本文整理汇总了Python中baselines.common.tf_util.adjust_shape方法的典型用法代码示例。如果您正苦于以下问题：Python tf_util.adjust_shape方法的具体用法？Python tf_util.adjust_shape怎么用？Python tf_util.adjust_shape使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类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个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: step
# 需要导入模块: from baselines.common import tf_util [as 别名]
# 或者: from baselines.common.tf_util import adjust_shape [as 别名]
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
# 需要导入模块: from baselines.common import tf_util [as 别名]
# 或者: from baselines.common.tf_util import adjust_shape [as 别名]
def make_feed_dict(self, data):
return {self._placeholder: adjust_shape(self._placeholder, data)} 示例3: _evaluate
# 需要导入模块: from baselines.common import tf_util [as 别名]
# 或者: from baselines.common.tf_util import adjust_shape [as 别名]
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
# 需要导入模块: from baselines.common import tf_util [as 别名]
# 或者: from baselines.common.tf_util import adjust_shape [as 别名]
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)