VGG16

# VGG16
modelVGG = Sequential([
    Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', input_shape=X[1, :].shape),
    Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2'),
    MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool'),
    
    Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1'),
    Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2'),
    MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool'),

    Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1'),
    Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2'),
    Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3'),
    MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool'),
    
     # Block 4
    Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1'),
    Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2'),
    Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3'),
    MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool'),

    # Block 5
    Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1'),
    Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2'),
    Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3'),
    MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool'),
    
    Flatten(name='flatten'),
    Dense(4096, activation='relu', name='fc1'),
    Dense(4096, activation='relu', name='fc2'),
    Dense(num_classes, activation='softmax', name='predictions')  
    ])