title: VAE autoencoder training fit_generator doesn't seems reduced the loss
body: <p>I have tried to use the fit_generator function. It's weird that when I use fit_generator, the loss seems just slowly going down by a little. Like I already trained about 5 days my loss just decrease from 129 to 119. But I have search how long should the model training and it said on the internet like only 8 hours. I do think it's my problem but i don't know what is the problem so I wish somebody could help me! And my data was only using 100 images only.</p>
  <p>Below is my training code</p>
  <pre><code>vae_model.compile(optimizer=adam_optimizer, loss = total_loss, metrics = [r_loss, kl_loss],experimental_run_tf_function=False)
  N_EPOCHS = 3000 
  zz = '/weights.h5'
  vae_model.load_weights(zz)
  checkpoint_vae = ModelCheckpoint(zz, save_weights_only = True, verbose=1)
  
  vae_model.fit_generator(data_flow, 
              shuffle=False, 
              epochs = N_EPOCHS, 
              initial_epoch = 0, 
              steps_per_epoch=tr_num / BATCH_SIZE,
              callbacks=[checkpoint_vae])
  </code></pre>
  <pre><code>LEARNING_RATE = 0.0005
  LOSS_FACTOR = 10000
  
  def r_loss(y_true, y_pred):
      return K.mean(K.square(y_true - y_pred), axis = [1,2,3])
  
  def kl_loss(y_true, y_pred):
      kl_loss =  -0.5 * K.sum(1 + log_var - K.square(mean_mu) - K.exp(log_var), axis = 1)
      return kl_loss
  
  def total_loss(y_true, y_pred):
      return LOSS_FACTOR*r_loss(y_true, y_pred) + kl_loss(y_true, y_pred)
    
  adam_optimizer = Adam(lr = LEARNING_RATE, beta_1=0.5, beta_2=0.9)
  </code></pre>
  <pre><code>
  </code></pre>