model.py 文件源码

def optimize(self, G_loss, D_Y_loss, F_loss, D_X_loss):
    def make_optimizer(loss, variables, name='Adam'):
      """ Adam optimizer with learning rate 0.0002 for the first 100k steps (~100 epochs)
          and a linearly decaying rate that goes to zero over the next 100k steps
      """
      global_step = tf.Variable(0, trainable=False)
      starter_learning_rate = self.learning_rate
      end_learning_rate = 0.0
      start_decay_step = 100000
      decay_steps = 100000
      beta1 = self.beta1
      learning_rate = (
          tf.where(
                  tf.greater_equal(global_step, start_decay_step),
                  tf.train.polynomial_decay(starter_learning_rate, global_step-start_decay_step,
                                            decay_steps, end_learning_rate,
                                            power=1.0),
                  starter_learning_rate
          )

      )
      tf.summary.scalar('learning_rate/{}'.format(name), learning_rate)

      learning_step = (
          tf.train.AdamOptimizer(learning_rate, beta1=beta1, name=name)
                  .minimize(loss, global_step=global_step, var_list=variables)
      )
      return learning_step

    G_optimizer = make_optimizer(G_loss, self.G.variables, name='Adam_G')
    D_Y_optimizer = make_optimizer(D_Y_loss, self.D_Y.variables, name='Adam_D_Y')
    F_optimizer =  make_optimizer(F_loss, self.F.variables, name='Adam_F')
    D_X_optimizer = make_optimizer(D_X_loss, self.D_X.variables, name='Adam_D_X')

    with tf.control_dependencies([G_optimizer, D_Y_optimizer, F_optimizer, D_X_optimizer]):
      return tf.no_op(name='optimizers')