CIFAR10_CNN2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CIFAR10_CNN2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN9nnZnaQvYmy1ib1dJJxYk",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SarraLKSC/CIFAR10_with_Tensorflow/blob/master/CIFAR10_CNN2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T6ZccWSAP8C1",
        "colab_type": "text"
      },
      "source": [
        "# **CIFAR10**\n",
        "This is my 2nd take on the CIFAR10 classification problem.\n",
        "In my previous attempt I reached an accuracy of 68% using a simple network: 3 block of conv-pool layers. This time i will use a deeper network, recreating a VGG3 architecture "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfOWn7zLFcNG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf \n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L97YGEb_G0nf",
        "colab_type": "code",
        "outputId": "5dee25c9-4d7a-4af7-c82a-90afc82ff989",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "(x_train,y_train),(x_test,y_test)=tf.keras.datasets.cifar10.load_data()\n",
        "class_names=['airplane' ,'automobile','bird' ,'cat','deer','dog' ,'frog' ,'horse','ship' ,'truck']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 13s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "thjp21LMHKKc",
        "colab_type": "code",
        "outputId": "0eaa4838-3858-4c30-ca56-c1f48fb689ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "plt.imshow(x_train[5])\n",
        "print(y_train[5])\n",
        "print(class_names[int(y_train[5])])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1]\n",
            "automobile\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "68rqJC2UHSXD",
        "colab_type": "code",
        "outputId": "358df6ae-1ea8-4e87-833e-6e841dc4f526",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "figure=plt.figure(figsize=(25,4))\n",
        "for idx in np.arange(20):\n",
        "  ax=figure.add_subplot(2,10,idx+1,xticks=[],yticks=[])\n",
        "  plt.imshow(x_train[idx])\n",
        "  ax.set_title(class_names[int(y_train[idx])])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x288 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jMF7LmEhIa4Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train=x_train.reshape(x_train.shape[0],32,32,3)\n",
        "x_train=x_train.astype('float32')\n",
        "x_train/=255\n",
        "y_train=tf.keras.utils.to_categorical(y_train)\n",
        "y_test=tf.keras.utils.to_categorical(y_test)\n",
        "x_test=x_test.reshape(x_test.shape[0],32,32,3)\n",
        "x_test=x_test.astype('float32')\n",
        "x_test/=255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0oreZAv5FWW",
        "colab_type": "code",
        "outputId": "59dda130-20c1-4e8e-a030-a411dc04bc29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "print(y_train[5])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hH8iJnwzJgpI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
        "model.add(tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "model.add(tf.keras.layers.MaxPooling2D((2, 2)))\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "model.add(tf.keras.layers.MaxPooling2D((2, 2)))\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "model.add(tf.keras.layers.MaxPooling2D((2, 2)))\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "model.add(tf.keras.layers.Dense(100, activation='relu', kernel_initializer='he_uniform'))\n",
        "model.add(tf.keras.layers.Dense(10, activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okHQ2neBJymU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='SGD',\n",
        "              loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "              metrics=['acc'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDthw0VCL81O",
        "colab_type": "code",
        "outputId": "f39bacf5-5d6c-47b7-e455-87761d289f45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.fit(x_train,y_train,shuffle='true',batch_size=64,epochs=100)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 2.0058 - acc: 0.2583\n",
            "Epoch 2/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 1.7144 - acc: 0.3711\n",
            "Epoch 3/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 1.6061 - acc: 0.4101\n",
            "Epoch 4/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 1.5197 - acc: 0.4417\n",
            "Epoch 5/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 1.4418 - acc: 0.4748\n",
            "Epoch 6/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 1.3716 - acc: 0.5009\n",
            "Epoch 7/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 1.3121 - acc: 0.5227\n",
            "Epoch 8/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 1.2588 - acc: 0.5493\n",
            "Epoch 9/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 1.2095 - acc: 0.5676\n",
            "Epoch 10/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 1.1676 - acc: 0.5792\n",
            "Epoch 11/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 1.1273 - acc: 0.5970\n",
            "Epoch 12/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 1.1006 - acc: 0.6073\n",
            "Epoch 13/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 1.0656 - acc: 0.6219\n",
            "Epoch 14/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 1.0456 - acc: 0.6314\n",
            "Epoch 15/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 1.0146 - acc: 0.6410\n",
            "Epoch 16/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.9957 - acc: 0.6484\n",
            "Epoch 17/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.9787 - acc: 0.6512\n",
            "Epoch 18/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.9633 - acc: 0.6604\n",
            "Epoch 19/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.9457 - acc: 0.6669\n",
            "Epoch 20/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.9299 - acc: 0.6713\n",
            "Epoch 21/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.9112 - acc: 0.6765\n",
            "Epoch 22/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.8977 - acc: 0.6830\n",
            "Epoch 23/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.8847 - acc: 0.6873\n",
            "Epoch 24/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.8740 - acc: 0.6912\n",
            "Epoch 25/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.8565 - acc: 0.6970\n",
            "Epoch 26/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.8522 - acc: 0.6967\n",
            "Epoch 27/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.8292 - acc: 0.7066\n",
            "Epoch 28/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.8322 - acc: 0.7051\n",
            "Epoch 29/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.8182 - acc: 0.7107\n",
            "Epoch 30/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.8086 - acc: 0.7164\n",
            "Epoch 31/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.8011 - acc: 0.7166\n",
            "Epoch 32/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.7874 - acc: 0.7211\n",
            "Epoch 33/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.7812 - acc: 0.7250\n",
            "Epoch 34/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.7696 - acc: 0.7277\n",
            "Epoch 35/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.7665 - acc: 0.7293\n",
            "Epoch 36/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.7561 - acc: 0.7312\n",
            "Epoch 37/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.7497 - acc: 0.7355\n",
            "Epoch 38/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.7412 - acc: 0.7373\n",
            "Epoch 39/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.7306 - acc: 0.7416\n",
            "Epoch 40/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.7275 - acc: 0.7428\n",
            "Epoch 41/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.7176 - acc: 0.7478\n",
            "Epoch 42/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.7087 - acc: 0.7522\n",
            "Epoch 43/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.7100 - acc: 0.7496\n",
            "Epoch 44/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6995 - acc: 0.7552\n",
            "Epoch 45/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6915 - acc: 0.7567\n",
            "Epoch 46/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6857 - acc: 0.7579\n",
            "Epoch 47/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6833 - acc: 0.7594\n",
            "Epoch 48/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6749 - acc: 0.7612\n",
            "Epoch 49/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6680 - acc: 0.7633\n",
            "Epoch 50/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6594 - acc: 0.7675\n",
            "Epoch 51/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6572 - acc: 0.7679\n",
            "Epoch 52/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6524 - acc: 0.7704\n",
            "Epoch 53/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6418 - acc: 0.7745\n",
            "Epoch 54/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6405 - acc: 0.7751\n",
            "Epoch 55/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6350 - acc: 0.7746\n",
            "Epoch 56/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6273 - acc: 0.7778\n",
            "Epoch 57/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6199 - acc: 0.7822\n",
            "Epoch 58/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6151 - acc: 0.7827\n",
            "Epoch 59/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6130 - acc: 0.7842\n",
            "Epoch 60/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6065 - acc: 0.7861\n",
            "Epoch 61/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6032 - acc: 0.7865\n",
            "Epoch 62/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.6024 - acc: 0.7894\n",
            "Epoch 63/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5928 - acc: 0.7883\n",
            "Epoch 64/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5925 - acc: 0.7924\n",
            "Epoch 65/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5846 - acc: 0.7940\n",
            "Epoch 66/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5803 - acc: 0.7954\n",
            "Epoch 67/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5764 - acc: 0.7976\n",
            "Epoch 68/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5732 - acc: 0.7966\n",
            "Epoch 69/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5648 - acc: 0.8017\n",
            "Epoch 70/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5586 - acc: 0.8020\n",
            "Epoch 71/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5551 - acc: 0.8039\n",
            "Epoch 72/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5541 - acc: 0.8034\n",
            "Epoch 73/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5514 - acc: 0.8057\n",
            "Epoch 74/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5443 - acc: 0.8075\n",
            "Epoch 75/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5381 - acc: 0.8090\n",
            "Epoch 76/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5344 - acc: 0.8114\n",
            "Epoch 77/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5332 - acc: 0.8110\n",
            "Epoch 78/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5268 - acc: 0.8134\n",
            "Epoch 79/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5291 - acc: 0.8126\n",
            "Epoch 80/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5232 - acc: 0.8163\n",
            "Epoch 81/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5183 - acc: 0.8163\n",
            "Epoch 82/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5128 - acc: 0.8163\n",
            "Epoch 83/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5085 - acc: 0.8211\n",
            "Epoch 84/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5100 - acc: 0.8202\n",
            "Epoch 85/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5018 - acc: 0.8219\n",
            "Epoch 86/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5043 - acc: 0.8201\n",
            "Epoch 87/100\n",
            "782/782 [==============================] - 4s 6ms/step - loss: 0.4996 - acc: 0.8241\n",
            "Epoch 88/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.4912 - acc: 0.8253\n",
            "Epoch 89/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.4924 - acc: 0.8250\n",
            "Epoch 90/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.4870 - acc: 0.8265\n",
            "Epoch 91/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.4841 - acc: 0.8272\n",
            "Epoch 92/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.4783 - acc: 0.8294\n",
            "Epoch 93/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.4847 - acc: 0.8285\n",
            "Epoch 94/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.4712 - acc: 0.8336\n",
            "Epoch 95/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.4668 - acc: 0.8338\n",
            "Epoch 96/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.4662 - acc: 0.8332\n",
            "Epoch 97/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.4661 - acc: 0.8328\n",
            "Epoch 98/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.4647 - acc: 0.8351\n",
            "Epoch 99/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.4572 - acc: 0.8373\n",
            "Epoch 100/100\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.4541 - acc: 0.8391\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f8010324b38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fUSFbzDrNy3L",
        "colab_type": "code",
        "outputId": "9b171a2b-2e63-46b7-fd73-1424b5912cf1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "model.evaluate(x_test,y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.6211 - acc: 0.7975\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6211456656455994, 0.7975000143051147]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DntB15nfXwtl",
        "colab_type": "text"
      },
      "source": [
        "# **Conclusion**\n",
        "As shown above, this model hit an accuracy of 83% on the training set and 79% on the testing set. This is a major improvement from the 68% accuracy reached by the 1st model I made. We can draw many conclusions from the comparision of the 2 models, first one being that a deeper network (with multiple dropouts) can achieve better results without manifesting overfitting. 2nd is: one hot encoding can prevent the model from making false ordinal relations between labels thus increasing its accuracy."
      ]
    }
  ]
}