Temas/Módulo 07 - Visión por computador/03 - Tratamiendo de imágenes/Cuadernos/ti_Cat_Dog_Kaggle.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<figure>\n",
        "<img src=\"../Imagenes/logo-final-ap.png\"  width=\"80\" height=\"80\" align=\"left\"/> \n",
        "</figure>\n",
        "\n",
        "# <span style=\"color:#4361EE\"><left>Aprendizaje Profundo</left></span>"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# <span style=\"color:red\"><center>Diplomado en Inteligencia Artificial y Aprendizaje Profundo</center></span>"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# <span style=\"color:green\"><center> Kaggle Dogs v.s Cats</center></span>"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**<center>Convoluciones</center>**"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<figure>\n",
        "<center>\n",
        "<img src=\"../Imagenes/Convolution_arithmetic.gif\" width=\"400\" height=\"400\" align=\"center\"/>\n",
        "</center>\n",
        "</figure>\n",
        "\n",
        "Fuente: <a href=\"https://commons.wikimedia.org/wiki/File:Convolution_arithmetic_-_Dilation.gif\">Vincent Dumoulin, Francesco Visin</a>, <a href=\"http://opensource.org/licenses/mit-license.php\">MIT</a>, via Wikimedia Commons"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## <span style=\"color:#4361EE\">Profesores</span>"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "1. Alvaro  Montenegro, PhD, ammontenegrod@unal.edu.co\n",
        "1. Camilo José Torres Jiménez, Msc, cjtorresj@unal.edu.co\n",
        "1. Daniel  Montenegro, Msc, dextronomo@gmail.com "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## <span style=\"color:#4361EE\">Asesora Medios y Marketing digital</span>"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "4. Maria del Pilar Montenegro, pmontenegro88@gmail.com\n",
        "5. Jessica López Mejía, jelopezme@unal.edu.co\n",
        "6. Venus Puertas, vpuertasg@unal.edu.co"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## <span style=\"color:#4361EE\">Jefe Jurídica</span>"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "7. Paula Andrea Guzmán, guzmancruz.paula@gmail.com"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## <span style=\"color:#4361EE\">Coordinador Jurídico</span>"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "8. David Fuentes, fuentesd065@gmail.com"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## <span style=\"color:#4361EE\">Desarrolladores Principales</span>"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "9. Dairo Moreno, damoralesj@unal.edu.co\n",
        "10. Joan Castro, jocastroc@unal.edu.co\n",
        "11. Bryan Riveros, briveros@unal.edu.co\n",
        "12. Rosmer Vargas, rovargasc@unal.edu.co"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## <span style=\"color:#4361EE\">Expertos en Bases de Datos</span>"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "13. Giovvani Barrera, udgiovanni@gmail.com\n",
        "14. Camilo Chitivo, cchitivo@unal.edu.co"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zX4Kg8DUTKWO"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dn-6c02VmqiN"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "id": "3sd9dQWa23aj",
        "outputId": "53be5853-8455-4541-dd7b-44e2f4905b09"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2020-10-24 15:48:30--  https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\n",
            "Resolving download.microsoft.com (download.microsoft.com)... 23.196.32.25, 2600:1408:24:49a::e59, 2600:1408:24:489::e59\n",
            "Connecting to download.microsoft.com (download.microsoft.com)|23.196.32.25|: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   147MB/s    in 5.6s    \n",
            "\n",
            "2020-10-24 15:48:36 (141 MB/s) - ‘/tmp/cats-and-dogs.zip’ saved [824894548/824894548]\n",
            "\n"
          ]
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "DM851ZmN28J3",
        "outputId": "04662670-0412-4177-b34d-47a828e4021b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "12501\n",
            "12501\n"
          ]
        }
      ],
      "source": [
        "print(len(os.listdir('/tmp/PetImages/Cat/')))\n",
        "print(len(os.listdir('/tmp/PetImages/Dog/')))\n",
        "\n",
        "# Expected Output:\n",
        "# 12501\n",
        "# 12501"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F-QkLjxpmyK2"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "zvSODo0f9LaU",
        "outputId": "44c9bd2e-002a-4566-8760-a2c31498d06d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "666.jpg is zero length, so ignoring.\n",
            "11702.jpg is zero length, so ignoring.\n"
          ]
        }
      ],
      "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",
        "## Uso de la función para este dataset en particular\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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "hwHXFhVG3786",
        "outputId": "7a82f9ef-7aad-44d9-d7c5-4190328bb3de"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "12499\n",
            "12497\n",
            "4276\n",
            "4310\n"
          ]
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-BQrav4anTmj"
      },
      "outputs": [],
      "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=['accuracy'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "fQrZfVgz4j2g",
        "outputId": "f10007fe-eb3e-4a86-83cf-e1ee38d9b7bc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 24994 images belonging to 2 classes.\n",
            "Found 8584 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "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",
        "\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."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "id": "5qE1G6JB4fMn",
        "outputId": "4f44de82-16c8-4e85-8030-a4c5fcd0d407"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "133/250 [==============>...............] - ETA: 1:22 - loss: 0.7639 - accuracy: 0.5642"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/PIL/TiffImagePlugin.py:770: 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:770: 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:770: 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:770: 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:770: 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:770: 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:770: 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:788: UserWarning: Corrupt EXIF data.  Expecting to read 2 bytes but only got 0. \n",
            "  warnings.warn(str(msg))\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "250/250 [==============================] - 238s 950ms/step - loss: 0.7162 - accuracy: 0.5872 - val_loss: 0.6193 - val_accuracy: 0.6424\n",
            "Epoch 2/15\n",
            "250/250 [==============================] - 237s 947ms/step - loss: 0.6182 - accuracy: 0.6603 - val_loss: 0.5757 - val_accuracy: 0.6942\n",
            "Epoch 3/15\n",
            "250/250 [==============================] - 239s 958ms/step - loss: 0.5743 - accuracy: 0.6983 - val_loss: 0.5854 - val_accuracy: 0.6817\n",
            "Epoch 4/15\n",
            "250/250 [==============================] - 240s 958ms/step - loss: 0.5474 - accuracy: 0.7216 - val_loss: 0.5190 - val_accuracy: 0.7441\n",
            "Epoch 5/15\n",
            "250/250 [==============================] - 241s 966ms/step - loss: 0.5295 - accuracy: 0.7366 - val_loss: 0.5170 - val_accuracy: 0.7506\n",
            "Epoch 6/15\n",
            "250/250 [==============================] - 241s 963ms/step - loss: 0.5146 - accuracy: 0.7445 - val_loss: 0.4838 - val_accuracy: 0.7676\n",
            "Epoch 7/15\n",
            "250/250 [==============================] - 236s 945ms/step - loss: 0.4987 - accuracy: 0.7562 - val_loss: 0.6400 - val_accuracy: 0.6972\n",
            "Epoch 8/15\n",
            "250/250 [==============================] - 235s 942ms/step - loss: 0.4870 - accuracy: 0.7635 - val_loss: 0.4761 - val_accuracy: 0.7718\n",
            "Epoch 9/15\n",
            "250/250 [==============================] - 230s 920ms/step - loss: 0.4762 - accuracy: 0.7744 - val_loss: 0.4532 - val_accuracy: 0.7916\n",
            "Epoch 10/15\n",
            "250/250 [==============================] - 225s 902ms/step - loss: 0.4622 - accuracy: 0.7835 - val_loss: 0.4306 - val_accuracy: 0.7943\n",
            "Epoch 11/15\n",
            "250/250 [==============================] - 223s 893ms/step - loss: 0.4500 - accuracy: 0.7895 - val_loss: 0.5374 - val_accuracy: 0.7611\n",
            "Epoch 12/15\n",
            "250/250 [==============================] - 223s 893ms/step - loss: 0.4418 - accuracy: 0.7977 - val_loss: 0.6683 - val_accuracy: 0.6665\n",
            "Epoch 13/15\n",
            "250/250 [==============================] - 223s 892ms/step - loss: 0.4336 - accuracy: 0.7980 - val_loss: 0.4450 - val_accuracy: 0.7866\n",
            "Epoch 14/15\n",
            "250/250 [==============================] - 223s 892ms/step - loss: 0.4230 - accuracy: 0.8036 - val_loss: 0.4702 - val_accuracy: 0.7716\n",
            "Epoch 15/15\n",
            "250/250 [==============================] - 223s 891ms/step - loss: 0.4194 - accuracy: 0.8070 - val_loss: 0.4314 - val_accuracy: 0.7997\n"
          ]
        }
      ],
      "source": [
        "# Note that this may take some time.\n",
        "history = model.fit(train_generator,\n",
        "                              epochs=15,\n",
        "                              verbose=1,\n",
        "                              validation_data=validation_generator)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "qH-6jIUjPDz4",
        "outputId": "2a49b6a5-e4da-4cad-dfcb-72047a123894"
      },
      "outputs": [
        {
          "ename": "OSError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-e628b6466ee2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cats_v_Dogs_Kaggle.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/saving/save.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile, options)\u001b[0m\n\u001b[1;32m    184\u001b[0m     \u001b[0mfilepath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpath_to_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\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--> 186\u001b[0;31m       \u001b[0mloader_impl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_saved_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\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    187\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0msaved_model_load\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/loader_impl.py\u001b[0m in \u001b[0;36mparse_saved_model\u001b[0;34m(export_dir)\u001b[0m\n\u001b[1;32m    111\u001b[0m                   (export_dir,\n\u001b[1;32m    112\u001b[0m                    \u001b[0mconstants\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSAVED_MODEL_FILENAME_PBTXT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m                    constants.SAVED_MODEL_FILENAME_PB))\n\u001b[0m\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: SavedModel file does not exist at: Cats_v_Dogs_Kaggle.h5/{saved_model.pbtxt|saved_model.pb}"
          ]
        }
      ],
      "source": [
        "model=tf.keras.models.load_model('Cats_v_Dogs_Kaggle.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "MWZrJN4-65RC",
        "outputId": "1d409908-3e97-4726-8125-003b3e8b20d6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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v0qT4gC4piWVat4Ydd4RBg6B/f9h55/jQriz01vpAd4/wmj8/hnnz1h7//HNW7riMV+bswOgvBjJq9EGMGtUT2IhvMYUjGMVgRtOfSayVF61aRcKmQ/fu0LcvdOhQecB17FjjB4+sjyeegKOOgtLSmG7XDrbeOkKy4rDVVvHlY0O5xxegF1+MMHz55QhmgO22g+OPh/33h/32i4ZrXTFfhx2dUj8NGDDAx48fX+wyRCq1fDm8++7arcDcByFAz54Rfv37R6Opf//4gGxR8St8dYKu4vjKlfkL22gj6NIFOneOkMqCzTfehMkrtmP0J7sw+v3teX16fDL32mIFg/dbzOBBJex3cEtad66ZbsNiGjs2Wsg77QRXXAHTp8OHH5YPH30Uv7+c5s2hV6/yoKwYoBtvnP913GNduRbhSy+V9wL06hWtwQMOiDDs0aP237eZvenuA9aar2Bs+BSMUh+99x48+mh5AL7/fnlvYrt28SGcC79cS3DTTUojxGbPjuGzz8rH0+l58woHXbt2sPnmEXZdupSP55vXpUssXw1z5sCTT8Lo0fDcc7Gvq317OOSQ2C956KGxuoZm0qTomuzaFf7xj/zvoaws3n8alumQfrmBWEcalJtvDm+8EUH4ySexTNeuEYC5MOzTp+67bhWMjZiCsfFyh7lzYdq06HL69FM47rjoWqyvSkrg+uvhsstiv93WW0P/ncvo32cJ/bvOo3+Hj9naP6T53M/WDr65c8v78lKdOkG3brDllvFziy0Kh101g25DLF8eLZ7Ro2P47LPoDR04MEJy8GD4xjfq/oN+XU2fDt/9btT+z39Gq219LFpUODRnzoy/406dIghzYdi3b/G3j4KxEVMwNnwrV8aHyNSpMeSCcNq0+NBJtWwJF10Ev/51EXrwckdXLlyYd5j2QXNOHXUMr8/bmmM3H8NfOl/OFp9PiVZgxc8aswiyXNilQzqva9d63VXpHkdO5kJywoSYv802EZA/+EEc7VrfzJ0Le+0Vrb2xY6Ffv9p5nZUr47V69KgXu1LXoGBsxBSMDYM7fP55/vD76KM1D1rs0QN22CG+Ved+9u0b+XDBBTB8eLRIhg6Nb/zrZeXKaOrMmhWttVzALVhQMPhYtCjvCdhlGDdzHhdxNW1tBX/p9juO6/Ua1qVz4cDbfPOaOYKjnpk1Kw5kGTUKXnghNvP//m8MxW4h5SxaFC23adOixu98p9gVFYeCsRFTMNYPpaXlR+cvWgQzZqwdggsXli/fpg1sv/2a4bfDDjGv0MELOc88A+ecAx9/DOeeC1dfHceMABFcixbFJ/SsWdH/mhtPp+fPz7/yVq1g002rPUxfujmn/aYPr/yrNYcd5gwdanTrVhNbtOFbuhR+8hO4+2446ST461+L3/hdsQK+971oJY4eHUf7NlWFglGna4gQrbXFi+HLL8uDLR0KzU8fW7Ik/7q7dYvA++EP1wzBXr3Wo2uptBTmzmXQZrN45+o5XHpHT/54S38eH/Ylt2xzA0esHBmht3Tp2s/t3Dmaot27wx57xM/cdLdusRNo001jH101mjbucc7fBRfE+7jrLhgyxOpNq6g+2GgjuPNO2HZbuPji+CLz2GNxlkYxlJTACSfEaRH339+0Q7EyajE2Amoxrp/p0+HCC6P1tXhx1ZdobNUqTknLDR07rjldcX6uO7RDhzwrc4/m5RdfRNflggXl4xV/puMLF651ovi45gM5s9mdTF79DX7Q4zVuPvQZum6fnUeXC78tt6zRpsrMmXDmmfDss3DQQfHhv74HbjQVDz4IQ4bEdnrqqQjLuuQOZ58drdY//hHOO69uX78+UldqI6ZgXDdffQVXXgk33RTnx510UhzkWFXQVXmhkdLS6KbMnfg1Z07h0FuwIP/RlzmbbBLNik6dyq+KkvvZrVt5S697d+jShdWlzbjuOvjNb+KqLX/4A5x2Ws3v03KH++6LD9XVq+G666JLt74dVFFfvfoqHHlkjD/++AbsH14PF18MV10Fl1wCv/1t3b1ufaZgbMQUjNVTUhItm0svjd1rp54aAdm9+zqsZPnytc9+zg0zZqx9geX27dcOtvRnvnmbbrreB6VMmxatgjFj4uCKO+6ouZbJnDnwX/8VB5XstVfsN6vrVk9j8MEHcNhh8ecybFhc3aW23XQT/PSn8fu79db6cxBQsWkfozRpzz0HP/sZvPMO7L03PP007L57ngXdozVX8bIfufFZs9ZcfpNN4rj8/v3jelrpWc3dutX5kRY77BAnUd95J/ziF3HS/OWXx3vfkANAH344DvJZsiRao+efH1c/kXW37bZxzuAxx8T+vo8+ilNvaiushg+PUDz2WPjLXxSK1aEWYyOgFmNhU6fCz38eVyzp0ye6/o45BgyPoBs3Lm7XkAZhxRMHu3XLf8HIbbaJVl49/aT57LM4IvLRR2HXXWPfUt4vA5X44gv48Y/hoYfgW9+Ce+6J00Rkw61cGftphw+PfY+33x77sWvSU09F1+0++8R4sY+IrW/UYpQmZcGCuObjLbfEQZa/v2wJ5+06htZv/QuGjovrUy1YEAu3bAm9e0fQDRy4ZvD16VMnV1KpDVtuCY88EsH43/8dB6L+9KexXTbaqOrnjxoV3bILFsDvfge/+lWea5bKemvdGu69N/7Mrrgijlh95JHoSa8J//wnfP/70Znx2GMKxXXi7hoa+LD77ru7hFWr3G+6doVvuvEqb2alfs7W/+dze+zmX9+GtVkz9112cT/rLPehQ90nTnRfvbrYZde6hQvdzz47NkGfPu7PPlt42S+/dB8yJJbt39/97bfrrs6m6t573Vu2dO/b1/2jjzZ8fZMnu3fs6L7ddu5z5274+horYLzn+UxVV2oj0KS7UktKYMoU/PVxPPG3Ffz8lcG8v6o3B/Msf+ACdu69JJpKuWG33arXXGqkxoyBs86KC3qfcgrccMOa59Q9+2zc82727Ljs3KWX1nz3nuT3yitw9NHRKh81av2vRjNjRvnRrq++Gp0hkl+hrtSit3Y0qMVYbWVl8XX6wQfdf/Yz9732cm/b1ieysx/Icw7uO2z0iT9x3H1eNvoJfVUuYPly90sucW/Rwr1LF/cHHnBfvNj9nHOildi3r/vrrxe7yqZp6lT3rbd2b9PGfeTIdX/+3LnRSuzYMVqNUjkKtBiL/qGuofEF46pVkWE1oqTE/fnn3c84w33zzf3rLtHWrX3O7of6WTu96s2s1Dt1LPGb/1jmq1bV0Os2AZMmue+xR2zOjTd2N3O/4AL3ZcuKXVnTNm+e+557xu/l2mur/7+0aJH7bru5t23r/uqrtVtjY1EoGLUrXWrMhx/GUZ/DhsV0jx5x49lCQ8eOBQ7odIfXXotLhTz8cFyav337uFXB3nuzYpdvc9NL/bnq2hYsXw7nnR9dfp061eW7bfh23jkO0Pjzn+MAnSuvjPMTpbi6dIkLew8ZEgc8ffBBnGZR2ek2K1dGN+zEidENWx/v5tGQKBhlg02cCNdeG4f0t2gBJ58cR9bNnBnDyy/HqQMVL/Sy0UZJUPZweraaS8+Px9JzwuP0nDeenq3m0X7w/nEDwsMOw9u0ZeRI+NWJsR/liCMiiLffvhjvunFo3jzOSTz//GJXIqk2beCBB+KI1auuir/3kSPzX16wtBROPDHuD3nvvXHDZNkwCkZZb2PHxl0dnnoqGnQXXBCnA+S7s0JJSVw5JReWXw/vLWbmO18x+cVWzCntCnw/G4BV0PEF6Pk+9Lw7btk0blwcfv7883DggXX5bkXqVrNm0YrfZpu4Ys13vxvn4261Vfky7nGe6SOPxIFUJ59cvHobEwWjrBP3CMKrr44j3rp0iXPczj238vOvWrSIrtUePWBg90/g44dgzAh4663oT913X1Z9/wRmfedYZi7ttHaAzozb5dxxB5x+uq66Ik3H6adHGB57bBypOno0DMiOo7zssrgwwEUXxZdSqRkKRqmWkpLY3XfNNXGhmF694E9/in/aap3/Pndu9AWNGBE7tiBOn7jxxrjFeffutAL6ZIOIlDvwwPi3OfTQuIrNAw/AJ5/El9Izz4yWpdQcBaNUasWKuFj0ddfFtbP79Yv9GMcdV41rby5cGJfcGDEidoCUlcURH1deGSvYeus6eQ8ijUG/fvD667Fv/Zhjovfm6KN1UfDaoGCUvL76Kv7hbrwxGnvf/naMDx5cxS2G3GOHx333xZW6V6+OnSQXXRS3Edhxxzp7DyKNzRZbxEXizzkn7iH6wAO6TF9t0CaVNcybF7eoueWWuJb2f/xHZNq++1bjW+mMGdGv88ILcS+nn/wkWoYDBugrrUgNadcuem2k9igYBYhMu/76uF3RypVx8eELL4wrqFWprCyOAPjlL2P6ttviumO6e62INEAKxibunXfiHMQRIyLHTj017uNX7XMDp0+Pi2u+9BIcfDAMHbrm8eQiIg2MgrGJKi2Ng0EfeyxOtD///LiZbbXvZl9WFi3DX/4yEnXo0AhIdZmKSAOnYGyimjePELziijhBOL3DQpU++ihC8OWX4ZBD4uTCXr1qq1QRkTqlYGzC/vSndXxCWVkclZO7Y+2dd8Jpp6mVKCKNioJRqufDD+Ns/jFjYNCg6Drt0aPYVYmI1DgdNiiVKyuDm2+OC5ROnAh33RXXhFMoikgjpRajFPbBB9FK/Mc/4lpUt9+uQBSRRk8tRllbaWmc5d+/P0yaFDdYfOIJhaKINAlqMcqa3n8/WomvvgqHHRatxGqfwyEi0vCpxSihtDRu6LbLLjBlSlxzavRohaKINDlqMQpMmxanXbz2Wlwl/Pbb899tWESkCVCLsSkrLY0LpO66K0ydCsOHw+OPKxRFpElTi7GpWr0a9t8/9iUeeWTcY0qBKCKiYGyyWraMy7mde27cJ1FXrxERARSMTdullxa7AhGRekf7GEVERBIKRhERkYSCUUREJKFgFBERSSgYRUREEgpGERGRhIJRREQkoWAUERFJKBhFREQSCkYREZGEglFERCShYBQREUkoGEVERBIKRhERkYSCUUREJKFgFBERSSgYRUREEgpGERGRhIJRREQkoWAUERFJKBhFREQSCkYREZGEglFERCShYBQREUkoGEVERBIKRhERkYSCUUREJKFgFBERSSgYRUREEgpGERGRhIJRREQkoWAUERFJKBhFREQSCkYREZGEglFERCShYBQREUkoGEVERBIKRhERkYSCUUREJKFgFBERSSgYRUREEgpGERGRhIJRREQkoWAUERFJKBhFREQSCkYREZGEglFERCShYBQREUkoGEVERBIKRhERkYSCUUREJKFgFBERSSgYRUREEgpGERGRhIJRREQkoWAUERFJKBhFREQSCkYREZGEglFERCShYBQREUkoGEVERBIKRhERkYSCUUREJKFgFBERSSgYRUREEgpGERGRhIJRREQkoWAUERFJKBhFREQSCkYREZGEglFERCShYBQREUkoGEVERBIKRhERkYSCUUREJKFgFBERSSgYRUREEgpGERGRhIJRREQkoWAUERFJKBhFREQSCkYREZGEglFERCShYBQREUkoGEVERBIKRhERkYSCUUREJKFgFBERSSgYRUREEgpGERGRhIJRREQkoWAUERFJKBhFREQSCkYREZGEglFERCShYBQREUkoGEVERBIKRhERkYSCUUREJKFgFBERSSgYRUREEgpGERGRhIJRREQkoWAUERFJKBhFREQSCkYREZGEglFERCShYBQREUkoGEVERBIKRhERkYSCUUREJFEjwWhmm5nZ29kwx8xmJdOtqnjuADO7uRqv8c+aqDVZ301ZnfpyICIiX2tREytx9y+AXQHM7HJgibtfn3vczFq4e0mB544HxlfjNfasiVqzepoBRwMzgX2Bl2pq3RVep+D7FhGR+qnWWktmNszMbjOz14Hfm9keZvaamb1lZv80sx2y5fYzsyey8cvN7C4ze9nMPjKz85L1LUmWf9nM/mZmU83sfjOz7LFDs3lvmtnNufXmsR8wBbgVOD55jS3M7DEzm5gNe2bzTzGzSdm8+5L39/0C9f3DzEYB72bz/p7VNMXMzk6eM8jMJmTrfcHMmpnZv82sS/Z4MzP7IDctIilP3CoAAAcQSURBVCK1r0ZajJXoAezp7qVmtgmwt7uXmNlBwFXAsXme0xfYH9gYmGZmt7r76grLfBPYEfgMeBX4rpmNB24H9nH36WY2opK6jgdGAI8DV5lZy+w1bgZecfejzaw50N7MdgQuyd7H52bWqRrvezdgJ3efnk2f7u4LzKwt8IaZPUJ8KRma1NvJ3cvMbDhwInATcBAw0d3nV3yBLGDPBujVq1c1ShIRkeqo7f1rI929NBvvAIw0s3eAG4lgy+dJd1/p7p8D84At8iwzzt0/dfcy4G2gNxGoHyVhlDcYs32ehwJ/d/evgNeBQ7KHDyBakbh7qbsvyuaNzOrB3RdU432PS+oAOM/MJgL/AnoC2wHfAcbklkvWexdwSjZ+OnB3vhdw9zvcfYC7D+jSRQ1KEZGaUtstxqXJ+G+Bl7LWWG/g5QLPWZmMl5K/xuosU8ghQEdgctYD2w5YDhTqdi2khOyLRbbPMj3I6Ov3bWb7ES2/ge6+zMxeBtoUWqm7zzSzuWZ2ALAH0XoUEZE6UpdHZHYAZmXjQ2ph/dOArbPQBfhhgeWOB850997u3hvoAxxsZu2AF4AfAZhZczPrALwI/MDMNsvm57pSZwC7Z+NHAC0LvF4HYGEWin2JliJE63EfM+tTYb0AfwWGs2aLW0RE6kBdBuPvgavN7C1qoaXq7suBc4FnzOxNYDGwKF0mC79BwJPJ85YCY4HBwPnA/mY2GXgT6OfuU4ArgVey7tAbsqcOBfbN5g1kzdZx6hmghZm9B1xDBCLZfsOzgUezdTyUPGcU0J4C3agiIlJ7zN2LXUONMbP27r4kO0r1L8C/3f3GYte1rsxsAHCju+9dneUHDBjg48dXecaLiIgkzOxNdx9QcX5jO7n9LDN7mzgVowNxlGqDYmYXAo8AFxW7FhGRpqhRtRibKrUYRUTWXVNpMYqIiGwQBaOIiEhCXamNgJnNBz5ez6d3Bj6vwXJqU0OqFRpWvQ2pVmhY9TakWqFh1buhtW7l7mtdIUXB2MSZ2fh8fez1UUOqFRpWvQ2pVmhY9TakWqFh1VtbtaorVUREJKFgFBERSSgY5Y5iF7AOGlKt0LDqbUi1QsOqtyHVCg2r3lqpVfsYRUREEmoxioiIJBSMIiIiCQVjE2Vmg8xsmpl9kF2ftd4ys55m9pKZvWtmU8zs/GLXVJXstmVvmdm63uezzplZRzP7m5lNNbP3zGxgsWsqxMx+mv0NvGNmI8ys4L1Ni8HM7jKzedkN2XPzOpnZc2b27+znpsWsMVWg3uuyv4VJZvaYmXUsZo05+WpNHrvAzNzMOtfEaykYmyAza07cfeR7QD/geDPrV9yqKlUCXODu/Yj7Wf64ntcLcQuz94pdRDX9EXjG3fsCu1BP6zaz7sB5wAB33wloDhxX3KrWMoy4tV3qQuAFd9+OuOdrffoiOoy1630O2Mnd+wPvU39uaDCMtWvFzHoC/wF8UlMvpGBsmvYAPnD3j9x9FfAgcGSRayrI3We7+4RsfDHxwd29uFUVZmY9gMOIG07Xa9nNuPcB7gRw91Xu/mVxq6pUC6CtmbUA2gGfFbmeNbj7GGBBhdlHAvdk4/cAR9VpUZXIV6+7P+vuJdnkv4AedV5YHgW2LcCNwC+BGjuSVMHYNHUHZibTn1KPgyZlZr2BbwKvF7eSSt1E/KOWFbuQaugDzAfuzrp+/2pmGxW7qHzcfRZwPdEymA0scvdni1tVtWzh7rOz8TnAFsUsZh2dDjxd7CIKMbMjgVnuPrEm16tglAbDzNoT96r8H3f/qtj15GNmhwPz3P3NYtdSTS2A3YBb3f2bwFLqV1ff17J9c0cSYb4lsJGZnVTcqtaNx/lxDeIcOTO7mNiNcX+xa8nHzNoBvwYuq+l1KxibpllAz2S6Rzav3jKzlkQo3u/ujxa7nkp8FzjCzGYQXdQHmNnw4pZUqU+BT9091wL/GxGU9dFBwHR3n+/uq4FHgT2LXFN1zDWzbgDZz3lFrqdKZjYEOBw40evvye7bEF+SJmb/bz2ACWbWdUNXrGBsmt4AtjOzPmbWijiAYVSRayrIzIzYB/aeu99Q7Hoq4+4XuXsPd+9NbNcX3b3etmrcfQ4w08x2yGYdCLxbxJIq8wnwHTNrl/1NHEg9PVCoglHAqdn4qcDjRaylSmY2iNgVcIS7Lyt2PYW4+2R339zde2f/b58Cu2V/0xtEwdgEZTvW/xv4P+KD5WF3n1Lcqir1XeBkovX1djYcWuyiGpGfAPeb2SRgV+CqIteTV9aq/RswAZhMfH7Vq8uXmdkI4DVgBzP71MzOAK4BDjazfxOt3muKWWOqQL1/BjYGnsv+124rapGZArXWzmvV31ayiIhI3VOLUUREJKFgFBERSSgYRUREEgpGERGRhIJRREQkoWAUERFJKBhFREQS/w8JhKj/tX57SwAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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FnngC+OwzO5JdjowYYcc1xoyJbGgBwAsv2AjDP/zBjmNSeA4fBnr1sn+zmTNjE1qATVEZMsSmQSxdGpvXLA/Y4ooBtrjy27gRGDjQ/lhbt7YP8G7dAOzdaxPRvvjCLl9/bcO7EhJsCNb55wOtWtnl/PPtkpTk969TIunp9u36hhvsWEo0jp/MmmVfFIYOBf7+98jvv7zJybFRmbNmWc/AlVfG9vUPHbLFert3B95/P7avHe84OMNHDK5TqdoZYQPnnLrlFmDkSGst/OTgQWuaLF5sAz/WrLGl8YOXRW/S5NRAa9UKOPPMmP9OxTl61Eb8HTtmZ8SNZom33Qa88YadQfmSS6L3OuVBYPL36NF2wgQ/PPWUjWRcvpwTyYMxuHzE4CrcsWM2qm7kSJuwPHw4cNddRQzDz8qyJtuaNXYJBNq339qM0YD69UMHWt260RsmVoy77rJZA59+aj2h0XTwoLVmk5Lsw5CTwUMbNcomy99zjwWXXw4csFbXFVcA777rXx3xhsHlIwZX8b77Drj3XpuM2a6ddR+WaG5Lbq413QoG2po1dgAjoFatvBBr2dK6IM8+266j2O04c6YdQ3ngAQvpWJg92yaAP/gg8I9/xOY1XTJ8OPD443Ym44kTw5izGGXDhgFPP22Ddlq39reWeMHg8hGDKzyqwHvvAfffD2zfbiPkRoywidBl2unOnaeG2Zo1tl5SsHr18oIs+NK8ud1XypZaRoZNXWvQwCYIx3LA5J/+ZKumLFhgx9bI/ksMG2bBdcst1qXqd2gBtopK06Z2fPKdd/yuJj4wuHzE4CqZI0fsm+fzz1sjaPBg6+GrVMnOJFu5ct7tsmyrnrkfsnEDsGGDdT9u2JB32b49/xmhTz89f6gF327a1HYYgqq1tP73PxuYEetv0ocPW2hWrWpjXapVi+3rxxtV4KGHrNX7xz/aWp6JiX5XleeRR4BnnwVWr7ZOgYqOweUjBlfprF1rxx6itYJ2vXrANddYsFxxhWXTTzIzgc2b84IsONg2brQlvgMSEmyQSCDIWrQAzjkHOOccjP3sXNw1sDJefNEGovghsIjv4ME2CKGiUrXW/Esv2fHG0aPjbwGXvXttikTv3sCECX5X4z8Gl48YXKWnal0omZlAdraNzQhcF3Y7nPtPnrQWyKxZNpChalXgssuA664Drr0WSE4uoqjcXGDXrvwttOBg27sXALAO56I9lqPraWmY1Xk4Es71Ai0QbM2bx6zf8O67gbFjbZbBL34Rk5eMK7m59iVo7Fhblf35530bo1Oshx6yQSNr1thh2IqMweUjBlf8ysqyqWMzZthlwwbb3r69tcR69bIh7CX6Zn7gALLWfo9ONzfDpl3VsOrqh9BwRxqwfr2lcECgpea1zvJdmjUrtPuxNI4csS7DSpVstY7SrELvqpwcO53PG29YKPz97/EbWoCdrSElxQaNvPmm39X4i8HlIwaXG1RtVP2MGTbHbOFC+6besKG1wnr1Anr0CO840aOP2jD/994DfvOboDv27bMAC3U5eDDvcYmJ9unVIqiV1rixNQUbNbLh/iU8ODNvng3DHzQIePHFEj3VWdnZQP/+1u3217/aJZ5DK2DIEOvS/PZbe+srKgaXjxhcbtq711ZSmDED+OQTG+hQrZodL7ruOjs+FuqMuPPn2yoI/fsDr78e5oup2guuXw98//2poRY8pB+w1lqDBhZigUsg1IJvF2ha/fnPdmynIpzAMCsLuPlmO6dWJFbgj6UffrCe5H79bIX6iorB5SMGl/syM+3DPtCluGWLbe/YMa9LsW1bW76nbVtrDH39ta1FV2aBUNu+3dZ0DFwXvB3cYguoWTNfoB2tk4I2/x0EVErEyqkbcHqLBna20HgbpVBGmZl2rrPp021AyuDBfldUcoMGAf/6l81xbNbM72r8weDyEYOrfFG14cqBEFu82LY1bmzD9pcvt1O9x3ze1JEjoQMt+OdduzBfu6A7Psc9GIN/YqAdS6tXz1pwDRvadahLYE5CnDt+3NaCnDXL32WcymrHDmt19e9vK65URAwuHzG4yreMDFsweMYMm681dKgd44pLWVnArl2474FKeGlyA3x271RcmpRmfVPBF29kZD4JCTYbvLiAq1/ft9PSHD1qC+Z++ql92N9+uy9lRMw999gE8u+/t3E8FQ2Dy0cMLoo3x45Zl+aePXY8rmPHvEutWrD5AhkZFmI7d54abIFLRoaNYCmobl3r30pJsevAJSXFJmxXqRLx3+nwYRtE8+WXNoLw97+P+EvE3LZtNjXwttus27CiYXD5iMFF8WjNGhsanpYGrFuXt/3ss/MHWYcOBSZnB8vJsfQLDrOdO+0Td9Mmu2zdmn9FfxE75lZYsCUnl3jE5MGDwNVX25Ja48cDN91U0n+N+HXnnRbEGzYUM7+whHbvtqXAvvrKurfbt7fBIO3axc/ISwaXjxhcFO8OHrQTGS5ZYkGWlmbZA1gP4QUX5A+zCy8sQaMpJ8fCLBBkmzbZqiSB2wWX16pUyfrFQgVb48bWHRkUbPv22WLCK1YAkyYVmH5QDmzebDMi7roLePnl0u0jN9dWolmwIC+svv/e7qtSxdad/uYbmz5w/vkWYP362TE2PzG4fMTgIhdlZFiABYdZYF3iqlWtq7FjRzvdfceOtspDqQYnnjyZv4VWMNgyMvI/PjHRwis5GXvrnI8rFj2FNfvq470hC3Ft70RrljRs6MRAknDddpu1JDdtCj0Fo6CjR+39CgTVwoV26hTADlN27gx06WKXDh3stDc//mhTByZOtCkdAPDzn9uUgj59rPc31hhcPmJwUXmgankSHGRLl9qHJGBD/y+6KH/LrGnTCHQ7HTtm8w82bbKA274d2L4dGRuOoMfiZ7DhZDKmozeuxJy85yQk2CCR5OS8S2ACd+DSsGFUjrVFw4YN9sVg4EBbrqqgHTvyWlILFthUjOxsu69Vq/xB1aJF8e/J1q12hu6JE+2kp4mJNn/x5ptt8EtEpnmEgcHlIwYXlVc5OdYFFQiytDTrsgsc0qpdG0hNzQuy1NTwWgzF2bHDVjHZtg2YOUNxaYeDPwVacLjl21ZwEreITQNo1MgKPessuwTfLritenXfDgD17w9MnmxdfIHjU4GwCswrrFbNWsBdulhYderkDbYpg9WrLcAmTrTXqVbNJuD36wdcdVV0s5/B5SMGF1UkmZn2LT0tzU7lkpZmA0ECgw8bNcofZKmpJftw3brVFkTevdtWNgl70eBDh04Ns8D8tr17ra/sxx9DT+QOqFq16IALtb1mzYhM8F6/HjjvPMvNnBzb1qBBXkuqc2cbYBHBJS7zUbWQnDjRAnTvXuDMM21NxX79gK5dIz+PncHlIwYXVXRHjlj3VaBVlp5uH8QBZ5+dv2XWoUPoE1Jv3GihdeCALcN1ySVRKDYrC9i/P3+YBS4FtwV+3rcvL00KSkiwT/jiAq/gJcRcuJdeshGggbCKSFdsKWRl2dnKJ04E3n/fuouTk4G+fS3E2raNTF0MLh8xuIhOtX+/HSMLtMqCRzKK2Oi24JZZ9ep2duBjx+xDs0MHf+vPJzfXWnShwq2o4Dt+vPB9nn566LCrW9eO3wVP+K5XL3pNrWIcPWqLUk+cCHz8sR1ba9Uqb2RiWZarYnD5iMFFFJ6MjLwgC1zv3p13f506tjpJmzb+1RhRx4+HH3KBy/79ofdVu3ZekAWHWsFtSUlRa6aFGpmYlmZfPEqDweUjBhdR6ahaKyw93QaB9Oljc5oqtOBVTXbtKvx61y57bEHVq58aavXr21DB006z0RfB10Vtq1q10ANbW7YA06bZSMjSHvticPmIwUVEMadqrbPAiiZFhVxgkldpVK1adNC9/rodjCuFwoKr/MzQIyKiPCI2XLNWLVv6pCgnTtjBw+PH7faJE3m3C7sO9zFROGUOg4uIqKILtJQcUb7OHkdEROUeg4uIiJzC4CIiIqcwuIiIyCkMLiIicgqDi4iInMLgIiIipzC4iIjIKQwuIiJyCoOLiIicwuAiIiKnMLiIiMgpDC4iInIKg4uIiJzC4CIiIqcwuIiIyCkMLiIicgqDi4iInMLgIiIipzC4iIjIKQwuIiJyCoOLiIicwuAiIiKnMLiIiMgpDC4iInIKg4uIiJzC4CIiIqcwuIiIyCkMLiIicgqDi4iInMLgIiIipzC4iIjIKQwuIiJyCoOLiIicwuAiIiKnMLiIiMgpDC4iInIKg4uIiJzC4CIiIqcwuIiIyCkMLiIicgqDi4iInMLgIiIipzC4iIjIKQwuIiJyCoOLiIicwuAiIiKnMLiIiMgpDC4iInIKg4uIiJzC4CIiIqcwuIiIyCkMLiIicgqDi4iInMLgIiIipzC4iIjIKQwuIiJyCoOLiIicwuAiIiKnMLiIiMgpDC4iInIKg4uIiJzC4CIiIqcwuIiIyCkMLiIicgqDi4iInMLgIiIipzC4iIjIKQwuIiJyCoOLiIicwuAiIiKnMLiIiMgpDC4iInIKg4uIiJzC4CIiIqcwuIiIyCkMLiIicgqDi4iInBKR4BKRs0Tka++yS0R2BP1cpZjnporIy2G8xlcRqvWXIjIzEvsiIqLYqxSJnajqjwDaAYCIPAHgiKqOCtwvIpVUNbuQ56YDSA/jNTpHolYiInJb1LoKRWSciIwVkcUA/iEiF4vIQhFZLiJfiUhL73E/tYBE5AkReV1E5onIRhEZGLS/I0GPnyci74rItyIyQUTEu6+nt22piLxckpaViPQVkVUislpERnjbEr3fY7V33/3e9oEiskZEVorIpIj9oxERUbEi0uIqQjKAzqqaIyI/A9BVVbNF5HIAzwC4IcRzzgNwKYAaANaJyCuqmlXgMe0BXABgJ4AFALqISDqAVwF0U9VNIvJ2uEWKSEMAIwBcBGA/gNkicj2AbQAaqWpr73E1vacMBdBMVTODthXc5x0A7gCAJk2ahFsKEREVI9qDM6aoao53+wwAU0RkNYAXYMETyoeqmqmqewHsBlAvxGOWqOp2Vc0F8DWAFFjgbVTVTd5jwg4uAB0BzFPVPV6X5gQA3QBsBNBcRP4pIlcBOOQ9fiWACSJyC4DCukBfU9VUVU2tU6dOCUohIqKiRDu4jgbdfhrAXK/10gvAaYU8JzPodg5CtwrDeUyZqep+AG0BzANwJ4D/eHddA2AMgA4A0kQk2i1XIiLyxHI4/BkAdni3+0dh/+tgraMU7+cbS/DcJQC6i0htEUkE0BfA5yJSG0CCqr4H4DEAHUQkAUBjVZ0L4CHY75UUod+BiIiKEcuWwj8AvCkijwH4MNI7V9XjInI3gI9F5CiAtCIe3kNEtgf9/FvYcau5AATWXTldRNoCeMMLKwB4GEAigPEicob32JdV9UCkfx8iIgpNVNXvGiJGRJJU9Yg3ynAMgPWq+oLfdaWmpmp6erEj/omIKIiILFXV1ILby9vKGbeLyNcAvoF14b3qcz1ERBRh5WpQgde68r2FRURE0VPeWlxERFTOMbiIiMgp5WpwRrwSkT0AtpTy6bUB7I1gOdHmUr2sNXpcqtelWgG36i1rrU1V9ZQVHBhccU5E0kONqolXLtXLWqPHpXpdqhVwq95o1cquQiIicgqDi4iInMLgin+v+V1ACblUL2uNHpfqdalWwK16o1Irj3EREZFT2OIiIiKnMLiIiMgpDK44JiJXicg6EfleRIb6XU9hRKSxiMwVkTUi8o2IDPK7puKISKKILBeRmX7XUhwRqSki74rItyKyVkQ6+V1TYUTkfu//wGoReVtECjvvni9E5HUR2e2d0DawrZaIzBGR9d71mX7WGKyQekd6/xdWisi0ws7CHmuhag26b4iIqHeqqDJjcMUp77xgYwBcDaAVgL4i0srfqgqVDWCIqrYC8HMA98RxrQGDAKz1u4gwvQTgY1U9D3Zi07isW0QaARgIINU7YWwigJv8reoU4wBcVWDbUACfquo5AD71fo4X43BqvXMAtFbVNgC+g51uKR6Mw6m1QkQaA7gSwNZIvRCDK35dDOB7Vd2oqicBTALQ2+eaQlLVH1R1mXf7MOyDtZG/VRVORJJhZ7H+T3GP9Zt33rduAP4LAKp6Ms7P/1YJQDXvrODVAez0uXwgxigAAAKHSURBVJ58VPULAPsKbO4N4E3v9psAro9pUUUIVa+qzlbVbO/HRQCSY15YCIX82wK28PlfAERsJCCDK341ArAt6OftiOMwCPDOQN0ewGJ/KynSi7A/pFy/CwlDMwB7YCc0XS4i/xGR0/0uKhRV3QFgFOyb9Q8ADqrqbH+rCks9Vf3Bu70LQD0/iymhPwKY5XcRhRGR3gB2qOqKSO6XwUURIyJJAN4DcJ+qHvK7nlBE5FoAu1V1qd+1hKkSgA4AXlHV9gCOIr66sn7iHRvqDQvbhgBOF5Fb/K2qZNTmBzkxR0hEHoV100/wu5ZQRKQ6gEcADIv0vhlc8WsHgMZBPyd72+KSiFSGhdYEVZ3qdz1F6ALgOhHZDOt+vUxExvtbUpG2A9iuqoEW7LuwIItHlwPYpKp7VDULwFQAnX2uKRwZItIAALzr3T7XUywR6Q/gWgA3a/xOxj0b9iVmhff3lgxgmYjUL+uOGVzxKw3AOSLSTESqwA5yf+BzTSGJiMCOwaxV1ef9rqcoqvqwqiaragrs3/QzVY3bVoGq7gKwTURaept6AFjjY0lF2Qrg5yJS3fs/0QNxOpCkgA8A/MG7/QcA032spVgichWsq/s6VT3mdz2FUdVVqlpXVVO8v7ftADp4/6fLhMEVp7yDr/cC+AT2xz9ZVb/xt6pCdQHwO1jr5Wvv0tPvosqRPwOYICIrAbQD8IzP9YTktQrfBbAMwCrY50tcLU8kIm8DWAigpYhsF5FbATwL4AoRWQ9rNT7rZ43BCql3NIAaAOZ4f2tjfS3SU0it0Xmt+G1lEhERnYotLiIicgqDi4iInMLgIiIipzC4iIjIKQwuIiJyCoOLiIicwuAiIiKn/D8xdhRYX80ScAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "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['accuracy']\n",
        "val_acc=history.history['val_accuracy']\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.show()\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.show()\n",
        "# Desired output. Charts with training and validation metrics. No crash :)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oDYDa_DdwDA6"
      },
      "outputs": [],
      "source": [
        "model=tf.keras.models.load_model('Cats_v_Dogs_Kaggle.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LqL6FYUrtXpf"
      },
      "outputs": [],
      "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 cat\")\n",
        "  else:\n",
        "    print(fn + \" is a dog\")"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "ti_Cat_Dog_Kaggle.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}