def _feedforward_unit(self, state, arrays, network_states, batch_size,
during_training):
"""Constructs a single instance of a feed-forward cell.
Given an input state and access to the arrays storing activations, this
function encapsulates creation of a single network unit. This will *not*
create new variables.
Args:
state: MasterState for the state that will be used to extract features.
arrays: List of TensorArrays corresponding to network outputs from this
component. These are used for recurrent link features; the arrays from
other components are used for stack-prop style connections.
network_states: NetworkState object containing the TensorArrays from
*all* components.
batch_size: int Tensor with the current batch size.
during_training: Whether to build a unit for training (vs inference).
Returns:
List of tensors generated by the underlying network implementation.
"""
with tf.variable_scope(self.name, reuse=True):
fixed_embeddings = []
for channel_id, feature_spec in enumerate(self.spec.fixed_feature):
fixed_embedding = network_units.fixed_feature_lookup(
self, state, channel_id, batch_size)
if feature_spec.is_constant:
fixed_embedding.tensor = tf.stop_gradient(fixed_embedding.tensor)
fixed_embeddings.append(fixed_embedding)
linked_embeddings = []
for channel_id, feature_spec in enumerate(self.spec.linked_feature):
if feature_spec.source_component == self.name:
# Recurrent feature: pull from the local arrays.
index = self.network.get_layer_index(feature_spec.source_layer)
source_array = arrays[index]
source_layer_size = self.network.layers[index].dim
linked_embeddings.append(
network_units.activation_lookup_recurrent(
self, state, channel_id, source_array, source_layer_size,
batch_size))
else:
# Stackprop style feature: pull from another component's arrays.
source = self.master.lookup_component[feature_spec.source_component]
source_tensor = network_states[source.name].activations[
feature_spec.source_layer]
source_layer_size = source.network.get_layer_size(
feature_spec.source_layer)
linked_embeddings.append(
network_units.activation_lookup_other(
self, state, channel_id, source_tensor.dynamic_tensor,
source_layer_size, batch_size))
context_tensor_arrays = []
for context_layer in self.network.context_layers:
index = self.network.get_layer_index(context_layer.name)
context_tensor_arrays.append(arrays[index])
return self.network.create(fixed_embeddings, linked_embeddings,
context_tensor_arrays, during_training)
def _feedforward_unit(self, state, arrays, network_states, stride,
during_training):
"""Constructs a single instance of a feed-forward cell.
Given an input state and access to the arrays storing activations, this
function encapsulates creation of a single network unit. This will *not*
create new variables.
Args:
state: MasterState for the state that will be used to extract features.
arrays: List of TensorArrays corresponding to network outputs from this
component. These are used for recurrent link features; the arrays from
other components are used for stack-prop style connections.
network_states: NetworkState object containing the TensorArrays from
*all* components.
stride: int Tensor with the current beam * batch size.
during_training: Whether to build a unit for training (vs inference).
Returns:
List of tensors generated by the underlying network implementation.
"""
with tf.variable_scope(self.name, reuse=True):
fixed_embeddings = []
for channel_id, feature_spec in enumerate(self.spec.fixed_feature):
fixed_embedding = network_units.fixed_feature_lookup(
self, state, channel_id, stride)
if feature_spec.is_constant:
fixed_embedding.tensor = tf.stop_gradient(fixed_embedding.tensor)
fixed_embeddings.append(fixed_embedding)
linked_embeddings = []
for channel_id, feature_spec in enumerate(self.spec.linked_feature):
if feature_spec.source_component == self.name:
# Recurrent feature: pull from the local arrays.
index = self.network.get_layer_index(feature_spec.source_layer)
source_array = arrays[index]
source_layer_size = self.network.layers[index].dim
linked_embeddings.append(
network_units.activation_lookup_recurrent(
self, state, channel_id, source_array, source_layer_size,
stride))
else:
# Stackprop style feature: pull from another component's arrays.
source = self.master.lookup_component[feature_spec.source_component]
source_tensor = network_states[source.name].activations[
feature_spec.source_layer]
source_layer_size = source.network.get_layer_size(
feature_spec.source_layer)
linked_embeddings.append(
network_units.activation_lookup_other(
self, state, channel_id, source_tensor.dynamic_tensor,
source_layer_size))
context_tensor_arrays = []
for context_layer in self.network.context_layers:
index = self.network.get_layer_index(context_layer.name)
context_tensor_arrays.append(arrays[index])
if self.spec.attention_component:
logging.info('%s component has attention over %s', self.name,
self.spec.attention_component)
source = self.master.lookup_component[self.spec.attention_component]
network_state = network_states[self.spec.attention_component]
with tf.control_dependencies(
[tf.assert_equal(state.current_batch_size, 1)]):
attention_tensor = tf.identity(
network_state.activations['layer_0'].bulk_tensor)
else:
attention_tensor = None
return self.network.create(fixed_embeddings, linked_embeddings,
context_tensor_arrays, attention_tensor,
during_training)