def export_ops(self, name):
"""Exports ops to collections."""
self._name = name
ops = {util.with_prefix(self._name, "cost"): self._cost}
if self._is_training:
ops.update(lr=self._lr, new_lr=self._new_lr, lr_update=self._lr_update)
if self._rnn_params:
ops.update(rnn_params=self._rnn_params)
for name, op in ops.items():
tf.add_to_collection(name, op)
self._initial_state_name = util.with_prefix(self._name, "initial")
self._final_state_name = util.with_prefix(self._name, "final")
util.export_state_tuples(self._initial_state, self._initial_state_name)
util.export_state_tuples(self._final_state, self._final_state_name)
def import_ops(self):
"""Imports ops from collections."""
if self._is_training:
self._train_op = tf.get_collection_ref("train_op")[0]
self._lr = tf.get_collection_ref("lr")[0]
self._new_lr = tf.get_collection_ref("new_lr")[0]
self._lr_update = tf.get_collection_ref("lr_update")[0]
rnn_params = tf.get_collection_ref("rnn_params")
if self._cell and rnn_params:
params_saveable = tf.contrib.cudnn_rnn.RNNParamsSaveable(
self._cell,
self._cell.params_to_canonical,
self._cell.canonical_to_params,
rnn_params,
base_variable_scope="Model/RNN")
tf.add_to_collection(tf.GraphKeys.SAVEABLE_OBJECTS,
params_saveable)
self._cost = tf.get_collection_ref(util.with_prefix(
self._name, "cost"))[0]
num_replicas = FLAGS.num_gpus if self._name == "Train" else 1
self._initial_state = util.import_state_tuples(
self._initial_state, self._initial_state_name, num_replicas)
self._final_state = util.import_state_tuples(self._final_state,
self._final_state_name,
num_replicas)