本文整理汇总了Python中dragnn.python.component.NetworkState方法的典型用法代码示例。如果您正苦于以下问题：Python component.NetworkState方法的具体用法？Python component.NetworkState怎么用？Python component.NetworkState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.component的用法示例。
在下文中一共展示了component.NetworkState方法的6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: testBulkFeatureIdExtractorOkWithOneFixedFeature
# 需要导入模块: from dragnn.python import component [as 别名]
# 或者: from dragnn.python.component import NetworkState [as 别名]
def testBulkFeatureIdExtractorOkWithOneFixedFeature(self):
component_spec = spec_pb2.ComponentSpec()
text_format.Parse("""
name: "test"
network_unit {
registered_name: "IdentityNetwork"
}
fixed_feature {
name: "fixed" embedding_dim: -1 size: 1
}
""", component_spec)
with tf.Graph().as_default():
comp = bulk_component.BulkFeatureIdExtractorComponentBuilder(
self.master, component_spec)
# Should not raise errors.
self.network_states[component_spec.name] = component.NetworkState()
comp.build_greedy_training(self.master_state, self.network_states)
self.network_states[component_spec.name] = component.NetworkState()
comp.build_greedy_inference(self.master_state, self.network_states) 示例2: testBulkFeatureIdExtractorOkWithOneFixedFeature
# 需要导入模块: from dragnn.python import component [as 别名]
# 或者: from dragnn.python.component import NetworkState [as 别名]
def testBulkFeatureIdExtractorOkWithOneFixedFeature(self):
component_spec = spec_pb2.ComponentSpec()
text_format.Parse("""
name: "test"
network_unit {
registered_name: "IdentityNetwork"
}
fixed_feature {
name: "fixed" embedding_dim: -1 size: 1
}
""", component_spec)
comp = bulk_component.BulkFeatureIdExtractorComponentBuilder(
self.master, component_spec)
# Should not raise errors.
self.network_states[component_spec.name] = component.NetworkState()
comp.build_greedy_training(self.master_state, self.network_states)
self.network_states[component_spec.name] = component.NetworkState()
comp.build_greedy_inference(self.master_state, self.network_states) 示例3: testBulkFeatureIdExtractorOkWithMultipleFixedFeatures
# 需要导入模块: from dragnn.python import component [as 别名]
# 或者: from dragnn.python.component import NetworkState [as 别名]
def testBulkFeatureIdExtractorOkWithMultipleFixedFeatures(self):
component_spec = spec_pb2.ComponentSpec()
text_format.Parse("""
name: "test"
network_unit {
registered_name: "IdentityNetwork"
}
fixed_feature {
name: "fixed1" embedding_dim: -1 size: 1
}
fixed_feature {
name: "fixed2" embedding_dim: -1 size: 1
}
fixed_feature {
name: "fixed3" embedding_dim: -1 size: 1
}
""", component_spec)
comp = bulk_component.BulkFeatureIdExtractorComponentBuilder(
self.master, component_spec)
# Should not raise errors.
self.network_states[component_spec.name] = component.NetworkState()
comp.build_greedy_training(self.master_state, self.network_states)
self.network_states[component_spec.name] = component.NetworkState()
comp.build_greedy_inference(self.master_state, self.network_states) 示例4: build_inference
# 需要导入模块: from dragnn.python import component [as 别名]
# 或者: from dragnn.python.component import NetworkState [as 别名]
def build_inference(self, handle, use_moving_average=False):
"""Builds an inference pipeline.
This always uses the whole pipeline.
Args:
handle: Handle tensor for the ComputeSession.
use_moving_average: Whether or not to read from the moving
average variables instead of the true parameters. Note: it is not
possible to make gradient updates when this is True.
Returns:
handle: Handle after annotation.
"""
self.read_from_avg = use_moving_average
network_states = {}
for comp in self.components:
network_states[comp.name] = component.NetworkState()
handle = dragnn_ops.init_component_data(
handle, beam_size=comp.inference_beam_size, component=comp.name)
master_state = component.MasterState(handle,
dragnn_ops.batch_size(
handle, component=comp.name))
with tf.control_dependencies([handle]):
handle = comp.build_greedy_inference(master_state, network_states)
handle = dragnn_ops.write_annotations(handle, component=comp.name)
self.read_from_avg = False
return handle 示例5: setUp
# 需要导入模块: from dragnn.python import component [as 别名]
# 或者: from dragnn.python.component import NetworkState [as 别名]
def setUp(self):
self.master = MockMaster()
self.master_state = component.MasterState(
handle='handle', current_batch_size=2)
self.network_states = {
'mock': component.NetworkState(),
'test': component.NetworkState(),
} 示例6: testBulkFeatureIdExtractorOkWithMultipleFixedFeatures
# 需要导入模块: from dragnn.python import component [as 别名]
# 或者: from dragnn.python.component import NetworkState [as 别名]
def testBulkFeatureIdExtractorOkWithMultipleFixedFeatures(self):
component_spec = spec_pb2.ComponentSpec()
text_format.Parse("""
name: "test"
network_unit {
registered_name: "IdentityNetwork"
}
fixed_feature {
name: "fixed1" embedding_dim: -1 size: 1
}
fixed_feature {
name: "fixed2" embedding_dim: -1 size: 1
}
fixed_feature {
name: "fixed3" embedding_dim: -1 size: 1
}
""", component_spec)
with tf.Graph().as_default():
comp = bulk_component.BulkFeatureIdExtractorComponentBuilder(
self.master, component_spec)
# Should not raise errors.
self.network_states[component_spec.name] = component.NetworkState()
comp.build_greedy_training(self.master_state, self.network_states)
self.network_states[component_spec.name] = component.NetworkState()
comp.build_greedy_inference(self.master_state, self.network_states)