4_vgg_dense_mbl_incptin(2).ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EkfYLtYqxvGx",
        "outputId": "a99d9ed1-7124-4fa6-ebfa-d10a041408f7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from pathlib import Path\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.utils import image_dataset_from_directory\n",
        "from tensorflow.keras import Sequential , Input, layers , losses , optimizers\n",
        "from tensorflow.keras.applications.vgg16 import VGG16\n",
        "from tensorflow.keras.applications.vgg16 import preprocess_input\n",
        "from keras.applications.efficientnet import EfficientNetB3\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from sklearn.model_selection import train_test_split\n",
        "import os\n",
        "import shutil\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.layers import concatenate\n",
        "from keras.layers import Input\n",
        "from keras.optimizers import Adam, Adamax\n",
        "from keras.layers import MaxPooling2D, Flatten,Conv2D, Dense,BatchNormalization,GlobalAveragePooling2D,Dropout\n",
        "from tensorflow.keras.applications.densenet import DenseNet169\n",
        "from keras.applications.mobilenet_v2 import MobileNetV2\n",
        "from keras.models import Model\n",
        "from keras import regularizers\n",
        "from tensorflow.keras.layers import Attention\n",
        "from tensorflow.keras.applications import InceptionResNetV2\n",
        "from tensorflow.keras.layers import Attention, Concatenate, Lambda, Layer\n",
        "# import tensorflow as tf\n",
        "# from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ],
      "metadata": {
        "id": "EELg_jMCvHCM"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = '/content/gdrive/MyDrive/Colab Notebooks/dataset/Curated X-Ray Dataset'\n",
        "class_names = ['COVID-19', 'Normal', 'Pneumonia-Bacterial', 'Pneumonia-Viral']"
      ],
      "metadata": {
        "id": "Vk98A7g5xz8m"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data_dir0 = '/content/gdrive/MyDrive/Colab Notebooks/dataset/Curated X-Ray Dataset/test_set'\n",
        "train_data_dir0 = '/content/gdrive/MyDrive/Colab Notebooks/dataset/Curated X-Ray Dataset/train_set'\n",
        "valid_data_dir0 = '/content/gdrive/MyDrive/Colab Notebooks/dataset/Curated X-Ray Dataset/valid_set'"
      ],
      "metadata": {
        "id": "GNxooTlJx2Z_"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datagen = ImageDataGenerator(rescale=1.0 / 255)\n"
      ],
      "metadata": {
        "id": "xeJBS921x2cX"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1.0 / 255,      # Rescale pixel values to [0, 1]\n",
        "    rotation_range=20,      # Randomly rotate images by 20 degrees\n",
        "    width_shift_range=0.2,  # Randomly shift images horizontally by 20% of the width\n",
        "    height_shift_range=0.2, # Randomly shift images vertically by 20% of the height\n",
        "    shear_range=0.2,        # Shear transformations\n",
        "    zoom_range=0.2,         # Randomly zoom images by 20%\n",
        "    horizontal_flip=True,   # Randomly flip images horizontally\n",
        "    fill_mode='nearest'     # How to fill newly created pixels after rotation or shifts\n",
        ")"
      ],
      "metadata": {
        "id": "8wC5VeZlx2eY"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set batch size and image size\n",
        "batch_size = 32\n",
        "image_size = (224, 224)\n",
        "\n",
        "train_dataset = train_datagen.flow_from_directory(\n",
        "    train_data_dir0,\n",
        "    target_size=image_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical',\n",
        "    classes=class_names,\n",
        "    shuffle=True,\n",
        "    subset='training'  # Use this if you have separate train and validation folders\n",
        ")\n",
        "\n",
        "validation_dataset = datagen.flow_from_directory(\n",
        "    valid_data_dir0,\n",
        "    target_size=image_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical',\n",
        "    classes=class_names,\n",
        "    shuffle=False,\n",
        ")\n",
        "\n",
        "\n",
        "test_dataset = datagen.flow_from_directory(\n",
        "    test_data_dir0,\n",
        "    target_size=image_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical',\n",
        "    classes=class_names,\n",
        "    shuffle=False  # Keep the original order of test images\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KdA_m5hxx2gi",
        "outputId": "2cbce4e0-d4f7-412d-cdfb-d01d4a8f29ae"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 6444 images belonging to 4 classes.\n",
            "Found 1381 images belonging to 4 classes.\n",
            "Found 1382 images belonging to 4 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_shape = (224,224,3)\n",
        "input_layer = Input(shape = (224, 224, 3))\n",
        "num_clases = 4"
      ],
      "metadata": {
        "id": "ESsTL7rUy97S"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = VGG16(weights='imagenet', include_top=False)\n",
        "# Freezing the top to intermediate layers means we are keeping the pre-trained weights and we are not training it from scratch.\n",
        "for layer in model.layers:\n",
        "       layer.trainable = False\n",
        "model_vgg = model(input_layer)\n",
        "model_vgg = GlobalAveragePooling2D()(model_vgg)\n",
        "output_vgg = Flatten()(model_vgg)\n",
        "#first model\n",
        "mobilenet_base = MobileNetV2(weights = 'imagenet',input_shape = input_shape,include_top = False)\n",
        "\n",
        "densenet_base = DenseNet169(weights = 'imagenet', input_shape = input_shape,include_top = False)\n",
        "for layer in mobilenet_base.layers:\n",
        "    layer.trainable =  False\n",
        "for layer in densenet_base.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "model_mobilenet = mobilenet_base(input_layer)\n",
        "model_mobilenet = GlobalAveragePooling2D()(model_mobilenet)\n",
        "output_mobilenet = Flatten()(model_mobilenet)\n",
        "model_densenet = densenet_base(input_layer)\n",
        "model_densenet = GlobalAveragePooling2D()(model_densenet)\n",
        "output_densenet = Flatten()(model_densenet)\n",
        "\n",
        "inception_base = InceptionResNetV2(weights='imagenet', include_top=False, input_shape=input_shape)\n",
        "# Freeze the base model layers for fine-tuning\n",
        "for layer in inception_base.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "# Add custom classification layers on top of the base model\n",
        "model_inception = inception_base(input_layer)\n",
        "model_inception = GlobalAveragePooling2D()(model_inception)\n",
        "output_inception = Flatten()(model_inception)\n",
        "# predictions = Dense(num_classes, activation='softmax')(x)\n",
        "\n",
        "# Create the final model for fine-tuning\n",
        "# model_inception = Model(inputs=inception_base.input, outputs=predictions)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SjIvs9CXyDp4",
        "outputId": "c6033d0b-11ff-4922-e84f-3c3fdcb87b3b"
      },
      "execution_count": 12,
      "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",
            "58889256/58889256 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
            "9406464/9406464 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/densenet/densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "51877672/51877672 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_resnet_v2/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "219055592/219055592 [==============================] - 2s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge the attention outputs\n",
        "merged = concatenate([output_vgg, output_inception, output_mobilenet, output_densenet], name='concatenated')\n",
        "\n",
        "# merged = concatenate([output_vgg, output_mobilenet, output_densenet], name = 'concatenated')\n",
        "\n",
        "final_layer =  Dense(4, activation = \"softmax\", name = \"output_layer\")(merged)\n",
        "stacked_model = Model(inputs = input_layer, outputs = final_layer)"
      ],
      "metadata": {
        "id": "0crLWRlgyHLt"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_layer =  Dense(4, activation = \"softmax\", name = \"output_layer\")(output_inception)\n",
        "stacked_model = Model(inputs = input_layer, outputs = final_layer)"
      ],
      "metadata": {
        "id": "buoyiZcIes3M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "tf.keras.layers.Concatenate()\n",
        "# stacked_model it is the final model ready for the predictions.\n",
        "# Model Building\n",
        "                                                                                                              #  The output of the stacked model\n",
        "# Model Compilation\n",
        "# So far we have designed our model, its time to assign some learning parameters and compile the model.\n",
        "\n",
        "# LR = 0.0001\n",
        "# optimizer = adam\n",
        "# We are trying adam optimizer and LR = 0.0001, the small rate of learning is the better starting for the pre-trained model.\n",
        "\n",
        "optm = Adam(learning_rate=0.0001)\n",
        "stacked_model.compile(loss='categorical_crossentropy', optimizer=optm,\n",
        "                  metrics=['accuracy'])\n",
        "# Step 6 Defining Callbacks\n",
        "# Callbacks are a tool for efficient training, but it’s not mandatory to use, and it gives us control over the training.\n",
        "\n",
        "from keras.callbacks import EarlyStopping,ReduceLROnPlateau\n",
        "EarlyStopping = EarlyStopping(monitor='val_accuracy',\n",
        "                              min_delta=.01,\n",
        "                              patience=6,\n",
        "                              verbose=1,\n",
        "                              mode='auto',\n",
        "                              baseline=None,\n",
        "                              restore_best_weights=True)\n",
        "# EarlyStopping: It stops the training if the model doesn’t get better results after some epochs.\n",
        "\n",
        "rlr = ReduceLROnPlateau( monitor=\"val_accuracy\",\n",
        "                            factor=0.01,\n",
        "                            patience=6,\n",
        "                            verbose=0,\n",
        "                            mode=\"max\",\n",
        "                            min_delta=0.01)\n",
        "# ReduceLROnPlateau It reduces the rate of learning (LR) if the model doesn’t get better.\n",
        "\n",
        "model_save = tf.keras.callbacks.ModelCheckpoint('./stacked_model_4.h5',\n",
        "                             save_best_only = True,\n",
        "                             save_weights_only = False,\n",
        "                             monitor = 'val_loss',\n",
        "                             mode = 'min', verbose = 1)\n",
        "# ModelCheckpoint : it saves the model at several epochs.\n",
        "\n",
        "# Step 7 Training\n",
        "# Now we are ready with the model and data, it’s time to start the training\n",
        "\n",
        "# nb_train_samples = 6444 # number of training-samples\n",
        "# nb_validation_samples = 1381 # number of validation-samples\n",
        "# nb_test_samples = 1382 # number of training-samples\n",
        "# epochs = 20\n",
        "# batch_size  = 16\n",
        "# Training\n",
        "\n"
      ],
      "metadata": {
        "id": "OjJ9_NKv0OVf"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stacked_history = stacked_model.fit(train_dataset,\n",
        "                              # steps_per_epoch = nb_train_samples // batch_size,\n",
        "                              epochs = 20,\n",
        "                              validation_data = test_dataset,\n",
        "                              # class_weight=class_weights,\n",
        "                              callbacks=[EarlyStopping, model_save,rlr])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IBFq8uva0STG",
        "outputId": "f813ea91-0f36-4118-8552-7a55eba0b340"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "202/202 [==============================] - ETA: 0s - loss: 0.8606 - accuracy: 0.6634 \n",
            "Epoch 1: val_loss improved from inf to 0.72828, saving model to ./stacked_model_4.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r202/202 [==============================] - 3332s 16s/step - loss: 0.8606 - accuracy: 0.6634 - val_loss: 0.7283 - val_accuracy: 0.6896 - lr: 1.0000e-04\n",
            "Epoch 2/20\n",
            "202/202 [==============================] - ETA: 0s - loss: 0.5777 - accuracy: 0.7714\n",
            "Epoch 2: val_loss improved from 0.72828 to 0.60252, saving model to ./stacked_model_4.h5\n",
            "202/202 [==============================] - 241s 1s/step - loss: 0.5777 - accuracy: 0.7714 - val_loss: 0.6025 - val_accuracy: 0.7323 - lr: 1.0000e-04\n",
            "Epoch 3/20\n",
            "202/202 [==============================] - ETA: 0s - loss: 0.5275 - accuracy: 0.7872\n",
            "Epoch 3: val_loss improved from 0.60252 to 0.54072, saving model to ./stacked_model_4.h5\n",
            "202/202 [==============================] - 239s 1s/step - loss: 0.5275 - accuracy: 0.7872 - val_loss: 0.5407 - val_accuracy: 0.7677 - lr: 1.0000e-04\n",
            "Epoch 4/20\n",
            "202/202 [==============================] - ETA: 0s - loss: 0.4797 - accuracy: 0.8071\n",
            "Epoch 4: val_loss did not improve from 0.54072\n",
            "202/202 [==============================] - 250s 1s/step - loss: 0.4797 - accuracy: 0.8071 - val_loss: 0.5572 - val_accuracy: 0.7576 - lr: 1.0000e-04\n",
            "Epoch 5/20\n",
            "202/202 [==============================] - ETA: 0s - loss: 0.4692 - accuracy: 0.8133\n",
            "Epoch 5: val_loss improved from 0.54072 to 0.46871, saving model to ./stacked_model_4.h5\n",
            "202/202 [==============================] - 236s 1s/step - loss: 0.4692 - accuracy: 0.8133 - val_loss: 0.4687 - val_accuracy: 0.8046 - lr: 1.0000e-04\n",
            "Epoch 6/20\n",
            "202/202 [==============================] - ETA: 0s - loss: 0.4515 - accuracy: 0.8191\n",
            "Epoch 6: val_loss did not improve from 0.46871\n",
            "202/202 [==============================] - 235s 1s/step - loss: 0.4515 - accuracy: 0.8191 - val_loss: 0.4952 - val_accuracy: 0.7938 - lr: 1.0000e-04\n",
            "Epoch 7/20\n",
            "202/202 [==============================] - ETA: 0s - loss: 0.4402 - accuracy: 0.8167\n",
            "Epoch 7: val_loss did not improve from 0.46871\n",
            "202/202 [==============================] - 236s 1s/step - loss: 0.4402 - accuracy: 0.8167 - val_loss: 0.4788 - val_accuracy: 0.8032 - lr: 1.0000e-04\n",
            "Epoch 8/20\n",
            "202/202 [==============================] - ETA: 0s - loss: 0.4325 - accuracy: 0.8268\n",
            "Epoch 8: val_loss did not improve from 0.46871\n",
            "202/202 [==============================] - 238s 1s/step - loss: 0.4325 - accuracy: 0.8268 - val_loss: 0.4762 - val_accuracy: 0.7952 - lr: 1.0000e-04\n",
            "Epoch 9/20\n",
            "202/202 [==============================] - ETA: 0s - loss: 0.4230 - accuracy: 0.8274\n",
            "Epoch 9: val_loss did not improve from 0.46871\n",
            "202/202 [==============================] - 237s 1s/step - loss: 0.4230 - accuracy: 0.8274 - val_loss: 0.4950 - val_accuracy: 0.7931 - lr: 1.0000e-04\n",
            "Epoch 10/20\n",
            "202/202 [==============================] - ETA: 0s - loss: 0.4182 - accuracy: 0.8304\n",
            "Epoch 10: val_loss improved from 0.46871 to 0.46871, saving model to ./stacked_model_4.h5\n",
            "202/202 [==============================] - 245s 1s/step - loss: 0.4182 - accuracy: 0.8304 - val_loss: 0.4687 - val_accuracy: 0.8010 - lr: 1.0000e-04\n",
            "Epoch 11/20\n",
            "202/202 [==============================] - ETA: 0s - loss: 0.4088 - accuracy: 0.8340Restoring model weights from the end of the best epoch: 5.\n",
            "\n",
            "Epoch 11: val_loss did not improve from 0.46871\n",
            "202/202 [==============================] - 237s 1s/step - loss: 0.4088 - accuracy: 0.8340 - val_loss: 0.5159 - val_accuracy: 0.7851 - lr: 1.0000e-04\n",
            "Epoch 11: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the loss and accuracy curves\n",
        "plt.figure(figsize=(12, 4))\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(stacked_history.history['loss'], label='Training Loss')\n",
        "plt.plot(stacked_history.history['val_loss'], label='Validation Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.title('Loss Curve')\n",
        "\n",
        "# Plot training & validation accuracy values\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(stacked_history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(stacked_history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.title('Accuracy Curve')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "aMvPsyXhOpe4",
        "outputId": "672f7ccd-e07b-4a79-d231-57b268004ea8"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}