Python dragnn_ops.bulk_fixed_features方法代码示例

# 需要导入模块: from dragnn.python import dragnn_ops [as 别名]
# 或者: from dragnn.python.dragnn_ops import bulk_fixed_features [as 别名]
def fetch_differentiable_fixed_embeddings(comp, state, stride):
  """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.

  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)
    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 

# 需要导入模块: from dragnn.python import dragnn_ops [as 别名]
# 或者: from dragnn.python.dragnn_ops import bulk_fixed_features [as 别名]
def extract_fixed_feature_ids(comp, state, stride):
  """Extracts fixed feature IDs.

  Args:
    comp: Component whose fixed feature IDs we wish to extract.
    state: Live MasterState object for the component.
    stride: Tensor containing current batch * beam size.

  Returns:
    state handle: Updated state handle to be used after this call.
    ids: List of [stride * num_steps, 1] feature IDs per channel.  Missing IDs
         (e.g., due to batch padding) are set to -1.
  """
  num_channels = len(comp.spec.fixed_feature)
  if not num_channels:
    return state.handle, []

  for feature_spec in comp.spec.fixed_feature:
    check.Eq(feature_spec.size, 1, 'All features must have size=1')
    check.Lt(feature_spec.embedding_dim, 0, 'All features must be non-embedded')

  state.handle, indices, ids, _, num_steps = dragnn_ops.bulk_fixed_features(
      state.handle, component=comp.name, num_channels=num_channels)
  size = stride * num_steps

  fixed_ids = []
  for channel, feature_spec in enumerate(comp.spec.fixed_feature):
    tf.logging.info('[%s] Adding fixed feature IDs "%s"', comp.name,
                    feature_spec.name)

    # The +1 and -1 increments ensure that missing IDs default to -1.
    #
    # TODO(googleuser): This formula breaks if multiple IDs are extracted at some
    # step.  Try using tf.unique() to enforce the unique-IDS precondition.
    sums = tf.unsorted_segment_sum(ids[channel] + 1, indices[channel], size) - 1
    sums = tf.expand_dims(sums, axis=1)
    fixed_ids.append(network_units.NamedTensor(sums, feature_spec.name, dim=1))
  return state.handle, fixed_ids 

# 需要导入模块: from dragnn.python import dragnn_ops [as 别名]
# 或者: from dragnn.python.dragnn_ops import bulk_fixed_features [as 别名]
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