def optimization(self): train_discriminator, train_generator, _ = optimizer( self.beta_1, self.loss_gen, self.loss_dis, self.loss_type, self.learning_rate_input_d, self.learning_rate_input_g, None, beta_2=self.beta_2, gen_name='generator', dis_name='discriminator_gen', mapping_name='mapping_', encoder_name='encoder') with tf.control_dependencies(tf.get_collection( tf.GraphKeys.UPDATE_OPS)): # Quick dirty optimizer for Encoder. trainable_variables = tf.trainable_variables() encoder_variables = [ variable for variable in trainable_variables if variable.name.startswith('encoder') ] train_encoder = tf.train.AdamOptimizer( learning_rate=self.learning_rate_input_e, beta1=self.beta_1).minimize(self.loss_enc, var_list=encoder_variables) return train_discriminator, train_generator, train_encoder
def optimization(self): train_discriminator, train_generator = optimizer( self.beta_1, self.loss_gen, self.loss_dis, self.loss_type, self.learning_rate_input_g, self.learning_rate_input_d, beta_2=self.beta_2) return train_discriminator, train_generator
def optimization(self): train_discriminator, train_generator = optimizer(self.beta_1, self.loss_gen, self.loss_dis, self.loss_type, self.learning_rate_input_g, self.learning_rate_input_d, beta_2=self.beta_2) return train_discriminator, train_generator