本文整理汇总了Python中deepchem.models.TensorGraph._get_tf方法的典型用法代码示例。如果您正苦于以下问题：Python TensorGraph._get_tf方法的具体用法？Python TensorGraph._get_tf怎么用？Python TensorGraph._get_tf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deepchem.models.TensorGraph的用法示例。
在下文中一共展示了TensorGraph._get_tf方法的2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: _build_graph
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import _get_tf [as 别名]
def _build_graph(self, tf_graph, scope, model_dir):
"""Construct a TensorGraph containing the policy and loss calculations."""
state_shape = self._env.state_shape
state_dtype = self._env.state_dtype
if not self._state_is_list:
state_shape = [state_shape]
state_dtype = [state_dtype]
features = []
for s, d in zip(state_shape, state_dtype):
features.append(Feature(shape=[None] + list(s), dtype=tf.as_dtype(d)))
policy_layers = self._policy.create_layers(features)
action_prob = policy_layers['action_prob']
value = policy_layers['value']
search_prob = Label(shape=(None, self._env.n_actions))
search_value = Label(shape=(None,))
loss = MCTSLoss(
self.value_weight,
in_layers=[action_prob, value, search_prob, search_value])
graph = TensorGraph(
batch_size=self.max_search_depth,
use_queue=False,
graph=tf_graph,
model_dir=model_dir)
for f in features:
graph._add_layer(f)
graph.add_output(action_prob)
graph.add_output(value)
graph.set_loss(loss)
graph.set_optimizer(self._optimizer)
with graph._get_tf("Graph").as_default():
with tf.variable_scope(scope):
graph.build()
if len(graph.rnn_initial_states) > 0:
raise ValueError('MCTS does not support policies with recurrent layers')
return graph, features, action_prob, value, search_prob, search_value示例2: _build_graph
# 需要导入模块: from deepchem.models import TensorGraph [as 别名]
# 或者: from deepchem.models.TensorGraph import _get_tf [as 别名]
def _build_graph(self, tf_graph, scope, model_dir):
"""Construct a TensorGraph containing the policy and loss calculations."""
state_shape = self._env.state_shape
state_dtype = self._env.state_dtype
if not self._state_is_list:
state_shape = [state_shape]
state_dtype = [state_dtype]
features = []
for s, d in zip(state_shape, state_dtype):
features.append(Feature(shape=[None] + list(s), dtype=tf.as_dtype(d)))
policy_layers = self._policy.create_layers(features)
value = policy_layers['value']
rewards = Weights(shape=(None,))
advantages = Weights(shape=(None,))
graph = TensorGraph(
batch_size=self.max_rollout_length,
use_queue=False,
graph=tf_graph,
model_dir=model_dir)
for f in features:
graph._add_layer(f)
if 'action_prob' in policy_layers:
self.continuous = False
action_prob = policy_layers['action_prob']
actions = Label(shape=(None, self._env.n_actions))
loss = A3CLossDiscrete(
self.value_weight,
self.entropy_weight,
in_layers=[rewards, actions, action_prob, value, advantages])
graph.add_output(action_prob)
else:
self.continuous = True
action_mean = policy_layers['action_mean']
action_std = policy_layers['action_std']
actions = Label(shape=[None] + list(self._env.action_shape))
loss = A3CLossContinuous(
self.value_weight,
self.entropy_weight,
in_layers=[
rewards, actions, action_mean, action_std, value, advantages
])
graph.add_output(action_mean)
graph.add_output(action_std)
graph.add_output(value)
graph.set_loss(loss)
graph.set_optimizer(self._optimizer)
with graph._get_tf("Graph").as_default():
with tf.variable_scope(scope):
graph.build()
if self.continuous:
return graph, features, rewards, actions, action_mean, action_std, value, advantages
else:
return graph, features, rewards, actions, action_prob, value, advantages