def export_ops(self, name):
"""Exports ops to collections."""
self._name = name
self._exported_ops = {}
ops = {}
ops[util.with_prefix(self._name, 'clfs_padd')] = self._padding
ops[util.with_prefix(self._name, 'last_step')] = self._last_step
for key, preds in self._predictions.items():
ops[util.with_prefix(self._name, key)] = preds
self._exported_ops.update({'predictions': [f'{self._name}/{k}' for k in self._predictions]})
for key, loss in self._losses.items():
ops[util.with_prefix(self._name, key)] = loss
for key, loss in self._l2_losses.items():
ops[util.with_prefix(self._name, key)] = loss
self._exported_ops.update({'losses': [f'{self._name}/{k}' for k in self._losses.keys()],
'l2_losses': [f'{self._name}/{k}' for k in self._l2_losses.keys()]})
ops.update({f'{name}/cur_epoch': self._cur_epoch,
f'{name}/global_step': self._global_step})
self._exported_ops.update({'epoch_and_step': [f'{name}/cur_epoch', f'{name}/global_step']})
if self._is_training:
for key, train_op in self._train_ops.items():
ops[key] = train_op
self._exported_ops.update({'train_ops': self._train_ops.keys()})
ops.update(lr=self._lr, new_lr=self._new_lr, lr_update=self._lr_update,
new_val=self._new_val, update_epoch=self._update_epoch)
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 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)
for name, op in ops.items():
tf.add_to_collection(name, op)
self._final_state_name = util.with_prefix(self._name, "final")
util.export_state_tuples(self._final_state, self._final_state_name)
def export_ops(self, name):
"""Exports ops to collections."""
self._name = name
ops = {util.with_prefix(self._name, "cost"): self._cost, util.with_prefix(self._name, "probabilities"): self._probabilities}
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 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 export_ops(self, name):
"""Exports ops to collections. The collection is managed by tensorflow"""
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 n, op in ops.items():
tf.add_to_collection(n, 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 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, params_size=self._params_size)
if FLAGS.num_gpus:
ops.update(memory_use=self._memory_use)
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 export_ops(self, name):
self._name = name
ops = {util.with_prefix(self._name, 'cost'): self._cost}
tf.add_to_collection(util.with_prefix(self._name, 'logits'),
self._logits)
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 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)
if self.model_type == 'test':
ops.update(logits=self.logits, y=self.y)
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 export_ops(self, name):
"""Exports ops to collections."""
self._name = name
ops = {util.with_prefix(self._name, "cost"): self._cost,
util.with_prefix(self._name, "kl_div"): self._kl_loss,
util.with_prefix(self._name, "input_data"): self._input_data,
util.with_prefix(self._name, "output"): self._output,
util.with_prefix(self._name, "targets"): self._targets,
}
if self._is_training:
ops.update(lr=self._lr, new_lr=self._new_lr, lr_update=self._lr_update)
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 export_ops(self, name):
"""Exports ops to collections."""
self._name = name
ops = {
util.with_prefix(self._name, "cost"): self._cost,
util.with_prefix(self._name, "kl_div"): self._kl_loss,
util.with_prefix(self._name, "input_data"): self._input_data,
util.with_prefix(self._name, "output"): self._output,
util.with_prefix(self._name, "targets"): self._targets,
}
if self._is_training:
ops.update(lr=self._lr,
new_lr=self._new_lr,
lr_update=self._lr_update)
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)
from __future__ import absolute_import