Conv_2_CIFAR10_Magnitude_based_Pruning_Gaussian_Glorot_initializations.ipynb

{
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   "cell_type": "markdown",
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    "id": "Uea3-3ZvMKS5"
   },
   "source": [
    "# Lottery Ticket Hypothesis: _Conv-2_ Convolutional Neural Network for CIFAR10 dataset\n",
    "\n",
    "Implementation of the hypothesis using the following architecture:\n",
    "\n",
    "1. __Conv layers:__ 64, 64 followed by _Max Pooling_ layer\n",
    "1. __Dense layer:__ 256, 256, 10\n",
    "\n",
    "smallest magnitudes in each layer are removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "xUSZzBD5MdcL",
    "outputId": "fbf989f2-745b-4152-911b-4953755191e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow 2.x selected.\n"
     ]
    }
   ],
   "source": [
    "# %tensorflow_version 2.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/opt/conda/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/opt/conda/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/opt/conda/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/opt/conda/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/opt/conda/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\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",
    "\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",
    "\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "aoIVNDPtMKTU",
    "outputId": "f515a0b1-452a-4130-bfd0-242f3ca88eec"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "CdI869wWMKTi",
    "outputId": "8e55f021-186a-4bf1-aaaa-7be7995a6a43"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_DEVICE_ORDER=PCI_BUS_ID\n",
      "env: CUDA_VISIBLE_DEVICES=0\n"
     ]
    }
   ],
   "source": [
    "%env CUDA_DEVICE_ORDER=PCI_BUS_ID\n",
    "%env CUDA_VISIBLE_DEVICES=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RpefuTJ2MKTt"
   },
   "outputs": [],
   "source": [
    "batch_size = 60\n",
    "num_classes = 10\n",
    "num_epochs = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "cpovn_jyMKT5",
    "outputId": "ae5ba05c-7890-4b02-8419-b778f82253e6"
   },
   "outputs": [],
   "source": [
    "# Data preprocessing and cleaning:\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 32, 32\n",
    "\n",
    "# Load CIFAR-10 dataset-\n",
    "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "zNbVLDY6M3tW",
    "outputId": "00d1ece3-f118-4dd4-a4fa-95e2f4918e01"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape = (50000, 32, 32, 3), y_train.shape = (50000, 1)\n",
      "X_test.shape = (10000, 32, 32, 3), y_test.shape = (10000, 1)\n"
     ]
    }
   ],
   "source": [
    "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": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "colab_type": "code",
    "id": "XByeYdcwMKUE",
    "outputId": "3961609c-7718-4728-8238-326752312b06"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "'input_shape' which will be used = (32, 32, 3)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "if tf.keras.backend.image_data_format() == 'channels_first':\n",
    "    X_train = X_train.reshape(X_train.shape[0], 3, img_rows, img_cols)\n",
    "    X_test = X_test.reshape(X_test.shape[0], 3, img_rows, img_cols)\n",
    "    input_shape = (3, img_rows, img_cols)\n",
    "else:\n",
    "    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 3)\n",
    "    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 3)\n",
    "    input_shape = (img_rows, img_cols, 3)\n",
    "\n",
    "print(\"\\n'input_shape' which will be used = {0}\\n\".format(input_shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pahIwSC8MKUO"
   },
   "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": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hKk5sNp6MKUX"
   },
   "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": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 86
    },
    "colab_type": "code",
    "id": "_-GPw1d8MKUs",
    "outputId": "d26d829a-177a-4dc8-fdc1-c20872bcec2d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dimensions of training and testing sets are:\n",
      "X_train.shape = (50000, 32, 32, 3), y_train.shape = (50000, 10)\n",
      "X_test.shape = (10000, 32, 32, 3), 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": {
    "colab": {},
    "colab_type": "code",
    "id": "M1kapfNVMKU3"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OxQX5ma2MKVH"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yx1hHreJMKVQ"
   },
   "source": [
    "### Prepare CIFAR10 dataset for _GradientTape_ training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F1gjoSY6MKVT"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "Nz5RdcIjMKVZ"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "Nl62ZgFkMKVi"
   },
   "outputs": [],
   "source": [
    "test_dataset = test_dataset.batch(batch_size=batch_size, drop_remainder=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kvdKx7gAMKVp"
   },
   "outputs": [],
   "source": [
    "# Choose an optimizer and loss function for training-\n",
    "loss_fn = tf.keras.losses.CategoricalCrossentropy()\n",
    "optimizer = tf.keras.optimizers.Adam(lr = 0.0002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ai3kCIYzMKVu"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "iCz54L2rMKV1"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NcJZj1ZfMKV8"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-TqIXiLpMKWB"
   },
   "outputs": [],
   "source": [
    "def conv2_cnn():\n",
    "\t\"\"\"\n",
    "\tFunction to define the architecture of a neural network model\n",
    "\tfollowing Conv-2 architecture for CIFAR-10 dataset and using\n",
    "\tprovided parameter which are used to prune the model.\n",
    "    \n",
    "\tConv-2 architecture-\n",
    "\t64, 64, pool  -- convolutions\n",
    "\t256, 256, 10  -- fully connected layers\n",
    "    \n",
    "    \n",
    "\tOutput: Returns designed and compiled neural network model\n",
    "\t\"\"\"\n",
    "    \n",
    "\tpruned_model = Sequential()\n",
    "    \n",
    "\tpruned_model.add(\n",
    "\t\tConv2D(\n",
    "\t\t\tfilters = 64, kernel_size = (3, 3),\n",
    "\t\t\tactivation='relu', kernel_initializer = tf.initializers.GlorotNormal(),\n",
    "\t\t\tstrides = (1, 1), padding = 'same',\n",
    "\t\t\tinput_shape=(32, 32, 3)\n",
    "\t\t\t)\n",
    "\t)\n",
    "\n",
    "\tpruned_model.add(\n",
    "\t\tConv2D(\n",
    "\t\t\tfilters = 64, kernel_size = (3, 3),\n",
    "\t\t\tactivation='relu', kernel_initializer = tf.initializers.GlorotNormal(),\n",
    "\t\t\tstrides = (1, 1), padding = 'same'\n",
    "\t\t\t)\n",
    "\t)\n",
    "    \n",
    "\tpruned_model.add(\n",
    "\t\tMaxPooling2D(\n",
    "\t\t\tpool_size = (2, 2),\n",
    "\t\t\tstrides = (2, 2)\n",
    "\t\t)\n",
    "\t)\n",
    "    \n",
    "\tpruned_model.add(Flatten())\n",
    "    \n",
    "\tpruned_model.add(\n",
    "\t\tDense(\n",
    "\t\t\tunits = 256, activation='relu',\n",
    "\t\t\tkernel_initializer = tf.initializers.GlorotNormal()\n",
    "\t\t)\n",
    "\t)\n",
    "    \n",
    "\tpruned_model.add(\n",
    "\t\tDense(\n",
    "\t\t\tunits = 256, activation='relu',\n",
    "\t\t\tkernel_initializer = tf.initializers.GlorotNormal()\n",
    "\t\t)\n",
    "\t)\n",
    "    \n",
    "\tpruned_model.add(\n",
    "\t\tDense(\n",
    "\t\t\tunits = 10, activation='softmax'\n",
    "\t\t\t)\n",
    "\t)\n",
    "    \n",
    "\n",
    "\t# Compile pruned CNN-\n",
    "\tpruned_model.compile(\n",
    "\t\tloss=tf.keras.losses.categorical_crossentropy,\n",
    "\t\t# optimizer='adam',\n",
    "\t\toptimizer=tf.keras.optimizers.Adam(lr = 0.0002),\n",
    "\t\tmetrics=['accuracy']\n",
    "\t)\n",
    "    \n",
    "    \n",
    "\treturn pruned_model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n1Q8QnB2MKWJ"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JguLJhLyMKWW"
   },
   "outputs": [],
   "source": [
    "'''\n",
    "# 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",
    "]\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JcXG7d4FMKWe"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oe0hsCAcMKWi"
   },
   "outputs": [],
   "source": [
    "# Initialize a Conv-2 CNN model-\n",
    "orig_model = conv2_cnn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5PB2kDj-MKWt"
   },
   "outputs": [],
   "source": [
    "# Save random weights-\n",
    "orig_model.save_weights(\"Conv2_CIFAR10_Random_Gaussian_Glorot_Weights.h5\", overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 399
    },
    "colab_type": "code",
    "id": "u7uOTLnVMKW2",
    "outputId": "b5c1f975-c071-432e-aa3b-365bfaa4b492"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 32, 32, 64)        1792      \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 16384)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               4194560   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                2570      \n",
      "=================================================================\n",
      "Total params: 4,301,642\n",
      "Trainable params: 4,301,642\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Get CNN summary-\n",
    "# orig_model_stripped.summary()\n",
    "orig_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "J5Z51mXtMKW-"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jOCYJdmlMKXD"
   },
   "source": [
    "### Layer-wise pruning:\n",
    "The Lottery Ticket Hypothesis follows a _layer-wise pruning_ heuristic:\n",
    "\n",
    "1. __conv layer(s):__ 10%\n",
    "1. __fully connected layer(s):__ 20%\n",
    "1. __output layer:__ 10%\n",
    "\n",
    "Therefore, there is a need to compute the percentage of weights being pruned for each layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "colab_type": "code",
    "id": "O9MnOecLMKXF",
    "outputId": "b5dc86c8-f9c2-4b1b-d978-c18fb5e26f4d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total number of trainable parameters = 4301642\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# number of convolutional parameters-\n",
    "conv1 = 1792\n",
    "conv2 = 36928\n",
    "\n",
    "# number of fully-connected dense parameters-\n",
    "dense1 = 4194560\n",
    "dense2 = 65792\n",
    "op_layer = 2570\n",
    "\n",
    "\n",
    "# total number of parameters-\n",
    "total_params = conv1 + conv2 + dense1 + dense2 + op_layer\n",
    "\n",
    "print(\"\\nTotal number of trainable parameters = {0}\\n\".format(total_params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jgRex1e8MKXL"
   },
   "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": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LHqAXqj7MKXP"
   },
   "outputs": [],
   "source": [
    "loc_tot_params = total_params\n",
    "loc_conv1 = conv1\n",
    "loc_conv2 = conv2\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": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nwX8dL-wMKXU"
   },
   "outputs": [],
   "source": [
    "# Lists to hold percentage of weights pruned in each round for all layers in CNN-\n",
    "conv1_pruning = []\n",
    "conv2_pruning = []\n",
    "dense1_pruning = []\n",
    "dense2_pruning = []\n",
    "op_layer_pruning = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "NVi_rZtGMKXY",
    "outputId": "218478c6-27a5-4de7-f288-cfa7111c8161"
   },
   "outputs": [],
   "source": [
    "while loc_tot_params >= max_pruned_params:\n",
    "    loc_conv1 *= 0.9    # 10% weights are pruned\n",
    "    loc_conv2 *= 0.9    # 10% weights are pruned\n",
    "    loc_dense1 *= 0.8   # 20% weights are pruned\n",
    "    loc_dense2 *= 0.8   # 20% weights are pruned\n",
    "    loc_op_layer *= 0.9 # 10% weights are pruned\n",
    "    \n",
    "    conv1_pruning.append(((conv1 - loc_conv1) / conv1) * 100)\n",
    "    conv2_pruning.append(((conv2 - loc_conv2) / conv2) * 100)\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_conv1 + loc_conv2 + loc_dense1 + loc_dense2 + loc_op_layer\n",
    "\n",
    "    n += 1\n",
    "\n",
    "    '''\n",
    "    print(\"\\nConv1 = {0:.3f}, Conv2 = {1:.3f}\".format(loc_conv1, loc_conv2))\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": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 86
    },
    "colab_type": "code",
    "id": "Yy2OQ6h7MKXe",
    "outputId": "ceb2ee40-1b13-4c7d-9fdb-6e3128414281"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "number of pruning rounds = 25\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nnumber of pruning rounds = {0}\\n\\n\".format(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7X1MRMkjMKXj"
   },
   "outputs": [],
   "source": [
    "num_pruning_rounds = n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sUaiIEotMKXp"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_StqHUjFMKXs"
   },
   "outputs": [],
   "source": [
    "# Convert from list to np.array-\n",
    "conv1_pruning = np.array(conv1_pruning)\n",
    "conv2_pruning = np.array(conv2_pruning)\n",
    "dense1_pruning = np.array(dense1_pruning)\n",
    "dense2_pruning = np.array(dense2_pruning)\n",
    "op_layer_pruning = np.array(op_layer_pruning)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zzjCYJz1MKXy"
   },
   "outputs": [],
   "source": [
    "# Round off numpy arrays to 3 decimal digits-\n",
    "conv1_pruning = np.round(conv1_pruning, decimals=3)\n",
    "conv2_pruning = np.round(conv2_pruning, decimals=3)\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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 86
    },
    "colab_type": "code",
    "id": "WXpjS1enMKX1",
    "outputId": "92a48358-48c0-41c2-bc90-7fdb6a7d300b"
   },
   "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,\n",
       "       99.622])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check-\n",
    "dense1_pruning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10.   , 19.   , 27.1  , 34.39 , 40.951, 46.856, 52.17 , 56.953,\n",
       "       61.258, 65.132, 68.619, 71.757, 74.581, 77.123, 79.411, 81.47 ,\n",
       "       83.323, 84.991, 86.491, 87.842, 89.058, 90.152, 91.137, 92.023,\n",
       "       92.821])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check-\n",
    "op_layer_pruning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 86
    },
    "colab_type": "code",
    "id": "eeMMpTHVPXhT",
    "outputId": "0c08ffcb-577c-4e30-c257-942fa3b50112"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10.   , 19.   , 27.1  , 34.39 , 40.951, 46.856, 52.17 , 56.953,\n",
       "       61.258, 65.132, 68.619, 71.757, 74.581, 77.123, 79.411, 81.47 ,\n",
       "       83.323, 84.991, 86.491, 87.842, 89.058, 90.152, 91.137, 92.023,\n",
       "       92.821])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check-\n",
    "conv1_pruning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4VRVmCKTMKYM"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lkQOrredMKYQ"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wBjdQjm0MKYZ"
   },
   "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": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-6U20dWuMKYa"
   },
   "outputs": [],
   "source": [
    "# Instantiate a new neural network model for which, the mask is to be created,\n",
    "# according to the paper-\n",
    "mask_model = conv2_cnn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_GflihdgMKYk"
   },
   "outputs": [],
   "source": [
    "# Assign all masks to one-\n",
    "\n",
    "for wts in mask_model.trainable_weights:\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": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "colab_type": "code",
    "id": "EccmHsOxMKYo",
    "outputId": "cc4a1ae4-eeec-4043-a204-4602d0178de9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mask model metrics:\n",
      "layer-wise number of nonzero parameters in each layer are: \n",
      "\n",
      "\n",
      "Total number of trainable parameters = 4301642\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.trainable_weights:\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": {
    "colab": {},
    "colab_type": "code",
    "id": "ufJE7mdRMKYu"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IxqXepWmMKYz"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "colab_type": "code",
    "id": "EP1nqG_dMKY3",
    "outputId": "38fb41bf-4662-4dfc-ebeb-c377d3fcde98"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "number of pruning rounds for Conv-2 CNN = 25 and number of epochs = 100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nnumber of pruning rounds for Conv-2 CNN = {0} and number of epochs = {1}\\n\".format(num_pruning_rounds, num_epochs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yFWFUgqSMKY7"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y5i6gg19MKY_"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U6EVU768MKZC"
   },
   "source": [
    "### Python 3 dictionary to hold model training metrics for each of _n_ rounds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "A0_CKJfWMKZE"
   },
   "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": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "PtB3L56iMKZJ",
    "outputId": "68b8343e-c654-4a7f-d06a-311b9e0bce9c"
   },
   "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, 25])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check-\n",
    "history_main.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "PtYfIVBXMKZP",
    "outputId": "d6328c4f-9b9f-4634-ca44-4ac537687644"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check-\n",
    "history_main[10]['accuracy'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oT3FmB3eMKZa"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M8i6fdUXMKZd"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prune_conv2(model, pruning_params_conv, pruning_params_fc, pruning_params_op):\n",
    "    '''\n",
    "    Function to prune top p% of trained weights using the provided parameters using\n",
    "    magnitude-based weight pruning.\n",
    "    \n",
    "    Inputs:\n",
    "    'model' is the TensorFlow 2.0 defined convolutional neural network\n",
    "    'pruning_params_conv' is the percentage of weights to prune for convolutional layer\n",
    "    'pruning_params_fc' is the percentage of weights to prune for dense, fully-connected layer\n",
    "    'pruning_params_op' is the percentage of weights to prune for output layer\n",
    "\n",
    "    Returns:\n",
    "    Python list containing pruned layers\n",
    "    '''\n",
    "\n",
    "    # List variable to hold magnitude-based pruned weights-\n",
    "    pruned_weights = []\n",
    "\n",
    "    for layer in model.trainable_weights:\n",
    "        x = layer.numpy()\n",
    "\n",
    "        if len(layer.shape) == 4:\n",
    "            # this is a convolutional layer\n",
    "            print(\"conv layer: {0}, pruning rate = {1}%\".format(layer.shape, pruning_params_conv))\n",
    "\n",
    "            # Compute absolute value of 'x'-\n",
    "            x_abs = np.abs(x)\n",
    "\n",
    "            # Mask values to zero which are less than 'p' in terms of magnitude-\n",
    "            x_abs[x_abs < np.percentile(x_abs, pruning_params_conv)] = 0\n",
    "\n",
    "            # Where 'x_abs' equals 0, keep 0, else, replace with values\n",
    "            # of 'x'-\n",
    "            # OR\n",
    "            # If x_abs == 0 (condition) is True, use the value of 0, otherwise\n",
    "            # use the value in 'x'\n",
    "            x_mod = np.where(x_abs == 0, 0, x)\n",
    "\n",
    "            pruned_weights.append(x_mod)\n",
    "\n",
    "        elif len(layer.shape) == 2 and layer.shape[1] != 10:\n",
    "            # this is a fully-connected dense layer\n",
    "            print(\"dense layer: {0}, pruning rate = {1}%\".format(layer.shape, pruning_params_fc))\n",
    "\n",
    "            # Compute absolute value of 'x'-\n",
    "            x_abs = np.abs(x)\n",
    "\n",
    "            # Mask values to zero which are less than 'p' in terms of magnitude-\n",
    "            x_abs[x_abs < np.percentile(x_abs, pruning_params_fc)] = 0\n",
    "\n",
    "            # Where 'x_abs' equals 0, keep 0, else, replace with values\n",
    "            # of 'x'-\n",
    "            # OR\n",
    "            # If x_abs == 0 (condition) is True, use the value of 0, otherwise\n",
    "            # use the value in 'x'\n",
    "            x_mod = np.where(x_abs == 0, 0, x)\n",
    "\n",
    "            pruned_weights.append(x_mod)\n",
    "        \n",
    "        elif len(layer.shape) == 2 and layer.shape[1] == 10:\n",
    "            # this is the output layer\n",
    "            print(\"op layer: {0}, pruning rate = {1}%\".format(layer.shape, pruning_params_op))\n",
    "\n",
    "            # Compute absolute value of 'x'-\n",
    "            x_abs = np.abs(x)\n",
    "\n",
    "            # Mask values to zero which are less than 'p' in terms of magnitude-\n",
    "            x_abs[x_abs < np.percentile(x_abs, pruning_params_op)] = 0\n",
    "\n",
    "            # Where 'x_abs' equals 0, keep 0, else, replace with values\n",
    "            # of 'x'-\n",
    "            # OR\n",
    "            # If x_abs == 0 (condition) is True, use the value of 0, otherwise\n",
    "            # use the value in 'x'\n",
    "            x_mod = np.where(x_abs == 0, 0, x)\n",
    "\n",
    "            pruned_weights.append(x_mod)\n",
    "\n",
    "        elif len(layer.shape) == 1:\n",
    "            # bias does not have to be pruned-\n",
    "            # print(\"layer: {0}, pruning rate = {1}%\".format(layer.shape, 0))\n",
    "            pruned_weights.append(x) \n",
    "\n",
    "\n",
    "    return pruned_weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv layer: (3, 3, 3, 64), pruning rate = 10.0%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 10.0%\n",
      "dense layer: (16384, 256), pruning rate = 20.0%\n",
      "dense layer: (256, 256), pruning rate = 20.0%\n",
      "op layer: (256, 10), pruning rate = 10.0%\n"
     ]
    }
   ],
   "source": [
    "# Sanity check-\n",
    "orig_model_pruned = prune_conv2(orig_model, conv1_pruning[0], dense1_pruning[0], op_layer_pruning[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F2zjLvjMMKZ6"
   },
   "outputs": [],
   "source": [
    "# User input parameters for Early Stopping in manual implementation-\n",
    "minimum_delta = 0.001\n",
    "patience = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JNMQK8tWMKZ9"
   },
   "outputs": [],
   "source": [
    "best_val_loss = 100\n",
    "loc_patience = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "TGIecrdVMKaA",
    "outputId": "a5fcfb3d-c71b-4712-be90-05cb6bc70e0f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total number of parameters in overparametrized, original, unpruned network =  4301642\n"
     ]
    }
   ],
   "source": [
    "orig_sum_params = total_params\n",
    "print(\"\\nTotal number of parameters in overparametrized, original, unpruned network = \", orig_sum_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EBrrbLM-MKaD"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BSOeplxmMKaG"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization:\n",
    "\n",
    "Instead of using weights saved as '.h5' file, which takes time to save weights and loading weights to and/or from disk, the optimized process makes use of preexisting models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate a model-\n",
    "winning_ticket_model = conv2_cnn()\n",
    "\n",
    "# Use Randomly initialized weights-\n",
    "winning_ticket_model.set_weights(orig_model.get_weights())\n"
   ]
  },
  {
   "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": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Qf-_IkmcMKaL",
    "outputId": "791231e9-fe69-45cd-8d6c-3137432b0069"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 1\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.4817, Accuracy: 46.7080, Test Loss: 1.2120, Test Accuracy: 57.690002\n",
      "Total number of trainable parameters = 4301637\n",
      "\n",
      "Epoch 2, Loss: 1.1120, Accuracy: 60.7740, Test Loss: 1.0353, Test Accuracy: 63.389999\n",
      "Total number of trainable parameters = 4301638\n",
      "\n",
      "Epoch 3, Loss: 0.9471, Accuracy: 66.8540, Test Loss: 0.9861, Test Accuracy: 64.970001\n",
      "Total number of trainable parameters = 4301639\n",
      "\n",
      "Epoch 4, Loss: 0.8390, Accuracy: 70.8060, Test Loss: 0.9177, Test Accuracy: 67.959999\n",
      "Total number of trainable parameters = 4301641\n",
      "\n",
      "Epoch 5, Loss: 0.7493, Accuracy: 74.0620, Test Loss: 0.8878, Test Accuracy: 69.450005\n",
      "Total number of trainable parameters = 4301642\n",
      "\n",
      "Epoch 6, Loss: 0.6683, Accuracy: 76.6060, Test Loss: 0.8936, Test Accuracy: 69.419998\n",
      "Total number of trainable parameters = 4301642\n",
      "\n",
      "Epoch 7, Loss: 0.5835, Accuracy: 79.9400, Test Loss: 0.8771, Test Accuracy: 70.630005\n",
      "Total number of trainable parameters = 4301642\n",
      "\n",
      "Epoch 8, Loss: 0.4996, Accuracy: 82.9520, Test Loss: 0.9352, Test Accuracy: 70.599998\n",
      "Total number of trainable parameters = 4301642\n",
      "\n",
      "Epoch 9, Loss: 0.4123, Accuracy: 86.2920, Test Loss: 0.9492, Test Accuracy: 70.840004\n",
      "Total number of trainable parameters = 4301642\n",
      "\n",
      "Epoch 10, Loss: 0.3293, Accuracy: 89.0060, Test Loss: 1.0324, Test Accuracy: 69.389999\n",
      "Total number of trainable parameters = 4301642\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 10.0%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 10.0%\n",
      "dense layer: (16384, 256), pruning rate = 20.0%\n",
      "dense layer: (256, 256), pruning rate = 20.0%\n",
      "op layer: (256, 10), pruning rate = 10.0%\n",
      "\n",
      "After Pruning Round = 1, total number of surviving trainable parameters = 3445558\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.3759, Accuracy: 86.7740, Test Loss: 1.0966, Test Accuracy: 70.260002\n",
      "Total number of trainable parameters (FT) = 3445558\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.1812, Accuracy: 94.3740, Test Loss: 1.2128, Test Accuracy: 70.020004\n",
      "Total number of trainable parameters (FT) = 3445558\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.1127, Accuracy: 96.7320, Test Loss: 1.3925, Test Accuracy: 69.690002\n",
      "Total number of trainable parameters (FT) = 3445558\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 2\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.2851, Accuracy: 53.7080, Test Loss: 1.0807, Test Accuracy: 61.470001\n",
      "Total number of trainable parameters = 3445535\n",
      "\n",
      "Epoch 2, Loss: 0.9388, Accuracy: 67.0300, Test Loss: 0.9582, Test Accuracy: 66.570000\n",
      "Total number of trainable parameters = 3445539\n",
      "\n",
      "Epoch 3, Loss: 0.8140, Accuracy: 71.7060, Test Loss: 0.9096, Test Accuracy: 68.470001\n",
      "Total number of trainable parameters = 3445541\n",
      "\n",
      "Epoch 4, Loss: 0.7197, Accuracy: 75.0340, Test Loss: 0.8961, Test Accuracy: 68.940002\n",
      "Total number of trainable parameters = 3445545\n",
      "\n",
      "Epoch 5, Loss: 0.6328, Accuracy: 78.1340, Test Loss: 0.8721, Test Accuracy: 70.480003\n",
      "Total number of trainable parameters = 3445547\n",
      "\n",
      "Epoch 6, Loss: 0.5474, Accuracy: 81.1860, Test Loss: 0.8845, Test Accuracy: 70.830002\n",
      "Total number of trainable parameters = 3445548\n",
      "\n",
      "Epoch 7, Loss: 0.4625, Accuracy: 84.3120, Test Loss: 0.9332, Test Accuracy: 70.150002\n",
      "Total number of trainable parameters = 3445548\n",
      "\n",
      "Epoch 8, Loss: 0.3827, Accuracy: 87.2720, Test Loss: 0.9486, Test Accuracy: 70.779999\n",
      "Total number of trainable parameters = 3445548\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 19.0%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 19.0%\n",
      "dense layer: (16384, 256), pruning rate = 36.0%\n",
      "dense layer: (256, 256), pruning rate = 36.0%\n",
      "op layer: (256, 10), pruning rate = 19.0%\n",
      "\n",
      "After Pruning Round = 2, total number of surviving trainable parameters = 2760270\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.3990, Accuracy: 86.1300, Test Loss: 1.0357, Test Accuracy: 70.490005\n",
      "Total number of trainable parameters (FT) = 2760270\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.2257, Accuracy: 92.7700, Test Loss: 1.1578, Test Accuracy: 69.650002\n",
      "Total number of trainable parameters (FT) = 2760270\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.1539, Accuracy: 95.4340, Test Loss: 1.2795, Test Accuracy: 70.540001\n",
      "Total number of trainable parameters (FT) = 2760270\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 3\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.2445, Accuracy: 55.2600, Test Loss: 1.0473, Test Accuracy: 62.900002\n",
      "Total number of trainable parameters = 2760140\n",
      "\n",
      "Epoch 2, Loss: 0.9292, Accuracy: 67.3580, Test Loss: 0.9523, Test Accuracy: 66.779999\n",
      "Total number of trainable parameters = 2760149\n",
      "\n",
      "Epoch 3, Loss: 0.8245, Accuracy: 71.2460, Test Loss: 0.9036, Test Accuracy: 68.510002\n",
      "Total number of trainable parameters = 2760152\n",
      "\n",
      "Epoch 4, Loss: 0.7496, Accuracy: 73.9660, Test Loss: 0.9082, Test Accuracy: 69.000000\n",
      "Total number of trainable parameters = 2760154\n",
      "\n",
      "Epoch 5, Loss: 0.6878, Accuracy: 76.2340, Test Loss: 0.8894, Test Accuracy: 69.590004\n",
      "Total number of trainable parameters = 2760156\n",
      "\n",
      "Epoch 6, Loss: 0.6244, Accuracy: 78.4080, Test Loss: 0.8827, Test Accuracy: 70.169998\n",
      "Total number of trainable parameters = 2760159\n",
      "\n",
      "Epoch 7, Loss: 0.5647, Accuracy: 80.6660, Test Loss: 0.9031, Test Accuracy: 70.120003\n",
      "Total number of trainable parameters = 2760160\n",
      "\n",
      "Epoch 8, Loss: 0.5064, Accuracy: 82.8120, Test Loss: 0.9105, Test Accuracy: 70.380005\n",
      "Total number of trainable parameters = 2760163\n",
      "\n",
      "Epoch 9, Loss: 0.4468, Accuracy: 84.8560, Test Loss: 0.9847, Test Accuracy: 68.699997\n",
      "Total number of trainable parameters = 2760168\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 27.1%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 27.1%\n",
      "dense layer: (16384, 256), pruning rate = 48.8%\n",
      "dense layer: (256, 256), pruning rate = 48.8%\n",
      "op layer: (256, 10), pruning rate = 27.1%\n",
      "\n",
      "After Pruning Round = 3, total number of surviving trainable parameters = 2211576\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.4736, Accuracy: 83.5080, Test Loss: 0.9840, Test Accuracy: 70.200005\n",
      "Total number of trainable parameters (FT) = 2211576\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.3169, Accuracy: 89.4860, Test Loss: 1.0331, Test Accuracy: 70.050003\n",
      "Total number of trainable parameters (FT) = 2211576\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.2531, Accuracy: 91.8900, Test Loss: 1.1143, Test Accuracy: 70.250000\n",
      "Total number of trainable parameters (FT) = 2211576\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 4\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.2132, Accuracy: 56.6960, Test Loss: 1.0199, Test Accuracy: 64.410004\n",
      "Total number of trainable parameters = 2211505\n",
      "\n",
      "Epoch 2, Loss: 0.9117, Accuracy: 68.1660, Test Loss: 0.9393, Test Accuracy: 66.860001\n",
      "Total number of trainable parameters = 2211512\n",
      "\n",
      "Epoch 3, Loss: 0.8140, Accuracy: 71.7140, Test Loss: 0.8975, Test Accuracy: 68.709999\n",
      "Total number of trainable parameters = 2211515\n",
      "\n",
      "Epoch 4, Loss: 0.7395, Accuracy: 74.3800, Test Loss: 0.8928, Test Accuracy: 69.239998\n",
      "Total number of trainable parameters = 2211515\n",
      "\n",
      "Epoch 5, Loss: 0.6772, Accuracy: 76.5860, Test Loss: 0.8849, Test Accuracy: 69.620003\n",
      "Total number of trainable parameters = 2211516\n",
      "\n",
      "Epoch 6, Loss: 0.6119, Accuracy: 78.9000, Test Loss: 0.8873, Test Accuracy: 70.420006\n",
      "Total number of trainable parameters = 2211517\n",
      "\n",
      "Epoch 7, Loss: 0.5454, Accuracy: 81.4800, Test Loss: 0.8844, Test Accuracy: 70.790001\n",
      "Total number of trainable parameters = 2211519\n",
      "\n",
      "Epoch 8, Loss: 0.4837, Accuracy: 83.6100, Test Loss: 0.9056, Test Accuracy: 70.720001\n",
      "Total number of trainable parameters = 2211522\n",
      "\n",
      "Epoch 9, Loss: 0.4265, Accuracy: 85.6660, Test Loss: 0.9514, Test Accuracy: 70.430000\n",
      "Total number of trainable parameters = 2211524\n",
      "\n",
      "Epoch 10, Loss: 0.3692, Accuracy: 87.7840, Test Loss: 0.9895, Test Accuracy: 70.240005\n",
      "Total number of trainable parameters = 2211525\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 34.39%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 34.39%\n",
      "dense layer: (16384, 256), pruning rate = 59.04%\n",
      "dense layer: (256, 256), pruning rate = 59.04%\n",
      "op layer: (256, 10), pruning rate = 34.39%\n",
      "\n",
      "After Pruning Round = 4, total number of surviving trainable parameters = 1772318\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.3980, Accuracy: 86.2660, Test Loss: 1.0152, Test Accuracy: 70.660004\n",
      "Total number of trainable parameters (FT) = 1772318\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.2543, Accuracy: 91.8480, Test Loss: 1.1170, Test Accuracy: 70.590004\n",
      "Total number of trainable parameters (FT) = 1772318\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.2006, Accuracy: 93.7560, Test Loss: 1.1827, Test Accuracy: 70.020004\n",
      "Total number of trainable parameters (FT) = 1772318\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 5\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.1780, Accuracy: 58.3360, Test Loss: 1.0058, Test Accuracy: 65.130005\n",
      "Total number of trainable parameters = 1772286\n",
      "\n",
      "Epoch 2, Loss: 0.8686, Accuracy: 69.7300, Test Loss: 0.9057, Test Accuracy: 68.409996\n",
      "Total number of trainable parameters = 1772286\n",
      "\n",
      "Epoch 3, Loss: 0.7631, Accuracy: 73.6000, Test Loss: 0.9060, Test Accuracy: 68.510002\n",
      "Total number of trainable parameters = 1772291\n",
      "\n",
      "Epoch 4, Loss: 0.6778, Accuracy: 76.7000, Test Loss: 0.8750, Test Accuracy: 70.320000\n",
      "Total number of trainable parameters = 1772292\n",
      "\n",
      "Epoch 5, Loss: 0.6098, Accuracy: 79.1220, Test Loss: 0.8760, Test Accuracy: 70.330002\n",
      "Total number of trainable parameters = 1772292\n",
      "\n",
      "Epoch 6, Loss: 0.5362, Accuracy: 81.8220, Test Loss: 0.8739, Test Accuracy: 71.170006\n",
      "Total number of trainable parameters = 1772293\n",
      "\n",
      "Epoch 7, Loss: 0.4615, Accuracy: 84.3540, Test Loss: 0.8899, Test Accuracy: 71.320000\n",
      "Total number of trainable parameters = 1772293\n",
      "\n",
      "Epoch 8, Loss: 0.3909, Accuracy: 87.2140, Test Loss: 0.9467, Test Accuracy: 70.570000\n",
      "Total number of trainable parameters = 1772294\n",
      "\n",
      "Epoch 9, Loss: 0.3261, Accuracy: 89.3620, Test Loss: 1.0098, Test Accuracy: 70.150002\n",
      "Total number of trainable parameters = 1772294\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 40.951%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 40.951%\n",
      "dense layer: (16384, 256), pruning rate = 67.232%\n",
      "dense layer: (256, 256), pruning rate = 67.232%\n",
      "op layer: (256, 10), pruning rate = 40.951%\n",
      "\n",
      "After Pruning Round = 5, total number of surviving trainable parameters = 1420629\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.3426, Accuracy: 88.4680, Test Loss: 1.0546, Test Accuracy: 70.770004\n",
      "Total number of trainable parameters (FT) = 1420629\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.2002, Accuracy: 94.0480, Test Loss: 1.1640, Test Accuracy: 69.880005\n",
      "Total number of trainable parameters (FT) = 1420629\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.1464, Accuracy: 95.9140, Test Loss: 1.2393, Test Accuracy: 70.120003\n",
      "Total number of trainable parameters (FT) = 1420629\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 6\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.1450, Accuracy: 59.5960, Test Loss: 0.9754, Test Accuracy: 66.080002\n",
      "Total number of trainable parameters = 1420619\n",
      "\n",
      "Epoch 2, Loss: 0.8445, Accuracy: 70.6700, Test Loss: 0.9046, Test Accuracy: 68.690002\n",
      "Total number of trainable parameters = 1420621\n",
      "\n",
      "Epoch 3, Loss: 0.7362, Accuracy: 74.5760, Test Loss: 0.8820, Test Accuracy: 69.489998\n",
      "Total number of trainable parameters = 1420622\n",
      "\n",
      "Epoch 4, Loss: 0.6505, Accuracy: 77.4300, Test Loss: 0.8688, Test Accuracy: 70.270004\n",
      "Total number of trainable parameters = 1420622\n",
      "\n",
      "Epoch 5, Loss: 0.5748, Accuracy: 80.1060, Test Loss: 0.8824, Test Accuracy: 70.510002\n",
      "Total number of trainable parameters = 1420622\n",
      "\n",
      "Epoch 6, Loss: 0.5013, Accuracy: 82.9920, Test Loss: 0.8883, Test Accuracy: 71.060005\n",
      "Total number of trainable parameters = 1420622\n",
      "\n",
      "Epoch 7, Loss: 0.4341, Accuracy: 85.6200, Test Loss: 0.9341, Test Accuracy: 70.670006\n",
      "Total number of trainable parameters = 1420622\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 46.856%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 46.856%\n",
      "dense layer: (16384, 256), pruning rate = 73.786%\n",
      "dense layer: (256, 256), pruning rate = 73.786%\n",
      "op layer: (256, 10), pruning rate = 46.856%\n",
      "\n",
      "After Pruning Round = 6, total number of surviving trainable parameters = 1139001\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.4455, Accuracy: 84.6160, Test Loss: 0.9922, Test Accuracy: 69.660004\n",
      "Total number of trainable parameters (FT) = 1139001\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.3016, Accuracy: 90.2720, Test Loss: 1.0261, Test Accuracy: 70.970001\n",
      "Total number of trainable parameters (FT) = 1139001\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.2410, Accuracy: 92.6540, Test Loss: 1.1061, Test Accuracy: 69.880005\n",
      "Total number of trainable parameters (FT) = 1139001\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 7\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.1480, Accuracy: 59.4100, Test Loss: 1.0166, Test Accuracy: 64.139999\n",
      "Total number of trainable parameters = 1138993\n",
      "\n",
      "Epoch 2, Loss: 0.8463, Accuracy: 70.6000, Test Loss: 0.9406, Test Accuracy: 67.369995\n",
      "Total number of trainable parameters = 1138993\n",
      "\n",
      "Epoch 3, Loss: 0.7359, Accuracy: 74.2980, Test Loss: 0.8888, Test Accuracy: 69.790001\n",
      "Total number of trainable parameters = 1138994\n",
      "\n",
      "Epoch 4, Loss: 0.6565, Accuracy: 77.2520, Test Loss: 0.9132, Test Accuracy: 69.510002\n",
      "Total number of trainable parameters = 1138995\n",
      "\n",
      "Epoch 5, Loss: 0.5811, Accuracy: 79.9260, Test Loss: 0.8867, Test Accuracy: 70.000000\n",
      "Total number of trainable parameters = 1138995\n",
      "\n",
      "Epoch 6, Loss: 0.5174, Accuracy: 82.2280, Test Loss: 0.8921, Test Accuracy: 70.240005\n",
      "Total number of trainable parameters = 1138995\n",
      "\n",
      "Epoch 7, Loss: 0.4556, Accuracy: 84.5880, Test Loss: 0.9228, Test Accuracy: 70.230003\n",
      "Total number of trainable parameters = 1138995\n",
      "\n",
      "Epoch 8, Loss: 0.3976, Accuracy: 86.8480, Test Loss: 0.9654, Test Accuracy: 70.389999\n",
      "Total number of trainable parameters = 1138995\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 52.17%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 52.17%\n",
      "dense layer: (16384, 256), pruning rate = 79.028%\n",
      "dense layer: (256, 256), pruning rate = 79.028%\n",
      "op layer: (256, 10), pruning rate = 52.17%\n",
      "\n",
      "After Pruning Round = 7, total number of surviving trainable parameters = 913509\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.4118, Accuracy: 85.9320, Test Loss: 1.0136, Test Accuracy: 70.060005\n",
      "Total number of trainable parameters (FT) = 913509\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.2827, Accuracy: 90.9340, Test Loss: 1.0531, Test Accuracy: 70.540001\n",
      "Total number of trainable parameters (FT) = 913509\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.2283, Accuracy: 93.0360, Test Loss: 1.1073, Test Accuracy: 70.389999\n",
      "Total number of trainable parameters (FT) = 913509\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 8\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.1276, Accuracy: 60.4620, Test Loss: 0.9814, Test Accuracy: 65.979996\n",
      "Total number of trainable parameters = 913502\n",
      "\n",
      "Epoch 2, Loss: 0.8216, Accuracy: 71.5940, Test Loss: 0.9188, Test Accuracy: 68.620003\n",
      "Total number of trainable parameters = 913502\n",
      "\n",
      "Epoch 3, Loss: 0.7107, Accuracy: 75.4940, Test Loss: 0.8708, Test Accuracy: 70.400002\n",
      "Total number of trainable parameters = 913502\n",
      "\n",
      "Epoch 4, Loss: 0.6218, Accuracy: 78.6720, Test Loss: 0.8731, Test Accuracy: 70.650002\n",
      "Total number of trainable parameters = 913502\n",
      "\n",
      "Epoch 5, Loss: 0.5476, Accuracy: 81.3780, Test Loss: 0.8866, Test Accuracy: 70.209999\n",
      "Total number of trainable parameters = 913502\n",
      "\n",
      "Epoch 6, Loss: 0.4795, Accuracy: 83.8800, Test Loss: 0.9056, Test Accuracy: 70.380005\n",
      "Total number of trainable parameters = 913502\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 56.953%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 56.953%\n",
      "dense layer: (16384, 256), pruning rate = 83.223%\n",
      "dense layer: (256, 256), pruning rate = 83.223%\n",
      "op layer: (256, 10), pruning rate = 56.953%\n",
      "\n",
      "After Pruning Round = 8, total number of surviving trainable parameters = 732833\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.4897, Accuracy: 83.1100, Test Loss: 0.9046, Test Accuracy: 70.970001\n",
      "Total number of trainable parameters (FT) = 732833\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.3593, Accuracy: 88.3820, Test Loss: 0.9561, Test Accuracy: 71.000000\n",
      "Total number of trainable parameters (FT) = 732833\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.3000, Accuracy: 90.4900, Test Loss: 1.0148, Test Accuracy: 70.990005\n",
      "Total number of trainable parameters (FT) = 732833\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 9\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.1173, Accuracy: 60.9140, Test Loss: 0.9736, Test Accuracy: 66.589996\n",
      "Total number of trainable parameters = 732828\n",
      "\n",
      "Epoch 2, Loss: 0.8183, Accuracy: 71.6700, Test Loss: 0.8982, Test Accuracy: 69.150002\n",
      "Total number of trainable parameters = 732829\n",
      "\n",
      "Epoch 3, Loss: 0.7039, Accuracy: 75.8580, Test Loss: 0.8762, Test Accuracy: 70.090004\n",
      "Total number of trainable parameters = 732829\n",
      "\n",
      "Epoch 4, Loss: 0.6186, Accuracy: 78.8060, Test Loss: 0.8792, Test Accuracy: 70.380005\n",
      "Total number of trainable parameters = 732830\n",
      "\n",
      "Epoch 5, Loss: 0.5432, Accuracy: 81.6580, Test Loss: 0.8913, Test Accuracy: 70.190002\n",
      "Total number of trainable parameters = 732831\n",
      "\n",
      "Epoch 6, Loss: 0.4802, Accuracy: 84.0300, Test Loss: 0.8957, Test Accuracy: 70.860001\n",
      "Total number of trainable parameters = 732831\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 61.258%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 61.258%\n",
      "dense layer: (16384, 256), pruning rate = 86.578%\n",
      "dense layer: (256, 256), pruning rate = 86.578%\n",
      "op layer: (256, 10), pruning rate = 61.258%\n",
      "\n",
      "After Pruning Round = 9, total number of surviving trainable parameters = 588143\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.4898, Accuracy: 83.2220, Test Loss: 0.9025, Test Accuracy: 70.800003\n",
      "Total number of trainable parameters (FT) = 588143\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.3681, Accuracy: 88.0600, Test Loss: 0.9529, Test Accuracy: 70.480003\n",
      "Total number of trainable parameters (FT) = 588143\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.3146, Accuracy: 89.9320, Test Loss: 0.9988, Test Accuracy: 70.760002\n",
      "Total number of trainable parameters (FT) = 588143\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 10\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.1308, Accuracy: 60.8420, Test Loss: 0.9826, Test Accuracy: 65.570000\n",
      "Total number of trainable parameters = 588140\n",
      "\n",
      "Epoch 2, Loss: 0.8211, Accuracy: 71.6120, Test Loss: 0.9034, Test Accuracy: 68.849998\n",
      "Total number of trainable parameters = 588140\n",
      "\n",
      "Epoch 3, Loss: 0.7035, Accuracy: 75.8720, Test Loss: 0.8892, Test Accuracy: 69.250000\n",
      "Total number of trainable parameters = 588140\n",
      "\n",
      "Epoch 4, Loss: 0.6189, Accuracy: 78.8160, Test Loss: 0.8977, Test Accuracy: 69.570000\n",
      "Total number of trainable parameters = 588140\n",
      "\n",
      "Epoch 5, Loss: 0.5502, Accuracy: 81.3720, Test Loss: 0.8940, Test Accuracy: 70.320000\n",
      "Total number of trainable parameters = 588140\n",
      "\n",
      "Epoch 6, Loss: 0.4900, Accuracy: 83.6360, Test Loss: 0.9027, Test Accuracy: 70.570000\n",
      "Total number of trainable parameters = 588140\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 65.132%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 65.132%\n",
      "dense layer: (16384, 256), pruning rate = 89.263%\n",
      "dense layer: (256, 256), pruning rate = 89.263%\n",
      "op layer: (256, 10), pruning rate = 65.132%\n",
      "\n",
      "After Pruning Round = 10, total number of surviving trainable parameters = 472169\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.5046, Accuracy: 82.9020, Test Loss: 0.9133, Test Accuracy: 70.820000\n",
      "Total number of trainable parameters (FT) = 472169\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.3853, Accuracy: 87.5540, Test Loss: 0.9448, Test Accuracy: 70.940002\n",
      "Total number of trainable parameters (FT) = 472169\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.3359, Accuracy: 89.2560, Test Loss: 0.9696, Test Accuracy: 71.100006\n",
      "Total number of trainable parameters (FT) = 472169\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 11\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.1300, Accuracy: 60.9500, Test Loss: 0.9962, Test Accuracy: 65.610001\n",
      "Total number of trainable parameters = 472167\n",
      "\n",
      "Epoch 2, Loss: 0.8221, Accuracy: 71.9080, Test Loss: 0.9093, Test Accuracy: 68.949997\n",
      "Total number of trainable parameters = 472167\n",
      "\n",
      "Epoch 3, Loss: 0.7101, Accuracy: 75.7680, Test Loss: 0.8839, Test Accuracy: 69.320000\n",
      "Total number of trainable parameters = 472167\n",
      "\n",
      "Epoch 4, Loss: 0.6265, Accuracy: 78.5380, Test Loss: 0.8804, Test Accuracy: 70.340004\n",
      "Total number of trainable parameters = 472167\n",
      "\n",
      "Epoch 5, Loss: 0.5613, Accuracy: 81.0060, Test Loss: 0.8918, Test Accuracy: 69.900002\n",
      "Total number of trainable parameters = 472167\n",
      "\n",
      "Epoch 6, Loss: 0.5069, Accuracy: 83.0780, Test Loss: 0.8955, Test Accuracy: 70.490005\n",
      "Total number of trainable parameters = 472167\n",
      "\n",
      "Epoch 7, Loss: 0.4554, Accuracy: 84.8200, Test Loss: 0.9163, Test Accuracy: 70.639999\n",
      "Total number of trainable parameters = 472167\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 68.619%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 68.619%\n",
      "dense layer: (16384, 256), pruning rate = 91.41%\n",
      "dense layer: (256, 256), pruning rate = 91.41%\n",
      "op layer: (256, 10), pruning rate = 68.619%\n",
      "\n",
      "After Pruning Round = 11, total number of surviving trainable parameters = 379272\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.4741, Accuracy: 83.9500, Test Loss: 0.9181, Test Accuracy: 70.779999\n",
      "Total number of trainable parameters (FT) = 379272\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.3659, Accuracy: 88.0520, Test Loss: 0.9923, Test Accuracy: 70.310005\n",
      "Total number of trainable parameters (FT) = 379272\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.3212, Accuracy: 89.7720, Test Loss: 1.0072, Test Accuracy: 70.450005\n",
      "Total number of trainable parameters (FT) = 379272\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 12\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.1490, Accuracy: 60.3020, Test Loss: 1.0050, Test Accuracy: 65.080002\n",
      "Total number of trainable parameters = 379271\n",
      "\n",
      "Epoch 2, Loss: 0.8415, Accuracy: 71.1900, Test Loss: 0.9304, Test Accuracy: 67.589996\n",
      "Total number of trainable parameters = 379271\n",
      "\n",
      "Epoch 3, Loss: 0.7242, Accuracy: 75.1320, Test Loss: 0.8894, Test Accuracy: 69.570000\n",
      "Total number of trainable parameters = 379271\n",
      "\n",
      "Epoch 4, Loss: 0.6419, Accuracy: 78.0660, Test Loss: 0.8901, Test Accuracy: 69.779999\n",
      "Total number of trainable parameters = 379271\n",
      "\n",
      "Epoch 5, Loss: 0.5747, Accuracy: 80.5200, Test Loss: 0.8811, Test Accuracy: 70.090004\n",
      "Total number of trainable parameters = 379271\n",
      "\n",
      "Epoch 6, Loss: 0.5184, Accuracy: 82.5320, Test Loss: 0.9222, Test Accuracy: 69.330002\n",
      "Total number of trainable parameters = 379271\n",
      "\n",
      "Epoch 7, Loss: 0.4682, Accuracy: 84.4260, Test Loss: 0.9117, Test Accuracy: 70.440002\n",
      "Total number of trainable parameters = 379271\n",
      "\n",
      "Epoch 8, Loss: 0.4254, Accuracy: 85.8260, Test Loss: 0.9306, Test Accuracy: 70.440002\n",
      "Total number of trainable parameters = 379271\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 71.757%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 71.757%\n",
      "dense layer: (16384, 256), pruning rate = 93.128%\n",
      "dense layer: (256, 256), pruning rate = 93.128%\n",
      "op layer: (256, 10), pruning rate = 71.757%\n",
      "\n",
      "After Pruning Round = 12, total number of surviving trainable parameters = 304796\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.4524, Accuracy: 84.7620, Test Loss: 0.9502, Test Accuracy: 70.090004\n",
      "Total number of trainable parameters (FT) = 304796\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.3512, Accuracy: 88.7880, Test Loss: 0.9990, Test Accuracy: 70.230003\n",
      "Total number of trainable parameters (FT) = 304796\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.3119, Accuracy: 90.1020, Test Loss: 1.0256, Test Accuracy: 70.279999\n",
      "Total number of trainable parameters (FT) = 304796\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 13\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.1853, Accuracy: 58.8400, Test Loss: 1.0258, Test Accuracy: 64.459999\n",
      "Total number of trainable parameters = 304795\n",
      "\n",
      "Epoch 2, Loss: 0.8662, Accuracy: 70.4160, Test Loss: 0.9420, Test Accuracy: 67.360001\n",
      "Total number of trainable parameters = 304795\n",
      "\n",
      "Epoch 3, Loss: 0.7456, Accuracy: 74.4640, Test Loss: 0.9033, Test Accuracy: 68.870003\n",
      "Total number of trainable parameters = 304795\n",
      "\n",
      "Epoch 4, Loss: 0.6614, Accuracy: 77.4160, Test Loss: 0.8944, Test Accuracy: 69.459999\n",
      "Total number of trainable parameters = 304795\n",
      "\n",
      "Epoch 5, Loss: 0.5926, Accuracy: 79.8440, Test Loss: 0.8946, Test Accuracy: 70.090004\n",
      "Total number of trainable parameters = 304795\n",
      "\n",
      "Epoch 6, Loss: 0.5371, Accuracy: 81.9200, Test Loss: 0.8938, Test Accuracy: 70.349998\n",
      "Total number of trainable parameters = 304795\n",
      "\n",
      "Epoch 7, Loss: 0.4895, Accuracy: 83.5960, Test Loss: 0.9063, Test Accuracy: 70.410004\n",
      "Total number of trainable parameters = 304795\n",
      "\n",
      "Epoch 8, Loss: 0.4445, Accuracy: 85.1900, Test Loss: 0.9447, Test Accuracy: 69.580002\n",
      "Total number of trainable parameters = 304795\n",
      "\n",
      "Epoch 9, Loss: 0.4078, Accuracy: 86.6340, Test Loss: 0.9521, Test Accuracy: 70.370003\n",
      "Total number of trainable parameters = 304795\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 74.581%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 74.581%\n",
      "dense layer: (16384, 256), pruning rate = 94.502%\n",
      "dense layer: (256, 256), pruning rate = 94.502%\n",
      "op layer: (256, 10), pruning rate = 74.581%\n",
      "\n",
      "After Pruning Round = 13, total number of surviving trainable parameters = 245103\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.4450, Accuracy: 85.1360, Test Loss: 0.9610, Test Accuracy: 70.209999\n",
      "Total number of trainable parameters (FT) = 245103\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.3469, Accuracy: 89.0120, Test Loss: 1.0082, Test Accuracy: 69.919998\n",
      "Total number of trainable parameters (FT) = 245103\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.3094, Accuracy: 90.1480, Test Loss: 1.0381, Test Accuracy: 70.000000\n",
      "Total number of trainable parameters (FT) = 245103\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 14\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.2158, Accuracy: 57.9200, Test Loss: 1.0632, Test Accuracy: 63.309998\n",
      "Total number of trainable parameters = 245101\n",
      "\n",
      "Epoch 2, Loss: 0.8935, Accuracy: 69.3140, Test Loss: 0.9497, Test Accuracy: 67.279999\n",
      "Total number of trainable parameters = 245101\n",
      "\n",
      "Epoch 3, Loss: 0.7655, Accuracy: 73.7480, Test Loss: 0.9242, Test Accuracy: 68.150002\n",
      "Total number of trainable parameters = 245101\n",
      "\n",
      "Epoch 4, Loss: 0.6768, Accuracy: 76.8740, Test Loss: 0.9073, Test Accuracy: 68.550003\n",
      "Total number of trainable parameters = 245101\n",
      "\n",
      "Epoch 5, Loss: 0.6079, Accuracy: 79.2500, Test Loss: 0.9002, Test Accuracy: 69.950005\n",
      "Total number of trainable parameters = 245101\n",
      "\n",
      "Epoch 6, Loss: 0.5511, Accuracy: 81.2540, Test Loss: 0.8955, Test Accuracy: 70.110001\n",
      "Total number of trainable parameters = 245101\n",
      "\n",
      "Epoch 7, Loss: 0.5026, Accuracy: 83.1540, Test Loss: 0.9198, Test Accuracy: 69.919998\n",
      "Total number of trainable parameters = 245101\n",
      "\n",
      "Epoch 8, Loss: 0.4612, Accuracy: 84.6620, Test Loss: 0.9301, Test Accuracy: 70.120003\n",
      "Total number of trainable parameters = 245101\n",
      "\n",
      "Epoch 9, Loss: 0.4258, Accuracy: 86.0020, Test Loss: 0.9311, Test Accuracy: 70.660004\n",
      "Total number of trainable parameters = 245101\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 77.123%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 77.123%\n",
      "dense layer: (16384, 256), pruning rate = 95.602%\n",
      "dense layer: (256, 256), pruning rate = 95.602%\n",
      "op layer: (256, 10), pruning rate = 77.123%\n",
      "\n",
      "After Pruning Round = 14, total number of surviving trainable parameters = 197198\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.4614, Accuracy: 84.4040, Test Loss: 0.9611, Test Accuracy: 69.779999\n",
      "Total number of trainable parameters (FT) = 197198\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.3727, Accuracy: 87.9960, Test Loss: 0.9986, Test Accuracy: 69.930000\n",
      "Total number of trainable parameters (FT) = 197198\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.3365, Accuracy: 89.1760, Test Loss: 1.0159, Test Accuracy: 70.340004\n",
      "Total number of trainable parameters (FT) = 197198\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 15\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.2514, Accuracy: 56.7820, Test Loss: 1.0724, Test Accuracy: 62.440002\n",
      "Total number of trainable parameters = 197193\n",
      "\n",
      "Epoch 2, Loss: 0.9222, Accuracy: 68.3040, Test Loss: 0.9716, Test Accuracy: 66.469994\n",
      "Total number of trainable parameters = 197193\n",
      "\n",
      "Epoch 3, Loss: 0.7907, Accuracy: 72.9600, Test Loss: 0.9258, Test Accuracy: 68.480003\n",
      "Total number of trainable parameters = 197193\n",
      "\n",
      "Epoch 4, Loss: 0.7000, Accuracy: 75.9720, Test Loss: 0.9026, Test Accuracy: 69.340004\n",
      "Total number of trainable parameters = 197193\n",
      "\n",
      "Epoch 5, Loss: 0.6297, Accuracy: 78.6460, Test Loss: 0.9085, Test Accuracy: 69.520004\n",
      "Total number of trainable parameters = 197193\n",
      "\n",
      "Epoch 6, Loss: 0.5729, Accuracy: 80.6380, Test Loss: 0.8953, Test Accuracy: 70.230003\n",
      "Total number of trainable parameters = 197193\n",
      "\n",
      "Epoch 7, Loss: 0.5246, Accuracy: 82.4440, Test Loss: 0.9080, Test Accuracy: 70.430000\n",
      "Total number of trainable parameters = 197193\n",
      "\n",
      "Epoch 8, Loss: 0.4842, Accuracy: 83.8600, Test Loss: 0.9204, Test Accuracy: 70.440002\n",
      "Total number of trainable parameters = 197193\n",
      "\n",
      "Epoch 9, Loss: 0.4487, Accuracy: 85.2140, Test Loss: 0.9386, Test Accuracy: 70.580002\n",
      "Total number of trainable parameters = 197193\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 79.411%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 79.411%\n",
      "dense layer: (16384, 256), pruning rate = 96.482%\n",
      "dense layer: (256, 256), pruning rate = 96.482%\n",
      "op layer: (256, 10), pruning rate = 79.411%\n",
      "\n",
      "After Pruning Round = 15, total number of surviving trainable parameters = 158763\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.5165, Accuracy: 82.8080, Test Loss: 0.9332, Test Accuracy: 70.050003\n",
      "Total number of trainable parameters (FT) = 158763\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.4158, Accuracy: 86.3840, Test Loss: 0.9731, Test Accuracy: 70.020004\n",
      "Total number of trainable parameters (FT) = 158763\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.3798, Accuracy: 87.6360, Test Loss: 0.9991, Test Accuracy: 69.550003\n",
      "Total number of trainable parameters (FT) = 158763\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 16\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.3206, Accuracy: 54.5020, Test Loss: 1.1212, Test Accuracy: 61.180000\n",
      "Total number of trainable parameters = 158762\n",
      "\n",
      "Epoch 2, Loss: 0.9900, Accuracy: 66.0160, Test Loss: 1.0174, Test Accuracy: 64.900002\n",
      "Total number of trainable parameters = 158762\n",
      "\n",
      "Epoch 3, Loss: 0.8691, Accuracy: 70.0320, Test Loss: 0.9628, Test Accuracy: 66.329994\n",
      "Total number of trainable parameters = 158762\n",
      "\n",
      "Epoch 4, Loss: 0.7813, Accuracy: 73.2460, Test Loss: 0.9410, Test Accuracy: 67.760002\n",
      "Total number of trainable parameters = 158762\n",
      "\n",
      "Epoch 5, Loss: 0.7098, Accuracy: 75.5160, Test Loss: 0.9256, Test Accuracy: 68.620003\n",
      "Total number of trainable parameters = 158762\n",
      "\n",
      "Epoch 6, Loss: 0.6544, Accuracy: 77.8120, Test Loss: 0.9177, Test Accuracy: 68.540001\n",
      "Total number of trainable parameters = 158762\n",
      "\n",
      "Epoch 7, Loss: 0.6056, Accuracy: 79.4840, Test Loss: 0.9060, Test Accuracy: 69.880005\n",
      "Total number of trainable parameters = 158762\n",
      "\n",
      "Epoch 8, Loss: 0.5652, Accuracy: 80.8840, Test Loss: 0.9098, Test Accuracy: 70.099998\n",
      "Total number of trainable parameters = 158762\n",
      "\n",
      "Epoch 9, Loss: 0.5265, Accuracy: 82.3520, Test Loss: 0.9188, Test Accuracy: 69.880005\n",
      "Total number of trainable parameters = 158762\n",
      "\n",
      "Epoch 10, Loss: 0.4950, Accuracy: 83.4680, Test Loss: 0.9375, Test Accuracy: 69.650002\n",
      "Total number of trainable parameters = 158762\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 81.47%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 81.47%\n",
      "dense layer: (16384, 256), pruning rate = 97.185%\n",
      "dense layer: (256, 256), pruning rate = 97.185%\n",
      "op layer: (256, 10), pruning rate = 81.47%\n",
      "\n",
      "After Pruning Round = 16, total number of surviving trainable parameters = 127969\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.5377, Accuracy: 81.6920, Test Loss: 0.9327, Test Accuracy: 69.520004\n",
      "Total number of trainable parameters (FT) = 127969\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.4615, Accuracy: 84.8200, Test Loss: 0.9479, Test Accuracy: 69.570000\n",
      "Total number of trainable parameters (FT) = 127969\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.4317, Accuracy: 85.8260, Test Loss: 0.9825, Test Accuracy: 69.550003\n",
      "Total number of trainable parameters (FT) = 127969\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 17\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.3563, Accuracy: 53.1180, Test Loss: 1.1664, Test Accuracy: 58.829998\n",
      "Total number of trainable parameters = 127959\n",
      "\n",
      "Epoch 2, Loss: 1.0135, Accuracy: 65.0600, Test Loss: 1.0352, Test Accuracy: 64.070000\n",
      "Total number of trainable parameters = 127959\n",
      "\n",
      "Epoch 3, Loss: 0.8886, Accuracy: 69.4360, Test Loss: 0.9767, Test Accuracy: 65.989998\n",
      "Total number of trainable parameters = 127959\n",
      "\n",
      "Epoch 4, Loss: 0.8009, Accuracy: 72.3580, Test Loss: 0.9458, Test Accuracy: 67.089996\n",
      "Total number of trainable parameters = 127959\n",
      "\n",
      "Epoch 5, Loss: 0.7320, Accuracy: 74.7420, Test Loss: 0.9288, Test Accuracy: 68.150002\n",
      "Total number of trainable parameters = 127959\n",
      "\n",
      "Epoch 6, Loss: 0.6781, Accuracy: 76.7540, Test Loss: 0.9086, Test Accuracy: 69.070000\n",
      "Total number of trainable parameters = 127959\n",
      "\n",
      "Epoch 7, Loss: 0.6329, Accuracy: 78.4600, Test Loss: 0.9087, Test Accuracy: 69.199997\n",
      "Total number of trainable parameters = 127959\n",
      "\n",
      "Epoch 8, Loss: 0.5934, Accuracy: 79.8220, Test Loss: 0.9077, Test Accuracy: 69.379997\n",
      "Total number of trainable parameters = 127959\n",
      "\n",
      "Epoch 9, Loss: 0.5611, Accuracy: 80.8940, Test Loss: 0.9181, Test Accuracy: 69.669998\n",
      "Total number of trainable parameters = 127959\n",
      "\n",
      "Epoch 10, Loss: 0.5301, Accuracy: 82.0520, Test Loss: 0.9289, Test Accuracy: 69.700005\n",
      "Total number of trainable parameters = 127959\n",
      "\n",
      "Epoch 11, Loss: 0.5037, Accuracy: 83.2040, Test Loss: 0.9547, Test Accuracy: 68.680000\n",
      "Total number of trainable parameters = 127959\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 83.323%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 83.323%\n",
      "dense layer: (16384, 256), pruning rate = 97.748%\n",
      "dense layer: (256, 256), pruning rate = 97.748%\n",
      "op layer: (256, 10), pruning rate = 83.323%\n",
      "\n",
      "After Pruning Round = 17, total number of surviving trainable parameters = 103213\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.5834, Accuracy: 80.4480, Test Loss: 0.9325, Test Accuracy: 68.809998\n",
      "Total number of trainable parameters (FT) = 103213\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.4949, Accuracy: 83.4780, Test Loss: 0.9527, Test Accuracy: 69.139999\n",
      "Total number of trainable parameters (FT) = 103213\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.4646, Accuracy: 84.6080, Test Loss: 0.9582, Test Accuracy: 69.459999\n",
      "Total number of trainable parameters (FT) = 103213\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 18\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.4139, Accuracy: 51.0740, Test Loss: 1.1934, Test Accuracy: 57.869999\n",
      "Total number of trainable parameters = 103204\n",
      "\n",
      "Epoch 2, Loss: 1.0733, Accuracy: 62.8440, Test Loss: 1.0810, Test Accuracy: 62.150002\n",
      "Total number of trainable parameters = 103204\n",
      "\n",
      "Epoch 3, Loss: 0.9539, Accuracy: 67.1420, Test Loss: 1.0153, Test Accuracy: 64.340004\n",
      "Total number of trainable parameters = 103204\n",
      "\n",
      "Epoch 4, Loss: 0.8694, Accuracy: 69.9780, Test Loss: 0.9868, Test Accuracy: 65.559998\n",
      "Total number of trainable parameters = 103204\n",
      "\n",
      "Epoch 5, Loss: 0.8035, Accuracy: 72.0960, Test Loss: 0.9495, Test Accuracy: 67.239998\n",
      "Total number of trainable parameters = 103204\n",
      "\n",
      "Epoch 6, Loss: 0.7510, Accuracy: 74.1620, Test Loss: 0.9438, Test Accuracy: 67.349998\n",
      "Total number of trainable parameters = 103204\n",
      "\n",
      "Epoch 7, Loss: 0.7056, Accuracy: 75.6140, Test Loss: 0.9302, Test Accuracy: 67.949997\n",
      "Total number of trainable parameters = 103204\n",
      "\n",
      "Epoch 8, Loss: 0.6675, Accuracy: 77.1180, Test Loss: 0.9359, Test Accuracy: 68.150002\n",
      "Total number of trainable parameters = 103204\n",
      "\n",
      "Epoch 9, Loss: 0.6356, Accuracy: 78.1500, Test Loss: 0.9238, Test Accuracy: 68.870003\n",
      "Total number of trainable parameters = 103204\n",
      "\n",
      "Epoch 10, Loss: 0.6053, Accuracy: 79.1480, Test Loss: 0.9262, Test Accuracy: 68.570000\n",
      "Total number of trainable parameters = 103204\n",
      "\n",
      "Epoch 11, Loss: 0.5775, Accuracy: 80.2880, Test Loss: 0.9428, Test Accuracy: 68.540001\n",
      "Total number of trainable parameters = 103204\n",
      "\n",
      "Epoch 12, Loss: 0.5556, Accuracy: 81.0980, Test Loss: 0.9503, Test Accuracy: 68.820000\n",
      "Total number of trainable parameters = 103204\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 84.991%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 84.991%\n",
      "dense layer: (16384, 256), pruning rate = 98.199%\n",
      "dense layer: (256, 256), pruning rate = 98.199%\n",
      "op layer: (256, 10), pruning rate = 84.991%\n",
      "\n",
      "After Pruning Round = 18, total number of surviving trainable parameters = 83307\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.6216, Accuracy: 78.8220, Test Loss: 0.9251, Test Accuracy: 69.059998\n",
      "Total number of trainable parameters (FT) = 83307\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.5448, Accuracy: 81.5400, Test Loss: 0.9501, Test Accuracy: 68.750000\n",
      "Total number of trainable parameters (FT) = 83307\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.5218, Accuracy: 82.2600, Test Loss: 0.9534, Test Accuracy: 69.110001\n",
      "Total number of trainable parameters (FT) = 83307\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 19\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.4686, Accuracy: 49.2860, Test Loss: 1.2351, Test Accuracy: 56.309998\n",
      "Total number of trainable parameters = 83300\n",
      "\n",
      "Epoch 2, Loss: 1.1253, Accuracy: 61.0400, Test Loss: 1.1167, Test Accuracy: 60.829998\n",
      "Total number of trainable parameters = 83300\n",
      "\n",
      "Epoch 3, Loss: 1.0092, Accuracy: 65.0680, Test Loss: 1.0536, Test Accuracy: 63.290001\n",
      "Total number of trainable parameters = 83300\n",
      "\n",
      "Epoch 4, Loss: 0.9267, Accuracy: 67.9860, Test Loss: 1.0082, Test Accuracy: 64.880005\n",
      "Total number of trainable parameters = 83300\n",
      "\n",
      "Epoch 5, Loss: 0.8618, Accuracy: 70.2180, Test Loss: 0.9775, Test Accuracy: 65.769997\n",
      "Total number of trainable parameters = 83300\n",
      "\n",
      "Epoch 6, Loss: 0.8079, Accuracy: 71.9300, Test Loss: 0.9593, Test Accuracy: 66.419998\n",
      "Total number of trainable parameters = 83300\n",
      "\n",
      "Epoch 7, Loss: 0.7624, Accuracy: 73.5440, Test Loss: 0.9512, Test Accuracy: 66.939995\n",
      "Total number of trainable parameters = 83300\n",
      "\n",
      "Epoch 8, Loss: 0.7244, Accuracy: 74.9940, Test Loss: 0.9455, Test Accuracy: 67.290001\n",
      "Total number of trainable parameters = 83300\n",
      "\n",
      "Epoch 9, Loss: 0.6903, Accuracy: 76.2400, Test Loss: 0.9354, Test Accuracy: 68.180000\n",
      "Total number of trainable parameters = 83300\n",
      "\n",
      "Epoch 10, Loss: 0.6613, Accuracy: 77.1400, Test Loss: 0.9369, Test Accuracy: 68.139999\n",
      "Total number of trainable parameters = 83300\n",
      "\n",
      "Epoch 11, Loss: 0.6366, Accuracy: 78.2120, Test Loss: 0.9359, Test Accuracy: 68.269997\n",
      "Total number of trainable parameters = 83300\n",
      "\n",
      "Epoch 12, Loss: 0.6132, Accuracy: 79.1320, Test Loss: 0.9520, Test Accuracy: 68.159996\n",
      "Total number of trainable parameters = 83300\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 86.491%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 86.491%\n",
      "dense layer: (16384, 256), pruning rate = 98.559%\n",
      "dense layer: (256, 256), pruning rate = 98.559%\n",
      "op layer: (256, 10), pruning rate = 86.491%\n",
      "\n",
      "After Pruning Round = 19, total number of surviving trainable parameters = 67346\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.6923, Accuracy: 76.3280, Test Loss: 0.9667, Test Accuracy: 67.540001\n",
      "Total number of trainable parameters (FT) = 67346\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.6201, Accuracy: 78.6860, Test Loss: 0.9454, Test Accuracy: 68.089996\n",
      "Total number of trainable parameters (FT) = 67346\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.5972, Accuracy: 79.6140, Test Loss: 0.9483, Test Accuracy: 68.279999\n",
      "Total number of trainable parameters (FT) = 67346\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 20\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.5254, Accuracy: 47.4860, Test Loss: 1.2849, Test Accuracy: 54.520000\n",
      "Total number of trainable parameters = 67332\n",
      "\n",
      "Epoch 2, Loss: 1.1650, Accuracy: 59.7740, Test Loss: 1.1505, Test Accuracy: 59.460003\n",
      "Total number of trainable parameters = 67332\n",
      "\n",
      "Epoch 3, Loss: 1.0486, Accuracy: 63.7100, Test Loss: 1.0823, Test Accuracy: 62.309998\n",
      "Total number of trainable parameters = 67332\n",
      "\n",
      "Epoch 4, Loss: 0.9717, Accuracy: 66.4780, Test Loss: 1.0502, Test Accuracy: 63.320000\n",
      "Total number of trainable parameters = 67332\n",
      "\n",
      "Epoch 5, Loss: 0.9132, Accuracy: 68.3700, Test Loss: 1.0132, Test Accuracy: 64.780006\n",
      "Total number of trainable parameters = 67332\n",
      "\n",
      "Epoch 6, Loss: 0.8634, Accuracy: 70.0820, Test Loss: 0.9998, Test Accuracy: 65.400002\n",
      "Total number of trainable parameters = 67332\n",
      "\n",
      "Epoch 7, Loss: 0.8235, Accuracy: 71.5700, Test Loss: 0.9950, Test Accuracy: 65.639999\n",
      "Total number of trainable parameters = 67333\n",
      "\n",
      "Epoch 8, Loss: 0.7896, Accuracy: 72.5760, Test Loss: 0.9712, Test Accuracy: 66.209999\n",
      "Total number of trainable parameters = 67333\n",
      "\n",
      "Epoch 9, Loss: 0.7602, Accuracy: 73.5140, Test Loss: 0.9586, Test Accuracy: 67.290001\n",
      "Total number of trainable parameters = 67333\n",
      "\n",
      "Epoch 10, Loss: 0.7337, Accuracy: 74.5280, Test Loss: 0.9511, Test Accuracy: 67.369995\n",
      "Total number of trainable parameters = 67333\n",
      "\n",
      "Epoch 11, Loss: 0.7123, Accuracy: 75.4460, Test Loss: 0.9536, Test Accuracy: 67.529999\n",
      "Total number of trainable parameters = 67333\n",
      "\n",
      "Epoch 12, Loss: 0.6930, Accuracy: 76.0600, Test Loss: 0.9496, Test Accuracy: 68.019997\n",
      "Total number of trainable parameters = 67333\n",
      "\n",
      "Epoch 13, Loss: 0.6731, Accuracy: 76.8180, Test Loss: 0.9536, Test Accuracy: 67.820000\n",
      "Total number of trainable parameters = 67333\n",
      "\n",
      "Epoch 14, Loss: 0.6565, Accuracy: 77.3460, Test Loss: 0.9599, Test Accuracy: 67.799995\n",
      "Total number of trainable parameters = 67333\n",
      "\n",
      "Epoch 15, Loss: 0.6422, Accuracy: 77.8980, Test Loss: 0.9535, Test Accuracy: 68.040001\n",
      "Total number of trainable parameters = 67333\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 87.842%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 87.842%\n",
      "dense layer: (16384, 256), pruning rate = 98.847%\n",
      "dense layer: (256, 256), pruning rate = 98.847%\n",
      "op layer: (256, 10), pruning rate = 87.842%\n",
      "\n",
      "After Pruning Round = 20, total number of surviving trainable parameters = 54509\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.6912, Accuracy: 76.2120, Test Loss: 0.9470, Test Accuracy: 67.849998\n",
      "Total number of trainable parameters (FT) = 54509\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.6388, Accuracy: 78.1000, Test Loss: 0.9557, Test Accuracy: 67.970001\n",
      "Total number of trainable parameters (FT) = 54509\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.6210, Accuracy: 78.6960, Test Loss: 0.9606, Test Accuracy: 68.059998\n",
      "Total number of trainable parameters (FT) = 54509\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 21\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.5675, Accuracy: 45.8620, Test Loss: 1.3235, Test Accuracy: 53.630001\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 2, Loss: 1.2072, Accuracy: 58.2260, Test Loss: 1.1825, Test Accuracy: 58.200001\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 3, Loss: 1.0922, Accuracy: 62.0160, Test Loss: 1.1170, Test Accuracy: 60.730000\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 4, Loss: 1.0178, Accuracy: 64.6140, Test Loss: 1.0747, Test Accuracy: 62.059998\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 5, Loss: 0.9599, Accuracy: 66.7040, Test Loss: 1.0466, Test Accuracy: 63.620003\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 6, Loss: 0.9111, Accuracy: 68.3940, Test Loss: 1.0168, Test Accuracy: 64.730003\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 7, Loss: 0.8713, Accuracy: 69.7560, Test Loss: 0.9964, Test Accuracy: 65.449997\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 8, Loss: 0.8355, Accuracy: 70.9380, Test Loss: 0.9883, Test Accuracy: 65.860001\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 9, Loss: 0.8038, Accuracy: 72.0420, Test Loss: 0.9811, Test Accuracy: 66.009995\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 10, Loss: 0.7777, Accuracy: 72.8980, Test Loss: 0.9665, Test Accuracy: 66.949997\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 11, Loss: 0.7536, Accuracy: 73.6920, Test Loss: 0.9691, Test Accuracy: 66.970001\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 12, Loss: 0.7342, Accuracy: 74.6420, Test Loss: 0.9623, Test Accuracy: 67.369995\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 13, Loss: 0.7170, Accuracy: 75.0000, Test Loss: 0.9603, Test Accuracy: 67.250000\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 14, Loss: 0.6981, Accuracy: 75.8740, Test Loss: 0.9665, Test Accuracy: 67.669998\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 15, Loss: 0.6846, Accuracy: 76.2480, Test Loss: 0.9606, Test Accuracy: 67.879997\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 16, Loss: 0.6702, Accuracy: 76.6900, Test Loss: 0.9558, Test Accuracy: 68.059998\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 17, Loss: 0.6589, Accuracy: 77.1600, Test Loss: 0.9595, Test Accuracy: 67.790001\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 18, Loss: 0.6457, Accuracy: 77.5980, Test Loss: 0.9715, Test Accuracy: 67.959999\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "Epoch 19, Loss: 0.6339, Accuracy: 78.0980, Test Loss: 0.9673, Test Accuracy: 67.970001\n",
      "Total number of trainable parameters = 54490\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 89.058%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 89.058%\n",
      "dense layer: (16384, 256), pruning rate = 99.078%\n",
      "dense layer: (256, 256), pruning rate = 99.078%\n",
      "op layer: (256, 10), pruning rate = 89.058%\n",
      "\n",
      "After Pruning Round = 21, total number of surviving trainable parameters = 44150\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.7037, Accuracy: 75.7900, Test Loss: 0.9629, Test Accuracy: 67.400002\n",
      "Total number of trainable parameters (FT) = 44150\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.6515, Accuracy: 77.4720, Test Loss: 0.9750, Test Accuracy: 67.349998\n",
      "Total number of trainable parameters (FT) = 44150\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.6357, Accuracy: 78.0620, Test Loss: 0.9784, Test Accuracy: 67.459999\n",
      "Total number of trainable parameters (FT) = 44150\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 22\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.6911, Accuracy: 41.4880, Test Loss: 1.4461, Test Accuracy: 50.089996\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 2, Loss: 1.3221, Accuracy: 54.3460, Test Loss: 1.2724, Test Accuracy: 56.029999\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 3, Loss: 1.1889, Accuracy: 58.8680, Test Loss: 1.1939, Test Accuracy: 57.760002\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 4, Loss: 1.1095, Accuracy: 61.5720, Test Loss: 1.1429, Test Accuracy: 60.039997\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 5, Loss: 1.0517, Accuracy: 63.6400, Test Loss: 1.1110, Test Accuracy: 61.129997\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 6, Loss: 1.0030, Accuracy: 65.2740, Test Loss: 1.0849, Test Accuracy: 61.779999\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 7, Loss: 0.9618, Accuracy: 66.6160, Test Loss: 1.0576, Test Accuracy: 63.090004\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 8, Loss: 0.9262, Accuracy: 68.0360, Test Loss: 1.0353, Test Accuracy: 64.389999\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 9, Loss: 0.8929, Accuracy: 69.1380, Test Loss: 1.0332, Test Accuracy: 64.450005\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 10, Loss: 0.8641, Accuracy: 70.0440, Test Loss: 1.0135, Test Accuracy: 65.240005\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 11, Loss: 0.8386, Accuracy: 71.0260, Test Loss: 0.9991, Test Accuracy: 65.900002\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 12, Loss: 0.8156, Accuracy: 71.8020, Test Loss: 0.9900, Test Accuracy: 66.239998\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 13, Loss: 0.7942, Accuracy: 72.5240, Test Loss: 0.9884, Test Accuracy: 66.379997\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 14, Loss: 0.7766, Accuracy: 73.0840, Test Loss: 0.9975, Test Accuracy: 66.239998\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 15, Loss: 0.7589, Accuracy: 73.7040, Test Loss: 0.9881, Test Accuracy: 66.659996\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 16, Loss: 0.7439, Accuracy: 74.1460, Test Loss: 0.9811, Test Accuracy: 66.799995\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 17, Loss: 0.7296, Accuracy: 74.7340, Test Loss: 0.9851, Test Accuracy: 66.610001\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 18, Loss: 0.7178, Accuracy: 75.1040, Test Loss: 0.9757, Test Accuracy: 67.229996\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 19, Loss: 0.7052, Accuracy: 75.5900, Test Loss: 0.9895, Test Accuracy: 66.709999\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 20, Loss: 0.6939, Accuracy: 76.1540, Test Loss: 0.9830, Test Accuracy: 67.009995\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "Epoch 21, Loss: 0.6838, Accuracy: 76.2780, Test Loss: 0.9828, Test Accuracy: 67.049995\n",
      "Total number of trainable parameters = 44132\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 90.152%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 90.152%\n",
      "dense layer: (16384, 256), pruning rate = 99.262%\n",
      "dense layer: (256, 256), pruning rate = 99.262%\n",
      "op layer: (256, 10), pruning rate = 90.152%\n",
      "\n",
      "After Pruning Round = 22, total number of surviving trainable parameters = 35844\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.7225, Accuracy: 74.9740, Test Loss: 0.9889, Test Accuracy: 66.680000\n",
      "Total number of trainable parameters (FT) = 35844\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.6855, Accuracy: 76.3140, Test Loss: 0.9847, Test Accuracy: 67.189995\n",
      "Total number of trainable parameters (FT) = 35844\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.6732, Accuracy: 76.8100, Test Loss: 0.9938, Test Accuracy: 66.970001\n",
      "Total number of trainable parameters (FT) = 35844\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 23\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.7249, Accuracy: 40.3500, Test Loss: 1.4963, Test Accuracy: 47.260002\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 2, Loss: 1.3687, Accuracy: 52.7940, Test Loss: 1.3148, Test Accuracy: 53.530003\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 3, Loss: 1.2301, Accuracy: 57.2880, Test Loss: 1.2242, Test Accuracy: 56.699997\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 4, Loss: 1.1429, Accuracy: 60.4520, Test Loss: 1.1648, Test Accuracy: 59.399998\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 5, Loss: 1.0792, Accuracy: 62.6460, Test Loss: 1.1223, Test Accuracy: 60.630001\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 6, Loss: 1.0271, Accuracy: 64.3940, Test Loss: 1.0949, Test Accuracy: 61.889999\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 7, Loss: 0.9831, Accuracy: 65.8560, Test Loss: 1.0736, Test Accuracy: 62.400002\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 8, Loss: 0.9447, Accuracy: 67.2620, Test Loss: 1.0498, Test Accuracy: 63.169998\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 9, Loss: 0.9120, Accuracy: 68.5220, Test Loss: 1.0379, Test Accuracy: 63.709999\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 10, Loss: 0.8819, Accuracy: 69.5740, Test Loss: 1.0258, Test Accuracy: 64.459999\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 11, Loss: 0.8567, Accuracy: 70.2100, Test Loss: 1.0092, Test Accuracy: 65.200005\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 12, Loss: 0.8325, Accuracy: 71.2460, Test Loss: 1.0075, Test Accuracy: 65.520004\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 13, Loss: 0.8128, Accuracy: 71.7540, Test Loss: 1.0000, Test Accuracy: 65.779999\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 14, Loss: 0.7946, Accuracy: 72.4300, Test Loss: 0.9910, Test Accuracy: 66.299995\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 15, Loss: 0.7783, Accuracy: 72.9340, Test Loss: 0.9900, Test Accuracy: 66.139999\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 16, Loss: 0.7631, Accuracy: 73.6160, Test Loss: 0.9931, Test Accuracy: 66.019997\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 17, Loss: 0.7488, Accuracy: 73.9780, Test Loss: 1.0032, Test Accuracy: 65.949997\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "Epoch 18, Loss: 0.7375, Accuracy: 74.3200, Test Loss: 1.0020, Test Accuracy: 66.239998\n",
      "Total number of trainable parameters = 35834\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 91.137%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 91.137%\n",
      "dense layer: (16384, 256), pruning rate = 99.41%\n",
      "dense layer: (256, 256), pruning rate = 99.41%\n",
      "op layer: (256, 10), pruning rate = 91.137%\n",
      "\n",
      "After Pruning Round = 23, total number of surviving trainable parameters = 29124\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.8200, Accuracy: 71.4960, Test Loss: 0.9994, Test Accuracy: 65.869995\n",
      "Total number of trainable parameters (FT) = 29124\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.7680, Accuracy: 73.5100, Test Loss: 0.9954, Test Accuracy: 66.229996\n",
      "Total number of trainable parameters (FT) = 29124\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.7537, Accuracy: 73.7120, Test Loss: 0.9975, Test Accuracy: 66.149994\n",
      "Total number of trainable parameters (FT) = 29124\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 24\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.7407, Accuracy: 40.3040, Test Loss: 1.5242, Test Accuracy: 47.279999\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 2, Loss: 1.4048, Accuracy: 51.5920, Test Loss: 1.3496, Test Accuracy: 52.619999\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 3, Loss: 1.2790, Accuracy: 55.8400, Test Loss: 1.2805, Test Accuracy: 54.949997\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 4, Loss: 1.2000, Accuracy: 58.3840, Test Loss: 1.2189, Test Accuracy: 56.959999\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 5, Loss: 1.1384, Accuracy: 60.5160, Test Loss: 1.1776, Test Accuracy: 58.340000\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 6, Loss: 1.0890, Accuracy: 62.2140, Test Loss: 1.1535, Test Accuracy: 58.920002\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 7, Loss: 1.0473, Accuracy: 63.5980, Test Loss: 1.1201, Test Accuracy: 60.720001\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 8, Loss: 1.0141, Accuracy: 64.7300, Test Loss: 1.0964, Test Accuracy: 61.510002\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 9, Loss: 0.9847, Accuracy: 65.7860, Test Loss: 1.0799, Test Accuracy: 62.320000\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 10, Loss: 0.9586, Accuracy: 66.5160, Test Loss: 1.0688, Test Accuracy: 62.550003\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 11, Loss: 0.9340, Accuracy: 67.6300, Test Loss: 1.0554, Test Accuracy: 63.260002\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 12, Loss: 0.9136, Accuracy: 68.2960, Test Loss: 1.0519, Test Accuracy: 63.500000\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 13, Loss: 0.8940, Accuracy: 69.1160, Test Loss: 1.0418, Test Accuracy: 63.980000\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 14, Loss: 0.8775, Accuracy: 69.4040, Test Loss: 1.0256, Test Accuracy: 64.570000\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 15, Loss: 0.8602, Accuracy: 70.0860, Test Loss: 1.0277, Test Accuracy: 64.690002\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 16, Loss: 0.8457, Accuracy: 70.4960, Test Loss: 1.0242, Test Accuracy: 64.639999\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 17, Loss: 0.8326, Accuracy: 71.0400, Test Loss: 1.0147, Test Accuracy: 65.139999\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 18, Loss: 0.8189, Accuracy: 71.4920, Test Loss: 1.0299, Test Accuracy: 64.470001\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 19, Loss: 0.8087, Accuracy: 71.8380, Test Loss: 1.0101, Test Accuracy: 65.330002\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 20, Loss: 0.7981, Accuracy: 72.2220, Test Loss: 1.0096, Test Accuracy: 65.669998\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 21, Loss: 0.7865, Accuracy: 72.6040, Test Loss: 1.0173, Test Accuracy: 65.459999\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 22, Loss: 0.7778, Accuracy: 72.9340, Test Loss: 1.0150, Test Accuracy: 65.279999\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "Epoch 23, Loss: 0.7703, Accuracy: 73.1940, Test Loss: 1.0101, Test Accuracy: 65.570000\n",
      "Total number of trainable parameters = 29099\n",
      "\n",
      "\n",
      "'EarlyStopping' called!\n",
      "\n",
      "conv layer: (3, 3, 3, 64), pruning rate = 92.023%\n",
      "conv layer: (3, 3, 64, 64), pruning rate = 92.023%\n",
      "dense layer: (16384, 256), pruning rate = 99.528%\n",
      "dense layer: (256, 256), pruning rate = 99.528%\n",
      "op layer: (256, 10), pruning rate = 92.023%\n",
      "\n",
      "After Pruning Round = 24, total number of surviving trainable parameters = 23708\n",
      "\n",
      "\n",
      "Fine-tuning, round = 1\n",
      "\n",
      "FT: Epoch 1, Loss: 0.7946, Accuracy: 72.2860, Test Loss: 1.0072, Test Accuracy: 65.690002\n",
      "Total number of trainable parameters (FT) = 23708\n",
      "\n",
      "\n",
      "Fine-tuning, round = 2\n",
      "\n",
      "FT: Epoch 2, Loss: 0.7657, Accuracy: 73.3140, Test Loss: 1.0018, Test Accuracy: 65.860001\n",
      "Total number of trainable parameters (FT) = 23708\n",
      "\n",
      "\n",
      "Fine-tuning, round = 3\n",
      "\n",
      "FT: Epoch 3, Loss: 0.7553, Accuracy: 73.7220, Test Loss: 1.0063, Test Accuracy: 65.889999\n",
      "Total number of trainable parameters (FT) = 23708\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Iterative pruning round: 25\n",
      "\n",
      "\n",
      "Epoch 1, Loss: 1.7991, Accuracy: 37.4920, Test Loss: 1.5701, Test Accuracy: 45.639999\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 2, Loss: 1.4728, Accuracy: 48.7740, Test Loss: 1.4168, Test Accuracy: 49.810001\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 3, Loss: 1.3418, Accuracy: 53.2460, Test Loss: 1.3253, Test Accuracy: 52.619999\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 4, Loss: 1.2597, Accuracy: 56.0700, Test Loss: 1.2683, Test Accuracy: 54.840000\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 5, Loss: 1.1958, Accuracy: 58.2300, Test Loss: 1.2161, Test Accuracy: 56.540001\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 6, Loss: 1.1429, Accuracy: 60.0340, Test Loss: 1.1835, Test Accuracy: 57.889999\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 7, Loss: 1.0996, Accuracy: 61.5440, Test Loss: 1.1520, Test Accuracy: 59.069996\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 8, Loss: 1.0616, Accuracy: 62.7540, Test Loss: 1.1319, Test Accuracy: 59.930004\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 9, Loss: 1.0283, Accuracy: 64.0540, Test Loss: 1.1198, Test Accuracy: 60.560001\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 10, Loss: 1.0013, Accuracy: 64.9200, Test Loss: 1.0966, Test Accuracy: 61.349998\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 11, Loss: 0.9749, Accuracy: 65.9980, Test Loss: 1.0853, Test Accuracy: 61.989998\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 12, Loss: 0.9523, Accuracy: 66.8060, Test Loss: 1.0629, Test Accuracy: 62.400002\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 13, Loss: 0.9313, Accuracy: 67.5860, Test Loss: 1.0570, Test Accuracy: 62.610001\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 14, Loss: 0.9130, Accuracy: 68.2660, Test Loss: 1.0437, Test Accuracy: 63.349998\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 15, Loss: 0.8942, Accuracy: 68.7640, Test Loss: 1.0406, Test Accuracy: 63.440002\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 16, Loss: 0.8773, Accuracy: 69.2660, Test Loss: 1.0321, Test Accuracy: 64.020004\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 17, Loss: 0.8634, Accuracy: 69.8780, Test Loss: 1.0240, Test Accuracy: 64.170006\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 18, Loss: 0.8496, Accuracy: 70.2400, Test Loss: 1.0195, Test Accuracy: 64.550003\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 19, Loss: 0.8384, Accuracy: 70.6540, Test Loss: 1.0195, Test Accuracy: 64.740005\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 20, Loss: 0.8262, Accuracy: 71.1600, Test Loss: 1.0199, Test Accuracy: 64.720001\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 21, Loss: 0.8164, Accuracy: 71.4660, Test Loss: 1.0141, Test Accuracy: 65.070000\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 22, Loss: 0.8069, Accuracy: 71.8260, Test Loss: 1.0101, Test Accuracy: 65.190002\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 23, Loss: 0.7988, Accuracy: 72.2260, Test Loss: 1.0096, Test Accuracy: 65.480003\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 24, Loss: 0.7903, Accuracy: 72.3140, Test Loss: 1.0102, Test Accuracy: 65.540001\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 25, Loss: 0.7829, Accuracy: 72.6420, Test Loss: 1.0068, Test Accuracy: 65.699997\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 26, Loss: 0.7764, Accuracy: 72.8700, Test Loss: 1.0090, Test Accuracy: 65.459999\n",
      "Total number of trainable parameters = 23697\n",
      "\n",
      "Epoch 27, Loss: 0.7696, Accuracy: 73.1200, Test Loss: 1.0093, Test Accuracy: 65.650002\n",
      "Total number of trainable parameters = 23697\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)c\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",
    "    model_gt = conv2_cnn()\n",
    "    \n",
    "    # Load winning ticket (from above)-\n",
    "    # model_gt.load_weights(\"Conv2_CIFAR10_Winning_Ticket.h5\")\n",
    "    model_gt.set_weights(winning_ticket_model.get_weights())\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_stripped, optimizer, x, y)\n",
    "            train_one_step(model_gt, mask_model, optimizer, x, y)\n",
    "\n",
    "\n",
    "        for x_t, y_t in test_dataset:\n",
    "            # test_step(model_gt_stripped, optimizer, x_t, y_t)\n",
    "            test_step(model_gt, 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.trainable_weights:\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(\"Conv2_CIFAR10_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",
    "    # Prune neural network-\n",
    "    # pruned_weights = prune_lenet(model_gt, dense1_pruning[i - 1], op_layer_pruning[i - 1])\n",
    "    pruned_weights = prune_conv2(\n",
    "        model = model_gt,\n",
    "        pruning_params_conv=conv1_pruning[i - 1],\n",
    "        pruning_params_fc=dense1_pruning[i - 1],\n",
    "        pruning_params_op=op_layer_pruning[i - 1]\n",
    "    )\n",
    "\n",
    "    \n",
    "    # Instantiate a Neural Network model\n",
    "    pruned_model = conv2_cnn()\n",
    "\n",
    "    # Load pruned numpy weights-\n",
    "    pruned_model.set_weights(pruned_weights)\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.trainable_weights:\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(\"\\nAfter Pruning Round = {0}, total number of surviving 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",
    "    # '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",
    "    \n",
    "    # Save weights of PRUNED and Trained model-\n",
    "    # pruned_model.save_weights(\"Conv2_CIFAR10_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 = lenet_nn()\n",
    "    mask_model = conv2_cnn()\n",
    "    \n",
    "    # Load weights of PRUNED model-\n",
    "    # mask_model.load_weights(\"Conv2_CIFAR10_Pruned_Weights.h5\")\n",
    "    mask_model.set_weights(pruned_model.get_weights())\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",
    "    for wts in mask_model.trainable_weights:\n",
    "        wts.assign(tf.where(tf.equal(wts, 0.), 0., 1.))\n",
    "\n",
    "    \n",
    "    # FINE-TUNE pruned model (to recover from pruning)-\n",
    "    # Question: Does fine-tuning help here? Because the weights will be extracted\n",
    "    # according to surviving weights. Hence, fine-tuning the surviving weights does\n",
    "    # not make sense!\n",
    "    \n",
    "    \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",
    "    # Fine-tune pruned model by training it for 3 more epochs-\n",
    "    for epoch in range(3):\n",
    "        print(\"\\nFine-tuning, round = {0}\\n\".format(epoch + 1))\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(pruned_model, mask_model, optimizer, x, y)\n",
    "            train_one_step(pruned_model, mask_model, optimizer, x, y)\n",
    "\n",
    "\n",
    "        for x_t, y_t in test_dataset:\n",
    "            # test_step(model_gt, optimizer, x_t, y_t)\n",
    "            test_step(pruned_model, optimizer, x_t, y_t)\n",
    "\n",
    "        template = 'FT: Epoch {0}, Loss: {1:.4f}, Accuracy: {2:.4f}, Test Loss: {3:.4f}, Test Accuracy: {4:4f}'\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 pruned_model.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 (FT) = {0}\\n\".format(model_sum_params))\n",
    "     \n",
    "    \n",
    "    # Save weights of PRUNED and Trained model-\n",
    "    # pruned_model.save_weights(\"Conv2_CIFAR10_Pruned_Weights.h5\", overwrite = True)\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 = lenet_nn()\n",
    "    winning_ticket_model = conv2_cnn()\n",
    "    \n",
    "    # Load weights of PRUNED model-\n",
    "    # winning_ticket_model.load_weights(\"Conv2_CIFAR10_Pruned_Weights.h5\")\n",
    "    winning_ticket_model.set_weights(pruned_model.get_weights())\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.trainable_weights,\n",
    "                                    winning_ticket_model.trainable_weights):\n",
    "        pruned_wts.assign(tf.where(tf.equal(pruned_wts, 0), pruned_wts, orig_wts))\n",
    "    \n",
    "    \n",
    "    # Since it's difficult to forecast the sparsity level which gives the winning ticket, save\n",
    "    # all sub-networks, and post training, pick the appropriate 'winning ticket'-\n",
    "    winning_ticket_model.save_weights(\"Conv_2_CIFAR10_Magnitude_Based_Winning_Ticket_Distribution_{0}.h5\".format(\n",
    "        history_main[i]['percentage_wts_pruned']), overwrite = True)\n",
    "    \n",
    "        \n",
    "    with open(\"/home/majumdar/The_Lottery_Ticket_Hypothesis-Resources/Conv2_Magnitude_Based_Pruning_log.txt\", \"a+\") as f:\n",
    "        f.write(\"Round = {0}, val_accuracy = {1:.4f}%\\n\".format(\n",
    "            i, history_main[i]['val_accuracy'][epoch]\n",
    "        ))\n",
    "    \n",
    "    "
   ]
  },
  {
   "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": [
    "# Save winning ticket:\n",
    "winning_ticket_model.save_weights(\"Conv_2_CIFAR10_Magnitude_Based_Winning_Ticket_Distribution_{0}.h5\".format(\n",
    "    history_main[i]['percentage_wts_pruned']), overwrite = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CsLyhh1bMKaV"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VBXN-D6TMKaZ",
    "outputId": "9a1ff76f-2739-47d9-91ef-e0ea46a5e1ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iterative-pruning for Conv-2 CNN using Lottery Ticket Hypothesis & Magnitude-based weight pruning is now complete.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nIterative-pruning for Conv-2 CNN using Lottery Ticket Hypothesis & Magnitude-based weight pruning is now complete.\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-KKhJ0fKMKac"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bNkOyPDAMKag"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bFBKZT-vMKai"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xvFUhqzBMKal",
    "outputId": "59ab0b32-d5bc-4c1e-99a8-083856d0c96d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/majumdar/The_Lottery_Ticket_Hypothesis-Resources'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WjlcGKVHMKaq"
   },
   "outputs": [],
   "source": [
    "with open(\"/home/majumdar/The_Lottery_Ticket_Hypothesis-Resources/Conv2_CIFAR10_history_main_Magnitude_based_Pruning_Gaussian_Glorot.pkl\", \"wb\") as f:\n",
    "    pickle.dump(history_main, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ppLmZMjFMKau"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OsBYYnb_MKa1"
   },
   "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": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QSxAra7BMKa4",
    "outputId": "8fde851d-a615-4b23-b8dc-b5fdfcf04787"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.13206556393234"
      ]
     },
     "execution_count": 54,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check-\n",
    "history_main[10]['percentage_wts_pruned']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xNdgwvMhMKa7"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mZpJx9dMMKa-"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IUJ1dwOLMKbB"
   },
   "source": [
    "### Visualizations for _Iterative Pruning Rounds_:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bOZ5K2gJMKbG"
   },
   "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": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7Wm25Jm7MKbK"
   },
   "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": 57,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_WUjOMj6MKbO"
   },
   "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": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hNEsVFD8MKbR",
    "outputId": "b97473ae-5168-4ef2-a792-c19c82141e7f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x560 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=(10, 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(\"Conv-2 CNN: Percentage of weights pruned VS Accuracy\")\n",
    "plt.xlabel(\"percentage of weights pruned\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FhOq59evMKbU"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w1iH9AVHMKbX"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Sc4BcshQMKbZ"
   },
   "outputs": [],
   "source": [
    "# Python 3 dict for training and testing loss visualization-\n",
    "plot_loss = {}\n",
    "plot_test_loss = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ssUgic2KMKbc"
   },
   "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": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NZe0qKq3MKbf"
   },
   "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": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uCKDEWElMKbl",
    "outputId": "1b86a507-9cf0-41de-ed56-296b81233e46"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x560 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=(10, 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(\"Conv-2 CNN: Percentage of weights pruned VS Loss\")\n",
    "plt.xlabel(\"percentage of weights pruned\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LCxxv2ahMKbs"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VLioC9-NMKbu"
   },
   "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": 64,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dif4-rEbMKby"
   },
   "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": 65,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u4UJfVvhMKb5",
    "outputId": "ba60c50a-9a37-4cbc-f124-67f1c791d8d8"
   },
   "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(\"Conv-2 CNN: Percentage of weights pruned VS number of epochs (Early Stopping)\")\n",
    "plt.xlabel(\"percentage of weights pruned\")\n",
    "plt.ylabel(\"number of epochs\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FLe0KyKTMKb7"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eLTSCEaxMKb-"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0-tQrHhtMKcB"
   },
   "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": 67,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AHtANfJMMKcF"
   },
   "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": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jsfhtooJMKcH"
   },
   "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": 69,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vnNOmxPzMKcK",
    "outputId": "bc72c0b5-57a7-45de-cac4-70e4893921d3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize starting accuracy VS percentage of weights pruned-\n",
    "fig=plt.figure(figsize=(10, 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(\"Conv-2 CNN: Percentage of weights pruned VS Starting Accuracy\")\n",
    "plt.xlabel(\"percentage of weights pruned\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Bly3r9nsMKcO"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "41HdFzlSMKcS"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KxQ__PodMKcW"
   },
   "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": 71,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cnzPlUx0MKcY"
   },
   "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": 72,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y9J82jrOMKcb"
   },
   "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": 73,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6ywrtLJkMKce",
    "outputId": "0952a982-9ac4-44cb-94c6-28e24d026312"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize Starting training & testing loss VS percentage of weights pruned-\n",
    "fig=plt.figure(figsize=(10, 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(\"Conv-2 CNN: Percentage of weights pruned VS Starting Loss\")\n",
    "plt.xlabel(\"percentage of weights pruned\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6TkNqPNTMKch"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ElBrb4p_MKcz"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ItL8unY0MKc2"
   },
   "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": 75,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qXByFXWRMKc6"
   },
   "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": 76,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SVSnE3ClMKc9"
   },
   "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": 77,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r0ntLSMWMKdA",
    "outputId": "71061594-671b-4e55-8179-04f62baf8b07"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize best accuracy VS percentage of weights pruned-\n",
    "fig=plt.figure(figsize=(10, 7), 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(\"Conv-2 CNN: Percentage of weights pruned VS Best Accuracy\")\n",
    "plt.xlabel(\"percentage of weights pruned\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mAAZOWbrMKdD"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9WyxkjuOMKdF"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Uur7ZSt6MKdI"
   },
   "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": 79,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SZHNOz-2MKdL"
   },
   "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": 80,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "flqdkmEnMKdM"
   },
   "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": 81,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XkmwHQw1MKdP",
    "outputId": "796b4c86-c496-41c6-b92b-5be67889f464"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize best loss VS percentage of weights pruned-\n",
    "fig=plt.figure(figsize=(10, 7), 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(\"Conv-2 CNN: Percentage of weights pruned VS Best Loss\")\n",
    "plt.xlabel(\"percentage of weights pruned\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Yryb4648MKdT"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JNDEnxftMKdV"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2giW2pNyMKdn"
   },
   "source": [
    "### Find the winning ticket(s):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1, val_acc = 69.3900% with 0.0000% weights pruned, needs = 10 epochs\n",
      "epoch: 2, val_acc = 70.7800% with 19.9013% weights pruned, needs = 8 epochs\n",
      "epoch: 3, val_acc = 68.7000% with 35.8322% weights pruned, needs = 9 epochs\n",
      "epoch: 4, val_acc = 70.2400% with 48.5876% weights pruned, needs = 10 epochs\n",
      "epoch: 5, val_acc = 70.1500% with 58.7990% weights pruned, needs = 9 epochs\n",
      "epoch: 6, val_acc = 70.6700% with 66.9747% weights pruned, needs = 7 epochs\n",
      "epoch: 7, val_acc = 70.3900% with 73.5217% weights pruned, needs = 8 epochs\n",
      "epoch: 8, val_acc = 70.3800% with 78.7637% weights pruned, needs = 6 epochs\n",
      "epoch: 9, val_acc = 70.8600% with 82.9639% weights pruned, needs = 6 epochs\n",
      "epoch: 10, val_acc = 70.5700% with 86.3275% weights pruned, needs = 6 epochs\n",
      "epoch: 11, val_acc = 70.6400% with 89.0235% weights pruned, needs = 7 epochs\n",
      "epoch: 12, val_acc = 70.4400% with 91.1831% weights pruned, needs = 8 epochs\n",
      "epoch: 13, val_acc = 70.3700% with 92.9144% weights pruned, needs = 9 epochs\n",
      "epoch: 14, val_acc = 70.6600% with 94.3021% weights pruned, needs = 9 epochs\n",
      "epoch: 15, val_acc = 70.5800% with 95.4158% weights pruned, needs = 9 epochs\n",
      "epoch: 16, val_acc = 69.6500% with 96.3092% weights pruned, needs = 10 epochs\n",
      "epoch: 17, val_acc = 68.6800% with 97.0251% weights pruned, needs = 11 epochs\n",
      "epoch: 18, val_acc = 68.8200% with 97.6006% weights pruned, needs = 12 epochs\n",
      "epoch: 19, val_acc = 68.1600% with 98.0634% weights pruned, needs = 12 epochs\n",
      "epoch: 20, val_acc = 68.0400% with 98.4344% weights pruned, needs = 15 epochs\n",
      "epoch: 21, val_acc = 67.9700% with 98.7328% weights pruned, needs = 19 epochs\n",
      "epoch: 22, val_acc = 67.0500% with 98.9736% weights pruned, needs = 21 epochs\n",
      "epoch: 23, val_acc = 66.2400% with 99.1667% weights pruned, needs = 18 epochs\n",
      "epoch: 24, val_acc = 65.5700% with 99.3230% weights pruned, needs = 23 epochs\n",
      "epoch: 25, val_acc = 65.8800% with 99.4489% weights pruned, needs = 31 epochs\n"
     ]
    }
   ],
   "source": [
    "for k in history_main.keys():\n",
    "\tepoch_length = len(history_main[k]['loss'])\n",
    "\n",
    "\tif k == 1:\n",
    "\t\tprint(\"epoch: {0}, val_acc = {1:.4f}% with {2:.4f}% weights pruned, needs = {3} epochs\".format(\n",
    "\t\t\tk, history_main[k]['val_accuracy'][epoch_length-1],\n",
    "\t\t\t0, epoch_length))\n",
    "\telse:\n",
    "\t\tprint(\"epoch: {0}, val_acc = {1:.4f}% with {2:.4f}% weights pruned, needs = {3} epochs\".format(\n",
    "\t\t\tk, history_main[k]['val_accuracy'][epoch_length-1],\n",
    "\t\t\thistory_main[k - 1]['percentage_wts_pruned'], epoch_length))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SjABF-VtMKdr"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [
    "IUJ1dwOLMKbB",
    "MgVkzKiDMKdY"
   ],
   "name": "Conv_2_CIFAR10-Magnitude_based_Pruning.ipynb",
   "provenance": []
  },
  "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": "3.8.2"
  }
 },
 "nbformat": 4,
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}