Kaggle_Cats_and_Dogs.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Kaggle Cats and Dogs",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ikramooe/DataScience-1/blob/master/Kaggle_Cats_and_Dogs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "flcTMv3JMSmB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf \n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt \n",
        "import os \n",
        "import zipfile \n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from shutil import copyfile"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZ8TQ1DiM5TR",
        "colab_type": "code",
        "outputId": "88cba61a-f3a1-4519-f2cb-a951e601d272",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "\n",
        "\n",
        "!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\"\n",
        "\n",
        "import zipfile\n",
        "\n",
        "local_zip = '/tmp/cats-and-dogs.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip,'r')\n",
        "zip_ref.extractall('/tmp/cats-and-dogs')\n",
        "\n",
        "\n",
        "print(len(os.listdir('/tmp/cats-and-dogs/PetImages/Cat')))\n",
        "print(len(os.listdir('/tmp/cats-and-dogs/PetImages/Dog')))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-05-25 13:02:42--  https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\n",
            "Resolving download.microsoft.com (download.microsoft.com)... 23.41.23.170, 2600:140b:4800:2ac::e59, 2600:140b:4800:296::e59\n",
            "Connecting to download.microsoft.com (download.microsoft.com)|23.41.23.170|: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  14.5MB/s    in 57s     \n",
            "\n",
            "2019-05-25 13:03:39 (13.8 MB/s) - ‘/tmp/cats-and-dogs.zip’ saved [824894548/824894548]\n",
            "\n",
            "12501\n",
            "12501\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KvdvqITLSn7X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import os \n",
        "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",
        "    \n",
        "\n",
        "except OSError:\n",
        "    pass\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8-vWt3ZYpjk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import random \n",
        "def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):\n",
        "  files=[]\n",
        "  for fname in os.listdir(SOURCE):\n",
        "    path = SOURCE + fname\n",
        "    if os.path.getsize(path)>0:\n",
        "      files.append(fname)\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",
        "    \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",
        "    \n",
        "    \n",
        "# YOUR CODE ENDS HERE\n",
        "\n",
        "\n",
        "CAT_SOURCE_DIR = \"/tmp/cats-and-dogs/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/cats-and-dogs/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)\n",
        "\n",
        "# Expected output\n",
        "# 666.jpg is zero length, so ignoring\n",
        "# 11702.jpg is zero length, so ignoring\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AszbOeJTlvis",
        "colab_type": "code",
        "outputId": "6208aa30-9501-4728-f462-ab28a6a2c424",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "print(len(os.listdir('/tmp/cats-v-dogs/training/cats/')))\n",
        "print(len(os.listdir('/tmp/cats-v-dogs/training/dogs/')))\n",
        "print(len(os.listdir('/tmp/cats-v-dogs/testing/cats/')))\n",
        "print(len(os.listdir('/tmp/cats-v-dogs/testing/dogs/')))\n",
        "\n",
        "# Expected output:\n",
        "# 11250\n",
        "# 11250\n",
        "# 1250\n",
        "# 1250\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11250\n",
            "11250\n",
            "1250\n",
            "1250\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-eCMKQRmBSI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# DEFINE A KERAS MODEL TO CLASSIFY CATS V DOGS\n",
        "# USE AT LEAST 3 CONVOLUTION LAYERS\n",
        "model = tf.keras.models.Sequential([\n",
        "tf.keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=(150,150,3)),\n",
        "tf.keras.layers.MaxPooling2D(2,2),\n",
        "tf.keras.layers.Conv2D(16,(3,3),activation='relu'),\n",
        "tf.keras.layers.MaxPooling2D(2,2),\n",
        "tf.keras.layers.Conv2D(64,(3,3),activation='relu'),\n",
        "tf.keras.layers.MaxPooling2D(2,2),\n",
        "tf.keras.layers.Flatten(),\n",
        "tf.keras.layers.Dense(215,activation='relu'),\n",
        "tf.keras.layers.Dense(1,activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='Adam', loss='binary_crossentropy', metrics=['acc'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlsuL7R_nXMJ",
        "colab_type": "code",
        "outputId": "7e71b9fb-8c9a-4064-f1c0-dbd8527a548a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "TRAINING_DIR = '/tmp/cats-v-dogs/training/'\n",
        "train_datagen = ImageDataGenerator(rescale=1./255)\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "                     TRAINING_DIR,\n",
        "                     batch_size=150,\n",
        "                     target_size=(150,150),\n",
        "                     class_mode='binary')\n",
        "\n",
        "VALIDATION_DIR = '/tmp/cats-v-dogs/testing/'\n",
        "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
        "validation_generator = validation_datagen.flow_from_directory(\n",
        "                           VALIDATION_DIR,\n",
        "                           batch_size=50,  \n",
        "                           target_size=(150,150),\n",
        "                           class_mode='binary')\n",
        "\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": "hz0sRo1BpKza",
        "colab_type": "code",
        "outputId": "74b084c3-7e71-46ac-e39b-cced37b2bb60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2994
        }
      },
      "source": [
        "history = model.fit_generator(\n",
        "train_generator,\n",
        "steps_per_epoch=75,\n",
        "epochs=15,\n",
        "validation_data=validation_generator,\n",
        "validation_steps=50)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Epoch 1/15\n",
            " 24/150 [===>..........................] - ETA: 57s - loss: 0.7232 - acc: 0.5178"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 80000 bytes but only got 0. Skipping tag 64640\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 6553600 bytes but only got 0. Skipping tag 49\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 1050744 bytes but only got 4951. Skipping tag 51\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 293339136 bytes but only got 0. Skipping tag 5\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 293863424 bytes but only got 0. Skipping tag 5\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 3368026112 bytes but only got 0. Skipping tag 7\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 134479872 bytes but only got 0. Skipping tag 7\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 295698432 bytes but only got 0. Skipping tag 10\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 296222720 bytes but only got 0. Skipping tag 5\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 3300917248 bytes but only got 0. Skipping tag 7\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 65536 bytes but only got 0. Skipping tag 3\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 14745600 bytes but only got 0. Skipping tag 4\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 25624576 bytes but only got 0. Skipping tag 4\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 317718528 bytes but only got 4956. Skipping tag 4\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 131073 bytes but only got 4952. Skipping tag 0\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 393216 bytes but only got 0. Skipping tag 3\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 287178752 bytes but only got 0. Skipping tag 5\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 287703040 bytes but only got 0. Skipping tag 5\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 131072 bytes but only got 0. Skipping tag 3\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 524288 bytes but only got 0. Skipping tag 4\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 286654464 bytes but only got 4956. Skipping tag 4\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:742: UserWarning: Corrupt EXIF data.  Expecting to read 12 bytes but only got 2. \n",
            "  warnings.warn(str(msg))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            " 59/150 [==========>...................] - ETA: 43s - loss: 0.6901 - acc: 0.5607"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 18350080 bytes but only got 0. Skipping tag 0\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:742: UserWarning: Corrupt EXIF data.  Expecting to read 12 bytes but only got 6. \n",
            "  warnings.warn(str(msg))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            " 76/150 [==============>...............] - ETA: 35s - loss: 0.6791 - acc: 0.5772"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 404094976 bytes but only got 0. Skipping tag 5\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 404619264 bytes but only got 0. Skipping tag 5\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 425459712 bytes but only got 0. Skipping tag 4\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 1385474 bytes but only got 6833. Skipping tag 513\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 3846701056 bytes but only got 0. Skipping tag 2\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 196867 bytes but only got 6833. Skipping tag 0\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:742: UserWarning: Corrupt EXIF data.  Expecting to read 12 bytes but only got 8. \n",
            "  warnings.warn(str(msg))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "103/150 [===================>..........] - ETA: 22s - loss: 0.6595 - acc: 0.6004"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 307363840 bytes but only got 0. Skipping tag 5\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 307888128 bytes but only got 0. Skipping tag 5\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 328728576 bytes but only got 0. Skipping tag 4\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 1385474 bytes but only got 5357. Skipping tag 513\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 196867 bytes but only got 5357. Skipping tag 0\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "117/150 [======================>.......] - ETA: 15s - loss: 0.6528 - acc: 0.6090"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 262146 bytes but only got 0. Skipping tag 2\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 262151 bytes but only got 0. Skipping tag 56\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:742: UserWarning: Corrupt EXIF data.  Expecting to read 12 bytes but only got 0. \n",
            "  warnings.warn(str(msg))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "17/50 [=========>....................] - ETA: 8s - loss: 0.5257 - acc: 0.7471"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: 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:725: 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:725: 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:725: 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:725: 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:725: 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:725: 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:742: 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": [
            "31/50 [=================>............] - ETA: 5s - loss: 0.5162 - acc: 0.7394"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 209715200 bytes but only got 0. Skipping tag 48\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 52428800 bytes but only got 0. Skipping tag 0\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 6468665344 bytes but only got 0. Skipping tag 0\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 1050744 bytes but only got 7027. Skipping tag 48\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 422313984 bytes but only got 0. Skipping tag 5\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 422838272 bytes but only got 0. Skipping tag 5\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 428867584 bytes but only got 0. Skipping tag 10\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 429391872 bytes but only got 0. Skipping tag 5\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 2031616 bytes but only got 0. Skipping tag 3\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 429916160 bytes but only got 0. Skipping tag 5\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 21299200 bytes but only got 0. Skipping tag 4\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 24313856 bytes but only got 0. Skipping tag 4\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 453771264 bytes but only got 7032. Skipping tag 4\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 131073 bytes but only got 7028. Skipping tag 0\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 416415744 bytes but only got 0. Skipping tag 5\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 416940032 bytes but only got 0. Skipping tag 5\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n",
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:725: UserWarning: Possibly corrupt EXIF data.  Expecting to read 415825920 bytes but only got 7032. Skipping tag 4\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 11s 228ms/step - loss: 0.5150 - acc: 0.7416\n",
            "150/150 [==============================] - 83s 550ms/step - loss: 0.6293 - acc: 0.6348 - val_loss: 0.5150 - val_acc: 0.7416\n",
            "Epoch 2/15\n",
            "50/50 [==============================] - 10s 197ms/step - loss: 0.4622 - acc: 0.7800\n",
            "150/150 [==============================] - 75s 498ms/step - loss: 0.5022 - acc: 0.7537 - val_loss: 0.4622 - val_acc: 0.7800\n",
            "Epoch 3/15\n",
            "50/50 [==============================] - 10s 197ms/step - loss: 0.4148 - acc: 0.8052\n",
            "150/150 [==============================] - 74s 496ms/step - loss: 0.4310 - acc: 0.8004 - val_loss: 0.4148 - val_acc: 0.8052\n",
            "Epoch 4/15\n",
            "50/50 [==============================] - 10s 196ms/step - loss: 0.4226 - acc: 0.7992\n",
            "150/150 [==============================] - 74s 494ms/step - loss: 0.3846 - acc: 0.8276 - val_loss: 0.4226 - val_acc: 0.7992\n",
            "Epoch 5/15\n",
            "50/50 [==============================] - 10s 199ms/step - loss: 0.4207 - acc: 0.8108\n",
            "150/150 [==============================] - 74s 494ms/step - loss: 0.3405 - acc: 0.8524 - val_loss: 0.4207 - val_acc: 0.8108\n",
            "Epoch 6/15\n",
            "50/50 [==============================] - 10s 200ms/step - loss: 0.3951 - acc: 0.8220\n",
            "150/150 [==============================] - 76s 509ms/step - loss: 0.2987 - acc: 0.8689 - val_loss: 0.3951 - val_acc: 0.8220\n",
            "Epoch 7/15\n",
            "50/50 [==============================] - 10s 195ms/step - loss: 0.4029 - acc: 0.8316\n",
            "150/150 [==============================] - 74s 492ms/step - loss: 0.2502 - acc: 0.8939 - val_loss: 0.4029 - val_acc: 0.8316\n",
            "Epoch 8/15\n",
            "50/50 [==============================] - 10s 194ms/step - loss: 0.5071 - acc: 0.8072\n",
            "150/150 [==============================] - 74s 492ms/step - loss: 0.1867 - acc: 0.9253 - val_loss: 0.5071 - val_acc: 0.8072\n",
            "Epoch 9/15\n",
            "50/50 [==============================] - 10s 194ms/step - loss: 0.4898 - acc: 0.8276\n",
            "150/150 [==============================] - 74s 490ms/step - loss: 0.1477 - acc: 0.9411 - val_loss: 0.4898 - val_acc: 0.8276\n",
            "Epoch 10/15\n",
            "50/50 [==============================] - 10s 208ms/step - loss: 0.5678 - acc: 0.8252\n",
            "150/150 [==============================] - 76s 506ms/step - loss: 0.1014 - acc: 0.9628 - val_loss: 0.5678 - val_acc: 0.8252\n",
            "Epoch 11/15\n",
            "50/50 [==============================] - 11s 218ms/step - loss: 0.6623 - acc: 0.8208\n",
            "150/150 [==============================] - 73s 487ms/step - loss: 0.0657 - acc: 0.9778 - val_loss: 0.6623 - val_acc: 0.8208\n",
            "Epoch 12/15\n",
            "50/50 [==============================] - 10s 193ms/step - loss: 0.7012 - acc: 0.8248\n",
            "150/150 [==============================] - 72s 481ms/step - loss: 0.0476 - acc: 0.9845 - val_loss: 0.7012 - val_acc: 0.8248\n",
            "Epoch 13/15\n",
            "50/50 [==============================] - 10s 195ms/step - loss: 0.8583 - acc: 0.8236\n",
            "150/150 [==============================] - 73s 490ms/step - loss: 0.0326 - acc: 0.9908 - val_loss: 0.8583 - val_acc: 0.8236\n",
            "Epoch 14/15\n",
            "50/50 [==============================] - 10s 193ms/step - loss: 0.8634 - acc: 0.8116\n",
            "150/150 [==============================] - 75s 503ms/step - loss: 0.0323 - acc: 0.9908 - val_loss: 0.8634 - val_acc: 0.8116\n",
            "Epoch 15/15\n",
            "50/50 [==============================] - 10s 194ms/step - loss: 0.8073 - acc: 0.8156\n",
            "150/150 [==============================] - 73s 488ms/step - loss: 0.0271 - acc: 0.9926 - val_loss: 0.8073 - val_acc: 0.8156\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1hhNBnsIAT6j",
        "colab_type": "code",
        "outputId": "81d9638c-2e3f-4952-e01f-d266ad4bdd27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        }
      },
      "source": [
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "\n",
        "loss=history.history['loss']\n",
        "val_loss = history.history['val_acc']\n",
        "\n",
        "epochs= range(len(acc))\n",
        "\n",
        "plt.plot(epochs,acc)\n",
        "plt.plot(epochs,val_acc)\n",
        "plt.title(\"training and validation accuracy\")\n",
        "\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs,loss)\n",
        "plt.plot(epochs,val_loss)\n",
        "plt.title(\"training and testing loss\")\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'training and testing loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BMjMzNUAxGdPhqCqL1u7ikffWs7mogonpPXnqmhGMT00IdmgmRPiT9POBVJ/n/d1ph6jq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\nYSbc2kbeZ5IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XaEyzEUi4ZwAb/aYL3XlHEZEficga4DHg7tpWJCLjRCRPRPKKiopOpF4TBqJ9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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}