CatsAndDogs_BigDataset_with_augmentation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CatsAndDogs_BigDataset_with_augmentation.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNaR/Wg91aSIrxeL5KzMmYV"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "lY80Tps9d0qW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import zipfile\n",
        "import random\n",
        "import tensorflow as tf\n",
        "from shutil import copyfile"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7NJA-lLt-uen",
        "colab_type": "code",
        "outputId": "075e2c79-4ca0-4087-8a7f-8b7df67c580b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "    \"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\" \\\n",
        "    -O \"/tmp/cats-and-dogs.zip\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-05-29 11:08:01--  https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\n",
            "Resolving download.microsoft.com (download.microsoft.com)... 2.22.94.11, 2a02:26f0:7b:985::e59, 2a02:26f0:7b:984::e59\n",
            "Connecting to download.microsoft.com (download.microsoft.com)|2.22.94.11|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 824894548 (787M) [application/octet-stream]\n",
            "Saving to: ‘/tmp/cats-and-dogs.zip’\n",
            "\n",
            "/tmp/cats-and-dogs. 100%[===================>] 786.68M  71.4MB/s    in 11s     \n",
            "\n",
            "2020-05-29 11:08:13 (70.4 MB/s) - ‘/tmp/cats-and-dogs.zip’ saved [824894548/824894548]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4f3_dyJ-_ZK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "local_zip = '/tmp/cats-and-dogs.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jwhDSORZ_iza",
        "colab_type": "code",
        "outputId": "a631981e-1243-44c8-8968-cc032589247d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(len(os.listdir('/tmp/PetImages/Cat/')))\n",
        "print(len(os.listdir('/tmp/PetImages/Dog/')))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12501\n",
            "12501\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZcbNqAl5VBm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "try:\n",
        "    os.mkdir('/tmp/cats-v-dogs')\n",
        "    os.mkdir('/tmp/cats-v-dogs/training')\n",
        "    os.mkdir('/tmp/cats-v-dogs/testing')\n",
        "    os.mkdir('/tmp/cats-v-dogs/training/cats')\n",
        "    os.mkdir('/tmp/cats-v-dogs/training/dogs')\n",
        "    os.mkdir('/tmp/cats-v-dogs/testing/cats')\n",
        "    os.mkdir('/tmp/cats-v-dogs/testing/dogs')\n",
        "except OSError:\n",
        "    pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xOZCNBsa5Y0f",
        "colab_type": "code",
        "outputId": "58d013e3-1102-47ef-e5b5-0dbc934c0f3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):\n",
        "    files = []\n",
        "    for filename in os.listdir(SOURCE):\n",
        "        file = SOURCE + filename\n",
        "        if os.path.getsize(file) > 0:\n",
        "            files.append(filename)\n",
        "        else:\n",
        "            print(filename + \" is zero length, so ignoring.\")\n",
        "\n",
        "    training_length = int(len(files) * SPLIT_SIZE)\n",
        "    testing_length = int(len(files) - training_length)\n",
        "    shuffled_set = random.sample(files, len(files))\n",
        "    training_set = shuffled_set[0:training_length]\n",
        "    testing_set = shuffled_set[:testing_length]\n",
        "\n",
        "    for filename in training_set:\n",
        "        this_file = SOURCE + filename\n",
        "        destination = TRAINING + filename\n",
        "        copyfile(this_file, destination)\n",
        "\n",
        "    for filename in testing_set:\n",
        "        this_file = SOURCE + filename\n",
        "        destination = TESTING + filename\n",
        "        copyfile(this_file, destination)\n",
        "\n",
        "\n",
        "CAT_SOURCE_DIR = \"/tmp/PetImages/Cat/\"\n",
        "TRAINING_CATS_DIR = \"/tmp/cats-v-dogs/training/cats/\"\n",
        "TESTING_CATS_DIR = \"/tmp/cats-v-dogs/testing/cats/\"\n",
        "DOG_SOURCE_DIR = \"/tmp/PetImages/Dog/\"\n",
        "TRAINING_DOGS_DIR = \"/tmp/cats-v-dogs/training/dogs/\"\n",
        "TESTING_DOGS_DIR = \"/tmp/cats-v-dogs/testing/dogs/\"\n",
        "\n",
        "split_size = .9\n",
        "split_data(CAT_SOURCE_DIR, TRAINING_CATS_DIR, TESTING_CATS_DIR, split_size)\n",
        "split_data(DOG_SOURCE_DIR, TRAINING_DOGS_DIR, TESTING_DOGS_DIR, split_size)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "666.jpg is zero length, so ignoring.\n",
            "11702.jpg is zero length, so ignoring.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qS3qhNF1AFvq",
        "colab_type": "code",
        "outputId": "7e1b10a5-87c0-48aa-d699-354b27e68123",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print('Total training cats: '+str(len(os.listdir(\"/tmp/cats-v-dogs/training/cats/\"))))\n",
        "print('Total training dogs: '+str(len(os.listdir(\"/tmp/cats-v-dogs/training/dogs/\"))))\n",
        "print('Total validation cats: '+str(len(os.listdir(\"/tmp/cats-v-dogs/testing/cats/\"))))\n",
        "print('Total validation dogs: '+str(len(os.listdir(\"/tmp/cats-v-dogs/testing/dogs/\"))))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total training cats: 11250\n",
            "Total training dogs: 11250\n",
            "Total validation cats: 1250\n",
            "Total validation dogs: 1250\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iu8QjQkOAUO3",
        "colab_type": "code",
        "outputId": "ed1c2ba4-ce06-4ccc-8c48-e0d2d56175f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "source": [
        "import keras\n",
        "model = keras.models.Sequential([\n",
        "                                 keras.layers.Conv2D(16,(3,3),activation='relu',input_shape=(150,150,3)),\n",
        "                                 keras.layers.MaxPooling2D(2,2),\n",
        "                                 keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
        "                                 keras.layers.MaxPooling2D(2,2), \n",
        "                                 keras.layers.Conv2D(64, (3,3), activation='relu'), \n",
        "                                 keras.layers.MaxPooling2D(2,2),\n",
        "                                 # Flatten the results to feed into a DNN\n",
        "                                 keras.layers.Flatten(), \n",
        "                                 # 512 neuron hidden layer\n",
        "                                 keras.layers.Dense(512, activation='relu'), \n",
        "                                 # Only 1 output neuron. It will contain a value from 0-1 where 0 for 1 class ('cats') and 1 for the other ('dogs')\n",
        "                                 keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 148, 148, 16)      448       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 74, 74, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 72, 72, 32)        4640      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 36, 36, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 34, 34, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 17, 17, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 18496)             0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 512)               9470464   \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1)                 513       \n",
            "=================================================================\n",
            "Total params: 9,494,561\n",
            "Trainable params: 9,494,561\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-HYuXDiJC-Pf",
        "colab_type": "code",
        "outputId": "956cfc8a-acad-4e3c-aa4a-1e262c9a9fa5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "TRAINING_DIR = \"/tmp/cats-v-dogs/training/\"\n",
        "# Experiment with your own parameters here to really try to drive it to 99.9% accuracy or better\n",
        "train_datagen = ImageDataGenerator(rescale=1./255,\n",
        "      rotation_range=40,\n",
        "      width_shift_range=0.2,\n",
        "      height_shift_range=0.2,\n",
        "      shear_range=0.2,\n",
        "      zoom_range=0.2,\n",
        "      horizontal_flip=True,\n",
        "      fill_mode='nearest')\n",
        "train_generator = train_datagen.flow_from_directory(TRAINING_DIR,\n",
        "                                                    batch_size=100,\n",
        "                                                    class_mode='binary',\n",
        "                                                    target_size=(150, 150))\n",
        "\n",
        "VALIDATION_DIR = \"/tmp/cats-v-dogs/testing/\"\n",
        "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
        "validation_generator = validation_datagen.flow_from_directory(VALIDATION_DIR,\n",
        "                                                              batch_size=100,\n",
        "                                                              class_mode='binary',\n",
        "                                                              target_size=(150, 150))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 22498 images belonging to 2 classes.\n",
            "Found 2500 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DF97_ROyDG00",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer = 'Adam',\n",
        "              metrics = ['acc']\n",
        "              )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2a5SvmwmEEjb",
        "colab_type": "code",
        "outputId": "46251816-8476-470d-80d0-0b664f1435a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        }
      },
      "source": [
        "history = model.fit_generator(\n",
        "    train_generator,\n",
        "    epochs = 10,\n",
        "    validation_data = validation_generator,\n",
        "    verbose = 1\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            " 44/225 [====>.........................] - ETA: 2:41 - loss: 0.7027 - acc: 0.5118"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data.  Expecting to read 32 bytes but only got 0. Skipping tag 270\n",
            "  \" Skipping tag %s\" % (size, len(data), tag)\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data.  Expecting to read 5 bytes but only got 0. Skipping tag 271\n",
            "  \" Skipping tag %s\" % (size, len(data), tag)\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data.  Expecting to read 8 bytes but only got 0. Skipping tag 272\n",
            "  \" Skipping tag %s\" % (size, len(data), tag)\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data.  Expecting to read 8 bytes but only got 0. Skipping tag 282\n",
            "  \" Skipping tag %s\" % (size, len(data), tag)\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data.  Expecting to read 8 bytes but only got 0. Skipping tag 283\n",
            "  \" Skipping tag %s\" % (size, len(data), tag)\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data.  Expecting to read 20 bytes but only got 0. Skipping tag 306\n",
            "  \" Skipping tag %s\" % (size, len(data), tag)\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data.  Expecting to read 48 bytes but only got 0. Skipping tag 532\n",
            "  \" Skipping tag %s\" % (size, len(data), tag)\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:788: UserWarning: Corrupt EXIF data.  Expecting to read 2 bytes but only got 0. \n",
            "  warnings.warn(str(msg))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "225/225 [==============================] - 198s 879ms/step - loss: 0.6494 - acc: 0.5996 - val_loss: 0.5648 - val_acc: 0.6896\n",
            "Epoch 2/10\n",
            "225/225 [==============================] - 190s 846ms/step - loss: 0.5859 - acc: 0.6846 - val_loss: 0.5732 - val_acc: 0.7448\n",
            "Epoch 3/10\n",
            "225/225 [==============================] - 190s 846ms/step - loss: 0.5455 - acc: 0.7178 - val_loss: 0.4420 - val_acc: 0.7800\n",
            "Epoch 4/10\n",
            "225/225 [==============================] - 190s 844ms/step - loss: 0.5186 - acc: 0.7440 - val_loss: 0.3974 - val_acc: 0.7800\n",
            "Epoch 5/10\n",
            "225/225 [==============================] - 190s 846ms/step - loss: 0.4924 - acc: 0.7538 - val_loss: 0.4383 - val_acc: 0.7956\n",
            "Epoch 6/10\n",
            "225/225 [==============================] - 190s 845ms/step - loss: 0.4782 - acc: 0.7671 - val_loss: 0.3867 - val_acc: 0.8212\n",
            "Epoch 7/10\n",
            "225/225 [==============================] - 190s 844ms/step - loss: 0.4617 - acc: 0.7775 - val_loss: 0.4105 - val_acc: 0.8288\n",
            "Epoch 8/10\n",
            "225/225 [==============================] - 190s 846ms/step - loss: 0.4560 - acc: 0.7826 - val_loss: 0.2904 - val_acc: 0.8352\n",
            "Epoch 9/10\n",
            "225/225 [==============================] - 190s 844ms/step - loss: 0.4329 - acc: 0.7986 - val_loss: 0.4765 - val_acc: 0.8168\n",
            "Epoch 10/10\n",
            "225/225 [==============================] - 190s 846ms/step - loss: 0.4216 - acc: 0.8058 - val_loss: 0.3902 - val_acc: 0.8528\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FAADtzVHOpj2",
        "colab_type": "code",
        "outputId": "851b00a4-4092-4db0-9f0e-18c149230b87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'r', label='Training Loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "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"
          }
        },
        {
          "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": "ACpWrKWHXAQj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}