def fit(self, sess, local_): for _ in range(local_): x, _ = next_batch_(FLAGS.bz) sess.run(self.optim, {self.x: x}) x, _ = next_batch_(FLAGS.bz * 5) return sess.run( [self.loss, self.rec_loss, self.kld_loss, self.fit_summary], {self.x: x})
def fit(self, sess, local_): for _ in range(local_): x, _ = next_batch_(FLAGS.bz) sess.run(self.optim, { self.x: x, self.z: gaussian(FLAGS.bz, FLAGS.z_dim) }) x, _ = next_batch_(FLAGS.bz * 5) return sess.run( [self.loss, self.loss_nll, self.loss_mmd, self.fit_summary], { self.x: x, self.z: gaussian(FLAGS.bz, FLAGS.z_dim) })
def fit(self, sess, local_): for _ in range(local_): x_real, y = next_batch_(FLAGS.bz) one_hot_y = one_hot_(y, FLAGS.y_dim) sess.run(self.a_optim, {self.x: x_real}) for _ in range(3): sess.run(self.d_optim, {self.x: x_real, self.y: one_hot_y, self.real_z: z_real_(y)}) sess.run(self.g_optim, {self.x: x_real, self.y: one_hot_y}) x_real, y = next_batch_(FLAGS.bz * 5) one_hot_y = one_hot_(y, FLAGS.y_dim) return sess.run([self.a_loss, self.g_loss, self.d_loss, self.fit_summary], { self.x: x_real, self.real_z: z_real_(y), self.y: one_hot_y})
def fit(self, sess, local_): for _ in range(local_): x_real, _ = next_batch_(FLAGS.bz) sess.run(self.a_optim, {self.x: x_real}) for _ in range(3): sess.run(self.d_optim, { self.x: x_real, self.real_z: z_real_(FLAGS.bz) }) sess.run(self.g_optim, {self.x: x_real}) x_real, _ = next_batch_(FLAGS.bz * 5) return sess.run( [self.a_loss, self.g_loss, self.d_loss, self.fit_summary], { self.x: x_real, self.real_z: z_real_(FLAGS.bz * 5) })
def latent_z(self, sess, bz): x, y = next_batch_(bz) return sess.run(self.fake_z, {self.x: x}), y