VGG12:

# Convolution Block 1

Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')
Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')
MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')

# Convolution Block 2

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')

# Convolution Block 3

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')

# Convolution 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')

# Fully Connected Block

Flatten()
Dense(512, activation='relu')
Dropout(0.25)
Dense(10, activation='softmax')

REGU:
   
# Convolution Block 1

   Conv2D(32, (3, 3), activation = 'relu')
   Dropout(0.2)
   BatchNormalization(axis=-1)
   Conv2D(32, (3, 3), activation = 'relu')
   MaxPooling2D(pool_size=(2,2))
   
# Convolution Block 2
   
   BatchNormalization(axis=-1)
   Conv2D(64, (3, 3), activation = 'relu')
   Activation('relu')
   Dropout(0.2)
   BatchNormalization(axis=-1)
   Conv2D(64, (3, 3), activation = 'relu')
   MaxPooling2D(pool_size=(2,2))
   
# Fully Connected Block

   Flatten()
   BatchNormalization()
   Dense(512, activation = 'relu')
   BatchNormalization()
   Dropout(0.2)
   Dense(10, activation = 'softmax')