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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bahiensed/ml/blob/main/11_RetrainingVGG16.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iBYM_gnQjzhy"
      },
      "outputs": [],
      "source": [
        "#import necessary libraries\n",
        "import os\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Config and Metadata"
      ],
      "metadata": {
        "id": "u2YjRaTd0EcB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#config and metadata\n",
        "#image size\n",
        "IMAGE_SIZE = 224"
      ],
      "metadata": {
        "id": "F3hRChg7kRQa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#image shape\n",
        "IMAGE_SHAPE = (IMAGE_SIZE, IMAGE_SIZE, 3)"
      ],
      "metadata": {
        "id": "4F131OJCDJ_J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#image batch size\n",
        "IMG_BATCH_SIZE = 1"
      ],
      "metadata": {
        "id": "RVF6-P08DMOE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#batch size\n",
        "BATCH_SIZE = 32"
      ],
      "metadata": {
        "id": "y9IgK9kXDOWL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#shuffle buffer size\n",
        "SHUFFLE_BUFFER_SIZE = 1000"
      ],
      "metadata": {
        "id": "nuquMwv5DP9f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#base learning rate\n",
        "BASE_LEARNING_RATE = 0.0001"
      ],
      "metadata": {
        "id": "GJxdhwCbDSfl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#trainging epochs\n",
        "TRAINING_EPOCHS = 20"
      ],
      "metadata": {
        "id": "yOs7-d00DU-z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#get data\n",
        "#add this line, as suggested in https://stackoverflow.com/a/72458771, to solve DownloadError\n",
        "setattr(tfds.image_classification.cats_vs_dogs, '_URL',\"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_5340.zip\")"
      ],
      "metadata": {
        "id": "SItpZUvpkXg2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#split raw data in training, validation and test groups\n",
        "(raw_training, raw_validation, raw_testing), metadata = tfds.load(\n",
        "    'cats_vs_dogs',\n",
        "    split=['train[:80%]', 'train[80%:90%]', 'train[90%:]'],\n",
        "    with_info=True,\n",
        "    as_supervised=True,\n",
        ")"
      ],
      "metadata": {
        "id": "4gC420YIkY6P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237,
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        "outputId": "710b9974-2fdb-46c9-ef77-184a84c835e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDownloading and preparing dataset cats_vs_dogs/4.0.0 (download: 786.68 MiB, generated: Unknown size, total: 786.68 MiB) to /root/tensorflow_datasets/cats_vs_dogs/4.0.0...\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bdf93e9150714cb6804a1146754c296f"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e424a4e375554f239255934dbc3bcdb1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "89a064a8b1374507b0b7f50e838a2aff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:1738 images were corrupted and were skipped\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shuffling and writing examples to /root/tensorflow_datasets/cats_vs_dogs/4.0.0.incompleteSIFZRO/cats_vs_dogs-train.tfrecord\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/23262 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "808621b3f9b8416aa204d1b49ad0d979"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDataset cats_vs_dogs downloaded and prepared to /root/tensorflow_datasets/cats_vs_dogs/4.0.0. Subsequent calls will reuse this data.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#resize image and convert it into a form that tensorflow can read more easily\n",
        "def prep_image(image, label):\n",
        "  image = tf.cast(image, tf.float32)\n",
        "  image = (image/127.5) - 1\n",
        "  image = tf.image.resize(image, (IMAGE_SIZE, IMAGE_SIZE))\n",
        "  return image, label"
      ],
      "metadata": {
        "id": "RPXWhWvBkcOj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#create training, validation and testing data from raw data\n",
        "training_data = raw_training.map(prep_image)\n",
        "validation_data = raw_validation.map(prep_image)\n",
        "testing_data = raw_testing.map(prep_image)"
      ],
      "metadata": {
        "id": "T_cJfmBJkfAE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#create batches\n",
        "training_batches = training_data.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)\n",
        "validation_batches = validation_data.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)\n",
        "testing_batches = testing_data.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)"
      ],
      "metadata": {
        "id": "03cdPn6zkilE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#import the original VGG16 model and remove its top layer\n",
        "original_model = tf.keras.applications.VGG16(include_top=False)"
      ],
      "metadata": {
        "id": "-7jrgLh_klll",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a766426-c5ba-4d4c-b453-603403061a36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 0s 0us/step\n",
            "58900480/58889256 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#don't retrain the original model\n",
        "original_model.trainable = False"
      ],
      "metadata": {
        "id": "8oei72Dtkpoi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#add first new layer\n",
        "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()"
      ],
      "metadata": {
        "id": "IRQZ3nhJkrsY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#add second new layer\n",
        "prediction_layer = tf.keras.layers.Dense(2)"
      ],
      "metadata": {
        "id": "_CvPW_E9ktin"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#combining the two new layers with the original model\n",
        "model = tf.keras.Sequential([\n",
        "  original_model,\n",
        "  global_average_layer,\n",
        "  prediction_layer\n",
        "])"
      ],
      "metadata": {
        "id": "EEkqesa-kv2J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#compile the model\n",
        "model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=BASE_LEARNING_RATE), #changed lr to learning_rate as lr is deprecated\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "4cpzQHiakzSP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#check compilation\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "UuGH-JvBk__r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08c705dc-deb1-4211-8538-c511667ac367"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " vgg16 (Functional)          (None, None, None, 512)   14714688  \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 512)              0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " dense (Dense)               (None, 2)                 1026      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14,715,714\n",
            "Trainable params: 1,026\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#use GPU and train model\n",
        "with tf.device('/device:GPU:0'):\n",
        "  history = model.fit(training_batches,\n",
        "                      epochs=TRAINING_EPOCHS,\n",
        "                      validation_data=validation_batches)"
      ],
      "metadata": {
        "id": "AVEoLxpolB0q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c59a458e-949a-412c-be89-208d1dd0f04b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "582/582 [==============================] - 143s 219ms/step - loss: 0.9069 - accuracy: 0.6288 - val_loss: 0.6263 - val_accuracy: 0.8401\n",
            "Epoch 2/20\n",
            "582/582 [==============================] - 129s 218ms/step - loss: 0.6528 - accuracy: 0.8557 - val_loss: 0.6754 - val_accuracy: 0.8874\n",
            "Epoch 3/20\n",
            "582/582 [==============================] - 128s 218ms/step - loss: 0.6270 - accuracy: 0.8860 - val_loss: 0.6249 - val_accuracy: 0.9011\n",
            "Epoch 4/20\n",
            "582/582 [==============================] - 128s 217ms/step - loss: 0.6266 - accuracy: 0.8893 - val_loss: 0.5818 - val_accuracy: 0.8925\n",
            "Epoch 5/20\n",
            "582/582 [==============================] - 128s 218ms/step - loss: 0.6097 - accuracy: 0.8876 - val_loss: 0.6085 - val_accuracy: 0.8990\n",
            "Epoch 6/20\n",
            "582/582 [==============================] - 128s 217ms/step - loss: 0.6127 - accuracy: 0.8903 - val_loss: 0.5792 - val_accuracy: 0.9007\n",
            "Epoch 7/20\n",
            "582/582 [==============================] - 128s 217ms/step - loss: 0.5720 - accuracy: 0.8934 - val_loss: 0.5633 - val_accuracy: 0.9024\n",
            "Epoch 8/20\n",
            "582/582 [==============================] - 128s 217ms/step - loss: 0.5604 - accuracy: 0.8963 - val_loss: 0.5620 - val_accuracy: 0.9037\n",
            "Epoch 9/20\n",
            "582/582 [==============================] - 128s 217ms/step - loss: 0.5381 - accuracy: 0.8968 - val_loss: 0.5362 - val_accuracy: 0.9050\n",
            "Epoch 10/20\n",
            "582/582 [==============================] - 128s 217ms/step - loss: 0.5327 - accuracy: 0.8989 - val_loss: 0.5475 - val_accuracy: 0.9063\n",
            "Epoch 11/20\n",
            "582/582 [==============================] - 128s 216ms/step - loss: 0.5115 - accuracy: 0.8981 - val_loss: 0.5168 - val_accuracy: 0.9084\n",
            "Epoch 12/20\n",
            "582/582 [==============================] - 128s 217ms/step - loss: 0.5120 - accuracy: 0.8987 - val_loss: 0.5183 - val_accuracy: 0.9084\n",
            "Epoch 13/20\n",
            "582/582 [==============================] - 128s 217ms/step - loss: 0.5101 - accuracy: 0.9001 - val_loss: 0.5157 - val_accuracy: 0.9080\n",
            "Epoch 14/20\n",
            "582/582 [==============================] - 128s 217ms/step - loss: 0.4939 - accuracy: 0.9011 - val_loss: 0.5004 - val_accuracy: 0.9076\n",
            "Epoch 15/20\n",
            "582/582 [==============================] - 128s 216ms/step - loss: 0.4963 - accuracy: 0.9038 - val_loss: 0.4986 - val_accuracy: 0.9089\n",
            "Epoch 16/20\n",
            "582/582 [==============================] - 128s 216ms/step - loss: 0.4784 - accuracy: 0.9056 - val_loss: 0.5068 - val_accuracy: 0.9089\n",
            "Epoch 17/20\n",
            "582/582 [==============================] - 128s 217ms/step - loss: 0.4821 - accuracy: 0.9060 - val_loss: 0.4824 - val_accuracy: 0.9101\n",
            "Epoch 18/20\n",
            "582/582 [==============================] - 128s 216ms/step - loss: 0.4714 - accuracy: 0.9062 - val_loss: 0.4546 - val_accuracy: 0.9080\n",
            "Epoch 19/20\n",
            "582/582 [==============================] - 128s 216ms/step - loss: 0.4751 - accuracy: 0.9057 - val_loss: 0.4712 - val_accuracy: 0.9089\n",
            "Epoch 20/20\n",
            "582/582 [==============================] - 128s 217ms/step - loss: 0.4651 - accuracy: 0.9075 - val_loss: 0.4684 - val_accuracy: 0.9063\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get image function\n",
        "def get_image_from_url(image_url):\n",
        "  # If the temporary test_image.jpg file already exists,\n",
        "  # delete it so a new one can be made.\n",
        "  if os.path.exists('/root/.keras/datasets/test_image.jpg'):\n",
        "    os.remove('/root/.keras/datasets/test_image.jpg')\n",
        "\n",
        "  image_path = tf.keras.utils.get_file('test_image.jpg', origin=image_url)\n",
        "  return image_path"
      ],
      "metadata": {
        "id": "gRyggd8RlEOX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print prediction function\n",
        "def print_predictions(predictions):\n",
        "    for (prediction, number) in zip(predictions[0], range(1, len(predictions[0])+1)):\n",
        "      print('{}. {} {:.2f}%'.format(number, prediction[1], prediction[2]*100))"
      ],
      "metadata": {
        "id": "bq-YP75NlJBE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#predict image function\n",
        "def predict_image(image_url):\n",
        "  #fetch image from URL using the above get image function\n",
        "  image_path = get_image_from_url(image_url)\n",
        "\n",
        "  #prepare image for use by the model\n",
        "  image = tf.keras.preprocessing.image.load_img(image_path, target_size=(IMAGE_SIZE, IMAGE_SIZE))\n",
        "\n",
        "  #create a new figure, or activate an existing one\n",
        "  #https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html\n",
        "  plt.figure()\n",
        "\n",
        "  #display data as an image, i.e., on a 2D regular raster.\n",
        "  #https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html\n",
        "  plt.imshow(image)\n",
        "\n",
        "  #transform image into an array\n",
        "  image = tf.keras.preprocessing.image.img_to_array(image)\n",
        "  image = np.expand_dims(image, axis=0)\n",
        "\n",
        "  #prediction result\n",
        "  prediction_result = model.predict(image, batch_size=IMG_BATCH_SIZE)\n",
        "  labels = metadata.features['label'].names\n",
        "  print(labels[prediction_result.argmin()])"
      ],
      "metadata": {
        "id": "GHwGvhNJlRWi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#make prediction\n",
        "predict_image('https://upload.wikimedia.org/wikipedia/commons/5/5f/Kolm%C3%A5rden_Wolf.jpg')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "ocgSPL_LlTOI",
        "outputId": "838c02a1-0772-4b5e-91a0-9a316cbeb2f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://upload.wikimedia.org/wikipedia/commons/5/5f/Kolm%C3%A5rden_Wolf.jpg\n",
            "172032/166544 [==============================] - 0s 0us/step\n",
            "180224/166544 [================================] - 0s 0us/step\n",
            "dog\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}