### COMPILATION
       def vae_r_loss(y_true, y_pred):
           r_loss = K.mean(K.square(y_true - y_pred), axis = [1,2,3])
           return r_loss_factor * r_loss

       def vae_kl_loss(y_true, y_pred):
           kl_loss =  -0.5 * K.sum(1 + self.log_var - K.square(self.mu) - K.exp(self.log_var), axis = 1)
           return kl_loss

       def vae_loss(y_true, y_pred):
           r_loss = vae_r_loss(y_true, y_pred)
           kl_loss = vae_kl_loss(y_true, y_pred)
           return  r_loss + kl_loss

       optimizer = Adam(lr=learning_rate)
       self.model.compile(optimizer=optimizer, loss = vae_loss,  metrics = [vae_r_loss, vae_kl_loss])```