MNIST_CNN_Classifier.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MNIST_CNN_Classifier.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I3Nr3YQNE0ka",
        "colab_type": "text"
      },
      "source": [
        "##Import necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LLRzEgUPERuW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "14b4a13f-d0bf-413b-fae8-49c9e721f0a7"
      },
      "source": [
        "from numpy import mean\n",
        "from numpy import std\n",
        "from matplotlib import pyplot\n",
        "from sklearn.model_selection import KFold\n",
        "from keras.datasets import mnist\n",
        "from keras.utils import to_categorical\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D\n",
        "from keras.layers import MaxPooling2D\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.optimizers import SGD"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_XCPjljE5Lk",
        "colab_type": "text"
      },
      "source": [
        "##Load & Prep Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VlcX39d9EWSc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load train and test dataset\n",
        "def load_dataset():\n",
        "\t# load dataset\n",
        "\t(trainX, trainY), (testX, testY) = mnist.load_data()\n",
        "\t# reshape dataset to have a single channel\n",
        "\ttrainX = trainX.reshape((trainX.shape[0], 28, 28, 1))\n",
        "\ttestX = testX.reshape((testX.shape[0], 28, 28, 1))\n",
        "\t# one hot encode target values\n",
        "\ttrainY = to_categorical(trainY)\n",
        "\ttestY = to_categorical(testY)\n",
        "\treturn trainX, trainY, testX, testY"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-C_AeU_EEZPP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# scale pixels\n",
        "def prep_pixels(train, test):\n",
        "\t# convert from integers to floats\n",
        "\ttrain_norm = train.astype('float32')\n",
        "\ttest_norm = test.astype('float32')\n",
        "\t# normalize to range 0-1\n",
        "\ttrain_norm = train_norm / 255.0\n",
        "\ttest_norm = test_norm / 255.0\n",
        "\t# return normalized images\n",
        "\treturn train_norm, test_norm"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "32y28FfrFBee",
        "colab_type": "text"
      },
      "source": [
        "##Build Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7OhASutEqfG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define cnn model\n",
        "def define_model():\n",
        "  model = Sequential()\n",
        "  model.add(Conv2D(32, (5, 5),strides=1,padding='same', activation='relu', kernel_initializer='he_uniform', input_shape=(28, 28, 1)))\n",
        "  model.add(MaxPooling2D((2, 2),strides=2))\n",
        "  model.add(Conv2D(32, (5, 5),strides=1,padding='same', activation='relu', kernel_initializer='he_uniform', input_shape=(28, 28, 1)))\n",
        "  model.add(MaxPooling2D((2, 2),strides=2))\n",
        "  model.add(Conv2D(64, (5, 5),strides=1,padding='same', activation='relu', kernel_initializer='he_uniform', input_shape=(28, 28, 1)))\n",
        "  model.add(Flatten())\n",
        "  model.add(Dense(1024, activation='relu', kernel_initializer='he_uniform'))\n",
        "  model.add(Dense(512, activation='relu', kernel_initializer='he_uniform'))\n",
        "  model.add(Dense(10, activation='softmax'))\n",
        "  # compile model\n",
        "  opt = SGD(lr=0.01, momentum=0.9)\n",
        "  model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "  return model\n",
        " \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "be63rbfLEsjQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# evaluate a model using k-fold cross-validation\n",
        "def evaluate_model(model, dataX, dataY, n_folds=5):\n",
        "\tscores, histories = list(), list()\n",
        "\t# prepare cross validation\n",
        "\tkfold = KFold(n_folds, shuffle=True, random_state=1)\n",
        "\t# enumerate splits\n",
        "\tfor train_ix, test_ix in kfold.split(dataX):\n",
        "\t\t# select rows for train and test\n",
        "\t\ttrainX, trainY, testX, testY = dataX[train_ix], dataY[train_ix], dataX[test_ix], dataY[test_ix]\n",
        "\t\t# fit model\n",
        "\t\thistory = model.fit(trainX, trainY, epochs=1, batch_size=100, validation_data=(testX, testY), verbose=1)\n",
        "\t\t# evaluate model\n",
        "\t\t_, acc = model.evaluate(testX, testY,batch_size=100, verbose=1)\n",
        "\t\tprint('> %.3f' % (acc * 100.0))\n",
        "\t\t# stores scores\n",
        "\t\tscores.append(acc)\n",
        "\t\thistories.append(history)\n",
        "\treturn scores, histories"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wp-2m0wDEitp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# summarize model performance\n",
        "def summarize_performance(scores):\n",
        "\t# print summary\n",
        "\tprint('Accuracy: mean=%.3f std=%.3f, n=%d' % (mean(scores)*100, std(scores)*100, len(scores)))\n",
        "\t# box and whisker plots of results\n",
        "\t#pyplot.boxplot(scores)\n",
        "\t#pyplot.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sSb7kNS4Edzd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def run_test_harness():\n",
        "    # load dataset\n",
        "    trainX, trainY, testX, testY = load_dataset()\n",
        "    # prepare pixel data\n",
        "    trainX, testX = prep_pixels(trainX, testX)\n",
        "    # define model\n",
        "    model = define_model()\n",
        "    # evaluate model\n",
        "    scores, histories = evaluate_model(model, trainX, trainY)\n",
        "    print(histories)\n",
        "    # summarize estimated performance\n",
        "    summarize_performance(scores)\n",
        "    model.save('final_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "puAXg76uExER",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "outputId": "8475766c-c59e-4b15-8e72-1818054b8a1c"
      },
      "source": [
        "# entry point, run the test harness\n",
        "run_test_harness()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/1\n",
            "48000/48000 [==============================] - 7s 148us/step - loss: 0.1649 - accuracy: 0.9494 - val_loss: 0.0613 - val_accuracy: 0.9809\n",
            "12000/12000 [==============================] - 1s 56us/step\n",
            "> 98.092\n",
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/1\n",
            "48000/48000 [==============================] - 7s 142us/step - loss: 0.0491 - accuracy: 0.9845 - val_loss: 0.0438 - val_accuracy: 0.9879\n",
            "12000/12000 [==============================] - 1s 55us/step\n",
            "> 98.792\n",
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/1\n",
            "48000/48000 [==============================] - 7s 142us/step - loss: 0.0334 - accuracy: 0.9896 - val_loss: 0.0330 - val_accuracy: 0.9892\n",
            "12000/12000 [==============================] - 1s 54us/step\n",
            "> 98.917\n",
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/1\n",
            "48000/48000 [==============================] - 7s 141us/step - loss: 0.0259 - accuracy: 0.9921 - val_loss: 0.0169 - val_accuracy: 0.9946\n",
            "12000/12000 [==============================] - 1s 55us/step\n",
            "> 99.458\n",
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/1\n",
            "48000/48000 [==============================] - 7s 142us/step - loss: 0.0189 - accuracy: 0.9944 - val_loss: 0.0167 - val_accuracy: 0.9952\n",
            "12000/12000 [==============================] - 1s 55us/step\n",
            "> 99.517\n",
            "[<keras.callbacks.callbacks.History object at 0x7fb330459c88>, <keras.callbacks.callbacks.History object at 0x7fb33041f5f8>, <keras.callbacks.callbacks.History object at 0x7fb33041f828>, <keras.callbacks.callbacks.History object at 0x7fb33041f860>, <keras.callbacks.callbacks.History object at 0x7fb33041fb70>]\n",
            "Accuracy: mean=98.955 std=0.518, n=5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "83NqwjzFFInM",
        "colab_type": "text"
      },
      "source": [
        "##Test Image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AyzNiqD5FLV5",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "acbe9a4f-04ed-4140-e8a7-cd0bbde9ea67"
      },
      "source": [
        "from keras.preprocessing.image import load_img\n",
        "from keras.preprocessing.image import img_to_array\n",
        "from keras.models import load_model\n",
        "from google.colab import files\n",
        "import matplotlib.pyplot as plt\n",
        "import \n",
        "\n",
        "def load_image(filename):\n",
        "    img = load_img(filename, grayscale=True, target_size=(28, 28))\n",
        "    plt.imshow(img,cmap='gray')\n",
        "    img = img_to_array(img)\n",
        "    # reshape into a single sample with 1 channel\n",
        "    img = img.reshape(1, 28, 28, 1)\n",
        "    img = img.astype('float32')\n",
        "    img = img / 255.0\n",
        "    return img\n",
        "\n",
        "def run_example():\n",
        "  uploaded = files.upload()\n",
        "  img = load_image('8.png')\n",
        "  \n",
        "  model = load_model('final_model.h5')\n",
        "  digit = model.predict_classes(img)\n",
        "  print(\"Prediction:\",digit[0])\n",
        " \n",
        "\n",
        "run_example()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-4c514c4f-2af5-4253-8819-e0d2e737367d\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-4c514c4f-2af5-4253-8819-e0d2e737367d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving 8.png to 8 (1).png\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/utils.py:107: UserWarning: grayscale is deprecated. Please use color_mode = \"grayscale\"\n",
            "  warnings.warn('grayscale is deprecated. Please use '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Prediction: 8\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": "hLVQpRWvK_ot",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}