Hands-On Deep Learning Algorithms with Python

Building the model

We build the model as follows:

vae = Model(x, reconstructed)

Define the reconstruction loss:

Reconstruction_loss = original_dim * metrics.binary_crossentropy(x, reconstructed)

Define KL divergence:

kl_divergence_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)

Thus, the total loss can be defined as:

total_loss = K.mean(Reconstruction_loss + kl_divergence_loss)

Add loss and compile the model:

vae.add_loss(total_loss)vae.compile(optimizer='rmsprop')vae.summary()

Train the model:

vae.fit(x_train,        shuffle=True,        epochs=epochs,        batch_size=batch_size,        verbose=2,        validation_data=(x_test, None))