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)
})