本文整理匯總了Python中dragnn.python.dragnn_ops.release_session方法的典型用法代碼示例。如果您正苦於以下問題：Python dragnn_ops.release_session方法的具體用法？Python dragnn_ops.release_session怎麽用？Python dragnn_ops.release_session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: build_warmup_graph
# 需要導入模塊: from dragnn.python import dragnn_ops [as 別名]
# 或者: from dragnn.python.dragnn_ops import release_session [as 別名]
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
# 需要導入模塊: from dragnn.python import dragnn_ops [as 別名]
# 或者: from dragnn.python.dragnn_ops import release_session [as 別名]
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