本文整理匯總了Python中baselines.common.tf_util.adjust_shape方法的典型用法代碼示例。如果您正苦於以下問題：Python tf_util.adjust_shape方法的具體用法？Python tf_util.adjust_shape怎麽用？Python tf_util.adjust_shape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baselines.common.tf_util的用法示例。
在下文中一共展示了tf_util.adjust_shape方法的4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: step
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import adjust_shape [as 別名]
def step(self, obs, apply_noise=True, compute_Q=True):
if self.param_noise is not None and apply_noise:
actor_tf = self.perturbed_actor_tf
else:
actor_tf = self.actor_tf
feed_dict = {self.obs0: U.adjust_shape(self.obs0, [obs])}
if compute_Q:
action, q = self.sess.run([actor_tf, self.critic_with_actor_tf], feed_dict=feed_dict)
else:
action = self.sess.run(actor_tf, feed_dict=feed_dict)
q = None
if self.action_noise is not None and apply_noise:
noise = self.action_noise()
assert noise.shape == action[0].shape
action += noise
action = np.clip(action, self.action_range[0], self.action_range[1])
return action, q, None, None 示例2: make_feed_dict
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import adjust_shape [as 別名]
def make_feed_dict(self, data):
return {self._placeholder: adjust_shape(self._placeholder, data)} 示例3: _evaluate
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import adjust_shape [as 別名]
def _evaluate(self, variables, observation, **extra_feed):
sess = self.sess or tf.get_default_session()
feed_dict = {self.X: adjust_shape(self.X, observation)}
for inpt_name, data in extra_feed.items():
if inpt_name in self.__dict__.keys():
inpt = self.__dict__[inpt_name]
if isinstance(inpt, tf.Tensor) and inpt._op.type == 'Placeholder':
feed_dict[inpt] = adjust_shape(inpt, data)
return sess.run(variables, feed_dict) 示例4: _evaluate
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import adjust_shape [as 別名]
def _evaluate(self, variables, observation, **extra_feed):
sess = self.sess
feed_dict = {self.X: adjust_shape(self.X, observation)}
for inpt_name, data in extra_feed.items():
if inpt_name in self.__dict__.keys():
inpt = self.__dict__[inpt_name]
if isinstance(inpt, tf.Tensor) and inpt._op.type == 'Placeholder':
feed_dict[inpt] = adjust_shape(inpt, data)
return sess.run(variables, feed_dict)