Convolutional Autoencoders - machine-learning

Question

I am writing a code for running autoencoder on CIFAR10 dataset and see the reconstructed images.
The requirement is to create
Encoder with First Layer
Input shape: (32,32,3)
Conv2D Layer with 64 Filters of (3,3)
BatchNormalization layer
ReLu activation
2D MaxpoolingLayer with (2,2) filter
Encoder with Second Layer
Conv2D layer with 16 filters (3,3)
BatchNormalization layer
ReLu activation
2D MaxpoolingLayer with (2,2) filter
Final Encoded as MaxPool with (2,2) with all previous layers
Decoder with First Layer
Input shape: encoder output
Conv2D Layer with 16 Filters of (3,3)
BatchNormalization layer
ReLu activation
UpSampling2D with (2,2) filter
Decoder with Second Layer
Conv2D Layer with 32 Filters of (3,3)
BatchNormalization layer
ReLu activation
UpSampling2D with (2,2) filter
Final Decoded as Sigmoid with all previous layers
I understand that
When we are creating Convolutional Autoencoder (or any AE), we need to pass the output of the previous layer to the next layer.
So, when I create the first Conv2D layer with ReLu and then perform BatchNormalization .. in which I pass the Conv2D layer .. right?
But when I do MaxPooling2D .. what should I pass .. BatchNormalization output or Conv2D layer output?
Also, is there any order in which I should be performing these operations?
Conv2D --> BatchNormalization --> MaxPooling2D
OR
Conv2D --> MaxPooling2D --> BatchNormalization
I am attaching my code below ... I have attempted it to two different ways and hence getting different outputs (in terms of model summary and also model training graph)
Can someone please help me by explaining which is the correct method (Method-1 or Method-2)? Also, how do I understand which graph shows better model performance?
Method - 1
input_image = Input(shape=(32, 32, 3))
### Encoder
conv1_1 = Conv2D(64, (3, 3), activation='relu', padding='same')(input_image)
bnorm1_1 = BatchNormalization()(conv1_1)
mpool1_1 = MaxPooling2D((2, 2), padding='same')(conv1_1)
conv1_2 = Conv2D(16, (3, 3), activation='relu', padding='same')(mpool1_1)
borm1_2 = BatchNormalization()(conv1_2)
encoder = MaxPooling2D((2, 2), padding='same')(conv1_2)
### Decoder
conv2_1 = Conv2D(16, (3, 3), activation='relu', padding='same')(encoder)
bnorm2_1 = BatchNormalization()(conv2_1)
up1_1 = UpSampling2D((2, 2))(conv2_1)
conv2_2 = Conv2D(32, (3, 3), activation='relu', padding='same')(up1_1)
bnorm2_2 = BatchNormalization()(conv2_2)
up2_1 = UpSampling2D((2, 2))(conv2_2)
decoder = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(up2_1)
model = Model(input_image, decoder)
model.compile(optimizer='adam', loss='binary_crossentropy')
model.summary()
history = model.fit(trainX, trainX,
epochs=50,
batch_size=1000,
shuffle=True,
verbose=2,
validation_data=(testX, testX)
)
As an output of the model summary, I get this
Total params: 18,851
Trainable params: 18,851
Non-trainable params: 0
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.show()
Method - 2
input_image = Input(shape=(32, 32, 3))
### Encoder
x = Conv2D(64, (3, 3), activation='relu', padding='same')(input_image)
x = BatchNormalization()(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = BatchNormalization()(x)
encoder = MaxPooling2D((2, 2), padding='same')(x)
### Decoder
x = Conv2D(16, (3, 3), activation='relu', padding='same')(encoder)
x = BatchNormalization()(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = BatchNormalization()(x)
x = UpSampling2D((2, 2))(x)
decoder = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)
model = Model(input_image, decoder)
model.compile(optimizer='adam', loss='binary_crossentropy')
model.summary()
history = model.fit(trainX, trainX,
epochs=50,
batch_size=1000,
shuffle=True,
verbose=2,
validation_data=(testX, testX)
)
As an output of the model summary, I get this
Total params: 19,363
Trainable params: 19,107
Non-trainable params: 256
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.show()

Answer 1

In method 1, BatchNormalization layers does not exist in the compiled model, as the output of these layers are not used anywhere. You can check this by running model1.summary()
Method 2 is perfectly alright.
Order of the operations :
Conv2D --> BatchNormalization --> MaxPooling2D is usually the common approach.
Though either order would work since, since BatchNorm is just mean and variance normalization.
Edit:
For Conv2D --> BatchNormalization --> MaxPooling2D :
conv1_1 = Conv2D(64, (3, 3), activation='relu', padding='same')(input_image)
bnorm1_1 = BatchNormalization()(conv1_1)
mpool1_1 = MaxPooling2D((2, 2), padding='same')(bnorm1_1)
and then use mpool1_1 as input for next layer.
For Conv2D --> MaxPooling2D --> BatchNormalization:
conv1_1 = Conv2D(64, (3, 3), activation='relu', padding='same')(input_image)
mpool1_1 = MaxPooling2D((2, 2), padding='same')(conv1_1)
bnorm1_1 = BatchNormalization()(mpool1_1)
and then use bnorm1_1 as input for next layer.

Answer 2

To effectively use BatchNormalization layer, you should always use it before activation.
Instead of:
conv1_1 = Conv2D(64, (3, 3), activation='relu', padding='same')(input_image)
bnorm1_1 = BatchNormalization()(conv1_1)
mpool1_1 = MaxPooling2D((2, 2), padding='same')(bnorm1_1)
Use it like this:
conv1_1 = Conv2D(64, (3, 3), padding='same')(input_image)
bnorm1_1 = BatchNormalization()(conv1_1)
act_1 = Activation('relu')(bnorm1_1)
mpool1_1 = MaxPooling2D((2, 2), padding='same')(act_1)
For more details, check here:
Where do I call the BatchNormalization function in Keras?