11-Training_Deep_Neural_Network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "66620c32-5acc-47ea-ba48-65843049d3c3",
   "metadata": {},
   "source": [
    "# Training Deep Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6b61d9f7-73dd-4cab-abc6-6b676f3648a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-31 20:26:55.300213: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-12-31 20:26:55.448395: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-12-31 20:26:55.449266: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-12-31 20:26:56.605724: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fde01643-b005-4443-9d2a-0537f46664c5",
   "metadata": {},
   "source": [
    "## The Vanishing/Exploding Gradient Problems"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d494841d-e61c-4f8c-b55c-6f62baf78c67",
   "metadata": {},
   "source": [
    "### Galrot and He Initialization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "994e172f-367a-4819-90eb-7909525ea941",
   "metadata": {},
   "source": [
    "By default, Keras uses Glorot initialization with a uniform distribution.\n",
    "When creating a layer, we can change this to He initialization by setting\n",
    "`kernel_initializer=\"he_uniform\"` or\n",
    "`kernel_initializer=\"he_normal\"` like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d8be3dc4-fba4-481f-beb9-59fd005f2664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.src.layers.core.dense.Dense at 0x7f64524bb090>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.layers.Dense(10, activation=\"relu\", kernel_initializer=\"he_normal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66792ee7-9556-4512-ae59-b5eeca1f67d2",
   "metadata": {},
   "source": [
    "If you want He initialization with a uniform distribution but based on\n",
    "$fan_{avg}$ rather than $fan_{in}$, you can use the `VarianceScaling` initializer like\n",
    "this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8ee5841d-ae77-49b1-b322-5e79d535620d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.src.layers.core.dense.Dense at 0x7f63dc1c6c50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "he_avg_init = keras.initializers.VarianceScaling(scale=2., mode='fan_avg', distribution='uniform')\n",
    "keras.layers.Dense(10, activation=\"sigmoid\", kernel_initializer=he_avg_init)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92ca6110-9745-4338-9e0f-fcf325598596",
   "metadata": {},
   "source": [
    "### Nonsaturating Activation Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5710c26a-589c-4400-b186-f351cf00becf",
   "metadata": {},
   "source": [
    "To use the leaky ReLU activation function, create a `LeakyReLU` layer and\n",
    "add it to our model just after the layer we want to apply it to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ba2b0cfc-b8f7-47ad-af55-e406cbd7d576",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(10, kernel_initializer=\"he_normal\"),\n",
    "    keras.layers.LeakyReLU(alpha=0.2),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "802e461f-e36a-4332-bd5e-a37266da14d5",
   "metadata": {},
   "source": [
    "For SELU activation, `set activation=\"selu\"` and\n",
    "`kernel_initializer=\"lecun_normal\"` when creating a layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c22c81c-898a-44f5-b0af-11f709745cfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "layer = keras.layers.Dense(10, activation=\"selu\", kernel_initializer=\"lecun_normal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75e1f9c-5a4a-4dc8-96a1-187faa5f45b9",
   "metadata": {},
   "source": [
    "### Batch Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1dfc995c-861b-4900-b343-5b4b661f6ee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28, 28]),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Dense(300, activation=\"elu\", kernel_initializer=\"he_normal\"),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Dense(100, activation=\"elu\", kernel_initializer=\"he_normal\"),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Dense(10, activation=\"softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "417c1a7f-25a6-4417-a382-6e8c33658ed9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " flatten (Flatten)           (None, 784)               0         \n",
      "                                                                 \n",
      " batch_normalization (Batch  (None, 784)               3136      \n",
      " Normalization)                                                  \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 300)               235500    \n",
      "                                                                 \n",
      " batch_normalization_1 (Bat  (None, 300)               1200      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 100)               30100     \n",
      "                                                                 \n",
      " batch_normalization_2 (Bat  (None, 100)               400       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 10)                1010      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 271346 (1.04 MB)\n",
      "Trainable params: 268978 (1.03 MB)\n",
      "Non-trainable params: 2368 (9.25 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04176fd5-d837-4bfd-b4b3-a20469630faf",
   "metadata": {},
   "source": [
    "As we can see, each BN layer adds four parameters per input: $\\gamma$, $\\beta$, $\\mu$, and\n",
    "$\\sigma$ (for example, the first BN layer adds 3,136 parameters, which is 4 × 784). \n",
    "The last two parameters, $\\mu$ and $\\sigma$, are the moving averages; they are\n",
    "not affected by backpropagation, so Keras calls them “non-trainable” \n",
    "(if we count the total number of BN parameters, 3,136 + 1,200 + 400, and\n",
    "divide by 2, you get 2,368, which is the total number of non-trainable\n",
    "parameters in this model).\n",
    "\n",
    "**NOTE:**\n",
    "\n",
    "$\\mu$ and $\\sigma$ are estimated during training, based on the training data, so arguably they\n",
    "are trainable. In Keras, “non-trainable” really means “untouched by backpropagation.”"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa2945b2-2bb2-41d0-8585-ce6640625657",
   "metadata": {},
   "source": [
    "Let’s look at the parameters of the first BN layer. Two are trainable (by\n",
    "backpropagation), and two are not:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "83206e4d-24ce-4b1b-97e0-3e88d5e3c33e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('batch_normalization/gamma:0', True),\n",
       " ('batch_normalization/beta:0', True),\n",
       " ('batch_normalization/moving_mean:0', False),\n",
       " ('batch_normalization/moving_variance:0', False)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(var.name, var.trainable) for var in model.layers[1].variables]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4a5cea4-2040-4282-b37a-71b4743144b4",
   "metadata": {},
   "source": [
    "The authors of the BN paper argued in favor of adding the BN layers\n",
    "before the activation functions, rather than after (as we just did). But this highly depends on the task. To add the BN layers before the activation functions, you must\n",
    "remove the activation function from the hidden layers and add them as\n",
    "separate layers after the BN layers. Moreover, since a Batch\n",
    "Normalization layer includes one offset parameter per input, you can\n",
    "remove the bias term from the previous layer (just pass `use_bias=False`\n",
    "when creating it):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "821ac608-c27b-4f84-9553-0a058f584c95",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28, 28]),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Dense(300, kernel_initializer=\"he_normal\",use_bias=False),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Activation(\"elu\"),\n",
    "    keras.layers.Dense(100, kernel_initializer=\"he_normal\",use_bias=False),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Activation(\"elu\"),\n",
    "    keras.layers.Dense(10, activation=\"softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb11f1f-62f7-40a3-a0f2-ee9b8609a8b5",
   "metadata": {},
   "source": [
    "When the input batch is 2D (i.e., the batch shape is `[batch size, features])`, this means that each input feature will be normalized based on the mean and standard deviation computed across all\n",
    "the instances in the batch.\n",
    "\n",
    "For example, the first BN layer in the previous code example will independently normalize (and rescale and shift) each of the 784 input features. If we move the first BN layer before the `Flatten` layer, then the input batches will be 3D, with shape `[batch size, height, width]`; therefore BN layer will compute 28 means and 28 standard deviations (1 per column of pixels, computed across all instances in the batch and across all rows in the column), and it will normalize all pixels in a given column using the same mean and standard deviation. There will also be just 28 scale parameters and 29 shift parameters. If instead we still want to treat each of the 784 pixels independently, then we should set `axis=[1,2]`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e8b1117-860b-4c7f-b92e-3a3eb917c80c",
   "metadata": {},
   "source": [
    "### Gradient Clipping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c75c5e-b6cd-4eee-969d-0fc34c272578",
   "metadata": {},
   "source": [
    "In Keras, implementing Gradient Clipping is just a matter of setting the\n",
    "`clipvalue` or `clipnorm argument` when creating an optimizer, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b7d369fe-b6cb-4293-be1d-21741276e2de",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.SGD(clipvalue=1.0)\n",
    "model.compile(loss=\"mse\", optimizer=optimizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c06ca98b-b49d-4a12-adb1-50257ac44850",
   "metadata": {},
   "source": [
    "The optimizier will clip every component of the gradient vector to a value between -1.0 and 1.0. This means that all the partial derivatives of the loss (wrt to each and every trainable parameter) will be clipped to -1.0 and 1.0. This threshold is a hyperparameter we can tune.\n",
    "\n",
    "Note that, it may change the orientation of the gradient vector. For instance, if the original gradient vector is [0.9, 100.0], it points mostly in the direction of 2nd axis; but once u clip it by value, we get [0.9, 1.0], which points roughly in the diagonal between the two axes. In practice, this approach works well. \n",
    "\n",
    "If we want to ensure that Gradient Clipping does not change the direction of gradient vector, we should clip by norm by setting `clipnorm` instead of `clipvalue`. This will clip the whole gradient if its $l_2$ norm is greater than the threshold we picked. For example, if we set `clipnorm=1.0`, theb vector [0.9, 100.0] will be clipped to [0.00899964, 0.9999595], preserving its oreintation but almost eliminating its first component. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f88dbab3-1eb4-41df-894a-e8ae1e9e9ca2",
   "metadata": {},
   "source": [
    "## Reusing Pretrained Layers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91588e2c-d91c-44de-8fe6-44d9fe2053f8",
   "metadata": {},
   "source": [
    "### Transfer Learning with Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae2b99b2-27e3-4508-b91a-253dbb1a5033",
   "metadata": {},
   "source": [
    "Suppose the Fashin MNIST dataset only contained eight classes -  for example, all the classes except for sandal and shirt. Someone built and trained a Keras model on that set and got reasonably good performance (>90% accuracy). Let's call this model A. Now we want to tackle a different task: we have images of sandals and shirts, and we want to train a binary classifier (positive = shirt, negative = sandal). Our dataset is quite small, we only have 200 labelled images. When we train a new model for this task (let's call it model B) with the same architecture as model A, it performs reasonably well (97.2 % accuracy). But since it's a much easier task (there are just two classes), we were hoping for more. While drinking our morning coffee, we realized that our task is quite similar to task A, so perhaps transfer learning can help. Let's find out."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bdafec2-fe58-4de2-be01-787d9b55dba1",
   "metadata": {},
   "source": [
    "But first of all we will train model A, so that we can reuse it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fc44297-66e8-46e0-ad31-0e1f4b7887e3",
   "metadata": {},
   "source": [
    "##### Training Model A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e35342ee-6e5c-45de-9ad7-2b5add83d40f",
   "metadata": {},
   "source": [
    "Let's split the Fashion MNIST dataset in two:\n",
    "\n",
    "- `X_train_A`: all the images of all items except for sandalas and shirts (class 5 and 6)\n",
    "- `X_train_B`: a smaller training set of just 200 images of sandalas and shirts\n",
    "\n",
    "The validation and test set are also split this way, but without restricting the number of images. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f91ea69d-ff76-4398-b584-bf7afa9d4953",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_dataset(X, y):\n",
    "    y_5_or_6 = (y == 5) | (y == 6) # sandals or shirts\n",
    "    y_A = y[~y_5_or_6]\n",
    "    y_A[y_A > 6] -= 2 # class indices 7, 8, 9 should be moved to 5, 6, 7\n",
    "    y_B = (y[y_5_or_6] == 6).astype(np.float32) #binary classification task: is it a shirt (class 6)?\n",
    "    return ((X[~y_5_or_6], y_A),\n",
    "            (X[y_5_or_6], y_B))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "59f608f1-6b01-4368-903c-284f9d235b1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()\n",
    "X_train_full = X_train_full / 255.0\n",
    "X_test = X_test / 255.0\n",
    "X_valid, X_train = X_train_full[:5000], X_train_full[5000:]\n",
    "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "598115ac-033e-4041-a143-2a6c4be3e5d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train_A, y_train_A), (X_train_B, y_train_B) = split_dataset(X_train, y_train)\n",
    "(X_valid_A, y_valid_A), (X_valid_B, y_valid_B) = split_dataset(X_valid, y_valid)\n",
    "(X_test_A, y_test_A), (X_test_B, y_test_B) = split_dataset(X_test, y_test)\n",
    "X_train_B = X_train_B[:200]\n",
    "y_train_B = y_train_B[:200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d852af4a-c8a9-4710-a1e4-2619fc367165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43986, 28, 28)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "89e2e239-1b32-4bd7-9fab-fe34d6be50dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 28, 28)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_B.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ddda0e6b-1743-40e6-a7e2-8ffae8d5fb56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 0, 5, 7, 7, 7, 4, 4, 3, 4, 0, 1, 6, 3, 4, 3, 2, 6, 5, 3, 4, 5,\n",
       "       1, 3, 4, 2, 0, 6, 7, 1], dtype=uint8)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_A[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "09b4f954-1db8-4b97-a842-9e0a6bf5d10b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 0., 0., 0., 0., 1., 1., 1., 0., 0., 1., 1., 0., 0., 0., 0.,\n",
       "       0., 0., 1., 1., 0., 0., 1., 1., 0., 1., 1., 1., 1.], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_B[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b1ba68b2-5b5d-47a5-a079-0f96a008a822",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "afee90c5-fd63-4932-a1ca-666dd7bdd1b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ea2157f-82b1-47d1-b033-26a3f519b98b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_A = keras.models.Sequential()\n",
    "model_A.add(keras.layers.Flatten(input_shape=[28, 28]))\n",
    "for n_hidden in (300, 100, 50, 50, 50):\n",
    "    model_A.add(keras.layers.Dense(n_hidden, activation=\"selu\"))\n",
    "model_A.add(keras.layers.Dense(8, activation=\"softmax\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aaaef9fd-b88d-413b-8ed7-e01a756d2424",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_A.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "                optimizer=keras.optimizers.SGD(learning_rate=1e-3),\n",
    "                metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e5ce1fa1-bbc0-4b40-8955-f453a34abbe7",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-26 21:18:06.510259: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 137940096 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1375/1375 [==============================] - 3s 2ms/step - loss: 0.5725 - accuracy: 0.8144 - val_loss: 0.3817 - val_accuracy: 0.8717\n",
      "Epoch 2/20\n",
      "1375/1375 [==============================] - 3s 2ms/step - loss: 0.3495 - accuracy: 0.8798 - val_loss: 0.3242 - val_accuracy: 0.8916\n",
      "Epoch 3/20\n",
      "1375/1375 [==============================] - 3s 2ms/step - loss: 0.3144 - accuracy: 0.8913 - val_loss: 0.2977 - val_accuracy: 0.8996\n",
      "Epoch 4/20\n",
      "1375/1375 [==============================] - 3s 2ms/step - loss: 0.2958 - accuracy: 0.8971 - val_loss: 0.2849 - val_accuracy: 0.9081\n",
      "Epoch 5/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2832 - accuracy: 0.9016 - val_loss: 0.2743 - val_accuracy: 0.9108\n",
      "Epoch 6/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2736 - accuracy: 0.9054 - val_loss: 0.2704 - val_accuracy: 0.9066\n",
      "Epoch 7/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2658 - accuracy: 0.9092 - val_loss: 0.2647 - val_accuracy: 0.9108\n",
      "Epoch 8/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2591 - accuracy: 0.9114 - val_loss: 0.2578 - val_accuracy: 0.9128\n",
      "Epoch 9/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2540 - accuracy: 0.9124 - val_loss: 0.2542 - val_accuracy: 0.9143\n",
      "Epoch 10/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2491 - accuracy: 0.9148 - val_loss: 0.2522 - val_accuracy: 0.9175\n",
      "Epoch 11/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2450 - accuracy: 0.9163 - val_loss: 0.2486 - val_accuracy: 0.9168\n",
      "Epoch 12/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2409 - accuracy: 0.9177 - val_loss: 0.2467 - val_accuracy: 0.9158\n",
      "Epoch 13/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2377 - accuracy: 0.9190 - val_loss: 0.2445 - val_accuracy: 0.9168\n",
      "Epoch 14/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2342 - accuracy: 0.9196 - val_loss: 0.2414 - val_accuracy: 0.9173\n",
      "Epoch 15/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2315 - accuracy: 0.9205 - val_loss: 0.2468 - val_accuracy: 0.9158\n",
      "Epoch 16/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2283 - accuracy: 0.9227 - val_loss: 0.2391 - val_accuracy: 0.9198\n",
      "Epoch 17/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2260 - accuracy: 0.9228 - val_loss: 0.2466 - val_accuracy: 0.9153\n",
      "Epoch 18/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2232 - accuracy: 0.9237 - val_loss: 0.2432 - val_accuracy: 0.9143\n",
      "Epoch 19/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2209 - accuracy: 0.9247 - val_loss: 0.2349 - val_accuracy: 0.9193\n",
      "Epoch 20/20\n",
      "1375/1375 [==============================] - 4s 3ms/step - loss: 0.2186 - accuracy: 0.9253 - val_loss: 0.2357 - val_accuracy: 0.9190\n"
     ]
    }
   ],
   "source": [
    "history = model_A.fit(X_train_A, y_train_A, epochs=20, validation_data=(X_valid_A, y_valid_A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6c60deec-d167-4f07-b803-13b42098de1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/anaconda3/envs/latest/lib/python3.11/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "model_A.save(\"my_model_A.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aa82c2d-c50f-4b3f-9fca-d87d09dc4099",
   "metadata": {},
   "source": [
    "Now we will train model_B (binary classifier) from and then we will train using transfer learning and then we will check whether transfer learning provides any benefits:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac3a5fbd-13ec-4407-92b4-1576e5aba81f",
   "metadata": {},
   "source": [
    "##### Training Model B from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6ebc67df-f39b-44e5-956c-d4bfcd43ca8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_B = keras.models.Sequential()\n",
    "model_B.add(keras.layers.Flatten(input_shape=[28, 28]))\n",
    "for n_hidden in (300, 100, 50, 50, 50):\n",
    "    model_B.add(keras.layers.Dense(n_hidden, activation=\"selu\"))\n",
    "model_B.add(keras.layers.Dense(1, activation=\"sigmoid\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "51232633-d39b-405e-8b8c-d3f74ea6bca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_B.compile(loss=\"binary_crossentropy\",\n",
    "               optimizer=keras.optimizers.SGD(learning_rate=1e-3),\n",
    "               metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "71cf1c3e-8093-447d-a74d-d699cd628823",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "7/7 [==============================] - 1s 59ms/step - loss: 0.7185 - accuracy: 0.6000 - val_loss: 0.5737 - val_accuracy: 0.6673\n",
      "Epoch 2/20\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.5255 - accuracy: 0.7600 - val_loss: 0.4362 - val_accuracy: 0.8347\n",
      "Epoch 3/20\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.3989 - accuracy: 0.8750 - val_loss: 0.3527 - val_accuracy: 0.8925\n",
      "Epoch 4/20\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.3214 - accuracy: 0.9050 - val_loss: 0.2962 - val_accuracy: 0.9168\n",
      "Epoch 5/20\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.2666 - accuracy: 0.9350 - val_loss: 0.2559 - val_accuracy: 0.9391\n",
      "Epoch 6/20\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.2261 - accuracy: 0.9600 - val_loss: 0.2257 - val_accuracy: 0.9493\n",
      "Epoch 7/20\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.1982 - accuracy: 0.9650 - val_loss: 0.2038 - val_accuracy: 0.9544\n",
      "Epoch 8/20\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.1756 - accuracy: 0.9700 - val_loss: 0.1857 - val_accuracy: 0.9615\n",
      "Epoch 9/20\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.1576 - accuracy: 0.9750 - val_loss: 0.1711 - val_accuracy: 0.9696\n",
      "Epoch 10/20\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.1427 - accuracy: 0.9800 - val_loss: 0.1592 - val_accuracy: 0.9706\n",
      "Epoch 11/20\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.1310 - accuracy: 0.9800 - val_loss: 0.1488 - val_accuracy: 0.9736\n",
      "Epoch 12/20\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.1209 - accuracy: 0.9850 - val_loss: 0.1401 - val_accuracy: 0.9757\n",
      "Epoch 13/20\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.1123 - accuracy: 0.9850 - val_loss: 0.1328 - val_accuracy: 0.9757\n",
      "Epoch 14/20\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.1049 - accuracy: 0.9850 - val_loss: 0.1262 - val_accuracy: 0.9767\n",
      "Epoch 15/20\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0986 - accuracy: 0.9850 - val_loss: 0.1205 - val_accuracy: 0.9797\n",
      "Epoch 16/20\n",
      "7/7 [==============================] - 0s 18ms/step - loss: 0.0930 - accuracy: 0.9850 - val_loss: 0.1155 - val_accuracy: 0.9797\n",
      "Epoch 17/20\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0878 - accuracy: 0.9850 - val_loss: 0.1110 - val_accuracy: 0.9797\n",
      "Epoch 18/20\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.0833 - accuracy: 0.9850 - val_loss: 0.1070 - val_accuracy: 0.9797\n",
      "Epoch 19/20\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0790 - accuracy: 0.9850 - val_loss: 0.1032 - val_accuracy: 0.9797\n",
      "Epoch 20/20\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0753 - accuracy: 0.9850 - val_loss: 0.0997 - val_accuracy: 0.9797\n"
     ]
    }
   ],
   "source": [
    "history = model_B.fit(X_train_B, y_train_B, epochs=20, \n",
    "                     validation_data=(X_valid_B, y_valid_B))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "01ace278-7151-469b-8327-2b3bf4ee5f4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " flatten_1 (Flatten)         (None, 784)               0         \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 300)               235500    \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 100)               30100     \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 50)                5050      \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 50)                2550      \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 50)                2550      \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 1)                 51        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 275801 (1.05 MB)\n",
      "Trainable params: 275801 (1.05 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_B.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d1a5f1-6997-4d8f-8ff8-ef3205cb2001",
   "metadata": {},
   "source": [
    "Now let's use the transfer learning. \n",
    "\n",
    "First we need to load model A and create a new model based on that model's layers. Let's reuse all the layers except for the output layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1469c501-be56-486c-9264-1531f296be52",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_A = keras.models.load_model(\"my_model_A.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9a223828-bbd3-4599-98fc-ccffd742129b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<keras.src.layers.reshaping.flatten.Flatten at 0x7f53701f3dd0>,\n",
       " <keras.src.layers.core.dense.Dense at 0x7f53706541d0>,\n",
       " <keras.src.layers.core.dense.Dense at 0x7f53d6335f50>,\n",
       " <keras.src.layers.core.dense.Dense at 0x7f53701dd610>,\n",
       " <keras.src.layers.core.dense.Dense at 0x7f5350711910>,\n",
       " <keras.src.layers.core.dense.Dense at 0x7f53701db590>,\n",
       " <keras.src.layers.core.dense.Dense at 0x7f5350708690>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_A.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e9f73a16-0d68-40f9-8226-38b4a8c38c7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_B_on_A = keras.models.Sequential(model_A.layers[:-1])\n",
    "model_B_on_A.add(keras.layers.Dense(1, activation=\"sigmoid\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e646c5d1-d34a-4169-a797-0738597f4c0e",
   "metadata": {},
   "source": [
    "Note that `model_A` and `model_B_on_A` now share some layers. When we train `model_B_on_A`, it will also affect `model_A`. If we want to avoid that, we need to clone the `model_A` before we reuse its layers. To do this, we clone model A's architecture with `clone.model()`, then copy its weights (since `clone_model()` does not clone the weights):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3010ea1d-3a56-4663-87b5-df3d5ad9d8dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_A_clone = keras.models.clone_model(model_A)\n",
    "model_A_clone.set_weights(model_A.get_weights())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2df53e90-da99-46f9-aeeb-61149acf69bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_B_on_A = keras.models.Sequential(model_A_clone.layers[:-1])\n",
    "model_B_on_A.add(keras.layers.Dense(1, activation=\"sigmoid\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcb5df3a-6a15-4eed-9065-47d6a82c95c6",
   "metadata": {},
   "source": [
    "Now we could train `model_B_on_A` for task B, but since the new output layer was initialized randomly it will make large errors (at least during the first few epochs), so there will be large error gradients that may wreck the reused weights. To avoid this, one approach is to freeze the reused layers during the first few epochs, giving the new layer some time to learn reasonable weights. \n",
    "\n",
    "To do this, set every layer's `trainable` attribute to `False` and compile the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3d573981-574a-47d1-ac58-49adf3fba1d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in model_B_on_A.layers[:-1]:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "80b2952a-b347-499e-97b8-491e9e979b2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_B_on_A.compile(loss=\"binary_crossentropy\", \n",
    "                     optimizer=keras.optimizers.SGD(learning_rate=1e-3), \n",
    "                     metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "728fea96-9966-4934-b6e9-338d6d7437bd",
   "metadata": {},
   "source": [
    "**NOTE:**\n",
    "\n",
    "We must always compile our model after we freeze or unfreeze layers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "262a2dd5-5734-4726-9506-2148ccfd58e3",
   "metadata": {},
   "source": [
    "Now we can train the model for a few epochs, then unfreeze the reused layers (which requires compiling the model again) and continue training to fine-tune the reused layers for task B. After unfreezing the reused layers, it is usually good idea to reduce the learning rate, once again to avoid damaging the reused weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a02bc058-8876-45f4-85db-3a9a4d880955",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "7/7 [==============================] - 1s 37ms/step - loss: 0.8463 - accuracy: 0.4500 - val_loss: 0.7631 - val_accuracy: 0.5101\n",
      "Epoch 2/4\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.7725 - accuracy: 0.5200 - val_loss: 0.6979 - val_accuracy: 0.5659\n",
      "Epoch 3/4\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.7047 - accuracy: 0.5800 - val_loss: 0.6425 - val_accuracy: 0.6258\n",
      "Epoch 4/4\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.6471 - accuracy: 0.6250 - val_loss: 0.5951 - val_accuracy: 0.6805\n"
     ]
    }
   ],
   "source": [
    "history = model_B_on_A.fit(X_train_B, y_train_B, epochs=4, validation_data=(X_valid_B, y_valid_B))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fdd83a19-faec-4da0-9393-d207a5f0071e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in model_B_on_A.layers[:-1]:\n",
    "    layer.trainable = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2c40c1df-741c-42d2-acbb-400944b69fcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.SGD(learning_rate = 1e-3)\n",
    "model_B_on_A.compile(loss=\"binary_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "85121235-57e7-4f10-a971-91be786b8653",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/16\n",
      "7/7 [==============================] - 1s 38ms/step - loss: 0.5373 - accuracy: 0.7250 - val_loss: 0.4243 - val_accuracy: 0.8276\n",
      "Epoch 2/16\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.3924 - accuracy: 0.8550 - val_loss: 0.3262 - val_accuracy: 0.9249\n",
      "Epoch 3/16\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.3027 - accuracy: 0.9200 - val_loss: 0.2662 - val_accuracy: 0.9554\n",
      "Epoch 4/16\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.2468 - accuracy: 0.9550 - val_loss: 0.2258 - val_accuracy: 0.9635\n",
      "Epoch 5/16\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.2085 - accuracy: 0.9750 - val_loss: 0.1953 - val_accuracy: 0.9746\n",
      "Epoch 6/16\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.1787 - accuracy: 0.9850 - val_loss: 0.1728 - val_accuracy: 0.9787\n",
      "Epoch 7/16\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.1572 - accuracy: 0.9850 - val_loss: 0.1560 - val_accuracy: 0.9817\n",
      "Epoch 8/16\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.1407 - accuracy: 0.9900 - val_loss: 0.1423 - val_accuracy: 0.9828\n",
      "Epoch 9/16\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.1270 - accuracy: 0.9900 - val_loss: 0.1305 - val_accuracy: 0.9838\n",
      "Epoch 10/16\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.1153 - accuracy: 0.9900 - val_loss: 0.1214 - val_accuracy: 0.9878\n",
      "Epoch 11/16\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.1063 - accuracy: 0.9900 - val_loss: 0.1126 - val_accuracy: 0.9878\n",
      "Epoch 12/16\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0975 - accuracy: 0.9900 - val_loss: 0.1059 - val_accuracy: 0.9878\n",
      "Epoch 13/16\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0908 - accuracy: 0.9900 - val_loss: 0.0998 - val_accuracy: 0.9888\n",
      "Epoch 14/16\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0848 - accuracy: 0.9900 - val_loss: 0.0942 - val_accuracy: 0.9878\n",
      "Epoch 15/16\n",
      "7/7 [==============================] - 0s 15ms/step - loss: 0.0791 - accuracy: 0.9900 - val_loss: 0.0897 - val_accuracy: 0.9878\n",
      "Epoch 16/16\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0745 - accuracy: 0.9900 - val_loss: 0.0859 - val_accuracy: 0.9878\n"
     ]
    }
   ],
   "source": [
    "history = model_B_on_A.fit(X_train_B, y_train_B, epochs=16, validation_data=(X_valid_B, y_valid_B))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a5d58d5-7829-4db1-9720-a071a1e791a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 2ms/step - loss: 0.0902 - accuracy: 0.9875\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0902191549539566, 0.987500011920929]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_B_on_A.evaluate(X_test_B, y_test_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fd198a06-66b6-41e3-a092-64ead87d7a5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 2ms/step - loss: 0.0965 - accuracy: 0.9815\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.09650509804487228, 0.9815000295639038]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_B.evaluate(X_test_B, y_test_B)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23a23832-6573-4fdd-a35c-6f9038d935b6",
   "metadata": {},
   "source": [
    "Well the model's test accuracy has improved a little bit from transfer learning. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f7202eb-2943-48d2-b50b-32d75a8c46c6",
   "metadata": {},
   "source": [
    "It turns out that transfer learning does not work very well with small dense networks, preasumably because small networks learn few patterns, and dense networks learn very specific patterns, which are unlikely to be useful in other tasks. Transfer learning works best with deep CNN, which tend to learn feature detectors that are much more general (especially in the lower layers)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85632ca8-9495-4364-b0ec-2c5d400d329a",
   "metadata": {},
   "source": [
    "### Unsupervised Pretraining"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77baabc1-1a7d-4e7d-8c44-1f17c749959f",
   "metadata": {},
   "source": [
    "### Pretraining on Auxiliary Task"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b21ae68d-36c3-47e8-bbe0-29827c6c4abd",
   "metadata": {},
   "source": [
    "## Faster Optimizers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba160831-b5fa-4ee7-982b-3dd8797f626e",
   "metadata": {},
   "source": [
    "### Momentum Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44b08e8b-5b7a-42af-85f3-fcb09fcf782a",
   "metadata": {},
   "source": [
    "Implementing momentum optimization in Keras is a no-brainer: just use\n",
    "the SGD optimizer and set its `momentum` hyperparameter, then lie back and\n",
    "profit!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0fb0afc8-dd43-41be-a25b-e8da55ab22d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.SGD(learning_rate=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff952a79-7ad6-4f54-87f4-1476bf62057c",
   "metadata": {},
   "source": [
    "### Nesterov Accelerated Gradient (NAG)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db892e3b-2436-4795-9283-89bd86adb640",
   "metadata": {},
   "source": [
    "NAG is generally faster than regular momentum optimization. To use it simply set `nesterov=True` when creating SGD optimizer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ab111e86-4e04-4c05-a186-9773decaffdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.SGD(learning_rate=0.001, momentum=0.9, nesterov=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96b3ba1a-ac6b-4e0b-9e81-5a2e20002e59",
   "metadata": {},
   "source": [
    "### AdaGrad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "25b5f2fb-4c63-4199-b225-c91cd992c1fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.Adagrad(learning_rate=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abba61d5-4668-46af-8c19-454ac56a6fd1",
   "metadata": {},
   "source": [
    "### RMSProp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d223f654-125c-4be4-8883-186f95ca3e13",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.RMSprop(learning_rate=0.001, rho=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "987b7772-3aa0-49df-9189-2ac86c692c9d",
   "metadata": {},
   "source": [
    "### Adam and Nadam Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01316997-e3f0-4d2a-83f9-d81c73658bca",
   "metadata": {},
   "source": [
    "The momentum decay hyperparameter $\\beta_1$ is typically initialized to 0.9,\n",
    "while the scaling decay hyperparameter $\\beta_2$ is often initialized to 0.999. As\n",
    "earlier, the smoothing term $\\epsilon$ is usually initialized to a tiny number such as\n",
    "$10^{-7}$.\n",
    "\n",
    "These are the default values for the `Adam` class (to be precise, `epsilon` defaults to `None`, which tells Keras to use `keras.backend.epsilon()`, which defaults to $10^{-7}$; we can change it\n",
    "using `keras.backend.set_epsilon())`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24de5bd5-7756-4cbb-9564-9f903da1dcde",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cae5e8a0-1579-47e2-a999-8c4c79f95e49",
   "metadata": {},
   "source": [
    "Since Adam is an adaptive learning rate algorithm (like AdaGrad and RMSProp), it requires less tuning of the learning rate hyperparameter $\\eta$.\n",
    "We can often use the default value $\\eta$ = 0.001, making Adam even easier to use than Gradient Descent."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da05961b-3ef7-4fad-ae21-a58b23aac59d",
   "metadata": {},
   "source": [
    "#### Adamax and Nadam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a92a99a5-411a-4bdc-81ea-9207ab48bd6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.Adamax(learning_rate=0.001, beta_1=0.9, beta_2=0.999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fda5c836-9358-4e10-a9c0-9193fa417f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.Nadam(learning_rate=0.001, beta_1=0.9, beta_2=0.999)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b28fcd3-1724-44dc-8e9f-25794dbe3f12",
   "metadata": {},
   "source": [
    "### Learning Rate Scheduling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d0528c-d628-4f49-bf72-09013396610f",
   "metadata": {},
   "source": [
    "#### Power Scheduling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a4d0b6c-2a55-4479-883a-38afcf39b590",
   "metadata": {},
   "source": [
    "Implementing power scheduling in Keras is the easiest option: just set the\n",
    "`decay` hyperparameter when creating an optimizer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f044b16c-9038-4f79-9500-d61e44262ce3",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.legacy.SGD(learning_rate=0.01, decay=1e-4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79ca5a72-c117-401d-b502-ea2c5aa6357f",
   "metadata": {},
   "source": [
    "The decay is the inverse of `s` (the number of steps it takes to divide the learning rate by one or more unit), and Keras assumes that `c` is equal to 1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75f97a36-e02f-4375-b879-fa7179db77b8",
   "metadata": {},
   "source": [
    "#### Exponential Scheduling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cde40346-d69f-48c4-9b2a-331f0f6f3ab5",
   "metadata": {},
   "source": [
    "Exponential scheduling and piecewise scheduling are quite simple too.\n",
    "We first need to define a function that takes the current epoch and returns\n",
    "the learning rate. For example, let’s implement exponential scheduling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5af5fce6-eae4-46da-bdbd-8db0a03f5ea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def exponential_decay_fn(epoch):\n",
    "    return 0.01*0.1**(epoch/20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c22b9c29-1de5-43dd-a6ea-41bdf9f162ec",
   "metadata": {},
   "source": [
    "If we don't want to hardcode $\\eta_0$ and s, we can create a function that returns a configured function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "693aaa64-3f66-4901-8a94-9c290036c629",
   "metadata": {},
   "outputs": [],
   "source": [
    "def exponential_decay(lr0, s):\n",
    "    def exponential_decay_fn(epoch):\n",
    "        return lr0 * 0.1 ** (epoch / s)\n",
    "    return exponential_decay_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65b5bce6-178f-44ce-8dfe-bb05985c259b",
   "metadata": {},
   "outputs": [],
   "source": [
    "exponential_decay_fn = exponential_decay(lr0=0.01, s=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9907c34-6742-4398-bc07-a0a6add4894f",
   "metadata": {},
   "source": [
    "Next, create a `LearningScheduler` callback, giving it a schedule function, and pass this call back to fit method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6bc3ecb6-3e93-4048-8826-48c2c4c78853",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_scheduler = keras.callbacks.LearningRateScheduler(exponential_decay_fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e978cff9-7893-4249-a969-2e4c5236cc21",
   "metadata": {},
   "source": [
    "The `LearningRateScheduler` will update the optimizer's `learning_rate` attribute at the beginning of each epoch. Updating the learning rate once per epoch is usually enough, but if we want to update more often, for example at every step, we can always write our own callback. Updating the learning rate at every step makes sense if there are many steps per epoch. Let's try it out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6e81b82e-912b-477d-996b-b01074ee4739",
   "metadata": {},
   "outputs": [],
   "source": [
    "K = keras.backend\n",
    "\n",
    "class ExponentialDecay(keras.callbacks.Callback):\n",
    "    def __init__(self, s=40000):\n",
    "        super().__init__()\n",
    "        self.s = s\n",
    "    \n",
    "    def on_batch_begin(self, batch, logs=None):\n",
    "        #Note: the `batch` argument resets at each epoch\n",
    "        lr = K.get_value(self.model.optimizer.learning_rate)\n",
    "        K.set_value(self.model.optimizer.learning_rate, lr*0.1**(1/(self.s)))\n",
    "        \n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        logs = logs or {}\n",
    "        logs[\"lr\"] = K.get_value(self.model.optimizer.learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0420b789-5f06-4829-80f8-20cdb110f898",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_decay = ExponentialDecay()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ca91684-429f-4c50-a34c-3e6c1ffd3ea1",
   "metadata": {},
   "source": [
    "Alternatively, we can use the `keras.optimizers.schedules` approach."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b827261f-2612-435b-9f06-bbec59939123",
   "metadata": {},
   "source": [
    "The schedule function can optionally take the current learning rate as a second argument. For example, the following schedule function multiplies the previous learning rate by $0.1^{1/20}$, which results in the same exponential decay ( except the decay now starts at the beginning of epoch 0 instead of 1):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d6fbbc02-286a-4c12-a3d0-6ff3f5374149",
   "metadata": {},
   "outputs": [],
   "source": [
    "def exponential_decay_fn(epoch, lr):\n",
    "    return lr * 0.1 ** (1 / 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58863b00-0887-41ab-a7cc-7f6675bf91d3",
   "metadata": {},
   "source": [
    "When we save the model, the optimizer and its learning rate get saved along with it. This means that with this new schedule function, we could just load a trained model and continue training where it left off, no problem. \n",
    "\n",
    "Things are not so simple if our schedule function uses the `epoch` argument, however: the epoch does not get saved, and it gets reset to 0 everytime we call the `fit()` method. If we were to continue training a model where it left off, this could be lead to a very large learning rate, which would likely damage our model's weights. One solution is to manually set the `fit()` method's argument `intial_epoch` argument so the `epoch` starts at right value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6fa5a18-705b-42ae-ad42-de2e8d9630b3",
   "metadata": {},
   "source": [
    "#### Piecewise constant Scheduling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1178004a-097e-4728-8acf-9eb14123f62f",
   "metadata": {},
   "source": [
    "For piecewise constant scheduling, we can use a schedule function like the following one, then create a `LearningRateScheduler` callback with this function and pass it to `fit()` method, just like we did for exponential scheduling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0de9cb2e-9656-403f-85db-ceaf9743d77f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def piecewise_constant_fn(epoch):\n",
    "    if epoch < 5:\n",
    "        return 0.01\n",
    "    elif epoch < 15:\n",
    "        return 0.005\n",
    "    else:\n",
    "        return 0.001"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "446c1e22-9a96-4f68-a92c-12a7f285f85d",
   "metadata": {},
   "source": [
    "More general Piecewise Constant Scheduling function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4ac8344c-1294-44cb-8402-3108962fd207",
   "metadata": {},
   "outputs": [],
   "source": [
    "def piecewise_constant(boundaries, values):\n",
    "    boundaries = np.array([0] + boundaries) # it is just adding 0 at the beginning\n",
    "    # print(boundaries)\n",
    "    values = np.array(values) # just converting the values (learning rate values) to numpy array\n",
    "    # print(values)\n",
    "    def piecewise_constant_fn(epoch):\n",
    "        return values[np.argmax(boundaries > epoch) - 1] # this will check whether boundary is greather than epoch and it find the index of that and we subtract 1 because array are 0 based and then we get the corresponding learning rate based on the index\n",
    "    return piecewise_constant_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3c80129f-c4f0-4da8-b07e-aa1447d030da",
   "metadata": {},
   "outputs": [],
   "source": [
    "piecewise_constant_fn = piecewise_constant([5,15], [0.01, 0.005, 0.001])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "994bac9a-b296-4dc2-9a1d-66e24f946cab",
   "metadata": {},
   "source": [
    "#### Performace Scheduling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32736930-2a9c-416d-ba25-bb117bbe0d01",
   "metadata": {},
   "source": [
    "For performace scheduling, use the `ReduceLROnPlateau` callback. For example, if we pass the following callback to `fit()` method, it will multiply the learning rate by 0.5 whenever the best validation loss does not improve for five consecutive epochs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3f9ff820-8f1c-426c-839a-be56d120b382",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_scheduler = keras.callbacks.ReduceLROnPlateau(factor=0.5, patience=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfb36648-fae2-4b07-b50d-d318f6afcdbe",
   "metadata": {},
   "source": [
    "Lastly, tf.keras offers an alternative way to implement learning rate scheduling: define the learning rate using one of the schedules available in `keras.optimizers.schedules`, then pass this learning rate to any optimizer. This approach updates the learning rate at each step rather than at each epoch.\n",
    "\n",
    "For example, here's how to implement the same exponential schedule as the `exponential_decay_fn()` function we defined earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b729ec13-7b81-4310-86b7-0a24392057cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 40000\n",
    "learning_rate = keras.optimizers.schedules.ExponentialDecay(0.01, s, 0.1)\n",
    "optimizer = keras.optimizers.SGD(learning_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bd4c44e-8aa0-4d33-a2ba-626c89e9b252",
   "metadata": {},
   "source": [
    "This is nice and simple, plus when we save the model, the learning rate and its schedule (including its state) get saved as well. This approach, however, is not part of the Keras API; it is specific to tf.keras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5baec78c-7172-4e57-b717-636f64d1cb23",
   "metadata": {},
   "source": [
    "#### 1Cycle Scheduling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5af623b-0e46-4105-8141-52eb18294ddb",
   "metadata": {},
   "source": [
    "As for 1cycle approach, the implementation poses no particular difficulty: just create a custom callback that modifies the learning rate at each iteration (we can update the optimizer's learning rate by changing `self.model.optimizer.lr`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9b038544-f87b-438e-8d4a-74cda5069349",
   "metadata": {},
   "outputs": [],
   "source": [
    "K = keras.backend\n",
    "\n",
    "class OneCycleScheduler(keras.callbacks.Callback):\n",
    "    def __init__(self, iterations, max_rate, start_rate=None, \n",
    "                last_iterations=None, last_rate=None):\n",
    "        self.iterations = iterations\n",
    "        self.max_rate = max_rate\n",
    "        self.start_rate = start_rate or max_rate / 10\n",
    "        self.last_iterations = last_iterations or iterations // 10 + 1\n",
    "        self.half_iteration = (iterations - self.last_iterations) // 2\n",
    "        self.last_rate = last_rate or self.start_rate / 1000\n",
    "        self.iteration = 0\n",
    "    \n",
    "    # this function starting with \"_\" means it is for internal use inside the class. It is not public method and it is not recommended to access publicly.\n",
    "    def _interpolate(self, iter1, iter2, rate1, rate2):\n",
    "        return ((rate2 -  rate1) * (self.iteration - iter1) / (iter2 - iter1) + rate1)\n",
    "    \n",
    "    def on_batch_begin(self, batch, logs):\n",
    "        if self.iteration < self.half_iteration:\n",
    "            rate = self._interpolate(0, self.half_iteration, self.start_rate, self.max_rate)\n",
    "        elif self.iteration < 2 * self.half_iteration:\n",
    "            rate = self._interpolate(self.half_iteration, 2 * self.half_iteration, self.max_rate,\n",
    "                                     self.start_rate)\n",
    "        else:\n",
    "            rate = self._interpolate(2 * self.half_iteration, self.iterations, self.start_rate,\n",
    "                                    self.last_rate)\n",
    "        self.iteration += 1\n",
    "        K.set_value(self.model.optimizer.learning_rate, rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70f009db-104c-4bd1-b3c6-24d3b9758f59",
   "metadata": {},
   "source": [
    "## Avoiding Overfitting Through Regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c48aa6e-c8a6-4ce0-ab47-9716f37bcfac",
   "metadata": {},
   "source": [
    "### $l_1$ and $l_2$ regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2596382e-0ea8-4efa-ab0c-b07ef387b435",
   "metadata": {},
   "source": [
    "Here's how to apply $l_2$ regularization to a Keras layer's connection weights, using a regularization factor of 0.01:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d43b2ba1-8bfb-48c8-a9a8-d4db5e020d7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "layer = keras.layers.Dense(100, activation=\"elu\", kernel_initializer=\"he_normal\",\n",
    "                          kernel_regularizer=keras.regularizers.l2(0.01))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f84cdecb-6764-437d-96a2-a19ba5ee40f3",
   "metadata": {},
   "source": [
    "The `l2()` function returns a regularizer that will be called at each step during training to compute the regularization loss. This is then added to final loss. \n",
    "\n",
    "Similarly, we can use `keras.regularizers.l1()` for $l_1$ regularization and `keras.regularizers.l1_l2()` if we want both $l_1$ and $l_2$ regularization, specifying factors for both."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fce329b-0820-4aab-a184-4b79b38d3fc2",
   "metadata": {},
   "source": [
    "Since we typically want to apply the same regularizer to all layers in network, as well as using same activation function and the same initialization strategy in all hidden layers, we may find ourself repeating same arguments. To avoid this, we can try refactoring our code to use loops. Another option is to use Python's `functools.partial()` function, which lets us create a thin wrapper for any callable, with some default argument values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "38e531f6-388c-4d8f-b8b5-81455e4a4f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "\n",
    "RegularizedDense = partial(keras.layers.Dense, activation=\"elu\", kernel_initializer=\"he_normal\",\n",
    "                          kernel_regularizer=keras.regularizers.l2(0.01))\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28,28]),\n",
    "    RegularizedDense(300),\n",
    "    RegularizedDense(100),\n",
    "    RegularizedDense(10, activation=\"softmax\", kernel_initializer=\"glorot_uniform\")\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39dc90b6-de67-4b1d-a3c0-255644fb9291",
   "metadata": {},
   "source": [
    "### Dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6de9d88b-7ff3-48f3-a9f0-700f6f1f75bd",
   "metadata": {},
   "source": [
    "To implement dropout using Keras, we can use the `keras.layers.Dropout` layer. During training it randomly drops some inputs (setting them to 0) and divides the remaining inputs by the keep probability. After training, it does nothing at all; it just passes the inputs to the next layer. The following code applies the dropout regularization before every `Dense` layer, using a dropout rate of 0.2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2a409af6-4e6a-4434-b86c-387cfb9a5944",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()\n",
    "X_train_full = X_train_full / 255.0\n",
    "X_test = X_test / 255.0\n",
    "X_valid, X_train = X_train_full[:5000], X_train_full[5000:]\n",
    "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]\n",
    "\n",
    "pixel_means = X_train.mean(axis=0, keepdims=True)\n",
    "pixel_stds = X_train.std(axis=0, keepdims=True)\n",
    "X_train_scaled = (X_train - pixel_means) / pixel_stds\n",
    "X_valid_scaled = (X_valid - pixel_means) / pixel_stds\n",
    "X_test_scaled = (X_test - pixel_means) / pixel_stds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "78b2833d-296e-4584-a95f-795c7b414afc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-01 14:36:45.516693: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 172480000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1719/1719 [==============================] - 8s 4ms/step - loss: 0.5790 - accuracy: 0.8026 - val_loss: 0.4100 - val_accuracy: 0.8464\n",
      "Epoch 2/2\n",
      "1719/1719 [==============================] - 8s 5ms/step - loss: 0.4254 - accuracy: 0.8454 - val_loss: 0.3493 - val_accuracy: 0.8690\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28,28]),\n",
    "    keras.layers.Dropout(rate=0.2),\n",
    "    keras.layers.Dense(300, activation=\"elu\", kernel_initializer=\"he_normal\"),\n",
    "    keras.layers.Dropout(rate=0.2),\n",
    "    keras.layers.Dense(100, activation=\"elu\", kernel_initializer=\"he_normal\"),\n",
    "    keras.layers.Dropout(rate=0.2),\n",
    "    keras.layers.Dense(10, activation=\"softmax\")\n",
    "])\n",
    "\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
    "n_epochs=2\n",
    "h = model.fit(X_train_scaled, y_train, epochs=n_epochs, \n",
    "             validation_data=(X_valid_scaled, y_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c15643-ec0f-445e-8e5b-3c70e1adca47",
   "metadata": {},
   "source": [
    "**Warning:**\n",
    "\n",
    "Since dropout is only active during training, comparing the training loss and the validation loss can be misleading. In particular, a model may be overfitting the training set and yet have similar training and validation losses. So make sure to evaluate the training loss without dropout (e.g: after training)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b761b8a6-7485-45b5-80b6-5f90bc05c600",
   "metadata": {},
   "source": [
    "If we observe that model is overfitting, we can increase the dropout rate and vice-versa. It can also help to increase the dropout rate for large layers, and reduce it for small ones.\n",
    "\n",
    "Dropout tends to significantly slow down the convergence, but it usually results in a much better model when tuned properly. So, it is generally well worth the extra effort and time. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b03ebff-4705-48b1-a245-57f24b10eb5f",
   "metadata": {},
   "source": [
    "**TIP:**\n",
    "\n",
    "If we want to regularize a self-normalizing network based on the SELU activation function, we should use *alpha dropout*: this is a variant of dropout that preserves the mean and the standard deviation of its inputs (it was introduced in the same paper as SELU, a regular dropout would break self-normalization)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb5d836-1d7b-44ce-9932-1b186ce6dfc3",
   "metadata": {},
   "source": [
    "### Monte Carlo (MC) Dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de937e0c-b29f-46fa-a888-a2b3d90d3532",
   "metadata": {},
   "source": [
    "Look at the following code. It is full implementation of `MC Dropout`, boosting the dropout model we trained earlier without retraining it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "009cb8e6-fa83-4853-9ed8-42a472b82cac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-01 14:39:13.034987: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 62720000 exceeds 10% of free system memory.\n",
      "2024-01-01 14:39:13.062752: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 31360000 exceeds 10% of free system memory.\n",
      "2024-01-01 14:39:13.073865: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 31360000 exceeds 10% of free system memory.\n",
      "2024-01-01 14:39:13.080418: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 31360000 exceeds 10% of free system memory.\n"
     ]
    }
   ],
   "source": [
    "y_probas = np.stack([model(X_test_scaled, training=True) for sample in range(100)]) # we are making predictions\n",
    "y_proba = y_probas.mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "688ec5bc-83e0-4962-a1ff-3c08e11dc6a0",
   "metadata": {},
   "source": [
    "We just make 100 predictions over the test set, setting `training=True` to ensure that the `Dropout` layer is active, and stack the predictions. Since dropout is active, all the predictions will be different. Recall that `predict()` returns a matrix with one row per instance and one column per class. Because there are 10,000 instances in the test set and 10 classes, this is a matrix of shape [10000, 10]. We stack 100 such matrices, so `y_probas` is an array of shape [100, 10000, 10]. Once we average ove the first dimension (axis = 0), we get an array of shape [10000, 10], like we would get with single prediction. That's all!\n",
    "\n",
    "Averaging over multiple predictions with dropout on gives us a Monte Carlo estimate that is generally more reliable than the result of a single prediction with dropout off."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e419a2f0-bf97-48a0-a14f-2b388597ef9c",
   "metadata": {},
   "source": [
    "For example, let's look at the model's prediction for the first instance in the test set, with dropout off:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f6d87314-8c78-4cfa-9554-3881908b5c55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 62ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.  , 0.  , 0.  , 0.  , 0.  , 0.02, 0.  , 0.09, 0.  , 0.89]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(model.predict(X_test_scaled[:1]), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860b762a-f103-43c1-b634-9ea73b7a2ef6",
   "metadata": {},
   "source": [
    "The model seems certain that this image belongs to class 9 (ankle boot). Should we trust it? Is there really a little room for doubt? Let's compare it with the predictions made when dropout is activated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "79cf30d1-e537-4f1e-b0aa-c6d8e4f638b1",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.1 , 0.  , 0.9 ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.08, 0.  , 0.92]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.02, 0.  , 0.28, 0.  , 0.7 ]],\n",
       "\n",
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       "\n",
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       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.04, 0.  , 0.24, 0.  , 0.72]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.02, 0.  , 0.6 , 0.  , 0.37]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.17, 0.  , 0.1 , 0.  , 0.73]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.1 , 0.  , 0.89]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.07, 0.  , 0.16, 0.  , 0.77]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.07, 0.  , 0.14, 0.  , 0.8 ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.13, 0.  , 0.25, 0.  , 0.61]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.06, 0.  , 0.93]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.2 , 0.  , 0.79]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.04, 0.  , 0.08, 0.  , 0.88]],\n",
       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.14, 0.  , 0.13, 0.  , 0.73]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.02, 0.  , 0.01, 0.  , 0.97]],\n",
       "\n",
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       "\n",
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       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.09, 0.  , 0.01, 0.  , 0.9 ]],\n",
       "\n",
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       "\n",
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       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.04, 0.  , 0.13, 0.  , 0.83]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.09, 0.  , 0.9 ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.04, 0.  , 0.2 , 0.  , 0.76]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.04, 0.  , 0.37, 0.  , 0.59]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.02, 0.  , 0.03, 0.  , 0.95]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.02, 0.  , 0.05, 0.  , 0.93]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.21, 0.  , 0.22, 0.  , 0.57]],\n",
       "\n",
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       "\n",
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       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.15, 0.  , 0.84]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.06, 0.  , 0.94]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.  , 0.  , 0.98]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.04, 0.  , 0.35, 0.  , 0.62]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.02, 0.  , 0.42, 0.  , 0.56]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.04, 0.  , 0.06, 0.  , 0.9 ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.99]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.03, 0.  , 0.46, 0.  , 0.51]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.18, 0.  , 0.81]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.17, 0.  , 0.82]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.02, 0.  , 0.18, 0.  , 0.8 ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.04, 0.  , 0.13, 0.  , 0.83]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.14, 0.  , 0.14, 0.  , 0.72]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.2 , 0.  , 0.79]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.02, 0.  , 0.08, 0.  , 0.91]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.19, 0.  , 0.8 ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.02, 0.  , 0.25, 0.  , 0.73]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.13, 0.  , 0.86]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.09, 0.  , 0.91]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.03, 0.  , 0.01, 0.  , 0.96]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.04, 0.  , 0.16, 0.  , 0.8 ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.11, 0.  , 0.88]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.16, 0.  , 0.12, 0.  , 0.73]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.02, 0.  , 0.21, 0.  , 0.76]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.02, 0.  , 0.04, 0.  , 0.94]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.03, 0.  , 0.16, 0.  , 0.81]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.6 , 0.  , 0.39]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.03, 0.  , 0.28, 0.  , 0.7 ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.15, 0.  , 0.01, 0.  , 0.84]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.28, 0.  , 0.18, 0.  , 0.54]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.05, 0.  , 0.94]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.09, 0.  , 0.91]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.05, 0.  , 0.28, 0.  , 0.67]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.04, 0.  , 0.26, 0.  , 0.7 ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , 0.  , 0.  , 0.01, 0.  , 0.14, 0.  , 0.85]]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(y_probas[:, :1], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "587c7c71-1fd6-4fae-9617-3bda47500eb8",
   "metadata": {},
   "source": [
    "This tells a different story: apprantly when we activate dropout, the model is not sure anymore. It still seems to prefer class 9, but sometimes it hesitates with class 7 (sneaker), which makes sense given they are all footwear. \n",
    "\n",
    "Once we average over the first dimension, we get the following MC Dropout predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6547a4ad-42bc-4403-a45c-5e01cadad080",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.  , 0.  , 0.  , 0.  , 0.  , 0.06, 0.  , 0.17, 0.  , 0.77]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(y_proba[:1], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5410ea6-72e2-4255-9d33-8eabb74c40cb",
   "metadata": {},
   "source": [
    "The model still thinks this image belongs to class 9, but only with a 77% confidence, which seems much more reasonable than 89%. Plus it's useful to know exactly which other classes it thinks are likely. And we can also look at the standard deviation of the probability estimates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "87d6d0c5-363e-49dd-a0b5-6226f79b00a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.  , 0.  , 0.  , 0.  , 0.  , 0.09, 0.  , 0.14, 0.  , 0.17]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_std = y_probas.std(axis=0)\n",
    "np.round(y_std[:1],2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7efd17-4b14-4659-a92d-fa5f568e8048",
   "metadata": {},
   "source": [
    "Apparantly, there's a quite a lot of variance in the probability estimates: if we were builiding a risk-sensitive system (e.g: a medical or financial system), we should probably treat such an uncertain prediction with extreme caution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b5ebc09a-e902-41ed-9972-92d0f2ba0909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8589"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = np.argmax(y_proba, axis=1)\n",
    "accuracy = np.sum(y_pred == y_test) / len(y_test)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57c2705c-bc06-4a94-93d5-d3342e40b00f",
   "metadata": {},
   "source": [
    "**NOTE:**\n",
    "\n",
    "The number of Monte Carlo samples we use (100 in above code) is a hyperparameter we can tweak. The higher it is, the more accurate the predictions and their uncertainity estimates will be. However, if we double it, inference time will be also doubled. Moreover, above certain number of samples, we will notice little improvement. So our job is to find the right trade-off between latency and accuracy, depending on our application. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00f75746-8780-45b5-a87f-2673e7282e40",
   "metadata": {},
   "source": [
    "If our model contains other layers that behave in a special way during training (such as `BatchNormalization` layers), then we should not force training model like we just did. Instead, we should replace the `Dropout` layers with the following `MCDropout` class (This `MCDropout` class works well with all Keras API, including the Sequential API). If we only care about the Functional API or subclass API, we do not have to create an `MCDropout` class; we can create a regular Dropout layer and call it with `training = False`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "63ce891f-03ec-4f0e-a91a-c876c13e6526",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MCDropout(keras.layers.Dropout):\n",
    "    def call(self, inputs):\n",
    "        return super().call(inputs, training=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "180db902-67ac-4a5a-a5be-175cdfed8284",
   "metadata": {},
   "source": [
    "Here, we just subclass the `Dropout` layer and override the `call()` method to force its `training` argument to `True`. \n",
    "\n",
    "Since MC Dropout is just regular dropout during training, it also acts like regularizer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ed5f3b-7be2-49bb-afc3-fa7463efa31b",
   "metadata": {},
   "source": [
    "### Max-Norm regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d5e8ac1-b45b-47ee-81ed-4e61afded10b",
   "metadata": {},
   "source": [
    "To implement it in Keras, set the `kernel_constraint` argument of each hidden layer to a `max_norm()` constraint with the appropriate max value, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d730170d-feb0-405d-bc02-2e2f4c6736a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.src.layers.core.dense.Dense at 0x7f611c0ca710>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.layers.Dense(100, activation=\"elu\", kernel_initializer=\"he_normal\", \n",
    "                  kernel_constraint=keras.constraints.max_norm(1.))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "055f8344-1204-4f58-a6a9-7733c2a9d37a",
   "metadata": {},
   "source": [
    "After each training iteration, the model's `fit()` method will call the object returned by `max_norm()`, passing it layer's weights and getting rescaled weights in return, which then replace the layer's weights. We can also contrain the bias terms by setting the `bias_constraint` argument. \n",
    "\n",
    "The `max_norm()` function has an `axis` argument that defaults to 0. A `Dense` layer usually has weights of shape [*number of inputs*, *number of neurons*], so using `axis=0` means that the max-norm constraint will apply independently to each neuron's weight vector. "
   ]
  },
  {
   "attachments": {
    "841ceb29-5232-497a-901f-bd9a9b0fd7a2.png": {
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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "059b26ac-0285-4563-baf4-5d0cb510a8ab",
   "metadata": {},
   "source": [
    "Below are few configurations that will work generally most of the time.\n",
    "\n",
    "![image.png](attachment:a3114163-3f4c-413d-81b0-010c1d399eca.png)\n",
    "\n",
    "![image.png](attachment:841ceb29-5232-497a-901f-bd9a9b0fd7a2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79438bb3",
   "metadata": {},
   "source": [
    "## Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91957d45",
   "metadata": {},
   "source": [
    "### 8.\n",
    "\n",
    "Practice training a deep neural network on the CIFAR10 image dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2da9171e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-02 15:16:23.862449: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-01-02 15:16:23.982358: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-01-02 15:16:23.983360: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-01-02 15:16:24.885233: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import os\n",
    "import math\n",
    "\n",
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2f610c2",
   "metadata": {},
   "source": [
    "a. Build a DNN with 20 hidden layers of 100 neurons each (that’s too many, but it’s the point of this exercise). Use\n",
    "He initialization and the ELU activation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "84167558",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential()\n",
    "model.add(keras.layers.Flatten(input_shape=[32,32,3]))\n",
    "\n",
    "for _ in range(20):\n",
    "    model.add(keras.layers.Dense(100, activation=\"elu\", kernel_initializer=\"he_normal\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03267079",
   "metadata": {},
   "source": [
    "b. Using Nadam optimization and early stopping, train the network on the CIFAR10 dataset. You can load it with\n",
    "`keras.datasets.cifar10.load_data()`. The dataset is composed of 60,000 32 × 32–pixel color images \n",
    "(50,000 for training, 10,000 for testing) with 10 classes, so you’ll need a softmax output layer with 10 neurons. Remember to search for the right learning rate each time you change the model’s architecture or hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "646a5e27",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(keras.layers.Dense(10, activation=\"softmax\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "381bc517",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.Nadam(learning_rate=5e-5)\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer,\n",
    "            metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1312a050",
   "metadata": {},
   "source": [
    "We also want to use early stopping, so we need a validation set. Let's use the first 5000 images of the original training set as the validation set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7437b573",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "156e5ae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50000, 32, 32, 3), (10000, 32, 32, 3))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_full.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4d55696d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50000, 1), (10000, 1))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_full.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d6964adf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6],\n",
       "       [9],\n",
       "       [9],\n",
       "       ...,\n",
       "       [9],\n",
       "       [1],\n",
       "       [1]], dtype=uint8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "22729537",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train_full[5000:]\n",
    "y_train = y_train_full[5000:]\n",
    "X_valid = X_train_full[:5000]\n",
    "y_valid = y_train_full[:5000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0c0815d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_labels = [\"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\", \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b6a7450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x480 with 40 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_cols = 10\n",
    "n_rows = 4\n",
    "\n",
    "plt.figure(figsize=(n_cols*1.2, n_rows*1.2))\n",
    "\n",
    "for row in range(n_rows):\n",
    "    for col in range(n_cols):\n",
    "        index = n_cols * row + col\n",
    "        plt.subplot(n_rows, n_cols, index+1)\n",
    "        plt.imshow(X_train[index])\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(class_labels[y_train[index][0]], fontsize=12)\n",
    "        \n",
    "plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a721401c",
   "metadata": {},
   "source": [
    "Now we can create the callbacks and train the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3f03b4fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stopping_cb = keras.callbacks.EarlyStopping(patience=20)\n",
    "model_checkpoint_cb = keras.callbacks.ModelCheckpoint(\"my_cifar_model.h5\", save_best_only=True)\n",
    "run_index = 1 # increment everytime we train the model\n",
    "run_logdir = os.path.join(os.curdir, \"my_cifar10_logs\", \"run_{:03d}\".format(run_index))\n",
    "tensorboard_cb = keras.callbacks.TensorBoard(run_logdir)\n",
    "callbacks = [early_stopping_cb, model_checkpoint_cb, tensorboard_cb]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7c0118f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-02 12:11:08.313541: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 138240000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1407/1407 [==============================] - 31s 18ms/step - loss: 4.6947 - accuracy: 0.1684 - val_loss: 2.1202 - val_accuracy: 0.2310\n",
      "Epoch 2/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/anaconda3/envs/latest/lib/python3.11/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1407/1407 [==============================] - 29s 21ms/step - loss: 2.0386 - accuracy: 0.2499 - val_loss: 1.9992 - val_accuracy: 0.2442\n",
      "Epoch 3/100\n",
      "1407/1407 [==============================] - 27s 19ms/step - loss: 1.9345 - accuracy: 0.2860 - val_loss: 1.8804 - val_accuracy: 0.3074\n",
      "Epoch 4/100\n",
      "1407/1407 [==============================] - 25s 18ms/step - loss: 1.8596 - accuracy: 0.3188 - val_loss: 1.8622 - val_accuracy: 0.3274\n",
      "Epoch 5/100\n",
      "1407/1407 [==============================] - 25s 18ms/step - loss: 1.7986 - accuracy: 0.3434 - val_loss: 1.7424 - val_accuracy: 0.3612\n",
      "Epoch 6/100\n",
      "1407/1407 [==============================] - 25s 18ms/step - loss: 1.7531 - accuracy: 0.3623 - val_loss: 1.7626 - val_accuracy: 0.3592\n",
      "Epoch 7/100\n",
      "1407/1407 [==============================] - 30s 21ms/step - loss: 1.7127 - accuracy: 0.3776 - val_loss: 1.7113 - val_accuracy: 0.3826\n",
      "Epoch 8/100\n",
      "1407/1407 [==============================] - 26s 18ms/step - loss: 1.6784 - accuracy: 0.3891 - val_loss: 1.6627 - val_accuracy: 0.3994\n",
      "Epoch 9/100\n",
      "1407/1407 [==============================] - 23s 16ms/step - loss: 1.6483 - accuracy: 0.4042 - val_loss: 1.6441 - val_accuracy: 0.4038\n",
      "Epoch 10/100\n",
      "1407/1407 [==============================] - 23s 16ms/step - loss: 1.6204 - accuracy: 0.4167 - val_loss: 1.6624 - val_accuracy: 0.3938\n",
      "Epoch 11/100\n",
      "1407/1407 [==============================] - 21s 15ms/step - loss: 1.5986 - accuracy: 0.4243 - val_loss: 1.6502 - val_accuracy: 0.3964\n",
      "Epoch 12/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.5749 - accuracy: 0.4341 - val_loss: 1.6075 - val_accuracy: 0.4172\n",
      "Epoch 13/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.5538 - accuracy: 0.4401 - val_loss: 1.6395 - val_accuracy: 0.4102\n",
      "Epoch 14/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.5370 - accuracy: 0.4468 - val_loss: 1.5779 - val_accuracy: 0.4290\n",
      "Epoch 15/100\n",
      "1407/1407 [==============================] - 19s 14ms/step - loss: 1.5208 - accuracy: 0.4542 - val_loss: 1.5729 - val_accuracy: 0.4400\n",
      "Epoch 16/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.5013 - accuracy: 0.4597 - val_loss: 1.5547 - val_accuracy: 0.4380\n",
      "Epoch 17/100\n",
      "1407/1407 [==============================] - 19s 13ms/step - loss: 1.4864 - accuracy: 0.4659 - val_loss: 1.5370 - val_accuracy: 0.4470\n",
      "Epoch 18/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.4726 - accuracy: 0.4707 - val_loss: 1.5692 - val_accuracy: 0.4396\n",
      "Epoch 19/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.4605 - accuracy: 0.4741 - val_loss: 1.5616 - val_accuracy: 0.4376\n",
      "Epoch 20/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.4485 - accuracy: 0.4762 - val_loss: 1.5738 - val_accuracy: 0.4364\n",
      "Epoch 21/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.4376 - accuracy: 0.4832 - val_loss: 1.5879 - val_accuracy: 0.4386\n",
      "Epoch 22/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.4255 - accuracy: 0.4877 - val_loss: 1.5415 - val_accuracy: 0.4538\n",
      "Epoch 23/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.4120 - accuracy: 0.4947 - val_loss: 1.5474 - val_accuracy: 0.4422\n",
      "Epoch 24/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.4036 - accuracy: 0.4952 - val_loss: 1.5354 - val_accuracy: 0.4468\n",
      "Epoch 25/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.3909 - accuracy: 0.4996 - val_loss: 1.5107 - val_accuracy: 0.4584\n",
      "Epoch 26/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.3828 - accuracy: 0.5066 - val_loss: 1.5485 - val_accuracy: 0.4444\n",
      "Epoch 27/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.3709 - accuracy: 0.5102 - val_loss: 1.5228 - val_accuracy: 0.4496\n",
      "Epoch 28/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.3648 - accuracy: 0.5105 - val_loss: 1.5208 - val_accuracy: 0.4538\n",
      "Epoch 29/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.3550 - accuracy: 0.5135 - val_loss: 1.5242 - val_accuracy: 0.4620\n",
      "Epoch 30/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.3407 - accuracy: 0.5187 - val_loss: 1.5179 - val_accuracy: 0.4674\n",
      "Epoch 31/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.3343 - accuracy: 0.5212 - val_loss: 1.5470 - val_accuracy: 0.4558\n",
      "Epoch 32/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.3255 - accuracy: 0.5248 - val_loss: 1.4939 - val_accuracy: 0.4686\n",
      "Epoch 33/100\n",
      "1407/1407 [==============================] - 19s 13ms/step - loss: 1.3161 - accuracy: 0.5262 - val_loss: 1.5536 - val_accuracy: 0.4552\n",
      "Epoch 34/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.3094 - accuracy: 0.5315 - val_loss: 1.5096 - val_accuracy: 0.4654\n",
      "Epoch 35/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2986 - accuracy: 0.5338 - val_loss: 1.5089 - val_accuracy: 0.4716\n",
      "Epoch 36/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2924 - accuracy: 0.5357 - val_loss: 1.5010 - val_accuracy: 0.4702\n",
      "Epoch 37/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2816 - accuracy: 0.5399 - val_loss: 1.5104 - val_accuracy: 0.4594\n",
      "Epoch 38/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2737 - accuracy: 0.5436 - val_loss: 1.5087 - val_accuracy: 0.4736\n",
      "Epoch 39/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2676 - accuracy: 0.5451 - val_loss: 1.5384 - val_accuracy: 0.4578\n",
      "Epoch 40/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2580 - accuracy: 0.5497 - val_loss: 1.5333 - val_accuracy: 0.4650\n",
      "Epoch 41/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2519 - accuracy: 0.5528 - val_loss: 1.4912 - val_accuracy: 0.4768\n",
      "Epoch 42/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2413 - accuracy: 0.5560 - val_loss: 1.5087 - val_accuracy: 0.4728\n",
      "Epoch 43/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2371 - accuracy: 0.5580 - val_loss: 1.5077 - val_accuracy: 0.4702\n",
      "Epoch 44/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2258 - accuracy: 0.5622 - val_loss: 1.5538 - val_accuracy: 0.4602\n",
      "Epoch 45/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2228 - accuracy: 0.5628 - val_loss: 1.5088 - val_accuracy: 0.4716\n",
      "Epoch 46/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2142 - accuracy: 0.5658 - val_loss: 1.5111 - val_accuracy: 0.4712\n",
      "Epoch 47/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2062 - accuracy: 0.5677 - val_loss: 1.5441 - val_accuracy: 0.4698\n",
      "Epoch 48/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2004 - accuracy: 0.5697 - val_loss: 1.5739 - val_accuracy: 0.4620\n",
      "Epoch 49/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1905 - accuracy: 0.5749 - val_loss: 1.5187 - val_accuracy: 0.4756\n",
      "Epoch 50/100\n",
      "1407/1407 [==============================] - 19s 13ms/step - loss: 1.1840 - accuracy: 0.5789 - val_loss: 1.5204 - val_accuracy: 0.4730\n",
      "Epoch 51/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1762 - accuracy: 0.5813 - val_loss: 1.6199 - val_accuracy: 0.4610\n",
      "Epoch 52/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1685 - accuracy: 0.5823 - val_loss: 1.5431 - val_accuracy: 0.4730\n",
      "Epoch 53/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1607 - accuracy: 0.5835 - val_loss: 1.5539 - val_accuracy: 0.4666\n",
      "Epoch 54/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1583 - accuracy: 0.5866 - val_loss: 1.5660 - val_accuracy: 0.4652\n",
      "Epoch 55/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1505 - accuracy: 0.5872 - val_loss: 1.5552 - val_accuracy: 0.4740\n",
      "Epoch 56/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1451 - accuracy: 0.5890 - val_loss: 1.5452 - val_accuracy: 0.4700\n",
      "Epoch 57/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1381 - accuracy: 0.5927 - val_loss: 1.5391 - val_accuracy: 0.4764\n",
      "Epoch 58/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1309 - accuracy: 0.5956 - val_loss: 1.5604 - val_accuracy: 0.4770\n",
      "Epoch 59/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1267 - accuracy: 0.5977 - val_loss: 1.5561 - val_accuracy: 0.4780\n",
      "Epoch 60/100\n",
      "1407/1407 [==============================] - 19s 13ms/step - loss: 1.1164 - accuracy: 0.5997 - val_loss: 1.5511 - val_accuracy: 0.4824\n",
      "Epoch 61/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1120 - accuracy: 0.6024 - val_loss: 1.6120 - val_accuracy: 0.4650\n"
     ]
    }
   ],
   "source": [
    "h = model.fit(X_train, y_train, epochs=100, validation_data=(X_valid, y_valid), callbacks=callbacks, batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8986d3bf",
   "metadata": {},
   "source": [
    "Now let's visualize the graphs in tensorboard. We can use the following command to launch tensorboard:\n",
    "```\n",
    "tensorboard --logdir=./my_cifar10_logs --port=6006\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "55678447",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-02 12:36:36.566284: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 15360000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 1s 3ms/step - loss: 1.4912 - accuracy: 0.4768\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.4911773204803467, 0.47679999470710754]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.models.load_model(\"my_cifar_model.h5\")\n",
    "model.evaluate(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "aef7733d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#type: ignore\n",
    "epoch_of_min_valloss = np.argmin(h.history[\"val_loss\"]) + 1 # because index starts from 0 \n",
    "epoch_of_min_valloss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "51db0ac7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5087246894836426"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h.history[\"val_loss\"][epoch_of_min_valloss]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9b0689a",
   "metadata": {},
   "source": [
    "The model with the lowest validation loss gets about 47.67% accuracy on the validation set. It took 41 epoch to reach the lowest validation loss, with  <30 seconds per epoch on CPU (without GPU). Let's see if we can improve performance using Batch Normalization."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "731f6558",
   "metadata": {},
   "source": [
    "c. Now try adding Batch Normalization and compare the learning curves: Is it converging faster than before? Does it produce a better model? How does it affect training speed?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43f0080a",
   "metadata": {},
   "source": [
    "The code below is very similar to the code above, with few changes:\n",
    "\n",
    "- I added a BN Layer after every Dense Layer (before activation function), except for the output layer. I also added a BN layer before the first hidden layer. \n",
    "- Changed the learning rate to 5e-4\n",
    "- Renamed the run directory to run_bn_* and the model fine name to my_cifar10_bn_model.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "02119441",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-02 12:50:57.681756: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 138240000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1406/1407 [============================>.] - ETA: 0s - loss: 1.8494 - accuracy: 0.3349"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-02 12:51:47.286805: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 15360000 exceeds 10% of free system memory.\n",
      "/home/dell/anaconda3/envs/latest/lib/python3.11/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1407/1407 [==============================] - 51s 23ms/step - loss: 1.8494 - accuracy: 0.3349 - val_loss: 1.6614 - val_accuracy: 0.4070\n",
      "Epoch 2/100\n",
      "1407/1407 [==============================] - 28s 20ms/step - loss: 1.6775 - accuracy: 0.4031 - val_loss: 1.5762 - val_accuracy: 0.4418\n",
      "Epoch 3/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.6029 - accuracy: 0.4300 - val_loss: 1.5248 - val_accuracy: 0.4586\n",
      "Epoch 4/100\n",
      "1407/1407 [==============================] - 29s 20ms/step - loss: 1.5501 - accuracy: 0.4494 - val_loss: 1.4876 - val_accuracy: 0.4636\n",
      "Epoch 5/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.5053 - accuracy: 0.4644 - val_loss: 1.4447 - val_accuracy: 0.4888\n",
      "Epoch 6/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.4660 - accuracy: 0.4797 - val_loss: 1.4361 - val_accuracy: 0.4916\n",
      "Epoch 7/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.4353 - accuracy: 0.4893 - val_loss: 1.4141 - val_accuracy: 0.4974\n",
      "Epoch 8/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.4071 - accuracy: 0.4990 - val_loss: 1.3807 - val_accuracy: 0.5082\n",
      "Epoch 9/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.3818 - accuracy: 0.5128 - val_loss: 1.3985 - val_accuracy: 0.5024\n",
      "Epoch 10/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.3611 - accuracy: 0.5166 - val_loss: 1.3651 - val_accuracy: 0.5170\n",
      "Epoch 11/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.3414 - accuracy: 0.5222 - val_loss: 1.3457 - val_accuracy: 0.5278\n",
      "Epoch 12/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.3180 - accuracy: 0.5332 - val_loss: 1.3968 - val_accuracy: 0.5008\n",
      "Epoch 13/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.2980 - accuracy: 0.5391 - val_loss: 1.3615 - val_accuracy: 0.5122\n",
      "Epoch 14/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.2780 - accuracy: 0.5465 - val_loss: 1.3602 - val_accuracy: 0.5232\n",
      "Epoch 15/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.2632 - accuracy: 0.5525 - val_loss: 1.3831 - val_accuracy: 0.5198\n",
      "Epoch 16/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.2515 - accuracy: 0.5555 - val_loss: 1.3798 - val_accuracy: 0.5176\n",
      "Epoch 17/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.2334 - accuracy: 0.5631 - val_loss: 1.3163 - val_accuracy: 0.5388\n",
      "Epoch 18/100\n",
      "1407/1407 [==============================] - 27s 19ms/step - loss: 1.2156 - accuracy: 0.5719 - val_loss: 1.3631 - val_accuracy: 0.5174\n",
      "Epoch 19/100\n",
      "1407/1407 [==============================] - 24s 17ms/step - loss: 1.2024 - accuracy: 0.5733 - val_loss: 1.3445 - val_accuracy: 0.5352\n",
      "Epoch 20/100\n",
      "1407/1407 [==============================] - 24s 17ms/step - loss: 1.1926 - accuracy: 0.5792 - val_loss: 1.3971 - val_accuracy: 0.5202\n",
      "Epoch 21/100\n",
      "1407/1407 [==============================] - 24s 17ms/step - loss: 1.1794 - accuracy: 0.5830 - val_loss: 1.3493 - val_accuracy: 0.5280\n",
      "Epoch 22/100\n",
      "1407/1407 [==============================] - 31s 22ms/step - loss: 1.1616 - accuracy: 0.5899 - val_loss: 1.3378 - val_accuracy: 0.5278\n",
      "Epoch 23/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.1510 - accuracy: 0.5927 - val_loss: 1.3178 - val_accuracy: 0.5456\n",
      "Epoch 24/100\n",
      "1407/1407 [==============================] - 29s 20ms/step - loss: 1.1361 - accuracy: 0.5997 - val_loss: 1.3243 - val_accuracy: 0.5458\n",
      "Epoch 25/100\n",
      "1407/1407 [==============================] - 29s 20ms/step - loss: 1.1249 - accuracy: 0.6034 - val_loss: 1.3439 - val_accuracy: 0.5402\n",
      "Epoch 26/100\n",
      "1407/1407 [==============================] - 29s 20ms/step - loss: 1.1109 - accuracy: 0.6064 - val_loss: 1.3619 - val_accuracy: 0.5294\n",
      "Epoch 27/100\n",
      "1407/1407 [==============================] - 29s 20ms/step - loss: 1.1025 - accuracy: 0.6094 - val_loss: 1.3297 - val_accuracy: 0.5410\n",
      "Epoch 28/100\n",
      "1407/1407 [==============================] - 29s 20ms/step - loss: 1.0927 - accuracy: 0.6141 - val_loss: 1.3404 - val_accuracy: 0.5450\n",
      "Epoch 29/100\n",
      "1407/1407 [==============================] - 29s 20ms/step - loss: 1.0827 - accuracy: 0.6183 - val_loss: 1.3330 - val_accuracy: 0.5516\n",
      "Epoch 30/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.0727 - accuracy: 0.6201 - val_loss: 1.3521 - val_accuracy: 0.5406\n",
      "Epoch 31/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.0624 - accuracy: 0.6263 - val_loss: 1.3450 - val_accuracy: 0.5438\n",
      "Epoch 32/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.0467 - accuracy: 0.6293 - val_loss: 1.3648 - val_accuracy: 0.5428\n",
      "Epoch 33/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.0396 - accuracy: 0.6322 - val_loss: 1.3695 - val_accuracy: 0.5456\n",
      "Epoch 34/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.0314 - accuracy: 0.6362 - val_loss: 1.3396 - val_accuracy: 0.5494\n",
      "Epoch 35/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.0205 - accuracy: 0.6402 - val_loss: 1.3416 - val_accuracy: 0.5484\n",
      "Epoch 36/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 1.0177 - accuracy: 0.6406 - val_loss: 1.3490 - val_accuracy: 0.5420\n",
      "Epoch 37/100\n",
      "1407/1407 [==============================] - 29s 21ms/step - loss: 0.9992 - accuracy: 0.6479 - val_loss: 1.4008 - val_accuracy: 0.5290\n"
     ]
    }
   ],
   "source": [
    "keras.backend.clear_session()\n",
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "model = keras.models.Sequential()\n",
    "model.add(keras.layers.Flatten(input_shape=[32,32,3]))\n",
    "model.add(keras.layers.BatchNormalization())\n",
    "\n",
    "for _ in range(20):\n",
    "    model.add(keras.layers.Dense(100, kernel_initializer=\"he_normal\"))\n",
    "    model.add(keras.layers.BatchNormalization())\n",
    "    model.add(keras.layers.Activation(\"elu\"))\n",
    "model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
    "\n",
    "optimizer = keras.optimizers.Nadam(learning_rate=5e-4)\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"])\n",
    "\n",
    "early_stopping_cb = keras.callbacks.EarlyStopping(patience=20)\n",
    "model_checkpoint_cb = keras.callbacks.ModelCheckpoint(\"my_cifar10_bn_model.h5\", save_best_only=True)\n",
    "run_index = 1 # increment everytime you train the model\n",
    "run_logdir = os.path.join(os.curdir, \"my_cifar10_logs\", \"run_bn_{:03d}\".format(run_index))\n",
    "tensorboard_cb = keras.callbacks.TensorBoard(run_logdir)\n",
    "\n",
    "callbacks = [early_stopping_cb, model_checkpoint_cb, tensorboard_cb]\n",
    "\n",
    "h_bn = model.fit(X_train, y_train, epochs=100, validation_data=(X_valid, y_valid), callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c8ba7dd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 1s 4ms/step - loss: 1.3163 - accuracy: 0.5388\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.3163434267044067, 0.5388000011444092]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.models.load_model(\"my_cifar10_bn_model.h5\")\n",
    "model.evaluate(X_valid, y_valid) #type: ignore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "24f1da51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#type: ignore\n",
    "epoch_of_bn_min_valloss = np.argmin(h_bn.history[\"val_loss\"]) + 1 # because index starts from 0 \n",
    "epoch_of_bn_min_valloss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ad8bd6a",
   "metadata": {},
   "source": [
    "- Is the model converging faster than before? Much faster! The previous model took 41 epochs to reach the lowest validation loss, while this model achieved the same loss in just 5 epochs and continued to improve until the 17th epoch. The BN layers stabilized training and allowed us to use a much larger learning rate, so convergence was much faster.\n",
    "\n",
    "- Does BN produce a better model? Yes! The final model is much better with 53.88% accuracy instead of 47.67%. It's still not good model, but at least it's much better than before.\n",
    "\n",
    "- How does BN affect the training speed? Although the model converged much faster, each epoch took approx 30s instead of 20s, because of the extra computations required by the BN layers. But overall, training time was shortened significantly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b6dd123",
   "metadata": {},
   "source": [
    "d. Try replacing Batch Normalization with SELU, and make the necessary adjustements to ensure the network self-normalizes (i.e., standardize the input features, use LeCun normal initialization, make sure the DNN contains only a sequence of dense layers, etc.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "93724cb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-02 13:53:58.402951: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 552960000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1407/1407 [==============================] - 27s 13ms/step - loss: 1.9157 - accuracy: 0.3142 - val_loss: 1.7980 - val_accuracy: 0.3538\n",
      "Epoch 2/100\n",
      "   1/1407 [..............................] - ETA: 14s - loss: 1.8739 - accuracy: 0.4062"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/anaconda3/envs/latest/lib/python3.11/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.7082 - accuracy: 0.3945 - val_loss: 1.6735 - val_accuracy: 0.3944\n",
      "Epoch 3/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.6188 - accuracy: 0.4268 - val_loss: 1.6489 - val_accuracy: 0.4148\n",
      "Epoch 4/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.5570 - accuracy: 0.4524 - val_loss: 1.6148 - val_accuracy: 0.4416\n",
      "Epoch 5/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.5026 - accuracy: 0.4731 - val_loss: 1.5404 - val_accuracy: 0.4576\n",
      "Epoch 6/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.4557 - accuracy: 0.4938 - val_loss: 1.5408 - val_accuracy: 0.4548\n",
      "Epoch 7/100\n",
      "1407/1407 [==============================] - 17s 12ms/step - loss: 1.4159 - accuracy: 0.5078 - val_loss: 1.5587 - val_accuracy: 0.4572\n",
      "Epoch 8/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.3730 - accuracy: 0.5194 - val_loss: 1.4883 - val_accuracy: 0.4814\n",
      "Epoch 9/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.3433 - accuracy: 0.5381 - val_loss: 1.5201 - val_accuracy: 0.4720\n",
      "Epoch 10/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.3060 - accuracy: 0.5476 - val_loss: 1.4859 - val_accuracy: 0.4912\n",
      "Epoch 11/100\n",
      "1407/1407 [==============================] - 17s 12ms/step - loss: 1.2806 - accuracy: 0.5561 - val_loss: 1.4982 - val_accuracy: 0.4936\n",
      "Epoch 12/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2601 - accuracy: 0.5669 - val_loss: 1.4859 - val_accuracy: 0.4870\n",
      "Epoch 13/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2237 - accuracy: 0.5760 - val_loss: 1.4941 - val_accuracy: 0.4918\n",
      "Epoch 14/100\n",
      "1407/1407 [==============================] - 19s 13ms/step - loss: 1.2005 - accuracy: 0.5848 - val_loss: 1.5247 - val_accuracy: 0.4940\n",
      "Epoch 15/100\n",
      "1407/1407 [==============================] - 19s 13ms/step - loss: 1.1759 - accuracy: 0.5937 - val_loss: 1.5171 - val_accuracy: 0.4938\n",
      "Epoch 16/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.3986 - accuracy: 0.5724 - val_loss: 1.4784 - val_accuracy: 0.5038\n",
      "Epoch 17/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1412 - accuracy: 0.6045 - val_loss: 1.5083 - val_accuracy: 0.4964\n",
      "Epoch 18/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1050 - accuracy: 0.6190 - val_loss: 1.4863 - val_accuracy: 0.5116\n",
      "Epoch 19/100\n",
      "1407/1407 [==============================] - 19s 13ms/step - loss: 1.0849 - accuracy: 0.6276 - val_loss: 1.5763 - val_accuracy: 0.4990\n",
      "Epoch 20/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.0677 - accuracy: 0.6347 - val_loss: 1.5231 - val_accuracy: 0.5006\n",
      "Epoch 21/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.0614 - accuracy: 0.6374 - val_loss: 1.5272 - val_accuracy: 0.5086\n",
      "Epoch 22/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.0535 - accuracy: 0.6361 - val_loss: 1.5333 - val_accuracy: 0.5012\n",
      "Epoch 23/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.0395 - accuracy: 0.6458 - val_loss: 1.5519 - val_accuracy: 0.4842\n",
      "Epoch 24/100\n",
      "1407/1407 [==============================] - 19s 13ms/step - loss: 1.0408 - accuracy: 0.6438 - val_loss: 1.5506 - val_accuracy: 0.4998\n",
      "Epoch 25/100\n",
      "1407/1407 [==============================] - 19s 13ms/step - loss: 1.0386 - accuracy: 0.6466 - val_loss: 1.5726 - val_accuracy: 0.5120\n",
      "Epoch 26/100\n",
      "1407/1407 [==============================] - 19s 13ms/step - loss: 1.0169 - accuracy: 0.6522 - val_loss: 1.5514 - val_accuracy: 0.5034\n",
      "Epoch 27/100\n",
      "1407/1407 [==============================] - 19s 13ms/step - loss: 1.0032 - accuracy: 0.6564 - val_loss: 1.5706 - val_accuracy: 0.4898\n",
      "Epoch 28/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.9937 - accuracy: 0.6598 - val_loss: 1.5229 - val_accuracy: 0.5006\n",
      "Epoch 29/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.9684 - accuracy: 0.6702 - val_loss: 1.6241 - val_accuracy: 0.5102\n",
      "Epoch 30/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.9608 - accuracy: 0.6687 - val_loss: 1.6159 - val_accuracy: 0.5022\n",
      "Epoch 31/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.9495 - accuracy: 0.6768 - val_loss: 1.6557 - val_accuracy: 0.5018\n",
      "Epoch 32/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.9389 - accuracy: 0.6812 - val_loss: 1.6991 - val_accuracy: 0.4960\n",
      "Epoch 33/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.0545 - accuracy: 0.6420 - val_loss: 1.5800 - val_accuracy: 0.5008\n",
      "Epoch 34/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 747.1775 - accuracy: 0.6460 - val_loss: 1.6298 - val_accuracy: 0.4556\n",
      "Epoch 35/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1628 - accuracy: 0.5962 - val_loss: 1.5724 - val_accuracy: 0.4936\n",
      "Epoch 36/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.0774 - accuracy: 0.6279 - val_loss: 1.5662 - val_accuracy: 0.4952\n"
     ]
    }
   ],
   "source": [
    "keras.backend.clear_session()\n",
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "model = keras.models.Sequential()\n",
    "model.add(keras.layers.Flatten(input_shape=[32, 32, 3]))\n",
    "for _ in range(20):\n",
    "    model.add(keras.layers.Dense(100, kernel_initializer=\"lecun_normal\", activation=\"selu\"))\n",
    "model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
    "\n",
    "optimizer = keras.optimizers.Nadam(learning_rate=7e-4)\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"])\n",
    "\n",
    "early_stopping_cb = keras.callbacks.EarlyStopping(patience=20)\n",
    "model_checkpoint_cb = keras.callbacks.ModelCheckpoint(\"my_cifar10_selu_model.h5\", save_best_only=True)\n",
    "\n",
    "run_index = 1 #increment everytime you train the model\n",
    "run_logdir = os.path.join(os.curdir, \"my_cifar10_logs\", \"run_selu_{:03d}\".format(run_index))\n",
    "tensorboard_cb = keras.callbacks.TensorBoard(run_logdir)\n",
    "\n",
    "callbacks = [early_stopping_cb, model_checkpoint_cb, tensorboard_cb]\n",
    "\n",
    "X_means = X_train.mean(axis=0)\n",
    "X_stds = X_train.std(axis=0)\n",
    "X_train_scaled = (X_train - X_means) / X_stds\n",
    "X_valid_scaled = (X_valid - X_means) / X_stds\n",
    "X_test_scaled = (X_test - X_means) / X_stds\n",
    "\n",
    "h_selu = model.fit(X_train_scaled, y_train, epochs=100, validation_data=(X_valid_scaled, y_valid), callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e110d2d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 1s 4ms/step - loss: 1.4784 - accuracy: 0.5038\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.4783544540405273, 0.5037999749183655]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.models.load_model(\"my_cifar10_selu_model.h5\")\n",
    "model.evaluate(X_valid_scaled, y_valid) #type: ignore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c428b725",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#type: ignore\n",
    "epoch_of_selu_min_valloss = np.argmin(h_selu.history[\"val_loss\"]) + 1 # because index starts from 0 \n",
    "epoch_of_selu_min_valloss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d99357b",
   "metadata": {},
   "source": [
    "We got 50.37% accuracy, which is slight better than the original model (47.67%) but not as good as the model with BN (53.88%). However, convergence was almost as fast as with BN model. Took 16 epochs to reach the lowest validation loss, plus each epoch took around 18s. So it's by far the fastest model to train so far."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3039a15",
   "metadata": {},
   "source": [
    "e. Try regularizing the model with alpha dropout. Then, without retraining your model, see if you can achieve better accuracy using MC Dropout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bfffbf18",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-02 14:21:33.468900: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 552960000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1407/1407 [==============================] - 29s 15ms/step - loss: 1.9010 - accuracy: 0.3259 - val_loss: 1.7681 - val_accuracy: 0.3884\n",
      "Epoch 2/100\n",
      "   1/1407 [..............................] - ETA: 17s - loss: 1.8923 - accuracy: 0.2500"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/anaconda3/envs/latest/lib/python3.11/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.6680 - accuracy: 0.4114 - val_loss: 1.6459 - val_accuracy: 0.4140\n",
      "Epoch 3/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.5786 - accuracy: 0.4426 - val_loss: 1.5924 - val_accuracy: 0.4358\n",
      "Epoch 4/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.5190 - accuracy: 0.4650 - val_loss: 1.5741 - val_accuracy: 0.4626\n",
      "Epoch 5/100\n",
      "1407/1407 [==============================] - 18s 12ms/step - loss: 1.4607 - accuracy: 0.4928 - val_loss: 1.5774 - val_accuracy: 0.4720\n",
      "Epoch 6/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.4105 - accuracy: 0.5101 - val_loss: 1.5219 - val_accuracy: 0.4716\n",
      "Epoch 7/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.3681 - accuracy: 0.5242 - val_loss: 1.5192 - val_accuracy: 0.4808\n",
      "Epoch 8/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.3233 - accuracy: 0.5391 - val_loss: 1.5019 - val_accuracy: 0.4944\n",
      "Epoch 9/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2899 - accuracy: 0.5527 - val_loss: 1.4876 - val_accuracy: 0.4928\n",
      "Epoch 10/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2580 - accuracy: 0.5621 - val_loss: 1.5094 - val_accuracy: 0.5006\n",
      "Epoch 11/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.2237 - accuracy: 0.5752 - val_loss: 1.4772 - val_accuracy: 0.5200\n",
      "Epoch 12/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1876 - accuracy: 0.5893 - val_loss: 1.5200 - val_accuracy: 0.4984\n",
      "Epoch 13/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1674 - accuracy: 0.5966 - val_loss: 1.5176 - val_accuracy: 0.5180\n",
      "Epoch 14/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1338 - accuracy: 0.6078 - val_loss: 1.4878 - val_accuracy: 0.5142\n",
      "Epoch 15/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1083 - accuracy: 0.6187 - val_loss: 1.5959 - val_accuracy: 0.5146\n",
      "Epoch 16/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.0779 - accuracy: 0.6299 - val_loss: 1.6571 - val_accuracy: 0.5172\n",
      "Epoch 17/100\n",
      "1407/1407 [==============================] - 19s 13ms/step - loss: 1.0614 - accuracy: 0.6361 - val_loss: 1.6048 - val_accuracy: 0.5178\n",
      "Epoch 18/100\n",
      "1407/1407 [==============================] - 19s 13ms/step - loss: 1.0381 - accuracy: 0.6460 - val_loss: 1.6564 - val_accuracy: 0.5150\n",
      "Epoch 19/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.0193 - accuracy: 0.6499 - val_loss: 1.6946 - val_accuracy: 0.5162\n",
      "Epoch 20/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.9950 - accuracy: 0.6606 - val_loss: 1.6687 - val_accuracy: 0.5142\n",
      "Epoch 21/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.9751 - accuracy: 0.6717 - val_loss: 1.6625 - val_accuracy: 0.5180\n",
      "Epoch 22/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.9540 - accuracy: 0.6761 - val_loss: 1.6820 - val_accuracy: 0.5130\n",
      "Epoch 23/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.9404 - accuracy: 0.6816 - val_loss: 1.7428 - val_accuracy: 0.5084\n",
      "Epoch 24/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.9235 - accuracy: 0.6865 - val_loss: 1.7450 - val_accuracy: 0.5132\n",
      "Epoch 25/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.9056 - accuracy: 0.6929 - val_loss: 1.7217 - val_accuracy: 0.5260\n",
      "Epoch 26/100\n",
      "1407/1407 [==============================] - 19s 13ms/step - loss: 0.8817 - accuracy: 0.7026 - val_loss: 1.7553 - val_accuracy: 0.5080\n",
      "Epoch 27/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1473 - accuracy: 0.6525 - val_loss: 1.6714 - val_accuracy: 0.4460\n",
      "Epoch 28/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 1.1230 - accuracy: 0.6123 - val_loss: 1.6873 - val_accuracy: 0.4994\n",
      "Epoch 29/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.9541 - accuracy: 0.6737 - val_loss: 1.7758 - val_accuracy: 0.5092\n",
      "Epoch 30/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.8754 - accuracy: 0.7010 - val_loss: 1.8105 - val_accuracy: 0.5168\n",
      "Epoch 31/100\n",
      "1407/1407 [==============================] - 18s 13ms/step - loss: 0.8308 - accuracy: 0.7179 - val_loss: 1.7676 - val_accuracy: 0.5222\n"
     ]
    }
   ],
   "source": [
    "keras.backend.clear_session()\n",
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "model = keras.models.Sequential()\n",
    "model.add(keras.layers.Flatten(input_shape=[32, 32, 3]))\n",
    "for _ in range(20):\n",
    "    model.add(keras.layers.Dense(100, kernel_initializer=\"lecun_normal\", activation=\"selu\"))\n",
    "model.add(keras.layers.AlphaDropout(rate=0.1))\n",
    "model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
    "\n",
    "optimizer = keras.optimizers.Nadam(learning_rate=5e-4)\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"])\n",
    "\n",
    "early_stopping_cb = keras.callbacks.EarlyStopping(patience=20)\n",
    "model_checkpoint_cb = keras.callbacks.ModelCheckpoint(\"my_cifar10_alpha_dropout_model.h5\", save_best_only=True)\n",
    "\n",
    "run_index = 1 #increment everytime you train the model\n",
    "run_logdir = os.path.join(os.curdir, \"my_cifar10_logs\", \"run_alpha_dropout_{:03d}\".format(run_index))\n",
    "tensorboard_cb = keras.callbacks.TensorBoard(run_logdir)\n",
    "\n",
    "callbacks = [early_stopping_cb, model_checkpoint_cb, tensorboard_cb]\n",
    "\n",
    "X_means = X_train.mean(axis=0)\n",
    "X_stds = X_train.std(axis=0)\n",
    "X_train_scaled = (X_train - X_means) / X_stds\n",
    "X_valid_scaled = (X_valid - X_means) / X_stds\n",
    "X_test_scaled = (X_test - X_means)  / X_stds\n",
    "\n",
    "h_alpha_droput = model.fit(X_train_scaled, y_train, epochs=100, validation_data=(X_valid_scaled, y_valid), callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "00ab08c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 1s 4ms/step - loss: 1.4772 - accuracy: 0.5200\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.4772435426712036, 0.5199999809265137]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.models.load_model(\"my_cifar10_alpha_dropout_model.h5\")\n",
    "model.evaluate(X_valid_scaled, y_valid) #type: ignore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df52a1d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#type: ignore\n",
    "epoch_of_alpha_dropout_min_valloss = np.argmin(h_alpha_droput.history[\"val_loss\"]) + 1 # because index starts from 0 \n",
    "epoch_of_alpha_dropout_min_valloss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9610b91",
   "metadata": {},
   "source": [
    "The model reaches 52% accuracy on the validation set. That's better than without dropout (47.67%). But it's not as good as the model with BN (53.88%). However, it's much faster to train. Each epoch took 18s, which is faster than the model with BN (30s). Also it took only 11 epochs to reach the lowest validation loss, which is faster than the model with BN (17 epochs)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8baa7a55",
   "metadata": {},
   "source": [
    "Let's use MC Dropout now. We will need the `MCAlphaDropout` class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7248e506",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MCAlphaDroput(keras.layers.AlphaDropout):\n",
    "    def call(self, inputs):\n",
    "        return super().call(inputs, training=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e46811e9",
   "metadata": {},
   "source": [
    "Now let's create a new model, identical to the one we just trained (with same weights), but with the `MCAlphaDropout` dropout layers, instead of `AlphaDropout` layers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0dece253",
   "metadata": {},
   "outputs": [],
   "source": [
    "mc_model = keras.models.Sequential([\n",
    "    MCAlphaDroput(layer.rate) if isinstance(layer, keras.layers.AlphaDropout) else layer for layer in model.layers #type: ignore\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c90d3dfd",
   "metadata": {},
   "source": [
    "Then let's add a couple utility functions. The first will run the models many times (10 by default) and it will return the mean predicted class probabilities. The second will use the mean probabilites to predict the most likely class for each instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d5b68f1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mc_droput_predict_probas(mc_model, X, n_samples=10):\n",
    "    Y_probas = [mc_model.predict(X) for sample in range(n_samples)]\n",
    "    return np.mean(Y_probas, axis=0)\n",
    "\n",
    "def mc_dropout_predict_classes(mc_model, X, n_samples=10):\n",
    "    Y_probas = mc_droput_predict_probas(mc_model, X, n_samples)\n",
    "    return np.argmax(Y_probas, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "218feeb6",
   "metadata": {},
   "source": [
    "Now let's make predictions for all the instances in the validation set, and compute the accuracy score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5b0cb682",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 1s 6ms/step\n",
      "157/157 [==============================] - 1s 3ms/step\n",
      "157/157 [==============================] - 1s 3ms/step\n",
      "157/157 [==============================] - 1s 4ms/step\n",
      "157/157 [==============================] - 1s 4ms/step\n",
      "157/157 [==============================] - 1s 3ms/step\n",
      "157/157 [==============================] - 1s 3ms/step\n",
      "157/157 [==============================] - 0s 3ms/step\n",
      "157/157 [==============================] - 0s 3ms/step\n",
      "157/157 [==============================] - 1s 3ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5198"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.backend.clear_session()\n",
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "y_pred = mc_dropout_predict_classes(mc_model, X_valid_scaled)\n",
    "accuracy = np.mean(y_pred == y_valid[: , 0])\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d83ba29",
   "metadata": {},
   "source": [
    "We got no accuracy improvement in this case (we are still at ~52% accuracy)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4013f105",
   "metadata": {},
   "source": [
    "So the best model we got so far is the Batch Normalization model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29c0f57a",
   "metadata": {},
   "source": [
    "f. Retrain your model using 1cycle scheduling and see if it improves training speed and model accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "db6bc3b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "model = keras.models.Sequential()\n",
    "model.add(keras.layers.Flatten(input_shape=[32, 32, 3]))\n",
    "for _ in range(20):\n",
    "    model.add(keras.layers.Dense(100, kernel_initializer=\"lecun_normal\", activation=\"selu\"))\n",
    "model.add(keras.layers.AlphaDropout(rate=0.1))\n",
    "model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
    "\n",
    "optimizer = keras.optimizers.SGD(learning_rate=1e-3)\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "98c301b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "K = keras.backend\n",
    "\n",
    "class ExponentialLearningRate(keras.callbacks.Callback):\n",
    "    def __init__(self, factor):\n",
    "        self.factor = factor\n",
    "        self.rates = []\n",
    "        self.losses = []\n",
    "    def on_batch_end(self, batch, logs):\n",
    "        self.rates.append(K.get_value(self.model.optimizer.learning_rate)) #type: ignore\n",
    "        self.losses.append(logs[\"loss\"])\n",
    "        K.set_value(self.model.optimizer.learning_rate, self.model.optimizer.learning_rate * self.factor) #type: ignore\n",
    "\n",
    "def find_learning_rate(model, X, y, epochs=1, batch_size=32, min_rate=10**-5, max_rate=10):\n",
    "    init_weights = model.get_weights()\n",
    "    interations = math.ceil(len(X) / batch_size) * epochs\n",
    "    factor = np.exp(np.log(max_rate / min_rate) / interations)\n",
    "    init_lr = K.get_value(model.optimizer.learning_rate)\n",
    "    K.set_value(model.optimizer.learning_rate, min_rate)\n",
    "    exp_lr = ExponentialLearningRate(factor)\n",
    "    history = model.fit(X, y, epochs=epochs, batch_size=batch_size, callbacks=[exp_lr])\n",
    "    K.set_value(model.optimizer.learning_rate, init_lr)\n",
    "    model.set_weights(init_weights)\n",
    "    return exp_lr.rates, exp_lr.losses  #type: ignore\n",
    "\n",
    "def plot_lr_vs_loss(rates, losses):\n",
    "    plt.plot(rates, losses)\n",
    "    plt.gca().set_xscale(\"log\")\n",
    "    plt.hlines(min(losses), min(rates), max(rates))\n",
    "    plt.axis([min(rates), max(rates), min(losses), (losses[0] + min(losses)) / 2])\n",
    "    plt.xlabel(\"Learning rate\")\n",
    "    plt.ylabel(\"Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e93a8bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_means = X_train.mean(axis=0)\n",
    "X_stds = X_train.std(axis=0)\n",
    "X_train_scaled = (X_train - X_means) / X_stds\n",
    "X_valid_scaled = (X_valid - X_means) / X_stds\n",
    "X_test_scaled = (X_test - X_means)  / X_stds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e9d85674-4565-4ab1-b9c5-0c0db4ca2bfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cddbb58e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-02 15:14:17.908726: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 552960000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "352/352 [==============================] - 7s 15ms/step - loss: nan - accuracy: 0.1516\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rates, losses = find_learning_rate(model, X_train_scaled, y_train, epochs=1, batch_size=batch_size)\n",
    "plot_lr_vs_loss(rates, losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6ba64be",
   "metadata": {},
   "outputs": [],
   "source": [
    "class OneCycleScheduler(keras.callbacks.Callback):\n",
    "    def __init__(self, iterations, max_rate, start_rate=None, last_iterations=None, last_rate=None):\n",
    "        self.iterations = iterations\n",
    "        self.max_rate = max_rate\n",
    "        self.start_rate = start_rate or max_rate / 10\n",
    "        self.last_iterations = last_iterations or iterations // 10 + 1\n",
    "        self.half_iteration = (iterations - self.last_iterations) // 2\n",
    "        self.last_rate = last_rate or self.start_rate / 1000\n",
    "        self.iteration = 0\n",
    "    def _interpolate(self, iter1, iter2, rate1, rate2):\n",
    "        return ((rate2 - rate1) * (self.iteration - iter1) / (iter2 - iter1) + rate1)\n",
    "    \n",
    "    def on_batch_begin(self, batch, logs):\n",
    "        if self.iteration < self.half_iteration:\n",
    "            rate = self._interpolate(0, self.half_iteration, self.start_rate, self.max_rate)\n",
    "        elif self.iteration < 2 * self.half_iteration:\n",
    "            rate = self._interpolate(self.half_iteration, 2 * self.half_iteration,\n",
    "                                    self.max_rate, self.start_rate)\n",
    "        else:\n",
    "            rate = self._interpolate(2 * self.half_iteration, self.iterations,\n",
    "                                    self.start_rate, self.last_rate)\n",
    "        self.iteration += 1\n",
    "        K.set_value(self.model.optimizer.learning_rate, rate) #type: ignore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4d2c306a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1e-05"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(rates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "46d8360a",
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "model = keras.models.Sequential()\n",
    "model.add(keras.layers.Flatten(input_shape=[32, 32, 3]))\n",
    "for _ in range(20):\n",
    "    model.add(keras.layers.Dense(100, kernel_initializer=\"lecun_normal\", activation=\"selu\"))\n",
    "\n",
    "model.add(keras.layers.AlphaDropout(rate=0.1))\n",
    "model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
    "\n",
    "optimizer = keras.optimizers.SGD(learning_rate=1e-2)\n",
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7084db22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-02 15:17:30.577230: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 552960000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "352/352 [==============================] - 5s 10ms/step - loss: 2.0650 - accuracy: 0.2852 - val_loss: 1.7596 - val_accuracy: 0.3846\n",
      "Epoch 2/15\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 1.7621 - accuracy: 0.3761 - val_loss: 1.6761 - val_accuracy: 0.4134\n",
      "Epoch 3/15\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 1.6293 - accuracy: 0.4222 - val_loss: 1.6204 - val_accuracy: 0.4260\n",
      "Epoch 4/15\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 1.5514 - accuracy: 0.4480 - val_loss: 1.6599 - val_accuracy: 0.4306\n",
      "Epoch 5/15\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 1.4992 - accuracy: 0.4707 - val_loss: 1.6490 - val_accuracy: 0.4462\n",
      "Epoch 6/15\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 1.4594 - accuracy: 0.4843 - val_loss: 1.6075 - val_accuracy: 0.4502\n",
      "Epoch 7/15\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 1.4226 - accuracy: 0.4948 - val_loss: 1.5683 - val_accuracy: 0.4488\n",
      "Epoch 8/15\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 1.3533 - accuracy: 0.5200 - val_loss: 1.4931 - val_accuracy: 0.4828\n",
      "Epoch 9/15\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 1.2794 - accuracy: 0.5454 - val_loss: 1.5452 - val_accuracy: 0.4842\n",
      "Epoch 10/15\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 1.2075 - accuracy: 0.5704 - val_loss: 1.5266 - val_accuracy: 0.4970\n",
      "Epoch 11/15\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 1.1417 - accuracy: 0.5900 - val_loss: 1.5088 - val_accuracy: 0.5148\n",
      "Epoch 12/15\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 1.0705 - accuracy: 0.6147 - val_loss: 1.5098 - val_accuracy: 0.5122\n",
      "Epoch 13/15\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 1.0031 - accuracy: 0.6395 - val_loss: 1.5261 - val_accuracy: 0.5230\n",
      "Epoch 14/15\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 0.9393 - accuracy: 0.6641 - val_loss: 1.5541 - val_accuracy: 0.5250\n",
      "Epoch 15/15\n",
      "352/352 [==============================] - 4s 12ms/step - loss: 0.9004 - accuracy: 0.6762 - val_loss: 1.5754 - val_accuracy: 0.5204\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 15\n",
    "onecycle = OneCycleScheduler(math.ceil(len(X_train_scaled) / batch_size) * n_epochs, max_rate=0.05)\n",
    "h = model.fit(X_train_scaled, y_train, epochs=n_epochs, batch_size=batch_size, validation_data=(X_valid_scaled, y_valid), callbacks=[onecycle])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f5c87c33-b842-404e-9b7c-b2236f468c62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 3ms/step - loss: 1.5754 - accuracy: 0.5204\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.5753862857818604, 0.5203999876976013]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid_scaled, y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8b3bf75-a63c-49ff-9718-b416a5aa69c6",
   "metadata": {},
   "source": [
    "One cycle allowed us to train the model in just 15 epochs, each epoch taking around 4s (thanks to larger batch size). This is several time faster than the fastest model we trained so far. Morever, performance has reached near to that of BN model, but remember that BN was much slower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cd93441b-6169-4935-8159-b6370c600338",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 1.5709 - accuracy: 0.5224\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.5708906650543213, 0.5224000215530396]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d59ab8a1",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "\n",
    "Batch Normalization is a great technique to accelerate training and improve performance of deep neural networks. However, it does slow down training at runtime. So if we care about runtime latency, we may want to use MC Dropout instead. It will not perform as well as BN, but it will be much faster at runtime. \n",
    "\n",
    "Learning rate scheduling can also help speed up training. \n",
    "\n",
    "Optimizers such as Nadam and 1cycle can also speed up training considerably."
   ]
  }
 ],
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