本文整理匯總了Python中dragnn.python.dragnn_ops.bulk_fixed_embeddings方法的典型用法代碼示例。如果您正苦於以下問題：Python dragnn_ops.bulk_fixed_embeddings方法的具體用法？Python dragnn_ops.bulk_fixed_embeddings怎麽用？Python dragnn_ops.bulk_fixed_embeddings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.bulk_fixed_embeddings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: fetch_fast_fixed_embeddings
# 需要導入模塊: from dragnn.python import dragnn_ops [as 別名]
# 或者: from dragnn.python.dragnn_ops import bulk_fixed_embeddings [as 別名]
def fetch_fast_fixed_embeddings(comp, state):
"""Looks up fixed features with fast, non-differentiable, op.
Since BulkFixedEmbeddings is non-differentiable with respect to the
embeddings, the idea is to call this function only when the graph is
not being used for training.
Args:
comp: Component whose fixed features we wish to look up.
state: live MasterState object for the component.
Returns:
state handle: updated state handle to be used after this call
fixed_embeddings: list of NamedTensor objects
"""
_validate_embedded_fixed_features(comp)
num_channels = len(comp.spec.fixed_feature)
if not num_channels:
return state.handle, []
tf.logging.info('[%s] Adding %d fast fixed features', comp.name, num_channels)
state.handle, bulk_embeddings, _ = dragnn_ops.bulk_fixed_embeddings(
state.handle, [
comp.get_variable(network_units.fixed_embeddings_name(c))
for c in range(num_channels)
],
component=comp.name)
bulk_embeddings = network_units.NamedTensor(bulk_embeddings,
'bulk-%s-fixed-features' %
comp.name)
return state.handle, [bulk_embeddings] 示例2: fetch_fast_fixed_embeddings
# 需要導入模塊: from dragnn.python import dragnn_ops [as 別名]
# 或者: from dragnn.python.dragnn_ops import bulk_fixed_embeddings [as 別名]
def fetch_fast_fixed_embeddings(comp,
state,
pad_to_batch=None,
pad_to_steps=None):
"""Looks up fixed features with fast, non-differentiable, op.
Since BulkFixedEmbeddings is non-differentiable with respect to the
embeddings, the idea is to call this function only when the graph is
not being used for training. If the function is being called with fixed step
and batch sizes, it will use the most efficient possible extractor.
Args:
comp: Component whose fixed features we wish to look up.
state: live MasterState object for the component.
pad_to_batch: Optional; the number of batch elements to pad to.
pad_to_steps: Optional; the number of steps to pad to.
Returns:
state handle: updated state handle to be used after this call
fixed_embeddings: list of NamedTensor objects
"""
_validate_embedded_fixed_features(comp)
num_channels = len(comp.spec.fixed_feature)
if not num_channels:
return state.handle, []
tf.logging.info('[%s] Adding %d fast fixed features', comp.name, num_channels)
features = [
comp.get_variable(network_units.fixed_embeddings_name(c))
for c in range(num_channels)
]
if pad_to_batch is not None and pad_to_steps is not None:
# If we have fixed padding numbers, we can use 'bulk_embed_fixed_features',
# which is the fastest embedding extractor.
state.handle, bulk_embeddings, _ = dragnn_ops.bulk_embed_fixed_features(
state.handle,
features,
component=comp.name,
pad_to_batch=pad_to_batch,
pad_to_steps=pad_to_steps)
else:
state.handle, bulk_embeddings, _ = dragnn_ops.bulk_fixed_embeddings(
state.handle, features, component=comp.name)
bulk_embeddings = network_units.NamedTensor(
bulk_embeddings, 'bulk-%s-fixed-features' % comp.name)
return state.handle, [bulk_embeddings]