Helper function for init_opt
(self, generator_loss, discriminator_loss)
| 164 | return discriminator_loss, generator_loss |
| 165 | |
| 166 | def prepare_trainer(self, generator_loss, discriminator_loss): |
| 167 | '''Helper function for init_opt''' |
| 168 | all_vars = tf.trainable_variables() |
| 169 | |
| 170 | g_vars = [var for var in all_vars if |
| 171 | var.name.startswith('g_')] |
| 172 | d_vars = [var for var in all_vars if |
| 173 | var.name.startswith('d_')] |
| 174 | |
| 175 | generator_opt = tf.train.AdamOptimizer(self.generator_lr, |
| 176 | beta1=0.5) |
| 177 | self.generator_trainer =\ |
| 178 | pt.apply_optimizer(generator_opt, |
| 179 | losses=[generator_loss], |
| 180 | var_list=g_vars) |
| 181 | discriminator_opt = tf.train.AdamOptimizer(self.discriminator_lr, |
| 182 | beta1=0.5) |
| 183 | self.discriminator_trainer =\ |
| 184 | pt.apply_optimizer(discriminator_opt, |
| 185 | losses=[discriminator_loss], |
| 186 | var_list=d_vars) |
| 187 | self.log_vars.append(("g_learning_rate", self.generator_lr)) |
| 188 | self.log_vars.append(("d_learning_rate", self.discriminator_lr)) |
| 189 | |
| 190 | def define_summaries(self): |
| 191 | '''Helper function for init_opt''' |