There is an important custom loss function that is needed for the generator network: the self-regularization loss. We'll define this function as though it's part of our helper functions in the generator:

1. Define self-regularization loss as follows:

def self_regularization_loss(self,y_true,y_pred):
    return tf.multiply(0.0002,tf.reduce_sum(tf.abs(y_pred-y_true)))

2. Create the summary helper function, as follows:

def summary(self):
    return self.Generator.summary()

3. Define the save_model_graph function as in previous sections, as follows:

def save_model_graph(self):
    plot_model(self.Generator, to_file='/out/Generator_Model.png')

4. Save the model h5 file if requested by the user with the following code:

def save_model(self,epoch,batch):
    self.Generator.save('/out/'+self.NAME+'_Epoch_'+epoch+'_Batch_'+batch+
                        'model.h5')