def construct_state_saving_rnn(cell,
inputs,
num_label_columns,
state_saver,
state_name,
scope='rnn'):
"""Build a state saving RNN and apply a fully connected layer.
Args:
cell: An instance of `RNNCell`.
inputs: A length `T` list of inputs, each a `Tensor` of shape
`[batch_size, input_size, ...]`.
num_label_columns: The desired output dimension.
state_saver: A state saver object with methods `state` and `save_state`.
state_name: Python string or tuple of strings. The name to use with the
state_saver. If the cell returns tuples of states (i.e.,
`cell.state_size` is a tuple) then `state_name` should be a tuple of
strings having the same length as `cell.state_size`. Otherwise it should
be a single string.
scope: `VariableScope` for the created subgraph; defaults to "rnn".
Returns:
activations: The output of the RNN, projected to `num_label_columns`
dimensions, a `Tensor` of shape `[batch_size, T, num_label_columns]`.
final_state: The final state output by the RNN
"""
with ops.name_scope(scope):
rnn_outputs, final_state = rnn.static_state_saving_rnn(
cell=cell,
inputs=inputs,
state_saver=state_saver,
state_name=state_name,
scope=scope)
# Convert rnn_outputs from a list of time-major order Tensors to a single
# Tensor of batch-major order.
rnn_outputs = array_ops.stack(rnn_outputs, axis=1)
activations = layers.fully_connected(
inputs=rnn_outputs,
num_outputs=num_label_columns,
activation_fn=None,
trainable=True)
# Use `identity` to rename `final_state`.
final_state = array_ops.identity(
final_state, name=rnn_common.RNNKeys.FINAL_STATE_KEY)
return activations, final_state
def construct_state_saving_rnn(cell,
inputs,
num_label_columns,
state_saver,
state_name,
scope='rnn'):
"""Build a state saving RNN and apply a fully connected layer.
Args:
cell: An instance of `RNNCell`.
inputs: A length `T` list of inputs, each a `Tensor` of shape
`[batch_size, input_size, ...]`.
num_label_columns: The desired output dimension.
state_saver: A state saver object with methods `state` and `save_state`.
state_name: Python string or tuple of strings. The name to use with the
state_saver. If the cell returns tuples of states (i.e.,
`cell.state_size` is a tuple) then `state_name` should be a tuple of
strings having the same length as `cell.state_size`. Otherwise it should
be a single string.
scope: `VariableScope` for the created subgraph; defaults to "rnn".
Returns:
activations: The output of the RNN, projected to `num_label_columns`
dimensions, a `Tensor` of shape `[batch_size, T, num_label_columns]`.
final_state: The final state output by the RNN
"""
with ops.name_scope(scope):
rnn_outputs, final_state = rnn.static_state_saving_rnn(
cell=cell,
inputs=inputs,
state_saver=state_saver,
state_name=state_name,
scope=scope)
# Convert rnn_outputs from a list of time-major order Tensors to a single
# Tensor of batch-major order.
rnn_outputs = array_ops.stack(rnn_outputs, axis=1)
activations = layers.fully_connected(inputs=rnn_outputs,
num_outputs=num_label_columns,
activation_fn=None,
trainable=True)
# Use `identity` to rename `final_state`.
final_state = array_ops.identity(
final_state, name=rnn_common.RNNKeys.FINAL_STATE_KEY)
return activations, final_state