dogs_vs_cats.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "dogs_vs_cats.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO0qv3udfMl3xKkqmGaDfwT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dlguswn3659/big_pie/blob/master/dogs_vs_cats.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2FIwqjxCtzlh",
        "colab_type": "code",
        "outputId": "9114aa37-1e40-43df-f305-876739e27e4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "# plot dog photos from the dogs vs cats dataset\n",
        "from matplotlib import pyplot\n",
        "from matplotlib.image import imread\n",
        "import csv\n",
        "import math\n",
        "# from google.colab import files\n",
        "# uploaded = files.upload()\n",
        "\n",
        "# import zipfile\n",
        "# import io\n",
        "# zf = zipfile.ZipFile(io.BytesIO(uploaded['dogs-vs-cats2.zip']), \"r\")\n",
        "# zf.extractall()\n",
        "\n",
        "# define location of dataset\n",
        "folder = 'train/train/'\n",
        "# plot first few images\n",
        "for i in range(9):\n",
        "\t# define subplot\n",
        "\tpyplot.subplot(330 + 1 + i)\n",
        "\t# define filename\n",
        "\t# filename = folder + 'sample_image' + '.jpg'\n",
        "\tfilename = folder + 'dog.' + str(i) + '.jpg'\n",
        "\t# filename = folder + 'cat.' + '.jpg'\n",
        "\n",
        "\t# load image pixels\n",
        "\timage = imread(filename)\n",
        "\t# plot raw pixel data\n",
        "\tpyplot.imshow(image)\n",
        "# show the figure\n",
        "pyplot.show()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nF4egOCQ7V4R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "a0b25cfa-eacb-4880-b0f3-16a81ce919e0"
      },
      "source": [
        "# plot cat photos from the dogs vs cats dataset\n",
        "from matplotlib import pyplot\n",
        "from matplotlib.image import imread\n",
        "# define location of dataset\n",
        "folder = 'train/train/'\n",
        "# plot first few images\n",
        "for i in range(9):\n",
        "\t# define subplot\n",
        "\tpyplot.subplot(330 + 1 + i)\n",
        "\t# define filename\n",
        "\tfilename = folder + 'cat.' + str(i) + '.jpg'\n",
        "\t# load image pixels\n",
        "\timage = imread(filename)\n",
        "\t# plot raw pixel data\n",
        "\tpyplot.imshow(image)\n",
        "# show the figure\n",
        "pyplot.show()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YrfvnSz_7cpR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "69ca4a70-7733-4822-fecd-98ce13633bda"
      },
      "source": [
        "# load dogs vs cats dataset, reshape and save to a new file\n",
        "from os import listdir\n",
        "from numpy import asarray\n",
        "from numpy import save\n",
        "from keras.preprocessing.image import load_img\n",
        "from keras.preprocessing.image import img_to_array\n",
        "# define location of dataset\n",
        "folder = 'train/train/'\n",
        "photos, labels = list(), list()\n",
        "# enumerate files in the directory\n",
        "for file in listdir(folder):\n",
        "\t# determine class\n",
        "\toutput = 0.0\n",
        "\tif file.startswith('cat'):\n",
        "\t\toutput = 1.0\n",
        "\t# load image\n",
        "\tphoto = load_img(folder + file, target_size=(200, 200))\n",
        "\t# convert to numpy array\n",
        "\tphoto = img_to_array(photo)\n",
        "\t# store\n",
        "\tphotos.append(photo)\n",
        "\tlabels.append(output)\n",
        "# convert to a numpy arrays\n",
        "photos = asarray(photos)\n",
        "labels = asarray(labels)\n",
        "print(photos.shape, labels.shape)\n",
        "# save the reshaped photos\n",
        "save('dogs_vs_cats_photos.npy', photos)\n",
        "save('dogs_vs_cats_labels.npy', labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_cyA3fak7nAh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "c677c508-a65b-4a1d-884c-b969a8d2f20c"
      },
      "source": [
        "# load and confirm the shape\n",
        "from numpy import load\n",
        "photos = load('dogs_vs_cats_photos.npy')\n",
        "labels = load('dogs_vs_cats_labels.npy')\n",
        "print(photos.shape, labels.shape)"
      ],
      "execution_count": 1,
      "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-1-020dacd59454>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# load and confirm the shape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mphotos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dogs_vs_cats_photos.npy'\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      4\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dogs_vs_cats_labels.npy'\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[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mphotos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    426\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\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    429\u001b[0m         \u001b[0mown_fid\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    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'dogs_vs_cats_photos.npy'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCwKfH4U7sJw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create directories\n",
        "dataset_home = 'dataset_dogs_vs_cats/'\n",
        "subdirs = ['train/', 'test/']\n",
        "for subdir in subdirs:\n",
        "\t# create label subdirectories\n",
        "\tlabeldirs = ['dogs/', 'cats/']\n",
        "\tfor labldir in labeldirs:\n",
        "\t\tnewdir = dataset_home + subdir + labldir\n",
        "\t\tmakedirs(newdir, exist_ok=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0T_64wW67xs5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# seed random number generator\n",
        "seed(1)\n",
        "# define ratio of pictures to use for validation\n",
        "val_ratio = 0.25\n",
        "# copy training dataset images into subdirectories\n",
        "src_directory = 'train/'\n",
        "for file in listdir(src_directory):\n",
        "\tsrc = src_directory + '/' + file\n",
        "\tdst_dir = 'train/'\n",
        "\tif random() < val_ratio:\n",
        "\t\tdst_dir = 'test/'\n",
        "\tif file.startswith('cat'):\n",
        "\t\tdst = dataset_home + dst_dir + 'cats/'  + file\n",
        "\t\tcopyfile(src, dst)\n",
        "\telif file.startswith('dog'):\n",
        "\t\tdst = dataset_home + dst_dir + 'dogs/'  + file\n",
        "\t\tcopyfile(src, dst)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3PF525474Uo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# organize dataset into a useful structure\n",
        "from os import makedirs\n",
        "from os import listdir\n",
        "from shutil import copyfile\n",
        "from random import seed\n",
        "from random import random\n",
        "# create directories\n",
        "dataset_home = 'dataset_dogs_vs_cats/'\n",
        "subdirs = ['train/', 'test/']\n",
        "for subdir in subdirs:\n",
        "\t# create label subdirectories\n",
        "\tlabeldirs = ['dogs/', 'cats/']\n",
        "\tfor labldir in labeldirs:\n",
        "\t\tnewdir = dataset_home + subdir + labldir\n",
        "\t\tmakedirs(newdir, exist_ok=True)\n",
        "# seed random number generator\n",
        "seed(1)\n",
        "# define ratio of pictures to use for validation\n",
        "val_ratio = 0.25\n",
        "# copy training dataset images into subdirectories\n",
        "src_directory = 'train/'\n",
        "for file in listdir(src_directory):\n",
        "\tsrc = src_directory + '/' + file\n",
        "\tdst_dir = 'train/'\n",
        "\tif random() < val_ratio:\n",
        "\t\tdst_dir = 'test/'\n",
        "\tif file.startswith('cat'):\n",
        "\t\tdst = dataset_home + dst_dir + 'cats/'  + file\n",
        "\t\tcopyfile(src, dst)\n",
        "\telif file.startswith('dog'):\n",
        "\t\tdst = dataset_home + dst_dir + 'dogs/'  + file\n",
        "\t\tcopyfile(src, dst)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcWp7s_j77id",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# block 1\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "# block 2\n",
        "model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "# block 3\n",
        "model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "model.add(MaxPooling2D((2, 2)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trc80xWx7-KS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define cnn model\n",
        "def define_model():\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Flatten())\n",
        "\tmodel.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
        "\tmodel.add(Dense(1, activation='sigmoid'))\n",
        "\t# compile model\n",
        "\topt = SGD(lr=0.001, momentum=0.9)\n",
        "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\treturn model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcd5dblI8BEa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define model\n",
        "model = define_model()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVHouvPO8Csm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create data generator\n",
        "datagen = ImageDataGenerator(rescale=1.0/255.0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6JG5MXnP8EgT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# prepare iterators\n",
        "train_it = datagen.flow_from_directory('dataset_dogs_vs_cats/train/',\n",
        "\tclass_mode='binary', batch_size=64, target_size=(200, 200))\n",
        "test_it = datagen.flow_from_directory('dataset_dogs_vs_cats/test/',\n",
        "\tclass_mode='binary', batch_size=64, target_size=(200, 200))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSX1Z4KW8HPK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# fit model\n",
        "history = model.fit_generator(train_it, steps_per_epoch=len(train_it),\n",
        "\tvalidation_data=test_it, validation_steps=len(test_it), epochs=20, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iy4bozN78KE1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# evaluate model\n",
        "_, acc = model.evaluate_generator(test_it, steps=len(test_it), verbose=0)\n",
        "print('> %.3f' % (acc * 100.0))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "35E1FywN8MTz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plot diagnostic learning curves\n",
        "def summarize_diagnostics(history):\n",
        "\t# plot loss\n",
        "\tpyplot.subplot(211)\n",
        "\tpyplot.title('Cross Entropy Loss')\n",
        "\tpyplot.plot(history.history['loss'], color='blue', label='train')\n",
        "\tpyplot.plot(history.history['val_loss'], color='orange', label='test')\n",
        "\t# plot accuracy\n",
        "\tpyplot.subplot(212)\n",
        "\tpyplot.title('Classification Accuracy')\n",
        "\tpyplot.plot(history.history['accuracy'], color='blue', label='train')\n",
        "\tpyplot.plot(history.history['val_accuracy'], color='orange', label='test')\n",
        "\t# save plot to file\n",
        "\tfilename = sys.argv[0].split('/')[-1]\n",
        "\tpyplot.savefig(filename + '_plot.png')\n",
        "\tpyplot.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uwrtNf3v8OwH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# baseline model for the dogs vs cats dataset\n",
        "import sys\n",
        "from matplotlib import pyplot\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\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        " \n",
        "# define cnn model\n",
        "def define_model():\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Flatten())\n",
        "\tmodel.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
        "\tmodel.add(Dense(1, activation='sigmoid'))\n",
        "\t# compile model\n",
        "\topt = SGD(lr=0.001, momentum=0.9)\n",
        "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\treturn model\n",
        " \n",
        "# plot diagnostic learning curves\n",
        "def summarize_diagnostics(history):\n",
        "\t# plot loss\n",
        "\tpyplot.subplot(211)\n",
        "\tpyplot.title('Cross Entropy Loss')\n",
        "\tpyplot.plot(history.history['loss'], color='blue', label='train')\n",
        "\tpyplot.plot(history.history['val_loss'], color='orange', label='test')\n",
        "\t# plot accuracy\n",
        "\tpyplot.subplot(212)\n",
        "\tpyplot.title('Classification Accuracy')\n",
        "\tpyplot.plot(history.history['accuracy'], color='blue', label='train')\n",
        "\tpyplot.plot(history.history['val_accuracy'], color='orange', label='test')\n",
        "\t# save plot to file\n",
        "\tfilename = sys.argv[0].split('/')[-1]\n",
        "\tpyplot.savefig(filename + '_plot.png')\n",
        "\tpyplot.close()\n",
        " \n",
        "# run the test harness for evaluating a model\n",
        "def run_test_harness():\n",
        "\t# define model\n",
        "\tmodel = define_model()\n",
        "\t# create data generator\n",
        "\tdatagen = ImageDataGenerator(rescale=1.0/255.0)\n",
        "\t# prepare iterators\n",
        "\ttrain_it = datagen.flow_from_directory('dataset_dogs_vs_cats/train/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(200, 200))\n",
        "\ttest_it = datagen.flow_from_directory('dataset_dogs_vs_cats/test/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(200, 200))\n",
        "\t# fit model\n",
        "\thistory = model.fit_generator(train_it, steps_per_epoch=len(train_it),\n",
        "\t\tvalidation_data=test_it, validation_steps=len(test_it), epochs=20, verbose=0)\n",
        "\t# evaluate model\n",
        "\t_, acc = model.evaluate_generator(test_it, steps=len(test_it), verbose=0)\n",
        "\tprint('> %.3f' % (acc * 100.0))\n",
        "\t# learning curves\n",
        "\tsummarize_diagnostics(history)\n",
        " \n",
        "# entry point, run the test harness\n",
        "run_test_harness()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ul4pXltW8SfD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define cnn model\n",
        "def define_model():\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Flatten())\n",
        "\tmodel.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
        "\tmodel.add(Dense(1, activation='sigmoid'))\n",
        "\t# compile model\n",
        "\topt = SGD(lr=0.001, momentum=0.9)\n",
        "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\treturn model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "huEBap_a8Ukc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define cnn model\n",
        "def define_model():\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Flatten())\n",
        "\tmodel.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
        "\tmodel.add(Dense(1, activation='sigmoid'))\n",
        "\t# compile model\n",
        "\topt = SGD(lr=0.001, momentum=0.9)\n",
        "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\treturn model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qTJkhXnr8cue",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define cnn model\n",
        "def define_model():\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Flatten())\n",
        "\tmodel.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
        "\tmodel.add(Dense(1, activation='sigmoid'))\n",
        "\t# compile model\n",
        "\topt = SGD(lr=0.001, momentum=0.9)\n",
        "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\treturn model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHNIRLy78lpK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define cnn model\n",
        "def define_model():\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Dropout(0.2))\n",
        "\tmodel.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Dropout(0.2))\n",
        "\tmodel.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Dropout(0.2))\n",
        "\tmodel.add(Flatten())\n",
        "\tmodel.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
        "\tmodel.add(Dropout(0.5))\n",
        "\tmodel.add(Dense(1, activation='sigmoid'))\n",
        "\t# compile model\n",
        "\topt = SGD(lr=0.001, momentum=0.9)\n",
        "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\treturn model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Din1dzsw8mj2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# baseline model with dropout for the dogs vs cats dataset\n",
        "import sys\n",
        "from matplotlib import pyplot\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.layers import Dropout\n",
        "from keras.optimizers import SGD\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        " \n",
        "# define cnn model\n",
        "def define_model():\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Dropout(0.2))\n",
        "\tmodel.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Dropout(0.2))\n",
        "\tmodel.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Dropout(0.2))\n",
        "\tmodel.add(Flatten())\n",
        "\tmodel.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
        "\tmodel.add(Dropout(0.5))\n",
        "\tmodel.add(Dense(1, activation='sigmoid'))\n",
        "\t# compile model\n",
        "\topt = SGD(lr=0.001, momentum=0.9)\n",
        "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\treturn model\n",
        " \n",
        "# plot diagnostic learning curves\n",
        "def summarize_diagnostics(history):\n",
        "\t# plot loss\n",
        "\tpyplot.subplot(211)\n",
        "\tpyplot.title('Cross Entropy Loss')\n",
        "\tpyplot.plot(history.history['loss'], color='blue', label='train')\n",
        "\tpyplot.plot(history.history['val_loss'], color='orange', label='test')\n",
        "\t# plot accuracy\n",
        "\tpyplot.subplot(212)\n",
        "\tpyplot.title('Classification Accuracy')\n",
        "\tpyplot.plot(history.history['accuracy'], color='blue', label='train')\n",
        "\tpyplot.plot(history.history['val_accuracy'], color='orange', label='test')\n",
        "\t# save plot to file\n",
        "\tfilename = sys.argv[0].split('/')[-1]\n",
        "\tpyplot.savefig(filename + '_plot.png')\n",
        "\tpyplot.close()\n",
        " \n",
        "# run the test harness for evaluating a model\n",
        "def run_test_harness():\n",
        "\t# define model\n",
        "\tmodel = define_model()\n",
        "\t# create data generator\n",
        "\tdatagen = ImageDataGenerator(rescale=1.0/255.0)\n",
        "\t# prepare iterator\n",
        "\ttrain_it = datagen.flow_from_directory('dataset_dogs_vs_cats/train/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(200, 200))\n",
        "\ttest_it = datagen.flow_from_directory('dataset_dogs_vs_cats/test/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(200, 200))\n",
        "\t# fit model\n",
        "\thistory = model.fit_generator(train_it, steps_per_epoch=len(train_it),\n",
        "\t\tvalidation_data=test_it, validation_steps=len(test_it), epochs=50, verbose=0)\n",
        "\t# evaluate model\n",
        "\t_, acc = model.evaluate_generator(test_it, steps=len(test_it), verbose=0)\n",
        "\tprint('> %.3f' % (acc * 100.0))\n",
        "\t# learning curves\n",
        "\tsummarize_diagnostics(history)\n",
        " \n",
        "# entry point, run the test harness\n",
        "run_test_harness()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFBFaWQ48qYl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create data generators\n",
        "train_datagen = ImageDataGenerator(rescale=1.0/255.0,\n",
        "\twidth_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True)\n",
        "test_datagen = ImageDataGenerator(rescale=1.0/255.0)\n",
        "# prepare iterators\n",
        "train_it = train_datagen.flow_from_directory('dataset_dogs_vs_cats/train/',\n",
        "\tclass_mode='binary', batch_size=64, target_size=(200, 200))\n",
        "test_it = test_datagen.flow_from_directory('dataset_dogs_vs_cats/test/',\n",
        "\tclass_mode='binary', batch_size=64, target_size=(200, 200))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOdC-yTb8zUe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# baseline model with data augmentation for the dogs vs cats dataset\n",
        "import sys\n",
        "from matplotlib import pyplot\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\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        " \n",
        "# define cnn model\n",
        "def define_model():\n",
        "\tmodel = Sequential()\n",
        "\tmodel.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(200, 200, 3)))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
        "\tmodel.add(MaxPooling2D((2, 2)))\n",
        "\tmodel.add(Flatten())\n",
        "\tmodel.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
        "\tmodel.add(Dense(1, activation='sigmoid'))\n",
        "\t# compile model\n",
        "\topt = SGD(lr=0.001, momentum=0.9)\n",
        "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\treturn model\n",
        " \n",
        "# plot diagnostic learning curves\n",
        "def summarize_diagnostics(history):\n",
        "\t# plot loss\n",
        "\tpyplot.subplot(211)\n",
        "\tpyplot.title('Cross Entropy Loss')\n",
        "\tpyplot.plot(history.history['loss'], color='blue', label='train')\n",
        "\tpyplot.plot(history.history['val_loss'], color='orange', label='test')\n",
        "\t# plot accuracy\n",
        "\tpyplot.subplot(212)\n",
        "\tpyplot.title('Classification Accuracy')\n",
        "\tpyplot.plot(history.history['accuracy'], color='blue', label='train')\n",
        "\tpyplot.plot(history.history['val_accuracy'], color='orange', label='test')\n",
        "\t# save plot to file\n",
        "\tfilename = sys.argv[0].split('/')[-1]\n",
        "\tpyplot.savefig(filename + '_plot.png')\n",
        "\tpyplot.close()\n",
        " \n",
        "# run the test harness for evaluating a model\n",
        "def run_test_harness():\n",
        "\t# define model\n",
        "\tmodel = define_model()\n",
        "\t# create data generators\n",
        "\ttrain_datagen = ImageDataGenerator(rescale=1.0/255.0,\n",
        "\t\twidth_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True)\n",
        "\ttest_datagen = ImageDataGenerator(rescale=1.0/255.0)\n",
        "\t# prepare iterators\n",
        "\ttrain_it = train_datagen.flow_from_directory('dataset_dogs_vs_cats/train/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(200, 200))\n",
        "\ttest_it = test_datagen.flow_from_directory('dataset_dogs_vs_cats/test/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(200, 200))\n",
        "\t# fit model\n",
        "\thistory = model.fit_generator(train_it, steps_per_epoch=len(train_it),\n",
        "\t\tvalidation_data=test_it, validation_steps=len(test_it), epochs=50, verbose=0)\n",
        "\t# evaluate model\n",
        "\t_, acc = model.evaluate_generator(test_it, steps=len(test_it), verbose=0)\n",
        "\tprint('> %.3f' % (acc * 100.0))\n",
        "\t# learning curves\n",
        "\tsummarize_diagnostics(history)\n",
        " \n",
        "# entry point, run the test harness\n",
        "run_test_harness()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8T9hl87v82Pj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define cnn model\n",
        "def define_model():\n",
        "\t# load model\n",
        "\tmodel = VGG16(include_top=False, input_shape=(224, 224, 3))\n",
        "\t# mark loaded layers as not trainable\n",
        "\tfor layer in model.layers:\n",
        "\t\tlayer.trainable = False\n",
        "\t# add new classifier layers\n",
        "\tflat1 = Flatten()(model.layers[-1].output)\n",
        "\tclass1 = Dense(128, activation='relu', kernel_initializer='he_uniform')(flat1)\n",
        "\toutput = Dense(1, activation='sigmoid')(class1)\n",
        "\t# define new model\n",
        "\tmodel = Model(inputs=model.inputs, outputs=output)\n",
        "\t# compile model\n",
        "\topt = SGD(lr=0.001, momentum=0.9)\n",
        "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\treturn model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MrWNaiP5872O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# vgg16 model used for transfer learning on the dogs and cats dataset\n",
        "import sys\n",
        "from matplotlib import pyplot\n",
        "from keras.utils import to_categorical\n",
        "from keras.applications.vgg16 import VGG16\n",
        "from keras.models import Model\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.optimizers import SGD\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        " \n",
        "# define cnn model\n",
        "def define_model():\n",
        "\t# load model\n",
        "\tmodel = VGG16(include_top=False, input_shape=(224, 224, 3))\n",
        "\t# mark loaded layers as not trainable\n",
        "\tfor layer in model.layers:\n",
        "\t\tlayer.trainable = False\n",
        "\t# add new classifier layers\n",
        "\tflat1 = Flatten()(model.layers[-1].output)\n",
        "\tclass1 = Dense(128, activation='relu', kernel_initializer='he_uniform')(flat1)\n",
        "\toutput = Dense(1, activation='sigmoid')(class1)\n",
        "\t# define new model\n",
        "\tmodel = Model(inputs=model.inputs, outputs=output)\n",
        "\t# compile model\n",
        "\topt = SGD(lr=0.001, momentum=0.9)\n",
        "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\treturn model\n",
        " \n",
        "# plot diagnostic learning curves\n",
        "def summarize_diagnostics(history):\n",
        "\t# plot loss\n",
        "\tpyplot.subplot(211)\n",
        "\tpyplot.title('Cross Entropy Loss')\n",
        "\tpyplot.plot(history.history['loss'], color='blue', label='train')\n",
        "\tpyplot.plot(history.history['val_loss'], color='orange', label='test')\n",
        "\t# plot accuracy\n",
        "\tpyplot.subplot(212)\n",
        "\tpyplot.title('Classification Accuracy')\n",
        "\tpyplot.plot(history.history['accuracy'], color='blue', label='train')\n",
        "\tpyplot.plot(history.history['val_accuracy'], color='orange', label='test')\n",
        "\t# save plot to file\n",
        "\tfilename = sys.argv[0].split('/')[-1]\n",
        "\tpyplot.savefig(filename + '_plot.png')\n",
        "\tpyplot.close()\n",
        " \n",
        "# run the test harness for evaluating a model\n",
        "def run_test_harness():\n",
        "\t# define model\n",
        "\tmodel = define_model()\n",
        "\t# create data generator\n",
        "\tdatagen = ImageDataGenerator(featurewise_center=True)\n",
        "\t# specify imagenet mean values for centering\n",
        "\tdatagen.mean = [123.68, 116.779, 103.939]\n",
        "\t# prepare iterator\n",
        "\ttrain_it = datagen.flow_from_directory('dataset_dogs_vs_cats/train/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(224, 224))\n",
        "\ttest_it = datagen.flow_from_directory('dataset_dogs_vs_cats/test/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(224, 224))\n",
        "\t# fit model\n",
        "\thistory = model.fit_generator(train_it, steps_per_epoch=len(train_it),\n",
        "\t\tvalidation_data=test_it, validation_steps=len(test_it), epochs=10, verbose=1)\n",
        "\t# evaluate model\n",
        "\t_, acc = model.evaluate_generator(test_it, steps=len(test_it), verbose=0)\n",
        "\tprint('> %.3f' % (acc * 100.0))\n",
        "\t# learning curves\n",
        "\tsummarize_diagnostics(history)\n",
        " \n",
        "# entry point, run the test harness\n",
        "run_test_harness()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oMDHSJfw8_iV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# organize dataset into a useful structure\n",
        "from os import makedirs\n",
        "from os import listdir\n",
        "from shutil import copyfile\n",
        "# create directories\n",
        "dataset_home = 'finalize_dogs_vs_cats/'\n",
        "# create label subdirectories\n",
        "labeldirs = ['dogs/', 'cats/']\n",
        "for labldir in labeldirs:\n",
        "\tnewdir = dataset_home + labldir\n",
        "\tmakedirs(newdir, exist_ok=True)\n",
        "# copy training dataset images into subdirectories\n",
        "src_directory = 'dogs-vs-cats/train/'\n",
        "for file in listdir(src_directory):\n",
        "\tsrc = src_directory + '/' + file\n",
        "\tif file.startswith('cat'):\n",
        "\t\tdst = dataset_home + 'cats/'  + file\n",
        "\t\tcopyfile(src, dst)\n",
        "\telif file.startswith('dog'):\n",
        "\t\tdst = dataset_home + 'dogs/'  + file\n",
        "\t\tcopyfile(src, dst)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Er04Kt99C07",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# prepare iterator\n",
        "train_it = datagen.flow_from_directory('finalize_dogs_vs_cats/',\n",
        "\tclass_mode='binary', batch_size=64, target_size=(224, 224))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIO4GC0r9E-4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# fit model\n",
        "model.fit_generator(train_it, steps_per_epoch=len(train_it), epochs=10, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fB6bLwbe9HUs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# save model\n",
        "model.save('final_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y-q2Ukmz9Jgx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# save the final model to file\n",
        "from keras.applications.vgg16 import VGG16\n",
        "from keras.models import Model\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.optimizers import SGD\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        " \n",
        "# define cnn model\n",
        "def define_model():\n",
        "\t# load model\n",
        "\tmodel = VGG16(include_top=False, input_shape=(224, 224, 3))\n",
        "\t# mark loaded layers as not trainable\n",
        "\tfor layer in model.layers:\n",
        "\t\tlayer.trainable = False\n",
        "\t# add new classifier layers\n",
        "\tflat1 = Flatten()(model.layers[-1].output)\n",
        "\tclass1 = Dense(128, activation='relu', kernel_initializer='he_uniform')(flat1)\n",
        "\toutput = Dense(1, activation='sigmoid')(class1)\n",
        "\t# define new model\n",
        "\tmodel = Model(inputs=model.inputs, outputs=output)\n",
        "\t# compile model\n",
        "\topt = SGD(lr=0.001, momentum=0.9)\n",
        "\tmodel.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\treturn model\n",
        " \n",
        "# run the test harness for evaluating a model\n",
        "def run_test_harness():\n",
        "# define model\n",
        "\tmodel = define_model()\n",
        "\t# create data generator\n",
        "\tdatagen = ImageDataGenerator(featurewise_center=True)\n",
        "\t# specify imagenet mean values for centering\n",
        "\tdatagen.mean = [123.68, 116.779, 103.939]\n",
        "\t# prepare iterator\n",
        "\ttrain_it = datagen.flow_from_directory('finalize_dogs_vs_cats/',\n",
        "\t\tclass_mode='binary', batch_size=64, target_size=(224, 224))\n",
        "\t# fit model\n",
        "\tmodel.fit_generator(train_it, steps_per_epoch=len(train_it), epochs=10, verbose=0)\n",
        "\t# save model\n",
        "\tmodel.save('final_model.h5')\n",
        " \n",
        "# entry point, run the test harness\n",
        "run_test_harness()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1X6sCeDM9SJp",
        "colab_type": "text"
      },
      "source": [
        "# **이제 sample_image로 final model 테스트**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o0t5GUtL9NBP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load and prepare the image\n",
        "def load_image(filename):\n",
        "\t# load the image\n",
        "\timg = load_img(filename, target_size=(224, 224))\n",
        "\t# convert to array\n",
        "\timg = img_to_array(img)\n",
        "\t# reshape into a single sample with 3 channels\n",
        "\timg = img.reshape(1, 224, 224, 3)\n",
        "\t# center pixel data\n",
        "\timg = img.astype('float32')\n",
        "\timg = img - [123.68, 116.779, 103.939]\n",
        "\treturn img"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xiSAR7q_9un9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# predict the class\n",
        "result = model.predict(img)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vAdurFU59whW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# make a prediction for a new image.\n",
        "from keras.preprocessing.image import load_img\n",
        "from keras.preprocessing.image import img_to_array\n",
        "from keras.models import load_model\n",
        " \n",
        "# load and prepare the image\n",
        "def load_image(filename):\n",
        "\t# load the image\n",
        "\timg = load_img(filename, target_size=(224, 224))\n",
        "\t# convert to array\n",
        "\timg = img_to_array(img)\n",
        "\t# reshape into a single sample with 3 channels\n",
        "\timg = img.reshape(1, 224, 224, 3)\n",
        "\t# center pixel data\n",
        "\timg = img.astype('float32')\n",
        "\timg = img - [123.68, 116.779, 103.939]\n",
        "\treturn img\n",
        " \n",
        "# load an image and predict the class\n",
        "def run_example():\n",
        "\t# load the image\n",
        "\timg = load_image('sample_image.jpg')\n",
        "\t# load model\n",
        "\tmodel = load_model('final_model.h5')\n",
        "\t# predict the class\n",
        "\tresult = model.predict(img)\n",
        "\tprint(result[0])\n",
        " \n",
        "# entry point, run the example\n",
        "run_example()"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}