本文整理汇总了Python中dragnn.python.dragnn_ops.release_session方法的典型用法代码示例。如果您正苦于以下问题：Python dragnn_ops.release_session方法的具体用法？Python dragnn_ops.release_session怎么用？Python dragnn_ops.release_session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dragnn.python.dragnn_ops的用法示例。
在下文中一共展示了dragnn_ops.release_session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_warmup_graph
# 需要导入模块: from dragnn.python import dragnn_ops [as 别名]
# 或者: from dragnn.python.dragnn_ops import release_session [as 别名]
def build_warmup_graph(self, asset_dir):
"""Builds a warmup graph.
This graph performs a MasterSpec asset location rewrite via
SetAssetDirectory, then grabs a ComputeSession and immediately returns it.
By grabbing a session, we cause the underlying transition systems to cache
their static data reads.
Args:
asset_dir: The base directory to append to all resources.
Returns:
A single op suitable for passing to the legacy_init_op of the ModelSaver.
"""
with tf.control_dependencies([dragnn_ops.set_asset_directory(asset_dir)]):
session = self._get_compute_session()
release_op = dragnn_ops.release_session(session)
return tf.group(release_op, name='run') 示例2: _outputs_with_release
# 需要导入模块: from dragnn.python import dragnn_ops [as 别名]
# 或者: from dragnn.python.dragnn_ops import release_session [as 别名]
def _outputs_with_release(self, handle, inputs, outputs):
"""Ensures ComputeSession is released before outputs are returned.
Args:
handle: Handle to ComputeSession on which all computation until now has
depended. It will be released and assigned to the output 'run'.
inputs: list of nodes we want to pass through without any dependencies.
outputs: list of nodes whose access should ensure the ComputeSession is
safely released.
Returns:
A dictionary of both input and output nodes.
"""
with tf.control_dependencies(outputs.values()):
with tf.name_scope('ComputeSession'):
release_op = dragnn_ops.release_session(handle)
run_op = tf.group(release_op, name='run')
for output in outputs:
with tf.control_dependencies([release_op]):
outputs[output] = tf.identity(outputs[output], name=output)
all_nodes = inputs.copy()
all_nodes.update(outputs)
# Add an alias for simply running without collecting outputs.
# Common, for instance, with training.
all_nodes['run'] = run_op
return all_nodes