CIFAR-10-Deployment-Using-FastAPI/model.py at main · Adarshgurazada/CIFAR-10-Deployment-Using-FastAPI · GitHub

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from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization
import tensorflow as tf

def create_vgg19_model(input_shape, num_classes):
    model = Sequential()

    # Convolutional layers with BatchNormalization
    model.add(Conv2D(filters=32, kernel_size=(3, 3), input_shape=input_shape, activation='relu', padding='same'))
    model.add(BatchNormalization())
    model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding='same'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu', padding='same'))
    model.add(BatchNormalization())
    model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu', padding='same'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Conv2D(filters=128, kernel_size=(3, 3), activation='relu', padding='same'))
    model.add(BatchNormalization())
    model.add(Conv2D(filters=128, kernel_size=(3, 3), activation='relu', padding='same'))
    model.add(BatchNormalization())
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.25))
    model.add(Dense(num_classes, activation='softmax'))

    METRICS = [
        'accuracy',
        tf.keras.metrics.Precision(name='precision'),
        tf.keras.metrics.Recall(name='recall')
    ]
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=METRICS)

    return model