myExercise/Copy_of_Exercise_6_Answer.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Exercise 6 - Answer.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "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/DayuanTan/AITensorFlowSpecialization/blob/master/myExercise/Copy_of_Exercise_6_Answer.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": [
        "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": "4c4c9f87-9e47-4c6c-f917-c1cf8972eed0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# If the URL doesn't work, 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 to the dataset\n",
        "\n",
        "# Note: This is a very large dataset and will take time to download\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": [
            "--2019-09-03 05:04:15--  https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\n",
            "Resolving download.microsoft.com (download.microsoft.com)... 104.84.156.142, 2600:1417:5e:6a2::e59, 2600:1417:5e:68b::e59, ...\n",
            "Connecting to download.microsoft.com (download.microsoft.com)|104.84.156.142|: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   121MB/s    in 7.1s    \n",
            "\n",
            "2019-09-03 05:04:22 (111 MB/s) - ‘/tmp/cats-and-dogs.zip’ saved [824894548/824894548]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DM851ZmN28J3",
        "colab_type": "code",
        "outputId": "934529fe-e82d-4ca0-a200-1217d86003da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "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": [
        "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": "15f14533-7589-48a7-95db-c2e202e31122",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):\n",
        "    files = []\n",
        "    for filename in os.listdir(SOURCE):\n",
        "        file = SOURCE + filename\n",
        "        if os.path.getsize(file) > 0:\n",
        "            files.append(filename)\n",
        "        else:\n",
        "            print(filename + \" is zero length, so ignoring.\")\n",
        "\n",
        "    training_length = int(len(files) * SPLIT_SIZE)\n",
        "    testing_length = int(len(files) - training_length)\n",
        "    shuffled_set = random.sample(files, len(files))\n",
        "    training_set = shuffled_set[0:training_length]\n",
        "    testing_set = shuffled_set[:testing_length]\n",
        "\n",
        "    for filename in training_set:\n",
        "        this_file = SOURCE + filename\n",
        "        destination = TRAINING + filename\n",
        "        copyfile(this_file, destination)\n",
        "\n",
        "    for filename in testing_set:\n",
        "        this_file = SOURCE + filename\n",
        "        destination = TESTING + filename\n",
        "        copyfile(this_file, destination)\n",
        "\n",
        "\n",
        "CAT_SOURCE_DIR = \"/tmp/PetImages/Cat/\"\n",
        "TRAINING_CATS_DIR = \"/tmp/cats-v-dogs/training/cats/\"\n",
        "TESTING_CATS_DIR = \"/tmp/cats-v-dogs/testing/cats/\"\n",
        "DOG_SOURCE_DIR = \"/tmp/PetImages/Dog/\"\n",
        "TRAINING_DOGS_DIR = \"/tmp/cats-v-dogs/training/dogs/\"\n",
        "TESTING_DOGS_DIR = \"/tmp/cats-v-dogs/testing/dogs/\"\n",
        "\n",
        "split_size = .9\n",
        "split_data(CAT_SOURCE_DIR, TRAINING_CATS_DIR, TESTING_CATS_DIR, split_size)\n",
        "split_data(DOG_SOURCE_DIR, TRAINING_DOGS_DIR, TESTING_DOGS_DIR, split_size)\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.jpg is zero length, so ignoring.\n",
            "11702.jpg is zero length, so ignoring.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwHXFhVG3786",
        "colab_type": "code",
        "outputId": "27aa7445-defc-43f4-e0bf-25b698a59ecb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "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",
        "outputId": "1bdada23-92e7-440b-a9f1-f632171a1d16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "model = tf.keras.models.Sequential([\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'])\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0903 05:04:49.576267 139826625247104 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",
            "W0903 05:04:49.707581 139826625247104 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": "fQrZfVgz4j2g",
        "colab_type": "code",
        "outputId": "857f4012-8371-43b2-b28b-69b71ccfa17a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "\n",
        "TRAINING_DIR = \"/tmp/cats-v-dogs/training/\"\n",
        "# Experiment with your own parameters here to really try to drive it to 99.9% accuracy or better\n",
        "train_datagen = ImageDataGenerator(rescale=1./255,\n",
        "      rotation_range=40,\n",
        "      width_shift_range=0.2,\n",
        "      height_shift_range=0.2,\n",
        "      shear_range=0.2,\n",
        "      zoom_range=0.2,\n",
        "      horizontal_flip=True,\n",
        "      fill_mode='nearest')\n",
        "train_generator = train_datagen.flow_from_directory(TRAINING_DIR,\n",
        "                                                    batch_size=100,\n",
        "                                                    class_mode='binary',\n",
        "                                                    target_size=(150, 150))\n",
        "\n",
        "VALIDATION_DIR = \"/tmp/cats-v-dogs/testing/\"\n",
        "# Experiment with your own parameters here to really try to drive it to 99.9% accuracy or better\n",
        "validation_datagen = ImageDataGenerator(rescale=1./255,\n",
        "      rotation_range=40,\n",
        "      width_shift_range=0.2,\n",
        "      height_shift_range=0.2,\n",
        "      shear_range=0.2,\n",
        "      zoom_range=0.2,\n",
        "      horizontal_flip=True,\n",
        "      fill_mode='nearest')\n",
        "validation_generator = validation_datagen.flow_from_directory(VALIDATION_DIR,\n",
        "                                                              batch_size=100,\n",
        "                                                              class_mode='binary',\n",
        "                                                              target_size=(150, 150))\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": "5qE1G6JB4fMn",
        "colab_type": "code",
        "outputId": "ee214918-9fde-4ce6-ae65-7714e825deb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Note that this may take some time.\n",
        "history = model.fit_generator(train_generator,\n",
        "                              epochs=15,\n",
        "                              verbose=1,\n",
        "                              validation_data=validation_generator)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            " 35/225 [===>..........................] - ETA: 2:05 - loss: 0.8282 - acc: 0.5200"
          ],
          "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": [
            " 80/225 [=========>....................] - ETA: 1:35 - loss: 0.7504 - acc: 0.5535"
          ],
          "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": [
            " 96/225 [===========>..................] - ETA: 1:24 - loss: 0.7372 - acc: 0.5578"
          ],
          "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": [
            "165/225 [=====================>........] - ETA: 38s - loss: 0.7058 - acc: 0.5815"
          ],
          "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": [
            "188/225 [========================>.....] - ETA: 23s - loss: 0.6991 - acc: 0.5862"
          ],
          "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": [
            "196/225 [=========================>....] - ETA: 18s - loss: 0.6961 - acc: 0.5885"
          ],
          "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 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 5357. 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": [
            "225/225 [==============================] - 163s 724ms/step - loss: 0.6876 - acc: 0.5957 - val_loss: 0.6114 - val_acc: 0.6704\n",
            "Epoch 2/15\n",
            "225/225 [==============================] - 152s 675ms/step - loss: 0.6058 - acc: 0.6690 - val_loss: 0.5619 - val_acc: 0.7164\n",
            "Epoch 3/15\n",
            "225/225 [==============================] - 151s 672ms/step - loss: 0.5733 - acc: 0.7001 - val_loss: 0.6015 - val_acc: 0.6956\n",
            "Epoch 4/15\n",
            "225/225 [==============================] - 152s 676ms/step - loss: 0.5493 - acc: 0.7214 - val_loss: 0.5424 - val_acc: 0.7232\n",
            "Epoch 5/15\n",
            "225/225 [==============================] - 151s 673ms/step - loss: 0.5287 - acc: 0.7356 - val_loss: 0.4903 - val_acc: 0.7644\n",
            "Epoch 6/15\n",
            "225/225 [==============================] - 151s 672ms/step - loss: 0.5164 - acc: 0.7470 - val_loss: 0.4858 - val_acc: 0.7552\n",
            "Epoch 7/15\n",
            "225/225 [==============================] - 152s 673ms/step - loss: 0.5021 - acc: 0.7556 - val_loss: 0.5992 - val_acc: 0.6816\n",
            "Epoch 8/15\n",
            "225/225 [==============================] - 151s 670ms/step - loss: 0.4938 - acc: 0.7588 - val_loss: 0.4675 - val_acc: 0.7748\n",
            "Epoch 9/15\n",
            "225/225 [==============================] - 151s 671ms/step - loss: 0.4824 - acc: 0.7697 - val_loss: 0.4562 - val_acc: 0.7856\n",
            "Epoch 10/15\n",
            "225/225 [==============================] - 151s 671ms/step - loss: 0.4703 - acc: 0.7762 - val_loss: 0.4414 - val_acc: 0.8084\n",
            "Epoch 11/15\n",
            "225/225 [==============================] - 151s 673ms/step - loss: 0.4618 - acc: 0.7837 - val_loss: 0.4436 - val_acc: 0.7960\n",
            "Epoch 12/15\n",
            "225/225 [==============================] - 150s 668ms/step - loss: 0.4506 - acc: 0.7902 - val_loss: 0.5423 - val_acc: 0.7468\n",
            "Epoch 13/15\n",
            "225/225 [==============================] - 150s 669ms/step - loss: 0.4440 - acc: 0.7937 - val_loss: 0.4266 - val_acc: 0.8000\n",
            "Epoch 14/15\n",
            "225/225 [==============================] - 150s 666ms/step - loss: 0.4366 - acc: 0.7980 - val_loss: 0.5111 - val_acc: 0.7448\n",
            "Epoch 15/15\n",
            "225/225 [==============================] - 151s 671ms/step - loss: 0.4285 - acc: 0.8010 - val_loss: 0.3978 - val_acc: 0.8156\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MWZrJN4-65RC",
        "colab_type": "code",
        "outputId": "ad81d394-c1b6-42f8-db72-d7ea2828ae87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        }
      },
      "source": [
        "%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",
        "plt.figure()\n",
        "\n",
        "\n",
        "# Desired output. Charts with training and validation metrics. No crash :)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vh\nsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQz\nM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vh\nsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQz\nM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vh\nsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQz\nM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vh\nsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQz\nM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vh\nsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8vhsDQzM8uxxmEp\naSNJ47JhhqSPC35er5bruF3S1jnznC7p6DWtt2B93SStlHRifa3TzMzWTq3XdAURMQcYACDpUmBR\nRFxdOI8kAYqI8iLrOL4W2xm6prVWcQTwEjAEuLWe1/05Sa0jYmVDrd/MzBpeg3XDStpS0puS7gbe\nAHpI+pOk0ZLekHRJwbzPSxogqbWk+ZKulDRe0kuSNsnmuUzS2QXzXynpVUlvS9ojm76BpAey7d6f\nbWtAkRKHAGcDm0vqUVDLQZLGZtsfkU3rIOkOSROy4dsVtRYsd6SkW7PxuyTdJOlV4ApJu2ef5TVJ\nL0jaKpuvtaRrJU3M1nuapP0l3V+w3gMk/a0+fidmZrZ61rhlmWMb4PsRMRpA0gURMVdSa+BpSfdH\nxJtVlukEPBMRF0i6BjgBuLKadSsidpN0KHAJMBg4E5gREf8laUdgbHVFSeoHbBgRY7IgOgK4XlJ3\n4CZgr4j4QNKG2SKXArMiYoesldy5Fp+9B7B7RJRL6pStc6WkwcBlwH8DpwI9gR0joizb3nzgRkkb\nZa3244HbinyOk4CTAPr06VOLkszMbHU09Ak+/6kIyswQSWNJIbYtsF01yyyNiMey8TFAvyLrfrCa\nefYE/goQEeNJLdrqHAncm41xA4A7AAAGJUlEQVT/ldTKBPgq8HREfJCtY242fV9gaDYtImJekfUW\n+ltBt3Nn4AFJE4GrgS8XrPfmiCir2F62zN3AUVl47gKMqG4DEfGniBgYEQM33njjWpRkZmaro6Fb\nlosrRrKuxx8Du0XEfEl3AetXs8xnBeNlFK9xeS3mKWYI0FXSD7Kfe0ravI7rKAdU8HPVz7K4YPxy\n4PGI+KOkLYHhOeu+DXggG7+3IkzNzKw0GvPSkY7AQuDT7BjhtxpgGy+QulSRtD3VtFwlbQe0johe\nEdEvIvoBvyO1Nl8E9pbUN5u3oht2JHB6Nk2SumQtwHmStpK0DvCdGurqBHycjR9XMH0kcIqkVoXb\ni4iPgNnABcCwuuwAMzOrf40ZlmOBN4G3gDtJwVbfbgB6SXoT+GW2vQVV5hkCPFRl2gPAkIiYSTqO\n+A9J40ndoQC/Arpl3ajjgL2y6ecDj5NCdmoNdf0W+F3WBV3YGr0FmAFMyLZ3RMF7fwHej4h3av7I\nZmbW0BQRpa6h3mQnDrWOiGVZt+8IYKvmeOmGpJuBlyLijtrMP3DgwBg9enT+jGZmBoCkMRExsDbz\nNvQxy8bWHngyC00BJzfToBwHzAPOKnUtZma2loVlRMwnnT3arEVEsWtDzcysBHxvWDMzsxwOSzMz\nsxxr1Qk+LZmkWcAHq7l4V9KlKs1Bc6oVmle9zalWaF71NqdaoXnVuya19o2IWt3RxWFpSBpd2zPC\nSq051QrNq97mVCs0r3qbU63QvOptrFrdDWtmZpbDYWlmZpbDYWkAfyp1AXXQnGqF5lVvc6oVmle9\nzalWaF71NkqtPmZpZmaWwy1LMzOzHA5LMzOzHA7LFkzSYElvS3pP0gWlrqcmknpLelrSm5LekPTj\nUteUR1IrSa9JeqTUteSR1FnS/ZLekjRJ0ldLXVMxkn6S/RuYKOkeSdU9F7dkJN0m6ZPsKUUV0zaU\nNFLSu9lrl1LWWKFIrb/L/h1MkPSQpM6lrLFQdfUWvHeOpJDUtSG27bBsobJnaA4FDiA993NI9qzP\npmolcE5EbAfsDpzexOuF9LDzSaUuopauB4ZHxDbAjjTRuiX1Ij1gYGBE9AdakZ5F25QMAwZXmXYB\n8GREbAU8mf3cFAzji7WOBPpHxA7AO8CFjV1UDYbxxXqR1BvYH/iwoTbssGy5dgPei4jJEfEZ8Ffg\nsBLXVFRETI+Isdn4QtIf816lrao4SZsCBwG3lrqWPJI6AV8H/g8gIj7LHkrQVLUG2mZPF2oHTCtx\nPauIiGeBuVUmHwZUPG7vDuDbjVpUEdXVGhEjCp7W9DKwaaMXVkSRfQtwLXAe0GBnrDosW65ewEcF\nP0+lCYdPIUn9gJ2AV0pbSY2uI/3nLS91IbWwGTALuD3rNr5V0galLqo6EfExcDWpBTEdWBARI0pb\nVa10i4jp2fgMoFspi6mDE4DHSl1ETSQdBnwcEeMbcjsOS2tWJLUHHgDOjohPS11PdSQdDHwSEWNK\nXUsttQZ2Bm6KiJ2AxTSdbsJVZMf6DiMFfE9gA0nHlLaquol0vV6Tv2ZP0sWkwx93l7qWYiS1Ay4C\nLmnobTksW66Pgd4FP2+aTWuyJK1LCsq7I+LBUtdTg68Bh0qaQure/qaku0pbUo2mAlMjoqKlfj8p\nPJuifYH3I2JWRKwAHgT2KHFNtTFTUg+A7PWTEtdTI0nHAQcDR0fTvhh/C9IXp/HZ/7dNgbGSutf3\nhhyWLde/ga0kbSZpPdJJEg+XuKaiJIl0TG1SRFxT6npqEhEXRsSmEdGPtF+fiogm2/qJiBnAR5K2\nzibtA7xZwpJq8iGwu6R22b+JfWiiJyNV8TDwg2z8B8A/SlhLjSQNJh1CODQilpS6nppExOsRsUlE\n9Mv+v00Fds7+Tdcrh2ULlR3APwN4nPTH5r6IeKO0VdXoa8CxpFbauGw4sNRFrUXOBO6WNAEYAFxR\n4nqqlbV+7wfGAq+T/oY1qVuzSboHeAnYWtJUST8ErgT2k/QuqXV8ZSlrrFCk1huBDsDI7P/ZzSUt\nskCRehtn2027hW1mZlZ6blmamZnlcFiamZnlcFiamZnlcFiamZnlcFiamZnlcFiamZnlcFiamZnl\n+H+e1Cm/3ee/4AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lcmp+HQC7AewSkaMAnBOHfXwG4HLAjnUhfM8vknkArvRe2x7AUQDWiEgbVV2j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\nyHkMMyIict7/A/DkVo3PQwLqAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LqL6FYUrtXpf",
        "colab_type": "code",
        "outputId": "129d72e3-944c-40d2-a397-b425307af753",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 416
        }
      },
      "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",
        "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=(150, 150))\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": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-f1daee99-1a78-47d0-8e20-196d9d8d614a\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-f1daee99-1a78-47d0-8e20-196d9d8d614a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "error",
          "ename": "MessageError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mMessageError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-cced66ba92c5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0muploaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0muploaded\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mupload\u001b[0;34m()\u001b[0m\n\u001b[1;32m     70\u001b[0m     result = _output.eval_js(\n\u001b[1;32m     71\u001b[0m         'google.colab._files._uploadFilesContinue(\"{output_id}\")'.format(\n\u001b[0;32m---> 72\u001b[0;31m             output_id=output_id))\n\u001b[0m\u001b[1;32m     73\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'action'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'append'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m       \u001b[0;31m# JS side uses a generator of promises to process all of the files- some\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/output/_js.py\u001b[0m in \u001b[0;36meval_js\u001b[0;34m(script, ignore_result)\u001b[0m\n\u001b[1;32m     37\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mignore_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m    104\u001b[0m         reply.get('colab_msg_id') == message_id):\n\u001b[1;32m    105\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;34m'error'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mMessageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'error'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mMessageError\u001b[0m: RangeError: Maximum call stack size exceeded."
          ]
        }
      ]
    }
  ]
}