Cat_vs_Dog_Classification_with_image_augmentation_.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Cat vs Dog Classification with image augmentation .ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "o6-WuA_PgeT3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Importing libraries "
      ]
    },
    {
      "metadata": {
        "id": "XdbTjXGV0OKh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.layers import Activation, Dropout, Flatten, Dense\n",
        "import os \n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MJuk3GbSgl25",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Preprocessing the images to increase the training data by image augmentation**"
      ]
    },
    {
      "metadata": {
        "id": "dC_zJZf_AU_q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1584
        },
        "outputId": "ec057d18-c5bc-45d6-d953-8ccb79904697"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.layers import Activation, Dropout, Flatten, Dense\n",
        "from keras import backend as K\n",
        "\n",
        "\n",
        "# dimensions of our images.\n",
        "img_width, img_height = 150, 150\n",
        "\n",
        "train_data_dir = 'data/train'\n",
        "validation_data_dir = 'data/test'\n",
        "nb_train_samples = 2000\n",
        "nb_validation_samples = 800\n",
        "epochs = 50\n",
        "batch_size = 16\n",
        "\n",
        "if K.image_data_format() == 'channels_first':\n",
        "    input_shape = (3, img_width, img_height)\n",
        "else:\n",
        "    input_shape = (img_width, img_height, 3)\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3, 3), input_shape=input_shape))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(1))\n",
        "model.add(Activation('sigmoid'))\n",
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer='rmsprop',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# this is the augmentation configuration we will use for training\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1. / 255,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True)\n",
        "\n",
        "# this is the augmentation configuration we will use for testing:\n",
        "# only rescaling\n",
        "test_datagen = ImageDataGenerator(rescale=1. / 255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_data_dir,\n",
        "    target_size=(img_width, img_height),\n",
        "    batch_size=batch_size,\n",
        "    class_mode='binary')\n",
        "\n",
        "validation_generator = test_datagen.flow_from_directory(\n",
        "    validation_data_dir,\n",
        "    target_size=(img_width, img_height),\n",
        "    batch_size=batch_size,\n",
        "    class_mode='binary')\n",
        "\n",
        "model.fit_generator(\n",
        "    train_generator,\n",
        "    steps_per_epoch=nb_train_samples // batch_size,\n",
        "    epochs=epochs,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=nb_validation_samples // batch_size)\n",
        "\n",
        "model.save_weights('first_cnn.h5')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "125/125 [==============================] - 103s 824ms/step - loss: 0.6133 - acc: 0.6810 - val_loss: 0.5933 - val_acc: 0.6775\n",
            "Epoch 6/50\n",
            "125/125 [==============================] - 105s 838ms/step - loss: 0.5932 - acc: 0.6930 - val_loss: 0.5134 - val_acc: 0.7150\n",
            "Epoch 7/50\n",
            "125/125 [==============================] - 104s 828ms/step - loss: 0.5728 - acc: 0.7010 - val_loss: 0.5313 - val_acc: 0.7306\n",
            "Epoch 8/50\n",
            "125/125 [==============================] - 104s 828ms/step - loss: 0.5581 - acc: 0.7085 - val_loss: 0.5292 - val_acc: 0.7137\n",
            "Epoch 9/50\n",
            "125/125 [==============================] - 103s 825ms/step - loss: 0.5414 - acc: 0.7310 - val_loss: 0.5562 - val_acc: 0.7137\n",
            "Epoch 10/50\n",
            "125/125 [==============================] - 103s 826ms/step - loss: 0.5402 - acc: 0.7405 - val_loss: 0.4934 - val_acc: 0.7733\n",
            "Epoch 11/50\n",
            "125/125 [==============================] - 103s 826ms/step - loss: 0.5287 - acc: 0.7420 - val_loss: 0.5116 - val_acc: 0.7392\n",
            "Epoch 12/50\n",
            "125/125 [==============================] - 103s 825ms/step - loss: 0.5143 - acc: 0.7570 - val_loss: 0.5216 - val_acc: 0.7591\n",
            "Epoch 13/50\n",
            "125/125 [==============================] - 103s 822ms/step - loss: 0.5185 - acc: 0.7590 - val_loss: 0.4928 - val_acc: 0.7681\n",
            "Epoch 14/50\n",
            "125/125 [==============================] - 103s 825ms/step - loss: 0.5144 - acc: 0.7485 - val_loss: 0.5299 - val_acc: 0.7578\n",
            "Epoch 15/50\n",
            "125/125 [==============================] - 103s 827ms/step - loss: 0.5040 - acc: 0.7665 - val_loss: 0.5429 - val_acc: 0.7290\n",
            "Epoch 16/50\n",
            "125/125 [==============================] - 104s 831ms/step - loss: 0.4922 - acc: 0.7755 - val_loss: 0.5300 - val_acc: 0.7539\n",
            "Epoch 17/50\n",
            "125/125 [==============================] - 103s 824ms/step - loss: 0.4777 - acc: 0.7820 - val_loss: 0.5103 - val_acc: 0.7863\n",
            "Epoch 18/50\n",
            "125/125 [==============================] - 103s 827ms/step - loss: 0.4725 - acc: 0.7815 - val_loss: 0.5296 - val_acc: 0.7707\n",
            "Epoch 19/50\n",
            "125/125 [==============================] - 104s 833ms/step - loss: 0.4848 - acc: 0.7805 - val_loss: 0.6525 - val_acc: 0.7659\n",
            "Epoch 20/50\n",
            "125/125 [==============================] - 104s 833ms/step - loss: 0.4884 - acc: 0.7670 - val_loss: 0.5159 - val_acc: 0.7565\n",
            "Epoch 21/50\n",
            "125/125 [==============================] - 104s 832ms/step - loss: 0.4812 - acc: 0.7760 - val_loss: 0.5365 - val_acc: 0.7396\n",
            "Epoch 22/50\n",
            "125/125 [==============================] - 104s 831ms/step - loss: 0.4700 - acc: 0.7805 - val_loss: 0.7826 - val_acc: 0.6819\n",
            "Epoch 23/50\n",
            "125/125 [==============================] - 104s 831ms/step - loss: 0.4808 - acc: 0.7880 - val_loss: 0.5091 - val_acc: 0.7785\n",
            "Epoch 24/50\n",
            "125/125 [==============================] - 104s 836ms/step - loss: 0.4731 - acc: 0.7850 - val_loss: 0.5514 - val_acc: 0.7681\n",
            "Epoch 25/50\n",
            "125/125 [==============================] - 105s 840ms/step - loss: 0.4774 - acc: 0.7840 - val_loss: 0.5986 - val_acc: 0.6917\n",
            "Epoch 26/50\n",
            "125/125 [==============================] - 104s 835ms/step - loss: 0.4690 - acc: 0.7820 - val_loss: 0.4889 - val_acc: 0.7621\n",
            "Epoch 27/50\n",
            "125/125 [==============================] - 105s 837ms/step - loss: 0.4632 - acc: 0.7895 - val_loss: 0.6669 - val_acc: 0.7215\n",
            "Epoch 28/50\n",
            "125/125 [==============================] - 105s 838ms/step - loss: 0.4766 - acc: 0.7875 - val_loss: 0.5027 - val_acc: 0.7746\n",
            "Epoch 29/50\n",
            "125/125 [==============================] - 104s 833ms/step - loss: 0.4432 - acc: 0.7970 - val_loss: 0.5386 - val_acc: 0.7720\n",
            "Epoch 30/50\n",
            "125/125 [==============================] - 106s 848ms/step - loss: 0.4395 - acc: 0.7995 - val_loss: 0.6684 - val_acc: 0.7621\n",
            "Epoch 31/50\n",
            "125/125 [==============================] - 105s 840ms/step - loss: 0.4608 - acc: 0.8025 - val_loss: 0.5156 - val_acc: 0.7435\n",
            "Epoch 32/50\n",
            "125/125 [==============================] - 104s 835ms/step - loss: 0.4404 - acc: 0.8050 - val_loss: 0.6410 - val_acc: 0.7396\n",
            "Epoch 33/50\n",
            "125/125 [==============================] - 105s 842ms/step - loss: 0.4367 - acc: 0.8105 - val_loss: 0.5907 - val_acc: 0.7735\n",
            "Epoch 34/50\n",
            "125/125 [==============================] - 105s 843ms/step - loss: 0.4429 - acc: 0.7975 - val_loss: 0.5519 - val_acc: 0.7902\n",
            "Epoch 35/50\n",
            "125/125 [==============================] - 105s 837ms/step - loss: 0.4268 - acc: 0.8105 - val_loss: 0.5308 - val_acc: 0.7422\n",
            "Epoch 36/50\n",
            "125/125 [==============================] - 106s 849ms/step - loss: 0.4588 - acc: 0.8050 - val_loss: 0.5068 - val_acc: 0.7759\n",
            "Epoch 37/50\n",
            "125/125 [==============================] - 105s 839ms/step - loss: 0.4343 - acc: 0.8075 - val_loss: 0.5394 - val_acc: 0.7634\n",
            "Epoch 38/50\n",
            "125/125 [==============================] - 105s 839ms/step - loss: 0.4446 - acc: 0.8105 - val_loss: 0.5683 - val_acc: 0.7370\n",
            "Epoch 39/50\n",
            "125/125 [==============================] - 106s 849ms/step - loss: 0.4330 - acc: 0.8065 - val_loss: 0.6087 - val_acc: 0.7034\n",
            "Epoch 40/50\n",
            "125/125 [==============================] - 105s 838ms/step - loss: 0.4398 - acc: 0.8005 - val_loss: 0.6295 - val_acc: 0.7354\n",
            "Epoch 41/50\n",
            "125/125 [==============================] - 105s 840ms/step - loss: 0.4693 - acc: 0.7980 - val_loss: 0.5150 - val_acc: 0.7772\n",
            "Epoch 42/50\n",
            "125/125 [==============================] - 106s 845ms/step - loss: 0.4759 - acc: 0.8020 - val_loss: 0.5825 - val_acc: 0.7370\n",
            "Epoch 43/50\n",
            "125/125 [==============================] - 105s 839ms/step - loss: 0.4496 - acc: 0.8080 - val_loss: 0.5719 - val_acc: 0.7370\n",
            "Epoch 44/50\n",
            "125/125 [==============================] - 105s 844ms/step - loss: 0.4467 - acc: 0.8020 - val_loss: 0.7461 - val_acc: 0.7176\n",
            "Epoch 45/50\n",
            "125/125 [==============================] - 106s 850ms/step - loss: 0.4421 - acc: 0.7985 - val_loss: 0.5214 - val_acc: 0.7785\n",
            "Epoch 46/50\n",
            "125/125 [==============================] - 105s 837ms/step - loss: 0.4429 - acc: 0.8100 - val_loss: 0.5310 - val_acc: 0.7746\n",
            "Epoch 47/50\n",
            "125/125 [==============================] - 105s 837ms/step - loss: 0.4442 - acc: 0.8135 - val_loss: 0.7421 - val_acc: 0.7630\n",
            "Epoch 48/50\n",
            "125/125 [==============================] - 107s 853ms/step - loss: 0.4177 - acc: 0.8140 - val_loss: 0.6572 - val_acc: 0.7494\n",
            "Epoch 49/50\n",
            "125/125 [==============================] - 104s 834ms/step - loss: 0.4312 - acc: 0.8075 - val_loss: 0.5598 - val_acc: 0.7759\n",
            "Epoch 50/50\n",
            "125/125 [==============================] - 104s 834ms/step - loss: 0.4270 - acc: 0.8130 - val_loss: 0.5670 - val_acc: 0.7202\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "DftrY_q3Qwlg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}