本文整理匯總了Python中utils.center方法的典型用法代碼示例。如果您正苦於以下問題：Python utils.center方法的具體用法？Python utils.center怎麽用？Python utils.center使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類utils的用法示例。
在下文中一共展示了utils.center方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _create_baseline
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import center [as 別名]
def _create_baseline(self, n_output=1, n_hidden=100,
is_zero_init=False,
collection='BASELINE'):
# center input
h = self._x
if self.mean_xs is not None:
h -= self.mean_xs
if is_zero_init:
initializer = init_ops.zeros_initializer()
else:
initializer = slim.variance_scaling_initializer()
with slim.arg_scope([slim.fully_connected],
variables_collections=[collection, Q_COLLECTION],
trainable=False,
weights_initializer=initializer):
h = slim.fully_connected(h, n_hidden, activation_fn=tf.nn.tanh)
baseline = slim.fully_connected(h, n_output, activation_fn=None)
if n_output == 1:
baseline = tf.reshape(baseline, [-1]) # very important to reshape
return baseline 示例2: _create_loss
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import center [as 別名]
def _create_loss(self):
# Hard loss
logQHard, samples = self._recognition_network()
reinforce_learning_signal, reinforce_model_grad = self._generator_network(samples, logQHard)
logQHard = tf.add_n(logQHard)
# REINFORCE
learning_signal = tf.stop_gradient(U.center(reinforce_learning_signal))
self.optimizerLoss = -(learning_signal*logQHard +
reinforce_model_grad)
self.lHat = map(tf.reduce_mean, [
reinforce_learning_signal,
U.rms(learning_signal),
])
return reinforce_learning_signal