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)
def import_ops(self, config):
"""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.CudnnLSTMSaveable(
rnn_params,
config.num_layers,
config.hidden_size,
config.hidden_size,
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)
def import_ops(self, num_gpus=1):
"""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]
self._cost = tf.get_collection_ref(util.with_prefix(
self._name, "cost"))[0]
self._kl_loss = tf.get_collection_ref(
util.with_prefix(self._name, "kl_div"))[0]
self._input_data = tf.get_collection_ref(
util.with_prefix(self._name, "input_data"))[0]
self._output = tf.get_collection_ref(
util.with_prefix(self._name, "output"))[0]
self._targets = tf.get_collection_ref(
util.with_prefix(self._name, "targets"))[0]
num_replicas = 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)
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]
self._params_size = tf.get_collection_ref("params_size")[0]
if FLAGS.num_gpus:
self._memory_use = tf.get_collection_ref("memory_use")[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")
# params_saveable = tf.contrib.cudnn_rnn.CudnnLSTMSaveable(
# rnn_params,
# self._cell.num_layers,
# self._cell.num_units,
# self._cell.input_size,
# self._cell.input_mode,
# self._cell.direction,
# 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)
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)
if self.model_type == 'test':
self.logits = tf.get_collection_ref('logits')[0]
self.y = tf.get_collection_ref('y')[0]
self._cost = tf.get_collection_ref(util.with_prefix(
self._name, 'cost'))[0]
num_replicas = self.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)
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:
assert(False, \
"This branch spells trouble as the fn RNNParamsSaveable()" + \
" is not available from cudnn_rnn ")
# params_saveable = tf.contrib.cudnn_rnn.RNNParamsSaveable(
#
# /home/rm/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/contrib/cudnn_rnn/python/layers/cudnn_rnn.py
# does have the function RNNParamsSaveable()
params_saveable = 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)
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.CudnnLSTMSaveable(
rnn_params,
self._cell.num_layers,
self._cell.num_units,
self._cell.input_size,
self._cell.input_mode,
self._cell.direction,
scope="Model/RNN")
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)
def import_ops(self):
"""Imports ops from collections."""
self._padding = tf.get_collection_ref(util.with_prefix(self._name, 'clfs_padd'))[0]
self._last_step = tf.get_collection_ref(util.with_prefix(self._name, 'last_step'))[0]
if self._is_training:
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]
self._new_val = tf.get_collection_ref("new_val")[0]
self._update_epoch = tf.get_collection_ref("update_epoch")
for key in self._exported_ops['train_ops']:
self._train_ops[key] = tf.get_collection_ref(key)[0]
for key in self._exported_ops['epoch_and_step']:
if key == f'{self._name}/cur_epoch':
self._cur_epoch = tf.get_collection_ref(key)[0]
else:
self._global_step = tf.get_collection_ref(key)[0]
for key in self._exported_ops['losses']:
self._losses[key.split('/')[1]] = tf.get_collection_ref(key)[0]
for key in self._exported_ops['l2_losses']:
self._l2_losses[key.split('/')[1]] = tf.get_collection_ref(key)[0]
for key in self._exported_ops['predictions']:
self._predictions[key.split('/')[1]] = tf.get_collection_ref(key)[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)
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]
self._cost = tf.get_collection_ref(util.with_prefix(self._name, "cost"))[0]
self._final_state = util.import_state_tuples(self._final_state, self._final_state_name)
def import_ops(self, num_gpus = 1):
"""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]
self._cost = tf.get_collection_ref(util.with_prefix(self._name, "cost"))[0]
self._kl_loss = tf.get_collection_ref(util.with_prefix(self._name, "kl_div"))[0]
self._input_data = tf.get_collection_ref(util.with_prefix(self._name, "input_data"))[0]
self._output = tf.get_collection_ref(util.with_prefix(self._name, "output"))[0]
self._targets = tf.get_collection_ref(util.with_prefix(self._name, "targets"))[0]
num_replicas = 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)
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)
from __future__ import absolute_import