Model training.ipynb

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  "metadata": {
    "colab": {
      "name": "classification.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TBFXQGKYUc4X"
      },
      "source": [
        "##### Copyright 2018 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "1z4xy2gTUc4a"
      },
      "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."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SjQkON5a_F7r"
      },
      "source": [
        "##Import TensorFlow and other libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L1WtoaOHVrVh"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import PIL\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.python.keras.preprocessing.image import ImageDataGenerator, load_img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3FSCmsc__NZ7"
      },
      "source": [
        "###Load data from google driver"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dlIl_qZRj6Nx",
        "outputId": "ddba1118-b487-4908-c535-8975f50c2284"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "import pathlib\n",
        "data_dir = \"/content/drive/MyDrive/dataset/Training\"\n",
        "data_dir = pathlib.Path(data_dir)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbtTDYhOHZb6",
        "outputId": "d6656896-162a-4872-ef54-88da6a334cd2"
      },
      "source": [
        "image_count = len(list(data_dir.glob('*/*.png')))\n",
        "print(image_count)\n",
        "print(data_dir)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11500\n",
            "/content/drive/MyDrive/dataset/Training\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xyDNn9MbIzfT"
      },
      "source": [
        "## Create a dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "anqiK_AGI086"
      },
      "source": [
        "Define some parameters for the loader:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H74l2DoDI2XD"
      },
      "source": [
        "batch_size = 10\n",
        "img_height = 250\n",
        "img_width = 250"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pFBhRrrEI49z"
      },
      "source": [
        "It's good practice to use a validation split when developing your model. Let's use 80% of the images for training, and 20% for validation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fIR0kRZiI_AT",
        "outputId": "5897accf-25cb-4a68-a73b-7f22813be801"
      },
      "source": [
        "train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "  data_dir,\n",
        "  validation_split=0.2,\n",
        "  subset=\"training\",\n",
        "  seed=123,\n",
        "  image_size=(img_height, img_width),\n",
        "  batch_size=batch_size)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 11500 files belonging to 2 classes.\n",
            "Using 9200 files for training.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iscU3UoVJBXj",
        "outputId": "ca5e6d72-12d8-4336-ec49-2ac1694f27f8"
      },
      "source": [
        "val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "  data_dir,\n",
        "  validation_split=0.2,\n",
        "  subset=\"validation\",\n",
        "  seed=123,\n",
        "  image_size=(img_height, img_width),\n",
        "  batch_size=batch_size)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 11513 files belonging to 2 classes.\n",
            "Using 2302 files for validation.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WLQULyAvJC3X"
      },
      "source": [
        "You can find the class names in the class_names attribute on these datasets. These correspond to the directory names in alphabetical order."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHAxkHX5JD3k",
        "outputId": "fb215f6d-5331-41b1-e22b-630fb835224e"
      },
      "source": [
        "class_names = train_ds.class_names\n",
        "print(class_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['clean', 'defaced']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Dr0at41KcAU"
      },
      "source": [
        "## Configure the dataset for performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOjJSm7DKoZA"
      },
      "source": [
        "AUTOTUNE = tf.data.AUTOTUNE\n",
        "\n",
        "train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)\n",
        "val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GUnmPF4JvEf"
      },
      "source": [
        "## Standardize the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEYxo2CTJvY9"
      },
      "source": [
        "normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9o9ESaJJ502",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "798b33e1-1b93-42ab-9fcd-bb33c0c70f46"
      },
      "source": [
        "normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))\n",
        "image_batch, labels_batch = next(iter(normalized_ds))\n",
        "first_image = image_batch[0]\n",
        "# Notice the pixels values are now in `[0,1]`.\n",
        "print(np.min(first_image), np.max(first_image)) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0 1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hqtyGodAMvNV"
      },
      "source": [
        "# Overfitting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BDMfYqwmM1C-"
      },
      "source": [
        "## Data augmentation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9J80BAbIMs21"
      },
      "source": [
        "data_augmentation = keras.Sequential(\n",
        "  [\n",
        "    layers.experimental.preprocessing.RandomFlip(\"horizontal\", \n",
        "                                                 input_shape=(img_height, \n",
        "                                                              img_width,\n",
        "                                                              3)),\n",
        "    layers.experimental.preprocessing.RandomRotation(0.1),\n",
        "    layers.experimental.preprocessing.RandomZoom(0.1),\n",
        "  ]\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Zeg8zsqXCsm"
      },
      "source": [
        "model = Sequential([\n",
        "  layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),\n",
        "\n",
        "  layers.Conv2D(16, 3, padding='same', activation='relu'),\n",
        "  layers.BatchNormalization(),\n",
        "  layers.MaxPooling2D(pool_size=2, strides=2),\n",
        "  layers.Dropout(0.1),\n",
        "\n",
        "  layers.Conv2D(32, 3, padding='same', activation='relu'),\n",
        "  layers.Conv2D(32, 3, padding='same', activation='relu'),\n",
        "  layers.BatchNormalization(),\n",
        "  layers.MaxPooling2D(pool_size=2, strides=2),\n",
        "\n",
        "  layers.Conv2D(64, 3, padding='same', activation='relu'),\n",
        "  layers.Conv2D(64, 3, padding='same', activation='relu'),\n",
        "  layers.MaxPooling2D(pool_size=2, strides=2),\n",
        "  layers.Dropout(0.2),\n",
        "\n",
        "  layers.Conv2D(128, 3, padding='same', activation='relu'),\n",
        "  layers.Conv2D(128, 3, padding='same', activation='relu'),\n",
        "  layers.BatchNormalization(),\n",
        "  layers.MaxPooling2D(pool_size=2, strides=2),\n",
        "\n",
        "  layers.Dropout(0.5),\n",
        "  layers.Flatten(),\n",
        "  layers.Dense(256, activation='relu'),\n",
        "  layers.Dense(2, activation='softmax')\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L4nEcuqgZLbi"
      },
      "source": [
        "## Compile and train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EvyAINs9ZOmJ"
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wWLkKoKjZSoC",
        "outputId": "795770a0-e4ee-4225-c6c9-a590a34a9c48"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "rescaling_6 (Rescaling)      (None, 250, 250, 3)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_30 (Conv2D)           (None, 250, 250, 16)      448       \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 250, 250, 16)      64        \n",
            "_________________________________________________________________\n",
            "max_pooling2d_15 (MaxPooling (None, 125, 125, 16)      0         \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 125, 125, 16)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_31 (Conv2D)           (None, 125, 125, 32)      4640      \n",
            "_________________________________________________________________\n",
            "conv2d_32 (Conv2D)           (None, 125, 125, 32)      9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_9 (Batch (None, 125, 125, 32)      128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_16 (MaxPooling (None, 62, 62, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_33 (Conv2D)           (None, 62, 62, 64)        18496     \n",
            "_________________________________________________________________\n",
            "conv2d_34 (Conv2D)           (None, 62, 62, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_17 (MaxPooling (None, 31, 31, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 31, 31, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_35 (Conv2D)           (None, 31, 31, 128)       73856     \n",
            "_________________________________________________________________\n",
            "conv2d_36 (Conv2D)           (None, 31, 31, 128)       147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_10 (Batc (None, 31, 31, 128)       512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_18 (MaxPooling (None, 15, 15, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_9 (Dropout)          (None, 15, 15, 128)       0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 28800)             0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 256)               7373056   \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 2)                 514       \n",
            "=================================================================\n",
            "Total params: 7,665,474\n",
            "Trainable params: 7,665,122\n",
            "Non-trainable params: 352\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LWS-vvNaZDag",
        "outputId": "4c74587c-bcfd-4a5d-beac-14b01f0147b1"
      },
      "source": [
        "epochs=60\n",
        "history = model.fit(\n",
        "  train_ds.repeat(),\n",
        "  steps_per_epoch=int(len(train_ds)/batch_size),\n",
        "  validation_data=val_ds.repeat(),\n",
        "  epochs=epochs,\n",
        "  validation_steps=int(len(val_ds)/batch_size)\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/60\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/backend.py:4930: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n",
            "  '\"`sparse_categorical_crossentropy` received `from_logits=True`, but '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "92/92 [==============================] - 4s 35ms/step - loss: 3.1607 - accuracy: 0.8109 - val_loss: 25.1391 - val_accuracy: 0.4957\n",
            "Epoch 2/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.7289 - accuracy: 0.8565 - val_loss: 2.9798 - val_accuracy: 0.7304\n",
            "Epoch 3/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.3316 - accuracy: 0.8728 - val_loss: 0.7237 - val_accuracy: 0.8174\n",
            "Epoch 4/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.2733 - accuracy: 0.9000 - val_loss: 0.3469 - val_accuracy: 0.8696\n",
            "Epoch 5/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.2380 - accuracy: 0.9109 - val_loss: 0.3327 - val_accuracy: 0.8783\n",
            "Epoch 6/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.2073 - accuracy: 0.9152 - val_loss: 0.2167 - val_accuracy: 0.9348\n",
            "Epoch 7/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1820 - accuracy: 0.9413 - val_loss: 0.1736 - val_accuracy: 0.9130\n",
            "Epoch 8/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1904 - accuracy: 0.9304 - val_loss: 0.2308 - val_accuracy: 0.9000\n",
            "Epoch 9/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1588 - accuracy: 0.9380 - val_loss: 0.3627 - val_accuracy: 0.9043\n",
            "Epoch 10/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1629 - accuracy: 0.9380 - val_loss: 0.0810 - val_accuracy: 0.9783\n",
            "Epoch 11/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1192 - accuracy: 0.9576 - val_loss: 0.1615 - val_accuracy: 0.9522\n",
            "Epoch 12/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.0974 - accuracy: 0.9620 - val_loss: 0.2283 - val_accuracy: 0.9174\n",
            "Epoch 13/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1427 - accuracy: 0.9533 - val_loss: 0.1313 - val_accuracy: 0.9565\n",
            "Epoch 14/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1705 - accuracy: 0.9424 - val_loss: 2.0740 - val_accuracy: 0.7739\n",
            "Epoch 15/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1735 - accuracy: 0.9446 - val_loss: 0.4188 - val_accuracy: 0.8043\n",
            "Epoch 16/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1623 - accuracy: 0.9511 - val_loss: 0.2068 - val_accuracy: 0.9304\n",
            "Epoch 17/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1098 - accuracy: 0.9587 - val_loss: 0.0846 - val_accuracy: 0.9826\n",
            "Epoch 18/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1257 - accuracy: 0.9522 - val_loss: 0.1037 - val_accuracy: 0.9696\n",
            "Epoch 19/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1127 - accuracy: 0.9598 - val_loss: 0.3905 - val_accuracy: 0.8783\n",
            "Epoch 20/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1137 - accuracy: 0.9663 - val_loss: 0.2974 - val_accuracy: 0.9304\n",
            "Epoch 21/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1535 - accuracy: 0.9533 - val_loss: 0.2968 - val_accuracy: 0.8826\n",
            "Epoch 22/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.1507 - accuracy: 0.9533 - val_loss: 0.1575 - val_accuracy: 0.9478\n",
            "Epoch 23/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.1327 - accuracy: 0.9641 - val_loss: 0.0837 - val_accuracy: 0.9652\n",
            "Epoch 24/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.1108 - accuracy: 0.9587 - val_loss: 0.1307 - val_accuracy: 0.9739\n",
            "Epoch 25/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.0968 - accuracy: 0.9674 - val_loss: 0.0535 - val_accuracy: 0.9826\n",
            "Epoch 26/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1030 - accuracy: 0.9707 - val_loss: 0.1082 - val_accuracy: 0.9609\n",
            "Epoch 27/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.1249 - accuracy: 0.9630 - val_loss: 0.1141 - val_accuracy: 0.9609\n",
            "Epoch 28/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.1328 - accuracy: 0.9685 - val_loss: 0.0699 - val_accuracy: 0.9696\n",
            "Epoch 29/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.2013 - accuracy: 0.9391 - val_loss: 0.4105 - val_accuracy: 0.8565\n",
            "Epoch 30/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.1496 - accuracy: 0.9576 - val_loss: 0.0822 - val_accuracy: 0.9696\n",
            "Epoch 31/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.0919 - accuracy: 0.9739 - val_loss: 0.0853 - val_accuracy: 0.9739\n",
            "Epoch 32/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.0855 - accuracy: 0.9793 - val_loss: 0.1156 - val_accuracy: 0.9652\n",
            "Epoch 33/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1205 - accuracy: 0.9630 - val_loss: 0.4099 - val_accuracy: 0.8087\n",
            "Epoch 34/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.1227 - accuracy: 0.9489 - val_loss: 0.2845 - val_accuracy: 0.8870\n",
            "Epoch 35/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.0781 - accuracy: 0.9674 - val_loss: 0.0887 - val_accuracy: 0.9696\n",
            "Epoch 36/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1100 - accuracy: 0.9598 - val_loss: 0.1744 - val_accuracy: 0.9478\n",
            "Epoch 37/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.0881 - accuracy: 0.9707 - val_loss: 0.2141 - val_accuracy: 0.9261\n",
            "Epoch 38/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.0915 - accuracy: 0.9696 - val_loss: 1.5837 - val_accuracy: 0.7739\n",
            "Epoch 39/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1730 - accuracy: 0.9446 - val_loss: 0.0941 - val_accuracy: 0.9652\n",
            "Epoch 40/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1300 - accuracy: 0.9522 - val_loss: 0.0881 - val_accuracy: 0.9783\n",
            "Epoch 41/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.0867 - accuracy: 0.9663 - val_loss: 0.1723 - val_accuracy: 0.9391\n",
            "Epoch 42/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.0859 - accuracy: 0.9663 - val_loss: 0.2902 - val_accuracy: 0.9652\n",
            "Epoch 43/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.1597 - accuracy: 0.9522 - val_loss: 0.0824 - val_accuracy: 0.9609\n",
            "Epoch 44/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1433 - accuracy: 0.9576 - val_loss: 0.1135 - val_accuracy: 0.9478\n",
            "Epoch 45/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1253 - accuracy: 0.9609 - val_loss: 0.3527 - val_accuracy: 0.8261\n",
            "Epoch 46/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1096 - accuracy: 0.9717 - val_loss: 1.0649 - val_accuracy: 0.6391\n",
            "Epoch 47/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.0960 - accuracy: 0.9652 - val_loss: 0.1946 - val_accuracy: 0.9435\n",
            "Epoch 48/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.0842 - accuracy: 0.9739 - val_loss: 0.0705 - val_accuracy: 0.9696\n",
            "Epoch 49/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1078 - accuracy: 0.9750 - val_loss: 0.0958 - val_accuracy: 0.9478\n",
            "Epoch 50/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1551 - accuracy: 0.9424 - val_loss: 0.0859 - val_accuracy: 0.9739\n",
            "Epoch 51/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.0883 - accuracy: 0.9761 - val_loss: 0.2934 - val_accuracy: 0.8913\n",
            "Epoch 52/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.0866 - accuracy: 0.9707 - val_loss: 0.1242 - val_accuracy: 0.9609\n",
            "Epoch 53/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1103 - accuracy: 0.9739 - val_loss: 0.3251 - val_accuracy: 0.8478\n",
            "Epoch 54/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1060 - accuracy: 0.9652 - val_loss: 0.1127 - val_accuracy: 0.9478\n",
            "Epoch 55/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.1211 - accuracy: 0.9685 - val_loss: 0.1107 - val_accuracy: 0.9565\n",
            "Epoch 56/60\n",
            "92/92 [==============================] - 3s 33ms/step - loss: 0.0933 - accuracy: 0.9696 - val_loss: 0.1402 - val_accuracy: 0.9565\n",
            "Epoch 57/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.0925 - accuracy: 0.9696 - val_loss: 0.2098 - val_accuracy: 0.8913\n",
            "Epoch 58/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.0933 - accuracy: 0.9630 - val_loss: 0.0970 - val_accuracy: 0.9652\n",
            "Epoch 59/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.0682 - accuracy: 0.9826 - val_loss: 0.0819 - val_accuracy: 0.9609\n",
            "Epoch 60/60\n",
            "92/92 [==============================] - 3s 34ms/step - loss: 0.0983 - accuracy: 0.9717 - val_loss: 0.1155 - val_accuracy: 0.9652\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lkdl8VsBbZOu"
      },
      "source": [
        "## Visualize training results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "dduoLfKsZVIA",
        "outputId": "f61d2468-ad5d-488f-ce83-78ca801b4911"
      },
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs_range = range(epochs)\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kOPkBi1yT8ir"
      },
      "source": [
        "Sau khi đào tạo mô hình xong ta sẽ lưu lại mô hình với định dạng h5. Từ mô hình này ta có thể phát triển các ứng dụng khác mà không phải đào tạo lại.\n",
        "After training, we save the model with h5 format. From these model, we can development other application without retraining."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04x3wQ2EN66i"
      },
      "source": [
        "model.save(\"/content/drive/MyDrive/dataset/final_model.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mpit1CZROFyU"
      },
      "source": [
        "## **Model evaluation**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aHktLT9OOLcS"
      },
      "source": [
        "model = keras.models.load_model(\"/content/drive/MyDrive/dataset/final_model.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "txjrhhgMw-ug",
        "outputId": "4f798f1c-5bd2-43e2-a283-a597981a5208"
      },
      "source": [
        "!pip install visualkeras\n",
        "\n",
        "import visualkeras\n",
        "from tensorflow.python.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D, ZeroPadding2D\n",
        "from collections import defaultdict\n",
        "\n",
        "color_map = defaultdict(dict)\n",
        "color_map[Dense]['fill'] = 'pink'\n",
        "color_map[Flatten]['fill'] = 'purple'\n",
        "\n",
        "visualkeras.layered_view(model, color_map=color_map)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: visualkeras in /usr/local/lib/python3.7/dist-packages (0.0.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from visualkeras) (7.1.2)\n",
            "Requirement already satisfied: numpy>=1.18.1 in /usr/local/lib/python3.7/dist-packages (from visualkeras) (1.19.5)\n",
            "Requirement already satisfied: aggdraw>=1.3.11 in /usr/local/lib/python3.7/dist-packages (from visualkeras) (1.3.12)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGBA size=1196x1334 at 0x7F06D02E7150>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZSImZSfDBDrm"
      },
      "source": [
        "### Function predict images:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xzIJo_ZjbgjA"
      },
      "source": [
        "def img_pred(img_path):\n",
        "  img = keras.preprocessing.image.load_img(\n",
        "    img_path, target_size=(img_height, img_width)\n",
        "  )\n",
        "  img_array = keras.preprocessing.image.img_to_array(img)\n",
        "  img_array = tf.expand_dims(img_array, 0) \n",
        "  predictions = model.predict(img_array)\n",
        "  score = tf.nn.softmax(predictions[0])\n",
        "  return format(class_names[np.argmax(score)])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9u8fYYNDnp8"
      },
      "source": [
        "test_data_dir = \"/content/drive/MyDrive/dataset/Test\"\n",
        "test_data_dir = pathlib.Path(test_data_dir)\n",
        "defaced = list(test_data_dir.glob('defaced/*'))\n",
        "clean = list(test_data_dir.glob('clean/*'))\n",
        "evaluate=[]\n",
        "for i in range (0,len(defaced)):\n",
        "  if img_pred(defaced[i]) == 'defaced':\n",
        "    label = 1\n",
        "  else:\n",
        "    label = 0\n",
        "  defaced_dic = {'class_true':'defaced','class_pred':img_pred(defaced[i]), 'label':label}\n",
        "  evaluate.append(defaced_dic)\n",
        "\n",
        "for i in range (0,len(clean)):\n",
        "  if img_pred(clean[i]) == 'clean':\n",
        "    label = 1\n",
        "  else:\n",
        "    label = 0\n",
        "  clean_dic = {'class_true':'clean','class_pred':img_pred(clean[i]), 'label':label}\n",
        "  evaluate.append(clean_dic)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PA_bZ0IVowVd",
        "outputId": "688ba97a-1ce3-4aed-c99f-2f47ab56ce45"
      },
      "source": [
        "TN, TP, FP, FN = 0,0,0,0\n",
        "for i in range (0, len(evaluate)):\n",
        "  if evaluate[i][\"class_true\"] == \"defaced\" and evaluate[i][\"label\"] == 1:\n",
        "    TP = TP + 1\n",
        "  if evaluate[i][\"class_true\"] == \"defaced\" and evaluate[i][\"label\"] == 0:\n",
        "    FN = FN + 1\n",
        "  if evaluate[i][\"class_true\"] == \"clean\" and evaluate[i][\"label\"] == 1:\n",
        "    TN = TN + 1\n",
        "  if evaluate[i][\"class_true\"] == \"clean\" and evaluate[i][\"label\"] == 0:\n",
        "    FP = FP + 1\n",
        "print(\"TP: \",TP)\n",
        "print(\"FP: \",FP)\n",
        "print(\"TN: \",TN)\n",
        "print(\"FN: \",FN)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TP:  940\n",
            "FP:  60\n",
            "TN:  1547\n",
            "FN:  18\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yj6AcxOY2aIr"
      },
      "source": [
        "Tỉ lệ cảnh báo giả\n",
        "FPR = FP/(FP + TN) "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wSTpQPCx2ZaE",
        "outputId": "abb704a5-aff0-4e9f-cd80-745e03087664"
      },
      "source": [
        "FPR = FP*100/(FP + TN)\n",
        "print(\"FPR: {:.2f}\".format(FPR))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FPR: 3.73\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CZkhj5gT2354"
      },
      "source": [
        "Tỉ lệ phát hiện: TPR = TP/(TP + FN)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jY4yC3Wk23lC",
        "outputId": "6470ec35-e05f-4c51-a6bd-330028d3e1cc"
      },
      "source": [
        "TPR = TP*100/(TP + FN)\n",
        "print(\"TPR: {:.2f}\".format(TPR))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TPR: 98.12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlIwmlFB8wfb"
      },
      "source": [
        "Tỉ lệ cảnh báo đúng"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VfO1qm4m6qZ8",
        "outputId": "f7c6a97f-6cbe-411c-b78b-a298926b0af6"
      },
      "source": [
        "PPV = TP*100/(TP + FP)\n",
        "print(\"PPV: {:.2f}\".format(PPV))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PPV: 94.00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltdhImb661Ok"
      },
      "source": [
        "Tỉ lệ bỏ sót FNR = FN/(FN + TP) × 100% = 100 − TPR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Toy01f-q6xK5",
        "outputId": "13073e59-f6e7-46a4-e675-7cb902989ee8"
      },
      "source": [
        "FNR = FN*100/(FN + TP)\n",
        "print(\"FNR: {:.2f}\".format(FNR))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FNR: 1.88\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zBVlbY6T7Ath"
      },
      "source": [
        "Độ chính xác tổng thể ACC = (TP + TN)/(TP + FP + TN + FN) × 100%"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XuYdV-ix69-j",
        "outputId": "7fd7cd11-c548-4491-cbd4-a21354c91804"
      },
      "source": [
        "ACC = (TP + TN)*100/(TP + FP + TN + FN)\n",
        "print(\"ACC: {:.2f}\".format(ACC))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ACC: 96.96\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x--AZZyT67vc",
        "outputId": "4c376f82-bb4f-4b14-8454-e41d7eae329d"
      },
      "source": [
        "F1 = 2*TP*100/(2*TP + FP + FN)\n",
        "print(\"F1 Score: {:.2f}\".format(F1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 Score: 96.02\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}