Exercise_5_Question.ipynb

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      "name": "Exercise 5 - Question.ipynb",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/soumyadiptapete/Coursera_TF_specialization/blob/master/Exercise_5_Question.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dn-6c02VmqiN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# In this exercise you will train a CNN on the FULL Cats-v-dogs dataset\n",
        "# This will require you doing a lot of data preprocessing because\n",
        "# the dataset isn't split into training and validation for you\n",
        "# This code block has all the required inputs\n",
        "import os\n",
        "import zipfile\n",
        "import random\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from shutil import copyfile"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3sd9dQWa23aj",
        "colab_type": "code",
        "outputId": "9e4f500e-8073-4462-ef10-152366ff96a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "# This code block downloads the full Cats-v-Dogs dataset and stores it as \n",
        "# cats-and-dogs.zip. It then unzips it to /tmp\n",
        "# which will create a tmp/PetImages directory containing subdirectories\n",
        "# called 'Cat' and 'Dog' (that's how the original researchers structured it)\n",
        "# If the URL doesn't work, \n",
        "# .   visit https://www.microsoft.com/en-us/download/confirmation.aspx?id=54765\n",
        "# And right click on the 'Download Manually' link to get a new URL\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",
        "local_zip = '/tmp/cats-and-dogs.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-05-14 12:36:24--  https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\n",
            "Resolving download.microsoft.com (download.microsoft.com)... 92.122.255.148, 2a02:26f0:ec:28c::e59, 2a02:26f0:ec:280::e59\n",
            "Connecting to download.microsoft.com (download.microsoft.com)|92.122.255.148|: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  19.9MB/s    in 38s     \n",
            "\n",
            "2019-05-14 12:37:02 (20.7 MB/s) - ‘/tmp/cats-and-dogs.zip’ saved [824894548/824894548]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3F3TVM8W5MD",
        "colab_type": "code",
        "outputId": "6fd7c218-4fab-4f27-9ee9-796ec38bf1bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x=os.listdir('/tmp/')\n",
        "x"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['PetImages', 'cats-and-dogs.zip', 'readme[1].txt', 'MSR-LA - 3467.docx']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gi3yD62a6X3S",
        "colab_type": "code",
        "outputId": "c0f50cce-78ea-4bb5-ee3c-303d098de9c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(len(os.listdir('/tmp/PetImages/Cat/')))\n",
        "print(len(os.listdir('/tmp/PetImages/Dog/')))\n",
        "\n",
        "# Expected Output:\n",
        "# 12501\n",
        "# 12501"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12501\n",
            "12501\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-QkLjxpmyK2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Use os.mkdir to create your directories\n",
        "# You will need a directory for cats-v-dogs, and subdirectories for training\n",
        "# and testing. These in turn will need subdirectories for 'cats' and 'dogs'\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",
        "except OSError:\n",
        "    pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvSODo0f9LaU",
        "colab_type": "code",
        "outputId": "cbb22182-8d46-499c-cbfc-b0ed4eb41860",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# Write a python function called split_data which takes\n",
        "# a SOURCE directory containing the files\n",
        "# a TRAINING directory that a portion of the files will be copied to\n",
        "# a TESTING directory that a portion of the files will be copie to\n",
        "# a SPLIT SIZE to determine the portion\n",
        "# The files should also be randomized, so that the training set is a random\n",
        "# X% of the files, and the test set is the remaining files\n",
        "# SO, for example, if SOURCE is PetImages/Cat, and SPLIT SIZE is .9\n",
        "# Then 90% of the images in PetImages/Cat will be copied to the TRAINING dir\n",
        "# and 10% of the images will be copied to the TESTING dir\n",
        "# Also -- All images should be checked, and if they have a zero file length,\n",
        "# they will not be copied over\n",
        "#\n",
        "# os.listdir(DIRECTORY) gives you a listing of the contents of that directory\n",
        "# os.path.getsize(PATH) gives you the size of the file\n",
        "# copyfile(source, destination) copies a file from source to destination\n",
        "# random.sample(list, len(list)) shuffles a list\n",
        "def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):\n",
        "# YOUR CODE STARTS HERE\n",
        "  fnames=os.listdir(SOURCE)\n",
        "  source_fnames=[]\n",
        "  for fname in fnames:\n",
        "    if os.path.getsize(SOURCE+fname)>0:\n",
        "      source_fnames.append(fname)\n",
        "    else:\n",
        "      print(fname+'is zero length, so ignoring')\n",
        "    \n",
        "  \n",
        "  \n",
        "  random.shuffle(source_fnames)\n",
        "  for cnt,source_fname in enumerate(source_fnames):\n",
        "    \n",
        "    if cnt< int(SPLIT_SIZE*len(source_fnames)):\n",
        "      copyfile(SOURCE+source_fname,TRAINING+source_fname)\n",
        "    else:\n",
        "      copyfile(SOURCE+source_fname,TESTING+source_fname)\n",
        "    \n",
        "    \n",
        "# YOUR CODE ENDS HERE\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)\n",
        "\n",
        "# Expected output\n",
        "# 666.jpg is zero length, so ignoring\n",
        "# 11702.jpg is zero length, so ignoring"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "666.jpgis zero length, so ignoring\n",
            "11702.jpgis zero length, so ignoring\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luthalB76ufC",
        "colab_type": "code",
        "outputId": "b8d3ee9b-c7a5-4437-e074-546d9a9f0a0a",
        "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"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11250\n",
            "11250\n",
            "1250\n",
            "1250\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-BQrav4anTmj",
        "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",
        "    # Note the input shape is the desired size of the image 150x150 with 3 bytes color\n",
        "    # This is the first convolution\n",
        "    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150, 150, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    # The second convolution\n",
        "    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    # The third convolution\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    # 512 neuron hidden layer\n",
        "    tf.keras.layers.Dense(512, activation='relu'),\n",
        "    # Only 1 output neuron. It will contain a value from 0-1 where 0 for 1 class ('horses') and 1 for the other ('humans')\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(optimizer=RMSprop(lr=0.001), loss='binary_crossentropy', metrics=['acc'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlNjoJ5D61N6",
        "colab_type": "code",
        "outputId": "453b5e97-a3ee-410b-8217-ab76178d7f73",
        "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(TRAINING_DIR,  # This is the source directory for training images\n",
        "        target_size=(150, 150),  # All images will be resized to 150x150\n",
        "        batch_size=500,\n",
        "        # Since we use binary_crossentropy loss, we need binary labels\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(VALIDATION_DIR,  # This is the source directory for training images\n",
        "        target_size=(150, 150),  # All images will be resized to 150x150\n",
        "        batch_size=50,\n",
        "        # Since we use binary_crossentropy loss, we need binary labels\n",
        "        class_mode='binary')\n",
        "\n",
        "\n",
        "\n",
        "# Expected Output:\n",
        "# Found 22498 images belonging to 2 classes.\n",
        "# Found 2500 images belonging to 2 classes."
      ],
      "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": "qZ14fAE3qCwY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
        "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"]=\"2\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KyS4n53w7DxC",
        "colab_type": "code",
        "outputId": "ff4e7082-53cc-4007-813b-5a2846b7b449",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2943
        }
      },
      "source": [
        "history = model.fit_generator(train_generator,\n",
        "                              epochs=15,\n",
        "                              verbose=1,\n",
        "                              validation_data=validation_generator)\n",
        "\n",
        "# The expectation here is that the model will train, and that accuracy will be > 95% on both training and validation\n",
        "# i.e. acc:A1 and val_acc:A2 will be visible, and both A1 and A2 will be > .9"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            " 4/45 [=>............................] - ETA: 1:19 - loss: 2.8617 - acc: 0.4945"
          ],
          "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": [
            "32/45 [====================>.........] - ETA: 24s - loss: 0.9768 - acc: 0.5479"
          ],
          "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 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 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 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 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 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 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": [
            "37/45 [=======================>......] - ETA: 14s - loss: 0.9368 - acc: 0.5532"
          ],
          "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 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 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 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 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 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 415825920 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:742: UserWarning: Corrupt EXIF data.  Expecting to read 12 bytes but only got 2. \n",
            "  warnings.warn(str(msg))\n",
            "/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",
            "/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 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 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 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 286654464 bytes but only got 4956. Skipping tag 4\n",
            "  \" Skipping tag %s\" % (size, len(data), tag))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "42/45 [===========================>..] - ETA: 5s - loss: 0.9046 - acc: 0.5608"
          ],
          "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": [
            "14/50 [=======>......................] - ETA: 11s - loss: 0.6262 - acc: 0.6757"
          ],
          "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": [
            "50/50 [==============================] - 15s 304ms/step - loss: 0.6397 - acc: 0.6472\n",
            "45/45 [==============================] - 99s 2s/step - loss: 0.8876 - acc: 0.5680 - val_loss: 0.6397 - val_acc: 0.6472\n",
            "Epoch 2/15\n",
            "50/50 [==============================] - 15s 296ms/step - loss: 0.6346 - acc: 0.6904\n",
            "45/45 [==============================] - 88s 2s/step - loss: 0.6599 - acc: 0.6373 - val_loss: 0.6346 - val_acc: 0.6904\n",
            "Epoch 3/15\n",
            "50/50 [==============================] - 14s 279ms/step - loss: 0.6059 - acc: 0.6856\n",
            "45/45 [==============================] - 87s 2s/step - loss: 0.6056 - acc: 0.6869 - val_loss: 0.6059 - val_acc: 0.6856\n",
            "Epoch 4/15\n",
            "50/50 [==============================] - 14s 282ms/step - loss: 0.5564 - acc: 0.7224\n",
            "45/45 [==============================] - 84s 2s/step - loss: 0.5635 - acc: 0.7160 - val_loss: 0.5564 - val_acc: 0.7224\n",
            "Epoch 5/15\n",
            "50/50 [==============================] - 15s 303ms/step - loss: 0.5537 - acc: 0.7340\n",
            "45/45 [==============================] - 91s 2s/step - loss: 0.5281 - acc: 0.7411 - val_loss: 0.5537 - val_acc: 0.7340\n",
            "Epoch 6/15\n",
            "50/50 [==============================] - 15s 292ms/step - loss: 0.5187 - acc: 0.7460\n",
            "45/45 [==============================] - 90s 2s/step - loss: 0.4770 - acc: 0.7682 - val_loss: 0.5187 - val_acc: 0.7460\n",
            "Epoch 7/15\n",
            "50/50 [==============================] - 14s 290ms/step - loss: 0.5096 - acc: 0.7492\n",
            "45/45 [==============================] - 90s 2s/step - loss: 0.4598 - acc: 0.7809 - val_loss: 0.5096 - val_acc: 0.7492\n",
            "Epoch 8/15\n",
            "50/50 [==============================] - 15s 292ms/step - loss: 0.4529 - acc: 0.7916\n",
            "45/45 [==============================] - 87s 2s/step - loss: 0.4357 - acc: 0.7923 - val_loss: 0.4529 - val_acc: 0.7916\n",
            "Epoch 9/15\n",
            "50/50 [==============================] - 14s 279ms/step - loss: 0.4488 - acc: 0.7892\n",
            "45/45 [==============================] - 86s 2s/step - loss: 0.4067 - acc: 0.8098 - val_loss: 0.4488 - val_acc: 0.7892\n",
            "Epoch 10/15\n",
            "50/50 [==============================] - 14s 279ms/step - loss: 0.4436 - acc: 0.7928\n",
            "45/45 [==============================] - 85s 2s/step - loss: 0.3814 - acc: 0.8284 - val_loss: 0.4436 - val_acc: 0.7928\n",
            "Epoch 11/15\n",
            "50/50 [==============================] - 14s 282ms/step - loss: 0.5719 - acc: 0.7340\n",
            "45/45 [==============================] - 84s 2s/step - loss: 0.3510 - acc: 0.8443 - val_loss: 0.5719 - val_acc: 0.7340\n",
            "Epoch 12/15\n",
            "50/50 [==============================] - 14s 275ms/step - loss: 0.5473 - acc: 0.7640\n",
            "45/45 [==============================] - 84s 2s/step - loss: 0.3204 - acc: 0.8562 - val_loss: 0.5473 - val_acc: 0.7640\n",
            "Epoch 13/15\n",
            "50/50 [==============================] - 14s 279ms/step - loss: 0.4825 - acc: 0.7856\n",
            "45/45 [==============================] - 83s 2s/step - loss: 0.2936 - acc: 0.8716 - val_loss: 0.4825 - val_acc: 0.7856\n",
            "Epoch 14/15\n",
            "50/50 [==============================] - 15s 297ms/step - loss: 0.4791 - acc: 0.7952\n",
            "45/45 [==============================] - 90s 2s/step - loss: 0.2560 - acc: 0.8928 - val_loss: 0.4791 - val_acc: 0.7952\n",
            "Epoch 15/15\n",
            "50/50 [==============================] - 14s 289ms/step - loss: 0.4892 - acc: 0.7936\n",
            "45/45 [==============================] - 89s 2s/step - loss: 0.2271 - acc: 0.9083 - val_loss: 0.4892 - val_acc: 0.7936\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MWZrJN4-65RC",
        "colab_type": "code",
        "outputId": "81824cc3-213f-4e39-a53a-acc09a4ad5e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        }
      },
      "source": [
        "# PLOT LOSS AND ACCURACY\n",
        "%matplotlib inline\n",
        "\n",
        "import matplotlib.image  as mpimg\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#-----------------------------------------------------------\n",
        "# Retrieve a list of list results on training and test data\n",
        "# sets for each training epoch\n",
        "#-----------------------------------------------------------\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)) # Get number of epochs\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation accuracy per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, acc, 'r', \"Training Accuracy\")\n",
        "plt.plot(epochs, val_acc, 'b', \"Validation Accuracy\")\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.figure()\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation loss per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, loss, 'r', \"Training Loss\")\n",
        "plt.plot(epochs, val_loss, 'b', \"Validation Loss\")\n",
        "\n",
        "\n",
        "plt.title('Training and validation loss')\n",
        "\n",
        "# Desired output. Charts with training and validation metrics. No crash :)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Training and validation loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ww2FpZmaWw2FpZmaWw2FpZmaWw2FpZmaWw2FpZmaWw2FpZmaWw2FpZmaWw2Fp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\nmZnlcFiamZnl+P93YVJVoTKAugAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0cmVomnz1VXjllTB1NkCHDmGW0SOPDJcFtG0bb51SqxRmCaMwE6llc+eWBdtr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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LqL6FYUrtXpf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Here's a codeblock just for fun. You should be able to upload an image here \n",
        "# and have it classified without crashing\n",
        "\n",
        "import numpy as np\n",
        "from google.colab import files\n",
        "from keras.preprocessing import image\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        " \n",
        "  # predicting images\n",
        "  path = '/content/' + fn\n",
        "  img = image.load_img(path, target_size=(# YOUR CODE HERE))\n",
        "  x = image.img_to_array(img)\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "\n",
        "  images = np.vstack([x])\n",
        "  classes = model.predict(images, batch_size=10)\n",
        "  print(classes[0])\n",
        "  if classes[0]>0.5:\n",
        "    print(fn + \" is a dog\")\n",
        "  else:\n",
        "    print(fn + \" is a cat\")"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}