lecture_15/Addition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to try and add 2 numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate_examples(n_examples):\n",
    "    xs = []\n",
    "    ys = []\n",
    "    for i in range(n_examples):\n",
    "        a = random.randint(0, 32)\n",
    "        b = random.randint(0, 32)\n",
    "        \n",
    "        result = a + b\n",
    "    \n",
    "        binary_a = format(a, '06b')\n",
    "        binary_b = format(b, '06b')\n",
    "        binary_result = format(result, '06b')\n",
    "\n",
    "        xs.append(binary_a + binary_b)\n",
    "        ys.append(binary_result)\n",
    "        \n",
    "    return xs, ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  '100100'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generate_examples(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import to_categorical\n",
    "def generate_npfy_examples(n_examples):\n",
    "    xs, ys = generate_examples(n_examples)\n",
    "    np_xs = []\n",
    "    np_ys = []\n",
    "    for x in xs:\n",
    "        np_xs.append(\n",
    "            to_categorical(\n",
    "                np.array(\n",
    "                    list(map(int, list(x)))\n",
    "                ),\n",
    "                2\n",
    "            )\n",
    "        )\n",
    "        \n",
    "    for y in ys:\n",
    "        np_ys.append(\n",
    "            to_categorical(\n",
    "                np.array(\n",
    "                    list(map(int, list(y)))\n",
    "                ),\n",
    "                2\n",
    "            )\n",
    "        )\n",
    "    return np.array(np_xs), np.array(np_ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "        [1., 0.]]),\n",
       "        array([[1., 0.],\n",
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       "        [1., 0.],\n",
       "        [0., 1.]]),\n",
       "        array([[1., 0.],\n",
       "        [0., 1.],\n",
       "        [0., 1.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        [0., 1.]]),\n",
       "        array([[1., 0.],\n",
       "        [1., 0.],\n",
       "        [0., 1.],\n",
       "        [1., 0.],\n",
       "        [0., 1.],\n",
       "        [1., 0.]])], dtype=object))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generate_npfy_examples(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Input\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import TimeDistributed\n",
    "from keras.layers import RepeatVector\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1238: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "WARNING:tensorflow:From /usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1340: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 100)               41200     \n",
      "_________________________________________________________________\n",
      "repeat_vector_1 (RepeatVecto (None, 6, 100)            0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 6, 50)             30200     \n",
      "_________________________________________________________________\n",
      "time_distributed_1 (TimeDist (None, 6, 2)              102       \n",
      "=================================================================\n",
      "Total params: 71,502\n",
      "Trainable params: 71,502\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(100, input_shape=(12, 2))) #encoder lstm\n",
    "model.add(RepeatVector(6))\n",
    "model.add(LSTM(50, return_sequences=True)) # decoder lstm\n",
    "model.add(TimeDistributed(Dense(2, activation='softmax')))\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.6939 - acc: 0.5100\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 767us/step - loss: 0.6919 - acc: 0.5183\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 827us/step - loss: 0.6932 - acc: 0.5083\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 831us/step - loss: 0.6939 - acc: 0.4933\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6917 - acc: 0.5250\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6922 - acc: 0.5250\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 937us/step - loss: 0.6972 - acc: 0.4950\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6940 - acc: 0.5100\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 898us/step - loss: 0.6936 - acc: 0.4967\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6956 - acc: 0.5017\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6946 - acc: 0.4933\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6915 - acc: 0.5583\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 964us/step - loss: 0.6924 - acc: 0.5267\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 968us/step - loss: 0.6919 - acc: 0.5267\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6926 - acc: 0.5200\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6914 - acc: 0.5367\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6913 - acc: 0.5250\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6939 - acc: 0.5383\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6878 - acc: 0.5833\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6922 - acc: 0.5433\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6910 - acc: 0.5383\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6893 - acc: 0.5267\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6930 - acc: 0.5083\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6901 - acc: 0.5300\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6953 - acc: 0.5100A: 0s - loss: 0.6951 - acc: 0.512\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6887 - acc: 0.5533\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6850 - acc: 0.5850\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6879 - acc: 0.5433\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6871 - acc: 0.5517\n",
      "(100, 12, 2) (100,)\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6806 - acc: 0.5500\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6867 - acc: 0.5483\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6877 - acc: 0.5550\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6874 - acc: 0.5550\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6901 - acc: 0.5233\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6804 - acc: 0.5667\n",
      "(100, 12, 2) (100,)\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6864 - acc: 0.5350\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1000us/step - loss: 0.6763 - acc: 0.5633\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6819 - acc: 0.5617\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6739 - acc: 0.5600\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6811 - acc: 0.5417\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6782 - acc: 0.5583\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6779 - acc: 0.5400\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6889 - acc: 0.5250\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6783 - acc: 0.5750\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6695 - acc: 0.5517\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
      "100/100 [==============================] - 0s 1ms/step - loss: 0.6750 - acc: 0.5500\n",
      "(100, 12, 2) (100, 6, 2)\n",
      "Epoch 1/1\n",
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     ]
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     ]
    },
    {
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     ]
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     ]
    },
    {
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     ]
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     ]
    },
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      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
    },
    {
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     ]
    },
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     ]
    },
    {
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     ]
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     ]
    },
    {
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     ]
    },
    {
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     ]
    },
    {
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      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
    },
    {
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     ]
    },
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     ]
    },
    {
     "name": "stdout",
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     ]
    },
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     ]
    },
    {
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     ]
    },
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     ]
    },
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      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
    },
    {
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     ]
    },
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     ]
    },
    {
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     ]
    },
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     ]
    },
    {
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     ]
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     ]
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      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
    },
    {
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     ]
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     ]
    },
    {
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     ]
    },
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     ]
    },
    {
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     ]
    },
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     ]
    },
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     ]
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     ]
    },
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      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
    },
    {
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     ]
    },
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     ]
    },
    {
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     ]
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     ]
    },
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     ]
    },
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     ]
    },
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      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
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     ]
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     ]
    },
    {
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     ]
    },
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     ]
    },
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     ]
    },
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     ]
    },
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     ]
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     ]
    },
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      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
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     ]
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     ]
    },
    {
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     "output_type": "stream",
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     ]
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     ]
    },
    {
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     ]
    },
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     ]
    },
    {
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     ]
    },
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     ]
    },
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      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
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     ]
    },
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     ]
    },
    {
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     ]
    },
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     ]
    },
    {
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     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " 96/100 [===========================>..] - ETA: 0s - loss: 3.6191e-04 - acc: 1.0000"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-e1c28c6142fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m             \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_xs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_ys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m    891\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    892\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 893\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    895\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1629\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1630\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1631\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1632\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1633\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m   1211\u001b[0m                     \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1212\u001b[0m                     \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1213\u001b[0;31m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1214\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1215\u001b[0m                         \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2330\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[1;32m   2331\u001b[0m                               \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2332\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2333\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    893\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 895\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    896\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1126\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1127\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1128\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1129\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1130\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1342\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1343\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1344\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1345\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1346\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1348\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1351\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1327\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[1;32m   1328\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1329\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1331\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for epochs in range(1000):\n",
    "    for batch_id in range(1000):\n",
    "        batch_xs, batch_ys = generate_npfy_examples(100)\n",
    "        print(batch_xs.shape, batch_ys.shape)\n",
    "        \n",
    "        if batch_ys.shape != (100, 6, 2):\n",
    "            continue\n",
    "        \n",
    "        model.fit(batch_xs, batch_ys, epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "example_xs = to_categorical([0,0,0,0,0,1,0,0,0,1,1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "1/1 [==============================] - 0s 4ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 1, 0, 0, 0]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_classes(np.array([example_xs]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pretrained_model = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "1/1 [==============================] - 0s 4ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 1, 0, 0]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretrained_model.predict_classes(np.array([example_xs]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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