AlexNet.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2TnX8vbgruFu"
      },
      "source": [
        "# **A notebook implementing the AlexNet from Scratch**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3SxWvIdsLG3"
      },
      "source": [
        "**Imports the required Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "qUT6NR0osKtF"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import time "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8pnOZWiwuvLJ"
      },
      "source": [
        "**Loading Cifar-10 Dataset using Keras dataset library**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "If7JfcaxsCy-",
        "outputId": "9058f6d6-d1cc-42dd-f1e5-50efe3448e3e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": [
        "(train_images, train_labels), (test_images, test_labels) = keras.datasets.cifar10.load_data()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RXldRXoJvDTm"
      },
      "source": [
        "**Classes which we are going to predit in an image**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "qtrVu0Pfu6ED"
      },
      "outputs": [],
      "source": [
        "CLASS_NAMES = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uIPGr7wDvgvA"
      },
      "source": [
        "**Create a Validation set from the first 5000 training images and labels**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "73g979UqvLjm"
      },
      "outputs": [],
      "source": [
        "validation_images, validation_labels = train_images[:5000], train_labels[:5000]\n",
        "\n",
        "#Remaining images and labels from total of 60K will be assigned to training set\n",
        "train_images, train_labels = train_images[5000:], train_labels[5000:]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SQh4Y1CAsJiZ"
      },
      "source": [
        "**Converting Training/Validation/Test set into dataset that can be used in tensorflow model training**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "lbppLsKkwYWa"
      },
      "outputs": [],
      "source": [
        "train_ds = tf.data.Dataset.from_tensor_slices((train_images,train_labels))\n",
        "test_ds = tf.data.Dataset.from_tensor_slices((test_images,test_labels))\n",
        "validation_ds = tf.data.Dataset.from_tensor_slices((validation_images,validation_labels))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WrcfVAa1b8e"
      },
      "source": [
        "**Visualizing the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "hHIJgryJx-yG",
        "outputId": "ee5b1458-8520-4136-cff9-a661e288f1ed"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize = (20,20))\n",
        "for i,(image,label) in enumerate(train_ds.take(15)):\n",
        "  ax = plt.subplot(5,15,i+1)\n",
        "  plt.imshow(image)\n",
        "  plt.title(CLASS_NAMES[label.numpy()[0]])\n",
        "  plt.axis('off')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z72q-DGj1hvJ"
      },
      "source": [
        "**Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "is6999JqyYqr"
      },
      "outputs": [],
      "source": [
        "def process_images(image,label):\n",
        "  #Normalize: Mean = 0, Std = 1\n",
        "  image = tf.image.per_image_standardization(image)\n",
        "  \n",
        "  #Resize the image from 32x32 to 227x227 for standard required input of AlexNet\n",
        "  image = tf.image.resize(image, (227,227))\n",
        "\n",
        "  return image,label"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bgJMKalW61vU"
      },
      "source": [
        "**Get the size of the train/test/validation set**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FcSqroTb3pmk",
        "outputId": "cd0117da-d357-4bb8-af90-6c753be17d19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "45000\n",
            "10000\n",
            "5000\n"
          ]
        }
      ],
      "source": [
        "train_ds_size = tf.data.experimental.cardinality(train_ds).numpy()\n",
        "test_ds_size = tf.data.experimental.cardinality(test_ds).numpy()\n",
        "validation_ds_size = tf.data.experimental.cardinality(validation_ds).numpy()\n",
        "\n",
        "print(train_ds_size)\n",
        "print(test_ds_size)\n",
        "print(validation_ds_size)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tr3pyWDx8nna"
      },
      "source": [
        "**Reszing and creating Batch sizes and shuffling the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "srqf5sTy5lm4"
      },
      "outputs": [],
      "source": [
        "train_ds = (train_ds\n",
        "            .map(process_images)\n",
        "            .shuffle(buffer_size= train_ds_size)\n",
        "            .batch(batch_size=32, drop_remainder = True))\n",
        "\n",
        "test_ds = (test_ds\n",
        "            .map(process_images)\n",
        "            .shuffle(buffer_size= test_ds_size)\n",
        "            .batch(batch_size=32, drop_remainder = True))\n",
        "validation_ds = (validation_ds\n",
        "            .map(process_images)\n",
        "            .shuffle(buffer_size= validation_ds_size)\n",
        "            .batch(batch_size=32, drop_remainder = True))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t-Zradf9HsmB"
      },
      "source": [
        "**Creating the AlexNet model Architecture**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "YwKRaZHb9gFB"
      },
      "outputs": [],
      "source": [
        "from matplotlib.cbook import flatten\n",
        "model = keras.models.Sequential([\n",
        "          \n",
        "          #Convolutional Layers\n",
        "          keras.layers.Conv2D(filters=96, kernel_size = (11,11), strides=(4,4), activation='relu', input_shape=(227,227,3)),\n",
        "          keras.layers.BatchNormalization(),\n",
        "          keras.layers.MaxPool2D(pool_size=(3,3), strides = (2,2)),\n",
        "\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), strides=(2,2)),\n",
        "\n",
        "          keras.layers.Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), activation='relu', padding=\"same\"),\n",
        "          keras.layers.BatchNormalization(),\n",
        "\n",
        "          keras.layers.Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), activation='relu', padding=\"same\"),\n",
        "          keras.layers.BatchNormalization(),\n",
        "\n",
        "          keras.layers.Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), activation='relu', padding=\"same\"),\n",
        "          keras.layers.BatchNormalization(),\n",
        "          keras.layers.MaxPool2D(pool_size=(3,3), strides=(2,2)),\n",
        "\n",
        "          #Fully Connected Layers\n",
        "          keras.layers.Flatten(),\n",
        "          keras.layers.Dense(4096, activation='relu'),\n",
        "          keras.layers.Dropout(0.5),\n",
        "\n",
        "          keras.layers.Dense(4096, activation='relu'),\n",
        "          keras.layers.Dropout(0.5),\n",
        "          \n",
        "          #Softmax Layer to define which class has highest probability\n",
        "          keras.layers.Dense(10, activation='softmax')\n",
        "                               \n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dHwof3ZEp8D6"
      },
      "source": [
        "**To check the Logs while training data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "TIqoIqiiOpBH"
      },
      "outputs": [],
      "source": [
        "root_logdir = os.path.join(os.curdir, \"logs\\\\fit\\\\\")\n",
        "\n",
        "def get_run_logdir():\n",
        "    run_id = time.strftime(\"run_%Y_%m_%d-%H_%M_%S\")\n",
        "    return os.path.join(root_logdir, run_id)\n",
        "run_logdir = get_run_logdir()\n",
        "tensorboard_cb = keras.callbacks.TensorBoard(run_logdir)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UMEzmrfqqMui"
      },
      "source": [
        "**Defining Loss function, optimizer, and display Model summary**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VrhBnVyi6ww",
        "outputId": "cf44d11f-c97d-49ea-fe7e-4b7533c7bce7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 55, 55, 96)        34944     \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 55, 55, 96)       384       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 27, 27, 96)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 27, 27, 256)       614656    \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 27, 27, 256)      1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 13, 13, 256)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 13, 13, 384)       885120    \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 13, 13, 384)      1536      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 13, 13, 384)       1327488   \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 13, 13, 384)      1536      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 13, 13, 256)       884992    \n",
            "                                                                 \n",
            " batch_normalization_4 (Batc  (None, 13, 13, 256)      1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 6, 6, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 9216)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4096)              37752832  \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: 58,327,818\n",
            "Trainable params: 58,325,066\n",
            "Non-trainable params: 2,752\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "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": "markdown",
      "metadata": {
        "id": "oeGs0Hhnq26W"
      },
      "source": [
        "**Training the Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HyJC2D1ZkEWc",
        "outputId": "58e9fdd9-450e-4089-85b5-cb22ef23517d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "1406/1406 [==============================] - 760s 533ms/step - loss: 2.0458 - accuracy: 0.3521 - val_loss: 1.3504 - val_accuracy: 0.5192\n",
            "Epoch 2/50\n",
            "1406/1406 [==============================] - 749s 532ms/step - loss: 1.4841 - accuracy: 0.4755 - val_loss: 1.2041 - val_accuracy: 0.5787\n",
            "Epoch 3/50\n",
            "1406/1406 [==============================] - 740s 525ms/step - loss: 1.2979 - accuracy: 0.5410 - val_loss: 1.0888 - val_accuracy: 0.6202\n",
            "Epoch 4/50\n",
            "1406/1406 [==============================] - 747s 531ms/step - loss: 1.1770 - accuracy: 0.5843 - val_loss: 0.9983 - val_accuracy: 0.6488\n",
            "Epoch 5/50\n",
            "1406/1406 [==============================] - 742s 527ms/step - loss: 1.0710 - accuracy: 0.6210 - val_loss: 0.9173 - val_accuracy: 0.6795\n",
            "Epoch 6/50\n",
            "1406/1406 [==============================] - 744s 529ms/step - loss: 0.9868 - accuracy: 0.6521 - val_loss: 0.8685 - val_accuracy: 0.6881\n",
            "Epoch 7/50\n",
            "1406/1406 [==============================] - 754s 535ms/step - loss: 0.9132 - accuracy: 0.6765 - val_loss: 0.8719 - val_accuracy: 0.6897\n",
            "Epoch 8/50\n",
            "1406/1406 [==============================] - 763s 542ms/step - loss: 0.8492 - accuracy: 0.7023 - val_loss: 0.7887 - val_accuracy: 0.7204\n",
            "Epoch 9/50\n",
            "1406/1406 [==============================] - 755s 536ms/step - loss: 0.7942 - accuracy: 0.7212 - val_loss: 0.7731 - val_accuracy: 0.7292\n",
            "Epoch 10/50\n",
            "1406/1406 [==============================] - 764s 542ms/step - loss: 0.7504 - accuracy: 0.7357 - val_loss: 0.7207 - val_accuracy: 0.7442\n",
            "Epoch 11/50\n",
            "1406/1406 [==============================] - 756s 537ms/step - loss: 0.7009 - accuracy: 0.7545 - val_loss: 0.7172 - val_accuracy: 0.7490\n",
            "Epoch 12/50\n",
            "1406/1406 [==============================] - 750s 533ms/step - loss: 0.6603 - accuracy: 0.7688 - val_loss: 0.6853 - val_accuracy: 0.7606\n",
            "Epoch 13/50\n",
            "1406/1406 [==============================] - 751s 534ms/step - loss: 0.6230 - accuracy: 0.7807 - val_loss: 0.6614 - val_accuracy: 0.7620\n",
            "Epoch 14/50\n",
            "1406/1406 [==============================] - 745s 529ms/step - loss: 0.5838 - accuracy: 0.7963 - val_loss: 0.6602 - val_accuracy: 0.7692\n",
            "Epoch 15/50\n",
            "1406/1406 [==============================] - 741s 526ms/step - loss: 0.5538 - accuracy: 0.8061 - val_loss: 0.6433 - val_accuracy: 0.7766\n",
            "Epoch 16/50\n",
            "1406/1406 [==============================] - 740s 525ms/step - loss: 0.5184 - accuracy: 0.8181 - val_loss: 0.6474 - val_accuracy: 0.7760\n",
            "Epoch 17/50\n",
            "1406/1406 [==============================] - 748s 531ms/step - loss: 0.4894 - accuracy: 0.8279 - val_loss: 0.6499 - val_accuracy: 0.7748\n",
            "Epoch 18/50\n",
            "1406/1406 [==============================] - 750s 533ms/step - loss: 0.4598 - accuracy: 0.8401 - val_loss: 0.6329 - val_accuracy: 0.7812\n",
            "Epoch 19/50\n",
            "1406/1406 [==============================] - 745s 529ms/step - loss: 0.4309 - accuracy: 0.8494 - val_loss: 0.6066 - val_accuracy: 0.7835\n",
            "Epoch 20/50\n",
            "1406/1406 [==============================] - 754s 536ms/step - loss: 0.3996 - accuracy: 0.8603 - val_loss: 0.6142 - val_accuracy: 0.7889\n",
            "Epoch 21/50\n",
            "1406/1406 [==============================] - 750s 532ms/step - loss: 0.3786 - accuracy: 0.8670 - val_loss: 0.5963 - val_accuracy: 0.7961\n",
            "Epoch 22/50\n",
            "1406/1406 [==============================] - 746s 530ms/step - loss: 0.3494 - accuracy: 0.8789 - val_loss: 0.6167 - val_accuracy: 0.7907\n",
            "Epoch 23/50\n",
            "1406/1406 [==============================] - 750s 532ms/step - loss: 0.3258 - accuracy: 0.8872 - val_loss: 0.5824 - val_accuracy: 0.8011\n",
            "Epoch 24/50\n",
            "1406/1406 [==============================] - 752s 534ms/step - loss: 0.3054 - accuracy: 0.8930 - val_loss: 0.6104 - val_accuracy: 0.7947\n",
            "Epoch 25/50\n",
            "1406/1406 [==============================] - 759s 539ms/step - loss: 0.2844 - accuracy: 0.9010 - val_loss: 0.5966 - val_accuracy: 0.7995\n",
            "Epoch 26/50\n",
            "1406/1406 [==============================] - 739s 525ms/step - loss: 0.2635 - accuracy: 0.9093 - val_loss: 0.6289 - val_accuracy: 0.7987\n",
            "Epoch 27/50\n",
            "1406/1406 [==============================] - 749s 532ms/step - loss: 0.2423 - accuracy: 0.9162 - val_loss: 0.5986 - val_accuracy: 0.8071\n",
            "Epoch 28/50\n",
            "1406/1406 [==============================] - 738s 524ms/step - loss: 0.2261 - accuracy: 0.9206 - val_loss: 0.6040 - val_accuracy: 0.8063\n",
            "Epoch 29/50\n",
            "1406/1406 [==============================] - 747s 531ms/step - loss: 0.2057 - accuracy: 0.9302 - val_loss: 0.6024 - val_accuracy: 0.8103\n",
            "Epoch 30/50\n",
            "1406/1406 [==============================] - 749s 532ms/step - loss: 0.1885 - accuracy: 0.9354 - val_loss: 0.6064 - val_accuracy: 0.8097\n",
            "Epoch 31/50\n",
            "1406/1406 [==============================] - 749s 532ms/step - loss: 0.1784 - accuracy: 0.9385 - val_loss: 0.6175 - val_accuracy: 0.8069\n",
            "Epoch 32/50\n",
            "1406/1406 [==============================] - 740s 526ms/step - loss: 0.1613 - accuracy: 0.9461 - val_loss: 0.6019 - val_accuracy: 0.8137\n",
            "Epoch 33/50\n",
            "1406/1406 [==============================] - 753s 535ms/step - loss: 0.1485 - accuracy: 0.9509 - val_loss: 0.6110 - val_accuracy: 0.8113\n",
            "Epoch 34/50\n",
            "1406/1406 [==============================] - 743s 528ms/step - loss: 0.1308 - accuracy: 0.9567 - val_loss: 0.6336 - val_accuracy: 0.8145\n",
            "Epoch 35/50\n",
            "1406/1406 [==============================] - 742s 527ms/step - loss: 0.1266 - accuracy: 0.9570 - val_loss: 0.6720 - val_accuracy: 0.8039\n",
            "Epoch 36/50\n",
            "1406/1406 [==============================] - 753s 535ms/step - loss: 0.1152 - accuracy: 0.9624 - val_loss: 0.6700 - val_accuracy: 0.8123\n",
            "Epoch 37/50\n",
            "1406/1406 [==============================] - 747s 531ms/step - loss: 0.1096 - accuracy: 0.9632 - val_loss: 0.6472 - val_accuracy: 0.8123\n",
            "Epoch 38/50\n",
            "1406/1406 [==============================] - 752s 534ms/step - loss: 0.0957 - accuracy: 0.9687 - val_loss: 0.6680 - val_accuracy: 0.8129\n",
            "Epoch 39/50\n",
            "1406/1406 [==============================] - 756s 537ms/step - loss: 0.0909 - accuracy: 0.9701 - val_loss: 0.6686 - val_accuracy: 0.8119\n",
            "Epoch 40/50\n",
            "1406/1406 [==============================] - 751s 533ms/step - loss: 0.0874 - accuracy: 0.9716 - val_loss: 0.6934 - val_accuracy: 0.8047\n",
            "Epoch 41/50\n",
            "1406/1406 [==============================] - 747s 530ms/step - loss: 0.0771 - accuracy: 0.9751 - val_loss: 0.6839 - val_accuracy: 0.8141\n",
            "Epoch 42/50\n",
            "1406/1406 [==============================] - 748s 531ms/step - loss: 0.0772 - accuracy: 0.9754 - val_loss: 0.6928 - val_accuracy: 0.8143\n",
            "Epoch 43/50\n",
            "1406/1406 [==============================] - 747s 530ms/step - loss: 0.0669 - accuracy: 0.9792 - val_loss: 0.6809 - val_accuracy: 0.8135\n",
            "Epoch 44/50\n",
            "1406/1406 [==============================] - 742s 527ms/step - loss: 0.0670 - accuracy: 0.9782 - val_loss: 0.7313 - val_accuracy: 0.8129\n",
            "Epoch 45/50\n",
            "1406/1406 [==============================] - 742s 527ms/step - loss: 0.0635 - accuracy: 0.9804 - val_loss: 0.7156 - val_accuracy: 0.8133\n",
            "Epoch 46/50\n",
            "1406/1406 [==============================] - 747s 530ms/step - loss: 0.0561 - accuracy: 0.9826 - val_loss: 0.7092 - val_accuracy: 0.8161\n",
            "Epoch 47/50\n",
            "1406/1406 [==============================] - 748s 531ms/step - loss: 0.0530 - accuracy: 0.9839 - val_loss: 0.7293 - val_accuracy: 0.8173\n",
            "Epoch 48/50\n",
            "1406/1406 [==============================] - 750s 532ms/step - loss: 0.0485 - accuracy: 0.9851 - val_loss: 0.6995 - val_accuracy: 0.8171\n",
            "Epoch 49/50\n",
            "1406/1406 [==============================] - 745s 529ms/step - loss: 0.0476 - accuracy: 0.9854 - val_loss: 0.7101 - val_accuracy: 0.8197\n",
            "Epoch 50/50\n",
            "1406/1406 [==============================] - 746s 530ms/step - loss: 0.0432 - accuracy: 0.9868 - val_loss: 0.7358 - val_accuracy: 0.8227\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f34ad052690>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "model.fit(train_ds,\n",
        "          epochs=50,\n",
        "          validation_data=validation_ds,\n",
        "          validation_freq=1,\n",
        "          callbacks=[tensorboard_cb])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSk-fvaaq_xU"
      },
      "source": [
        "**Test the Model's accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "LmjlEF8DlIZz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5ca4b4a-9ed9-4f14-82ce-2792828c5669"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "312/312 [==============================] - 27s 86ms/step - loss: 0.8405 - accuracy: 0.8060\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8405038118362427, 0.8059895634651184]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "model.evaluate(test_ds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "__c_7dQhhU3G"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "collapsed_sections": [],
      "machine_shape": "hm",
      "name": "AlexNet.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}