CIFAR-10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "38c62d81-f237-448e-ad5b-db6d5b1f56fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the necessary libraries\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers\n",
    "import tensorflow.keras as keras\n",
    "import time\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1705f692-117b-4f90-a59a-3755ce5bbc8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the CIFAR-10 dataset\n",
    "(train_data,train_labels),(test_data,test_labels) = keras.datasets.cifar10.load_data()\n",
    "labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e4e8887-83a1-4643-90bc-84b0275b4735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "58a822cfc5374ce6b427d5ee5d2e2020",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=24999, description='k', max=49999), Output()), _dom_classes=('widget-int…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import interact\n",
    "\n",
    "@interact(k=(0, len(train_data)-1))\n",
    "def myplot(k):\n",
    "    fig, ax = plt.subplots(figsize=(1, 1))\n",
    "    ax.set_title(labels[train_labels[k][0]])\n",
    "    ax.imshow(train_data[k])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4c9c16e-7d4e-4b99-a079-67f06712e231",
   "metadata": {},
   "source": [
    "# Preparing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "235660a8-7516-4c9b-a65c-a532c8e454cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50000, 32, 32, 3), numpy.ndarray, (10000, 32, 32, 3))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape, type(train_data), test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b87ce780-0e66-49ff-be45-d860b6872f10",
   "metadata": {},
   "source": [
    "Here, the variable **train_data** is an array consisting of 50000 images, each of size 32 * 32 with 3 colour channels. Similarly, the variable **test_data** is an array of 10000 images, each of size 32 * 32 with 3 colour channels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "051e08b9-ca3e-4595-b8cc-d7b6f2644ad6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 1)\n",
      "[9]\n",
      "255\n",
      "255\n"
     ]
    }
   ],
   "source": [
    "print(train_labels.shape)\n",
    "print(max(train_labels))\n",
    "print(np.max(train_data))\n",
    "print(np.max(test_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b905966-5fc2-4fa4-995c-bbb1d8467148",
   "metadata": {},
   "source": [
    "Here, the variable **train_labels** contains 50000 labels for each of the image in train_data. There are a total of 10 labels which are defined in the previous block of code. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f53526-a2f2-4bb4-9691-a731226e7c83",
   "metadata": {},
   "source": [
    "In the next cell, I am confirming whether the dataset is labelled correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "39574d6e-b156-4e88-9cc3-2a0bc80421db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_data[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(labels[train_labels[i][0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6a163b94-35d6-45bd-a54a-0a917b82c7a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalizing the input images by dividing the each pixel value by the maximum value 255 to the interval between 0 and 1\n",
    "train_data = train_data.astype('float32') / 255.0\n",
    "test_data = test_data.astype('float32') / 255.0\n",
    "\n",
    "#converting the input labels in train_labels and test_labels into categorical vector (one-hot encoding)\n",
    "train_labels_cat = keras.utils.to_categorical(train_labels,10)\n",
    "test_labels_cat = keras.utils.to_categorical(test_labels,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bb87d860-d880-49ef-a3f8-6cea754e1e56",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data augmentation\n",
    "data_aug = keras.Sequential(\n",
    "    [\n",
    "        layers.experimental.preprocessing.RandomFlip(\"horizontal\"),\n",
    "        layers.experimental.preprocessing.RandomRotation(0.1), # rotates by up to 36 degrees. \n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "810f819f-50dc-4ab4-a51f-74085c3954ed",
   "metadata": {},
   "source": [
    "Data augmentation is done so that the model can learn to be more robust to variations in the input data which can lead to the improvement of its accuracy and performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43774132-e3be-443e-a162-3549c0f866e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#to plot the data augmented images\n",
    "plt.figure(figsize=(10, 10))\n",
    "images = train_data[4:13]\n",
    "for i in range(9):\n",
    "    aug_images = data_aug(images)\n",
    "    ax = plt.subplot(3, 3, i + 1)\n",
    "    plt.imshow(aug_images[4].numpy())\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5f8f8b5a-a820-4647-b733-ec0c57249035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 32, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#specifying the input shape\n",
    "input_shape = train_data.shape[1:]\n",
    "input_shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ff6e7bf-0909-4201-bba4-ecf9b03c5f31",
   "metadata": {},
   "source": [
    "# Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f2d9e62b-d081-4282-b1ad-7a660fbb966e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_model1(input_shape, num_classes):\n",
    "    inputs = keras.Input(shape=input_shape)\n",
    "    \n",
    "    # Image augmentation block\n",
    "    x = data_aug(inputs)\n",
    "\n",
    "    x = layers.Conv2D(32, 3, strides=2, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "    \n",
    "    x = layers.Conv2D(32, 3, strides=2, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "    x = layers.MaxPooling2D(pool_size = (1,1))(x)\n",
    "    x = layers.Dropout(0.2)(x)\n",
    "\n",
    "    x = layers.Conv2D(64, 3, strides=2, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "    \n",
    "    x = layers.Conv2D(64, 3, strides=2, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "    x = layers.MaxPooling2D(pool_size = (1,1))(x)\n",
    "    x = layers.Dropout(0.3)(x)\n",
    "\n",
    "    x = layers.Conv2D(128, 3, strides=2, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "    x = layers.MaxPooling2D(pool_size = (1,1))(x)\n",
    "    x = layers.Dropout(0.4)(x)\n",
    "\n",
    "    x = layers.GlobalAveragePooling2D()(x)\n",
    "\n",
    "    outputs = layers.Dense(num_classes, activation='softmax')(x)\n",
    "    return keras.Model(inputs, outputs)\n",
    "\n",
    "\n",
    "model1 = make_model1(input_shape=input_shape, num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b02ef6c4-8fb2-4e13-82ea-084247052496",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " sequential (Sequential)     (None, 32, 32, 3)         0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 16, 16, 32)        896       \n",
      "                                                                 \n",
      " batch_normalization (Batch  (None, 16, 16, 32)        128       \n",
      " Normalization)                                                  \n",
      "                                                                 \n",
      " activation (Activation)     (None, 16, 16, 32)        0         \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 8, 8, 32)          9248      \n",
      "                                                                 \n",
      " batch_normalization_1 (Bat  (None, 8, 8, 32)          128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " activation_1 (Activation)   (None, 8, 8, 32)          0         \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 8, 8, 32)          0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 8, 8, 32)          0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 4, 4, 64)          18496     \n",
      "                                                                 \n",
      " batch_normalization_2 (Bat  (None, 4, 4, 64)          256       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " activation_2 (Activation)   (None, 4, 4, 64)          0         \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 2, 2, 64)          36928     \n",
      "                                                                 \n",
      " batch_normalization_3 (Bat  (None, 2, 2, 64)          256       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " activation_3 (Activation)   (None, 2, 2, 64)          0         \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 2, 2, 64)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 2, 2, 64)          0         \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 1, 1, 128)         73856     \n",
      "                                                                 \n",
      " batch_normalization_4 (Bat  (None, 1, 1, 128)         512       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " activation_4 (Activation)   (None, 1, 1, 128)         0         \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPoolin  (None, 1, 1, 128)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 1, 1, 128)         0         \n",
      "                                                                 \n",
      " global_average_pooling2d (  (None, 128)               0         \n",
      " GlobalAveragePooling2D)                                         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 141994 (554.66 KB)\n",
      "Trainable params: 141354 (552.16 KB)\n",
      "Non-trainable params: 640 (2.50 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f3327c5-740b-4b35-8791-cfebf1728a52",
   "metadata": {},
   "source": [
    "# Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ceb56896-f35e-4730-a43b-95612178ca02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "625/625 [==============================] - 16s 23ms/step - loss: 1.8515 - accuracy: 0.3331 - val_loss: 1.5354 - val_accuracy: 0.4454\n",
      "Epoch 2/20\n",
      "625/625 [==============================] - 15s 23ms/step - loss: 1.5481 - accuracy: 0.4375 - val_loss: 1.4196 - val_accuracy: 0.4806\n",
      "Epoch 3/20\n",
      "625/625 [==============================] - 14s 23ms/step - loss: 1.4442 - accuracy: 0.4785 - val_loss: 1.5388 - val_accuracy: 0.4473\n",
      "Epoch 4/20\n",
      "625/625 [==============================] - 15s 23ms/step - loss: 1.3752 - accuracy: 0.5045 - val_loss: 1.3264 - val_accuracy: 0.5309\n",
      "Epoch 5/20\n",
      "625/625 [==============================] - 15s 23ms/step - loss: 1.3203 - accuracy: 0.5250 - val_loss: 1.1812 - val_accuracy: 0.5814\n",
      "Epoch 6/20\n",
      "625/625 [==============================] - 15s 24ms/step - loss: 1.2794 - accuracy: 0.5435 - val_loss: 1.1677 - val_accuracy: 0.5851\n",
      "Epoch 7/20\n",
      "625/625 [==============================] - 15s 24ms/step - loss: 1.2458 - accuracy: 0.5556 - val_loss: 1.2870 - val_accuracy: 0.5370\n",
      "Epoch 8/20\n",
      "625/625 [==============================] - 15s 24ms/step - loss: 1.2233 - accuracy: 0.5664 - val_loss: 1.2336 - val_accuracy: 0.5698\n",
      "Epoch 9/20\n",
      "625/625 [==============================] - 15s 23ms/step - loss: 1.2019 - accuracy: 0.5741 - val_loss: 1.5015 - val_accuracy: 0.5205\n",
      "Epoch 10/20\n",
      "625/625 [==============================] - 15s 24ms/step - loss: 1.1751 - accuracy: 0.5823 - val_loss: 1.1450 - val_accuracy: 0.5940\n",
      "Epoch 11/20\n",
      "625/625 [==============================] - 15s 24ms/step - loss: 1.1557 - accuracy: 0.5911 - val_loss: 1.0678 - val_accuracy: 0.6278\n",
      "Epoch 12/20\n",
      "625/625 [==============================] - 15s 24ms/step - loss: 1.1436 - accuracy: 0.5963 - val_loss: 1.1872 - val_accuracy: 0.5751\n",
      "Epoch 13/20\n",
      "625/625 [==============================] - 15s 24ms/step - loss: 1.1286 - accuracy: 0.6029 - val_loss: 1.2267 - val_accuracy: 0.5770\n",
      "Epoch 14/20\n",
      "625/625 [==============================] - 15s 24ms/step - loss: 1.1164 - accuracy: 0.6078 - val_loss: 1.0148 - val_accuracy: 0.6371\n",
      "Epoch 15/20\n",
      "625/625 [==============================] - 15s 24ms/step - loss: 1.0998 - accuracy: 0.6128 - val_loss: 1.0510 - val_accuracy: 0.6252\n",
      "Epoch 16/20\n",
      "625/625 [==============================] - 15s 25ms/step - loss: 1.0900 - accuracy: 0.6156 - val_loss: 0.9422 - val_accuracy: 0.6670\n",
      "Epoch 17/20\n",
      "625/625 [==============================] - 15s 24ms/step - loss: 1.0775 - accuracy: 0.6220 - val_loss: 0.9884 - val_accuracy: 0.6527\n",
      "Epoch 18/20\n",
      "625/625 [==============================] - 15s 24ms/step - loss: 1.0725 - accuracy: 0.6245 - val_loss: 1.0287 - val_accuracy: 0.6304\n",
      "Epoch 19/20\n",
      "625/625 [==============================] - 15s 24ms/step - loss: 1.0638 - accuracy: 0.6277 - val_loss: 1.1158 - val_accuracy: 0.6007\n",
      "Epoch 20/20\n",
      "625/625 [==============================] - 17s 27ms/step - loss: 1.0615 - accuracy: 0.6285 - val_loss: 1.5394 - val_accuracy: 0.5359\n"
     ]
    }
   ],
   "source": [
    "epochs = 20\n",
    "\n",
    "model1.compile(\n",
    "    optimizer=keras.optimizers.Adam(1e-3),\n",
    "    loss=\"categorical_crossentropy\",\n",
    "    metrics=[\"accuracy\"],\n",
    ")\n",
    "start_time = time.time()\n",
    "history1 = model1.fit(\n",
    "    train_data, train_labels_cat, epochs=epochs, batch_size=64, validation_split = 0.2, shuffle = True\n",
    ")\n",
    "\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time - start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4746730c-51aa-42c8-a3e3-8217370186fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_dict = history1.history\n",
    "history_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ec265f05-c7e5-4b76-a767-449846b75da0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a622b0dc60>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history_dict['accuracy']\n",
    "val_acc = history_dict['val_accuracy']\n",
    "loss = history_dict['loss']\n",
    "val_loss = history_dict['val_loss']\n",
    "\n",
    "epochs = history1.epoch\n",
    "\n",
    "# to plot the training and validation loss against number of epochs\n",
    "# \"bo\" is for \"blue dot\"\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "# b is for \"solid blue line\"\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "559de732-0748-4e17-96d6-7de4aed77bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a622bae710>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to plot the training and validation accuracy against number of epochs\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3346abde-d888-4de5-8caf-518ab17b8205",
   "metadata": {},
   "source": [
    "# Evaluating the model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c415ed55-4b02-4178-977f-cb96e466b9bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 1s 10ms/step - loss: 1.5446 - accuracy: 0.5268\n",
      "Test accuracy: 0.5267999768257141\n",
      "Time taken:  300.3428153991699 seconds\n"
     ]
    }
   ],
   "source": [
    "loss1, accuracy1 = model1.evaluate(test_data, test_labels_cat, batch_size=128, verbose=1)\n",
    "\n",
    "print('Test accuracy:', accuracy1)\n",
    "print(\"Time taken: \", elapsed_time, \"seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8738fa23-9be7-42b8-9067-9886e4e2968e",
   "metadata": {},
   "source": [
    "The model architecture in make_model1 has the following layers: \n",
    "\n",
    "Convolution layers of various filters (32, 64, 128)\n",
    "\n",
    "Batch normalization layer : This layer is used to increase the stability and speed of the training by normalizing the activations in each layer\n",
    "\n",
    "Activation layer : This layer introduces non-linearity in the model\n",
    "\n",
    "Pooling layer: This layer reduces the spatial dimensionality of the input and extract important features from the data.\n",
    "\n",
    "Dense layer: This dense layer along with softmax activation function classifies the input data into 10 classes where the softmax function gives the probability distribution of the input image among 10 different classes.\n",
    "\n",
    "The test accuracy of the model defined in make_model1 is 52.68% and the time taken to train the model is 300.343 seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f1d59bbe-0458-4817-a605-51f1845ee9e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_model2(input_shape, num_classes):\n",
    "    inputs = keras.Input(shape=input_shape)\n",
    "    \n",
    "    # Image augmentation block\n",
    "    x = data_aug(inputs)\n",
    "\n",
    "    x = layers.Conv2D(32, 3, strides=2, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "    \n",
    "    x = layers.Conv2D(32, 3, strides=2, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "    x = layers.Conv2D(64, 3, strides=2, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "    \n",
    "    x = layers.Conv2D(64, 3, strides=2, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "    x = layers.Conv2D(128, 3, strides=2, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "    x = layers.GlobalAveragePooling2D()(x)\n",
    "\n",
    "    outputs = layers.Dense(num_classes, activation='softmax')(x)\n",
    "    return keras.Model(inputs, outputs)\n",
    "\n",
    "model2 = make_model2(input_shape=input_shape, num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "87d48860-5d8e-4fc3-b6bb-92172b6b178e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "625/625 [==============================] - 15s 22ms/step - loss: 1.5754 - accuracy: 0.4284 - val_loss: 1.4307 - val_accuracy: 0.4822\n",
      "Epoch 2/20\n",
      "625/625 [==============================] - 13s 21ms/step - loss: 1.3094 - accuracy: 0.5283 - val_loss: 1.4437 - val_accuracy: 0.4916\n",
      "Epoch 3/20\n",
      "625/625 [==============================] - 13s 20ms/step - loss: 1.2031 - accuracy: 0.5697 - val_loss: 1.5147 - val_accuracy: 0.4823\n",
      "Epoch 4/20\n",
      "625/625 [==============================] - 13s 21ms/step - loss: 1.1261 - accuracy: 0.5970 - val_loss: 1.2879 - val_accuracy: 0.5614\n",
      "Epoch 5/20\n",
      "625/625 [==============================] - 13s 21ms/step - loss: 1.0774 - accuracy: 0.6145 - val_loss: 1.3488 - val_accuracy: 0.5449\n",
      "Epoch 6/20\n",
      "625/625 [==============================] - 13s 21ms/step - loss: 1.0293 - accuracy: 0.6343 - val_loss: 1.2735 - val_accuracy: 0.5681\n",
      "Epoch 7/20\n",
      "625/625 [==============================] - 14s 22ms/step - loss: 0.9959 - accuracy: 0.6453 - val_loss: 1.4024 - val_accuracy: 0.5382\n",
      "Epoch 8/20\n",
      "625/625 [==============================] - 14s 22ms/step - loss: 0.9578 - accuracy: 0.6585 - val_loss: 0.9748 - val_accuracy: 0.6565\n",
      "Epoch 9/20\n",
      "625/625 [==============================] - 14s 22ms/step - loss: 0.9288 - accuracy: 0.6716 - val_loss: 1.6785 - val_accuracy: 0.4752\n",
      "Epoch 10/20\n",
      "625/625 [==============================] - 14s 22ms/step - loss: 0.9090 - accuracy: 0.6785 - val_loss: 1.0763 - val_accuracy: 0.6260\n",
      "Epoch 11/20\n",
      "625/625 [==============================] - 14s 22ms/step - loss: 0.8900 - accuracy: 0.6848 - val_loss: 1.0405 - val_accuracy: 0.6394\n",
      "Epoch 12/20\n",
      "625/625 [==============================] - 14s 23ms/step - loss: 0.8651 - accuracy: 0.6931 - val_loss: 1.1658 - val_accuracy: 0.5979\n",
      "Epoch 13/20\n",
      "625/625 [==============================] - 14s 23ms/step - loss: 0.8568 - accuracy: 0.6963 - val_loss: 0.9158 - val_accuracy: 0.6770\n",
      "Epoch 14/20\n",
      "625/625 [==============================] - 14s 23ms/step - loss: 0.8287 - accuracy: 0.7061 - val_loss: 0.9981 - val_accuracy: 0.6581\n",
      "Epoch 15/20\n",
      "625/625 [==============================] - 14s 22ms/step - loss: 0.8105 - accuracy: 0.7135 - val_loss: 0.9782 - val_accuracy: 0.6653\n",
      "Epoch 16/20\n",
      "625/625 [==============================] - 14s 23ms/step - loss: 0.8102 - accuracy: 0.7136 - val_loss: 0.9075 - val_accuracy: 0.6844\n",
      "Epoch 17/20\n",
      "625/625 [==============================] - 15s 23ms/step - loss: 0.7873 - accuracy: 0.7210 - val_loss: 0.9686 - val_accuracy: 0.6705\n",
      "Epoch 18/20\n",
      "625/625 [==============================] - 14s 23ms/step - loss: 0.7830 - accuracy: 0.7215 - val_loss: 1.0363 - val_accuracy: 0.6525\n",
      "Epoch 19/20\n",
      "625/625 [==============================] - 14s 23ms/step - loss: 0.7727 - accuracy: 0.7264 - val_loss: 1.3779 - val_accuracy: 0.5865\n",
      "Epoch 20/20\n",
      "625/625 [==============================] - 14s 23ms/step - loss: 0.7625 - accuracy: 0.7311 - val_loss: 0.8563 - val_accuracy: 0.7044\n",
      "79/79 [==============================] - 1s 9ms/step - loss: 0.8655 - accuracy: 0.7085\n",
      "Test accuracy: 0.7085000276565552\n",
      "Time taken:  277.5758273601532 seconds\n"
     ]
    }
   ],
   "source": [
    "epochs = 20\n",
    "\n",
    "model2.compile(\n",
    "    optimizer=keras.optimizers.Adam(1e-3),\n",
    "    loss=\"categorical_crossentropy\",\n",
    "    metrics=[\"accuracy\"],\n",
    ")\n",
    "start_time = time.time()\n",
    "history2 = model2.fit(\n",
    "    train_data, train_labels_cat, epochs=epochs, batch_size=64, validation_split = 0.2, shuffle = True\n",
    ")\n",
    "\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time - start_time\n",
    "\n",
    "loss2, accuracy2 = model2.evaluate(test_data, test_labels_cat, batch_size=128, verbose=1)\n",
    "print('Test accuracy:', accuracy2)\n",
    "print(\"Time taken: \", elapsed_time, \"seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1cce742-21bb-46b7-87d0-5ea5fb52932f",
   "metadata": {},
   "source": [
    "In the model architecture defined in make_model2, I have removed the MaxPooling layer as well as the dropout layer to observe the model performance when compared with make_model1.\n",
    "\n",
    "The test accuracy of the model defined in make_model2 is 70.85% and the time taken to train the model is 277.576 seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "491b42ee-9e75-405b-b5c7-02b23196870f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_model3(input_shape, num_classes):\n",
    "    inputs = keras.Input(shape=input_shape)\n",
    "\n",
    "    x = layers.Conv2D(32, 3, strides=2, padding=\"same\")(inputs)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "    \n",
    "    x = layers.Conv2D(32, 3, strides=2, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "    x = layers.MaxPooling2D(pool_size = (1,1))(x)\n",
    "    x = layers.Dropout(0.2)(x)\n",
    "\n",
    "    x = layers.Conv2D(64, 3, strides=2, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "    \n",
    "    x = layers.Conv2D(64, 3, strides=2, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "    x = layers.MaxPooling2D(pool_size = (1,1))(x)\n",
    "    x = layers.Dropout(0.3)(x)\n",
    "\n",
    "    x = layers.Conv2D(128, 3, strides=2, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "    x = layers.MaxPooling2D(pool_size = (1,1))(x)\n",
    "    x = layers.Dropout(0.4)(x)\n",
    "\n",
    "    x = layers.GlobalAveragePooling2D()(x)\n",
    "\n",
    "    outputs = layers.Dense(num_classes, activation='softmax')(x)\n",
    "    return keras.Model(inputs, outputs)\n",
    "\n",
    "\n",
    "model3 = make_model3(input_shape=input_shape, num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d89b3a33-8535-4f73-ba6d-8671102ba387",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "625/625 [==============================] - 16s 23ms/step - loss: 1.7916 - accuracy: 0.3559 - val_loss: 1.6150 - val_accuracy: 0.4185\n",
      "Epoch 2/20\n",
      "625/625 [==============================] - 13s 21ms/step - loss: 1.4334 - accuracy: 0.4799 - val_loss: 1.5108 - val_accuracy: 0.4795\n",
      "Epoch 3/20\n",
      "625/625 [==============================] - 13s 21ms/step - loss: 1.2981 - accuracy: 0.5322 - val_loss: 1.2015 - val_accuracy: 0.5751\n",
      "Epoch 4/20\n",
      "625/625 [==============================] - 14s 22ms/step - loss: 1.2195 - accuracy: 0.5649 - val_loss: 1.2888 - val_accuracy: 0.5401\n",
      "Epoch 5/20\n",
      "625/625 [==============================] - 277s 443ms/step - loss: 1.1467 - accuracy: 0.5929 - val_loss: 1.3695 - val_accuracy: 0.5196\n",
      "Epoch 6/20\n",
      "625/625 [==============================] - 11s 18ms/step - loss: 1.0937 - accuracy: 0.6127 - val_loss: 1.1947 - val_accuracy: 0.5772\n",
      "Epoch 7/20\n",
      "625/625 [==============================] - 12s 20ms/step - loss: 1.0465 - accuracy: 0.6309 - val_loss: 1.0919 - val_accuracy: 0.6116\n",
      "Epoch 8/20\n",
      "625/625 [==============================] - 12s 20ms/step - loss: 1.0036 - accuracy: 0.6467 - val_loss: 0.9874 - val_accuracy: 0.6503\n",
      "Epoch 9/20\n",
      "625/625 [==============================] - 13s 20ms/step - loss: 0.9716 - accuracy: 0.6580 - val_loss: 1.0005 - val_accuracy: 0.6452\n",
      "Epoch 10/20\n",
      "625/625 [==============================] - 16s 26ms/step - loss: 0.9429 - accuracy: 0.6676 - val_loss: 1.0469 - val_accuracy: 0.6398\n",
      "Epoch 11/20\n",
      "625/625 [==============================] - 18s 29ms/step - loss: 0.9157 - accuracy: 0.6799 - val_loss: 0.9091 - val_accuracy: 0.6800\n",
      "Epoch 12/20\n",
      "625/625 [==============================] - 17s 27ms/step - loss: 0.8893 - accuracy: 0.6858 - val_loss: 0.9910 - val_accuracy: 0.6446\n",
      "Epoch 13/20\n",
      "625/625 [==============================] - 17s 27ms/step - loss: 0.8722 - accuracy: 0.6927 - val_loss: 0.9424 - val_accuracy: 0.6695\n",
      "Epoch 14/20\n",
      "625/625 [==============================] - 18s 29ms/step - loss: 0.8519 - accuracy: 0.7023 - val_loss: 0.9266 - val_accuracy: 0.6755\n",
      "Epoch 15/20\n",
      "625/625 [==============================] - 20s 32ms/step - loss: 0.8331 - accuracy: 0.7048 - val_loss: 0.9830 - val_accuracy: 0.6638\n",
      "Epoch 16/20\n",
      "625/625 [==============================] - 17s 27ms/step - loss: 0.8192 - accuracy: 0.7115 - val_loss: 0.8492 - val_accuracy: 0.7021\n",
      "Epoch 17/20\n",
      "625/625 [==============================] - 17s 28ms/step - loss: 0.8071 - accuracy: 0.7172 - val_loss: 0.9019 - val_accuracy: 0.6834\n",
      "Epoch 18/20\n",
      "625/625 [==============================] - 17s 27ms/step - loss: 0.7850 - accuracy: 0.7236 - val_loss: 1.1696 - val_accuracy: 0.6128\n",
      "Epoch 19/20\n",
      "625/625 [==============================] - 15s 24ms/step - loss: 0.7805 - accuracy: 0.7258 - val_loss: 0.9750 - val_accuracy: 0.6583\n",
      "Epoch 20/20\n",
      "625/625 [==============================] - 15s 25ms/step - loss: 0.7698 - accuracy: 0.7282 - val_loss: 0.8645 - val_accuracy: 0.6924\n",
      "79/79 [==============================] - 1s 10ms/step - loss: 0.8863 - accuracy: 0.6884\n",
      "Test accuracy: 0.6883999705314636\n",
      "Time taken:  570.3346924781799 seconds\n"
     ]
    }
   ],
   "source": [
    "epochs = 20\n",
    "\n",
    "model3.compile(\n",
    "    optimizer=keras.optimizers.Adam(1e-3),\n",
    "    loss=\"categorical_crossentropy\",\n",
    "    metrics=[\"accuracy\"],\n",
    ")\n",
    "start_time = time.time()\n",
    "history3 = model3.fit(\n",
    "    train_data, train_labels_cat, epochs=epochs, batch_size=64, validation_split = 0.2, shuffle = True\n",
    ")\n",
    "\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time - start_time\n",
    "\n",
    "loss3, accuracy3 = model3.evaluate(test_data, test_labels_cat, batch_size=128, verbose=1)\n",
    "print('Test accuracy:', accuracy3)\n",
    "print(\"Time taken: \", elapsed_time, \"seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3457fc43-bcf3-4d30-9c68-5fe5515763dc",
   "metadata": {},
   "source": [
    "In the model architecture defined in make_model3, I removed the data augmentation of the input images to observe the model performance when compared with make_model1.\n",
    "\n",
    "The test accuracy of the model defined in make_model3 is 68.84% and the time taken to train the model is 570.334 seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d5216293-3c34-4e1a-b601-468c928dc9b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 4ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "y_pred = np.argmax(model1.predict(test_data), axis=1)\n",
    "cm = confusion_matrix(test_labels, y_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecda2bc5-cb49-45d2-8580-baff10da2ec6",
   "metadata": {},
   "source": [
    "The confusion matrix is a summary of a classification model's predictions, displaying the number of correct and incorrect classifications for each class in tabular form. It provides valuable insights into the model's performance, highlighting which classes it excels at predicting and which it struggles with. In this case, the model demonstrates higher accuracy for classes like 'airplane', 'automobile', 'frog', and 'ship', but faces challenges in accurately predicting 'bird', 'cat', and 'dog' classes. Further improvements could be achieved by fine-tuning the model architecture and exploring better data augmentation techniques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "78e08867-a219-44e4-9f8b-f375e7f44e81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    airplane       0.51      0.78      0.62      1000\n",
      "  automobile       0.59      0.88      0.71      1000\n",
      "        bird       0.67      0.27      0.38      1000\n",
      "         cat       0.57      0.09      0.15      1000\n",
      "        deer       0.59      0.31      0.41      1000\n",
      "         dog       0.80      0.07      0.14      1000\n",
      "        frog       0.35      0.89      0.51      1000\n",
      "       horse       0.68      0.58      0.63      1000\n",
      "        ship       0.57      0.70      0.63      1000\n",
      "       truck       0.57      0.69      0.62      1000\n",
      "\n",
      "    accuracy                           0.53     10000\n",
      "   macro avg       0.59      0.53      0.48     10000\n",
      "weighted avg       0.59      0.53      0.48     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(test_labels, y_pred, target_names=labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8bb404-33eb-4d93-9c87-2523ad7fcaa9",
   "metadata": {},
   "source": [
    "Based on the classification report, we can analyze the performance of the model in classifying the different categories:\r\n",
    "\r\n",
    "\r\n",
    "**Precision** : Precision represents the ability of the model to correctly identify the true positives among the predicted positive samples for each class. It is calculated as the ratio of true positives to the sum of true positives and false positives. For example, the precision for the 'ship' class is 0.87, indicating that when the model predicts an image as a ship, it is correct 87% of the time.\r\n",
    "\r\n",
    "**Recall** : Recall, also known as sensitivity or true positive rate, represents the ability of the model to correctly identify the true positives among all the actual positive samples for each class. It is calculated as the ratio of true positives to the sum of true positives and false negatives. For instance, the recall for the 'cat' class is 0.39, indicating that the model correctly identifies only 39% of the actual cat images.\r\n",
    "\r\n",
    "**F1-score** : The F1-score is the harmonic mean of precision and recall. It provides a balanced measure between precision and recall, especially when there is an imbalance in the dataset. The F1-score for the 'truck' class is 0.74, which considers both precision and recall.\r\n",
    "\r\n",
    "**Support** : Support represents the number of occurrences of each class in the test dataset. It is the actual number of samples for each class. For example, the 'airplane' and 'automobile' classes have 1000 samples each.\r\n",
    "\r\n",
    "### Overall Accuracy and Macro/Micro Average:\r\n",
    "\r\n",
    "The accuracy of the model on the entire test dataset is 0.68, which means it correctly predicts 68% of the samples. The macro average F1-score (0.67) represents the unweighted mean of the F1-scores across all classes, giving equal importance to each class. The weighted average F1-score (0.67) accounts for class imbalance by considering the number of samples in each class.\r\n",
    "\r\n",
    "Observations:\r\n",
    "\r\n",
    "The model performs relatively well in distinguishing 'ship', 'automobile', 'frog', and 'horse' classes, as they have higher precision, recall, and F1-scores compared to other classes.\r\n",
    "\r\n",
    "1. The 'cat' and 'deer' classes have lower precision and recall, indicating that the model struggles to correctly classify these categories.\r\n",
    "\r\n",
    "2. The 'bird' class also has relatively lower precision and recall, indicating room for improvement.\r\n",
    "\r\n",
    "3. The 'ship' class has the highest precision, but its recall is relatively lower, indicating that while the model correctly identifies many ships, it also misses some actual ship images.\r\n",
    "\r\n",
    "4. The 'automobile' class has the highest F1-score, indicating a good balance between precision and recall.\r\n",
    "\r\n",
    "5. The 'dog' class shows moderate performance, with a decent F1-score but slightly lower precision compared to recall.\r\n",
    "\r\n",
    "In conclusion, the model exhibits varying levels of performance across different classes, with some categories showing better discrimination than others. Further fine-tuning of the model architecture and data augmentation techniques may help improve the overall accuracy and performance, especially for classes with lower precision and recall. Additionally, investigating misclassified samples and exploring advanced techniques, such as transfer learning, might lead to further enhancement of the model's performance on this classification task."
   ]
  },
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    "Please note that the presented classification report corresponds to model1."
   ]
  },
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   "source": [
    "# Computing time and accuracy"
   ]
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    "There are various number of factors that can effect the computing time as well as the accuarcy in training the model. The factors that can be considered are as follows:\n",
    "\n",
    "**Model architecture**: If the model build is more complex,i.e., if more number of different layers, more number of neurons are added, after fine tuning, we might get an efficient model, which in turn might give an improved prediction accuracy but it might take more time to fit the model. In this case, it took more processing time as there was a more trainable parameter to compute and execute the results. Also, I have observed that mindlessly defining layers will not improve the model performance, in fact, it can decrease the model's accuracy.\n",
    "\n",
    "**Batch Size**: In the above model defined, I tried to change the batch size. It was observed that when increase the batch size, the model was trained faster hence decreasing the processing time but it resulted in the lower accuracy than before.\n",
    "\n",
    "**Epochs**: More number of epochs increases the processing time and also it might lead to overfitting.\n",
    "\n",
    "**Regularization**: Adding a dropout layer is one of the form of regularization techniques. Dropout layers are used to randomly drop out some percentage of nodes / neurons in every layer during training the model. After this, the model has to run with the remaining nodes and try to optimize the weights and biases values and avoid overfitting. Adding a dropout layer of 0.5 means that 50% of neurons are dropped after each training so that the model must understand the input data and the pattern even with less amount of information provided in the next step. This may lead to an increased performance but it might also increase the processing time.\n",
    "\n",
    "**Data Augmentation**: Data augmentation, as mentioned before, is done to apply various image transformation(such as flipping, rotating, zooming, changing the brightness, contrast, saturation) to the input data, so that the mdoel can recognize variation of a single image belonging to a particular class and make better predictions. It also increases the number of input data fed into the model in addition with the input training data which increases the computing time. But in this case, in the make_model3, I removed the data augmentation and observed that the test accuracy was more when compared with make_model1. Thus, fine tuning of the model architecture is required to get more prediction accuracy along with the data augmentation."
   ]
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   "id": "169663bb-a9f8-48fd-8ae0-9a21fbc01b83",
   "metadata": {},
   "outputs": [],
   "source": []
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