LeNet_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lottery Ticket Hypothesis: _LeNet_ Dense Fully-Connected Feed Forward Neural Network for MNIST\n",
    "\n",
    "Implementation of the hypothesis using the following architecture:\n",
    "\n",
    "__Fully Connected Layers:__ 300, 100, 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import tensorflow_model_optimization as tfmot\n",
    "from tensorflow_model_optimization.sparsity import keras as sparsity\n",
    "# from tensorflow.keras import datasets, layers, models\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.layers import AveragePooling2D, Conv2D, MaxPooling2D, ReLU\n",
    "from tensorflow.keras import models, layers, datasets\n",
    "from tensorflow.keras.layers import Dense, Flatten, Reshape, Input, InputLayer\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.initializers import RandomNormal\n",
    "# import math\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_DEVICE_ORDER=PCI_BUS_ID\n",
      "env: CUDA_VISIBLE_DEVICES=2\n"
     ]
    }
   ],
   "source": [
    "%env CUDA_DEVICE_ORDER=PCI_BUS_ID\n",
    "%env CUDA_VISIBLE_DEVICES=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 60\n",
    "num_classes = 10\n",
    "num_epochs = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data preprocessing and cleadning:\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "# Load MNIST dataset-\n",
    "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "'input_shape' which will be used = (28, 28, 1)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "if tf.keras.backend.image_data_format() == 'channels_first':\n",
    "    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)\n",
    "    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)\n",
    "    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "print(\"\\n'input_shape' which will be used = {0}\\n\".format(input_shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert datasets to floating point types-\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "\n",
    "# Normalize the training and testing datasets-\n",
    "X_train /= 255.0\n",
    "X_test /= 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert class vectors/target to binary class matrices or one-hot encoded values-\n",
    "y_train = tf.keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = tf.keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape training and testing sets-\n",
    "X_train = X_train.reshape(X_train.shape[0], 784)\n",
    "X_test = X_test.reshape(X_test.shape[0], 784)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dimensions of training and testing sets are:\n",
      "X_train.shape = (60000, 784), y_train.shape = (60000, 10)\n",
      "X_test.shape = (10000, 784), y_test.shape = (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nDimensions of training and testing sets are:\")\n",
    "print(\"X_train.shape = {0}, y_train.shape = {1}\".format(X_train.shape, y_train.shape))\n",
    "print(\"X_test.shape = {0}, y_test.shape = {1}\".format(X_test.shape, y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare MNIST dataset for _GradientTape_ training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create training and testing datasets-\n",
    "train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n",
    "test_dataset = tf.data.Dataset.from_tensor_slices((X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = train_dataset.shuffle(buffer_size = 20000, reshuffle_each_iteration = True).batch(batch_size = batch_size, drop_remainder = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset = test_dataset.batch(batch_size=batch_size, drop_remainder=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose an optimizer and loss function for training-\n",
    "loss_fn = tf.keras.losses.CategoricalCrossentropy()\n",
    "optimizer = tf.keras.optimizers.Adam(lr = 0.0012)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select metrics to measure the error & accuracy of model.\n",
    "# These metrics accumulate the values over epochs and then\n",
    "# print the overall result-\n",
    "train_loss = tf.keras.metrics.Mean(name = 'train_loss')\n",
    "train_accuracy = tf.keras.metrics.CategoricalAccuracy(name = 'train_accuracy')\n",
    "\n",
    "test_loss = tf.keras.metrics.Mean(name = 'test_loss')\n",
    "test_accuracy = tf.keras.metrics.CategoricalAccuracy(name = 'test_accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'end_step parameter' for this dataset =  100000\n"
     ]
    }
   ],
   "source": [
    "# The model is first trained without any pruning for 'num_epochs' epochs-\n",
    "epochs = num_epochs\n",
    "\n",
    "num_train_samples = X_train.shape[0]\n",
    "\n",
    "end_step = np.ceil(1.0 * num_train_samples / batch_size).astype(np.int32) * epochs\n",
    "\n",
    "print(\"'end_step parameter' for this dataset =  {0}\".format(end_step))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the parameters to be used for layer-wise pruning, NO PRUNING is done here:\n",
    "pruning_params_unpruned = {\n",
    "    'pruning_schedule': sparsity.ConstantSparsity(\n",
    "        target_sparsity=0.0, begin_step=0,\n",
    "        end_step = end_step, frequency=100\n",
    "    )\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = tf.keras.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pruned_nn(pruning_params):\n",
    "    \"\"\"\n",
    "    Function to define the architecture of a neural network model\n",
    "    following 300 100 architecture for MNIST dataset and using\n",
    "    provided parameter which are used to prune the model.\n",
    "    \n",
    "    Input: 'pruning_params' Python 3 dictionary containing parameters which are used for pruning\n",
    "    Output: Returns designed and compiled neural network model\n",
    "    \"\"\"\n",
    "    \n",
    "    pruned_model = Sequential()\n",
    "    pruned_model.add(l.InputLayer(input_shape=(784, )))\n",
    "    pruned_model.add(Flatten())\n",
    "    pruned_model.add(sparsity.prune_low_magnitude(\n",
    "        Dense(units = 300, activation='relu', kernel_initializer=tf.initializers.GlorotUniform()),\n",
    "        **pruning_params))\n",
    "    # pruned_model.add(l.Dropout(0.2))\n",
    "    pruned_model.add(sparsity.prune_low_magnitude(\n",
    "        Dense(units = 100, activation='relu', kernel_initializer=tf.initializers.GlorotUniform()),\n",
    "        **pruning_params))\n",
    "    # pruned_model.add(l.Dropout(0.1))\n",
    "    pruned_model.add(sparsity.prune_low_magnitude(\n",
    "        Dense(units = num_classes, activation='softmax'),\n",
    "        **pruning_params))\n",
    "    \n",
    "    # Compile pruned CNN-\n",
    "    pruned_model.compile(\n",
    "        loss=tf.keras.losses.categorical_crossentropy,\n",
    "        # optimizer='adam',\n",
    "        optimizer=tf.keras.optimizers.Adam(lr = 0.0012),\n",
    "        metrics=['accuracy'])\n",
    "    \n",
    "    return pruned_model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a pruning step callback to peg the pruning step to the optimizer's\n",
    "# step. Also add a callback to add pruning summaries to tensorboard\n",
    "callback = [\n",
    "             sparsity.UpdatePruningStep(),\n",
    "             # sparsity.PruningSummaries(log_dir = logdir, profile_batch=0),\n",
    "             tf.keras.callbacks.EarlyStopping(\n",
    "                 monitor='val_loss', patience = 3,\n",
    "                 min_delta=0.001\n",
    "             )\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow_model_optimization/python/core/sparsity/keras/pruning_wrapper.py:183: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.add_weight` method instead.\n"
     ]
    }
   ],
   "source": [
    "# Initialize a CNN model-\n",
    "orig_model = pruned_nn(pruning_params_unpruned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Strip model of it's pruning parameters\n",
    "orig_model_stripped = sparsity.strip_pruning(orig_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save random weights-\n",
    "orig_model.save_weights(\"LeNet_MNIST_Ramdom_Weights.h5\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save random weights-\n",
    "orig_model.save_weights(\"LeNet_MNIST_Winning_Ticket.h5\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 300)               235500    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 100)               30100     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 266,610\n",
      "Trainable params: 266,610\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Get CNN summary-\n",
    "orig_model_stripped.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Layer-wise pruning:\n",
    "The Lottery Ticket Hypothesis follows a _layer-wise pruning_ heuristic, where the fully-connected (fc) layers are pruned at the rate of 20% per iterative\n",
    "pruning round.\n",
    "\n",
    "Therefore, there is a need to compute the percentage of weights being pruned for each layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total number of trainable parameters = 266610\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# number of fully-connected dense parameters-\n",
    "dense1 = 235500\n",
    "dense2 = 30100\n",
    "op_layer = 1010\n",
    "\n",
    "\n",
    "# total number of parameters-\n",
    "total_params = dense1 + dense2 + op_layer\n",
    "\n",
    "print(\"\\nTotal number of trainable parameters = {0}\\n\".format(total_params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# maximum pruning performed is till 0.5% of all parameters-\n",
    "max_pruned_params = 0.005 * total_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "loc_tot_params = total_params\n",
    "loc_dense1 = dense1\n",
    "loc_dense2 = dense2\n",
    "loc_op_layer = op_layer\n",
    "\n",
    "# variable to count number of pruning rounds-\n",
    "n = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lists to hold percentage of weights pruned in each round for all layers in CNN-\n",
    "dense1_pruning = []\n",
    "dense2_pruning = []\n",
    "op_layer_pruning = []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dense1 = 188400.000, Dense2 = 24080.000 & O/p layer = 808.000\n",
      "Total number of parameters = 213288.000\n",
      "\n",
      "Dense1 = 150720.000, Dense2 = 19264.000 & O/p layer = 646.400\n",
      "Total number of parameters = 170630.400\n",
      "\n",
      "Dense1 = 120576.000, Dense2 = 15411.200 & O/p layer = 517.120\n",
      "Total number of parameters = 136504.320\n",
      "\n",
      "Dense1 = 96460.800, Dense2 = 12328.960 & O/p layer = 413.696\n",
      "Total number of parameters = 109203.456\n",
      "\n",
      "Dense1 = 77168.640, Dense2 = 9863.168 & O/p layer = 330.957\n",
      "Total number of parameters = 87362.765\n",
      "\n",
      "Dense1 = 61734.912, Dense2 = 7890.534 & O/p layer = 264.765\n",
      "Total number of parameters = 69890.212\n",
      "\n",
      "Dense1 = 49387.930, Dense2 = 6312.428 & O/p layer = 211.812\n",
      "Total number of parameters = 55912.169\n",
      "\n",
      "Dense1 = 39510.344, Dense2 = 5049.942 & O/p layer = 169.450\n",
      "Total number of parameters = 44729.736\n",
      "\n",
      "Dense1 = 31608.275, Dense2 = 4039.954 & O/p layer = 135.560\n",
      "Total number of parameters = 35783.788\n",
      "\n",
      "Dense1 = 25286.620, Dense2 = 3231.963 & O/p layer = 108.448\n",
      "Total number of parameters = 28627.031\n",
      "\n",
      "Dense1 = 20229.296, Dense2 = 2585.570 & O/p layer = 86.758\n",
      "Total number of parameters = 22901.625\n",
      "\n",
      "Dense1 = 16183.437, Dense2 = 2068.456 & O/p layer = 69.407\n",
      "Total number of parameters = 18321.300\n",
      "\n",
      "Dense1 = 12946.749, Dense2 = 1654.765 & O/p layer = 55.525\n",
      "Total number of parameters = 14657.040\n",
      "\n",
      "Dense1 = 10357.400, Dense2 = 1323.812 & O/p layer = 44.420\n",
      "Total number of parameters = 11725.632\n",
      "\n",
      "Dense1 = 8285.920, Dense2 = 1059.050 & O/p layer = 35.536\n",
      "Total number of parameters = 9380.505\n",
      "\n",
      "Dense1 = 6628.736, Dense2 = 847.240 & O/p layer = 28.429\n",
      "Total number of parameters = 7504.404\n",
      "\n",
      "Dense1 = 5302.989, Dense2 = 677.792 & O/p layer = 22.743\n",
      "Total number of parameters = 6003.523\n",
      "\n",
      "Dense1 = 4242.391, Dense2 = 542.233 & O/p layer = 18.195\n",
      "Total number of parameters = 4802.819\n",
      "\n",
      "Dense1 = 3393.913, Dense2 = 433.787 & O/p layer = 14.556\n",
      "Total number of parameters = 3842.255\n",
      "\n",
      "Dense1 = 2715.130, Dense2 = 347.029 & O/p layer = 11.645\n",
      "Total number of parameters = 3073.804\n",
      "\n",
      "Dense1 = 2172.104, Dense2 = 277.623 & O/p layer = 9.316\n",
      "Total number of parameters = 2459.043\n",
      "\n",
      "Dense1 = 1737.683, Dense2 = 222.099 & O/p layer = 7.452\n",
      "Total number of parameters = 1967.235\n",
      "\n",
      "Dense1 = 1390.147, Dense2 = 177.679 & O/p layer = 5.962\n",
      "Total number of parameters = 1573.788\n",
      "\n",
      "Dense1 = 1112.117, Dense2 = 142.143 & O/p layer = 4.770\n",
      "Total number of parameters = 1259.030\n",
      "\n"
     ]
    }
   ],
   "source": [
    "while loc_tot_params >= max_pruned_params:\n",
    "    loc_dense1 *= 0.8   # 20% weights are pruned\n",
    "    loc_dense2 *= 0.8   # 20% weights are pruned\n",
    "    loc_op_layer *= 0.8 # 20% weights are pruned\n",
    "    \n",
    "    dense1_pruning.append(((dense1 - loc_dense1) / dense1) * 100)\n",
    "    dense2_pruning.append(((dense2 - loc_dense2) / dense2) * 100)\n",
    "    op_layer_pruning.append(((op_layer - loc_op_layer) / op_layer) * 100)\n",
    "\n",
    "    loc_tot_params = loc_dense1 + loc_dense2 + loc_op_layer\n",
    "\n",
    "    n += 1\n",
    "\n",
    "    print(\"Dense1 = {0:.3f}, Dense2 = {1:.3f} & O/p layer = {2:.3f}\".format(\n",
    "        loc_dense1, loc_dense2, loc_op_layer))\n",
    "    print(\"Total number of parameters = {0:.3f}\\n\".format(loc_tot_params))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "number of pruning rounds = 24\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nnumber of pruning rounds = {0}\\n\\n\".format(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pruning_rounds = n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert from list to np.array-\n",
    "dense1_pruning = np.array(dense1_pruning)\n",
    "dense2_pruning = np.array(dense2_pruning)\n",
    "op_layer_pruning = np.array(op_layer_pruning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Round off numpy arrays to 3 decimal digits-\n",
    "dense1_pruning = np.round(dense1_pruning, decimals=3)\n",
    "dense2_pruning = np.round(dense2_pruning, decimals=3)\n",
    "op_layer_pruning = np.round(op_layer_pruning, decimals=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20.   , 36.   , 48.8  , 59.04 , 67.232, 73.786, 79.028, 83.223,\n",
       "       86.578, 89.263, 91.41 , 93.128, 94.502, 95.602, 96.482, 97.185,\n",
       "       97.748, 98.199, 98.559, 98.847, 99.078, 99.262, 99.41 , 99.528])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dense1_pruning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note:\n",
    "\n",
    "For _tensorflow-model-optimization_ package to work, the pruning rates should be between 0 and 1, hence convert them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dense1_pruning = dense1_pruning / 100\n",
    "dense2_pruning = dense2_pruning / 100\n",
    "op_layer_pruning = op_layer_pruning / 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.2    , 0.36   , 0.488  , 0.5904 , 0.67232, 0.73786, 0.79028,\n",
       "       0.83223, 0.86578, 0.89263, 0.9141 , 0.93128, 0.94502, 0.95602,\n",
       "       0.96482, 0.97185, 0.97748, 0.98199, 0.98559, 0.98847, 0.99078,\n",
       "       0.99262, 0.9941 , 0.99528])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dense1_pruning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a mask:\n",
    "A mask is created where all parameters equal to one.\n",
    "\n",
    "This will be used later by _GradientTape_ for training the defined neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate a new neural network model for which, the mask is to be created,\n",
    "# according to the paper-\n",
    "mask_model = pruned_nn(pruning_params_unpruned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Strip the model of its pruning parameters-\n",
    "mask_model_stripped = sparsity.strip_pruning(mask_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign all masks to one-\n",
    "\n",
    "for wts in mask_model_stripped.trainable_weights:\n",
    "    wts.assign(\n",
    "        tf.ones_like(\n",
    "            input = wts,\n",
    "            dtype = tf.float32\n",
    "        )\n",
    "\n",
    "    )\n",
    "    # wts.assign(1.)\n",
    "    # wts.assign(tf.where(tf.equal(wts, 0.), 0., 1.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mask model metrics:\n",
      "layer-wise number of nonzero parameters in each layer are: \n",
      "\n",
      "235200\n",
      "300\n",
      "30000\n",
      "100\n",
      "1000\n",
      "10\n",
      "\n",
      "Total number of trainable parameters = 266610\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nMask model metrics:\")\n",
    "print(\"layer-wise number of nonzero parameters in each layer are: \\n\")\n",
    "\n",
    "masked_sum_params = 0\n",
    "\n",
    "for layer in mask_model_stripped.trainable_weights:\n",
    "    print(tf.math.count_nonzero(layer, axis = None).numpy())\n",
    "    masked_sum_params += tf.math.count_nonzero(layer, axis = None).numpy()\n",
    "\n",
    "print(\"\\nTotal number of trainable parameters = {0}\\n\".format(masked_sum_params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "number of pruning rounds for LeNet NN = 24 and number of epochs = 100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nnumber of pruning rounds for LeNet NN = {0} and number of epochs = {1}\\n\".format(num_pruning_rounds, num_epochs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python 3 dictionary to hold model training metrics for each of _n_ rounds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Method - 1: Nested Python 3 dictionaries:\n",
    "history_main = {}\n",
    "\n",
    "# for x in range(num_pruning_rounds + 1):\n",
    "for x in range(num_pruning_rounds):\n",
    "    history = {}\n",
    "    \n",
    "    # Neural Network model, scalar metrics-\n",
    "    history['accuracy'] = np.zeros(shape = num_epochs)\n",
    "    history['val_accuracy'] = np.zeros(shape = num_epochs)\n",
    "    history['loss'] = np.zeros(shape = num_epochs)\n",
    "    history['val_loss'] = np.zeros(shape = num_epochs)\n",
    "    \n",
    "    # compute % of weights pruned at the end of each iterative pruning round-\n",
    "    history['percentage_wts_pruned'] = 90\n",
    "\n",
    "    history_main[x + 1] = history\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check-\n",
    "history_main.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check-\n",
    "history_main[10]['accuracy'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# User input parameters for Early Stopping in manual implementation-\n",
    "minimum_delta = 0.001\n",
    "patience = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_val_loss = 100\n",
    "loc_patience = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "orig_sum_params = 266610"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 1\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.2133, Accuracy: 93.6467, Test Loss: 0.1119, Test Accuracy: 96.500000\n",
      "Total number of trainable parameters = 266610\n",
      "\n",
      "Epoch 2, Loss: 0.0855, Accuracy: 97.3233, Test Loss: 0.0857, Test Accuracy: 97.330002\n",
      "Total number of trainable parameters = 266610\n",
      "\n",
      "Epoch 3, Loss: 0.0591, Accuracy: 98.1617, Test Loss: 0.0716, Test Accuracy: 97.790001\n",
      "Total number of trainable parameters = 266610\n",
      "\n",
      "Epoch 4, Loss: 0.0435, Accuracy: 98.5533, Test Loss: 0.0710, Test Accuracy: 97.989998\n",
      "Total number of trainable parameters = 266610\n",
      "\n",
      "Epoch 5, Loss: 0.0348, Accuracy: 98.8550, Test Loss: 0.1074, Test Accuracy: 96.790001\n",
      "Total number of trainable parameters = 266610\n",
      "\n",
      "Epoch 6, Loss: 0.0280, Accuracy: 99.0767, Test Loss: 0.0817, Test Accuracy: 97.629997\n",
      "Total number of trainable parameters = 266610\n",
      "\n",
      "Epoch 7, Loss: 0.0218, Accuracy: 99.2600, Test Loss: 0.0927, Test Accuracy: 97.860001\n",
      "Total number of trainable parameters = 266610\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 7s 109us/sample - loss: 0.0227 - accuracy: 0.9924 - val_loss: 0.0950 - val_accuracy: 0.9774\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0125 - accuracy: 0.9959 - val_loss: 0.0759 - val_accuracy: 0.9824\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0111 - accuracy: 0.9959 - val_loss: 0.0966 - val_accuracy: 0.9807\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0099 - accuracy: 0.9967 - val_loss: 0.0861 - val_accuracy: 0.9814\n",
      "Epoch 5/100\n",
      "60000/60000 [==============================] - 6s 99us/sample - loss: 0.0077 - accuracy: 0.9976 - val_loss: 0.0881 - val_accuracy: 0.9812\n",
      "\n",
      "Round = 1, total number of trainable parameters = 213370\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 2\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.1527, Accuracy: 95.3367, Test Loss: 0.0874, Test Accuracy: 97.369995\n",
      "Total number of trainable parameters = 213370\n",
      "\n",
      "Epoch 2, Loss: 0.0585, Accuracy: 98.1450, Test Loss: 0.0806, Test Accuracy: 97.419998\n",
      "Total number of trainable parameters = 213370\n",
      "\n",
      "Epoch 3, Loss: 0.0374, Accuracy: 98.8067, Test Loss: 0.0734, Test Accuracy: 97.680000\n",
      "Total number of trainable parameters = 213370\n",
      "\n",
      "Epoch 4, Loss: 0.0269, Accuracy: 99.1000, Test Loss: 0.0638, Test Accuracy: 97.889999\n",
      "Total number of trainable parameters = 213370\n",
      "\n",
      "Epoch 5, Loss: 0.0212, Accuracy: 99.2983, Test Loss: 0.0685, Test Accuracy: 98.009995\n",
      "Total number of trainable parameters = 213370\n",
      "\n",
      "Epoch 6, Loss: 0.0150, Accuracy: 99.4983, Test Loss: 0.0711, Test Accuracy: 98.089996\n",
      "Total number of trainable parameters = 213370\n",
      "\n",
      "Epoch 7, Loss: 0.0145, Accuracy: 99.5583, Test Loss: 0.0836, Test Accuracy: 97.849998\n",
      "Total number of trainable parameters = 213370\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 105us/sample - loss: 0.0150 - accuracy: 0.9948 - val_loss: 0.0931 - val_accuracy: 0.9800\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0076 - accuracy: 0.9977 - val_loss: 0.0919 - val_accuracy: 0.9811\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0052 - accuracy: 0.9984 - val_loss: 0.0969 - val_accuracy: 0.9791\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.0078 - accuracy: 0.9973 - val_loss: 0.0826 - val_accuracy: 0.9826\n",
      "Epoch 5/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0064 - accuracy: 0.9976 - val_loss: 0.1027 - val_accuracy: 0.9789\n",
      "Epoch 6/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0029 - accuracy: 0.9990 - val_loss: 0.1052 - val_accuracy: 0.9805\n",
      "Epoch 7/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0078 - accuracy: 0.9976 - val_loss: 0.0979 - val_accuracy: 0.9828\n",
      "\n",
      "Round = 2, total number of trainable parameters = 170778\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 3\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.1377, Accuracy: 95.8783, Test Loss: 0.0902, Test Accuracy: 97.150002\n",
      "Total number of trainable parameters = 170774\n",
      "\n",
      "Epoch 2, Loss: 0.0513, Accuracy: 98.3667, Test Loss: 0.0692, Test Accuracy: 97.750000\n",
      "Total number of trainable parameters = 170774\n",
      "\n",
      "Epoch 3, Loss: 0.0315, Accuracy: 99.0267, Test Loss: 0.0594, Test Accuracy: 98.259995\n",
      "Total number of trainable parameters = 170774\n",
      "\n",
      "Epoch 4, Loss: 0.0220, Accuracy: 99.2917, Test Loss: 0.0710, Test Accuracy: 97.959999\n",
      "Total number of trainable parameters = 170774\n",
      "\n",
      "Epoch 5, Loss: 0.0160, Accuracy: 99.5200, Test Loss: 0.0720, Test Accuracy: 98.229996\n",
      "Total number of trainable parameters = 170774\n",
      "\n",
      "Epoch 6, Loss: 0.0123, Accuracy: 99.6000, Test Loss: 0.0739, Test Accuracy: 98.129997\n",
      "Total number of trainable parameters = 170774\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 7s 108us/sample - loss: 0.0131 - accuracy: 0.9954 - val_loss: 0.0883 - val_accuracy: 0.9812\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0062 - accuracy: 0.9980 - val_loss: 0.0889 - val_accuracy: 0.9805\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0051 - accuracy: 0.9985 - val_loss: 0.0887 - val_accuracy: 0.9811\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0039 - accuracy: 0.9987 - val_loss: 0.0965 - val_accuracy: 0.9791\n",
      "\n",
      "Round = 3, total number of trainable parameters = 136701\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 4\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.1236, Accuracy: 96.3283, Test Loss: 0.0772, Test Accuracy: 97.649994\n",
      "Total number of trainable parameters = 136697\n",
      "\n",
      "Epoch 2, Loss: 0.0421, Accuracy: 98.6383, Test Loss: 0.0658, Test Accuracy: 97.889999\n",
      "Total number of trainable parameters = 136697\n",
      "\n",
      "Epoch 3, Loss: 0.0256, Accuracy: 99.1817, Test Loss: 0.0608, Test Accuracy: 98.079994\n",
      "Total number of trainable parameters = 136697\n",
      "\n",
      "Epoch 4, Loss: 0.0175, Accuracy: 99.4367, Test Loss: 0.0687, Test Accuracy: 98.049995\n",
      "Total number of trainable parameters = 136697\n",
      "\n",
      "Epoch 5, Loss: 0.0121, Accuracy: 99.6333, Test Loss: 0.0789, Test Accuracy: 97.930000\n",
      "Total number of trainable parameters = 136697\n",
      "\n",
      "Epoch 6, Loss: 0.0097, Accuracy: 99.7050, Test Loss: 0.0774, Test Accuracy: 98.059998\n",
      "Total number of trainable parameters = 136697\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 106us/sample - loss: 0.0101 - accuracy: 0.9965 - val_loss: 0.0718 - val_accuracy: 0.9839\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0049 - accuracy: 0.9986 - val_loss: 0.0846 - val_accuracy: 0.9815\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.0026 - accuracy: 0.9994 - val_loss: 0.0729 - val_accuracy: 0.9842\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0060 - accuracy: 0.9981 - val_loss: 0.0858 - val_accuracy: 0.9822\n",
      "\n",
      "Round = 4, total number of trainable parameters = 109441\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 5\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.1102, Accuracy: 96.6567, Test Loss: 0.0658, Test Accuracy: 98.059998\n",
      "Total number of trainable parameters = 109437\n",
      "\n",
      "Epoch 2, Loss: 0.0353, Accuracy: 98.9233, Test Loss: 0.0605, Test Accuracy: 98.159996\n",
      "Total number of trainable parameters = 109437\n",
      "\n",
      "Epoch 3, Loss: 0.0204, Accuracy: 99.3700, Test Loss: 0.0568, Test Accuracy: 98.369995\n",
      "Total number of trainable parameters = 109437\n",
      "\n",
      "Epoch 4, Loss: 0.0134, Accuracy: 99.6017, Test Loss: 0.0643, Test Accuracy: 98.169998\n",
      "Total number of trainable parameters = 109437\n",
      "\n",
      "Epoch 5, Loss: 0.0093, Accuracy: 99.7167, Test Loss: 0.0747, Test Accuracy: 98.099998\n",
      "Total number of trainable parameters = 109437\n",
      "\n",
      "Epoch 6, Loss: 0.0072, Accuracy: 99.7850, Test Loss: 0.0725, Test Accuracy: 98.329994\n",
      "Total number of trainable parameters = 109437\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 106us/sample - loss: 0.0080 - accuracy: 0.9974 - val_loss: 0.0788 - val_accuracy: 0.9820\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 99us/sample - loss: 0.0042 - accuracy: 0.9989 - val_loss: 0.0887 - val_accuracy: 0.9804\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0020 - accuracy: 0.9996 - val_loss: 0.0854 - val_accuracy: 0.9820\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0046 - accuracy: 0.9985 - val_loss: 0.0808 - val_accuracy: 0.9832\n",
      "\n",
      "Round = 5, total number of trainable parameters = 87634\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 6\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.1033, Accuracy: 96.9467, Test Loss: 0.0659, Test Accuracy: 97.809998\n",
      "Total number of trainable parameters = 87631\n",
      "\n",
      "Epoch 2, Loss: 0.0314, Accuracy: 99.0683, Test Loss: 0.0554, Test Accuracy: 98.199997\n",
      "Total number of trainable parameters = 87631\n",
      "\n",
      "Epoch 3, Loss: 0.0172, Accuracy: 99.5217, Test Loss: 0.0541, Test Accuracy: 98.259995\n",
      "Total number of trainable parameters = 87631\n",
      "\n",
      "Epoch 4, Loss: 0.0106, Accuracy: 99.7100, Test Loss: 0.0644, Test Accuracy: 98.129997\n",
      "Total number of trainable parameters = 87631\n",
      "\n",
      "Epoch 5, Loss: 0.0071, Accuracy: 99.8183, Test Loss: 0.0625, Test Accuracy: 98.369995\n",
      "Total number of trainable parameters = 87631\n",
      "\n",
      "Epoch 6, Loss: 0.0061, Accuracy: 99.8117, Test Loss: 0.0653, Test Accuracy: 98.329994\n",
      "Total number of trainable parameters = 87631\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 107us/sample - loss: 0.0057 - accuracy: 0.9982 - val_loss: 0.0750 - val_accuracy: 0.9835\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0046 - accuracy: 0.9986 - val_loss: 0.0664 - val_accuracy: 0.9845\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.0701 - val_accuracy: 0.9836\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.0039 - accuracy: 0.9988 - val_loss: 0.0764 - val_accuracy: 0.9842\n",
      "Epoch 5/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.0025 - accuracy: 0.9992 - val_loss: 0.0851 - val_accuracy: 0.9825\n",
      "\n",
      "Round = 6, total number of trainable parameters = 70188\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 7\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.1032, Accuracy: 97.0567, Test Loss: 0.0650, Test Accuracy: 97.939995\n",
      "Total number of trainable parameters = 70186\n",
      "\n",
      "Epoch 2, Loss: 0.0302, Accuracy: 99.1000, Test Loss: 0.0572, Test Accuracy: 98.159996\n",
      "Total number of trainable parameters = 70186\n",
      "\n",
      "Epoch 3, Loss: 0.0165, Accuracy: 99.5950, Test Loss: 0.0598, Test Accuracy: 98.129997\n",
      "Total number of trainable parameters = 70186\n",
      "\n",
      "Epoch 4, Loss: 0.0096, Accuracy: 99.7717, Test Loss: 0.0570, Test Accuracy: 98.379997\n",
      "Total number of trainable parameters = 70186\n",
      "\n",
      "Epoch 5, Loss: 0.0062, Accuracy: 99.8567, Test Loss: 0.0625, Test Accuracy: 98.339996\n",
      "Total number of trainable parameters = 70186\n",
      "\n",
      "Epoch 6, Loss: 0.0045, Accuracy: 99.9017, Test Loss: 0.0700, Test Accuracy: 98.209999\n",
      "Total number of trainable parameters = 70186\n",
      "\n",
      "Epoch 7, Loss: 0.0034, Accuracy: 99.9183, Test Loss: 0.0741, Test Accuracy: 98.239998\n",
      "Total number of trainable parameters = 70186\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 106us/sample - loss: 0.0041 - accuracy: 0.9988 - val_loss: 0.0790 - val_accuracy: 0.9830\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 99us/sample - loss: 0.0035 - accuracy: 0.9992 - val_loss: 0.0712 - val_accuracy: 0.9828\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.0811 - val_accuracy: 0.9824\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 8.8835e-04 - accuracy: 0.9998 - val_loss: 0.0876 - val_accuracy: 0.9819\n",
      "Epoch 5/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0028 - accuracy: 0.9994 - val_loss: 0.0845 - val_accuracy: 0.9827\n",
      "\n",
      "Round = 7, total number of trainable parameters = 56236\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 8\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.1077, Accuracy: 97.0517, Test Loss: 0.0613, Test Accuracy: 97.979996\n",
      "Total number of trainable parameters = 56234\n",
      "\n",
      "Epoch 2, Loss: 0.0305, Accuracy: 99.1317, Test Loss: 0.0545, Test Accuracy: 98.279999\n",
      "Total number of trainable parameters = 56234\n",
      "\n",
      "Epoch 3, Loss: 0.0167, Accuracy: 99.5633, Test Loss: 0.0566, Test Accuracy: 98.320000\n",
      "Total number of trainable parameters = 56234\n",
      "\n",
      "Epoch 4, Loss: 0.0096, Accuracy: 99.7750, Test Loss: 0.0590, Test Accuracy: 98.269997\n",
      "Total number of trainable parameters = 56234\n",
      "\n",
      "Epoch 5, Loss: 0.0057, Accuracy: 99.8933, Test Loss: 0.0632, Test Accuracy: 98.269997\n",
      "Total number of trainable parameters = 56234\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 105us/sample - loss: 0.0054 - accuracy: 0.9985 - val_loss: 0.0676 - val_accuracy: 0.9843\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0050 - accuracy: 0.9990 - val_loss: 0.0638 - val_accuracy: 0.9844\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0024 - accuracy: 0.9995 - val_loss: 0.0689 - val_accuracy: 0.9832\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0012 - accuracy: 0.9999 - val_loss: 0.0740 - val_accuracy: 0.9831\n",
      "Epoch 5/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0026 - accuracy: 0.9993 - val_loss: 0.0804 - val_accuracy: 0.9826\n",
      "\n",
      "Round = 8, total number of trainable parameters = 45067\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 9\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.1123, Accuracy: 96.8233, Test Loss: 0.0635, Test Accuracy: 98.099998\n",
      "Total number of trainable parameters = 45063\n",
      "\n",
      "Epoch 2, Loss: 0.0335, Accuracy: 99.0217, Test Loss: 0.0566, Test Accuracy: 98.269997\n",
      "Total number of trainable parameters = 45063\n",
      "\n",
      "Epoch 3, Loss: 0.0183, Accuracy: 99.5000, Test Loss: 0.0537, Test Accuracy: 98.409996\n",
      "Total number of trainable parameters = 45063\n",
      "\n",
      "Epoch 4, Loss: 0.0112, Accuracy: 99.7350, Test Loss: 0.0583, Test Accuracy: 98.290001\n",
      "Total number of trainable parameters = 45063\n",
      "\n",
      "Epoch 5, Loss: 0.0069, Accuracy: 99.8517, Test Loss: 0.0614, Test Accuracy: 98.360001\n",
      "Total number of trainable parameters = 45063\n",
      "\n",
      "Epoch 6, Loss: 0.0044, Accuracy: 99.9250, Test Loss: 0.0701, Test Accuracy: 98.180000\n",
      "Total number of trainable parameters = 45063\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 107us/sample - loss: 0.0041 - accuracy: 0.9991 - val_loss: 0.0804 - val_accuracy: 0.9810\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0058 - accuracy: 0.9986 - val_loss: 0.0700 - val_accuracy: 0.9820\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0018 - accuracy: 0.9998 - val_loss: 0.0672 - val_accuracy: 0.9833\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0010 - accuracy: 0.9999 - val_loss: 0.0715 - val_accuracy: 0.9836\n",
      "Epoch 5/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0024 - accuracy: 0.9993 - val_loss: 0.0819 - val_accuracy: 0.9828\n",
      "Epoch 6/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0018 - accuracy: 0.9997 - val_loss: 0.0777 - val_accuracy: 0.9841\n",
      "\n",
      "Round = 9, total number of trainable parameters = 36132\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 10\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.1222, Accuracy: 96.5867, Test Loss: 0.0706, Test Accuracy: 97.799995\n",
      "Total number of trainable parameters = 36124\n",
      "\n",
      "Epoch 2, Loss: 0.0369, Accuracy: 98.8983, Test Loss: 0.0587, Test Accuracy: 98.089996\n",
      "Total number of trainable parameters = 36124\n",
      "\n",
      "Epoch 3, Loss: 0.0217, Accuracy: 99.4050, Test Loss: 0.0553, Test Accuracy: 98.329994\n",
      "Total number of trainable parameters = 36124\n",
      "\n",
      "Epoch 4, Loss: 0.0130, Accuracy: 99.6783, Test Loss: 0.0580, Test Accuracy: 98.159996\n",
      "Total number of trainable parameters = 36124\n",
      "\n",
      "Epoch 5, Loss: 0.0090, Accuracy: 99.7883, Test Loss: 0.0620, Test Accuracy: 98.180000\n",
      "Total number of trainable parameters = 36124\n",
      "\n",
      "Epoch 6, Loss: 0.0056, Accuracy: 99.8800, Test Loss: 0.0665, Test Accuracy: 98.129997\n",
      "Total number of trainable parameters = 36124\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 7s 115us/sample - loss: 0.0050 - accuracy: 0.9990 - val_loss: 0.0734 - val_accuracy: 0.9807\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0068 - accuracy: 0.9983 - val_loss: 0.0641 - val_accuracy: 0.9825\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0027 - accuracy: 0.9997 - val_loss: 0.0679 - val_accuracy: 0.9826\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0019 - accuracy: 0.9998 - val_loss: 0.0744 - val_accuracy: 0.9820\n",
      "Epoch 5/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0019 - accuracy: 0.9997 - val_loss: 0.0787 - val_accuracy: 0.9821\n",
      "\n",
      "Round = 10, total number of trainable parameters = 28975\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 11\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.1364, Accuracy: 96.3317, Test Loss: 0.0751, Test Accuracy: 97.479996\n",
      "Total number of trainable parameters = 28969\n",
      "\n",
      "Epoch 2, Loss: 0.0442, Accuracy: 98.6483, Test Loss: 0.0662, Test Accuracy: 97.909996\n",
      "Total number of trainable parameters = 28969\n",
      "\n",
      "Epoch 3, Loss: 0.0269, Accuracy: 99.2400, Test Loss: 0.0574, Test Accuracy: 98.169998\n",
      "Total number of trainable parameters = 28969\n",
      "\n",
      "Epoch 4, Loss: 0.0183, Accuracy: 99.5033, Test Loss: 0.0613, Test Accuracy: 98.129997\n",
      "Total number of trainable parameters = 28969\n",
      "\n",
      "Epoch 5, Loss: 0.0121, Accuracy: 99.7100, Test Loss: 0.0624, Test Accuracy: 98.239998\n",
      "Total number of trainable parameters = 28969\n",
      "\n",
      "Epoch 6, Loss: 0.0084, Accuracy: 99.8283, Test Loss: 0.0656, Test Accuracy: 98.269997\n",
      "Total number of trainable parameters = 28969\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 108us/sample - loss: 0.0068 - accuracy: 0.9984 - val_loss: 0.0673 - val_accuracy: 0.9825\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 100us/sample - loss: 0.0078 - accuracy: 0.9982 - val_loss: 0.0670 - val_accuracy: 0.9827\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0040 - accuracy: 0.9995 - val_loss: 0.0723 - val_accuracy: 0.9817\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0029 - accuracy: 0.9996 - val_loss: 0.0770 - val_accuracy: 0.9822\n",
      "\n",
      "Round = 11, total number of trainable parameters = 23255\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 12\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.1480, Accuracy: 96.1833, Test Loss: 0.0780, Test Accuracy: 97.659996\n",
      "Total number of trainable parameters = 23240\n",
      "\n",
      "Epoch 2, Loss: 0.0503, Accuracy: 98.4750, Test Loss: 0.0666, Test Accuracy: 97.909996\n",
      "Total number of trainable parameters = 23240\n",
      "\n",
      "Epoch 3, Loss: 0.0333, Accuracy: 99.0067, Test Loss: 0.0631, Test Accuracy: 98.099998\n",
      "Total number of trainable parameters = 23240\n",
      "\n",
      "Epoch 4, Loss: 0.0233, Accuracy: 99.3450, Test Loss: 0.0640, Test Accuracy: 98.199997\n",
      "Total number of trainable parameters = 23240\n",
      "\n",
      "Epoch 5, Loss: 0.0168, Accuracy: 99.5517, Test Loss: 0.0653, Test Accuracy: 98.029999\n",
      "Total number of trainable parameters = 23240\n",
      "\n",
      "Epoch 6, Loss: 0.0121, Accuracy: 99.7000, Test Loss: 0.0666, Test Accuracy: 98.110001\n",
      "Total number of trainable parameters = 23240\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 106us/sample - loss: 0.0095 - accuracy: 0.9977 - val_loss: 0.0716 - val_accuracy: 0.9828\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0143 - accuracy: 0.9964 - val_loss: 0.0679 - val_accuracy: 0.9801\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0075 - accuracy: 0.9985 - val_loss: 0.0693 - val_accuracy: 0.9807\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0054 - accuracy: 0.9991 - val_loss: 0.0772 - val_accuracy: 0.9803\n",
      "Epoch 5/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.0045 - accuracy: 0.9991 - val_loss: 0.0799 - val_accuracy: 0.9807\n",
      "\n",
      "Round = 12, total number of trainable parameters = 18667\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 13\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.1566, Accuracy: 95.9650, Test Loss: 0.0901, Test Accuracy: 97.309998\n",
      "Total number of trainable parameters = 18654\n",
      "\n",
      "Epoch 2, Loss: 0.0556, Accuracy: 98.3433, Test Loss: 0.0705, Test Accuracy: 97.909996\n",
      "Total number of trainable parameters = 18654\n",
      "\n",
      "Epoch 3, Loss: 0.0371, Accuracy: 98.9550, Test Loss: 0.0665, Test Accuracy: 98.009995\n",
      "Total number of trainable parameters = 18654\n",
      "\n",
      "Epoch 4, Loss: 0.0279, Accuracy: 99.2233, Test Loss: 0.0647, Test Accuracy: 98.089996\n",
      "Total number of trainable parameters = 18654\n",
      "\n",
      "Epoch 5, Loss: 0.0208, Accuracy: 99.4400, Test Loss: 0.0651, Test Accuracy: 98.070000\n",
      "Total number of trainable parameters = 18654\n",
      "\n",
      "Epoch 6, Loss: 0.0159, Accuracy: 99.5717, Test Loss: 0.0667, Test Accuracy: 98.110001\n",
      "Total number of trainable parameters = 18654\n",
      "\n",
      "Epoch 7, Loss: 0.0126, Accuracy: 99.6950, Test Loss: 0.0677, Test Accuracy: 98.129997\n",
      "Total number of trainable parameters = 18654\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 7s 116us/sample - loss: 0.0104 - accuracy: 0.9973 - val_loss: 0.0730 - val_accuracy: 0.9808\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0161 - accuracy: 0.9957 - val_loss: 0.0712 - val_accuracy: 0.9806\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0099 - accuracy: 0.9979 - val_loss: 0.0743 - val_accuracy: 0.9795\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.0074 - accuracy: 0.9986 - val_loss: 0.0754 - val_accuracy: 0.9812\n",
      "Epoch 5/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0058 - accuracy: 0.9989 - val_loss: 0.0778 - val_accuracy: 0.9811\n",
      "\n",
      "Round = 13, total number of trainable parameters = 14995\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 14\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.1804, Accuracy: 95.1967, Test Loss: 0.0926, Test Accuracy: 97.180000\n",
      "Total number of trainable parameters = 14976\n",
      "\n",
      "Epoch 2, Loss: 0.0657, Accuracy: 98.0350, Test Loss: 0.0758, Test Accuracy: 97.599998\n",
      "Total number of trainable parameters = 14976\n",
      "\n",
      "Epoch 3, Loss: 0.0468, Accuracy: 98.5800, Test Loss: 0.0693, Test Accuracy: 97.899994\n",
      "Total number of trainable parameters = 14976\n",
      "\n",
      "Epoch 4, Loss: 0.0357, Accuracy: 98.9283, Test Loss: 0.0646, Test Accuracy: 98.059998\n",
      "Total number of trainable parameters = 14976\n",
      "\n",
      "Epoch 5, Loss: 0.0281, Accuracy: 99.1950, Test Loss: 0.0665, Test Accuracy: 98.040001\n",
      "Total number of trainable parameters = 14976\n",
      "\n",
      "Epoch 6, Loss: 0.0226, Accuracy: 99.3883, Test Loss: 0.0683, Test Accuracy: 98.079994\n",
      "Total number of trainable parameters = 14976\n",
      "\n",
      "Epoch 7, Loss: 0.0184, Accuracy: 99.5117, Test Loss: 0.0690, Test Accuracy: 98.009995\n",
      "Total number of trainable parameters = 14976\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 107us/sample - loss: 0.0156 - accuracy: 0.9956 - val_loss: 0.0716 - val_accuracy: 0.9803\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0292 - accuracy: 0.9913 - val_loss: 0.0731 - val_accuracy: 0.9784\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0176 - accuracy: 0.9956 - val_loss: 0.0730 - val_accuracy: 0.9783\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0142 - accuracy: 0.9964 - val_loss: 0.0757 - val_accuracy: 0.9793\n",
      "\n",
      "Round = 14, total number of trainable parameters = 12048\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 15\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.1929, Accuracy: 95.0717, Test Loss: 0.0963, Test Accuracy: 96.970001\n",
      "Total number of trainable parameters = 12028\n",
      "\n",
      "Epoch 2, Loss: 0.0685, Accuracy: 97.9050, Test Loss: 0.0776, Test Accuracy: 97.589996\n",
      "Total number of trainable parameters = 12028\n",
      "\n",
      "Epoch 3, Loss: 0.0500, Accuracy: 98.4567, Test Loss: 0.0734, Test Accuracy: 97.659996\n",
      "Total number of trainable parameters = 12028\n",
      "\n",
      "Epoch 4, Loss: 0.0393, Accuracy: 98.8500, Test Loss: 0.0716, Test Accuracy: 97.790001\n",
      "Total number of trainable parameters = 12028\n",
      "\n",
      "Epoch 5, Loss: 0.0325, Accuracy: 99.0450, Test Loss: 0.0694, Test Accuracy: 97.839996\n",
      "Total number of trainable parameters = 12028\n",
      "\n",
      "Epoch 6, Loss: 0.0269, Accuracy: 99.2567, Test Loss: 0.0696, Test Accuracy: 97.909996\n",
      "Total number of trainable parameters = 12028\n",
      "\n",
      "Epoch 7, Loss: 0.0231, Accuracy: 99.3500, Test Loss: 0.0733, Test Accuracy: 97.829994\n",
      "Total number of trainable parameters = 12028\n",
      "\n",
      "Epoch 8, Loss: 0.0198, Accuracy: 99.4700, Test Loss: 0.0749, Test Accuracy: 97.829994\n",
      "Total number of trainable parameters = 12028\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 7s 117us/sample - loss: 0.0175 - accuracy: 0.9953 - val_loss: 0.0799 - val_accuracy: 0.9788\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.1755 - accuracy: 0.9672 - val_loss: 0.1311 - val_accuracy: 0.9708\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.0708 - accuracy: 0.9859 - val_loss: 0.1022 - val_accuracy: 0.9738\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.0493 - accuracy: 0.9892 - val_loss: 0.0923 - val_accuracy: 0.9759\n",
      "\n",
      "Round = 15, total number of trainable parameters = 9685\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 16\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.3353, Accuracy: 92.5983, Test Loss: 0.1562, Test Accuracy: 96.500000\n",
      "Total number of trainable parameters = 9650\n",
      "\n",
      "Epoch 2, Loss: 0.1132, Accuracy: 97.4717, Test Loss: 0.1109, Test Accuracy: 97.169998\n",
      "Total number of trainable parameters = 9650\n",
      "\n",
      "Epoch 3, Loss: 0.0793, Accuracy: 98.0750, Test Loss: 0.0954, Test Accuracy: 97.449997\n",
      "Total number of trainable parameters = 9650\n",
      "\n",
      "Epoch 4, Loss: 0.0621, Accuracy: 98.3983, Test Loss: 0.0870, Test Accuracy: 97.579994\n",
      "Total number of trainable parameters = 9650\n",
      "\n",
      "Epoch 5, Loss: 0.0511, Accuracy: 98.6750, Test Loss: 0.0824, Test Accuracy: 97.669998\n",
      "Total number of trainable parameters = 9650\n",
      "\n",
      "Epoch 6, Loss: 0.0435, Accuracy: 98.8150, Test Loss: 0.0797, Test Accuracy: 97.680000\n",
      "Total number of trainable parameters = 9650\n",
      "\n",
      "Epoch 7, Loss: 0.0379, Accuracy: 98.9717, Test Loss: 0.0798, Test Accuracy: 97.739998\n",
      "Total number of trainable parameters = 9650\n",
      "\n",
      "Epoch 8, Loss: 0.0337, Accuracy: 99.0683, Test Loss: 0.0786, Test Accuracy: 97.799995\n",
      "Total number of trainable parameters = 9650\n",
      "\n",
      "Epoch 9, Loss: 0.0301, Accuracy: 99.1733, Test Loss: 0.0803, Test Accuracy: 97.790001\n",
      "Total number of trainable parameters = 9650\n",
      "\n",
      "Epoch 10, Loss: 0.0270, Accuracy: 99.2967, Test Loss: 0.0830, Test Accuracy: 97.719994\n",
      "Total number of trainable parameters = 9650\n",
      "\n",
      "Epoch 11, Loss: 0.0249, Accuracy: 99.3067, Test Loss: 0.0837, Test Accuracy: 97.729996\n",
      "Total number of trainable parameters = 9650\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 107us/sample - loss: 0.0229 - accuracy: 0.9938 - val_loss: 0.0859 - val_accuracy: 0.9771\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.1401 - accuracy: 0.9704 - val_loss: 0.1333 - val_accuracy: 0.9678\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0666 - accuracy: 0.9852 - val_loss: 0.1137 - val_accuracy: 0.9708\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0501 - accuracy: 0.9881 - val_loss: 0.1085 - val_accuracy: 0.9714\n",
      "\n",
      "Round = 16, total number of trainable parameters = 7780\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 17\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.4705, Accuracy: 91.1317, Test Loss: 0.2077, Test Accuracy: 96.240005\n",
      "Total number of trainable parameters = 7758\n",
      "\n",
      "Epoch 2, Loss: 0.1523, Accuracy: 97.1833, Test Loss: 0.1376, Test Accuracy: 96.849998\n",
      "Total number of trainable parameters = 7758\n",
      "\n",
      "Epoch 3, Loss: 0.1041, Accuracy: 97.7567, Test Loss: 0.1114, Test Accuracy: 97.199997\n",
      "Total number of trainable parameters = 7758\n",
      "\n",
      "Epoch 4, Loss: 0.0804, Accuracy: 98.0983, Test Loss: 0.0997, Test Accuracy: 97.439995\n",
      "Total number of trainable parameters = 7758\n",
      "\n",
      "Epoch 5, Loss: 0.0669, Accuracy: 98.3150, Test Loss: 0.0927, Test Accuracy: 97.479996\n",
      "Total number of trainable parameters = 7758\n",
      "\n",
      "Epoch 6, Loss: 0.0572, Accuracy: 98.5350, Test Loss: 0.0897, Test Accuracy: 97.500000\n",
      "Total number of trainable parameters = 7758\n",
      "\n",
      "Epoch 7, Loss: 0.0505, Accuracy: 98.6817, Test Loss: 0.0878, Test Accuracy: 97.529999\n",
      "Total number of trainable parameters = 7758\n",
      "\n",
      "Epoch 8, Loss: 0.0452, Accuracy: 98.8033, Test Loss: 0.0871, Test Accuracy: 97.629997\n",
      "Total number of trainable parameters = 7758\n",
      "\n",
      "Epoch 9, Loss: 0.0409, Accuracy: 98.9100, Test Loss: 0.0871, Test Accuracy: 97.519997\n",
      "Total number of trainable parameters = 7758\n",
      "\n",
      "Epoch 10, Loss: 0.0379, Accuracy: 98.9750, Test Loss: 0.0880, Test Accuracy: 97.610001\n",
      "Total number of trainable parameters = 7758\n",
      "\n",
      "Epoch 11, Loss: 0.0350, Accuracy: 99.0750, Test Loss: 0.0894, Test Accuracy: 97.549995\n",
      "Total number of trainable parameters = 7758\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 107us/sample - loss: 0.0327 - accuracy: 0.9912 - val_loss: 0.0913 - val_accuracy: 0.9751\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.1689 - accuracy: 0.9718 - val_loss: 0.1491 - val_accuracy: 0.9676\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0787 - accuracy: 0.9845 - val_loss: 0.1252 - val_accuracy: 0.9705\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0588 - accuracy: 0.9872 - val_loss: 0.1146 - val_accuracy: 0.9713\n",
      "\n",
      "Round = 17, total number of trainable parameters = 6260\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 18\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.6104, Accuracy: 90.6367, Test Loss: 0.3099, Test Accuracy: 95.910004\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "Epoch 2, Loss: 0.2315, Accuracy: 96.9500, Test Loss: 0.2025, Test Accuracy: 96.599998\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "Epoch 3, Loss: 0.1572, Accuracy: 97.4850, Test Loss: 0.1585, Test Accuracy: 96.879997\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "Epoch 4, Loss: 0.1197, Accuracy: 97.7683, Test Loss: 0.1357, Test Accuracy: 97.059998\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "Epoch 5, Loss: 0.0967, Accuracy: 98.0217, Test Loss: 0.1201, Test Accuracy: 97.159996\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "Epoch 6, Loss: 0.0814, Accuracy: 98.2717, Test Loss: 0.1126, Test Accuracy: 97.290001\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "Epoch 7, Loss: 0.0703, Accuracy: 98.4317, Test Loss: 0.1072, Test Accuracy: 97.309998\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "Epoch 8, Loss: 0.0625, Accuracy: 98.5633, Test Loss: 0.1036, Test Accuracy: 97.290001\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "Epoch 9, Loss: 0.0565, Accuracy: 98.6483, Test Loss: 0.1009, Test Accuracy: 97.489998\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "Epoch 10, Loss: 0.0518, Accuracy: 98.7850, Test Loss: 0.0999, Test Accuracy: 97.409996\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "Epoch 11, Loss: 0.0480, Accuracy: 98.8667, Test Loss: 0.1005, Test Accuracy: 97.430000\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "Epoch 12, Loss: 0.0449, Accuracy: 98.9050, Test Loss: 0.1002, Test Accuracy: 97.389999\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "Epoch 13, Loss: 0.0422, Accuracy: 98.9817, Test Loss: 0.0998, Test Accuracy: 97.470001\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "Epoch 14, Loss: 0.0401, Accuracy: 99.0317, Test Loss: 0.1017, Test Accuracy: 97.439995\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "Epoch 15, Loss: 0.0380, Accuracy: 99.0650, Test Loss: 0.1024, Test Accuracy: 97.449997\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "Epoch 16, Loss: 0.0362, Accuracy: 99.1517, Test Loss: 0.1038, Test Accuracy: 97.430000\n",
      "Total number of trainable parameters = 6239\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 107us/sample - loss: 0.0349 - accuracy: 0.9912 - val_loss: 0.1072 - val_accuracy: 0.9732\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0604 - accuracy: 0.9818 - val_loss: 0.1176 - val_accuracy: 0.9700\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0484 - accuracy: 0.9859 - val_loss: 0.1183 - val_accuracy: 0.9694\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.0451 - accuracy: 0.9873 - val_loss: 0.1189 - val_accuracy: 0.9702\n",
      "\n",
      "Round = 18, total number of trainable parameters = 5037\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 19\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.6629, Accuracy: 88.9017, Test Loss: 0.3431, Test Accuracy: 95.169998\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 2, Loss: 0.2618, Accuracy: 96.0883, Test Loss: 0.2281, Test Accuracy: 96.260002\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 3, Loss: 0.1812, Accuracy: 96.7850, Test Loss: 0.1827, Test Accuracy: 96.529999\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 4, Loss: 0.1407, Accuracy: 97.2583, Test Loss: 0.1547, Test Accuracy: 96.820000\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 5, Loss: 0.1156, Accuracy: 97.5383, Test Loss: 0.1386, Test Accuracy: 96.959999\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 6, Loss: 0.0991, Accuracy: 97.7300, Test Loss: 0.1277, Test Accuracy: 97.059998\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 7, Loss: 0.0876, Accuracy: 97.9133, Test Loss: 0.1225, Test Accuracy: 97.040001\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 8, Loss: 0.0792, Accuracy: 98.0783, Test Loss: 0.1185, Test Accuracy: 97.119995\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 9, Loss: 0.0727, Accuracy: 98.1900, Test Loss: 0.1147, Test Accuracy: 97.199997\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 10, Loss: 0.0677, Accuracy: 98.2983, Test Loss: 0.1134, Test Accuracy: 97.099998\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 11, Loss: 0.0635, Accuracy: 98.3517, Test Loss: 0.1144, Test Accuracy: 97.150002\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 12, Loss: 0.0604, Accuracy: 98.4183, Test Loss: 0.1116, Test Accuracy: 97.180000\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 13, Loss: 0.0575, Accuracy: 98.5000, Test Loss: 0.1109, Test Accuracy: 97.169998\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 14, Loss: 0.0550, Accuracy: 98.5517, Test Loss: 0.1108, Test Accuracy: 97.169998\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 15, Loss: 0.0530, Accuracy: 98.5550, Test Loss: 0.1112, Test Accuracy: 97.190002\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 16, Loss: 0.0512, Accuracy: 98.6117, Test Loss: 0.1110, Test Accuracy: 97.180000\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "Epoch 17, Loss: 0.0495, Accuracy: 98.6367, Test Loss: 0.1148, Test Accuracy: 97.220001\n",
      "Total number of trainable parameters = 5016\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 107us/sample - loss: 0.0484 - accuracy: 0.9865 - val_loss: 0.1136 - val_accuracy: 0.9713\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0704 - accuracy: 0.9791 - val_loss: 0.1289 - val_accuracy: 0.9666\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.0608 - accuracy: 0.9824 - val_loss: 0.1275 - val_accuracy: 0.9676\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.0580 - accuracy: 0.9833 - val_loss: 0.1255 - val_accuracy: 0.9685\n",
      "\n",
      "Round = 19, total number of trainable parameters = 4057\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 20\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.6981, Accuracy: 87.7017, Test Loss: 0.3648, Test Accuracy: 94.770004\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 2, Loss: 0.2816, Accuracy: 95.5833, Test Loss: 0.2506, Test Accuracy: 95.670006\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 3, Loss: 0.1994, Accuracy: 96.3500, Test Loss: 0.2015, Test Accuracy: 96.060005\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 4, Loss: 0.1577, Accuracy: 96.7867, Test Loss: 0.1743, Test Accuracy: 96.300003\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 5, Loss: 0.1322, Accuracy: 97.1050, Test Loss: 0.1583, Test Accuracy: 96.590004\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 6, Loss: 0.1147, Accuracy: 97.3133, Test Loss: 0.1468, Test Accuracy: 96.730003\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 7, Loss: 0.1028, Accuracy: 97.4367, Test Loss: 0.1416, Test Accuracy: 96.720001\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 8, Loss: 0.0942, Accuracy: 97.5817, Test Loss: 0.1361, Test Accuracy: 96.760002\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 9, Loss: 0.0873, Accuracy: 97.6733, Test Loss: 0.1334, Test Accuracy: 96.669998\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 10, Loss: 0.0822, Accuracy: 97.8200, Test Loss: 0.1316, Test Accuracy: 96.839996\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 11, Loss: 0.0777, Accuracy: 97.8850, Test Loss: 0.1317, Test Accuracy: 96.790001\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 12, Loss: 0.0742, Accuracy: 97.9333, Test Loss: 0.1290, Test Accuracy: 96.879997\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 13, Loss: 0.0715, Accuracy: 98.0067, Test Loss: 0.1292, Test Accuracy: 96.949997\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 14, Loss: 0.0691, Accuracy: 98.0717, Test Loss: 0.1301, Test Accuracy: 96.900002\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 15, Loss: 0.0669, Accuracy: 98.0933, Test Loss: 0.1290, Test Accuracy: 96.889999\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 16, Loss: 0.0652, Accuracy: 98.1717, Test Loss: 0.1288, Test Accuracy: 96.910004\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 17, Loss: 0.0636, Accuracy: 98.2083, Test Loss: 0.1305, Test Accuracy: 96.900002\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 18, Loss: 0.0622, Accuracy: 98.2950, Test Loss: 0.1298, Test Accuracy: 96.940002\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "Epoch 19, Loss: 0.0610, Accuracy: 98.2650, Test Loss: 0.1352, Test Accuracy: 96.849998\n",
      "Total number of trainable parameters = 4036\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 106us/sample - loss: 0.0600 - accuracy: 0.9830 - val_loss: 0.1335 - val_accuracy: 0.9690\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.1021 - accuracy: 0.9675 - val_loss: 0.1521 - val_accuracy: 0.9624\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.0855 - accuracy: 0.9738 - val_loss: 0.1506 - val_accuracy: 0.9622\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.0823 - accuracy: 0.9755 - val_loss: 0.1493 - val_accuracy: 0.9633\n",
      "\n",
      "Round = 20, total number of trainable parameters = 3271\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 21\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 0.7527, Accuracy: 86.1250, Test Loss: 0.3975, Test Accuracy: 94.029999\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 2, Loss: 0.3160, Accuracy: 94.7783, Test Loss: 0.2758, Test Accuracy: 94.880005\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 3, Loss: 0.2293, Accuracy: 95.5833, Test Loss: 0.2258, Test Accuracy: 95.349998\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 4, Loss: 0.1853, Accuracy: 96.0117, Test Loss: 0.1980, Test Accuracy: 95.590004\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 5, Loss: 0.1581, Accuracy: 96.3783, Test Loss: 0.1801, Test Accuracy: 95.779999\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 6, Loss: 0.1397, Accuracy: 96.5983, Test Loss: 0.1675, Test Accuracy: 96.050003\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 7, Loss: 0.1272, Accuracy: 96.7983, Test Loss: 0.1594, Test Accuracy: 96.060005\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 8, Loss: 0.1180, Accuracy: 96.9300, Test Loss: 0.1567, Test Accuracy: 96.050003\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 9, Loss: 0.1108, Accuracy: 97.0450, Test Loss: 0.1539, Test Accuracy: 96.139999\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 10, Loss: 0.1058, Accuracy: 97.1133, Test Loss: 0.1506, Test Accuracy: 96.110001\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 11, Loss: 0.1018, Accuracy: 97.1900, Test Loss: 0.1490, Test Accuracy: 96.220001\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 12, Loss: 0.0982, Accuracy: 97.2767, Test Loss: 0.1496, Test Accuracy: 96.139999\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 13, Loss: 0.0955, Accuracy: 97.2717, Test Loss: 0.1471, Test Accuracy: 96.279999\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 14, Loss: 0.0929, Accuracy: 97.3367, Test Loss: 0.1462, Test Accuracy: 96.370003\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 15, Loss: 0.0907, Accuracy: 97.3767, Test Loss: 0.1454, Test Accuracy: 96.330002\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 16, Loss: 0.0889, Accuracy: 97.4533, Test Loss: 0.1468, Test Accuracy: 96.260002\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 17, Loss: 0.0872, Accuracy: 97.4517, Test Loss: 0.1486, Test Accuracy: 96.150002\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "Epoch 18, Loss: 0.0855, Accuracy: 97.5050, Test Loss: 0.1460, Test Accuracy: 96.330002\n",
      "Total number of trainable parameters = 3254\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 107us/sample - loss: 0.0846 - accuracy: 0.9756 - val_loss: 0.1465 - val_accuracy: 0.9627\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 1.1412 - accuracy: 0.7031 - val_loss: 0.9222 - val_accuracy: 0.7389\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.7322 - accuracy: 0.7509 - val_loss: 0.6652 - val_accuracy: 0.7437\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.5459 - accuracy: 0.7533 - val_loss: 0.5471 - val_accuracy: 0.7459\n",
      "\n",
      "Round = 21, total number of trainable parameters = 2639\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 22\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.2425, Accuracy: 60.5050, Test Loss: 0.9550, Test Accuracy: 65.709999\n",
      "Total number of trainable parameters = 2608\n",
      "\n",
      "Epoch 2, Loss: 0.8869, Accuracy: 66.1750, Test Loss: 0.8475, Test Accuracy: 66.309998\n",
      "Total number of trainable parameters = 2608\n",
      "\n",
      "Epoch 3, Loss: 0.8156, Accuracy: 66.6417, Test Loss: 0.8087, Test Accuracy: 66.479996\n",
      "Total number of trainable parameters = 2608\n",
      "\n",
      "Epoch 4, Loss: 0.7825, Accuracy: 66.8367, Test Loss: 0.7896, Test Accuracy: 66.449997\n",
      "Total number of trainable parameters = 2608\n",
      "\n",
      "Epoch 5, Loss: 0.7643, Accuracy: 66.9133, Test Loss: 0.7788, Test Accuracy: 66.419998\n",
      "Total number of trainable parameters = 2608\n",
      "\n",
      "Epoch 6, Loss: 0.7527, Accuracy: 66.9950, Test Loss: 0.7725, Test Accuracy: 66.439995\n",
      "Total number of trainable parameters = 2608\n",
      "\n",
      "Epoch 7, Loss: 0.7446, Accuracy: 67.0700, Test Loss: 0.7711, Test Accuracy: 66.379997\n",
      "Total number of trainable parameters = 2608\n",
      "\n",
      "Epoch 8, Loss: 0.7391, Accuracy: 67.1300, Test Loss: 0.7700, Test Accuracy: 66.139999\n",
      "Total number of trainable parameters = 2608\n",
      "\n",
      "Epoch 9, Loss: 0.7348, Accuracy: 67.1383, Test Loss: 0.7638, Test Accuracy: 66.439995\n",
      "Total number of trainable parameters = 2608\n",
      "\n",
      "Epoch 10, Loss: 0.7317, Accuracy: 67.1467, Test Loss: 0.7630, Test Accuracy: 66.529999\n",
      "Total number of trainable parameters = 2608\n",
      "\n",
      "Epoch 11, Loss: 0.7292, Accuracy: 67.2050, Test Loss: 0.7615, Test Accuracy: 66.610001\n",
      "Total number of trainable parameters = 2608\n",
      "\n",
      "Epoch 12, Loss: 0.7271, Accuracy: 67.2100, Test Loss: 0.7597, Test Accuracy: 66.680000\n",
      "Total number of trainable parameters = 2608\n",
      "\n",
      "Epoch 13, Loss: 0.7253, Accuracy: 67.2433, Test Loss: 0.7607, Test Accuracy: 66.559998\n",
      "Total number of trainable parameters = 2608\n",
      "\n",
      "Epoch 14, Loss: 0.7236, Accuracy: 67.2883, Test Loss: 0.7598, Test Accuracy: 66.579994\n",
      "Total number of trainable parameters = 2608\n",
      "\n",
      "Epoch 15, Loss: 0.7220, Accuracy: 67.2817, Test Loss: 0.7607, Test Accuracy: 66.559998\n",
      "Total number of trainable parameters = 2608\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 108us/sample - loss: 0.7213 - accuracy: 0.6729 - val_loss: 0.7604 - val_accuracy: 0.6654\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.7232 - accuracy: 0.6725 - val_loss: 0.7629 - val_accuracy: 0.6650\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.7213 - accuracy: 0.6722 - val_loss: 0.7610 - val_accuracy: 0.6657\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.7203 - accuracy: 0.6727 - val_loss: 0.7612 - val_accuracy: 0.6660\n",
      "\n",
      "Round = 22, total number of trainable parameters = 2117\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 23\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.2609, Accuracy: 60.0767, Test Loss: 0.9638, Test Accuracy: 65.720001\n",
      "Total number of trainable parameters = 2100\n",
      "\n",
      "Epoch 2, Loss: 0.8903, Accuracy: 66.1600, Test Loss: 0.8499, Test Accuracy: 66.459999\n",
      "Total number of trainable parameters = 2100\n",
      "\n",
      "Epoch 3, Loss: 0.8158, Accuracy: 66.5900, Test Loss: 0.8077, Test Accuracy: 66.449997\n",
      "Total number of trainable parameters = 2100\n",
      "\n",
      "Epoch 4, Loss: 0.7817, Accuracy: 66.7633, Test Loss: 0.7888, Test Accuracy: 66.430000\n",
      "Total number of trainable parameters = 2100\n",
      "\n",
      "Epoch 5, Loss: 0.7630, Accuracy: 66.9067, Test Loss: 0.7755, Test Accuracy: 66.529999\n",
      "Total number of trainable parameters = 2100\n",
      "\n",
      "Epoch 6, Loss: 0.7513, Accuracy: 66.9800, Test Loss: 0.7697, Test Accuracy: 66.570000\n",
      "Total number of trainable parameters = 2100\n",
      "\n",
      "Epoch 7, Loss: 0.7437, Accuracy: 67.0233, Test Loss: 0.7648, Test Accuracy: 66.489998\n",
      "Total number of trainable parameters = 2100\n",
      "\n",
      "Epoch 8, Loss: 0.7379, Accuracy: 67.0117, Test Loss: 0.7607, Test Accuracy: 66.579994\n",
      "Total number of trainable parameters = 2100\n",
      "\n",
      "Epoch 9, Loss: 0.7339, Accuracy: 67.0567, Test Loss: 0.7608, Test Accuracy: 66.430000\n",
      "Total number of trainable parameters = 2100\n",
      "\n",
      "Epoch 10, Loss: 0.7309, Accuracy: 67.0850, Test Loss: 0.7594, Test Accuracy: 66.489998\n",
      "Total number of trainable parameters = 2100\n",
      "\n",
      "Epoch 11, Loss: 0.7287, Accuracy: 67.0917, Test Loss: 0.7575, Test Accuracy: 66.529999\n",
      "Total number of trainable parameters = 2100\n",
      "\n",
      "Epoch 12, Loss: 0.7266, Accuracy: 67.1317, Test Loss: 0.7573, Test Accuracy: 66.540001\n",
      "Total number of trainable parameters = 2100\n",
      "\n",
      "Epoch 13, Loss: 0.7249, Accuracy: 67.1767, Test Loss: 0.7583, Test Accuracy: 66.349998\n",
      "Total number of trainable parameters = 2100\n",
      "\n",
      "Epoch 14, Loss: 0.7238, Accuracy: 67.1833, Test Loss: 0.7573, Test Accuracy: 66.529999\n",
      "Total number of trainable parameters = 2100\n",
      "\n",
      "Epoch 15, Loss: 0.7223, Accuracy: 67.1967, Test Loss: 0.7578, Test Accuracy: 66.519997\n",
      "Total number of trainable parameters = 2100\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 7s 109us/sample - loss: 0.7216 - accuracy: 0.6722 - val_loss: 0.7562 - val_accuracy: 0.6657\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.7283 - accuracy: 0.6701 - val_loss: 0.7624 - val_accuracy: 0.6658\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 98us/sample - loss: 0.7251 - accuracy: 0.6713 - val_loss: 0.7638 - val_accuracy: 0.6635\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.7237 - accuracy: 0.6714 - val_loss: 0.7635 - val_accuracy: 0.6643\n",
      "\n",
      "Round = 23, total number of trainable parameters = 1707\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 24\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.2648, Accuracy: 59.8967, Test Loss: 0.9632, Test Accuracy: 65.580002\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 2, Loss: 0.8898, Accuracy: 66.0950, Test Loss: 0.8521, Test Accuracy: 66.089996\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 3, Loss: 0.8164, Accuracy: 66.5050, Test Loss: 0.8085, Test Accuracy: 66.399994\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 4, Loss: 0.7835, Accuracy: 66.7367, Test Loss: 0.7891, Test Accuracy: 66.320000\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 5, Loss: 0.7654, Accuracy: 66.8183, Test Loss: 0.7796, Test Accuracy: 66.349998\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 6, Loss: 0.7543, Accuracy: 66.9167, Test Loss: 0.7726, Test Accuracy: 66.540001\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 7, Loss: 0.7468, Accuracy: 66.9433, Test Loss: 0.7702, Test Accuracy: 66.409996\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 8, Loss: 0.7417, Accuracy: 67.0033, Test Loss: 0.7665, Test Accuracy: 66.529999\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 9, Loss: 0.7379, Accuracy: 67.0233, Test Loss: 0.7661, Test Accuracy: 66.439995\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 10, Loss: 0.7351, Accuracy: 67.0750, Test Loss: 0.7648, Test Accuracy: 66.509995\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 11, Loss: 0.7328, Accuracy: 67.1050, Test Loss: 0.7676, Test Accuracy: 66.329994\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 12, Loss: 0.7311, Accuracy: 67.1133, Test Loss: 0.7649, Test Accuracy: 66.500000\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 13, Loss: 0.7297, Accuracy: 67.1150, Test Loss: 0.7646, Test Accuracy: 66.509995\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 14, Loss: 0.7288, Accuracy: 67.1167, Test Loss: 0.7633, Test Accuracy: 66.479996\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 15, Loss: 0.7278, Accuracy: 67.1250, Test Loss: 0.7641, Test Accuracy: 66.409996\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 16, Loss: 0.7266, Accuracy: 67.1450, Test Loss: 0.7669, Test Accuracy: 66.339996\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "Epoch 17, Loss: 0.7262, Accuracy: 67.1350, Test Loss: 0.7654, Test Accuracy: 66.489998\n",
      "Total number of trainable parameters = 1697\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 6s 108us/sample - loss: 0.7257 - accuracy: 0.6713 - val_loss: 0.7649 - val_accuracy: 0.6645\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 1.1986 - accuracy: 0.5735 - val_loss: 1.1395 - val_accuracy: 0.5645\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 1.0391 - accuracy: 0.5740 - val_loss: 1.0361 - val_accuracy: 0.5688\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 6s 96us/sample - loss: 0.9911 - accuracy: 0.5742 - val_loss: 1.0208 - val_accuracy: 0.5676\n",
      "\n",
      "Round = 24, total number of trainable parameters = 1383\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, num_pruning_rounds + 1):\n",
    "    \n",
    "    print(\"\\n\\n\\nIterative pruning round: {0}\\n\\n\".format(i))\n",
    "    \n",
    "    # Define 'train_one_step()' and 'test_step()' functions here-\n",
    "    @tf.function\n",
    "    def train_one_step(model, mask_model, optimizer, x, y):\n",
    "        '''\n",
    "        Function to compute one step of gradient descent optimization\n",
    "        '''\n",
    "        with tf.GradientTape() as tape:\n",
    "            # Make predictions using defined model-\n",
    "            y_pred = model(x)\n",
    "\n",
    "            # Compute loss-\n",
    "            loss = loss_fn(y, y_pred)\n",
    "        \n",
    "        # Compute gradients wrt defined loss and weights and biases-\n",
    "        grads = tape.gradient(loss, model.trainable_variables)\n",
    "    \n",
    "        # type(grads)\n",
    "        # list\n",
    "    \n",
    "        # List to hold element-wise multiplication between-\n",
    "        # computed gradient and masks-\n",
    "        grad_mask_mul = []\n",
    "    \n",
    "        # Perform element-wise multiplication between computed gradients and masks-\n",
    "        for grad_layer, mask in zip(grads, mask_model.trainable_weights):\n",
    "            grad_mask_mul.append(tf.math.multiply(grad_layer, mask))\n",
    "    \n",
    "        # Apply computed gradients to model's weights and biases-\n",
    "        optimizer.apply_gradients(zip(grad_mask_mul, model.trainable_variables))\n",
    "\n",
    "        # Compute accuracy-\n",
    "        train_loss(loss)\n",
    "        train_accuracy(y, y_pred)\n",
    "\n",
    "        return None\n",
    "    \n",
    "    \n",
    "    @tf.function\n",
    "    def test_step(model, optimizer, data, labels):\n",
    "        \"\"\"\n",
    "        Function to test model performance\n",
    "        on testing dataset\n",
    "        \"\"\"\n",
    "    \n",
    "        predictions = model(data)\n",
    "        t_loss = loss_fn(labels, predictions)\n",
    "\n",
    "        test_loss(t_loss)\n",
    "        test_accuracy(labels, predictions)\n",
    "\n",
    "        return None\n",
    "\n",
    "\n",
    "    # Instantiate a model\n",
    "    model_gt = pruned_nn(pruning_params_unpruned)\n",
    "    \n",
    "    # Load winning ticket (from above)-\n",
    "    model_gt.load_weights(\"LeNet_MNIST_Winning_Ticket.h5\")\n",
    "    \n",
    "    # Strip model of pruning parameters-\n",
    "    model_gt_stripped = sparsity.strip_pruning(model_gt)\n",
    "    \n",
    "    \n",
    "    # Train model using 'GradientTape'-\n",
    "    \n",
    "    # Initialize parameters for Early Stopping manual implementation-\n",
    "    best_val_loss = 100\n",
    "    loc_patience = 0\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "    \n",
    "        if loc_patience >= patience:\n",
    "            print(\"\\n'EarlyStopping' called!\\n\")\n",
    "            break\n",
    "        \n",
    "        # Reset the metrics at the start of the next epoch\n",
    "        train_loss.reset_states()\n",
    "        train_accuracy.reset_states()\n",
    "        test_loss.reset_states()\n",
    "        test_accuracy.reset_states()\n",
    "        \n",
    "        \n",
    "        for x, y in train_dataset:\n",
    "            # train_one_step(model_gt_stripped, mask_model, optimizer, x, y, grad_mask_mul)\n",
    "            train_one_step(model_gt_stripped, mask_model_stripped, optimizer, x, y)\n",
    "\n",
    "\n",
    "        for x_t, y_t in test_dataset:\n",
    "            # test_step(x_t, y_t)\n",
    "            test_step(model_gt_stripped, optimizer, x_t, y_t)\n",
    "\n",
    "        template = 'Epoch {0}, Loss: {1:.4f}, Accuracy: {2:.4f}, Test Loss: {3:.4f}, Test Accuracy: {4:4f}'\n",
    "    \n",
    "        # 'i' is the index for number of pruning rounds-\n",
    "        history_main[i]['accuracy'][epoch] = train_accuracy.result() * 100\n",
    "        history_main[i]['loss'][epoch] = train_loss.result()\n",
    "        history_main[i]['val_loss'][epoch] = test_loss.result()\n",
    "        history_main[i]['val_accuracy'][epoch] = test_accuracy.result() * 100\n",
    "        \n",
    "        print(template.format(epoch + 1, \n",
    "                              train_loss.result(), train_accuracy.result()*100,\n",
    "                              test_loss.result(), test_accuracy.result()*100))\n",
    "    \n",
    "        # Count number of non-zero parameters in each layer and in total-\n",
    "        # print(\"layer-wise manner model, number of nonzero parameters in each layer are: \\n\")\n",
    "\n",
    "        model_sum_params = 0\n",
    "    \n",
    "        for layer in model_gt_stripped.trainable_weights:\n",
    "            # print(tf.math.count_nonzero(layer, axis = None).numpy())\n",
    "            model_sum_params += tf.math.count_nonzero(layer, axis = None).numpy()\n",
    "    \n",
    "        print(\"Total number of trainable parameters = {0}\\n\".format(model_sum_params))\n",
    "\n",
    "    \n",
    "        # Code for manual Early Stopping:\n",
    "        if np.abs(test_loss.result() < best_val_loss) >= minimum_delta:\n",
    "            # update 'best_val_loss' variable to lowest loss encountered so far-\n",
    "            best_val_loss = test_loss.result()\n",
    "        \n",
    "            # reset 'loc_patience' variable-\n",
    "            loc_patience = 0\n",
    "        \n",
    "        else:  # there is no improvement in monitored metric 'val_loss'\n",
    "            loc_patience += 1  # number of epochs without any improvement\n",
    "\n",
    "    \n",
    "    # Resize numpy arrays according to the epoch when 'EarlyStopping' was called-\n",
    "    for metrics in history_main[i].keys():\n",
    "        history_main[i][metrics] = np.resize(history_main[i][metrics], new_shape = epoch)\n",
    "        # history[metrics] = np.resize(history[metrics], new_shape=epoch)\n",
    "     \n",
    "    \n",
    "    # Save trained model weights-\n",
    "    model_gt.save_weights(\"LeNet_MNIST_Trained_Weights.h5\", overwrite=True)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    # Prune trained model:\n",
    "    \n",
    "    # print(\"\\n% of weights to be pruned in round = {0} is: {1:.4f}\\n\".format(i, wts_np[i - 1]))\n",
    "    \n",
    "    # Specify the parameters to be used for layer-wise pruning, Fully-Connected layer pruning-\n",
    "    pruning_params_fc = {\n",
    "        'pruning_schedule': sparsity.ConstantSparsity(\n",
    "            target_sparsity=dense1_pruning[i - 1], begin_step = 1000,\n",
    "            end_step = end_step, frequency=100\n",
    "        )\n",
    "    }\n",
    "    \n",
    "    \n",
    "    # Instantiate a Nueal Network model to be pruned using parameters from above-\n",
    "    pruned_model = pruned_nn(pruning_params_fc)\n",
    "    \n",
    "    # Load weights from original trained and unpruned model-\n",
    "    pruned_model.load_weights(\"LeNet_MNIST_Trained_Weights.h5\")\n",
    "    \n",
    "    # Train pruned NN-\n",
    "    history_pruned = pruned_model.fit(\n",
    "        x = X_train, y = y_train,\n",
    "        batch_size = batch_size,\n",
    "        epochs = epochs,\n",
    "        verbose = 1,\n",
    "        callbacks = callback,\n",
    "        validation_data = (X_test, y_test),\n",
    "        shuffle = True\n",
    "    )\n",
    "    \n",
    "    # Strip the pruning wrappers from pruned model-\n",
    "    pruned_model_stripped = sparsity.strip_pruning(pruned_model)\n",
    "    \n",
    "    # print(\"\\nIn pruned model, number of nonzero parameters in each layer are: \\n\")\n",
    "    pruned_sum_params = 0\n",
    "    \n",
    "    for layer in pruned_model_stripped.trainable_weights:\n",
    "        # print(tf.math.count_nonzero(layer, axis = None).numpy())\n",
    "        pruned_sum_params += tf.math.count_nonzero(layer, axis = None).numpy()\n",
    "    \n",
    "    print(\"\\nRound = {0}, total number of trainable parameters = {1}\\n\".format(i, pruned_sum_params))\n",
    "    # print(\"\\nTotal number of trainable parameters = {0}\\n\".format(pruned_sum_params))\n",
    "    \n",
    "    '''\n",
    "    # Sanity-check: confirm that the weights are actually pruned away from the network-\n",
    "    print(\"\\nRound = {0}, % of weights pruned away = {1:.2f}%\\n\".format( \\\n",
    "                                                i, (orig_sum_params - pruned_sum_params) / orig_sum_params * 100))\n",
    "    '''\n",
    "    \n",
    "    # 'i' is the index for number of pruning rounds-\n",
    "    history_main[i]['percentage_wts_pruned'] = ((orig_sum_params - pruned_sum_params) / orig_sum_params) * 100\n",
    "\n",
    "    # Save weights of PRUNED and Trained model BEFORE stripping-\n",
    "    pruned_model.save_weights(\"LeNet_MNIST_Pruned_Weights.h5\", overwrite = True)\n",
    "    \n",
    "    \n",
    "    # Create a mask:\n",
    "    \n",
    "    # Instantiate a new neural network model for which, the mask is to be created,\n",
    "    mask_model = pruned_nn(pruning_params_unpruned)\n",
    "    \n",
    "    # Load weights of PRUNED model-\n",
    "    mask_model.load_weights(\"LeNet_MNIST_Pruned_Weights.h5\")\n",
    "    \n",
    "    # Strip the model of its pruning parameters-\n",
    "    mask_model_stripped = sparsity.strip_pruning(mask_model)\n",
    "    \n",
    "    # For each layer, for each weight which is 0, leave it, as is.\n",
    "    # And for weights which survive the pruning,reinitialize it to ONE (1)-\n",
    "    for wts in mask_model_stripped.trainable_weights:\n",
    "        wts.assign(tf.where(tf.equal(wts, 0.), 0., 1.))\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    # Extract Winning Ticket:\n",
    "    \n",
    "    # Instantiate a new neural network model for which, the weights are to be extracted-\n",
    "    winning_ticket_model = pruned_nn(pruning_params_unpruned)\n",
    "    \n",
    "    # Load weights of PRUNED model-\n",
    "    winning_ticket_model.load_weights(\"LeNet_MNIST_Pruned_Weights.h5\")\n",
    "    \n",
    "    # Strip the model of its pruning parameters-\n",
    "    winning_ticket_model_stripped = sparsity.strip_pruning(winning_ticket_model)\n",
    "    \n",
    "    # For each layer, for each weight which is 0, leave it, as is. And for weights which survive the pruning,\n",
    "    # reinitialize it to the value, the model received BEFORE it was trained and pruned-\n",
    "    for orig_wts, pruned_wts in zip(orig_model_stripped.trainable_weights,\n",
    "                                    winning_ticket_model_stripped.trainable_weights):\n",
    "        pruned_wts.assign(tf.where(tf.equal(pruned_wts, 0), pruned_wts, orig_wts))\n",
    "    \n",
    "    \n",
    "    # Save the weights (with pruning parameters) extracted to a file-\n",
    "    winning_ticket_model.save_weights(\"LeNet_MNIST_Winning_Ticket.h5\", overwrite=True)\n",
    "    \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iterative-pruning for LeNet-300-100-10 using Lottery Ticket Hypothesis is now complete.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nIterative-pruning for LeNet-300-100-10 using Lottery Ticket Hypothesis is now complete.\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/majumdar/The_Lottery_Ticket_Hypothesis-Resources'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"/home/majumdar/The_Lottery_Ticket_Hypothesis-Resources/LeNet_MNIST_history_main_Experiment_3.pkl\", \"wb\") as f:\n",
    "    pickle.dump(history_main, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"/home/majumdar/The_Lottery_Ticket_Hypothesis-Resources/LeNet_MNIST_history_main_Experiment_3.pkl\", \"rb\") as f:\n",
    "    history_main = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.13206556393234"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check-\n",
    "history_main[10]['percentage_wts_pruned']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizations for _Iterative Pruning Rounds_:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python 3 dict for training and testing accuracy visualization-\n",
    "# key is 'percentage_wts_pruned' and value is accuracy\n",
    "plot_accuracy = {}\n",
    "plot_test_accuracy = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# populate 'plot_accuracy'-\n",
    "for k in history_main.keys():\n",
    "    epoch_length = len(history_main[k]['accuracy'])\n",
    "    plot_accuracy[history_main[k]['percentage_wts_pruned']] = history_main[k]['accuracy'][epoch_length - 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# populate 'plot_test_accuracy'-\n",
    "for k in history_main.keys():\n",
    "    epoch_length = len(history_main[k]['accuracy'])\n",
    "    plot_test_accuracy[history_main[k]['percentage_wts_pruned']] = history_main[k]['val_accuracy'][epoch_length - 1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of training and testing accuracy VS percentage of weights\n",
    "# pruned-\n",
    "fig=plt.figure(figsize=(9, 7), dpi= 80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.plot(list(plot_accuracy.keys()), list(plot_accuracy.values()), label = 'training_accuracy')\n",
    "plt.plot(list(plot_test_accuracy.keys()), list(plot_test_accuracy.values()), label = 'testing_accuracy')\n",
    "\n",
    "plt.title(\"LeNet : Percentage of weights pruned VS Accuracy (at convergence)\")\n",
    "plt.xlabel(\"percentage of weights remaining\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python 3 dict for training and testing loss visualization-\n",
    "plot_loss = {}\n",
    "plot_test_loss = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# populate 'plot_loss'-\n",
    "for k in history_main.keys():\n",
    "    epoch_length = len(history_main[k]['loss'])\n",
    "    plot_loss[history_main[k]['percentage_wts_pruned']] = history_main[k]['loss'][epoch_length - 1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# populate 'plot_test_loss'-\n",
    "for k in history_main.keys():\n",
    "    epoch_length = len(history_main[k]['loss'])\n",
    "    plot_test_loss[history_main[k]['percentage_wts_pruned']] = history_main[k]['val_loss'][epoch_length - 1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of training and testing loss VS percentage of remaining weights-\n",
    "# pruned-\n",
    "fig=plt.figure(figsize=(9, 7), dpi= 80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.plot(list(plot_loss.keys()), list(plot_loss.values()), label = 'training_loss')\n",
    "plt.plot(list(plot_test_loss.keys()), list(plot_test_loss.values()), label = 'testing_loss')\n",
    "\n",
    "plt.title(\"LeNet: Percentage of weights pruned VS Loss (at convergence)\")\n",
    "plt.xlabel(\"percentage of weights remaining\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot number of epochs (Early Stopping) VS percentage of weights pruned-\n",
    "\n",
    "# Python 3 dict to hold number of epochs vs % of weights pruned-\n",
    "plot_num_epochs = {}\n",
    "plot_num_epochs_test = {}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# populate 'plot_num_epochs'-\n",
    "for k in history_main.keys():\n",
    "    num_epochs = len(history_main[k]['accuracy'])\n",
    "    plot_num_epochs[history_main[k]['percentage_wts_pruned']] = num_epochs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# populate 'plot_num_epochs_test'-\n",
    "for k in history_main.keys():\n",
    "    num_epochs = len(history_main[k]['val_accuracy'])\n",
    "    plot_num_epochs_test[history[k]['percentage_wts_pruned']] = num_epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize percentage of weights remaining VS number of epochs (Early Stopping)\n",
    "fig=plt.figure(figsize=(9, 7), dpi= 80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.plot(list(plot_num_epochs.keys()), list(plot_num_epochs.values()), label = 'training set')\n",
    "plt.plot(list(plot_num_epochs_test.keys()), list(plot_num_epochs_test.values()), label = 'testing set')\n",
    "\n",
    "plt.title(\"LeNet: Percentage of weights pruned VS number of epochs (Early Stopping) until convergence\")\n",
    "plt.xlabel(\"percentage of weights remaining\")\n",
    "plt.ylabel(\"number of epochs\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try to visualize the STARTING training and testing accuracy VS percentage\n",
    "# of remaining weights-\n",
    "\n",
    "# Python 3 dict for visualization-\n",
    "plot_starting_accuracy = {}\n",
    "plot_starting_test_accuracy = {}\n",
    "\n",
    "# Key:    percentage of weights pruned\n",
    "# Value:  starting accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# populate 'plot_starting_accuracy'-\n",
    "for k in history_main.keys():\n",
    "    epoch_length = len(history_main[k]['loss'])\n",
    "    plot_starting_accuracy[history_main[k]['percentage_wts_pruned']] = history_main[k]['accuracy'][0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# populate 'plot_starting_test_accuracy'-\n",
    "for k in history_main.keys():\n",
    "    epoch_length = len(history_main[k]['loss'])\n",
    "    plot_starting_test_accuracy[history_main[k]['percentage_wts_pruned']] = history_main[k]['val_accuracy'][0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize starting accuracy VS percentage of weights pruned-\n",
    "fig=plt.figure(figsize=(9, 7), dpi= 80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.plot(list(plot_starting_accuracy.keys()), list(plot_starting_accuracy.values()), label = 'training starting accuracy')\n",
    "plt.plot(list(plot_starting_test_accuracy.keys()), list(plot_starting_test_accuracy.values()), label = 'testing starting accuracy')\n",
    "\n",
    "plt.title(\"LeNet: Percentage of weights pruned VS Starting Accuracy\")\n",
    "plt.xlabel(\"percentage of weights remaining\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try to visualize the STARTING training and testing loss VS percentage\n",
    "# of remaining weights-\n",
    "\n",
    "# Python 3 dict for visualization-\n",
    "plot_starting_loss = {}\n",
    "plot_starting_test_loss = {}\n",
    "\n",
    "# Key:\t\tpercentage of weights pruned\n",
    "# Value:\tstarting loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Populate 'plot_starting_loss' Python 3 dict-\n",
    "for k in history_main.keys():\n",
    "    epoch_length = len(history_main[k]['loss'])\n",
    "    plot_starting_loss[history_main[k]['percentage_wts_pruned']] = history_main[k]['loss'][0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Populate 'plot_starting_test_loss' Python 3 dict-\n",
    "for k in history_main.keys():\n",
    "    epoch_length = len(history_main[k]['loss'])\n",
    "    plot_starting_test_loss[history_main[k]['percentage_wts_pruned']] = history_main[k]['val_loss'][0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize Starting training & testing loss VS percentage of weights pruned-\n",
    "fig=plt.figure(figsize=(9, 7), dpi= 80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.plot(list(plot_starting_loss.keys()), list(plot_starting_loss.values()), label = 'training starting loss')\n",
    "plt.plot(list(plot_starting_test_loss.keys()), list(plot_starting_test_loss.values()), label = 'testing starting loss')\n",
    "\n",
    "plt.title(\"LeNet: Percentage of weights pruned VS Starting Loss\")\n",
    "plt.xlabel(\"percentage of weights remaining\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try to visualize: percentage of weights remaining VS BEST accuracy within each iterative\n",
    "# pruning round\n",
    "\n",
    "# Python 3 dict for visualization-\n",
    "plot_best_accuracy = {}\n",
    "plot_best_test_accuracy = {}\n",
    "\n",
    "# Key:\t\tpercentage of weights pruned\n",
    "# Value:\tbest accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Populate 'plot_best_accuracy' Python 3 dict-\n",
    "for k in history_main.keys():\n",
    "    epoch_length = len(history_main[k]['accuracy'])\n",
    "    plot_best_accuracy[history_main[k]['percentage_wts_pruned']] = np.amax(history_main[k]['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Populate 'plot_best_test_accuracy' Python 3 dict-\n",
    "for k in history_main.keys():\n",
    "    epoch_length = len(history_main[k]['accuracy'])\n",
    "    plot_best_test_accuracy[history_main[k]['percentage_wts_pruned']] = np.amax(history_main[k]['val_accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize best accuracy VS percentage of weights pruned-\n",
    "fig=plt.figure(figsize=(10, 9), dpi= 80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.plot(list(plot_best_accuracy.keys()), list(plot_best_accuracy.values()), label = 'training best accuracy')\n",
    "plt.plot(list(plot_best_test_accuracy.keys()), list(plot_best_test_accuracy.values()), label = 'testing best accuracy')\n",
    "\n",
    "plt.title(\"LeNet: Percentage of weights pruned VS Best Accuracy\")\n",
    "plt.xlabel(\"percentage of weights remaining\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try to visualize: percentage of weights remaining VS LOWEST loss within each iterative\n",
    "# pruning round\n",
    "\n",
    "# Python 3 dict for visualization-\n",
    "plot_best_loss = {}\n",
    "plot_best_test_loss = {}\n",
    "\n",
    "# Key:\t\tpercentage of weights pruned\n",
    "# Value:\tbest loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Populate 'plot_best_loss' Python 3 dict-\n",
    "for k in history_main.keys():\n",
    "    epoch_length = len(history_main[k]['loss'])\n",
    "    plot_best_loss[history_main[k]['percentage_wts_pruned']] = np.amin(history_main[k]['loss'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Populate 'plot_best_test_loss' Python 3 dict-\n",
    "for k in history_main.keys():\n",
    "    epoch_length = len(history_main[k]['loss'])\n",
    "    plot_best_test_loss[history_main[k]['percentage_wts_pruned']] = np.amin(history_main[k]['val_loss'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize best loss VS percentage of weights pruned-\n",
    "fig=plt.figure(figsize=(10, 9), dpi= 80, facecolor='w', edgecolor='k')\n",
    "\n",
    "plt.plot(list(plot_best_loss.keys()), list(plot_best_loss.values()), label = 'training best loss')\n",
    "plt.plot(list(plot_best_test_loss.keys()), list(plot_best_test_loss.values()), label = 'testing best loss')\n",
    "\n",
    "plt.title(\"LeNet: Percentage of weights pruned VS Best Loss\")\n",
    "plt.xlabel(\"percentage of weights remaining\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the iterative pruning round for which training and testing accuracy is highest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iterative round = 8 has highest training accuracy = 99.9183% at percentage of weights pruned = 83.0967%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Find highest training and testing accuracy for all iterative pruning rounds-\n",
    "best_accuracy = 0\n",
    "iterative_round = 0\n",
    "\n",
    "\n",
    "for k in history_main.keys():\n",
    "\tepoch_length = len(history_main[k]['accuracy'])\n",
    "\tif history_main[k]['accuracy'][epoch_length - 1] > best_accuracy:\n",
    "\t\tbest_accuracy = history_main[k]['accuracy'][epoch_length - 1]\n",
    "\t\titerative_round = k\n",
    "\n",
    "\n",
    "print(\"\\nIterative round = {0} has highest training accuracy = {1:.4f}% at percentage of weights pruned = {2:.4f}%\\n\".format(\n",
    "    iterative_round, best_accuracy, history_main[iterative_round]['percentage_wts_pruned']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iterative round = 8 has highest testing accuracy = 98.3000% at percentage of weights pruned = 83.0967%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "best_test_accuracy = 0\n",
    "iterative_round = 0\n",
    "\n",
    "\n",
    "for k in history_main.keys():\n",
    "\tepoch_length = len(history_main[k]['val_accuracy'])\n",
    "\tif history_main[k]['val_accuracy'][epoch_length - 1] > best_test_accuracy:\n",
    "\t\tbest_test_accuracy = history_main[k]['val_accuracy'][epoch_length - 1]\n",
    "\t\titerative_round = k\n",
    "\n",
    "\n",
    "print(\"\\nIterative round = {0} has highest testing accuracy = {1:.4f}% at percentage of weights pruned = {2:.4f}%\\n\".format(\n",
    "    iterative_round, best_test_accuracy, history_main[iterative_round]['percentage_wts_pruned']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig=plt.figure(figsize=(10, 9), dpi= 80, facecolor='w', edgecolor='k')\n",
    "\n",
    "for k in history_main.keys():\n",
    "    plt.plot(history_main[k]['accuracy'], label = 'training_accuracy_epoch-{0}'.format(k))\n",
    "    # plt.plot(history_main[k]['val_accuracy'], label = 'testing_accuracy_epoch-{0}'.format(k))\n",
    "\n",
    "plt.title(\"Conv-2 CNN - Training Visualization [Scalar Metrics]\")\n",
    "plt.xlabel(\"number of epochs\")\n",
    "plt.ylabel(\"accuracy accuracy\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig=plt.figure(figsize=(10, 9), dpi= 80, facecolor='w', edgecolor='k')\n",
    "\n",
    "for k in history_main.keys():\n",
    "    # plt.plot(history_main[k]['accuracy'], label = 'training_accuracy_epoch-{0}'.format(k))\n",
    "    plt.plot(history_main[k]['val_accuracy'], label = 'testing_accuracy_epoch-{0}'.format(k))\n",
    "\n",
    "plt.title(\"Conv-2 CNN - Testing Visualization [Scalar Metrics]\")\n",
    "plt.xlabel(\"number of epochs\")\n",
    "plt.ylabel(\"validation accuracy\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}