ/7NqAyNuUZ1pQjucQqp01E+vepdn16urJNd8zE1Zmys19a7Z4WxK6SsfQ3FAJTSFlRkIsKLZoWY65MpmXgyPGN1qUY9C5uOkpHSK21nLv7HK969YNceugBzp49y2x7F1tPwFS9iGDT6hg3mdDMhdgsiT5Q2YAzCq7i3LlTvO51j7G7u8uLz77A9b09Dg/nqfxVTW1TOIZBmcymzHa2U3YPZzk6OmI+X1JZx3R3m4ODI5r5gr1r11m2DYeHhxBhsVjQtmmxJtJnMFn6lsUybcxT4laH5KXLniCDcbT8sbIAcGCEcJzodGPzhu53IyW6G6c/SxLyh42TSNdquUsH2izYlONf6Zr5wEEqxn6viMHJ3Z8n5WhePXwDGQSGm7tsMhCKAfrcc89xcLDfiR9XrlxhMplwdLTg7rvPEfyCgLA93eG73vNt/G8/8VPU1h2Lxb4Zj8ON2vkkI+KV7rFJcb6R4bLpu+E70V1HVv+HVSJtPssgzzucPK8SNUNywZbweaP9osL1KiyLAAFEdSWP8Ia7pN8k3U8yUetIMHlHrVyYOHg5NlmdqkkBsEnw4+/+L/8DhwcLjFScO30GsRZbV5yZzVgsFkSNOHFEE3G25tpLz/PhTzzO1WbBqVzcaJLLbajqlsnhuLV3gr6btt5Z+bjJ/RI1pUgahoaUHNfDq3bn0C/AbA3Y2KebMyizSc1Rc/Lip+56ncK8agQVNUSyUls8CyUl3np0ykr2k9sUwHH+jHLWtJw9s8X/8/g1PqqXedPrzvPaN57m/oeXfPhfLPjQ/2twVYpddjZgXMphHIKhWSqqhlYD9bbl1O4W0zrF+zbNEkONREEc1BWYmYBpQBy+DdQToW18IqESUAdOJoSgBA9gMOJYNj6Ro+jQoCzbNquIQmg8EzOBJlJPLRWeySQwmwLWcfW0cvCycthEDo8MxpcYdUFDJrvGpQwdmhZSaTGLTJqinXMpFrkNNKJYJ4jN5Df0ZMgv06JEJcXMB1UiKT+zhoiRkr0hLcpKoUKD91NI2TSyghxiTOEpJJIcvSK53waTxpbhoB0hhYgED8WzpYGQwxSURNJiDusQl0jUVm2oxeO9I8wh+IidGubLgEYheMeiNSx8oJUUupTeK4uztydsQySRXWPJWU8qYiamMRNpm18yZ2qgn7irrECrJsU/tXUfXlFI8pBID5Wvch3nXJcNSCBtGrRh8i1vd0cay5iUN29xBna2ay4+cDcPP3I/Dzz4ANvnzvbEOW8v3rnNaVAfkicIpV0uiQhut+b06XvY2W04c/+jPLA35+jai1y7+hLXL7+Aq2YsDw9oFXa2ZyyPDjl/z90cHh7ifcs9917gueee4+y507x8+QpnT53mcLFksVhw9fIVds/sJkMvpPptGt/Vwdb2LnvXrvPEJz7J0VHD73/ycfYPFmgTCQpRY17/c5xkdM9VUpNJMpJXNkjgBBIy+PsGWVC7c43IDY+7Hdg0P68S6Js7/5jnd3CFlTANTR6r4dwplHl19X7D/roiAQ2O6+fafKUbqOAiwr/89feyWCywlcNZw97RUSpXTOtrZpNtXr52hZ2tXXa3T/HpZ67z7d/wtfyjn/hFpluTlTJ0Y9+GjX/Wj1n/7ph3eoMyvf4cNzrmZlLqbbrXsAwbPRNDe/wmjIUh7mjynIzklVeYssbYsCrBlw0xuni94XU43umH1yykuRDnPNem/KCDCi/ZL4bE2aDE/H+xYgNpEhSBN73z7Rxevsrk1CmMTcp1ZQVpj6jcDmY2wVMT2iUzU7O1tcW1F57jB37gr/Dur/wafvA735NUtWjSDoBDYqh9iidnLD6GrqMP7enVLXD78qeYH3pygfQZNfJ3JVNJiU9Mz18ycfRDSGecRKHOJDgay7SqmS+PjnkGNrZHl4w7L/TI6npph67eB0PXCWHdtx2xWWIOWnwQZAlHB4b9vYbZTsUDZyb8rt0jWMtsVlFZmNQ2pwWLeHK6OSJVVWFMSot1tO+hIqWMW6YQC2sN8yZk17CCa/GNYl2FDcmSmdQW1DA/8qgHjSalV1MheJhOk9IYyTvCIUSf0ip675m6GkPg1OmK2szZ3U0kajJ1XHNCeFkxVgnLCfgW32rO42vz5kMlq0sisDEq1imd+0QsgkNiSxsVK0r0AZcXVIUQsWIIktYORHGE0GRvRo4NlYjk3NSodkn013WmkLcTR5XQpjJCzpSA0sbUh8PAW1LeEYlgq4qoTVqQrOTsGNrl+E3HW4yJWJdSBC5DJM4NS29olo65BznsJ4MQU95y4wzRKz4mg+j0qdu0YNBlJRjpMkfYyuUc2n34jB1s462alH3JWTQcq8ovFKJsu0mqqqoVYlLGqG7xHpLycceYjZ8+jrrf4CCFPFml27a+XCOFBBkuXLqXhx++xIUHHmDn3F3Mtk+DsRjjUEroR0TbJb5d4tuG6APNfMnVvWucOXMf9dZ5JrNtQqyZbd/HdKdlsePY2amwzT62qnm+OeRtb/gi6rpi79rLBIF6MuOFyy/wyKtfxWx3yukz53nN6+Dlq3vIZ57jwQcvsn/vXSxVOT2bMJlMePbZZzl711kODw85f+/DGFfRLhve+JbXc+X5F6lnUz70oY+xNAtoQb1gtV8sOVTcShovU+oSIBs1RRHt1rJsEFMKOiGqEJShelfO4fh3txvHiRTd/4OjumNvpGQOj9HVA1bqbZ2wbSJ0MIiVFkkpBuX4+qtVci7H3pXhczqED73vd6gqR9s07B3u4UPk9OnTLBYLJlXNweF1Tu3sIqq0zRHb2zOuvHiNP/ud38qP//TPH99Uh35R9bqSfCOFf/1ZN/22/v2mvncSAd507kkq+PrvxwxMLRxI2JQO+CTc0eRZdZiKLLHa9NwpV/D6boDpl76jHVejT4bI6t+ykerlhtByr2xxM1i0KIIjrZbHWr7pPd/EuXvPE0OadKaTKrnkQqDZ3ycawW1tMd2aEpqGo6MjZrunaA6v87Z3vYMH3vQ2nv7d90GTwyzNYPCK2r1pYqUj0+t9qHsJMlvufi7GiWavnsKm1dgFJbtIykiQNcRsuMyMo1HPZFJx+vRpXvf2L+VLvvgd/Nd/62+uZMQoSNtuW9b2acx12EenDwf24QJDYVVhTs/ZXyUZV3Iz4sLnBP/q1/Z48v4dXvMgvOriac5cuJ8HHrjAZ648jb9+lXO7E87d55lUAW0SoWgjhCCE1qTE+SQFLCgcLS2IZxtHKx5Vz2xmOVooy0NLVQuRBqMW6xRtJbk9KmUyU1TTddpYEZZzNFoQg0hg2fhMahJJWi7b7IZPC56MFVQMcemx00h7bcndD2xxsGxTLudKePkK7DUtYidpoVWbJ2eJeLWIERoEg0/xvK4malpsFZcpAt8YR9SAEYOdOmKTXNMWwZf+jkGjYKTOaxgEFU3eGw9SuTzQr6aEslFznH8mdGLQ6HL3S2S5nlmqmBZYauwJH+RxJSjqBOMt0QoxeqydEHXRTS7W5UwZNmdVCDWKY9lEmmVgucjk0IBIQMTSRM/EGsQKiyhYidQTxU1vda/NdTXYDKWQsKqq0Lbp6qOqqm6NRwnXMBbUpLSESEpfKc72W7Rrr2KV+lLVLk44hJBCCXwyhAAQoTKWNobBphb9xN1n1egzlJRcygLsnD7FfRfv5eFHHuSuCxfY2j2TFq2aCkyNiYGonugbYjPHhJbl4TWuv/gMhwdzZm7G2XvvI+27vuTUfQ8TvKGqlHCw5NTMsjh/jk996lO840u/DOeV60f7XHjwIV585tOcvnSeN7zlDXzkAx/iwqWLiDF8+olPwPQc9126yGw249577+WZ556laRZsWWE6c1hb0RzO2Zp6ts/dh3VTmmCw09/ni66+zM5sysd+/1NcfullfAwsFg2yFiM9JELGpDSTKRSkj/eFnjwU7976VszrSB6I4wTnpM+3E+V5Bt+Q1jKtk6gkdm0q+9C7eyPCWGo0jVKAyIpqu05Ah+c6hFaOc5pN3paToKp89KMf5dq160yqGmMttXXs7e1x8eIlmqND7j5zjomreOHlyzz79FM88MiraeMSUcf3/5Xv42//nR/rnnmYOnL9/icR0fLbK/WBdUJ8o+NPIuE3e4/141dIeIpX7FXtz8Lmu6PJc0JupFxJTtNEt2LlrpCyhOE+MSuvSNdgfe7WQtJL53fDhP2Dl2y4ojk1ON2qxLKlh0WwzlDXU3buvodLd9/D1mQ35a8lrd43CrpYsvSeaquGhRBjlRQxETRC2N/jUy++ny/5yq/iY+//Lc5hWTDotErH+FWgbduUs1DpVOLhM5eq1MEzoKRQiU5FTu7qQmdX3EeDlyUMBopJDiX5C3/pe/mVX/9NDvav8i3f+h5e99hr+Nlf+r9o2jmlkEofx5qE7ZQX4yQvwXrbJas8lWPT9hHFMwCrK6BvBz74UUfUyN7hNXZ3Kv7dx1q++MFX8SE8T7/Y8MY3WJ596TmaQ8tLzzXM6gkaIz7CMkCMDarCEQa/EOaLgLUVrg6cdhXGOZzx1Etl/8jiBCI1IpGJc7QRKuuwLmBdYLnwGFthTd6BUASNHkjE1uc606CIccTQ4lxKpaahpXakNgw1Yg1H+3Nmp3bYjxHnA1sTB6dqjg4bQoxMdhyN90S1OE393moqU0RZNkdMXEVsk4LZBBCXXf0aiY3v4mxVFULEx5jCQHyDMzUhxrxdrWAlZ+5oEzmPyCD0ShFrcaqdkRtRjOsnN2uFSJvUU6OoF3xbAsSS0RYR8FllNoLBEbXBYBMRVkm7LiKERoiu4nChiHqa1tE2KcwkqhDz1snRxLQLH4pRxeRNkrZrzZvk3HoYZ1OMeJdLOIKGbgMR5xxETRlLsqqsmmK9xQxSddFPbi6r1CU1WwnNKOeW741Kt5lJUb9RpcqL/wBiHrs1HdSRPkvpP2ncNs5w38XzPPjwQ1x45BG2z9yFVJPc/xUtqfYQnFh8VaMaiIsFe9f2ufjYO5icPkPTthxdu8q5+x/BSEXbXmdawW//3sd57FUP8tgb3szZu84zm53mN379X7A9m/LqS5f4wHPP8W+/5W387oc/wj3nzvDCCy/wyIUHuOvue/HtkhiU/asv8+mrV3nrO/8Iy7bhvb/5Pi48cIm9F57h3NlTtMuG557+JJde/UbO7E6ZuNdwtH+Nhx55mOlOzft+60NcPzxixzma4FPMfSF7ZQt6Skx58gaW+k91mQlm6IldadN1rKjOQ4+fri4ivDOIcynLKsE7yX2/rrKcFK5x7C4DJXq4SUpcI8orBHRwt/K/J/X9FZJHKXv53hKjP1a+8rkV5f57L1DXNUfzOVWVFqzu7u4QfcvZu+/CVhUv7+1x7tw5TOWY711lvn/E9WbB008/ReuPqKtdRPpsLytxwyeQ2BsZUCcdc1J/OUmFLp83EeeT1OqNbTVsm1S9lIQDGm++797h5Dm9xJG0vbDLD2hZfXs1H1k2zYj0IRzramXfYCU/aXrBuvQvFBdKl3i0u89wMxFILkrJRJDiDrKChsik3uL7/8KfYeK2oa4wrU8TpiZjx9bJXVupoD6AeuJ0i9oZ/DIQzYSLd2/z2i9+G3NVFk6RsDZq5ecxYgiiXf5PCcmdWQi7aLaqS+5PpVMOTOzE5/65ukEyra7vOn+up0JSz27vcO1gnze+9Z3ErTP8d3/3x/BBaRdznn36KT7xkQ9kVX4YaJHraUNrC2BJBMdq2Umwbzty2w53dlzPoDJ8aUrqwtuBL3qL5557tnj+mTlXXoYXnvB85sKz1Lbh7lMVz1w/ZPu05bmnG7yvudJE2oUQxaAIRiaEGIiNsEBRK0wQVNOCodB6qCrmRw6dK0eNR+oa53za6COrcOJaJpMZqi34yMw6/ETyFtUlNV5KFWcUQgxorBExND5Q1WAMTGcW5wJHi4a6MojZ5uW9liNfEdSzbJXQNMwkYmYVPrbMphWHfkkbhNpOkCgs5gHBU5leIfaa4pmjgpr0nlmE1g925DOCczUEgzW2n/w1e1xI4RiK4r0n4JIHKPs4Uqq5RE4rcTQawIBRC5a0PfqkStlHLOzveyQoGm23LbWqIsHgQ4TokzqqefvpKPgA1lZJyYwwX4KIRaJh/yh2IQxeobKTtDAypGfAWvyyJUaDcxBUiO3ti3lOYRKZXBqLasSTsmGE1qc80CJEm9YhGJe33KZXon30GE1KcBN8t5iuI84aU+7okNXRnD0jhSz1anWXQcLQKdRYMLkfpPYXyJuUhBConOXUuR0eePgSDz76CKfvvod6axvBYqxLOcmLAhANYiuMKO18H7d9lntefYrpfRdh3jC//gynz9/P7u55AoGjwz0uv/A4Lz31NH/k33oX73vfB3nt61/HJz/xcR589GFsG7mukfsfepQnn/gU9124wD/75f+TUE351O99nGuHc77yq7+Sylo+/JHf4/0f+RAX7r2PrTPn+OZv+nY+9eTHqGzNT/6D/523vvkNTLenvOqhB4l7R2ydusAjr3o1Tz31KR565EHuu+c8l59/iceffpEnnniKw2WTcmyH5P0QNV38vqpgYkr9WvLmJ9GoX+Cu2qdBtWvqczcPsDpfFDLSLfSUYULY24dubgZguB7oxqrp8P+TSNzwHpA8wnnLmxuqsrA+g69eZ/VzapfeEOglo02k0opw/fAAa20K3WhbVBXfeJqmZWt7ht3ahiC0i8BOvQNWuXRxm5cuX+OZ5x7n67/26/n5X/o5prPdJCqazc+wfu91QvtKavWNDKwbGTubvt9UHjjuMRiWcWgQDI3CshPqzeAOJ8/FctZMiCn++xXyVXIbl/jckhmjpDhbf8ihtWkywS492gjJ3UJiubL2Mqw32nonKS7hB1/zKi498iimrpJiE5XQthAiMbTE1jOpaoJGQnOEmVRI22DcNuLyrmUB3v2WN3Df+fvRw8t5F7XjzxIGqTdKJ+gW2g06c6cQDKzctDMaqU7XrLLieuqukQddIaXUevHggO/5S/8+3/xt30xoF8z3Xk4dNsKlR1/Ds5evrVjYiRwPkvtnw2b4VGXIXVeWlUKa18/or72OcML3twJf9PbT7O8LZ/yUJz7e8EN/73He+FvP8fo310y2hI987IDLLym2Msw10s5rlt4TCLicNzf1tZzeTSSlPZM8SfnAYq/h2lXDYs9QzQK70uBmlkoNMVh8G6lsxeKowYfsmnJpMSEmEr1gbEjeEKm7KKAmeJwYrE1KlTUt+8uGiauZbFU06jlcLtlvYf8oYuwWcdGm8As1qG0xtaDSsjWp8K3iYyR4sJOIti73v4hBaGNImToA37TYyiKVMLUOEUvbRmIAgyeYCm0HC1I0hfmoMbTqsc6Cr5iUnQVF0i5TWhRjS5sSSKd+LSUnvFK7lqqK2Bp21HBtqUmJGHS3RlrqSpjY5AZPacpMRyZ9zrphqhROvWhSTm0VSYq+ghMFiaSdGQUNkjZ6sS4p0yjLpbJ3cHtinlUVBhNi27YY6dUjSEoyQGx9ygZEMgBU0/oHZx2thG4jKleEHhGcycpmTjlXVW5lYivGdQknMCVvcjqli79WVSpn0ZjzIefd10qIwj0X7+XSow9x94X72No9i9gJYhx0uxw2aTMUA82iwUhNffYRJndXbB/N+dQHfoPXvOFRDidTXnrpCqcfFvaf/TgvX/4MH37/e/ljX/cn+cAHP8hkVvNLv/xP+Yqv+Aq2p9vs7b9EM1fETTk4OmLr1Bm++uu+kV/91V/l9W9/E4cHe1y9fJUHH3mYy9euc+mhR7h89YDz1YwPfOj9/N+//CucPjPje//y9/Hk4x/jwx9+P1/2ZV/O/sEB01kDzrJTT3j9m9/GbPsM8/2rvPaTH+N3fvsDfOhjT/DS5Wt5V0ZJWWFScyYxSTqHZZfvVjWkOta+bYtxuq4YbiIfQ+Kckz12bXY7kfrUcUX8JCF5fb4chr1sOm5FqIllThve+7Ovg3WSJ53hfjwTxvp5MUZe9/rHeP9v/yuWyyVRU4pE33ouXbqEagUy5fx993Dh7otcfvE5Xrj8AnsHe5w6e4aHH36Ml69c4Zu//hv4hX/6K7mNN6n0m7GJ0Jey3ei4m7neJmX5s/FwDEOR1nnc8DqrIT43xh1OngeqbyGHZMV4UG/FxQFJgS7WtNP+Kj3hGsTZKN1mNBs7RnYJQkpjUjYMGIaJhBL3K72bWET4pm/5BnZ3zlLv7CZ3mZQdpFqcc7Stor7FVA6vgeU8UAssF8ltvmw88eCQ3cmMX/y1f87XvfUtCC02RcJ2scaJEBmkTUu+ijq82tn65+46h6RRtDyTZo0uSh/tnYbBYT7RpOY6Yzlz9jS/+DM/zb5v0PkSa+Do8FqK0W0jk9ByyglXMjmJgxYo8chpM5ZeXU4EWehrPZXLDM4Nql22E80qQvE6aC60khdsbuhRtwpHi4DVbcL8KtcuW56/DPrEIY8/M2d+6NhfRB57veEt75zy/LMLPv7BlnaeFgM2EqkkK5KkRZwT46in0CzgwCpVcOztLWkXFbUDEw3iDXrQEI3DLzxBLDqXvP13RGIiKhIDoDQaURyTKf0xkWQAACAASURBVCnjhQrOVgSfNgbxTUuwSfWumCXF1QZs7aCqkbhA3f/H3ZvHSZbV1b7fvfcZYsrIeajKyho7a+iZnum5aVoQVBBEUa9eARXF1wLiA73iVbmI793rAHy4XAfEifYKCg1eFAFBoBvopqeq6qmqusbOrMqhco6M4Qx77/fHPicyKququ+GjFJ+3+1NdlZERJyLOsM/a67d+a4UszGtWFw3C+vihpCBSwCBD6eLWM8arqVM83yM2CUYrMM59ArlWJvalQiiBbxSJaQEShUIoRawTFArd9ncXLsbYWtDWXRvGnUFrza2iHU2elT3WUgRzn1ttwQc8CIsQFl1ZvxQJbAQ6AW2dz3HRE/iBoOCDFgIvKKATJynRSeqqBpmmWSlJYlJsIknS1Gn8rXaLOiMy/XUGaKSTtmCcRrhpXXXsQox2H2e233zlYTuWs7k8ApH5MnewjFaJdmiKlzV4SiEco2PBaoPn++SyapMRAlJ1LHBEBsGy6G86QHv7hgeQVyuy+dAaF+Utga6+Klt3bmdsyxYGhkYJihVHSGSzhbDGxcYnCVYboijCKoMvfIgipg7vZ2hDH4888Cg9A4NsHL+C1uwhrEkphQXuuv2lPPDow9xy421ErVXK5W76uvu476tf49prrmJ0z24+9L7f5fY7v49Ts6c4fGSSl7385fiqQN9AH5RCHvrKv3L5VVdT7a6wc3w3X//61/EKRd71336Lw08+yQd+/w+56/ab2T1+MXsf2UtY8ulaXqanfyNDwwPMzM5hCwHlrhIbxjZxR08vN99yPY89vI9DxydpNmImnp3JjpPzTTc6u9aFI5oEHWBZZUC7wxawDTQkHfcF0VF1zbTSxv1Zsxn8jzs/n3tkd/s2SMrZR9G+h533lecp/a+XH3SCwrPkCmsb+I6/wdkM7tpnPx+7m//7P//MG7nnL/6cYlhEW02z2WJkeIh6Y5Wt20cZ7u1FpIp6vcZA3yBBMWDvvkeI6jGlriLzViMo8+yJo2zZMQ763E4b5/qsneNcLPG5gGvnc8/FOJ9v2+c6Vvm/jdGuh0X5WeO62+5615D258ilGi+AZe8c39PgOSOU202Dbau0dczzC91W/trOkdvP5df62aCTtRsvnAXJ8jJWJ5iv9g2gjGOVwkKBOIrceyiFJCCJG1kMbKbz047JTeoNKGjKff2YJKZQDPCsptTTC77jbDFZ6Q06ynHWxSQL0B2BIp3j+Vdp7juqjhPRZKBbZGDcz8ILyuUyH7rnHhYW5ygXKmgJ1kjKxQq15RV836d7YCO12Rknq8gAtJQCbbLJ9xwfp20BaNfkGDnP3Dbu58ymihw05wU5YzMdH3li4YUZi1MxMo1ZmFXMzRkq3ZbmakCEpbaiGN6k2LEzZXSnT2UooqdP4i9HHDjgMTEbZCU645hmJUCkkDqniXotxWqIW06DKqxESUm92aTHL2FTd7OMIrA2BQWFskcYWtI4IiiFxInGFz5JPjmq/HrTSGGwOqXg+4hYgxDEniGONanUeKrAQr0BXsjqcopNQpc2ncU4p57BsxZSi/AlyAQpNVVfYDHUlSCuZyVVz2I1TqhhwejE2dwpgfIlQkusduyYSJ1ntMRJenI4t+b4kHs0Z6V9KbLGYgsd6XO5rZlQmbQpO3HSOEX1+EgZ01P1UFoTrfq0pMFXHhpNEEiKBUgSTcFXlGxKo2mpt1yJwLnzWDCQxBprnVkExjVkkqfvZQDf5ud3DpaFAzbGGHqLFyglhUz3al2lw1hD6PlZQE22jzNzfSHWHDTyigiKtvY4Z5eBtkWdtU5qZq3FUznLKdYaD7PrO29cVN7abcoYpzMTQq59ng4W1JcKpSSbto0xtm07/cMj+MUSUrgG2fx9a0vzJEmEpxRzp6dZmp9jdMs20HBg3yNcdsUlHDp4kL1PHeINP3sHhw8/DfEKvdVu9u3bR2IsOy7aRbm/n+acZXRzL0cOHWLzlo2Mju/h/9xzD5deejl/9Rcf5a1v/xXSSDA5OcnVt9+JXV1haXaBy6++kaXVBgUSStVetmzbTlxb5h8+8ucMb93CW956N7XT04wMD/L5f/48u3btAmFI4hZhscxgb4wOoNWMKZVKzE9NoW1Kpa/CrWPXkySabz24lyOHj5PEGp17e2b+zvm1b6STHUiT0RHKzaYiix1H0L7ftAFOR1+NAyRn3pONvnBzL9h2QA50yA6/TUB7rpJ/5+/OxX4+1132fCx2ZxPt+vdce548Yxvnu59bEqo9VaZOncqIBMvJU6colyvo9CDbv++VJM0mPj69lSrRVIvB3kEmpk9Rirqw1tJqxdz9iz/PZ/75iwjhtb/V+d73hcgwzrUAWb8gfq79dr5hjMkcmTRKCr5x3/0cnzzMhqFuisVebrrtJfiycAajvJ51Fm3Q/MKZbPgeB8+wBpzzf+d6rE7d6/rnnktRa62Tc7jbtzjjNYa1qO0zT4SO8vD5Pl/21Bz4SSVZmptlYKifUrUPIRRaJ9nm8uYkhTYpSop2UlShWGS1tkJcq2GlZGjTNqJGnRPPHGR7pZsd19/K8Qe+QhpHCKEc2M/esx7HdMu8+12s8y48++TrLHkjHAOmMVmi27rnZky2JxVSWiqVKn/5yU+QNOuUSmWMtgS+swXQaUqhUKLVahApiba0vVpLYUgtihzYFeAjs5VMx7HI92V2bHN5jnuWaB9ZkW1DW2enlp8D7XMlrwBcQPB85MmY3l6fSFsqVcHuXR7N2BKlmjiJ2HNxgY3bFV7YYGBjQH93ne3CpzafcuRUft458GyNcqVuEdNsJIBBpIq45VwMURJprIv0NprEQmzc/tHGpxSkKM/iFyy+8ojTBKUhsoIkNUjPkb/WOEZXSQPCh8z9wMtic+rNhN5iidVGHSkltRVLVIekabIbSUqaCkQa4AcGbTTKpBRLAWnq2G5rLaWCIo40aaRds5wGITU6c7gIjAuZkJ5HmiYOVGrXiGusa6RUqULnJehs4WjJfKGNa8JLjZsYJZLUaKS1becHa20bYDuZgkQIQ6thKRZ9pEjp6kpRSApKYoVz0qiUJOWKRdqAxaWYxAiE1fhCEiHASiwaY1wTo8WiUwsyq6E4ZIyRYKQ7Rq6HwOJnlSTf0/QUBNVifEHO3TWAm8sqfCeHyxo4lXTXqs58uHPg7KRmrnRv7BqwytlIyCzkVMbOm6wvxVfues5kNtg1p4h8OJmGBuE5Xb7t0EQL0X4fay0DwwPsuuRitu64iMGhjaiw4M5Po7FW01iYYerZEyzMzSGkpbunh65KielnJ3j64EFuuvF6Tp+eZ+9jj7Nr96WsJLBzx3ZOHjvIgQMHGN91CRR76C75/OZv/ha/+77f4eN/9/fccusdfPyej3LJVTewdcdW5uaWeOlLX85XvvxVbrvzTo4dPMCBhx+htrpMz/AwW4sbOHHsENVigB8odl1+NU/vf4SRTWP4SE48dYhDhw5x820vpl5bYW5umqVawLatRSYnJ9k0MoSyhrnJCSIdUy4XeeLAYTaPbcWiiRPDnst3U+3r44l9T9BcbTlfaPJ97RYzJvOWth2sRF6RbB/DdWxnDkZz8AeQ+QNli9JvD4j8uw1h3DX4HO9/PiB8xmaeB+B16mlf6OgMrznfZzkfgD4Xy3wuAO+FHklqKRQKNFp1CoUSzWaLKIpoNpvMTE0Rt1psHBmmlbQYHtjIUm2RWqtBs9lkeHiY2dk5Qr/Eu371bfzBB/8Ek8bn/a6dC5PzAvpzMPjrv9PzMdJnPFcaMB6rqyvc/7X7+Oif/hkrq0tY7SqKSkharSbDg8P84e9/gB//8R/j9T/1BqztrESsvW8+jHCWjy90fM+D53ONnJHOx5kQrON5Z7xm7UUyA2FS5Azomowj1y52Ds+KTHV9biZICOF2pNsAfUODdFf6KHZ3I4ymsZxpG7PnO7P6jGlJUqyw1JeWsJ4EoZidnXGR3l09DA4Ok6wu8Rcf+0su3TRCn/KA1HmUWos1gj27dzF58AhKSkQGEIAMRD/HJJJJHLBn+nba7NvabB95wuB5iv6hfn7vA+8nqa/geQFSeAQFrw3QjdB4AopByB3XX0e/UhjtYpcbcdTWowvrhBlnmsTbdrJVXqZaO26mozDc/pBnLazO+NuuLbQuxJiZLeEXUkIfxnfBRZdbnLesIEkUYdkQhIJKwae+3OLQgQL3H4b9jyZYoUDmUb+ORXfgwcPmN4YE4kRnHcIaKRXSehAZGjYltj4Kie9BECoEKUYnCKkwVqGNqwKQpfBZa7OEJeNkEqT4AMagfDdRCBvQbBiQHlpIorokjSUmtlk1xIHfWKeUle8Y58y9IgwtVhdo6QgVWnxfYAoeOtJu4WYVgVQkOUOLRCUgpWs+9IseeAq8BM8XtFrOJ73ZiF0jISlBKAhCaEUJzbpENwTGSATGsQs54OqYqA0JpUAhfUWo3PcMbEqpVCAsWZakRXsxga8QQuF7kUsnFBIpFHEzRdoAacGzhlRotJZZgEp+Y/NoW+xJmTUzZ3aWSIxJ6fV9CkGKH2i6C5Zyl6RavjDMs5drjMXaIkNK1Y7UllIishj2zhtnDig8qbAi71QByLTJmTwlB7rZKjdbDXdoTLNXCSHa1QOLIEUijXFEhXJnqUS0G6OldAKvnZftZsf4dvoGerOwFwU6QWiYmzrOoacPsrxUI40arKysMHHyJFdcdglhUOLSiy8mbjb444/+LdVyhdu+7xWY2jzPLs/R213h6quv5cHHHmdsq+Lw1DLbNgzyx3/0Ia67+UY2btpId7WXP37/H/DGX/q/sPogGzZu5LP3foK/+9hf8jNvfBNz8zV2bB9ndn6W17/+9dxzzz3EySoHDzzD6JYEX0jS1HD8+FEmJyd5/Y+8lvu/8m88Oz3DymqNG667ltNzU2wc28HR48fZNDSITlrUag0izzVnzp8+TWpT5uaXECqku7vK9vHtHD9ynNVaA6tBtRPlbDuApt1TIoRrOMStTh373AGUrWoTQfm9MjVr9xyTL4IuxLAKd17pbEF87nCdM15yDpC2nildP/6jvt+5wONzPZ7/rvOx2+98KV/+/D8RJS1arRZSSkZGRiiXyxSKHjt2XEJXucjBI0cZGelmdNMWSpUKhw8fJCdoTy/O8/hjTzI5cYSR4dEzvNrPYtvPA3LP9Zz1z+tkgPO/n+v4GAMriy3e9KafolVv0Kw3CItF0tgQxzFWQKlQxPM8lus1hLV88pP3smHDMDe+5GXtHowz32ONLP12xvc0eF4fgLH+uOTyDUHuwNHxWtYfzNyxY92BEWdKMc4A3Hk9lTwt6zw79wwNtuXHfuqn0SbBsmZvZ4xGGI21xqWZxQlxmuJ5PlILIpuiY0mhEDLU3Q+4RjGTpKRxxOrMSX7rv3+QD/2XdyCt6yp3zI1l266dTBw4TCpASdvWt0gEqT3TZ9Ltt45VbLshIC+HZvIN8hPZ3dQaccxn/uHvMY06VqekqePvZdbhnsaOXTcCpB8wNryB+ukZtm/fzpFjx8j1m5166jSjlp1usvMYrC2HHMDIosQ7jo9oH6D86ObfeQ1A6wsIntPIsrqqGRkX9G9QFEsecQKFYgEaLeceIS2FzM7s4S/HzM74CIpgtUun9CWWFKQiTnQmR4gdINAZi5e9X6wNNlU0pEWLAJNapNSZdtRSKgiKpQI6EqxGKSbFNaqR3WSsIUm1q9AISZA5QyhrKQUSqQxauAjk2MpMNmIhTlG40l6uW/UDEL4mCAugUzAuVjYxCTaFKNFoK/CFJJEuZTFNBLGOHagUoh3BLoxAKoFf8JDKUKpIVNGiVJkkaVHVmt4eD4Qm8CUmSYlixfIM1AKP5qpGp85+LTHOig/ycp2lXAoohIYgSBFCUykqdAJpFKOFoK/soaUTKlhhiRJLrZayUpM0WpCmoXP3sJZEWEwqs3S7tWGtxklKJdZqx8zm7awaSr4gkC0GuouUggjfF3RVFcEF8nmGDvZZyrYmts22WUmiU3zVafWn8hcSZZHdvuetNf1J2W44tsZpaC20GWfn19xu34bsd9I6J6E0k8EJz52TQgjXgK1UB4izdFWr7BjfSk9/n7MsFAbbWOHEscPMz86xuLhIbaWOwC0YJ06eYvOmjTz++JP0Dw7RP9DDxz72Oa697iZuuuNOurp7qc0cYGjDCE8+9ABd/YNs234RXZUKv/ub72X7znHGRkf5+lfvY25mmjTW3Hbn93HoyacpFQI+9fef4Ide/SriOOVf//ULxKurnF6q88a738wfvf/DNJMUK4vsfewxCtUeDj6+j607LmLT6AY+OzvPhz78x2zevJk7X3IXpZJHY3kZY1NKxYA0jTly7DBX7B7nY5/4HIODvRyfnGL3+FamTs4wPj6OJyVHj0/Q3V1lbGyUk5Mz1GuNduJc7vsshGjPJdZYpFLtVMmc3Uy1zhgZ3Z5vHTmioG3F6mRQF87nKHtfu9Zody5W85yvPI8k4TsdIt9mtg3zAj7DuT9H5/3wufertPCWX76bL33un3LdZeaY4Xz0B4eHWF1ZolgucdVVVzM3O8vGwVGKXonQD7j3s5+hUu0l8HyKpS7e8nNv5pP3/mP7OlsveTjvd38BQPr5ft+5DW0FrWbMG37y9cxOzyCUpBCEICXNeiNzGPEBy9LSEmEY0NXVhRWwvFTjr//qo9x8x/eDaB+O9nvlP3dWW17IuHCiuhcwcqCbO210fukcNHcOm/3X/ml9ecau4S2drbQNHQfTIW5nlZU9zzHGa74QZxde2i9sb+evP/JnhJUejHA34SSKMXFCmiROE5iVyWToIzyFXypk9lygm01atWVkmuDhXDcWF5cpKM1rf/JHUb5je8IwzMqptBkgxVrTItn3Uh1aqbPH2ReyzeQbImt4cgBN8JnP/ROm3kSkLawVhGERKRRK+W0myfd9iihC6RGvLNLV28vho0fPYJfX2gMz0JxfKOf4dGuf6RxlM9pzw1mPg3NauZCJg4kWhF4RL4iodBWJmpKZUw1OTzeZnzZMPN3k5BHB3m+m/NMnG0xPqXZanlIK4SsQDoS4SUSRJgKsTxJDmjj5Uc4aWyuJWpo0EZhIIxJnUxV4rvlNWgva0GpF2Y1FYG1mtWYMrVRjpAKrMLFAaoHVKcWCTxBAGFoC4cC+0gLdMEgkgQhco5B1JfQgCJDSc8ykcLISHUmM9kgSQ6OeolsFbFOSZjrp/HoQ1gXnSGOxqXaNaUrhaQ/biDOZhaTg+fhhSl+vZqDfsmHMY/NoyOZhn+5KTH+XYWhE0NOt6eqyhGWL8lJC31CUCQUMHpbAg8CzlLsMQ8OG4SEPYWPXVxBJytJHyhZhMQVhaDSaRHVYXhDETdCpII4TEusSEY0z3iCfVjulBw5cZv7C1oWjGA1SGSpFyXCvoq8rpVSwlErQSlosrDYvxKnrkvmcCr2tc/WkQgnZlmC055zspuUrB5R9T+FL2V58I9fkFBKBJ6SLnbYu0TLXLJI5oiCdFt2x1JIE0wYdmgzAkc0HSiEyYJ+mKcYYBscGCAsewjRJkxanJw/zyY/9FV/90teYnDjF8lIN5QmQkiSNuPbqq+gbGKK7b5ibrr+Wv/m7zxAjGBodorvksTD5OJs3b+X05LNsvfgSdl5yGZ/99L08+OCD3PF9L+cX3vJLPLR3P7svuYydOy/i6LFn8RJ3Tn/la/czv7zII488xoaNG9n3+NPUW01+/ufeBFHEl/7tKxx75hDR7DxveOuvMDc9hfB8uspVkAHHjx5mzyWXMjN/mumZUzz80GPs2LGDKE6pnZ6i2lWgu3eIlSjl1msv5ZsP7+f4xDSJNVyyayenZ2Z44ulDCCmp12uUuip093RRLPko5eR90ltbfJzJ8LmgGoNrJHfHS7igFZH1llhcJSsjN7TNUj4vFOv87zTO9fmfD7SeZ0NngJbn2y8vVAOcf57zgdBKtYtqbx/lcplisUilUqFWX0Epxb7HHkUnKVGzhUk15a4udl15LbsuexHjF+3h6iuvYnh4lEajzszMFEkz5T2//Zvt5VJntSGXceV/On/f+ed8n/1cspP1+yKvfP3sz/xnfvTVP8jJkyepVquMjIxQqVTp6erC931GRkaoVqvEcUylWqGrUmF5aYna4goGWF5q0GzWz9pn7n3OhSaff3xPM89xB53uVgVZxG/28PpSfSd7mbs7r00MbtLV4uyVjzzrAGcWYe6fbiXdBuIZAs+lHPbsE+S6G66nXlth0FisSRHGEicxvgS0wZoUHadgLYlIqEcRSAeUlRB4RmIaDU6ns5SLBUa3bwdjCJTiFf/pp/ncPX/lShTWSR/6+wZRMmdpnR+2ES7mV2SLgRxItk/UdRY+FpxEoMP0uVwqEdVr/Jc/+BDdfT3EqysI6aOE7y4W4U4fpZzeWlhoSRDVCi++5Rbu++r97RhvyxqwFeQ6T9E+TqYNojPv0XaITXahdqwKncWSOxRZQNy6JkJXphBi7T2/26NYcCl7vh/SjFrMT6dMzSiqVViYSagt+hx6QnP8SIKlAEKjcgsVt0eyphuDFE7KYDGkSeaNa1xjXw5MDC6AJNU68zR2tmlxalBSEEeOWYpSBVqQtiyxdaDaaCcnQUOagGjFiGKAh8YmMZVSkThJUJ5PbCCQgjiQbjtWZ+eR0wxrUnzhfH0rMqDVauHbArWaxRiPNFHopInRsn3+WgvSalfqtxahldO1JRbjuZqDblqUVayKFCV8wgCCrgIBFqkjUqspFkv4OCsymaQUgyKUUoLAp6ETQEGs0A2D1U7DXCprqt2CsCwp+BYlfUzD0tIJYZrQX4FSsUBjVbC6EtGMiixHllacuDRIk0sSnB2d86LLF4ppVi1w84YUOMY5T9OzklLB0D9g6O1VlLGYVLC8qmiKgLCaXpBz1/NykOx+VkpkpX6F1Ulm+ed8UIQEKS1apwS+5xp3lEJ6zr8ZjUs/zeZIjW2z0UK4/aekRx5Ykt+EZRYYJTJLOsiCVsQaGBfZeZY/7vo4nM98Y2mJSink6f1PM7NYcyFE1rJp02ZWV1f4+gNfp+R5TE/NUiqVeemdL+HEsSN4YZldO3ewc+duorjO4EAfp+emWFhYYGLiJDfeOsDwxlG+cd/9HDtxyjVmxQmTR49xzTXXsHPHVuaXlxESuitdXLR1G5//wpdYnlvgJbfcyJ/+yUcol7vo6+sjTRpsGBllw+gG9j3wIBMTE1y0fZxXvvrV3HX77WzZNs6D33iI1ZUlXvvqV1H0PVZrC1x37dUcPnCAHbv3EFNAaU0oBBdt6ufIBDy89xAXbR5BWkOlq0SSSnp6emi1EgrlAr30sbxUo15vEsdxG/w4ULzWJCjtWgqkth0yv87bnXHntc3uM5isuVBeGF7ufIzn+UDcubTP6/XF5wOBzze+I8B91lhXUe9gf8+rCdYxb/i5n+eDv/8+hJR40kO3WkRxzMjQMD09VaTRxK06W8a28bV/+Qxx7MKtxkZHWTp4iDRNieOI+cV5vvD5z/OOd/06nufR3dWDJeVzn/0MO8Yv4h8/9WkmJk/QNzjEyMgIAp+t2zZxw423Uu4qkQlazyud6dznZ+xjY0kRmNTwuh9+NVGzRaoFAwMDWK3p7qoyvzCLMYZqtYuVlWWkVCRJQhiGLC4sEZYLxK2IchhQb7YoloLs/YCz5Ln5ffeFH+fvafBsOkCRyOypwDlC5Lre57tEOy+GvIySS0A6v7xov48TlbdLLYK2U4TJyo25+rm9asm9p7PXfPGLX+Tut70LEzuXjTiJkNKVJ1XmQ2okGG1QQYiyHtK6lWC1p8rCyjKUqxSKAaYZceiJxxm/aBcyXuG//PcPMn3gII/tfRiTpCAs//rFL7rO6PZxd6yg3zGBpbkuUDhWJ9WmbRclBC7ZK9sHvu+DTdi4aZRX/uTPcNUNlyKaq46xSX0S49LGPD9o72MhBNIIpB/w0CP7uO/fvkIrkwEgnC6xjQ07jo3J4LNmzR3Dtv+Xf5s1PXSaf8d1tPKZIDnT7v67TF7f2SgWBMvLCTr2iRoJWnsMj1rCMKWvr8jkMzEHn9BYCmhUOxQGoVEE2TkrHICWmfRHW4RQJLEmVB5JmiICgdbOoSDRds2fXAg8KfGER5rERKnApBbb9EgSTZyCzBoSERabSPdzK0EaRZoawhAKnsLEEeVSgdU4cfrqbNFkrAtgWW02AUkhCBypbS0yLbC83ETokEYCUd05VqRxgrUeqbYo4ZNmFnDKChLIAh6EkyZlJX4sCCNpNi2+VgiRUsTi+wlhf4FaBPFKgzltSFZ9opbGUyGpFRhpCLssXmowscEqSVNrPBsSlAx+kCI9iTUKrRtIGZKIJgXhIWRC4JURxjl8VEoFViLpGoBFiJEaJQxWpBjPgU6MxWCQeO3oaCf/0mgBnpLYFKy2lEqCgYGA4bEWA90RyYrPwqzmyKzCL4GJLkzDYBgELknQz4CsBSMNvidJlQfaEkjnPmK0S1XzfZc6qJTCptox8dbJNLRea9LJHTWcBt01KjuXDpOVXd0QmTZcG0OaBf54GYhJkgTf9zHWYqwlxbjGQys4cXSS3t4qXkZ41JotikGZHTu2MDU1RZqmLC7MMbZhIxuHhpienSHwQ5rNmP/3Ax/hphtv4+abridU0De4gbnpE5QrBWq1GpViiU/9wyfxVUipVGHzli088fQBusKQL99/H0oaTk2fphVrNmwY4eI9u3hi/+P8yCteztGJE9QbMcYIbr7pDu677yvceu01vO//+R/8wCtexhe//EVuuOo6lhYW+Y13/iqn5xb5+Mc/zuZNWzBoPn3vP3LtFZfSimqkSZOFlWXsMwe59OqbefKRh/lf93yal994Gc3Ysu/Jo7Saq1zzokto1CKmZ0+hpM/yaoNWq4UVgqAYEMepY/2hLdc7E0A62Z9zU1l7Xv57KWW7qVTYNXcOd+VeCPY5T41UZ3zO82mHz6eteF0F7QAAIABJREFUXf+7b0fyIYQ4I2nwhYy29ao92z/7hYwzME52PH7wB17B/3jff6NcLHH1tdcwcWKSLVu2IC3UlhfYsu0iTk0+y5P7HmF5aYmR0c10F4t8a+8BpqZO0oxagMMCJop542t/iKuuvZxSzxhPPPEEQwMjPPrI46ysLhEWu4kjw+TkJGkETz3xFP94778gpJMYvubHfoKbb77lDOlX5wKgk2W21s0hKI9f+pk3cvzEUXRqWaotcfHOPXR3d7EwP83M9JT78togfEfm1Wo1giCk2WwipMH3CnhFSRzHbBocQSh/TdJ6nsXdt7NI+p4Gz5n0393gs6iUrM3kDG1rp462c1jhZBYiA8uQnVysabLche8milycIXJBXvaoIGM6MkuTzt0uWKNEJQIlFbuuuITVxjKeshTKJYxxsakKC9Z5MSvlYY3BxDFSa/xKBREEPHvqJCJJ8MQqmwcuosYcGwf6KFWL2CQmrS/w2rt/ibn3/B5zMyeJkpjjx4/RJ13al9TuJiKlk5mkWXpXX18ftVqNOE0w2rS/S9uposPKq39wgLgV8f6P/hleGKCadaxfwCQxxdDHI0BI5fxahXC+wFIRmSaqpDi5sAAdjFE+oeYLHp2t8kwG2o3Asa75Ucwmn4znd9rxvP0oj1TPjtX6S8Dk/+9YbF2I0T/iUVsxaG1YXLD45Qa7L62SpJpiSTO6NcSoGhPHDM1aC08V2Dkq2NireGB/wlzNZpHGFmOcZl5K6dwMpCJKNApJkmiUcFl6QmgHOtXaZKoTDaFAtwwaQRqnJLFECYWOE1ASZZ31mDUCIX0QmoInCISHkoaCj/NxjS2+5xFbhZTGRVonKSg3jSRphMAQeD4agUlDkpYgbUauypIKrJEYmyebZY1FxoJU+MaQ6gRPKlKbudJk5XwjBT6JSzr0BSorEUd1Q9RIWFnyaa4omq0ySRJRKluCIMWgM0tICVKQxppKd0iURHhFQVABGWisb0gMhL6g1N+DjQ1C1jAiRQYOEFIKaC3U6O0rslRvEaaWWAgUilRKlBJEUUTghaRGEyiPNE4dqMxmWmMF1hcopdm6STK2JWXT5gi/7DN7yjJxwrAUGQJtGRgJL9DZS9s9o9NVQ6eJ82yWqt1Y7QdrmlgpnIxI+R5WO2cLIRVWCLR2Ti/WuAWSk2EYwtzjOwPVuXbaaA1KEgQBKmtAszZrSpTuHMJaPKXac5fWmmYj5aEHHufE0Sle9qpXsG3Hbjz/JE8fPEKpXGBqdoaTp2aZnDzFxOQppmbmedvdb+Yr/3Y/4+O7KHgJQ8PDlPu6WFk4ydz0BOHoGF2VIs1GwvTJacq9A2wc286Xv/Z1Lr78CrrLJb759Qf45y99Ba0t5WKB66+7lt7ePmanTnPw8FEmJp7lrjvv4tAzR9i/fx/XXXcdX/vm/dx15+287Ad+gDiKuPdTn2bjpo2MjW1l4sQpbrjxdp49dpTp07P4vs/E8Qluv/UahPTYtHGUYrnEycNPUh0ZoagEf3Pvl/jD9/7frPzp33J0YoonnzpOsRhQW2nSWw2IWxFz84uMjW5yCwtbA9ZkGm2iigz8CSj4AWnqqknuuGRMLALrjC3ca5XIzJ46CJDv8nDniAsBOx8Qfu7Xns04rwfO5wPSnUzqeuB8Lja8k9EWHbrzs6UYOSPKusfPfu8zfi8tAwP9zM0tMr+wykXbLuLI0YNs3bqVOE44duQQp06dYmZ6mkq5i/rSIkr3ITxJd3eVZtPp4guFArIYMrJjD08cXaS73GRo40Yay8ssrS7heyELi3P09PQwNDBAV1cBz1OcOHGCgYEBVlZW+MiH/4RPfvwTVLtCfuO33kvgh64X4QzC0lVpE625//77+fAHPwCpoVlvEQQel198Ma1mk8XZOkka0dNTpVTsIgyLHDz4NOWuSntbvu8W/8tLC6jMN/6n3/CjCCMzydz5NfCdYTTPN76nNc95k5m1IKzr5bfZyZmvbp9rhSs6zj+PzMM4e7rKy0x2LcUnTxPsdGmQHV3jwroVtnvumSxqW1uMZmjTWJu90c0IJSRxc5W4sYpOE2yqSaLY6VZT7fRirRblkU1suuRFbLvuRqLF00w9e4QXXXc70ydPslSvU1+uoRoLXHn1DTz4xGOM7bmSPVddy50vvYugUKRQ6HErX885A+hiiPIEQlqWFxadL2pmZJ+L1rws3jh/fM+ePfT19HLr938/QhpkHBFlsbsO1IAfFCmVuwnDojs4NkUIF9Erjc/++76eAWXr2NRs5DZ1a8fHFbfXJBc2qzC4IBQtzq1rhowtzxdA2RGy+cQtZPuN1icVfrfGtj0Ntu00pJHi2ROCwZEy5cCwYaib/lKZjZt8Lr8mYOvuBje/QvK6n9a8/e4i73xTLzeMp1gliY3FSOUmGuO5gAPrGGRtUxKbkGoPbTMJjzJ4gUZJiy+cL7hUAmXACg8lPAQBOoU0ShC4pD+hXRNrLqEoFTz8QIBNCTxF6BWIG64hVAhFHMekqcGiiBO7BmosBCLEJmDqCSLyMIkm1dIx6EJjhUHL3AnF4BmdsYYuoCevMFhriY12CZyZxZtVBhkqfJXglwVh4EBYswHNGjTrOB/rgkV6gjjVtOICtZpicdFSX4UUH+PF9IxaeoadjaAMJImBJAlZilMWak2WkwTtF5lvGuYaKTXhEamY4dGAgdGUsW0pw5ugt8fH8zUlXxOIhEqo8ND4RpBGph1PjfXAeigJBZWweYNg/KKUK3dLdmzvo78nJJUxhgBjAlIMSevCnL05A5ZbkDnwnBEHQmX+1Lk9oW0HnRjydFMHpIIgIA8ekEpghAGVHXvpFkcyA8tKOTePXK6Bp5yLhnWgOEkSEqPb4MBY95mszZI4M7DvSUBb5mbmOXbsGIuLizQaDSYnpyiWqtTrdbaMbSYMiiytRuzatYcTR49x4tQsW8Y2c/Ptd1IoFGgtLyCEYsPWcU4+O8Gey17Enj176Omu8OnPfJaDR46ytLTEA998iIW502wcHaWn2s9PvP7H+bVffxe7d+8hTmNe/dpXse/Rx3jtD7+O++77Jr7v8yd/+ufs3buXn3vHu8BIfvFNb6K3u5eJZ5/lJXfcgRKSZ545yIkjh9k8voOw0seje5/g2MkJTk6eYtPoCKenppGeondkIyMDffzKL76Rl99+C7//ob/gda/9fga6yyzVVunt7Wf79u2AoFKp4PshCwtLACRR2p77c4tBJ72wZ50DOdCTyllGWk84D3dwREVWUZWZq0qeQPndHM+nnT3fOBe4PR/gfr7Hn68J7lzPXy8XOXsb53v8/EMIQc/AEKHvsW3LNk5MnsCkKSeOHeP0zCxxnLCyvIzv+wwODFD0AlJT49HHHmN+foEoitr+7dt37Obk5BS7x3dS6qlSq9VpxZpWq8XUqQlGhjcS+gETExMcfOYQUX2VMCywXFthaWUZFQaUCkVWahG/8a538s5ffQvGOArU9bpIUgN///FP8ItveAMffP8fgREcPTaBkJIdW7YyNTHJs8dOUPADtm7ZzujwCKUgpKtUZuvW7VQqVZIopbFcp15fpdFqUigU8PwCBc/nxptubwPnsxcbaybH3874nmae8yFyCpLMS7kDMp/v63au1ywOWAnAMx2spZRrk+86DVHnhtt+omdtNfs9Z64Yb9izidXVVUqFIsYYTs/OEtgUIyWkCSIoIJSHIgUURgiC3gFWWwlXXn4Zj+/by6Zdl7FSn+cbX/8ae/ftp6uri4ZQbBwZZvCiDbzyR36M173mVTRrK7z5zW/ms5/6FHf/3JsZGt7GqWdPUO7robaySqlU4hWv/iEOHT3OvgcfcvIJYyiWS+goQQlLGAb09A5wenYWWQjZvH0bb37rW6j6Ia1m07E/wgPh4XkBXlYmSdOUNI2RUoF0oRL0DfLEg99wN1hxxm5i/bzzXAsfY9dkHKZzNZ8xyrl1u8n2fx4kanGhDjLXrV8g9nlguESx2iRaSdGnNPNzMZdtH6ZQKNCME5JmC68kGdlm6akatmzpI7YxTx2d4asHY1qpYHREcPl1Cadn4PjT0FhV2cLRgpAM9YbccHU/Dz44TxRn7hjCNaHlJtieh5NoaIExKcJ6eFKAdGAcssqAcKx24AuE1g5U4ybPxMQITxE1UtI0JUkl2rjUPGMNKIVKLcZoUgMqAqwiNnHGShmsBq0V1qSorDvfs5mOVbpQllj6COUkK9KHJHUNk+jU2fdZQdJ0rhphKLFxSiNWJJF0YRtaoDyBpxwYsKZAfSnCpKC8AnXdIixAUAqwJqFQEnT1COaXmujIJ0lSCkGAlpooSlGyzGytTsUEeGGKqCjKoaCsoL4iWfKdd3CauvJ2UfmYyLHckbBonSClIvM8AU8SKkt3j8+WsZRNo5bBLb34RYFZEZTLFi8wzpPbc5Z/F2J0smhCiAzUZrr7fK7MFtvW0Lafy5ljz1NtZqFd3s90yp63pouO4xhxngjk/LUI2nONOydxhAfuWveECwgCSIyBjNU2xuBJQ2N+AasTNmwYYf/+/WzbspmJqWmmF+Z5/KkjjA0PMzs7y9z8MiPDw4QBJKaF57mgIqkjBkeGmTp5gif3Pcnw8Bjj4+M8+I1vEQnF2IZB+nr62b65wr4nD/Cpe/8PfV/t4b3v+a9s3XEF1161k7e+7W285z3vxSKJYsN1199EsdLF+3/7d9i/fy9Li3UeevhRrrjyGj772S8QR01e+YqXcXphifu+8RCXX345yhpKgYfvCWZmZtm2Zyc6jpifOEy5Z4DlZpP5uWlGBgd54KFHufKyXew9eJSJyZP09/XSjCMWFmsUi0V85RFFzayim5fKnSwxT8v1lNe+JyqlXKqtdXOvlWuN6VK6qoKQwi2UjEVJsV5Vd0HGCwHO63+/HtS+0DL+84HmF8p+fyfjfJ//xTfdyJN7H2VuYZaunl5MuUxXucjpmVlOn57NKuLaWRVIwQOP7SOOY8IwpK+vj/6BYSpdJVqpZvySK5iYOEmaWKanj9KKEnbt2oWQTYwxzMzOoPyQvgxcd3VXWVpZxQqPpJUwPT3D4vISvd09hGHIO375LXzww/+T2dNLJBZ+5zd+ixPHDjlvfGuYnZlh49gYy4tLfPOBr3HN1ddx043X0V2qksSGrr4eJicnmVta5PT8HKdOnSJKmrR0i1JQJMgqJkhBd2+JQrmP9Sz+2v5yjl7rPaCfb3xPM89w7jjN9bLuzp+FEGcwx/l5Za3TzSoBnhD4wnkOK3vmxQKAsU4mkNHLOdtsRc5AZ9sUdHgTu/GqV7+a3RddRDF0By+NWwwOb6TVbGK0xRgHDLVOMEZSHByma3Sc6uAoaW2VZw4e4lv3P8CBp/dR7h+kp6vIi++4k+MHn6Sr0o2UHsvzc/zBBz5AVylgoKfK23/lrdz3rUe56bbbefTwM2jp0TJQ7OlhbM9lPP300/zau3+D1SSibg3d3d284x1v5/JrrmZ46w7Gtm9FVcpceuWLeM3rX8ev/dd342UToBcGGCtJ296dDq6mIguZsAYpPaTv0Wq1OPjEU1x65dVtFvgMiYtd23fnspHL5LJYnGQnFda5Zli33NHCNY56GSg3Zy6PspMfVEdJ4EIxz5NHEiya6pDgxpcW2DCiKBfLVLwizZWYJLWUCnDJeJGeksczh2Z56Okm93wrRnZ5DI1E/ODrBP/pZ0d51et8Nu0QRDYhFQkJKTdfO8LDX3g9H/lfL+Odv7yLIGw6MGxdzLbNGD+tBSqbwIyWkOaaM4OSrsFKCIEVBulZEAYRuOQ9qQCpqLc0Te3CV6JUEaU+SSSJoxSTSOeYYXMJhkBKkCq3acykVsLg+ZKg4OP7mrAgCbKqiBCZqwYmA71pW26kdYISMtNUCmxkMNInqlnqS5Zm3RI1EpYWItdoqATGSqJUkdQt1nhI6yGI8UKPILRUexSVqqBUcYEzoecjFSjfI9GaNIFWKqi3mhTLBcKShx9IBAk6jmjEETIsOrcM38Mv+EhPEqcxqbBrCYbCaYFLoST0BAWTUgwEQ4MRW7Z5bNsxRLEY4ktFmihWl1qOnfZjwsKZyXrfzaEzbaK1llYSt8EosJbKSAau5ZqrSA62kiQ5w5Wj7aTB2jybpmmb2ep8v/x50gqkUE4iksfqQtt9RgjRbhTMwXQhDF2ZFkupq8jOXdtISBgYGEBYzemFZWZn59g0sgFjYMf2MXaNb2Nyeh7fkzz66CMoD0Ti5D7NZhOv3E+p2o22lmp3L08degohfK664cVs27KZQqHIu37tV5mZmmZ2dpqe7i4OHnyGd/zqO3nx9Zdx91vvptVsslJr8mu//k4aKfzDp+5ldXGBj3/847zyB3+Au+66iztfcjuf+cy9fOMb32DL5g0cP36ciROTHHz6AHsfe4RNW7dx0e6LsfjsGR9HJxo/KNLSgumZCTaObeKVr3gJp+fm0YRcf+01jG/dTBiG2NRQKpWIo4hWM0ZbQ7Neh9yzOzumuQf6GY4ZSrYXJJ1/Oo+llLLda9EO0rkg+dzn1jA/Fwg+n+75OwWy69/vhWqlX9h2z729cy0ShBD8xE+8nrBQ4NljhxnsH2JswwjTp04xMzWJ5/mYVFOqFKnXllharTMzfZIgCPCUYnx8N804oVjuQlpDbWkGFQZMz52g2j/E+O5xavUIFfjMLc4RtRJqi0ssLSySWMPy8jKrS4u0WjGHjx3l+KlT1GorKOU0yNMzC/z0T76Bt999N2/7hV/gqf2PEjdbnD49y/LyCuVKN4EXMtzXww/d9UpuueV2No9sp2dwFBmELC3XkSLACohbEQiFsG6eSrWm1WiSGk2oQv76b/837f605zgGToHw/xPNcz7ylLszms04N+sshHAMxbqYakmeRpfppwXtUjEdADt3DchFGB2wnPVrDbVuee1LxTMT05hCFybTFq+stvD9AklzFTBI1YW1EMcxJtXoKcO2a7ew2IyRPb0snjpBtezRX+2l1y+SmIR6cxHbPYDneZRKJZYbK3iex+joKEi4YmmWW1/5w7z73b/NJz77OZqthGqlTLlcppEaVmaneOLkHH/yif/N048/w//8vffQPzTIrssuofnEk/T29nLbzp1oa7jsyisJS2Wn9ZYSm6aEWWMggPICpO8CWpK4hdAaLxQYodDG4+sPPsT+hx50TLB1+y1n+tePzsedVoy2NZLbv46dToXF2JxFdtICc4a3dsc2O+huzdn+39+tsX9fzKalkMERwdh2j9D2MHUiIjYR00sR47tD+oIQaRKK1nLqGcGiTdh+qc9rXnMFG4cGseEiq9EivWFAtLBMLNz3k0Jw0+WbKIwmxLV50sYKqYbEuBK6J0EbQRBYTJxmzggeNhHOnztb9OU3PiHXGmuUEs7Rw4fYJERZ814rteAFJA1BEltSkyKEB1aicYl+nuckC0HFJ9GawDjLPusJPCOQMs1u0D6h8jFxhI4TPOm5YAVr0TpCSA9DinOyccytEgJBiJQpuimwFUWSAmlK0grRkSAxmlD5pNaiY0XaMiBTwi4fITwKpZRiVVEsRnRVAppxhIwMcVPSXGyhCh7WCLR2jL0nFIXAw+g6QSDRsaIRW1qRQUcpceTRbMQkSSYrsaC0IE4N2uBYvtBVQTzfEkcJA8Nwye4qm8ZCSv0VpEpZWoiYOrVKbVkzPdukVPIQwqDTC8NtSCnxlDojQTA/V8DJMZwGP+9ch8Dzz9aJem6eQIr2NGoyJjqP1ibrMcnn85z8MEZjpCBOnC5fZHaG7SAWz4FqvxNc5wt23+OGm65i25ZNiIlZtBT4vqKrWCBKYh5/+gCD3d0YYan2Vqmt1Gm1GrzpjT9OuRBSKvpMTE3T2zNAAKw06tQWWxybmOLm2+7k7e98N7PLqwSqSBw1efXrfhJhE+645Tbu/cw/cvkVV4AwVHu6+cAfvZ9rr3sxt916M+//4IdZrq1w6/ffhcZn27ZtLMwt8tDDD/KFf/0SfUPDXLJzJwPDoxw4cIhH9z5JUKmQak19dZnhvh6OTJziw3/2V1x19aVcfuWVrNQWGRzcTFqfZ8+VV9J/7z9x6JljKGm56vI9fPmrDyICxcL0Is1mk/7+fuJW5GQanufmWuuuXYzTkHdWPNYDsvY+zo+FdG5VzmpVZAth79sChf8R44U2/J3rc76QBsHnGt+uvCL/+9yv6ySIzu+dvH5bAF1dXWijqDdbLC3OsHXzGFvHtlApljhy9AB9Pf00VpuMjvVQLFeplKvIMKZa7UYouPrKK+ju7uXo4SMcP3yMkdEt9PUPgxHUlmokSUIrahD4Lv66u6cL4QcsL6/SPzDEYm2agd4eduy4FptaJqZOMXlylksu3snW7dt46FuP0FPtZWVlnq5qLxMTR9y+byoGBwYYHxuhWulm1+XXAbCyskKt1WBk8zZarRZzs6eZP37MVfaEazwvFouuuhs5zfbWHSN09w8jzLn32/pF/bczvufBs83wa1bBa7syrMmZ3RPyr22MCx8QttMRI99W1vwnnM+ztWRNhW7kjJwrF3buSNveZg7M1jPO+cTS29tNnFqUjDBGQKoxtkUcNYgbq45FE67ZIkkSdH2OJ7/2ZdTgMKVqFzIs0t3TT/emYR5/7GF6RgaZWphDSZ+HH3uIK3btxnohGI3yFfHqKps2b2V64gS/8e53Mz89hVcsgdUUCgWEVyQY3cDS0hJBqcTgxnEqxQIzs7OstiLe997fYf9TBxAWbrrlZo49cwgTRwipXKk0C4fovLi1TlzMqxWYKCYoWECjlKBa6cKSZlKbzC4QkTlmZPsKQZL9nENdm+mf18v1TQb0TN5kSL6QWtv/hrWVlcXdq63NmhFf0Fn27z+GR33iluTpvZqJ48t4sszyYg1UzMVXKbZZhTEpcaSBIksrmmefjbjjrqt5zV0vpxgo6rUJHn1iP6dPneDHX7GNYyeHuf/BA+yfWeWP/uYbNOUJpp89yaf/RSKUY+s9X9KIUoQw2c71UR5IoZFIFBYrJIk1WVe4aTeZer4L5VFqzRs80sbpjrVFG0HLSufpjF2zF8xKBY5xsu7cDMAYiZJZUI/nYaJW29YwNSmCmELJQ2hNqjTSOH9obTr8VaQT5xgjsBICC36gSBqGwINW7NFaco2ZCEVzVRMUfIwwhF0Cr+BRrhp8X6BNSiEArWFluYnRHo16QiP2KaouWqsR0nPsRaEYEjciYt9gbYpSRVaWLSvLKYKANHEAOU1EZngLVgXUohQjJELy/3H33kGSHfed5ycznytf1b57vLdwBGEI0HtvRWqXpCiK1FEUtdqQQhe6C+3e7a4kSruUVlKcFCdHGVIUKUog6CnQgTAEQBBmAAzGu+5pb6vLP5t5f7yq7p7BDAiG7gjGZUTH1FQ9U5UvX75v/n7f3/eLbSXYjqHUJ7DtBE8qin0xAwOSfH8GLEUQGWrVkNnZNmEzy4BtEZmU7tHynx/ahlKKpAtIHWWt0dugZ0ucasvrrlJDL/IoWC8uXDfLoAuyzZoKQY/eEcdxSgHoFsJCN8LdzZpordPxqdPzxXGcuqgK0eVMplFyAIQgTtK5++A1e7j+hoPk81mGhvo4NzHL0tIKOklYXFjiusMH+N6p01x38CBxHDM0NESxUmbXzm2UKxXGz54m2zdImPhcPHORb3/nPm570e2YMOYb3/gmH/rAz/Mnf/lJVqt1Wm0fS0iyuRJTswtcf/31XHvtYertgI997GN84P0/x0MP/4ByscQf/o9P8J73f5B773mQc6dOUsh7rKws8c53vYvf/N/+DzzXplQp8on//vs8/PDDvPZN72BhdoFjp85jhMWhPTuxBgf4xY/8En/zF3/Gjm3byOaKazUHURzz1re9jt/+xF+Sz2XYNDrIls0jLK02KBdL1GpdwOO30dG6KyN0F+UqfR7acj1qbOR6oWbPLGfjZ1p0swE9njQCdHIF+defRNuYcTbPAkjTdvnnG7HCj9r3uYKtZ6MkPTf+dC+A9+wSdVeKPhspGBwokySG8+MTjA6ntME46tBXKfCiW17K2fNnmJ45w7mJ+4mUS6lcJNGaoONz+vRp+gcqhAaK/QMUi0W0iZm5OMHCwixBEHYdc2MqlQoL8zNs27YdkDQ7TaJOg4naMisrK9iOIpvJs9ysc+SJp9i5YxuVvhITF8YplgsoO4NAEUYd+iolTBxRr7UZ6BtmeX6GweERvGyGfKlEo1mn0WziRzGW7M5XUUQQBGm9gu+jhKKvr8Rf/u1n0syoeCZee+b/f7wF3089eL68qQ3AGZ7Jm+2BNckzB1piUm6WRCBkKv2VdEGbXNM9lpdwwdZ1nM1a+lJK+Yx+NsawY+8eCuUSrflZKrsPsFprsrK6QsnN0mgG2DrCtlcwJDhOPj1uJg9xh+bCQpqyNgkjm0YZ3bKdyfMXQVq88IW341qwsLTE1Pw0W4dHGShvYXF+Ade1CYMs09PTBJ0WwnKJQ00mkyGXyRITY+KQXM4iDJv0e4I3v/0ddPwGb7A9vvPVO9m/fy9eNkPGc9i7d2+XK0Qa3UlM+tCyLZTyuuYjCoNAeQmWVUTaDkGiiSxFLlcgjA0h67f9xuVN9xYH1phGadln98Eq2agHLbrXZ+N+3Qcrlxvg9LZIMw7PN+Xu1pc7NKuas8ckK9UCc9UAjMXW3YrtOxJsW9AJDNq2WV4JmV+IabRsBvM7sKWHYhNCKgrlRfbcUuEFe/YhRIn2wn6eeGSSe+4/zj0PzjM/7xALg0oScjmf97xhB296ywFa7ZDP/8vTfPPeFRxAaRCWQFmpRKIlFXG3AEuotPw2imI8qZAmxHXc1EiouxCJpUWYSIwSJFJjSY9Uvkqi45TOo0lSTo0NyATbFSQGZG8MWQ5REqGUjQ1YWRtXp9QNRyrCEITRWMIiDFIgpDCgXHS3sFArQafWIFvI0g7BJB6R6aCJiKIIz7ZJdITtSVwvoTLg4uUibDdMAVsAYQTEmVkQAAAgAElEQVT1uiEJDGGSRYcJy5EP0uC6LsqKabUS8nmLKATXK7Gy1KFZs2k3JXHMGtVFoFASwsAQGYiS1PzFsiXSMmTKmkyuxchQFmVpCkVFrmhjLEkUG6pLbU6eWWFxFhpL4IkIZSDoJIjIftYx9v9VW3MNlJIk7lq399R5NjykN3KZUwpHKo/YA8dpNiMBY1BX4Tb3Cr+NXF+wQQqoldZY3ShmHMfdgtb1+UB3gTpJWrkvLEU+n+fQNbvp66+ANgwPVDh1eiKliiSCfL7IqTPnGRke4sz5CV79ytt48IEHGBwuM7plK0szE1QGhwnDkIJb5HsPP8GO7bv5b//148zMzPHO976Pb37rWywsLNGKYKhSotaJeOLppxkZGkaYmPGLF2i1Wpw4cYy3vf2dfO9732N4ZIwP/vzP8ZKXvJR/veu7HDq4l1q9zezsPM3WY7zlzW/knnvuYf/+w2QyGf7qr/+O2flF5qtNpLLoK5cwQvHNu+7m4tmzfOD97+HJJ46zZ88uOpFPnAT0SSgO9nPTdfvQMsPCwgojQ0OcOjeBQOH7bSqlPlphtOZOmhiNkAaF6lqdp9mnHmCW3XlVdYszlVgfI0pKYt0zM5KY2KSAmsuDTz/ZdiVQeaV2OXDeOD6vVOB3NaD9o77Lc3n/2cF+9574MSkgMjFcd+MLOHvyDMVShVazg6tSMzPX9jjy2L002iFnJhcp5MuAwHEzOJZDJpfh4tQktiMpFfuIg/Sevnj2DDMzk+Tz+VTD3/bo+C1WVpYBaLZbtFot4tkYv9Ok0+mQJAn9/QPML8wyNDzAar1JX/8gq9UqhUIBx3FotUP6+vq5OHWRdquBsS0UgsmJSfwzp7j1pS9j557DzM/PUHAznFs4TasdYExC4Ke1XfZahtwwMjbKpz/7tyltbEOfPxvt5cdtP/XgWdLVGeXKgOgZ7wkJRqNJI3FCCGJj0gIzkT7MEQZpBEqKtWP3qAJiA6FZdrVIzQYKyNU6WhqYnZ7mTe9+B+32KqvLq3SCgEyuyMSpk/QVK9Src2id4LeaCONhOW5aSR6GWI5DpViivjTH5KmTjE9M8Np3vouTjz3M7Nx0CqoH+olHN5OrFBk/fw5p2bjaIQhbbNmyhXbHp1iu4DfrOJYi8OspgLYluUyWlaUOsQ5JQshmc1iWxZve/W6CVoPBvn5WlufpRJpisUjkBxgrBVhKpX5jQRx0aRERcbtN1KqRyecwjoctJJbt8vU7Po8QKWXiSnaXvciwbVJt57UHoTFr/OQ0Ir0OtnvHsIBEiEsUPHoThoJ16bsrXqGfcDOCvtGE/crw9KM+K4sWcaxo1hJWl0EnPp2mjeUZhIwZ3WYRacH3HngY225weNtWLNdhst5k+6YhpJvFljH25iy3b7qG21+7k+/f9zDTi/OcfHqZJx5R1Nohr3nVdl744kGws7Qagq/efTe2sZAqNeURcUrpCLqrcSVTIXopBZZtp9J0nptylJ2U6xskCVEkUzpErBBOjDIQhhrLEkQmRDhWaqGsBbZJaQxxknSVTxyk0AgFJAkm0iRS4hhFELXJuB4YQ2xiEA7toIMyFsaIlP4hE4QAk2iSMEYoG78TYymXTqdNHCdoI3FcheuB61ooLySTTyiXIV+UaKEIAoEfRzSqgk4LDBaxlgRBGumVBnw/1SYVMqCRWLgZQ6Pm02oI4sgQR2mBlDYa17NItCYMINapbKDRKX874wqyZUGmHNFXcXGyoESC59kkJkbHCe1GxNLMKktzEAUurpFYGQ0tTZSkZkfPR+txX+OuyoaRoss/T7N6a2ZMap0yYUzKeRVWqniRRii7+2mN7jphCpEezzJpxEh2DWN6AYoepzk2KYUg6QLxNAqdFhxGUbRGI0mSBMuyMCItbLz+luvZsmUTnuelZkpJwEB/mX17d3Jx+kFiHbF39y5mZuaIqZHPZRFS8t73vRu/Xce1HU4ePUFl0xiPPHgXCzPTWFaWV73mjfzzHV/gzju/xA+PH+VNL3slk8ttjp8dxz/6NH6zA3HIi2+7mRtuuIFXvfZ1PPrDRxgfH0faDkefeoqVeoNvfOWr+DE8eew0zdUqxhzmhtHtPPHEvezZs4ed27cxNTXF5+/4AtPLDbSS3HzDYVTkc+eXv0G73uJ7DzxGeaCfn3nb6xGOhY2kurBK0Gkx0D/Ke975Zv7us1/hXG0VGygU8py/MEXGzdJptlL3TpXaNfsmQSYGq7t4USK953qSrb1rm3QlVjcGkExXTtKY1OwmzTRc2THuJ9l+nBT8j4owbzzmjzru1aLCz3a+jYWJV1fbgLSeRz4rqL/8u2k0P/P2t/OFO75CNl9g384tnDhyhKGBYRD9NGortHWdleoyWpSolAfQsabRrqUFg5UKc3PzDAxtQiURy6sL1OtVtNY02200hlarhW25CCFSVZuLk9jKotVpkcm6a/fp/Pw8uVwWowXDw6OMT15ksFJiZGQkDbiYGknskc9l8IMARwqCoEN/3wgLC/OceuoYQTvEynmsrqTfodGs4wfttfEZBD7FYpFyscQ7f/ZdFItDV4zMP/Ma9rZZz7A9l/ZTDZ5FL/3Oujaz4EcPzN42Pa70xtKFXra/lxgWosvVIqVx9Oy6e9FSAWCeyScC1qxr00kmYc/ha4hNRCaTZ3ZqgpFtOygNj6DrqyxNX6TdapHJZMm4LogEJR0iv4OlXPqGhmmuVgmtDHYuxG+2OX3sKYY3b4fERxBzzz3f49bbX4LfaRO12zi5HE2/k7oYBk2KhSLTExforxS4ePY0w6MjaQEAhrmFedAaJSRxp4FjWxiTpk0zhTyL1RqelSEO65jIx+iEqBNhe16qTSstSAS2EsSdiEhaDGzfS9w1cojjiCBJmDl5lBCzXhnPen/DZaC4m/FOuouTHmg2l4EG2btupjuxdA/8DHWU9XXPegrxeWrf+UrEwWvAJJIgyoD2CVqKs8dD6ksSqWLaHdi2O+ZFtxe49oYA4hZHn1pgZm6S7cNFitsK7NiVoWWanJk9RWN8noO7XkC+MowvWuy8JsdYTTGYj+krK779iM1373+MQmGOXLHIX/71KWIhSXSIJW2M1hhhUFKl0Tq6HHERI5WNNhqBRMchZCwMIcJzCQKzNlFoFWCLLIlJUpOdGGzXQccJwrIINCSdVGpPSIOwUmWGME6/Q08WK0ajHYlj26n+chRjlEUUaIRtrcsnatVNA1ugDCaGREuMLUDFyIzAU5JsNotwI5QK8AqSfEmSzRgytiKbbWFCDz+ICDoWrbok7ig6JiSTBdu1iIIYrSXaKCQJWkukJ2g1NFGYyuDZlkAKjZ3p/jYgDMAk6Z9jCZRjcDMx+ayi1CdxVIw2FkIqwiih0QyxlaLRCXFtj/nlFvW6gxcJtBtgSQujLephb6H6k2+x0UixHlUGSBKN1iaNMBqD6pr0WGrdsrsXrRQidcCM4wjkekSvB8ohvT9t2+5youUaQO7RMJSUaWlyd3vLskhZcCnQjnSCJQW2ba+pd4xtHWXP/h2USsV1u2BgbHSQcxOTFAoFpqZm6LRbtNttwjCmXmtQLlfYsXWMRnWZk08eY8+hQ9jKYW5mli/d8S8cevFbWFltcGZ6ldVWi9FNO/iFd72TxsOPMFbJ0/IDto0OsWvXNpaXl9m0aYx//sxnmJydY8+ePVx76CBf+MIXefnLXsGJU6dZXKnT7vhs2bqV6blZXlZwqVQqvPzlL+XTn/4MJ0+dYWZhDg2M2IZmdYF6AtMLqzTbbaSA+x58io9+6Ocp5Pu4/+67qBQLTE+Ms2PPbqQtuPWGA3z5rnt5tNmkXMzTbrdxbQcdBqnedjdYIWWPgtW9+N2MQg8kx/rSkutexmFt7k00Rqq14nkhxVUAyk+mPRsYvvx7XQ5YL48+X+nYVztW773LX18JvG3c93JQ92zR78vP+Wz0DYA4NvzR//xTdu/axsXJMzRWq9Tm57n55hfQqq3iekW+99iXGBsbwbJdpBTEcUS+UCCKNdt27eNAocyJo0eZnDqHiTSZjEMul0NaFtXqCnEUkSQJXsZhYLCP6koNrcF1XTrtAMdx6ARtHMejr7/ManWJQ1u3sFJtEBlBvlzizPGnyRcrgCGKNe1mh2Imx+axMcYvThOGEX7QYW7yAoObNtGqNzh86BBf/PKdxGGUZlATTaVSoVgs8pGPvo/XvOHdawv7y/vlau3HXfT9dIPnbuSy50LXg9CXxjPXOycdXGYdZG0AT+nbXavR7gpDdTu0B87XaS9dANdFbD0gtrFosVfYokRaNGEMPPbEk7z6Da9mdXUR7BztMEDVq5yfGqfP8wibbdpWjWzfAMbJEAmTVvgHhuriPEGhiI58tmwa5eLJe5gqFNOTxSFLK4sM9w3y9GM/YOeufRQKGSqDw/zwhw9zcOdW5hbmKA7C4f17OHHsODt372K5tko2jjFGMzo8SKsTIKXFwtRFlOVR6quAFDheBtf1aDXqSMdCJxFJFJMtFIiSVI6rq1WCNBAnPjkLdKuJtFziKLVHPjcxRf/wKM2JC6nNK5dGjnvZAGPS6HEvktFbGPW278a31kFx9/olGwC06q7C1xdDVx43zxeAvnDcorWUoBMLaRlc26JlEhzpsTRvsB2NYytWlyQXJ9vs2euweU+O5EyCYwmqfp1CDH6gWVmOMW3D94/UeeDsefrL53DyknanSp8LS1ULnRW86d9rHn2gxq//zirNVcnpeYOHQ3/Fxm/5COOgRcxgf8RqE2qNTJqFkRaCdGGFA8pSGCWxijYdEWF7giiMsS1F1I4RMkRqjeVZKbj1YxIrdZJDShJtQZw+nI1lsN1UdUIIRRREhHGCJRV+ohFoLEfgOB6tlsGYCKkl2iRkPY9Op4MRFkmcIIQEBV7G4BQ0+T5Jon0yriKRq+RKNkLGWBa4tqboGbKeJueV8Ot1fE+yGMcYY6OVIeNIHC8FXoEU+H4aVQ6DdVMaqRySUGNJg2sL7KyFVAkiloSxJlZg2WDZCY6XMDBoU6gY8l6CoY3CJo4EtWaCiS3aHclKLUBKSeg3qC5LREcQK4UWPkJKUqqzRxg8T/bcXZOojVGxtNoaQp1GeiOdCvAl2qBUlxaHxmZdszY1uem5YyZrUWVIlUSEASUVQZIQx3EqY6dT/rqUCkV6z/fk0GwhMTKl2EndVXXoAu9M1uPg/p0M9FXShZToUka0wLMkOdfm8KGDSG24ODlDFEN9tUYYtEmSmFbdJ0kiMlmPB+97kMPXXsNgsZ/89uv57Be/RoDBljaFcomlpRqfv/MrXH/jNbzn3e/kP/3n32PHTdvp1Gu86x1v4c4vfol8vsjr3/gGvnP3PczOzpLrq3DfDx9hftUn40ne/rLXEYYB508d5w/++M/4pQ9/ECkExUoZlclSGRhmqT3Lq177ao4cOcap6Xksy8K2HaIo5OaXv4LXv/cj/Pff/Bgr1SrVapWhTaPMXZyk0Q5x8jmGh4eYmppm1/YdLCzWaDaba2ZVxpiuUGqqrqG17how9azjzRo1rrcwUt3iTOiNDRBSobrXp6fdbYiQ5idf7HolMHz55xvbRrC0cUHwXMD/s0cy1xcZl7/3bMD8SoD7UqrIM4/1bPQUaUta7RrnLpxjeuIiXrHAyNAo89Ua1cVZbC9Ds9mkUu4nDFJJ2r37DlJrtTGWzdTEReLkNKurVYb7+2l12vT3D3Dx4kUG+/tBGwYHB1ldXQWTFhI7TmqBvZYt0hplBEtLyzRqDXbs2smZUycol8vUww5nThzHcmzisEMcx2zZtJnJZJwo9Dl19hRKZdCEWLZNJwpYnJsnSAwP3Hs3FpqB/j4GxShaKmYunOJXf+OjvOQlr3vGAmWNbntZX/1b1ng/1eAZ1jnMG1sv+JgyLc36e6IHw9LPehPFerRznS+LWI8yS7GuS7mu0LEhwiwlUnfB29p36D1YWLswt952C2Eco+KQ0tgWRndvx59fYtvmLZw58hi2Sbdt1xvkiv0YoQmjCJFEtFo+nuMwsHM7jx95lFgabn7BDZyenuDokcc5cewkb33rm8nmytx/z7e47RWvw6st01ytcvRkxP59e+gfHKC1ukLUbNNsNsnZaaRPZXMIJVhdmiJfKbFtzz4ujp+n3N/XTTV3EEKQzXpEIjWakKqrAakFGcehFRmECZCRQYYddJAg+zLEcYLfaiNcxWNHnmB64gKiy1XVG/qxB5E3Wq6rbkVRj74BPYvqjezmS1/DpZPKRh1nvbb9+mLnecp8Y2KN74PRHTYPuFRXwMokFMsBA0OGYlHTrFlMTQuePiJIkg4jIw6Dw1DKlqgHq9RrPsHxmKdXHYJ6i+UgIn/NBTpOiSiIWJivc3FZ0AktChnFrl1FElHlbDbg4rjhF2/axlvfdZAt2yvoYJCTRx/lhw9/n1e8di9+PeG3/+gciysZhJNgbCu9YrYg8VywYrAUriMAieVoTCiJY6trNW+ITJTqRmdsZGwI/HTM6O7D2ck52BmN54ZkMgqRtFiaj4hUgUTFmEiQLXvoOCIUgkjFSGWhpSAJNI1WK1VmIAZhIVUHN2fjFiOKQ4byILztwBv48z/7JrtvCbEHIYgkSrSREvLlUUoZRUZL3AHJartJsxGCtLCzCdlChJtJEMIiqRriwBD6EbGUOF6GIACMxG9HZF2N40Gx4ICMkGRZrQfkrHRCzmckxbxgsNhhaFBiuxphuyz5ivmlJkHgohNBGBhIJK2WJIoc7MTBNgbpxihl0fIFK7GhFWms50Xui9RRrmuVLaDr3henfHDTLSReCyKwBqhklwPbk6dbk7c063KEa+BZqVRtKElS23ad4AcB0I0md+tQlOl6vDo2wmg8x0V1H4Y9UGdbDlu3jTA0Okwun0l1qYEoCrrFnAmH9+3gru8+wqFrD7O4tMzZs2fZtm0Ltpel6fucP32CbC7DyKatPP7kSc5Nr/LHn/pndu4/TBAnHNi1jShKcF2XX37f+3H7skSrK5w9cYYX3niIsUo/gzu34LoOIyNjdKKYv/nk3/Enf/In3PTyN+PrGCUkjrRpNgM++bkvYTuK//wbv8LJP/4jvvq1b+DYgp//hQ/xoQ99iE997gsc/5tP0eyELDTqvPWlL0opIkGH177khXz605/l1ltv4q77H0W3VnjT61/B+NnTbN21j9pqg5nZFeqNNtlMHsuW+O0mURCAFhgSEikxSTcK2psjpbik4FPobj1QDwSadUrPxsyB1hrHsbrXIwGsNanSn2S7EpB8roV5G81grrTf1Y5zJYB+ObC9ZBF62Tk27nclSsflx9247ZV+/+WZ8b/7zOd4x5vfRLFSoX9gjKm5OaqLKf1ifPIi5f4BPMcj1AG2V8KPEy6Mn2F4cITFxUWUSpXLJqensG07tevWCX4Y4Hgu9XodIQS+77O4sEyr1aJSLtNs1YnChHKlSBilVMB8vkAYhhw6dJDZ+SUW52fIZ12GxzYzPTmJ52VYWligv9iHZynGNm1icnqGUqGfMAhI2gmWclhaXkJaiv6+Mm6uj1Ab5mfGuellN/HSl76RjVbyvX7ZmPVa79NL+y2dy547YPipBs9CiA3uc2kzCKTYGLU0XTBt0kmadZWNXnRz4/HW0/mp5rMUXdm6taptEN2iQdGNcGqtU0m7jd9J6JQP3QtHSytNRUQRfqtDv7ExoeGpHzxE3lVov00CVKur5AdG8dtt7EIZ6Wj8jk+n06C8aZjTJ04yMjLCjW94IxcnLvLkD3/AvkMH+aX/5SN87tN/z84t27jlppsplUqsLC6wa89utm7fxaljR5mdn2PL8CAvuPVaJsZn2Lx7L7VOh2KxyPmTxymVKoS+z/mF05SLOSwSOkGbTKUPadmYJCaxNMqkRSNoiKWh5XewLY84SaOUOoZMuUKMTadRJY7boByefPTRtAiFdVG/XtYA1sFyCm57NByx9oBdKxbs0S/WqB7dS9PdV13Ca+6mlUX3WpA6ZZneuHiemBuJgOIADPQXMAlU3Jh92zoMb1IUiwZLOkyc08zOWKwuatpVyG5OkIRMzQkOXJeHxPDU423OnNS4SnLoxgx95TJKxixMNVhagP5CHq0ijG2Ym/axMoYXv22YqVN1bth2kFtvfyFecR+J9tm6v8itrx1GEuFJi88c2MOdd57mb748gybEy2ZBCALt40rQKEzSpU9pSRiRasBigJiMk8UPY8IgjfJZmQxJs4MGbMdBWIJ8CbJ5TXFAMn13noIBaddZiSJUpkjH14R+gJNzUqdNtW7EYIRIjS+MAJVWUAs3Ynh3lv6hBtHDQ/zBXd8ip1xO/kBz6wchqw3GZPCcCn35In2lHKbTYnmpQ8M3eAWXJLLwnIBSCbRQLK00CHyXqAUYmyQ2BEGAJW2kBMtRuFmBqzSWSFLnSx2SzwmM1JjYkLUSirZkc79DQfnkSg7NKGY1DMgVMqysRMShi98x+C1NsyZotwwFF4o5jUrAODahEURoQg1h/Pyw93v85hSYpioXlkgL82Kd4Fr2WlHgRtDRK+zr7d97yGutSUTX2KjLZe490HrqGsKktrqRTgiTOFXg0BqhVFromSRYlkJImebAlEQ46SzjeQ5jW8boH+wjm89hOQ7KGMLQJ+lmEIqlLLfefA2PPHWCVrNJJlukXMxQqzXYu2c3E1PTDA72U2/5tFotfvt3fp+phRXOTC3yppfdxmNHnybyxnC8Ag88dpw3vOnl3PmtO/jN3/g1jp0+z8XJsxy+5o3ce9/9bNq8nfsfuod8rsDhF7+WbZtH2bd1lPm5WSzbwSSapZVlFpsB/+m3/5AtmwZ541veysrCLJMTFzny+CMMDm5i7+5tnB+/wP/1h5/gjm98i5bboOGvcOdd9/HrH3gXf/a5LzHRX+aVNx3mwoVzvOz22zh98hxPPH2cs+MTzC36bBrp5/y5caIoQdoS4/foWgbPcYnjeG0RIhGw4drYShF3U9+alG7VAyCWZV0CDtPXvejt80fbuLxtjChf3q5Eqei15wJWn+2YG9vVAPnl4P25nONKm1yJbtJ7L1Nw+ewd/8R/+OhHefzx7xOGIVLZ+J0WpXIf5XKZTqfD8OAwUgqW5qfYvmkHA/3DTE3N4FgWK8s1XNsh0UG6cJaS1ZUqtm0Tx3Gq6iUE9XqdbM5Bk2aZMgWPYqHMar3O5s2bmJycIpPJ8OTR4wSJZte2bZw99RQr1RpuJoNtYvygTRAljG3fzOnjp8gUS1iWIAxDVutVjJWO1XK5jO1myeRLHDnyGKvVWX7nd/+p+7svvRZXWwQ9Nz701dtPtUnK5cAZuul80zPU2FDI1/3bKHOt1/41azxYgB4pvLev1inXsxd+7p2j15F2l64guqlMLbo3lDRrJg9CGnbs2IaShkgI+jeP4jfr7L3+Rs6ePIHfaeNHqXtPej6Fcm2COCaKAhq1FY6fn+Dfv+fdDI2N0QoDVttNcoUS/YMD3Hf3dzh8zbU89fRR8pk+qkvzfOO730Y7Lvd88yu8+FWvxrE9Hnn8Ib5/933kygW0BMfxOHPqJFMzM9i2ol5bYefePegwol6rkiQBvu+TRCG16gqg8bJZLMclCDtYloXjWOQKeUycIKIOypYoy6Ndr9Gpr5AkCU62gIg6a25/PT75pddu3RxFIEjEOkDeeL2u1IxgLZK98bia1DwlHRPriydEz379+ZnEN++K2HNAs2V7i+27Q/Zf1+b2V2bYvzeDl9XUVxPmZyLCQFCuwL6dkr2Dghs32awuJQwOZxno9ygUDFt32mzeEzDQb1idD5g8XePIEcX5kw65coZdhytsO1DCjw2FvgKj23LsuKbAxMI5GkunCaJpktYMIm4jRZFOJ0BlXLYePMAHP/xiMoUY5UmMiBEywnIktuugLIckMcSBRiQ2cSBIwjTqLIRNpBNSkxGF0IIkCrBshbIk0o7xPJ9sOaEyCF4mIGNH5PMhSewwYOXYu7UfrQS255D4MUJLRBKDiUhMjJGKRKT2zFprtBPhDiY4ssPS9xUnx1fJ2hYDRYXn5igUy2QzFbKqzFBlmKHSAEIIPC+L55ZoBxoSTdaO6C8LbGnI2S6EFs26Rkc9rVuB0RZREmNIUFIjYoNlOfhaECc2MQbbSnCVwbEFjudiQsHSHASd1GlQEbF1OE/OE2RciIKQsJM6RTYagiiw8TuGwE8wRmCIibRAR10joOdperYsC9M1eknBMKC69AjbwfSUFqJozd2vx2nucZC1TiPnvW0dqda2TYukY+Ke/brWKMtay0DFGGKjSTBEcUwURVhKoQyXADgdpwB9dGyQ4dFhcrkcrpNDWutGKlprlGPjuBlGhkuU8xlyuSzNjo8jLE6cOU/JVQwOjbBn70HGRoeorjZoRDA41E9/uUTFydChgj00it03hnE8Os0mf/4H/4XP/ONnue7Qdt73/vfiui5btuzg3vsfJE40Dz55ihv2bOHA5mG2Dg9wy40v4Gfe+jYOHz7Mr3zkQ3zk536WSsZmYnqRv/zbv+epY6cZGN1Cs+Zz6OBedg8W+as//Z985q/+mrPnp8lWBjFuntDELDebDFRKzC8sc/zMBXK5AhcnJ/jK1+/m2IlJ2s2E/koRjcG1Xfr6ykRRRKjASLVGrzAkKV+522c9BZWNmto9YG3bNkJJkJdup2wLkOn1k+m8LtVPfuH3o3jIvbYxAnw1fvHlnz/X8z9buxI1Y+N5Nv5dbf/umTYsWK583rXMLILBwUH+6Y47OHT9Cynkiqn5j3IolUqYxGChiOOE5eUqlUIfQ0MDSBny3p99L7t2HyKbLZPLZ2i3fUrFPDpOCCKfQqlIFEXEcUKchDiuhd+JwaTjwg8i5hcXCcOAhbl5RkZGqNdrKGHIO4r55SUOXnsLg8PDSBHj+z6jw0NIz+H0xDQzq6tUBivYrkOu4LBzz278ZojjwuDwGLZrcfTok1Srs/zX3/ttpLDZaMF9pWj/pe9JxGU28kkdaykAACAASURBVD8OWvipjjwn9LSAN6wcBGvFDeoKA79nww3rhWYbjTh6XGjVTemv7St6boZpdFSI9ERKcUmxmiVShrS4bPAry2F0+3aksnnB7S+nVqtR8LKMbN7Ezv0HuXDkCHEQkeRLSMehHYQk7TaNVg1bx0Qtnx0Hh/nSv36N3bt38dB99zO2ZZSxrZsZGRqktrLM1l3b2XtoP/NzUyycm+bAjlHu+87d7Nm7n8nZJS7OTvPq17yDcl+Or37xS+y4/jaWFxfZuXM7B/btY2pmmqyXo7G4RBj4FEoVLFvSbLdwpKDV6pAvlJFKIZQhV+7HGEHbr3U1qjXuwBjBco3q/Cz1mWlyxTxWLkOoE2582av43d/9bcbHx/GbLT7+8Y+zurpEpxOwTrBJo7Kq239ppPm5p0qEMGvXcO29jdmF57FIcGO79cXQNxIjjUPQbpLNS8JWRKQMUWzjR4JWS6FFgLRi2qHh3LTF+EREo5Vl8UIDY0eU8i6uEzEwlCcIQ46fWWVhRtNseVxzSDLQLxjY4qJ1TN+ARbFsIRINWnD+/Bn+fDZh59ZzNGtnGSpnqVKiUauxf3eR63buZLUu2LorYfKiQSdp8atUGinpyoPZRElMEMQEUYyUYCsLiLs8ZIsgToiNJo5jMo4NWiNUjJUV5DyHIGiRkTb7d+yArOHppy7S6sQcf2qeTVtchBsSJBFlyyGIJO1OSBQrHMvQ1zfCyfMrlEqGN9y+h1xB8Nj3p4iiDHa2wf/+6x/l9z7+CTLlLIXsGMJ1UFowNlAhTDqErYCpus/03DJRYGO0RDoxOooo5D2azYSw5WISSWJCDKndtm3biF66WkIiNO1WgOkYvEKWOAhxspDP24QmwPcjorZLbcVHRxJtSYbHsszWQkYrLlE7oYOiFQs6DU0UW6nyiAXSEiQiIY406ATHsYjDnmb9T771CvDWdH2VQGizFlHqAaseeEqSZC0k1gNgPYkoy7LWIptJr6iwx6cmjSQLkWqB6x69wwikkOlCxrahCxQiDLZQa1Fty7LwMi7Do6OU+0pkMi5Cdq2RrvBs0HHC7s39nD1/kT07dxD7NY6dOMXPvP52lmpNIjQjm7Zwy83X8cVvPcCOPYf53N9/mve9/9/RbC+Ttw8QNapMLY/z5YkjvPFFn2D76AiFQonxCxN87KO/wotf9ioGR0c4eXKWHUMVto6McnZimnOz81ycmsUVEEnFl//VsHv7FnZtGSW32mBioUrWmeLi+fO0203+4i8+SSZjc++/fp3jx47Q9gaIM6OY2JBow4Fd2+iEms9/7dvUajWCKCaTyxNGEdKAVIq+SgUpQSuB67rdjMF69DJJopRKY3pAgzSjisBosTY392gbPRfCXiGhErKr152qn/QMa5SAxPzkKUcbx+Xl7WqFeJeD2H9L+3H2vxof+3IgfDmV42qfXWm7yyOrn/rUpzh+7CQffO97cLPZVKLUgOU5qetko8bxp5dZXa0SA2ObN/GiW27jumuu5/S5p5HCZm5ujjAMyXgeC3PzuBmPMI6IoxCtNX4YkiOfyt0KiKKEfD5PpVKhVq0i0MxMTZErFFITk61bsRyHYr7C4uIieApLOSx3Gly3dQs2kphU3lSgOHT4AGGkmF+YIgwl1eo023eMccstr7oqZ/1KC6orReuvtO2ztZ9q8JyamQCkvOSNPLsewE2juGlLqQA9A+nLD7Ye4xQiNSnp9ZPAQphLH1TCgKUu7UhbpikthMboSwsCPM+j0WhRyhQJEo1fr2OSiJxnUW+2aXQ6DJTLxGFEmGhcW6bFUJ7CN/Cnn/kH3tXq8LMf+QWefvxxxifPUcg6oAyNehUtJHd96Q7afkymkKe/f5Cde29gZIdBuQ6xNrz6dW/m+9/+BoOjm5iZnOS6212K5T0sLVUpZxTDW3YwXMozs7BIudJHFAdY0sEoO60S37YNpWyiKECYCIlNx+9gC4iVhZXLs7A4R7tax1UCr6+CETFxp40s5nnlK1/Fz7zyxUQ6oVQsstpoEBlQGHbsOcCJM6ewSCWPNtIzftQYSDOBXddI0c0xdNOEV4pX97jszyeQltJQqxlmxjVh26ZQNGzfD54VoxzIOoJSSdBuKRxPMT5r6AwmNBJBECeMX0zw44Sjx2zCwDDU12b37ixBxyOREbfdrti+yUVagtnpVZIwSCOjdZem38LEGQp9WR599CyPnRYUi4Zcrkk2M0++aHPP4/P88MQMlYrN5q0OU5Npul3aFugQk2hs1yEKQuJEECYxriNBGmzPkJiErO0QdyRRJ8aWCmWlcX87I7FsgevRlTq0qAWa2YvnOXh4B7miwI49ZqoNzk62KXmG2154DVLE2MLi8PX7iFstBgb6WKzWsV8l+OHjx1geb/HQ+QWWayExPiUR84O7v84gDtZAwNJSnaFCH8MDFWwHZOKwUjUsVleZn22yNG+jjSCfsbCUpN6KqS9b1DuCMI6wHIXW6wVSKe9HomNDLKAdgJGSeruDYyuGKgopfRxh04xgYcZHJZJqNcLYDpl8TH9RYTmCVREzFxiilkRrk2pp21b6PW2Nci1iGVIoGvxWDEbh2s+TPbcES6q01qBrmCG7k2+ysfDPpA6ASZJgEF3ZOIWWco0rLYRIeexaE+l4zVJ745/ucg3tLtDu6TlLpdYKDaFLJ0l5X2kxYpJQKhXoG0ir7DOZTJc6EmGSpMvftXCdDCZJqC4vMj8xzqGtIzx4apYX3XgNE+NTlPIF2hFptX6lj+tueTHveM1RvnHvI3zh7u8yOzNFThnqpx7GRpKxID88yMMPP8zExXOU+wq4mSwf+MWPsHPvHr710BGMMVQ2jXHXQ4/wittu4chjj3Boz052797LQw89BDpkYHiIo0+fxFWSgoQzc1X+4V/uYPNAP4cO7iWfz/LUsfP88n/4KN9/4Afc/+RpYt9nIOvx4JOnOD8+wfZNg/z8v3sD11xzkIxt4boeSRxiMKwsLbLvwEGmJi9Q8LLQvSaqG20Tykr7SKQUDKMNUrE+/pFrxYQbudAbObwbo9NamJQ3rU3qRPQ8tCsBZ7gyKLoaN/ny1//Wdnnx2tW2ee7vdxc6a/SYqz9IL41OJ+zbv4uHHn+Mt7/5LegkxO+E9A8OUszlsLXCGxjDKvTx4Q9/mOuv2c9bXnkzlVI/Gccln88RRQFRHKBiQZyEBLVOqrHsOLTbPrmsR7NRI4nTgtN8Pk91ZYlysYQxhmq1yvDwMAJFw/c5c/ocSlgMDRbJF3O0mw2kgsHBQfr7+xkd7CMy4Lg57IxNogW16gLnJsepVuvYjuHPP/mpblDtmb/72XjqvT5c71XWjL+eS/vppm1sgD/SdKkZKaUqhU0beEK9tL5kndLRa+KK0Wc2qGes8/Yu4cfodAUOrCl19D7v3aTKtth54CCDuw+w6/B1xLaiE4Vs2rqNWnWVxx56ENd1yZb7Ur/1WDM8tpVWo4lUEAUdDr7wBfz+n/4xB2+9gaeePEKc+Nx03XWMbhkjCUK+cueXmZq4wHU33MS+Q4fZOjyK1AnnL16kXMriN6pMTk7Sji1KAyPc/a1vUV1tMHX6JI7jsGXLFoZGxijkskxMzzA4OIjI5AjaHcanZii4NiObN9FqtTBSoaOYMNZoJVCOwsvmsITEbzawEij2V1CuQ6mYS/lHm7eyVG3zwN3fpVeAWWs0SHoDGMH5Mydw0WQ8j9/6rf8TYTtrCx3TpcGkatJiXZoOLrsh0odsYrocmqsQPXq0keezBTXF2ac0Z086HH/K5dhRh0fuNTz4/YSzTytOHItZWJBYtsHLBwwPWgyOxRw8bNi2FeZXbKanFI6bIIRmeVkxOxMSxS1yOU0UtoiShEceqXLuWJ1Hvh/y3a91+NevznLyqE8YhuQqDgMjGRJjGBvNEcQRQ5uzlDbZDO/JoioBkZXgFQ1bduQQIgEj8ANDEGr8TowQCiEMWU+QLwpyBYOXjeirWHjZBOnEWBmNFhLLTZUovKzB8dICwkYtZrWaMD8bkkSauQsneMnLr6MRdLCVwLEs2iG0QpvzF1cZn1rmHz//VartNnd++36+/u2HufMrP+TUmUVOj8+z6rdoEaJESDHn8OTRk3QI2XJwDADHlSACpIR6rUXTT2g3BYuLLu2OTRhpluo+y03Jak0yVxe0I4G0JdJWCEuiHAupBMJIkkgTJwY/tmkFFrWmpN1S6ChCWoqMJwmjNu2OpBZKVgJJw/dYqMZ0WoKcUUT1CFdZgCBrC7IW5L0kVQIppIsN6So8z8FgYRmBQ0LWi56XsetKq6vpzFqUNzaGKE7QSfdfY0hkWt9gpER3qR2ml/IXXYqWkmlWwugNqf6Us2xZ1lok25YSuQGU9ebpHkjeqPKw1qSg1F+gf6CPXC4tFFS2iyTV3O50gjUaWbteY3l2nqWlVRamJ1hdnOX4yRPcfMMuOu0WxVKZRrPDUrVOqVThphsPUynl+ZP/8Qf84i//KntGy2zry3LDvi0MD1T4zV/9j9TDhFyxxMjIGHfe8UUe/OFT3P3gYyit+fUPf5DNo1sYKBTxW3XK+QK/9rFf5gf33stgscgn/+qvObBnD3GkmW+H+EohjE2gLa678UYQimPHTjE1P83uHXsYH18gYww37dtFq9UGy2VpaQElNOViDs9xmJmZ2WB7DiZJmJuZxrM9Go0GlmWlxZQ9EKzFOnA2KdgxSAwydY/FrHFcNxZ79q5DHMckXUdeYa2rK/y4dIf/t9pGKsOztStxYH9UtPK58ZF/FN3i6iD5Sn129XN2QZDQXA6c0z7Qqb291muvU+fkrp6/pfjy1+5i++5dZHJZ2o0abjbL4I5dRH6Hs0cf5b/9r7/CO19zO088/gibhofQQpLNuDhO6uCntSYIAlzXJfAjRoZGiUI/dQ01hlwuR6mYJ47SbfygzdLyApZlUa1WyRXybN28jeGhAa49fJharYbjZgjDmHK5jBCCheUlmu2QPXsPpZKVicY1klbo4+ZSC/F/+MdPY6nMFfvzahmI9WvwzP7+/03kOQXMvR/UmzzXzU+gVzTY4zxfgbfUHWc9+9g1xQ0BsluLtKZt1n3Z2ybZeDwhSEjpBimndv07HD95lg//1n9lcHCY1vIEYRjR8Wvk8x4FdzPzE+Pp95eSdnOVcyePMnbNNSxMTjC6cxtnTj1JpW+QpaUFpsYv8PZ3vwuAO77wBcYGB7Esi2Ixz5lTx4ikh0wiQiPoKxTo6+vnm9/6Ojff+jKisM2+615AY2GWL/zLHVx77fUoy2J1dYU45yGFRaFQYOLCORIM/cUyTj5Pp9MhUyiSdJawB0cwXoYwaBP7HSwvk0aWooSF2Vn6+waI2yEzp8+w89rDOOUS7VYDpOKRhx9Ii76MWau2FsJ0J+K0BUHAJ37vdzAYSoUSzXYLreNn8NvXotOXvrv+3uXyKJfYdff+ff4KV+YmE8YnHVqrEUZBs2ZYqdpYEia8mMogbNqqkBaMbVJce6PLwNAQtWqb+uo8x05Atmizd7+gWk2YncwSRB36BlwKRYGXFZy9UGNu1qPdSfCbEi8H/grkyxo366CKMfsO5ehbCPEyhnwnHbOeQ5cHqvCyNvlykXyhg9Y2U+MJCkUQaYyMQEAmZ2FbmowHCZpcNjWqsGwrtalODElsEDpBWpokiZDKItESv5068sXGo+M32eGWKCYupt1i/4F9nDo3Q5jYHD9xlrbf5vYXbmf5WJ3HnzhKtQq+HyCVjZQOtVaHxHg4QhN1NaTf8+bX89hjRyhkS9Q60zSbHhmlqNeqzC5VOT0+x+qKpFqXxElM1jEox6PpG4JQ0Il1ql1uDEImSFIw4Fg2QafroKg1cZB0ud7pYsJISRKDnwgcO0u7ExFrhQaasWF1SXD+QouM8mi0AhZXJQIL5WgGhj1C3xBFCUKBVBZBJ8TyFM1Gm1hn8ByJm3l+CgY3AlUpZWp2ozXKUmv3ljTpdmEUdRVR0nstTuI1e2fLsrv/KpIkBXWiy51dUzkC3G5kOjYa1T2ukCnojrRGWjZ0VR+Uba1xnXP5DFu3bqW/v4LnuEgrrU1Jur8hjmNiFdNcXWZ+bpL52TkanTYZ2+H6vZvSoqUto9iWRz0OOXXqFLfccjOeZ/HK17yOP/3rf4Ig4P/+i7/iV/7jrwHg5bIc3L+Lj33wA3z8d/4Lp86c5p577uP8xCLlUpa5Rod3vfHVPP7Yw4xt3kK5XOSGG24gly3y0MOPIEi4+fab/x/u3jxasqO+8/xExF1yf/n2rfYq1SZRSEL7gpBYzA5jwBhsY2O84KUxdHu6Zzw97tNzTrun2+4e2mDa7oMN0zbrGGNjYYPMIgntaK2SSrWvr96+5n6XiJg/4uZbiipJeNqIM3FOnVeZeW/mzYjIuL/4/r6/75df/vVf5Y8/+Qm++JW/Yf/4MAtLdXp6yjxz7Cxfv/ub3Hb7jVyxezfjYyP8b7/3+3ziM/83hw8+Q6A8Pvtnf8bBgwdptFu85c2v57qbbiJqNjl79rwLXliTCqyvrDjKjE5WA+c4jjOn3TVZOljL5FprnR67cFSXSK+pF0gpnVuo8hGZ1JHGIvTG4rqXY939x35md56/2DGX+8wX4h5f6vh/bPsBaoZdc+Xsmqi4x13QaSM3ev24+IFieb7J4OAQc9PTDA4M89AjT3DlVdcyNz+DSdr09Fa4+4t/zdJKnVarTZxoklgzMjKENd7qHCsWi8zMzdHb1+c224CnfILQFRuHoaJSrtJqtfC9kE2btrpiwzjBEx4LCzM0Vlaw0pLP5ynkS1yYmEakEUtDc3hSs2nTJqRQHDryHKgCrdo0113/CvqHtmLXsQYutTG6+LH7+4OvG/vDxQs/1sHzKj0++7LrYlyATIZuDT4XWfi8vpNkNqG6jnbgkGhn3+2IHl11hhe9HuGCwfWHKqV47/t+liuvuYZoeQZfQb3ZoNxTdrus2Rm0SYjjmM7yMgXpUcjnOfXcYY6fOUXYk6e10mB0aJid27eyY+smjh1+lsHhITaNj1GpVEjjDtYKZuZW2LSpzGxtha07djE1NUOz1WLnzn2Uy2WOPHeQ4S07iFLLO97xDrbv3kM+n3eUlXYDz7PkylWGhoZYqtfoKQS0hE8uCDEISpXq6g9Nego/LGdpVEnO89i8czv1xRVEPs+2q65kcXYOFQZ4hTyeVHz7W/fSn20qLNlEzsZIIVZl6mz2uFlfWZWh84R0SAc2s4TtTnBXFHi51g26LxdmeC8TAj23DJ2WReUTtu4JMElCKzK0ahJfGW58TZ6x8TbLs4FD6/yEQjlFG5/NW0tg62zb7bG8LDlxxGI0VPsUVgqGB5ps3TVIa8Fn+x5La7nE80dXaNQlvcUcRnjEUcreXWPk8ysYG61SjpbnOhw7bjIXvZRiwTI8XCGNJFHUIk1ygCAMchiTEBQMxZKH7yeUywqddMjlXWBUW4nQqU8Q5EiTGGG8rFhQIZREJwlRZGlr0ImirywZ2TzG17/5Pf6Pf/+7/J//7lNESYwQgmZHsG3zECfOLFGulliaj8iVc9TrLaLIkkR1ZK6IjSxYjdSKTT39fPovvoGfL7JjrIbp1ZxuzRAngrm5JeYWmyzM+yyvRKADcoEln/dBxCB8jLDkCoJ2SzkhRWsRSiItRGmC6XKGsnS0l6VItbYYPJYbglIqiaTA4jZGxkoQgkZLk5geLiwkNNuWxIaonKWaA20S5hNNFBkCE5Bag40FzUiQNgIsGr8gKVVeegrxf2RTShFFkSsAs87VT1tXY9K9GVshiNIEX7k5KdcjxsauUi26ihxdu25jLbF2UniiKyepJFJJArLi7uz9pTFgDK6c242B1QZPuMB6YHiA4dFhwnwOP+dllCOL1prUphgEK7U6plPjwvlpoighCALyuTxDfRX8XEipUEYWijTna+zYtp3xHftZWFhkcWGOsmd51a3Xc/d9D/LfP/OnbtOUxARCMDY8wre/9V36eyo8+NBj9FXznF2OeP0NB3j88cdptyKOnDiJER6f/NPPkaA4sPUs1193HU8+8QTlUpX/6+N/xIEDV3Hh5Al2bt/M9MQEezeNcGxiivse+D4f+Jn3cH5iEj+N+O1f/QVyxQq1Wo2ZmTnqnZSffttr2TxUYfP2HfzdX3+VufmFLFPgCgIxWTAgullcgUGvBtFJrAmCYBUttNaSCotnXSGVxJDgkxqNMZBYizQWZNdZ0mUWlAiwuHGB7B75cmmEvkDrxgUXK1u8WMC0IZ5YV0jZfe1S7XIc5Euh0Bdfyw9H4bjU9bv+vxQS3z3W8xQrK0v09VdpRAlRu0GlUuHJe79OEHh0pKR/aIy+0S2k7QRf+DS0JkkMQ8NDLC2uoKTPtq07WKq38I0hCALmF+coBDmStOMKjHNFllcWUWqGzeOb6Kn2sXPnbk6ePM5jjz2G8gV33HEHA/3DLDcW0HHC0tIS/f19zM/P8+RzR5lrdvD8gMXFBV59++sR4QrFxRz//vd+HyPN6s1/fQbhUv24kTVw+eNeavuxDp7Xl4VZ69ym/HWvdxdzLjP5pZRYs3HCumAr24GJjBebWYvCeirHJd7Pgs24t0AmWu9z8tR5rlqYJfTcTddHEkhL6nkUCwGh6sffv4/DDzxEK4p45pFHKG3fzO4DV5IPfcK+CufOnqa/t4d2bBgcHaderzM6OEC5UiVJI/qHR/DCkKU55yHfbrd507vfx9zMFA0d0D9+Bb2jm2i2OvSPj7FrbIzJeo1Wq4XWmvMXznLzdTfw9OHjbBkZZGRgkCDw0Ymh1YmoVAtY7WN0RKvVQElnj+x5Hq1GHZO6hXZgdBiZGRbEOmFpbpZKLke5mOPpo0d5/Sv2IJIYk1mdQHdSmywoXmsiuynajMtordMVTS9B2XihZi5D+5KIVerIj7pV+3wSa7CeoKfP4iHYNJ6ysOhx6liATWLarZTJ6YTFuQL5fJP+njJKeJSKAZt3KUpVn8nzK/RUiuzcZdi6zefU6YjISBr1iM17hxnfHNDudBgag4PPJNRqhoXZiPlpTW3Mudi5RR8KfshUrUnc8Tk3kRJ1BNKDMExZWY7o1APStkH4Bm0VQUEifYkfplTKEAYRXilHIZd3yhCJRSeGJE4ylz6DEQaTpmAESZSSCg+040z2DfTwvYefJxLwp3/wcUqFEkst5Yw3CoKpuTppJ0IqRU/FJ1ru0D88wPzyApH2MRosGg+P/kqR589Psmmwl+MLTRYNiIZP2omYnzvH7HxC0vKw0hC1PQhSvFAgfIMX+iQdg0IQG4PyJAoPkyZ4UhJr4cx8RBY4+xAGEpMatHZqGFEHVurQjpxEYqfhAkVfGAJpCUJBrWlISfGCEC0s/WWfNDXUVwyeAKFyaJnSSgTWpphYEqUKq1NUXlLseVmmLlGaIDwfugGRsQQZOtwtADTGOJ676nKinbRc94bdRaCAzAp6o3qDMdbZv3eLvDzlqFZSrCr1WOtsdzFrgYCjfUAQ+mzdNk7fQC9hGOKpAGsFSAlWksSGNIpprCyyvDDL0vwSsdEU83nCXEAxVyTfUwYhHE0By3PPPc/OvVcisSzMzfGmN7+ex55+Hj/ucPrESVCSPds3M7+8zOatY4yNDPGFL3yBXJDj5Pw81+/eytzcLHv2XMHE5CxzC/NUS2W2jIyQL1fYs3MT5yemGOjp5dT0SS7MzFAu9TA02Mvho6fxhKa3YtnUX2ZuucHDDz3Ir//KL/P0oYNcuDDFQmMKKaCvWuXG6/dw+tQxfvrdb+LC+XOUy2U8BZ4XrAVpgo3251KAllghMNriSUWsU6SSq3Cl7BpbZMWA1qZYKzGkzpmU7nquQSiX1bUpvq9WHXd/GK3cf4p2Ob7yxZTLFwtuLz7vckVml7uGy73PpR6/FF70i3/mGoTU/a12aTxx7DIPQriNbqsVkQs92s0mQwN9nDh9Cl/k2HPDLcStBkePPs/RZ4/yh3/0cSqDFVq2RWrK1GoXOHXqFAJFsVwmLBcZrvby3p96P1/+/F+glKVdb9HT10+96QJyo50FfLFYZmZqhrynaEU57nzLu1manuTpJ5+i2Wzx9ne8hQfuf5BCMcDzfOKkQ8fzWFpeYeeOfTRbHb72tS/T11fBz0nwfKR1wGd3U7R+bC/f32tjc7kiy5fSfqw5zxe3y6VHNvCbbSbk3qVhrOtAxcbJ2b03vlCaaZWHay+iimSDFccxsXIFg92bTOBJOs0ajXqN1KREVtNIOtStZTkf8so33cWrbrmOHWOjpO0I0gTPk3gqQEmYuXAe3/dpdCJWWjVSI3jo3u8weWGC3uFBDtx4G7JY5fypY6BTsDGd1hIPP/IIm7bu5upbX8cjh48yvPkKPC9gcGCAaqWXcxcmGRoaYm5mmiTt0OmkPP3kk5R7HJlfZaYqlWIJqx1alCQa31er3MRWvUGr1WRubpanv/tt0qVJkpUZypUijz71BDnp8Z//9L+tygm68TF0/SF/8J9TLukGzhvHJaPXvOCsELxccnQv1EqVlAPXJFyxB+KWZXrCWT4LFSMwNBoS2+4lJytMnIl49HuKCyc7RA2P/oFeyj1Oomd0XLHnyibbtqUMjxc4cJVlsCcgacH0hUXmFzssL9ec1JRu0VO0+KFhcrLFwlwLm0gq+RImlcRtidUBjUaLwNf0VAKUNUTtGB2DkIZCxV8VxtdJjOcLFJqo0SFJEoS1bkOWgtUpnq9I0hahp/EDV+SF8EljSxxLbCqxRhH6gvP1DqpUJud5nF2JmJhfREiL70vqjTqdSHPF/j3IMKTdFJQqLu0cxYokFbSSFtKC8jTTtQY1pbjQaNMyhlaqWa5FtFoe0/MhUa2INgGdlqNZFXxDMXR6pCbVDuGUGqEEKBcMWE8SG+3wOW3RWJRaQ1Gl75QxkILEChoNQ21FsLzsCputTsn5kAssUnXl9jw6WmCMwMeg0oSiHxLmPEdxsQmFQGGTmCRym47Ak+QrGWynKAAAIABJREFUUMi/PPP64hvQKlqlnNyc53n4nkcYBM7hLysQFNLZredzOVfgLdcsvtffzJTysmDXkujUaYevvua4tUoJPE+SC3yCwCP0FUHg4fuKIAjo7e1hYKDfrbtKrsrcde8FBqfVvby4xMz0Ap22y3D4vqLUU6HQ10cYFBxlrVggand481vextzsNFGnxiuueiXveu+7idKUQBn6SwWK+ZCnnjvB0uICp06dItGWnXuv5NzsIgf2bGbHvt2Mjm+h2Y4YHR0m1Zbx0WFuvPEafJnyn/7jf6C20uDGm29m56ZxbrnuWn7lF95PrVbDDwOWWimNFgz1Vbh6324OPXeKv777b3nj699A3rO84lUHGOzvI241mJ08x+jYCD19vSwvLDM5OUm5XF7tQyWyYj/s6r9EO1UTbQ2xTknXoUTr3de6fai1RnjKZV2ywGstAPUQGe1DSrl6bwVW3V9frvZiQe7FVIYf5lrXH/tSznupKPKLIdkXH3epc9Y/7tJrui0IfDxPuUyB5zEzPcfgUD9+oJAopienKJcK6E6Dgf5e7rrzDq66cgc7d+9hpdZgaWGJI0cPs23bdvbtvZIbbriZt779nfzCBz7IG+66gzMnj/COd76ND//aR1lcnkdKSTFfxFoYGh1ly/Zt1BorKE/yz37rN5mfPc2zTz7N0dMnCf2QoJDnuUOHKVbKKN9zYgpG0my1mJ6a4IEHv02n3aZcDKn2DPLlv/pKtt/bmBV4oULBF+r37mv/v6FtpDjETmR85W67XCfZLD3VpRpY4yKw1cBXdHWhuxwut3O21q6mENcH3SJDpLufobF4Yg0PF36AX67y+re+kySNqC1NOy6Yp4iSBEyKH+SJdZvS4DBv/qUPOi5ep8Py4hJKKcLQJwxDGs2IsFDEKg8tJMViEa3TVe3UK3bvZ6HeoNw7wOTkDFfs3Mbp54+hrSRninzn7r/n7PFD5BanuOM9v8TVN95KodpPFCe0Ik2lp4eJM6cZGNnM4Ogm5uaX6O+B4dFR6vUG5Z4KngrwVI6l+SmEki4daw2dToIfOutNmxpqzQbTU+fxczkKA1tIbYI1irvueB3/Qls++uFfwyCxSkGaZImBzPyi23frxnPN+dE1hUOihQVPCoy1zmBBgF4XKGe3+IvebaMGh3yZ0oeDowG7rxHMzTU4d9yj1Q558nGBSRWDYwlDA4OEuSJbtgTMTZzj8GHBPd4yb31rjtDTNGpN5i6kFEp58vk2PZUAL58wUu1BCMXiTJO5mQYzEy0WFlOW5xVh6DE26hHkoZAPWJhr4JFQyPnMTdVotRS1hiHn5SGMkTJBE9BpK0LfyVbFSYQXKoJQoDzfCeNrj3xJIYyk3YrJ5QMa9Rb1WowMQ8IwJIk0vhDUGy2UCtDaBZxWu4xRqpuIvOTkfIonFNZqZ9duXP4h5+dpxC2OHz9Lux2Tlz6T001uuvFaHnjkEFIahJa0rMETCj+naHdimgn4SuFHEbW0iO4oomaKsQahPYT08XyFsZrYaLcpFAJhJHErdSuBSFFSOEMYlEPUrbP7SYwllCCkQ+80OPnKGKLEkCrhzDxQTmHEh3Jg8VSIsoZ6x2JsSn+hTKPWwbeKKI4ACEJFvqJQIsFSIIlwcoE5D2nN5RJq/+RNCEGcxuSCcNWyOXsBqdRqEZlex4U1Gf8xiuPVwNlaC9q4DbBkFVWWUiCEe18lRFYAbvE9hRROWm09B1UC0nOudUIIPF9SLhfp6a0SBEF27JqGdGo0JtW0W02Wl5epNZr4vk/oh4T5ImGhiBCCKI2x1lLu7SfWzzuqioGBwVEe+O63OHfyLNft380TTz/Jnddfy/Y9e/nj//oZ+stF8oUSZ86c4aFHH2OwJ8/k9By+H/Lq19zJn33uS+R8ye5dW4ijlBtuuIG7bn8Nv/TBX+LsxFnGR8f4rd/4MJ/81B/x0PceYG5qlkq1Qjkn8cKApWZC7cJxdm4ZwSYph597jit27aHZjkgbNXw0Fyam+Nf/y8foKRY4fPYMuVyRIMitBk3W2swNzqH9zuxEOxk7BCr011we0272ViCkxAQZhUMLbOq46EIojF6zUCejUnYzClEWeKjMpOrFOMT/1O1yQfGliv4vPueFAqgXo3tcjq7xUs69ODC/2ODlUoHf+uBxbVPQpT1ewrEw1aTG8ok//M94UqFTy/j4GOcvnGNi8jxKSRYW5hgeHiaKNU8/+wQrtSXyPSVuvuV2GvUWW7Zt5bprr+Ob3/4W9973Dwz3DdHT08cj99/P29/9bj70mx/j/JnTfP0rX2Z4ZJAk7WB0lWpPmdOnjtOYn6O3UqY1Ncno2DCv3H8VA+UiUdzh0e8/gh8EmNSwdetW5hbmCT0Pz/dRgeaPPv1Ztm7ZhedtpM+8NNTe/sCaugF4FT+cPtePdfC8Ximyy2024KqyrUMXpBQbOC/gXANlF8fMJlMWv2VNrvKoTSaDBxlqbZ1Uk1scXOGKq1SVbnlWGXfMGEja1GtwYXKCUPSjpMU4NS+kFXQsaC2QuQK9lZwL6pXACwP6qxV0arBphBUCa2IindKKOpTLFQAKxRLKQjuO6HQiRsY20WzHeFgW55t88i/+kre+713MLiwxPb3CkYOn2LbrRn7v13+V937kV+gfGKXRrrO42GT+7HGG+nqZmDiDEFvJ54sYqejt76PUU0EKD8/PIZRPpX8YLSQ2irDlEHIldKdJu91GeIqcl2P7la9g3yuv59TBxwlKFazVfOfe71KqFLHLEe//xQ/yhc99nsgYrNGrk7bLsOj+xNczLtbzkz2cQ6TMUoDd8VNi/WLg1nyVvYPOntPCbbjk5Rk9P4KmWF4IyBc1vb0evheRJu7mvnVnP0oB0tLXX+CK/WVOT08xuSB44uAihVDyxDMRoVeiWrEUe0Na/TAaR9RKTXr6Kmhtef6YYnZa02pIPN8wOOjhLwryOUO1WnRazTpiYFwyLEu02x3G/YDF+TYnj2g67YAoTlE4UwM/8LCxRStnjmGsJh941FbaGF1AqBjfE9TrkVu8VJ7GUgtr8/iB5+yL/RBsQGxdAIsweL5EypCQDrt3jTM5sUy+VEH4OSanF5xNvYZirsjoljGOHztDTUcYbfnGA0+grUUbTaAEV+3fy+HnjhNFCbu2b0a22kgMA6bIZGsJXZcYQoQM6CSxmxvCEngBUZQgjUckBFGUoGSIEhaBJkGgtaOcSBwH1wgX6EmVYFILXggmdWQjkaCkj8WQ0qVypRjh0zaSONK00oDUxJRknkUvplBSRB2DEQEtG+MXAeNMl2pLllZDgrCExuL5BmtfnuVZKImP72THsh+qlHKV5+x5nqthkGKVxywzrWAvDJDGaTobKbCZU50QAqEEnlR0f/WekKsmU+v1o4MgACBKHKc6LBQ23Cg9T1LIBeRyudUAaE1ezVHr2p04y5J18DyPfJgjnw+pDPSRr/SBSdBpRFCqMHH+DHEc06iv0Gh3OHHsJCuzC+w/8Ao+/elPk1c+33viOb73vYfJ+TA0uoUw73PVVVdx7Mw5jp08Qynns9CO+MyXvsKunVsZKOU5ePQY40MjHD38PI88/CArjTbX7tvNFfv28tGP/RZDw2N0UsO+K/dx+uRJegdGmJ+fZ2x8hMX5WfI9fczNL7Jn/z5m5+ZQnkLkS1SLecaHKnieK9zN5QqcPHGWmelp15dC4vkSZQGl0Mbgk8n7CbcJdFkkl1UNc75z1MyUUbDZJimTF3TjYlf50Vi7qgHdRZ4FGY1SKsASpT96pZjLFYh128X0i25h7Prn1qO3L4RevhRkc2OB2osjnBc/t57mdPF1XvxZG79D994oVtHZOHYGbVIKPOVzbuI8ut2hXq8zMXmecrFIpVqhqAKm5+cJPJ80iZmdmmFsbIyTJ0+wd9dOjh8/xSMPPczDDz3AvgNXs23TFbSaK8jAZ++Bq/ji57/A0EAfjUaD97z3vaRpyt/dfTdbNm+j1emwa/suPva//g5TizWuf+U19G/ewute/Ro+9tHf5G3veBtbtu7A2pRzk9Pc+urbOHb8CJ/8xKeoFMs/0IcXby4uh9675+0l+90BIhbFDxc4w4958LwmLwfYtZ3BenS4ixrDxjSjWUcb6HZe9/8OPBZObYONWX+TodcWu4EyIIRASCcgLy2QBdWhpzj85GNcs+/9LCycX72mWrNBzg9IbEypWF77AGswWJrNJlEUoZIIgjz9o+P4fp6+XBFjUoIwRKYpuh1R6a1SKFdoNztMnTiHKfZx9/1P8o4PfJAvfubTnDg3SV+xyGvf/ka+8q2v8abbbuXbX/0a19ba5PtG6SQR0gtYrtcYGhoj7kR4CFomRXo+1YwvKJRPqsELCthU0yHF9wJUhroJKbONhiSJOzz6wL0M9JQQnkOj9l65n9tvu4V7v/Y3/Pmf/SkyDAgCj2ZHryqnXHqcV0scNhzjxspuSAmm1mHX3WXPWjeJU0BIt2kx68Dolyt7WOopsbzQQYoc07NLDA1WGB4L6XQ6NKMGadtQLJeQKmFsW4E3vdEjFZreiiaJIvZfKZiYSDg7Z2mfsEidUu2xbN8L27bFzE7D9PmYxQWfJLJUqgqbWuIopRF7BLkOg8OC3vEq1cE8oiBZqTlKh1+QtBYCjh+35DyfTpxZ71qDF/ikqeOkhn6RdqtNIItELYkxzj0wTTTFSp7AU5BaZ7EdJYDEypBGzSkwSJVmwadFoyl4MFDJozYJJmYu0KrlSGyCZyWeJ1HK59jxMxgDRngYa+ipVFlcWsFi2b5rF4cPHaOcz7PUrnN+Yp7tA0WuvXoLPeUhLjz1dSZVHm0sSTtCKmcAIDPDeIFPM01R0gejSHXiCqhCD0yK5wuXMREABh+BJyW+J4mVptWO8fBcVbbvYbFobZBSOTMKC3WtaWu3LYzjBE8ox5kOfRozLRpaEHuasBQQd5oYIYk6ljTOuTUHQ2o82pFAL6cvOMf+qVr3xtTlykov47pmiGIXabQi04O2dh2tzWI8iULhr1LicGhzV6oOx6u1WFT3tmWyonDhigIBckHgEOLEZUW6+s+xsRTKJYJc6Iy0Mq5jmqbYTGUjjmPiOHZFS/k85Z4eNu+6gu279pBYy9yFc6ysBPQOjTK8aRsnj51kbm6OVGseuO9+No+OcP7sBFIL3nDH7XTqbe559ClasWZ+eYmgE1DrxOzZvZdjx0+x1IK0NkuzHbF9fIRjp85w16238cQzh3jk8cf5qfe+j3vuuYfvH3yG+YU5/usnP8Gv/cZHuPU1r+b/+eKXeNe7fpKJqSnmFxc4c36aoFhhaWGaN73+dRw8eBA/X+DC+QtUQ0nOi3jnO34Cm7Ro1Vs8d/AZent7kXqI+bn2KkLfrQlSygW/XddHABWGq4GvNq4oNDEaD5dxtNY63XbpZzKDFmP0Bkv2LtXPaUWvFeIZa1apTi/H3L0cCnlxMPtCKfwXCqJfamr/UtdxuUD6cjSSi9HVSyHR6/+upzB0578QglwuXH2PJEkoFAqkwmJMiXw+T31lhcbiMp1cSLW/wkOPPUy10kOuUKDZbDI+Ps6FiXOUi0XORR1edePN7Nq9H3Ag3/OHDjE9tcj4+GZGhwaZnprg/PnzVMpVDrzyWiYmz1Ot9hIWfK695kZmlxd59tBh9ljNv/7f/xV33HkHx48fp3dwkEajwTfvuRtBsIFic7n+v9zjtbjv0oFzt89WM9g/JNL2Yx08A3Stsrtugto6RNlfDag2TqzVhRxWo+y1oGxjsG0EbqG5KMASJkOjrcJmaSgrXeGKkAKnrwgShU4T7njdT7DSquF5PjpxVrv4jr4Ryhw6SfECV+qoszSojjp4xiCCEK0T2rUGtuiKcFJr8JSkWaujMo5aikIVS9zyjp/k6JFTjFeGeOTB7/HUkZNY4VFrLfHlP/8Kb3zP/8Qff/JPeOsb72Rp8jz56ghJkpLzPKYnppBKMTm9wLve+RaOnTzL3i3b0VKhY7dwSgkTx5/jyfvuJSiUkVGNLbv30LNr36qqAkqi45j9rzjA4szcKmexVq9z/z3fyMB+QRLFAATdnf6GESMrFsyKMNe90o191TqaR3ccFc6hcL20XTfEEIZsF8klCwh/lE2nFjyfTl0zORPgexHDm5xySNoRWBUT6wax9skVLKWqoVZ36e2+gZBCOaHcC8XjHeYCydnjIe2WotaUnDqtiFoRuXzI1h0KmzRIbQBG4kmf5brh8FKb26t5KjlJbbnB+TNLJDpHz4BFKY9Gp0Wt5iTDPDyUssSJwRpXXCJRpB2LNorYWjxlSRMPQ0qQ90gWI6TQYD10AlFssEaSxhEmATwNgcKKCCnAGkiyjMk1N76S5qMdVs6tUAh9isUyy/U6rWaThBQhQ6xRKDQLS8uZSoDk+KmzdEiwHUlvvkKt00T5vTRaMQ898k16dpWZnksR2qHGylNILIWSwuoYg6VoBUanJL6l0YophgE6cbQLKywoFwgUAhdwCE9hrUGkzkFNZiizFi6wNtoirIAUlPQx1hAbQdpO0YFCRhphoY0lkAFGp7StxMYJSga064aoLWm3UoTy8ASkNiaNfay5nIbMP22zpluInc1l7dzkkGs35m7NhxBi1fhEa02gvDVUCIMnFcK4NVdaAdpJdlpjUb7ESIGya4Vt3eDMBdmODrLeOU4phZZQKhVR6+kd2abeWk0URURxe/U6qr19jGzZzrY9ewkq/YQa6rUlok6LnoERwlyJQqnEIw8+xNVXX01fX5ViuYckSfjAz/8sX/jzL9BXDOn1NOVcSJLGmLam1WrxyJNPUos1t91wNUnU5uTZM/z2P/8oB598iqGBAaanpzlw4AB33/01br75Vg6eOIW1lvu+ez/ve897aKcxd956G2fPnKG32sfOLePU2x0WlhapdRIefORRbr/5Jj77uS/R31Nky/AIxiYcff4ISZJw6JmDLKw0yHuSTWNjHD8+RbvdwZNuEyhMt9B9LTjT61D6JE2xUpFmbpYa62hNWRAspMEzEt29l1qRBcauANwF1g7Vtga6BYZdI5YfZXuxoPal8JUvh1b/Y5HoFzvuUoj3+tdeiNd88TWuPe+yaJfbwEhPsjg7SxIl1GorbN+xFVB0kjbSJpw4cYJ9e/eyUquzZ89unn7yKeKoSb5YYHr6BG95y9uYn1vm6cceoNPqMDS8mUpfD48+8Qy7xgc4tbJEdWiA62+4mYMHD2JMhytfcTW15QUWlxcISiUGenu5Yuc2KmHAT//sB3jqsUew1rK4NEegnJ501Em42D774nY5ZH8NlX5hDvRqP7/gp1y6/VgHz91g6eLmkEe7qt6gEFkfdbkBa050bmchLquiIewax/ZSTQj33sKyZtebPd9T7WN05xUUcx4Sk0n6pCjldnmrxRTZeVJKcj09tJsNkkjiBRIvCEi1o4porRHGEgYe02dOocIcfqFEf7mMwrLjFTdQCEJ4/igL589ipcQmWVGH5zvt595Bqrv24ANHj5ykNDhC//Yr6MmHVIshxhhKpRL33HMvN95yM0tLS+QqVTzpEAQjBH/4ua9z/R2vJk4iktoKr+zZxPyp0wzs2EXOD9HWEORzaBFT6Otx1dl+jicevg8bp6R0Ny1uSKy17Nu7lyPHjq+iFl3JP/fHyQa+lAIMnb3xxUznbqpKsXED+XIhzyuLKy6F7SmKCpaXYepcC2PcbloFHp1Oi7Tg02x0mJkvc/5sTCEfE3iWTppy0/U5du2ocOSZOhdOG+LYobO7tluUpygUE7Zvl84opxkwP9tEIqgdjWk0YWmhw/mJmJVGnelzluWVmJ6eiP6eAvPzAVIK4pYlTlOklS5wjgwi9bHGEpsUm4BJBB2RkaakB1agPVdAlkYpRgvQCp1ojBao0FCqhvhhggwEWhhMKvHmQ5ZaMY89cojl5Yg2gjTqEMeSHTtHeP7EFFJIHIYd4SuJ0TA0UmFibgWTRvT4CinaTLUNeQRTSzV8D2aXUmglWCuJtUEFitCXKAH4KYWyM/7QoSWJU2QkkSJAp2CQSGFQHniZq5+77xiMdZrVGJwKSRKDFzguvdV4njNSMcohrMIajJKonEfa0RgpsZndubYG40tMGhN1DEIHRFbSaRrwQZoUPy8RRtBqOYrXy9GkAikV4FBlT6qMpyzAGHzfI020o8dJhxo73rdFYDNdZ42XWXyTudb5GUrp+lchhcIau4qUbihaE2t20DqTuhNZqr1cKlLtcyob1trMCjyjbyQpsXaFl9L3KZZK9I6OsmnHDnqHxgnK/Wjl0RfXKVf76R0colVfYqCvj/rgMlGaELVjvvqXX6Vc7aFaKTAyMkReJ2zePM5iJ+Xs5Axvecub+O699zHS14eNO/SUizw/cY5d27bzu7/zO3zoQx/ivu/dz8LcPL4n2bP3CtI0ZWV2gc9//vNcfWA/tZUWSimePXyI1772tRw7M0Gt0eCWm27izJkzSGG45uprue/+e7PfguYV11zH/d/9BimCa665hmeeOsj5qVmgiudBKecTx/EqncZkFJrUGgfqWAs6UycQAqMEcZRihAONPM8jNQYlQVqnMKOkWgOiskAlTV0Niy8VJqtLSsioEHKVNPmytJdCi7gUirs+WH2hc1/qa5cLxC93/uXoGT8YHF/+PPe4Wy9x+WLIrVt2MD91nna7xfzcIkIY2lEL0ZGMb95Eo9lk956ddJotZqYnabZbKCFpNFpcuf8VPPDAQ2AVp08dp9Zp0TjUpK9U4vEnHmXH9l0cP3mcmYkznDpzlk1j45w5cYz5+QWGhnupLa9QzBcpFoustJvc+51v4fse5b4qi4uLYJoksb7s9b9Yv74Qkn+pvu/i0z/savtjrbaxfj+wWkyWpaO6E12tC5jXG2Z0H8p1iLMVP7hD6Z6/nhrQdcdanzIQSuJhIUOihRAsryxy9OlHueevvkKz0c6O78I1rshCKUVqNbFOiJOI5soK1oDyA5da7HSw1tJqtGgvL5N0Gpw8ephEx2idECiIO20G+4dozE/jByG333UHYbTC0Ngwb7rrNnfzCQSnzx7i9JlTRCsNzp4+x8LUDMuT08jUkHbaLC8scujg88RxSn9fL2kUE5YKCAupSQDBX379HvZdfz2f+eR/4Td+9cP83V99mUPNiJ7BYcIMYZZSUiyV8Qt5ipUKhd4+Tpy5wPS5M7zqxlvWjd8q5ZwjR45kfWcpBP4GqozcMMIu26Dl2vKb1dEjRXc0s38bxpIs7bsxYH65Jvj0hOW5QytMT9cYGdb4UnHuDEydNywtpTTrgiTSNNsJE1NtWi2P4cEKfX3jKNVDb0EyPFqi2hcShILRrZaxrZpcscOVexSvvqufW+4cYfvuKkObexnZkePALYNs219m2y7YtlVRHvJoJ036hvNceWMvu/f6LC9KDj/fxg+gWLGUen2MTEm103I1VlIsS/KVlFKPoVBOsXTwkaRRik5T4o4halmadRfciMDpVAeFlELV0DNkKFYb9A0ZBoZhcNCQK6REwjLbjDl2fpaOtfSEHgqPTf0e3zj0fk43/gU/8/7t+EaBVEjhYTJUU5mYC/U/4MTSv+Qj/+bV5JWPQDE62Mtio0YYKlItQRuCUKGEwfcVqUwwOsHQJAwMubwkLEryVYlfBM8X5ANLUBCEeUNYsvhFQSdN0MYhbVprrHHzLQyLoCxKCYyySAHKExkqKhBK4SuB8DR+oLBKYJVHB03iCbQgU4fwaUcpcVs7qTRjsFKQxBCnlk6kabVenrnr+z6+8lBC4iuPIPDxlcLLlDa01viBh+dnQXLmFOj7/oYqfyGcrbvGuu9m9Oq62m02480mqSFN3RqwyvXUmWNdlloVGZSyecsoAwMDq5/bvUHGcUykDTp2fZrL5xkYGWNoZJxib5UUS6fdRFlL7+hOqkMjxHGM7+UZGhlhaXGZ+flZTJJy4JprSVLDlfv20mo38CoVap2YlXqda665hsefepIP/eIHGR4eZmaxxrFTZ5FById+/hd47Z13ceTIETaPb+LnPvAzDA30Mzs9x9DIKH2DAyzPr/Cdb99PHLVptxq8+Y1vZWpqhkLgjCX+7p5/4JnnDnPq9Bn6+nvZvHkz+/bvhbBIMQzwCiXSKOXLn/sS3/nu/ShhSaIO1mh27N5Kx1gaaUor1XSMIQFS4zT0tTVorBsLazZQMKwUdJIYjSU1elUJxQjt7mfG3S+tdapS3QxEV4nDE6CU/7JRNrrth6VVvFB7KYDOC73/D3P+5egdlwqiu89fitJwqWLN9c8bY5iamqHVbuD7brwqlQpYhUGzfes2Nm8ZZWZqmvmFBUbGNzE4NMLYpnHe/rbX8sjDD/L8kUOcPnmElZU646ObeNWrXsWzzx1i2/btKKG44zU/wcHnjiKs4cy5s8RxzI5dOygUShSLRZKog/QEI8Pj1GtLpGlMJV+kv6eE0byo3OHFa8jGgsmNqH0XSH3Bvn/BVy/dfqyRZwGXRYy79surxidsDIYz/Qx3jFg73lq7yp9WrEnOAQh5CR4UThkAnXkUCuEKhKxFSEm5t59iueJQlTR2GqiwlvoyTppJSoHNnLqSdjPjEnr4YZ4kSVBALh+QaI2XD7HKTfTF+Tm2by+R1BZIWMHbuh2vWOJj/+w3+ORf/S03vf2tPPTw42zefyV/+Nmv4wV58kRENmRsfICzJ0+w/1UHaLQSFhYWKBQKlMtl/CDk+UOHuPbW16CtyVy/DMfPTjHX7PDg9w+hheKvHjnEq294EP/mO9nrtyj19iFFiBAWFWY6sNpSa0ecOfIMT33/IZxOQXdX11XZUIRZoNGKYxfkrgt0V0dNSIcgm42joLEu97+udTWcV5cK60LwbgD9Q1KY/oe2Z58xGJ0nalt27pHkCh3qKx4L8xIhDWEoaSxpKn0dPCUZHqlQyeWQnmB5LqFSKBHmc2jRYMd+xdguH6sVTzxq0Sn4foTRKcvLhnw+j/Kcs2OqEnqfjis9AAAgAElEQVT7faanOtSXJEFREpQkuZzHypxkpW7ZNOYxPppnebnBwmKCF3gsTBraHUkugHyuQ7XfIwid/evSvKFTi7F1FzR2Iu2UExSIQBBIS1g0hMUALxdT7Q0xtPF9DSLF93PYKKWWIYdBPqDTSUnTGF8EHFuo87rt/4l7j32CN9z+Tr7x1U+x2LZ0dIKxhvNT0xQC+OW3f4ovfucEZUIMmooPjeYixviMDo1DcZlaGpGkFiGhk7bwfUnOz+FLx8PWpCiREooAYTWR8AhCSxBKVGaF3m5K2m2B0WtrQ5ePnBiNtWCkE0cTGX/I/d5BYjKZRYlShqglqFlNTrhKitRKkm6iDG81mBZZmlFaVk1YXibWhkt5rkv7aq0dDcA6vXAlPXRqNug+X8y37AbF3eBWrsu+CWMvcXPb+PnKCqwUeMpzcy0L1IIgYNOmccq91dUiQ6UUaeZwp7Um0RopIVcs0T80Sv/4Jkr9mwnzVTQaY7RD14M8oTS0m3X8MGDT5nHKvf10Oh327b+Ku//hHvDzPPH4c7z1zlvRVlIoFbnttts4/Nwz/Mff/wNAIoVmZmqSvbt28vFPfoJtmzZx0w038vzhZ3n8se+TK5b4/sEjPP3cUSYuTOPnfeJWh6G5RULfJwwd8FIoFPGRtFqtTCLU5y+/ejf/8rd/i//y8Y+TD30+/aWvcu1VO1heXuHo6dNsHxtGWk1PtYwICoxvH2D47Axnz09v4Jd3W3esumMkhHCbvDR12tvZeAq5RsvpUgHWj1NqHWcda7FmzaGva6f+cqhtvJTCvPX0zouf657TfW7938u934u17jy/HM/64vdeHzC/FJrJpdDny3G711//G9/yZh747jdJ55dYWlnGLCwQ5jwa9YTz58654xGMjvdx8uRJ9u7bTW/fINdcczVzU7PccPvrCT2fv7/7q9Rqs/T1VBkaGeTwoWe57sabef7gY/SXKwR5j2qlhJW5DIQwHD32PPkwx/jmTTz55BP0VIukacrp06cplUq87V1vuuT1X/xdLza7Wf/9187p0jjUhteVfGmZ7hdqP9bBMxnfWQjxAwiiWRc0dzu4K3uUeWyv8gbURX1kRUb9EMC6BUJklqTdprHZ69Lpy3fPty6l4NmUdn2ZZ5/4Pq+66WqnF6sTfOkjpZP0US5fC1aik3TDwIe+q3L2latEj+MOeCHl6hC5QFKrNejprVKrNYg6hoXFWcqlApv2HiAs5Pnw23+C3/293+enPvIbNJdWeOAv/xtvefu7OHz+GQohvOHtb+eBe+/j0e9+h57RMbZu34YWCt2JabRiCpUqJ44eY8eunRgh0WlKJcyR7x8iTTsQFgmkx+e/+ne8d3wnI3IEP+fj+wornN6qQGGiDgM9ZbYMD3O8VGJ5eYlVtD8bJSE0ngyyIqS1ieuGyI1XdwPEujEVOK7s+gTD+jHs0m5ccOPGyGMtcH65JKB37xXMLcTkvCKpThke8+ntt9R6LUniM3kK6isplRXN8LgkjRM6UpB2oNKX4nltJk7PsGv/EGo8wfd9aospu3ZalmstwnM+vdU8nQRa9Tqp0ZRKBXp6AxqFiELgcfCZlLGlhHIF8sMCnRr8vGJ8S5HeIcXgll522YTJkzGPPaQ4dzbGz0HfkGR0zFDIxQShYmUwZH5WU6mDH4ZEiSvGCgIPK1OCUJDPKXIlQ5ATKD8mDAt0Oh08P0dtOSKX9ymIkFy1Srtep1wpkEpDo9VEtxUnZi27+z/C7u0ltm0fYP65CWfmIATWSgIvx7fvP09BeKQ2Jef5VAsefpijtVynZgQ9tkUQSozMCqGSjP9nIdGSOE3xPKcXDFDyQnL5lGqfj7ER+bzFCk2z5tFsaVorPiCdtbHv+M7SCAJfgpCY1H1OZA1WuxQ5juqOrzwSG+EFCuNJYilIDU7BQneDZGc/LYTAFwpEJgsGWYr95SkYBHedJlt/rbUkaeoCo4zXCu75rqGGlHJVuahLpegGaZCBFUplxdasghYX0zW01o6OAauBcfYiSMn41k30jwwR5nxshnoiFOgErV1Bd/YFCHMFqn39GJOCdps1GfoIPGQgyQmPpFPH8zwqpTLFQsDzB5/lwLWvYmZ+jn/12/+c3/6ff4ebb7qdv7/3YYaGRmhFHRZWlpldXKGvt0I+yNNOquzdfxWD/f0cP36cs2fPcctNN/PkMwdZWFpmaaXOjm07+IVf/Hled/st/Ot/+++Ymp/mH757P4P9vQwN9PPa193F3X/7dcIwZM/ObVjl0Wo0GR6s8qk/+RNuvuVWPvvZ/06SaL754NNcvX8rvT0VQiXpLZcc37xQpFzpZf+Vu5mcnEEgMdZsQBxVljno9nuXRuNJiemqnSgvk3SUTs5Ma4y0kHHhpQWbHbsB8ZMOtRDdFOCPuP1/QZJ/GHrFS22XCl4vd8zlAutLcXov9Vz3+YsDyssF/D/3c+/jiUceIE1nUUoyNDDCmTNn2LRlM5MTF9i9dw+tVotDzxykXCrxjne9k/sffJy+3kEW5ub5h6//DcutlMkzx0ijFnPzi9x022vJ5Qosz89w1xtfxzNPP8788gpnL8xj4gUq1SF8D5aWllB9g0xOThKEHlfs2sfZ8+eYm51mZHSIn3znuy/Z35frhxfqs7W/ZpWiIURWSyG68Oo/rv1YB88ChypanDWov64zDNkOQnQLAtVlCa5d7u3FCLZ7r7WFW+CCciEyRY11g+NsS7uT3KGjeAG1dsKV+zYxOTnJ4MgQ+TDEaImVWfGi8EjSlit+0RaI3WJvJakBk3aordTxPJ+gUKSQyyOFIiwWqHghcW2RGIv2O/RVK5w+fpQwV8Av5KgOjfEf/u3vstKos7K4QPvOm1henoc7Xk1Jx5w9e55TJ46xdc8eOjNzbN6+g9rCEsJTJGnMQF8/gfJpdxJyRhB7ktuv3s/3Jmvc8oa38ezBJzny2H185MMfhXwOY5qE5X2krUUUrtDE8zyKXpGDB5+kMLyF1PPJF0usNJuQjR/CMjY4xNTcnJMbtBtVNbBi3dA53LqLdnRvh5lk95p+c3b8msSM2y153aC5i2i/TMHzgRthZVGSJgnTSy2KdY++fo9yJWB2tsHweMDOnWWUL0mSBGM7zM7UWFxJ8LyYLeOS+HzCwtw849vAK8SkHUFPRXJuLuHQMykD1ZBUN8iVFOWeAJNAsSTp6y0yPupx6lids6c9+vva6AjyssjYkKZWjxBSUagIyqWAXLGF8i2+ryj3WEZHBL39lsHhMsakFIqWIN8Bq5zaiRA0m5ogyAIfafDDBD/IoxON9HxUIEh0ivt1WloLkLQKzC/NEXqSqGOotxoM91ZptxpOgSaxPHtylv5SHUHGl8XQEdDfn2diYpHYQk4qCkCjWXMol4FmWqfoCXxp8QPtdk05RRIL2u02OXykculApXyCUICJqOY8ipWEYsnPpCZ9rG5TKPsYbZGpTxQZPAVpCniCwDP/L3XvHWfZVV/5fnc46ebKVV3dXZ2DWhJCSEgCoQASWYQhDB6cPdgPG4w9PA/G9vBssP3mOYzDGHuejaOYMcE2Bh6IpCyh2Eq0Qre61bmrunLdeOLe88e5t7q6qG6JeR6E9+dTn6q699xzzz1n37PXXnv91iIjxun6pCqhiLPci1zInHVGgjQSpS1GaozKsFaSJgaT5hN9gwCrEd2Jn0AiZS4FESIje5FERysHHtslIaTMXRiMMSBy//We04jospK6Gwnda0L1gjrAZPngpbqpdqobuNJrUgHGonVuh9bzKE7TGOW4YC3GGkaG+vFLxe6+HITUy2xnkiSkXc1tajJq/X25Q4QRKCdAK0nSaaMcF6EUmc1yDbuUWOUwvnGC+x98lFtvvZ3NmycIs4RtWzczNz8JWvLEvu/wsU/8Jjff/Glefd31HD5ykGarQx+CW2+/jU4UMxB43HDDjTz+nSewQpNYydjIEG978/XcdMO1PPzII3zwfT/JgQMH+WLhyzx18BinF5a44/Y7cT2HKIlZanQ4NTXNG9/waq66/GW88spX8ou/9Eu88pVX8fijj/Gq3ds4dOhZXn7NNYStJkHBwSvUUI6DFQ59fX0Ui0FuzWjzlDlDb6VO5j7la7F5JsXVXeDsSKTJC4qV6tIT3clND6RpkWubraOwViyTSkLkRYkvdjsfCF6L1T2f5nl1eyEs9P8KS73yeFb/vfqxtdjz3j7Mec6/tRbtOExNnmZsdJjDx47TaC4x0F+j2e4QxhFxnFCvN9CO5E1vfjN333EvV7/iSlphxNJCg4GhUZKpSV7/5jfxhX/+PBNbdiNweeqxx9gw0c+eSy7j6tfeyF/8yaeYWYgJqlXWrVvHqZNHUdql1W7TidqMDo8xOXWSkyePU63kzh/lSm1NOcpabPz5Jib5ubBnr4KsnnCc8yw9f/uB1jxbcslFb3k+63aOrHvSJCCsyH9Ebi2Xv3Dll6Mrfra9qJUzg0Jv6UqKril8jyc1Z3fKHpjuMS0rZ9zDQ4P0l0qcOHKUJIryUBPVY00NWdrOPamNwZoEk6RkUYgxKTaJyaIQz3XRWqP9Yj6ACui0W3ieR2lwFK9QIEpiHJUnOU2fOoFjDVm7Rbu1gJaGkZERhtePMr55K3t27WZ2dpavfukL6FKJvoFBhgb6aLXaOFojrWbXjp1smdjEQ9++G2sNqTGErTam3aS/Pc1NP/IjXHbF1VzyytcwevkrsbqfmckprEko9Q2TIUjDNpmJ0Nrl7W95EyZZ4sf/42/QP7IBTe5GAKCFZGp6OpcVILoTFokQlpVzv1zZbLrP94QcOXu9ss/LFb8t+XWz9PrDmedezM4dthw2bBth884ayggOHUw4cTxh/5MtZk9qMpsytM5nYnOJDRMlKjUH37dMneiw99sO37wl48H7FXd807Dv4YxsUREtWE49ZwnDCrVimXYYslgXzM54HHy2xeJMxOJ8RKFSpDaSsXlbjcUlwx23JTx43wwL803iLGJ+KeboZIf9Tzd46MFZnnraYXY+xdoML4BiMWN4tER5IGB4YpjKaJHxzX2MrPcZ3+wxuk5wwcV9bN3tsWGbw9ZdZdZvKNHXLxkaLeI4yfJymTUu7TmX2QMVtNAUPI9SwWHP9gmqhTJTC036+2v4WtFf8rn04h1sXDdOLAwlCYEwGJHx/vddz651fQw5HplNOW1CiqUaxcCnk3QYGqzge1AIDG5BUKhavBI4Tkap6BAUHFxXoT2D1BGuE1EbEJSqhkrVoVhMCEoJtT6FX5SUSwLfByM7KFeABq276c8apAuFksArKrTMI4qNVaSZIYkzhE2Q1uC4IEUeimJEt2hLSlIjyFJBrs7qSj3IAabnSnzXUvGy8/ax/12tJ7XQUp2VwKoQSCyuzv8Sqsu9dFlqyO+pWuuzUgNFl5VM07QbuHEmSvcsJknloFcomcdyS4HWueeztZZiMaDSV8PVmiy1CCXz5VipkDJ36TBJHgXvOA6JyTDGUKj25/vOQpygiHR8pPLRjkcadsCkIAWOF3DB7l3Mz8/z0MOPUS6Uefc7387SQp0tGzeh/IDf//3/ghv4fPpzn+GRR59gYGiUhx97nJGBPq67+mq2bNlKs9Pi1jvv5NTMPFdf/hIGarkc4/4HH+Rr3/oGjz62l07c4tqrX8XEyCBLS0scOnqMeqPJxRdfRJRFlMsu17ziFdQbLd7zw+/lyf3P8txzR3j7O97OqcnTTGzcwOOPP0r/QImB4fUU+2oI1S36NobBgX7SNF2WzuSrAfnKX5Zla4KT3mpBb5zLwVieTog9QzIZAUIrMvIx15ieHCQf99Lsxem3z9fOx/D22vlkFav39S91TGvJSM73XudiydeSQq11nL3t1m/cwOJiHSVzp6Dq4CjFIODVN9zI8PAwpVKRZrPJZz73WWxmmZyc5PXXXE2xGLBl40Z279zJ7NQMN73uTezatZvt2zZz9ateQSHo49G9j+Di8xM/+ZN8/P/6GKOjY5ycPEUchjgyX4UqlUq4vkOlUmF4cIR160Y5fvwoUbI2cIYzGKzXz43JljFZ/ndGnsuR/wDfvULC2hOQtf4/X/uBBs9wdiHY6tbrGL347JX2ZT2wbFnDim5Fh1UrlgyFyKu8jTiPIF2QL09JQUZGfWmBZ556mrHRcYJiCcdxujcYQxYnZFnXLN7YXPMsHNxCGc/ziFpNoigkNhlOqUSlVsX1PaRWVKr57EsqTStM6R8cJiiWCMoV/GKB6ZlZTpw8BkmHsLXE3OljNE4dYfLQPp59Yi9escCOXTu55PKXMXnyBF/83D9y4tBzTE5NMzd5kumTp3nu0CGuveoKWvPzGCRaO2zbtZML1vezLpzlNW98Lb/927/Jjq1buOf2LxB1QrTvgZA4hTJCB1ijQCiKfkBtYD1f++LnabY6gMXaXP8dGkPW1Tgba79rhWB1J/xu3TlnRXbnwbBdfXMXZBvArtFJVkt2vl8tsxWGyxUcXWJ+2nD8oMfeu1KeuA86LUVmHL6zb5L5pQ5RZDGZpNJX4rJLy7xkj8umbR6XXuZwyUWS4bJLYy6mPpOwsGRo1psoJ2W4XzA45BCHKa2mz8FDEXNHE+ZnO5RqBbS/yJbNmtGNLsWSAwrKhSKddoKNHObmXI4f1UydhDR00EKjNHhOQJwYwJCkHdxAYj2DX/Uo14r0jw1QHi7Tv24Av6yRHkhXIBREccyyc2YGnamM5+5xUKJEisTzfUzmMD0zg7YprlBMLS7hOj6FksdAbYhnnz3KQODzkR+/gh2DBQayjHf+p7/hlif/kf/0kffT50G/1JxqJEzOzVMs13jj665Bhgl9QUbJSxgbcqmVJdUhDzdwMTZCOSmOthR8ge8bCo4l8ARKxnhCIqzBVZJOGxwX3MDiBgKlRfeGbXM9cppS1BrtWBxlwGYEjgOpzS32lCZDEAQBrqspFEpIaZAqjzBXbu60k9kUpEVqidR5FqbGYmWC4ws8/0W6PZvc99tgUY7OU/yUyAOedF4ohsj9g1dqmaXsMs1dYGwyS2pyVr0Hqq3tgl6tlkGYlTmLjD0T5d3TM/fS8gCksLiuXmbVevUlUiuU4+QphFaQZSnK8SgEJdxCgWJtGIFGSBdEbhlqkjZhcwknKBG2l3CUplSrcc1rbuSqV1zOYmOR+x54iL/97/+Da6++ggzB7GyLoYFBRoeGuf5Vr6JZb3D44H5etucC2q0Wz+5/mvXr13Pk6DFmlzq88rKXMDQ0hFY+J09P85d/9XcszLa45ZZbePLpZ7jvwYdohx18rVhcbOJoxZ133sXrrn01W7Zs4wtf+ALfvvdetm3biSthdmaeT998M1defhmBlGzauI7B/gEG143TaodIK0mNJbOCcjlYLuBctv6T4DnqLIazV+y3UgfdK8S0ApJuym0PtKyU2PSuZy4VsEDuKuMpjfMiWNX12mowuvLxc22/lrziXID2fwV8P9+xrcQkq59fzSKvfl52V82fD+CvZKWNybjiylfgSE1/3yBBEJB2WlT7a2RJvuJZKfexa/eFKCVoh00cHP7rJ/+YTZu2EIZtXnP9dVx37VU88sBDbNm4nrjdobG0yPZtmzlx6gRaQala4+FHH+OJJ57A1Q5hmqA9Fy8IGB9bT6vZ4eCzz1EqVSn4Rd74lpuWQ+JWSlx7x2zPKnI1WPvdn3slWF5ZELvWuXi+icr52g84eF6leaEn1RDLvs9nNs3RVXYWk2mWmU1BvhwKZxtmG7Jl/1Jr7TLQzrDLM2voic7JlyW7N3nXKTC6bgObt28Hp5usFEXEcYzIDMZmYAQmzTBkSNchyxLCdou4HZGGbZSUuF4BqTxMZsnSBC0F7eYCcRiRhG2q5TJSK2aWmshu7nuzUSdqNDh+9Gg3Fc3SaLcIowTll1m/aSsD4xNMT04xum6MV1x/DdZIZo8cJWks8tzDdzB7YB9HnnyUId8QzpxEC0GURGzesYdrLr+EH7p8N6frIf/tt3+bd2/u4+VvugmtHUgzTBojpCXNci9nKwVvePPr+PCv/QY/9oEPUvR8hBAM1QZWaDiXlwa6N9vef3atK921jxHLj/Qu88qvQbriP2k5q8DUsNLD4/vbLtzdpn/dYaLOFBNbiviuIo0KCKUpliVLs5YD+yUHDrY4ORUxO91hdqHO0Jjm2hssb32b5SVXpEi/xcOP13nkcclzUw59gxa37BBmkChDsdShOuQiBIRtwenTbWaOpsyfUpRcl/XDll0TknWD/WBdrEnBQKdl6CwZkkUJiaHgKyplhen645o0odFMsMIj7RYSSeEQxglogVSGZmuB4kABHUiitEOWJfjd8IV6vQWxZt/dETYt0+y0ieKUjtVESnHw1AwLnYhi0UGaDK/kETebWK+fSat4zdUTfOhTt/HUfJv9yXN41KG0g+H103zzC5/lF371vRSDFtVqlff96EvZst2weCSXU3kVS9s0sTKhXEwplSL6+w21qiAo5rHYSik8v4SxCWmaUe8kNNuaZw8ugdSUKw6+B55nUTql4BaII4uJLWmoSDOFSQRapxRKBs8Hr2hwfYtUGQVfYaTFK7pYmeEWHMpVSbHPUiinCCfGLSjcgqE2LKkMpRRrGuuA5+XFgq774vReqRWyazdnrSVNE2S3kKwHnqzoppcJSWZz//Kkywb17q9nmOWcbTdSIrsuDSuLEa21xN3oZ9fxMUDSfTwnK3LLwYLvnhn0lEQ5LlI7IBxEt54iTMJcNoSk3WlCmhJHHVRQRPqlXFpgM5AqB+4CguoQolug3Wo3uOSlL+O6q1/J8PAwe3bv4TvPPMex40cYHu1ndHiAd7z1bTyz70kKpQLXX389rbCFkA4X7NrNc0cPc+fDTzI4OMD+/fu55Vt3sP/YEa5/9WuZmj7N7Pw080tt/vFLX2HfgYNEVnDjjTeiAp/KwAjXXXcd37z1G3zwA++nWqux0Gjwnaefoq9aI0kSKuU+7vn2vVjX5aa3v5M9l7+csNVkZGgUv1TFcT2kdvALxXyikXU9jqxYLkDtgWFrLUmSkzuZFURJhkGSGfIEQhRa+bk0qTuZsbbruJHm11oYi6cV2go8qdFWIGU3jv373FYnBq7VzgeIe/31fKztuR4X53luNRh/oYBttZzknNtx9pi4etvVjLtSiunpaX7oR/8d0s2/j8VqlSPHTkJmmJubxfEcBIYbrns1g4MjlNwiT+1/msHBYcpFnzRJ+PpXv8RD376Hcl+J1uIcSRyye/sOnn32EDe96c189rOf42u3fJ6DBw+waWI9QyMjDA2OEfguGMvs3Bztdofx8fVUq2XiNONnP/DBs0jQtVnjHEif/entOc/vysfO2s8a5/p7aT/Ymufl2ZjoLvHnrhmCnm9zTkUazp4FGJGz0aZ7gxes0idJcWY5kpwh6UG1le8NvZMNUuqu24bNQ0wEJFGLOK7ywN5HGBofpb4YUO2v4giFMSBSC45ASEESJ2gJGEPSbjNfXyAo+hT8Eko5+EGe+FMolXC9IF/iDDu0my08V5EkEYGjieOEqemjbBxaRz1OiMKQCy6+iFu//EX6xjdQKpXoH5tA2oRyktE/MkCr2aEmXeqHj+OkCc1DUwxpSykQ1Po30FyYoRiUqR87xMDWi2k2ZzFa0l+t8b43XM2/f+1ViK4dV9pepLM4l3vdSo00+WCkpMZxHPqLBWYm5xncupvWgaeZW5w7c2PJz+jykn7vEUFeLLWake69rlcQKMiXDLOVN8AV2/fKE3sYvTtXelHa7osrPHv4CJGtUu1z2bK1zrGjAeWBjEpfyvy05tQhQ2MuRbsdXCdlbH2BkmwyfomPE/golTE6WmbvI7NEiUF3BMWyw/y8x6G5kLGNgpF1Dk6hTXnAMFA09FeqFKv9dJotJEW0MPTXKqRpTLORkBmBgybNJMKGFHxFZgUl3cJkkqFBD0dbolggY0tzMY9kT1oRoc0L5Nqk+AWBSSRRI8EYi5QucZbRWlyiHUnSpuXEPsni/BAFxyHOWrTbMUZYSqUSV16+m3sf2sfJZgfH0fzRX36QpSmfn3/ff+A/vOu9fPrzn+W5A3+D7/YRZW28uAmJ4GVX3MDj37qLdQMhB47czfGnvoTbP8rIlo3EzWFue+LTNAptoigDJ8YNQPsWKQyeI8isolsHzOJiHUgYXjfE0mLE/FydoFCGLKZQlBSCCGkVaQRJFIKShJGhIDVZnIESBErQV9M0GxGy6tGKUuLEYBQ4WhAlMa5vcbyMoAB+IKk3YryGolGP6Ov3cd0M39UsNmKSUBClFi0tRq89WP7vbkJKrDHIXqy2zCclqbFkInfhyL+D+XdYC0lm80IyQ57w52uFFAZrVdeeLh8IMyXIklzTrLoFO0rmCaVpkmBsbpmWWSCzSDKENQjHpVAuIkQeG61FNwSkCwwMOaMdttokcQdfO2gnQPkl/MogJoswVuQSPeFA2sr1u1nK/ORx5ufn6atWWZifIcsUb3r7O3j0vruZmp7n4KHDzM41sAIOHTrE7/7u7/D+n/sAH/noL/MXf/4pqgODJFnGV79xO9sv3EPRVzgqo1juY9fEJgYGBvjrT/8V23ftZLHVYbSvhvJcdlxwEQf37+POe79NoRBw/97vcM+DjzI+UOXO2+7i9rseYOOGMXwl6EQho+OjBMoSRm3KhSLrJ9YxdeIkQalIJ2pTKawjbTQRSBwvQGsFKh8nkySfnMSZQcve5CcfP5PMnmGhTU4aKeV0UyAzhARshsmSLigWWDIcoUklaJVPkPIV4i5LLb//fXclK75WO5/WGc6A73MV5J2vrR5mVr5+LTC+mu0+1/GubOdyl1i5/VrbrD4WKSVDQ0NYk/HWd7yDz//D5/j23Xfyqutu5MI9O7n3zrty54tCgb2P3M/OC17C1t07eXb/fqRwePTRx4iW5lEljWnk9/PJyUlc1+Xp/U9iopR777yLRx57iJ0XXMyh5w7wxKOP84pXvRLXdVgYnAAAACAASURBVDGZxBioVqvMzs5SqhQYHBxmeva53IBgFaNs7RlwvPK8vRApzXklL6vOT6+I8IW2H2jwnH+olcVfYpmTFAbUSmu55cAUwNLVwJ6xq+vKnpdvGCvfA0AKeRZrvbJjL//d1Sz2qjetFURhh43rxmnGEZvKVbRVmCxDSScvBstSjBVox0dgiVuzOI5Dpa8vDw4oVvCLZVrtJp7nkTkuUdjOZ1cCvEqRtN1BWAjjEKlChqp9xNrl1Mkj9Jd9HrznLt787vfw6AP34ZZqBJU+WrOnQVjcWh+u4xMMj/DUqUkWjhyg6ij0YIWKdijUxvCcCiJJ0Z02T3/r8+x83b9BSsniwiyeylllqRW2HSIVZEkCGRhpQIjcI9haDh06RH//IAuL09SnTiHTTl4YddZsvzfNWck8Z3SzFvJtVrDNvWuyrHO2dMOW87aSm+vZna+U6bxYbhuHjhxnbGg9xw7lUdmpUCSRod0UHD8MUyfywpv5KYMRgjRSTB2NyJoujQWDX26zaZskTRt8/is/xuGDT/Kt/+9hZqcgWrC064ojByxL84LBUcvQYMK6LWVKQZXWXIpNNZ7n0c5iWo0OjqNwlEscxWjtYMKUgbKDEhmKmHLRQQlJ0OejPYNQlqhtiaOEzHYwiSRJc2uyZHGBoKDRnkQncV7wGAuiTkwUQxwmRJHHpz/+X7jyLb9BTJM4UyQi90SJ2iFbt47x7h96ObfesZdP3fwNrr34ZXScWd5x08v42d96NT/3iYtwZus0pqZIJo7j+hrX9Xlq3z28+l3X86Uvf56TJ+6nNv5SOvES9edOsv+5Ge76aoOXvAWCgiDNLJWKxCYh5ZJHYvJUwdyjFhaWErTymD5dZ3omwwty1wKpErxAMjTq0G7ly9pxJJDSkKWaMM3Aze3ppGMIyhnlWh5jXAwljaYkMyIHoGRoP6Xa71CpWoRMcQKN6wqCssKRCa4HjisoGkvLldhM5JXg5sXpvHEcn7Eb6zJyK12JkiRZXrY3XTciukuknpuHAOUyqlyHbAXEaYLT1Uhr7QCCtOvh3PPfz0QO0G2aB8QoLZFakxiDyTI830dqtSzl6A16vWNdqY8Wjsb1ixQqfdg0wjgOqvv+JotJ0whMRr0+T7Fcob64gBMUGF43zsLsPIuzpxleP0GYZly4exvPHj5Co9nhZKvJ9m07+J3f+32qlSHm52eZO3ycGEtiJXsf/Q4Xbl7HqakpLr/0cmq1CsePH+PJfQeYm2tQrvrsuuBCEgyuq9n39CEKpQKdqM3uLRM8fvAE042Qv/3MPzFULtBotFhoJJQCiQmbNK1l44Yx3vHud3Hq2FGiOGV84wQLi4t0GkukqV2+fr4fsFhvLQPjnoSjp1+WUpBlCQqJox2MTXNEkVpQCinsMhgTJieMsjS/+3pCYdN8EiNMXoDWYwl71+DFaudiIJ/vmNYC12sB2JUFaCvlLqv3tdZxrbQIXP2+Kwm7tfDHWu+zevvzgcWVFpK9SWe9XsdBcvXLr2Rh8iT3z82ghGDn9u0YY6lWa2zeOM7+p54gizO+s+8RdmzcSm3jRhZOzvIT7/kpnjt8gIcfe4jFxZSLX3oxDz20l8f2fYeh0XGe3Pc4L7ngQjzpMHX8JJValaDgsVRvMj09zeDwAL5fZGZ6kl/79U8sf46VPs898Pwv3Xrnq3eOvhfgDD/g4Bm6y/3kjLOkywQLgdtjF1d0MiM4E80tu8w0Zzyee9v3mGtWsJ89OUjvb+wZ26Xey/OTLVHWghQkJqG1OAM248CDj3DqyAmuu/Ea+vrLOYOiHTzPxcQxJglJkw7a8dBKY4xGORrH8ZBSMzSyjnqjRZKluMLD0z5pFhGGbYqlMq1WB61cBBmtTpOs2cDEHS694d089cA9PPzte1C1UToLkxx+/H62bN3ByZOzDFWrVEfHUEgqoyM8eu+9DIchQ84WNgwNkjiaeHEpX8p2ChTSlJOHnmTLnpdSqQ5iienU57FJhNIOcdTB8zyM0KB0d+DShHGHLZs2cf++Z7j2ta/lmb33MT83Bct69Xw6k1/Pnndub1UgZwJXSzS6l3FZfqG6V6tbbtjjmYEzrhvaimVnlLTLeL0Y7Sufcdl8wQmOHSlz6kSHpekCjbpBakvox2jH4AZFLt2pOH5sgT/6q6v41pcP8t6f2slbrn0MKyUvuyzhwgvhm1+/hS3ba1zx6gm+8tezhO2QomtIYsviKU256jA6KKgNexAneMWAViMkcFwyFWNMipQOUkK16FJMXZo06S8VKJU9WmGHgdEKmc1zwrIsIYyjvG4gtUQZtFsxWjh5Ip9RtGNLZUATJykSj1a9RZZZCoGHzuDijdu56A2/StnzaYQZiTVgDdYm/MIv/Qx//9//lItf+hZ+9sffwMd/6W00symWFlKcSyK++um/43WvfxuqIhkcG2fq6WPccc+tdNqLXP/um1gILVdc8waqfaOYsIOnq0zNnmC4PyOLBb4VDA2XqbdDHBIGR2sIJ0MYQX8lw3VzkOWIiCiDqRmNUgKSJros6K9oNmwsMzO9wIKGTjvDdxx0t6BNCkViMrAKz4fBfgflxCzVQ7RTwBhLvZGhlMSXglpN0z8gcJw4TwOVlsXFiFLFR4oUJSTCxljjYGyGRKALkky/OJ33rOIxWC4AUz3w1QvcSFKkVnTIMGlemJZ2QlyllyUc+eAkly2isixFdJMJewOkUhLHdfJJGBapJEpJtJBoKUlsrp3usWZRFOHFMU7X6lOqfOInhKATd9BZCkLTPzyEFeC4pZw4EQpMjBKgC4PEchav7uN4Phu37gSbcXppgUZ9gTjKCKMM33fJkhQt8qFyoFbl8acOgjUkmUU7itBaXNflx9/zXp7d9wjvetub+fq37uShvXu5+JKXcPs9D7BhdIDxDUU6nRaTk6eZPH2K5kIDz/P5N295E16xxNe+eguVcsBSvYkAlqKUTr1N2bXUShWMsIwNDvK2m17P+PoRnnz0GCPrt5KmMYHvE0URjaUmnSgEIbFCIYRCWkti8+uhlCBLkxwoWHCUQliDUiBMvkogu/dkhSTrpkwmWZqD4wyEsKQ2JcHk9UaORmTZGfJJgM1eJNai256PNV4NTle/7lys8Gr/6nNttxrIrtTvrgWS1wLQK1+7mj1eS36y1uMrJzNp126y9xnqjQZpltBuNeiEMcViiZMnT1LwA3bt3kljcQlRLHDw2ecQwBvf+Ho+c/Nn6CsFvHXP6+m/YoC+8XX0VQts3jDOX/zdzRw7dJQ0ijl8+AhxmnLg2UNEUYewkwP2UiFnqSc2bqG+NE+5WGFubpbt23fwy//po3zuM1/u4ra1teGrP/O5tllrkrL6tavbamL1+doPPHiGXicwy/HYxlpS8oM3XQeNXpfOsCvsy7onagVzDSx7lwopv+u5s98z1zwLcaYY8QzTbZFIpPJwXZc4TQjrdTqtFl7XC9nRmiiK0IDUGk+V0DoHkJm1ZEaAkKRxRGMxzYsXY2h1OsTWMjd1AmENhb5BvKCMdDxOHz1OtZozZL7rsfee21lamOe1b3gLn735ZoSF8fFxFpoZu3btwnUDDj71JFsuvJCrr7uG3Tt2M/nUk9z1qZt5/P69/MxHP5x3tKF1PPvgvcyamJ1bdiKlJBEZjnAolPuxSUgchSjHI0kSrATPzeO+U2tIwpDp2Xnu+KfP8dh9D7A0c2IZBGvyz4vo6tDFiiTGM2f8bA1S9z/zXY+JLpjOr/PKZMjlPXXnO2rV67+f7Yc/uJENlSvp23SKN7z8LsbXhYytT7jopRWufV2Jz/7NaX7s58rs3jFBvZ3gOAv85P+xid/9+Ck2b7UcOuQyNS3ZBVz+svUUix4dIZmrT1OqZbhK0FgyzLc8fv03387vfeJLKKfN8HqXQjkG47I0l+AWFEGxiNaCOLYoC2EY4vsexbKH9iX9XhHHVXhaoISDMIq52UWanZB2K8Yi0FaDtViTkVlBGIcoB5QricMWWjqU/RIOmiSTfOrPnwGh6VhNJhTWZFiZkhBy8NgJrr5yK9/88q2857N/zFOHj/OHr7+OaHKBh+++i2plkMmFI0z072B69igPP3EbUGfrhRMIXJQ1eP4wSWhwPZ+F+eM8+ci9+N4mXv2ql7N+fIFNOys0wibTM8dBRFSqVZCCqBNSLlVIspStfWVOnVigE1mETSiXfYb6BeUSGBtRqfq02x2CskPmZpQGFDYTtDotTOLk8cZWYXVKsSQpV30mJw1uCH4qSWKBH4DjdwhKBYQRJJkkifII6zQxaA1pAlksaTZyAKq1pBiklMsvTlJbz52h96OU6ibxaSSglKDTBbqmywJrrZd9oVNhUUKeFZCiu24YWjvLy+NKqeVioDiOuwO7QCNwpUYqSNMU7XgoR1Iul9FaE6Up9XodKwQ+As9VxGQgFFr7xGFIRQs6zQ61sSJGJNjMIKTOpWbSJ7UxWI1fqdGuL+A4HkuLixTLFTw3ICj5NA4dxq9W2bZjO8Vv30+jpUiNYGyozOHTS/ilMs1mk1K5QKMZcttX/hnP8/j4b/0OH/mlX2B65iT333cPExvX89EP/yLvfPe72LRlB16QsGXjBpSSzCwtsTA3z71f+yYXX7CTuSeeWh7462GIwLJ+LE9C9FxNmnWoVCpMnTxB3+AISRphkThugaJbpL64RBpFdNptdDckJbMZNksRMl+LdXXer4TpLlOLfMSUWiGNQdmeD3RuJ5gzlYokSZAqZ+riNMFF5jVBsuvAwZlQIalfPGjxQuQWa8k3Vr/+fPKH8+13pQxkre1XP/5CihFXA+G1tj+fXMX0Iu5XgE6tPe699ZtsWDfOU/ufYXExZf36cRYWFnjmwNNIoRkZGaGvVuHg/gM8dP8DfOR9P87WHTtwy2WGhoaYnZmhf6BC4GjW91WZmp8B4MorXs7U6dMMDw0wN79ImhoKfsBifZGhoSEG+8s4rqRaLVOt9tOKYmaPzHDfPd/gFa+44QWtXKwlfVlrUrRSM75ykrL6fCnEmqYD52o/4AWDeVvdaXT3JtANlfpuYAvL0dzCrnDhMGeW9HsOHYizO/Jq6YZCLCdi5Q8ahOyefAtZGtNuNKnVakxs3kSn3WVCVO66oZSTM3hdwCy1j1V5Vbh0chbFYjBZStxp0VqYI5yfYW7yJIMDQyjXJ4tC5mdOMjt1Ejdwabc7pFmGcjRp2KbgFXjkwfu4/FXXcvn1r2bLjh0EpTL9hQLCGILAQycZGQq/r8Lml17CT/3B7/Pum24kXWohWx2iFDZcfRUXXHcNG7ZupV1foDU3Q31uMl/uE6AdF4REKBehFVGckhkgS1FCcfvXv8Krrr+Bsc07MFKdYe7pdeRuVbYFukWCFvFdQHot4NwDydmyl8OZ7VY2KfLBF+z39EX4l25TB5sgDzI64vB/f3IXi0sl1g+P8rrXXsLQaB8f+MgEaSLJMsvs9AmaCyX+8A8eYmS8ztVXj3HxRbllV6MZ8eAjz5DEcNsd9/KBXx/hQ5/YwWVvSJElw199aSePP3CUY4cSjhwICcMZgkqMU0opVV2K5UJui6jBqgxVlLgVB6fqYVSGX/BQSuD6eWW+V3Dza6RkN3LZxfMCyuUqBb+M6/p42sP3SmSZwKZQ6rrHuIGLV9YEpJw6miFQREkKxuIX83CTivB4+O6vcc83T3DzP3+OVnORHVuHePqRBzhy6gDBQIkn9z3BA3c9yP4H7+Pp++/Dhm0mto8xsGkTpf4toArE4RyJhSjxGRu/jMuuvIK//NQnuePuB/CUYNNYgCfB931c318ujPJLPonJQ1usMPi+oa9mqFUkfQUoFRw83+n6E1tc16HgGcpFw/CQZHAItmzx6Rs2eL4kTTokSV7cliVpHvXtSUqBIPDzOPBKKSDwBKnRtJsZcZySRjFa5dO/uJPSbqRIo7A2wQ+gVPMpll+cviulzJMUpcDK3BVjuVisW+jn6jPxzMvLyl0A1lu+1/pMxLcxOUvZs5jKMrO8vzP76UrrRJ5gl5qcLU4zQ6HgU6pWcDwXST4BDMNcMpQkSdeWLZcPSNelWqqgAw+/3IdJLVIrhPJA5kXbIjNI7aDdAK9QZWlhHtfJU9CQedT68Ngw1WKBUqnE+vF17N65g7e95SYsgte95nqydpud2zbRarQQ1pBZw0svvYh6GPFH//XPeeqZw8w2LU8eOsaP/uwvsv2CS7jsiqs4fnyKtlGcnpnnne98G9/e+ziTM4sox2NhoUG5VEDZfJKyrlZkaWkB13UZqJUZGRqivjTHzNRJWu0GjuPkDL3O5TKeX0SIbiGgTREWXO3guw6OkrhK4giJrx2kyC3DxArLQCnEWQ4cvSV+11F4rsb18uc8z8tj3B0HLdUZEsNKEGo5fOX72VYXlvUeW73N6rYWkDrXdt/L+64EbGvtZzUgXs1Ify+yk/OB+rVA/9Hjp/jFD76fMI5J0g4jI0O02y2UzldRrBGMrx+jvrTAYn2B0cEBLty8kd27d1MZGGZgeIh6q0mp1odb9MARXPua17I0N40QgtOnT3Pk8GGqlT4GBgYoFov09/cTeAFDI+t45tmDzM/O0V8uMb/U4uDT36FcLvIXf/bfltOJX2g73/ldfR7PNwFa7bL2fO1fBXg+/wzQ0nPaUGLtk75ao7Ryn7mM4+wkr3Ox0JL8PSQrBLrkM8mJLTuZa0W41TJhEi+b9psk9xANk4w0tdQXZ2nOThG2FpBpbgO0tLREFoWE9SVa9QWidpssipg8dZL60mLOFAZFpBsw1zH0bd7F5de9ibEdL6EjiqhijY07L+TggWeYPznF9Klp3CDg2MlZjDGUB4bRWrP/oUdwpUD7HoWBGnr3pbhBBR3UKAyOYrRP38QEVkmsSYnjkDSKCTsNHLdAYrp+2FmGVM6yFVKSJHQ6Ha68+moevO0bTB89QDHwyItIemy/XZ7MnI9P6xUGim4h0pm49RfWqV9Mrd3KVh0YYKExxWNPHGb3rq382p/O89M//Rb+/JN38sDd+3nJjqvQsp/6QkgW9VGp1NiwrZ9StU6xErH9koy5KYNfg0svHef+e+a5fPfLaLQUvvC4+ILt/MEnX8rXv7iPhx54mF/5fzbw0U+8mU1bttFop1idENpFOnGbpWaDdrMFQhEnliSFSqXC2NAg/aUitaBKSfkMlKv4VuJ7imqxQLFQpr+/j75aBYmlVHDxPA8pc0s1kQo8p0jBK1D0A8qFIoHjELYtpfJIzjZbQWzahO2IwHqsq/hctecS7t/7KT724ffTimFgoMZLL9nGxMZBdu0Y5qKLtlAp+syfnsEPLNuuuJDh7RcyMXEFaWeJJG3jlydA5NOrTrjEwtIib3nLa9i6aZyH7jxBfaFNIVC4rg/CIoVHwfdQRlMoFBBC4AcOQeBRKzpMrCtRrroMDA8gtY/rBGRhilBQKTsMj/gMDjjUqoq+qke1qkjjNnFiabcNSvr0VUsgYqqVlGIpYmhQUiknFIoxJgtpLrWRVuMpzfCQRymwDA+UqBV8XFdTLkj6auAFGcg2rh+/KH03tSC6gDdnEmVe4KdU7krRTRMUIl+Wk0qByIv2zlqCthYpRS4LEGDsmWS7nN3O49+tNSiVe7vntSm5/jmxhhRLajMKRR/fz8FtZi3K5AA6SZJlQJ5/9yXFQhkcSbHYT9Rp5plUmcGmISZp5tZ42kW6LkLnWvWBkXWgHYqVKkMjI4SLSwyPjjA4PECxGHDRnt20mvP8wxf+iUa7xZ233Q7A4YOHKXkO5WLAQqPFN++4F6xkqdEmSQwqi1GAp11OzMzy1VtvpW0FTx04xKmZJf7yf3yRE9PzVGtVbr/jPhygUW9RcjX9WpElIbVyBVcb+qoVNowNk4ZtkiTBcwNqfQMEpTKFUoCUAtf3ENbiSolNM1wl0SL32neVzrXPSuXpt1ovx8KvvG8mae5hlBmTyw85U2hmrSWT+eSGrpe3MHmRZ8/v29qzXa++n201WHoh8o3VbeX5OBf4Ph+47umTV+9rrfdZi1HuPbb6dc8XeS44UwS3+hycRRBmhtu/+Q20NdQXF3lw73cYHxtjoFJkemqOJMldk8J2h0qxgmMVb33ja7n+xteTlXyKpYAk6lAq+HiOQCIo99XYtms7L7nkUtKww8zMDENDA7RaLTzHpxB4nD59GrA0Gg36+weo9vXx3PFJDu5/koX5JcIwJgrhG7f805rHvfpcr3Wdz6V3X/06If7/pQvCvxLZBpizCr9y8jLveMrmS4RW5Ev50lqEPCPVgG5ntnQlGGcYzO6uUSvg3GoZwPJFsHmhRK+aWIgzuukkSdh7351MbNvF3jvuRRc9Xv+GG/J38DysyXCcXPOplItQeTW0UJJWYwGHDGJwHEWxOLC8hElmONls01cbQpCgXY+RQol2fYnH9t5HZsB1FEmzydTxw+zZcxEplrjZJk1TSr5HGqeE9QZiaJBtF15AEPiYTkrW7FC48BIWb7uFwtbd6CzE6y/juAGtpUXSMMw/v++jRV4sZoVBaAfHcdHaRUhNkhrCJCaKEvpHh/m3P/rDXHPjjTz1xKPcdtttPHPgAL7r4whDFEUo0U2ClAbMmaCTnlmdWCG5wfYcVljxqF1mpAXnvjHmjLZ90WQbuza6vPc9T5Ao+NvPaj728/APf7eZ/c9AqTDE75z6LDfc9CY++Qf7uPO2I/zn/zfmXe+6gpMnZjl5YgZ/NuBXfm+QyaP9vOFVD/OfP+lTDF7Kvv2H6HPfyM2fu4W3vneUsY3XUHDu5tc++Awf+8MOUydjlDSkWUhQ8mgsdNCOQxwpauUiFc9jsORT0w6+E3SZP42Vuc4x0xFJErFQajInE+biGCU9pIWk06HkugTKI440frGAshmezD2KHSFxdYEn7p+j2U4wOCQ2xBE+1mZkNmNuqc3BYw9z3VXv4k//+peRZFQGxpF+QKF/GtIYkjZJCG7RwytXKZTGcJ0hYlPAKQ4gVIw1GlePIB0X18vYtnUP//6f/5A3vf6VoI9z9PQCjie7HsK5pjnsJHiOQxpnpEmP3XFwnNxNoq/cnzs8CE2WCoTy6CspDJIkyfB9B9nNl3aFwS8qWp0M0dUHO65gcLAAaOIoQ+BQb9cpB5JiMQCb0GkkaE9jLRiTEsbTVAcChLJ0Wm1KfkBiDcWSpFh9cfquFj3bx268swVrzXK6ZJ4ymDvgSKmx3XujBYwVpEm8zF7m6XQSS15olS0PbnR/9/z18xAVrTWZtTiyx2Dn2/YkG1mWYbvERBYnuX+x0CgnD5pyXB9MiOsXcQMfR/v5hF0XuseeYk0P3EmEUFTK/YRRk0KhRJYl1PqGCFt1jhx8hvLACIHvsmfPTmrFIt+4825OnJ5DmEXqrZSS6xBn0IjaWCkQYZK/nzXdRFwouQ5g0eT+1iDIwhgrQJsM33UwYYiwBkdLPGsolwuYOMHRMeWyx4aREYZH+nnZpS+hE4Ws27CJdRs20mw2KZYqtFqtXGKRJssTXEkuy0jTdBkkCyHyVQVrkebMNbDWkq0IrkktZCbLpR9SkqTp8kTKWkNmIBUJDjovKBf5Sq6WilRYbPrikRjnA1DnAsu959Yi186njV65zUowvNY+1zqWF0r2rJQbnGvfljOwZy3NNOTg+q477uTbd97Kwtwc1b4B5usNbvn6N9mz4wKGBzQHDx4gcz2GygEo2Dyxga17LmbD1s2EYcjphRkqKsAEhlYU0mo02TCxEa9S5G3v/LfcfeeHGBjuY3Z+josvvpgn9z3N5OQknudRKBQ5evwY68aGCQKP5w4dZWZ2imptgDBKENbwmZs/y7XXvp6g4IE9O0jpXNKVlROW1cxz7+9eQaDonasXIMM5X/tXAZ5lFwj1AHPPy9daS5ZPeJd1rhbOEcOdLwtmJkOKs5norDtYrEn/d9kScZ6ZpM0MJklZWlpiw+aNuL7H00/t5z0/8l72Pf4YjtbdYiOLxseKmDgJIczy4gybRxlnWZZrpFVu5TJdbzJSCtAmJEHhCYEXBCRxjHY9wLJp4yaOHzlKp1HHCofO/BJe4CNjg+zr5+jhg2zYME7Uipk7dITxiU2Y1FLwSgQmYNEtwdAYWSpI6k3cUhHpuJDE2DgljSJCPyBwJSaThGEb3y+AtGRZTIZgenKKb91+D7fe9nUu2nMxd/zj3y/PMitSEcdtEnK5jee5tDodPvknf8YHf+FDpHFewHP2NPDsAbYnuzFdycbKQO6zX3Hmt+nOn14st41nj/fz87+6k6XF/RTLe8iyQ3zko3+NbcFPfPgK/uNPfZnf+9138Ku/+nWGN8A//F2ZZ544ySuuXWShc4y7bvEZGRnjFz4sGayOMXVimJ/54/v40IfeyLH2Xn794z/MP/zVo0xsfpJOmPJbf3wZ9z/0MONjFVzHJQxT0iyjWqtSiALGB8sMV2qUvQGsdnGLfWDzm5My3bADZUnTJoUgwXcWGC+2mGkv0AxjjJIkjiIo+ERJQiYc0iikHJRxdA5KMgnKSDZP7OLZA5PM1xs41kHL3MnGsSk7d0/wa7/yfn7jY3/Cv3vHH/D5r/0y6zfvpOANE9cuQNAias2SJTGuW0AVB5G6H4Gbr+KYRUgLGEKUdDAmJY0TWnHCT//EdXzjWycIyqexIwEDYwXCOERIl7Ae43kuIrUYckbcILBJvkwNAq1zkGyNJo4jisUiZIYsyd01cpYTbJphhaFcUqgsoq9PMT5SphBIAi8ljVLaYUKxWCBoRhRKijTMGBvwyKr5uVLKJY5jUCXqSxHVsmBuTiKVpBGmSC0pFl6k27M1uI7Tvc/lRbfW5iBsGVxlGUo53fuhJDVdDTMZVuZxzWkvzc5xcIUiTbL83oJZHuyybmx3lmXLgBvIb+rkTKZWingZvLHsGhGHIXVACIXnF3J/JSFwPY8sS8iSCKtlLiGzGXmhoos1BmOTXI6iHcJOHeVoGkuL+IUi0kCxVKEYFGjPUVFccAAAIABJREFUTdO3bjOl6gCD/UMs1JfoP3yE4yenODk9RyfMKPlloplZ4t59SkJk0twVyApMnOACg+UCzU5IPe1OPozI731xhJSCStEhs/nkxFUG4wnG+gboq5UoBT7bJiaYmT7FxK6LmNi5h1Ktn2onYnFhGtf1CcNwWdfac9ywgFQ9vbkmTOJlv2fsGbCc0U0VTPMC8CxLUcohM4Y0M91rkY+hSIHAYIwgE+YsYCcEiK5E5/vd1gK8z9dWg97Vz631/7kkFmtig/McR0/7/Hwgbi12+vmO/VwtCmP+/tN/izAxpZLP4aOThK2YzKYElQ0sLT7LzOIifeUCB55rsWnDRrbtuZTxTduYOj3DpvVjFKsV4k6DonRpxQmVWh+P7XuSRjtDSJfxLeM05hepVfp46MG9hGHI9u3bqdfrTE4dY8vWDUgDS0stXN+jUCjgO4ogCFhYmqfgV7n5b/+Mn3n//8lK+mutScO55DEr+8K52vfqrrG6/UCD555O1gA9pYS1ljyOBFQXBGfW5JIAIZYBtFqBmnLfZ/IYXXP2CV7+kq9gQixZF4x3I2q7N5fVF68H0q21JElEGnYIWyGL9SVG9Rj33HkX1WqZLMvwPBf5P7l783jLrrra9zvnXO3uTn/qVJ3qUpVUSEICoUnoiaCCAl4VVFREuFevXtQnnT4FfFdEfReuEUEEASGAgOiFII2CtEoTIB1pKiFtpVKVak+/u9XN5v4x1z7npKgK4MdHcd/8I+dkN6vOXmvtOcccv/EbwwU4Z5GkBDIFq7HarB8zkt5HImy0OHTHLUx2WvTWTpK6Kdpb5jHOYZ1GBQFhIFnLSzozWyjuOMhjn/AY7rrjWzzmyU/gvnvvJVQh0zNbyK1mfrtnKKbP2UWWlUQyotAF6AKRGcpuRjQxRRCGZKsDwtSHDJhGAmLDCgogDAKc1TgX+U54W6GU4r6Dd3H55Y/nqre8mTDr+/MiBIXdVMCzliLLCIXgZb/5G2AtgRRsm9/F4cP3g/AbCX8+Nu4D77jh1h02Tr3lNwPnkexDutF98F3fbv+h4xW/9Rl+/gWX8LGr4Wk/ehd/9uaIl78AXv2HF3H+Oefxe699DtsvfDGhgHIQ8cJfinjvm+5keuyH+Ld/rXjNa5/KF6/5HPcdmeYb1wu++rWbEcuKV7/qs7zyZZP88wdu4MX/tYHNH8nx5QPcuXwbU1ubrK0NmO741LaqMuxujnHu9j3E8TQybiJUGycVPmPaIQw+JKhmqQIV45whSsYRrmIq6DLRGuKMJY1DtPZVkapy3sWjtgcLI0EYBoRK8Qe//0HuWgtJRIR0gkA5Sm0RtsGN++/jZb/5et71jit57vNfRLMRkA1KwjhABBMIExG1Oh5MqabX3wUxVZkjgxhdtYjiNiGGShfEAWhdMjE2wbDv2L//dt74V7/ITYe+RL+bUxaQFT1KrYmkYnK8SSdVZE4yzEqU817vayurhFsjlBI0opBYRYAmywekYUhgJNo5WmnIytqQsXaDmJzxuTnAMtmJGY8tgYEjQ4WKFI6SdjMhCCQ2LJiemFiPTC6rnEanSSyg3RAEKLJsFUtE6Aq2jMdMdJKzcu+moQ8jMa4GVLVoKokinLZY6Uv764uYkkhhUcKzuajRouYt7IQQFNqzxLrMacRefiGDTWmEbLBHo+MGUq3bixaFr4gkzgcNKRmg9RCTDbCLkDZLHwFuK1pRg2zQJ4gb6EGXLO+TJB2ixGuew6gBzlcZtbNEcZvFY/fRGZ+gyIYMsy5RmjAxO8uw1+Xw3fuZ3r4XqwTTkx1uv6MgDEPmpsYpK8PaIGd+ssnRtYJSa5TzgVwCSVwvEo04ZFh6W7hUgJOK8WZIVWREQUDSSAmUZKzVpNAFcahoNlIm2i0m2g3m52dwtmJ6ZiszMzPESmLLnCBJiBotqm4XMUoTZFMSJL7KKpREu1HTmG/EdHVMMvj1MqilOlpXOOuwNcc0auzcDPSkCMBZ/zoMTgRepqNAugdbjX2/xmYweSq4PfX3zePB4H8jOv7U947uz++kqz7dsddtAk95bvPPh/pcm38/E0gcPTfSqZ86pJRce+21LBw9zsyWDicW+xRVSWlyTFViXcXd997HDz/9R7n7nm+RJA201rznnX/NL7/4hQyGXXpVzuc/90UeecnDEbOzdGZmiKIEEyd88IP/yNev+TwnThwD7Thn725mJsZZWl3h+LHDVBp0ntEIAhrNhBMnl9k2v5PVlUWyYsjYWJuicCANX/7S13nhi4akafqgc3DqRuF0hOepzPP6BmP0GjYwxHeSeTzU+IEGz+Ar/KPh5RgO6zZOoqIuGXkrh3VLpAd9IaxDSg/MIhVQGv1tN67/r90AbcJbJQnpIVkAaPvtX8LRF00phXKGf/vql9i751wuuOThBJFP2ZNSIusFCaMZ+ceroIGCdUAijOURl1/G/mu/xo49+xh2V2hIcEajh10IUqQMEVFAJQMCm3Po/vtAaL7x5S/SarS567a7MEXO1Dm76PeHjDfHWF5aoMwLUiGx2rK6epK1hUWyw4d55I7drB07gmg2IBZ1N7pnCm1WECQpUoUMh0NkoKgyvb6oojzwGQ6H/NLP/TwvfeEvU2hHGCic1jgh6iAbX84Fz1QE9XlzgLCOBw4fJBAKJ5WXHOhag+6+HSivVxdOvU9q0Lx5nE1B/+++9NX8+Ttfzzve8TLSxkcxkeJP37SMdZP80OV/ztC0SJvwqEfBC39lG432Ev/jrzXnXZbxo1fMcf29H6KZwL9+dZGn//Acn/9kyGe+8kae8tg/5Xdfc5LdO4dMX7CTPVvvpqdzZoOESjSowgEDXTEc5kw359iZzJA2zoGogZJpDWy8JZ0zBlv5xdbHzfsGLqsdSqYYERF3mt6CrMoJhERacLakmUq0UusVm1BFhJEgcBWdVohb8ruXNFVsmW5zz+EFNP6apy3Dr/7qi3n+LzyNzvw5DJYtIQE4CWETYSxRECCiCCMclCWBirCmQjWbWJ1hDYR1fHXAkDtvuxGpHI+97BI+/rGP8uin7GRlMCDPKmwRYJ2gV+ZMN0NaqmLCOfq64viKYWASeqsDWrFvnmw0EuIkoCxLEhQmzxEqJY0Ug+GAhkxwtqI51mS8HdGIFFWeMxGFJK0UgeEBC/0SQgT9fp/ORAchfW4poSCOGrX8zDCVes/3HfOGQycztrUb7JqdZHKueVbu3WoEimoP5/VAKSGwwUYc97oEzniXECUVQrh1ZlNt8pUNa2lLFEWURm+QICMm03mQhnVQg7pAKrQ1OGPXeyuMMSC8HAclPcMqJMOyWNdll1FAq9XEOUdvcZHm5Dhx0lxnvW1ZYIVFBQlSJRgxpNUZQ2vNsN+l3Z6gzIck6RCjHYESZL0uUXOc2bntPPz8RQbDjJXFIYcXT5Jrx2WPegR//09fBuFIpSBWAZc+8uF887bbkMaTHlEcITR0mk2KbMD8zAS9MkU5mOi0a9LGMjc7R6ORYIyjnUbsmJujnUakzQZbd+0kCht+DbKOwepJjHWEkUJrL09yztXVQYXGYG1ddTUGb93t03JHQNFLa8BZi3uQE4omlN4px4tNHEYIH2IhJUL6TIP17APr5T3S8e0JwN+ncWqJ/0y62NNWmU/z3HcjAfluZBmnph+eCRB/Nyz0md43+nk6P2jnHNoK3vPeq8iMYXWlx8Rkm5XVJUpdoGRAtz9kcmqGlZU1cIZ9+/Zw9cc/wdOe+iRu++Y3QEjuXzzGIy6+mE6nTVEZoOTfPvM5JrbO896r/pqLLt5HEkUErYj+ygp5ZYlj/71MogrR6TA9M8kd9x5n0M9Zba564wUriYKAbdu2s3//LSTNcP3zPVQ4zKnM8whvbT43D3kPiAejhu8FRP9Ag2d/A/jmEeW8F/B6WAb1hO48A60QCOkotdfJKlFrMXCIYCNREGcJBcDIx9lfmIDah1gKhPB6YyHr1CXnJ3lpwNiKzXKuzRfq11/6Uv7+gx9ibHqSYa9H+7y9WJ1v2g063xkdhj5Fy5h1sG+tJStLbrr2WkCBtCSdcZQYR2KQQYpRipyA2FpcPiRKG/SWlggCiWy2QKX0uieZmtnG2uoqO3fspqoMC8eO4irN6sqAWISMz2xlcv4cwsufRHXsBPs/chUXpYLG/G7CYIyiEsRpgmr5En2pKyIpqPIS7TS20pRGE0YNgkCyurLEu975blQrpr08YOSeITZNPML5GsJIey6BoGa3hKibiYxBG1Ay4LGPu5xrv3YNwm0UbmwNkDcHd2/sJjd9QTb9drYaV17/hj/ByAkmtx+htE10vsjFl17Cz/3ENfz27z+LmW1380cvvZsbbnJ87dcP8id/Cdtnt3HvHbdzbOE4O2e3sDq5yjve1KL94gWCCcmXP/xaXv+6C/nwJyNuvuEg522LuHP5EFOdJt3SUAzXkCEYp0mClJ3hDJNjF+HkGBaDdhp0fb85g3MGgaOwBuHqCV0LECGlHRKoBGsEOEUg22hbEYQB2KZPY5KiZrcshGCF4PgDh+gPBboWtCcShv0BkZJUVjM+Ps2znvsCtsRLPOayJ1L1MqZmzqHf74LOsDSIowZGV+gyQzhJGCmMcLia5RYiRYQ5Zf8Yx47dw0RL8eG3f5wk2EXpFFRbSGzETKhpxIa+y0niJsXAMmsKdsqYyUZMJkumteT4asmhsEW+VBBFFmsdSsckosI5hSWipSTFMKdpHbbKmAwkYeRYy9boDxOaQUBlBaGWSKcJtaMpFd1hgXIx0kgaqkGU+pJ+nmcEOCabLVIKnHHIuQkikbKlkzLdDgnS6Kzcu9aBtQ7tHNYYH8FcL4BSGyyOJI7r8CNBEITrgCxQgbc0q7WyI3JB243qlbbea9uCB+ejhc95T+lQeku8qipRYYAKE7KixIxIE1g/vpSBn6uBXGfMzs6SNBJcGHDnrTcQBAF7mpcgWlAOvTwjSBpeBiQUCDCZl8GVZUmjM4EIA0KXEiUpw+GQ+d3ncf+9d6GCgG07d9EeG+e6677GsRNrNJsN4uVlbrr5VmY6EcJZkIpACu6881sEGBrNlGZrgrKoCMOQbrfLrm3TNJsJk6pJEkdEKqDZbFLkQ6bGxyAAaXxDaZpK4iggaTTZOr+DbreLlFDkQ6IowVUlQgSoyqc3rvW69Pq+MTJQIYWu6utAPf/6ypyqdeUjOU5VmbrK6gmpUHrLwpF1oSd/HE5KKjOKYfcVAmWdD7URASLwm5qzNU4Hms4Eps40TmU3zySbeKhjnU7mcerzZ5IbnG5sBpIPxXafTsohpeT6a68l661SZDnHBgZdHcc5y+zMNgbdNe668wYefuETOHz0ViY7be655yC7t+/ihuuuZ9hdo9FocPz4EXbuSOj3HVNTM1TG8IjHPApTlbztyjewcOwIH/iHq8mdJu50KBaP85hHXUZ3rY8xhlv238o99z/AoD9ACcUDhw7hqJiammG1P2Bx5W7ipIWwgm/edB1PfPxTTsuub97UnHr+NlcgTj3/Xv47WgO/ncH+buUv8AMOnkfgaFSCP7U/cnRSlPABJ87hS4dOgLS+kcG4dYC6+WYelav8yfeezw6JrIGukg6lAgKpkNIDN1unKaE12kqc8BNIGIW84Fd+jb/6iysZn5mn1enQarW5/tprufTih+OEwYUG6QRCegYglCFhmKCrfL1bPKGOvjWGIKpLp3pIEHlf2sJZOqmqmUPfOd3vDTyzoR2hKilVQJB4L+b7jhxh2/Z5+svL2EFOK4qxTqIaMUJAFEVEO+Z52HOfz9KB2+kpyVxjD1Y1KMuSdGKcqNWqrd8ExlkwvvERI6i05WP/+A9889b9POtHn8gzf/odPOrCC3jJS17Im974V6TST9JmPRjFX1HnXO2Ru0Etj64Rwi+uX7vmq0SnNBgJJ05hmN2Ddo1OeI3hZlB9tsar/kzw4z95GYfuvIHVfInuoM/Xb76e1/zlOGOTN9Fx+7jxCxG/+Qcn+MdPL/CS5yW87s1dzj034vzdj+G+g7eze+95vP19cMcBxz9+5Cf58icO8tXP3Mqx+xb4n29+GEO7zLlb5lnq99gzfxFf3X8bc3v9PdoUHWbinWjTAWfq61CBFD5i19ULIV7S5KwF4XwaphLowmCU3/g5IdAYcNJfNxwqAClCjHMEgURrAYEmlA1OrvpQFmc1UgniwBEIDXHKwsoiH/jge/hvL/wFsv4Cb3rD+3nJy36fME3ROiAKDbbo+XJ/0kRK0IRYYUAbVFWgiwGN5gx5IZiJZnjDH76X83dv5U/f8kme/8JfJC5v5ty5nYDl8MIy4/1VkihkcuZcxlGMx0PCOMOJIZNWE6kpsmSB8ck2VhvaLT95h0p6dlIqQgJsA7qLyzSMY3vQotcvyXWXI/0TVI0UJcapSDBW0Ygctp/RiFKmmhFjnQYmiMgKDRU0wpQGhqioaMcKEQRgS9KZFu1Q0UkD1oZnB4CMGsqsUwjlQDi0s5iiWLeflKMIb/HgfhBvQ2fW59f1UYNe4zbmuNFrRmCb2s3DuY1AB2MsxhSUVYM6thAzanirvaCdc4hAEcnY63ydI1YBrtmk2131YSrZkKQ1DioGERAIi9E5OEMYxuS69LrhSmPKCpMPSBpNwl4PKWH3vgvYf9P1GBfQGZtg68wsWMGhB47Aju1UlaaxuESe51izab1RkkaceNZdCsbHGkyONZjfPkc+HACSVhLTakQ00hbGJrTbTaIgpBF7xjKKIma272TL/A6Wl5eZmpoiSlLQmmI4AKnIeis46XX0Kowp8pyV1TUfKCMjSmdwzqcKjuR2Izu5vPLg2eFwdfS5pL6uztUV2Bq4YZBO+uRZ5zbJbfzzpTU47XyI0FkaD1XW///y3zrdc6dzyPj3SgU2A8dTgeFDbQ486Ha8/e3vJM8McQAri6vkWUm73WFiokUSRix3F9m993xu338dT3rc41GBwVQ5D7/wheAUw+4artLce9dtTI5NY/OSLMsoBgOOHTnKytIS27bMsjzoUvT7lIMB01tmWe0bvnzNdQghGOYD2s0xiqrk4kueyk03fJ44CX3VZ5CTJAlTU1P0ez2uvvpqnvLkp2Pr7/vpgPPmz7h5bK6Mnfa8uI0o7u8FMG8eP9DgeQS3znSLnenms8Kz1MqCFa6WbWzcxKOd9OgYo8Q6EQaIQOGK0gNEKQmiEKRE4v0rtdaYoo8oKwSSKE34+Re+iI9d/VGCiVm+sf9GHv/0K/jb97+X5z3/ZwmSEFOV6AJUGHmdXz2pOiGIk5QwipECokDRXTnpm0xchZIxURCt75Qmx8brG8inlA16Q4QVGGN57CWP4F8/8y/80LP+EwcPHsSWBZ2JWb7yz59iz85dqPGIJE1IwhZIh5K+xCZiyeSevRQ6Ix8OKAofKOHCkFBFxGFEXhRUZQ5IkvYk+bCLI+Dk8aP87PN+kZu+8tv83d9+hNf9zmuIcLzzL97Kn135Rl72ipcxOz7B6upKPbluNPBZB2zSRFrcSLRcXxfQ1qKEIEoShlm23ib4YAA9Yq3sunvg6DD6FHD9/RyPOP/lvPRFV/Ibr54iibfz2Esu4fNf/BK7zjuP7r07uf8D9/DO8m4uuSLlS1+a4n0ffho//+J/4sefuYwMb+TSJ2uq2xc4f980EzMRxcoBznvcc3n0ZTs4eejLdEUX6WKKrEBmhpu+dQBXtmiolK5eZNw1CNUYgUioTI42GmtLkApRl2ShruZI3581CrIxtiCQ4KzGrpfOwDkvd/IpZY4KQxgpitxbjQntaEYN4ijADiwTrXHaLUMnDTmyMMQJy8PmZ2k0I3aev5s7D9zLK17xCo4sLTIttxKIVcJAgW0igpRs2IMoREpLIATWFsgoJmpPsbJwB6tHv8grf/NT3HzgEH/8Wy8iCuGq932A3/qFJ7N1zw6KQR9FjOtM0GpPIyJv0+V0Tln1IeoRp4LtpaXVmKc0JXEc46hoRB2/AdCSOAh9o2AuGHdTJJlmXIREZUGZjFPGbcbGW7QmmqTtDs2kxerKIovBcVqtFp00QYUJcRyTFRlY48NGjEKIEBUJtMmIqzWW1/o4Mnp5yeJKdlbu3RG5YEoNwhMKYiTjqEMzRn0QQvjmtDAM/eOAqN0ZolpGYYzZcHvYBLRHMd+jhXHUMCjrxd4Y6+VEdbqdrpsGR79vgAZFoCLCOFp35FhZWWHpxAkuuvSxDAd9mpMpZT5AqdwDRRVgZejdfoQhTrzThikzGp0xyjAh7y7TbLfJ8iEygEsvfyp33rafxYUTxHHKtu1bmZzqcPjQMYRQzM/NcejIEcIgpt/v02j6a766ukqn0/FNeAhEFJCGAWNT00gpiaIAW2marZQkGcPZilh5uihppmzftZeZrfMkrTbTU7M4anJFhQSBB8ztsXHWltbI85zJ6SmOHzlKFITosvKbIRxK1t1CmxhPrTWB12z4x6SsyQdLKANUPS+I2k7QOAvOk0mOelPlLKW2NanjAf/ZcjkajdMByO/EFJ/62tHYjBUeSkKweXyvjXzfzdjMVJ/KWp/6N52qEc5LSygcZZVx/5GjzM7MMdaBwWDI0uIKSSMmDpp8+jMf4ElX/BhHF0/yYz/2DJ79rGdSVRXFsEuZZxCm5HnO2OQE2hrybEh/ZZWl40dYWlmm0Wr7yPcopjM1QZSkrK71yIZ9KqPp9dbIBjnzO3Zw0SWP4LZbvkRZeulnqxmQZRnLy8sAZN1Fbr/7DrbP72Ksma5vjE/3eTc/diobfbpzcrrz+L1eqx9o8Hzq8A0odj2qeRQjKtkQ8TuhcCNDfuEQ+GaW01H+/pi+i3iks1OBb5gS2ut/jZDI2l5OCEkYBZTOICqNMRqtFe9599tJGx12nnchb/+b3+GTH7+al/zGbyACb4FlhbdqGrEyUeKtv6JQYRCEcUASKHrLi0RRjEJQFAVpqqiMA2dI0yZojQoUToFxEMUxrvZavvWO/cydcw7d7irHjx6hmcYU3QFbt83SnBojimKKLCfZOg3WYHpD0skmmS5pxCkT5+ziwM03UQlLYSxpIigrQ5atEiQpumZmTJWBUDgV8o53/A0P33cOt9x+E0963FO49pYbadf1gde88uU0haDRaHDp5Zfz+X/5dO2aIurWP1tP2BvynA0JxkastnGOLMtAjB6vd4vrEo6N98NGtQLOvOn6fozXvPZKfuvlc/zdO45zxXOGCAuPvPzp/K83Gk5c/SkORPBrL5iEbwSU5SI//fNf5hWvLXjc5ftQgeCO2w2f+vAC217yGBbu6vPl2/+Fb910DT/87IRHPl4RDDuUXUHPlJSrjidesY+V3lGuveYw7WZE0mqAdZT9HjK2VNUy4ChcjyhKCVyT0ipCFdag2Zd1ERHOlIDy9641WFMhoxiBD1DR1oI1gKEsHFHssxz9ZajQtkSJmKrIWOspRCUIQwVSsGVuF4cO38uV//0PeexjL+OfPv8RrnzbH9Oe2EaZWZwJMTrH5gVRKwXrZVtCSETUwQmBc4as1+NfP3YfSVCwswVveMtVXHDeuXzzzkM8+fGXYAqJiseZ2NLEaYeLIlAKiUC7BFGlSNOhqry0ZEpqUCCU1177SOkQIUKc9RNlpYZ0khRVGkwQEdqK6eGQtpol7oSE7QYyHcdax1SYMtGaQJPRSCZAeQlGnPhNia+yhAiZ1I12EAwWEO4Evf4SpRngyrMDQeIgpLKGMFTewzdQUPchjBaadVAsBEkc1yzrxmNSepJgJN0azblKqY2ehk3Hk1JiKu0rTMorbLX28d/OWPJ+TrfXY2xywlcUpcS6jb+pMhppQi8pK3366+TMHCeOHmFsqiBMU8amt/u5iwqcl/pZESIjBUVGUawhg4A89xWXIIrIBorhICNOve/59NZtIARxnHpQ3BpDyZiiKBgbKwBopk2WV5e9blhAGsWoOEJJwdraGlump2imPrwoiiKklLQaidfbt1u005jhoMfWbdsYn93K1MwWut1V8qyPCrdTlUPWlk8yuWU7WXdAmKaYsmJ1bZEwDOn1BnTabR6wh9eb/WQdsy3kg0FkENSRUq727683R84JCld5+7vaQ7s0Flu7gYxkh9p4Bs9Yh8X6UDIpieTZScccje/EyD4Ui3kmEPXdgu9Tj3em579bsHY6ecfmx089zoNfJ0AJ9u//Jv1+n353QKfVpigzlFK0222czihKb5KQJBEqSGgmE7zxyjez57x9TI+3CQNHGCdMzsxz9MRRosYkSRrhVESrM8Wtt97Ks37iudx/5DhxGOFshRMKC9x5x+1oa/wmrzVBkiTsPe98du19OKWuGG+3KSvDzOwExnjrYQwEKuU9b3krDxy7j+e94EU86clXMDs+gan14w/Ftj/UtRo9Z6z9NlXC9zJ+oMHzt3/0B9uW+CCNb6fevbZ50+vkQ9D39dAWpNY+bhRZWxwJbO1rqZz3i0XUZcdAEUUR2jrazQmKquTwrTfyJ6++nT0XnMvBe+5h30UXYOuFMoh8N7WSfjFACrSpsAg6nRbDtR5xq0MoJc4ZGPYJooSy30XFCa7KMVIRBb77NJASKyxRHOKUJKZBlve45+5vsfecHSwtLTG2ZYp2Z9InfSnFWLtBXuVMzW7BtNqISNEJWjjnmAx2sry8RNxqIKTBIghCiQpCQmkQUbKu2xZCcN+BA3Qmpvjmt+7mEZc9hVv238G2iQla4x2O3ne/19UBx48e4eTRI+w7bx+33X0XiVQIZ9Z15P56wCYFx/pjgLd7wm+aEBvXdgSSN7PQIy30D8JQMZw4oXjef05phhcxLGDXRJenv+AgD3vdjzEzkfC5K4/wpquv5S/et4M/++99Lr50B/0+3HnvAfbtO5fdF5d89ON/y+c+Mc3xhSGf/JcLGG88k4NLH2Tf/HaOf7WHUT2Orx2nVx2ib9b41o0lv/f6H+VfP/8FDhZ3EkwndI8qnth6PKbKuWflHhpxm7l4jmYwi7MxRhhqESRRA47qAAAgAElEQVTO5ljr9WmhGsNaEEZ75rHejAopMNoSxBECRVl6LadsBIQSLjp3Oyf3nyBIUtpRSVn2KYxlqtng4MH9ZMsVM+d2uPdIj7991/sYygXitI2LQ5+MFqVIZ9FS+kbfUQ3KaWxlcGHEjvlzueGbt3Py6CLLXdi3dzsHDt6LcY5v3nwbey64mNJGiEZAVVqMgCDwns1ag4gU1jRQsff89T61EoLa99o5nPPNahYLwhKOjWGKHNcJIY4wRU7aCgmsQUYxqAZShwSxotKWsJMS4XsoDIZAeADtnAcpTiicUusVk5A5plRCrNqsLK/ROksRx1JKnBk17TqsrlAIotD7FSsVYq1nio2164uZEAIl5TrLbGtZx2gIoahKg1QP1jCOKoFSSOymcr9S3hM+DOuy7tD716MCiqKkqgzW4t1MgO7qGqrdII7S+l6uUFGCNRVR2MBWJSO/aRGkIAMkAlMVWFvinKPR7JAXGUEQUJVDVCBoj40jUMRxzOTUFobDnLXVRcI45cTCAkpIJqYmSQa+ujLW7jDe9d7Lwnlni4sufjjHjx/HWks+HNTeyQVhFBJFEdMzM2TZgJnZORppxDlj52PrTUK/30cIQbvVobtykiCISJotD16FJcRhbMHa2hpxnHLy5CLdbrf+DCOG3jPHIzmGqMmmEZtXObzGvarwTnq1T77VCBn4SoDz1UrnvNOUNRJtCqLAVxgi6T9rHKizKtsYjVN1yg/Fzn4vx/yPkIJ8L2Dtu9FVb37tqay5MZb3vOPtSKkQUmGcJVYhU5MzAJRDQRALVrtd9HDAddd9kqc+5Re54GGP41Wv+iN++jnP4Kd+6se8tl4awjBlZXmZnfPbGOtMcOLYSX7kOT9D2mgwVhr27NrDkePHkE5y8OAh1rrLCBRbt23n3nvvZsfYLtLWLtZWcwIZUFbefajb7VMUBTvmtyOEYGm5j5xK2Dq7nfe+7a28913v4CW//XKueOJTH2RreTrG+VQN85nAtYD16ur3elX/j0gYHA3nPHweNQsaARaJdWI91nV0sjY50mFcrddlQ5e3ccyNaE1TaWxRIKx38UD4Sd1qg9UGY6sH6ZjKskSXFd3VZYIgYG5ujuc85zm04iZPuuJpACgVEgReh+qcj4n1i4UBJxhrtemvddcvZp7nZFmBUxH94YBWq0N7bIJmIyGNEw+8rcPV/qhCSUxVYTDEUUoQBBw8cJ8va1pHVeQU2ZB8OGAwGNBqt72OO1AI4a2g/PmwzO85p5aWKMIgXgfKI1uqMJBUGgbDkunpacY7HW66/jqmJse45bYb6K2skA8qtPBaZV27NFrg4N13IYC/+Mu3YNxGWpLzVUQ2q5SFYN0GUNQTtgC+k2nzRjMh6z/P1mgEcODWLbz/nRlFtcD1X7kZvdQlcpYbbr6VL33lRnb/px5v+vR5JOMB/8+Vl9JO29x///1EgUJXAUGYsW1HxEpvibe94/EE6YB04noCEbPQL/i1/7lIa1+TRz9hB0VuyDPN054b8I3rP8u2nSnnXbiTR51zMY+Rj8KVUOmMJFQsLa9xfOUYvWIZY3NcVeD0AF0VVMMey6uHObZ8L1XVRUmDkJpiuILTfVyVgTHI0DdqmdqqUAiLziqyYUUkCirh6GYDunlJYQWxkvQHK1SlJEwDFhZK7r79Rl73ht/jqne/H6vzdVcFIQTaGuxwAFXl73VtsfkQFYUoISiV5Jd/+wqe+zPPJgxDH2ndGEMh+MbXb8LkBVRD8mHmF3xr0FVJkQ9Bl5jSWzMZnPdt1garK1xVUhU5Wpd+wpX+O4bzTKcLJBpHVvpY7sqBFDW4yPuUvTXybhdpBUKGoLyMQNjaC5s6yttU/nhV5eeWSqMCgQhbNFszzE1tYdfcvrNy72ZlQSiVT/hUilCq+vs6AiP193eT5GJd71xr50cBJMayXh0azXF2M9gWXmMnpbc6jMOQUCniuqKmala60WgAkiLLPShX4fpcXxpNNhiyurrK0aNHyfOCIhv6pscR6Dalr1pJBSLGGYsuh2ALhK0wuiRtjUEYglQM+z2ExoeuAFVZcvz4UYoyY2xinN1799Ee67Dn3H3s3HMOVng9965du4jThLm5OS699FK2bJ3jwodfwGDQY+fO7bTbTdodb8HY6XSYm5tj165dRFHE+MQMaZoSJQnj0zOEYUwcxx4E104jxhjSRosiH1JlQ9I4ZeHYA1gtCAQcO3aEOPSsb1F4JlxYHxLmI783HFCstRgpyYwht5pcG0wdODayhHL4pk+9rjOvrQTrvqA0ColCRShYl/JU1lCZB6f2fr/GmUr3Z5JxnGl8J3C7+b7/9/6N/97xnVjVzcMKyAtL1l9lbW2NdnuMdmuMMFQcPXQ/i2srlKUGZzBlydjYOEVR8PFPvps4bTI/s5dbbrmHI8dWWesXdNdy+oMhQRhR6IpskNOZmqAx1mZscoILLr6QV776D3jBi17MxMwWTpxcRAUxRggWTy6wb9/D2LZrnum5Pfz1X76aOI4oCu+UMxwOvXS12yUrcpS0zM5tIcsqZqcm0QPNX115JW+/6q1Ulf42sHw6cvRMkoxTmwX/f8c8U8sy1v/P1Yxk/TktkFtNLNSmx5xvHKPW0UJdOqxdAQBvnbZhuO+cw23yRnSjEBW5qaHNbuzSvYelxAnjG7Bq7di9B++jPT3NiRMn+NIXPs/5F10IcqOZwjnhPaelB+ZRFJEPM+8WEvjyWpxs+BrmlcEJQb/bBSxxHJMEIdo6jHVEUYBAoIQvk+WV76puj3mf3KXFE6RJAxcERGGD2W3bKMuSIPKNSFIAMgBpPWCO2zTGpv1ntRXOlIRxEysCKlMyLAx33XUv7Qg++4WvEDjLw867iM9+7Gpuv/sAb3nzn/Put76NVrtNt9uty/hunVlTwCtf+ltIBENricUGw+w2AWPnfM6gG12j+sqJmqI29TUW68d1XmsphG8Y/A/Wmv17RrsdcfWHbuQjn/o5vn7LR5ncInj3+2POlY/mj9/3GX7tZbD7Zx7FwomTaEoCdz+CBtOzW/gvzz/E6/78Tqba0/zp77TIwgf4zOe+ys1/9DBe/XrFvr1zHF25i9e96TwC2WWlt0raTNDaM6bn7DyHW++6g/13L/GoixdJGvMs39/AuorVQeXj46WiKioKm9daUkOoDMOiy0DkRLKBzgvQBrRDuRjKEq0cQSJ8rHEdvGCrCmEVRhjiOOZnn3U+n7zlGkonkVJx4b6dXHPLAZJAYXRFpCRpHLI6cERunif90AX0ukuErRY4i6k0YQCkDZyuAOk3wyrwGlwEYdDh3AvO558+cjU/+dPP5Ibrvo4xBaiYH3/WZegi924KRmCjGvgaW8tAHFXW95KAqInFILXGGc8Ki6ieUFXsdbFS+oAWRg1cMdoqpDJYAq8hr7TXUnlXXUzk5xiPI31FxJfMFUZrwEJVIWzNvsoQG/jqShCnSCeYDs+OVd2pzX4jWcVIS+VquxxTh2yM3jNqQMP56G0pvM3bhnWWQwrv/yyEIFLqQW5FyuFlabWO2eIwrp7PA0Uch+RF5iUaYURVeTJDlxYlQ8ASRDETU5OeYKjJlGZrHJwEYXCVxkbKg+JKYysPxsNGB4FCVpqst4YSgjBtcOSeW2k2J2h2GoxNb6Hf75OmDdbWVinyiuGgR15kzMzOcmw9IVHQbqe0Oh3OaXiv3LTZJs9zZue2cezI/cRJsM4qp2lKq9Wh0W4RxzHNZpPhMKfV6qBrt6bZ2TkGgx5jkzPoyjI+s42lY0doth3jnTEWTp4kbo7j9BHuO3ySY8eOY6yj0tV6BoLVllxXGBFgjE/tNdr5KHZt1p1SVCBqiaFDhUENwsW6DZ4EgigAqz0xNbruQq2DEnuW0qlOxzafuhZslkF8J7nGqczlQ1Wvv5e/7zu95nRM6amNgqd7z6nvlVJyw3XXs7C0SFX69TaOY8bHpllQIYPekFVT0klaiEDQ7/ew1qACyU37P8fD9z2Fbj/jNb/3/6LSkMmpMS68+ELOP3cHcRzhjOOWW29mfvteJrKMAwfvI68E//KFr/G1f7uGqbmdLB0/RBQowuYYlzzmcSyeWORjH/ofDNbW0K0G7VaTOAro9/vEcYjFV5uEk8RRg23z89xxr3eWGWYDPnv1pzh2+BB/8Oo/WfeBPnWMXH5G40wa6BHjfKbz+lDjBxw8s17e916SXgsrhbc4U8I7AdhNLOOo8WxkayTY+DLp0WN2tJi59d2yxbPNznmfW1mXamv8hpUO6yxWSYwzaG9qiYoiZud3sLjaY0xK9l1yEXv0Bezeez5RLMn6S9iyAmUIVOTZOuNIGqlfiOMIJbzOTAUSHOspUfH0ZL34ZnVDnKMsc5ARUiqUkARxglLal8uiAClS32EdhBRZjgoDrJDrDhyB0RRFRpS0CKIEU1Z+IyEj0lbq2WbriGRAPrTossJaXdtXwfkXP4Kin5MNPstHP/Q+pienoCy5bN8evvBvX+Wz//xp7rnvAL/+kv/GO9/21voiev1jgKtDYRyxkLz1ne/mJb/6n9ksx9l8HYWoYfMmCQ7UYHrTPS7wPqbylNedzdGOp3jb37Z41f/99zz/V6ZxrYxHPPUAS0eO8+lrLudVr7yeH3l2gXWS4w8UXPSwOb74hVvYuneKK/8mYTJ+Lh/9h4OoyZvYMdHg61+J0foOouYCN927hEDQyE7w6duO8qRLtzPMhzSbikC1KMyQmekWO3Z1GLOPI7+3T2gkeZUTFoJECdpmhobrkPUHCAWBjHBGIE1KpKdppC3KnkE1BNYEhCokrzRhECAwWFkvvpVFKoepLAhJkWt27djCzlbK8YGm2y+57e77MVqTWc1MJ8HaCmcV5+w5l7QVMzc5RmWXSauQyo1DXiCTGCcdVoUIY7wOLojQWhOqkCo7QVO1eMxFT+B3X/sWptsxpVZok1MN+giZghJgHbbQvlfCVgQqoqpKhHNUhSYO/P1oKk3ZHyIDgaj89zuOQ5yKavmAwJH441lb34MSN5IgKIlUyoMPYWq22Uu/hAlBCIzzFSwhBE6DswZK7UMmogBbhoiqQgSKQAT+7z8LwzmHlaLWugqUAlv5CsNIFyusAGN9A6bbcMfwJIE/p/776+dp69x6cMdIB+2ZZTZFR9e9D3Us+GhWkHj2PxQSazWD3hCcQEUxKogIRLBuX9fr+QpbHHqJTKkrGq2Kk0cO0OyOMTGzhbLIMWVG0uwQJU1fzkZhnEZXBZ3xCfrLy/RXThLHMe2xcR8GFERsm9rCwvHDxElKEEdsndxJ1h+wsrzMvvMexpEjx9i6bQtSBKx21+h0xkmCiDj2gABgenYbzWaTIAgYZn3arTEarSZhGDI9O4MzmiLvU1UZRVEwPj5JUfksgCAIaDQSrHNMz82TZRm93iJVVdFpj9HpdDh05JtEUUQx8NrlalOTm3MCJzS6suvgcRTUBbV+XPtN34idHl0r4UQd/OibOJ0Dh9hUyd0Amt/mtnKWxkNpm8+kiX4ozfPmcWof1eb3n1Em8F2A7tO95kwM6uaq7YM2DsLWun74yN+9H+ECpPQON07AsZMLhHFCZVYZH2sShgm23yMMva97GgaMNVvccc+X2Dl/CXHcJmkmnDy2hDb3cuKBVT7e/yLbZqeZ27aVz37xH7hw9xRXfegjXPLIR3P4vgNEoaK3dIQgTHjaM36EG2+4icXjJ3jggUMeHCvJ0onjbLnoItZWvStOmVe02wFxGGEs3HHnbew77wLajSZZtw/W9zfcc9sB/umfP8rzfvaXwJhTzsnGNTmTLnrkQe+cr1H/e9IGf+DB86kjYINyHwFo2LTDdHXYA6OT6N9nNxDYhgbaeXs769d9BA5hPStrrUBqzaj45Bt8fHKbkB5oGxmRNjvIZIwn/ciTycuMLTNzHF44TmssJeuvYXSJzjIKQMgWAkmSePmFc1DlBUYpEN4n1UNDhVWCMAi9PGX02SuNjfxkF8UxQgiyXo/WxBjDodeiBUFEGMb+o0qBDCOajQbGhURxylhnhkpnSBVSlhrnNLgQWTNECkdlcqrSawCrso/Wjm4/p1tmvP5PXs6JO77FgUMH6QhobJvj0PIyQgiu+KGn8I9f+RLPefwTed+7r6JyEG6S0qzfnkJisfzar7yIX/4vv8L73vUuRrWCzWN0/Xws7EYT4egs+WOKdTtDKR7s3bh+3c7C+MTHF/jIh4/x7g/v4tixY3z2n0sueyo86pHnstLfyzOf9Q2qYsjXv3KEF/3iq1lZ/BSPfMw+0jTnUx/vc9Xb/p44DmmNQWkGWAvv/18v4zOfuIbF3hI/8ex5VtQx9o5vQaoeRbfBoFyq0x4Pcc7unSweX2L3xF7O+9lnceIz7/EhN0UOYYi1JUIFpKFCRRKHwWjojLWZtFCUJXEz8tWW2tdVGImpNfxCeasyKzROGnxioUHLkChWPObcMT5x06IPVag0SavJYDBAhillv0+z0+AZz7yUbVu3s/9rGc94/hiVTSl6BwjiOazxjjNCV+ACjAKpImLASEtR9lk+uMCJ44e5/NGPZOHkUdZW+sQotu+YRcgIpSzaaYQxCAXaWLSp6rkhJG3GGCfBlAgiotihTUlVGi8PkbFvKhEgkDjtKAFRGISowyQc6EAgMBgRIAPl/w0h0aYiDCOwPv3OaoOkbsiyBrISV/pQERkJVMMzgM45KkrvYXwWhnMjdxWBoAZBDkrrCDZZQAX1Jt9Y6xvLtF5nk4Xw3vmjzyC8t6H/nU2d7jU7FEXRJmCwAUYsvnJlBSStJuNT476sm2UUZYkrvfY5Kyr6/SGhEhRZTlUUxHEMAlYXFwgbnsWP0wThoDMzh1ExuhjUn9ngapA56A+wriQrC3buvZSV1ZNYbWklAVmvS9JoU2Y5U9MzvpJRVkzMTJN3++RFlzhRNNKYyekpnBVs2bbVs2pZijYlUzOzZHlOZ2KCaed7DSYmJojjGK29k0aS+nL2WM3o53nOlu3nECQNH1hUn7dBb4myKDh48CBxkoCKmZiYYHFxcb35z3+++txK6b27hUAF9Xmunx85mCgkhS59rwxiXQIp2WCZtXXroTabQeP3AhK/X+NUDfCZHjsdc3u612yWHo3G5vee7jgPdezR8b/Tc2cmhcS3v7YOxnHGsba8jDMFg2GGFAFxFGGtJlaKc3duZ3ltEV0WVDojjmOstsztnecXfv7F7N17Dnt3b+XvP/gxbt1/jH6vR6BiegPNwsk1jjywQPaN60lczu03Xcf8zkfQ70n6Kyf4ief9ItffcC2hLbjv3gN02imLJ4/SW1kmjmMaSYquCk4cP+5DgaqSuNVGojh58iTbd+xGVwW97go7du1haWHRf0Zj6a71+cRHP86jLnsie3bsOuN5/LaKwRnO4Oi938v4P0LzHCAIhV+o3OjE1BNxQB2SYjff4LXmWfjS3alJoes7DjEq9ftSpLZQAVp4Bw5tzbotUmU0lc7QZe7ZWu3DA/I857GPu5yDh+7jssddTtBs86M/8izCMMShkcL7MYdhSJ7nSCWodE5VZpRVBpL1JhYpfWOfVAK0xlTal86UIggVxAESHw6gAklR5jTaDWxZopSg1WqRpjHNZooxFXGcUpQaS0AYB8RpQm/YI4gaUO+4rFMIiWeXde4ZD535sqcTlBWs9vqsLpzktq9fi+ytcefhQ3zpi19GBCHf+tYdNfvvSyXPfsJTyGp2IlaBr2TXw9bnHufTHiXwgauu4rnPfa7Xgp7m2q+7a9Q/bS3R2DxGX4yRrl3gF2iFWGejv9/j3677DX79pTG3Xftojt4zwQ//yBw7ds4w7DuWD99Eq/lojt7n+MInDYojHFtssbjc4+d+/BBXPHOMP3/7VrQJ+OnnTVCuzNBI4CW/8kZe8Mt7+Knn7eQRe/6Mfq9gcnKelaWc1f6AWLSZjhRveBV0Tzq2TcyzcOhTHP+XqxBxhdMZKgmIA0UUeRARSEWkUgLZrN1ULCWGII6wpUMYWZftBSiBCORGR76xddBP6BuIrMUKy1inxf/1X58BwmuCM2Po94fEKmBhcQ0RNGkkMZ/+5NcJgoTdeztc8+l/4tjx24mbW5Euoxzcj8lWEDIhajRQCKp8lVKvUHTvZ3XhRg7dc5x3v+tjZAuHSSzs2TVLIwpJ0xRdFuRrQ7qLK/RW+gyW+9jMYIqKMi9gBBasQxIDDhVGhGGTZjpJHI6DTVGygbMhuvCLVyw8WKp0jqtK//lL4wOUigqRV8jCIkogt5huDpnGDDJMnoEpkcL7pZfa24iVhaEoKt8bocLaV96izhLzPBrG+aAoYQ1a+J8AOB8/XRUFVvvNiakq4jol1dWMpVDS2yBuSrMb6dn9c14XPWJkRwufpI4Fdw6sIwwDXKlpNptMzcyyZes2tm3fwczsLJ1Ox4NkIC8LDh4+ysljxwGBUIpGq43FcNdtN4P1CYdBGLG2vMDa0QNU+QCrDVWZE8oIXWZgNSJoMDExQ5Z3acYRY60mtirJ8j6BikiaDYywxGlEmMRMT0/T7a8xM7udsfFJwtgz32maUlWaZrOFlJKpyRkanTZTszM0GilTs7Ps2Ok1z0oFBIGi311DioAkbZLEDYqiYGp2S32efAhLPszIBkMOHzxMnpV0mi1EZeivLXHi5KKXY4zkNoykNwHCWZJAkUaSSAqUs0RSEAnF/+buveNsy+oy7+8KO51Uueqmvvd2vnSiaQlNE5QgEpo0CMoojCDCBAccHPQzM8qM4ziKM0b8OAZUeBFFB2nFITkkFbqBphvoRjreDjdW3cp10g4rvH+sfU6de7lNmEDzvqs/1d1VJ++9z1rPen7P73mSWKNVaBRvZAlKhObPSGmkCGvmqCoqg/bxLNmOlDIwiqPbv83j6wH2b0bKdy6wPhcwj9eZCYnnI73mJPN5Ptb7fCz2I/3+yO/97Oc7+7XC9+vP3vMn7GxuYJwkSxpAHeJJaLo9evwka5sDNjY2EMLT6+8wtzjD69/wEzz5KU9lfmkR4wQ3vvB7+Yk3/SN+9FUvoMq3qUwIXeu05zg4t59/9obXIhtTdLe7XHbxVRy++IkcP7XNXXd8gbWNNfr9Lo00ZXVlBSEdZVngvSdN04CVrKTZbBPHKUjPvsU5Wo0mWZZw6tQKzgtm5uYw1mFdqITYwvPO3/9t8qr8mmM0+f+Tt/n6x9X4h4nz+q1u+r6jmWcvdptNxhcw1Gb7tbaKOuq5BmzeTV74NbMsRk1ogpF/A/XjtAzaujEzXUcQWyHwWBQe0CPRbdjNeU/cDJOatCUPH72XmblZyrzg+LEHmZqaQisohyEApdVuUeYFQkKe58R1FHIolQfbH6VUiDfF4705q5tUxQnSOxQKL314t0qh0xSNYrO3yuz0Epvbm8RxTJK2SLM2SRKh4wZRktBsdQJ7k6RYW2FMCGqROsKbCu8MxluUcFBZnAyRt2c2d7jlM7dw80c+zBe/+EUOHDyEIuJZz3oaor6IzUTqkReWxAcWeWZ2lu951jN533v/bHz7ZFqgROCd5f1/8T5SrSiNHTtnPNKQdUl3fI3UJv2i/gd2p5RHU7xxwzW/wU0fewzHT97C8591Ix/55N8gCnjw5Jfp5wkvevET+MotQ37sxw7x95/7W2b3aHw14L+95zEUuefvP3k3P/+Lglv//hr+5c9/hmIr4rrHH+Twvkt42rUf483/7pXMT13GD7/xs/z++2a4+sgTecONX+blr57lta+/H61ezNqJu9l39Rzv+Jk/5iWveiK3bdzCU575TFr3SJQ5GCYQF4ELescit1gnSARn2VFlWYK1hOS32n9WKYV1Bi01pjR470jSiGJQMKj6eJkwFSkGFVRWorVHaU2kDXMNgY8UrTTmt3/vXTz/6dfy3Fc8gXYrwROD0sSpw5gSX27R7fWREqI0oxjs0GhIZqcPcarzIIWAh9eH7N+zxNHja7QbFVoWDHoJrjJsd0tSIfFKIqVFSB/KmS3wFbjIoVTQfiqdIITB+SBHsDiUV8QeCj8I+mkbmtvSqE2Z97C2QKIw3iOlo6w8USwxeQVO4HBUZXgNKMiHMjTseknsBE56TH2c+/mQRpyETbae0BB/m0ckBd454ihCeB9irYscHYW01VED2+6m1RNLRWkMvn7PURRhpQzzVS3JcID0u+4Zuo7sttYDrmaaQyUplJdrCzvniCJFnCZkWYYXoNOMtDUFQL/fpbOxCcJx/MRp+v3hmP3ubm+zsXwanAvHVSiqcsj65ip79h1CJxm2HFIOBlhToYUiaU+R72zgnERbj2pNs7O+go4jGq0pqqrEOcPePQcYDgrmk4yV5VMcvPhSsixje32Dne0tskYDncQUgwEmjmhNdWqnkeDOIXxwvtBJiow0ZT5E+BDhHunw/ne2NlBCI0VMlDXpba0RxRlSStZWzrC45wBnTj5Eu93kTHeLMytr4G3NIIQqaaxl2CgqEHZXDzoC18YBk975LhAcWtXa59pa0PtdScZoM2RdVVvVVUihxzaE5wsH+b89zgVP54LVbwR0z32ecxnfycTMR2KDxxWTc/ygH4kR/WY+y7mP2QXJYS5+pM/ipeKzt9xMaQ1YQ+Utxlk6zRCCNjs7gxCwtn6Gubk5hkXO+vo6Vio++/nPkTQiHnPpRSgtKascL+DaJz2OI1cfIe9vc/sts9x2xzIuzXjxq17E3/3tPSRTM6ytbbC070I6UzN4JMPhkMoUbG+sEqeh4j2q/jebTYqiYO+eWYRTTE1Ps729jZibQ8WCMye30EnM1voKF1xyEcsnlukOd3DA5vYGR+923H333Vx79TVQOxvVNN3XPZajMXYEOk/V+huN72jwbP3uTkFOllZgfN0476lEaAoUXtY+sB459jMTo36depwtIh9PJNQXuK+BnSdYV412I8IhhGRmYZHN7Q1e+AM/wpkzxzl18gzXX38DH/zw/2B45ZVcfs1jSbIW3e2ToaM7H4Rw7aEAACAASURBVLI57KGUotFooFWEVGD6PYTRlCJYZHlrSBtNlNT1JkHU7IygqooQQkEoHysddvlxI0XJiLk0BaHYkzXIhz2SLELIZtBsiogky0jStGYLiwAelAr+vcIFWypfp4YJjZOS08tnWF5dQwvFfXfdjdaa2U6bB7/6JVpesAEkQOxckFWwe148DiUkqxvrPP7xT+S9730v2p81PY8DcOqTNGYqAqjevU+4x24wyqiJMHh915PJhMPG2Pv5/8D1978zItnkpc+9ix988dP5hy//AZTXIvgKlYSXvOxx3HfXA1x05UU88/p/zOfv/AMefGiHJ3zX43nWNR+nm0uecM0F3PR7x/n1v7yFqbmUO24tePFzjvLs53yA9e1V/uNb4anPuZdf+f0L2LOU8vRrPoawmj/8zRX+6rNv47nP/GXe+Z7H8dkP7ZA8yaIvr7j3s3Dfuz7Fdz054+KrjzF/8xMpymDFiBxSOkGSeMoCVKxw1pGmafB6di5k2tQuB85aoigadz1LqTFVYEttFXoVlhqOk8MM5T1lVSIpcN5hXYyylvXlMzx8apN3PHSKC65c5LFaMr1PMeiXSAn9fp/Z2XmUihjunMFVMYYGxmjW147xtl/6Mk97ylP5n5/6NJtrm0xnCY22QEQRQhq89LTTGKRESIsUMZ7ah1grvAzWkaZuBvRQT76hNAgOoRU5DukjjDW1/jmiMkXYYFhwVABYG3zMi2ERZA8ugBMZgZQaZwRiWKCkwBtHZSqiZkazHSFwOOspBkPiSGONR+pHpzAoBchaX4szIARZkobGurqxOZpoEEzjeFc3O5J1SElVVSGGe+TLKgRChwZLagBgRLDCG5EjCjFmnUdgZVQhjOrQlTRNUXbEtAmM9TTbMDXVZ3F+jrm5OaSUoXmvMug4ohklbG9ucd9X7mRxzxJZ1qQY9Cm6m5TO0mx0gmQmjvFVjhtFZ0URq6eOMbd3D6YCUafAKqUYDvvESczO1jbTMzO021P0+330oiLPh1hrqXr98H5rDbDWITMg1hE6bdDd2iQui2CXKCVpo0Gvu8nW1hZlWSKlZHpuEVVvOITSOGvp9XaIYkVVlKyurrK0OE+e5xjjgi59gi0drXHOe5x1qCjCAbaO5cbvArLJRquwHsBofnY2BNucq2fWWocKr51oLn0UxiPpW7+Zx02O0WN3G1137/dI7OYjvY//lfH19NnAWQD+3M981mMtrK2uk2VNus6y1J5iem6JM2dWmZubZWN9nSxLiJRkfXWVfQcOsrHT51nPfiHPe8Hz6bQbdTaDp93poHVMv99FKpieWeKpz3o6T3iy5Z77vsT28irb3QfJN1YBx8L8Ae6/+zYuveww997zIPMLM+xs9Wi2W0BwKouUHrtsLK+ss3dpDwB79y3RamSkcejTarRbbG1tEmWNYPFbV/5nZmYZDgo+/j8/yGWXXU4WJ4BBCHnWcZw8NqM5ZfKYOf+/Fqb2HQ2eoQZOY8cGMVLI41FIHxKtGDGPYmK3OAJzI5WHAyH8RANiMOMW+BDv7TyGQHULRJ1s5dD1xaO1IEozkiRhfs8Sd/3DbVxyzXfxin/yej7wgb/k+1/3z7jw4ovIe9tUgx7eWnQk8ZFCyuBfGxYfTaOR4eIMrYKmN0mzoB2sDDpRdclF462lwtNIQjldJLXntBOgI6I4RusYR0aUxLiyYDFNKONG/Z41UiriKMUZS5YkID2C0NgkFVhToqVGRYGRdkJiEGz3ujjn+MSnPs7a5jInHnqI7uYG1ofz8T1PvoF/9cY38cof/IFwPgItgfEhoKY/zPnFt/0SP/2vfzLIVqQiNwXS7drJ2bpJUwDGmho0jwBx+Peka8roepC1dGP0PCNwrerGRDcG0ZNw/ds7fuzNT+Cy677KY/fN8ODJJ3JsdYfFhb0c/VLKddc+k7/48/dwyxc7POdp/5JnfF+HT3y0pBkv080Tnv2kvdzy+Yf40Bcu48SJE2yuNGm2c/78r5/KK77v81xwKfzGHx1kuXuM/VNw/ASsrv4hh/f8C3ac5NU/9NPc9OHH4B7aRsRHufJIi8dc+kIed8OXeejhirThWf5qyufTP+eS9oXMzWvEiWlE7wDKuLqRpCRtJJRVgc0NZWmYWuwg0gQqgzC19EZKcBYVKYSMkBaSROGd4hk3XMU7PnovUbMN1iK8xyE5tTlkZbvHJYcWUXIHVzoOH76BT33wT3jBP3k1Qmo2to7TydpsbDyI1LM0G9MYtxGcFyLHiTtO8SM/eAORm+KLX/wicZRwYmOHt/2LF7IwP4NxFa6wgdW0DhHXLKrUCCUQUiFk8BUWvpYGWIe3JaYsxt3eVTUcL1SBobB408eZkBYqFFhj6ypZiNAsq13wEsdpaEQzFqEsLlJYJUCDL8MG2WHrJFSHsxZvAjix1aNy6aLqkq8nfA6kB+PHOtsR+9gY9yIERxMldtMGhfcTgC8E5ATdtENVBilDs7efANYIMM4iZQ2useGaqoGzlwIdR0QiDiy2DAz4aHZYqJZYXNhgZW2VRiOtg6YyVJSQxDHeFiyf2iBrZxy46AjGh4bF2JToOKIqcgZba8goDZ0nSjDYXmf/RZeS93eohn3StIExFhQ0khZ5NaDd6lBax8b2FlPNBptba6RpWrPOQxYXl7DWQKSIdIxKNFVV0JieCUBUeJQXxM0WRXc7bLhUCBeytiLJmlTGYm1FqznFztpa7cOu2dheo9VqsbG2CkhOnloO4H4EEpyvr/MwLwt1tvY2JDaG4ze2oRNQGYP0cixRsH43zGYSuEmhQXq8EWM3Klt7QT8a41zgeT4g+q0+1+Tvj9TM98183q/XSHju802Oc1ns3c3JLub52vfrefvb3063t8Wg26PTjFjb2GB7MMTkdlzV7/WHxGmGcZajDx/lkosuRUvB2vpp5heOALVdZdQk7w8wVRE2ppEgLyTN6ZQLLnwMv/rWn+MVL72Rn/vF/8Q73/ku1rtdnLuBmz97O7O3fI4v3/Y5okhT5P0gQ1Wesiyx1hHr4Gg0zA3JYMBwOOTSiy/h5OlTVFXFoLuD0AkHDzdQSmCMJc0kxhmyLOOuO79aX6ejY/rI4VLjjfx5zq0Tu4TcNzO+48GzJUwsk/ApjBHjWbtuUIOpQM+edUEFq57Q+R2A9W4zigA0Cs85vpRu5L5RS0Z0hPOCNGtxan2dKef40Af+Ci8FL3vlK+mVJbFWVN6TFz2Et1SDAaYK7IGQAucta2urTE9PE8cxUaOBsGWQj+j6XRiDjgXgwoKhZZB4pNm4wUNKGQAxHh2nxHESFqcoQWhFK4oI1kwOb12I4pZ6nCAnRN3sIRxKReAExpT0+0NWV1e56aabiOOYY/fcj0Hx/S97Je84+Zsce+C+mokQfP7mm3n5Fz4PBAs6j99t5KylFP/+Z34Wj2d+eobNnW5o/BkD3jD8BNA9d0ixa481OUY8txWB4QxpkxPlqno3+WgK+q960n1k0VO48ftv4pd+/mruvPkkL3npFfzab3yOTvtXuOVzER/8H58ky+AVP7LAc194hic+bj//6Hvu5if/i0K5S/nCnffy9p/dz1t+cUgrm2Eg/4HP3/tSBt0pRPYZinyet/zYGjf+0BB4Mz23TdKDn/7HeyBP+N5/fCvvfP+lxJ2cd7//19m3uITbc4K8qIjay5idhNs37uHKpWmig+sk3ftJH3oWpa9wylIWBmFDaqdxQYOv0+ADLn2CUxBFAlMKnAitZSJy4ANDK4ZbVM6xvdMlFRInHe0kMJqdxLK5tsZlRw5x/fVP4if+6Vv5sdc9iZNn7mdpbj+LM3Osb26Q6CaRVmxtPMzU1AxSRvze236Tjl7gHX/0cZ58w3fTzBIqr+hWhlhU5P0BohFR4YiVhpjg1SzDBtraIH3CurCZ8x6MwdQSDFeEEr/SEmeDIw9OoSIZ5AcCknaMTCNEZUMTsR95wnsajcZ4krbeYUqLjoLrn8fiVEycRngdU5YWLSRaBtAilKot8zxSPjqyjcnytBDBUz6OE0pjsPXfrfdEcYQxJcZaqBsGdRwF6dxIclbHduO/tmFqpKEdybl2mb2ahZZxmM6lotGMSSKNc8HJJI5DHDdS0MTXVZCKRqNBpDUojVCSojTMLy7S2+myfPIY1kuSrM2gu0OcNcirAlcZKqOweUnamQZTYkRMf9AljkKYijeWZqcdQllETLAa1ZTlgEo4FJ5Op8XO2nqYk5SiHObMzM/RG/ZxznBg4SIGwy6NrEk5zOlubzE3t0B/sAPesbN2hkajQZplOOPJmpKqKOl3t4nTRmj2tVCZkrm5BR66/z4AIq05vnwaqWLarRbDYT7uIBmBKylHes6wYQypouE8iJpowQdZkhBqLGdUI9tW4YPnuQwJn26ix8gYi3dqF0Q6/7/kXvC/O84nv5gE0ZNNe48k6/h6DYOP9PhvdaNwPrb4W20kHH1Pzv59/BuVFRy966sY45hZWGRrcxNXQeEKWlOd2v8+bHaFkCRJhlKKrN3hkssvo9/N6W1tk83PUBTFblJl3QC80+vSaDTY2toiloopo/A64ZIL95M2Mi6YbuM9HDp0IUt79tLb2eHBB76KlsFxxxhLFAfksHdpia2dLlnWCHLAssf2dpdYR2PLxDiGrbUVOrNzLC8vA9Dv9pEikCMPH3uAKy674rznZlJm8/XOtRxLGr658f+JhkFLWLAMwePXBqXrWbfbiZLKuXQ9tcbYi92mstHwQlA5O/alHNlTylrfJaSn2QqNd1ZJBv0d7KDHVBSztX6GdmuaXl4w1e6Espkd4LVEO1e7alTgDFmSoqQmazZQkUbpCIEmSppESRKYFB2aC4UHHSmiNCKKIpqtDlEjDVrmpEGctZFJk7TVIW00UDomiZv17S2UThFKo1WMjgKwTmLNKJrWY1EyTJrBX0NSliXbm1ssr6zSbnb4wJ+9jwfuu5fPfOQD/NSPvpq7vnRrffz8OKjmt37910ijOJRe62M32tFaQmnmJ978ZrqDPsOyxNfSjckiiSCE3YTY7knWuV5MGYHlXYmGnTj7XuwG4nh2gTvUevhHh3jm8IFFLr9yi+979mE++1nHK19zHX/zqc/xy7+6wO3/8DgeeCCiPV3yrvdfy0xryFXXzBDFKR+77Sls9Qt0PEM1gB/9V6t0BxucOLbJf/7JTe49/kGe+72/R6dxIfvnOrzpbUOmZ+H+B8/wM/+1ye++90ay62Bq6hC3fe6fs39vh8sWr+axVx5meaWLVxmCaWxpUcawd7HFsQd63POVZTr3XgI4pFDESlOZAiEESaqYXWiHFEFL8IiVAmEdVWHCJGc9ZV5gS09VeXZ2dpjd08ZqR5JotAzsSV4U9Pp9fKRQeM6c3uKBBx7mmiv3cfzEOhcffkxgHlWDqc4ccRoTyYpG1kH4lF7vND/2L17JztDyule9hj/7xM2srHU5tbpCWwgef90RlIoQTqCEJmyyHVqruhFPIbwLEgIPw+GQfr9fV6IiUIq03SBqJEitibNkPMEO+wNsHtg8FTvSRkQ2FZO0YnQMppZymKoI4TGjSVuFmPORbYSrDGVeYaqKMs/J85y8qGqQGZq0UIybzr7dYzJsqrKhWjAoi/Bdqzf41loqW+d/ChVCNESo1o01onXfxmSZ37mQrqpFiOkura0DOGq3oDrWecSABoAtQ4y1jmo3EktVVVRFHm6XQc4xkn5prUnTFCl08NLPcwb5AFNWRHFM3h/Q626xvRks3qYX9zHsDmi22oHNNh6HQMkEnSUMNtaoTElRFFgbJCZJrNlcWw2fxxj6vS6DzS22dzZRwhMnms7MNN57ms0madogbTboTM+RNTqkzRaJjiBS2LrxNE1TtrfW6O90UVIyGPTQSYozFWU+IFIRzlZYb1g5eYLhsGCYdymrCo/irnuOcmZrY9yrMFrDRoxwAAu1RSC1Zat1tcuTw1MhlQNXooQlEiBxREoQKYGwZhzQJRWBnCEA6dFrjEDJo6HXfySAdD6WcfRffw5uOEsz/Ah//0bv4Zu5ffK1zwXh576v8z/2bBnJ2RIPyZve/EaWT59mMBiwcvIEZd4jtwXWOaphSaPVIo6C/DPVEVJoGkmHYpDzsY98nP379xLHmkgH2dX6+nq9f9IIgnQqz3OytEmz0+biJ19PLCyv/dF/QpwIsjQN31lnmJ9pkTVTrrjqaq668hp0nTshhSZLm5xZWydLm+g4YjgcMjPdodlqUVU2eDvnQ0xpWd9cZ2FuBmOCvl4pWVf0DadOPFDPM2JcQZnU50spxxs6pRRS1GmoIuR0TP7+zY7veOZ5tL/yE7qU8a6h/t3VJSVXJ9FNambDvBDAlvHUbHINjBmBrADOx8BZjEzTgsajPyxQEg4fvJBLr7mO59/4Atb7Oa97y79hY2ebqakp+t0d8u4aoiqIlCY3JVNTU2ysrzO/tESn06GsJ3tb9nDOMLUwi5cRYvyGfV0WgzhKQvd1khLFKbZ+cypNECIwz0lSN8/U8dtVVY27nUeylFEU8EjLWVXhszhr0CrBYjHGsHLiOMtnlpme2cOePXuYarV5+L67qKzD1p6ru8c1HM8f/+dvYmFhgXJ1pQbA9fGbOH+/9qu/SpakpFIwu/8CTh8/HkgpvwuSR44Yrq4Q17zgWc9zrvxCMOoaJsBxIc87cT1a5cPtzS5Vvs3jn/4QT37a1fzxbwz4/tdcgzaSdOmrfOj9O7z0h/dw8UUdhl5y6xdu57GXzdGYSbhocQ/3n9hAlE2s8eyZk/RWHE97Rkm/2+PTNz+flYdO8prXPsDCIXjm90o6i47HX9PhsqUBdx/t88d/9Vd893e/iGF1HyeWH8R0n8Djr5XYYcxXjn4JmQmSdsHyquPIzMUMbt5m+soFopmEogrVAO1g/UyXLEtozCYIofFlAUmM8IKyKKmKMviYa431BhkntBoJnRm45tKCmeaJ0Dhb+4xmjSZlf5O5qb0c39imMhV//6mbKTR86E/fSpHnzCxcQd7bxLkNdtZPkCQJUZyiI890egHD7gpt3+An3/FOZqzASdjTTnntjzyF1dWjOJ+gRBw8lpMIbyu2NnZoNJJg1WU8Ue1jrBDEcRJSBqUInr8eIhVhsQglyFpNrLfoPMJWBhDYvmFYdomzFF94BBJpJYN+yfp2lyzSzO6pY+W9pyoccawpcs92v4/WkjjSGGuJAKkinJUIGdx9dBRRFsNH5dq13o0lFGGExbqqTNA6y+A5PzLBN97iPSipMa7C14vWGEC5UCPEE+Y45ygJbhxaqRBQNdI6Cz/uhHfeByBYM9pS18moXqLrUq9Qevw+KlPgrWWnu8XmZpvO1BTdnR2iKADryhpi4Xjg/rs5eOFF7D90mF6vx/LD9xOnCaV1eFOEhkgTrDpdWVAUW3jvidMGQkU4DDu9HdIswSGwuSFNU4ZFzr4LDgOhMTxNQzqh1jpUF5MGOglrT4LEpxWmLEA4OjOLVMMuSdmgqiqSRpOkKACJkDFIRTkcUpR9nHFsrm/gqiHVMCdNG1x44YV88c67qUqHwIYqUK0Vj+Pgj+69r/MA4tHSNgYZYQ4NwT6jTZ81FUKKOm1T1TpaE3y7nQVnkfW6IhVYb8f9QV+vdP5/c5xPozwpUfl6rPLk4yf1xV+vofAbvYdvxCyf7z1/vfsEQmnX9CD8bfd+Qgg+e9utXDi9wN/edSfeVBTlcOzNbswQlyRoKZGNFg5Pd3snbJ2sp9/tcqq6m/WVUzSzQ/T6XbIso9PpkA/zENSWpmysrdNshDC0OIpoL7UwrsfhAxfjjKWw/YBdNDzu6qt445t+nDMrm7zvff+dvXsPsbWxwjAvyfOc6ZkOtrJsb27SaKYMCk9ve5WsmdLb3qIzM02/12N6poOWETMzM6EfQIF3GmMcd37xNp77nJcwUrSMGOfJ44L3qPpvfuJv49vhWwLP39HMs4PQFMgEsKVuDhzf6xxt08RFNvpwI8ZkkqXE+7EuOrClI9q0duiYBNYuNA6dOn6Ck8fu5x3veDuXXnyIj33iE0gd0+9tYcshG+srDMucqhgws7iXtN3m6muuYX5+nmGREycpU1NT7N13kFarQ5H3EBKSOCVrNEmzBq12EOYDRFGCjJPavipMXs1mi6nODGnaIMlS0jQlTRqoKKHZniJLm2TNNq1mh7SRkWXBgkzrAMpD9dqHpiUMEk+WZWSdOe574CQ3ve8mfvZnf5ZjJ08E3V4kxpOwGR0rHzYXVjpObqyOWbzRdbdrmR8AfCPL8Fj+w8/8bB0PO2r4mzyDEyB64vyPbfDOee7RtRDAc4hf94zSJXfP+6MUdMV998CZU4JL9j+Rmz+4wTOfN2TjeI8oabLYfAxv/DcJKye3+ek33sa7f/chHrj9coi63HXPMVbOrJEmS6ipPqiSv/6TnChSLCx2OLz4A7zrDz/O/P6L+a+/PsW/+/cH+cmfeCXXX9thLplju9iiX0ie96Sf4+d+6gMkjQs481CXSy7aYFq1mWs3ecZ1j+eGK65k7+JeKguHOi1mkmswOsYLhY4cOtHoTDG/b5q4petFAMqiwg8G9Ld3GPYLujs5w77DGUvayEJDkwYRCYZ+wPrWOrIoSCJJFCms8SwsXcB2t09nqsni7BTze5eIDXz0o5+kkSRU1TYCS6RjdBTiiAH6/QE6alBqz8AOORCFCGnrPJ25vTSyNu25BdJWhlMWEWuEVkRZSqvdBhhbQjocQglUrEFHFKbCe0HeKyn7xTi9TvigS9Y6ImnFtGaaxI0Ma8Dkhnyroswr8kEZNudaMD89RbvTIFIaYQSJTtAa8kHBYDAkL0vWdzbZHKyTmwEqFkhhsaZk0O3R7xVsrO0w3MkflWvXWze2HhuVPJ2z+DqgYASqRwvNKELb1S5BIyvDypoaENe75RFAq89DLBWxDOEnml1NtdaB0ym9x3lFiaQzPUuWZWG+SzNGq6QQIiRuC1EzrZ5Os0U1GNDd2aKqinFC3uz8AlVRUg4HbK5vcPcdX2L19Cm0jmi2p6nyADIqF5ht4S1rZ1bY3DiDVmldQg7Mc6PZQkahUtjImmSNJu3pKbJWm7TdpDMzTXN6mqTRDMA5bQZCI4qRQuF9CM1yzpE1OiA9xoRQmumZBYqiIG13iGNNnOixnV9ZlvT7XZQWrCwvs7q8gnSW2754J2mjgcfsgjXhxuywkB6pINJy/BmkqOUVPkgUsfVmyBhMFXSxlfUYN9JG786+IVxM19dGfZvz4x9vH13wfC6jfD5W91xwfe7jgfPedr7XO9/4VgDZufd9JB10OG/6LHZ18rZeXvChv/hLTi4fw1UOhMGasGky3mGx9LZ3KMuSI0eOMBgM8F4QqRitI3Z2drj2cU/k2LFTuLJEetje3qbVbtdzAgz6XbTWGGdpNptILdi39xCHD1zIwuK+oIlWgt72FsZ6nPDMzExxeu00vc0zrK6coNvtgrcoLRgOh8wtzNFoNBn2ByGHQkrSOAFhwIYG5mD3OwDhxnPEqJfszi9/hcIWeEIk/aQuf3SeHkmvfm5j5jc7vqPBM+zuqrwfgeAgCfjaRrCvzzCOd5YTDxt/gRx4HM6Hxh0/8oeuJQBeCZLWAmmrzdIFF/A9z3kht3z6UzzrGc9gcXERDQy6q6RaI33F9tYGOo6YnZql2WyjVES73UbrINtoz84zvbBEoz1HEsVjZjhMVoY4a9KYmkJEMcL54KdaGRqNJnHUYKQVHP1EUUQaxSQ6Io5jYqXHt2mtiWMdJlFToLxFeIfEoAToOCGOUw5ddCkvevkrec4LXsifvf8v+eXf/C1e/8afotmZQ/lducakA4YEXvkDPzzesAi/C2hH98F7rn/ykzEOfvwNb6g1dTUrMiHTGEku7LkAuL7ox5uic067FEETPnpvj45K9Dyj7CH9YZqNx/Nrbz+NjFN+9Ac83a0LuP1LDzC391o+/ekh+/bPcOTanA/8xT186bZ1Ds1eyH1H13j41M2snYZDB/fz3JccoDM3xJgdPn3L+zDGcvSBe7nt1h6fuukYv/u77+HQ7Mt5yfO/wlOvvp0ff+02p9Qv8OpXvYyD8UFu/nzFiZ3jPNS7j3u3buW7rv97rrv2VlpZyUuLVxLddzFXXL+fbKkRGkqVoLQhy1U3YtJmWrN8gkgrvJAhea0dpBxFUVIWDlcKXGmwRUnVsywtzDMTKYo4o1sUlENDKQQnVtZxMqI0gqH1PPs5z+BVr3oFx+4+StycRdc9CTpSJMmFSJGRJA2ytI1O2iwtXsJjHrvEi550Hd4bKqA/XCfRXVwlsEKifDwubVJ4jAQVxzip0K0EmaU4BFXlsKYMvtfCo7TDOI8pDK5yFIMCZ2xgsaWsN2mWKAoVoDoYnpDEJ0gaCY1OQpxpjDO1pjf4ZqftjNnFKQ5esITSJVYNMbpPt7+NMxU7Gz2cEfT7AzY3NzizffJRuXRHmmdc3S/id/8eQK9GeYKnc+1UMppfq6rC+OD4AKG5Z/QdVkLQUBFZpGnEEZEA4R1JpMb3Mc5RGUflPPjwPhYXptmzd55Op4OKYuI0JU6Tel4IXuVSCIwxdHeGtY1iWVcVUgJdIBnkQ7rdLmdWl1lfOwNoZmfm8YSwqiSOqVwoFcs6fj7JGkxNLxI1UpSKSNImUZqh0pRIx4GYyDKaU7O0WrO0pmaJdIYUMUnSJk4yWtNzZM2wAcRZrCvHrFjSaCKQ9Da3a79bQ5w26Pe7gETqmLwMfTFF3iNSmvW1NdZXTzM9M0faaHH84WOsb/dZX98KqbMi9LtA3fxZn8NRuuXYTcM7xESWwWiMmhilgFiFHykCow0O5wxKeIS34EzICHAmgG/rdj26v83jGzHCk+Nc0DQak04jk+B0dNzO/X1ykzn5c+5jH+m5Jn24zweGz/05Vy89+XkqZ/lXr38d3fUNNlZW8RPnoizLYBmXV1hbcfrUCb54++3MTM+B9AzyLtaF6+zTf/dx2XZe2gAAIABJREFUDu5botUK12yj0WA4GDA11cY7R14U6NpP/8zaKo1mk7TZQrdmGebh2lY6ptkMG8c8z1ld74ZGwMuOYK0fy2m1DjKQsizrZmOFFJpWZ5Zub0iSNbFYEIZut0uz2dy1uXTBIrGqClwBx4+dQvr0rGvgXBnO5IbpW6kmnG98R8s2HAo5AYd8reEQ5/l8IzA2sh2Rj1AuEX5XHgAE+y03ovCD7nb8/DLY1c3NzPG05z6HJz/1KRTe8tnPfY6XvOC5rG6ssRQp+t1NZmZmGCQJwkNeWCIMyycfZrrVpjM7x9TMHNZV9La3qCrLIB+yOL8AwGAwYKrVBtUcMwyu9uMsyoIoStBJRJIkFGWfLEmJUxUa2P35Tb6DBZHHmAJkYOGF1jBu7NCIUYiJt0hfsm9pnrm5p3H/vfdy4MLLaXcWOHLbrdz2yY8RlzkGj5IqpM0B2gn++5+8m/gcRwwYAWKPR/LQ8WMAmDolUNTnYbJZcAyk6/N7VuCr3zWjE/7sL4SZsK6DEKQz6fL4KKk2+LVfWcbJVarhx9nuwpt+ZJmbPns1L7j+oxw8nANf4a2/IDl2/AR2cAXPftFXcYVjrfoCT3rqBTzw0GnSSLE9OIb3KWdWwSu45w7L/oPw5dsf5AlP6nDiaMry9mle8+oP80tvP8Kp5Xu46f/xXNDczw+/4RN88tYX8eKX7+Wxh57CsFijIWI++bd9Hn74QaKBYv7gDFZVgMNag3U+WGklNeNows5eeEHRz3G2vi0JDbBz81NUeUHUSDGmwjgQBSAcjWbKQrvB0a0+WmR476nygtmZNseOnSSVobLwzne/jxuecJiXv+I5IEMstRcD8u6Q9swM5WAVpZsgPFWxSeVzhuUOX3jweG0p5qiKksMX7mdQ7SBFjDUOTLC7lATXHOc8KtKgFdZW6DRDSI2qex689Gir0Do0xRWDCmsEwjiSaDe4IzCrQfvsw+QRmmmUQspdCzVvPNZbMPUCKYMkwTjDnrl9CK0wdaKpjGOmZzRlUUtJ0ojGzKNz7Y6ab613wXFopEOWdZgTIYBES0lpDMaY4Itd2xf6WtccS4lSmkiFRdKUVS0ZCy5JlfM4HLaqwkbHORwSL0byNY/wnosP72NxcZE4Tc+WpNUNjdYHZwyUxEtBVVXMzswhpMI5Q1WVNJptpjszVEXBYBiirh988CitdpvMO/LhAIDZxT0gFFrHqM40rgrR7lHWxOY5cbM5XoCTbAprK2Sc4MqKNGsFR6UoQsehedtXoOuocCEEzlTgBSrSVGUeGvNcSBrod7eJogTjLI1GK9g+Wj+WfUipWV1dQQuIdIbWmqmpKR64/162trbGLDByFzBPgrVGEjMsqzFQFlIhnCcSrnaHAe3DGhon8a7TRi2rsQSJlLMjtjlImLy19fwrcfb8bgbfjvE1YNh7hPPseEdD67oycfZ9vx6AHj8v4EebEQeOiEpbZOVpekUpDV4qlHcI71BOMxQQ4+lpQWICGzSqv4j6dUdNsqq2EZz8HOfTO59fflI/q9T8yMtfgTBDbFHWyXCOYlhS2pJYChppTH+YU5WeONasb64xPTVHI20y3WmH5r9I0e93mVmYJ8kaxFrVQT9FSPIcDpmfmwvBccZw4MABtrY2mZ2dZTgMkqdmo8H6+ipFadjpG+65/yjv/qN3YPIhZ1ZPh88mRaiupClpkpLnBVornDWkcUxR5QF493OShqC0jlgoVlZWmJ6aZWdnh6IoiLSm1WphreWO2z7JJYcPAHp8DM8CyBPH/nxVh291fEeD55GjxmhIzo5o9uPDwfjIeDFy6DibVtdejEHzWU1kfqTRAqjTButntdYilGK732VrfZnbb/083/P8F/BDP/QqVre77DkwRzkY4K3hxLFlTD5kamGOpb17Ea5gYc8ecALvKmQNTjqtNtOzc2z2cxppiMnszC4EJw0XGhSrqhiXSp2tkLFGCU+ZD0kbTaIkRorRhCrxrg5VwdVi4MD6OG8QwkNl8DaUgIWKa8ZG1YuQD+DUFEghSXTM1NwiO5s7fOTvPslznvlMBqsr3HnsJGpzuW4S2p14rr/uOm697Qs1hDnrVIzP4ZErruTOf/jKeYHsSN88grt+QgstJjZKk4yz86H86yeY7jrDpr599N9HSbMB/Mx/vphTx07QmbXEao5Lrj7IQqtPu5Pz7r9+Mffd9UVSFTM0d7KVf5VDB8Hm38vLnvcXfPX+Z3PXgw9x6oEpfv83PZvbQz7yyYs4/pDlaU/vsjizyKnNM8Qu4ronLvKuP4c77jjF6/7RKd781llOH9tg4YDg9W9IueGJ7+X9H7mKrTN7uG/r4yw0p1nc06Gz2Oei1nXYFYOTKjBLQoKpsBasLTE2bEaSRoKONUJKhHMM+30imSG8wGrQaUzpLEhBVLsnKB/RTGJ+69+9iue95Y/I0piqKFESNje7dDoaW0icSDhyYD+f/tw9XHfFLDvbp2jEU3hbYqWnMj16gwFpCs32LN5UJM1p0qlFjj58mqGQeC+4cN8e2lMalWh6mzmusDXILTGJIlNJsOtKYyrjkDJFxKC0RCcaW1qsEUhKrAll53RKoqQOfrgojC/RIjg4CEBFKjQAeo+W4fiUuQ1JgXgQFf3+gO5Gyexcm6QZYcrA1uma3Yx9isPSjjN6rsDlPeZmp2imGTv5xqNy7ZamwtUWZ0KIOpCq3iAQ0iatC37BKR4nRdik1I4XWU0iCCGQOJwV9MqcylhkpOueCUuSRFjj68ag4Jyh5O68Bo6pmQ779++h3W6NGw9N7eGnR6XZESMex6SxZnZ+kYXFeVZXVykGOc2pWaqqYtgfUJYV29tdiuJBDl54mKP334tSEQcPX8D07HyYd62tg2AsjXYLU5RIBFGzFazZBKHioGO0ksiogZAlVd2zIqTDu5AREKdNqiInyzKKqqIaDNGJYrCdEytJbkPPia3KQHbYoFfOmm2sdYTMW0lZ5sRRSAB0IhAuSsKD993D6uoqw2HQx4+AX/g6Bl/m0IwYY2xJrCKEC42Z1lVINdEk5RwWj5C7ZJS1Fq3qpsO6EmFqiZx1JuzoCXOw8SXO1baOjxJpMRrOOSoLUVHwvOe8mL/5zEfJHMGE6jxSjPNpk4UQAUccX+VjP/Q64sccIr/3FLJaISHh57p38PYHDnPrtTOsrnZ5wf6DnNnaYX7Z8sCBITdmc3x6X5Ns/iBX/eDLmH3K44HdpvbRK7kJJvR8m47z6a4nhxCCP3/PH5NIy2Z/yMZmlyLvjSsK7akOO1tdimFJpMKcFeawnDxNmZ6ZY/n4Q8RpijEhkKjf7aKEAOfZ2tik2W6QJAnT0x2Gw2HYDBtDFEVEcdg4t1otjClZWV8jjRLuuOdB/uZDH+bEsQdBOtbX17D1dzeKEqSUJHFGljUoy4KyGDK3MEtpHHMzs6yv7WCtxRQl1N+TznSG7kWYqkIAzVaLbr/H/qVFvvzlW3jx9782uH/Vx+pcP+xzz+/55Dnf7PiOBs9+AjwBCCERY6YxXHyWkbY5TPIjwDUWhNdjBJhH1nfj1xhRoeMY6BElLQLj4iFtNNjqDtifNnnPH/0Rl191BbNzC1xy+WUURZ/Z+TnmF2Y59tBDmLxAdiSd6QWKYkgSpVSDLtvb2ywtLeGtId/ZIVOSOG4RJdN4AaYscWVBORyys7UZyhimqi/YaZRUdKanEUKSRDFVVYYFXErwVc1AS3DhuHhnwubCWZwHrdKabq+jlUcNHXXpw1sXfA6FZH52ip2tdZ7/jKfxh3/wTr7wpc+HAGM3AqhhExNHMXfc+aWzztlIhC/YZYAPXXBgdEjrUjp1Y+eoZdOPwfGEJH18Pyb+Nnqe8fMxkvF87ZDfQMrzf3P85OtOIaIGr37NYd73p/fw2++7G6MW+Pn/ejl/8s73MjsPRR/mZyTYJr2yx2/9+gdpZwnPuu7vmJ01/MZv7+e3fmGZT9/+Tzmx+n6efsMNvPumj3Pjcz3F9gab/YSfe0vEmeXTxBHEbfj939ig34Prr3qAD33sAv7wDwp+4Nl3Ilnh8is3uf/oJh+85XLarRmGvqTZOE3Zncb7mCjRbG91SVsRroJyx7BT5SRJwdRsBt4jtQgl7bwKgRbGoJIkeDg7F3SxVYluOFxhOLN2khk3pFtURD7IihLpUKrJzJSkmzu+cv/dXHHkCNPze5DeYX2O94IkShDWoIWhyrsMpEULgTF9is2cl77gmXz8bz/DsO/Y2FmjGEIji5A6RwgNVW0DJ+qmMu8wRYGXEldVgEKqYEMWZCkOdAMVV9jKUQ37OOUxw4pi0AslQ2tI4qCZdh5UkoIvEGistTgseVnQaKYMB45EJ2RLTXQaZqmQrifI+zkyMiAMrXZCN+/hS0kji2ilmsobuv1HR1WndTRecEZs+wioJkqjZV3Wdo7SWqI4xhuLM4ZmlmGqAD5zW5EXRSAtRIwXoEzYXGg18nj2oR8jGi1FPmxo6k3Rof17WZidDwu7DdecsAZBaB6srKuZcEkaxUgE3a1tzP4lOrNzDHa26XU3yfOSJE0pSxPcUKKIjdUzJEmG0IrjDwv27r+AohiiVYrxhjhpgIqJsgDspQpVQVuVJM023osg8a2ClZfyOZV1Qddc+ylXxSCwjM7hyuCRaysTSuHDfrBZdRatgytI2mhjrUVFCaYoKMuSOI7x1rLd74NQlIWj2WyyvnKK06vrnFrbRqAYDoc4bKgKCkGk5TgsRiECA+48Ua1VljasBVIF5w0n6gRf5/CE4CycDY0yhLXRGAM+uBNoEfThOBc2TyL01Vjrzj8hf5vGCCi1I8WTn/VcXvWa13Ljs1/MRz75USJbMDSSYa9P5Rz75qbRvqISAikLjOwwc3ITNzzNzde+ghkhSMo1vosOn77jGPsTzd9n8Jl9jt85upc97gwX3b7Nx33J3l7J4dLw4Z5lrsz408Oe7/vwV2nJ2yg+dSv/Vvd4zU6FeOELuOSX30KeZKRGUUb10nwOQB6Bu9GG6JGbCQUf+eBfI4Riu7tDHMdYCd3tAh0Juhs7RElMr7eDr80FpJQUhcFubdLrDoijFB1lDPMuVx25nIMHD5LnwyCjUI4iH1AWRbCIjIJFZSNLEEIRdabG8tG+rWgkKYPc8q4/eAdT7TYPP3AUIQR5MaTVagAh8XJmdpYyryiKHK01SZwiPFTOsLG2RpH3kVEd6OYNeVXSMoZ2s80ZsUJVVXR3tpienWFta5O5LUlMifHRWcdvfDwnjtkjacr/fyPbCMB2xCzX5dD6tuDaHMau/nnXSQNcsK8DoloagAxgLcTE7r6O9bvNa07Ur+sdca3L2RiUXPmYq1heOcZ3P/uZHDlyhK1enyof4q3lnrvuRnrDoQsvpJ+XJFnG9naXdisFHTO1uI84jhnkOe1OE6+CI4Z2DkyJUjElnkFZ0d3cpMyHSOdZmJuj0WwjpSJptkKYw8gZwBPYGmqQaCpEHIX4alfrDG1gEfz4ixkWq9EX0nsfHEqEwNc7SS9LBAmXXX4Jnzh5jA/89V/yb3/qP/DLb/uPtBU4u1sNmJufZ/n0aZpZg24+CJsW78dG46ONzR/83u+fdV7dxKZo7GziPUYEplMBdlTjGp3Z+v+tAD0h3fCE+7vwZPXE+bWP+3aPt/wXyeUXNFg+/iC/98fXgVnhp95wD6977aVcdckcKycrksaQn//XDV7/833+7L/FVNbSbrUp3TbdoeChk33+02/HfOGu3+F//iVc/tbj/ODLHsNHP/15/su/voj13kkyNlAmRbqIrAN54bjqqoQnfXeX/XufR168g04cY90KD90Dv/I7+7n7K2fYs9QnWVpk8/JlPvfuu7jxaTdidzZYOnCAstrGVwVTUykz0TQYgw+r427JUAikAZ8qbG6QUQh2cF6E8jqKWCv275nhe65o8P6v5iTKkUUR7XaMsJa90zMUp06Rioi7776f31o9yRVX38BVTz+C8xu4SqEjQRK38HaA9BE6Elx3zZspHRTAlG5wcL6JFS44VQwGuMqgdEwpQaQJXsga3IFzBUInwdvd7VZchrZAxRFaV2EDjMIPKgwFljom2lYIJyiMRUsZ/NMpKXOD0CXaK8pBn3Z7qtYLK0QUqklSgncKJxw6VngRY6qCIh+g6NNQHXyZs2UlG72CQZGzubb1qFy7umZyrbXYcZNYkGblxmBMSA70HlCSYR2HbYxBuuAdq5RBaIWj1nyL0OsQR7vMspQSZ21YHKtyPJ8pFZIBZaI4cHgfzekOxvy/3L13nGZZWe/7XWvHN7+VO/d0N5MDM6ikgWEEhJEgBzk4JBVFOAJmjyjnKFk5KngURRGuYEAFiYJHBQlDmoFhcuzpmc7VXV3xze9OK9w/1n6rq8e593q89zLq+ny6q7q66q2qvdZe+1m/5xcKAuE5UMRowHUK0BKzKTSUCE/SH41Jx0PIC4ajHqpwomhjBXv27iOu1Bj0u2hTsLIywveh3Zrm4F130Z6ZZWp2Fj/0UUGILAqiet0J6LRB+h5ID6sdzzdPC6q1OkU6RIYRUVilKFL3HLKawAux1jLqr2+m80kpMSIs+c8WlY8Jqw0iKlQabbROSAcjVwTpgkGvgy6cPd1oNOLM0gnQltFgSJookjQjzxXgBIeB79acLySeFAgctzmQnuOP+h6qcEBKEEiUzjdtEifFg9a6tCYFoV3R7Am/1AYZpJAIATLw0cpQGE2utAuqepQ4z5NhdMG1z3wWF150BacHff78g3+E71V49Y+8BiPgx378FVx2+UV8/gtfZ2P1ENf/6C9wYD4kveNe+k/9YUwa0d0bE16wnQd7Q5RpcaQ3YltTc9mui+kOHuC6QxvMzc0y3DbFTx19EN+v8+xsQJBroMf3NC6nMxjzk7tSfrm/m/7icW5oj3hL43Ie/MDHWPuLz7O8o8XX1DI/f/ft9L0AjzG+qTKIFLGyKB45q+Bs4echhOZz//A58kGPPEkptCXQhizLCANLYZ0myCsNB3JlMHmOL52A2yqLNQPac/vZs3MXRx+8j//0gy+myHKq1QpWW/woRhcKYwuCwPGTz7qRZJs85LW1U0zNTIOSDHsdukuLrJ8BpXLnwV6vkWUFURzj+T7pOKdarzHs9zFWYbRDhiu1KkXpq1+JIkbDhB07djghb56TJBndbpdKVHH78XBMc6rJRVdeTuF7SHUuuizcO5v/nhyuttJf/jUI9L/p4hm2wO8ItHB0DPGwwurhSXKbF6L8o3GqYr15sew5p7yzrcmzr+v8AT1kGGF9n26R8JznPps01yiVMh520UWOsYqF+RkwimF/A52l9CVcdPnjUHmGL91jJx2PkZ4kLwp8C34YuPZlnqIoGI0G9NbXKLKUeq1KvVJ1IpXYeZV6QUAQuUWnCr1pSSeli/Z2RbIT+QiXsuAU6dbD87YoojnbygCHohjjHgoefolAKz7xsc9w6uQi1133bP76o3/Fju27WV467q6NcPZHp0+fdocS7+wt7m8RAXo4f+3OoF92Atwfr5yXieUdnHXf8Ccc6Enb13LW8B9XOAOb3PbJYWjrvLv34X/jPvj/fCwtjXjfrxcYcr73GbfxpKfBG37pCpJuyOHegzz1mc/nRc/5CrKR88H/qfjAn1/D4ZNfZsd0hVe/bIPGYyzvescy07M5z3kJPPsHqoyKFlecf4CLL1rlB15yhD/7/TqZsfjl0W/U92jNCtbObPD9z5jlY391I83I4EkPXXg02lV+9ReXabUVv/Krz0X73+B1L+iSefDXN/4xly0k/PIP/xi+HxFHPtKXmDRHWSciFIG/yTfVeYERBk85kZe0jtdqSy6NosCLfLZtm+MHnv84Pn3wVpTRaG2Q1oIsOLJ4ojTAD5FWsbo+4Bd+9lf47E1/gE+AMgVZqvFkhIlaCM8QRjkD5QrnEEiLlHGiIC2wmcKLKkCGShRB6GENKKuI/IC8SJ3YsRo6YVNeMO4n6EK5trYxIBXWGoqymDNG4UlBmqZkqcbqAm0Foe8jhCaoVDFWEWgPG1pqtQrKKrySghUEEmk9VGHQ5ARhSFEU5CpxlKowYTAsOHxihaWNlLBZ4YorDjDd9Inrj87anRQ/WVGAEKR5gZFOIOySB2GUZc61JC+cj36JkOZ57pQO1kKR43seHp4Tmk7QbGtJVUHkR6V9qOM2T4TQWmsUgosuuZD9Bw7QmprCqAJV5M6bWChHR1AWKwLCOEaXqvtKFJJkKTrNqTbqtD2PUX9Arg31RosgCGi1Wo7m5gty5URIJ48fJapG1FZXkb7Hnr37mN+xAxPGjJM+lUqNemt6MzDCWlsKnyx5kRDEVYdEG4XnR2A1xkgM4/LhbYk8521tPXf/YCxGSpSRVMMK1iQO3S1cEMyot8E4zRgN+vi+T6+7wcmTJxmNRui8II58Ot2+a5/7jocfBb4LiZEOYfY856phlCbVRXlQMZuUDWMM2phNKpze8pywxlEHXbvbYqxy9mAlAOMcVRyHe9I51LosoB8l1MJaSyADPvzRT/HjL7ue3fUmw2RI6IX0V5cRQYj1Ak4eX+Tlz30e7/vhl7L2jqvZbjI62ZipvefRP7KEn9b51Jk7+c/7v4vHHDpJ5ekzfPwfDzH2H+T3Rxu8OE/4iAePv3OFC0Wb1yKpHTifq6sr/O3BFnQ6XC/gGZ5Ajh4gCiM+vTrmxnGHx9VzTquC7OQq+4uUozufyL2VOu2XXMPj3vlm6oWHkprQUFrA2nN+v80Cz+YgQj7wB7+DzsaMhgn1Zg2Jx0KjzenTJzAa4kqdosip1OrkabaZKDlKMqZaMZVghlf/xOuYmWvzja98hQMHDrh0zHKtBxg8X6Ayw2icUqvVqNcqjEYjwFGIiqJgZmYGhAeeYb23xnXf/1z+5hN/w+z8Ap1OB2sFzUYbbQq3H9qCQa+HKjKiSozv+zQaLZIsZdzvEwoXI48nKZIh1akZjDGMx2OC0EP4FkWBDKpIYH5+Hm1KYHULFQb+ObK8FUB8uKjwXzr+TRfP5/xSgpJusUV0NkEfyyJr8+vKvzaDrixoO0E4J0K2iYtDaUu3BQ0NPMn5F13KOLWM0z7P+cEXs7BzHj+o0Vs7Ta0+Ig4DlFGM84K1tTUajQaR79CZeuxz/203sbBzN83WDHG1SmgFvnAIsTEZo0FKpVKj0IagEjI/v41arQaqIM9TWq0ph9BKn6had1Z1MsQoS6EVsV/dbKsKZ3iINM4WBumBJ12ggHHiEKsd0UVbA0pTqMSJH4zBWo1RHvgRvhfiScFznn4tQRwy+OGX86E/fC/d9TVOHHwAEVb4xje+QJGlm1LOwWgInO0GTMSXQmoCPDKjMeJs4YsUZYw2m8QNcAXvBK12loTlhP0/jAm9Z0LX2aTBP4z28Z0cN98AlVgz3Ghxw/+StNs1JIcYdSyE8INPv5GorsGHxlTA2976Jf77L72WX/6Zv6E9Dacfgs/eIVk7M0NSrINN+R9vOsn/+tQ9CLFCJXAHyswf4hVVwkiSp4rR0FBrR7zu1ZbX/eKDjg8uQt7/iQpvfO0IaWMiKrz3t29mLusy3YTf/KDPxsZjeePPfZP7T3+IP/qpVzoqTApZmhLEzhfWR2IpI3+FgCgouzhu5rUn8Eo/WGEVOgcvMlx64QV87wUn+NLBU4yynEpiiHzNtp07OHrsJHlu8T1LI6jge3XG62eot7cTRJCOPIJqBV8KtB6x1u1QC6tU8hQPQTWI6Ccpr3rFs2lM1yAMiaXvgieKMhxCK5TnIQQEYYxVCqsNo96QM2cGCAVTMzHtah2rFHlWoJRLTxTCUqlXyLLMCQKD4CzfL4yRvsAPq1gvxLOKRI9RY0VQq4EcM+rnFFoTEyMiSyEKx8/1fEQAflBhYCWtHQ32XjxDr9ejUQtIVU5YeXRCUgyQFjkGgdIW6XsYpcqHjfOYtVaglCu6Auk72o7SoA1xKAmkQMrgrOisjIEWoqRZGSiMRpmCMKxhhEFZyq6ZYGHHPI+9/CJmZmYwSpHnOWkyQBiBxCGh4/EYZEBcrzskSRfEoU+uvM0Dved5CF8S+QFa5SB95rbvoJUlrG902b93hsXFxTJpTbCxepT5bTtYXTlDlqaMx0Oq1Tqz2+Y2xYDGCiq1OlYrvCDEqALrK6QfIGQAMkRlfYTnEYZx6a3vo60mrjfJx0PSdFzGFAeO+mEM2WhUdj98jp06STWusLG6wrEjh0jGpRtI4Wzm4jAAY6jEIZmq4JdI2qQbJ6SPsgZPOMcTcAEXznbclNzu0tfZCzDGFfOTg8GmQ0qZLGe38N7dHuuEnUaY8to732eH6P2/E2L9a8cmICQEu2Zb/MSP/BBvf+fvMNNeYDwc0R+PyEyKrTUYvuYN3H3vN7lcas4TbYrLr6AW1fmhb32Ud059N9t6p3nHOCC5/xhfuXQvl/3jCX53b4UXRFWuGkj+rOXzye6ImcsuIlnK+KPuKZ6zfoI/W8uJdgTcfabglfND/nBjjj+sDnho1iMsmsyxzgubMwy7ih9fCfj9aoHqb/A0NeaWz3yJxfd9hA2juOLQLeTbI2RQJUw1qV+m5lq7Keox0iMfjRiPh6AKtu8+j6BS4/7776PXP0m/20Fri9KWVqvlrAwF5EoTCg9joDvocuCqS1hePcPCju08/dnfT1yvApAlifNXl44qGgQBtUodPBcuhXQ6rUqlhpQZ2kBRpAjhMdee5uLLLmfXjTeysGsH99x9v7u3cQdDhEUXoLKBoyjlmtRYut0N6pU6YcUnzzzmpuc4cWoRIyT5OCGcqpCmY8LQRxeWIPAZj4foImF6aoFAG5g8gyYL4xH47JP18khc6H/p+DddPD/8FOCCM8oTRCl0m3zeuf6/YgIub37dRADhim23/jzOFaIBm+rihx64j+e88MX8xcc+xmWPvYSwUgdp2L13D9L3qUQRp4YD5ua3YfKMM2dO09eW1vQUnU6P2flt6KIgHfVIkwHT09PCZaoRAAAgAElEQVROxKCUM//Xmn6vw/TMDDYfUKjEPYSqNRpTs+6kb0D6AdL3sRqk75En49KGLnCIj0rxfA9jysOBy011iTmeR5Znm0IfYxTWKlSWgrHkqkBrg+f7WGMJghgw+AIqjRq//pY3c9/9h7j2qU9l2F3lSU+9hvf8zm9jzLlh5pNpcnHZbl4kzrnIoksv5rOhNW5zdQ9Rj7JIFmdfx+AQaMO583rOPD3s+4stH5+g3PAvKb3//xmjjVmG3RyXhpbxqY+M+PifBVhdIw4zrFHkfUjJSDoePNTmNS/5K7RJiSpNWvWA779yDQR8+PNTNGs+n/vbIwgRYu002BHnXTDgxDEoCp9kbNDC0owjXvez+yjEbSxsn+XFP/pYXvFKwx333oIxe/Blh/XuKjubPuH+A8Qbh0myBrvO6/C+D83T+7bPl5Zu5lm7r3Zex9IduIJQOOs1KVHWdRsMLv0tCGJ3D0qBCAJ0liNwaLXWiqgCL3/ePr7wwBJDVRANA2Z2NVhfXaIeRoTCsGP7PA8dPoLNYbAIUzMhhTZUmjWQgUMkUs273vJ3qCInKg9ZeZ5TjWKsXqHaeCzddIDwcf4afoi0lsDGYBQ2qLhVlSiycYbNcVZmwnlJGzy8qk8ch8gM0glHVTvhGNaSjzKk50SGnpCoLHdBRbh1G0aRuz99iKlSiZ2IcJz0kRqEEbgoa7HJ0ZWVGkHdCRqVkZxZPkVhCibBRt/p0c8VSOloCtLdvR4eAunQeVyAiivGFEZD5PtlvSo33TqKTCFC31E/yn3XljQxd1K2eH5AkuYEfilO1CClZfeeHTTiKuvr6y4qXeLQWmuw1omORqPRprA7KxS9Xo9MOTeJPE2ISh516Pv0eh08L6AeRBjlfvY4jun1es5CtFKh0+85f9k4xpcexw8fIggCTmcnybKE5VOnqTdazG1bQKucUb/H3M69pUAbBAHWaIwaYvIMK4yLWheuOPa8AK2dgE8ZQxiGFEVG4AtMkTPorZNlGeNRjzOnFkuXg4I4qqIKFzXvC+m43nlGnhU4ZxAfq9RmAaC1xlMgAw9hJ25GlJqgLRxQaZGmPAh67q0qX2finJLnzg1GCFdEK+2KY0o/aWlLjvSWZ/VEj/SdHg93UXjJK17F8dM9vvTlzzPWCYEMuGDPd/MzL34eb13Yz4Zo8qJL9vP1bsq1d93Pol3hs62LeFJ+iJfE+3hNuIOf7N3KzZ2Ch67czw15ikksl1XbvM0UrDZGvPqe+7mnEuBHAV+7aDtDmfHse7u06nX+4UTIjPT4A+Nj83nuzLpcOx5zbOMEB7IWv0eXp/m7+bZ3hide+BguvnuRo0YRNCTei17L3ccPk1z2GJ78mf+DSFbxfZ8kzzYfbh4QRRGe1jQbU4yzlIrnDl9BEFBvNshTjfBgMOwwToab9nNGgFLOivHW227h1PISe/dfwO4924mr7nvValXyNGMwHJGIhCgKwQyot5pElRppkjAcD9FRhNYWLQyBH5KmY6bmZwlqFf7LT/0cSaHwgi9y8003YHEe6hM7TC+oUKs3AVwKMpAXY6zOUEbSaLewi8fJsgTj+zSRdHvrpElOGPnkStGo1Zmeb3P5FU/Ebva+3dDGYM1ZX+hHEoX+a0WD/6aL563j4V0gW6LQj/TrGmwpRptUZKWlFOLsxs1kM3FFml/yGkPftQ237dnP3QcP8l3ffRUP3neQlfUVrnnms6h5Af1ul6GEesUl7fS7GwRxQLteJZCW0PNIRz3q7SmqjZZTKBduoYZRDSEs416HbNBjY2ODqUYDjKIYj/DiKlp6rjDWlkoYus1eSjCaokiJoirIkvhgBFpZPM/HGoOQjttslHJFpeeBMYyHG05omOeo4qzPKDik2g8id6JQBQQhoR/xc7/0RpaXl5mbmeWix+yjJkoUe7NELedmS+F7dg62BJqIszSKCZhsN1t9jiP9cH7XVjrG5PW2/r+WzjZIiAlP/dzie2JL9GhR7176asnvv6uP2ohdKlMAr3vjlfzWm76JtZ5TMiOoUEVZjZSK1GTs3C55xnPnOe/AHq56vM+Lnv41Xvr0BN8GCKr4VMi8PoUQvOHNM1xwYBtP+Z5DtHcUDDuC0Pd4z7vvQnotGq01XvPTa9x6L6zdfT3/80+/xsUHnsf3PeHTPHgy4+d+9QC///7D1NszTE+3Ga6e4Em7Xk638EiKNbprBXEcoq0m1RoKR9fxgxB8DcqJvqwvNtFJXaIieqxQ6YjmTAMCuOZ7r+TAB25ncTBGSUWmCtcZ0QJPWjprp5mdboFKefXL38HXjn2EteUTVJptotoMmIJ8tM5XbzhCKBzSGfkSiUP3nv/cJ9PtLYNQjLIhcTCNthJhNH7YQCmB5wHakOsU4QualRp+mGKURUaCbFRQlRJqPoXOQEvSPCcUhrASk44z8iJHYanVYxdCABSZcsEFgevwYCXpOCOuRmirCGJBrAXaSlSm3HrwA/IkRaQCK+Hk0hlW1zao1nxa7TrbprafE0rxnRympE25PUZirXEOCxMXjBJZlMIifZ8A53etDI4ihAvaEJ7EqsK18aXAL3nRsOVBpYWzqEK7bwe0p6eYn51BGc3po8dRSlFr1lBpwmjQxxPSFZZKM84S/PU+uTYMBj02OgOsFZw8dYYd84ogCokqVSq1BukoYWV5idb0PL7vo7MUPwrZMTWD70ui06cBGAx7hGFItd4kiirI1LWnjz30IFOzMwyHQ7bt3Ea12nB7sx8ghUtelHnuAJNhHz+OycYJAsuwv8HsjvMYbqyxurpKe3oWGVforS6zdOq0Q/FUQadzCLQirFTp9XpUq1W27dhDno7pbKwxNTtFb33d/b5JTppmZVy62ozddiLB8jCirLOfFGcdHiR2s3ixxjmlaAUIuWm1OLHH8zyv5LAHzg1KuDhvWeqLlBGO3mVsSVtxz9mtvtHfqXG2MHLdaS3h9m/eyGioCKM626faLJ5+kKjS5F2iz9uvupJ7jx3m4kLwG9WcG5oVvhaHfPTMduKNhHgm5t3iPKLpbTzx2CHeqT2eXtvHyy6f5de+8XX+vDnLZ2cuJWXEu4Zn+LCCcL0GlxbcYSsc2F/n9K238V56bNTnGJ9Z4aZWi5f2A/4kWOcSK7m7t8SPyRrB3bcTKp/PR3Wemlf5vZVFrls/gv1awb3zz2T7dMzNsea6+28gsDmFiMikJdaWuFJjeWWNuT17qVarVCoV8iIlTcdEFdcZqVarFLndpFqEoY/0fHwvJG74jMcDPvhXH+b1r/0pNjoDdu1cwBhFu91GW4suFN1OhzTLuPjii1laO40nnbsO1on6ijQjE+7eDqKIZK3D+Rdfws0338KXv/QPeJNUUV+itaJebzrqT1GAcIdJdEalUeP48ZMsbN+NlAFFoTGRJBAGD4/BYIDvuS5MParQbrQJA8P01LwLcLJik5ngSelC3LZwnh9p/IdDnidDbFZdW+3QrGN6liIzURZgAjYFa2e5z44cILecMjZb++WNNqFyZFphRMjBI8d55atexb7z93PBJZc5dMBCmgwJgoAo8piZnuLBQwdptBtEUYTOnRp8MBjQagg6Z06TDwbUpqdRhaU9PUWrPY3v+7TnFhhFMcWoR26cCj+QCqNzinEfhKTWmkVrTRhGpYl4tik4oeSjaeO4fyKO0KV1zGTzkoDV2nGisYxHfSSOs2ylAOETRDWQnhN76S1tDCnxgoDbbr2ZdruNlRHfc80zOXz7zaz2OsDWdL+ztJetlnVbtJ6u8C1pFY6HfhZx1uXD2i/n0ArcXG75fCNKqk45fbL8JmLLa09K+nMoPI8Sb+PvP77Cj79e8Je/G2KtRKmc33vLLYSej1YSr6KQgULoAD2SyNDytl+HtW5GNeiztPI1sm/lKCJCz8fqAD8YMcoHRBZe/TM7OXbXKu0Zw1OeVnDwHnjdz0m+9/ub/MR/rrB0ep3ffG+TZiOmGVzJocZHSfOYm27+JM953jw33bbMHv8BfuNNz+HU4lcQgwbRzftYq69y4nTOrl0zSAHjvqbalPgEuGhrA9KgdYrxFX7YxiqDSV1sr85LRMpY/HoVHYd4+HRWV3jzq57Kq37nnxgnBctrKXu2zZEMc5ZW3XqKfBCFoR5AOj5D2t+gNrUbL4oRfoWPvu0LHF1ZxZMQegEasFZRqcHxoyepzAm0SPFjyZn+ESId0WrHxMYgqZTFAngywI8dpaDSrGKk2yt04YpfYQReEOAJg841YRxhS6QwimOMVdhckBUpqgAhFWmaUpMxRaYYjcd4UuL5Pl4owGqE77G+ssJ4VDBVzNCu1zizskZuFMLCXHuOHZcsOESl6kOqCeSjQ9sQ1sVpB57ngocmvr5lO88JCgUSsYX/OyFxmU2qhofYtLub8BAnvHiv9ImeWKC5jqAr1pqNOlEUkYzGJElCXK0TioDl3iqdtQ5xHLOz3kQKj97qBsYY4jgmzTTLq11qcczMdBPP8xwy53nUanUwhv5oyLEjh5iamqFSqdFstxn2+yTJyIVNxTHSDzlx7AhzC9vRCBrNNjOz8yyfOY3RcOzIIUb9Hrv276fabDnxY7VFun6KzvISJs9Y7W7Qak4xGvYZJxkL8/Mcu/8uiqJgfX2dZDBi5dQxlpaWycbOhcCUz4FCK5qtKRrNNv1ehyJLSMcJaZqSDoZl4aqJg9ABB27WXAErbEmTAWudn7YnpRMNSlnSPiRY4Yw0PAnaoK1LVYCzPGelik23DiehkUgcT1QptUn5s9YFjRkEylg8IfDEo+MUsxVFFMbwgb/4EHEUMFOr8F3fczX908uYnTPkpzb4peqYcEfE7ccbXPvky8k//yVW6PFbM5IeI967pmhIjT10jA8+/lpufeBWFgfrvObLd/PgVMjjDp2hfdUe9ENj/tS22Hf0XvZOz/K+UyFX7ohZCSUrjf1cF0u+dWbI4/ZfwdUnbudnrWSPbPMaM0D7KVb7hNEUZ0xOzR7lPv9Cfn5pibQ6y1+LNS5NBNlSm+2hoVu7hG/NR8zsvoAn/d37sFOzpEWOiAOGwy4rKyssbNvO7OwsR48dZtgfkJI78TI5fhCQpilR1CDwI3fwsgaTaA7ddhOvfuXXueiii3jxD16P70tmp9qEYUilVqXdbNFZWy/nW5SWlpr19XWmZmbwgoBed8jp1VWOLy6RDfu0Z2ZAGcJAUomqDEZDgqCC77vgliI3SM8yVZ8iK1JCP0Aptw+sr68SB07/ZYXB05Zedx1rJzx8g5YG62ne8uvvIi90qVU7ux4eHjCzdTySPeH/zvh3UTxv8rhKKFEg/ploUG9BoTXnXqRJkffPLp4t+bKTfwuB5/tc/YxnMTO/HT8Omd62gyzLqE3NowtFFPscP7yCVgGBlAQC+t0e8dwccRRhMARxhUIrarWaiyQeD0F66KIgT8aMlLMaqlTrxHHEqLPGKElpNRqYIsUWKSKqOW/Pat15gBrHJ5SeQKkcIX20Vqi8QAhIh66VmSVDfN8JnYwuEMa6r0cgpe+CHYRwJHvfA+kjxCTtyCK8YNM1o16Ned4LfpDRaMQ/ffUGfuaVr+TSq67ixq99ldSozSLVF9Lx6zjbTlLloWRC2djqwAE4f2nOFsNnv1ZsFs6T14KzfOnJXJ5zM2y+cXlvk8bNI835d2q8+JUxP/LyZ5L0/o4HbjmPe+5aIjcKKap4cUKjVefSx3n80Z++gCdc8pekOezYvsDGygZetIxQbX7jjTmGebQ6yV//zU9y10Pv5mUveSy79z2OL33707z8uTnv++5LuP0mwYELQz7+l2c4f3/Ci194EYu9gvUTe2DXKX7mDZ/nv/9GQLVRZe/Oa7n6CQEbnR45B8H0+eUfzXjrb5/gtnSdFz63g3coYHf0YvqjDXTJUQvjwCXtWR8R+YjCR4sNtPbwbex8bQPpDjEGROgjfQ+d5uR6RKPR4JLLJXWZkZiAZq2NNR5pXlCr1UgzTaYdn3Y6jvjmx27h7pP38vo3XoVfqWCTPr/5x5+lQCCMhxKaWhASSMF0tco/fOYf8cKAx1y0n507t+M3Qrx2xHp/jelGiq+3OX4p1olfiozA8xknOQKHqHrChYJ4Y0E6Ts5p9RXWONcR6w6mAuUENTIm8H1HxRIWKS3VSoVKJcJIi84KLB6DXo61ktOLPW744j1cctlOPC9ACc1jL7yASiXCWkEUxWTjFM/zN2Owv9MjlKUjhFF4nsDz/LMiY23wS79l3/fJygILDL6Um3uJKIstXXomlxcSIct0Nevudd93nEpbOrl4nmBjvcPdd99Pc7ZNNQyoS5e8qpUlVzDXaNBotMiSlM5gTLvZcGtonGz6HWdpQZ5mbo/LMkajkRMUhhGrq6uMRgkLc/MACGGpVp2GRGPJRiN2795LrdFASkmaFwwGA2am5xgNBgg8xsM+D9x7D76Q1JpNVhaPcej+eyiyhDvvvpcL9p3PaDxgOBwihGVmZo71jQ3m52YYjVOGzQ4nTp5k545t5IVlNHJdHWHMZjBKEASkwwGj0YjRyIkGhScZdQfEYchgNC49y92Ol+c5ooxGdpfbvY5nXd9OKYWU4HuSopjwkx2SLMpQLWvAE5OurFd27lw3U0gocn0Oiielj7ZmszMqpTxHvP+dHEKctXSbFEX1egNjDH0jHUq5awF9eAVTGG4ZrHJNY4YL612OHPM4sG0/twXwB0WH69N1vrdd4cvdkLXaAnNrp9i3doh12+QPA4UuCuqN3SykfYow5f5oinvWhkw9Zj9/+M1vsj+a5YvxdjZ2T/H8Bxa5Uvu8MF3ifdbwX7F8c2EXB0XEh5NTvG5Fcl8zYiHIOdad4VWh4gtTNepFzit6IS+aTbhk/zytRPG8+4d4Sx0Ode7nwW3nc9/TnkzRTxCeBeXCT9Kh60w26i2sNuRaORpn4JeHVcFwOCQMc3zfZ1Q6u1gCYj/k2EMP8Zm//RQvvf4lHH/oGOft20MgJIfXVkm6XbqrZ1g6fZpqrUVhNPVGg36/T6c34NOf+DTrnR7TCzOsLa9xy83f5uqnPIkorJBkTiSdZRlhVCEZjRwlrhYzKjJqlSrD4YAocFSkJEvxgpDdu3dz+tRxtIF7D92LNR6Fyqj4NcLA4xfe8PNccP5lTlPBuSjyw63/tq6PrR/7v/PR/r8a/y6K563jkW5MvYUWsDVUY/L+I31NqX8oOdTOykj7Nb7v+T/AQydP8QMvvRZPRjzmMXs4ubhInowZdrtUaiFSQhxHdDfWyI1lamoKneUEYUCiFHHgI8OYsFrDswarDb4vycZ9dJHRbLYdLzIIUbklrDaIESgB9Zk2g5WTVIXG2BxpDMPhgGrpBmCMsxcq8jFKFa5Vng2RUjr/xTRFqazkRFsKpfD9ECsDvEoVIX2EFyJlydKckOvLRTRpFRvtOJe+UawsnWBqdgd/8alP8/ofeRnCamLOFsLGWkIoYWd30AlxNe1kHoKyQwBuoapyEgTCoSVbVK/hBHHeAl2fc8CZfHzLydG5c0jkFn6PeRQVgysrhvf/8d+xfhoO37eE1YaZHRG1apNDh8ZkWZdrn1vnor1/yd99/qV87lM3cOjOJfZcWueCC3YyVW/x7Bdcwe/89lf52Mdfx+Kpb3D+k/ezsdHn6x/9ED0Db3o3nEnv5Pb7n83b3vGPXP5UjzAU7Ljsmxxo+EhxmMUTA9757ian7rmOK664lSdc/knuuOPJVKcD+muWx132QwzHX+cvPwQ33wiffM8S8xfAh39+jUbok6SK0VpCUVXISDhPZK9C6IcMhhG5GjPVqqKloRjm1JotCpVihBPu+LrAZpqRKfBszCufcyV/9Pf3cnRllX3TLfxA0O/2kb4gDCpUKxHdcY9hplnvZBRqjcDUed9bf52RhMh6BNYSScfhn55u0mr6POsZT6Y5W6fdbHD05BK247HcWSaekvT8IVKsUvenqVQD+qt9bA5BFJL2C9f1qUCeZmysjNi+MOXWlfQJK5psmBIFIQaP3GqCwEla40rDFWW5j/BlaefnipdCK6R2/H6dF7TrLRqNFlPNefbt20GtVqPZilCJK0asBiUUfugTypDhMCGQjw7nWQjXx5eec9awaHwgCIMScSyRySLDkwJtFP6kGyZK0VmZNBcEwabTjx8GLhnQllZ/3lmRmzGu8BmPUzrdIceWlrlw/14uuPDAppjP8wXVMKDRaFCpVBgMBmx0+oAr1E8tr7O60WG27Ti7k5ZwHMc0my6FbGOjSxzHDEZDHjz8EHML29m7dy9eEJBlGSpxtlpFUZS8b01WFAw6a0RRhWG/R5KlnF7sIIRl+dRpLn/sY+n3+4wGI1qtFpdfcimHDz7E6sY6eWFZmJ/m3oPfwhOCw8eW8IVBK8uO7Qs8dOwk7VoDvxIx02yXHrqKU4snqEc1Op11hLRkWcKoPyBNxsRxTKVS4/jyYPPaCsxZVJ8te7ku6XvCEnhO6DlxGlClCFQY0MaFsbiuAyXVQ7CVOqSVwAqJNi7X052SnUOTQqOts8d7tIYQk0CRsz+DNBbtS3whyDp95OGTcNEOvru9i4tvvpXz8dmoKv7TXMDbC8tFl+7j2ju+yg4VsH2uQqKqXDSf85fHFvmN6jz3XraLn73x2/zuhY9HpZblM8dp9wv2PPNqBp9b5Pvu/zaf8COe3+8SdIY8zQt4jx5zvU74bGeOaz2fT+udNGLJnCz4lcUVirn9XDc9xcuPH+E3I8WNCzO8vfsgr+1pZiqzaE+y/+aD/OjCNn5LneBFTLNj/wJLwyoXfHWRG/cmdEyBTBJajSaddEy72WRmYQEpBP1xH7TnAng8j3q9Tp7nDAYDwsDFzhsN/aSLEIJ2e5qTJx7k4L13MNOcZmN1lbxed+mdacLnPvUJLrz8CnRekGjXkW8223SWV5FFxvGj97K22mJ56QyeGHD3HTdRr7UpigI/8snSgpEaOB9pP6Lb3WB+YRfz8/NUKjHd3hoVUx6+i5zmbJOTR3KGZsjKmWWmZqbLTkjBM551Ldc87dlY49yPto6tDhpbUeZNl7ItxfK/BmT7d1E8T4ojp/gvLwicUxhtFYhN2vsPv40n/ydLcoGTwJSc2SRjdtcCe3bv5vyLL6Nab1MUGalShJFPb32VbNzFo0EUBtRqNbZt30GeJHTWV9Fak+Qp1WqVaq1GqzVFmqZYKVBFgVQCYXIKpRkK1wZttmfoD7pU4xphFGFMwXA4JqjPUqgMmyYIJD5nW6KUSLLKC4fsZWMn/ssS1KgPRmG0QkY1KvUWfmCx0kMGlTKhSzCJ9DZWukhc6zibkwWmtcZ6AoSg2x84Wzydc/2LX0I67HPRhZdw9NDBUoRoEHLCp/OckJOtQkzn7+o2VYOUHpStP43Fw262Au0mR1mWP8ukPSM3rev+2brY8jHP8xz2bCczLx813uiom/OPH4FX/QJc+1yP97wtQ/tNdu85xexu+OlfPI+bvzXgDf+txle++nEW12P272ozzrqsbEiGI4kyi/zsf9/O/Q/dyP79+7njrsNoBE940tWcWFmmEaQcXhzyic9+heQUPP6Kqzh45wPs298G0ePHXjTg4ze8mKLzRf7rr36cHX/k88E/b5KkgiNLd3Px3n28+1ffyZ98eop4puAVT4A/ee9O9kffx5N/5E+56c9eQq3SYlTJOb7Yo9UKaNRisn5BgqYaT+FVPJSSSE8SRBKdp6gkc0lyWjPWGdVqjBcYdJFz1YVNon+SzFbq9IYD5qfmaDQaeIFE5YZef0yzVue2Ow/ymRtO8itvgbtv+Bz337lOixrT83U6y12uueYp3HfbrSS54NnfewlUU8KggtKShR1TFPkYPfKYbjahCCAw5HlOop3ivhZW6dkhK3odbxhwSesAUsK2OCLLlPNAtQJVQFZolE2JooAiyUlKDm5RFISeT1rkoCGuhMRV54k6GiRIK1FFghQxo3QERcH0rnmaU86WqbcxYjDW9AcJ22cb1JoVxnmCTg2h5xGEj07xrCeKW2Px/fIeR2wKfaDkRUuBto6779r1ZXhK2cHzfYdwGkrHDuP2Aik9rJQoC4XSzrbTaHzplfdwxvraBkeDAM+TLMzPur2uyBklYwb9LlHos7q6zsraGmBYW+1wx70HWV5bI/ArrHUH1KshDVUDY4mrjr5RrVaZF4JKGHEqX2M06HHm9CJZ4drIg24PGfhcfOEBTp08TqfTY35+nkMPHaHRaGCtQ87GgyEr62vEcUy13uTBBx9kYWGOJCsIpGKt1+OhYydpNBp0uutIP8QKifByBsMRRsDy8jLVRp1GrUIYx3Rnp7DKsm3bNmySYGqKuFphY2ONbJyxtraGFDC7ZxdYj1GabCL8m9Hx5R7uSYkRovQopwykoXTWMOcIGCfJja7DK1Bliqzr9Jbdg0CipWHzUSQ9rHJot5aU6+Bcu8Hv9Jg4gmwthLRw2phUKn5mz27eP7ed9uIax6o56+fvpqgvsH094f6D9/BfqrPEt6xy44X7+POZEW/rdbm+3uN1R5b5k+Y8fy+avPqb32apvY/vuucWro3mubbq8zhWeOLiN7knnuWabVUWki4vX1HcYnt8tDlNa9rykmKKp5iEb190KQxHvP3UffyoX+UlpsG4VSXpL7I7GfOQl/P2wSlum97GnZUmr6it8/gNzRMrLTK1wRPlGBW0ueP0IrXuEj05x9xKwkZsoFJjoz9iz55doCHNcsbjIVOtJuNxzrgzxPd98iID4Q62UkoqVeelHAQRg+GQKBk5s4HEQN2yurJCnubMb5tnbtsCg94aqjCoIqVeazHo9bnjpluYmpvl6dc8k/bUHCLwuf2Ob3PixHGMtvR6PbQpyMYJtXqbLB3T7/fd3u/5pMmQ06dPOYpIVGU0GuF5gjj06Q9S/vhBy33FKo34fP62yLihpTh/93m85tVvcMJqacpD4j8XAz6Sy8bD//0fkvO82QIEKN0XxBYUc9PBYcvXeIhNB+GtqLeuTmYAACAASURBVLPAuTxM/ISREk8IrnvB9XzmM5/kF9/8Tv76k3/DW9/0ZlZWzxBEDfqdDtKPaDQs6DGnjh+mPT1PnucYIZmam2E0GnHq5FE8z2N22y6MzqlUIqTKUULg+3V8z/EXwzDE8wWjYddxx0YDRK1GoXN8321i2kIQViCKsEqBNSTjAWFUJR8PCOMKJi/Q1gUQTNplRiuCoIKNJIEfYoPIxd3iHliiLJCB0iO1LHotKJVtIhJWSDwr8MKQHTu3U6/XsUHEVY9/MlOxZPXocTqLJzYXoS650tYWZxHsySHH+psOKU7pfTZQW4hJa1Cf02qbzLu1DhV3m7x+WGvFbH7e1rUiRbDlpPkowc7A45/oses8SSQFXjTmp/5bxIELG9z8NcX2nQOarYR8aND1Dtc89Sq2X3wzN3wMPv4OyR98NCXLlqjHlrVTBSfyO/nqTXfge3DZBftYX12h6KeMW5JUDWhf4PH45Inc/LVv8vf/BL/+7idwcvFePvnlBj/47M8SKcnXbrmMBm0+8PFvIaKDiP40GxE85/onsLj2T+zy9/IP3/DZFe9k9dYhX/i9F5L0NCvFIt0lhZQerUaVajMmT1JqjQg1TlFWopVE5IYCRaUekueKSDguvOe5pDmvK4iCgDD2OX+6ysm1Ae1qk0E6RloPm2uwGhlIuoOEO+86zjgf8Gu/8m52Ts/x1ZseJJA+G2vrhH7EF7/8RbbPzbJ75zYWtlWZn5lmOBwT1SMwgqIMdlDK0I6mKLwRMQ2SNMPLfUI/Ym3YY26ni0E+unKU3Qt7iase1TKOVBkXw11vNNFKMux3obBYLTY3fiMNreYU/dJv11POItIl5CmM9vFDg/AM0ovJ8wTrafK0wBOCSuQRBVUQBRtdxWy1zSgYkiYFoniU1q91NKwo8JzjivCQZURzrgsEAVgIhIsnkhaMsJsWfuCCToqiwBhKNyCzWVgrbSnSnCgKXBDHJChly+cIITh67ASnTp3CSI/Z6SbNeg2lCzY2ukS1Kp1Oj/Ulx4POsoKHHjpMa3aKwJdOTJhr6lYzGg2Q0h2uA89H+w5ljqKIbrfL0SMnSfJsM/xh587t3HTjBivr69RqVfqjlEajyWg8Jo6rKA3VZpM9tRoAt99xJwcPHiQMqzSbdaanWqx3e0SVJsMkRRhFux7ieZCOhxSFoshzRuMxG/0+YeDAmE7HpcONkpy5mSZZlrmUQAv9vltfszPT1GoNhoOR03dv+jC7+fF8f7N17fsuZEVbZyc4KZon4RCT/XRyKNoMjvBkaWnnKE5CSic81G7vlSV3WluQkeOo+gKKskNojONQP1pjcpigvA7GGKzwmLvhVl7UUNhxjxOV7+G71nI+5p2h1o6Z2XUJyliEHzL3lW/TXJhnY/0o1V2P4QP7d/ObR5Z4eTrgE3uewZtO3MyvbT+fy1qCn3ugz/JjnsLbNs7Qo8ZbTx3n+mqNeGrMNZ02v+zHvELAR/2Ab1TbnDh0K9fFki+YGgtMcV21yxcrCbc8NOYImq/T4gtrY8iW+ZN0O59fUUxfuY2Tp3xes7HMb0fbGCQ5Tekziqa5IN7Ozw8HvEocZff+A2gZEEUBjWqD9d6A8/YeYPHMScZJh4Vt21BFQZaN6HUTarXYrQed0WzUSPOMKy68GKUUq2trDDpn0AsLHNi/jygOiLwIY3MWduxjNBwiPKjWG6ydWmJmZoYkyTnRPcX+/efxrVu+xZnlk06LkKbkeV7GcdfwfZ/OeIzRsL7RpRKHJImjVc1MzztkPHE2kXnsHGdmnnslj/38rawOR/xeUSFdl9zY7LFn+zS2pIc9PInxkd4+3Nd5Mv7jhqQI4YQPJTUDe7YonmAzk7dyU1XsPtETZ2OaTYk0e9YS+AFhrcFzfuhlnDq2yOvf/Da6yYirLr6QEyeP0Gw2kVKQjHPy4QYCg1EGL6qytr7CiZPHeMpTr+XBxRPUqk3iuMqefedx/NgiYRgw6A4478B5BMaSJAnNlkvhqdRrFLmmGtVAZwTVGJWmxDX3/aTvY5SCstiVYYAtMsjcJix1gRoblFVIG6ALjV+pEHkhWRZCWEFkKSaqOispITdRBwsuE15Yl25lnfG9u37e5slNYjE6wygJniSKQ84sL/Osp1/LsWMn+MnX/yKjTpf3v+d/cPj2m1lb28Bh+hFSng1ZOLcgPtsHcEW6t4kuw9aC2f6zlgpC4XmTDfHcVssEWHZF9rk3ijGaRwl4Jst95uYKBgOD6Vd508+P+ek3HmNhh4eNA6rN89i5+wTGjDizfB8bHZ+vfl4RyBl8sUqrUWPHrgu59+77qMQ1nnrlE7jjgdtoTAUMOzWihiJXIy7Yt4dC9zDtJQ5+Fb7+DyFX3/Atzt8Z8Euvv44PfeRufu0XH+C1P36c9/xhjx/98au5+ct3cfe9D/HdlRqPe6LP+Y3LSGxGEq6TBrtIgyVqw/MYDod0BkN27p6nUnW817xIqTY98n6HBx5cxvd99u7chiogDCM8ExCHPgiL9GSJQhUEzRiJ5tLL9/PuN+3kN97/De676xQLC3Okwz5pMcYXMWlh8X2PvPApErjhy2fYv11SKAg8MNpFVzcrVfLROnffu0GzdhGNRkS15oS1ftikyBIWtteQKNK8g7AVEm9AEIVUa01Mrthf202RKULPp5jRkBmSYUJUrbhCOPARnguBIRR4sU+1FVIUztoriEKyJCXPU6JKhEAy6g5JsoIshTh0qKwXh2QDxThJWe0OCSMBYc5UvY3woRG7QIJI+PSzAUpp+sOU8Sh/VNau0vlmMRv6QWlpWeohtDsEe56DKHxtwRNlN4/NB1iuitLxSG6ikZvdJTRBOEkIPSsilL7n4qM9n3azgeeN6XUHnFhZ5fAR5xDQqFc4cvgkoyxnPEqwQuJLl2oYBSG7F7Y7gbMxeMJ3YkdVkAyGxHGMFaLkXAaEWeYCcio+CIMuMpRWnDx2kpWNDYpCMzMzRVStU63XqNYbADRaTVqNOpVKBel7SOuMSVZW15ifn6fWqCPCCqdPn6E3SIg88P0ErzxgSClBCGTgExrXhcwyB17U63WyLGVl7f/k7j2jLLvKc91nzpV3rl25q7PUUqMICEtgAQKRkw0mOhyMje0LNsYBAxeOfX3B1x7GCR+DcSIeksEGAwZJBiOyZOVWVquDOlau2nnlOef9MXdVC3zGuMc/LvLwGkNDGlUdVHvNNdc3v+99n3esz15fohIFNJt1dszPUK1WMapkeb1DkiTbn12pFa7j2JhtbNS2MQoHgee5Y4azQMpxke3I8WHBTg3EFs5Oj9MEt+6X0pRbhlG99W6VKCEoVWnJK4xDq4yxqb6P0pT+cC/rhTI/ULhLKVFxytB0aA8KhIiYP3IXiZMRXHANuzfPMpV6fG/9LA/lA1qTNTY3Nukph13uiFEwza/Va/yBO8Gwew/f27mPn3XrpGbAjzQEraVFXjos+Ba3cKM/zacmEqpzszwxKPjlVswNnuHGu9f5/Krh/JkLcOU6TupwxWTKdXEDVa5yvFbnZcOYZz/uMoJjN3GmmOVlM7tY6xzl3Q+cYf/cHt5o9vIWdZj/+7zdXLZaYWFB8InNEQvxGa4cOPzb0ln2zO3B9yv0RjHdbockHlCNalQqNXrdTVxp8YPzOxp0Oh0wkjhP8AvJK17+k1QrdSarVY4dfZDde/ZSDXyGvQFJbAN+fE9QJEMmZubRGtKkwHNcBA5ZPGSYxJSl5sjhh+h2EvqdR/CCiGq9BgI6nQ7u0O4JUSWgXmuC0HbKn8ZsdpYQYp7pmQn6G5LhsM/URIvvScG9ieQix5DqLt9tt/m5u1b5UuspXKVK7nj+j/CXTcmXP/lRZGnAOJwcDpmp+Ofqih8omrcOnFt71n+0+/zYCZT+N65Agi8gMIbIWB1tYCAAPAwBhmj8T0VAVQoqSKpCUhWGmsB+D0EFQV0I6o5DxZWEUQWRZzxw5AQDXdJb73LRRQf50WuvZX7XbkotGQ175HlKr9djY2WJMo0tH9qUzCzs5P4H72d+do4otDo8P6xS8yXS81jv9RDSG48f7MJTZc7a0iJFlmGUwvErOK4lbvQ7y6gss1gVx7X6O20opUNSaJxKg0JLjB9BWMXxKyBc8lLZzVeVyChC+j7C822wQJmjxkD8IktRZYZSObooKfMMVRSgsBGdxqCFNeYUqsR1w/EmaOUbnU6H5z33Wp5yzdMYlTF/8e7fYePMWTY3N7FdZIHjnBuTbMXv2oV5btSLFI8aE6rvW8yOsKlseozPM8ZilMrCMimVgVIIlJRox6EUFnenHYEYc4eNADXunmvpod3HZokf3P9kkuEEP/L4H0Oi+atPvI73/Z6LUh7xWsnN37qFP3i7YvXEyxmNJJPzJb/63x/Hr/7mi1g/fRE/8+yUn3/ZnZTKMNEK+IfPHGd+fga1nrNraoK5YI4iCdk1dy3/+olNfuHVJ1lcBeO47JuvsHSkYN8TJnFkl1EBH/rYhSx2Ktz0nZtp7o645gU7eNrTXs4tt2xwen2F3qDLTG2W5fQENDZwXRsAMd+eJs8MqnCRwrPauTyArMZ5exYIwgqL6wVHTm0Q1hycqktuUtzApTQF/TjBuAKlCsrcUJaaemioVxwm55rs3TMLosSTEWmpkMoBHLpJn/1TDeZn6pRZn9APyLVAOFbR6bo+9doM1WZEVPdRpqQsc7RybCDCRsojD3d56MFHWN48yuLqMmmckY1KumsDpHRs2qF0KVWB64SM0gTX9el2bSy2yjJc41IkCXG3h07HqLVCoHLD6pkNhiP7zI5GI3obXeJsxOZmlygK8AOJ40h6nZw0VxSiHBu8YHV9hTvuu4MTpzY4+vAaK0speVqSl4Zev+TUsRU++qlvPSZrd0unvHWI9V0P1whcpQhdB18KpFa4UuA7VuvsCMuFdoTcfhlJ18Fxx5MupewEStvDsQ0IEVaqISwb2hlLu3xHUo9CJuo1qrWIuXaLShjgCUk8ylhcWWd1bZPBKCZNUwqlaTQa7Nuzh7kpi50rlaA3GuJIa7zzKxHCcwmikHo9IvQdmo06rWZELYhsMp8jcYS00ei9PolS1OsT7N65h/nZORbmdzA7PcXM1CRRNJbBaUOW50xNTXPJJZcwPz+LUIZRf8RwMGI4sulruigx2hIuPN/KR8JKRKtep163xnFjNGVZkCUj8jTm7OJpWyAL232uVGpMNltkWcHqZs/uj8ru/7ocT++k3i4KHs19BruHZlmGUoa8VPbfSiOwgRl5odDKhqloY4kdaaEptUFzTiJnjLHeFmObMMLYHAHXGFzpnDsg/NAvq+38X3UVA79G4YDBxSUjoEQoh5+//14eoMZmw2FRx9QdyZWdIaGf0QpcBonD0sYGy66DXpjhvKLFwpl7udvp8yKh+ZhjiGsjvJkK3xAtjjYCxJ5dvPq2k3xkrc+L1zSvyds8VJ3kpyYWaA5G/EVjF7eqnHsWptjZqvELfbiiEXCiVSN2esRz8/yNUbx/tMhPzjbZu+syXvHIWS5pF3xo+nw+GUesEPPI8SUOFF0OFJqX7t7N3tOrbMZDsiJGCIHnO/hBRL/fp1aNUKWh2+0icBiNRtZLABRZjhdIvnzDlzBhnerULFc94zns3nMeYVihUq3TarTYtbCTiTAi7g9wlKAaVUAb/DBglMVkZYLreKxvDOgNhmiVUq1G5ONAoPkdO9m1ax7ftVQbz3PI8hFFYVMPrU9CEoYRyBAcmJiYoFQ5Z5Iej68IRp7DReE0v9NL+bWDLQ6agI5RXPuv9/OBGx5EL/c5tpHz3r/9KGY0pNPvI8W4lToGT+jx4U6PU5u0samnj05h/t+5/lN3npt6PEoSW4QGZ1tfK603B1d6P/CwjEdS48Lt3K+V6LGLXEqJ9EN+/73v5TOf/zJPfc7zeMqTr2StN6BVqRLHA/JiRBz36K0vQ1mwsHMnJ48dZb2fMNGsMlpfZro9TWdzibLQxP0unWWP+tQUvheyf99ePDdgYnKGlZUV2u0pjOuB0mSDDmnsU2k0iYIAEVQxumQ4HCJdz8YVj18wBitQKJSycgphCMOINLaaQy+C9ZVV6hNtXB0SRC5ZZh3nspBQbo1ES4oisw75wiCFHheo54wh5fjFVmS5Zd+6jpWbSMH+/ftRuaJZCbjrjjs5fHYJt4hpz+xgfXUJbRRGjU0qUlgurLGaZhv2IDHakKtzMH+DtOaj7VtnF7LjgECTqcKOIB/ViQ6DYPuFAdjY8jFH1verBGGEkZDGMZHrEYTV/59X6f/6esVLvoUx4OhvccNNr+DZV34aPyq5/9AEl1y+xuUXX0kvv5uPf/zTrG9CLaiQ5EeBMxgR4+AxP1+l3u7TW34uOy/8Ghsr+2jWNMcevJHJWpvJmTp33v9p9l82w+Z7B/zJXyQ4Zcznv/NkfuzJ9+C1VxArDbLeCqs9hyce3MeTfvxBbri5zpevf4AzZz/NnrkKi2dGBJFh2B9R6BEX77uIjehe3CMLHDucMdmawJGKOJb4UtBfXcMRNe489BBPf9aF+JUIWYZ89RuHuOZpl+NrF6NKapUKkKDKHCkjdFFa2YMxXHDBXjY6R7n3/sNM1Ct0Nq0RKi4Vxmh2TO1k19468zNNrrvuHrRwUFrjOLaLORolnLdvjo1HhhQmo78RU6nWEUGOA+zdt5up7rTdH1yFH4aUiUKhKEvJ4SOnWNjfRomUUT/DFC6VKGBU9olqPmvri/QHKRhJs9pgY3OIMYJLHrefxOQgNJV6DTe0CMzIBIRhSFlopqYceuubbHYzIs8jqlRwnBpFVlDxMjs1KupEosLZo2vWiFPaLs6OhTrDvsILDE+/4sLHZO0aYyzycqxjVmVpSQ/Ccr2LskS4DiovKI09gCh1jrohjNx+ERljxmEgNsZ76znWCltYy7G5UrgUpcWpaWFQKseokmrgk/iBLQixXdFQBHiplVi4rku9WqFerzPRqo/3ThelMgbDjDQvaNUjKC0SL1MFjvSYm9tBmqb4vmR1pUNa5NQqIbFJiZ3x9E1rdu3axeRkc9wAObfvbJsYx42RqGJT2YbDmEGcMBjFFsVVFCjf7l/aWGOl7/sYxbbeWRmDlLZh4jsufuDiCGg1qwx7XWq1CvVaRLNRAcdqWZMk/b69+1xAyLkJgBinAdqOMRRFaakaQpNl5fYe7QsHPQ6vEQDGmi31WDanlKIcf18bDWPToef4lFpZY7bW+K5LWdr9f8sw+sO+fnA0v/WuKYSiokq0gEgbpppNer0ecdHl505t4J1o8aNFl9gRtBo1DnZTPh9U6JcK8pJy1OGPjM87g4zEhDzn9FHeePklfONMxNWTM3RPHeFXTY2XmJAjpyUfrEzh+QmN9YTf39xk6UCDY/cvs3M244LFgDfIGHlsxD8uHuHJjTneU9vgXd2CqHuYX/My/s6fZbM/wNn0OT4/oKP6XLqwwMnDPd5xXsjJu9a5nZSWU2e11ebM2gleH0zxZ8Mhy2dKGvVJ9u/az0avw/T0NKNhMo5kt+ZQYxSO9MiKlFajiSo0nlOQxX2Gg5CqP8HMzAymLBglQ1zGUqwS9uy5AN/1iJOEaq1JvdmgPpFz6tQinU6HHbumeOYzns+RU0e5967baLQjpiZnOPXIcfI8pdQKR/hIx8UYRZZlhJUIacR23WMDVFwMitEwoRO4XBMf5ZU/cg1vf/AIzx8N+NThGpmTsVSNuGFKcPmm4COPfwqLjz/A4t5L+McyYTjq8o7feuv3SXm2iuita/vZ4b+QbMN35bg7ucXqsydchEA6Y2OK8//RchcgxkQJKTTS84lqdWb27Of2B4/QmmrTmmhw8uwybuCTpyOS0YBhb5V0NKBeqbK5sUa/1wOgXQto1Wvccuu/EV3+eLrdTarVunWSdjeYctq2G5p7jDI7/pydnSXNC8o8w/UDTG7Tcco8JgO8KMSVPsNhH2EkI0/QaDSsmc7Y8AntuTaAQCuKLHvUQhA4vre9mcdFQZGnFmCuU4vkci0GS44RXQiPwoBrDFqXYJzt6F0jPOugL1MwHlmRE4Q1Is9BO5p7Dx/m4YcfZsfcDEGzysN33sIgLtDC8n2VYByoYD93x/XxAp/SaPIsRQrbpZKOwRVb4SY2IMVzBFJ7SCHBlXi5RglFiI0gF0JQC2pk6dDycYWDJwSOKpFeSBhEuK7HeY+/nEq9waFbboY4/iGs1H9/lYPKWK8vePYTv4DWFVAhX/5gxmdVFa2P4jkw7ARUKxlZLPn2oZ/mWVd9jCTz8UXKoTtSfuWFbaR3PX/+8QjPF/jNgkhHbIxGvPapm+y6AIbLMYHfRGQZRnosL8V88RtP5as3/jNTjZBf/u0ZFh+5g5u/cAVPesYOjiwtMT1bYdgvWQtq1BopbrlAPDjLW38x4+OfvZSuuI7zFmbxTkkWzy7j+1M4bsjdDx8lSXOufdb5vOwNV+JWm+iyz5/87j/jiRmu/5fvcdUTziMMI+I8Q4mcwHFRuExW6gz6KdLRdIcFjamQsycz9u3bw9LZI0jlYKQiK0tOn13hwktaCKlwQ4FOxo+zAZRB+IJTx48TkuPpgKAacfTYaWqVkKAScc+9h6lXHEapQbiKvfvnkZ7GkJPJmB2XVpDBGo3JBivfWSceKtIlGzk9OVmlUfHYt38nvldhOBwyPTeNEIY8K+j3Ch46fJSLLzpAU0hGI02aplC3rGJVWC3omeM9rrpiBoyHIcegcHQFBZy/p0aRK+ZnSjxH0Gq1rLHLaJJ0xOJSZ0zE+eFfjuNYg6602Jutl5nCdiyNMZDZ7zuOs627BYMUlrThux4wloqVNrEQxvIq6Vm6g7QbgJT2YC3G7MtyHNGtx7rbasUn0A5FoYDAyhR8D6VBeC71sEK9WsNzAyvxU1aulZcFo6SgUnXQRiK0xcFV6zUczyWq1vDDAGXA8Vz6gx6dzT75GDOaloa8KDBGkKb5NtvY/swlEmf7a0IIRqOEoijIsoI0zywHtzxnnNNIAj8iz0qmZmeYmJig2azjui7+eEKmtcaRkGUJjuMQ7tmB53k06w3yPKUoFJv9EVlusYl6HHgCtuEgBDhCjg3a9rMwSEqtLB2pLK15U9hAG6nO+V4sYUZSKsbdaFtcq/EakAYc3yNO7VRUmQLvUWSVTGtKJFle8JioNrblf7Al77NR5IAWSL+NydcpEJzu9cGNmM5inGCamaLHI66kqVz00NCiIKlP0FnpEjVcWo0ZXrs64nl6wIvqLm+NHT5elDzzsiad0QkSJ2LKn+GmMqHvZLiz84iFmPfnbZJb7+XqlQqv9gWfOe/ZlPcf4w/TCp12nRc+9cdwOAs3nuaXw4wz513Is5I+qyeOEUZVZCjYtfwAqhZw6P5lPhF3eMOhGT4nff6b1uxuzHNyNORnFp7KXafv4zVdw4eKAY3pedIiY3pmgsWzq/T6Hcq8IEkSZmdnWV9ft1Pe7W6rodPrcvftN+Ff8gT83oj1w0doTbYQ2uDtmCNXA5JBQqMa0OsNCOoNTJ6z2RlRGoVQOdUopNNNqLXneOhr16NRRL7PqdNHcd2QJMup1CJ0qXAcH3AsDlO4BK6kWgmJY0sS81CkKiF0fPy+4ZumReWWY1R9zSVuROQM+LrJKYsaV53usnPHHMXqJK0Tm1x3z5f58+8oXv+m/85Xvv4dXvKca7CpmN9vwrZ14VjXrzXyv0rxDFtEBYAxZuRR+lhgW7Py6FPmlvkB2JYI2K97GDdg4cKL2RwkjIYJb3rbO1lcWrKyCp0xGg6J+ytoVVCvRnQ21+n1Oqxudrjk4AGOHz+OH/vs2bOHzsYmE207JhwMYmqViOWVVeI4ZmJyijNnzjA3N8fczl24rkut1gDpkBmJSksqYWQNHcMxS9cL6HRWmGxP01vfJKwVeNLdjsodpANCLyTXmmI8liuKAukEuNKh3+8SOBKkjyptbLB0pOUlGo0phI16LROCIECXhX1RZSP8MLKfrJTkeWa7DEWJ8HzKIsM4PnGScM3VVzM3NUVvvcs/fuyvaLgaraSVS4zdzpnRZOPP3EHjS02pSwptCKUc6/wMRpTjca+F+dsACxu0IXOL+AukwPUlAls8p3F3jEpyKIscUygcYXWS6XBAWebc/I0NGu023TOnaNYbP4xl+u8ub1u/XXLpUyQPHBqhU01mDK5wcBoxedrgLz+TUJ58Npz/XQ498AVKY/jEFyQHLzrIi59+jEylCLfkV39hxIELjvCe/3GAmVnBy562SGtylnywzkpXMdXusW8eDt+VMYyXSMt1ds/vJWxlnFlbYveB53Jy9XqecwG0ai/l0mdUuPfop1leOsu1V/08L3/1h/nnL/4st959mE++8aOEz30KnHeU8NkFpoRycUTYO8Cu/XO4+Bw5kpDFIbt21Nhci3nuNVexc2Yn9amQB+89zka3w6WXHsA4Akc7JLHmxn+7hWdcfQVSK0rHYe8uQW/RdtAUOVoJCm2fcY3ClR5T03UKVZIVORoJokRK19IoIo+F+Vlm5tvEccz55y2AI8FIrmy3rCG2KClKSW+zjxs18Y1BZgkyDVnvDEibPtPufvREjtP26ScZrVbIqNtnfaVPraYpEo2KR1RbFdxA0p7yePrc5UgJRVHg+AW1qIEpcu4+fJydszNMtye56qqL8UTKKM6pN3w8z07JlFKM+iOEEMzNT9Db7FOUQ5JMURaGmbkaBxohS2cHj8naNcYWzVobyjHOUynbUbTPIQhpJVaPNp5pJVHK8ubdcffRcRx7GB7znbW2ISrSGRN2tAAhv6/gEUJ8n+Es9H2rw7UqAbR7LpzFcRxrwvZsZ1zlBWmSkKQ56ZhKNNE4H9+3aMIwDO37Qmkc16HVauP7IdPTMaPRiMXFRaSUxGnGerfHKB4Qx0OEsIVyqRWitAQLd0wrKMuSJEnI85LBaEiS2CI6LXJKbXF5WVkSOJJqtUqlGtJqNdmxY46oWrXJStKzDAAAIABJREFUtoE7Tlgr0UpRlrZBEgV2zThCUhQOnc4mZ5bX7MFBCOQ2d9x2fp3xu1CPsW1SWPLFVsff8SxaLhxTN0pTbvtMiqL4d+bNrSaNlBJdlPZggtU2SxhPLBUKdywRPHeY+OFfZryOzk04bCcatMypvvj5FF/8LH1tDx5S5kxLjw21jnQkKJfIcVkvh+wO5rg2zvnSpDXp56bOxyckrz5/L7e5+7jw8E285XiX39m/n98IDP/whJDPfu+7fKm6nz9NqvQmUwZ3r+DqJSadgnS9y183ItbuuZNjseJD5y3wgU6fX/7O3extGt5yacGdx2tUjx/miUry2gP7ueHaa7jn41/iLQdn8LTk7/IJxFLGDekx3n7FK7lr9QzJqUP8Wa3Jk47ewbX1KoPeCqYM8LIMz3M4fN9D7DlwAGNsEmGSpSwvL9uk2LFe3gJzBL7wOPvIYVZXTlKr1FmI2sy2JpmZmaG3sUGjXsVXmtPDLmFUZdOcJqg2bMMMgeOFFIXin77w98TpGiqz0544jilygZQl9XrVplaWNmCnWo3w3AmCIGBquo3nSPLCsLR4hh2TE2x0UzzfpzI0fG7B51VnA66bcHnnWsxv5DE7heGgMQStBiqO2enlfKr0uLxZ4x2//DN87+j9DLod/uiPf4/v3PANtNmqTM6tT207eP/h1fafungujR53Jx/F7h3jkLZGgFpskcm2HnQ9pmls6Wz19u93hYPBsHPvPl569VNpTk5x/PhR6s0GaTKgLDLWzp7E9+3Ia6O7QZamNGtNBvGIzc1NzjvvPDZW11C5sm57DOura0RRhPB9Go0mYnWNLB2xc2YGz5GcfPhBqtUqWZwgPZf25BT1ZouydNClIQisfi5XBRPtGZwgwBOQxSmZMORlSRQEFFnKsOzTaE0y7Pfww9CGAmiNGIPw+/EGYWMKXcQEUuK3p0Ab1tdWEELQak+SZRm63kTnCc3mBGdXlggDn0q1RpGM8MMKSltZh87sqNIJ7H0Y9Dt8/u8/zSc+/ml+4nkv4tBdd3BmdZmZapU8tppwKcZJVEiUUeg8H3enNAqHHIPUAleXaFkgzLj41gqjsaYhoXGFi0FQlAZJuX1AOnegsiQRbQQ6SXFd21VRgz7rvT6uI3DcxyalzUGAVIRTJUunPaKaQHshOs8whcPMgsfb3tvjV14Er3/tv3L5ZJMsHfKZG6DRCrnj5iVQkpe/zufTH9S0pyI2NhRv+oUH+PMP7+O6f5vgVU8xrDuKv/zwxXz2w/dDAz7wt6/njkNf4W1vXuSr376Mb970CO96u+bT//Jtdu+v8NAhSW/+IVbvO8Z7fnMvr31zh1e8/MP86V9dxN986AaufYHLM/+4DZ9pMHPey3nzuz7Jr799hv3ty7j53kNc/91HuHjPPpp1Bz8QSM8j8l2GiSYWQzZObzI9HbG3PsNwOECEgj957zf5qZc9mxc+70o2VvosrvZotds87con8ZFPfoNLiln27prnxOKmleAYxWBYcPCSi3GCgprvcqpQSCFRGqTnIKVBMuSBY5pbbznNwQsmMcLB0S6djk2h00hwFJ7j4lddhoMExzVEHviBYG62SjQ5gwx94vU1Rpsx9VqE60uak1OUeU6ZKSr1iMBzYNydS+KCalNz332naExMUAk91k6fZdRPmZmeZNfCHLgC4SSsne3wvdvvoz6X87QrnkbkheiixPd9KzEQikrkj6OwC+rVCpubPVThEvnRY7J2t03aWmO0DR7yvDHxZtxtLooCV7jnCizpjrFpj+LGO/a53iqcH03McaRPkthD/DahxxjKQtt9YHzI2MJhbhV1xpQIY9MNcaQ1RaNsMIhRJJltRGSllYdtdnr0BzGVyTrpyGovw0jj+6E1+0lJrTFHWSpGoyGTk5PUKlXcwMc/cYr1xbOszszSbFqzoHDsz+l5waMIFSVKGYajEUmmSIt8uxBN8xKlwfcdZmZm2LEwR1SLCD2fZrOO9NxxY6VmAy7i4RhHKm14yXjNZZmlgahSkySpLZi3PkvHwZeuLXrLRx1MxvIK20GG7aCULeyg62zLPvKysGN0rW1hKWxiIY6V/hVj2Ys05/ITSj32DIhzqDwHY6fB8rEonr8fO2bMVhFvKJFc9eH3cV37UwjHQZcCk0tOC42rHahWMIM+mQIPmPZqXB6foTF9IZ90a9SrdY4WKU/aCNk9u8kf7L6K67sbvNeL+exwALt3Up2/kE889XIeuvE2Ju47TQ3Bje0Zbs1z7ppQDFuSwXrON4qcBx66h4E7xTtFj5/Kmrxjdj8f79yNv28n3zrbI+lrnvqlr/E/hku8+eQsx5pzrDjHeK0IqM4+kZW7buEhtc49RLzbb9BxS2qDhF8UM7TLnL+Nl0lOa1qtSZJRjOv7bC5adKJSBWmaEoYhzWYTx7H0IC0dBlmHv1wL+L92pEQPPEQkIg4BnYqiqM9zoD3Pw+ka11xwEXGaMxFFTF94ASrJuO72Wzm+/AjSdciSHM8LMCq16zOwce6+7yGlS9CMCIKAKKwxGHapVEMCv4IxmtC3k/fVjXWCyEdlOWfOnOXfLr2An1k6wsc3Q17iKx5fNFlVq7yONTYHU5ysC66UAS8lY8cgIP3gZ/jX/gaXXfUk6rLCn3/8U+zatZdWvc5TLzmAcSTuOJ54S7L0HzG6/qcunjNtKIV1rAMEfoA2Cj3emKWUuML+CBrAmO0AjWLs/HWNxHMkwnGZ3LGbJz/7hczPz5OmMQ9++9s8/TnPo9fZBKNwhaBar5N0l+n3etRqNWZ2LNgUnSThzJkz3HbbHbzguc8h8HyGgwGdlVW0gDSJCXwPk6UcO3qE3Tvm2X3BQZIkYWV1CVdq0iQh0BHdtTUEBkTPvkCb9uWSlwVeY8KaRCSM4iHxcMDE9AxJkhDHMfFwQFipEacpeWq/JoyxlA4NmytLhMMRZ88scenB81laWsLzPNrttk2WSoe4jk+ZDGi2JonTjMnJaYyxo06pob+5RlipIqWL9AOrkUoTXOnjOB4Le3bzp+9/HytnF5nYt4dvfPqTjIYDa9iTW+Yfx0bEYmxioBB4wsGM76nG3tbQiHE65LkIX/t7HMqtE6EGbxw7izFjN7Uco+/s3RfCA8fFBCGVqEqpC1zpoYPgh7pmty77UhIkm4K/+NLV/PovfZVyAKHrkcaStROKX38NlKbKfacb/NXrCr70vQtY65yl2xmCX/CH77sIL8qp1bq85icv5DUvvYN4GPCBP+5y7y0d/uC3pnjjH0C1+Qi/9DYQpcPZ9Q9z802Gdktw1/fOZ7JxjBe/oIHnJcTpgIWDiqMnHuLKKw/w2+87gi4avOGdHseODXnNz17CR953hKteVLJn3iN+YIM/euWLmPRqmJpLkip27zuIpKD0JvjI5+6hO0jwQ4MiYa1nSAcGlEKMD7kFbQJZcPpDN7BzOuSSiw9y0YXzgOL2W+/n0oPns7p4mumaB8aQ6dKOno3g+MMnOXDFbp7x3Kdz+CPX2/h4BElWkuclU802vbjL9Tc9wmb3LPtnFxiNEhzfIfRqpHFMkiW0J1tIHNqTDkHgkfRTykJQ5oLO+kkmmiFes4UkGRdsDrnKrQZbpbiuT54VlCrGCwOC0OPhe09SrUTUPReTSc7fu4czS6fZtTCFMQUyDJHCY2qmxhWXXcL1X/8XrjyYUhpwXA2UYAQPPrREq94gLVLqlSaDckS9UsWJBKH/2KzdR3fttqZ6ulQ4UlIae5jeMhWCne4VyoCx6XhbnF8pXBCCTCtc1xJ3BAJH2uLNdV1cT45H62Zb17xFbLDpftm29KHUGiMEDgYjxdiY5lKrVggciec5hGFou92AltaQuN4f0ayPu7rdkka1guMIPN8nCAK8MAAjqTXGoTeA1iVCK06dXWR9fZV63corXEduF85GQJbn46mIIQgCsvH/qxAClI00z5Wm3mwxNzfDzMwUQSWiEoRUq1W0gDAMqVbtvXaqFcq8AM9+7nmanUPJoXn4xBmyfNzdZSs0Sm5rtHOj8LSDFOBJW0w7QoIjiLPUyumkwZHOdsqglC5aq3NTXG3lN4UpUKVGSwehCwLPpSwUhbYyEIxri2pj8B2JC2gjyVWJKn74uo1Hc/9/kN1bUYZMeLg0aJU9tHCJTYmPZqA1WpfMIhk5klEJUZHTImXi5N08Q85wRit2NNrcbhKm4xrvHx3louYCb7rt23y1Mc3T2z3243P4c1/mN52SPxEBr5yd5fYyw8wf4Ld6K4iR4NW7ppnUJ/g0TY52N1km5etFlRd+6z7Mnh1cvJ5wo3R5X9DibzZO8aagyY2VgNnlw9Qfdx4X75/jNd0lPtHz+cqOnfzx4jrBVU/kpq9+k1eEKYtpl3saLXb2fZoLLYaJ9St0uht0NtapNxskiSW7pGlqiTq+SxiGuHmBUg2+6SuuPtunEk5wlc7xcqiWGfcsn+K5nQ1ekNf5xuLX2RtU+adeB+fmOqpdpzdOCI1Tu2cKIajXmxRFQb3WJE6G2xOOKKwy2Z4ijmOmJi36d3193VJftKJSCYg7ECion1rkGq/KIw88wvt3z/Hq/oi/6iY0wg0CXSVPCt4ymfDuNYl8Qo3hqSbJzj5fLhz+j8o0H7v9PvypCt/+0j/TWe+x6/xpfuvBB7j1m7diTD4mtNh3y3+Z4nnLkCGlxAkCGrWa1f9qjXAcfNdFjDvOjmvZxkWeo8extspY7V0ljKhWaxy4/HJe+pIXsrTR4ZY7buea5zwHz/cZ9IeodAhAMuziBiG6WGPQ7dHpdHAchziOmZydY3Z2lsXFRXSpSEd9m+gzGPLgQ4fp9Lo2KjYKOL20zPTCApubmxgh8DyPTKRkWYJwJIPOOlGlQZEkCOHYgjkKGHY75HlOrsptFulwOGR2fo72zDQzcwtUKiFhGLGytEgYhqwsLWGJGjl79uxmY32Vq5/6FHSR056epd/tEDgug0Gf9vS0/TNHI1ZXl2m3p8jz8ajVdcnKkkqtiiOkpR9k1pjjBArtCCr1Cj/xqp+kVanxd3/3d1x+8EKuT60JoMSmDpYIW9AL+WiyHGrM31ZjnbURFh+o0dux4Y7YGtvbSGRnTO4oBUhpT4dKabRS2x0t3/NxqzV27t5N2GiwubrB5uY6cZYx6Gc8FpeQBVO7UjYXq7zxJd8mMRE+Bt0w+NWYd/3J8/mtN30HH8NtNw5xpeHHn/oA7/kQ7NgVceZ4wWbxALt3VrnyR+G2O+7i/3zHlfzZnz3APXclvP2dz+Edv3GIn3sV3Pu1BA+H40PFf/upy/jHv7+HePgxKvK1JG6Vz31+jkdO3sWxm17O5c/5HFPVHaSjLrt27GFzYwXd0/jtAe965Y28/unPonHLNJsbHcL6JiqTiLWYtdUhh08rgsY0Nx66j+PHHmZioo3rRKx1E4TToBEIHC/DqVao1WoMhyNqtSr9QZckdzl8WnDf8XuYve0Uz3z2Fbh1wa6FOseOSeo1j8g3qNwyYrWEQ3cfY+eBaQ4c2Ge7lkgKoxBCEoYhhQHPcZmuVnjeq1/IXV/5JpKIOCm459u38riLDuAHdYYDa9KriSrpcESl6pPEKWliqEkHhUN/aZUg8PFESK+TMOz3mZxsIYUhT1KGwxH1IKQ0iizPiMIJ5nZMIB1NZ61Pt7POzrl54jTHMSALjUoKwkaF6UaFJ1xwCUsPbzA7PQkVl6WNLpUwYm5miijyUarCAw+eZhCPePrVB8H16HWHj8na1cp27TzfsoGdRyW2udLSgJRS22l4AEKLMZFBkhUWkaak7RZvmc62DH4lNolOSklZ6HFXWltEnpRjA5pBmRLQjPM3qIShlVtgJ47KaHzPsemGRhJ4HlEQWEOeUWSFspKfrCRNSmoVF9dx6A76tD2fIIjswaAo8aMQ17PEi8nJKUxho4tDP+L08ga9Xo/mRAuhDErFtqsr2KZcbGtHlaYsS9I0JSuULfyFoFarMTExQRRF+J5Ptd4krIZUq5Etyl2JLgucwCWRoHIo84KyzCnylDwriUcpSWbZ2WZMChAGpNAEnmfpTOP7IQ3ocTG81bn3pUdGCeNC20iBKsrtX2OTLjVKwihPUUYgHXC0NYFmhSYuFbk2CCNwHYOnJUJayoY0MCwy9NYN+yFfP4ghO8futdxyjeFHP/8xbnzpj5OLEmHAD2r0spilJMZg5XZTwKn8FJ7n08hznuR0uKDTZ33tGJPNC5H9NVZEhZudmK/RoH/Z43heXuJ3T/MgIb9U9Di/WeXa/ohVCnT/GE4+4Ld7Hk/sSbLZSW5OPRQNftsb8rm1DT7f2INSsN47wcCb48/LHv+wq8VHs330nVPsVVNED9/L8cU+67MzZDsSwlGfU0rw1uMP8vLWLDMLksHDZ/ntYZv1jSHv3pkzs3OOtdVlzpw5gecFrK9u4Hgu9Xodz3NI05w0KzA6w6u28LXislHGbWGdF5V9Ds5ewdNXj/JPpeGFJNyVuVwsz3KtaHNeGvKScIrfK3LuSDNc36M3GFKtVhlXpHie9WPFyZCZmSlGo4Qg8PB8nzRNqTUaZEli8zu0YjgY4LoQjwb4nsPG0jK/X0YI2eBnp1Jef2KRN7uCu1SLR3Kft8wt8Nen7+eTgyb4ij86usnbdkScXBb8ZjHAZA43XBziN5osnj1FJazxa2/+FR6++xB5nuJ7HloUjzp4/Rcpnhdm52zMalGC41AJbXrX1vhPYk/3Roy7FWqsyVIluC6Vao16VEE6DsIRLJ4+w8233kbQaPD8F7+QfrfHYNAj7awipKLIE5JBj9XlFaKKh+c5TLYnOXXqFKM4plKpELg23nLp7CKt1iSra6vMzM6xb7yJXXzJ5Tx8/93sXtjJytIy+/fs5cFuZxzuIGnU68RpBqGPcB10lnLi6MPMzM/RX+8zMdFmENsTmo8hciTVRt1uTqXGrUiEdKm3p5mcmkMiOHbyczzhsscjhCCLBzSm2pRZSa1WobuxabFQYUDTmyAZjkAIKpXKeBRb4Pt2hGoXkCHLCoTRuH5EqaxhRhc5jpKoUmGU5uTJR1hdPsOn/+ffkpQ5OTbOOBlLFY3QiPF/byNhAIQdIVphvktGiTTjREJj8B7VMVBCUIxfpADOeJzrCEEh7AsiAFSpyOOYo6dOIV2Hfq+LHgP9Ed4PccWeu4SB9TNVpDZo4eHJBFX6qFxz8KIG73n3dTSbFfpDQTkmUHzxpsfxz194kB1tn4kpj/e8u887fxcck1KvB8zM3stbf69PnsB558e4E2uct/8A8xfA0ukNrgwaKF3jo/8wzSOP3IXApV6OOP+CBSbXD/Dk3zjK86+G+blF3v6egHQ1obMouOjgHiZueioX/4zLoNcnVwWze6boDwcMBxlHjxbccP1d3HkiRukeG92YRn0CU1itejWq0GpFnDqzihv4oAtW11eIIpvCN9WcoNAWwSWFwLiSIPIRpuC8fVPc8PWU5TXJUx6/l9vvO02aZMSmQDgVgihCkCMpUIQEfkBRKiYbFU4trbAwNc1bfueFuDP78Vt3UgxSJJrHX3ohIgSdDTFOROT5hIEiHWMdK/UK7dmI9aVN2pHVlfrVOjXXYa3XAdfjyJHjPO6i/Zhc4UvJRr/LzMwMCIe9B1rWgJsXeD7Eo4JSKYxRSOkh0CANGyvLuIXPMy69kPtvPsQfvv9GXvnjV5KonGFrmcdddD5BvoARcOGFswjH4fTJTSanGlZn/BhcW0WuUuVYFuBSaoXnepR5gVYajaTU5TZtQgi2Xz6u6+Jwjriz/UyIczIC15VkWTb+e8YUC8F2N8wwThx0fQrHFn8eIFz796H0dqhUkhXgWjoIRYHru4CkFCVCWolJURp0aegNezQciTRWc+kIb2zUtGEhQgiq1QrF9DSFsiE7ozglLwuSLCN9VFf80YeKPM/tZDBLUbk1CnqOwwirua3XGtRqNbsXjzvuUWQJLULYP8+VAqNLHKEojaIoMjtpKwu6mxts9kekSWEldWNZjSPHpnltY7m3QsXM2EhY6i1MXYkdDmiUUeM9V243n5RSdlqLoUgLXMe3/gBtmdSpLhnkOUpYGUfgSTwhcF1gbJwstMbRLlJqG1X/GF2PJpBsTUe0AEcLimueyY5ogaPJIlUH2tkAZ2GO8uwGJYpEZ8whGRpNP8/tZ1oUNF2NFh7HZJfbpCFdHzHb3svvzzTh/of4R+lxP/CVfXt5y8Y676qE3BkViFX463zE83dN8uHdF7B2x3G+I1q8evcMr3vwLubbM8x0Ul7bX+SvL306964c5g3xBq9rR1C5gA/2HuTyjRymG1R1nWYe847uCjhTdLyCy55/MX9/w13cHhq46ifoVvZxwYn7uHkjQZUFDz3wEN3OCq5XJY432CLp2PASi3qUGIQMMOWQwKnw4ZriQKL5ojPJ+d0+8xoucBtcp1J+zov510IxSkquFiW/Wx1y52QNk5ekebktZdqSE41GIyqViu0sr9lguFq9Qr1eJQgqpP0e1UabZG2FwPdI4y6htDHyw9GIIPUJSsW39CpPXEpZqO5lnxjQPrCDmx5a5nnucf7lcZdyKt9kT2snP909y2ce3OA5T6iRnm5wf7rJ6W4Vv58RhhX6nT6v/6nX06rX+H/+9P084eJdfPDD/zCWgSrkf0Bu9J+a82wM+FLYdBvXpcwLezIuSnQ23ujaTarNJqV0WO4PSFVBqTTdJGG1N+D02hpZWeBGVfYdOIgwElUkHH3kKPFwQG/1DP3BBotnztDvbGKMYXZukolGE2+csBWGIVONBipLGPR79DpdfN/Hq1SIs4wsy3jC4x9P6Pt865vf4OHjp3j+i3+MUa/L4YcfZH2zy0WXXU4yijnvwotwhB3JJklCmsbMLMzj+QF5POLw/ffieZYbXavViPsdyiwnCjzAdja0yiniPp3NdQbJgKc/4xlMzs4hA5dWu017YgaJ4YH77qff79FqNsZGEqi1mkjHIqe2+J9GQFSt2cABPxjD/wOr2xuHqui8IB11KXJLrxgUOcvdIWeXlildl1K6xOPT/VbHSFNghMYIGwGuzblxI0KRocmNJgcKAwWQIYiBgTEkxpALSIwhM5pca1IMPa3J0ZRGk6iCuCjoxzGr6+ssL68wSnKSvCApcuLisek8K+3abFislrtUlnctnJA77xzxo9dcRDJycL3EmoUCwaufdpZP/hFceOGzeMOr6vzG26CzGbM2Ai+SDEcpAVBxn8ji0iHe+rtggjNI+ii3z1dvOMH//MA9zE79CJ/94hf5zq1Xc/0DF1Ed9HjBMz7LiUfO8DefqfCBD+1hIriCX/mxIVpWuaj/q4hdkzx8+gQEksCfYXN1gOe47NszSYDm1a+6iv/+xmfyvKvOZ65dJwwtt1tpcDzJYBjTbNRwx7rkWq1BHCd0ej2SoqQ7GDIoDEpLilxhtEJrwfT0JAooRYnr+vhSkQuFwXDovocwKsf1ChSgUaRFgSsdapFDhuAXf/oKitBlFG/Smq/TnKqzMTrLcucs/c0NsHJGMmULDy/0mWw3iSoeaTZkYjri0D2n+ejHvkY2GpBoRaPm0apF7N65gFEa7Rs6ySatVo10ZLvBK2e6xL0BWVygS8HCznkqVZfJiTqVZkTgerSn27RaLQZK8083PcRtazFXvuhS7t9cZUPH9LoV1jcSNjaHrG70yTKf1cUBujR89+a76XbSx2TtMsZZAWAkSVFSashLTa5tJPcWEqwsLRdej6VZoWc9BkIIvO0gJFtIFUVhNbfGHqiF65AWOUmeEWcpaZ6Pix5Lf1BKYRxJoTRJUdKJY/pZRoFluQOgFZ4Ab6y3NWOPS2GstlcJ64kYJClBtYbjupSjmE53k3g02qZlFGnGcNAjzxIkmkoYUanUiKIAr2Lj1PU4PGRLp62x4STKaIpckauS0WjEsMiJ84I8z/Ecl70L81R8fzskJajWrFHK83AcMaZtWOqAOzb6GWX35yzLyJXG8QLWujG94Wi7MInCEN/1rIbZGIpxxDnjoDBlNIXSpGVJYQxeYEfprpC40qHqSFzHyuwyVZJpwSgpENIlUIpaxcokM12S2KjIbamIMpApzVBphkVBqcFIa/TVGh6LeJ+tfIF/Dw/A0kekAMfl6kNfYRcuvhKsG4GztIkjNQaNMoZsPD2d8CKE4zAtYORW+apocV1nDT9VzPoeVbdCrR4RTTRob5RMNpv8QjyAPXNcVywzOrkMM9PMT4X8WuIQr58l3NngJcM1Dt1+F/e1JxktD1iZX+A4LsND36VkP7eKCb45MJx98Ha+e+CC/5e79w6y7CzPfX/fyjvvzrl7co4KI40GSUgIISFEkkDEg0FcAw7YBhwABxljOPjKXCPCwRYYhIGDTRAWCFtCOY40Gk3OMz0dpvPunfdeeX33j7V7NHLdW/apOldQ95vqqq6ZXVOr9/56fe963+f5PWzfuZX+WpHuZSP8Q9PnSt9CSTg0pueZ+ffn6TTb6HEitv38AXLPP8nv92T54rp2dh9+kdmF6TjQJAhpa+ug6TSoVCpUq2VqVYdMNk8mFzcCNDWDHQS8x0+z3bd5hzvP1906+poBXt8zwF255YxGEiPZyRlhcb8hebanm7oIsV0PIeOJ4Pn7gu+TTCZjvKSZxLR0/MAlCiGXyWMZBp39A9jNOslEnESYNUxqToPFuVmazQYJv8ZEUCQfzbHDSHCqMU++06RtwOHSRJJfrF7H7KkJ8tNNxvYe5KbmNG/e0EFbI4dhV0GxSLo2v/+xD9EMIJdPk2vr4pOf/TvyeopP33Hn+YJ/aUr2X12/1p1n12mgmga6YaEIDc+LfyWTpoUkxPMc7IaCZlkYQNow4puPIhF2AKHHuk2XcvL4CeZLVbqGR0hVZkjIdjq6+iAKqJULmFYcE+t5HjKEwG9gmiZBJPD9Or7vY5oa0o/IpLPUqw0WFgt4dp1sJo0XhOze/TTrNmxk1doNFOfneeqph0Co6GaCK3cqzZUWAAAgAElEQVRdxrnRMwhDY/++g3T09lCtN+LOqaZg16pMFgp09/bTm0yQy6R4Yf8B1q9ag6pZgMSpN8l19SKQhK6DahkIESEDD7vRQM2qtLf1UVw4RyaVoqFCNp1j2fJh6vUaUSTJ5/Ox1nAJeN/C+IR+gGpqGJYVd6/dVsEpFRRNo+k4oMTfB36IovvkE2l++0MfpDg3R31hlpOjo3FaoRKjBF9C6QXnP0+5tDEjiURFjeFKLSJKFBtx1HjkGqmCmA8Nqoid8kqrm6HrOpqmoSvxvweBF0fKLjnKW0+PQtNA+dUYBn2pIkSAUFx0K8etNxfZszdL2fboyvusWOPhBC4pPU9IEVXtJKSOG7Zx2YaHEVLwO+8TaJHO5z+t0vOW98L8A9AXMRAGPHW8gaaB1m5w6MAckzOwaqvG7NE6P/j6L9h5Y46Z2jRBw8DPFllsfJTNq+7itg9CunucyB3nj/4Wygslfqh8kplJh4suXcNDxx9hg34N/al+Thw/wroNa1GEgfB1NqzNIXyf5/bWKLrtWFZEzXbBtvHlktFIRVNVStUKpkggFJhbWCCTz+LV64SKRl862yIJmBTLRVKmJAoEi9OzhFGAJiPWb1jH2eNjuHUo1CokUGgKCKXAlAFT8yU0Idlx3Qrc6Xns8gSy0ERTBH3dXSi6RWkqIN+f4d8ffoSRFT00fZd1K5bhN+MiLpVNUK83Wb2snc7cdtA0Jk5PkumysH1Bvi1NLp9B+DZt2XW4QfzgKAOFYqNMo6GQzuVp683GYUeuR6NcwQ8EtqfSkXdJpbJks1WuefVGmq5L0oSFhRqmmUCTKpYVcap4ioXqDFf03YCuJZkYK0AkafgLv5K9q7ZIDJ7vX/B3sY45YcQP1UvG7SiK0FQTogBFU3ADP4701vWXiEcIojA6H54howDXi+kpqogT6lQ1TvlbKnSagRObiFvF7VJRtKT/1VrFc8qMC1shIxw/QLUsmp6HaOHnlCiWUVSaHp4fkcu1USoX0Gp1dC3WQeuJJJqh4/shmqbhsxRvrZBMWuRyGaqNVhc9ih1zQRCc7+KFgWx139XzxA+FmD4khCBA4BOh6Boosb7UNE0MyySRSsX66tCHICSQsZzAD1w8145pTKFPo2FTKFXP87bDMATpY6oGQkhs10GJYqRnGLwkI7kwKtt1XVTi648iQc0PcMMIu4WWE4pP2oyJE6qM8YuqUHECHxHFDzRL5kPfi6kbwHnN9VJ8t+/HyYWv9Pp/l22I8zAFRYbY/WsZNzIookKfG1EKPTwEmqaTU4zYi+O6aL5Dj2Uw7Qak+5ZzbG4K0ZbHmXFpRBHhwiJB4KK6VSbX9DFXWCCKBBcdrHHrcC9zzRofGpvmG6uXM3tyEnnlZu6crfLg4jTWim5+dLpB/1CSe3yFVR0DJOuneePNgyiHDT4x8zB3Ll/L+x57hj9L9PJt2ybonOcaMkzYRc7JiNszGT5WKfFdtYyZ16hJn59aCfZqCu5ihGc36VmzHBlp3H777ezZs4eFhQXa2jvJJE30VIbTx4/FdK/KInpHP9mONlY6Pm/oW8efTp9kd8bghbEie7xR7l+2mU94adY1JRdr8Hsb2xBlB8MOyLR1tig6EeVKjVymrVU8WxQWymTSJsmUFdcUKjQaTRKmRdFroAuH8lwZ3dEozI3xFTJk1DYi4fFUxyAzhSqvjzQWulJ0VW3saoHj0QZyjVOUJmaJjCrVQMdUQr4126RUPE1v2yDS1tmUqSFcuPW2dzO0aguphIUqDQ4cfIYPfuIj9HX3nZ9Q/Eed/H+2fq2L51y+HYid0VLGSTZeS1tjmhbFxXmqlRKiocY3YlUh0TJz6BK8KOLsySN0tLXRMTRM//LlLN+4gaZjU6zMY1dKhL6D3ai0AkkigjAeb/lhFJuSOjtxnCYyiLASMfM19F0Ge7upOw5CMwg9h1x3HwiFsTMnadg2xfIMyWwviYTJs8/tJZdJkMnmMTSd6mKJwLepVqukEgmiKCCTa6NcmIsTCYVCNp3BdRyyLfC/H3rYtRKKEY/6Ij/Atx1CIGEmwA9BC+gZWMHC5Anceg1VSGZnZ2jPtRHp4vyYVGtpFTVNjwNNglhbHAoFz/dRNB3LSuK6Pp7nnDfqhX7Q6o5YGKbCsReOsG7jOnY/Mc07330b48cPX6ABXEIItm5qSouSEi2NbbWXPfEBaJr+MvTV0lLEy80g8HKudxxAEL8+1lYu3Ujly5BXr+jS3NhU6ib5qz8Q+CmD1btqvO+97+bs0X18+1tHichSr9sIYYIs0DuoMHlOkk0Y+E0XiYordf7kryTK33ydhCv59wffz/UfuJdyDb5yVyftQ2nspsny7QXKZwI2XDPEvhdc1q0Y4sV9hxhaYWBFwzzw1F185u+zdCTbOHxgnEYdVq1ay9jkKNUChF6GijpJtgdW7WjiPgK93e04TRdV16jYRc4+dxRMyfVXb+Q79x+hUA1Jp9LU7QAjER/iQgHH98gkU3hhgGlZaJZK5IdYloXrBFTrNVSh4Mm4M/Hhd76Or3z7YS7fdTnn7r2PpKlx6OQZUki6utv45te/y8hgB8fPlZGEZLMd1JqLKBpYNY/STJ1qoxnr61yf0LcYHMnTkTUIw5AbX3cDKh7lUhPPBqstJCHTREQYpg6extT4IlMLJdatHeCZZw5x6UXbmR2v0JgvM1sqsHrtCnzXI9Oex/YbLBvqp2m7aIqJrkMUemg61BsuXV1dZFqEhHKphpVoo9QokDYzzM1UUTSVVFJHMwQLMx6265LW2omigMALWLF8EMNUsZLWr2TrLnVjl9YSFk7XdfwoBFVBveDAUZQIhHJe17zUwREtcyFaXBirQqAqgKK97Pc/nkapBKEXm2xbPgsllMiWZCsK4oCPJQ+MlBJF1XGXjOFCkGiZkRRFR9NjuZahatgtqV+xVmOgq510MkWj0SCUEZ7vkvQ9DDOBphkEUStCXMbFoqrqpFMpvNBvTdUiwiA6H2EdhdB0nfOBKBdSMJbey0q1iozi+59lJdF1lUQiQTrTInggCPyohUuN3zPPiSeahBGB62Hb9kvyltYhLyNBoEgUGWFpGjKUuOFLVKKlz0dHQahxkUskqbkOdugRhAI/jOUylq6RQKAJgSpi0zaoeJ6Prsfna+DHJtelmG5VaTGVFHGe/y2EQFeVmNTxCq+lfQGcf3BYOluEiNGLulDxZcQ7C1Pc25lnbd8ge2bPkZARMvBx8GkCnlDpVDP4+CRQqIwd4HO6QWE+RDMsCmETSuP0+AqdqSTV8QW+ZqrM1ZqcbTNpS2jYuuT73Um+T5nenZt4zy+P8Imsx6fIoay/mMuqz/HimjQ7KhZv1zSuP1rhSdnOs6P38U5pweAWPn5ukZFVHVw7K+lPb+SX5kEuiRbpHrqUVRPjBCJHNdnOqy4d4ZfPn+Wty02mjp8k0EKyuQ7sekgu5dGYPMf+Pc9TrZRoNpv09/dz6badNKsVCoUCrhsyvzCNqg3wF506n/YXWd7exsNByD/aRd5FJ+8bm8baMMCZsQp3ZxWq1TozM9MkE1kWFxdjIo4myWRy2E6dbDqDrmq0tecYHOqnUbcZHEozNTWF6zo0Gg18zyUozXDNVBOViN821vCBHpfPT7qUk4O8dX6e8e0bKB2fZGNScnaqSD7bTfngORQz4H7Xxc908QFNEM6WKPeuZHtvJ6UXD5BYP4Bvq6y1QvbuOcDgyAiBb1N3XDp6uvnWP36XW29648vDU/4XDIPqHXfc8b97D/9vW/f8/dfuEKjno1CllDScepzgVK3h+0HsEg5CvCCI2ZiI8x1ORUisRBISCXZevhM/dHGCiHqtRujV8Z0Gjt1EKCqNWjkeNTgummkhUXHsJooax/jqqorrOqiahu979A8N4ns+uq6QzGaZPHuatRs3Mzp6hmwmx65dVzMxPspCoUR7ewbHDfHCiLMnj5Fvz+KFcOLUWXo6siSSSSrlEkIFGUQ0mg001SCdyxJ6Hq7roOsmUYsBGhIhQglhhBeGZFLJWL+mqLhS0pw5x+zCPAnTQNcVOjo6cT0vvnbPIyJCVeMOBiJ2j7t+iKLrWIaFG8Tx3YqAIIwPRrdWRzUNoihs6aM1MokMZlLFqdVIZTLkTJXOfJ62XJ58Nkc2naa9o51UOkVPTxcJwyCTTJNNZ7CSyfgASadJWwlMy4qlMHocp6vrOrqqoWs6SksjqF6YEHkBy1u0WKtxh+pCrrcCmsr73n/7X77Se/fzf/FndyRTIV/52mo++7kxbnjzav7md5p89++nuOurk7z3t9Nke2oc3WsSKYJIeqQSJsmch13U+eY/vI4HnpuAUCMIXQgVhK7wk4de5G+/soMrLitTmGtjYH2BwWUmE6dqvHrHCErmHHZThUQ73SOCxfos5cUCXV2b6O1cxsziJFtXXkp35xYmyi+gaeC6Pldd3UUqbKOt26LhBGxLX8b8Qo3e7jYO7t9Prq2XS7dtYcXAChTNZ7BTo1H38KI4gjlUBbqmEkaSSs2j7tqkE+k4oarZxPMjVC0JwsfM6KxbuZy5YhXPDci3B1w0MsTMuVOMji0ymFX4jbdt5ZLNw0jDoFEvMTZWpeq6hKqK4niogcLGiwb5ypcfYcvm5axY0QESDMskaSmEjoHTbKIpEPghmq6hagrJhIZGilNnpunr7ebM2bNU6i5zpTpCqjz2zAsMDI/wi58/idRUrESGtRvW8MQz+5icm6XW8FG92ASTyVmcOnmOzq4U87MVdEshn++kXC2hGBJT1Yg8n3TGJJUwgIjOjm4cLyDyBYcPnqK/uwsvXKA/PxIXg76NlTAxzQSO7bPm2ne94nv3n7919x3/MV75woLwvDEuCl/G0QeQMkSGceyvoelLiRWYuoGmaqgtnbJhGC9jCXthSBhJwsBHEbGeWkRxCReGEaqmxZhCJS7oJCKOjZZgGjpp00QoakxcUhR0LTYHGroOErwgpCuXI5dNoSgS3/PxbYfID3AdhzDwiIJYT+x7HrbTxHN9bNeh0rSpNVw8N4jhP6E8TwhxHPd8IIpnuzTsJq7r4fsege8RSQhlyEVbNjEyPEwilSCZMDBNHVVTY2M1xCZ3IfFsG99uIFEQkYxj32s2Y1MFqnW71UGNoPVeLPlHXN+Lw6kFKFEs4RCKQENB11RMJQ5sqvseNS/CiyRB6GMIFQVJSlHQhABFEEoZx3K3yCES8CKJjEKQIbomsDQNTRExklT6qEJFVxQUYlSpAD7ysT96Rfeu4wd3wEuNlaUz4uUUjvjL1wPWbt7GsW9/h0UloCklCU1FSIGuKESENIUfN3oiybpEmmLg4QQhauiTjlRqQjJkWAR2AxHYZD2D8pphlCCiXm+gJTL0lHwWpSDy6vxrVuP3t1/Fz/Y/zd7j+3hb3WP67Dw/OTfO3VWfgpHg2b3P89HtF9GYHmfydIGRW97Ew3tPk1yzlsxUkWvXdDOVzPLNI8c4NTjCj3WPd/Uvxzk+T7edYaC2wOq+ARZzBvlcJ0Ze49brX0tbW55Hdz/LmtVraNSbWFaS7euW8eqrr+a5PXtaBK6Its485ajC363dzNnpBQarc3y2N8fZAD7fm2XZqRofHdE56HioQqAbFoHnkMvnEEJB13WSSYvAlzSdOtlsO8NDIwR+iKoJyuUi3f2DBDJi9OhRbpg8y11BksFIY3vHBrqtBd4vs/TfNsLwkQKf7Ezzdi3J1hvXQyLi8LjD0WbIFbrFXZT4s9V9LMsM8vnqLJc4Nts39vG6uTNc17CgEPCUbvIdWeXp3fux7YCG7dLf281P7/s5Tcfk1ltvfClqvrUSpv5f2re/1p3nLVu2sTB/DtNKMjE3xzvf/W7uvuc7FM+dIwok6WQCLwgJoohcJs3UQgHdiTEshqJhWhpq4LKqo525uSlWbFiPpqpUqxVy7VmmKjPUazGSTteN2JykaNi2i+c1yGSyBDIik83GEaVB0DJeRMzOz1Gv2QwNDeC5Dm94y20c3reHi7dfRL67h3vvvZdly4aJEFhmhk1b1nB07/OkMjnauwYxLIPy4hwSwfzsHKZp4jseQtXxvIAgajAzOUEmmydlabhaDaO9GyOdRZMiDkkJI0QYEKGgtAx0STPPw4eOcPFFW6iVK/i+z8LCAolM3MlOJJPxYeV56LqOIE5uXNKL2Z6Doikoaizyj6RARaAnEgRBhJVI4fo2jt0g05XHPBtx1TXXku4Y5qF7v4XWMgTU602iyCf0XUwzEbvhzQTNljHxfHpVEMWj0AtHJjIOSVgysiDjAaEiBGHotzrb8UsD14kNWrqGF/hIP4yvO4pASBz7V6N5/tRnDdIJhdFxj0olz2/ffopMoptKqURvv82G7Rv52zsKqIxz/y/ezM033EuxJNATFrmeJInMSULXQ4tMLF0gonjE3Ji3+MhbDuEqAsQot5zO8vYPFNiys42TZ23ceZ0r3zDCb757L4uVTj70p3DJljwL5YPU68vw7Sbz6lEGurrYJNZxbHyCerHCwT0+a7YqNBsK7VaTxx49QqhLiodPMDw8yHylxBNPTPOqK7fR02Gy4fWb2XnxSkIn5B9+9CznCirjsyUQOtmkjhBWiwcckGhNMWTkEYqQ66/ZxXxhgfnpKXQRoi/P8GffuAvUNGHgoppZEG3gL/KlT30M31HZcdllnHrgIaQMcCPYuCHD+999FUO/tQNDmEyNT1OtlUlnepiYmGBoaIhszqTRrJFOduB5MW/UtT3q9VlWLR/g3FSBfKoTTfPovXgD3/vRz/HqEQf3HGSgV2e4tx2Jx+7nDzAyMsIjv3yBpx4/ztVXbcBzA+oNh+uvvRwRJDD1BhOnChw4vI/X33QJCSXF0wcPsmG4F6XSIAgl+8ZeZMf6K+lsz1Oqltmwfoix2jmOFea4Krsaw9QolmdQG1mcRkA296vpPF/YsVvqIiutoi1iKYRDYKhKLDlYeogVAoFE0Q0ULU4KVRQVpIKmanGiGfJ8CIcMYwmAkKBEYcyrFwqhjFrShSBOxWuh7UIEnheb2fwgQlFAUWMtthOGiMjBTKQIQ4nrB6jRS4EepqGRyCRIZdL4noaqG3jNJq4bnxdhGGI7TYTvoWkGumEQqWAl02QTAeONQjxyVuICQVEFjh3fZ1zXxXV9Gq6L58ZaZ7eVAhuGIb29vfQP9JDOpkinEvH0zjBbCD5JREwP8t04RlMKFdeu4fkuqqqyWKwxV2pgOwHx8C7mnL90j4ylL0EQoAmFWiQJQ0hqSswQD3ycIKLhujSD2CuiSmI2t5AYQonDqaII6UUvNZ8U5aW02NY0T1Vic60fvjTmVhQtNu8DMghBvPyB6pVaF47dX5pMXjjFFC3MKaieinvjTRyPNAaFD4pOPlIoSxdNQkpRKYchoFBD5YznkAkjHFUhUHXmPZfNSopUGCJVFS/S6IwqvOVUgzCS2IZFzYiojHTxgLGC350/xgtJg7WP/ZJ3dA3yhc0DPL/vJHdKn39ZvpYzZ8+hrV3NQt2ndGSG5au3Yoxs4u0/f4LvXZZnkjMUvWPct3+YN3SmyPd1o1rw6eQq+pb7FFWJ0aFw+KmIy6er3LpjO6sXp3naTfDFJ54l0Z7F9RpYlsW2bdvYt3cPbuTxxGNPsnygj7FzU+Ty3ZQKFQaWj/Cb40f4SnWOoaxFVlZoLu9l2g74b3mfpKNiJixKpUWIJIlUGtu2UdVYTrSwsEgmk6G/ZzDGTToOpmnieZJsIkP57DTh2DyfdGw+dvWrmWo02eaF/F/FMm+s5lhZnUI5Y/PVwS6+eWyU31Mc7p4Iue+hs/TrFl9MLfDVhMon/XZmFwKscIK/1DVOhSF79i3yi11dqH05vrEwzRsv3Uzq2b1UCrPc+8//BCjkunq4+xtf5+Mf+yN8u4mqvYQE/f+NbCPblsSPOlg+MsLA8AB2bZ53v+l6crkc9z/yLPNzixw6dohIM3HrdUxdpauri5nFWWphQOj4pNUsQyNDFEsLdM3lOTsxztq1azl28DS6KnAcm6Sh09W3ksWFOWYWJrGMBO2dnQjVIJk0adTraJqCVLVWkpNOYWGRXGc7ruti+yFHD+zhxLHjhEFEed+LbNiwjp6BAcbPnEXRFJ569GF27drF6bFxIiI0VeeyK67izInjDCwbxhA6lWaVuek52vMxE7HiBBiagt8I6ejoQDNMdDUed5pGktAP8GslanOzpDraCWWAveBzybbNpLN5EukczfIijuOg6waapSOjKO76yBDP9WIIeRiimVb8cOD66FYCRY0Pv2w+xcz4WQoL8wwODjI1PoplJWnYLvXCLJphMTt+nLQhKBUWGe7rYuzMGLt2XIwTuIyOTnDmxHE6u/tYuWyI50dH8Ro2yZRF6IUslIqYukEynaJZLbFq1RqOnTp9nrDhIbDtOkkjNulI3yEIIqRQiVTB9osuxnN8Tp8+iW/bJEydFWvWceL4KWr1MjU/+E922f83K9R9OoZ76fRG+IefKLz6io/yp3/9Ga6+cgSCNirzKj/42iWMTa7hwLMHMa0sgRpiajof+I0O/uSvjvLxP83wj18zmDzjoxJhaVYrzMCLU/qiNN+/x+Y9730/jebdvO9tcOU1bXz5eoexUUgkQi7ZBnNzc+iWTqQVSWdz2HYDqesM9veCEZBK52iGUyhGjmQg2bL5OorrZygeXeDP3z6Bqtrc+bn3MV9Z5PRogfZ8komZErlkmhMTc3z0/dfihk1OHC/zwpECD+4ZRdcsSoUKuqUDAaDg+yE7LlnPC7ufY92qboQfsnf/GW572wdBTSIxQU3g+wGKqIDv854PvIm7Jr/OfT//JYGmsba/m6mJKb7z8HfRcxmevOtvKZUCdKVJoeywYnmGns7tJPNJTh45wdptG3nkgce48podzEwWURWV3u52UskkdjNmKpcbi2jJkN/6zXdRmD9HtrOdyHZ4/rljrF23ms1blxHIkE2b34TdsLE0nX37jtHWlgcE1UqR0JMkkjqvvvpiEnqGE6f2s3l1D1L12X/sMDWnRueKkHroMl8ap1mKUPITnD5X59Eji1xy0Rnu3VthZ3YVzfQiebMDp2n/SvbuhcVzGIYthKWLqesIGaIQYehaq1hRUYRGGPkIVUUVcUdSVfXzxZ2qqKBKaKEFvcCnlRmKKmISQtTqdAdB0Ip7VhCaiirjLnEsq3upqFtaQRDg+gLd10mYeqy3lhKjVaRDLL1QVJVk0iKbz0GUxvVsQj/X8rnElABF09ENC82IpX9eJNFsF0JJPt9CHvouoQpBKFs4UZ/Ij419jUbsj/E8jyCCMIwbCRvXrqKrsx3L1LGMOMxEicI4DEZZ4tXHDYz4+uOfa2G+QKVS4+TENI7jv6Qx1zTCwItlIKqOjDx01cCNHOphgJQRSUPFROJ5HhW7SSgFjh8TRjShEBChRMR4OyLcMEJjCREYF5ihFEhF4HkBfkuGJ6I4uOZlRaoULR11RCQFYRT8SmQb8PLRuxAvMcQvXEtjej2QvLs+x4vpfuZllQaQEIJQ19ECn9WpNJN2k1CCFkZouo4aSeq+x4huksAnCiQ2IZ6mkYskkRLQrZos09M8W5/FPLnA3fo56q4kWj1Io1HjR90aDz53gn8T/VRyTR50UlwfDPK3B0/xxUGTtoJLVFvgmUMz/MtwFRLb+Zef/ZjLt1zLzsoUyeuu5G2RzeQv97G+vcqL903xtaEkd744y7VXXMVIl0fuied4as06Jp97jkFh8qX330C0agWXFGY5M1PESutoi4t0JXQ6V69EqBoNJyRy6+xct4xnDoSY2Q6+XjqKs3IbTzfnCZouqiqwbRsrEZLNtJNKJzh16gQDA0NEUURnewf1ep2IWLamp3XKpUW6u3sJHJv6k4/y6JXXcs/0BO+RKX559BRPeC5Gz3oa586wZfkgnx2+mOjQWd5Zm0C5fhlfrsMLo1N0rL+EDRsDfh4MYZkwf8rAO3UEbft6Hto7zq2vXcsfHj3KyJMGlazHtUqS4/c/yztuu44TtsPu3bupNF18PG547XX88R//LgjjvKrhP0pD/7P1a10879l/hNe9ehdHjh2jr6srtpdFIcVSjRuvuphjp0ZZt7yXjp4+nt93iMriAu1tOT70G++iXCwBkGnPszA7w7W7Lub46VF2bNnA1PhZguIsvpQYpsHY0XP09HRh2zYdqQSeYuJFkr6ODqq1CoZpIqMAZMztFKpC3+BAjGHJ5iiNjyGTFhs3b8DQE0RIHDfg0IGDmCrs3LmTwswEu59/nquvuZaf/uReBkaGwHMIPJ9ysYBpJhAyIpPL4bQSgFKmRUd7F2HkghZLGRRFwTBi17SVSdMUgma5iOfYpPKd2HYFx/dwFwpMjY7R0ZmlrauHMHCx9CQyeknTqOs6RD6qbiHDANfxMZMJNN0gCCL8yCFsCBr1Gh2ZHGOH9zM6NknKUJgrLBIheOSxx9m8di323ALvettbsETE48/sZs+ex0lYGd7/4Y/w9ONPsHHjRpSwyeSpo3Rv3Mb46ePsvPZVfOFLX+Lzn/kLHnroIa588y3IyGekv5t3vutt/P09/8zqFcMM9I/wP775LVau38hAR5YHHn8G4bgsX7OKVMIiTAcMDbyKjRvW8cuHHuGRZ57lS3f+HWXX4amHHvzVbN5Qp1qfob2thuPluPfHf82Nr7mKg0ee4tJLb+aNr3mAvP4cfT0Q6W0UvSq93Tn+7C920FjYj5FeSdicRrMafPpvVvJ/fuocXmijKzpuGCEVDU1GINPc9sb/iRcIQgRPPlpi29oSqgbf/9GH+OBtX+ehA2/m+Sd/xC9+WmHVsi1svPQ0p49PsXWjxDI8PGcCjdU88JNT3PzmLp478K+0rVmBfibPZ/5kHem2LJVaFTU0SaVUalWXZCrB6bMTrF67glB66JrGxk3tXHLRAG+/6VL+j0/fg5lsIwwEQRiiaWoL/WlTKTYpzFaYn1mks7edro5OsHOSNhEAACAASURBVCXClAQtmksUNVAlJHMJrrpuK+uWtzNdqvKBD7yR1NabUKwcNDwSQ2nmyieZGrMoV0pkM+fI51LIOUlXfzeT4xM0bJvukV4qxRKhp+H5gkO7n2frxRfz3LO7ybfnuWL1KiYmFpienufsxAIb1g5z/Q27EEJQKdWp1+tksyH1sk3UZrJ6zQoUJaKrcxjXK9NwbB75wW6mZ2e47nUXceJogVOnFti163KW9e9AURqETQ9dDTC9Nnr70+w5Ns+qwQHKiTO85cMH+fJnlpFJBnQF/aAJpP6riZZfMgfGRZ0kCmKjdhAELUKEiipUECCUGAunCQUvCFCFgqXrOC2Sj6IoqErLlNzC2inEkyWhxHs2bBWLS2sp8jlCEoWSKHwJgxcXd3Ehp2px0qjnBjhKPFlIGwJNFVit9DwZRtiBS9pQMLXYaBz4PslEGpkE3/Va/4+ITXxmohUWolF3bFQRyyfymTRu00FIBdF6KFCCABHGTOfYzC5i1F4Qns+3TSQsVoz0Y+kaUeAhpdGiaBA3ZFoTsjD0W9JAl2q5QrNhY9s2C4ulOGFV0c6/N4IIS1PRVQWiMJYYBj6uVFFlRMY00KKIMIyoBT71IOZ2x6gMNSY2nZfMSPwgJiAtUVDClhFQkbTQegpKGKPvIhFPCiPZKjRaPFI/iIvWJcTzf5TzvBLr5TXyS7i6C9F1S6jEpe89qbGzMsZ9uS6Slsmi2yRDQHskWWw0sRSBr8XotEYQkEIlUFWSYcCyVDtH/QU0CW1SktEMRCQ5JwJqjQWyloEfQuhU2RqF2KMuh4OQxWKBbcnlzKkJVvfDJ/wmVWsSM1KxqgY1XxIUC7x6/RyPX3UzxhMn+YCaobrwItU+wfhPn6H7YpP+S5Zz23P7+X5PJ5ONBvm37GT1vbv58dphtr31dfzi/sf45C3r2PFMnjvue4x3FFx+tizH7zYDlvX28cSTBzG7NMJA42ypSEd7F6qis27TNg4dm6A7nOKHpsFb33AD3/7W3VTrNUZGVjM/P8G6dVv55nf+kde95jqSySTj45P09naSzbfRNzDIqZNn8DwHXc/hhCGKAo3pBf4m2cPPnjxIV0LnCbfCZ1SDL+RHqNdP89MVyzg4P0N+PmJVSiEdKrx33uCeqWm2XLeKM/ce5BbRyWMr+1hwJN2X5jn94jM8+9wEP0iUuKGxjS81OtmXUbl5XTcdTZVPjs3SnDzH6KlJtlxyOetXruaBhx9Etin84J9+xMrVK7hsx1X/j16r/2yJ/5UXv9Jr88oV0tKhLZend6iP7nSShKljJBMYioJqGExNz3PxRVt46JePcOXVr+bZPS8w1N+HIUI6enriwJFmEyudxXUaLC4WKS4UcMMAz44PxVBKhKphWgm6+0aoOjbbr7wmHv8HPq7tYBoKzWoFQ9UQSOrNBitWraZer6NI4jhXRUFPJaksFCiVF0gYaSIhyGdS1CoVFAWuvu71PPLA/SxbuYYXX9xPT2cO13ZImhbLVi7j8MGDpHJ5LEOnp6OderNBZ1sbZiJPtquTVDqPoqlIIeJ0Qk1vZfMBapzodfbwQSZPn8YwVdZt2kQ6n8OxbVQtPuh03UTKuEPj+gGmmUDRDEIBQtFRdYNysYSmKTi1CnZpgd2PP8LlV+yiWKvzg3vuobe3H02JeNMtt/LMM88wOzeHaRggfFYPryDT1UlxZgJFNWn4AdIPMTSN7u5Oas2AY0dPk+roRBWC5StXUikVKRdLnDk7ytvf9iYe/ff72Lx5K9XFRQZXrubg4UOEYUgu38GTz+9DMwxc26FYLLNs2RDSs7Edj9Ub1nHoxecZGh6mUKzg+JJfPPLYK279/ruv6nLjmhUslDx++7+N8ZXvroW907S96koajTH2vLCe7uF76c4sY3J0jI9/6Eu8ZtPvInI9XLr5Ih5+8SF0U+V170vwuiuu503X/wv5dJpSTUdqRUSQQSouRCqSEBeDd79P5b4fV2J/FnDFqyyefsxBM3QUPc0f/2UbdXuUf/g8fP+BV9Gei2jWmhjS4t5fHGBu2uYP/+AmPv+lRe792m7yuUHu/OBqNE2jadusWbOOgeEBHn3oYbK5FJ093Rw5skB7tk4qJyh7KVb09+I3A6bmT/Pjn47z4MEqiioQukRGOisH0lSqNo7nMl2v40uFb3/2Fm65/TdwZR4ZhESqgfDj/arqScLSEay+LaAaMQbJc5CqgXA9Ar/EsSf+mal9x8h2Zeno6KFUrBA0m+w/eoxlI4Os2bAezQjxg4Cf/vOjXHrppXT3pqjXy7S3t2OoaVKZFKXyArVmDUUYeJ7HYH8v1XINz/aoNpu0d+RAmCh6E78RkurMkLaSuL7H4mwJVKgUI3TFI9eW5/0fuYc7//x1REoWMxHR3dVLGPkcPnwQRdHobOvBC20UoZHNpWnWayRSSaoVj0cf3cv/fHyC8V8BtuCWqy+RF3Jy9fOmK4FU446z0uLlLOlKw8g/bxZUpAoiiuOvW13KpZ/iwvNmqZBZMhouPdSf5w4voe1aHdclyYhEwdDja4pa2DzdUMmYJtl8jmQyiaa81KUOZUS+LcPVr7qUgYEeJCEJI4kX+LGRUY2vUwpahXpcxLqBj+v4NJs2hw4fZ262GGPhwvg6bdvFbumiPc+jVm3QdGxqS7rnKOTaqy/nta++glw6g6YrJC0LKxFHs2uaBkRoCoRhgF1rUK3WKBQKVMslFuaLTM2WmKl4eGEUF7MKcZqiqsRnmxdgh2GsB5cRKVWgayKeIkYCR0pKTfc8s1tGcSBViES0usMhEk3E94ylOHRoTSBELF2I4LxUAxmfN0vymoh4QhFEMapUSNAUhUPnFl7RvVuqO3Lpui9MyVwqii78WtpzEO/rDjPk/vwAVV1Blx4dgYZHQKRpsTxSFdQENP2AQNXIqQIZggh9NEUhI5U4sVCozMsQlXjelhIKTSSeZTLrBkxkOpnbeTnVUyewzpxgsruPnw9s5ZbqYXaMLXB/Ms3yhMJAVYctm3DnRvlqNYdfegGRgW+k1/C5ZR2888UxRkd6eG5ZN6VFmzfVijwx1MPhJx9hUHTwx6mAieEtfPRUhddea6AtGyB46hh/eHKed9sD/ME2nb94cT+7tl/DbfseZ1qYVHv7WLduHbWJM8xPjvK57ddx8b9/j6s3bCS0DOyaTYTk9a+/gS/d9T/47//984yOT3PvD79LNtdGOmGRb+uIUzrDkFQ6jVAkl120g7GxcbY+/Sw3BwF/tG09z+gBhRmX34oCfkwPhVVQ3LOHNR+9Ds5V8eo6smjw2uI4Xzsyx6bfegMNUSKVSnL7lx9kk5vlJmWSkU27CDcOkzg5wdy+M+gbVvDVU7MkAoduJc3d6/Oo2U7m56cxrDS1SimWzvYM8eef+j3Wrl9DMpl82SQlnzL/S/v219ow+Ccf/4M7rrr6Gp7f+wLVWoOeznbac1nm5mfR9QSKqvCut9/K448+Sn9/L7NzM/hOkzD06epsI/LDOJFQQGdHO83SIl3teRLJBAESp+HgeAGe52I7LmrkU6uUqcxNMX7iCKlMjsLiIqquEgUBuWyexVKJZDqDqmkkkmkMw8R2bDShUKyUaVRqKEJBVw1836evv48wCFBUnXKlwmJxERH6uM0mq9auJvQ9zGQKqShMnztHW3sbgdtk47ZLMDUDRwrGz4zSPTBILp9HUVUUIVAUQSjj6FWpKvEDgBKPHwyhsOfphxleNkLv8DCeF5BMZ7GbTRQFhKohFEHgByiqghAKgR+bZPzAx23WaTaqBJ7N/NQU46dOkDLg5PgUs+PjnDk7zc8eeBhDVzlx/CS93Z3k0haWqZDNtrFy1TIWpqdIZTJ0taXQzQzzs5MoocPFOy7noYcf4vSZMzSrRQIpeOa53Tz2xBMcP3aMTVu3cOTQIZ586hl+ev8DrN60nrFTx0hns4wM9LJ//z462zKs27SR2fEzFMoVLr1sB+fGR9m2ZSMb1qzm9MmjyMjjkkt2ohk6N77xza+46eqFF//qjrpdQqu7/Nu/BgyuWc2B+8/wxbtO8eMflpH+DFsukrilKpYR8uyhB2hb18P165M8Pz3K1TdvJ4pC1m0OePSRQwR6wNlRyWMH3sA/fu0IquoRRBkkLqCSNAKKs0n8qElbrpNPfy7gNTdsIpkrcMu7hjm21+OSq6Z49D6LEwfhO9+cpKfH5w/fdZo1V52jfXQz5aTN9vyVHLrnEeYrLv9033VsSV/MqdHTXLFrJwcOHCNlWdhunePHTtHb30OjcYqrb93M+HyI5oTke3vpbE/S0ZFn+5YhfvOdW9i5VqfLbLJYdBmdLuOFCtWmSwqVNb15vvjdv8a2I5QwjA26MkJRDQQKgZyiODVPpn8dfmWRKFSJQheMDAiJIk2yvcNkuxLMj55CUXXae3P4yQrduQG6+3rxApdlywZw6xo7r76IWnURoapIFPoH+ygWfcZOH6CtM097vhvbbyCjmPsqNUm+rx0jY6JoMDk2g2UmmBgbI3BC8iMdLIwXKBTLDA300Sgv4hFx6PABrt21js7BNrLtCp7rIBTJ3r3Ps3XzBnwv5qfPzCywdfMWFotzIBXCEDrbOxgZ6ue6HVtY85q3vuJ7995/+uYdsdEvvjdIBJGMzcNqywOBUFCERFEFiiriopNY46yoSsySb5n3RIslLGU84o9keL4oPs+av6DY0VqvUaVEU5TYwCXjyGxki9RDhJAxFlNRVYQCgRfgh3FwSMoyMHQNZIShaViGxtBwP5lMCithoigqhmFgWWYsINE0VE3HsBIITUUKFUXV8AOXUrHMuakZGg0v1jr7Hp4XYjuxttltkTY8Lw7KUcIIQ4lZ/jsu2UJ3RzsyCuKxf8uYpwjZMoDHgSpu08au1anVqjiNGtVqgyCQLJTr1ByJkJJUQiWhaRiKih8GNPwQBGgCLCFJmzoGL5FCFAUSqk4gPXQp0ZGkdB1NRKQ1FV0VaEJiCEgoCroAQYSqKggkiDhuO4xkS4ahvqy7K1t/gpZnpTVaOk8r+Z2P//Erundt17/jwsLZC4P4ei4ols/7aOA8bjDWrUsuesfbOfKNvyejGTTx8QSxgVTRGFYUnCCiokO7YqCFklQUZxToxJ9nUtepRBGGJjAQ+EIgAaFAp1DJIenwm3SfPsn24jw7dI3XODZfmzrLyobk8VBDT6VZ1CyOyYDhy1fT62u8Z0hiDQ9hGYKHR6eJzqUZG3L5xJmzdE2Mcmc14ivtEbUbrqVv9wvclEhw1xuv5CO6oDC3j4GOTShpF29eY+f4GChFPn7zNayUSS6dmOAvE518XM3j1mtMnTnIiVKZa0KV59oMdmy9iH977FE6cnnWrF/NNdfcyLXX38SXvvBZRvr6+OG9PyaXy1Kr1BhevjxODxaQzWYYmxhD0xQK5Qo7B4f5zcoZLt64mYOH57EmTvL5LZv4o2aS4sosZa1B4mSdiRcnqZ0oYG3dRuX4ST66eoSuTSu5+LEnaTw0xqsSGTas3QJ5g82bO7n7nEff3lFm61UGV46wbuwwRzU4ubaf++0yoW7hOg659i5cu0YQ+ORzbTi1eT704dtIpbuRMjpP8QGwDO2/tG9/rUNSPvSRj3Dk2DGSqQxnx8dQkimSmTSJRJJVa1YyOTHBg488QmdXN2dHT1NcWKBZLVOv1dj7wj5UGZHMZWhUykyOn41xQorO4uIi9fIiVsJgdmaeStWmUq4ys1CkWKlQa7oUFmbZ/eiDhK5DvVrD8VzKtSrdfb0YyQSd3V2cOHGCc1MThGFIrdmgr7uX9vZ2TMtgw6aNXLZrJ7VSAafZZGJijLZsG1oUkMnlKVRqpBIWmVw7RA5CKPQMDZFt78CyLCbOjhNIsDQdPZllsValUq3G0HzPo14pY+mxfEMVAiEjArdO6DpYbW286/YP0zu8giASKJpKFAWYiSReEMdNRBFITTnPUoYIVUYQRSwuLpJLWVQWC5w99gK1udMszk6i1ao8efAIzx04QN/wAIHQOTM+zi8eeJBI0QgDh/WbNvLUE4/T39NNs1zgyKHDtCUVujraWazU+d73vkdhdoZ0NsPPH3ycs6eP8vo3vZG33XoL2bY8P/zJj4n8JldecTlvuulG9j37NJlMhsHhAQ7s3093dzfNWoG5s8fp6Wzjso3LIPTo6uoi8F3u//lP6e7uJmGYPPRv/8rc1PivZO+6xy7nox/Suf3DDf7y4xexdcUM3d0G77n59QgRcuPNl3Di2SEiNcFiXaEjl+Ta9WncDoOnnmrwN399ilwmiSYClq912LJlAPBJWkd4+sAW9p25hkMnN9PRHZFOesjI5M+/UmBgEP78Tpexcx71xgm2Xq5w8dYtfOaLBW5/76d46JchQgvQ/Qxf/NMCdmTwhdsFV374Fq67YTO5XMAbbryKlSvhZ/c9yr/Ju+jsyfKDH/6Mzq4sc6U5FgpN1q/fTE97N6rSw/j+Gldt28xrP/AWPLmHz3zhyzx04CfMzE5gO0VuuOFGPvyhW/jOnW9l1wqoeHWaqJQVn4/94TsIGw2SVhciKqAERQxdQ/EXEaKBisnb/m/q3jRArqpO///ctfaqrq7et+z7ToBAIGFHRBARQQRBdHAbHdTRcdRRzDiO4zijo6KOCyIouICCCiqybyEBEkL2rdNJ72vt213P+b+41QnOq/m/YfzVm36T7uque3Lvc77neT7PW27Dyh1Aqhb4NdzKDKp0QESxS/3o0iXVOofWli5c28Gxq6h6jmr4BN/98b30dS/mycdfxJLT5PNFHCVLuVagvaWJifEcpdIgXX3zmJ6exIwERJlEOM7RI0NIT0MXCortUZ6p0NmWppi16OnrJh6Pc3DbHlQlRHNbksP9wzz0yHMsXrqEFSuXsGxZLy2pVhQZRtE1agWLJYuXkS+WsF2DuXPaSSabyGazNKUyAFi1Oq5rY/tl4qn/i6oJcIWK5wfWitk2PeAkY1jVwNBOFVJ4bnDUL9XGUbjjIEUwvBEEwmX2ZwUh4VO+1MAComIqChFNwyAQfYpohNEUBUVIdCRhLbCMGAroqoauSXRVYOgqYTUoaPB9n2olsDxIX+BLKJZrJNIJIpHQyfdUdA09EkILhdFMAzMUwYhE0XQTwwhwoEEhSoRyuczkdEBjKlVqCFeA56LJQHxZtovnCnRNEtEV4lGdaNgkHAmmluVSKegrMHQ0XcH23IY/2MNxAgydY1nYrhMUWJRrhEIhIpEQmh5BU4K/cXZSbHs+lteY8qs6ISPIwTiOE9zfhYIQIFyB5diYEmKGTlhVMBpFLT7yLyavnhRB+UzDQiPgL1jNcjYo+joerkrQZqgTTJo1KTE07S880W/ka1YMz55iKEKeDPr7vn/yNEN4Pq7r4zk+jmVTr9Yo1+scb+7msj8+jLA9LN9EoiJ0nbzvcMK1mZE+cVcBz8WVLq4ig9PfZByEICIFUVPHE0EhS0hILCkwpEndcVElxFFok5IkEFI0HCHZaNoslBUuU8vIkEEqHkbxa+x6Yhs7B48zXJvmVT/Fm6wopTXr6F3Vwq6JOp9dthZ5/qXUuzIk587n1se24V+/kV/GO+metHntT0+hz4SxlApff9wi/NpxPDPOwovPRtn2GjTN4eFaiWUVk7fMaeKJhUmWh8OcuOx0bsPi167Nd7/5n6xeuYRNZ67mxnf/LXa1zPT4MBXLJdHagar4rFt7Ou95782cefoZ+J5HrVqlUq7R091NIV8hqYXwmmNcU2/i4kMnuGNNN3M0g59HPE5fptBj9PPok0d4cH0faz59G/+05iwK/aPkjx+ksv0EH/7jc9QcjQ9e2M2umQgzB3fwz4P7sY6W2VcvcxdlVpx/Gp4XpcPzyCxYSi5v07V4OWYiSVtXN6oGtWoJx3IxDYV/+9o/42oJHMc6eeIlpeD/z7L9q548f/hDH9gyNjLKwvnzqdWrHO4fIKqrzJk7j9GxaXRVZXJ0hIHBYQaHh3nnDe/G8QSZTDOu75FMxHnu2adwfMnyFauYyRdQVQVXSNKJNKNTk5TKFabLNcxEM7liFcfxqVkOim4ihUb/8UHmzZ2Pj4IZCiGEpFqtUK2UaW5pBcclHI1QKBRQFYVkMkmqqYmR4REO7NtHIhXH1E3mLV2G5zgMnhiipb2VarHM8tXrePWVrWhKCCOkIxyfjs4uioUi69efjud5dHf3IIXG5NgkxWKZTEum8QBq0DI0HdfzQfgI10PRNUKhGCeGh0i1Z3AtG9e2KeRm0KVE4ON5Al03kFJBzO7C3QCL5LoOSMHEyBDdzXHquRmUUIQd219iJldEQ+Vt73of48ePsnDpEk5fu4ZoNMaTTz7Jmy67EB+TqeHj9PV20dnZiZSSfKlCKt1MvlTFtSxGskVQVIqVEisXz2N4aJAlC+fjeB7VSpnW1k5MQ3LOeRfQN6eXoRMnKOSmmZ6aQTNiJKJhpiYnGRsdJpFIYBUnmZya4bSVyxkaPE4hO4XneiQTEaxalevf+6E3fHq3+crbtmi+RGKwdds073lPihUdHv/+7b0oQrL2TSPosRKq8FA8F9WPseeVSb70XxaqgLdfN4eDB/cRarJ49Odw7mVlXnkBTowIztygUnctmvVm/uH2S/iXf9nHDTeHWb+5i7/78CZG9nexcIFCpbCcZFOJYv0Y77vJYsXyItseHseX0NyqYRBGrcdQQoL2JdswdZ9y6xSjB17hvl95vHLQ4tLLT8OuDdEZmUNnezv1Wonm5iZSmQSVSpnFS9dSz0umpqcY3DFMKNLBho3LIS9IRnrZtes4CjG2b32RTZs2s2HDPK7etIAHHt2NrcCyOSlO37AcNRJGdStUCmNoCMxwAiFsRLXKP37/MW69cjHReApfDSGxUBQNz66Bm0NXPSaLWdTqDC42Y2NTrNu8mbAR4rnBQXZsfQwRd+nt7kZRakip0NHaQ9WuU61WsWp1Ik2S8WMVQokwrusRjZj4rmT9aesZGx3hRP8gTakETW29hJM+wlXQTYgnM/i+Q1dPB/gWG89bhyYlhUqVvo4OhsZHMFWTRDzO7r17yeUd+np7KBctXBW622JMzNRpTodRFBWhakxPj5GMJ/E9wbzN73zD1+7P7/zBFgh8smrjaRJYDIJ6Z1VVGuQJDV8GgktRVEBB0RQMXW9YBAAUJIENQFFAVRUUlZN8d9kIqumaiueLv7Bx+L4fTHJVNQiiIfGFHwhqRcEXCjTCPiDRNBNFUxpUHoEH+KpOojnOhjPX0t3ejm6qAQ5Ta6DyFBXN0DF0E0XTg/uo8BtUhsD+MDE+wcDxEXRFQZESX3jUHZeq5VN1HTQkIS0Q+UHLlqBu2yAh3ZympSVNcyqFpIG89iSeZwUFML5PpVSmWqk0GmctPDd4kFcqZSZmythuo5lVManZHo70EQ22ve3Y1B0XS4DledQ9D0cKaq6LLQRVz8f1gyZEIRXqvgzCjBIczyMwrisIKXF9idqYGvuNMhQpA0LKLLbuf3pDZ08PFEVB0VScRoOsoih85A1G1VUtZ8useH69zxlOWYSkH5TXAPiej2s5CE8AAql61DKdNK9YxPHH/4AmFXShokkfTzWJSx9L0REErO2aotGjhyhVy1Q0tdEO7xFTNaKaSk3TadY0Qp6gWdcoCg9TSDJmmKrv4qKiCod5fhhdd1mkaWS9KEdmZkhlmimXy9QjJvcPZFlfkvxe9Vm7ZD7fzo5QNAQ/T2S4eWaC7y9dysSfHuZXUvIHK8kfhMM3i1M86hs8Zhj8NBrlHUNFuq5p5h05k19Nlrmsdy43vLKT629eQmxuhHd3a7zTqHPeRISfKEnOicWZMeEq3WRmssj7bv8yn/vip6lmxyiOHad3/kKefPoZfM/BceGZp55iaOAwiVgSTdPI52cwjAiLFi/AtV1ywudDw8MsKgn2TRucWLGIT1dqnDtygM41q1h3yYWcnZ/G/e1OwqPDLDw+yvHzN7Hk8jXM37qPm3Sb7vVncfXBQ6xNdvG1d59DuCXOGSWX/nA3dw/u53vxBLVYBtt3yLS1YNfKVCt5PB8qxRz1modhwsqV87js6huJaHFM81Qr4uw9KaRr/6t1+1ctnr/7H1/fMlUokkrG8X2HZCIJngtSUK2WKRSqHB0e4123/A1r1qzjl/fdQzgcIpNOEovEsOoV+o8OkGnOoAL9R46Qz2ZJpJLYlkVTUweWbzM8Ok6pVCGbL2F7wdFbuVyjUCyxcOkidu/Zg2mEkFJB1zWk6+J7ProKiWSSfDFPR2cH4UiYQwcOoKgK7R0dhEImvuthRGJMjIyQyqRxGraIfLlCvZglFk/Q3pahUiqiCIdMVzfJZBpN+GQ6OnEldPX1MjI8Qksqg1WtoGgSRVHRI3EMw6CUn0HTDRRdCx5OZphoPIJnuXhWjcLMFIbwkYZOLNGEZoQCf7KmoiLwpI/veZw4doRauUi6pZPjB3ax66Vd/OjHP0AoOiNDw6ihCGPjkzzz/NPULYcPve8mnGoRq1bi8ivehuq5uK4Hqk5PXy+jg8cYm5lG2ja246OaMY72H2F8pkJ2Jke5aiE9m/nz5jJ8/BiFcon+I8dYsaCDarnErl2vMDw8QkQT2ChEQmHmL1zEicHj+MLHqlVIxSIkkkmS6TTRsIEvPHxf4gsXFUmyKc1br3/PGy5A2trv2nL6OoNbb4bzF8zh2redzw8/l2dBQnDnA+fx4VsPs+ycCN/eUuPmd7yf8UmF7903yKOP3cLxE/v47R+GeP+H+wh5DudtTvPw/TX+7fsLCSdGyGebufmKI5x9QZYVK+ex/fmdzF1QxwxViURdakwTi56Frkv27h5B0cq86dwoQ/3DnLketj4LptCpVStkOjQmczUOHrG5/HKdSD3E0k3zOf9Sm2UrVvPQT7ZzxZozsGsGVatAKJJkYnycVDrNVDaHrtbYf3AfZ56xgapdRHU03JLLnDlLqVSrnLF+Hb/73SOsP7+dF5/aR3dHuaP54AAAIABJREFUN3tf3MNX/vFKPvzWNfzLlx/k6otWc2TXC6TiKpZb4+4v/heJTBEKZe7+3nd57+YFHDtaY/vTv6bFrKBXR6jVxvCHjjA2eoSRvVvR8yVOHJ9g2YoFLJy7hON7j4FeY+XKbv7tK/186JaL2N3/R277xh7md5dZcNZynh/cT1HbRy0iWNa8hJp0yWerLFjQi2Ya6AbUrDK6oSEU6GjrwXXzPPnEM6RSabKFGfaPztCcaGZmYoxkLElXRy/Dg2O0dXThChXdEwjgxMAwbe1ttLYlME2NdDpOdjpLteKgSoV6zUaqNp4lGRgYIplKMTYyzZor33hG+W/uvWuL9H00VTvVCjiLAVM0fAGKoTa8sWrDV+yhagq+8FGERNUIjv+lJGrogVXBF4R1IxBXIrBhhHQdKQW6qp0kI4iGABIiaAm1GnhQIQLxralBBXwobKBrKoZm4AMoDQEOeDIQsO2dbbztLZewZPkS4ok4mhFCUVVUfKT0GzaFRvhRgpBegxbh49oOrmNTKpY5cmQIz/OoOwGvHwU8P7BD+I4HSErVCo7nBzYOX2K5Ls0tzfR0dWKGdEzTbFgiBOVy6eTE2XNsarUahXwxaGI0VBzHZXQyx+h0GYmKbgSEJCED7rXvBxxm6UvcxlRVCIloVHKjBla+wCMu8TwReJJf19YoGpxs6Ut8GqIZ8IWK4/oouo7wZ1n/gKKgzwYBVQXP9wIOtxCojU2MjoLe4PB/6BOfesPF8+sLL2a/BpPo4DNTAK/x/J79O1RF4jku9UoNYdlYzW2s/cB72f2Du1FkcPpjmBrTvo+CJIxBXhNoiobvBUVlhqJRFz66qlITPrauoTkeVd/DRKUsBBJJs2aQ9yxsVHTpEVPAxyMhDTxTY7VT5EBnB62RMCVFDU4udMlTmkc4YfD00QEWTwySpYNsNMJbhUJy2uaKSoViuoXzwibDJ8YZKbpohGlpSTOazfOtlWkUO8bPjw7zi/VrSfglbjytg3B7jAfvG8XSe9HzQ6TGZnipp5s7JiZ5Id3OcK3IxzsMXvzs17lxwyImn32W56oWzvgUVcumb8F8jh45xEUXnc973vdBZiYnsXyBYYZpTqWZnBrBdwQpwqwaHeaiiMPGhWnOPEvnoiaJPjcBdjvvH5zhgTGP69Y2sSjaRPLypdz5xEF+MDrNT+wKn1m2nvKcpTw4Os7G4SLO48/TFNP5/EujHF3Uw/66QT2kUqgUSTc3ofga8VSaQr6ErgqyuRzxRBQDly997Rs0t7QQMrVAzzXubYYRNKuGjf+dbeOvWjz//rcPbunqaGPX3v2cv+FMFs+fh2KGGR4cQkUSTqSZmpymOx1n6wvbsGslkvEE2ekp5ixaTjKZ5qb3vJfszBSVSoViqYRl1Wjv6iE7OY0ekvQfGaZoW0xly8Hu3YWa7VF1ggDE2Og4La0ZWlsyOFadmuUQi0dwbJtKrUw2l6OjrYt8rkA2m6Ul00I0EqVYKjA+Nsbx40PkCjmuf+e72P3aq8SjUXr65iNdG9uuUiqW0BRJuWJTrZdJxJtQVZWoaVK2PMxQGNU0WLZiOUf2H0KoOlNDJ2jp7CLZlKaQm6GppR3fczFMA8MIARqmqjAxMoJbyuPZdWwhicebMI0oqmFguS66VNFDYUAl1dxMPl9gfHyMkWNHaIpHeOG5p4kmU+zafRBTD5Or5DncP0a2ZrNuxUIGB/rp7e2htaMNXJvlK5cwMjrOHx58gInREXbvPsDqNetASsYnp3CEYGY6S1dPH5MzOT7yiU/x/PPPYpiCtrZWRsenyM1kWTi3leZkGNOMUa6UKGVnSKZbiCfi7O0/QSE3g+dBrVwiEgvj2YKyVcFzKpTzeZqaW9ENBdMw0QyTK6676Q0XIH/44X9tmd9eQ/FbGd7XzhnrDrHnWD+rUgu58nMvYPkKn/rklZx5WZ377n2B3/35OFKHHc8c45obOnnrO6pMTmT5wdcEL+yo8pnbW+hZlKIl00ki5bH5LR5/c1MJWdnD1m3w1R+8H9QhotpCxib7qVoHmS4cZPG8FsZHPDrmRLGE5MtfcHFNcJUMdV8Bv0zZg0IBLj5tAUWtQqbFI9KcZm6rxf49KTZt6MKvQFNTgv6j/SSTMWLJJhQk4zM2fV1Rtm7fiaElMGIeqmISi4VRTJ1sdoZEMkpISbFmwzIqVp2+Ze2MT0wRSSe5+vLTsewsC+fNY+zAANnhGlHTJhOLc+LoMUJSYaQwwpymMl4VWhf0kmxpo3L4BHv3vcjC1hS2EiFbGmTf7kO4wkf6UC7UqZdg6OVx3nrdCsy6zdKuczh/RQ/z+pYStVtpqk5y5vzNWNN1CkWbxact487v/ISVq5eQy+Xp7urCsgPUWqlcx6lVGRxyeHnrLs45fwPpVILudJx8fgqpeOSLZeLhJLd+4C76WttQNIldcfCMGoqqs3TZWoYGBpCeSbVSIZVpYnJ0hv4Tg/T09KEqJlufO8jixYt5+sU9nL3pMvrOuuQNX7u//PEPtry+XOJksx2BeNV1DSkEGgqK4gfeZ6kiEURDIcK63iAySExNRdf1oMJZD0Sq1shsaEpw8qVIiVQUXM/D8T0838eVwbTUcz00KdBVlZCuojaIwlIlEIhuI9gYVISAqiKRaOgkmlNcdvF5LF66AF2RCMdGlS7CcfDtMlalhPQchG3jOnWk7+ML0fBlCzRVR9dVcrkCRwcG8RwP1fORvosemLBxXJtCtYpl2ZgqCBTqth+0V6oK7R3t9HZ3Ek9EMU0DUw3waZFQBM9z8Rt+aVUNGvxUQ8eybeq1OsWSRaFqYxoqqgyKUTzhBaJZVdAa/lpdAUNVAiRgYFUOgoG+QPXlyWZESSCuRUNQCxlM3hVNxW70Jdi+jw8IKfF8EUxpG5PpwMAbiOXXT3R9GRR2+cLHUIO0shTiDS9JKdesLbN9BSfFsy+ClkoZ/E6zGMbZJkjhekgBmq4ihY9fraMCNVeh84YbOPjQ/bhSIBwPTdWoS0mToaF5KpbiI5WAgrVIj3NM1olgEJGSiC+wNUlIgq0KVClJagaicf3jKIT0oIFXMU2E54ErkLrB0kKOWHaKNTZEytCmRVBUk2RN40yrwHNScJoZpqkzzYPC4oflCc6VJh1TFZ5mmq9f/T7Y9gxhu8Z716xh/8QBrjg8QvzdS1jwXJX/VqtsUqOEVp3Pp/6wj2vmQLKnncjLWbikiz1b97E01YpbLPLF9Rfzlv0HuX3uFfzLi9v5m3gS1E4+eewoj8UzvPnv/oaZgRE6NNj+wmM0JzN0NWeYLBaIN2doDgv6mjqYKRZpGS3xtcVz2XCoxCeG89wvkrxn46X8aXCYJwdr/Oi8lZRrNqmeDr59QOcGMcHNnX1c86ZLiFdm2PXr3/HLRJSPLOliudD45qEi289dy76ho4iIzszoENJ3qdse6AqmpuN4HjdfewVnrDmd9atWcu6FF7JoxSqSsSja7L1J0062GAOE/pfi+a+attGeiMvVq1eRy88wb948msIGzU1pBk4cx7Ydjg6MMFXIs3b9aQz1HyZi6FRKeZozTcQMnVAohGFqbNx8CeOjE2QycTLpJLViEamZPP38VlKpFGXX54mtrxI8Dk4FDmY/zKgKuiJJRBVi8SY8oKevl/mLlzA2OcGyZcvIz2RZtnQxxWKR9vZ2ZsYniSaieI5LW0c7B/YfJB6Loikqre1t6KpGfnoKRbrYVh0jHGPJitXUq2U0I4Rphujs6SWdaQ8wSpEwQ0ePM9J/FB9JtZzjkiuvwG5MemOJJF7DZxiOxKhXazz56O/obW8lk0oRbWrGDMXA0FB1rQEtd9DDYeyax+EjBzl8+ADPPPJbvHqFbK5EsdFupUkPhyAwoag6ruuRDJu0JEL0dLdyyQUXcuTofjozGVq6uuibs4TJiSGyk2OMjYyy9qxzaW1t42e/fADDMCiUHY4cG6BSqdDS0oLq1lmybCnF7Ci25bGkr5V0IokWCnNwaIz2ZIRFS1ax9eUdVNUobilLdiaHqSsMDE5z+UWnIX2PjrYWKuUa5VwWGYujSUE43sRPfv/G0zaiSouMRIo4VpJ77jKpVCro0SjViRKP/cxi27Ewbhje8ymLP94PN7317dx371N4uQK/2f4htv/5XkLNHnq8i1uvG6AvBe+4BITQWbKug3y1ytLNC7jk8t0oimD7iwv50AcOk2lK8873uQjdIpU0aUl148kqR3bkqdV99h1wGNkK+8cUepMKI3kDU1Ox/Trf3PJxvvzlb/LzHRsozEygC5UP3jzJtqfey+AzWeKJDC09vRzduwfV0IlGEoQjChNT04QMk0xTGoAjh48xNDrCmhUrmClmCYejNLekqFbLxGMxWltbqFQqWDWVvt7l/Pbh+3jgoZe48KKNrFy2hld3PsOrLx9k7VUQr/ZhLM+zwLiAp15+mPbWDD+8c4ZnD9zC84++wM4nBuhe2cZbzr0aN+vw4ksvs/GsS1A1GBwdws1ZhNrjDO0dIhKDe+99ka9+5ha+ftfd3PjmmxjpfoZVc8/hC//0S75z+99R9ov4ZT+g5xgRrIrHsaET2KLGvK6FeFYFzdARnsKBg4d417vexfDYEIMDg9StKps3n0u2WKKcz1HN15nOl2hpiWLZglAoxP6Dx0ikoghP0tk5j5mJY3S096ErKtlqgXqpgtRMOjo6sfwCl/7j79/wtXvVOafJWQEyW2gyix57fRGFrgTUi9lje003Eb77P7ipEr3ROOc3fo7VYK9L5RQ2U3huAxEXeHvd2bIjBTQpAs+tquD5Ek8G01ND1TA0HdRTNAXDCAWeZlPlvM1nc8HmMwkZUC6XURWJqar4fuB7xA9EoG3VUHWNSCyFYkYwQqGgQtyIYHsuoyeGeOiRJxgfmwmsJI6NoYBGsLHwPQ/btvF8HwVwXQchJKVanZaeTi66YCML5s6hOZPCNDQ826IplaBWq1IrlYPSEdelWK4Ews/3KFarDAyMMTKWI6qFQFVwZPBZ+Z5AFRLHdYHAA+1Lge0G09TZ6zE7ZbZd52SIUGuwsm3PPWnJQKq4fsByhuDfiIZtw5tlIjeuvzt7jaQETcGxA6Rg4I0PaB003veNpm1M5cvyL6rlxSkLh+MEAVXXdbGqtZP+fQgm0bZto6sarl3HKlew6vWgHl5z6Rod548f/zCGJ9EJgrGu9MmYIWzHxkUhHfD50KQKwiWJQgVJTNWp46MKiVQ0NC3wo9umQdkJJs9CBBx8pE8zUAeIRpmxLLJC4GgQ8lXqCJYDJTXKtxSod7azJNrMcafM4PQUffEIfqmEGo9D1OQtg3V+0WnyNq+FzyxN4etj9MS6cHIxInMMqr94ltiN5/DxJ47wjesvp3LnAyRvOo1yci7Fb/yGfz//Eu7ok3z2/qf4t5tPg2fHWV8VbItGyc7vo/zUa3zx7Zs48cJ2WmWVCVchlYpxfCqPrcKqNavZvHYR9ekZ9t9zL5cCl1/3AZ46PMw5u59hwb9/hHIpQiI6xMM/38u3XcFV9WFeqsX42WUXklMkn/jTn/m4pZOvetihOJ/Wc1xYhZdW9BJr7cGv2+w/doTOtgyK41L0bBYtXMaxE0cx1BB1p86Hb3k/hVyeZGsTF152KV0dnRiNkyBFUQJLWuOeApCImP+rdftXHRgMxaOMjo+DbrB/3yF+9dAf8bwq0UQcz3HRTI2mZJKDe/YyNV3g2MQMk0UPT6isO/10DF0nk8mw/enHOOOMFezasYOt215m94ED5GZyGIpE1QyquRyL5nSQSYRRFfk/HhQaNV9iS6g5BtPZEqVCmanJAtu2vszkyBS//MWvqXke8Uw7pXKV4eFhkk1puvvmUShWqNseSxYtJhKLBjd6JcD8rDr9DFQ12PkUi1UK2Ty5mWmaWzIkU2mMSBTHcTFNEwS0zekmX5hGNQ3SqSaOHz0a3ChVA9c/9SDyFJ3J8XEWz58fbCAiEarVOi4B2zlkhnEdDzMcRlVM9h05QntnLz+74xu0JyO8vO8Yw5N5dDNO0fKxhUbVEdhCwRESwwh2dNffcCPnrF/L7h0vUisUKJfLHHn1VcrTg/zmgftZuGAxE4UKpqZTLReYHBvHqtrULQcpoeZ4AXtJ1dCMEK4naYqFqFRq5IoF6uUS8zoyZNp6OHz8GEIxaWtpZnhsmpVr11H3Na5/x6XUXMlMLsvw8DBmJIqrafzg7vtQpI9l/d8UTejUqVseq5ZLJmrT7PmFxtc/NMXtn9cozU9x5S1JvvntRazpew+f+tillHeOccuNrTRrYS5d9X02Nf8DrSNv5rsfG+DLfzcHX4fv/S6MP9fDUsJ85NZvcPGSL7DtP99DWvo89+wAt3+ljZtvjCOrHZiVy6jWVcKGzqqeS+icE8PWHNatbuHNm+Hpb76Vd24Kk1Rs1i2q0//c5/ibLd9hwlMJ7evAfmYFM08u4CvvX4c9XOTY9FE8pchvf/cAO3a/xE0f/ilP79rBow8/i2dHEYbP8eFj7D5mI3yPSbvE/DVL6erqwIgqFGsl2jvnMjQ0xWuvHmL37sP8+bGnGJs4yI3vvoH//OX5LFpdp3NOjU99/u/ZdP5i3rHpVu748RCdzhpe2X+A6y77KAljNQULsvuXcssH+nnTle/ngkVv5fHf7+bArkFc12VqYoZUpJPupm4qcpLjcjvT/aP8+NsvcsObV/HVL97NxFE48xMhJooSUfMwi5DsjGBYHnteHuQjt/2E73/n19x1zwtcfN4FPPjrF9j3yiFaOluCAqKkydVvv5Gx8cP4tkcul0dVDHLZEo88+Hv27h3CEkmWLFlGpm0Olq1SqTm0JTO0JXvxbYtSbpjlK9ZzaGA/RbuCToiRkRyKSPGnBx9Gs/9vGgY1TUMRyl+0DOrqLLAseKmqftIHHQqFAjFCQMLAPzWAEAJcwEPBkZK6K3B9D7/Bm5/1GapqwIoOwmvgq+AoQbtgTUBV+NQcF1t4gSBXQVMFKB4GPmFFEtUUYgrEDYW0oWMKh1q5xtFjQ5SrNuFIAiMWxXYF5Xydaq1MsVhmMltgeqYcoOYqVarFEvVqGbuSxyrmiUSi9HZ3YTsOvu8TMfRggiwEUggs26ZUd6i5QaugLlVqdZuaLwnF4sQTCYQiGrYNDdXQG41sGuFoFDMcxowG7YehWIRQJEpzUxOpZAxfKNR9H0sI6q4LnsQRkprnYSkqdVfgC3D9WQzbKTSgJ3xsx8N1fKQIKCmvDwoKThEoVFXFVBUMVW94nWeve2DD8ITAagQdpZTBlNpxTxaqzL4CQR6I7P+L16wYOlWko+J5DfRegzU+63ENskMN4oYnqddtauU69boTBHfrFm7Foj8cY90P7qUSSeKbEYr4WCjUHA9bVdHQcNECzqEqCaHhhA3iWghE8J6+ogICU4CvqSiujyFcQoBUVBKGig5YjWuXrVvU0RCKSljVUTSFjKoSNkK0qB4fEw63Toxw3pF+zhgsEnFd5mppNL2Fmm6STHaz8fQN9KRiHOyMYXZazJmYw4FHDvLBYp5n9uTIRxweemaIb/79xajCQ5zVi22bFL9+D/G1HXzmuV/BwUN8qUclW2ri0WGXfxVFxOU9/OrRp1l88xJ+kcjzYLvNg5u6+G87x7dHxvlkdpzP2S5X7dzP8f/8Cff8/H4+qaXpN1oZOjHD9QsrLLh2I7XHHyR+7ChWJcaVc5sJl2b46AevJ+6rWKNHGPr1E3w+3syPmhWe04v8OmnQe+ZGftOkMzgxRXM4wqFjR8lk0hQKBXwkzbEww4f20BePc8s738btf/9RYjGN9eecxXmXXEQmk0Y31ZMbfLVxEvD6Kvf/9Vr7a548z+/tlKNjE4RMnR9+71t88QtbiIdDzOvpwIzEaW3v4lcPPUS1ZmP7Al8q9HS20NuSol4usOmscylXigi/Rr4QtD/1dHdiGBrDU3kGB46RSDYRCscBwdHBcUZyRSQKET2YZrRn0oxP5wgbOul4IOwcIbH8IHhomjpCeJx34QUYpk5fVw+1eoVzztrAwf4jmD5EE3HK5TI9PV0888wzrFq6nJ6eHgqFAsVikWQomARXag598+dRsmw2bjgLX1GJhiOkUmkkCjW7zv13/pAFc+YEnjPPYd05m4gmmtGMCIoWx3XzOJaL8BxMQ2Vy5DipVCpYGKpJNJkKrCCqSt2x0bUoT257kRceeYhjRw6yc+9+PB+6+3oZHhxGNI7BAtvbqRulqUguOGMVhlvinLM3cvz4cc4973wO7NzOyPQ0b7r2Zvbsehm7XKUpncHyAu7r4NAEv/vjE2w4ewPPvvQqV111JS88+SfwHVLxOImQyvy53bQkgo1DPJFg7Zo1PLntVbJVh0NHToCQaIaOU6+xYtE8apVpFs/tZWRkiLnzFzM5mSVbKhEzVfRwjN9v3feGT+9a9JA0kdx6W5i1a3v45q0HySzvYUFY40SxiR2HdrPu7HZe2DHJ5z97Gv/61YMsXxThtUM5Wn24+7Hr+fTHfsmnPnkZbrifD32gn5uvW8TiZRbzek/jgtVpwqn5CN/h/DP/g3PnNnPRxzPo8+tUajO864oaX/gPE8eu8vTD8PVvrefOn+xkQQccG4M12XX86uFDtCyo8/1f3sL+J6fYeMHVlCdOoKphXj70C/ri5/L1+3/Ije9dy+P3v8YD98EP7t5IzFlIKCZZ1LmCba+9QGsiQ6Fe5/FntnP7F27jhSdeRNch3pRmcmaMqZExLn7TlTz1+NPEoikmJ6dp7+qlb24rU5MjnHb6ElKxDnbt3kt7Zx+/e/B3LF/UgeMJ5i5cSm5mkpLn8uiLv+dj776a+3+RpXNxlpuvuZ7P3f0VJrenuPxNb2Xzhna+/s27uPxv57H1kZ1ccc37uPOOH/Gx//gcr3zrMSJpnZAeZ8KcIX1Bjckjx8ic0HjlNZv7/+TywhNbGD56iFwhy5ln9zF5fIpjAzqHdo5z7luWkookiCV9BodH6OlqxSVMcaLIH/7wNFdedTWf++x3+MlPb+epJ7YilRpLF84jmWyip683CH+GwoQMk5de3sOixT3k8g7ZyVEyLd3MTOeYN68byy2gKiGSySYKhQKXfOHBN3ztvn3zBqkI/9QEWT3VvuWJQCBFQ+EgHEcw6TRN8+Tk2HaCIhRFb6DnGjaO2Um07zcEmxQnv0dKJbBsNMgIAonjeThOID50qSBVgS4VQJwkQZimietLTF1vlLqAaWiYukFbbyerVy8nnozR191BW3sTmqIwMjLK8LEBbN+npa0V04hSt8rEIlF0XaPuCGKxCIoEy3FxPMn0dJYnH38B37Ybk0yXsuVSsqxAMCsQaUyiLcfGdoNmxs0Xb+S8TWejGirJeIxI2Ax+fz8YiqgEmwjHtfA9Sb1eP4lSGx4a48/PvobtNOwGioquaTiefXKS7Db4zb53imoSlNy4BK2e7smTA0UE3mjN0HEbU2v/deUmhq7i+VCz66c+cymxGwFOpRGYDGwygThHzPJxJZqiIJVTnOgDozNv6NrNFqry9cQNOFW+44kA6+r7Er/RWGlZVjBhVlUcy6UwPU0iGcOu1SkXSzSlkpSmpylNT5NKJXCrNmpEYcdtn0K6FVKKRPF9Yqj46CiKF3QlKAppTUM4LmEkUjVwhYuhqVi+JKQqVIXAUHQiKpi+wDB1wp6grCiMC0lN8XEVFcP3CTcQtMmG3UZVoAWTiuqgJluZqdSJSZeqZrLT9nkJnaoZo9oVJh2KUq8KkqpgY6qdgwmFtrpDydbps4s40QR3rjYoPLSbpreeR/+Ro4QGC7S+ez4f+dbL/Pifb+Pnj73GO9okRrHMe7ft5XbDY/6mM7nyqMa9R/Yy2qryvdR8BoYOcs/qBfxqxwQfvWIud4tWbjkbpO2i1KN4QkOplVj35ymeOKeNa5/Yz5p6jW9/4l387PvPIyZPYJuCW665imr/cTZPDhIPt/OlEckXlkeD0wNTZeLYCbRohJWxJhxVp+BWWbdsIW0tHUTb2mhu7yASj2P7goihkUin6Zgzj6Z0kmg0TChknkRVIiSqHpyMzZ62xUL6//uc5zu/d8eWmVwRTdX5zW8fZvncDvYcGWQ6XySkSQYGjtHoRqLqeEgF6tUalq9QrdbYf/AwRsSkWiqhSY/lK1dx8OAhhkeGsSyLrrYWhOeRbg7E5YmRMep+cMM3dZXmeIx4xCAdDT7cUimYYiqahh6NBTtvIVEVnf7+AQr5HH29fZSqNfqP9TOTLdHS1c7Y5DSLliykWirR09NH75w5JJJpQKGzZy7Sl4RNBQSUanU2X3Ip05PTtDalicXC2J5PzbZRge45c+jftwsjYpKIJWjp7EIxQoQjCUqlKVRVR/iCaq3O5PAQ0ZABjZ13MpUIeKahCKau4wpJ1ld56O7vc2J4CKGHKeaK1B2PfKEIikJzUxqr7qApNILZGiFNIaopWLUC83s6MU2dYrnK0889T61exTDDTI2OkE4keGnnDsYmJ3HrFiOT00xOZVHDYWZyeWKGzuCxo5SrNo7rEw3pNCWiLFswn6Z0gqnJKYxwiMHBUYxwlLae+UxNz5DLB7tMTVcp5nK0tzbheoJcoURvVwe2gFgsxv6Dh9E0lfd99JNvuG/0tQfu2DI5WWbnDsGvHpzizdeu4PltdU7rCfHw3mnKosbUaJVlG5cyt0fh0otsxmse99z1MT64oY1f3D/Ft757IdHiXK674RF+9Nt15CsD7Hwpy+1fG+In9+7gR995munjz/HbHSrPjhZpGlWY0yOIN0s+/8WbuOnSF3n79Wk65lngeHz2H2pce4NOS1yytOMCrrvsdN7y5muR2jiZJhfVjRKJt2LE44SKzVx763f44N9+jg+8+wG+8tV/Q/FepSu1lJAS4tD+fhLpTkqeweETk6ye38GV17wb4U/TvboTU3ZxvPAULz02xNmbNiKFSrleQpJl+aqlWFaZ9pZmPBTu/ekvicYjnHnWWdRreeIUwcszAAAgAElEQVTxCLWqgyNAcU2OjuyltTvJzuNH2VMa4PqPncN3P/sEB+pP8+WPfItlS5IMHDiMEYUbPnAW81e+mS/f8QBe0WDptZ1oFYtQdJwZpYwXGeL085dTOiyw96/kmUf7WdrXw1e/dBOqa3HHN57hrDNW8ut7nmXD6RfS3NFKqK3M2jkbOHzsZZpaM4yPTZLNV9ClQs2rE4/G2Lr9BS64YD2KapFMzWPThg389L47OeOMNdTykt45C/jpTx+jo6uFQydG6VuYpruzEyEcZqZyxOMRJianaGvtxrMl5WKVe+/5LW//+JY3fO3e+6Pvb5kNfSkIbNtFb5QxSaWBkVODcFigYQVSqKCoOH7QT6coCrqhBbxhEQg9XwqkJ9BUFQkBJQj95JQ0QKRJVEUJfKrIgDcsBT7guB62AMsT1H2BIyWWG3h0nUYznqmqoBAcz3suc+f3kUzEURWBqakB7Wd0gpGJGaRqsmDBAtra0phmCMtyGRubolyu4wsXTVVxXI+B44NUSxUGB0cbpShOg6sPGhLhOjiuoGQ75GoWNdsjrGvois/q9WuZO7cbVVEIhQL2tColZsgAQAqJbppoDfa+oetEYlFCho5t2UwVKrhW0D6o4eO4bhDKVJSGFcbH92RQea6p2I3goGy0K/q+j6YGKDnZ8JEH2LkgKDg7edZUFSF8hN8Q6aqGQOKKgDEdMo2TAiPA7MmAVKKArqknCRuSWR+04O/eaM6z5W6BU2SY19M2FGZDhBLN0PC9YHOoaQqu6xCJRjBDIVxfYOga8WQTHkHwzfUkk5PjaLpKOZtHX7sGlixneDKLLNt4motEQcWkjEtCGFi+g9F4X1vTGlYWHU0GpSmarqIBUgRWH1MIihJmFEkNQZMCGVUj1pj0t+sGGhJL1XCFglAIaDeej+dWWRSPo9bKdCqCGzXJ1Z7DzkIFP9OK59iosTBTvsvgdI4Bv0bdszFbmtg5meUcR+WJwXGS/UdovaCTHw+ZfHlcYf2qi9g0N8sqNYTz1HbOr3fxUD5H5mOX8YGfbucXN1xA/cAhBs49m1vn+Ny8YDHrXzvCXWekKaRDtIyMIZbOJXqowuNPDLNoXSdqdy/X1RRG/rSX2644jVePFxh5bA/3KWVe6ulgkR2js+zwJctGX7SE3Ngo96Vd2nvnMzw8SG1yGiUaYpnexKB0SRohehf2sWzhQroXLqS5s53W7h5CkSip1gzp9k5aO3tobk4TjUaIRMKAcrKtWdOUk2uXxldDU//fDwx+9Quf3eL6gqob1J16Tp10Msw7rrmWE8cOoxg67Z3dHB44gSdl8B9XCWwAuVKVjvYMhVwRTRXYns8nP/0ZqvUa+w/2s3bNaby27yB93Z2gKkzOzFCoVKjYwa5fN8N4To1IJEQyFqWltRldEw3cjUfNcmhJJhoTAI/mRIR6tc6BffvIFQrUSlWOHj3K9OQUk8PDnHnW2ZSK5aDcRAh8oZBIJenp7SWZiqNrBr7wQVGxXY9UKgOqRn5mgnAsQTgSJRaPIVSVHdu3kYpFaenuQTUjxBJJdDMCXplIKBI0e6kKxewUugpN6SaiyRSO6wQ3aSOEQMFTQoRV+N0Dv+LIoX7GR0bRDJ1qrc7seUTdqiNp3CxVFRUwNOhIJ5nX105IUxGKQldXL81dnbiWjW6Y5KZzQQpcKpx25ul0tHVSLpZp7V3Azldeoae1mWKtTlM8RsWq0dnWQiQapjkWIZqI4VgurZ1dqBJ86RMPRzkyOEi57hCKRKjV67iWi6JA1AghEURMk7rlkM40c2Iki6arWLbDhz7+xt7EAX79vf/c8vHbVvPmDy5g/fmjXHnFOfzohzvZMZQlZGokRZS6ZnLF5QYDL41x9y8K7D3UxEP3HuaqW97FktA0rzz1ONtee5HPfecCRg7PsHHTZi5eexGP3LuVf/zXc3n+0SHedf067vvxN1noTnDXQwdorce5+srzeObhe1jCUmq5s3j8njE+86nTOSMmEU3r+MKHB3jTGSnqnqBUHqE8pPD737zEz374CHPmdxOPpTlw4DU++v5rePGpl/n0R5czMfYahUGVq66+htzUOB1zMuiKw7rTluGUoihdPn+4+7fYuSg7Dr3EC08+RUrJsHjVMlAdWjua6T/QT1f3cnL5MWKJFE889hj1qkumtZUNZ2yi/8gB/vToi8xM57nmumsJhTQe+P1rrF3bwwP3PcbHr/9nWiLL+Pl//Z7NV3Vyx3/kmBp9mbNWn0UkLFA0yd4DR7n3v5/i4itWcMevn+P0M+eSze2hx1jLP336Od728XXseqRAU6aJvsXN/P1/7+filWlu+8IfOXhoFxO5GS669ELuvOtZPn/vU9z5wPMYNcHcvhquTBDWTcJGEstx2ffaUZYsXYmueczt7aFiWahYTI2NUKlXiSeTHBsuEaJGzZ3m+MQRwkKltz0D0mPva0c5cHg/NU6wd+AwF19wGVJ4vPzKy/T2dNPenmbNFe97w9fu/T/54RZfengNLJ2uqUR0FUX4hHQDIYN4XiNlgxAKEomuapi6GrB+G8Eyv+HHnfUl+8qpogpVnw3qyJNYNAnUHQfbA9v1cYSk6gQhOUVVcYXElQJHSFzPRzRsHlIIDE3F1DRM0wgCcxJ6ertIJOOMjY6gayqVao0TI+OUKjZtLWk6O9sQvk+xUKZYrjA2nSUSigYlLYpCvVZmfCpPoVglny/iOw66puK7LpbtUXd9ijWLuheIVkVRCBtqMHFEsnLtKlpb06iqSjikYxgawvfxhRfYC/4/7t4zyrKrPNd91lxp51g5dFdX59xq5SyEEkIiCQwYTDBgY4yNTTjGNgY5XN+L7XMwTseHIMAGY5OzCBLKsdXdanWrU1V3VVcOO+eV5jw/1q6SOOP+8C/Zx3uMHqNrdPWuXXvNPdc7v+/9ntc0EZqGlGEF3jQMdEMnCHx8X1IrlSmWaqAkjucRqDCuXMruoJ7SutY90eUch17M8EwTClxNC0W2XGNrazoyCNMAAykxu2LTD9aGCUMRHAQyDKURApTCkxIZBEhYt0XoXTHtS4lUGr6UuEF42PmdD3/0JV27HccPB127b4CmvdCK1/Uwrt0QodBfwxEqFJFoBEPXcVwXBQip0XFaVIolfNcFXZAfGERi0Dcwguc6zJeW2HzgYuS+3TSzPRSmFrCUg2YlMAJJVNfpiVi0AkgoSUQTNJE0lKQn04/WauCqsPTnAGXAQWFoFoO6RoSwE5NAENcFbemH9BShE9MhIn2UbtAJPPKWTd31qMuALBouihIuL9Mlm4pldjcbjHbajK5UUG6LZDxHvVrj3dMVDsdtTqkoD2XTvGfPMOYj02SbGfZEU7RaS7zp4ecYkyk+Oefyl4MzJPaN4iyUuGZkO+/5/kP8/ehGzh17jq8ogz87V+Cn7ipOpYfkeJSPLsENzgAfPz/NbybHYHOSV93zba62c+wZMNj55Ank5h18T3fxOhoJBTk3wtcv3s5qxOTI2RP09+UJPMWBUkDTtLm22GGw4qJJl217NnHdDdewddM4my+7lt6NmzCiSexEnEg8jmFapHNZcpkk0XiUeDwKqLDyjIahd6N4eaHq/F9GPH/pH//27rlyE9s20ZEEUtDfk6ZUKhK1TX7p9Xfx0EMP0XI6dPwX0DSu66JpGrV6m2jUYGRwkGajTKlU5NFHHyIWjaCZBrZpsrC0TKlapVJr4LQcNoxupFSr43setqEhHSeka3Q6BK5D4EtMy8LEx3dc4rbA1hS2qeN4HrYhKFc7tNt1LNMgEY2wMDeD53iMbdmMLjQMK8a27dvZsXUrqXyeC+cmGd88zvnJCaKJJDLwSPX2kU2naDtt4qksdixK4IaDKAcvuZzFC1PU6w1GN2/FjCYRpomhFI12G9Bw2x0IHBqFIsPjm1gplLAjMRAixAoZMcrVIr/91jdw7twkmtJx/A6lchX5f3h/wk1HRyqJpknSkRi5pEmrXidmGahAcmpykohp4ktJLJFgdqFMJGLjuC5zF2ZxA4/p+QWy6TT1apn/70//iEeePkJxZYlENMbYxo2USgX27diKYep0HJ/BDeOsrC5Rr1a5+dZbGRzdyMTkBc6dPweahh8EoCSbNgxTrRQY7O8jnc1Sq7d46IljRGyTcqXGh/7g4y+5APn6X3367j2vrvI3nzjDM0/5fPUzJ3nnG9P8yXvezahZ5DfefDmd1dOcfKTA/pftZHRrD/n4FO2qy+f+7ads7Q8Y2XMVz8yc4ZL4Qd73gQcZGPA5s3SC1961lZwt+O8ffhuNZoVH77sXOxLhH/7x3bz+97/HdjXKpz9/jlf96nasuodfaXHzxa/l+MQqG/VBssMnOHBJL+lhnR07dtGoSKLJDOcnp7jzdbexurJMsbRCLhuntz9FzIiwff8VfPqvv8ZieYI7X3czy0stiqUC3/7WtzEiFo3SeYqrRZK9iq0D27n+xivwlUlPLooXRNg4MEoi2c/s7FE2b93MZ/7uy1x2+X72H9zFt776A5LxHFOz59izbzMHLj3IvT+4n3rd5ekn7ucD7/8wP/rOfTz+6H1Mnz/MW9/4dtKRFBeP6Fx88UGazRb//O1/5dVvvIyBgT4ue9Uw93zyG2wbTLAhv5uVCw3Ot2f52EeuZLRP44Y73sDkfSe4cOIs//NP3sWx06d59y9fQa1U45ff92r6cwabhtP8xe+/h8u2xTh56nn2XXEVpcVZ9u/ZxqnTp9m+dy8jfb1UKyt8/0ffZ2jTVsr1EvmsZGWpwujmfqZnprn5lmuZqpzlh98/zXVX72J1pUEibbE8u8zmbeP0jWQ58fwih446rMwfoSnrbBgb4udPP8klF+9j09W/9JKv3R/88+fvjnX9uRogDB3H9fA08JT8hb1BBRLVbdlDuPd6fjhs5gZBKN4EGIQVTEOIkLKhJAot9BHLkJ0su2xnNI3A9zFNA6GBZYQVQiXDATZNaIgutSNuGsQNnbhtYiMIpMT1FeVWh/nVAoZh0ZNJ0Gk0iEQtmh0PTegkUykSthmi6Gotzpw5y+zCMkGg6OvJ4TgOtUqVM5PTBFKnWipTr9TpdDqUmm1qjk/T9Wk6DgqwNUgIjaSpE9GBIEAJndFNG0mnUgSBRywaQYcwZMY0AIVpmF2bQei7FF2Rp9Bot1qsFsqcm1lBEhZdIKwii1ABAkE4Q+N7GIaOjkAXob8WFJoGnhuA0hC6gfQljueGHGcBqJC64XoeEg3HD9b53r6UEBby6QIrQnH5IpoFayl6XctGoMK1YOo67//QS0vbaHXcuxXh+lTyBSb12h/LsrqccrEu/qPRaLjmFNh2SL0wLQM7EiGVSuF5HolYHFPoOB0XN/BwfNg4tp1KrUIymWKxVMbcvo2JfIYThRKa16YtJYuBQBfQUAqlCywNRIiIJh9N40qI4NHoRpzHNZ0YEltK0HU8GeICXSWx0DBMA933MUyDiGZgGwLDk6ACImj4QNQIKVMEAZYmSKiAvKGRajcYF3Cd2+blFZe85/IXMUFT82mkbcYqHoePL3JRR+NVOw1iTZ9Woc58o0oz3cN+5fBBaXLf4SbfrHh8SjV4+3Kbk4bPm7QBHg0c9kiN4X0X8ZGVOd556S6M7z/LY6efZ3JkN58oz/PO5Qhvu/IqTt57hEd27eXny0UWagvs7+vneL3Irs3b+MbKFL6pcXryLMrzsc04xEzaEYurpM7c8gW23HIFiY0DbN65j8zIVjbs2Uk8k8VOxIhEYuR7ehFCJ51JkM+lyffk0fXweluWFc7LGaL7+dBe5I//L1R5/vZXPnt3o9mm2XJIp1MIXdBotGi3GuRyfTz48EOMb9vO0sIyjuuHoP6u50hDcdsrbiaqaSwtzBGN2HjtFoGC0aFhjhw5TE9PjiDQ8AOP8bFx5pYLFCpV8CVR2yBumUQjduj1DXyUESMejaDj05PLoGtB6BNzfQxddSFKAkcqOm5Au9PBNnWuuvxyOm2XUyeO8ea3v5MTx56lVm/QblVpN5voehgTG0sk8P0Q1VRrtHC8Dpu2bKdSXGFxcZ5MKkOz1cKOpej4Ass2Gdo4jkJDkz52PEq1VCQai1Asl3j+8GEito4uBNFkCgnY0RjCinFuZo6zTz+MCgImTjxLT0+WiGXRaLfx5AueMQg3y750AuV5+EpxcOc47WqJnnyWWNQmHo+Q6+knHk9y5NjzFCplyrUKGhrRqE2xXGNkaJBY1KZQKhP4PpMTE9jxBAYBEV2g2wYDPRnOn5tgx7YdrFZrnDp1hr58nkg0xtmTJyh0YGp6iuWVApdccgmVchnLtLA0D8u0SCZsUtlezk5MEInFmV8u4frw+x//xEsuQN5wcebun37haQ4c3E11Icnc4jKPn+gQ9wSj14G9/SCfu+cR/viD7+WP/scPoVxl6/AefvW913LTKy3e/aljvOamW1k6I9i+bzutdIfJyQqJ9kEuG9jPLVfexu+8/1OMb7iFn/7oJxh2mc9/5nHu/oODvOePH+QvfvedvPymXdz7o0cZ3l9hsOcgzz31HAvVAre/7CYOnVjgDz76JFtG4qwuKxaWZ7nlNXdw6PAsklWsaJYTJ58LA2twGdz2Mu754r8y2jvA8mKJjVt6+dF37+O9738vmzcP0qh7LFQzHLj0BkzNZbo0y9//zTf4278/xPKFI0jhMr6lH2EIaNlcc/3VfOzub/ChD/wG2y/dQUpX9PT1MnVulrgRQY84PHH/Md793jdSKC3Tatd45WtewchAH7MTCyxMTXPr615DKqNjDxa5+a4rOfrUA7zhrfdz7LnDPPkIXLv/5bzj1f0YYoWL+m9kYWaBwz+XfPsfv8nw9gP0bUjy2DOP87o7X8t99x3iwEWbKc74JI0EBy7az/EjjzM5fZa7XnkNs0sr/OzH9/NHn3yYTdsOEDRLPPLoD7ns6qtIxVIUVpd58NtH6Lg9PPH0MS679BJ2bB1BeQ4//dZx3vaOWyiWWoyPDeJ5Or1GiqrXQGtbzK0I6qsBW3ZIRodH+eZXnuZdv/Im7EgfI5fd9tIf/L70ubs7XohbC9BQgUIGCiUEUUSInRMCJcNulB/4aCGnCEUomhEauhbi3AzdQBcaXqDR8Xw6QYAM6AaRgC5DIWzrgohphgJThT5iS4RCXXZDPQxNw9YVad0gbhlETYGlG+gCFDqljkPH93E8DxVIytUKpm6QSiXAMGh0SQsR28LpOExOTBGNxzh+9jzTs0uAIJVKEMiApdUSC3MFjj1/hvn5FRypqLkBLdel1R2WC1BI38cSAlML52A6QUAzkATSI5qIE4/H6e3vJRGLgPLRupYHGQRo4oV28Yv3XCklruPSbLaZXSyHZCQR3teUYt0XLqWG7wUITQel4QcugXwh+jxkc4csaMf3CBQgwipzSNIIcYE+0M2FCWvma4xoFdqag25XgC5lRBch89k0dFAqtNHpa9HjGqbQ+I0PfuQlXbuuH9yN0tC0MB5e4xeFked5YVJPV/DrhoEuBKZphgc2KYnFYhiWhTAMhG6ysDhHIpUiUJJoPI6hCxJxG9uMEM/miafzGFaUwf5BbMPAjsRpbRzlQiTOYqWKKxWu6nrREbR0iec5tKSL72vUu6E+WSGwNY2mUgjLph14YfW/e5DRFHhSYQgN4UsCXcMNAqIixBi2kZhKhcP3mkDTdRZlmAcRmDotJXGlT1vTidFgl25yVbvBw6ksZ2fPUdcDTnY6fLlXx3QtisLgWruHedXkQdHm2cAnqTk8ctceLundzTdXa2hjW7Cm5ng4UeGCE5BND7BYr/IvG4d49+FVvpUwOTo+zrmZs6w0l/iWbuM88CD/vDnPmaEhZg89SSSWYHlhlf7BYUq6IBmPs3B+Ft0SRG2bjt8hl8nSqlWJmoodt76cTXv2sGnffjbu2UN2uIdGu0MyncG0bQzTwrQ0hob6SCYTRCIRHMfBNM3uULPCMI11ktBaRwJeOGyZuvZ/v3i+/+tfurvRaGIn05RKJQ7u2c3iwjzpVJJ2o4EEzp2dJJfL0Gy3cFnzygGaRn15Gd9tsn/PdgrLyxSLJbZs3kRPTy8XHdhJu9mk1axw4OAVLCwvsVysUO342JZJzLaIGBqZdAKn3cDQDfrSCRqNKqlElEQy2Q0BkCTTSXzXwbIMnLZH2+tmailoNFrMT08zNzeNDHzm55bQpMPSaplAaQwO9LH/wEUcPnSYrdt2sFpYZbVYQWgSw4yiScniwiwiUPQN9BOJZRBWlOnZBZxWHcM0MLUA1wkjvn2nTaftgJJIp00sHieeymOaJovLK+R6+1GGxYXnn2Uol+H81CR//PH/xpEnn2Bubg6pDFqOt4b0xAAGMzEShuLS/dvYNtTD6uoqmpKYIqDTbOP6AdVqmQ1jm6nVmyyvVhCmRaHeZuPIMNt3HqBYLrJr927OnDnJxuE+Dj17gn17tnHlpZexsHiBmBXhV9/1GyzOLzG7NE+n08HQbTLZDH29Oc7PLnLq1BlS6SyrxRKFQoFINE4iFsVSko0bR0K+dqnCarlCoeZSbLVxleITn3jpfaNvvf21d3upIRbOn2fjlRVmyorP/+2rGM0Nct8PS7zzdfv47P98lGpQIOFXufPG63jsiSOM60mGM9v4xBvfxf/6m0+xpS9Lf8pkdbJIquUxls+QToPlmzitedCbnDh0kg996INcd9lexrIXs3VDB2UU+Mq/fJVbX7sX6Wb4yQM/hsgq7/ztu3jN7Z/lV151ORtTOV59110cP/4I19/0Gmwp+cQffZ6L9m8ln0px4tlj7NoxTCYzSK1xgVuu2Qtam207NjHYM8bmLaNhvcPVmT5zklff8Qru/fbXmJmZJhrEWKkusXk0BlqLdKYHXU8TsaMITOwM3HTLTpbmznPkySMcPXaKE6fOMTKWYfu2cUwMtuzeTHF5lepqmxsuuhgjbbJ7zx5U08WLgvRNisvLXDhbpyeyiXxsC3fdvIs7b7ia6162m3rhMHZygOqyxdjICJMTc1x28S4uuuggm8byeI5LX0+Os5Mh1zzXlyUWDTAswRc//49cd/tNZNM5ypUi+XSSO159Czdfu5U7b7+Kc1PzzExOs2FoFwODWdK5NLmBYRLxBjdcfwl2Isbhp+e44eVXEwR17v/Jz9mzcztzs3MsLy9x5a3XUi3VGBoeZag3yp5tWS7ecyltx6bjRjh/dpmB3l42X/+ql3ztfuXz/+tugYYuFaYQ2KYBIsSVqW5rP9DClr/vKyQ6us56FUfoBoJwgFtpWtePrAFhNUyHMCxFBdiGQcQ0sYXA832UlKDo8pDB8XxcFYrJiCHIRWwSuhmi0wQ4XVtfJ1A0PW9NG6EpUNJHD0ISTyB0Oo5kcblAq9PGQOLWm1Q7HYRuUinWKFVqpGIJGvUmy/NLzC0XWVhdZWZuCdcLkLpOoGQYuNFlBssgTDx0paThuLSCgJYrcWVAxw8oV+sMDPSxcaQf2zZR0gcChAhZ19L3CQK/G56iUIFCN7o3dKUwbQPP9SiW62EioaRLK5Eo2Y1QJ9yrw2TGtXCaEKkmVYDrBThBgKDrV+7aS1gbquvaPujaZ0JYtIbQusLzRYNqohuUsuYr1jQNdNFlKb/goUZ76RMG2453Nyg8z+2KorUDmkLoXaqCJtY5z7quo4mwUm8YRjeoR60PysbiMQb6+rFjMWQQYBo6rueRSKRpOk3S6TS+4+AFkkgqTa53gHT/KBvGtpJKJalpPlPoLIyOshSNc77RRsegJQOaKKJYWCrAkuFz13yfAIVjWPgiPNy0NQ1dQks3yes6tcAkTUAbRUxCW0k0TUdTWnjdA4mQkpZS0PVT15UfBhTZFkHgoXSL1aBNk4BtHY9mIk3LiNA7PIYMFAdqLq2VBX5QnaUYjZNIpPhWz0H+qbrKdyYcHpo6y55klu9WpohWW5xwfDaoAF8V+fL+vUwmN/JFtUTZV8wvXMCpt8lk80RiJvP5DLPS4uiJZ0JfuJ0g3zdA06mifFB+wPtGdpCbnmfIk/QM9tJjG9x6+ZUMbd3BvutuJr1xE6nBERL5HnoGB8n09pDKpOjNZ0nEI+Ty2XULhmEYoXDWBaJr3/F9H9u2u0t7DbP4wiHr3yue/1PTNm7aNaiuuOIq/uGfvkVHCISU7N+1lXQ8Tr26ih8EFItlmo5P09OouS9MBUOIU+ozPUYHU/QkkhimYLVYZWB0FNfx0YTCawZMLi/ScqBSb+EiMHUNU1NIT5JLG/TlEwgMolGLwHFJRi08TGrNBp1OJ4wvdT08X6PtuRRa4HU3LwVs6kuh4ZLK5dExcTtNor2D/No73k6jXWN8804a9SYbt4xz7NAzLK8uETUNnEBy6eVXMjQ8QNAJh2ni6TR2NM+F8xMcefQnbN26laGhIWKZkBrSaTWZmZtH0w3mz51mw9AoUuj0DAyRzGeJRVPM1er8yYd+hxt2beHWW6/nwx/+IPMLK+zctZ2FxSJL9TbF1Qp9uSS+02Js4zC7du9l8twZVCA5f36KZDyC5wZEYza+7zM+uhF0g0qzzdxiiYIHTrXM4ECeu267jYcefYShwTyFQgHbMtmxfTdCD3ju+VPkszm2btrCd773Pe7+f/6Su//sT/E8j6htE4/H2TQ6ghJw/OQZ6i2HaqNOPGpjRKL81m++j5/+2z9x7TVXcmbiLIHS6GgWpyanmZheWPtwvOTEgqf+/DfUvz15mPJMnbrssHC+Rae5QrIvRbUNTr3Gq1/Wy9kpi75+k8cPTfOO172MogjY0lvi0CGHlKGze9s4vX0G9YjG5n0+c0drHH7mBKVymz/7+Ef54f0/ZueuXSxMl+h0Wlx91aX87P4HueyKqwkCF0fWmTo/z+BQhHqpiam3ufji23ng548ytmUYYWjMLk6ybeNmJmaXmJ6ao7hc45bbr6fRKSBqPtsO7KdaKpJNZak3O5w5c6brJTV5+IHn8Y7Mcr61wIGDV/D4Y4e57JaLuBAeHLwAACAASURBVPNNN/L4958gNpAg058hleqjND9DoHSipkU8Kmh5Dm6tSYsAM2oSMS1i0TTVWg0tkMQSGX784x/x6+96N6dOH+f+x+7nHa9/By0r4FN/8k/80ptvR2ktvvZvP+GOV97G2OgIjzx5H9u2HkRPONhuAkv3KVWabBgb5ey5CRLJHPmeGKl0nBMnTpBLbuL+e7/HroO7aLo1aAsuu+Ii5hcnOXDzdUwdneDkMxM8/OQJ3vVrr8VUiqXVAj09Q4xt3sDTR59i4vhpXnHbnbSrOk23wKmJU8wun8cLUoz39WHnL+W6S3u4cG6GDWMbufdH32W4bwgPi/EtvRx69Cg9AwMYpqLRWeHKi27jiaefQrrw5r/9yUu+dl9/7aVKBRKphRXiQBNhUEmXjet1B9YMw0CoEBkn0ei4LmjhIJxlG+GQsZTYQqBLidBCUaJEeNMytbD9HwQBfnfKwnU9HKeDL3TaXkjeMAyDmK6jBwEBCs+XeEgCTYQphF2Chx8E2JZB1LBwHAfHcei4HoGuYRiCdDKK8n36BgcY7ulhdmGRpWKVbDZLo1XH1k2yyQStRp35xSVEzKZabFJrt0NWeS5Dq9MO4679sLpr6vp6iMyancHzPCzLIKobxBNRbrzxBi6/ch/5VAzpd4hFLeh6nIMgDF0RaAhDRyKwY8luaqPA8drUShV+/vCzHD1yIvR8EAplzw3JGagQRee6YRpeWAfuDgj6AV43uRGp1tFyUspup5T1QcJwGDB8P6WU62ISwLBMbF3HDbpR4VLhBn4I60Z0WcWsvzbTNDkxu/KSrt1Ko63WuORr4T4aAtd11wcdgXUs2VplXkBYrOnGygeBH6bNuQFO4IFUdDoddE3gdjohgaTRwtINOm6HwvwySEk0GmV2doYIBpVKhWx6gEpxgcZqCVfVmDh1kqXlAvVOk061zKBjkXdqjGodEkpiCkFUSqKaoK0JOprEVOHrjqCQmiJrbEP4Zwk0QUQIHBV2d5RSuIagT2i0XAhMgS01GiokgNi6Qc11kUInbli0PAeEIKPbNF2Tv9k8iKVl2btQ4qB/li2uxjwBX4uM8VRnASMxSqbXZ7Rh0zANLu/N8c3lRba2JLXA4ynZ4k+NLN/d1svY8BBPP3OKRn2FCDqB02FXbgh76zBOLEd5eR6t3kJEDaKLVfT+BFuaNmppmsApUsUkddEu8oP9HLz2ahJjGxntGcFOZekohac8kuksSmkEyieVSmGaOrZtracE6rqxrgWFEBhd28aLD0drX699Dtc+vzFL/3et2//U4vngcFwNDY2wUq4yu1RibMtWSisLKOkikCQsGyU0ZhZqNDyJ86LUI10TxHWNbMwgn48RMyzS6SROICmtFujpzVGphfg6T0K55lBs+HSkv+7x7Uvb9CZjCOHT6bToS2ZIpBPUGk0KlTb9vRkKhSL9AwPMzc3h+gHNTsBKWwujXzUNpTSGUxa2FfbLLDuKZRts37UfOxHj0ssuo39wlE1j26g1qiQSGY4cepiTJ0+SSGVIZzO4rTZ7du1mYGSYbLaHTgDzZ48RERqNep1YLk0+04udTCKEwfL8HHOry9QLKxy85Ar8QGDHbEzTwIwkePtb38w77ryNs88dZXLqFPFYEj9wKVVqDA6OUqzUmZ2fY6Q/SzwSp+22WS1U8DSolpvkcwl8PwAU0WiUZstheHgQlKRcq7G0WmOlKUkno2wcHCIVN8MUrcBBCPA9l607dlCvFojHswR+iG0KXI+FUp2l1RKrq2U6jkM6lWDLlnHanSbTF2YJFARKI52MYwpFu+WwbWM/Y8ODoa8wnefZs+e4cGGZ+WLlP0w8z/3bJ9WnP/1p3v+B9/O7v/WHXHTVNfjuKhMzbfbu6uPsoeP84V/8KR/8wB9QWPb5wTc+ioo63PCKT/Erf76RS60tFJspYirDPV/8BruuvoNPfuU7/Pc376anP4+nN+jPbiKol3ngiad573vey0/uf4jZ2Sne8rZXI3UDWh7Ccvj9j3yOv/nsR5GBiaW7/N1ff439BwcYHBzk8JHnOHdylt/6wHt5/vlZMD168wlOPHuK7bt2cvrs0/Tlt3HTbdfw4Q/+IR/6vXeiAvB9h2g0Sqm8QmWlwfY922l5CU6dP8mGfJrZmQWyuQSpWJRMug+n02LT6DhnZ8+SSCRwnBZCs/Edn1OTZ9mxays7tu3m8UOPsHnDFurlApMTs+QHU0yePc/83CJvfddbOfz0USwdMol+0CGRiFMoljhw0V6Wl1fptOukYlk8z2V1tcSOfbsorC7gui5uR9Kou/QNZqgVKxh6hJnZ8+zYO8JI/3YmJyepN1dIREcQdptkLM2pU88zPLqReExQKLaZmJjluusP8qFf/0c++PE7USrA8GDpwhkGD9zKVz7/AKcnTvGG1+2n3CoTMUxOnl/iFa+4nEwkxflzZ9m6bSfv+8g3+Z33vwG9PklucISFhef48y9dYENPFGe1zf/7qfczffR53vuln7/ka/cVl+1Xa0NWrh8Ku6ArMqKWQcTQ0dHoBF5oxQgCTF3H0nVMTWIb+jrdwNRDES0ChRLauojxA0k78DCFvj5AWHPbuH4oxC0R0iIsoaHpGh1f4nRDTTptF9O0kIGLaZr4MhT5uh5aCISS3dRD1oVTEAS4HQdFgBGLECiNSr1Ns91G1zR0TWBbJrphIKXEUxpS+nheQMf3iCdTpOMx9C5eT9NAdG+daz9DSUJomSkwg1CktaXkljtv4crLD5CwQCNAaCHpIWJaeG4ntOr54XNYVgTPl+i6iTANTCOCsAzm5gv8+GePMTu3FN6futaKTtvFV4Au6HSFnRAiTCHUwso2gNs98KxbObrXZ43brHWtAWukjLB9bWB144tlt9IeqBeCctb+7xqOMAjCwX6t65d+qVF15XpLrVUQ//8qimsHnLWgGSklmlizq3QvZhAeEsPgHrl+SPI87wUeudCpVqvrB6dWq4WmadSrNWzDpF6poqPTKFao12qYmqJWbWJZFssLywwPj3Lm1DHczipHjhyh1XRYLjRJNFfZicWwJvFUi6yusAOId6+NRGHZMUTg4ErFoGlRdToodBwBcU2Q0gQ1PwxuMYROTPpoVoSO59JWErM7YFsH0pbAUgZlL7xMLUMR8zV0zSWrSRISfHQ8XdCWBjVDUvd1vqt0DpKmF8lXt+aIRBOk02la7QZbnQgUSzxhV9GakrmVJUYGRhnqH2KXmeDyU4eYa9ZAM0gRY1L4dAyY8hQ9Y0PEh4fYsGc/+y6/BqIRkvk0kViUZCaJpoU2UE0XRCIRBBqRSCS0tqzRgQK5fhBfs2QIEVqT1kTzC3znX/z7eijefwXxfNXmrNq7dz8zM9OYsRzHT52mXW/T05NkaLCfVquJqWuslJrMLFWRpk7b656yw/kVtvTlkW6Vod4s9WaNetMllUoxNDTI8soqrVYLhE40EufgdXdwz5fuAcA2Qw9a0tJJR8INoS+fIh6L4FSq+Mk+3GaVarWKHUvgtFtINKoNj0qg43leOHmtK2xNI58wcDyPVDIdIpmEQS6TpX/zZm6//Q727L+ERx59gFtufx0TEyf41y9+lqefeZb3vOtdDA0NMdg/QLnRYvfu3XzhnnvY3J9n255dZLNZLMNEBh6RdBYMk2KpzBe+9BU25BPoZowDl1xDKh0hFouhiQjf/Ncv4dXKeLUVnjz0JNs2DDEzs0BPTwYngAsLK/i+ZKQnTSoZx0VQKJVYLNTIZVIUCzWiUYtsJoEvAzLpFAsLBeJRk9VClWIbHE1HaAGZeJKY7pJIJJCBh4Fix5ZxbrvjFp589Gn6BgdIJpMkY3Gmpmb44UMPMrtQJlCwbet2ZqfOcdOtL+fJx59Y37wWVopYBuzavIFkLM5AfxJdhslhmhlhpdwk1T/K17/zfYD/EPH8ydt3qk3ju5hfOkVp3mfTnst4bP5bvOqOa4l6HRYemieeyfLyN27jM5/5Ebfd8Uvc951nSJgxlquzvPLdWznyUJN/+eITfPwTb+Wjv/9levLwiY/8Hk7qGSYOVzj28GEOXHE9bb/J1t2bOH+mwLYdG5g9M0PvliwLCwuoZgxP+nRaNfI9Edp1n1QuSqvdwXdSNNpzvO3tb2GldIHFc9M8f3KaTVtHWV6psWvHTnTT4NDTz3DJFRfj4zI9fZ6eXC/tSpO2VmfTyC4S/RqNpQbFaoVUOotQkErm6RsZoVVfwkgG1ApN3vm+L/OFz7+LqBFldWmRdDpLuVTHd1uks3EW5ovs3T1KyzXxOm2mpxbYtXcr9UoHgJ7+FOcn5ti9YycnTj9HfrCXhek5Mql+0skkP/jhfbgNycWXHeD737kX04pwzY0HSWWSFFYX2Lt3N6eOT7J9xzhHnzvGnr07KC61MGMuqUwaz3NJJfO0WzUc12fDxhHmF5aQvkbQbmMLCyuq4coApy1B2pRXZ2j5Pvv37+UHP/g5Pfk0EdMinU+Rz49SdJZZLs8z9RzEzADdkKR6TE4+d56x8c1I2eCxp6Y5cOAWfvDjH9CTi5PLRDENm2fPznO4/tKv3Tsv26ccBRKBpgIMIYgaa5VmSSAEgdKQ6oXobtM00QnboXo3PMQwdCx0nEDiqzBFzdBDb2nbD1nQfqDW+fFChlHdmhR4+LheQMOX+Gt8YsvEVGFAihZ2pcMwlcBHoKMFPsK08KWHRdjOdp1Q7Nqmhu9LJNAhQHo+0pM4voMmw2ErXddJRyN4OtRaHVxf0mm66BFBKhYlG4ujy1DEqMDH1DWQCq+L4fO8AF0PhXggPTSpCAyDl938Mq68fA8JQ8MPHNyOSz6bIRIxaNTrqMCh1XExrQi6odGod2h3wI5HSSQjGFaEiBnhqUPP8sATxzG1UAg63U6r43nrN//1xEYUsotj8+UvdmSDIGRoQ+gpd7vR3BFDR9e0rvUlDFSxtG5lnVCA691uwVrK4ItF9NqwIN3vPT1ffEnXbqPjqbWq84vDX9a+Xjs8QPj+rQnjFw/ACiG61XRwO21M06TTcdD1UHTbtt1lkYeV+zDgrIpphgUiQ+g0m020bgFPOqHoLq2s0Gq0aNRqmJqO22pTKpXozeSYnrmA5wqymTjPHX2a+akJCoUCnXKJEXz6Oi2SRniYTKOTQ9JREltTSAGRwMDER2iQ3LSf6syp0BIRwlhIKoErJL7UsA0dL5C0hU5EKpTyaRsmehCg6QZCAH5AUwYMCYsYkogw8GSAEGBKSRTFeWHwtkiasf5eAhEwcr7Knr4Uz1RX8OJJbixXedLUWUn2crNlsm3+JB1lUO0GyrSFzYpQ1EezRCJxtu/cy9DOrey76loWCzWsiE00mUCPGGR7e7BNk3g8im3bWJa1fsABha7pONJHVwrTsNY7KWsFAEMPD89rYnptjbx4faytCYBExPy/XzxfPpZSCoO+vj5ct47TUcysVhgeHiYdt4joGoHrMTW/jNQMlstViq0A1fVuaUDcEPTnErRrNYb6Mrz1rb/CZ+/5MpYlCVGiCtfx6XiSSq1DU9dRfoDSwk3HAHaOZjCFxmB/D8XVArYdxTR1njk+S9SGdDYeonokrJY7NFW4qaZsjWzUQgYOuq5hGBZe4GNYEVyl4zge8WSMd7/310lne7n4kiv52QMPM3XmFM89/QAH9x9A2QkOXnE57WYLT9M4f/48rbkZbrnzLoaHcmHSl2Xhy7AKrEdiPPT0MT71yU8SC9oMZ21ef9ebaRaneerYSe5/4DAf/9hv86OfPUhhZY5SocRgX450PIIZSbFSKlEs1RgdGWCgp4dipcLc3BypVIqBgX5WVlZoNFshYL7jkUxEMDRBodoGXeAZUUrVFgLJ6OgohdUlLtq5nUzcZHpuHt/3sYTitptv5PP3fJO2hA++/y0cO3qY6665nu88+CSPP3WMuGlgRWwGevPMLyzT8hxuuvlmlmemmDw/SSKbYTiXI2EbbBofYWb6AuPj4xQKZUrNNseeO03VD2NnVSBfcgHyD7/+BmUbFzh9fJmDV2Z44kSLV73ilczOfZ1Nm/rBSvDjR1b4yy/Mssdy+c23Xk612GJxWTI0KujLx9CUQImA3kyGWCzGV7/xUy65JkpxaoRURufaK8Y5/PwcZk8SS0VYnlni1juu4guf+SL7du7m5rfs5fyhSR57eppX3nYn73vvX/GRj92C344yWfgZQ/Zm9l6S5zv/eoZLrtyLCipkc1uYv7DC5vFRfvyTn7L3xkFEKY+vJSiXWuRyObT4OYrzBjHLRjPqVCsButTYtWucvlQSP5pmsbrMEz99nv/xtWe5589vQZdj+HqVTeNZjj5/nA07t5CJpzCUTm2lxvJyhUqlxoULF5hf9Dl9aopXvuoibnz5fqbPzbFpfJR6zSFhR8HU8bw2lmXx3LPH2b/nIMVCGXRBu1nnuZOnsEWOSqWGpgsu2j+Gkj6jY4OsFssEgaQvn6dUKtE/1E/gCKrNMufPTnPwkp0k4xmmJ6foH8hRrFTZOr6ZH/74AU5PTPJLr78ZO5KkU9dxggqdpqR/MEGz6rJj51ZOnj5DabWApmfJ9io6sw6ZdJ5Ff4lsNotpxKk321gDiyyf1RndMESj1EITPoaZpt5cxXMVS9NLDA1HedVfPPmSr92bLt6jdD0UUmsDeyFmrpsxKFVYzelizrTusJWmaRh6mGS3FtPsdqk4qlu51kXIibZ1QcwwcD0nrOwFEsf3aDkuSguFsSsVCIWlQdywCXw3tMgFGh3fw1UBaHoXO9UVx6FdF1sXXT5+txqLhmmIMClRCAIvDNNw1+wOWjjyGLNMhKHT6DjUmh1MTRCxLXLRCEoDTwYEvha2vbte544fCjJL6OhKkrQtXN/DV6BHbV59561s3jBAp93Ea7dxAp/RgRz5TIJGo0G5VKXW6pBIJMgm41RrTTzdJpXLkYyZNJttetJpqvUG3733MZZXSuuWDLfr3V07xARdf7hSQeh7Ft3k2W7FmO6Qp9e1Xwi0dXFp6mJdVKw9NC1E4PEi28OaZjC7Fom1yl24NmSI05OS04ul/xDbxotj5YF1wfzCEOQLoT8vHhZb+z7fC4VXJBLp+shDa5Hnh52OMEFz7af+YvXSc8KimWmGFJVOq40mwteQiMYprKxiaALpB7i+i5KSerFMu9ZE1wTPHznK+PhmFueXKBWKNNo1TE1y+tljnF+YIF+qM6hp5IQi2mkRQYIQGKhQLKuQtKJpGkklSZoCz5M0Ab9LYulIn5Rh0pKSlpToukYEHVNpIV1F0/ACF83QiHqA0tA1SUZpDAmLNg4Cg45QzCuwuu+xMAQpHwITlj0DSwOpHOY1HaXBooxQNnSc4R5ufMuvE+vpI5ZPkevJ0yYgGrXJ9w+hdEjEIkQikVAAK9Bta93CtZ6IaZhoIuTF65oIkbqBAu2FTsjaUODamv0/xfMLFt+wQyGl/HeHpPynFs/XbO1V1WYLpQJ6Mll8p4NumeQzWQZHNjI7PwNewBNHJ9m1ezM+gieOT/DCbyTQNElcQH9KJ5fNkE0m0DTF3MIiujCJJqLE7BgLKwXKtTYFF1Aa8ajJRQcOUFlZpLKyxJtf/1oOHz5MIFtIjLCtiKDSaFCp1Wk7AX4g8RT88jt/k89/5u8wUIz1Z0mlozTrTUwh6HgufiBZKjtYEZtGx2Xnzm28+d2/xv0/uQ+/ukyhUGHfrm1MT03x/t/9EPfefz/1Zpude3dzeuI0548+x0c+8iHMaIJULo8ViZBIpvElfPe+h/mz//Z7xNMJrrn8Egpnn0FJyXVXHOC5508iNAsdRX5gACue5JGHnyJia/ia5Jorr+WxRx5lfPN22s0yEklUNymVy+G7qQNKUK1WAcj39dHXP8iZc5PU6m3qrQAZNXEabdIxCyEMLMMkETFRfpu3v+XN3PMvX2dkoJeefIZnjx7nda+/i7nZC0QsnWajQ73dYW65zLnpOXTTwI5F2bllI/Pnz7Ha9hjsyTLc08u52XkuuWgX11xxORMnTzJ1YZJUKoXSdBZWGpQbVZaKdSqNFq4XvOQC5Jsfu0qdXFrk6PFl5hcE03MuL9+a46Zbb6VeOEPUsrHNDl4gyOcGiKehVm5x708f4cqrruWRBx/nztdexeLsDI2aha/gq/c+z199bjOPfr1AayHJxz95FWeftZmcLBCJBaQyFtFkknzW4NOf+ibXXLWb0uIp9r5H8ORfJxjaOMipZ1fYdskIf3fPo4z1WxzcN8bO/SnOzJ9kNLGTU6ef5U3vegXLFywmjk9yw9W7aAYSZQq2bBzi3OQEgxtyuH6UQHgM9vbx9LGzJOwogWryvR98n0v2HcC2auzbv5F7fzrBUK6faDKLpySyIxgey+G5itmZFTwX0rks4+MZqjXB/Mw5dNsEX7Bz21YeffwUQxtM8rk+mvUG7XqDhhemwQmRYHgwzYXpWeKRNJG4wfJygWQ6jSU0evpT1KoOqXScdsuh03GIRGxKlTapdIRqqUwiG0PhEzETBK6k2WzQbNQY27KNdrOGMAyS0QStdofluRUSmTRLC1VuveUgX/jcV7j+hmuorhZZKNcQpsZA/xC+65HNpWm16/jzbQ5PnGXTzi2kMiaeq8j25FGBR61eBGVSWC0xNDqCciWnz06QyaRIJk0OH5riYz+aeMnX7h1X7Fc6YXvXAxQSXRgE3YG8EGEn16kbUv0iMUJ1B/ZQCsMQRCwD0a0wr6XU2bqB43o0uqmCngICGe4xXdpPIEFoiqiuIxS0nA4tNAwBSoZkC78bAqLJcHDN0sIhOinB8b0wxERKkArDFOsCiW5FVdd10GS3a7/mle3+Dn4oPHVN4Bk6AWHsdLvjExCgtHBg3NQEQqz5iMEQOk7go0mNnoE8t738OnRTQxdhRHQqGcXzXJxmi2wmQbXlohCkEzbSD3ju9BnGNmxi996dZDMpzk6cp1yqkkvGeerwKWYWV3D9FywXuh52Ol9cVQ2CANMy0KTC64alrFVb13ygUgVETGtdVENYqVyjbKw9vCBYv7aCF/5tTTAbQuD6YUiZEAIVgFLBSy6ey422MtZE81o1+UW+1zUhvHbYWKs6riUwvtiGsiaqPNdfD7SBkNhhGMZ6EqYpXqA1vNhTrZRCBeHzut1Yd2MNSyhDOkqn1QzFuFS0ndBT3Ww2ETJkprfrDTzPoVmtUVhcpSed5amnnmbz2CZKhUUW5i/w6M8foL66TLTTJh+xsR2fqKYhZEA/DtKO4nkeMekTBRK6SSUI8DQNX0Ak0GlpHhYCicJQClsohNSIoaHpOu3AI2NZdFyPhFAklY6vAtro+CgCDSylkBpIpfCFSQOdouzgajFmUGx90+vZffBiIj39RBJJ4vkMwrKIpbLohoEiTCnVNIVlWSQSia4NSyJ0HdE9oL+4k6CUwnixMO4OxGpaSFCzLGv9mq4NuL748WJBvX4A/K8ini/akFbZbI5Go0E+m2L3zj0cP36UwPVYKVbYs3MHhXKJer1JodZhvtSkvT6oJ7H1sE0ovYD+pGCgJ4fQVBilmojTanXouA6ZdI4AmF9YYrmp0Wy3sHVBLhklCDx6s2mG+/LU6mUsM0Sf1BtVEvEUmmGwvFLAjidYLZQpOxI/0IhYgu1DeWJRm9tfcSsP/uRHGJE4mqaxXKoxtVig3vYJ0Ojp66VRKTOYSxONQCaTYaCvl7mZGTRNku0ZoV6vMTl5jn3btxBIj7mVClUl+PCH/5BsXw+bd+zhocce58//8Pe44tLLKBaWiEhJceF8yLL02vTl8+sbR73t0PYV5XKZfF8fhUIdvBbCCEH9QknikSi6Gd48o7qJaRsoqVEoFDFNEzMaYbXUpOMHNFyFJgROALaQ9CWimIbP2NgGzk3Nk45bjIyMkMn2c+jJx9i3bx8zSyuM9KTJ9Azw7e/+jCuv2IEuDBYLNc5dmGF4cIjl1RXGNw4xkE0zNTXFnkuvorK8hGVZDI4M87+5e+8Yybb8vu9zbq6cOk93T/fk/OblRL73NjEtl2ISbYugRVugYZOGBDjQNGjDIghaAiRbMmQBlkTQpigYJLHkksuw3F0vN7y3+3Ke8CZ3T890rOrKdeumc/zHqdtd0/sI+K9Zay8w6OmqrntvnXurzvd8f9/f99usbzD0AwQQ+i06XR9lOTTbPsVikTeu3iGRD7/0fboq1HNH5zlRKbJ4TlCeOcrL39hgqmJS3wqpVBQ7O4q/9bmn+IN/+4ccPnmKL339Ms//4Bm6210++2NZbtzvsLXSZflIlcPnbfq7JeYWXQ4fs3CTJe6+t46R81i51cLMKGzy+KHPYxeO8Po7t/nVf/yX/P3/fIrCoRa7t2ZJhif4vT/6KqdPZNhuOExNzUC7Trfb4t994Ze4s7XOibPThJsV1jfeZyt8m6/+UZ1PvfQCje2QG1dXWZg/jnB9rm4nPPekye/+87fIWg7/5T/4WYQD+XyRv/rTN3nhpXOs7zQ4cmKSdv8yt66GLC0dodsKmJtd4NJ7N5ianeTa9VWeePw89zdu4Fklrl6/zESlSrGaZ2lhkZW1VR658BS9zg49v0c2n+GdNy/x1BOPaq2hkdDv+wRBQLMVYjsxU7UpIhlhIInDDEIkVGpZhBAMhhE5L0Nzt0+24FHMFXn/0mXCoXYQmJ7N49iaBbEsg34not3tkSgT14TaZJVCMUu322XYD7X7gjQwlEG3VyeTyWJZDkGkwUrGsJC2jZAK01IMfYkpJI3dLbxsBoCrl1d4/sXn6DW7+MGAbMZGYWEZgk/8+h99TzTPodThGCMrYB2bIcQeuEobyrRTgYGJwEKQcR2iKMJ2zD1AFsQJ0hxFPQcBoCcsx9LNPWEYYtrWHhsspBqBFAc5sl0Lk1j7R48Y7yRRRDLRLJ5S2EKD1j3gzihhTiU6ohqDZOR/rCUHCTYGyhB7Jd4o0QyjKbQEwRY6arwndRy4NWLhrRFLlYLVaGQdNxwxYLrhTgPISiHPSy8+6Zb8ewAAIABJREFUy+REifpOE8MyOXlkHs+GV964QqVa5MjiIbIZl4lqgbX7W3zllfdwbYfPvPgYC/Oz3L27yfU799la32Bjq4Fp2sg42WPCxcjaKQVvqXxDKQ18wzjauxZGWhkwDJBKS1BGEgM9BjFxCqSVwjZNoiTRrLRKr71CmBowSzSrn4yOBdp2MOvYvLe6+VDv3fYgUCplE1NmER5gHsfZxnTxlLKU6Sal1j3HUTLyA2YPYIMGx6n9WTSSzKSgzhxFtKcgO70W442MoD9TcRhijJxVlGKvKjCeVRHHkb5/Y/3ajOsyHPqIWNFo7dLe2aFaLmMKiIcBG1ub2IFBt9ulXd/CMZR2KMvXKJYy7G5ss3rrNrWJChsbG7z9xpuUcx6i49NPhuRNm0ICju1RMgx6QY88ERnhMFAhJhbKkGSkwrY9lIz1QtGw2VQxW0rSKlSZnZ3h2R/6DCW7xMIT56GQwXEz2FmPQq0yAshZkArbdfaqADr1zyRl9E3DwB41AMcyfoBJ1n0Gcm8xMx6GM76wTK/zXprmWFVivPKQLpoytvHvP3g+P1scGfEYDPodirkcec/Ctgza/ZDp6UkyrsntlXUiKfByJa7d3yKKJTYGkVJkLItD0wX8zi6ZjMfTFy7w4eVL5PNZWv0+s7UKdibPZrNFFCoa/ZDFI8d46+13UVJyeLpMp9VmeX6Gv/eLP8+ffvHPaTabVCslhnFCvz9ARhEdP2K3F1IfDDmzNM/axgYFRzd2HDk8R8G2iOwM9+9tEAmTG2t1bNehH4QYSmFaNhdPLmHJmNOnl2nUO7zyyls88shREjIgEogDLl68wNqdO8R2lpsr9zh14jhXbt3ll37lV/g3//v/xuzCYW7fvo0lDO7dXaOWM8i5FtO1CraAfhCSCMG5J57lL/78ywziBD9K8GwbVIKJGE1KgqNHllhZWWEqnwMV4oxsXzDBEoKtRouhsvATCOL0BhSYhEznHEpFj3KpiMRlfn6Odq/L/Mw0QeBz5dJV2t0O0xMlpmcW2d6t8+RjjxIMBrx36RJb7QH3dppMFjM8+/hFnnj8IjevXOL6/R3mpybY3q5TqVXxfV2+T6KQXnuXwTDEsG12Gm09QXsu7918uF/iAL//9xZVU2zz9HMXedtehVgy3zzJ2vUGpw4dpd6tcPnKd7ixUefv/uxP8ief/2tqywN+4KUiXqZCvhDxp793j42NPPMlDzdb4Oj5PIsLkxhCuwgcO3oG0+oy9GMsQ9LYalDIVHFLFkaugHBhd22LwnQR06lhJ13wAwq1WXr1Hpfeu8nqyjrrDYFIOuzU2yyfrDJZmWHQtfjKt95ncSrHG2/c5tOfmqZWWeKj6/eZma2xevcOudIcQoYkUUCumCMMh5DA0aOHCIKIfK5Iu7VFdXqCXN7gzu11Tl04xu79dTw3x0RtjnevXGVp/giOO8TMCGzhksnncDMWwXDA9KEJUC79ZhfTtVhdWSebKzEznWXl6hqHT8yxdrfP+699hG0HfO7nPkGjOaBSLDIMenRbPuub60xMT2GbgkE3pDSZY2e7wVRtCsuyyFcKvPbyaxw/eZooSgj8Lo6XpVLO0h/2CfoRmUyGVm9Xfz4SQdHJkhjQbra0lEUIMEx6vY62R3Iz2K4EZWOaBvXNJpVKBd/3aXea2IZDkigMVxAHkM+VGIRtomHE1MQkf/GFL5OvFPi1P7v10O/dn3ruMSVU2vi1r5eNlUQJyFiOZoqEBhNC6O/otDdrGMUoDBJ085lUI2mAjMk5HgoNMsMwxBK6VyHWCg0NRgxBkOjJzTZHHrexxI9iYpmMjqknOy9t9hk1VEVynw3UjXgakEsxloqHwLFNpBJIFY9s20aNZChMoYgjiYwTAhljO54mYlCEUuFHMcM4ZhgrhFB7jg1BpMfKMCWe6WIZOvzi4oWzHDkyz6WrN1lYOMSFk8t4luBL33qbWqXEscUpbNdiYXaanj/gC3/1KrGEl565wNxEmZW1TTbqLd58411CqZgolvfizNOxSBcMURKPFhr7YNBA4NppE1zygFxh3GlDyxPknsbZYD+Oe/wYiZKIkeXX3r0xigQ3GAXiCMGlh9ww2PVDlb6n8fN+IKZ77H2n91DqDjIOqFKWOn18fMxSYJY2qaVOHQ+CY/UAqB4/ljHG3KfPpWNp2LpakF6n1FpNSn0uQRBgWAYGgmikp7YdiyAIcMYcJuI40XHrQcxwOCSXy6KkpN8bEIYhuXyW1k4D27LwbIvbt28zPTWBMCzW768xU5tkc3OTnGMzHPS5v7pK6AfkcjldwVCSeqONUJKpqRk26tuce/QxrIxLvlrGKRQIpAatmUIex3MxLAvLsfcYYcuyRk2yzneN7d54y5E7Bg+C3vRvYb8xcFw+A+y7qRzQtaevHW8uHW8s/b4Az8drrgqGCabrEkURs1MVXAPmpiokEq7dvMWhhXlUGDIIQkw3w+bGLj3fpzWUVPJZev0BZ84cxSai22lydOkEmYzD5tY6llI0O31qtQpxLMH1sN0Mjc6AK9dv0fNjJssZyhmHqWqFbmuDz3zqh9jYabC1voFlmdhulrXVOwwiaA2G9HoBn/nkc9y+dYdOt8mFU6c4e/oE16++z+TMIrfu3OVeo8fKxvboBjIwFYRCkDVNJvMuM9MTnL9wildefhXLsnj2uRf4y7/6MnMzkzxy5gQbjV2azSbDQYDjGJiZDNubDQJpsLVd1zG5jkM3GJI3oVb0mKhoDe211W0iBcNIYlk2TT/CEJKy69IZBiTpCgz2gg8soGYLpqYnGAz6WEbM7NQU97Ya1Psx/SAa2UZJskA551HIGxxdPoJt26ysrHDy9AnW1hu06nWmD82xeus2GDZYNj/9uU/z7W9/m6WFQ+QyLkGsWL27zvyJMwz9DsvTE8hoSKc3oNttYwqBkoInXniJL3zhCyShot1pMTtZxbZNNnZaIASeLSiUKnztzSsPHYD8zj9aVknR4Su/e52njpxidzPP5JTHu9fWOPfoBGsf1Zk7cZx/9u9e5syJKo8/d5JPPprjjz9/lc69DpbwmSxPcm9lBdsrcfTIPK3OOrZTYLPT5vq1Nn/nx5b5p1/aIlADYhPmLJO8ZzNnD/nZ/+AFMqZJp9um4iVIz0DEA84+copv/PFb/If/488iuz79lqTd2tVl7n5MoTxNs3kPyzGZOzRJ634dp1Igd3Ge+fISwrQJI4mTyZKEO/zGf/oP+aEf+wS5vMfExBx37tyh0+pTyhfIlz1Wb21SLpeRwqZSLXD5wxXquzsUShaTtXlmJqdo1Ntceu8qXsbGMEKKxSwb99c4fuwIubzJYCBYWDhEs9lGGJL7d3fww4RO12fxyDG21leRUZFjZyYoZ7JcvXmFU+eOEgeKYj7Hyup9pmcXcOwYL+diIAkCH6V0cp0Qiigx8TIGfi+gseUTMaRcLBLJiEI2y+5uk4mZWS5/eInpyRqTkxP0BhHHTi7SaXTY2WngZUz8XoRpCiISarUa9a0dpqcmefvND5iYmiabzdPutTER9Lo+SBPTSXQzrzII4whFgB9ElPIVPvubX3z4VnXPXlS2aWkgphKENDBM9iUZ7E9iupvA2PM9ti0L2zCJVaydNIRuUDNNhQGgdDMdKaudaH0uhokRSWzL0M13cTySZSgGodaRKhI9mdo61dTGQBiKJB6FOhk6hAUhtLuPYZBECYap9mQarqmBRyw0w2075uj7ziSSClPF2IZ2EElihRFLhGtjCEUnSJvxNLuayiMAMLS3NehGyzgOEcLUDgmWyZOPnadUrlIp5yl4DoWczR9/5VUOzUxgoKjWKhxdnKZUyPLO5Ts0OgOePHecXq9DEClW7tzntXcvUamU8BxXB60YAhlrEB1Eob4mpoGQuvTNaHw1EytJYg3ehKndI0BgWSZK7VcTlNLNhM6IjVVj4NMA3QSHZubjOEaJfdBhoBsG5ah8/tHGwwXPgzBRyZjE5OMAU4p3UiCWspbjj6WPjwM52F+wSSl1Ut3Y4gEedCFJt4PstDFWtZDJmA5XJQ+woiljuvd8qjcfOYNol5r02CNQbqQgUo5ds5G0atRAqhKpK0OuQxToio9pWwx6fcJgiG05e1pvz/OQcYwaySBIJMo09psjTZN+b4BU2ifcGt1zhmHgui6hTHAsLXnZ89UWAsux98CrIdCe4iPXnZRxdxx79H7lAwuQ8fFIxyldqIz/Pl4lGLehS6/TAwB9bJ+GYeBZ4t9/8Hzx8LSK44S7W7uEEqpZwTMXz7KzcY8Xf+B53nn/A1rdHt3egJmJCr3egMnJGpEUrG9vE0uLmZky9XqHQ4sLdHe38bsdpqeniWJ9M9aqZS59dJVyuYZhmrQ6XZodn64f0gkl1VyGI7NVnn7qCb71jZcp5DKcu/gob732bToD3Y1bKFZZWd8kiKHrByxMZnjm0adoNjcYDAb02h2eevYZwiDhoxs3+fD6Gm4hh2vqLtnnf/BFPvjwHZIkYXl2hkG/zdLiIT64cgtFQhhI/DBgslbCUBIvk8ECev0OluliWRarWw0agwjHEJSzHr1hQBQlFD2DjGuScS0Gw4jbmwMSAxKZ6tvQAGLUNY4hRiU+g6wjCCKBaSV4EioZC9NSlCpFet0h9c6AoRSEiQbZAFVP4ShBuZSlWptkp75FuVigkK+xXt+h3e5iWBatdh/XsfjN//kf8b/849+kXC6xMDeFjHxmp2Z578MPOXn6PF/9+jc5fXSe08ePEUQhW1tbmE5GG52XJlhZWaHfC/it//Vf8Ku//J9x7NQy9++u0el0mawWyGazfOuDOw8dgPz9n7HUh68k/NzfeYx//Tsf8t/+8hKf/+M2T38KjNDjndfqfPrF87z1dpPTx0r8qz94E1mC/+nXf4KMrbj34SYb19d45PGjdBsZ/vCvvsUPP3eGucNz/P4XX+Hi0WO8f/kOzz5zjMmqRy5XYKJqsb62y0DFeNJi5vAZbOrsNtv85V/fYuXuCp944THefvcW/rBPrxvzo595jHanyc/8rcf5zneuECqTUtng2MlFdjcanH9siVAEXDj/JNfe+pDf/j/+mM/+wjne/toav/hf/8fcu9bk0nvXmJh02b7fJgwUn/jRxwiHCe1uB6lslBLEccjczBRb2+sUawVkYoGKyOYdNu41aOz2OXZsDsN2UCph7f428dAgl7UwpUGnHeCHJifPL9Pv1tne3GFhaRrLzOCrIdW8AyO7sm6ri99NePeDd3jmmafo93ts7bQo5vK88s0POLo0y+zCBFOzk1y+dpeZ6RymSrh1vcHy0hTTszmUNGi0uoSDBD+IAMmxEwv89v/1b/ns536U/rDNcNDnjStv81/8/H/FreuXUbGgWCtimg62YbC53WBl5S4XzpwkiGI8zyOOA/q9kK31BoePHKbb75Pz8gz6uyilKyWdTotarUan1een/snD93n+1GNnVeqeMN4cppvL9N+kE8/IPhVrBKIsc1/3rEY6ZBIAPdmFKtnzFzYRGKYGXbYw9oCelIluOksgkDFxoh6wCxsHSGESa41yWpZP2b0U0MQRruvqiVVJrbs2DJRhPgCGpJRYpkOcaMu4fhCSJFqKpqTEsm2EMPCHIYGMx9hYqQNbpGAgY6LRe/AsE8vSUoduoEvvp48foZzPYlsWh+Ym+dZr72oCxrSZOzTN0xfPoJKYbm/Ibq/P0uI0qyv3MRG8/cEV1rd3OTI/TybjEccxnV4XgxFQNNQoQW1fHjAOiPVjBrHcbzJUSuGMgFUY79vMpYsCMaYDNkaLHSF0BLcG3wYi9c9N5KgCAXJ0Ttc3Hr7bxkFgpG374j3da8r+plsKZg+C3nRLm9TS+2scVMex9oNOtz3AOgbU0sfTMUwZbSHEnu+5VA9qsR8AdMmo+VFpz2rdnKulTKncZJyBhTHrRKUwhNbDe2PsbgpQ02OkLL1h7wPO1C0sSRIsYX7XMWKVYJk2KnWxGD2uRp/zkZ34Pusr1N5i4SA7bNs2idxfwIzr0cfv4/RcYR8Apw5cKfBNX/tx1/LgYir9+3FJB/D9oXn+rV/9B+oP/u/f4269SydUmCrhpz/5NLdvXGF5YZ7DC/O8+f5lFpaXePX111g6tMCjFy/w7W+/zMLcArWpSVQU8ubbb5CvzuB5Lp5lsn5vhcXlkywsLPD666+jDAOESWO3xTAKMb0s97c6KCCTcRiEEReXF3n6yYus3b3L5vZ9qpUptrfrnL/4CO+/9wHX725gWaBiqGVtTp5axrUUk5PTdNs9Nrc32Kp3sR2HVm9AsxdydHmWqVKJYSyxVMDlG6s8efEipojY3NrCMF0KOYc3L62yUMtiZfNATOwPKJVKFPMZtjZ38HJZYmkSBgHlvEen36M36FMulvWqN44p16oM/JD13Ta7vVCnZAnjgS+ayYkqrUadXDZLMBgQKd2NrpRgwlVUCjkMEeEPY+JY0omgH4/uHyHI2TZTlRyO0A0Sqa7p2NEj3L61hleo8P5Ht2kPeiTSoOSauK5LqVqGcEgpnyEcdjhyeIl81iUIAhLbYfvefRbn53jsiSe4fOUaV65c4ezFJ5FC8I1vvszQj+kMhtiuQ9Y1cE0b1zNH2rOQle3eQwcgy7ah5guQyZeZeiYk6faZVi/Sqd9i/qTkrZdDnn/iDGWzw9bmgE986jl+9V9/kc+9cJij5woUqlM0rt3EScpcu3mfJPSo1gwWZudYXbnP2nabo7NTHF6aIVtN2FhrUypnqczU2NnqIKKEKIZEBOTzeTq7A849eZQr79wg67lkS3lMS1GoZPnw8k0KXpZSyUUJmKoWGEpJY32XY4dnGPiSJIrZaDWwXYNe28LOdLl9p4thFQl7LYJul9rMDJPTZZ549BjBMGa326KQ8YgD8IoWq7fWdeNeYFObgZnZRbbWNsgUMzieIOj61A4tIOMh0dAkV/D4iz/6Co1ml+NnTzM1kUVFIeVMmSu3b7N8bJ6zF8/xtT/9Cs+99BS9YQ/HsTCFjt1t7nQolSq0dhuYQhKbJo5pc+XKFbJugVgKpqaLBEPFjRs7fP7zr/Cbv/WTeI6WoNg5F1tYdNs9hAlZ22ar3uTGjft4pkNlOU8pV6W/G/Dqa1/nycd+ACMb0Om1uXH7DlFQ5AefOM/m9i6B7zM/d4g33rpK6A85fWaR+ys7fPonH2V3KyQIfIrVPHEkiYZaxtDvdPmFf/mdh37vvvTIKWWaprbbMnTYFOw7LYx3qKclaMO2ELEcazhTOtpbSDKGjTDUKE5aEkv2QIxpORBHWCNr1URJ4kjqoA8pHpjwZBIhE/ZY41hJtDpBPcDSCcASYI50uPHIYs0x9P4kSltbjdLmJLphzkTo2G10c5ItDGQswR5JAAwTXykMqRBJTKIkw2hEOgiJUAaW0Gy8EErX7AwtV/EHgQbotsFUJceF0+e5vXGfoR+Sy2dYPjzPiYUZdhttYgNur25QKedh6LNyf4eNnYYOeikWyDj2SBtqYxm64TGO9p0lJPtATUqddihG0hmhFIbS+vVIKcwUNIz53iql9poEx0FlakMXS0ksFYkSOJaxx/ilDG56Ha6tP1zmuTeMVHpspXScuDwAfsfPL33sIOgaL+Onj6Wgbpy9TLfxz0TKHI+7N+wB2dFx0ns1ZaJTIJ6CVdM0SeLRPsS+BSFKjBpc9+fstFk0BdKpznpP8zuqAkSjeyG12hPGg8dPzz2170vHSIhR/Hs6RqPFcaoJT0FtEI0Y41FT5DjQNQxjDxyPL2LGFzfpc6lMZvw6jH/fpGM6DqrT/R20nhtfSP1NFYT0fY8z198Xso0ff/aUunJlhZ1uwNAQSGXy4mOnaK2v8COf+TRf/epXeeqpp/jmq29y4eJ5drc2CMIYaQoWjp9g/dJ7VKtlihMzzC8u88Ybr+O6Lu12m16nRW1ykvfevs3ZC0tsbu3QHUY0h5J+oD1JTVPfhJ5rM10u4febZB2HR8+f4+VX3qBQcNlsBhw9PMFus0dvEHLq6AxH56eZyBV5/doNji3McOvWHSzH5unnXuS1V99i8fAcb7z7AbWJCmXH4vnnf4Ct+jZ37u9w5vgRbn10iZtr65Q8k/n5RT5creOIhLV7GziORSmXIZf1EEhkFFEul7XlTadHrpghShS9Xg/Lssjl8jTrDfL5PN1Bn41Owu4gIJvNMhgMsCwDNUqJEklE3gbPMQgx6Q8jImVgIXDQX8ATRQslJIVShZ16k06oE79ioGhCpeji+wqpQuYPzTDo9pidmWDpyFH+8E++xvRUmfV6i0gKBIpCzmVhpsbmzi6lXI5a0eXYkWXurd3lxLEjCJGwsbHD/Pw8tpCYTp4vff0bmKarFznrO/jhEDuT06tYoZuHhBCkDbb3GoOHDkB++dFFZRgGA2niWRZLs/P83tde5UefOs7qRpNDFY/tpo/K1Pm5T1/A35S0Iig7FlHJpl/oUi5XEEnC21+9SdXLcuH0UdbrEcpSxJHg6996n5eOLiBqLhMTeYRlMpQDihNVeu0hqJAgVLzy8qv84i/9JJ1GiIoTWt0O+YJuVpMyZna6RhIavPbaB1SrNYSQLB2ZpdlsUCh6NDttlDXANgoU82UQNpZjkrNdDNegvrFNGIPtwM5mnUD6XDh9kfc/usXh+Txe1qFSnqHTbiKEoNns4zkZZmZrBAzJZ7MEgwgMhW2ZmLZBNDSJkz7ZUoZoGNHv+8hAkMlatHd7VCarbG82qNaK9Hs+hjKQQpLz8vR7XVo7PrOHa/iDHr1ORBhBHA3AzNNs7GCYionpCepbdSrVGRYWipiOhe0ofD+g29S2fP6wg+255ByP7e1tPC/LwNcTRTAAzIjNnQ2q1XmkGuIYgusfrXHixAm+8hdf5aUfexoSAYZBIZNlMOhhCIetzV2kSpidmOWP/ux1Ljx6jm57nas3urxx9S7/zX/yOMsnjvCpX/vDh37vfvbx00oaAiH15BbJBDAOTEKjCSxtIFQj5lkorNGiXI4+h57Q1nXRiJWSan8/47pU00DrbxMFpkESK6TYtxnTDWDmmK5Ts85BMmIWTRNH6GAQX2om1ESQxCGu66JiLUlQloVrao31fgkYpNCR11EyYhbRfy9GMjEpwLPMUSR4hJICQyjMka57FP5HogQY+1pLU0AYxgRBgJS6ya9SzCG1VQWTlQoL05OoRFcy85UJrn50i62dHYShMIRFIeuScVxymSy2owGTkiMgYxv6OzjW4S1qFPSRsofDJNpb5KQAIo61rlkcAIGmae75Ro/rhsHYS4JUSqfaYRh7gHqfndbhMomCG5sP122j64dqPPDiQcs9sQdO0+dS0DXOYh78f/paYA/UpY+lgPKBxeQYuwz7TaWp28NehWQEDlPAm+qAU3CdMrwPfE4wMK39dMQ95wm5zyanTOw4IB3f9lQ2ah9g743ViB02DC3NGAfm6TGjJH7gvVqGyXA4xPM8/b7U/nmNyyWU1Mcel0qk2ziwTeVW41rl8etwkFUeB8IfF4gzzlofPG76PsbPUwjx/RGS8h/90DMqCAL+n1c/YJBIhIRSLsOw7/PYiRmiKOJv/9zP8aWvfJlr1+9w+vRxRJJQLGTY2t7FD3102wPMzc3tdcm2220GQ59Dc4tcv3kDJQTdXkTTjwBIpEBY+ktUjEqMDlAtORAJqkWXUilHtVplcfEIb7zzJk+cOcvNtTtkrAyVnMAfDhkOInY6PYoZB1uAXSgwCCWffukF3nzrHc489jS9nXt8cOlDrEyB+tYmpXwegN1OnYWJGr0AVrdbGAKCMKLb8ZmaKo78Vw1KOZdERrSbLbLZLE42R7PZxLZtsq6HGNnfOI7D3a0mu8GIKUFPBDp5ykQmCbaAyZytvxAtk74fIEyLXj8kEaAMwZRnI1SsmSbLpd3z8SNdiq1mDCxDECQxCRAGilJefykcWZjh9r06tmnQ7AVEtkvOiHDcPI12i0fPnaLd7VFxTc6cPcEH77/H0uIC87PztNp1kgiK5Ry7u7usrG3iR5JmL2S72WY4jGE0GRazGYZRQBwpEPoD1A8fvlXdrz1/Qh1eqNDthFRLk9RqAVfebyJdC2Xa/OV33qBayNEzZwiDTYrAmePLbGysUastsCvW+alfOI9jOoTDiLAR8y/++bd57JlpFt0SR5anWV1tcf7JU/z+51/TaY35GU7OF6lUXBaXi4SGiRkKukGbxGthtCvMLy/g+z6N5i6HDlUQpmYLbGFwf2uLXL5IHBmUcxaDfsy9zTpnzh6nWW9gWRbDaICwErKZAvmMw50b95ladNnYalEpLRIaDXqDDTIZj/WNHu3eKqaQ7K6U+Imf/jT3VzfZ3mly7MQyG2tbZN08wogwHRNii1KlxjDqkss7ZDMupmVhGTZ+v4/l2WQ8G0vYtPsDZCCJ/D6DKMC0bWrlGlKFBBGU81V2d1t0Gz7zxwr4fp9csYTqSwIZ0eg2cYRNOPDBspicLHBvtYubd8i4Bp7rEoUx2byBa9msrO0ABrOzU/S6EWG4S8Yu4quAYSek1eiwurbOIxfP0G71mJieQJkBg1ZAxnERjkN30GWiVEPGQ+5v7HBoaY5hL6FQFdRyHnfub7Ldknzw5g2Er/iRH3mKH/7Hf/LwZRsXTyvXtIhFsqcHTk/iIIOz/28/hMJQkPBg6dw2TIbRcFT2RzNUsdYwp5N4GjqSGBDGEUEi8cbsxDAEwVADwlhJEiUxDR3OohINrKNEW4glCBxTO0rYpmAYxsRCYWPsfS8EQy0DsVyTMBixpqZBFGkAI5WWt1mm5t0NwyCWEiUMgijCSM99BCwty8JIWXFTfz9blo7wjqKIfhDq8zcNbGUQJBGJUmQcB88xiElYnp+nVsqztr5DoBQeqd1fhkRJcllPu49E+wDNMkySONTWfpa512yWuk3EY0ynUrpBUKaOGoncs5iTMhlpusWeRja9zqkUg7H7INVGa22tIlFaggMaW1/bfLgx7iKxAAAZI0lEQVSyjUGY7DUMHgTL6fvX5ya/CxgfZCXH2eF0O7i/8deNVz5S4D2+2ExB+cc1Mo4zpONAb1wfLYTYs80bZ8IfANLsg8XxLUkSHNfer/aMAHYSa8C8B0iFQI59HjOeC2jc4TgOAkUUJw+ML1LtgeLUls+2bWSi9pr8QGu1LXu06EvHDh5gjcclQ+liY1ymMQ6A0799oKF1bJFzcAGU7mOcXT/ITKdg/ftC8/zskao6ceIUN+6s8+HKBv0wZLZa4uLFi1y/dpm851IrFei1tjg0O8Nup8vi4hK9TgtTGJQnarRaHXZ26oRhwPT0NPl8nrWVu0ihaDR3GfoJiWHtra6CIOLY0iJJ6FNvdlhvdEedzZJqKc/R5cNs37+DsDKUMyYJEi+TpVHfIU4kzzz+KFv37zK3eJitlTU+9dmf4Gsvf5N33r7M2aMzvPDJT3HpymX8fo9nnn2B73z7G3heFoliZ2cHicFEpUoiI+LBgMzEFG+/9T7Hjh/hvcu3KZUKDDpt8oUctpAsLxxic2MUPuI4dAY+pqldMQwZk83miWKtK+oNfW5t9IgU1Koldprt1MEIBNhAwYB8TuiuWgx6vR7doSTGYKpcwBKSVrtHEOmgA9s1CIYJAoUQUK0V6HSHbPb1QkQIQd5UnD26gFfI0dztcOPuNsVygU8+/TgffPg+R46fIohCes0tSGCyVsQ2BSdPnqTZbqNiRTBosdFojRYNFtfXNukGiq1GEzuTxR/0yTgWTzz+GO+/8y7DOEZKzQLF8uGD53/ykxfVRNmmWM4TSc2GR4MhYSxYOjLJF791DRa69IcJO/f6HD45w269Tdm0UVseZ6Zn+WD9NuXlEvNTOuBjfqJCOBB8/cvf5sf/7ucYNCQd/wbnTrzEztYmhhRE2HiVDJe+cxkzNrlze4Wjy8vcW9+mkLHxkxiZ2BiOYmqqRrFgsXqnTmU6x6H5RdbXN7HMhCQ2aXR2GXQCjh9e5v333+f46SMolTA3M8n6ZoOZqQka9Sa31tYIRZOLjx/lo0sr+EHI3GyVSrGMgeD2nWuUxEk+eO8W5x5d4sz5YyQiwnEspIShH7PbaHPs+GFu3bxHrTaJVEOS2MSwBKYMMaTJ3Z1tStk8nYFPIiPKlQxTU1NEfR8sk3w+z87WFpmMi+3mKWSzNFs7hMHoy9QSFAoF6o0uYuQq0Om1mZ09hGlC4EcIK0u5rLi/1uLajbucO3uYdrNDxitw9959ogSOHZtA4WEoyOQMiLMEYZsglGSyLuEwwHILmFaCihXNZpdB36c/6LCwsMC99S2WFmfZ2W2Sd3Pkyh6dZo9yrcydm3oBbijF7Rvb/NpXP3ro9+6nHz2jtaOjyWmPkYO92G09ue9PYmrUvKfQ0gU9IY7AxWiCt0yhtdFSpwSGI+mFtpJTe1poYC+2W6Eb92IEiYx0+MRIV2wkWgqSpF7PQicW9oYBjmFimwYqiUaAUT8fqoREgue6yCBAmAZS6IZHJSWJ3N8n7FuUfZeeVSr6SaJdJqQOYImiCNc2QWkHASVjncI4wl+xTHTTllJYSvtUq1inEtqOiWFZ5FwHZ8SG9X2fWEKllKdarmDZWueKMhiGgT6fkTPIx8kIklSyYpp73tnCNEhCDWBSa7kUHmqJixwxisbe85IHWcpU1mEKocd4TMpgCkGk9Ljd3Gp+T2Qb48D4YMl+/HnQGnkhRs0/PMiMjmvrU6Z1HPDus/jx3vMH2c1xMJhWSz4O1KXHHmdA09enP8cZ5XELto9zodg711HFSBjf3dA4DtYPnsvePRTr5kiF/C7AmY6PHr/R4mq0iFZSO79oWZF6YFExzvaOj0cKbtNzGx//g5WA8TE6uCgZB+MfN54H5Srj1YDvG+b5+WMT6tD8Ihv1Fu9evYuPwjUkBS9Lozeg7ICw9VfuTLXI0uICtcoEndYuSvmUyzM0210GfhffH9JsN5g/dJhOp0O726HfCwiCiEwmi+OYSBWzOL+AlDGDfp84jun2tL54p77LD3/yE1z+6BaGEXDy2Am+/vK3WJqfp1arcfXqRxhSUpiosjw3w872JpXJOaIwplKbYHPzPsNem+WlRXL5IrdWVskXStTrdbrtLlOzE1rYnyuSBAFxrBuM6q0WjUaDudlZLt9Yw3UU63Uf2zAoZl0KeYt+p0up4GEJh8FwgB/FeJbDMIzJZFw8R8dr5jNZdts+VsZmo9GnNUxItYxCQBaYKuoGCMMYxdtGCUFk0A8iykWX4SAgk3EJggjLMfGDhEIugxzFYxpCsdOO6IH2I5URrgHTRY9iqYxMtHVOaHoMBz1efPwsX//m22QKDpVykWLG5PTZx7j83lssHT3C2v17TE1MUsxnKeSKXL1xncPzi9xavcvb11d0HLs0KJby+N0OS/NzVIoujWaH7XqHMEm+J8zzf//JY+rw3Dy3btcpV1xCv8PxwxWIK/zBN19lcvIQG4Pr/PzPPI5UBodmagw7Q9pBn8Tw2VgLWHmnzuH5w7x97x4Xzlb56699xK//xoucPf8UtHcZZgr8zj/8M77z/i3OHJ3mR376LO++c4/ZiSW6rQBl9jl3YQ5DOSTDmHCQYHox9fUWGdejvtPFQLHRCPSkbyoWp6awc4r33rlBruCxfGaBYtkGIXAMbVo/tzDBys0NyhOT1Jvr3Ot8yEz2MDKOeevSFQ5NLjM9XcBxqyTtDoXqHMNYcvXyNjJu88M//hjBwMfvh9SmJ/B9n2w+x9Dvo6Sg1+/g2FkM0yIMAiqVIomIiENFNuvRafXJOi7bu7tMTNQQRoSSNp7n0PPb9MJd6v27FMUcM7XDDPsB/iDCsLUESTkwHPjkC1VUEhMPfQrFLP3BkFanj4wM/LBPvuCRy5awzIhYSqJhjDIEWddjOAzptNpkvByO4xFLhWFogy/H0nGxQiqSKCaTtRn4iiDwITEYdCLaLZ/ZwwW8rI3v+3zr65f4gefOka1m2Vzv8pUvf8jZ44f4H77y/veEeRZC7AFlxkuno8cMBaalUCIFlvthESaCMImxRl35Eh364JqGboCONYsshI4chn2gIqXEUPssUZiMXDvihMQwMM2RZABtDSeVwDZ0YEsQxnvBKc7ID9oUBuHYhG9KHV3tWTaDJNGgefRcGEfogDJtu2YK7VyAHNN/jvTTSRIjDYs4UQRxggIsU41Ap0Ap7WqxP+lrDbVlpNpj7Xa0XzLWqXV7gGFEedumSalQIJPJYNkGKAMMnYpoGeYD5fmD4CiOY904ucem6sf9RBMbmpU392K2o0Q3aqa/a5Cc7nMUrkI6Y7B3XF1NSDXx+8DmYcs2xpnn8Z8HKyUHAfDB38d/ptvf9NpxsDbeQDh+HVJAOA5a0218f+Ms98cx0eN/93EOEgfB/EGAe3AcwijGNB7UfqcSC8PUAHjv/aFwbOuB46X/xvcpGGPLjTFrwNE+DwL2g42A464cB1n7dPzGAfH4+zoo6Th4buNjeRBQj4//94Xm+fnlqooV1CYmuHr7PvXuEMtzkUmCQULWy1AoFBBJgEhiji7Po1RCvjjJwO9gGSaDYYBSCa1WC2HaDIchW1s7WJbB0NcdxuVKBnuUXT87M41hGGxsbFCtVml3+oSBj2OaiCRkdv4wg2GfgqfDUgZBiOXY2JYW4m9ubmG5Dk9cOMXqRh3fH9IfDDh1/Ai9QcD8RIXZhSWuXfmQW/fWKWRzZHMejpfj1q2bZHN5DAGO4+C6Ht1eD8+1uLe9gyNMbNvlg7tNlICcUNTyDp6jfZdNS5cn/WFCmIBtZ4jjEM8R5LIe4TDCzXhEkQ92jrvbTfz4wS+KWsallDMQSYxlWQRBRM+XGhgIm5lDc9y6vUoEZEztwwp60si6FpZlsN0e0o4FtoDpapHd3TaWJZgs51FKsXhojiRRVKtlWrvbzM5OsbXd5NS58zS3tpieneHDD94lCAKK2QyzC4skwOrKHXZ3d5mfn+fiI4/z+b/4Cnc3dgilopDPk3Et4qGPZ8D506cJ4og33rtC53sAnv+7F84qz0uwnQxBKKi3ukxmLXabbWJnBql8hmHAxYsTvHPrOnMzOc4dmUUJiZcxkb4klJJCMsFwN+T66n2eOH+a3/3Sa3zpVo9febLC3HyJheUj5D0X3ASZBDr0gAyHDuUZhAnbG3WCXkwm59FuDcgXMpSqJZRKqNUm+fMvfod+L6KYL3DlozsUS1mKhSxRKFmcLzB7uMTc/CSBLzGUJBwG1OstOo0WiSlInD6PPnqeO1e3ufDEMS59dINjR+YZDrqYloslFV6uhmXpyNpmawfXy2ORoISNjIcAmp23XCzLIU6GDLuBLhd6WVqtFtOzZfq9ANs26TS72FYGy8vQ2L6Hm7OwrQyu5dD3IzrdXSYmJxGmYHtzF9fKMjdfodPpUCgUdANNFOAPYq5dXefU8UUsRzAcxuRyLtKIuXe3zvTMJO12m0I2w+tvvseFR86SKzi4wqQf6TGr13dRKqFQKOF5HoPBEBUn9Pt9QJIrFnAcj2joEychpmOQJBLbciFOkLFLY7dFHEIUDblybZunnjpM3rHx4yF/+599/eEzzxdPK90glzZQgWWmaYLqAfZrbzIVYi+220gntDFgkU5slr3PXhkISB6cSG1rPwVQolndVGsqUuHuWBpeEiuCOMI2BX6gwbAhtNBESu1Ha47cPAzDYBgn+jFTjNg7RZwC+FGkdcqCS6nt75RSWIZNNNI/h6Noa2AUcqGIYy0bCeIIKTQLbxmaFTRQGEhs08KxTJyRq0EURQyj/fS/RI3cCdS+ptRzLCrlAq6rS+iGsHToCewx9akVWjqGKZhVSuGY1p6NYMos75fcR9puKfecNIKRTtoyvluukP4/GlvojP9NLBUovUAyDOOhg+d+EKuPA7bpuHwc8E3PPQVPB1lq4AGQmG4HGc7079LrdvB1B89n/JjjMpLx/YzvY3/xajwAwnVFaF8eAWMgWKm9xv3xfSg10jab3528KKW24UqZasMwiKNEu3DBnrvLXmOkKb4LkB48vySWWLb5seOa/hwHwuk+/qaFRfoeD95/6XUev44HGerxazG+je/r+4J5fnqprLKlChnPpdvzWd2qc68xwLZscpZFNw6YyGbpBkNOLMzxiReeYO32LXZ2dpg7fJTrN1fw/b5ujhv69Hq9UaSs7hyOghjLMnBSk3HbolTI8OKnfpw3X/82K6urlAplDMOilBGUiy6BytDvDxCWwez0JN1On0K1TLfdYXNzCxPBMFaE4ZB8scDdO+vYGY9yIcvxY4fZXl/jxIlT9AZdPrp6i+LUHBtrK7huBts2kTJmYW6aRr1Jq9vlxNGjNJtNOv0+lqEYxiYfbXSQUuICOVvxwrNPcvvmVSqVKoOgz0YzZr3R1g04QMm1cICMayGlolhwUKbDoNfhTkshZLLX+FPxTCZKOdq7HVwHEmngh5JS3qbe1Xo9pKIndRHXMRRHZqfo1Hc0s2NbKNNgraWTxFwhyGcsBn6IMgS2Upw+scTy4Vnm5xe59O6bZPMlzp07x199+ctYlkWlUGBiZoZvfudNjs1OMTFZ4+T5R3n5la/T3O2SL2Qpl8tcunmbgR+z1R5gjWx/pisFRDjgsUdO0fcH9PyE1688fKu63/rxR9VMLUMndLm7eo/q5ASdts+h6Sz9XkQ+A6Vyluvr9/ng9jZOVfLi+cMsTy1RyLlsrDSwhEFMRK5WxGRfy6iUIlswqdd3kXHEzkaHRCnm56bJVyrkcoJy0SNRgsZWB8fOc+WjG8ShSxCETNXymJ6gOlEhiBMyuYRctkirGeC3h9xd3cIuDHnk0WPYZp5S0aG528O2BAKLrGuQGBG9wOfORy0qk2WOnFhCSd0Q2Kr3yZUzRIOEXC6DFEo3UwlB7McIW+K4GfqtPr4fYQpIhIEVgR+HVPJFhKGDSQZ+gDQD7m+vsrkZc+L4Ua5dv8nQ76II6XQHLM0fY3JihihK6PYCcjmPOEzIlW08x2VzrcHS8jRSgD+IcT0DL2sjw4RgGIFS+LGunOxs9ZmeLRAEIbGEbrdPpVLCUBZhGCMswerqHRYX5xn6kiAIsW0LESt6gz6Lc4e4u3qPfq9HFJmcfXKJqBsTqgDDsEikj7AtRKybq9y8N3LEMYlkROxHlCcL7O60KOYL/PBv/OlDv3c/+cgpDUKkRI6BigfAhVKatRW6TM8opc9zHNJpPB75zTqWPZKAjDX+jU2yaYOhPUpzi2UEykKqmIw5atALhtouL23SQusnhaEYhrGWI0TaMNNU+thRWk4fMVtRHBOPyTA0S6yT+DQQVSPXD4MojAmSUfOSaY/A7Qgwj4F+pRTC0lrrSBp7ID+Vu1iGwDUNso6DJEEw0sdKgVRqXxohtX90CqTSJrJcxiObcbEMU0tMkjGWUYxYc8w9l40kih8AIDByNhkBqWRsvo/jGMsw9hoHpZR74zM6KZ0eKPbT+B4AR2P7ilQaq8JoAZJwY7P1PWGe9anvL9gebHzkuwBvOl7jgOzjGN90+zhQ9nGLib/JMm38NX8T2z0OgMe3jzuXj3uP4xKPcQZ6nK1OF8PpcawxKUj6unQsTNMkjOK9z58p9h05EqXvk7QZUd+TOuV5fEExPh4HgfPHpf4dXGgcbPD8OCY//X38sY8D7WnD6Pj5pOfy/xU8f3fg9/+PtoEfkHUD6q0Wk9MTzJWLtFsDhjKmHUQszc5wb30TgGoxT7fZw7A9coUC9a0NijkLA5dmvQGWzaAfYJsWwoB8dt/z0e9LLEcbe/d7AZfee51uqz1qtggwDZN2b0CpWuETzz/Pv/k/f49iscil5i6VSoW1yxuUSyUs12NjfYdOP2R6foYbq3V6kUJEAcXJOVqtNkKYZLNZDM/DzW2xs9ulPQRHBYh+RN5x2Nyu0+8PQAi2G3XiYUA+kyE0TLrNkFiBQKEMg1qlwI2bV6iWyrTbXfA8gn6H2YkSm402EkHHjzEN6AUJhYwAHGana7gLS2TW7tLpdLnX1hq6/jBBqC7lUg7b1F/Mve0uluUiiYhiuddwCArHMKlv7+A5+gOXjKQ1RcsgFhCECZ1AgUQ3I5hQnZ3n26++wUTpErlsmdOn5nnjjTd48tkXeOeNVwmlZO3uKp5pcHtzC6+U41/+q99marpKv9tDCsh6GY4uHOb67duYCianJtna2iJXLELssXZ/nWohQzHrfi9uXe7c63FzpaE9uW2DvAVThxwMS1DI5Xj7w/vMdROydoUnlnJM1bKYEWzdr9MtugR9n2CYUJkqkMvYdBsDlCGYX5wlin0syyIMJEHis1yuaZutwRDDjPGHgGXRb/tIITFEwpGTxzEM6HTa5EvuKLzBwFa6zN0LmviyhSoEHL9YplZbRiWg5ID/t7376XGbiMM4/sx4bO+uN6RbdbeFFrhzQuLC++DG6+Gl8A64cUWi4gAvAgkkFui2ye7Gf2bGHMZJvFYPw6UU6fs5JpHiOJHz+Dczv/GjU1lZ2dGpuaj1wScrDX3Ut998rx9+/lWfvWjUdd/pq6+/1MXFY119+FjbzSs9f/qxuvZWhYna3nmdndcyK6lqzlSensrUUY9MI2cL3V3fqg2dKlOqvduq27Vqx07GS86c6u/frbpdr5c//qKfXl7ri88/1ZOnV3rxkdHV1XO1bavz5kR11eh+t1FzXin0g7abXk1T667dqe8HtTuvs1BpGAqNIciaUpqqJ9vbnerGaRxTd4T1eqWL9Vp/Xt8ojIM229d6dvlEF48uJTk5ly64Ve3kKqOz1Yl+++sPFVWh6nSl5tzobrNT2weVpdPm1Y0un63V94Pe3NyrrtP0J2MKWdcreKuyrrR9vdMYnW5u3vwnv93h0Iv2WN2c/+lK0qhU+TVGqoyRtcc5nb0ZZafh/qosFUOY2oZNw7mSiiItFhqCV+enkBqmoVVXyIe0qO2+S5t/uKJUDFFj8ApRGmKn0hZKi/+t+s6rdE4+RA0hqo9+OnajIcYUDqcQaXzQSV1p8D4Fd6UOIdZKYYjahT7NsR6NYpRi7A/BozBWcThuBRxC0G7oVRWVChvlpoBfOemkrlRMW01579X7QcYd++GGcZSfNroIZlQZ09bf6c9fqqbuFceh77SpyT4Upc0WjxtshPHh/FVr0xbaklJ43oem2fP7QNL7NNI47hfETTdOoysOjz2sKBr5cUxVdWtVyqY57NNrKvewUvguLCvLy57Je8tOFvPq7zx87s0XsC2rxMs58fOK9PxGcbnwcP8e82NchuVlJfZtlev5Mc8/4z4cLqut8xGEEIJ8iGm0aPrdleWx1dz8PdOahXQNqKtyNqJk0mhDGA8bzhzmNpviwXVjHlSX52Teb3nf5WP5OeedP+bf0fw1ywr12yr686r+8lz/m2Lye115BgAAAN4n7/72EAAAAPifIjwDAAAAmQjPAAAAQCbCMwAAAJCJ8AwAAABkIjwDAAAAmQjPAAAAQCbCMwAAAJCJ8AwAAABkIjwDAAAAmQjPAAAAQCbCMwAAAJCJ8AwAAABkIjwDAAAAmQjPAAAAQCbCMwAAAJCJ8AwAAABkIjwDAAAAmQjPAAAAQCbCMwAAAJCJ8AwAAABkIjwDAAAAmQjPAAAAQKZ/AEAjZeUB3W77AAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -169,12 +171,14 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -655,9 +659,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -669,9 +673,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch06_Multilayer_Perceptrons/Dropout.ipynb b/Ch06_Multilayer_Perceptrons/Dropout.ipynb index 498f03d4..6fb65351 100644 --- a/Ch06_Multilayer_Perceptrons/Dropout.ipynb +++ b/Ch06_Multilayer_Perceptrons/Dropout.ipynb @@ -797,8 +797,8 @@ " [ 8., 9., 10., 11., 12., 13., 14., 15.]])\n", "tensor([[ 0., 1., 2., 3., 4., 5., 6., 7.],\n", " [ 8., 9., 10., 11., 12., 13., 14., 15.]])\n", - "tensor([[ 0., 2., 4., 0., 0., 10., 12., 0.],\n", - " [16., 18., 0., 22., 0., 0., 28., 30.]])\n", + "tensor([[ 0., 2., 0., 6., 8., 0., 12., 0.],\n", + " [16., 0., 20., 22., 24., 26., 0., 30.]])\n", "tensor([[0., 0., 0., 0., 0., 0., 0., 0.],\n", " [0., 0., 0., 0., 0., 0., 0., 0.]])\n" ] @@ -914,16 +914,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch 1, loss 0.0034, train acc 0.681, test acc 0.776\n", - "epoch 2, loss 0.0021, train acc 0.808, test acc 0.768\n", - "epoch 3, loss 0.0018, train acc 0.832, test acc 0.809\n", - "epoch 4, loss 0.0016, train acc 0.847, test acc 0.827\n", - "epoch 5, loss 0.0015, train acc 0.855, test acc 0.815\n", - "epoch 6, loss 0.0015, train acc 0.861, test acc 0.812\n", - "epoch 7, loss 0.0014, train acc 0.867, test acc 0.842\n", - "epoch 8, loss 0.0014, train acc 0.872, test acc 0.839\n", - "epoch 9, loss 0.0013, train acc 0.875, test acc 0.849\n", - "epoch 10, loss 0.0013, train acc 0.876, test acc 0.850\n" + "epoch 1, loss 0.0034, train acc 0.675, test acc 0.779\n", + "epoch 2, loss 0.0020, train acc 0.807, test acc 0.775\n", + "epoch 3, loss 0.0018, train acc 0.832, test acc 0.827\n", + "epoch 4, loss 0.0016, train acc 0.844, test acc 0.831\n", + "epoch 5, loss 0.0015, train acc 0.855, test acc 0.788\n", + "epoch 6, loss 0.0015, train acc 0.859, test acc 0.843\n", + "epoch 7, loss 0.0014, train acc 0.867, test acc 0.832\n", + "epoch 8, loss 0.0014, train acc 0.871, test acc 0.857\n", + "epoch 9, loss 0.0013, train acc 0.873, test acc 0.805\n", + "epoch 10, loss 0.0013, train acc 0.878, test acc 0.848\n" ] } ], @@ -964,9 +964,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -978,9 +978,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch06_Multilayer_Perceptrons/Forward_Propagation_Backward_Propagation_and_Computational_Graphs.ipynb b/Ch06_Multilayer_Perceptrons/Forward_Propagation_Backward_Propagation_and_Computational_Graphs.ipynb index ad4d76b8..25ba9010 100644 --- a/Ch06_Multilayer_Perceptrons/Forward_Propagation_Backward_Propagation_and_Computational_Graphs.ipynb +++ b/Ch06_Multilayer_Perceptrons/Forward_Propagation_Backward_Propagation_and_Computational_Graphs.ipynb @@ -488,13 +488,27 @@ " * What are the advantages and disadvantages over training on a smaller minibatch?\n", "\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -506,9 +520,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch06_Multilayer_Perceptrons/Implementation_of_Multilayer_Perceptron_from_Scratch.ipynb b/Ch06_Multilayer_Perceptrons/Implementation_of_Multilayer_Perceptron_from_Scratch.ipynb index 6a2b6c85..3f807fe9 100644 --- a/Ch06_Multilayer_Perceptrons/Implementation_of_Multilayer_Perceptron_from_Scratch.ipynb +++ b/Ch06_Multilayer_Perceptrons/Implementation_of_Multilayer_Perceptron_from_Scratch.ipynb @@ -181,16 +181,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch 1, loss 0.0031, train acc 0.699, test acc 0.779\n", - "epoch 2, loss 0.0019, train acc 0.820, test acc 0.825\n", - "epoch 3, loss 0.0017, train acc 0.840, test acc 0.844\n", - "epoch 4, loss 0.0015, train acc 0.856, test acc 0.853\n", - "epoch 5, loss 0.0014, train acc 0.863, test acc 0.840\n", - "epoch 6, loss 0.0014, train acc 0.869, test acc 0.848\n", - "epoch 7, loss 0.0013, train acc 0.876, test acc 0.813\n", - "epoch 8, loss 0.0013, train acc 0.882, test acc 0.840\n", - "epoch 9, loss 0.0012, train acc 0.883, test acc 0.858\n", - "epoch 10, loss 0.0012, train acc 0.887, test acc 0.871\n" + "epoch 1, loss 0.0031, train acc 0.703, test acc 0.700\n", + "epoch 2, loss 0.0019, train acc 0.817, test acc 0.787\n", + "epoch 3, loss 0.0017, train acc 0.844, test acc 0.842\n", + "epoch 4, loss 0.0015, train acc 0.856, test acc 0.773\n", + "epoch 5, loss 0.0014, train acc 0.863, test acc 0.783\n", + "epoch 6, loss 0.0014, train acc 0.870, test acc 0.826\n", + "epoch 7, loss 0.0013, train acc 0.876, test acc 0.861\n", + "epoch 8, loss 0.0013, train acc 0.881, test acc 0.851\n", + "epoch 9, loss 0.0012, train acc 0.885, test acc 0.833\n", + "epoch 10, loss 0.0012, train acc 0.886, test acc 0.782\n" ] } ], @@ -218,7 +218,7 @@ "\n", "\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", @@ -431,7 +431,7 @@ "z\n", "\" id=\"DejaVuSans-116\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -444,7 +444,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -460,35 +460,35 @@ " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", @@ -567,7 +567,7 @@ "z\n", "\" id=\"DejaVuSans-114\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -578,7 +578,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -592,35 +592,35 @@ " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", @@ -658,114 +658,114 @@ "z\n", "\" id=\"DejaVuSans-115\"/>\n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", @@ -802,7 +802,7 @@ "z\n", "\" id=\"DejaVuSans-105\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -810,7 +810,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -821,91 +821,91 @@ " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", @@ -933,14 +933,14 @@ "z\n", "\" id=\"DejaVuSans-99\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -950,40 +950,40 @@ " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -991,7 +991,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1002,35 +1002,35 @@ " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", @@ -1063,7 +1063,7 @@ "z\n", "\" id=\"DejaVuSans-100\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1072,7 +1072,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1084,32 +1084,32 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" @@ -1191,9 +1191,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -1205,9 +1205,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch06_Multilayer_Perceptrons/Model_Selection_Underfitting_and_Overfitting.ipynb b/Ch06_Multilayer_Perceptrons/Model_Selection_Underfitting_and_Overfitting.ipynb index c6787f8e..e3664f58 100644 --- a/Ch06_Multilayer_Perceptrons/Model_Selection_Underfitting_and_Overfitting.ipynb +++ b/Ch06_Multilayer_Perceptrons/Model_Selection_Underfitting_and_Overfitting.ipynb @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -84,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -94,18 +94,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor([-0.6245])\n", - "tensor([ 0.0000e+00, -6.2449e-01, 3.8999e-01, -2.4354e-01, 1.5209e-01,\n", - " -9.4978e-02, 5.9313e-02, -3.7040e-02, 2.3131e-02, -1.4445e-02,\n", - " 9.0209e-03, -5.6334e-03, 3.5180e-03, -2.1970e-03, 1.3720e-03,\n", - " -8.5679e-04, 5.3505e-04, -3.3413e-04, 2.0866e-04, -1.3031e-04])\n" + "tensor([0.1063])\n", + "tensor([0.0000e+00, 1.0626e-01, 1.1290e-02, 1.1997e-03, 1.2747e-04, 1.3544e-05,\n", + " 1.4392e-06, 1.5292e-07, 1.6249e-08, 1.7265e-09, 1.8345e-10, 1.9493e-11,\n", + " 2.0712e-12, 2.2008e-13, 2.3385e-14, 2.4848e-15, 2.6402e-16, 2.8054e-17,\n", + " 2.9809e-18, 3.1673e-19])\n" ] } ], @@ -141,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -150,9 +150,7 @@ "\n", "dr=factorial(ok)\n", "\n", - "dr2=torch.from_numpy(dr)\n", - "\n", - "poly_features = poly_features.double() /dr2.t()\n", + "poly_features = poly_features.double() /dr.t()\n", "\n", "labels = torch.matmul(true_w.double(),poly_features)\n", "\n", @@ -178,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -196,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -267,24 +265,26 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "final epoch:train loss tensor(25.5834, grad_fn=) test Loss tensor(28.1122, grad_fn=)\n" + "final epoch:train loss tensor(31.9768, grad_fn=) test Loss tensor(26.1930, grad_fn=)\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ8AAAEMCAYAAAAbELt5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdd3xV9f348dc7OyEhgSy2CXspIAgi4h6AA+vedddWq7XVVn911H5btdq6FYsCYh2oIIqKgiiIygx7Q5gJkEEgIXvd9++PcwNJSEJCcu+F5P18PPLgnnM+55z3yQ33fT/jfI6oKsYYY4w3+fk6AGOMMS2PJR9jjDFeZ8nHGGOM11nyMcYY43WWfIwxxnidJR9jjDFeZ8nHGGOM11nyMcYY43UtOvmISFcRmSAiU30dizHGtCQeSz4iEiIiS0RklYisE5GnaykXJSJTRWSjiGwQkeGNOOdEEckQkbXV1o8SkU0ikiwij1asV9VtqnrnsZ7PGGPMsfFkzacYOE9VBwADgVEicnoN5V4BvlXV3sAAYEPljSISJyIR1dZ1r+Wc7wKjqpX1B94ARgN9gRtEpG/DL8cYY0xTCfDUgdWZNC7PvRjo/qkykZyIRAJnAbe59ykBSqod6mzgXhEZo6rFInI3cCVOMql+zvkiklBt9VAgWVW3uc85BRgLrK/vtcTExGhCQvXDGmOMqcuyZcv2qWpsTds8lnzgUK1jGdAdeENVF1crkghkApNEZIC77IOqml9RQFU/FZFE4GMR+RS4A7iwAWF0BFIqLacCw9zxRQP/BAaJyGOq+my1+C8DLuvevTtJSUkNOKUxxhgR2VnbNo8OOFDVclUdCHQChopI/2pFAoBTgXGqOgjIBx6tVgZVfR4oAsYBl6tqXvUyxxhflqreq6rdqice9/YvVfWeyMjIpjidMcYYN6+MdlPVbGAu1fpjcGohqZVqRFNxklEVIjIS6A9MB55q4Ol3A50rLXdyrzPGGOMjnhztFisiUe7XoThNZRsrl1HVNCBFRHq5V51Ptb4YERkEjMfpp7kdiBaRfzQglKVADxFJFJEg4HpgxjFckjHGmCbiyT6f9sBkd7+PH/CJqn4FICIzgbtUdQ/we+ADd2LYhpNgKgsDrlXVre59b8U9QKE6EfkIOAeIEZFU4ClVnSAi9wOzAH9goqqua9IrNcaYGpSWlpKamkpRUZGvQ/GokJAQOnXqRGBgYL33EXuS6dENGTJEbcCBMaahtm/fTkREBNHR0YiIr8PxCFUlKyuL3NxcEhMTq2wTkWWqOqSm/Vr0DAfGGONJRUVFzTrxAIgI0dHRDa7dWfIxxhgPas6Jp8KxXKMlHw/al1fMS99tZmPaQV+HYoxpgbKzs3nzzTcbvN+YMWPIzs72QESHWfLxIJdLeeX7LSzamuXrUIwxLVBtyaesrKzO/WbOnElUVJSnwgI8PMNBSxcbEUxkaCBbMprknlhjjGmQRx99lK1btzJw4EACAwMJCQmhTZs2bNy4kc2bN3PFFVeQkpJCUVERDz74IPfccw8ACQkJJCUlkZeXx+jRoznzzDNZsGABHTt25IsvviA0NLTRsVnNx4NEhJ7x4WxJt+RjjPG+5557jm7durFy5UpeeOEFli9fziuvvMLmzZsBmDhxIsuWLSMpKYlXX32VrKwjW2m2bNnCfffdx7p164iKimLatGlNEpvVfDyse1wE36zdi6q2iI5HY0zNnv5yHev3NG3/b98OrXnqsn71Lj906NAqw6FfffVVpk+fDkBKSgpbtmwhOjq6yj6JiYkMHDgQgMGDB7Njx47GB47VfDyuZ3w42QWl7MurPlm3McZ4V6tWrQ69njdvHnPmzGHhwoWsWrWKQYMG1ThcOjg4+NBrf3//o/YX1ZfVfDysR5zzKKIt6bnERgQfpbQxprlqSA2lqURERJCbm1vjtpycHNq0aUNYWBgbN25k0aJFXo3Nko+H9YwPB2BLRh5ndI/xcTTGmJYkOjqaESNG0L9/f0JDQ4mPjz+0bdSoUbz11lv06dOHXr16cfrpNT3r03Ms+XhYbEQwrUMC2Jxe87cPY4zxpA8//LDG9cHBwXzzzTc1bqvo14mJiWHt2rWH1j/88MNNFpf1+XiYM+ItwoZbG2NMJZZ8vKBHfDhb0nOxSVyNMcZhyccLesRFcMBGvBljzCGWfLygx6FBB9bvY4wxYMnHK3rGVwy3tn4fY4wBSz5eERcRTERIgNV8jDHGzZKPF1SMeNtsNR9jjBcd6yMVAF5++WUKCgqaOKLDLPl4SY84G/FmjPGu4zn52E2mXtIjPoIpS1PIyi8hJtym2THGeF7lRypceOGFxMXF8cknn1BcXMyvfvUrnn76afLz87n22mtJTU2lvLycJ554gvT0dPbs2cO5555LTEwMc+fObfLYLPl4SY84Z8Tb5vRcSz7GGK947rnnWLt2LStXrmT27NlMnTqVJUuWoKpcfvnlzJ8/n8zMTDp06MDXX38NOHO+RUZG8uKLLzJ37lxiYjwzLZg1u3lJxYi3dbvtkdrGtFiTLoEVHzivy0ud5VUfO8slBc7yWvfzcopynOX1M5zl/CxneZN7Spzc9Aadevbs2cyePZtBgwZx6qmnsnHjRrZs2cLJJ5/Md999x1/+8hd++uknIiMjm+BCj85qPl4S3zqYfh1aM215KneNTLRn+xhjvEpVeeyxx/jNb35zxLbly5czc+ZMHn/8cc4//3yefPJJj8djycdLRIQbh3Xhr9PXsiIlm1O7tPF1SMYYb7v968Ov/QOrLgeFVV0Oiay63Cq66nLE4Rmqa1P5kQoXX3wxTzzxBDfddBPh4eHs3r2bwMBAysrKaNu2LTfffDNRUVG88847Vfb1VLObJR8vGjuwI898vYEPF++y5GOM8bjKj1QYPXo0N954I8OHDwcgPDyc999/n+TkZB555BH8/PwIDAxk3LhxANxzzz2MGjWKDh06eGTAgdjQ36MbMmSIJiUlNcmxHvtsDdNXpLL4/11AZGhgkxzTGHN82rBhA3369PF1GF5R07WKyDJVHVJT+RY94EBEuorIBBGZ6q1z3jSsC0WlLqYvT/XWKY0x5rjjseQjIiEiskREVonIOhF5uo6y/iKyQkS+auQ5J4pIhoisrbZ+lIhsEpFkEXm0Yr2qblPVOxtzzobq3zGSUzpF8uGSXXbDqTGmxfJkzacYOE9VBwADgVEiUttzWh8ENtS0QUTiRCSi2rrutRznXWBUtbL+wBvAaKAvcIOI9K3vRXjCjUO7sDk9j2U7D/gyDGOM8RmPJR91VExmFuj+OeKrvoh0Ai4B3qnlUGcDn4tIsLv83cBrtZxzPrC/2uqhQLK7llMCTAHGNvBymtTlAzvQKsifact3+zIMY4wXtIQWjmO5Ro/2+bib01YCGcB3qrq4hmIvA38GXDUdQ1U/BWYBH4vITcAdwDUNCKMjkFJpOdW9DhGJFpG3gEEi8lgN8V8mIuNzcnIacLqjCwsK4OxesXy/IR2Xq/n/YRrTUoWEhJCVldWsE5CqkpWVRUhISIP28+hQa1UtBwaKSBQwXUT6q+qh/hgRuRTIUNVlInJOHcd5XkSmAOOAbpVqVI2NLwu4t47tXwJfDhky5O6mOF9lF/aNZ+aaNFbvzmFg56imPrwx5jjQqVMnUlNTyczM9HUoHhUSEkKnTp0atI9X7vNR1WwRmYvTH1N5MMAI4HIRGQOEAK1F5H1Vvbny/iIyEugPTAeeAu5vwOl3A50rLXdyr/Opc3vF4e8nzFmfbsnHmGYqMDCQxMREX4dxXPLkaLdYd40HEQkFLgQ2Vi6jqo+paidVTQCuB36oIfEMAsbj9NPcDkSLyD8aEMpSoIeIJIpIkPs8M47xsppMVFgQpyW04bv1DZufyRhjmgNP9vm0B+aKyGqcBPCdqn4FICIzRaRDPY8TBlyrqltV1QXcCuysqaCIfAQsBHqJSKqI3KmqZTg1pVk4I+o+UdV1jbqy+nKVQ9ZWyN9X4+YL+sSzKT2XXVmee2aGMcYcj2yGg3o45hkOCvbD84lw8TMw/L4jNu/MyufsF+bxxKV9ufNMq5obY5oXm+HAV0LbwBXjoMfFNW4+KboVPePDmWNNb8aYFsaSjyeJwMAbIaa2e2KdUW9Lduwnu6DEi4EZY4xvWfLxtOwUSFtb6+YL+sRT7lLmbsrwYlDGGONblnw8bdZjMK326eMGdIqiXesQpi3z+ehvY4zxGks+njbiIbjkxVo3+/kJt41I4OfkfaxOzfZiYMYY4zuWfDyt02BIGFFnkZuGdaF1SABvzt3qpaCMMca3LPl4WmE2bP8JinNrLRIREsitwxOYtT6N5IwmmTnIGGOOa5Z8PC11KUy+FDJqfGLEIbePSCA4wI+3frTajzGm+bPk42kdB8OtMyC2V53FosODuf60Lny+Yje7swu9FJwxxviGJR9PC2sLXc+GkMijFr37rK4AvDk32dNRGWOMT1ny8YYdP0PamqMW6xgVyk3DuvDhkl32lFNjTLNmyccbpt0Fi9+qV9GHL+5Fu9YhPDptNcVl5R4OzBhjfMOSjzdc/wGc/Wi9ikaEBPLPX/VnS0aeDb02xjRblny8oeNgiOp89HJu5/WOZ+zADrw5L5nN6bUP0TbGmBOVJR9vyNwE66Y3aJcnL+1LeHAAD05ZSUFJmYcCM8YY37Dk4w1rp8Gnt0N5/ZNIdHgwL143kE1pB/nDlJW4XPbcJWNM82HJxxsG3w73J4E07Nd9bq84Hr+kL7PXp/PC7E0eCq5l2JdXzL68Yl+HYYxxC/B1AC1C6/bHvOvtIxJIzsxj3LytdIgK5eZhXRCRJgyuZbj/w+Wowse/Ge7rUIwxWM3HO4oOwsqPIKvho9dEhKcv78fIHjE88flarhy3gIVbszwQZPNVWu5i+a5sVqRkU1Lm8nU4xhgs+XhH8UH4/F7Y8dMx7R7o78ek207jX1edTFpOETe8vYgbxi/iy1V77F6getiUlktJmYuSMheb0mz0oDHHA2t284aI9vD75dC6wzEfIsDfj+tO68LYgR15f9FOJv2yg99/tII2YYFc1LcdXaLD6BgVSv+OkXSPC2/C4E98K1OyK70+wMmdjj7VkTHGsyz5eIOfP0R3a5JDhQT6c9fIrtwxIpFftu5jypIUZq1PI7ugFAB/P+GL+0bQv+PhD9itmXm8NW8rf72kD1FhQU0Sx4lkZUo20a2CEIGVKTncYt0+xvicJR9v2fQNlJdA37FNcjg/P2Fkj1hG9ogFoKCkjJ1ZBdwyYTF/nb6Gz343An8/oaTMxQMfrWDdnoN0bhvGA+f3aJLzn0hWpWQzoHMUAqw6jp4W+8bcZE6KDuPSU469RmzMicr6fLxlyXj4+WWPHT4sKIA+7Vvz+CV9WZWaw4dLdgHw+g9bWLfnIJ3ahPLewh0UlR7uI9q+L59RL89nQfI+j8Xla7lFpSRn5jGgUxQDO0exNTOPg0Wlvg6LnMJSXvpuM89/uwlVz9zDtS+vmIc+Xsn+/BKPHN80TzPX7GXpjv0eP48lH2+58m247SuPn2bswA6M6B7N899u5Lv16bwxbytXntqR5648hX15JcxYtQcAVeVvM9axMS2XJ75YS2l58xwFtmZ3DqowsEsUAzpHoQprUnMObc8vLmNvjvefn/Tj5kzKXMqu/QWsqhRPU5q6LJXpK3YzfcVujxzfND8ZB4v4w5SVPPH5Wo+fy5KPt7SKgaBWHj+NiPB/Y/tTXOri7veSiI8I5qnL+jGiezS920Uw8eftqCqz16fz4+ZMzu8dx9bMfD5cvMvjsfnCqhTng31Ap0gGdIoCqg5AePjTVVzy6s8Ulnh31OD3G9KJCgskyN+PGSv3eOQc36xNA2CW+9/j2f78Eh6dtpqMg0W+DqVFm7RgByXlLjam5Xp8ZKglH2/J2Ajz/w1FnvmWW1nX2HDuP687fgIvXDOAyNBARIQ7zkxkY1ou32/I4O9frqdXfARv3TKYM7pF89KczeQUHG6OOpBfwo+bM3lzXjJ/mLKCT5JSPNY8dCzqO93QypQDJESHERUWRGRYIF1jWh1KPlvSc/lmbRr780uYscp7tYPSchdzN2ZwQZ94zu4Vy1er91DexNMn7c4uZFVKNrERwSzduZ+M3OP7Q/1f32xkytIU3v5pm69DabHyist4f9FOhneNxt9PPP5/wpKPt2RugB/+D3JSvXK635/XnUX/73xGdI85tG7swA7EhAfz+49WsDu7kL+P7Uegvx+PX9KXnMJSXv1hC+kHi3jyi7UMe+Z7fj1xCc9/u4n5W/bx56mruWtyEpm5vp+iZt2eHIY+M4d36vFBtSolhwGdow4tD+gcxcqUbFSVN+dtJSzIn64xrZi8YGeDk2t2QQl//3I9q1IaNoghaccBDhaVcUGfeC4f0IGM3GIWb6/5xuG1u3P4YWN6g44P8K27tvN/Y/ujCrPXNfwY3rIyJZuPk1IIC/JnytIU8ottIl1fmLJkF7lFZTw6ujcjuscwY9Uej37hbNHJR0S6isgEEZnq8ZP1GgP/by/E9/P4qcBpfouLCKmyLjjAn1tOP4nC0nKuGNiBYV2jAejboTXXDenM5AU7OOv5uXy4eBdXDe7IR3efzqqnLiLprxfw5KV9+Sl5H6Nens/kBTvq1TxSUubi0WmrGf3KT+zKKqizrMulzN2UwR3vLqXnX7/hnveSWJC874g//pT9Bdw2aSn78kp4/ttNJGfU3jSQllNE2sGiQ81tAAM7R5GZW8yS7fuZsWoPNw7twl0ju7J+78EGPz3271+uZ+Iv27nizV947LPV9e7Yn7MhnSB/P0b2iOGCPvGEBfnz5aojm95Wp2Zz3X8XctfkpAYnuG/X7qV3uwgu7hdPYkwrZq07PpveXC7lyS/WEhcRzH9vGUxuURnTlnvnC9qJKCO3iJlr9jZ5UigpczHh5+2c3rUtAzpHMXZAB1L2F7KigX93DeHR5CMiISKyRERWicg6EXm6hjKdRWSuiKx3l3mwEeebKCIZInJEb5mIjBKRTSKSLCKPAqjqNlW981jP1yABwRAU5pVT1eW2MxL49fCT+Oslfaus/9NFvUiIacUlp7Tnhz+dw7NXnsLwbtFEhgbi5+c02X39+zPp1CaUp2asY9iz33PVuAU8M3MDb85LZsqSXSTt2H+oOSy7oIRbJixmytIUdmXlc9VbC9iw92CNMX2zZi/n/mcet09ayurUHC4f2IGlO/Zz4zuLueil+bzz0zYyc4s5kF/Cryctobi0nA/vHkZYsD9/nrq6SpNVQUnZoVkfKoZVD+xSteYD8PDUVfiLcPdZXbliUAciQgKYvHBnjfHNWZ/OrycuYce+/EPr5m7K4LMVu7nrzETuHJHIJ0mpnPefeUcdOaiqzNmQzvBu0bQKDiA0yJ8L+8Yzc01alal/tmbmcdukpUSFBREbEcyfp66u99RAGblFJO08wOj+7RERRvVvx8KtWWQXHH+j3j5JSmF1ag5/vaQPI3vEMrBzFJN+2WGzuNdgT3Yh17y1kN99sJwvmrif8MtVe9ibU8RvznLuR7yoXzzBAZ7rjwTP3+dTDJynqnkiEgj8LCLfqOqiSmXKgD+p6nIRiQCWich3qrq+ooCIxAGFqppbaV13VU2udr53gdeB9yqvFBF/4A3gQiAVWCoiMyqfw+NUYcGr0LYr9LnMa6etLjIskKfH9j9ifWxEMHP+eHad+/aIj+Dz+0awJSOPb9em8e3aNCYv2EFxpQ/F2IhgLu4Xz4LkLFIPFPLydQPp16E1t05cwrX/Xci4mwYzrGtbAv39yDhYxBNfrGXWunT6tG/NazcM4uJ+7QgK8KOotJwvV+3h/UU7+cfXG3j2m43EhAdxoKCU9+8cxtDEtjx1WV8e+ngVkxfs4MZhXfjvj9sY92MyrYICuGX4SWTkFhPgJ/Rt3/pQfH3aRxDk70fK/kJuHNaF+NZO7fBad80v45I+xLU+XGP8JCmFR6etxqVwzX8X8v6dw+jYJpS/fraG7nHhPDKqF8EB/lwzpDO//2g5d05OYvIdQxma2LbG3+HWzDx2ZhVw18iuh9ZdPqADX6zcw09bMhneLZod+wq4a/JSBHj/rmFsy8zjzslJvD43mT9e2POo7/GsdemowuiT2wEwun87xs3bynfr07lmSP0fauhJqsqynQd4ftYmhia05fIBzr1Od5yZyAMfrWDe5gzO6x3Puj05/OOrDaRmF5BXVEZhaTnXn9aFxy/pQ4B/82q4UVX25ZWQW1RKblEZoUH+9IgLR0TYk13I9eMXcSC/hN7tIvjbl+sY0T2G2IjgRp93Z1Y+r/6whV7xEZzTy7lvMCIkkPP7xPHV6j0e+117NPmoUzfMcy8Gun+0Wpm9wF7361wR2QB0BConhrOBe0VkjKoWi8jdwJXA6GrHmi8iCTWEMhRIVtVtACIyBRhb7RyeJQLL3oWEkT5NPo0lIvSMj6BnfMShG1YLS8rZX1DCsp0H+HbtXqYuSyU00J8P7h7GaQnOh/DU357BLRMWc/OExfgJxLcOIbeojNJyF38Z1Zu7RyZW+QMPCXQ+0K8Z0pnkjFymLd/NDxsyePryfoc+2K8Y2JEZK/fw/KyNTPxlO6kHChndvx0lZS5enrMFgJM7RhIS6H/ouMEB/vTp0Jo1qdnce9bhWSduOf0kJvy8nQ8W7+Ih9wf8+PlbeWbmRkb2iOHhi3pxz/+SuG78Qk5LaMveg0VMvfcMggOcY/dqF8EHd53OdeMXcvukJbx/1zAGdWlzxO9vzoYMAM7vHXdo3cgesUSGBnL3e0lUfOGPCA7go3tOJzGmFYkxrfjVoI68OTeZUf3a0bdD6yOOW9k3a/bSLbYVPdzTLJ3cMZKOUaHMWpdWr+TjcikzVu3hle+3UFhSTs92EfSKD2doYjRn94wlKKDhH0RFpeWk7C9g2758VqVkM2PVHlIPFBIRHMDTY/sdmql9dP92tGsdwjs/bWdzeh7/mb2JqLAgzuweQ3hwADmFpby7YAfb9+Xz+o2DiAgJbHAsjfX9hnQe+2wNF/aN54Hzexz6AtMYyRm5/PGTVayuNuw+NiKYM7vHsGznAQ7kl/DenUOJCAlkzKs/8dSMtbx50+BGnffbtWk8MnUVAoy/dUiVGfMvH9CBmWvSWLgt69DN7E1JPD2CyV3rWAZ0B95Q1b/UUTYBmA/0V9WD1bb9GTgD+BS4H7hQVfNqOcZXqtq/0rqrgVGqepd7+RZgGPAU8E+cGtE7qvpstWNdBlzWvXv3u7ds2dKg665RaSEEhjb+OMe5wpJy/P3kiA+pnIJSZq1PI/VAIakHClB1BkZ0jT32uej25hQy6uWfiG8dzN8u68cZ7gEWyRm5/G/hTk5LbHvEDAKz1qWxN7uQ20YkVll/26QlzNuUSYCf4FLFpXDJKe158doBBAf4syurgBvfWUTqgULuGJHIk5dVbboEp5/puvEL2Z9fwlk9Yykrd1HuUgL8/AgN8mfJ9v1EhQXy9QMjq+w3c81eknYcICYiiNjwYE5LaEtCzOGh+QfyS7jwpR8B6BYbTlRYIMEB/hwsKiWnsJSycqVbbCu6x4Xz0pwt/Pbsbjx8ca9D+//9y/W8v3gn/71lMMWlLgpLyxCEAH8hwK/ifVLyisuZ+PN21u89SN/2rendLoLNGblsSc+juMxFVFggl57SnvN6x9E1JpxObUIREVL2F7AlI4/84jIS3AmzpMzFt2v38tVq56bFisTqJ3Bmj1iuGNiBi/q1Izy46nfgN+Ym88Is5/lVo/q149krT6ZNq8PTQn24eBdPfLGWHnHhPH5JX7rHhRPfOpiDRWUk7djPkh37aR0SyFWndqJdZOMTQ2WfLE3hselr6BAVwt7sIgL8hdtHJHLj0C50bus0q5eUufh0WQqTftlB5zahPHB+jxq/iICT6Cct2MG/vt1IqyB/fnN2N+JbBxMRHMj+ghJ+3rKPn919nxNvO+3Qcd6cl8zz327izZtOpV1kCF+s2M3SHQcY2CWKC/rEMbxrDDmFpWzLzCM1u5DEmFaHvoiVlLlYsesAn6/cw0dLdjGgUySv33jqofgrFJWWc9o/5nBx/3b8+5oBx/T7EpFlqjqkxm3eGj4rIlHAdOD3qlpTn0w48CPwT1X9rJZjTAHGAN1UNbOWMgnUM/mo6v31iX3IkCGalJRUn6LGB/KKywgN9Mffr3HPOdqamcfUZakIzhx57SJDuP60LlWOm5ZTxGcrUrntjATCgmpuONidXchDH68kK6+YQH8//EQoc7koKnVRVFrOQxf25IahXRoc35Lt+3n7p23kFDgJp6isnNYhge6h9JCckcfenCJEYOYDI+lTqblx2c4DXDVuQb3O06lNKI9c3IvLTumAn/vaS8td/Jy8j+nLdzN7fRpFpU5Ta4Cf4Oeexqk23ePCuahvPD3jI0iIaUXX2Fa0rqPGkl1Qwh8+XsmYk9tzzeBONT6/av7mTO77YDm57pFxoYH+FJWVowqB/kJpueIncF7vOE7pFMWu/QXsyiogI7eIvOIy8tz7JUS3IiG6FV2iw4gNDyY6PIjw4ACyC0vZn19CfnEZbVsFERcRwoa9B3l9bjJn9Yxl3E2nsi+vmJe+28wXq/agCn3bt2ZE92i+XZdGyv5CTu4YSeqBAg4UlHJ2z1hG9nC+GKk6/XJbMvLYnJbLnpwizu8dx7NXnXzEICFwEpRLtUrLQFm5i1+9uYA1u52aUnCAHwM6R7Fudw75tdyzFujvtFrs2JdPvvsL4q3DT+Kx0X1qrc0+O3MDgf5+Vb7INMRxkXzcgTwJFKjqv6utDwS+Amap6ou17DsSGIdTi8qtLXHUknyGA39T1Yvdy48BVK/p1KbJks++ZKffZ8SDTTbRqDGVVSSmLtFHDm5ZsesAZS4lLMif0EB/FCh36aHZLQTB309IjGlVZ9NafnEZG9MOsi0zn2378il3Kd3jwukeF05EcADb9+WzfV8+JWUuLurXjp7x4R55AOKB/BLW7z3Itn35bM/MJzI0kKGJbRnUJYq0nCKmLE1h6rJU9uUVExcRTEJ0K9pFhhAeEkBEcABlLmVnlhNryoHCeg3ouPLUjvzrqlMIrJQIdmblM2tdGrPXpbNs1yu26zwAACAASURBVAH6d4jkjxf15JyeseSXlPO/hTt5+6dtVUZDBgf40TU2nB5x4ZzbO5YrBnZs8O8oOSOXV793kuHF/eKJCAmkuKycxdv2k7RjP7ERwXSNDadDVCjJGXkk7dzP2t05nBTdirN6xHJG9+g6vwQ0BZ8lHxGJBUpVNVtEQoHZwL9U9atKZQSYDOxX1T/UcpxBwIfApcB24ANgq6o+XkPZBI5MPgHAZuB8YDewFLhRVdfV5zqaLPmkrYXJl8E1k6DrOY0/njGmTmXlLkrLldAg/zrLqSp5xWXsyyshr6iMqLBA2rYKIjTQnwMFJWTkFlNS5uKUTpF1Jomi0nKCA/yOKFNW7qKw0ryKYUEBja6pnwjqSj6eHu3WHpjs7vfxAz6pSDwiMhO4C+gK3AKsEZGV7v3+n6rOrHScMOBaVd3q3vdW4LbqJxORj4BzgBgRSQWeUtUJqlomIvcDswB/YGJ9E0+Tiu8Hf97mDD4wxnhcgL8fAXXnHcAZSBMREljjAIbo8GCiw+s3qqzy4JbqcUQ0s9F5jeXVZrcTlfX5GGNMw9VV87FU7G3L/wef3ePrKIwxxqcs+XhbQRbs3w4u786ibIwxxxN7kqm3nfkH58cYY1owq/kYY4zxOks+3qYKH90Iv7zq60iMMcZnLPl4mwj41WPspzHGNGPW5+ML1/3P1xEYY4xPWc3HGGOM11ny8YXUJHhtMOxe7utIjDHGJyz5+EKrGIjrY30/xpgWy/p8fKFNAlz3vq+jMMYYn7Gajy/ZvHrGmBbKko+vfP93eHWgr6MwxhifsGY3X2k/AMpLnTnerO/HGNPCWPLxlb5jnR9jjGmBrNnNl1wuKCnwdRTGGON1lnx86eWT4bsnfB2FMcZ4nTW7+dLpv4Xobr6OwhhjvM6Sjy+dcb+vIzDGGJ+wZjdfUoW8DCgt9HUkxhjjVZZ8fGnnAvh3D9i10NeRGGOMV9Ur+YjIgyLSWhwTRGS5iFzk6eCavfh+MOpfEN3d15EYY4xX1bfmc4eqHgQuAtoAtwDPeSyqliI0Ck6/F6K6+DoSY4zxqvomH3H/Owb4n6quq7TONEbRQUhb4+sojDHGq+qbfJaJyGyc5DNLRCIAl+fCakHmPQsTLnJuODXGmBaivkOt7wQGAttUtUBE2gK3ey6sFmTgjdD1HFAXNv7DGNNS1Df5DAdWqmq+iNwMnAq84rmwWpB2Jzs/xhjTgtT3q/Y4oEBEBgB/ArYC73ksqpZm7ypIX+frKIwxxmvqm3zKVFWBscDrqvoGEOG5sFqYT34Nc5/xdRTGGOM19W12yxWRx3CGWI8UET8g0HNhtTC/egsi2vs6CmOM8Zr61nyuA4px7vdJAzoBL3gsqpamy+nQ5iRfR2GMMV5Tr+TjTjgfAJEicilQpKonfJ+PiHR1z9gw1aeBlJfBuumQmuTTMIwxxlvqO73OtcAS4BrgWmCxiFzdFAGISIiILBGRVSKyTkSebsSxJopIhoisrWHbKBHZJCLJIvIogKpuU9U7GxN/kxA/+PJBWPE/X0dijDFeUd8+n78Cp6lqBoCIxAJzgKaoMRQD56lqnogEAj+LyDequqiigIjEAYWqmltpXXdVTa52rHeB16k2Ek9E/IE3gAuBVGCpiMxQ1fVNEH/j+fnBXT9Y05sxpsWob5+PX0XicctqwL51UkeeezHQ/aPVip0NfC4iwQAicjfwWg3Hmg/sr+E0Q4Fkd02nBJiCM3Lv+BHTHfxtDIcxpmWobwL5VkRmichtInIb8DUws6mCEBF/EVkJZADfqeriyttV9VNgFvCxiNwE3IHTBFhfHYGUSsupQEcRiRaRt4BB7tF81eO6TETG5+TkNPCKjkHRQfjxBUhZ4vlzGWOMj9V3wMEjwHjgFPfPeFX9S1MFoarlqjoQZxTdUBHpX0OZ54EinBteL69UW2rMebNU9V5V7aaqz9aw/UtVvScyMrKxpzo6/yD46d/2bB9jTItQ78doq+o0YJoHY0FVs0VkLjAKqDJoQERGAv2B6cBTQEOeQb0b6FxpuZN73fEjMAQe2QrB4b6OxBhjPK7Omo+I5IrIwRp+ckXkYFMEICKxIhLlfh2KMyhgY7Uyg3BqXmNxJjSNFpF/NOA0S4EeIpIoIkHA9cCMpoi/SVniMca0EHUmH1WNUNXWNfxEqGrrJoqhPTBXRFbjJInvVPWramXCgGtVdauquoBbgZ3VDyQiHwELgV4ikioid7qvowynpjQL2AB84n4m0fElexd8dg/sXubrSIwxxqPq3ezmKaq6Ghh0lDK/VFsuBd6uodwNdRxjJk04SMIjAlvBtnnQ+1LoONjX0RhjjMf4PPmYSlpFw582gdhDYo0xzZs9vex4Y4nHGNMCWPI53mRugvHnwM4Fvo7EGGM8xpLP8SY8HgJCwVXm60iMMcZjrM/neBMaBXd84+sojDHGo6zmc7xylTuPWjDGmGbIks/xKGMjPJ8Im7/1dSTGGOMRlnyOR227Qr9fQesOvo7EGGM8wvp8jkcBQXDZK76OwhhjPMZqPsezg3udRy0YY0wzY8nneLVvC7zYGzYcf/OfGmNMY1nyOV5Fd4dRz8FJZ/g6EmOMaXLW53O8EoHTf+vrKIwxxiOs5nM8c7lg+0+QtvboZY0x5gRiyed45iqDj2+CRW/6OhJjjGlS1ux2PAsIglumQ2xvX0dijDFNypLP8c4eKmeMaYas2e1EsGYqLHjN11EYY0yTseRzItg610lAqr6OxBhjmoQ1u50IxjwPgWH2lFNjTLNhNZ8TQVArJ/G4yn0diTHGNAlLPieKbT/CKwMhO8XXkRhjTKNZ8jlRtO0K0d2gtMDXkRhjTKNZn8+JIqoz3Pq5r6MwxpgmYTWfE01RDqyzJGSMObFZ8jnRLHwDpt5ufT/GmBOaNbudaIbdC71GO81wxhhzgrLkc6IJa+v8GGPMCcya3U5EqvDtY/DLK76OxBhjjonVfE5EIpC9y9dRGGPMMbPkc6K67n2bbscYc8KyZrcTVUXiKcz2bRzGGHMMLPmcyFZ+BP/uCQf3+DoSY4xpEEs+J7Iuw+D034KftZ4aY04sLTb5iEhXEZkgIlN9Hcsxa9sVLnwawuNg0iUw9U5fR2SMMfXiseQjIp1FZK6IrBeRdSLyYC3lHnJvXysiH4lIyDGeb6KIZIjI2hq2jRKRTSKSLCKPAqjqNlVtPp/WPS6AhDN9HYUxxtSLJ2s+ZcCfVLUvcDpwn4j0rVxARDoCDwBDVLU/4A9cX61MnIhEVFvXvYbzvQuMqr5SRPyBN4DRQF/ghupxNAtnPgRDbocNX8Gmb30djTHG1MljyUdV96rqcvfrXGAD0LGGogFAqIgEAGFA9d7zs4HPRSQYQETuBl6r4Xzzgf01HH8okOyu6ZQAU4Cxx3ZVJ4CfX4RFb/o6CmOMqZNX+nxEJAEYBCyuvF5VdwP/BnYBe4EcVZ1drcynwCzgYxG5CbgDuKYBp+8IVJ6FMxXoKCLRIvIWMEhEHqsl7stEZHxOTk4DTudj174HN0/zdRTGGFMnjycfEQkHpgF/UNWD1ba1wamFJAIdgFYicnP1Y6jq80ARMA64XFXzGhuXqmap6r2q2k1Vn62lzJeqek9kZGRjT+c9kZ3AP9DXURhjTJ08mnxEJBAn8Xygqp/VUOQCYLuqZqpqKfAZcEYNxxkJ9AemA081MIzdQOUpoDu51zVfC16DZZN9HYUxxtTKk6PdBJgAbFDVF2sptgs4XUTC3OXPx+kbqnycQcB4nBrS7UC0iPyjAaEsBXqISKKIBOEMaJjRsKs5wWyeBdvn+zoKY4yplSdrPiOAW4DzRGSl+2cMgIjMFJEOqroYmAosB9a44xlf7ThhwLWqulVVXcCtwM7qJxORj4CFQC8RSRWROwFUtQy4H6ffaAPwiaqu88D1Hj9u/gyunuDrKIwxplaiqr6O4bg3ZMgQTUpK8nUYxhhzQhGRZao6pKZtLXaGg2bv6z/BvOd8HYUxxtTIkk9zVXQQSvKgOM+58dSe/2OMOY5Y8mmurnobLvoHFGTBxzfBtnm1l3W5vBaWMcaAPUyu+WvdEe6ZB227HbmtvBQ+uwdQuOZd78ZljGnRrObT3PkHQIdBUFoA8/4FBe4ZiEqL4OWTYXcStDvFtzEaY1ocq/m0FHnpMO9ZiO8HfS6FknzoNRr6Xw0JI3wdnTGmhbHk01K0OwX+uB5ad3CWW0XDpS85r10uyN0LkTXN+2qMMU3Pmt1aCpHDiSc/C7K2Ht42/TcwaTTYPV/GGC+xmk9L4nLBl7+HdV9ASS48tN6p7Qy8EbqfD+oC8fd1lMaYFsCST0vi5+f09fS7wnnqaUUzW7dzfRuXMabFseTT0tQ2pLpgP+xe7jyO2xhjPMz6fIxj8Vvw4TVOf5AxxniY1XyMY9At0ONiCG3j60iMMS2AJR/jiOrs/IAzL1xIa9/GY4xp1qzZzVS17F14fQjkNO+HvRpjfMuSj6mqy3Doac1vxhjPsuRjqortBZe/BkFhsC8ZVrzvTEBqjDFNyJKPqd2qj2DmI1CY7etImqekiTDzz76OwhifsORjanfe43DPjxAe6yynLDm8rbTIeVCdOTaZm+CrhyBpgmfPU1oEJQWePYcxx8CSj6mdCMT2dF5vmQMTLoR1052k8/oQmP+Csy0vExa87r1kpAqrP4XcdO+czxPKS6Db+XDvz547R3YKPNMe1k713DmMOUaWfEz9dDsXLn0Zel8GweEw+NfQ3T0bwqav4fu/Q9pq78RSXgKf3QWzHvPO+Tyh3clwy2cQ18dz5ygtgMG3QfsBnjuHaX4WvQUbvvT4aSz5mPrx84chtzsPpwM46xFIHOm8HnwbPLQOTjrDWT7WZp7t8yE37ejlAoLhqglw9l8advzysmOLy1OK8+CjG2CNh2omX/4B0tdb8jEN8/3fIXWpx09jycc0jYp+oeQ58OpASF/XsP1z0+H9q2DBa7BrMaz8sOZy+7c7/SUnX+2MzKuvpInwQtfjp//j3Uth7j+dZFta6JlznP8knP3nqo/PMKYuLhdc8h/oNcbjp7LkY5pWm0ToPBQiOzVsv4h4uGmqM8hh+Xsw52lwlR9Z7sfn4e3znVrD9vnOZKhHs+B12LkQBtzQtB/0pUXOsfMyG75v+wEQ3Q3umQun3tJ0MVV20nDYuwpeOxWKcjxzDtO8+PlBt/Ng5wLni54HidoDxI5qyJAhmpSU5OswTjzlZfDpr2HIHc7zgmpyYAfsWQF9xjp/+ODUgoLDIajVkeXz98Gelc7x/tMLEkbC1UcZMfb+VRAcUfuM3sdKFb580Knl3fnd4fib0sI3nKazK95o2H7ZKXBwNwSGQfpa6HO58zs1pi4H90DGBnj/SrjyHTjlmkYdTkSWqeqQmrbZ3G7Gc3L3wr7NUHyw9jJJk5wZtbuc4dR+4PC/NWkVc/ixDzd9Cm27Hj2Om6c5NR5V5wO5obWy2ohA38udn4YkHpfrcPl1053a3N0/QGDokWU3fQN56U7sIvU/x/rPYfbj8Mg2aH9K/fczLduicc7/x0e2Ov/XPMia3YznRHWG3y2Cfr9ylrfOPXLOuPOfhDtmHZlwdi2CD65xHn4HTr/Fxzc7/T0V2g9wajR1KStx/g0MhYWvw0v9nWcXNVZ+ltMxG9vn8Ki/1CSnKe5oFo+DF7o7E7gGRUDUSc7rmtz2Fdy/tGGJB+CU65yk2yraqQVlbm7Y/qZlGngjXDne44kHLPkYT/NzP5a7rBg+/y3MeerwtvIyZ3uHgUfu5yqDrGQ4sNNZzt7l9F8EhBwuo+rUnNbPqPnc6eudprkd7ntpul8IY144HFNjpC6Bn1+CQnciy9kNk0Y7E7MeTVwfJyGHtHZqcTdOOTL55qQe7s8qLXQSd0OExx1Oih/fBN82cGSgaZkq/jZTk+CL++r3ZeoYWZ9PPVifTxPZl+zcoxPf1+knGXcG3PsLtOt/ZNmKv8vK3/jLSiAgqGq5t0ZC20S49r0jj5GxEX58Dsb8x6kBNLX8LAhrezjGZe9Cj4ugdYfGH/vrh2HF/+CPG9wDMJ6CB1dDm5OOvq+rHFZ/7DwqPaoLbPvRqSF2PLXxcZnmq7zU+aLWfoDz71d/cFolYnoc8yHr6vOx5FMPlnw8YNuPsPi/cNXbNQ8sACcBff0n50O0/5U1lyk8ACFRVZOUywVlhTUftzjX+VbX7Vxn+cBOZ3j4aXc27noaojivauf/h9dBRDu47JXD64oOOrWr7hfAwb2wbxOcdObh+6zqkrXVGeF2+eueG0lnmp/MzfDGafCr/8LJ14D4Nby5t5q6ko81uxnf6Ho23PBh7YkHoCDLudO6rpkTQts4SWr3Mqd/SBWm/wam3FTzUO0V78P/rnCa8QD8AmDpO7Dq4/rHvmwyfHpbzcO2d/xS+z1K4PQ3PdvROWeF+H4Q07NquZDWh5vNWreHrufUL/EAtEmA+5ZC70uc5ZIC2DbPSWLG1CayI9w6A7qe6zRNNzLxHI0lH3P8ahXjdLZf8Le6y81/AT64FtLWOv9hEs9y7lWQGv68+451/oNlbXU+jCPaOcOQe1xY/7hK8p3EWNPotGXvwtxnndpXbc57AjoNPbx8/pMw/D7n9d5VMHE07N9WdZ/8fU4fU3bK0ePz83fm5Atr6yznpcF7YyH5u6PvW6G8rObkbZqvoFbOl8KK/sdlk2H6vR47nQ21Nse30Kijlxn8a6c/I76vs1xXU1PrDk4z3SunQJfT4br34Vz3HHEuF5TkQkhkzfvm73P6rIb/zvmpyUX/cGottQ29DmsLZz185HpV5yc3HYqyIaxaH1VxLsz5G4THOyOS6rL2MwhufXhIelQC3PpFw6bZ+enfTm3p5s+cZzuZ5m/HzxAQCp0GO8sFWU6ztKu8aQbpVGN9PvVgfT7NUOoypzO+YloggE9vd+5Junmas7x2GvgHQZ/LnJrAc11g0M0w5vljP29ehpMYAiuN2ktdBv/7FVz/vlNrq+2enoN7nSa4o3lzuDPTxA11NP8dzepPYddCGHST02xXMY+fcZSXOl8GTruzfveaNVZZsdNE7IEkcMg7FzrzJt72VZMd0vp8jKmu0+CqiQfglGuhXaUbMuc+A6umOK/9A+DyV+vXgb93NUy4yBndV93nv4MJF1RdF9nJuZN876q6byatSDy7FtfdJHb3XLjs5arr9m05fC31cco1zhxfMx6AH/6v/vs1RnGu03fXFPZtOTxisimVl8Lb5zn3jK38EDbPbvpzVJebDm+eXvcw/rwM58GPjZlG6eqJMObfx75/A1nyMaZCr9FwQaX7kO78zmmWq3Dy1c6jEI4mrK0zs0NkR2d5zVQn6QAMvRtG/qlq+Yh46DnamZFg/Rd1HztjI0waBQterXl7SYGTvMLjqq7f8KUzEKP6h5PLVfUG1w+vgw3ub74izuzhN02tWr6mJ9sW5zl9UsfST1Rx0+/nv3XO39j59/asdGp/dX1YF+U408g0VEGW02wb1xfuWwKne65PBHCSSqsYZ8RndLfayyVNhCXjG/dMrajOENe76rov7ndqeJ6gqvZzlJ/BgwerMQ2Sl3n49azHVSddUnf5slLV1Z86/x7Nyo9Ui/Oc17sWq+5Ldl5nbVN9trPq8veP3Cc3Q3X/DtXy8sPrXC7VD65V/d+VzmtV1deHqS4eX/u5v/qT6hunO+cvKVDdu9pZv2aa6t+iVLf/5CzPeVp10VtHv5asbarPJagmTVJN36C6c9HR96lNbobq0omq5WWq8/+jWrC/9rJL3lZ9qrVq2rpjP1+F9PWqy95r/HFqMumSo//tVKj4OzgWWducv5vqv7P8rMN/G8cASNJaPldb9IADEekK/BWIVNWrfR2PaUYqT09yUT2arfwDnJpVfQy4/vDrL+53bgK8/gNniPXAmw8PvKisoomxOM+pOZ33BPS82KntUamZ775FR+6buQlmPgyXvAR9LnUGPQSGOdMLLXwdHljh3IcV19f55uxyOU2PbRJqv4ac3U7NsHUH6H8VJJ7t3CxcoeKGYle5M79dqxhngAg4sz/UND/fsndh/vPOsPSRf3TWucqdOQarl+9/tdOcFVvtm35dctOdPpHqg2AWvObcK9b/qqYdnKEKp93lzPYBTpPfkvHOc7M6DDpcrmKuwOhuTm2uMNuZ0bwhts+HLx+AhFXO7QsVKkZMekJtWamxP0BnYC6wHlgHPFhLuShgKrAR2AAMb8Q5JwIZwNpq60cBm4Bk4NEa9pta13Gt5mOOW3tXq+5ZVf/yWVtVJ45xakz1lZuu+spA1eQfqq7Pz1JdOaX2/SpqcekbVF8eoJq5xVlOmuTUOrJTat5v2WTVt0Y6NSuXS/U/fVWn/87ZVrBf9Z8dVec+6ywX5zs1j4qyFTWxCl/cr/qP9qpFB514vrhfde+aqmUK9ju1paOZ8aBTSysprLo+P6tqTddTCrNVn++mOvvJw+uKcp33Zs005/rfHKE6/tyGH7us1Kk5Va4ZNwHqqPl4Mvm0B051v44ANgN9ayg3GbjL/ToIiKq2PQ6IqLauey3nPAs4tXLyAfyBrUBX9/FXVY/Dko8xR9GIphfdOlf1w+udxKfqfMgtesv50K5JylLnQ7TC/u2Hy5YUqC58UzVtrbO8caaTyFKTaj5W1lanKU5V9cBO1X/3dpJfhbxM1Rf7qf7wT2c5c7MT28G0w2Uqks2elTU3aVZwuZxzHIvSItXtPztNo6pOwl44zrneynL2HH4vystVs1NVP7zB+Z2pOsm3tt+rD/gk+RxxIvgCuLDaukhgO+4h37Xsdw3wPRDsXr4b+KaO8gnVks9wYFal5ceAx6rtY8nHmONJYU79Et7BNOdbf96++h+3MpdL9bu/qaYuc5Y3fOUks8zNzvIvr6n+s8ORtZ2afP9/Tp9bboZTk/rgOtUl7zjbykpVdy9Xzdl9OI45T6vuWuIs71rsnHfzbGd540zVv8dWTYKV5Waojhuhum1+/a67LrtXqH7+O9UdCxp/rGrqSj5eGe0mIgnAIGBxtU2JQCYwSURWiMg7IlJlvhVV/RSYBXwsIjcBd+AkpPrqCFS+LTzVvQ4RiRaRt4BBIvJYDXFfJiLjc3LsKZDGeFVI6/pN7xIR7/Q31Xfi2JDWVZdFnBGOFZOu9rjImcw1uruz3HEwDLsXyuoxu/PJ18DZf3H6TPz8oSQPcA/3Li2A8efAmk+dZVcZ/PwypK1yljsMgus/hJNGOMvtB8CvZ9T+bKvSfOcetOrXA84TSN8+Dxa95SwXZsMzHWGp+6GL+fvg1VOdUZjgzGyQssQ5phd5/CZTEQkHfgT+qaqfVds2BFgEjFDVxSLyCnBQVZ+o4ThTgDFAN1Wt9bnF7kT3lar2dy9fDYxS1bvcy7cAw1T1/vpeg91kaoxpFFVn4ES7/s7NzRWfu42ZP01ruSesKAdm/RV6jYHeY5z7p+Y+68z1lzDCmYz364ede9a6nnPs568Hnz3JVEQCgWnAB9UTj1sqkKqqFTWiqcCjNRxnJNAfmA48BdQ7cQC7cQY/VOjkXmeMMd4h4iSCystNccyahETC2NcPLwdHwKhnDi+Htjn6o+e9wGPNbiIiwARgg6q+WFMZVU0DUkSkl3vV+Tij4yofZxAwHhgL3A5Ei8g/GhDKUqCHiCSKSBBwPVDL08eMMcZ4gyf7fEYAtwDnichK988YABGZKSIVT9z6PfCBiKwGBgLPVDtOGHCtqm5VVRdwK7CzphOKyEfAQqCXiKSKyJ2qWoZTU5qFM5T7E1Vd17SXaowxpiFsYtF6sD4fY4xpOJtY1BhjzHHFko8xxhivs+RjjDHG6yz5GGOM8TpLPsYYY7zORrvVg4hkUsvw7jrEAPs8EM7xrCVeM7TM67Zrbjkac90nqWpsTRss+XiIiCTVNsSwuWqJ1wwt87rtmlsOT123NbsZY4zxOks+xhhjvM6Sj+eM93UAPtASrxla5nXbNbccHrlu6/MxxhjjdVbzMcYY43WWfJqYiIwSkU0ikiwiRzybqDkQkc4iMldE1ovIOhF50L2+rYh8JyJb3P+28XWsniAi/u4n737lXk4UkcXu9/xj96M7mg0RiRKRqSKyUUQ2iMjwlvBei8hD7r/vtSLykYiENMf3WkQmikiGiKyttK7G91ccr7qvf7WInHqs57Xk04RExB94AxgN9AVuEJG+vo3KI8qAP6lqX+B04D73dT4KfK+qPYDvqeHBgM3EgziP56jwL+AlVe0OHADu9ElUnvMK8K2q9gYG4Fx7s36vRaQj8AAwxP1UZH+cZ4E1x/f6XWBUtXW1vb+jgR7un3uAccd6Uks+TWsokKyq21S1BJiC8xC8ZkVV96rqcvfrXJwPo4441zrZXWwycIVvIvQcEekEXAK8414W4Dycp/BCM7tuEYkEzsJ5MCSqWqKq2bSA9xrnSc+hIhKA81yxvTTD91pV5wP7q62u7f0dC7ynjkVAlIi0P5bzWvJpWh2BlErLqe51zZaIJACDgMVAvKrudW9KA+J9FJYnvQz8GXC5l6OBbPdDC6H5veeJQCYwyd3U+I6ItKKZv9equhv4N7ALJ+nkAMto3u91ZbW9v032GWfJxxwzEQkHpgF/UNWDlbepM4yyWQ2lFJFLgQxVXebrWLwoADgVGKeqg4B8qjWxNdP3ug3Ot/xEoAPQiiObploET72/lnya1m6gc6XlTu51zY6IBOIkng9U9TP36vSKKrj73wxfxechI4DLRWQHTpPqeTj9IVHuphlofu95KpCqqovdy1NxklFzf68vALaraqaqlgKf4bz/zfm9rqy297fJPuMs+TStpUAP94iYIJwOyhk+jqnJufs5JgAbVPXFSptmAL92v/418IW3Y/MkVX1MVTupagLOe/uDqt4EzAWudhdrVtetqmlAioj0cq86H1hPM3+vcZrbTheRMPffe8V1N9v3upra3t8ZwK3uUW+nAzmVmucaZIqB6QAAAoxJREFUxG4ybWIiMganX8AfmKiq//RxSE1ORM4EfgLWcLjv4//h9Pt8AnTBmQX8WlWt3pHZLIjIOcDDqnqpiHTFqQm1BVYAN6tqsS/ja0oiMhBngEUQsA24/f+3dz+tNkVhHMe/PykRJcXEgDCRQikDUsobMCDlz0CZmZhJkXgDJhRDIklhLAa3DHTd/Jl4BSZMpCQlHoO1bl1SJNY93b6f2Vlntc7Z7Xa/s/c++3loP1wX9L5OcgE4RPt35wvgBO3+xoLa10luA3tp1avfAueBB/xi//Ygvky7BPkJOF5VM3/1uYaPJGk0L7tJkoYzfCRJwxk+kqThDB9J0nCGjyRpOMNHWuCS7J2twC1NCsNHkjSc4SNNiCRHk0wneZnkWu8b9DHJpd5X5nGS1X3u9iRPe0+V+3P6rWxK8ijJqyTPk2zsyy+f05PnVn9YUJo3ho80AZJspj1Nv7uqtgNfgSO0gpYzVbUFmKI9fQ5wAzhdVVtplSZmx28BV6pqG7CLVpEZWuXxU7Q+UxtodcqkebP491MkDbAP2AE86yclS2nFHL8Bd/qcm8C93mNnZVVN9fHrwN0kK4C1VXUfoKo+A/T1pqvqTX/9ElgPPPn/myX9muEjTYYA16vqzA+Dybmf5v1tPay59ce+4rGveeZlN2kyPAYOJFkDkGRVknW0Y3S2ivJh4ElVfQDeJ9nTx48BU72r7Jsk+/saS5IsG7oV0h/y1480AarqdZKzwMMki4AvwEla87ad/b13tPtC0MrcX+3hMltpGloQXUtysa9xcOBmSH/MqtbSBEvysaqWz/f3kP41L7tJkobzzEeSNJxnPpKk4QwfSdJwho8kaTjDR5I0nOEjSRrO8JEkDfcdwAKbi/amwPwAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -308,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 19, "metadata": { "scrolled": true }, @@ -317,17 +317,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "final epoch:train loss tensor(46.7403, grad_fn=) test Loss tensor(54.2278, grad_fn=)\n" + "final epoch:train loss tensor(32.0082, grad_fn=) test Loss tensor(26.3281, grad_fn=)\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -351,24 +353,26 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "final epoch:train loss tensor(24.8952, grad_fn=) test Loss tensor(28.2323, grad_fn=)\n" + "final epoch:train loss tensor(32.0402, grad_fn=) test Loss tensor(26.4105, grad_fn=)\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -422,9 +426,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -436,9 +440,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch06_Multilayer_Perceptrons/Multilayer_Perceptron.ipynb b/Ch06_Multilayer_Perceptrons/Multilayer_Perceptron.ipynb index 7c8102a7..0e54ccd7 100644 --- a/Ch06_Multilayer_Perceptrons/Multilayer_Perceptron.ipynb +++ b/Ch06_Multilayer_Perceptrons/Multilayer_Perceptron.ipynb @@ -1152,9 +1152,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -1166,7 +1166,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.12" } }, "nbformat": 4, diff --git a/Ch06_Multilayer_Perceptrons/Numerical_Stability_and_Initialization.ipynb b/Ch06_Multilayer_Perceptrons/Numerical_Stability_and_Initialization.ipynb index 9062565e..2eddee35 100644 --- a/Ch06_Multilayer_Perceptrons/Numerical_Stability_and_Initialization.ipynb +++ b/Ch06_Multilayer_Perceptrons/Numerical_Stability_and_Initialization.ipynb @@ -75,8 +75,8 @@ "One major culprit in the vanishing gradient problem\n", "is the choices of the activation functions $\\sigma$\n", "that are interleaved with the linear operations in each layer.\n", - "Historically, a the sigmoid function $\frac{1}{1 + e^{-x}}$", - "(introduced in :numref:`chapter_mlp`)\n", + "Historically, a the sigmoid function $\f", + "rac{1}{1 + e^{-x}}$(introduced in :numref:`chapter_mlp`)\n", "was a popular choice owing to its similarity to a thresholding function.\n", "Since early artificial neural networks were inspired\n", "by biological neural networks,\n", @@ -422,9 +422,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -436,9 +436,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch06_Multilayer_Perceptrons/Predicting_House_Prices_on_Kaggle.ipynb b/Ch06_Multilayer_Perceptrons/Predicting_House_Prices_on_Kaggle.ipynb index d2c57672..66e24373 100644 --- a/Ch06_Multilayer_Perceptrons/Predicting_House_Prices_on_Kaggle.ipynb +++ b/Ch06_Multilayer_Perceptrons/Predicting_House_Prices_on_Kaggle.ipynb @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -61,7 +61,7 @@ "" ] }, - "execution_count": 11, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -96,7 +96,7 @@ "" ] }, - "execution_count": 12, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "metadata": { "attributes": { "classes": [], @@ -176,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "metadata": { "attributes": { "classes": [], @@ -200,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "metadata": { "attributes": { "classes": [], @@ -233,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "metadata": { "attributes": { "classes": [], @@ -325,7 +325,7 @@ "3 4 70 RL 60.0 WD Abnorml 140000" ] }, - "execution_count": 16, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -347,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": { "attributes": { "classes": [], @@ -391,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "metadata": { "attributes": { "classes": [], @@ -425,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "metadata": { "attributes": { "classes": [], @@ -440,7 +440,7 @@ "(2919, 331)" ] }, - "execution_count": 19, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -465,7 +465,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": { "attributes": { "classes": [], @@ -503,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -543,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -573,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -620,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -651,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 15, "metadata": { "attributes": { "classes": [], @@ -699,7 +699,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": { "attributes": { "classes": [], @@ -711,1023 +711,1025 @@ { "data": { "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - "\r\n" + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" ], "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - "fold 0, train rmse: 0.169580, valid rmse: 0.156457\n", - "fold 1, train rmse: 0.162256, valid rmse: 0.188862\n", - "fold 2, train rmse: 0.163859, valid rmse: 0.168451\n", - "fold 3, train rmse: 0.167941, valid rmse: 0.154711\n", - "fold 4, train rmse: 0.163324, valid rmse: 0.182826\n", - "5-fold validation: avg train rmse: 0.165392, avg valid rmse: 0.170261\n" + "fold 0, train rmse: 0.170056, valid rmse: 0.156666\n", + "fold 1, train rmse: 0.162478, valid rmse: 0.191762\n", + "fold 2, train rmse: 0.164140, valid rmse: 0.168976\n", + "fold 3, train rmse: 0.168314, valid rmse: 0.154802\n", + "fold 4, train rmse: 0.163237, valid rmse: 0.182972\n", + "5-fold validation: avg train rmse: 0.165645, avg valid rmse: 0.171035\n" ] } ], @@ -1764,7 +1766,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 17, "metadata": { "attributes": { "classes": [], @@ -1804,7 +1806,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "metadata": { "attributes": { "classes": [], @@ -1816,741 +1818,743 @@ { "data": { "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - "\r\n" + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" ], "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - "train rmse 0.162384\n" + "train rmse 0.162959\n" ] } ], @@ -2576,7 +2580,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -2586,7 +2590,7 @@ "" ] }, - "execution_count": 29, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -2623,9 +2627,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -2637,9 +2641,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch06_Multilayer_Perceptrons/submission.csv b/Ch06_Multilayer_Perceptrons/submission.csv new file mode 100644 index 00000000..2ba2beb5 --- /dev/null +++ b/Ch06_Multilayer_Perceptrons/submission.csv @@ -0,0 +1,1460 @@ +Id,SalePrice +1461,119431.61 +1462,154010.33 +1463,198657.38 +1464,217229.11 +1465,177596.84 +1466,193196.11 +1467,194944.16 +1468,187407.11 +1469,195579.61 +1470,123789.1 +1471,196171.69 +1472,103134.22 +1473,105728.336 +1474,150360.77 +1475,92990.52 +1476,306451.06 +1477,247267.55 +1478,282202.75 +1479,270789.25 +1480,401992.06 +1481,299222.66 +1482,211573.95 +1483,192545.64 +1484,176792.27 +1485,202095.11 +1486,211598.19 +1487,291210.53 +1488,244759.8 +1489,197456.55 +1490,236688.02 +1491,210228.47 +1492,83216.41 +1493,196809.84 +1494,275795.34 +1495,270761.78 +1496,218478.27 +1497,172503.05 +1498,163125.61 +1499,165191.27 +1500,170141.95 +1501,206878.86 +1502,158731.69 +1503,267272.25 +1504,245158.2 +1505,237971.34 +1506,206184.34 +1507,254149.12 +1508,205711.95 +1509,163616.39 +1510,171714.72 +1511,147533.05 +1512,192255.17 +1513,151157.73 +1514,168680.23 +1515,211594.92 +1516,163300.12 +1517,166306.67 +1518,136083.66 +1519,238457.25 +1520,127957.4 +1521,130121.6 +1522,202224.77 +1523,95212.62 +1524,96542.71 +1525,109080.695 +1526,101575.65 +1527,111772.58 +1528,134811.5 +1529,140159.12 +1530,211330.05 +1531,142849.78 +1532,94513.78 +1533,137576.84 +1534,112960.21 +1535,155775.16 +1536,102737.73 +1537,84539.25 +1538,153689.53 +1539,189032.0 +1540,110258.81 +1541,143868.98 +1542,133149.1 +1543,169863.4 +1544,73317.44 +1545,94056.76 +1546,141582.2 +1547,135841.77 +1548,120909.54 +1549,122030.75 +1550,137145.34 +1551,108021.01 +1552,141964.83 +1553,148977.45 +1554,98541.805 +1555,187149.31 +1556,75742.29 +1557,89071.49 +1558,83319.62 +1559,80420.9 +1560,118905.75 +1561,135954.62 +1562,127354.016 +1563,117568.64 +1564,170507.1 +1565,162753.34 +1566,231255.38 +1567,82536.17 +1568,247012.56 +1569,146132.31 +1570,144775.0 +1571,115941.08 +1572,146774.58 +1573,221223.28 +1574,143071.9 +1575,232098.83 +1576,260971.92 +1577,194284.98 +1578,145245.03 +1579,147618.53 +1580,205437.89 +1581,158233.28 +1582,129732.35 +1583,299279.44 +1584,239166.48 +1585,155457.05 +1586,62220.6 +1587,89866.47 +1588,144980.33 +1589,108016.43 +1590,131952.86 +1591,83243.74 +1592,140788.86 +1593,123005.0 +1594,106672.65 +1595,119762.6 +1596,212663.22 +1597,186233.28 +1598,199395.72 +1599,166035.11 +1600,172115.78 +1601,46699.105 +1602,111309.164 +1603,56798.27 +1604,242532.19 +1605,248913.47 +1606,173724.28 +1607,184052.64 +1608,236375.73 +1609,189716.97 +1610,172163.34 +1611,156632.14 +1612,193970.52 +1613,203897.84 +1614,124380.43 +1615,83429.82 +1616,71510.85 +1617,88457.445 +1618,115588.836 +1619,148401.1 +1620,200185.88 +1621,142800.6 +1622,147021.53 +1623,247199.81 +1624,239543.89 +1625,111044.86 +1626,207890.22 +1627,210094.58 +1628,267607.53 +1629,184585.97 +1630,282174.62 +1631,205025.89 +1632,216716.7 +1633,186365.69 +1634,209492.11 +1635,195383.69 +1636,175865.06 +1637,217163.02 +1638,218315.16 +1639,208285.92 +1640,244318.58 +1641,194762.3 +1642,230931.31 +1643,222505.33 +1644,229044.25 +1645,223669.36 +1646,176510.88 +1647,168688.27 +1648,138722.02 +1649,144799.44 +1650,136531.05 +1651,134225.0 +1652,108117.87 +1653,103818.74 +1654,152890.89 +1655,123472.586 +1656,145283.97 +1657,153196.19 +1658,153504.92 +1659,106286.43 +1660,165812.67 +1661,374184.4 +1662,310376.1 +1663,316849.66 +1664,378369.66 +1665,286354.2 +1666,303453.47 +1667,314004.25 +1668,296279.72 +1669,266502.12 +1670,297711.28 +1671,276858.4 +1672,358232.53 +1673,288285.5 +1674,259387.89 +1675,209115.8 +1676,206484.02 +1677,227137.62 +1678,374429.12 +1679,329351.16 +1680,273660.66 +1681,236771.22 +1682,299469.78 +1683,205013.0 +1684,201110.5 +1685,195424.17 +1686,184127.62 +1687,184754.66 +1688,216829.19 +1689,215046.83 +1690,217441.81 +1691,208262.69 +1692,253816.45 +1693,174172.45 +1694,199694.25 +1695,176220.11 +1696,268233.9 +1697,180845.28 +1698,318157.4 +1699,289054.06 +1700,257443.4 +1701,268739.5 +1702,254604.61 +1703,262531.84 +1704,272070.84 +1705,245025.62 +1706,339337.8 +1707,216202.31 +1708,216415.67 +1709,271014.44 +1710,230369.53 +1711,266442.0 +1712,259562.12 +1713,266763.12 +1714,223450.22 +1715,205719.98 +1716,196181.23 +1717,192844.39 +1718,138474.72 +1719,215568.5 +1720,229635.67 +1721,187888.83 +1722,132756.17 +1723,179925.42 +1724,217529.05 +1725,234428.84 +1726,191612.73 +1727,160246.47 +1728,187912.98 +1729,171961.38 +1730,155409.61 +1731,118268.36 +1732,149063.97 +1733,110066.29 +1734,117744.73 +1735,128031.64 +1736,100375.98 +1737,289546.22 +1738,240060.55 +1739,256840.17 +1740,237457.4 +1741,209684.5 +1742,182489.34 +1743,186538.28 +1744,281657.34 +1745,208829.52 +1746,193880.4 +1747,221645.02 +1748,233744.42 +1749,168018.19 +1750,138485.8 +1751,256540.72 +1752,104167.53 +1753,165356.0 +1754,214724.05 +1755,179931.98 +1756,143312.78 +1757,106649.97 +1758,167568.48 +1759,179786.66 +1760,188563.45 +1761,188377.19 +1762,177467.52 +1763,176901.95 +1764,108030.555 +1765,162911.78 +1766,200839.16 +1767,239382.53 +1768,143137.19 +1769,173100.25 +1770,157733.28 +1771,111768.69 +1772,126865.266 +1773,138895.33 +1774,173372.31 +1775,126533.95 +1776,120165.5 +1777,95965.0 +1778,132008.83 +1779,119639.49 +1780,170661.92 +1781,117104.61 +1782,84021.38 +1783,157124.17 +1784,97729.51 +1785,118956.54 +1786,157474.7 +1787,168030.97 +1788,56541.016 +1789,103444.14 +1790,85257.875 +1791,227980.3 +1792,162143.98 +1793,140055.36 +1794,159760.39 +1795,122805.02 +1796,113869.51 +1797,137250.44 +1798,110690.445 +1799,94886.24 +1800,110521.516 +1801,138154.86 +1802,151155.77 +1803,166577.69 +1804,141838.89 +1805,136807.14 +1806,114822.39 +1807,135795.19 +1808,131478.47 +1809,118888.16 +1810,141991.62 +1811,72637.77 +1812,86849.28 +1813,110902.36 +1814,81006.586 +1815,43376.105 +1816,81124.61 +1817,127229.336 +1818,148119.89 +1819,126535.55 +1820,63980.805 +1821,95616.8 +1822,152582.9 +1823,35763.08 +1824,132444.89 +1825,140237.34 +1826,95088.68 +1827,96651.44 +1828,134273.08 +1829,157495.42 +1830,140095.17 +1831,140473.84 +1832,97501.74 +1833,131205.78 +1834,111480.81 +1835,140775.77 +1836,106446.19 +1837,78571.055 +1838,101618.19 +1839,84534.586 +1840,155616.84 +1841,125977.19 +1842,94295.95 +1843,160644.97 +1844,130127.54 +1845,158259.33 +1846,149273.45 +1847,179428.55 +1848,34925.715 +1849,113926.664 +1850,111311.43 +1851,135258.9 +1852,130881.37 +1853,136574.16 +1854,164583.08 +1855,151394.98 +1856,241336.45 +1857,140693.73 +1858,135695.23 +1859,109462.76 +1860,165293.89 +1861,110098.98 +1862,312475.97 +1863,307963.94 +1864,307980.44 +1865,295426.38 +1866,299441.47 +1867,245202.05 +1868,290003.8 +1869,220232.67 +1870,235215.16 +1871,253003.2 +1872,200080.12 +1873,244442.48 +1874,155391.06 +1875,206922.97 +1876,221273.03 +1877,218133.83 +1878,225932.36 +1879,128698.94 +1880,136494.03 +1881,261146.06 +1882,254319.92 +1883,198889.53 +1884,210427.25 +1885,235696.56 +1886,268529.84 +1887,230454.39 +1888,268990.78 +1889,196608.44 +1890,140677.72 +1891,137762.06 +1892,86460.74 +1893,119446.83 +1894,131673.88 +1895,143139.3 +1896,119126.81 +1897,124618.75 +1898,114912.09 +1899,141880.7 +1900,130142.46 +1901,156892.77 +1902,146131.56 +1903,195005.64 +1904,129560.766 +1905,196272.48 +1906,151892.94 +1907,213841.56 +1908,116658.9 +1909,147839.97 +1910,129622.18 +1911,219407.69 +1912,271835.66 +1913,197494.58 +1914,51284.184 +1915,252826.53 +1916,42890.715 +1917,242792.94 +1918,142771.64 +1919,186692.89 +1920,180151.06 +1921,323431.4 +1922,277408.72 +1923,216880.17 +1924,259270.0 +1925,220640.86 +1926,313345.7 +1927,131730.77 +1928,180013.36 +1929,107123.19 +1930,123875.09 +1931,153662.36 +1932,149093.0 +1933,180459.36 +1934,196588.6 +1935,195667.7 +1936,197513.12 +1937,201530.6 +1938,189616.16 +1939,217818.9 +1940,180621.44 +1941,192487.78 +1942,193491.05 +1943,195075.16 +1944,299409.25 +1945,297255.6 +1946,157044.62 +1947,250490.69 +1948,188797.92 +1949,231361.39 +1950,195793.16 +1951,255246.95 +1952,220108.92 +1953,204285.86 +1954,202889.42 +1955,137601.0 +1956,289468.34 +1957,171410.4 +1958,288326.34 +1959,154353.69 +1960,101308.72 +1961,124269.88 +1962,95854.37 +1963,116170.72 +1964,117981.16 +1965,138607.75 +1966,127594.375 +1967,276050.56 +1968,348787.25 +1969,317155.6 +1970,345340.1 +1971,367170.38 +1972,321818.84 +1973,264772.16 +1974,298319.88 +1975,370084.03 +1976,275663.75 +1977,332806.56 +1978,312414.22 +1979,286273.25 +1980,213003.4 +1981,298501.88 +1982,220091.34 +1983,206235.33 +1984,180708.72 +1985,237495.69 +1986,230907.38 +1987,191345.1 +1988,203912.02 +1989,211656.25 +1990,230960.66 +1991,229230.81 +1992,237011.4 +1993,192936.06 +1994,228304.19 +1995,203591.28 +1996,276614.4 +1997,283371.03 +1998,295785.12 +1999,273365.3 +2000,285512.34 +2001,275669.06 +2002,247830.84 +2003,262515.38 +2004,279438.72 +2005,224358.66 +2006,217962.2 +2007,260030.81 +2008,220266.81 +2009,206893.9 +2010,210264.64 +2011,148373.86 +2012,186901.0 +2013,185291.64 +2014,197408.06 +2015,201202.8 +2016,206984.52 +2017,199426.16 +2018,124165.984 +2019,144351.17 +2020,95714.99 +2021,92530.11 +2022,202673.77 +2023,145304.73 +2024,283822.16 +2025,319398.12 +2026,172860.7 +2027,169647.3 +2028,159800.39 +2029,173338.98 +2030,244334.53 +2031,225662.5 +2032,224114.78 +2033,232509.56 +2034,173407.61 +2035,234157.94 +2036,210948.53 +2037,210072.33 +2038,244486.69 +2039,187114.1 +2040,299610.53 +2041,254272.53 +2042,217366.33 +2043,177545.66 +2044,196912.62 +2045,198452.03 +2046,144053.66 +2047,146238.47 +2048,149565.0 +2049,165837.66 +2050,163422.52 +2051,90500.56 +2052,134435.47 +2053,151874.7 +2054,64432.723 +2055,157253.3 +2056,154594.33 +2057,112195.73 +2058,238535.56 +2059,153293.81 +2060,184536.69 +2061,190685.73 +2062,122824.586 +2063,99911.74 +2064,145258.84 +2065,108939.2 +2066,190013.44 +2067,145358.52 +2068,152084.11 +2069,62685.16 +2070,91036.336 +2071,108596.23 +2072,169067.06 +2073,144941.56 +2074,187736.25 +2075,152873.7 +2076,119586.39 +2077,151767.17 +2078,113676.84 +2079,139084.92 +2080,101531.82 +2081,107746.9 +2082,139805.39 +2083,137055.62 +2084,91261.164 +2085,111463.04 +2086,148963.67 +2087,126995.92 +2088,102354.94 +2089,70735.82 +2090,121495.56 +2091,98854.125 +2092,146377.05 +2093,107425.44 +2094,111157.586 +2095,134854.17 +2096,67822.38 +2097,94716.836 +2098,145851.0 +2099,44128.383 +2100,103532.06 +2101,122843.016 +2102,119288.77 +2103,92902.16 +2104,120587.2 +2105,128590.57 +2106,56545.74 +2107,216747.95 +2108,116789.01 +2109,108878.6 +2110,127643.92 +2111,135275.34 +2112,136180.42 +2113,108400.57 +2114,109600.92 +2115,147617.73 +2116,116229.836 +2117,155923.11 +2118,104032.65 +2119,92068.75 +2120,111776.49 +2121,85651.75 +2122,116445.664 +2123,76049.38 +2124,155025.56 +2125,151372.23 +2126,175599.88 +2127,159440.17 +2128,115058.03 +2129,82173.86 +2130,131178.22 +2131,166635.1 +2132,116473.81 +2133,134287.81 +2134,131742.56 +2135,103194.984 +2136,46813.387 +2137,118029.016 +2138,137361.33 +2139,162567.55 +2140,142016.69 +2141,142961.34 +2142,129614.1 +2143,141306.75 +2144,99926.9 +2145,147733.28 +2146,156606.06 +2147,181383.23 +2148,143058.02 +2149,146018.5 +2150,224993.02 +2151,103324.805 +2152,159627.8 +2153,182004.66 +2154,90521.13 +2155,149281.75 +2156,251528.47 +2157,253307.11 +2158,236699.75 +2159,215004.1 +2160,199864.03 +2161,252055.6 +2162,345979.56 +2163,321588.06 +2164,243866.36 +2165,183280.33 +2166,165530.95 +2167,228763.0 +2168,207792.3 +2169,200351.6 +2170,216713.22 +2171,156837.4 +2172,154881.56 +2173,168293.61 +2174,239122.61 +2175,274015.16 +2176,276028.9 +2177,238583.8 +2178,227423.28 +2179,151617.61 +2180,225329.72 +2181,201261.2 +2182,229664.08 +2183,201447.06 +2184,127783.984 +2185,129034.47 +2186,157412.42 +2187,165306.72 +2188,158363.06 +2189,303844.47 +2190,48868.152 +2191,51416.36 +2192,70633.8 +2193,100918.06 +2194,99734.984 +2195,95761.414 +2196,91534.96 +2197,114890.13 +2198,147373.25 +2199,183564.98 +2200,139054.86 +2201,146963.7 +2202,164573.08 +2203,156928.84 +2204,161251.36 +2205,103554.516 +2206,136943.42 +2207,188142.22 +2208,239067.67 +2209,213035.97 +2210,122501.74 +2211,105985.19 +2212,110352.6 +2213,91648.53 +2214,144347.31 +2215,98160.914 +2216,148956.22 +2217,42180.41 +2218,60478.254 +2219,60053.535 +2220,56656.305 +2221,255340.0 +2222,242112.69 +2223,281674.75 +2224,235462.44 +2225,135840.94 +2226,217771.42 +2227,211455.0 +2228,258656.72 +2229,248843.67 +2230,165880.16 +2231,220204.3 +2232,206250.33 +2233,202090.98 +2234,249975.42 +2235,226040.77 +2236,262327.1 +2237,318124.03 +2238,218499.75 +2239,113394.47 +2240,170405.72 +2241,173420.75 +2242,136209.11 +2243,134821.45 +2244,107382.6 +2245,97962.97 +2246,132487.0 +2247,118212.74 +2248,118373.75 +2249,123179.77 +2250,131920.97 +2251,115283.4 +2252,199900.89 +2253,169788.9 +2254,188227.58 +2255,215589.77 +2256,178759.25 +2257,229537.86 +2258,172044.4 +2259,199166.55 +2260,167268.89 +2261,184834.69 +2262,183466.06 +2263,312293.88 +2264,387193.72 +2265,204168.9 +2266,249496.92 +2267,320257.66 +2268,316887.0 +2269,164372.08 +2270,205949.58 +2271,223034.47 +2272,226700.14 +2273,178349.55 +2274,210792.78 +2275,188398.72 +2276,182823.12 +2277,202428.47 +2278,166271.27 +2279,120934.78 +2280,102304.43 +2281,182700.94 +2282,192038.84 +2283,114835.56 +2284,120792.89 +2285,123263.53 +2286,125097.695 +2287,302024.62 +2288,269950.9 +2289,315350.84 +2290,351060.2 +2291,293892.8 +2292,345339.38 +2293,359553.97 +2294,324381.8 +2295,370222.1 +2296,280697.88 +2297,296322.66 +2298,302227.03 +2299,328541.1 +2300,302764.66 +2301,281204.25 +2302,252965.14 +2303,241230.44 +2304,242138.66 +2305,205224.95 +2306,202111.72 +2307,214940.58 +2308,218497.89 +2309,252692.31 +2310,211552.34 +2311,217402.22 +2312,194830.08 +2313,194874.77 +2314,182373.31 +2315,199177.05 +2316,212732.77 +2317,202414.98 +2318,195570.17 +2319,195305.23 +2320,190046.44 +2321,242967.22 +2322,199395.55 +2323,186495.8 +2324,204476.84 +2325,223714.84 +2326,192897.48 +2327,216992.34 +2328,232000.19 +2329,207785.22 +2330,210506.11 +2331,323035.7 +2332,321380.06 +2333,289960.44 +2334,255991.25 +2335,266851.97 +2336,293810.66 +2337,207278.23 +2338,267577.1 +2339,232468.92 +2340,326601.4 +2341,248903.9 +2342,236465.23 +2343,241986.62 +2344,225706.53 +2345,234318.95 +2346,213446.72 +2347,207987.92 +2348,233333.75 +2349,194496.66 +2350,284993.28 +2351,242248.97 +2352,251438.22 +2353,271630.0 +2354,145267.97 +2355,146473.56 +2356,171814.22 +2357,207827.86 +2358,199323.4 +2359,143088.9 +2360,115386.27 +2361,160997.3 +2362,255479.06 +2363,138128.34 +2364,175714.81 +2365,217896.78 +2366,192733.86 +2367,206185.66 +2368,215198.47 +2369,206454.72 +2370,184728.28 +2371,165577.47 +2372,202801.28 +2373,263940.1 +2374,288789.34 +2375,204993.14 +2376,281184.56 +2377,282309.56 +2378,161271.56 +2379,232923.64 +2380,145169.92 +2381,197659.88 +2382,216855.7 +2383,217035.89 +2384,250443.8 +2385,175644.73 +2386,135657.42 +2387,133885.31 +2388,99258.56 +2389,126189.44 +2390,146494.25 +2391,151045.02 +2392,105292.61 +2393,170869.2 +2394,144772.05 +2395,218036.81 +2396,142081.94 +2397,217597.3 +2398,140408.92 +2399,48376.02 +2400,49900.11 +2401,106550.61 +2402,138445.72 +2403,155692.75 +2404,156336.12 +2405,175175.55 +2406,141346.12 +2407,125044.95 +2408,157061.88 +2409,125155.66 +2410,194520.19 +2411,108267.76 +2412,159099.86 +2413,125890.42 +2414,142447.2 +2415,148124.23 +2416,116174.72 +2417,107821.56 +2418,125899.35 +2419,135784.4 +2420,129179.42 +2421,149734.86 +2422,117944.984 +2423,126935.234 +2424,168462.02 +2425,262515.9 +2426,151705.69 +2427,122173.664 +2428,197843.62 +2429,100079.86 +2430,130335.0 +2431,127653.02 +2432,141030.97 +2433,138425.5 +2434,137742.22 +2435,160673.89 +2436,94187.625 +2437,95604.8 +2438,122887.125 +2439,115260.875 +2440,120556.8 +2441,95735.58 +2442,99236.56 +2443,134134.34 +2444,151574.89 +2445,66929.11 +2446,135943.97 +2447,192815.06 +2448,130331.84 +2449,100733.375 +2450,162061.83 +2451,141892.5 +2452,187151.95 +2453,76525.46 +2454,123936.01 +2455,131744.72 +2456,115405.8 +2457,114234.26 +2458,119390.445 +2459,87067.125 +2460,158656.88 +2461,113991.43 +2462,128801.61 +2463,113738.31 +2464,177562.3 +2465,129574.195 +2466,116018.13 +2467,158939.42 +2468,81058.41 +2469,71409.234 +2470,215867.08 +2471,199813.34 +2472,171505.44 +2473,110932.88 +2474,86674.3 +2475,218758.75 +2476,95536.18 +2477,117425.54 +2478,166654.17 +2479,112638.78 +2480,146018.55 +2481,128258.46 +2482,129587.21 +2483,102715.88 +2484,134980.56 +2485,120075.92 +2486,153448.19 +2487,244939.78 +2488,156156.36 +2489,159947.98 +2490,153672.08 +2491,77833.5 +2492,194635.9 +2493,169760.72 +2494,160479.61 +2495,85867.49 +2496,247784.58 +2497,148426.73 +2498,102185.35 +2499,85078.91 +2500,122997.58 +2501,131380.95 +2502,153387.6 +2503,91764.09 +2504,198612.56 +2505,228759.61 +2506,266373.16 +2507,280619.1 +2508,265256.1 +2509,236016.25 +2510,233455.81 +2511,191283.78 +2512,218361.6 +2513,231236.33 +2514,226647.45 +2515,157589.23 +2516,190983.7 +2517,154532.27 +2518,169447.03 +2519,226981.44 +2520,222391.03 +2521,198814.0 +2522,238638.4 +2523,124780.766 +2524,153871.66 +2525,152929.39 +2526,142642.98 +2527,110172.06 +2528,123152.305 +2529,146682.92 +2530,137772.56 +2531,253573.66 +2532,235214.5 +2533,220138.25 +2534,233217.86 +2535,277353.44 +2536,247639.14 +2537,228326.02 +2538,196704.8 +2539,199768.48 +2540,199483.06 +2541,202565.39 +2542,179985.23 +2543,135392.39 +2544,113532.39 +2545,148600.06 +2546,137132.2 +2547,155598.67 +2548,152889.9 +2549,184543.12 +2550,578503.7 +2551,146331.7 +2552,123216.59 +2553,52809.906 +2554,88328.01 +2555,106473.93 +2556,88741.02 +2557,120400.664 +2558,180781.39 +2559,118896.0 +2560,156188.14 +2561,125963.34 +2562,128589.51 +2563,122562.59 +2564,188574.08 +2565,149564.4 +2566,161795.16 +2567,127479.72 +2568,200259.53 +2569,201952.69 +2570,121594.414 +2571,202758.78 +2572,143017.77 +2573,232853.86 +2574,235906.08 +2575,124171.36 +2576,128561.23 +2577,150753.53 +2578,67099.26 +2579,42748.484 +2580,103329.66 +2581,162733.64 +2582,116499.69 +2583,254707.98 +2584,167067.28 +2585,225445.17 +2586,219947.61 +2587,209614.33 +2588,138359.11 +2589,151469.78 +2590,226160.66 +2591,263153.12 +2592,218176.5 +2593,268936.16 +2594,173842.72 +2595,200986.67 +2596,295511.97 +2597,213993.81 +2598,279088.4 +2599,293643.7 +2600,221971.16 +2601,138847.12 +2602,82740.266 +2603,98272.266 +2604,84453.47 +2605,84651.375 +2606,157992.61 +2607,243003.34 +2608,231934.31 +2609,182167.25 +2610,89755.266 +2611,150182.17 +2612,170906.9 +2613,129719.21 +2614,121475.29 +2615,156990.02 +2616,164308.7 +2617,207015.25 +2618,233985.25 +2619,224252.8 +2620,211677.05 +2621,189574.95 +2622,203348.55 +2623,237603.69 +2624,293197.28 +2625,264458.44 +2626,187064.61 +2627,161276.17 +2628,341045.94 +2629,358374.06 +2630,298698.72 +2631,356424.78 +2632,332468.94 +2633,260146.95 +2634,342850.66 +2635,164998.27 +2636,208453.03 +2637,187919.36 +2638,256036.64 +2639,198660.62 +2640,168724.02 +2641,108332.305 +2642,191750.11 +2643,125086.64 +2644,136383.28 +2645,108199.99 +2646,99343.516 +2647,121048.336 +2648,143000.05 +2649,145916.4 +2650,115103.77 +2651,146224.42 +2652,324005.34 +2653,253855.61 +2654,259256.12 +2655,334302.16 +2656,302668.16 +2657,295505.62 +2658,273689.56 +2659,280032.2 +2660,307383.47 +2661,302864.34 +2662,313772.3 +2663,280240.34 +2664,245456.19 +2665,291891.75 +2666,262948.84 +2667,195636.78 +2668,196273.08 +2669,198529.12 +2670,272229.22 +2671,203632.11 +2672,206739.25 +2673,208698.5 +2674,217986.16 +2675,176799.7 +2676,209719.1 +2677,210595.5 +2678,256750.56 +2679,268058.47 +2680,269080.44 +2681,331935.25 +2682,297293.22 +2683,392904.6 +2684,293222.38 +2685,294023.1 +2686,254661.19 +2687,279109.72 +2688,219935.77 +2689,200947.23 +2690,346001.5 +2691,196406.27 +2692,139743.14 +2693,216916.69 +2694,139557.2 +2695,213551.81 +2696,200920.31 +2697,207445.75 +2698,211465.83 +2699,177241.56 +2700,158868.06 +2701,167322.16 +2702,133300.97 +2703,130270.19 +2704,152039.19 +2705,105225.71 +2706,106356.69 +2707,137370.8 +2708,119470.875 +2709,93061.445 +2710,124396.72 +2711,299373.34 +2712,341370.66 +2713,180835.89 +2714,157929.05 +2715,179027.83 +2716,158323.31 +2717,215458.45 +2718,228031.08 +2719,165294.62 +2720,190068.44 +2721,140103.45 +2722,174679.8 +2723,162538.72 +2724,123351.47 +2725,133493.67 +2726,145116.78 +2727,182760.12 +2728,180816.25 +2729,149593.84 +2730,140110.55 +2731,117016.414 +2732,118691.67 +2733,173237.4 +2734,150930.94 +2735,131949.78 +2736,149372.05 +2737,103670.62 +2738,143658.9 +2739,171147.69 +2740,144972.11 +2741,140985.86 +2742,181438.5 +2743,158346.81 +2744,170112.47 +2745,148489.42 +2746,131263.22 +2747,151849.38 +2748,117154.164 +2749,133191.73 +2750,120023.48 +2751,130805.56 +2752,220014.95 +2753,183663.8 +2754,283997.88 +2755,135359.84 +2756,95356.49 +2757,81793.0 +2758,69642.125 +2759,147578.47 +2760,137799.92 +2761,155437.0 +2762,152491.98 +2763,221533.1 +2764,150542.33 +2765,275074.97 +2766,124270.78 +2767,63421.76 +2768,115892.21 +2769,128597.125 +2770,144393.33 +2771,98609.92 +2772,102888.984 +2773,160560.05 +2774,132076.0 +2775,125205.51 +2776,138653.02 +2777,150910.42 +2778,100219.89 +2779,122260.086 +2780,94006.91 +2781,79756.61 +2782,90053.67 +2783,82480.17 +2784,119950.19 +2785,139516.72 +2786,48537.766 +2787,108772.29 +2788,70003.32 +2789,188170.58 +2790,80025.375 +2791,119410.15 +2792,51644.582 +2793,142702.03 +2794,86173.34 +2795,104674.91 +2796,77947.74 +2797,210377.89 +2798,118712.64 +2799,114962.9 +2800,63508.273 +2801,91129.93 +2802,125725.37 +2803,175501.61 +2804,125974.125 +2805,84810.13 +2806,75548.586 +2807,160131.81 +2808,164827.8 +2809,107565.555 +2810,128708.82 +2811,182434.7 +2812,174182.66 +2813,183133.34 +2814,178548.22 +2815,94526.99 +2816,208610.6 +2817,153149.47 +2818,125058.89 +2819,178656.4 +2820,152339.73 +2821,116432.31 +2822,200509.48 +2823,310455.28 +2824,188671.39 +2825,181413.28 +2826,132095.9 +2827,136797.42 +2828,247695.3 +2829,206631.81 +2830,243606.3 +2831,194065.94 +2832,248183.73 +2833,270540.28 +2834,224938.45 +2835,233193.58 +2836,217405.9 +2837,176084.14 +2838,163134.62 +2839,205917.36 +2840,207578.47 +2841,208206.8 +2842,240677.61 +2843,154106.67 +2844,173450.19 +2845,112024.836 +2846,220218.25 +2847,222000.58 +2848,215119.97 +2849,213172.23 +2850,273236.66 +2851,229969.56 +2852,243989.98 +2853,251357.19 +2854,151629.53 +2855,212513.14 +2856,211105.9 +2857,198181.66 +2858,213618.92 +2859,129059.61 +2860,128859.516 +2861,141955.33 +2862,221423.73 +2863,123513.51 +2864,250193.05 +2865,148374.61 +2866,168432.52 +2867,82027.56 +2868,113926.57 +2869,109936.375 +2870,151456.42 +2871,92337.17 +2872,24059.82 +2873,99384.82 +2874,129794.37 +2875,103877.37 +2876,161238.05 +2877,105371.016 +2878,132057.4 +2879,143420.31 +2880,94731.39 +2881,127152.82 +2882,150414.52 +2883,178371.78 +2884,192395.28 +2885,165379.83 +2886,232797.17 +2887,98791.695 +2888,122813.68 +2889,44857.188 +2890,65766.836 +2891,146442.33 +2892,40533.652 +2893,70617.5 +2894,40424.07 +2895,269030.03 +2896,249338.52 +2897,207524.39 +2898,162522.66 +2899,213793.19 +2900,177876.17 +2901,203851.8 +2902,199438.39 +2903,302054.84 +2904,313556.56 +2905,86204.61 +2906,215904.39 +2907,118072.32 +2908,133011.45 +2909,159608.3 +2910,56355.184 +2911,83514.83 +2912,163099.42 +2913,83319.0 +2914,70564.69 +2915,74294.92 +2916,85706.92 +2917,208221.98 +2918,106986.03 +2919,240563.05 diff --git a/Ch08_Convolutional_Neural_Networks/Convolutional_Neural_Networks(LeNet).ipynb b/Ch08_Convolutional_Neural_Networks/Convolutional_Neural_Networks(LeNet).ipynb index 0d188c7b..9268bc6e 100644 --- a/Ch08_Convolutional_Neural_Networks/Convolutional_Neural_Networks(LeNet).ipynb +++ b/Ch08_Convolutional_Neural_Networks/Convolutional_Neural_Networks(LeNet).ipynb @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 0, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -71,15 +71,13 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABNgAAAE5CAYAAABClp38AAAABmJLR0QA/wD/AP+gvaeTAACgOUlE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\n", + "image/png": "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\n", "text/plain": [ "" ] }, - "execution_count": 2, - "metadata": { - "tags": [] - }, + "execution_count": 1, + "metadata": {}, "output_type": "execute_result" } ], @@ -159,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 0, + "execution_count": 2, "metadata": { "colab": {}, "colab_type": "code", @@ -178,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 0, + "execution_count": 3, "metadata": { "colab": {}, "colab_type": "code", @@ -224,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -288,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 0, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -301,15 +299,13 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIcAAAE1CAYAAADNkvPAAAAABmJLR0QA/wD/AP+gvaeTAAAmjUlE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\nIpEoE/o/NLu01jBF4s8AAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ "" ] }, - "execution_count": 6, - "metadata": { - "tags": [] - }, + "execution_count": 5, + "metadata": {}, "output_type": "execute_result" } ], @@ -351,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -359,8 +355,7 @@ }, "colab_type": "code", "id": "ECMwOKTzdbhr", - "outputId": "9a7f2ac9-c95b-4bbb-e902-dcfbfd16301a", - "scrolled": false + "outputId": "9a7f2ac9-c95b-4bbb-e902-dcfbfd16301a" }, "outputs": [], "source": [ @@ -382,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -396,13 +391,11 @@ { "data": { "text/plain": [ - "device(type='cuda', index=0)" + "device(type='cpu')" ] }, - "execution_count": 5, - "metadata": { - "tags": [] - }, + "execution_count": 7, + "metadata": {}, "output_type": "execute_result" } ], @@ -440,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 0, + "execution_count": 8, "metadata": { "colab": {}, "colab_type": "code", @@ -480,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": 0, + "execution_count": 9, "metadata": { "colab": {}, "colab_type": "code", @@ -536,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -551,12 +544,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "training on cuda:0\n", - "epoch 1, loss 0.0091, train acc 0.103, test acc 0.100, time 4.3 sec\n", - "epoch 2, loss 0.0077, train acc 0.230, test acc 0.547, time 5.4 sec\n", - "epoch 3, loss 0.0038, train acc 0.611, test acc 0.590, time 5.4 sec\n", - "epoch 4, loss 0.0030, train acc 0.693, test acc 0.680, time 5.4 sec\n", - "epoch 5, loss 0.0026, train acc 0.740, test acc 0.709, time 5.4 sec\n" + "training on cpu\n", + "epoch 1, loss 0.0091, train acc 0.100, test acc 0.100, time 7.0 sec\n", + "epoch 2, loss 0.0090, train acc 0.120, test acc 0.100, time 6.8 sec\n", + "epoch 3, loss 0.0047, train acc 0.518, test acc 0.622, time 6.8 sec\n", + "epoch 4, loss 0.0032, train acc 0.685, test acc 0.713, time 6.8 sec\n", + "epoch 5, loss 0.0027, train acc 0.732, test acc 0.700, time 6.9 sec\n" ] } ], @@ -618,9 +611,9 @@ "version": "0.3.2" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -632,9 +625,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/Ch08_Convolutional_Neural_Networks/Convolutions_For_Images.ipynb b/Ch08_Convolutional_Neural_Networks/Convolutions_For_Images.ipynb index 00c70226..e0f38bb4 100644 --- a/Ch08_Convolutional_Neural_Networks/Convolutions_For_Images.ipynb +++ b/Ch08_Convolutional_Neural_Networks/Convolutions_For_Images.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": { "scrolled": true }, @@ -242,7 +242,7 @@ "" ] }, - "execution_count": 14, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -300,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -330,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -340,7 +340,7 @@ " [37., 43.]])" ] }, - "execution_count": 16, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -378,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "metadata": { "attributes": { "classes": [], @@ -414,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": { "attributes": { "classes": [], @@ -434,7 +434,7 @@ " [1., 1., 0., 0., 0., 0., 1., 1.]])" ] }, - "execution_count": 18, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -457,7 +457,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": { "attributes": { "classes": [], @@ -483,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "metadata": { "attributes": { "classes": [], @@ -503,7 +503,7 @@ " [ 0., 1., 0., 0., 0., -1., 0.]])" ] }, - "execution_count": 20, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -523,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -539,7 +539,7 @@ " [0., 0., 0., 0., 0.]])" ] }, - "execution_count": 21, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -576,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": { "attributes": { "classes": [], @@ -589,11 +589,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "batch 2, loss 5.672\n", - "batch 4, loss 1.359\n", - "batch 6, loss 0.395\n", - "batch 8, loss 0.134\n", - "batch 10, loss 0.051\n" + "batch 2, loss 9.997\n", + "batch 4, loss 2.100\n", + "batch 6, loss 0.525\n", + "batch 8, loss 0.159\n", + "batch 10, loss 0.056\n" ] } ], @@ -633,16 +633,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor([[ 1.0121, -0.9671]])" + "tensor([[ 1.0080, -0.9622]])" ] }, - "execution_count": 23, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -692,13 +692,27 @@ "\n", "\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -710,9 +724,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch08_Convolutional_Neural_Networks/From_Dense_Layers_to_Convolutions.ipynb b/Ch08_Convolutional_Neural_Networks/From_Dense_Layers_to_Convolutions.ipynb index 871211b3..97064401 100644 --- a/Ch08_Convolutional_Neural_Networks/From_Dense_Layers_to_Convolutions.ipynb +++ b/Ch08_Convolutional_Neural_Networks/From_Dense_Layers_to_Convolutions.ipynb @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -128,7 +128,7 @@ "" ] }, - "execution_count": 8, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -229,7 +229,7 @@ "" ] }, - "execution_count": 5, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -298,13 +298,27 @@ "1. What goes wrong when you apply the above reasoning to text? Hint - what is the structure of language?\n", "1. Prove that $f \\circledast g = g \\circledast f$" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -316,9 +330,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch08_Convolutional_Neural_Networks/Multiple_Input_and_Output_Channels.ipynb b/Ch08_Convolutional_Neural_Networks/Multiple_Input_and_Output_Channels.ipynb index 542262e9..0363ec85 100644 --- a/Ch08_Convolutional_Neural_Networks/Multiple_Input_and_Output_Channels.ipynb +++ b/Ch08_Convolutional_Neural_Networks/Multiple_Input_and_Output_Channels.ipynb @@ -817,9 +817,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -831,9 +831,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch08_Convolutional_Neural_Networks/Padding_and_Stride.ipynb b/Ch08_Convolutional_Neural_Networks/Padding_and_Stride.ipynb index b606fb6d..5a64e155 100644 --- a/Ch08_Convolutional_Neural_Networks/Padding_and_Stride.ipynb +++ b/Ch08_Convolutional_Neural_Networks/Padding_and_Stride.ipynb @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -62,14 +62,14 @@ "" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", - "Image(filename=\"img/padding.png\")" + "Image(filename=\"../img/padding.png\")" ] }, { @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -112,7 +112,7 @@ "torch.Size([8, 8])" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -149,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -158,7 +158,7 @@ "torch.Size([8, 8])" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -190,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -200,13 +200,13 @@ "" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Image(filename=\"img/stride.png\")" + "Image(filename=\"../img/stride.png\")" ] }, { @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -238,7 +238,7 @@ "torch.Size([4, 4])" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -257,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -266,7 +266,7 @@ "torch.Size([2, 2])" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -311,13 +311,20 @@ " \n", " 4. What are the computational benefits of a stride larger than 1." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -329,9 +336,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.6" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch08_Convolutional_Neural_Networks/Pooling.ipynb b/Ch08_Convolutional_Neural_Networks/Pooling.ipynb index ddea551c..6de042da 100644 --- a/Ch08_Convolutional_Neural_Networks/Pooling.ipynb +++ b/Ch08_Convolutional_Neural_Networks/Pooling.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 1, "metadata": { "scrolled": true }, @@ -246,7 +246,7 @@ "" ] }, - "execution_count": 36, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -302,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -330,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -340,7 +340,7 @@ " [7., 8.]])" ] }, - "execution_count": 38, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -359,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -369,7 +369,7 @@ " [5., 6.]])" ] }, - "execution_count": 39, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -394,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -423,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -432,7 +432,7 @@ "tensor([[[[10.]]]])" ] }, - "execution_count": 41, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -453,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -463,7 +463,7 @@ " [13., 15.]]]])" ] }, - "execution_count": 42, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -483,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -494,7 +494,7 @@ " [13., 15.]]]])" ] }, - "execution_count": 43, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -519,7 +519,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -552,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -565,7 +565,7 @@ " [14., 16.]]]])" ] }, - "execution_count": 45, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -609,9 +609,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -623,9 +623,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch12_Optimization_Algorithms/Convexity.ipynb b/Ch12_Optimization_Algorithms/Convexity.ipynb index b876ad6a..9d3031a8 100644 --- a/Ch12_Optimization_Algorithms/Convexity.ipynb +++ b/Ch12_Optimization_Algorithms/Convexity.ipynb @@ -76,7 +76,7 @@ "\n", "\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", @@ -106,10 +106,10 @@ " \n", " \n", + "\" id=\"m190ba525f3\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -146,7 +146,7 @@ "z\n", "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -155,7 +155,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -183,7 +183,7 @@ "z\n", "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -191,12 +191,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -208,10 +208,10 @@ " \n", " \n", + "\" id=\"m185810b59d\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -224,7 +224,7 @@ "z\n", "\" id=\"DejaVuSans-46\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -234,7 +234,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -265,7 +265,7 @@ "z\n", "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -275,7 +275,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -295,7 +295,7 @@ "z\n", "\" id=\"DejaVuSans-49\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -305,12 +305,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -320,12 +320,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -334,92 +334,92 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", " \n", @@ -427,12 +427,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -441,12 +441,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -454,12 +454,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -469,12 +469,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -485,12 +485,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -501,12 +501,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -516,12 +516,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -531,12 +531,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -545,137 +545,137 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", " \n", @@ -683,12 +683,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -697,12 +697,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -710,12 +710,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -725,12 +725,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -740,12 +740,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -755,12 +755,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -770,12 +770,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -785,12 +785,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -799,75 +799,75 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" @@ -944,7 +944,7 @@ "\n", "\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", @@ -974,10 +974,10 @@ " \n", " \n", + "\" id=\"m1f9973bd5b\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1014,7 +1014,7 @@ "z\n", "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1023,7 +1023,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1043,7 +1043,7 @@ "z\n", "\" id=\"DejaVuSans-49\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1052,7 +1052,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1080,7 +1080,7 @@ "z\n", "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1088,12 +1088,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1101,12 +1101,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1118,10 +1118,10 @@ " \n", " \n", + "\" id=\"m813711ea49\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1167,7 +1167,7 @@ "z\n", "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1178,12 +1178,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1194,12 +1194,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1210,12 +1210,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1225,12 +1225,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1239,94 +1239,94 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" @@ -1370,6 +1370,14 @@ "execution_count": 4, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jeon/anaconda3/envs/ai_safe/lib/python3.6/site-packages/numpy/core/_asarray.py:136: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", + " return array(a, dtype, copy=False, order=order, subok=True)\n" + ] + }, { "data": { "image/svg+xml": [ @@ -1657,13 +1665,13 @@ " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13546,7 +13554,7 @@ "\n", "\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", @@ -13576,10 +13584,10 @@ " \n", " \n", + "\" id=\"m8cbe2b918c\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13616,7 +13624,7 @@ "z\n", "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13625,7 +13633,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13645,7 +13653,7 @@ "z\n", "\" id=\"DejaVuSans-49\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13654,7 +13662,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13682,7 +13690,7 @@ "z\n", "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13690,12 +13698,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13703,12 +13711,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13720,10 +13728,10 @@ " \n", " \n", + "\" id=\"m2e363a7d2d\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13736,7 +13744,7 @@ "z\n", "\" id=\"DejaVuSans-46\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13746,7 +13754,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13777,7 +13785,7 @@ "z\n", "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13787,12 +13795,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13802,12 +13810,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13817,12 +13825,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13831,101 +13839,101 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", @@ -13964,17 +13972,17 @@ "z\n", "\" id=\"DejaVuSans-97\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", @@ -14006,17 +14014,17 @@ "z\n", "\" id=\"DejaVuSans-98\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", @@ -14037,15 +14045,15 @@ "z\n", "\" id=\"DejaVuSans-120\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" @@ -14149,13 +14157,27 @@ " * Can you find the 'right' value of $\\lambda$ without a lot of trial and error? \n", "1. Given a convex set $X$ and two vectors $\\mathbf{x}$ and $\\mathbf{y}$ prove that projections never increase distances, i.e. $\\|\\mathbf{x} - \\mathbf{y}\\| \\geq \\|\\mathrm{Proj}_X(\\mathbf{x}) - \\mathrm{Proj}_X(\\mathbf{y})\\|$." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -14167,9 +14189,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch12_Optimization_Algorithms/Gradient_Descent.ipynb b/Ch12_Optimization_Algorithms/Gradient_Descent.ipynb index 9488326e..d7179fe6 100644 --- a/Ch12_Optimization_Algorithms/Gradient_Descent.ipynb +++ b/Ch12_Optimization_Algorithms/Gradient_Descent.ipynb @@ -114,182 +114,70 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"m5dfa6d0f4d\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -378,81 +244,66 @@ "Q 22.75 39.890625 18.8125 39.015625 \n", "Q 14.890625 38.140625 10.796875 36.28125 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-35\"/>\n", + "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -472,61 +323,46 @@ "L 29.78125 35.203125 \n", "L 44.28125 54.6875 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-78\"/>\n", + "\" id=\"DejaVuSans-120\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"m0a578ed873\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -554,28 +390,24 @@ "Q 49.859375 40.875 45.40625 35.40625 \n", "Q 44.1875 33.984375 37.640625 27.21875 \n", "Q 31.109375 20.453125 19.1875 8.296875 \n", - "\" id=\"BitstreamVeraSans-Roman-32\"/>\n", + "z\n", + "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -597,28 +429,23 @@ "L 4.890625 17.1875 \n", "L 4.890625 26.703125 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-34\"/>\n", + "\" id=\"DejaVuSans-52\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -633,6 +460,7 @@ "Q 47.40625 15.53125 47.40625 23.390625 \n", "Q 47.40625 31.296875 43.53125 35.828125 \n", "Q 39.65625 40.375 33.015625 40.375 \n", + "z\n", "M 52.59375 71.296875 \n", "L 52.59375 62.3125 \n", "Q 48.875 64.0625 45.09375 64.984375 \n", @@ -651,28 +479,24 @@ "Q 23.390625 74.21875 37.203125 74.21875 \n", "Q 40.921875 74.21875 44.703125 73.484375 \n", "Q 48.484375 72.75 52.59375 71.296875 \n", - "\" id=\"BitstreamVeraSans-Roman-36\"/>\n", + "z\n", + "\" id=\"DejaVuSans-54\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -687,6 +511,7 @@ "Q 46.921875 13.96875 46.921875 20.515625 \n", "Q 46.921875 27.09375 42.890625 30.859375 \n", "Q 38.875 34.625 31.78125 34.625 \n", + "z\n", "M 21.921875 38.8125 \n", "Q 15.578125 40.375 12.03125 44.71875 \n", "Q 8.5 49.078125 8.5 55.328125 \n", @@ -704,6 +529,7 @@ "Q 6.78125 9.90625 6.78125 20.515625 \n", "Q 6.78125 27.484375 10.78125 32.3125 \n", "Q 14.796875 37.15625 21.921875 38.8125 \n", + "z\n", "M 18.3125 54.390625 \n", "Q 18.3125 48.734375 21.84375 45.5625 \n", "Q 25.390625 42.390625 31.78125 42.390625 \n", @@ -713,36 +539,32 @@ "Q 38.140625 66.40625 31.78125 66.40625 \n", "Q 25.390625 66.40625 21.84375 63.234375 \n", "Q 18.3125 60.0625 18.3125 54.390625 \n", - "\" id=\"BitstreamVeraSans-Roman-38\"/>\n", + "z\n", + "\" id=\"DejaVuSans-56\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -768,7 +590,7 @@ "Q 10.890625 67.625 15.140625 71.796875 \n", "Q 19.390625 75.984375 28.609375 75.984375 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-66\"/>\n", + "\" id=\"DejaVuSans-102\"/>\n", " \n", + "\" id=\"DejaVuSans-40\"/>\n", " \n", + "z\n", + "\" id=\"DejaVuSans-41\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -861,182 +804,70 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"m06ce2379c7\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1125,81 +934,66 @@ "Q 22.75 39.890625 18.8125 39.015625 \n", "Q 14.890625 38.140625 10.796875 36.28125 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-35\"/>\n", + "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1219,61 +1013,46 @@ "L 29.78125 35.203125 \n", "L 44.28125 54.6875 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-78\"/>\n", + "\" id=\"DejaVuSans-120\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"ma9067ad49e\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1301,28 +1080,24 @@ "Q 49.859375 40.875 45.40625 35.40625 \n", "Q 44.1875 33.984375 37.640625 27.21875 \n", "Q 31.109375 20.453125 19.1875 8.296875 \n", - "\" id=\"BitstreamVeraSans-Roman-32\"/>\n", + "z\n", + "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1344,28 +1119,23 @@ "L 4.890625 17.1875 \n", "L 4.890625 26.703125 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-34\"/>\n", + "\" id=\"DejaVuSans-52\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1380,6 +1150,7 @@ "Q 47.40625 15.53125 47.40625 23.390625 \n", "Q 47.40625 31.296875 43.53125 35.828125 \n", "Q 39.65625 40.375 33.015625 40.375 \n", + "z\n", "M 52.59375 71.296875 \n", "L 52.59375 62.3125 \n", "Q 48.875 64.0625 45.09375 64.984375 \n", @@ -1398,28 +1169,24 @@ "Q 23.390625 74.21875 37.203125 74.21875 \n", "Q 40.921875 74.21875 44.703125 73.484375 \n", "Q 48.484375 72.75 52.59375 71.296875 \n", - "\" id=\"BitstreamVeraSans-Roman-36\"/>\n", + "z\n", + "\" id=\"DejaVuSans-54\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1434,6 +1201,7 @@ "Q 46.921875 13.96875 46.921875 20.515625 \n", "Q 46.921875 27.09375 42.890625 30.859375 \n", "Q 38.875 34.625 31.78125 34.625 \n", + "z\n", "M 21.921875 38.8125 \n", "Q 15.578125 40.375 12.03125 44.71875 \n", "Q 8.5 49.078125 8.5 55.328125 \n", @@ -1451,6 +1219,7 @@ "Q 6.78125 9.90625 6.78125 20.515625 \n", "Q 6.78125 27.484375 10.78125 32.3125 \n", "Q 14.796875 37.15625 21.921875 38.8125 \n", + "z\n", "M 18.3125 54.390625 \n", "Q 18.3125 48.734375 21.84375 45.5625 \n", "Q 25.390625 42.390625 31.78125 42.390625 \n", @@ -1460,36 +1229,32 @@ "Q 38.140625 66.40625 31.78125 66.40625 \n", "Q 25.390625 66.40625 21.84375 63.234375 \n", "Q 18.3125 60.0625 18.3125 54.390625 \n", - "\" id=\"BitstreamVeraSans-Roman-38\"/>\n", + "z\n", + "\" id=\"DejaVuSans-56\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1515,7 +1280,7 @@ "Q 10.890625 67.625 15.140625 71.796875 \n", "Q 19.390625 75.984375 28.609375 75.984375 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-66\"/>\n", + "\" id=\"DejaVuSans-102\"/>\n", " \n", + "\" id=\"DejaVuSans-40\"/>\n", " \n", + "z\n", + "\" id=\"DejaVuSans-41\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1596,178 +1482,81 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"m0ea4affc13\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-120\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"mf22789cbc6\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-49\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-51\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-102\"/>\n", " \n", + "\" id=\"DejaVuSans-40\"/>\n", " \n", + "z\n", + "\" id=\"DejaVuSans-41\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], - "text/plain": [ - "" - ] + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" }, - "metadata": {}, "output_type": "display_data" } ], @@ -2552,221 +2117,70 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"m5214340cb5\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2855,81 +2247,66 @@ "Q 22.75 39.890625 18.8125 39.015625 \n", "Q 14.890625 38.140625 10.796875 36.28125 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-35\"/>\n", + "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2949,348 +2326,75 @@ "L 29.78125 35.203125 \n", "L 44.28125 54.6875 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-78\"/>\n", + "\" id=\"DejaVuSans-120\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"m78aeabe228\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-102\"/>\n", " \n", + "\" id=\"DejaVuSans-40\"/>\n", " \n", + "z\n", + "\" id=\"DejaVuSans-41\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -3422,492 +2686,52 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"mc40abf0f0a\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-8722\"/>\n", " \n", + "\" id=\"DejaVuSans-52\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-120\"/>\n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"mf5b2f28282\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -4404,190 +3446,70 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"m72c2d43d8d\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4676,81 +3576,66 @@ "Q 22.75 39.890625 18.8125 39.015625 \n", "Q 14.890625 38.140625 10.796875 36.28125 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-35\"/>\n", + "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4770,88 +3655,49 @@ "L 29.78125 35.203125 \n", "L 44.28125 54.6875 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-78\"/>\n", + "\" id=\"DejaVuSans-120\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"mf175cbf651\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "Q 39.75 74.21875 46.484375 68.546875 \n", + "Q 53.21875 62.890625 53.21875 53.421875 \n", + "Q 53.21875 48.921875 51.53125 44.890625 \n", + "Q 49.859375 40.875 45.40625 35.40625 \n", + "Q 44.1875 33.984375 37.640625 27.21875 \n", + "Q 31.109375 20.453125 19.1875 8.296875 \n", + "z\n", + "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-52\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-54\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-102\"/>\n", " \n", + "\" id=\"DejaVuSans-40\"/>\n", " \n", + "z\n", + "\" id=\"DejaVuSans-41\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -5276,363 +4066,57 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"md814062f54\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-8722\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-50\"/>\n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-120\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"m872e733328\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-102\"/>\n", " \n", + "\" id=\"DejaVuSans-40\"/>\n", " \n", + "z\n", + "\" id=\"DejaVuSans-41\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -6110,221 +4675,70 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"mca9c16e08d\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6413,81 +4805,66 @@ "Q 22.75 39.890625 18.8125 39.015625 \n", "Q 14.890625 38.140625 10.796875 36.28125 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-35\"/>\n", + "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6507,348 +4884,75 @@ "L 29.78125 35.203125 \n", "L 44.28125 54.6875 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-78\"/>\n", + "\" id=\"DejaVuSans-120\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"ma5dc6ef1ea\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-102\"/>\n", " \n", + "\" id=\"DejaVuSans-40\"/>\n", " \n", + "z\n", + "\" id=\"DejaVuSans-41\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -7008,13 +5272,20 @@ " - Apply this to the problem above.\n", "5. Apply the algorithm above to a number of objective functions (convex or not). What happens if you rotate coordinates by $45$ degrees?" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -7026,9 +5297,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch12_Optimization_Algorithms/Mini-batch_Stochastic_Gradient_Descent.ipynb b/Ch12_Optimization_Algorithms/Mini-batch_Stochastic_Gradient_Descent.ipynb index 99dfc4e2..891444da 100644 --- a/Ch12_Optimization_Algorithms/Mini-batch_Stochastic_Gradient_Descent.ipynb +++ b/Ch12_Optimization_Algorithms/Mini-batch_Stochastic_Gradient_Descent.ipynb @@ -125,7 +125,8 @@ " \n", " # Initialize model parameters\n", " net, loss = d2l.linreg, d2l.squared_loss\n", - " w = torch.ones(()).new_empty((features.shape[1], 1),requires_grad=True)\n", + "# w = torch.ones(()).new_empty((features.shape[1], 1),requires_grad=True)\n", + " w= torch.rand((features.shape[1], 1), requires_grad=True)\n", " b= torch.zeros((1,),requires_grad=True)\n", " l= torch.zeros((1500,1),requires_grad=True)\n", " def eval_loss():\n", @@ -147,7 +148,7 @@ " d2l.plt.plot(np.linspace(0, num_epochs, len(ls)), ls)\n", " d2l.plt.xlabel('epoch')\n", " d2l.plt.ylabel('loss')\n", - " return ts, ls " + " return ts, ls" ] }, { @@ -166,571 +167,612 @@ "name": "stdout", "output_type": "stream", "text": [ - "loss: 0.245498, 0.147121 sec per epoch\n" + "loss: 0.605671, 0.028616 sec per epoch\n" ] }, { "data": { "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - "\r\n" + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" ], "text/plain": [ "
" @@ -766,833 +808,620 @@ "name": "stdout", "output_type": "stream", "text": [ - "loss: 0.243159, 2.395513 sec per epoch\n" + "loss: 0.242423, 0.259268 sec per epoch\n" ] }, { "data": { "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - "\r\n" + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" ], "text/plain": [ "
" @@ -1624,640 +1453,518 @@ "name": "stdout", "output_type": "stream", "text": [ - "loss: 0.246469, 0.086600 sec per epoch\n" + "loss: 0.249161, 0.011021 sec per epoch\n" ] }, { "data": { "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - "\r\n" + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" ], "text/plain": [ "
" @@ -2289,837 +1996,645 @@ "name": "stdout", "output_type": "stream", "text": [ - "loss: 0.243249, 0.310116 sec per epoch\n" + "loss: 0.242561, 0.035637 sec per epoch\n" ] }, { "data": { "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - "\r\n" + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" ], "text/plain": [ "
" @@ -3150,1337 +2665,1348 @@ { "data": { "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - "\r\n" + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" ], "text/plain": [ "
" @@ -4515,13 +4041,27 @@ "## Exercises\n", "* Modify the batch size and learning rate and observe the rate of decline for the value of the objective function and the time consumed in each epoch." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -4533,9 +4073,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch12_Optimization_Algorithms/Momentum.ipynb b/Ch12_Optimization_Algorithms/Momentum.ipynb index 2a13d976..1729c283 100644 --- a/Ch12_Optimization_Algorithms/Momentum.ipynb +++ b/Ch12_Optimization_Algorithms/Momentum.ipynb @@ -47,717 +47,52 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"mc747566e45\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-8722\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-52\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-120\"/>\n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"m6b34c8e762\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1168,862 +1035,52 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"m25b25ab04e\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-8722\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-52\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-120\"/>\n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"m460926c5fd\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -2445,717 +2171,52 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"m5e1fcd1a0b\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-8722\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-52\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-120\"/>\n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"m0d46fc64d7\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -3555,953 +3148,52 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"m97d9764b50\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-8722\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-52\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-120\"/>\n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"ma8cff741a2\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -4920,244 +4287,41 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"ma222ddae87\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5172,6 +4336,7 @@ "Q 47.125 21.390625 47.125 36.375 \n", "Q 47.125 51.421875 43.28125 58.90625 \n", "Q 39.453125 66.40625 31.78125 66.40625 \n", + "z\n", "M 31.78125 74.21875 \n", "Q 44.046875 74.21875 50.515625 64.515625 \n", "Q 56.984375 54.828125 56.984375 36.375 \n", @@ -5181,69 +4346,21 @@ "Q 6.59375 17.96875 6.59375 36.375 \n", "Q 6.59375 54.828125 13.0625 64.515625 \n", "Q 19.53125 74.21875 31.78125 74.21875 \n", - "\" id=\"BitstreamVeraSans-Roman-30\"/>\n", + "z\n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-49\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-51\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-52\"/>\n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-109\"/>\n", " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-101\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"m245210f178\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-46\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-54\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-56\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + "\" style=\"fill:#ffffff;opacity:0.8;stroke:#cccccc;stroke-linejoin:miter;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", + "\" id=\"DejaVuSans-103\"/>\n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -6064,7 +5310,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "loss: 0.245, 0.019 sec/epoch\n" + "loss: 0.244, 0.005 sec/epoch\n" ] }, { @@ -6073,95 +5319,46 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"m58bc1aae89\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6176,6 +5373,7 @@ "Q 47.125 21.390625 47.125 36.375 \n", "Q 47.125 51.421875 43.28125 58.90625 \n", "Q 39.453125 66.40625 31.78125 66.40625 \n", + "z\n", "M 31.78125 74.21875 \n", "Q 44.046875 74.21875 50.515625 64.515625 \n", "Q 56.984375 54.828125 56.984375 36.375 \n", @@ -6185,35 +5383,31 @@ "Q 6.59375 17.96875 6.59375 36.375 \n", "Q 6.59375 54.828125 13.0625 64.515625 \n", "Q 19.53125 74.21875 31.78125 74.21875 \n", - "\" id=\"BitstreamVeraSans-Roman-30\"/>\n", + "z\n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", + "\" id=\"DejaVuSans-46\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6242,29 +5436,24 @@ "Q 22.75 39.890625 18.8125 39.015625 \n", "Q 14.890625 38.140625 10.796875 36.28125 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-35\"/>\n", + "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6282,54 +5471,44 @@ "L 54.390625 0 \n", "L 12.40625 0 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-31\"/>\n", + "\" id=\"DejaVuSans-49\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6357,18 +5536,43 @@ "Q 49.859375 40.875 45.40625 35.40625 \n", "Q 44.1875 33.984375 37.640625 27.21875 \n", "Q 31.109375 20.453125 19.1875 8.296875 \n", - "\" id=\"BitstreamVeraSans-Roman-32\"/>\n", + "z\n", + "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-112\"/>\n", " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-111\"/>\n", " \n", + "z\n", + "\" id=\"DejaVuSans-99\"/>\n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"m5965bb1f69\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-51\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-108\"/>\n", " \n", + "z\n", + "\" id=\"DejaVuSans-115\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -6886,7 +5984,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "loss: 0.266, 0.014 sec/epoch\n" + "loss: 0.250, 0.005 sec/epoch\n" ] }, { @@ -6895,95 +5993,46 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"mdd601e0d16\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6998,6 +6047,7 @@ "Q 47.125 21.390625 47.125 36.375 \n", "Q 47.125 51.421875 43.28125 58.90625 \n", "Q 39.453125 66.40625 31.78125 66.40625 \n", + "z\n", "M 31.78125 74.21875 \n", "Q 44.046875 74.21875 50.515625 64.515625 \n", "Q 56.984375 54.828125 56.984375 36.375 \n", @@ -7007,35 +6057,31 @@ "Q 6.59375 17.96875 6.59375 36.375 \n", "Q 6.59375 54.828125 13.0625 64.515625 \n", "Q 19.53125 74.21875 31.78125 74.21875 \n", - "\" id=\"BitstreamVeraSans-Roman-30\"/>\n", + "z\n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", + "\" id=\"DejaVuSans-46\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7064,29 +6110,24 @@ "Q 22.75 39.890625 18.8125 39.015625 \n", "Q 14.890625 38.140625 10.796875 36.28125 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-35\"/>\n", + "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7104,54 +6145,44 @@ "L 54.390625 0 \n", "L 12.40625 0 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-31\"/>\n", + "\" id=\"DejaVuSans-49\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7179,18 +6210,43 @@ "Q 49.859375 40.875 45.40625 35.40625 \n", "Q 44.1875 33.984375 37.640625 27.21875 \n", "Q 31.109375 20.453125 19.1875 8.296875 \n", - "\" id=\"BitstreamVeraSans-Roman-32\"/>\n", + "z\n", + "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-112\"/>\n", " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-111\"/>\n", " \n", + "z\n", + "\" id=\"DejaVuSans-99\"/>\n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"md09e7e8961\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-51\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-108\"/>\n", " \n", + "z\n", + "\" id=\"DejaVuSans-115\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -7703,7 +6653,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "loss: 0.245, 0.018 sec/epoch\n" + "loss: 0.245, 0.005 sec/epoch\n" ] }, { @@ -7712,96 +6662,46 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "L 0 3.5 \n", + "\" id=\"m5984afa1ef\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7816,6 +6716,7 @@ "Q 47.125 21.390625 47.125 36.375 \n", "Q 47.125 51.421875 43.28125 58.90625 \n", "Q 39.453125 66.40625 31.78125 66.40625 \n", + "z\n", "M 31.78125 74.21875 \n", "Q 44.046875 74.21875 50.515625 64.515625 \n", "Q 56.984375 54.828125 56.984375 36.375 \n", @@ -7825,35 +6726,31 @@ "Q 6.59375 17.96875 6.59375 36.375 \n", "Q 6.59375 54.828125 13.0625 64.515625 \n", "Q 19.53125 74.21875 31.78125 74.21875 \n", - "\" id=\"BitstreamVeraSans-Roman-30\"/>\n", + "z\n", + "\" id=\"DejaVuSans-48\"/>\n", " \n", + "\" id=\"DejaVuSans-46\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7882,29 +6779,24 @@ "Q 22.75 39.890625 18.8125 39.015625 \n", "Q 14.890625 38.140625 10.796875 36.28125 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-35\"/>\n", + "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7922,54 +6814,44 @@ "L 54.390625 0 \n", "L 12.40625 0 \n", "z\n", - "\" id=\"BitstreamVeraSans-Roman-31\"/>\n", + "\" id=\"DejaVuSans-49\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7997,18 +6879,43 @@ "Q 49.859375 40.875 45.40625 35.40625 \n", "Q 44.1875 33.984375 37.640625 27.21875 \n", "Q 31.109375 20.453125 19.1875 8.296875 \n", - "\" id=\"BitstreamVeraSans-Roman-32\"/>\n", + "z\n", + "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-112\"/>\n", " \n", - " \n", - " \n", + "\" id=\"DejaVuSans-111\"/>\n", " \n", + "z\n", + "\" id=\"DejaVuSans-99\"/>\n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + "L -3.5 0 \n", + "\" id=\"m70187ca95d\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + "z\n", + "\" id=\"DejaVuSans-51\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" id=\"DejaVuSans-108\"/>\n", " \n", + "z\n", + "\" id=\"DejaVuSans-115\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -8531,9 +7332,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -8545,9 +7346,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch12_Optimization_Algorithms/Optimization_And_Deep_Learning.ipynb b/Ch12_Optimization_Algorithms/Optimization_And_Deep_Learning.ipynb index 19df449d..c949bdf5 100644 --- a/Ch12_Optimization_Algorithms/Optimization_And_Deep_Learning.ipynb +++ b/Ch12_Optimization_Algorithms/Optimization_And_Deep_Learning.ipynb @@ -58,7 +58,7 @@ "\n", "\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", @@ -88,10 +88,10 @@ " \n", " \n", + "\" id=\"m84352672ec\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -155,7 +155,7 @@ "z\n", "\" id=\"DejaVuSans-54\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -165,7 +165,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -211,7 +211,7 @@ "z\n", "\" id=\"DejaVuSans-56\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -221,7 +221,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -241,7 +241,7 @@ "z\n", "\" id=\"DejaVuSans-49\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -251,7 +251,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -282,7 +282,7 @@ "z\n", "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -292,7 +292,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -316,7 +316,7 @@ "z\n", "\" id=\"DejaVuSans-52\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -341,7 +341,7 @@ "z\n", "\" id=\"DejaVuSans-120\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -352,10 +352,10 @@ " \n", " \n", + "\" id=\"m912cbc80d1\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -392,7 +392,7 @@ "z\n", "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -404,12 +404,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -421,7 +421,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -437,7 +437,7 @@ "z\n", "\" id=\"DejaVuSans-55\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -449,12 +449,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -466,12 +466,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -483,12 +483,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -571,7 +571,7 @@ "z\n", "\" id=\"DejaVuSans-107\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -580,238 +580,238 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", @@ -958,7 +958,7 @@ "\" id=\"DejaVuSans-108\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -976,12 +976,12 @@ " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", @@ -1034,27 +1034,27 @@ "z\n", "\" id=\"DejaVuSans-100\"/>\n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" @@ -1124,7 +1124,7 @@ "\n", "\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", @@ -1154,10 +1154,10 @@ " \n", " \n", + "\" id=\"m2702df2829\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1210,7 +1210,7 @@ "z\n", "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1221,7 +1221,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1252,7 +1252,7 @@ "z\n", "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1263,12 +1263,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1278,12 +1278,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1293,12 +1293,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1308,12 +1308,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1323,7 +1323,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1354,7 +1354,7 @@ "z\n", "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1379,7 +1379,7 @@ "z\n", "\" id=\"DejaVuSans-120\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1390,15 +1390,15 @@ " \n", " \n", + "\" id=\"m686628c70b\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1407,12 +1407,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1420,12 +1420,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1433,12 +1433,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1492,7 +1492,7 @@ "z\n", "\" id=\"DejaVuSans-41\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1501,118 +1501,118 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", @@ -1781,7 +1781,7 @@ "z\n", "\" id=\"DejaVuSans-117\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1798,12 +1798,12 @@ " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", @@ -1868,7 +1868,7 @@ "z\n", "\" id=\"DejaVuSans-98\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1888,8 +1888,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" @@ -1938,7 +1938,7 @@ "\n", "\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", @@ -1968,10 +1968,10 @@ " \n", " \n", + "\" id=\"m20dc803993\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2008,7 +2008,7 @@ "z\n", "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2017,7 +2017,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2037,7 +2037,7 @@ "z\n", "\" id=\"DejaVuSans-49\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2046,7 +2046,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2074,7 +2074,7 @@ "z\n", "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2082,12 +2082,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2095,12 +2095,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2123,7 +2123,7 @@ "z\n", "\" id=\"DejaVuSans-120\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2134,10 +2134,10 @@ " \n", " \n", + "\" id=\"m6ca6ead572\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2168,7 +2168,7 @@ "z\n", "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2177,12 +2177,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2190,12 +2190,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2249,7 +2249,7 @@ "z\n", "\" id=\"DejaVuSans-41\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2258,85 +2258,85 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", @@ -2560,7 +2560,7 @@ "z\n", "\" id=\"DejaVuSans-116\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2578,8 +2578,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" @@ -2952,14 +2952,14 @@ "L 3 -3 \n", "M -3 -3 \n", "L 3 3 \n", - "\" id=\"m5b88f9e2f5\" style=\"stroke:#ff0000;\"/>\n", + "\" id=\"mbb896517c6\" style=\"stroke:#ff0000;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5271,7 +5271,7 @@ "\n", "\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", " \n", @@ -5301,10 +5301,10 @@ " \n", " \n", + "\" id=\"mdd0110a304\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5341,7 +5341,7 @@ "z\n", "\" id=\"DejaVuSans-50\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5350,7 +5350,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5370,7 +5370,7 @@ "z\n", "\" id=\"DejaVuSans-49\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5379,7 +5379,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5407,7 +5407,7 @@ "z\n", "\" id=\"DejaVuSans-48\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5415,12 +5415,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5428,12 +5428,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5441,7 +5441,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5480,7 +5480,7 @@ "z\n", "\" id=\"DejaVuSans-51\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5488,7 +5488,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5512,7 +5512,7 @@ "z\n", "\" id=\"DejaVuSans-52\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5520,7 +5520,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5551,7 +5551,7 @@ "z\n", "\" id=\"DejaVuSans-53\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5574,7 +5574,7 @@ "z\n", "\" id=\"DejaVuSans-120\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5585,10 +5585,10 @@ " \n", " \n", + "\" id=\"m7452227ebd\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5601,7 +5601,7 @@ "z\n", "\" id=\"DejaVuSans-46\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5612,12 +5612,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5628,12 +5628,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5643,12 +5643,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5658,12 +5658,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5719,7 +5719,7 @@ "z\n", "\" id=\"DejaVuSans-41\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5728,81 +5728,81 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", @@ -6050,7 +6050,7 @@ "z\n", "\" id=\"DejaVuSans-116\"/>\n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6074,8 +6074,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" @@ -6128,13 +6128,27 @@ "\n", "[1] Wigner, E. P. (1958). On the distribution of the roots of certain symmetric matrices. Annals of Mathematics, 325-327." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -6146,9 +6160,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch12_Optimization_Algorithms/RMSProp.ipynb b/Ch12_Optimization_Algorithms/RMSProp.ipynb index db613f02..5724f8cd 100644 --- a/Ch12_Optimization_Algorithms/RMSProp.ipynb +++ b/Ch12_Optimization_Algorithms/RMSProp.ipynb @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -56,886 +56,6 @@ "text": [ "epoch 20, x1 -0.010599, x2 0.000000\n" ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -971,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -1007,14 +127,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "loss: 0.242, 0.012 sec/epoch\n" + "loss: 0.246, 0.005 sec/epoch\n" ] }, { @@ -1027,7 +147,7 @@ "\n", " \n", " \n", " \n", " \n", @@ -1051,7 +171,7 @@ " \n", " \n", " \n", - " \n", " \n", @@ -1059,10 +179,10 @@ " \n", " \n", + "\" id=\"m840c4d909c\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1105,13 +225,13 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1151,13 +271,13 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1186,13 +306,13 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1206,13 +326,13 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1377,7 +497,7 @@ " \n", " \n", " \n", - " \n", " \n", @@ -1385,10 +505,10 @@ " \n", " \n", + "\" id=\"m4e6a3d7fee\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1404,13 +524,13 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1426,13 +546,13 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1459,13 +579,13 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1515,13 +635,13 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1537,13 +657,13 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1607,21 +727,21 @@ " \n", " \n", " \n", - " \n", " \n", " \n", @@ -1647,7 +767,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1657,7 +777,9 @@ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1690,9 +812,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -1704,9 +826,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch12_Optimization_Algorithms/Stochastic_Gradient_Descent.ipynb b/Ch12_Optimization_Algorithms/Stochastic_Gradient_Descent.ipynb index f983db8f..c64fc6d8 100644 --- a/Ch12_Optimization_Algorithms/Stochastic_Gradient_Descent.ipynb +++ b/Ch12_Optimization_Algorithms/Stochastic_Gradient_Descent.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -69,729 +69,742 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "epoch 20, x1 0.102315, x2 -0.103776\n" + "epoch 20, x1 0.220980, x2 0.068481\n" ] }, { "data": { "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - "\r\n" + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" ], "text/plain": [ "
" @@ -837,13 +850,20 @@ "* Using a different objective function, observe the iterative trajectory of the independent variable in gradient descent and the SGD.\n", "* In the experiment for gradient descent in two-dimensional space, try to use different learning rates to observe and analyze the experimental phenomena." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -855,9 +875,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch12_Optimization_Algorithms/d2l/__init__.py b/Ch12_Optimization_Algorithms/d2l/__init__.py new file mode 100644 index 00000000..9272ba5b --- /dev/null +++ b/Ch12_Optimization_Algorithms/d2l/__init__.py @@ -0,0 +1,7 @@ +from .base import * +from .figure import * +from .data import * +from .model import * +from .train import * +from .ssd_utils import * +__version__ = '0.1.1' diff --git a/Ch12_Optimization_Algorithms/d2l/base.py b/Ch12_Optimization_Algorithms/d2l/base.py new file mode 100644 index 00000000..25258b5d --- /dev/null +++ b/Ch12_Optimization_Algorithms/d2l/base.py @@ -0,0 +1,74 @@ +"""The base module contains some basic functions/classes for d2l""" +import time +import torch +import numpy as np + +__all__ = ['try_gpu', 'try_all_gpus', 'Benchmark', 'Timer', 'Accumulator'] + +def try_gpu(): + """If GPU is available, return torch.device as cuda:0; else return torch.device as cpu.""" + if torch.cuda.is_available(): + device = torch.device('cuda:0') + else: + device = torch.device('cpu') + return device + +def try_all_gpus(): + """Return all available GPUs, or [torch device cpu] if there is no GPU.""" + if torch.cuda.is_available(): + devices = [] + for i in range(16): + device = torch.device('cuda:'+str(i)) + devices.append(device) + else: + devices = [torch.device('cpu')] + return devices + +class Benchmark(): + """Benchmark programs.""" + def __init__(self, prefix=None): + self.prefix = prefix + ' ' if prefix else '' + + def __enter__(self): + self.start = time.time() + + def __exit__(self, *args): + print('%stime: %.4f sec' % (self.prefix, time.time() - self.start)) + +class Timer(object): + """Record multiple running times.""" + def __init__(self): + self.times = [] + self.start() + + def start(self): + """Start the timer""" + self.start_time = time.time() + + def stop(self): + """Stop the timer and record the time in a list""" + self.times.append(time.time() - self.start_time) + return self.times[-1] + + def avg(self): + """Return the average time""" + return sum(self.times)/len(self.times) + + def sum(self): + """Return the sum of time""" + return sum(self.times) + + def cumsum(self): + """Return the accumuated times""" + return np.array(self.times).cumsum().tolist() + +class Accumulator(object): + """Sum a list of numbers over time""" + def __init__(self, n): + self.data = [0.0] * n + def add(self, *args): + self.data = [a+b for a, b in zip(self.data, args)] + def reset(self): + self.data = [0] * len(self.data) + def __getitem__(self, i): + return self.data[i] diff --git a/Ch12_Optimization_Algorithms/d2l/data/__init__.py b/Ch12_Optimization_Algorithms/d2l/data/__init__.py new file mode 100644 index 00000000..f56ba615 --- /dev/null +++ b/Ch12_Optimization_Algorithms/d2l/data/__init__.py @@ -0,0 +1,5 @@ +"""The data module contains functions/classes to load and (pre)process data sets""" + +from .fashion_mnist import * +from .base import * +from .nmt import * diff --git a/Ch12_Optimization_Algorithms/d2l/data/base.py b/Ch12_Optimization_Algorithms/d2l/data/base.py new file mode 100644 index 00000000..d759658a --- /dev/null +++ b/Ch12_Optimization_Algorithms/d2l/data/base.py @@ -0,0 +1,103 @@ +import random +import collections +import numpy as np +import zipfile +import torch +import os + +class Vocab(object): # This class is saved in d2l. + def __init__(self, tokens, min_freq=0, use_special_tokens=False): + # sort by frequency and token + counter = collections.Counter(tokens) + token_freqs = sorted(counter.items(), key=lambda x: x[0]) + token_freqs.sort(key=lambda x: x[1], reverse=True) + if use_special_tokens: + # padding, begin of sentence, end of sentence, unknown + self.pad, self.bos, self.eos, self.unk = (0, 1, 2, 3) + tokens = ['', '', '', ''] + else: + self.unk = 0 + tokens = [''] + tokens += [token for token, freq in token_freqs if freq >= min_freq] + self.idx_to_token = [] + self.token_to_idx = dict() + for token in tokens: + self.idx_to_token.append(token) + self.token_to_idx[token] = len(self.idx_to_token) - 1 + + def __len__(self): + return len(self.idx_to_token) + + def __getitem__(self, tokens): + if not isinstance(tokens, (list, tuple)): + return self.token_to_idx.get(tokens, self.unk) + else: + return [self.__getitem__(token) for token in tokens] + + def to_tokens(self, indices): + if not isinstance(indices, (list, tuple)): + return self.idx_to_token[indices] + else: + return [self.idx_to_token[index] for index in indices] + + +def data_iter_consecutive(corpus_indices, batch_size, num_steps, ctx=None): + # Offset for the iterator over the data for uniform starts + offset = int(random.uniform(0,num_steps)) + # Slice out data - ignore num_steps and just wrap around + num_indices = ((len(corpus_indices) - offset) // batch_size) * batch_size + indices = torch.tensor(corpus_indices[offset:(offset + num_indices)], dtype=torch.float32, device=ctx) + indices = indices.reshape((batch_size,-1)) + # Need to leave one last token since targets are shifted by 1 + num_epochs = ((num_indices // batch_size) - 1) // num_steps + + for i in range(0, num_epochs * num_steps, num_steps): + X = indices[:,i:(i+num_steps)] + Y = indices[:,(i+1):(i+1+num_steps)] + yield X, Y + +def data_iter_random(corpus_indices, batch_size, num_steps, ctx=None): + # Offset for the iterator over the data for uniform starts + offset = int(random.uniform(0,num_steps)) + corpus_indices = corpus_indices[offset:] + # Subtract 1 extra since we need to account for the sequence length + num_examples = ((len(corpus_indices) - 1) // num_steps) - 1 + # Discard half empty batches + num_batches = num_examples // batch_size + example_indices = list(range(0, num_examples * num_steps, num_steps)) + random.shuffle(example_indices) + + # This returns a sequence of the length num_steps starting from pos + def _data(pos): + return corpus_indices[pos: pos + num_steps] + + for i in range(0, batch_size * num_batches, batch_size): + # Batch_size indicates the random examples read each time + batch_indices = example_indices[i:(i+batch_size)] + X = [_data(j) for j in batch_indices] + Y = [_data(j + 1) for j in batch_indices] + yield torch.Tensor(X, device=ctx), torch.Tensor(Y, device=ctx) + + +def load_data_time_machine(num_examples=10000): + """Load the time machine data set (available in the English book).""" + with open('../data/timemachine.txt') as f: + raw_text = f.read() + lines = raw_text.split('\n') + text = ' '.join(' '.join(lines).lower().split())[:num_examples] + vocab = Vocab(text) + corpus_indices = [vocab[char] for char in text] + return corpus_indices, vocab + +def load_array(dataArray, labelArray, batch_size, is_train=True): + """ Constructs a pytorch dataloader""" + dataset = torch.utils.data.TensorDataset(torch.from_numpy(dataArray), torch.from_numpy(labelArray)) + return torch.utils.data.DataLoader(dataset, batch_size, shuffle=is_train) + +def get_data_ch10(batch_size=10, n=1500): + data = np.genfromtxt('../data/airfoil_self_noise.dat', delimiter='\t') + data = np.array((data - data.mean(axis=0)) / data.std(axis=0)) + data_iter = load_array(data[:n, :-1], data[:n, -1], + batch_size, is_train=True) + return data_iter, data.shape[1]-1 + diff --git a/Ch12_Optimization_Algorithms/d2l/data/fashion_mnist.py b/Ch12_Optimization_Algorithms/d2l/data/fashion_mnist.py new file mode 100644 index 00000000..7f10ff03 --- /dev/null +++ b/Ch12_Optimization_Algorithms/d2l/data/fashion_mnist.py @@ -0,0 +1,42 @@ +import os +import sys +import torchvision +from torchvision import transforms +from torch.utils.data import DataLoader +from ..figure import plt, use_svg_display + + +def load_data_fashion_mnist(batch_size, resize=None, root=os.path.join( + '~', '.pytorch', 'datasets', 'fashion-mnist')): + """Download the Fashion-MNIST dataset and then load into memory.""" + root = os.path.expanduser(root) + transformer = [] + if resize: + transformer += [transforms.Resize(resize)] + transformer += [transforms.ToTensor()] + transformer = transforms.Compose(transformer) + + mnist_train = torchvision.datasets.FashionMNIST(root=root, train=True, transform=transformer, download=True) + mnist_test = torchvision.datasets.FashionMNIST(root=root, train=False, transform=transformer, download=True) + num_workers = 0 if sys.platform.startswith('win32') else 4 + + train_iter = DataLoader(mnist_train, batch_size, shuffle=True, num_workers=num_workers) + test_iter = DataLoader(mnist_test, batch_size, shuffle=False, num_workers=num_workers) + return train_iter, test_iter + +def get_fashion_mnist_labels(labels): + """Get text labels for Fashion-MNIST.""" + text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat', + 'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot'] + return [text_labels[int(i)] for i in labels] + + +def show_fashion_mnist(images, labels): + """Plot Fashion-MNIST images with labels.""" + use_svg_display() + _, figs = plt.subplots(1, len(images), figsize=(12, 12)) + for f, img, lbl in zip(figs, images, labels): + f.imshow(img.reshape((28, 28)).numpy()) + f.set_title(lbl) + f.axes.get_xaxis().set_visible(False) + f.axes.get_yaxis().set_visible(False) diff --git a/Ch12_Optimization_Algorithms/d2l/data/nmt.py b/Ch12_Optimization_Algorithms/d2l/data/nmt.py new file mode 100644 index 00000000..e4d8ded1 --- /dev/null +++ b/Ch12_Optimization_Algorithms/d2l/data/nmt.py @@ -0,0 +1,78 @@ +#import urllib2 +import zipfile +import torch +import requests +from io import BytesIO +from torch.utils import data +from .base import Vocab + +__all__ = ['load_data_nmt'] + +def load_data_nmt(batch_size, max_len, num_examples=1000): + """Download an NMT dataset, return its vocabulary and data iterator.""" + # Download and preprocess + def preprocess_raw(text): + text = text.replace('\u202f', ' ').replace('\xa0', ' ') + out = '' + for i, char in enumerate(text.lower()): + if char in (',', '!', '.') and text[i-1] != ' ': + out += ' ' + out += char + return out + + url = 'http://www.manythings.org/anki/fra-eng.zip' + print("Downloading fra-eng.zip from '{0}'".format(url)) + + + headers={"User-Agent": "XY"}#dummy user agent + response = requests.get(url,headers=headers ,stream=True) + handle = BytesIO() + + for chunk in response.iter_content(chunk_size=512): + if chunk: # filter out keep-alive new chunks + handle.write(chunk) + + + with zipfile.ZipFile(handle, 'r') as f: + raw_text = f.read('fra.txt').decode("utf-8") + + handle.close() + + text = preprocess_raw(raw_text) + + # Tokenize + source, target = [], [] + for i, line in enumerate(text.split('\n')): + if i >= num_examples: + break + parts = line.split('\t') + if len(parts) == 2: + source.append(parts[0].split(' ')) + target.append(parts[1].split(' ')) + + # Build vocab + def build_vocab(tokens): + tokens = [token for line in tokens for token in line] + return Vocab(tokens, min_freq=3, use_special_tokens=True) + src_vocab, tgt_vocab = build_vocab(source), build_vocab(target) + + # Convert to index arrays + def pad(line, max_len, padding_token): + if len(line) > max_len: + return line[:max_len] + return line + [padding_token] * (max_len - len(line)) + + def build_array(lines, vocab, max_len, is_source): + lines = [vocab[line] for line in lines] + if not is_source: + lines = [[vocab.bos] + line + [vocab.eos] for line in lines] + array = torch.tensor([pad(line, max_len, vocab.pad) for line in lines]) + valid_len = (array != vocab.pad).sum(1) + return array, valid_len + + src_vocab, tgt_vocab = build_vocab(source), build_vocab(target) + src_array, src_valid_len = build_array(source, src_vocab, max_len, True) + tgt_array, tgt_valid_len = build_array(target, tgt_vocab, max_len, False) + train_data = data.TensorDataset(src_array, src_valid_len, tgt_array, tgt_valid_len) + train_iter = data.DataLoader(train_data, batch_size, shuffle=True) + return src_vocab, tgt_vocab, train_iter diff --git a/Ch12_Optimization_Algorithms/d2l/figure.py b/Ch12_Optimization_Algorithms/d2l/figure.py new file mode 100644 index 00000000..a3917a56 --- /dev/null +++ b/Ch12_Optimization_Algorithms/d2l/figure.py @@ -0,0 +1,145 @@ +"""The image module contains functions for plotting""" +from IPython import display +from matplotlib import pyplot as plt +import numpy as np + +__all__ = ['plt', 'bbox_to_rect', 'semilogy', 'set_figsize', 'show_bboxes', + 'show_images', 'show_trace_2d', 'use_svg_display', 'plot', 'set_axes', 'Animator'] + +def bbox_to_rect(bbox, color): + """Convert bounding box to matplotlib format.""" + return plt.Rectangle(xy=(bbox[0], bbox[1]), width=bbox[2]-bbox[0], + height=bbox[3]-bbox[1], fill=False, edgecolor=color, + linewidth=2) + +def semilogy(x_vals, y_vals, x_label, y_label, x2_vals=None, y2_vals=None, + legend=None, figsize=(3.5, 2.5)): + """Plot x and log(y).""" + set_figsize(figsize) + plt.xlabel(x_label) + plt.ylabel(y_label) + plt.semilogy(x_vals, y_vals) + if x2_vals and y2_vals: + plt.semilogy(x2_vals, y2_vals, linestyle=':') + plt.legend(legend) + plt.show() + + +def set_figsize(figsize=(3.5, 2.5)): + """Set matplotlib figure size.""" + use_svg_display() + plt.rcParams['figure.figsize'] = figsize + +def _make_list(obj, default_values=None): + if obj is None: + obj = default_values + elif not isinstance(obj, (list, tuple)): + obj = [obj] + return obj + +def show_bboxes(axes, bboxes, labels=None, colors=None): + """Show bounding boxes.""" + labels = _make_list(labels) + colors = _make_list(colors, ['b', 'g', 'r', 'm', 'k']) + for i, bbox in enumerate(bboxes): + color = colors[i % len(colors)] + rect = bbox_to_rect(bbox.numpy(), color) + axes.add_patch(rect) + if labels and len(labels) > i: + text_color = 'k' if color == 'w' else 'w' + axes.text(rect.xy[0], rect.xy[1], labels[i], + va='center', ha='center', fontsize=9, color=text_color, + bbox=dict(facecolor=color, lw=0)) + +def show_images(imgs, num_rows, num_cols, scale=2): + """Plot a list of images.""" + figsize = (num_cols * scale, num_rows * scale) + _, axes = plt.subplots(num_rows, num_cols, figsize=figsize) + for i in range(num_rows): + for j in range(num_cols): + axes[i][j].imshow(imgs[i * num_cols + j].asnumpy()) + axes[i][j].axes.get_xaxis().set_visible(False) + axes[i][j].axes.get_yaxis().set_visible(False) + return axes + +def show_trace_2d(f, res): + """Show the trace of 2D variables during optimization.""" + x1, x2 = zip(*res) + set_figsize() + plt.plot(x1, x2, '-o', color='#ff7f0e') + x1 = np.arange(-5.5, 1.0, 0.1) + x2 = np.arange(min(-3.0, min(x2) - 1), max(1.0, max(x2) + 1), 0.1) + x1, x2 = np.meshgrid(x1, x2) + plt.contour(x1, x2, f(x1, x2), colors='#1f77b4') + plt.xlabel('x1') + plt.ylabel('x2') + +def use_svg_display(): + """Use svg format to display plot in jupyter.""" + display.set_matplotlib_formats('svg') + +def plot(X, Y=None, xlabel=None, ylabel=None, legend=[], xlim=None, + ylim=None, xscale='linear', yscale='linear', fmts=None, + figsize=(3.5, 2.5), axes=None): + """Plot multiple lines""" + set_figsize(figsize) + axes = axes if axes else plt.gca() + #if isinstance(X, nd.NDArray): X = X.asnumpy() + #if isinstance(Y, nd.NDArray): Y = Y.asnumpy() + if not hasattr(X[0], "__len__"): X = [X] + if Y is None: X, Y = [[]]*len(X), X + if not hasattr(Y[0], "__len__"): Y = [Y] + if len(X) != len(Y): X = X * len(Y) + if not fmts: fmts = ['-']*len(X) + axes.cla() + for x, y, fmt in zip(X, Y, fmts): + #if isinstance(x, nd.NDArray): x = x.asnumpy() + #if isinstance(y, nd.NDArray): y = y.asnumpy() + if len(x): + axes.plot(x, y, fmt) + else: + axes.plot(y, fmt) + set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend) + +def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend): + """A utility function to set matplotlib axes""" + axes.set_xlabel(xlabel) + axes.set_ylabel(ylabel) + axes.set_xscale(xscale) + axes.set_yscale(yscale) + axes.set_xlim(xlim) + axes.set_ylim(ylim) + if legend: axes.legend(legend) + axes.grid() + +class Animator(object): + def __init__(self, xlabel=None, ylabel=None, legend=[], xlim=None, + ylim=None, xscale='linear', yscale='linear', fmts=None, + nrows=1, ncols=1, figsize=(3.5, 2.5)): + """Incrementally plot multiple lines.""" + use_svg_display() + self.fig, self.axes = plt.subplots(nrows, ncols, figsize=figsize) + if nrows * ncols == 1: self.axes = [self.axes,] + # use a lambda to capture arguments + self.config_axes = lambda : set_axes( + self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend) + self.X, self.Y, self.fmts = None, None, fmts + + def add(self, x, y): + """Add multiple data points into the figure.""" + if not hasattr(y, "__len__"): y = [y] + n = len(y) + if not hasattr(x, "__len__"): x = [x] * n + if not self.X: self.X = [[] for _ in range(n)] + if not self.Y: self.Y = [[] for _ in range(n)] + if not self.fmts: self.fmts = ['-'] * n + for i, (a, b) in enumerate(zip(x, y)): + if a is not None and b is not None: + self.X[i].append(a) + self.Y[i].append(b) + self.axes[0].cla() + for x, y, fmt in zip(self.X, self.Y, self.fmts): + self.axes[0].plot(x, y, fmt) + self.config_axes() + display.display(self.fig) + display.clear_output(wait=True) diff --git a/Ch12_Optimization_Algorithms/d2l/model.py b/Ch12_Optimization_Algorithms/d2l/model.py new file mode 100644 index 00000000..f6e68003 --- /dev/null +++ b/Ch12_Optimization_Algorithms/d2l/model.py @@ -0,0 +1,104 @@ +"""The model module contains neural network building blocks""" +import torch +import torch.nn as nn +import torch.nn.functional as F + +__all__ = ['corr2d', 'linreg', 'RNNModel' , 'Encoder', 'Decoder', 'EncoderDecoder'] + +def corr2d(X, K): + """Compute 2D cross-correlation.""" + h, w = K.shape + Y = torch.zeros((X.shape[0] - h + 1, X.shape[1] - w + 1)) + for i in range(Y.shape[0]): + for j in range(Y.shape[1]): + Y[i, j] = (X[i: i + h, j: j + w] * K).sum() + return Y + +def linreg(X, w, b): + """Linear regression.""" + return torch.mm(X,w) + b + +class RNNModel(nn.Module): + """RNN model.""" + + def __init__(self, rnn_layer, num_inputs, vocab_size, **kwargs): + super(RNNModel, self).__init__(**kwargs) + self.rnn = rnn_layer + self.vocab_size = vocab_size + self.Linear = nn.Linear(num_inputs, vocab_size) + + def forward(self, inputs, state): + """Forward function""" + X = F.one_hot(inputs.long().transpose(0,-1), self.vocab_size) + X = X.to(torch.float32) + Y, state = self.rnn(X, state) + output = self.Linear(Y.reshape((-1, Y.shape[-1]))) + return output, state + + def begin_state(self, num_hiddens, device, batch_size=1, num_layers=1): + """Return the begin state""" + if num_layers == 1: + return torch.zeros(size=(1, batch_size, num_hiddens), dtype=torch.float32, device=device) + else: + return (torch.zeros(size=(1, batch_size, num_hiddens), dtype=torch.float32, device=device), + torch.zeros(size=(1, batch_size, num_hiddens), dtype=torch.float32, device=device)) + +class Residual(nn.Module): + + def __init__(self,input_channels, num_channels, use_1x1conv=False, strides=1, **kwargs): + super(Residual, self).__init__(**kwargs) + self.conv1 = nn.Conv2d(input_channels, num_channels,kernel_size=3, padding=1, stride=strides) + self.conv2 = nn.Conv2d(num_channels, num_channels, kernel_size=3, padding=1) + if use_1x1conv: + self.conv3 = nn.Conv2d(input_channels, num_channels, kernel_size=1, stride=strides) + else: + self.conv3 = None + self.bn1 = nn.BatchNorm2d(num_channels) + self.bn2 = nn.BatchNorm2d(num_channels) + self.relu = nn.ReLU(inplace=True) + + def forward(self, X): + + Y = self.relu(self.bn1(self.conv1(X))) + Y = self.bn2(self.conv2(Y)) + if self.conv3: + X = self.conv3(X) + Y += X + Y =self.relu(Y) + return Y + +class Encoder(nn.Module): + """The base encoder interface for the encoder-decoder architecture.""" + def __init__(self, **kwargs): + super(Encoder, self).__init__(**kwargs) + + def forward(self, X, *args): + """Forward function""" + raise NotImplementedError + +class Decoder(nn.Module): + """The base decoder interface for the encoder-decoder archtecture.""" + def __init__(self, **kwargs): + super(Decoder, self).__init__(**kwargs) + + def init_state(self, enc_outputs, *args): + """Return the begin state""" + raise NotImplementedError + + def forward(self, X, state): + """Forward function""" + raise NotImplementedError + +class EncoderDecoder(nn.Module): + """The base class for the encoder-decoder architecture.""" + def __init__(self, encoder, decoder, **kwargs): + super(EncoderDecoder, self).__init__(**kwargs) + self.encoder = encoder + self.decoder = decoder + + def forward(self, enc_X, dec_X, *args): + """Forward function""" + enc_outputs = self.encoder(enc_X, *args) + dec_state = self.decoder.init_state(enc_outputs, *args) + return self.decoder(dec_X, dec_state) + diff --git a/Ch12_Optimization_Algorithms/d2l/ssd_utils.py b/Ch12_Optimization_Algorithms/d2l/ssd_utils.py new file mode 100644 index 00000000..2bb155da --- /dev/null +++ b/Ch12_Optimization_Algorithms/d2l/ssd_utils.py @@ -0,0 +1,689 @@ +import os +import errno +from tqdm import tqdm +import torch +import json +import numpy as np +from PIL import Image +import torch.nn as nn +import torchvision.transforms as transforms +import torch.optim as optim +import torch.nn.functional as F +import sys +d2l = sys.modules[__name__] + +# Defined in file: ./chapter_preface/preface.md +from matplotlib import pyplot as plt + +import d2l +import json +import time +from collections import namedtuple +import cv2 +from IPython import display + + +##################################### Display Functions ################################################# + +# Defined in file: ./chapter_crashcourse/probability.md +def use_svg_display(): + """Use the svg format to display plot in jupyter.""" + display.set_matplotlib_formats('svg') + + +# Defined in file: ./chapter_crashcourse/probability.md +def set_figsize(figsize=(3.5, 2.5)): + """Change the default figure size""" + use_svg_display() + plt.rcParams['figure.figsize'] = figsize + +# Defined in file: ./chapter_crashcourse/naive-bayes.md +def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5): + """Plot a list of images.""" + figsize = (num_cols * scale, num_rows * scale) + _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize) + axes = axes.flatten() + for i, (ax, img) in enumerate(zip(axes, imgs)): + ax.imshow(img.numpy()) + ax.axes.get_xaxis().set_visible(False) + ax.axes.get_yaxis().set_visible(False) + if titles: + ax.set_title(titles[i]) + return axes + +def read_img(img_str: str, target_size: int) -> np.ndarray: + img = cv2.imread(img_str, cv2.IMREAD_UNCHANGED) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = cv2.resize(img, (target_size, target_size)) + return img + + +def draw_boxes(img: str, boxes: list) -> np.ndarray: + for box in boxes: + cv2.rectangle(img, (int(box[0] - box[2]/2), int(box[1] - box[3]/2)), + (int(box[0] + box[2]/2), int(box[1] + box[3]/2)),(0, 0, 255), 2) + return img + + +def draw_grid(img: str, pixel_step: int) -> np.ndarray: + x = pixel_step + y = pixel_step + + while x < img.shape[1]: + cv2.line(img, (x, 0), (x, img.shape[0]), color=(255, 255, 255)) + x += pixel_step + + while y < img.shape[0]: + cv2.line(img, (0, y), (img.shape[1], y), color=(255, 255, 255)) + y += pixel_step + + return img + + +def draw_text(img: str, texts: list, locations: list) -> np.ndarray: + for text, loc in zip(texts, locations): + cv2.putText(img, text, (int(loc[0]-(loc[2]/2)-5), int(loc[1]-(loc[3]/2)-5)), cv2.FONT_HERSHEY_COMPLEX, + 0.3, (255, 0, 0), 1) + return img + + +################################ Functions for making an inference on an image using trained model ########################### + +PredBoundingBox = namedtuple("PredBoundingBox", ["probability", "class_id", + "classname", "bounding_box" + ]) + +def invert_transformation(bb_hat, anchors): + """ + Invert the transform from "loc_transformation". + """ + + return torch.stack([anchors[:, 0] + bb_hat[:, 0] * anchors[:, 2], + anchors[:, 1] + bb_hat[:, 1] * anchors[:, 3], + anchors[:, 2] * torch.exp(bb_hat[:, 2]), + anchors[:, 3] * torch.exp(bb_hat[:, 3]) + ], dim=1) + +def non_max_suppression(bounding_boxes: list, iou_threshold: float = 0.1) -> list: + filtered_bb = [] + + while len(bounding_boxes) != 0: + best_bb = bounding_boxes.pop(0) + filtered_bb.append(best_bb) + + remove_items = [] + for bb in bounding_boxes: + iou = jaccard(torch.tensor(best_bb.bounding_box).unsqueeze(0), + torch.tensor(bb.bounding_box).unsqueeze(0)) + + if iou > iou_threshold: + remove_items.append(bb) + bounding_boxes = [bb for bb in bounding_boxes if bb not in remove_items] + return filtered_bb + + +def infer(net, epoch, background_threshold=0.9, device = "cuda:0"): + + img = np.array(Image.open('../img/pikachu.jpg').convert('RGB').resize((256, 256), Image.BILINEAR)) + X = transforms.Compose([transforms.ToTensor()])(img).to(device) + + X = X.to(device) + + # background_threshold = 0.9 + + net.eval() + anchors, class_hat, bb_hat = net(X.unsqueeze(0)) + + anchors = anchors.to(device) + + + bb_hat = bb_hat.reshape((1, -1, 4)) + + bb_hat = invert_transformation(bb_hat.squeeze(0), anchors) + bb_hat = bb_hat * 256.0 + + class_hat = class_hat.sigmoid().squeeze(0) + + bb_hat = bb_hat[class_hat[:,0] < background_threshold, :] + + + bb_hat = bb_hat.detach().cpu().numpy() + class_hat = class_hat[class_hat[:,0] < background_threshold, :] + + class_preds = class_hat[:, 1:] + + prob, class_id = torch.max(class_preds,1) + + prob = prob.detach().cpu().numpy() + class_id = class_id.detach().cpu().numpy() + + + id_cat_pikachu = dict() + id_cat_pikachu[0] = 'pikachu' + + output_bb = [PredBoundingBox(probability=prob[i], + class_id=class_id[i], + classname=id_cat_pikachu[class_id[i]], + bounding_box=[bb_hat[i, 0], + bb_hat[i, 1], + bb_hat[i, 2], + bb_hat[i, 3]]) + for i in range(0, len(prob))] + + output_bb = sorted(output_bb, key = lambda x: x.probability, reverse=True) + + filtered_bb = non_max_suppression(output_bb) + + img_str = '../img/pikachu.jpg' + img = read_img(img_str, 256) + + # img = (X.cpu().numpy().transpose(1,2,0)*255) + # img1 = np.ascontiguousarray(img, dtype=np.uint8) + + img = draw_boxes(img, [bb.bounding_box for bb in filtered_bb]) + img = draw_text(img, [bb.classname for bb in filtered_bb], [bb.bounding_box for bb in filtered_bb]) + plt.imsave('ssd_outputs/img_' + str(epoch) + '.png', img) + + +########################################## Functions for downloading and preprocessing data ############################################## + + +def gen_bar_updater(): + pbar = tqdm(total=None) + + def bar_update(count, block_size, total_size): + if pbar.total is None and total_size: + pbar.total = total_size + progress_bytes = count * block_size + pbar.update(progress_bytes - pbar.n) + + return bar_update + +def calculate_md5(fpath, chunk_size=1024 * 1024): + md5 = hashlib.md5() + with open(fpath, 'rb') as f: + for chunk in iter(lambda: f.read(chunk_size), b''): + md5.update(chunk) + return md5.hexdigest() + + +def check_md5(fpath, md5, **kwargs): + return md5 == calculate_md5(fpath, **kwargs) + + +def check_integrity(fpath, md5=None): + if not os.path.isfile(fpath): + return False + if md5 is None: + return True + return check_md5(fpath, md5) + +def makedir_exist_ok(dirpath): + """ + Python2 support for os.makedirs(.., exist_ok=True) + """ + try: + os.makedirs(dirpath) + except OSError as e: + if e.errno == errno.EEXIST: + pass + else: + raise +def download_url(url, root, filename=None, md5=None): + """Download a file from a url and place it in root. + Args: + url (str): URL to download file from + root (str): Directory to place downloaded file in + filename (str, optional): Name to save the file under. If None, use the basename of the URL + md5 (str, optional): MD5 checksum of the download. If None, do not check + """ + from six.moves import urllib + + root = os.path.expanduser(root) + if not filename: + filename = os.path.basename(url) + fpath = os.path.join(root, filename) + + makedir_exist_ok(root) + + # downloads file + if check_integrity(fpath, md5): + print('Using downloaded and verified file: ' + fpath) + else: + try: + print('Downloading ' + url + ' to ' + fpath) + urllib.request.urlretrieve( + url, fpath, + reporthook=gen_bar_updater() + ) + except (urllib.error.URLError, IOError) as e: + if url[:5] == 'https': + url = url.replace('https:', 'http:') + print('Failed download. Trying https -> http instead.' + ' Downloading ' + url + ' to ' + fpath) + urllib.request.urlretrieve( + url, fpath, + reporthook=gen_bar_updater() + ) + else: + raise e + +# The following code is used to download pikachu dataset from mxnet aws server in the form of .rec files +# Then this .rec file is converted into png images and json files for annotation data +# This part requires 'mxnet' library which can be downloaded using conda +# using the command 'conda install mxnet' +# Matplotlib is also required for saving the png image files + +# Download Pikachu Dataset +def download_pikachu(data_dir): + root_url = ('https://apache-mxnet.s3-accelerate.amazonaws.com/' + 'gluon/dataset/pikachu/') + dataset = {'train.rec': 'e6bcb6ffba1ac04ff8a9b1115e650af56ee969c8', + 'train.idx': 'dcf7318b2602c06428b9988470c731621716c393', + 'val.rec': 'd6c33f799b4d058e82f2cb5bd9a976f69d72d520'} + for k, v in dataset.items(): + download_url(root_url + k, data_dir) + + +# Create dataloaders in mxnet +def load_data_pikachu_rec_mxnet(batch_size, edge_size=256): + from mxnet import image + """Load the pikachu dataset""" + data_dir = '../data/pikachu' + download_pikachu(data_dir) + train_iter = image.ImageDetIter( + path_imgrec=os.path.join(data_dir, 'train.rec'), + path_imgidx=os.path.join(data_dir, 'train.idx'), + batch_size=batch_size, + data_shape=(3, edge_size, edge_size), # The shape of the output image + # shuffle=True, # Read the data set in random order + # rand_crop=1, # The probability of random cropping is 1 + min_object_covered=0.95, max_attempts=200) + val_iter = image.ImageDetIter( + path_imgrec=os.path.join(data_dir, 'val.rec'), batch_size=batch_size, + data_shape=(3, edge_size, edge_size), shuffle=False) + return train_iter, val_iter + + +# Use mxnet dataloaders to convert .rec file to .png images and annotations.json + +def download_and_preprocess_data(dir = '../data/pikachu/'): + + if os.path.exists(os.path.join(dir, 'train')) and os.path.exists(os.path.join(dir, 'val')): + return + + train_iter, val_iter = load_data_pikachu_rec_mxnet(batch_size) + + os.mkdir(os.path.join(dir, 'train')) + os.mkdir(os.path.join(dir, 'val')) + os.mkdir(os.path.join(dir, 'train/images')) + os.mkdir(os.path.join(dir, 'val/images')) + + annotations_train = dict() + train_iter.reset() # Read data from the start. + id = 0 + for batch in train_iter: + id+=1 + + X = batch.data[0].as_in_context(ctx) + + Y = batch.label[0].as_in_context(ctx) + + x = X.asnumpy() + x = x.transpose((2,3,1,0)) + x = x.squeeze(axis=-1) + plt.imsave(os.path.join(dir, 'train/images', 'pikachu_' + str(id) + '.png'), x/255.) + an = dict() + y = Y.asnumpy() + + an['class'] = y[0, 0][0].tolist() + an['loc'] = y[0,0][1:].tolist() + an['id'] = [id] + an['image'] = 'pikachu_' + str(id) + '.png' + annotations_train['data_' + str(id)] = an + + import json + with open(os.path.join(dir, 'train', 'annotations.json'), 'w') as outfile: + json.dump(annotations_train, outfile) + outfile.close() + + + annotations_val = dict() + val_iter.reset() # Read data from the start. + id = 0 + for batch in val_iter: + id+=1 + + X = batch.data[0].as_in_context(ctx) + + Y = batch.label[0].as_in_context(ctx) + + x = X.asnumpy() + x = x.transpose((2,3,1,0)) + x = x.squeeze(axis=-1) + plt.imsave(os.path.join(dir, 'val/images', 'pikachu_' + str(id) + '.png'), x/255.) + an = dict() + y = Y.asnumpy() + + an['class'] = y[0, 0][0].tolist() + an['loc'] = y[0,0][1:].tolist() + an['id'] = [id] + an['image'] = 'pikachu_' + str(id) + '.png' + annotations_val['data_' + str(id)] = an + + import json + with open(os.path.join(dir, 'val', 'annotations.json'), 'w') as outfile: + json.dump(annotations_val, outfile) + outfile.close() + + +################################################## PyTorch Dataloader for PIKACHU dataset ######################################################## + +class PIKACHU(torch.utils.data.Dataset): + def __init__(self, data_dir, set, transform=None, target_transform=None): + + self.image_size = (3, 256, 256) + self.images_dir = os.path.join(data_dir, set, 'images') + + self.set = set + self.transform = transforms.Compose([ + transforms.ToTensor()]) + self.target_transform = target_transform + + annotations_file = os.path.join(data_dir, set, 'annotations.json') + with open(annotations_file) as file: + self.annotations = json.load(file) + + def __getitem__(self, index): + + annotations_i = self.annotations['data_' + str(index+1)] + + image_path = os.path.join(self.images_dir, annotations_i['image']) + img = np.array(Image.open(image_path).convert('RGB').resize((self.image_size[2], self.image_size[1]), Image.BILINEAR)) + # print(img.shape) + loc = np.array(annotations_i['loc']) + + loc_chw = np.zeros((4,)) + loc_chw[0] = (loc[0] + loc[2])/2 + loc_chw[1] = (loc[1] + loc[3])/2 + loc_chw[2] = (loc[2] - loc[0]) #width + loc_chw[3] = (loc[3] - loc[1]) # height + + + label = 1 - annotations_i['class'] + + if self.transform is not None: + img = self.transform(img) + return (img, loc_chw, label) + + def __len__(self): + return len(self.annotations) + +###################################################### Functions for showing graph of loss function ############################################## + +def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend): + """A utility function to set matplotlib axes""" + axes.set_xlabel(xlabel) + axes.set_ylabel(ylabel) + axes.set_xscale(xscale) + axes.set_yscale(yscale) + axes.set_xlim(xlim) + axes.set_ylim(ylim) + if legend: axes.legend(legend) + axes.grid() + +class Animator(object): + def __init__(self, xlabel=None, ylabel=None, legend=[], xlim=None, + ylim=None, xscale='linear', yscale='linear', fmts=None, + nrows=1, ncols=1, figsize=(3.5, 2.5)): + """Incrementally plot multiple lines.""" + d2l.use_svg_display() + self.fig, self.axes = d2l.plt.subplots(nrows, ncols, figsize=figsize) + if nrows * ncols == 1: self.axes = [self.axes,] + # use a lambda to capture arguments + self.config_axes = lambda : d2l.set_axes( + self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend) + self.X, self.Y, self.fmts = None, None, fmts + + def add(self, x, y): + """Add multiple data points into the figure.""" + if not hasattr(y, "__len__"): y = [y] + n = len(y) + if not hasattr(x, "__len__"): x = [x] * n + if not self.X: self.X = [[] for _ in range(n)] + if not self.Y: self.Y = [[] for _ in range(n)] + if not self.fmts: self.fmts = ['-'] * n + for i, (a, b) in enumerate(zip(x, y)): + if a is not None and b is not None: + self.X[i].append(a) + self.Y[i].append(b) + self.axes[0].cla() + for x, y, fmt in zip(self.X, self.Y, self.fmts): + self.axes[0].plot(x, y, fmt) + self.config_axes() + display.display(self.fig) + display.clear_output(wait=True) + + +################################## Functions for finding the overlap between anchors and predicted bounding boxes using IoU ####################### + +def center_2_hw(box: torch.Tensor) -> float: + """ + Converting (cx, cy, w, h) to (x1, y1, x2, y2) + """ + + return torch.cat( + [box[:, 0, None] - box[:, 2, None]/2, + box[:, 1, None] - box[:, 3, None]/2, + box[:, 0, None] + box[:, 2, None]/2, + box[:, 1, None] + box[:, 3, None]/2 + ], dim=1) + +def intersect(box_a: torch.Tensor, box_b: torch.Tensor) -> float: + # Coverting (cx, cy, w, h) to (x1, y1, x2, y2) since its easier to extract min/max coordinates + temp_box_a, temp_box_b = center_2_hw(box_a), center_2_hw(box_b) + +# print(temp_box_a.shape) + + max_xy = torch.min(temp_box_a[:, None, 2:], temp_box_b[None, :, 2:]) + min_xy = torch.max(temp_box_a[:, None, :2], temp_box_b[None, :, :2]) + + inter = torch.clamp((max_xy - min_xy), min=0) + return inter[:, :, 0] * inter[:, :, 1] + +def box_area(box: torch.Tensor) -> float: + return box[:, 2] * box[:, 3] + +def jaccard(box_a: torch.Tensor, box_b: torch.Tensor) -> float: +# print(box_a.shape) + intersection = intersect(box_a, box_b) + union = box_area(box_a).unsqueeze(1) + box_area(box_b).unsqueeze(0) - intersection + return intersection / union + +def find_overlap(bb_true_i, anchors, jaccard_overlap): + + jaccard_tensor = jaccard(anchors, bb_true_i) + _, max_overlap = torch.max(jaccard_tensor, dim=0) + + overlap_list = [] + for i in range(len(bb_true_i)): + threshold_overlap = (jaccard_tensor[:, i] > jaccard_overlap).nonzero() + + if len(threshold_overlap) > 0: + threshold_overlap = threshold_overlap[:, 0] + overlap = torch.cat([max_overlap[i].view(1), threshold_overlap]) + overlap = torch.unique(overlap) + else: + overlap = max_overlap[i].view(1) + overlap_list.append(overlap) + return overlap_list + + +#################################### Functions for saving and loading trained models ############################################# + +def save(model, path_to_checkpoints_dir, step, optimizer, loss): + + try: + os.makedirs(path_to_checkpoints_dir) + except: + pass + + path_to_checkpoint = os.path.join(path_to_checkpoints_dir, f'model-{step}_{loss}.pth') + checkpoint = { + 'state_dict': model.state_dict(), + 'step': step, + 'optimizer_state_dict': optimizer.state_dict() + } + torch.save(checkpoint, path_to_checkpoint) + return path_to_checkpoint + +def load(model, path_to_checkpoint, optimizer): + checkpoint = torch.load(path_to_checkpoint) + model.load_state_dict(checkpoint['state_dict']) + step = checkpoint['step'] + if optimizer is not None: + optimizer.load_state_dict(checkpoint['optimizer_state_dict']) + return step + + +############################ Object Detection Related Functions ############################## + + + + +import itertools +import math +def MultiBoxPrior(feature_map_sizes, sizes, aspect_ratios): + """Compute default box sizes with scale and aspect transform.""" + + sizes = [s*728 for s in sizes] + + scale = feature_map_sizes + steps_y = [1 / scale[0]] + steps_x = [1 / scale[1]] + + sizes = [s / max(scale) for s in sizes] + + num_layers = 1 + + boxes = [] + for i in range(num_layers): + for h, w in itertools.product(range(feature_map_sizes[0]), range(feature_map_sizes[1])): + cx = (w + 0.5)*steps_x[i] + cy = (h + 0.5)*steps_y[i] + + for j in range(len(sizes)): + + s = sizes[j] + boxes.append((cx, cy, s, s)) + + s = sizes[0] + + for ar in aspect_ratios: + + boxes.append((cx, cy, (s * math.sqrt(ar)), (s / math.sqrt(ar)))) + + return torch.Tensor(boxes) + +def MultiBoxTarget(class_true, bb_true, anchors): + + class_true +=1 + + class_target = torch.zeros(anchors.shape[0]).long() + + overlap_list = d2l.find_overlap(bb_true, anchors, 0.5) + + overlap_coordinates = torch.zeros_like(anchors) + + for j in range(len(overlap_list)): + overlap = overlap_list[j] + class_target[overlap] = class_true[j, 0].type(torch.LongTensor) + overlap_coordinates[overlap] = 1. + + + + new_anchors = torch.cat([*anchors]) + overlap_coordinates = torch.cat([*overlap_coordinates]) + new_anchors = new_anchors*overlap_coordinates + + return (new_anchors.unsqueeze(0), overlap_coordinates.unsqueeze(0), class_target.unsqueeze(0)) + + +def MultiboxDetection(id_cat, cls_probs, anchors, nms_threshold): + + id_new = dict() + id_new[0] = 'background' + for i in (id_cat.keys()): + id_new[i+1] = id_cat[i] + + cls_probs = cls_probs.transpose(0,1) + + prob, class_id = torch.max(cls_probs,1) + + prob = prob.detach().cpu().numpy() + class_id = class_id.detach().cpu().numpy() + + output_bb = [d2l.PredBoundingBox(probability=prob[i], + class_id=class_id[i], + classname=id_new[class_id[i]], + bounding_box=[anchors[i, 0], + anchors[i, 1], + anchors[i, 2], + anchors[i, 3]]) + for i in range(0, len(prob))] + + filtered_bb = d2l.non_max_suppression(output_bb, nms_threshold) + + out = [] + for bb in filtered_bb: + out.append([bb.class_id-1, bb.probability, *bb.bounding_box]) + out = torch.Tensor(out) + + return out + +############################ Functions for Multi-Scale Object Detection Jupyter Notebook ########################## + +# def bbox_to_rect(bbox, color): +# """Convert bounding box to matplotlib format.""" +# return d2l.plt.Rectangle(xy=(bbox[0], bbox[1]), width=bbox[2]-bbox[0], +# height=bbox[3]-bbox[1], fill=False, edgecolor=color, +# linewidth=2) + +# def show_bboxes(axes, bboxes, labels=None, colors=None): +# """Show bounding boxes.""" +# def _make_list(obj, default_values=None): +# if obj is None: +# obj = default_values +# elif not isinstance(obj, (list, tuple)): +# obj = [obj] +# return obj +# labels = _make_list(labels) +# colors = _make_list(colors, ['b', 'g', 'r', 'm', 'c']) +# for i, bbox in enumerate(bboxes): +# color = colors[i % len(colors)] +# rect = bbox_to_rect(bbox.numpy(), color) +# axes.add_patch(rect) +# if labels and len(labels) > i: +# text_color = 'k' if color == 'w' else 'w' +# axes.text(rect.xy[0], rect.xy[1], labels[i], +# va='center', ha='center', fontsize=9, color=text_color, +# bbox=dict(facecolor=color, lw=0)) + +def get_centers(h, w, fh, fw): + step_x = int(w/fw) + cx = [] + for i in range(fw): + cx.append((step_x*i + step_x*(i+1))/2) + + step_y = int(h/fh) + cy = [] + for j in range(fh): + cy.append((step_y*j + step_y*(j+1))/2) + cxcy = [] + for x in cx: + for y in cy: + cxcy.append([x, y]) + + return np.array(cxcy).astype(np.int16) + +#################################################################################################################################### diff --git a/Ch12_Optimization_Algorithms/d2l/train.py b/Ch12_Optimization_Algorithms/d2l/train.py new file mode 100644 index 00000000..8566d4fd --- /dev/null +++ b/Ch12_Optimization_Algorithms/d2l/train.py @@ -0,0 +1,352 @@ +import numpy as np +import math +import time + +from .base import try_gpu, Timer, Accumulator +from .figure import set_figsize, plt, Animator +from .data import data_iter_consecutive, data_iter_random +from .model import linreg +import torch +import torch.nn as nn +import torch.optim as optim +import torch.nn.functional as F +from torch.autograd import Variable + + +__all__ = ['evaluate_loss', 'train_ch10', 'train_2d','evaluate_accuracy', 'squared_loss', 'grad_clipping', 'sgd', 'train_and_predict_rnn', 'train_ch3', 'train_ch5','MaskedSoftmaxCELoss','train_ch7', 'translate_ch7', 'to_onehot' , 'predict_rnn', 'train_and_predict_rnn_nn', 'predict_rnn_nn', 'grad_clipping_nn'] + +def evaluate_loss(net, data_iter, loss): + """Evaluate the loss of a model on the given dataset""" + metric = Accumulator(2) # sum_loss, num_examples + for X, y in data_iter: + metric.add(loss(net(X), y).sum().detach().numpy().item(), list(y.shape)[0]) + return metric[0] / metric[1] + +def evaluate_accuracy(data_iter, net, device=torch.device('cpu')): + """Evaluate accuracy of a model on the given data set.""" + net.eval() # Switch to evaluation mode for Dropout, BatchNorm etc layers. + acc_sum, n = torch.tensor([0], dtype=torch.float32, device=device), 0 + for X, y in data_iter: + # Copy the data to device. + X, y = X.to(device), y.to(device) + with torch.no_grad(): + y = y.long() + acc_sum += torch.sum((torch.argmax(net(X), dim=1) == y)) + n += y.shape[0] + return acc_sum.item()/n + +def squared_loss(y_hat, y): + """Squared loss.""" + return (y_hat - y.view(y_hat.shape)).pow(2) / 2 + +def grad_clipping(params, theta, device): + """Clip the gradient.""" + norm = torch.tensor([0], dtype=torch.float32, device=device) + for param in params: + norm += (param.grad ** 2).sum() + norm = norm.sqrt().item() + if norm > theta: + for param in params: + param.grad.data.mul_(theta / norm) + +def grad_clipping_nn(model, theta, device): + """Clip the gradient for a nn model.""" + grad_clipping(model.parameters(), theta, device) + +def sgd(params, lr, batch_size): + """Mini-batch stochastic gradient descent.""" + for param in params: + param.data.sub_(lr*param.grad/batch_size) + param.grad.data.zero_() + +def train_and_predict_rnn(rnn, get_params, init_rnn_state, num_hiddens, + corpus_indices, vocab, device, is_random_iter, + num_epochs, num_steps, lr, clipping_theta, + batch_size, prefixes): + """Train an RNN model and predict the next item in the sequence.""" + if is_random_iter: + data_iter_fn = data_iter_random + else: + data_iter_fn = data_iter_consecutive + params = get_params() + loss = nn.CrossEntropyLoss() + start = time.time() + for epoch in range(num_epochs): + if not is_random_iter: + # If adjacent sampling is used, the hidden state is initialized + # at the beginning of the epoch + state = init_rnn_state(batch_size, num_hiddens, device) + l_sum, n = 0.0, 0 + data_iter = data_iter_fn(corpus_indices, batch_size, num_steps, device) + for X, Y in data_iter: + if is_random_iter: + # If random sampling is used, the hidden state is initialized + # before each mini-batch update + state = init_rnn_state(batch_size, num_hiddens, device) + else: + # Otherwise, the detach function needs to be used to separate + # the hidden state from the computational graph to avoid + # backpropagation beyond the current sample + for s in state: + s.detach_() + inputs = to_onehot(X, len(vocab)) + # outputs is num_steps terms of shape (batch_size, len(vocab)) + (outputs, state) = rnn(inputs, state, params) + # After stitching it is (num_steps * batch_size, len(vocab)) + outputs = torch.cat(outputs, dim=0) + # The shape of Y is (batch_size, num_steps), and then becomes + # a vector with a length of batch * num_steps after + # transposition. This gives it a one-to-one correspondence + # with output rows + y = Y.t().reshape((-1,)) + # Average classification error via cross entropy loss + l = loss(outputs, y.long()).mean() + l.backward() + with torch.no_grad(): + grad_clipping(params, clipping_theta, device) # Clip the gradient + sgd(params, lr, 1) + # Since the error is the mean, no need to average gradients here + l_sum += l.item() * y.numel() + n += y.numel() + if (epoch + 1) % 50 == 0: + print('epoch %d, perplexity %f, time %.2f sec' % ( + epoch + 1, math.exp(l_sum / n), time.time() - start)) + start = time.time() + if (epoch + 1) % 100 == 0: + for prefix in prefixes: + print(' -', predict_rnn(prefix, 50, rnn, params, + init_rnn_state, num_hiddens, + vocab, device)) + +def train_ch3(net, train_iter, test_iter, criterion, num_epochs, batch_size, lr=None): + """Train and evaluate a model with CPU.""" + optimizer = optim.SGD(net.parameters(), lr=lr) + for epoch in range(num_epochs): + train_l_sum, train_acc_sum, n = 0.0, 0.0, 0 + for X, y in train_iter: + optimizer.zero_grad() + + y_hat = net(X) + loss = criterion(y_hat, y) + loss.backward() + optimizer.step() + + y = y.type(torch.float32) + train_l_sum += loss.item() + train_acc_sum += torch.sum((torch.argmax(y_hat, dim=1).type(torch.FloatTensor) == y).detach()).float() + n += list(y.size())[0] + test_acc = evaluate_accuracy(test_iter, net) + print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f'\ + % (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc)) + + +def train_ch5(net, train_iter, test_iter, criterion, num_epochs, batch_size, device, lr=None): + """Train and evaluate a model with CPU or GPU.""" + print('training on', device) + net.to(device) + optimizer = optim.SGD(net.parameters(), lr=lr) + for epoch in range(num_epochs): + net.train() # Switch to training mode + n, start = 0, time.time() + train_l_sum = torch.tensor([0.0], dtype=torch.float32, device=device) + train_acc_sum = torch.tensor([0.0], dtype=torch.float32, device=device) + for X, y in train_iter: + optimizer.zero_grad() + X, y = X.to(device), y.to(device) + y_hat = net(X) + loss = criterion(y_hat, y) + loss.backward() + optimizer.step() + with torch.no_grad(): + y = y.long() + train_l_sum += loss.float() + train_acc_sum += (torch.sum((torch.argmax(y_hat, dim=1) == y))).float() + n += y.shape[0] + + test_acc = evaluate_accuracy(test_iter, net, device) + print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec'\ + % (epoch + 1, train_l_sum/n, train_acc_sum/n, test_acc, time.time() - start)) + +class MaskedSoftmaxCELoss(nn.CrossEntropyLoss): + def forward(self, pred, label, valid_length): + # the sample weights shape should be (batch_size, seq_len) + weights = torch.ones_like(label) + weights = SequenceMask(weights, valid_length).float() + self.reduction='none' + output=super(MaskedSoftmaxCELoss, self).forward(pred.transpose(1,2), label) + return (output*weights).mean(dim=1) + + +def train_ch7(model, data_iter, lr, num_epochs, device): + """Train an encoder-decoder model""" + optimizer = optim.Adam(model.parameters(), lr=lr) + loss = MaskedSoftmaxCELoss() + tic = time.time() + for epoch in range(1, num_epochs+1): + l_sum, num_tokens_sum = 0.0, 0.0 + for batch in data_iter: + optimizer.zero_grad() + X, X_vlen, Y, Y_vlen = [x.to(device) for x in batch] + Y_input, Y_label, Y_vlen = Y[:,:-1], Y[:,1:], Y_vlen-1 + Y_hat, _ = model(X, Y_input, X_vlen, Y_vlen) + l = loss(Y_hat, Y_label, Y_vlen).sum() + l.backward() + with torch.no_grad(): + grad_clipping_nn(model, 5, device) + num_tokens = Y_vlen.sum().item() + optimizer.step() + l_sum += l.sum().item() + num_tokens_sum += num_tokens + if epoch % 50 == 0: + print("epoch {0:4d},loss {1:.3f}, time {2:.1f} sec".format( + epoch, (l_sum/num_tokens_sum), time.time()-tic)) + tic = time.time() + +def translate_ch7(model, src_sentence, src_vocab, tgt_vocab, max_len, device): + """Translate based on an encoder-decoder model with greedy search.""" + src_tokens = src_vocab[src_sentence.lower().split(' ')] + src_len = len(src_tokens) + if src_len < max_len: + src_tokens += [src_vocab.pad] * (max_len - src_len) + enc_X = torch.tensor(src_tokens, device=device) + enc_valid_length = torch.tensor([src_len], device=device) + # use expand_dim to add the batch_size dimension. + enc_outputs = model.encoder(enc_X.unsqueeze(dim=0), enc_valid_length) + dec_state = model.decoder.init_state(enc_outputs, enc_valid_length) + dec_X = torch.tensor([tgt_vocab.bos], device=device).unsqueeze(dim=0) + predict_tokens = [] + for _ in range(max_len): + Y, dec_state = model.decoder(dec_X, dec_state) + # The token with highest score is used as the next time step input. + dec_X = Y.argmax(dim=2) + py = dec_X.squeeze(dim=0).int().item() + if py == tgt_vocab.eos: + break + predict_tokens.append(py) + return ' '.join(tgt_vocab.to_tokens(predict_tokens)) + + +def to_onehot(X,size): + return F.one_hot(X.long().transpose(0,-1), size) + +def predict_rnn(prefix, num_chars, rnn, params, init_rnn_state, + num_hiddens, vocab, device): + """Predict next chars with an RNN model""" + state = init_rnn_state(1, num_hiddens, device) + output = [vocab[prefix[0]]] + for t in range(num_chars + len(prefix) - 1): + # The output of the previous time step is taken as the input of the + # current time step. + X = to_onehot(torch.tensor([output[-1]], dtype=torch.float32, device=device), len(vocab)) + # Calculate the output and update the hidden state + (Y, state) = rnn(X, state, params) + # The input to the next time step is the character in the prefix or + # the current best predicted character + if t < len(prefix) - 1: + # Read off from the given sequence of characters + output.append(vocab[prefix[t + 1]]) + else: + # This is maximum likelihood decoding. Modify this if you want + # use sampling, beam search or beam sampling for better sequences. + output.append(int(Y[0].argmax(dim=1).item())) + return ''.join([vocab.idx_to_token[i] for i in output]) + +def predict_rnn_nn(prefix, num_chars, batch_size, num_hiddens, num_layers, model, vocab, device): + """Predict next chars with a RNN model.""" + # Use the model's member function to initialize the hidden state + state = model.begin_state(num_hiddens=num_hiddens, device=device, num_layers=num_layers) + output = [vocab[prefix[0]]] + for t in range(num_chars + len(prefix) - 1): + X = torch.tensor([output[-1]], dtype=torch.float32, device=device).reshape((1, 1)) + # Forward computation does not require incoming model parameters + (Y, state) = model(X, state) + if t < len(prefix) - 1: + output.append(vocab[prefix[t + 1]]) + else: + output.append(int(Y.argmax(dim=1).item())) + return ''.join([vocab.idx_to_token[i] for i in output]) + +def train_and_predict_rnn_nn(model, num_hiddens, init_gru_state, corpus_indices, vocab, + device, num_epochs, num_steps, lr, + clipping_theta, batch_size, prefixes, num_layers=1): + """Train a RNN model and predict the next item in the sequence.""" + loss = nn.CrossEntropyLoss() + optm = torch.optim.SGD(model.parameters(), lr=lr) + start = time.time() + for epoch in range(1, num_epochs+1): + l_sum, n = 0.0, 0 + data_iter = data_iter_consecutive( + corpus_indices, batch_size, num_steps, device) + state = model.begin_state(batch_size=batch_size, num_hiddens=num_hiddens, device=device ,num_layers=num_layers) + for X, Y in data_iter: + for s in state: + s.detach() + X = X.to(dtype=torch.long) + (output, state) = model(X, state) + y = Y.t().reshape((-1,)) + l = loss(output, y.long()).mean() + optm.zero_grad() + l.backward(retain_graph=True) + with torch.no_grad(): + # Clip the gradient + grad_clipping_nn(model, clipping_theta, device) + # Since the error has already taken the mean, the gradient does + # not need to be averaged + optm.step() + l_sum += l.item() * y.numel() + n += y.numel() + + if epoch % (num_epochs // 4) == 0: + print('epoch %d, perplexity %f, time %.2f sec' % ( + epoch, math.exp(l_sum / n), time.time() - start)) + start = time.time() + if epoch % (num_epochs // 2) == 0: + for prefix in prefixes: + print(' -', predict_rnn_nn(prefix, 50, batch_size, num_hiddens, num_layers, model, vocab, device)) + +def train_2d(trainer): + """Optimize a 2-dim objective function with a customized trainer.""" + # s1 and s2 are internal state variables and will + # be used later in the chapter + x1, x2, s1, s2 = -5, -2, 0, 0 + results = [(x1, x2)] + for i in range(20): + x1, x2, s1, s2 = trainer(x1, x2, s1, s2) + results.append((x1, x2)) + print('epoch %d, x1 %f, x2 %f' % (i + 1, x1, x2)) + return results + +def train_ch10(trainer, hyperparams, data_iter, feature_dim, num_epochs=2): + # Initialization + w1 = np.random.normal(scale=0.01, size=(feature_dim, 1)) + b1 = np.zeros(1) + w = Variable(torch.from_numpy(w1), requires_grad=True) + b = Variable(torch.from_numpy(b1), requires_grad=True) + + if trainer.__name__ == 'SGD': + optimizer = trainer([w, b], lr=hyperparams['lr'], momentum=hyperparams['momentum']) + elif trainer.__name__ == 'RMSprop': + optimizer = trainer([w, b], lr=hyperparams['lr'], alpha=hyperparams['gamma']) + + net, loss = lambda X: linreg(X, w, b), squared_loss + # Train + animator = Animator(xlabel='epoch', ylabel='loss', + xlim=[0, num_epochs], ylim=[0.22, 0.35]) + n, timer = 0, Timer() + + for _ in range(num_epochs): + for X, y in data_iter: + X, y = Variable(X), Variable(y) + optimizer.zero_grad() + output = net(X) + l = loss(output, y).mean() + l.backward() + optimizer.step() + n += X.shape[0] + if n % 200 == 0: + timer.stop() + animator.add(n/X.shape[0]/len(data_iter), + evaluate_loss(net, data_iter, loss)) + timer.start() + print('loss: %.3f, %.3f sec/epoch'%(animator.Y[0][-1], timer.avg())) + # return timer.cumsum(), animator.Y[0] \ No newline at end of file diff --git a/Ch14_Computer_Vision/Anchor_Boxes.ipynb b/Ch14_Computer_Vision/Anchor_Boxes.ipynb index ae2792ce..31a4754e 100644 --- a/Ch14_Computer_Vision/Anchor_Boxes.ipynb +++ b/Ch14_Computer_Vision/Anchor_Boxes.ipynb @@ -209,12 +209,12 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", "
\n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", + "\" id=\"m74c2209a3c\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -298,7 +298,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -339,7 +339,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -373,7 +373,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -424,10 +424,10 @@ " \n", " \n", + "\" id=\"mc8a544d5fd\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -440,7 +440,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -470,7 +470,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -485,7 +485,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -534,7 +534,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -549,7 +549,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -816,7 +816,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -880,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1380,7 +1380,7 @@ "" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1410,7 +1410,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "attributes": { "classes": [], @@ -1450,12 +1450,12 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", + "\" id=\"m8ae891bc7a\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1555,7 +1555,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1596,7 +1596,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1630,7 +1630,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1681,10 +1681,10 @@ " \n", " \n", + "\" id=\"m22566ecb3a\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1697,7 +1697,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1727,7 +1727,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1742,7 +1742,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1791,7 +1791,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1806,7 +1806,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2127,7 +2127,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2165,9 +2165,21 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "../d2l/ssd_utils.py:510: UserWarning: This overload of nonzero is deprecated:\n", + "\tnonzero()\n", + "Consider using one of the following signatures instead:\n", + "\tnonzero(*, bool as_tuple) (Triggered internally at /pytorch/torch/csrc/utils/python_arg_parser.cpp:766.)\n", + " threshold_overlap = (jaccard_tensor[:, i] > jaccard_overlap).nonzero()\n" + ] + } + ], "source": [ "labels = d2l.MultiBoxTarget(ground_truth_class.clone(), ground_truth_bbox, anchors)" ] @@ -2181,7 +2193,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "attributes": { "classes": [], @@ -2196,7 +2208,7 @@ "tensor([[0, 1, 2, 0, 2]])" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -2218,7 +2230,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "attributes": { "classes": [], @@ -2234,7 +2246,7 @@ " 1., 1.]])" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -2252,7 +2264,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "attributes": { "classes": [], @@ -2269,7 +2281,7 @@ " 0.9200, 0.9000]])" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -2294,7 +2306,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "attributes": { "classes": [], @@ -2321,7 +2333,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "attributes": { "classes": [], @@ -2361,12 +2373,12 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", + "\" id=\"m337ca9cec1\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2442,7 +2454,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2483,7 +2495,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2517,7 +2529,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2568,10 +2580,10 @@ " \n", " \n", + "\" id=\"m95e238535a\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2584,7 +2596,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2614,7 +2626,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2629,7 +2641,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2678,7 +2690,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2693,7 +2705,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3091,7 +3103,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3122,7 +3134,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -3133,7 +3145,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -3143,7 +3155,7 @@ " [1.0000, 0.9000, 0.5500, 0.2000, 0.9000, 0.8800]])" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -3202,12 +3214,12 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", + "\" id=\"mcf8bd4336d\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3267,7 +3279,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3308,7 +3320,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3342,7 +3354,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3393,10 +3405,10 @@ " \n", " \n", + "\" id=\"mb3771c2a44\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3409,7 +3421,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3439,7 +3451,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3454,7 +3466,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3503,7 +3515,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3518,7 +3530,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3824,7 +3836,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3868,13 +3880,20 @@ "* Verify the output of offset `labels[0]` by marking the anchor box offsets as defined in this section (the constant is the default value).\n", "* Modify the variable `anchors` in the \"Labeling Training Set Anchor Boxes\" and \"Output Bounding Boxes for Prediction\" sections. How do the results change?" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -3886,9 +3905,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch14_Computer_Vision/Multiscale_Object_Detection.ipynb b/Ch14_Computer_Vision/Multiscale_Object_Detection.ipynb index 1cdc69cd..dda10c7b 100644 --- a/Ch14_Computer_Vision/Multiscale_Object_Detection.ipynb +++ b/Ch14_Computer_Vision/Multiscale_Object_Detection.ipynb @@ -133,12 +133,12 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", + "\" id=\"ma78680eaa6\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -566,7 +566,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -607,7 +607,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -641,7 +641,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -692,10 +692,10 @@ " \n", " \n", + "\" id=\"m420e131966\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -708,7 +708,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -738,7 +738,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -753,7 +753,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -802,7 +802,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -817,7 +817,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -879,7 +879,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -948,12 +948,12 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", + "\" id=\"md6b72f9b08\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1093,7 +1093,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1134,7 +1134,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1168,7 +1168,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1219,10 +1219,10 @@ " \n", " \n", + "\" id=\"mf3ad580b44\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1235,7 +1235,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1265,7 +1265,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1280,7 +1280,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1329,7 +1329,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1344,7 +1344,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1406,7 +1406,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1475,12 +1475,12 @@ "z\n", "\" style=\"fill:#ffffff;\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", + "\" id=\"madb6d161d0\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1548,7 +1548,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1589,7 +1589,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1623,7 +1623,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1674,10 +1674,10 @@ " \n", " \n", + "\" id=\"md324b70fbb\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1690,7 +1690,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1720,7 +1720,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1735,7 +1735,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1784,7 +1784,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1799,7 +1799,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1861,7 +1861,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1919,13 +1919,20 @@ "\n", "* Given an input image, assume $1 \\times c_i \\times h \\times w$ to be the shape of the feature map while $c_i, h, w$ are the number, height, and width of the feature map. What methods can you think of to convert this variable into the anchor box's category and offset? What is the shape of the output?" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ai_safe", "language": "python", - "name": "python3" + "name": "ai_safe" }, "language_info": { "codemirror_mode": { @@ -1937,9 +1944,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.6.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/Ch14_Computer_Vision/Neural_Style_Transfer.ipynb b/Ch14_Computer_Vision/Neural_Style_Transfer.ipynb index e42ff35a..dd7967c6 100644 --- a/Ch14_Computer_Vision/Neural_Style_Transfer.ipynb +++ b/Ch14_Computer_Vision/Neural_Style_Transfer.ipynb @@ -1,803 +1,2350 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - }, - "colab": { - "name": "Neural_Style_Transfer.ipynb", - "version": "0.3.2", - "provenance": [], - "collapsed_sections": [] - }, - "accelerator": "GPU" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kaj3YpiT6lqj" + }, + "source": [ + "# Neural Style Transfer\n", + "\n", + "If you use social sharing apps or happen to be an amateur photographer, you are familiar with filters. Filters can alter the color styles of photos to make the background sharper or people's faces whiter. However, a filter generally can only change one aspect of a photo. To create the ideal photo, you often need to try many different filter combinations. This process is as complex as tuning the hyper-parameters of a model.\n", + "\n", + "In this section, we will discuss how we can use convolution neural networks\n", + "(CNNs) to automatically apply the style of one image to another image, an\n", + "operation known as style transfer :cite:`Gatys.Ecker.Bethge.2016`. Here, we need two input images, one content image and one style image. We use a neural network to alter the content image so that its style mirrors that of the style image. In :numref:`fig_style_transfer`, the content image is a landscape photo the author took in Mount Rainier National Part near Seattle. The style image is an oil painting of oak trees in autumn. The output composite image retains the overall shapes of the objects in the content image, but applies the oil painting brushwork of the style image and makes the overall color more vivid.\n", + "\n", + "![Content and style input images and composite image produced by style transfer. ](../img/style-transfer.svg)\n", + "\n", + ":label:`fig_style_transfer`\n", + "\n", + "\n", + "## Technique\n", + "\n", + "The CNN-based style transfer model is shown in :numref:`fig_style_transfer_model`.\n", + "First, we initialize the composite image. For example, we can initialize it as the content image. This composite image is the only variable that needs to be updated in the style transfer process, i.e. the model parameter to be updated in style transfer. Then, we select a pre-trained CNN to extract image features. These model parameters do not need to be updated during training. The deep CNN uses multiple neural layers that successively extract image features. We can select the output of certain layers to use as content features or style features. If we use the structure in :numref:`fig_style_transfer_model`, the pretrained neural network contains three convolutional layers. The second layer outputs the image content features, while the outputs of the first and third layers are used as style features. Next, we use forward propagation (in the direction of the solid lines) to compute the style transfer loss function and backward propagation (in the direction of the dotted lines) to update the model parameter, constantly updating the composite image. The loss functions used in style transfer generally have three parts: 1. Content loss is used to make the composite image approximate the content image as regards content features. 2. Style loss is used to make the composite image approximate the style image in terms of style features. 3. Total variation loss helps reduce the noise in the composite image. Finally, after we finish training the model, we output the style transfer model parameters to obtain the final composite image.\n", + "\n", + "![CNN-based style transfer process. Solid lines show the direction of forward propagation and dotted lines show backward propagation. ](../img/neural-style.svg)\n", + "\n", + ":label:`fig_style_transfer_model`\n", + "\n", + "\n", + "\n", + "Next, we will perform an experiment to help us better understand the technical details of style transfer.\n", + "\n", + "## Read the Content and Style Images\n", + "\n", + "First, we would import the required dependencies and read the content and style images. By printing out the image coordinate axes, we can see that they have different dimensions." + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "kaj3YpiT6lqj", - "colab_type": "text" - }, - "source": [ - "# Neural Style Transfer\n", - "\n", - "If you use social sharing apps or happen to be an amateur photographer, you are familiar with filters. Filters can alter the color styles of photos to make the background sharper or people's faces whiter. However, a filter generally can only change one aspect of a photo. To create the ideal photo, you often need to try many different filter combinations. This process is as complex as tuning the hyper-parameters of a model.\n", - "\n", - "In this section, we will discuss how we can use convolution neural networks\n", - "(CNNs) to automatically apply the style of one image to another image, an\n", - "operation known as style transfer :cite:`Gatys.Ecker.Bethge.2016`. Here, we need two input images, one content image and one style image. We use a neural network to alter the content image so that its style mirrors that of the style image. In :numref:`fig_style_transfer`, the content image is a landscape photo the author took in Mount Rainier National Part near Seattle. The style image is an oil painting of oak trees in autumn. The output composite image retains the overall shapes of the objects in the content image, but applies the oil painting brushwork of the style image and makes the overall color more vivid.\n", - "\n", - "![Content and style input images and composite image produced by style transfer. ](../img/style-transfer.svg)\n", - "\n", - ":label:`fig_style_transfer`\n", - "\n", - "\n", - "## Technique\n", - "\n", - "The CNN-based style transfer model is shown in :numref:`fig_style_transfer_model`.\n", - "First, we initialize the composite image. For example, we can initialize it as the content image. This composite image is the only variable that needs to be updated in the style transfer process, i.e. the model parameter to be updated in style transfer. Then, we select a pre-trained CNN to extract image features. These model parameters do not need to be updated during training. The deep CNN uses multiple neural layers that successively extract image features. We can select the output of certain layers to use as content features or style features. If we use the structure in :numref:`fig_style_transfer_model`, the pretrained neural network contains three convolutional layers. The second layer outputs the image content features, while the outputs of the first and third layers are used as style features. Next, we use forward propagation (in the direction of the solid lines) to compute the style transfer loss function and backward propagation (in the direction of the dotted lines) to update the model parameter, constantly updating the composite image. The loss functions used in style transfer generally have three parts: 1. Content loss is used to make the composite image approximate the content image as regards content features. 2. Style loss is used to make the composite image approximate the style image in terms of style features. 3. Total variation loss helps reduce the noise in the composite image. Finally, after we finish training the model, we output the style transfer model parameters to obtain the final composite image.\n", - "\n", - "![CNN-based style transfer process. Solid lines show the direction of forward propagation and dotted lines show backward propagation. ](../img/neural-style.svg)\n", - "\n", - ":label:`fig_style_transfer_model`\n", - "\n", - "\n", - "\n", - "Next, we will perform an experiment to help us better understand the technical details of style transfer.\n", - "\n", - "## Read the Content and Style Images\n", - "\n", - "First, we would import the required dependencies and read the content and style images. By printing out the image coordinate axes, we can see that they have different dimensions." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "XfcEW5z56lqm", - "colab_type": "code", - "colab": {} - }, - "source": [ - "import sys\n", - "sys.path.insert(0, '..')\n", - "\n", - "import numpy as np\n", - "\n", - "import d2l\n", - "import torch\n", - "import torch.nn as nn\n", - "import torchvision.transforms as transforms\n", - "import torchvision.models as models\n", - "import torch.optim as optim\n", - "\n", - "from PIL import Image\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "1" - }, - "scrolled": true, - "id": "9Z4vV1Ka6lq1", - "colab_type": "code", - "outputId": "39fe86f1-52e4-4fba-bfea-0839c571b648", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 241 - } - }, - "source": [ - "d2l.set_figsize((3.5, 2.5))\n", - "content_img = Image.open('../img/rainier.jpg')\n", - "d2l.plt.imshow(np.asarray(content_img));" - ], - "execution_count": 208, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "2" - }, - "id": "_0_4r4iK6lrF", - "colab_type": "code", - "outputId": "5d023a03-3785-4cf5-a0f0-c7401d5d29d9", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 249 - } - }, - "source": [ - "style_img = Image.open('../img/autumn_oak.jpg')\n", - "d2l.plt.imshow(np.asarray(style_img));" - ], - "execution_count": 209, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "I_81mNpu6lrU", - "colab_type": "text" - }, - "source": [ - "## Preprocessing and Postprocessing\n", - "\n", - "Below, we define the functions for image preprocessing and postprocessing. The `preprocess` function normalizes each of the three RGB channels of the input images and transforms the results to a format that can be input to the CNN. The `postprocess` function restores the pixel values in the output image to their original values before normalization. Because the image printing function requires that each pixel has a floating point value from 0 to 1, we use the `clamp` function to replace values smaller than 0 or greater than 1 with 0 or 1, respectively." - ] - }, - { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "3" - }, - "id": "9DSWgmWM6lrX", - "colab_type": "code", - "colab": {} - }, - "source": [ - "device = d2l.try_gpu()\n", - "\n", - "rgb_mean = torch.tensor([0.485, 0.456, 0.406]).view(-1, 1, 1)\n", - "rgb_std = torch.tensor([0.229, 0.224, 0.225]).view(-1, 1, 1)\n", - "\n", - "def preprocess(img, image_shape):\n", - " img = transforms.Resize(image_shape)(img)\n", - " img = transforms.ToTensor()(img)\n", - " img = (img - rgb_mean) / rgb_std\n", - " return img.unsqueeze(0)\n", - "\n", - "def postprocess(img):\n", - " img = img.cpu()\n", - " img = (img * rgb_std + rgb_mean).clamp(0, 1)\n", - " img = transforms.ToPILImage()(img.squeeze(0))\n", - " return img" - ], - "execution_count": 0, - "outputs": [] + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "XfcEW5z56lqm" + }, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '..')\n", + "\n", + "import numpy as np\n", + "\n", + "import d2l\n", + "import torch\n", + "import torch.nn as nn\n", + "import torchvision.transforms as transforms\n", + "import torchvision.models as models\n", + "import torch.optim as optim\n", + "\n", + "from PIL import Image\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "1" }, - { - "cell_type": "markdown", - "metadata": { - "id": "p4SPO-rp6lrf", - "colab_type": "text" - }, - "source": [ - "## Extract Features\n", - "\n", - "We use the VGG-19 model pre-trained on the ImageNet data set to extract image features[1]." - ] + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241 }, + "colab_type": "code", + "id": "9Z4vV1Ka6lq1", + "outputId": "39fe86f1-52e4-4fba-bfea-0839c571b648", + "scrolled": true + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "4" - }, - "id": "qi7PquEq6lri", - "colab_type": "code", - "colab": {} - }, - "source": [ - "pretrained_net = models.vgg19(pretrained=True).features.to(device).eval()" + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hLXulwIY6lrq", - "colab_type": "text" - }, - "source": [ - "To extract image content and style features, we can select the outputs of certain layers in the VGG network. In general, the closer an output is to the input layer, the easier it is to extract image detail information. The farther away an output is, the easier it is to extract global information. To prevent the composite image from retaining too many details from the content image, we select a VGG network layer near the output layer to output the image content features. This layer is called the content layer. We also select the outputs of different layers from the VGG network for matching local and global styles. These are called the style layers. As we mentioned in :numref:`chapter_vgg`, VGG networks have five convolutional blocks. In this experiment, we select the last convolutional layer of the fourth convolutional block as the content layer and the first layer of each block as style layers. We can obtain the indexes for these layers by printing the `pretrained_net` instance." + "text/plain": [ + "
" ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "d2l.set_figsize((3.5, 2.5))\n", + "content_img = Image.open('../img/rainier.jpg')\n", + "d2l.plt.imshow(np.asarray(content_img));" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "2" }, - { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "5" - }, - "id": "VQo4fz0b6lrs", - "colab_type": "code", - "colab": {} - }, - "source": [ - "style_layers, content_layers = [0, 5, 10, 19, 28], [25]" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SzX0lnS66lr1", - "colab_type": "text" - }, - "source": [ - "During feature extraction, we only need to use all the VGG layers from the input layer to the content or style layer nearest the output layer. Below, we build a new network, `net`, which only retains the layers in the VGG network we need to use. We then use `net` to extract features." - ] + "colab": { + "base_uri": "https://localhost:8080/", + "height": 249 }, + "colab_type": "code", + "id": "_0_4r4iK6lrF", + "outputId": "5d023a03-3785-4cf5-a0f0-c7401d5d29d9" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "6" - }, - "id": "loGrHS156lr3", - "colab_type": "code", - "colab": {} - }, - "source": [ - "net = nn.Sequential()\n", - "for i in range(max(content_layers + style_layers) + 1):\n", - " net.add_module(str(i), list(pretrained_net.children())[i])" + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bCm29Foi6lr8", - "colab_type": "text" - }, - "source": [ - "Given input `X`, if we simply call the forward computation `net(X)`, we can only obtain the output of the last layer. Because we also need the outputs of the intermediate layers, we need to perform layer-by-layer computation and retain the content and style layer outputs." + "text/plain": [ + "
" ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "style_img = Image.open('../img/autumn_oak.jpg')\n", + "d2l.plt.imshow(np.asarray(style_img));" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "I_81mNpu6lrU" + }, + "source": [ + "## Preprocessing and Postprocessing\n", + "\n", + "Below, we define the functions for image preprocessing and postprocessing. The `preprocess` function normalizes each of the three RGB channels of the input images and transforms the results to a format that can be input to the CNN. The `postprocess` function restores the pixel values in the output image to their original values before normalization. Because the image printing function requires that each pixel has a floating point value from 0 to 1, we use the `clamp` function to replace values smaller than 0 or greater than 1 with 0 or 1, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "3" }, - { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "7" - }, - "id": "k38_e52z6lsD", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def extract_features(X, content_layers, style_layers):\n", - " contents = []\n", - " styles = []\n", - " for i in range(len(net)):\n", - " X = net[i](X)\n", - " if i in style_layers:\n", - " styles.append(X)\n", - " if i in content_layers:\n", - " contents.append(X)\n", - " return contents, styles" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "r5xu2d4z6ltN", - "colab_type": "text" - }, - "source": [ - "Next, we define two functions: The `get_contents` function obtains the content features extracted from the content image, while the `get_styles` function obtains the style features extracted from the style image. Because we do not need to change the parameters of the pre-trained VGG model during training, we can extract the content features from the content image and style features from the style image before the start of training. As the composite image is the model parameter that must be updated during style transfer, we can only call the `extract_features` function during training to extract the content and style features of the composite image." - ] + "colab": {}, + "colab_type": "code", + "id": "9DSWgmWM6lrX" + }, + "outputs": [], + "source": [ + "device = d2l.try_gpu()\n", + "rgb_mean = torch.tensor([0.485, 0.456, 0.406]).view(-1, 1, 1)\n", + "rgb_std = torch.tensor([0.229, 0.224, 0.225]).view(-1, 1, 1)\n", + "\n", + "def preprocess(img, image_shape):\n", + " img = transforms.Resize(image_shape)(img)\n", + " img = transforms.ToTensor()(img)\n", + " img = (img - rgb_mean) / rgb_std\n", + " return img.unsqueeze(0)\n", + "\n", + "def postprocess(img):\n", + " img = img.cpu()\n", + " img = (img * rgb_std + rgb_mean).clamp(0, 1)\n", + " img = transforms.ToPILImage()(img.squeeze(0))\n", + " return img" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "p4SPO-rp6lrf" + }, + "source": [ + "## Extract Features\n", + "\n", + "We use the VGG-19 model pre-trained on the ImageNet data set to extract image features[1]." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "4" }, - { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "8" - }, - "id": "PumUWnzc6ltP", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def get_contents(image_shape, device):\n", - " content_X = preprocess(content_img, image_shape).to(device)\n", - " contents_Y, _ = extract_features(content_X, content_layers, style_layers)\n", - " return content_X, contents_Y\n", - "\n", - "def get_styles(image_shape, device):\n", - " style_X = preprocess(style_img, image_shape).to(device)\n", - " _, styles_Y = extract_features(style_X, content_layers, style_layers)\n", - " return style_X, styles_Y" - ], - "execution_count": 0, - "outputs": [] + "colab": {}, + "colab_type": "code", + "id": "qi7PquEq6lri" + }, + "outputs": [], + "source": [ + "pretrained_net = models.vgg19(pretrained=True).features.to(device).eval()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "hLXulwIY6lrq" + }, + "source": [ + "To extract image content and style features, we can select the outputs of certain layers in the VGG network. In general, the closer an output is to the input layer, the easier it is to extract image detail information. The farther away an output is, the easier it is to extract global information. To prevent the composite image from retaining too many details from the content image, we select a VGG network layer near the output layer to output the image content features. This layer is called the content layer. We also select the outputs of different layers from the VGG network for matching local and global styles. These are called the style layers. As we mentioned in :numref:`chapter_vgg`, VGG networks have five convolutional blocks. In this experiment, we select the last convolutional layer of the fourth convolutional block as the content layer and the first layer of each block as style layers. We can obtain the indexes for these layers by printing the `pretrained_net` instance." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "5" }, - { - "cell_type": "markdown", - "metadata": { - "id": "6zyxJkFL6ltZ", - "colab_type": "text" - }, - "source": [ - "## Define the Loss Function\n", - "\n", - "Next, we will look at the loss function used for style transfer. The loss function includes the content loss, style loss, and total variation loss.\n", - "\n", - "### Content Loss\n", - "\n", - "Similar to the loss function used in linear regression, content loss uses a square error function to measure the difference in content features between the composite image and content image. The two inputs of the square error function are both content layer outputs obtained from the `extract_features` function." - ] + "colab": {}, + "colab_type": "code", + "id": "VQo4fz0b6lrs" + }, + "outputs": [], + "source": [ + "style_layers, content_layers = [0, 5, 10, 19, 28], [25]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "SzX0lnS66lr1" + }, + "source": [ + "During feature extraction, we only need to use all the VGG layers from the input layer to the content or style layer nearest the output layer. Below, we build a new network, `net`, which only retains the layers in the VGG network we need to use. We then use `net` to extract features." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "6" }, - { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "9" - }, - "id": "EUpeG2mw6ltb", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def content_loss(Y_hat, Y):\n", - " return torch.mean(torch.pow((Y_hat - Y), 2))" - ], - "execution_count": 0, - "outputs": [] + "colab": {}, + "colab_type": "code", + "id": "loGrHS156lr3" + }, + "outputs": [], + "source": [ + "net = nn.Sequential()\n", + "for i in range(max(content_layers + style_layers) + 1):\n", + " net.add_module(str(i), list(pretrained_net.children())[i])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "bCm29Foi6lr8" + }, + "source": [ + "Given input `X`, if we simply call the forward computation `net(X)`, we can only obtain the output of the last layer. Because we also need the outputs of the intermediate layers, we need to perform layer-by-layer computation and retain the content and style layer outputs." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "7" }, - { - "cell_type": "markdown", - "metadata": { - "id": "2JYVVxZg6lth", - "colab_type": "text" - }, - "source": [ - "### Style Loss\n", - "\n", - "Style loss, similar to content loss, uses a square error function to measure the difference in style between the composite image and style image. To express the styles output by the style layers, we first use the `extract_features` function to compute the style layer output. Assuming that the output has 1 example, $c$ channels, and a height and width of $h$ and $w$, we can transform the output into the matrix $\\boldsymbol{X}$, which has $c$ rows and $h \\cdot w$ columns. You can think of matrix $\\boldsymbol{X}$ as the combination of the $c$ vectors $\\boldsymbol{x}_1, \\ldots, \\boldsymbol{x}_c$, which have a length of $hw$. Here, the vector $\\boldsymbol{x}_i$ represents the style feature of channel $i$. In the Gram matrix of these vectors $\\boldsymbol{X}\\boldsymbol{X}^\\top \\in \\mathbb{R}^{c \\times c}$, element $x_{ij}$ in row $i$ column $j$ is the inner product of vectors $\\boldsymbol{x}_i$ and $\\boldsymbol{x}_j$. It represents the correlation of the style features of channels $i$ and $j$. We use this type of Gram matrix to represent the style output by the style layers. You must note that, when the $h \\cdot w$ value is large, this often leads to large values in the Gram matrix. In addition, the height and width of the Gram matrix are both the number of channels $c$. To ensure that the style loss is not affected by the size of these values, we define the `gram` function below to divide the Gram matrix by the number of its elements, i.e. $c \\cdot h \\cdot w$." - ] + "colab": {}, + "colab_type": "code", + "id": "k38_e52z6lsD" + }, + "outputs": [], + "source": [ + "def extract_features(X, content_layers, style_layers):\n", + " contents = []\n", + " styles = []\n", + " for i in range(len(net)):\n", + " X = net[i](X)\n", + " if i in style_layers:\n", + " styles.append(X)\n", + " if i in content_layers:\n", + " contents.append(X)\n", + " return contents, styles" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "r5xu2d4z6ltN" + }, + "source": [ + "Next, we define two functions: The `get_contents` function obtains the content features extracted from the content image, while the `get_styles` function obtains the style features extracted from the style image. Because we do not need to change the parameters of the pre-trained VGG model during training, we can extract the content features from the content image and style features from the style image before the start of training. As the composite image is the model parameter that must be updated during style transfer, we can only call the `extract_features` function during training to extract the content and style features of the composite image." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "8" }, - { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "10" - }, - "id": "Lyo2GUE-6ltk", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def gram(X):\n", - " num_channels, n = X.shape[1], X.nelement() // X.shape[1]\n", - " X = X.reshape((num_channels, n))\n", - " return torch.matmul(X, X.t()) / (num_channels * n)" - ], - "execution_count": 0, - "outputs": [] + "colab": {}, + "colab_type": "code", + "id": "PumUWnzc6ltP" + }, + "outputs": [], + "source": [ + "def get_contents(image_shape, device):\n", + " content_X = preprocess(content_img, image_shape).to(device)\n", + " contents_Y, _ = extract_features(content_X, content_layers, style_layers)\n", + "# print('contents_Y:',contents_Y)\n", + " return content_X, contents_Y\n", + "\n", + "def get_styles(image_shape, device):\n", + " style_X = preprocess(style_img, image_shape).to(device)\n", + " _, styles_Y = extract_features(style_X, content_layers, style_layers)\n", + " return style_X, styles_Y" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6zyxJkFL6ltZ" + }, + "source": [ + "## Define the Loss Function\n", + "\n", + "Next, we will look at the loss function used for style transfer. The loss function includes the content loss, style loss, and total variation loss.\n", + "\n", + "### Content Loss\n", + "\n", + "Similar to the loss function used in linear regression, content loss uses a square error function to measure the difference in content features between the composite image and content image. The two inputs of the square error function are both content layer outputs obtained from the `extract_features` function." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "9" }, - { - "cell_type": "markdown", - "metadata": { - "id": "4vSKJ8tV6ltq", - "colab_type": "text" - }, - "source": [ - "Naturally, the two Gram matrix inputs of the square error function for style loss are taken from the composite image and style image style layer outputs. Here, we assume that the Gram matrix of the style image, `gram_Y`, has been computed in advance." - ] + "colab": {}, + "colab_type": "code", + "id": "EUpeG2mw6ltb" + }, + "outputs": [], + "source": [ + "def content_loss(Y_hat, Y):\n", + " return torch.mean(torch.pow((Y_hat - Y.detach()), 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2JYVVxZg6lth" + }, + "source": [ + "### Style Loss\n", + "\n", + "Style loss, similar to content loss, uses a square error function to measure the difference in style between the composite image and style image. To express the styles output by the style layers, we first use the `extract_features` function to compute the style layer output. Assuming that the output has 1 example, $c$ channels, and a height and width of $h$ and $w$, we can transform the output into the matrix $\\boldsymbol{X}$, which has $c$ rows and $h \\cdot w$ columns. You can think of matrix $\\boldsymbol{X}$ as the combination of the $c$ vectors $\\boldsymbol{x}_1, \\ldots, \\boldsymbol{x}_c$, which have a length of $hw$. Here, the vector $\\boldsymbol{x}_i$ represents the style feature of channel $i$. In the Gram matrix of these vectors $\\boldsymbol{X}\\boldsymbol{X}^\\top \\in \\mathbb{R}^{c \\times c}$, element $x_{ij}$ in row $i$ column $j$ is the inner product of vectors $\\boldsymbol{x}_i$ and $\\boldsymbol{x}_j$. It represents the correlation of the style features of channels $i$ and $j$. We use this type of Gram matrix to represent the style output by the style layers. You must note that, when the $h \\cdot w$ value is large, this often leads to large values in the Gram matrix. In addition, the height and width of the Gram matrix are both the number of channels $c$. To ensure that the style loss is not affected by the size of these values, we define the `gram` function below to divide the Gram matrix by the number of its elements, i.e. $c \\cdot h \\cdot w$." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "10" }, - { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "11" - }, - "id": "Pc0X7tUf6ltt", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def style_loss(Y_hat, gram_Y):\n", - " return torch.mean(torch.pow((gram(Y_hat) - gram_Y), 2))" - ], - "execution_count": 0, - "outputs": [] + "colab": {}, + "colab_type": "code", + "id": "Lyo2GUE-6ltk" + }, + "outputs": [], + "source": [ + "def gram(X):\n", + " num_channels, n = X.shape[1], X.nelement() // X.shape[1]\n", + " X = X.reshape((num_channels, n))\n", + " return torch.matmul(X, X.t()) / (num_channels * n)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4vSKJ8tV6ltq" + }, + "source": [ + "Naturally, the two Gram matrix inputs of the square error function for style loss are taken from the composite image and style image style layer outputs. Here, we assume that the Gram matrix of the style image, `gram_Y`, has been computed in advance." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "11" }, - { - "cell_type": "markdown", - "metadata": { - "id": "6Xmm1KFk6lt6", - "colab_type": "text" - }, - "source": [ - "### Total Variance Loss\n", - "\n", - "Sometimes, the composite images we learn have a lot of high-frequency noise, particularly bright or dark pixels. One common noise reduction method is total variation denoising. We assume that $x_{i,j}$ represents the pixel value at the coordinate $(i,j)$, so the total variance loss is:\n", - "\n", - "$$\\sum_{i,j} \\left|x_{i,j} - x_{i+1,j}\\right| + \\left|x_{i,j} - x_{i,j+1}\\right|$$\n", - "\n", - "We try to make the values of neighboring pixels as similar as possible." - ] + "colab": {}, + "colab_type": "code", + "id": "Pc0X7tUf6ltt" + }, + "outputs": [], + "source": [ + "def style_loss(Y_hat, gram_Y):\n", + " return torch.mean(torch.pow((gram(Y_hat) - gram_Y), 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6Xmm1KFk6lt6" + }, + "source": [ + "### Total Variance Loss\n", + "\n", + "Sometimes, the composite images we learn have a lot of high-frequency noise, particularly bright or dark pixels. One common noise reduction method is total variation denoising. We assume that $x_{i,j}$ represents the pixel value at the coordinate $(i,j)$, so the total variance loss is:\n", + "\n", + "$$\\sum_{i,j} \\left|x_{i,j} - x_{i+1,j}\\right| + \\left|x_{i,j} - x_{i,j+1}\\right|$$\n", + "\n", + "We try to make the values of neighboring pixels as similar as possible." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "12" }, - { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "12" - }, - "id": "HKKmMGjk6lt8", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def tv_loss(Y_hat):\n", - " return 0.5 * (torch.mean(torch.abs(Y_hat[:, :, 1:, :] - Y_hat[:, :, :-1, :])) +\n", - " torch.mean(torch.abs(Y_hat[:, :, :, 1:] - Y_hat[:, :, :, :-1])))" - ], - "execution_count": 0, - "outputs": [] + "colab": {}, + "colab_type": "code", + "id": "HKKmMGjk6lt8" + }, + "outputs": [], + "source": [ + "def tv_loss(Y_hat):\n", + " return 0.5 * (torch.mean(torch.abs(Y_hat[:, :, 1:, :] - Y_hat[:, :, :-1, :])) +\n", + " torch.mean(torch.abs(Y_hat[:, :, :, 1:] - Y_hat[:, :, :, :-1])))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "CY75M1uB6luA" + }, + "source": [ + "### Loss Function\n", + "\n", + "The loss function for style transfer is the weighted sum of the content loss, style loss, and total variance loss. By adjusting these weight hyper-parameters, we can balance the retained content, transferred style, and noise reduction in the composite image according to their relative importance." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "13" }, - { - "cell_type": "markdown", - "metadata": { - "id": "CY75M1uB6luA", - "colab_type": "text" - }, - "source": [ - "### Loss Function\n", - "\n", - "The loss function for style transfer is the weighted sum of the content loss, style loss, and total variance loss. By adjusting these weight hyper-parameters, we can balance the retained content, transferred style, and noise reduction in the composite image according to their relative importance." - ] + "colab": {}, + "colab_type": "code", + "id": "B54gvc-o6luB" + }, + "outputs": [], + "source": [ + "content_weight, style_weight, tv_weight = 1, 1e3, 10\n", + "\n", + "def compute_loss(X, contents_Y_hat, styles_Y_hat, contents_Y, styles_Y_gram):\n", + " # Calculate the content, style, and total variance losses respectively\n", + " contents_l = np.array([content_loss(Y_hat, Y) * content_weight for Y_hat, Y in zip(contents_Y_hat, contents_Y)])\n", + " styles_l = np.array([style_loss(Y_hat, Y) * style_weight for Y_hat, Y in zip(styles_Y_hat, styles_Y_gram)])\n", + " tv_l = tv_loss(X) * tv_weight\n", + " # Add up all the losses\n", + " l = np.sum(styles_l) + np.sum(contents_l) + tv_l\n", + " return contents_l, styles_l, tv_l, l" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "czPQCJhY6lud" + }, + "source": [ + "## Create and Initialize the Composite Image\n", + "\n", + "In style transfer, the composite image is the only variable that needs to be updated. Next, we define the `get_inits` function. This function creates an optimizer and sets the parameters to be updated as `X`. The Gram matrix for the various style layers of the style image, `styles_Y_gram`, is computed prior to training." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "15" }, - { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "13" - }, - "id": "B54gvc-o6luB", - "colab_type": "code", - "colab": {} - }, - "source": [ - "content_weight, style_weight, tv_weight = 1, 1e3, 10\n", - "\n", - "def compute_loss(X, contents_Y_hat, styles_Y_hat, contents_Y, styles_Y_gram):\n", - " # Calculate the content, style, and total variance losses respectively\n", - " contents_l = np.array([content_loss(Y_hat, Y) * content_weight for Y_hat, Y in zip(contents_Y_hat, contents_Y)])\n", - " styles_l = np.array([style_loss(Y_hat, Y) * style_weight for Y_hat, Y in zip(styles_Y_hat, styles_Y_gram)])\n", - " tv_l = tv_loss(X) * tv_weight\n", - " # Add up all the losses\n", - " l = np.sum(styles_l) + np.sum(contents_l) + tv_l\n", - " return contents_l, styles_l, tv_l, l" - ], - "execution_count": 0, - "outputs": [] + "colab": {}, + "colab_type": "code", + "id": "xh48eP-N6lui" + }, + "outputs": [], + "source": [ + "def get_inits(X, device, lr, styles_Y):\n", + " optimizer = optim.Adam([X.requires_grad_()], lr=lr)\n", + " styles_Y_gram = [gram(Y) for Y in styles_Y]\n", + " return X, styles_Y_gram, optimizer" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "FpF5u_uX6luw" + }, + "source": [ + "## Training\n", + "\n", + "During model training, we constantly extract the content and style features of the composite image and calculate the loss function. Also, we use `StepLR` to perform weight decay." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "16" }, - { - "cell_type": "markdown", - "metadata": { - "id": "czPQCJhY6lud", - "colab_type": "text" - }, - "source": [ - "## Create and Initialize the Composite Image\n", - "\n", - "In style transfer, the composite image is the only variable that needs to be updated. Next, we define the `get_inits` function. This function creates an optimizer and sets the parameters to be updated as `X`. The Gram matrix for the various style layers of the style image, `styles_Y_gram`, is computed prior to training." - ] + "colab": {}, + "colab_type": "code", + "id": "76bO2YR26luy" + }, + "outputs": [], + "source": [ + "def train(X, contents_Y, styles_Y, device, lr, num_epochs, lr_decay_epoch):\n", + " X, styles_Y_gram, optimizer = get_inits(X, device, lr, styles_Y)\n", + " scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=lr_decay_epoch, gamma=0.1)\n", + " \n", + " for epoch in range(1, num_epochs+1):\n", + " optimizer.zero_grad()\n", + " contents_Y_hat, styles_Y_hat = extract_features(X, content_layers, style_layers)\n", + " contents_l, styles_l, tv_l, l = compute_loss(X, contents_Y_hat, styles_Y_hat, contents_Y, styles_Y_gram)\n", + " l.backward(retain_graph = True)\n", + " optimizer.step()\n", + " scheduler.step()\n", + " \n", + " return X" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kojDlcFv6lu2" + }, + "source": [ + "Next, we start to train the model. First, we set the height and width of the content and style images to 150 by 225 pixels. We use the content image to initialize the composite image." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "17" }, - { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "15" - }, - "id": "xh48eP-N6lui", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def get_inits(X, device, lr, styles_Y):\n", - " optimizer = optim.Adam([X.requires_grad_()], lr=lr)\n", - " styles_Y_gram = [gram(Y) for Y in styles_Y]\n", - " return X, styles_Y_gram, optimizer" - ], - "execution_count": 0, - "outputs": [] + "colab": {}, + "colab_type": "code", + "id": "bvrto0O06lu3" + }, + "outputs": [], + "source": [ + "device, image_shape = d2l.try_gpu(), (150, 225)\n", + "net.to(device)\n", + "content_X, contents_Y = get_contents(image_shape, device)\n", + "_, styles_Y = get_styles(image_shape, device)\n", + "output = train(content_X, contents_Y, styles_Y, device, 0.01, 500, 200)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 258 }, + "colab_type": "code", + "id": "2uBbwpSBDstd", + "outputId": "5d6ea47d-56d8-42e9-eb9f-fd14fe7255b8" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "FpF5u_uX6luw", - "colab_type": "text" - }, - "source": [ - "## Training\n", - "\n", - "During model training, we constantly extract the content and style features of the composite image and calculate the loss function. Also, we use `StepLR` to perform weight decay." + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "16" - }, - "id": "76bO2YR26luy", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def train(X, contents_Y, styles_Y, device, lr, num_epochs, lr_decay_epoch):\n", - " X, styles_Y_gram, optimizer = get_inits(X, device, lr, styles_Y)\n", - " scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=lr_decay_epoch, gamma=0.1)\n", - " \n", - " for epoch in range(1, num_epochs+1):\n", - " optimizer.zero_grad()\n", - " contents_Y_hat, styles_Y_hat = extract_features(X, content_layers, style_layers)\n", - " contents_l, styles_l, tv_l, l = compute_loss(X, contents_Y_hat, styles_Y_hat, contents_Y, styles_Y_gram)\n", - " l.backward(retain_graph = True)\n", - " optimizer.step()\n", - " scheduler.step()\n", - " \n", - " return X" + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kojDlcFv6lu2", - "colab_type": "text" - }, - "source": [ - "Next, we start to train the model. First, we set the height and width of the content and style images to 150 by 225 pixels. We use the content image to initialize the composite image." + "text/plain": [ + "
" ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "d2l.plt.imshow(np.asarray(postprocess(output)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "SKR-01-S6lu7" + }, + "source": [ + "As you can see, the composite image retains the scenery and objects of the content image, while introducing the color of the style image. Because the image is relatively small, the details are a bit fuzzy.\n", + "\n", + "To obtain a clearer composite image, we train the model using a larger image size: 600 by 900 pixels. We increase the height and width of the image used before by a factor of four and initialize a larger composite image." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "attributes": { + "classes": [], + "id": "", + "n": "19" }, - { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "17" - }, - "id": "bvrto0O06lu3", - "colab_type": "code", - "colab": {} - }, - "source": [ - "device, image_shape = d2l.try_gpu(), (150, 225)\n", - "net.to(device)\n", - "content_X, contents_Y = get_contents(image_shape, device)\n", - "_, styles_Y = get_styles(image_shape, device)\n", - "output = train(content_X, contents_Y, styles_Y, device, 0.01, 500, 200)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "2uBbwpSBDstd", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 258 - }, - "outputId": "5d6ea47d-56d8-42e9-eb9f-fd14fe7255b8" - }, - "source": [ - "d2l.plt.imshow(np.asarray(postprocess(output)))" - ], - "execution_count": 224, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 224 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" - }, - "metadata": { - "tags": [] - } - } - ] + "colab": {}, + "colab_type": "code", + "id": "Pl81PuJJ6lu8" + }, + "outputs": [], + "source": [ + "image_shape = (600, 900)\n", + "_, content_Y = get_contents(image_shape, device)\n", + "_, style_Y = get_styles(image_shape, device)\n", + "X = (preprocess(postprocess(output), image_shape)).to(device)\n", + "output = train(X, content_Y, style_Y, device, 0.01, 300, 100)\n", + "d2l.plt.imsave('../img/neural-style.png', np.asarray(postprocess(output)))" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 258 }, + "colab_type": "code", + "id": "BE-pcC8PZViG", + "outputId": "995cedc4-a5cd-4da7-ff1a-99c397aa1923" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "SKR-01-S6lu7", - "colab_type": "text" - }, - "source": [ - "As you can see, the composite image retains the scenery and objects of the content image, while introducing the color of the style image. Because the image is relatively small, the details are a bit fuzzy.\n", - "\n", - "To obtain a clearer composite image, we train the model using a larger image size: 600 by 900 pixels. We increase the height and width of the image used before by a factor of four and initialize a larger composite image." + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 226, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" }, { - "cell_type": "code", - "metadata": { - "attributes": { - "classes": [], - "id": "", - "n": "19" - }, - "id": "Pl81PuJJ6lu8", - "colab_type": "code", - "colab": {} - }, - "source": [ - "image_shape = (600, 900)\n", - "_, content_Y = get_contents(image_shape, device)\n", - "_, style_Y = get_styles(image_shape, device)\n", - "X = (preprocess(postprocess(output), image_shape)).to(device)\n", - "output = train(X, content_Y, style_Y, device, 0.01, 300, 100)\n", - "d2l.plt.imsave('../img/neural-style.png', np.asarray(postprocess(output)))" + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "BE-pcC8PZViG", - "colab_type": "code", - "outputId": "995cedc4-a5cd-4da7-ff1a-99c397aa1923", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 258 - } - }, - "source": [ - "d2l.plt.imshow(np.asarray(postprocess(output)))" - ], - "execution_count": 226, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 226 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "haMOPu3W6lvA", - "colab_type": "text" - }, - "source": [ - "As you can see, each epoch takes more time due to the larger image size. As shown in :numref:`fig_style_transfer_large`, the composite image produced retains more detail due to its larger size. The composite image not only has large blocks of color like the style image, but these blocks even have the subtle texture of brush strokes.\n", - "\n", - "![$900 \\times 600$ composite image. ](../img/neural-style.png)\n", - "\n", - ":label:`fig_style_transfer_large`\n", - "\n", - "\n", - "## Summary\n", - "\n", - "* The loss functions used in style transfer generally have three parts: 1. Content loss is used to make the composite image approximate the content image as regards content features. 2. Style loss is used to make the composite image approximate the style image in terms of style features. 3. Total variation loss helps reduce the noise in the composite image.\n", - "* We can use a pre-trained CNN to extract image features and minimize the loss function to continuously update the composite image.\n", - "* We use a Gram matrix to represent the style output by the style layers.\n", - "\n", - "\n", - "## Exercises\n", - "\n", - "* How does the output change when you select different content and style layers?\n", - "* Adjust the weight hyper-parameters in the loss function. Does the output retain more content or have less noise?\n", - "* Use different content and style images. Can you create more interesting composite images?" + "text/plain": [ + "
" ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" } - ] -} \ No newline at end of file + ], + "source": [ + "d2l.plt.imshow(np.asarray(postprocess(output)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "haMOPu3W6lvA" + }, + "source": [ + "As you can see, each epoch takes more time due to the larger image size. As shown in :numref:`fig_style_transfer_large`, the composite image produced retains more detail due to its larger size. The composite image not only has large blocks of color like the style image, but these blocks even have the subtle texture of brush strokes.\n", + "\n", + "![$900 \\times 600$ composite image. ](../img/neural-style.png)\n", + "\n", + ":label:`fig_style_transfer_large`\n", + "\n", + "\n", + "## Summary\n", + "\n", + "* The loss functions used in style transfer generally have three parts: 1. Content loss is used to make the composite image approximate the content image as regards content features. 2. Style loss is used to make the composite image approximate the style image in terms of style features. 3. Total variation loss helps reduce the noise in the composite image.\n", + "* We can use a pre-trained CNN to extract image features and minimize the loss function to continuously update the composite image.\n", + "* We use a Gram matrix to represent the style output by the style layers.\n", + "\n", + "\n", + "## Exercises\n", + "\n", + "* How does the output change when you select different content and style layers?\n", + "* Adjust the weight hyper-parameters in the loss function. Does the output retain more content or have less noise?\n", + "* Use different content and style images. Can you create more interesting composite images?" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "Neural_Style_Transfer.ipynb", + "provenance": [], + "version": "0.3.2" + }, + "kernelspec": { + "display_name": "ai_safe", + "language": "python", + "name": "ai_safe" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/README.md b/README.md index 3ce82bc0..c21a7035 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,19 @@ This project is adapted from the original [Dive Into Deep Learning](https://d2l. Note: Some ipynb notebooks may not be rendered perfectly in Github. We suggest `cloning` the repo or using [nbviewer](https://nbviewer.jupyter.org/) to view the notebooks. +## Installation +Many of you will not have Python 3.6 already installed on your computers. Conda is an easy way to manage many different environments, each with its own Python versions and dependencies. This allows us to avoid conflicts between our preferred Python version and that of other classes. We’ll walk through how to set up and use a conda environment. + +Prerequisite: Anaconda. Many of you will have it installed from classes such as EE 16A; if you don’t, install it through the link. +### Creating a Conda Environment + ```conda create --name python=3.6``` + +### Entering the Environment + ```conda activate ``` + +### Setting the Environment + ```pip install -r requirements.txt``` + ## Chapters @@ -141,7 +154,6 @@ Note: Some ipynb notebooks may not be rendered perfectly in Github. We suggest ` If you like this repo and find it useful, please consider (★) starring it, so that it can reach a broader audience. - ## References [1] Original Book [Dive Into Deep Learning](https://d2l.ai) -> [Github Repo](https://github.com/d2l-ai/d2l-en) diff --git a/d2l/ssd_utils.py b/d2l/ssd_utils.py index c2fd6254..2bb155da 100644 --- a/d2l/ssd_utils.py +++ b/d2l/ssd_utils.py @@ -597,7 +597,7 @@ def MultiBoxTarget(class_true, bb_true, anchors): for j in range(len(overlap_list)): overlap = overlap_list[j] - class_target[overlap] = class_true[j, 0] + class_target[overlap] = class_true[j, 0].type(torch.LongTensor) overlap_coordinates[overlap] = 1. diff --git a/requirements.txt b/requirements.txt index e2f1f0a1..8616d9f4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,76 @@ -matplotlib==3.2.1 -numpy==1.14.3 +argon2-cffi==20.1.0 +async-generator==1.10 +attrs==20.2.0 +backports.functools-lru-cache==1.6.1 +bleach==3.1.5 +brotlipy==0.7.0 +certifi==2020.6.20 +cffi==1.11.5 +chardet==3.0.4 ConfigArgParse==1.2 -torch==1.5.0 -torchvision==0.6.0 -tqdm==4.45.0 +cryptography==3.1 +cycler==0.10.0 +decorator==4.4.2 +defusedxml==0.6.0 +entrypoints==0.3 +future==0.18.2 +idna==2.10 +importlib-metadata==1.7.0 +ipykernel==5.3.4 +ipython==5.8.0 +ipython-genutils==0.2.0 +ipywidgets==7.5.1 +Jinja2==2.11.2 +json5==0.9.5 +jsonschema==3.2.0 +jupyter-client==6.1.7 +jupyter-core==4.6.3 +jupyterlab==2.2.7 +jupyterlab-pygments==0.1.1 +jupyterlab-server==1.2.0 +kiwisolver==1.2.0 +MarkupSafe==1.1.1 +matplotlib==3.2.1 +mistune==0.8.4 +nbclient==0.5.0 +nbconvert==6.0.2 +nbformat==5.0.7 +nest-asyncio==1.4.0 +notebook==6.1.4 +numpy==1.19.2 +opencv-python==4.4.0.42 +packaging==20.4 +pandas==1.1.2 +pandocfilters==1.4.2 +pexpect==4.8.0 +pickleshare==0.7.5 +Pillow==7.2.0 +prometheus-client==0.8.0 +prompt-toolkit==1.0.18 +ptyprocess==0.6.0 +pycparser==2.20 +Pygments==2.7.0 +pyOpenSSL==19.1.0 +pyparsing==2.4.7 +pyrsistent==0.17.3 +PySocks==1.7.1 +python-dateutil==2.8.1 +pytz==2020.1 +pyzmq==19.0.2 +requests==2.24.0 +scipy==1.5.2 +Send2Trash==1.5.0 +simplegeneric==0.8.1 +six==1.15.0 +terminado==0.8.3 +testpath==0.4.4 +torch==1.6.0 +torchvision==0.7.0 +tornado==6.0.4 +tqdm==4.48.0 +traitlets==4.3.3 +urllib3==1.25.10 +wcwidth==0.2.5 +webencodings==0.5.1 +widgetsnbextension==3.5.1 +zipp==3.1.0