CNN_Image_Classifier.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CNN_Image_Classifier.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.2"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alokproc/Image-Processing-using-CNN/blob/master/CNN_Image_Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "4S5HLG46qIDF",
        "outputId": "45eccf1c-a25c-4394-c679-c8472eab3650",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras.datasets import cifar10\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D\n",
        "from pathlib import Path\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "MqbbyqrNqIDU",
        "outputId": "96159a34-0f79-4676-bcd9-96e7107988d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# Load data set\n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "\n"
      ],
      "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 [==============================] - 5s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "6HB6uA4aqIDb",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# List of names for each CIFAR10 class\n",
        "cifar10_class_names = {\n",
        "    0: \"Plane\",\n",
        "    1: \"Car\",\n",
        "    2: \"Bird\",\n",
        "    3: \"Cat\",\n",
        "    4: \"Deer\",\n",
        "    5: \"Dog\",\n",
        "    6: \"Frog\",\n",
        "    7: \"Horse\",\n",
        "    8: \"Boat\",\n",
        "    9: \"Truck\"\n",
        "}\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "QnBjARuUwh-7",
        "outputId": "c07e4a85-f550-4579-e4ca-6d560614728b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "print(cifar10_class_names)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{0: 'Plane', 1: 'Car', 2: 'Bird', 3: 'Cat', 4: 'Deer', 5: 'Dog', 6: 'Frog', 7: 'Horse', 8: 'Boat', 9: 'Truck'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "iA6fDWoVqIDe",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "MOYKU1ZcqIDg",
        "outputId": "4c5c799f-76d9-4fb9-b860-bea59c7f3f19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2657
        }
      },
      "cell_type": "code",
      "source": [
        "# Loop through each picture in the data set\n",
        "for i in range(10):\n",
        "    # Grab an image from the data set\n",
        "    sample_image = x_train[i]\n",
        "    # Grab the image's expected class id\n",
        "    image_class_number = y_train[i][0]\n",
        "    # Look up the class name from the class id\n",
        "    image_class_name = cifar10_class_names[image_class_number]\n",
        "\n",
        "    # Draw the image as a plot\n",
        "    plt.imshow(sample_image)\n",
        "    # Label the image\n",
        "    plt.title(image_class_name)\n",
        "    # Show the plot on the screen\n",
        "    plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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PVTmSBOVkUT9L2ngBQL3EW4o1IvX9GpFkoYVieBzr4gUAcxHF58gh/oYunF6m\ntuoyP+C2oXArrxuvnqRjmIuvn1ikY9bSyZ2/AuB33f0WtNpx32VmdwD4DIDPu/t1AOYB3NfxUYUQ\nm855g99bnO1QmW3/OIDfBfDt9vZHANyzIR4KITaEjr7zm1m63aH3JIAnALwBYMH9Hz/cHQPAP6MI\nIS47Ogp+d2+4+z4AOwHcDuC3Oj2Amd1vZgfN7OCZIi/+IIToLhe02u/uCwB+BOCfABg2s7OrQTsB\nTJExB9x9v7vvH+qPdDwQQnSV8wa/mY2Z2XD7cQHA7wF4Ba2LwB+2n3YvgO9vlJNCiEtPJ4k92wE8\nYmZptC4W33L3H5jZywC+YWZ/DuBZAF8+347cMmhktwZttdx+Oq7SDCeypOrh1lQAkB/i8tXwGP8E\nMpLiiSejpXCixcIcb++0cIrLeeVlPv2NOpcP4fya3ayHfVwp869cuVykXmCG+7+0whNPyuQrXjai\nBg+kwskqANBMcQmrVuPz2NMXlkzzWV4vcDjHfbwWw9T2zlt427Abbr6F2nZfd11w++13cHnz2PFi\ncPs/vMFjYi3nDX53fx7ArYHth9H6/i+EuALRf/gJkVAU/EIkFAW/EAlFwS9EQlHwC5FQzCPZY5f8\nYGazAM7m9W0F0LkusXHIj3ORH+dypflxtbuPdbLDrgb/OQc2O+juXNyXH/JDfmyoH/rYL0RCUfAL\nkVA2M/gPbOKxVyM/zkV+nMtvrB+b9p1fCLG56GO/EAlFwS9EQtmU4Dezu8zsV2Z2yMwe3Awf2n4c\nMbMXzOw5MzvYxeM+bGYnzezFVdtGzewJM3u9/Xtkk/x4yMym2nPynJl9qAt+7DKzH5nZy2b2kpn9\nu/b2rs5JxI+uzomZ5c3sZ2b2y7Yf/6m9/Roze7odN980s0jedwe4e1d/AKTRqgF4LYAcgF8CuKnb\nfrR9OQJg6yYc93cA3AbgxVXb/guAB9uPHwTwmU3y4yEA/77L87EdwG3txwMAXgNwU7fnJOJHV+cE\ngAHobz/OAngawB0AvgXgo+3tfwng367nOJtx578dwCF3P+ytOv/fAHD3Jvixabj7UwDWFqm/G60q\nyECXqiETP7qOu0+7+y/aj5fQqhQ1iS7PScSPruItNrxi9mYE/ySA1b2MN7PyrwP4OzN7xszu3yQf\nzjLh7tPtxycATGyiLw+Y2fPtrwUb/vVjNWa2G63iMU9jE+dkjR9Al+ekGxWzk77g9x53vw3ABwH8\nqZn9zmY7BLSu/GhdmDaDLwLYg1aDlmkAn+3Wgc2sH8B3AHzC3c+p29XNOQn40fU58XVUzO6UzQj+\nKQC7Vv1NK/9uNO4+1f59EsCzbqWkAAABH0lEQVR3sbllyWbMbDsAtH+f3Awn3H2mfeI1AXwJXZoT\nM8uiFXBfc/dH25u7PichPzZrTtrHvuCK2Z2yGcH/cwDXt1cucwA+CuCxbjthZn1mNnD2MYDfB/Bi\nfNSG8hhaVZCBTayGfDbY2nwEXZgTMzO0CsC+4u6fW2Xq6pwwP7o9J12rmN2tFcw1q5kfQmsl9Q0A\n/2GTfLgWLaXhlwBe6qYfAL6O1sfHGlrf3e5Dq+HpkwBeB/BDAKOb5Mf/BPACgOfRCr7tXfDjPWh9\npH8ewHPtnw91e04ifnR1TgDcjFZF7OfRutD8x1Xn7M8AHALw1wB61nMc/XuvEAkl6Qt+QiQWBb8Q\nCUXBL0RCUfALkVAU/EIkFAW/EAlFwS9EQvl/BjruNcNLOIoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jx8i+B5Cf5O/t5b07g+N9fdwPX9QbHo8UOp2NrvxCJBQFvxAJRcEvREJR8AuR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\nSCgKfiESioJfiISi4Bciofx/Mfn8GJhHkpMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+esgY9wDzBr+I3AbgMgArReQAgK8AuExELkR58b0BAJ+veo+EkJOCeYNfVa8NNN9cB18I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\nBj8hkcLgJyRSGPyERAqDn5BI+V+sPPZazem1XQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ronlx+M8B/AyAPxm2PwrgPbtioRBiV9jSM7+Z5YcVeucAPA7g+wAW3P3a95JX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\nyv8F68mAcFpKhu4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yI6v5BsRlL4slH5UjSUvF8Ke82Llikl1sjWtEzgOAXDO8xs3IueoRWz7Sk6sZ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\nSBQFvxCJouAXIlH+P6orKppnayxJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J1AxkZCM78JF8GvRn+HVQyoSvg6Ecd34ow1WHjZFLrhRZkDz437pA1spbkeuD\nKEztSJLTUpZz568BuMfdb0anHfe7zOxOAL8P4LPufg2ACwA+vOyzCiHWnEsGv3d4VXHMd/85gHsA\n/Hl3/H4A710VD4UQq8KyPvObWbbboXcSwEMAXgAw4/6zRORXAGxfHReFEKvBsoLf3VvufguAHQBu\nB3D9ck9gZveZ2SEzO3S+fMktASFEj3hdu/3uPgPguwDeBmDY7GdlanYAGCdzDrr7AXc/MBqpkiOE\n6C2XDH4z22Rmw92f+wH8MoCj6LwI/Fr3YR8C8K3VclIIceVZTmLPGID7zSyLzovF19z9L83sWQBf\nNbN/D+DHAL54ySNlMkChSIxc1jCWDEJkQwBoknZGANCOPO2YvDJGcn7+4c18u2NLnksvxyd4C6eJ\nMvf/QjOSLNQOJ7nUIlJZ0/hz9ljyUaT1VpbYogk6EckxksuEgYjk20f874skEa3L8iSckYhEOBCp\n/VfMcx9zZBkbDX4NLJIEo/brqOF3yeB39yMAbg2Mn0Dn878Q4k2IvuEnRKIo+IVIFAW/EImi4Bci\nURT8QiSKxWqqXfGTmZ0D8FL311EAPOWtd8iP1yI/XsubzY/d7r5pOQfsafC/5sRmh9z9wJqcXH7I\nD/mht/1CpIqCX4hEWcvgP7iG574Y+fFa5Mdr+f/WjzX7zC+EWFv0tl+IRFHwC5EoaxL8ZvYuM3ve\nzI6b2cfWwoeuHyfN7Gkze9LMDvXwvF8ys0kze+aisQ1m9pCZHev+P7JGfnzSzMa7a/Kkmb27B37s\nNLPvmtmzZvYTM/uX3fGerknEj56uiZkVzewxM3uq68e/7Y7vMbMfdePmT81sZdVx3L2n/wBk0akB\neDWAAoCnAOzvtR9dX04CGF2D874dwG0Anrlo7D8D+Fj3548B+P018uOTAP51j9djDMBt3Z+HAPwU\nwP5er0nEj56uCTplDwa7P+cB/AjAnQC+BuD93fH/AeC3VnKetbjz3w7guLuf8E6d/68CeM8a+LFm\nuPsjAKaXDL8HnSrIQI+qIRM/eo67n3H3J7o/z6NTKWo7erwmET96indY9YrZaxH82wG8fNHva1n5\n1wF8x8wOm9l9a+TDq2xx9zPdn88C2LKGvnzEzI50Pxas+sePizGzq9ApHvMjrOGaLPED6PGa9KJi\nduobfne7+20AfgXAb5vZ29faIaDzyo/OC9Na8HkAe9Fp0HIGwKd7dWIzGwTwdQAfdffX1Djr5ZoE\n/Oj5mvgKKmYvl7UI/nEAOy/6nVb+XW3cfbz7/ySAb2Jty5JNmNkYAHT/n1wLJ9x9onvhtQF8AT1a\nEzPLoxNwX3b3b3SHe74mIT9UqR5yAAAA8klEQVTWak26537dFbOXy1oE/+MAru3uXBYAvB/AA712\nwswGzGzo1Z8BvBPAM/FZq8oD6FRBBtawGvKrwdblfejBmpiZoVMA9qi7f+YiU0/XhPnR6zXpWcXs\nXu1gLtnNfDc6O6kvAPg3a+TD1egoDU8B+Ekv/QDwFXTePjbQ+ez2YXQanj4M4BiAvwGwYY38+F8A\nngZwBJ3gG+uBH3ej85b+CIAnu//e3es1ifjR0zUBcBM6FbGPoPNC83sXXbOPATgO4M8A9K3kPPp6\nrxCJkvqGnxDJouAXIlEU/EIkioJfiERR8AuRKAp+IRJFwS9Eovw/30wEj7iHpdsAAAAASUVORK5C\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fAzAC4GcAPujuvNAagGzKsK8nfMhNUmut7WR45dgz/L0rN8RX0mtLvC3U5hw1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\n8AsRKf8XipoUeK17i54AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gXgArADwB4GPuzhOtAQBySBHOkVcs8Hx2yIVtM7N85Tip8ZXS2Rme8y0fWVUe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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+F41MLwDomTtu2qwyct1zdpusObU/jpWdOa6Mj5i2Q08+Fh6j9uvy0leF1pXrrO+wMyc3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\nPe0bya6q8LsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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U7C4E2weW9Jl9pp0V+IyTQXzFsnAQEWCvipen7NX3qjOOatVRCRzVIZu1r9kzM+FAIi8I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\nQiLl/wEsfflDrxGTPQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "Un1qfsmUqIDk",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Normalize data set to 0-to-1 range\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "x_train /= 255\n",
        "x_test /= 255\n",
        "\n",
        "# Convert class vectors to binary class matrices\n",
        "y_train = keras.utils.to_categorical(y_train, 10)\n",
        "y_test = keras.utils.to_categorical(y_test, 10)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "xfYTLRRLqIDm",
        "outputId": "d58e220b-aaea-42fb-badb-2ef7a315cc4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "cell_type": "code",
      "source": [
        "# Create a model and add layers\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), padding='same', input_shape=(32, 32, 3), activation=\"relu\"))\n",
        "model.add(Conv2D(32, (3, 3), activation=\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3), padding='same', activation=\"relu\"))\n",
        "model.add(Conv2D(64, (3, 3), activation=\"relu\"))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512, activation=\"relu\"))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(10, activation=\"softmax\"))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "TlKZ8_aFqIDp",
        "outputId": "2e892cf2-4f55-4671-bc2f-33784b70621f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "model.compile(\n",
        "    loss='categorical_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "# Print a summary of the model\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 30, 30, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 15, 15, 64)        18496     \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 13, 13, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2304)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               1180160   \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 1,250,858\n",
            "Trainable params: 1,250,858\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "OAxQa-GeqIDt",
        "outputId": "b29b2906-1d7f-43de-bbfe-40321f2ebbbb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1142
        }
      },
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "model.fit(\n",
        "    x_train,\n",
        "    y_train,\n",
        "    batch_size=32,\n",
        "    epochs=30,\n",
        "    validation_data=(x_test, y_test),\n",
        "    \n",
        "    shuffle=True\n",
        ")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/30\n",
            "50000/50000 [==============================] - 294s 6ms/step - loss: 1.5446 - acc: 0.4327 - val_loss: 1.1843 - val_acc: 0.5723\n",
            "Epoch 2/30\n",
            "50000/50000 [==============================] - 292s 6ms/step - loss: 1.1476 - acc: 0.5909 - val_loss: 0.9404 - val_acc: 0.6714\n",
            "Epoch 3/30\n",
            "50000/50000 [==============================] - 293s 6ms/step - loss: 0.9844 - acc: 0.6503 - val_loss: 0.8643 - val_acc: 0.6904\n",
            "Epoch 4/30\n",
            "50000/50000 [==============================] - 291s 6ms/step - loss: 0.8833 - acc: 0.6927 - val_loss: 0.8061 - val_acc: 0.7218\n",
            "Epoch 5/30\n",
            "50000/50000 [==============================] - 289s 6ms/step - loss: 0.8244 - acc: 0.7108 - val_loss: 0.7562 - val_acc: 0.7342\n",
            "Epoch 6/30\n",
            "50000/50000 [==============================] - 289s 6ms/step - loss: 0.7727 - acc: 0.7281 - val_loss: 0.7329 - val_acc: 0.7500\n",
            "Epoch 7/30\n",
            "50000/50000 [==============================] - 290s 6ms/step - loss: 0.7275 - acc: 0.7466 - val_loss: 0.7218 - val_acc: 0.7497\n",
            "Epoch 8/30\n",
            "50000/50000 [==============================] - 290s 6ms/step - loss: 0.7028 - acc: 0.7516 - val_loss: 0.7023 - val_acc: 0.7555\n",
            "Epoch 9/30\n",
            "50000/50000 [==============================] - 291s 6ms/step - loss: 0.6739 - acc: 0.7644 - val_loss: 0.7001 - val_acc: 0.7541\n",
            "Epoch 10/30\n",
            "50000/50000 [==============================] - 290s 6ms/step - loss: 0.6527 - acc: 0.7713 - val_loss: 0.6950 - val_acc: 0.7601\n",
            "Epoch 11/30\n",
            "50000/50000 [==============================] - 291s 6ms/step - loss: 0.6301 - acc: 0.7783 - val_loss: 0.6747 - val_acc: 0.7646\n",
            "Epoch 12/30\n",
            "50000/50000 [==============================] - 290s 6ms/step - loss: 0.6057 - acc: 0.7887 - val_loss: 0.6661 - val_acc: 0.7744\n",
            "Epoch 13/30\n",
            "50000/50000 [==============================] - 291s 6ms/step - loss: 0.5899 - acc: 0.7925 - val_loss: 0.6549 - val_acc: 0.7787\n",
            "Epoch 14/30\n",
            "50000/50000 [==============================] - 290s 6ms/step - loss: 0.5766 - acc: 0.7977 - val_loss: 0.6591 - val_acc: 0.7768\n",
            "Epoch 15/30\n",
            "50000/50000 [==============================] - 295s 6ms/step - loss: 0.5654 - acc: 0.8003 - val_loss: 0.6598 - val_acc: 0.7792\n",
            "Epoch 16/30\n",
            "50000/50000 [==============================] - 297s 6ms/step - loss: 0.5486 - acc: 0.8046 - val_loss: 0.6582 - val_acc: 0.7852\n",
            "Epoch 17/30\n",
            "50000/50000 [==============================] - 301s 6ms/step - loss: 0.5370 - acc: 0.8108 - val_loss: 0.6545 - val_acc: 0.7840\n",
            "Epoch 18/30\n",
            "50000/50000 [==============================] - 299s 6ms/step - loss: 0.5306 - acc: 0.8141 - val_loss: 0.6680 - val_acc: 0.7761\n",
            "Epoch 19/30\n",
            "50000/50000 [==============================] - 298s 6ms/step - loss: 0.5210 - acc: 0.8154 - val_loss: 0.6745 - val_acc: 0.7774\n",
            "Epoch 20/30\n",
            "50000/50000 [==============================] - 297s 6ms/step - loss: 0.5031 - acc: 0.8228 - val_loss: 0.6560 - val_acc: 0.7811\n",
            "Epoch 21/30\n",
            "50000/50000 [==============================] - 296s 6ms/step - loss: 0.5013 - acc: 0.8226 - val_loss: 0.6514 - val_acc: 0.7835\n",
            "Epoch 22/30\n",
            "50000/50000 [==============================] - 298s 6ms/step - loss: 0.4929 - acc: 0.8262 - val_loss: 0.6914 - val_acc: 0.7768\n",
            "Epoch 23/30\n",
            "50000/50000 [==============================] - 293s 6ms/step - loss: 0.4806 - acc: 0.8305 - val_loss: 0.6690 - val_acc: 0.7846\n",
            "Epoch 24/30\n",
            "50000/50000 [==============================] - 295s 6ms/step - loss: 0.4799 - acc: 0.8315 - val_loss: 0.6761 - val_acc: 0.7805\n",
            "Epoch 25/30\n",
            "50000/50000 [==============================] - 294s 6ms/step - loss: 0.4709 - acc: 0.8341 - val_loss: 0.6545 - val_acc: 0.7830\n",
            "Epoch 26/30\n",
            "50000/50000 [==============================] - 293s 6ms/step - loss: 0.4655 - acc: 0.8364 - val_loss: 0.6989 - val_acc: 0.7802\n",
            "Epoch 27/30\n",
            "50000/50000 [==============================] - 297s 6ms/step - loss: 0.4598 - acc: 0.8402 - val_loss: 0.6706 - val_acc: 0.7940\n",
            "Epoch 28/30\n",
            "50000/50000 [==============================] - 295s 6ms/step - loss: 0.4488 - acc: 0.8426 - val_loss: 0.6637 - val_acc: 0.7884\n",
            "Epoch 29/30\n",
            "50000/50000 [==============================] - 292s 6ms/step - loss: 0.4506 - acc: 0.8424 - val_loss: 0.6818 - val_acc: 0.7861\n",
            "Epoch 30/30\n",
            "50000/50000 [==============================] - 295s 6ms/step - loss: 0.4411 - acc: 0.8464 - val_loss: 0.6900 - val_acc: 0.7831\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f64729cb940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "CViAkxQnqID2",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Save neural network structure\n",
        "model_structure = model.to_json()\n",
        "f = Path(\"model_structure.json\")\n",
        "f.write_text(model_structure)\n",
        "\n",
        "# Save neural network's trained weights\n",
        "model.save_weights(\"model_weights.h5\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "yX2kNBApqID7",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.models import model_from_json\n",
        "from pathlib import Path\n",
        "from keras.preprocessing import image\n",
        "import numpy as np\n",
        "\n",
        "# These are the CIFAR10 class labels from the training data (in order from 0 to 9)\n",
        "class_labels = [\n",
        "    \"Plane\",\n",
        "    \"Car\",\n",
        "    \"Bird\",\n",
        "    \"Cat\",\n",
        "    \"Deer\",\n",
        "    \"Dog\",\n",
        "    \"Frog\",\n",
        "    \"Horse\",\n",
        "    \"Boat\",\n",
        "    \"Truck\"\n",
        "]\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "CxjbS0z8qID9",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Load the json file that contains the model's structure\n",
        "f = Path(\"model_structure.json\")\n",
        "model_structure = f.read_text()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "E-gQzQgjqIEB",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Recreate the Keras model object from the json data\n",
        "model = model_from_json(model_structure)\n",
        "\n",
        "# Re-load the model's trained weights\n",
        "model.load_weights(\"model_weights.h5\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "lZxTQByfqIEE",
        "outputId": "7f8eda1a-fba8-4714-b8bb-38d8587dd5c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        }
      },
      "cell_type": "code",
      "source": [
        "# Load an image file to test, resizing it to 32x32 pixels (as required by this model)\n",
        "img = image.load_img(\"frog.png\", target_size=(32, 32))\n",
        "\n",
        "# Convert the image to a numpy array\n",
        "image_to_test = image.img_to_array(img)\n",
        "\n",
        "# Add a fourth dimension to the image (since Keras expects a list of images, not a single image)\n",
        "list_of_images = np.expand_dims(image_to_test, axis=0)\n",
        "\n",
        "# Make a prediction using the model\n",
        "results = model.predict(list_of_images)\n",
        "\n",
        "# Since we are only testing one image, we only need to check the first result\n",
        "single_result = results[0]\n",
        "\n",
        "# We will get a likelihood score for all 10 possible classes. Find out which class had the highest score.\n",
        "most_likely_class_index = int(np.argmax(single_result))\n",
        "class_likelihood = single_result[most_likely_class_index]\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-9c33ae20b594>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_img\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"frog.png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Convert the image to a numpy array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mimage_to_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimg_to_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/utils.py\u001b[0m in \u001b[0;36mload_img\u001b[0;34m(path, grayscale, color_mode, target_size, interpolation)\u001b[0m\n\u001b[1;32m    102\u001b[0m         raise ImportError('Could not import PIL.Image. '\n\u001b[1;32m    103\u001b[0m                           'The use of `array_to_img` requires PIL.')\n\u001b[0;32m--> 104\u001b[0;31m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpil_image\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcolor_mode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'grayscale'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'L'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode)\u001b[0m\n\u001b[1;32m   2528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2529\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2530\u001b[0;31m         \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2531\u001b[0m         \u001b[0mexclusive_fp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2532\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'frog.png'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "yFSN6upYqIEJ",
        "outputId": "4d3258ac-d8e2-4edd-ac2e-7e7ae86463ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        }
      },
      "cell_type": "code",
      "source": [
        "# Get the name of the most likely class\n",
        "class_label = class_labels[most_likely_class_index]\n",
        "\n",
        "# Print the result\n",
        "print(\"This is image is a {} - Likelihood: {:2f}\".format(class_label, class_likelihood))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-df5ddfe12c2f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclass_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclass_labels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmost_likely_class_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Print the result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"This is image is a {} - Likelihood: {:2f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_label\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_likelihood\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'most_likely_class_index' is not defined"
          ]
        }
      ]
    }
  ]
}