myExercise/Exercise_5_Question.ipynb

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  "metadata": {
    "colab": {
      "name": "Copy of Exercise 5 - Question.ipynb",
      "version": "0.3.2",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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    "accelerator": "GPU"
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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/DayuanTan/AITensorFlowSpecialization/blob/master/myExercise/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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5ff098e5-2020-49e7-9fb2-9a225aa8bc06"
      },
      "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": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-08-04 20:56:56--  https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\n",
            "Resolving download.microsoft.com (download.microsoft.com)... 173.223.192.195, 2600:1402:f000:69c::e59, 2600:1402:f000:6a0::e59\n",
            "Connecting to download.microsoft.com (download.microsoft.com)|173.223.192.195|: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  97.6MB/s    in 8.4s    \n",
            "\n",
            "2019-08-04 20:57:05 (94.1 MB/s) - ‘/tmp/cats-and-dogs.zip’ saved [824894548/824894548]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gi3yD62a6X3S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "55929e48-2dee-4feb-e214-fbf2a7a818d5"
      },
      "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": 3,
      "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",
        "    #YOUR CODE GOES HERE\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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "e50798d2-a2fc-40b8-9279-719efe717b9d"
      },
      "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",
        "    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",
        "# 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": 7,
      "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": "luthalB76ufC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "f167e5a0-2ba8-475c-c399-d9c690fad51b"
      },
      "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": 8,
      "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": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "outputId": "a91f1d01-82ea-499d-c625-cd39b59dc423"
      },
      "source": [
        "# DEFINE A KERAS MODEL TO CLASSIFY CATS V DOGS\n",
        "# USE AT LEAST 3 CONVOLUTION LAYERS\n",
        "model = tf.keras.models.Sequential([\n",
        "# YOUR CODE HERE\n",
        "    tf.keras.layers.Conv2D(16,(3,3),activation=\"relu\",input_shape=(150,150,3)),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(32,(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(512,activation=\"relu\"),\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": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0804 21:03:45.362765 139913593243520 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "W0804 21:03:45.567185 139913593243520 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlNjoJ5D61N6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "f511e4de-90d4-493e-952e-0dabba003847"
      },
      "source": [
        "TRAINING_DIR = \"/tmp/cats-v-dogs/training/\" #YOUR CODE HERE\n",
        "train_datagen = ImageDataGenerator(rescale=1.0/255.) #YOUR CODE HERE\n",
        "train_generator = train_datagen.flow_from_directory(TRAINING_DIR,\n",
        "                                                    batch_size=100,\n",
        "                                                    class_mode='binary',\n",
        "                                                    target_size=(150, 150)) #YOUR CODE HERE\n",
        "\n",
        "\n",
        "VALIDATION_DIR = \"/tmp/cats-v-dogs/testing/\"\n",
        "validation_datagen = ImageDataGenerator(rescale=1.0/255.)\n",
        "validation_generator = validation_datagen.flow_from_directory(VALIDATION_DIR,\n",
        "                                                              batch_size=100,\n",
        "                                                              class_mode='binary',\n",
        "                                                              target_size=(150, 150))\n",
        "\n",
        "\n",
        "\n",
        "# Expected Output:\n",
        "# Found 22498 images belonging to 2 classes.\n",
        "# Found 2500 images belonging to 2 classes."
      ],
      "execution_count": 10,
      "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": "KyS4n53w7DxC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "85bcb31c-89a1-470a-a943-ce2867e8d6bb"
      },
      "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": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            " 48/225 [=====>........................] - ETA: 58s - loss: 0.9552 - acc: 0.5471"
          ],
          "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": [
            " 64/225 [=======>......................] - ETA: 51s - loss: 0.8884 - acc: 0.5605"
          ],
          "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": [
            "121/225 [===============>..............] - ETA: 32s - loss: 0.7728 - acc: 0.5978"
          ],
          "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": [
            "139/225 [=================>............] - ETA: 26s - loss: 0.7589 - acc: 0.6091"
          ],
          "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": [
            "160/225 [====================>.........] - ETA: 19s - loss: 0.7355 - acc: 0.6190"
          ],
          "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 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 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 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": [
            "204/225 [==========================>...] - ETA: 6s - loss: 0.7006 - acc: 0.6378"
          ],
          "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": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r205/225 [==========================>...] - ETA: 6s - loss: 0.7001 - acc: 0.6381"
          ],
          "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": [
            "225/225 [==============================] - 77s 340ms/step - loss: 0.6868 - acc: 0.6459 - val_loss: 0.6371 - val_acc: 0.6120\n",
            "Epoch 2/15\n",
            "225/225 [==============================] - 70s 312ms/step - loss: 0.5067 - acc: 0.7487 - val_loss: 0.4839 - val_acc: 0.7664\n",
            "Epoch 3/15\n",
            "225/225 [==============================] - 70s 311ms/step - loss: 0.4340 - acc: 0.7992 - val_loss: 0.4439 - val_acc: 0.7972\n",
            "Epoch 4/15\n",
            "225/225 [==============================] - 70s 311ms/step - loss: 0.3787 - acc: 0.8316 - val_loss: 0.4385 - val_acc: 0.8000\n",
            "Epoch 5/15\n",
            "225/225 [==============================] - 70s 312ms/step - loss: 0.3193 - acc: 0.8597 - val_loss: 0.4073 - val_acc: 0.8248\n",
            "Epoch 6/15\n",
            "225/225 [==============================] - 70s 311ms/step - loss: 0.2559 - acc: 0.8916 - val_loss: 0.4303 - val_acc: 0.8216\n",
            "Epoch 7/15\n",
            "225/225 [==============================] - 70s 309ms/step - loss: 0.1917 - acc: 0.9233 - val_loss: 0.5469 - val_acc: 0.8000\n",
            "Epoch 8/15\n",
            "225/225 [==============================] - 70s 310ms/step - loss: 0.1349 - acc: 0.9485 - val_loss: 0.6016 - val_acc: 0.8220\n",
            "Epoch 9/15\n",
            "225/225 [==============================] - 69s 308ms/step - loss: 0.0890 - acc: 0.9680 - val_loss: 0.6805 - val_acc: 0.7888\n",
            "Epoch 10/15\n",
            "225/225 [==============================] - 69s 307ms/step - loss: 0.0593 - acc: 0.9792 - val_loss: 0.8949 - val_acc: 0.8276\n",
            "Epoch 11/15\n",
            "225/225 [==============================] - 69s 306ms/step - loss: 0.0682 - acc: 0.9795 - val_loss: 0.7460 - val_acc: 0.7992\n",
            "Epoch 12/15\n",
            "225/225 [==============================] - 69s 306ms/step - loss: 0.0510 - acc: 0.9860 - val_loss: 0.9535 - val_acc: 0.8272\n",
            "Epoch 13/15\n",
            "225/225 [==============================] - 69s 308ms/step - loss: 0.0501 - acc: 0.9860 - val_loss: 0.7453 - val_acc: 0.8048\n",
            "Epoch 14/15\n",
            "225/225 [==============================] - 69s 306ms/step - loss: 0.0410 - acc: 0.9880 - val_loss: 0.9695 - val_acc: 0.8088\n",
            "Epoch 15/15\n",
            "225/225 [==============================] - 69s 305ms/step - loss: 0.0583 - acc: 0.9844 - val_loss: 0.9436 - val_acc: 0.8180\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MWZrJN4-65RC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "0bd54ce5-5394-4b11-ae2b-8087c74657af"
      },
      "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": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Training and validation loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkc\nlmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZm\nZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkc\nlmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZm\nZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkc\nlmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZm\nZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkc\nlmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZm\nZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkc\nlmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjkclmZmZjnWOywl\nbStpUvaYLenDoteb1XMZN0vaLWee8ySNWN96i5bXVdJKSWc11jLNzGzjVLG+C4iIj4G9ACRdBXwW\nEb8snkeSAEXE6hLL+FY91vPb9a21hpOA54FTgRsbedmfk1QRESubavlmZtb0mqwbVtIukl6XNBKY\nCnSX9AdJEyRNlfTjonmfkbSXpApJn0q6RtJkSc9L6pLNc7Wki4rmv0bSS5LekHRQNr6DpLuz9d6V\nrWuvEiWeClwE7Cype1Etx0qqzNb/cDZuS0l/ljQle/xzodai950i6cZs+FZJN0h6CfiZpAOyz/KK\npGcl7ZrNVyHpV5Jey5b7XUlHSbqraLlHS/prY/ybmJnZulnvlmWOPsA3I2ICgKTLIuITSRXAeEl3\nRcTrNd7TEXgyIi6TdB1wJnBNLctWROwn6QTgx8Aw4HvA7Ij4qqQ9gcraipLUC9gmIiZmQXQS8D+S\nugE3AIMi4n1J22RvuQqYGxF7ZK3krevx2bsDB0TEakkds2WulDQMuBo4GfgXYDtgz4hYla3vU+A3\nkrbNWu3fAm4q8TnOAc4B2GGHHepRkpmZrYumPsDnH4WgzJwqqZIUYrsDfWt5z9KIeDAbngj0KrHs\nv9UyzyHAHQARMZnUoq3NKcB+Bxt7AAAGKElEQVSd2fAdpFYmwIHA+Ih4P1vGJ9n4I4DfZuMiIuaX\nWG6xvxZ1O28N3C3pNeCXwBeLlvu7iFhVWF/2npHA8Cw89wEerm0FEfGHiBgYEQM7d+5cj5LMzGxd\nNHXLcnFhIOt6vBDYLyI+lXQr0K6W9ywvGl5F6Rqr6jFPKacCnSSdnr3eTtLODVzGakBFr2t+lsVF\nwz8FHoqI/5W0CzAuZ9k3AXdnw3cWwtTMzMpjQ546shWwCFiY7SMc2gTreJbUpYqk/tTScpXUF6iI\niB4R0SsiegHXklqbzwGDJe2YzVvohn0EOC8bJ0lfyFqA8yXtKmkT4Mt11NUR+DAbPqNo/CPAuZLa\nFK8vIj4A5gGXAbc0ZAOYmVnj25BhWQm8Dvwd+Asp2Brb9UAPSa8D/5atb0GNeU4FRtcYdzdwakTM\nIe1HvFfSZFJ3KMC/A12zbtRJwKBs/A+Bh0ghO6OOun4OXJt1QRe3Rn8PzAamZOs7qWjabcC7EfFm\n3R/ZzMyamiKi3DU0muzAoYqIWJZ1+z4M7NoST92Q9Dvg+Yj4c33mHzhwYEyYMCF/RjMzA0DSxIgY\nWJ95m3qf5Ya2BfBYFpoCvtNCg3ISMB+4oNy1mJnZRhaWEfEp6ejRFi0iSp0bamZmZeBrw5qZmeVw\nWJqZmeXYqA7wac0kzQXeX8e3dyKdqtIStKRaoWXV25JqhZZVb0uqFVpWvetT644RUa8rujgsDUkT\n6ntEWLm1pFqhZdXbkmqFllVvS6oVWla9G6pWd8OamZnlcFiamZnlcFgawB/KXUADtKRaoWXV25Jq\nhZZVb0uqFVpWvRukVu+zNDMzy+GWpZmZWQ6HpZmZWQ6HZSsmaZikNyS9LemyctdTF0nbSxov6XVJ\nUyVdWO6a8khqI+kVSfeXu5Y8kraWdJekv0uaJunActdUiqT/l/0NvCbpdkm13Re3bCTdJOmj7C5F\nhXHbSHpE0lvZ8xfKWWNBiVqvzf4OpkgaLWnrctZYrLZ6i6Z9X1JI6tQU63ZYtlLZPTR/CxxNuu/n\nqdm9PpurlcD3I6IvcABwXjOvF9LNzqeVu4h6+h9gXET0AfakmdYtqQfpBgMDI6If0IZ0L9rm5BZg\nWI1xlwGPRcSuwGPZ6+bgFtau9RGgX0TsAbwJXL6hi6rDLaxdL5K2B44CpjfVih2Wrdd+wNsR8U5E\nLAfuAE4sc00lRcSsiKjMhheRvsx7lLeq0iT1BI4Fbix3LXkkdQS+BPwJICKWZzclaK4qgM2zuwu1\nB2aWuZ41RMRTwCc1Rp8IFG6392fgnzdoUSXUVmtEPFx0t6YXgJ4bvLASSmxbgF8BlwJNdsSqw7L1\n6gF8UPR6Bs04fIpJ6gUMAF4sbyV1+m/Sf97V5S6kHnYC5gI3Z93GN0rqUO6iahMRHwK/JLUgZgEL\nIuLh8lZVL10jYlY2PBvoWs5iGuBM4MFyF1EXSScCH0bE5KZcj8PSWhRJWwB3AxdFxMJy11MbSccB\nH0XExHLXUk8VwN7ADRExAFhM8+kmXEO2r+9EUsBvB3SQ9I3yVtUwkc7Xa/bn7En6V9Luj5HlrqUU\nSe2BK4AfN/W6HJat14fA9kWve2bjmi1Jm5KCcmRE/K3c9dThYOAESe+RureHSLq1vCXVaQYwIyIK\nLfW7SOHZHB0BvBsRcyNiBfA34KAy11QfcyR1B8iePypzPXWSdAZwHDAimvfJ+L1JP5wmZ//fegKV\nkro19ooclq3Xy8CuknaStBnpIIn7ylxTSZJE2qc2LSKuK3c9dYmIyyOiZ0T0Im3XxyOi2bZ+ImI2\n8IGk3bJRhwOvl7GkukwHDpDUPvubOJxmejBSDfcBp2fDpwP3lrGWOkkaRtqFcEJELCl3PXWJiFcj\noktE9Mr+v80A9s7+phuVw7KVynbgnw88RPqyGRURU8tbVZ0OBk4jtdImZY9jyl3URuR7wEhJU4C9\ngJ+VuZ5aZa3fu4BK4FXSd1izujSbpNuB54HdJM2Q9G3gGuBISW+RWsfXlLPGghK1/gbYEngk+3/2\nu7IWWaREvRtm3c27hW1mZlZ+blmamZnlcFiamZnlcFiamZnlcFiamZnlcFiamZnlcFiamZnlcFia\nmZnl+P8SFHm70e2UKAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6/vjc0GrZ0sM7JgozEZFEqVYN9trLb6mwaZN34eVcP1yyZPPBLzmP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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LqL6FYUrtXpf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "outputId": "abde6e67-748a-4d79-9e21-57b8d4b14abc"
      },
      "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": 17,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-17-e23a17d42966>\"\u001b[0;36m, line \u001b[0;32m13\u001b[0m\n\u001b[0;31m    x = image.img_to_array(img)\u001b[0m\n\u001b[0m      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    }
  ]
}