def fit(self, sess, local_): for _ in range(local_): sess.run(self.inc_step) x_real, _ = next_batch_(FLAGS.bz) z = gaussian(FLAGS.bz, FLAGS.z_dim) for _ in range(3): sess.run(self.d_optim, feed_dict={ self.real: x_real, self.z: z, is_training: True }) sess.run(self.g_optim, feed_dict={ self.real: x_real, self.z: z, is_training: True }) x_real, _ = next_batch_(FLAGS.bz) return sess.run( [self.d_loss, self.g_loss, self.fit_summary], feed_dict={ self.real: x_real, self.z: gaussian(FLAGS.bz, FLAGS.z_dim), is_training: False })
def fit(self, sess, local_): for _ in range(local_): x_real, _ = next_batch_(FLAGS.bz) z = gaussian(FLAGS.bz, FLAGS.z_dim) for _ in range(3): sess.run(self.d_adam, feed_dict={self.real: x_real, self.z: z}) sess.run(self.g_adam, feed_dict={self.real: x_real, self.z: z}) x_real, _ = next_batch_(FLAGS.bz) return sess.run([self.d_loss, self.g_loss, self.fit_summary], feed_dict={ self.real: x_real, self.z: gaussian(FLAGS.bz, FLAGS.z_dim) })