Myujth

I am learning the tutorial here. My Model part is:

input_img = keras.Input(shape=img_shape)

x = layers.Conv2D(32, (3, 3),
 padding='same', activation='relu')(input_img)

...
x = layers.Conv2D(64, (3, 3),
 padding='same', activation='relu')(x)
shape_before_flattening = K.int_shape(x)

x = layers.Flatten()(x)
x = layers.Dense(32, activation='relu')(x)

z_mean = layers.Dense(latent_dim)(x)
z_log_var = layers.Dense(latent_dim)(x)

def sampling(args):
 ...

z = layers.Lambda(sampling)([z_mean, z_log_var])

decoder_input = layers.Input(K.int_shape(z)[1:])

x = layers.Dense(np.prod(shape_before_flattening[1:]),
 activation='relu')(decoder_input)

x = layers.Reshape(shape_before_flattening[1:])(x)

x = layers.Conv2DTranspose(32, 3,
 padding='same', activation='relu',
 strides=(2, 2))(x)
x = layers.Conv2D(1, 3,
 padding='same', activation='sigmoid')(x)

# This is our decoder model from letent space to reconstructed images
decoder = Model(decoder_input, x)

# We then apply it to `z` to recover the decoded `z`.
z_decoded = decoder(z)

def vae_loss(self, x, z_decoded):
 ...


# Fit the end-to-end model
vae = Model(input_img, z_decoded) # vae = Model(input_img, x)
vae.compile(optimizer='rmsprop', loss=vae_loss)
vae.summary()

My question is: the end-to-end is vae = Model(input_img, z_decoded) or vae = Model(input_img, x). Should we compute loss on input_img and z_decoded OR between input_img and x? Thanks

x is changing throughout the model, where x = layers.Conv2D(1, 3,padding='same', activation='sigmoid')(x) you set x to be the last layer of your decoder model.


When doing z_decoded = decoder(z) you chain your decoder straight after the encoder, z_decoded is actually the output layer of your decoder, thus, the same x as earlier. Also, you create the link between the actual input and the output.

Computing the loss would yield the same results on both (as they both represent the same layer).

In short - Both vae = Model(input_img, z_decoded) and vae = Model(input_img, x) are the end to end model, i would suggest using the z_decoded version, for readability.