AlexNet-Architecture.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ab0287d4",
    "outputId": "66f623a0-54a0-4d59-a703-663ed2ee0b13"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: tensorflow==2.8 in /usr/local/lib/python3.7/dist-packages (2.8.0+zzzcolab20220506162203)\n",
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      "Requirement already satisfied: tf-estimator-nightly==2.8.0.dev2021122109 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.8) (2.8.0.dev2021122109)\n",
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      "Requirement already satisfied: tensorboard<2.9,>=2.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.8) (2.8.0)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.8) (57.4.0)\n",
      "Requirement already satisfied: flatbuffers>=1.12 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.8) (2.0)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.7/dist-packages (from astunparse>=1.6.0->tensorflow==2.8) (0.37.1)\n",
      "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py>=2.9.0->tensorflow==2.8) (1.5.2)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow==2.8) (0.6.1)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow==2.8) (2.23.0)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow==2.8) (1.35.0)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow==2.8) (0.4.6)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow==2.8) (3.4.1)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow==2.8) (1.0.1)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.9,>=2.8->tensorflow==2.8) (1.8.1)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow==2.8) (4.2.4)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow==2.8) (0.2.8)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow==2.8) (4.9)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow==2.8) (1.3.1)\n",
      "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow==2.8) (4.12.0)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.9,>=2.8->tensorflow==2.8) (3.8.1)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow==2.8) (0.4.8)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow==2.8) (3.0.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow==2.8) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow==2.8) (2022.6.15)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow==2.8) (1.24.3)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow==2.8) (3.2.0)\n",
      "Reading package lists... Done\n",
      "Building dependency tree       \n",
      "Reading state information... Done\n",
      "libcudnn8 is already the newest version (8.1.0.77-1+cuda11.2).\n",
      "The following package was automatically installed and is no longer required:\n",
      "  libnvidia-common-460\n",
      "Use 'apt autoremove' to remove it.\n",
      "0 upgraded, 0 newly installed, 0 to remove and 18 not upgraded.\n"
     ]
    }
   ],
   "source": [
    "#Importing other libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "#Importing Tensorflow\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import os, cv2, random\n",
    "!pip install tensorflow==2.8 \n",
    "!apt install --allow-change-held-packages libcudnn8=8.1.0.77-1+cuda11.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "287cb57b"
   },
   "source": [
    "## AlexNet on CIFAR-10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c4de53f7",
    "outputId": "57a786e9-635e-42a2-d554-40c0a604605a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
      "170500096/170498071 [==============================] - 2s 0us/step\n",
      "170508288/170498071 [==============================] - 2s 0us/step\n"
     ]
    }
   ],
   "source": [
    "(x_train, y_train),(x_test, y_test) = keras.datasets.cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1a6f9cd2",
    "outputId": "e7422bd7-6732-4a59-9e24-1d13b9636163"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 227
    },
    "id": "abb69dc5",
    "outputId": "a5d800f3-a4dd-4274-cfa4-d8741986bb7b"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(x,id) = np.unique(y_train, return_index=True)\n",
    "for i in range(10):\n",
    "  plt.subplot(2,5,i+1)\n",
    "  plt.imshow(x_train[id[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a42c0f21",
    "outputId": "2ea60870-fa20-4af1-9f2a-c5098ea1e0ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Maximum Value\n",
    "np.max(x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b0bf0a0b",
    "outputId": "a1e84420-2c14-4739-d693-a3e22131a59d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Minimum Value\n",
    "np.min(x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "104dbb6f"
   },
   "outputs": [],
   "source": [
    "train_ds=tf.data.Dataset.from_tensor_slices((x_train,y_train))\n",
    "test_ds=tf.data.Dataset.from_tensor_slices((x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "13ce413b"
   },
   "outputs": [],
   "source": [
    "def process_image(image,label):\n",
    "    image=tf.image.per_image_standardization(image)\n",
    "    image=tf.image.resize(image,(64,64))  \n",
    "    return image,label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "41bf5735",
    "outputId": "370a387a-43f4-4fcf-ca41-f36493ba8d65"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size: 50000\n",
      "Test size: 10000\n"
     ]
    }
   ],
   "source": [
    "train_ds_size=tf.data.experimental.cardinality(train_ds).numpy()\n",
    "test_ds_size=tf.data.experimental.cardinality(test_ds).numpy()\n",
    "print('Train size:',train_ds_size)\n",
    "print('Test size:',test_ds_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "726c5718"
   },
   "outputs": [],
   "source": [
    "train_ds=(train_ds.map(process_image).shuffle(buffer_size=train_ds_size).batch(batch_size=32,drop_remainder=True))\n",
    "test_ds=(test_ds.map(process_image).shuffle(buffer_size=test_ds_size).batch(batch_size=32,drop_remainder=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "64c48905"
   },
   "outputs": [],
   "source": [
    "model=keras.models.Sequential([\n",
    "    keras.layers.Conv2D(filters=128, kernel_size=(7,7), strides=(4,4), activation='relu', input_shape=(64,64,3)),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.MaxPool2D(pool_size=(2,2)),\n",
    "    keras.layers.Conv2D(filters=256, kernel_size=(5,5), strides=(1,1), activation='relu', padding=\"same\"),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.MaxPool2D(pool_size=(3,3)),\n",
    "    keras.layers.Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), activation='relu', padding=\"same\"),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Conv2D(filters=256, kernel_size=(1,1), strides=(1,1), activation='relu', padding=\"same\"),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Conv2D(filters=256, kernel_size=(1,1), strides=(1,1), activation='relu', padding=\"same\"),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.MaxPool2D(pool_size=(2,2)),\n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.Dense(4096,activation='relu'),\n",
    "    keras.layers.Dropout(0.5),\n",
    "    keras.layers.Dense(4096,activation='relu'),\n",
    "    keras.layers.Dropout(0.5),\n",
    "    keras.layers.Dense(10,activation='softmax')  \n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "17533f1e",
    "outputId": "59008e24-22fa-447a-b55c-b632abd32183"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 15, 15, 128)       18944     \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 15, 15, 128)      512       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 7, 7, 128)        0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 7, 7, 256)         819456    \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 7, 7, 256)        1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 2, 2, 256)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 2, 2, 256)         590080    \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 2, 2, 256)        1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 2, 2, 256)         65792     \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 2, 2, 256)        1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 2, 2, 256)         65792     \n",
      "                                                                 \n",
      " batch_normalization_4 (Batc  (None, 2, 2, 256)        1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 1, 1, 256)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 256)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 4096)              1052672   \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 4096)              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 4096)              16781312  \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 4096)              0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                40970     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 19,439,626\n",
      "Trainable params: 19,437,322\n",
      "Non-trainable params: 2,304\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(SGD, self).__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='sparse_categorical_crossentropy',optimizer=tf.optimizers.SGD(lr=0.001),metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eb2e0bb4",
    "outputId": "29729ba6-8023-46a2-f90c-c63ffa4771a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "1562/1562 [==============================] - 36s 14ms/step - loss: 1.9327 - accuracy: 0.2957 - val_loss: 1.5103 - val_accuracy: 0.4614\n",
      "Epoch 2/25\n",
      "1562/1562 [==============================] - 20s 11ms/step - loss: 1.5421 - accuracy: 0.4441 - val_loss: 1.3393 - val_accuracy: 0.5176\n",
      "Epoch 3/25\n",
      "1562/1562 [==============================] - 20s 11ms/step - loss: 1.3675 - accuracy: 0.5092 - val_loss: 1.2336 - val_accuracy: 0.5597\n",
      "Epoch 4/25\n",
      "1562/1562 [==============================] - 20s 10ms/step - loss: 1.2481 - accuracy: 0.5546 - val_loss: 1.1530 - val_accuracy: 0.5892\n",
      "Epoch 5/25\n",
      "1562/1562 [==============================] - 22s 11ms/step - loss: 1.1507 - accuracy: 0.5924 - val_loss: 1.1017 - val_accuracy: 0.6107\n",
      "Epoch 6/25\n",
      "1562/1562 [==============================] - 20s 11ms/step - loss: 1.0648 - accuracy: 0.6211 - val_loss: 1.0582 - val_accuracy: 0.6313\n",
      "Epoch 7/25\n",
      "1562/1562 [==============================] - 20s 11ms/step - loss: 0.9927 - accuracy: 0.6507 - val_loss: 1.0191 - val_accuracy: 0.6467\n",
      "Epoch 8/25\n",
      "1562/1562 [==============================] - 20s 11ms/step - loss: 0.9215 - accuracy: 0.6758 - val_loss: 0.9875 - val_accuracy: 0.6553\n",
      "Epoch 9/25\n",
      "1562/1562 [==============================] - 20s 11ms/step - loss: 0.8603 - accuracy: 0.6988 - val_loss: 0.9991 - val_accuracy: 0.6544\n",
      "Epoch 10/25\n",
      "1562/1562 [==============================] - 20s 10ms/step - loss: 0.7991 - accuracy: 0.7193 - val_loss: 0.9630 - val_accuracy: 0.6734\n",
      "Epoch 11/25\n",
      "1562/1562 [==============================] - 19s 10ms/step - loss: 0.7478 - accuracy: 0.7352 - val_loss: 0.9543 - val_accuracy: 0.6737\n",
      "Epoch 12/25\n",
      "1562/1562 [==============================] - 19s 10ms/step - loss: 0.6864 - accuracy: 0.7594 - val_loss: 0.9556 - val_accuracy: 0.6820\n",
      "Epoch 13/25\n",
      "1562/1562 [==============================] - 19s 10ms/step - loss: 0.6401 - accuracy: 0.7757 - val_loss: 0.9714 - val_accuracy: 0.6801\n",
      "Epoch 14/25\n",
      "1562/1562 [==============================] - 20s 11ms/step - loss: 0.5899 - accuracy: 0.7915 - val_loss: 0.9696 - val_accuracy: 0.6867\n",
      "Epoch 15/25\n",
      "1562/1562 [==============================] - 19s 10ms/step - loss: 0.5417 - accuracy: 0.8128 - val_loss: 1.0071 - val_accuracy: 0.6773\n",
      "Epoch 16/25\n",
      "1562/1562 [==============================] - 19s 10ms/step - loss: 0.5009 - accuracy: 0.8246 - val_loss: 1.0037 - val_accuracy: 0.6874\n",
      "Epoch 17/25\n",
      "1562/1562 [==============================] - 19s 10ms/step - loss: 0.4574 - accuracy: 0.8396 - val_loss: 1.0480 - val_accuracy: 0.6805\n",
      "Epoch 18/25\n",
      "1562/1562 [==============================] - 19s 10ms/step - loss: 0.4183 - accuracy: 0.8524 - val_loss: 1.0697 - val_accuracy: 0.6884\n",
      "Epoch 19/25\n",
      "1562/1562 [==============================] - 19s 10ms/step - loss: 0.3822 - accuracy: 0.8653 - val_loss: 1.1021 - val_accuracy: 0.6822\n",
      "Epoch 20/25\n",
      "1562/1562 [==============================] - 19s 10ms/step - loss: 0.3420 - accuracy: 0.8808 - val_loss: 1.0852 - val_accuracy: 0.6879\n",
      "Epoch 21/25\n",
      "1562/1562 [==============================] - 20s 10ms/step - loss: 0.3063 - accuracy: 0.8943 - val_loss: 1.1177 - val_accuracy: 0.6885\n",
      "Epoch 22/25\n",
      "1562/1562 [==============================] - 19s 10ms/step - loss: 0.2838 - accuracy: 0.9001 - val_loss: 1.2175 - val_accuracy: 0.6878\n",
      "Epoch 23/25\n",
      "1562/1562 [==============================] - 19s 10ms/step - loss: 0.2568 - accuracy: 0.9096 - val_loss: 1.1798 - val_accuracy: 0.6935\n",
      "Epoch 24/25\n",
      "1562/1562 [==============================] - 19s 10ms/step - loss: 0.2303 - accuracy: 0.9192 - val_loss: 1.2659 - val_accuracy: 0.6905\n",
      "Epoch 25/25\n",
      "1562/1562 [==============================] - 19s 10ms/step - loss: 0.2051 - accuracy: 0.9283 - val_loss: 1.2633 - val_accuracy: 0.6912\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(train_ds,epochs=25,validation_data=test_ds,validation_freq=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 573
    },
    "id": "a84a044b",
    "outputId": "f0d36109-fb46-496b-a8a8-8284e18c359d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting The Graphs for Accuracy and Loss of Model1\n",
    "sns.set_style(\"darkgrid\")\n",
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "#Train and validation accuracy\n",
    "plt.plot(epochs, acc, 'b', label='Training Accurarcy')\n",
    "plt.plot(epochs, val_acc, 'r', label='Validation Accurarcy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Training and Validation Accurarcy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "\n",
    "#Train and validation loss\n",
    "plt.plot(epochs, loss, 'b', label='Training Loss')\n",
    "plt.plot(epochs, val_loss, 'r', label='Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6TJYfUeuF9fF",
    "outputId": "ac200578-ca0a-43a9-dac8-e414f9af5fb8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score =  0.6935096383094788\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy Score = ',np.max(history.history['val_accuracy']))"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "AlextNet on CIFAR-10 and Cats and Dog.ipynb",
   "provenance": []
  },
  "gpuClass": "standard",
  "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}