def fetch_differentiable_fixed_embeddings(comp, state, stride,
during_training):
"""Looks up fixed features with separate, differentiable, embedding lookup.
Args:
comp: Component whose fixed features we wish to look up.
state: live MasterState object for the component.
stride: Tensor containing current batch * beam size.
during_training: True if this is being called from a training code path.
This controls, e.g., the use of feature ID dropout.
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, []
state.handle, indices, ids, weights, num_steps = (
dragnn_ops.bulk_fixed_features(state.handle,
component=comp.name,
num_channels=num_channels))
fixed_embeddings = []
for channel, feature_spec in enumerate(comp.spec.fixed_feature):
differentiable_or_constant = ('constant' if feature_spec.is_constant
else 'differentiable')
tf.logging.info('[%s] Adding %s fixed feature "%s"', comp.name,
differentiable_or_constant, feature_spec.name)
if during_training and feature_spec.dropout_id >= 0:
ids[channel], weights[
channel] = network_units.apply_feature_id_dropout(
ids[channel], weights[channel], feature_spec)
size = stride * num_steps * feature_spec.size
fixed_embedding = network_units.embedding_lookup(
comp.get_variable(network_units.fixed_embeddings_name(channel)),
indices[channel], ids[channel], weights[channel], size)
if feature_spec.is_constant:
fixed_embedding = tf.stop_gradient(fixed_embedding)
fixed_embeddings.append(
network_units.NamedTensor(fixed_embedding, feature_spec.name))
return state.handle, fixed_embeddings
def fetch_differentiable_fixed_embeddings(comp, state, stride, during_training):
"""Looks up fixed features with separate, differentiable, embedding lookup.
Args:
comp: Component whose fixed features we wish to look up.
state: live MasterState object for the component.
stride: Tensor containing current batch * beam size.
during_training: True if this is being called from a training code path.
This controls, e.g., the use of feature ID dropout.
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, []
state.handle, indices, ids, weights, num_steps = (
dragnn_ops.bulk_fixed_features(
state.handle, component=comp.name, num_channels=num_channels))
fixed_embeddings = []
for channel, feature_spec in enumerate(comp.spec.fixed_feature):
differentiable_or_constant = ('constant' if feature_spec.is_constant else
'differentiable')
tf.logging.info('[%s] Adding %s fixed feature "%s"', comp.name,
differentiable_or_constant, feature_spec.name)
if during_training and feature_spec.dropout_id >= 0:
ids[channel], weights[channel] = network_units.apply_feature_id_dropout(
ids[channel], weights[channel], feature_spec)
size = stride * num_steps * feature_spec.size
fixed_embedding = network_units.embedding_lookup(
comp.get_variable(network_units.fixed_embeddings_name(channel)),
indices[channel], ids[channel], weights[channel], size)
if feature_spec.is_constant:
fixed_embedding = tf.stop_gradient(fixed_embedding)
fixed_embeddings.append(
network_units.NamedTensor(fixed_embedding, feature_spec.name))
return state.handle, fixed_embeddings