def build_inference(self, handle, use_moving_average=False):
"""Builds an inference pipeline.
This always uses the whole pipeline.
Args:
handle: Handle tensor for the ComputeSession.
use_moving_average: Whether or not to read from the moving
average variables instead of the true parameters. Note: it is not
possible to make gradient updates when this is True.
Returns:
handle: Handle after annotation.
"""
self.read_from_avg = use_moving_average
network_states = {}
for comp in self.components:
network_states[comp.name] = component.NetworkState()
handle = dragnn_ops.init_component_data(
handle,
beam_size=comp.inference_beam_size,
component=comp.name)
master_state = component.MasterState(
handle, dragnn_ops.batch_size(handle, component=comp.name))
with tf.control_dependencies([handle]):
handle = comp.build_greedy_inference(master_state,
network_states)
handle = dragnn_ops.write_annotations(handle, component=comp.name)
self.read_from_avg = False
return handle
def build_inference(self, handle, use_moving_average=False,
clear_existing_annotations=False):
"""Builds an inference pipeline.
This always uses the whole pipeline.
Args:
handle: Handle tensor for the ComputeSession.
use_moving_average: Whether or not to read from the moving
average variables instead of the true parameters. Note: it is not
possible to make gradient updates when this is True.
clear_existing_annotations: Whether or not existing annotations
should be cleared when processing a new batch.
Returns:
handle: Handle after annotation.
"""
self.read_from_avg = use_moving_average
network_states = {}
for comp in self.components:
network_states[comp.name] = component.NetworkState()
handle = dragnn_ops.init_component_data(
handle, component=comp.name,
clear_existing_annotations=clear_existing_annotations)
master_state = component.MasterState(handle,
dragnn_ops.batch_size(
handle, component=comp.name))
with tf.control_dependencies([handle]):
handle = comp.build_inference(master_state, network_states)
handle = dragnn_ops.write_annotations(handle, component=comp.name)
self.read_from_avg = False
return handle
def build_inference(self,
handle,
use_moving_average=False,
build_runtime_graph=False):
"""Builds an inference pipeline.
This always uses the whole pipeline.
Args:
handle: Handle tensor for the ComputeSession.
use_moving_average: Whether or not to read from the moving
average variables instead of the true parameters. Note: it is not
possible to make gradient updates when this is True.
build_runtime_graph: Whether to build a graph for use by the runtime.
Returns:
handle: Handle after annotation.
"""
self.read_from_avg = use_moving_average
self.build_runtime_graph = build_runtime_graph
network_states = {}
for comp in self.components:
network_states[comp.name] = component.NetworkState()
handle = dragnn_ops.init_component_data(
handle, beam_size=comp.inference_beam_size, component=comp.name)
if build_runtime_graph:
batch_size = 1 # runtime uses singleton batches
else:
batch_size = dragnn_ops.batch_size(handle, component=comp.name)
master_state = component.MasterState(handle, batch_size)
with tf.control_dependencies([handle]):
handle = comp.build_greedy_inference(master_state, network_states)
handle = dragnn_ops.write_annotations(handle, component=comp.name)
self.read_from_avg = False
self.build_runtime_graph = False
return handle
def build_training(self,
handle,
component_weights=None,
unroll_using_oracle=None,
max_index=-1):
"""Builds a training pipeline.
Args:
handle: Handle tensor for the ComputeSession.
component_weights: If set, this is a list of relative weights
each component's cost should get in the pipeline. Defaults to 1.0 for
each component.
unroll_using_oracle: If set, this is a list of booleans indicating
whether or not to use the gold decodings for each component. Defaults
to True for each component.
max_index: Training will use only the first max_index components,
or -1 for all components.
Returns:
handle: to the ComputeSession, conditioned on completing training step.
outputs: a dictionary of useful training tensors.
Raises:
IndexError: if max_index is positive but out of bounds.
"""
self.read_from_avg = False
if max_index < 0:
max_index = len(self.components)
else:
if not 0 < max_index <= len(self.components):
raise IndexError('Invalid max_index {} for components {}; handle {}'.
format(max_index, self.component_names, handle.name))
# By default, we train every component supervised.
if not component_weights:
component_weights = [1] * max_index
if not unroll_using_oracle:
unroll_using_oracle = [True] * max_index
component_weights = component_weights[:max_index]
total_weight = (float)(sum(component_weights))
component_weights = [w / total_weight for w in component_weights]
unroll_using_oracle = unroll_using_oracle[:max_index]
logging.info('Creating training target:')
logging.info('\tWeights: %s', component_weights)
logging.info('\tOracle: %s', unroll_using_oracle)
metrics_list = []
cost = tf.constant(0.)
effective_batch = tf.constant(0)
avg_ops = []
params_to_train = []
network_states = {}
for component_index in range(0, max_index):
comp = self.components[component_index]
network_states[comp.name] = component.NetworkState()
logging.info('Initializing data for component "%s"', comp.name)
handle = dragnn_ops.init_component_data(handle, component=comp.name,
clear_existing_annotations=False)
# TODO(googleuser): Phase out component.MasterState.
master_state = component.MasterState(handle,
dragnn_ops.batch_size(
handle, component=comp.name))
with tf.control_dependencies([handle, cost]):
args = (master_state, network_states)
if unroll_using_oracle[component_index]:
handle, component_cost, correct, total = comp.build_training(
*args)
else:
handle = comp.build_inference(*args, during_training=True)
component_cost = tf.constant(0.)
correct, total = tf.constant(0), tf.constant(0)
weighted_component_cost = tf.multiply(
component_cost,
tf.constant((float)(component_weights[component_index])),
name='weighted_component_cost')
cost += weighted_component_cost
effective_batch += total
metrics_list += [[total], [correct]]
with tf.control_dependencies([comp.advance_counters(total)]):
cost = tf.identity(cost)
# Keep track of which parameters will be trained, and any moving
# average updates to apply for these parameters.
params_to_train += comp.network.params
if self.hyperparams.use_moving_average:
avg_ops += comp.avg_ops
# Concatenate evaluation results
metrics = tf.concat(metrics_list, 0)
# Now that the cost is computed:
# 1. compute the gradients,
# 2. add an optimizer to update the parameters using the gradients,
# 3. make the ComputeSession handle depend on the optimizer.
grads_and_vars = self.optimizer.compute_gradients(
cost, var_list=params_to_train)
clipped_gradients = [(self._clip_gradients(g), v)
for g, v in grads_and_vars]
minimize_op = self.optimizer.apply_gradients(
clipped_gradients, global_step=self.master_vars['step'])
if self.hyperparams.use_moving_average:
with tf.control_dependencies([minimize_op]):
minimize_op = tf.group(*avg_ops)
# Make sure all the side-effectful minimizations ops finish before
# proceeding.
with tf.control_dependencies([minimize_op]):
handle = tf.identity(handle)
# Restore that subsequent builds don't use average by default.
self.read_from_avg = False
# Returns named access to common outputs.
outputs = {
'cost': cost,
'batch': effective_batch,
'metrics': metrics,
}
return handle, outputs
def build_training(self,
handle,
compute_gradients=True,
use_moving_average=False,
advance_counters=True,
component_weights=None,
unroll_using_oracle=None,
max_index=-1):
"""Builds a training pipeline.
Args:
handle: Handle tensor for the ComputeSession.
compute_gradients: Whether to generate gradients and an optimizer op.
When False, build_training will return a 'dry run' training op,
used normally only for oracle tracing.
use_moving_average: Whether or not to read from the moving
average variables instead of the true parameters. Note: it is not
possible to make gradient updates when this is True.
advance_counters: Whether or not this loop should increment the
per-component step counters.
component_weights: If set, this is a list of relative weights
each component's cost should get in the pipeline. Defaults to 1.0 for
each component.
unroll_using_oracle: If set, this is a list of booleans indicating
whether or not to use the gold decodings for each component. Defaults
to True for each component.
max_index: Training will use only the first max_index components,
or -1 for all components.
Returns:
handle: to the ComputeSession, conditioned on completing training step.
outputs: a dictionary of useful training tensors.
Raises:
IndexError: if max_index is positive but out of bounds.
"""
check.IsFalse(compute_gradients and use_moving_average,
'It is not possible to make gradient updates when reading '
'from the moving average variables.')
self.read_from_avg = use_moving_average
if max_index < 0:
max_index = len(self.components)
else:
if not 0 < max_index <= len(self.components):
raise IndexError(
'Invalid max_index {} for components {}; handle {}'.format(
max_index, self.component_names, handle.name))
# By default, we train every component supervised.
if not component_weights:
component_weights = [1] * max_index
if not unroll_using_oracle:
unroll_using_oracle = [True] * max_index
if not max_index <= len(unroll_using_oracle):
raise IndexError(('Invalid max_index {} for unroll_using_oracle {}; '
'handle {}').format(max_index, unroll_using_oracle,
handle.name))
component_weights = component_weights[:max_index]
total_weight = (float)(sum(component_weights))
component_weights = [w / total_weight for w in component_weights]
unroll_using_oracle = unroll_using_oracle[:max_index]
logging.info('Creating training target:')
logging.info('\tWeights: %s', component_weights)
logging.info('\tOracle: %s', unroll_using_oracle)
metrics_list = []
cost = tf.constant(0.)
effective_batch = tf.constant(0)
avg_ops = []
params_to_train = []
network_states = {}
for component_index in range(0, max_index):
comp = self.components[component_index]
network_states[comp.name] = component.NetworkState()
logging.info('Initializing data for component "%s"', comp.name)
handle = dragnn_ops.init_component_data(
handle, beam_size=comp.training_beam_size, component=comp.name)
# TODO(googleuser): Phase out component.MasterState.
master_state = component.MasterState(handle,
dragnn_ops.batch_size(
handle, component=comp.name))
with tf.control_dependencies([handle, cost]):
args = (master_state, network_states)
if unroll_using_oracle[component_index]:
handle, component_cost, component_correct, component_total = (
tf.cond(comp.training_beam_size > 1,
lambda: comp.build_structured_training(*args),
lambda: comp.build_greedy_training(*args)))
else:
handle = comp.build_greedy_inference(*args, during_training=True)
component_cost = tf.constant(0.)
component_correct, component_total = tf.constant(0), tf.constant(0)
weighted_component_cost = tf.multiply(
component_cost,
tf.constant((float)(component_weights[component_index])),
name='weighted_component_cost')
cost += weighted_component_cost
effective_batch += component_total
metrics_list += [[component_total], [component_correct]]
if advance_counters:
with tf.control_dependencies(
[comp.advance_counters(component_total)]):
cost = tf.identity(cost)
# Keep track of which parameters will be trained, and any moving
# average updates to apply for these parameters.
params_to_train += comp.network.params
if self.hyperparams.use_moving_average:
avg_ops += comp.avg_ops
# Concatenate evaluation results
metrics = tf.concat(metrics_list, 0)
# If gradient computation is requested, then:
# 1. compute the gradients,
# 2. add an optimizer to update the parameters using the gradients,
# 3. make the ComputeSession handle depend on the optimizer.
gradient_norm = tf.constant(0.)
if compute_gradients:
logging.info('Creating train op with %d variables:\n\t%s',
len(params_to_train),
'\n\t'.join([x.name for x in params_to_train]))
grads_and_vars = self.optimizer.compute_gradients(
cost, var_list=params_to_train)
clipped_gradients = [
(self._clip_gradients(g), v) for g, v in grads_and_vars
]
gradient_norm = tf.global_norm(list(zip(*clipped_gradients))[0])
minimize_op = self.optimizer.apply_gradients(
clipped_gradients, global_step=self.master_vars['step'])
if self.hyperparams.use_moving_average:
with tf.control_dependencies([minimize_op]):
minimize_op = tf.group(*avg_ops)
# Make sure all the side-effectful minimizations ops finish before
# proceeding.
with tf.control_dependencies([minimize_op]):
handle = tf.identity(handle)
# Restore that subsequent builds don't use average by default.
self.read_from_avg = False
cost = tf.check_numerics(cost, message='Cost is not finite.')
# Returns named access to common outputs.
outputs = {
'cost': cost,
'gradient_norm': gradient_norm,
'batch': effective_batch,
'metrics': metrics,
}
return handle, outputs
def build_training(self,
handle,
compute_gradients=True,
use_moving_average=False,
advance_counters=True,
component_weights=None,
unroll_using_oracle=None,
max_index=-1):
"""Builds a training pipeline.
Args:
handle: Handle tensor for the ComputeSession.
compute_gradients: Whether to generate gradients and an optimizer op.
When False, build_training will return a 'dry run' training op,
used normally only for oracle tracing.
use_moving_average: Whether or not to read from the moving
average variables instead of the true parameters. Note: it is not
possible to make gradient updates when this is True.
advance_counters: Whether or not this loop should increment the
per-component step counters.
component_weights: If set, this is a list of relative weights
each component's cost should get in the pipeline. Defaults to 1.0 for
each component.
unroll_using_oracle: If set, this is a list of booleans indicating
whether or not to use the gold decodings for each component. Defaults
to True for each component.
max_index: Training will use only the first max_index components,
or -1 for all components.
Returns:
handle: to the ComputeSession, conditioned on completing training step.
outputs: a dictionary of useful training tensors.
Raises:
IndexError: if max_index is positive but out of bounds.
"""
check.IsFalse(
compute_gradients and use_moving_average,
'It is not possible to make gradient updates when reading '
'from the moving average variables.')
self.read_from_avg = use_moving_average
if max_index < 0:
max_index = len(self.components)
else:
if not 0 < max_index <= len(self.components):
raise IndexError(
'Invalid max_index {} for components {}; handle {}'.format(
max_index, self.component_names, handle.name))
# By default, we train every component supervised.
if not component_weights:
component_weights = [1] * max_index
if not unroll_using_oracle:
unroll_using_oracle = [True] * max_index
if not max_index <= len(unroll_using_oracle):
raise IndexError(
('Invalid max_index {} for unroll_using_oracle {}; '
'handle {}').format(max_index, unroll_using_oracle,
handle.name))
component_weights = component_weights[:max_index]
total_weight = (float)(sum(component_weights))
component_weights = [w / total_weight for w in component_weights]
unroll_using_oracle = unroll_using_oracle[:max_index]
logging.info('Creating training target:')
logging.info('\tWeights: %s', component_weights)
logging.info('\tOracle: %s', unroll_using_oracle)
metrics_list = []
cost = tf.constant(0.)
effective_batch = tf.constant(0)
avg_ops = []
params_to_train = []
network_states = {}
for component_index in range(0, max_index):
comp = self.components[component_index]
network_states[comp.name] = component.NetworkState()
logging.info('Initializing data for component "%s"', comp.name)
handle = dragnn_ops.init_component_data(
handle, beam_size=comp.training_beam_size, component=comp.name)
# TODO(googleuser): Phase out component.MasterState.
master_state = component.MasterState(
handle, dragnn_ops.batch_size(handle, component=comp.name))
with tf.control_dependencies([handle, cost]):
args = (master_state, network_states)
if unroll_using_oracle[component_index]:
handle, component_cost, component_correct, component_total = (
tf.cond(comp.training_beam_size > 1,
lambda: comp.build_structured_training(*args),
lambda: comp.build_greedy_training(*args)))
else:
handle = comp.build_greedy_inference(*args,
during_training=True)
component_cost = tf.constant(0.)
component_correct, component_total = tf.constant(
0), tf.constant(0)
weighted_component_cost = tf.multiply(
component_cost,
tf.constant((float)(component_weights[component_index])),
name='weighted_component_cost')
cost += weighted_component_cost
effective_batch += component_total
metrics_list += [[component_total], [component_correct]]
if advance_counters:
with tf.control_dependencies(
[comp.advance_counters(component_total)]):
cost = tf.identity(cost)
# Keep track of which parameters will be trained, and any moving
# average updates to apply for these parameters.
params_to_train += comp.network.params
if self.hyperparams.use_moving_average:
avg_ops += comp.avg_ops
# Concatenate evaluation results
metrics = tf.concat(metrics_list, 0)
# If gradient computation is requested, then:
# 1. compute the gradients,
# 2. add an optimizer to update the parameters using the gradients,
# 3. make the ComputeSession handle depend on the optimizer.
gradient_norm = tf.constant(0.)
if compute_gradients:
logging.info('Creating train op with %d variables:\n\t%s',
len(params_to_train),
'\n\t'.join([x.name for x in params_to_train]))
grads_and_vars = self.optimizer.compute_gradients(
cost, var_list=params_to_train)
clipped_gradients = [(self._clip_gradients(g), v)
for g, v in grads_and_vars]
gradient_norm = tf.global_norm(list(zip(*clipped_gradients))[0])
minimize_op = self.optimizer.apply_gradients(
clipped_gradients, global_step=self.master_vars['step'])
if self.hyperparams.use_moving_average:
with tf.control_dependencies([minimize_op]):
minimize_op = tf.group(*avg_ops)
# Make sure all the side-effectful minimizations ops finish before
# proceeding.
with tf.control_dependencies([minimize_op]):
handle = tf.identity(handle)
# Restore that subsequent builds don't use average by default.
self.read_from_avg = False
cost = tf.check_numerics(cost, message='Cost is not finite.')
# Returns named access to common outputs.
outputs = {
'cost': cost,
'gradient_norm': gradient_norm,
'batch': effective_batch,
'metrics': metrics,
}
return handle, outputs
def testBulkFeatureIdExtractorExtractFocusWithOffset(self):
path = os.path.join(tf.test.get_temp_dir(), 'label-map')
with open(path, 'w') as label_map_file:
label_map_file.write('0\n')
master_spec = spec_pb2.MasterSpec()
text_format.Parse("""
component {
name: "test"
transition_system {
registered_name: "shift-only"
}
resource {
name: "label-map"
part {
file_pattern: "%s"
file_format: "text"
}
}
network_unit {
registered_name: "ExportFixedFeaturesNetwork"
}
backend {
registered_name: "SyntaxNetComponent"
}
fixed_feature {
name: "focus1" embedding_dim: -1 size: 1 fml: "input.focus"
predicate_map: "none"
}
fixed_feature {
name: "focus2" embedding_dim: -1 size: 1 fml: "input(1).focus"
predicate_map: "none"
}
fixed_feature {
name: "focus3" embedding_dim: -1 size: 1 fml: "input(2).focus"
predicate_map: "none"
}
}
""" % path, master_spec)
with tf.Graph().as_default():
corpus = _create_fake_corpus()
corpus = tf.constant(corpus, shape=[len(corpus)])
handle = dragnn_ops.get_session(
container='test',
master_spec=master_spec.SerializeToString(),
grid_point='')
handle = dragnn_ops.attach_data_reader(handle, corpus)
handle = dragnn_ops.init_component_data(
handle, beam_size=1, component='test')
batch_size = dragnn_ops.batch_size(handle, component='test')
master_state = component.MasterState(handle, batch_size)
extractor = bulk_component.BulkFeatureIdExtractorComponentBuilder(
self.master, master_spec.component[0])
network_state = component.NetworkState()
self.network_states['test'] = network_state
handle = extractor.build_greedy_inference(master_state,
self.network_states)
focus1 = network_state.activations['focus1'].bulk_tensor
focus2 = network_state.activations['focus2'].bulk_tensor
focus3 = network_state.activations['focus3'].bulk_tensor
with self.test_session() as sess:
focus1, focus2, focus3 = sess.run([focus1, focus2, focus3])
tf.logging.info('focus1=\n%s', focus1)
tf.logging.info('focus2=\n%s', focus2)
tf.logging.info('focus3=\n%s', focus3)
self.assertAllEqual(
focus1,
[[0], [-1], [-1], [-1],
[0], [1], [-1], [-1],
[0], [1], [2], [-1],
[0], [1], [2], [3]])
self.assertAllEqual(
focus2,
[[-1], [-1], [-1], [-1],
[1], [-1], [-1], [-1],
[1], [2], [-1], [-1],
[1], [2], [3], [-1]])
self.assertAllEqual(
focus3,
[[-1], [-1], [-1], [-1],
[-1], [-1], [-1], [-1],
[2], [-1], [-1], [-1],
[2], [3], [-1], [-1]])
def testBulkFeatureIdExtractorExtractFocusWithOffset(self):
path = os.path.join(tf.test.get_temp_dir(), 'label-map')
with open(path, 'w') as label_map_file:
label_map_file.write('0\n')
master_spec = spec_pb2.MasterSpec()
text_format.Parse("""
component {
name: "test"
transition_system {
registered_name: "shift-only"
}
resource {
name: "label-map"
part {
file_pattern: "%s"
file_format: "text"
}
}
network_unit {
registered_name: "ExportFixedFeaturesNetwork"
}
backend {
registered_name: "SyntaxNetComponent"
}
fixed_feature {
name: "focus1" embedding_dim: -1 size: 1 fml: "input.focus"
predicate_map: "none"
}
fixed_feature {
name: "focus2" embedding_dim: -1 size: 1 fml: "input(1).focus"
predicate_map: "none"
}
fixed_feature {
name: "focus3" embedding_dim: -1 size: 1 fml: "input(2).focus"
predicate_map: "none"
}
}
""" % path, master_spec)
with tf.Graph().as_default():
corpus = _create_fake_corpus()
corpus = tf.constant(corpus, shape=[len(corpus)])
handle = dragnn_ops.get_session(
container='test',
master_spec=master_spec.SerializeToString(),
grid_point='')
handle = dragnn_ops.attach_data_reader(handle, corpus)
handle = dragnn_ops.init_component_data(
handle, beam_size=1, component='test')
batch_size = dragnn_ops.batch_size(handle, component='test')
master_state = component.MasterState(handle, batch_size)
extractor = bulk_component.BulkFeatureIdExtractorComponentBuilder(
self.master, master_spec.component[0])
network_state = component.NetworkState()
self.network_states['test'] = network_state
handle = extractor.build_greedy_inference(master_state,
self.network_states)
focus1 = network_state.activations['focus1'].bulk_tensor
focus2 = network_state.activations['focus2'].bulk_tensor
focus3 = network_state.activations['focus3'].bulk_tensor
with self.test_session() as sess:
focus1, focus2, focus3 = sess.run([focus1, focus2, focus3])
tf.logging.info('focus1=\n%s', focus1)
tf.logging.info('focus2=\n%s', focus2)
tf.logging.info('focus3=\n%s', focus3)
self.assertAllEqual(
focus1,
[[0], [-1], [-1], [-1],
[0], [1], [-1], [-1],
[0], [1], [2], [-1],
[0], [1], [2], [3]])
self.assertAllEqual(
focus2,
[[-1], [-1], [-1], [-1],
[1], [-1], [-1], [-1],
[1], [2], [-1], [-1],
[1], [2], [3], [-1]])
self.assertAllEqual(
focus3,
[[-1], [-1], [-1], [-1],
[-1], [-1], [-1], [-1],
[2], [-1], [-1], [-1],
[2], [3], [-1], [-1]])
def build_training(self,
handle,
component_weights=None,
unroll_using_oracle=None,
max_index=-1):
"""Builds a training pipeline.
Args:
handle: Handle tensor for the ComputeSession.
component_weights: If set, this is a list of relative weights
each component's cost should get in the pipeline. Defaults to 1.0 for
each component.
unroll_using_oracle: If set, this is a list of booleans indicating
whether or not to use the gold decodings for each component. Defaults
to True for each component.
max_index: Training will use only the first max_index components,
or -1 for all components.
Returns:
handle: to the ComputeSession, conditioned on completing training step.
outputs: a dictionary of useful training tensors.
Raises:
IndexError: if max_index is positive but out of bounds.
"""
self.read_from_avg = False
if max_index < 0:
max_index = len(self.components)
else:
if not 0 < max_index <= len(self.components):
raise IndexError(
'Invalid max_index {} for components {}; handle {}'.format(
max_index, self.component_names, handle.name))
# By default, we train every component supervised.
if not component_weights:
component_weights = [1] * max_index
if not unroll_using_oracle:
unroll_using_oracle = [True] * max_index
component_weights = component_weights[:max_index]
total_weight = (float)(sum(component_weights))
component_weights = [w / total_weight for w in component_weights]
unroll_using_oracle = unroll_using_oracle[:max_index]
logging.info('Creating training target:')
logging.info('\tWeights: %s', component_weights)
logging.info('\tOracle: %s', unroll_using_oracle)
metrics_list = []
cost = tf.constant(0.)
effective_batch = tf.constant(0)
avg_ops = []
params_to_train = []
network_states = {}
for component_index in range(0, max_index):
comp = self.components[component_index]
network_states[comp.name] = component.NetworkState()
logging.info('Initializing data for component "%s"', comp.name)
handle = dragnn_ops.init_component_data(
handle, component=comp.name, clear_existing_annotations=False)
# TODO(googleuser): Phase out component.MasterState.
master_state = component.MasterState(
handle, dragnn_ops.batch_size(handle, component=comp.name))
with tf.control_dependencies([handle, cost]):
args = (master_state, network_states)
if unroll_using_oracle[component_index]:
handle, component_cost, correct, total = comp.build_training(
*args)
else:
handle = comp.build_inference(*args, during_training=True)
component_cost = tf.constant(0.)
correct, total = tf.constant(0), tf.constant(0)
weighted_component_cost = tf.multiply(
component_cost,
tf.constant((float)(component_weights[component_index])),
name='weighted_component_cost')
cost += weighted_component_cost
effective_batch += total
metrics_list += [[total], [correct]]
with tf.control_dependencies([comp.advance_counters(total)]):
cost = tf.identity(cost)
# Keep track of which parameters will be trained, and any moving
# average updates to apply for these parameters.
params_to_train += comp.network.params
if self.hyperparams.use_moving_average:
avg_ops += comp.avg_ops
# Concatenate evaluation results
metrics = tf.concat(metrics_list, 0)
# Now that the cost is computed:
# 1. compute the gradients,
# 2. add an optimizer to update the parameters using the gradients,
# 3. make the ComputeSession handle depend on the optimizer.
grads_and_vars = self.optimizer.compute_gradients(
cost, var_list=params_to_train)
clipped_gradients = [(self._clip_gradients(g), v)
for g, v in grads_and_vars]
minimize_op = self.optimizer.apply_gradients(
clipped_gradients, global_step=self.master_vars['step'])
if self.hyperparams.use_moving_average:
with tf.control_dependencies([minimize_op]):
minimize_op = tf.group(*avg_ops)
# Make sure all the side-effectful minimizations ops finish before
# proceeding.
with tf.control_dependencies([minimize_op]):
handle = tf.identity(handle)
# Restore that subsequent builds don't use average by default.
self.read_from_avg = False
# Returns named access to common outputs.
outputs = {
'cost': cost,
'batch': effective_batch,
'metrics': metrics,
}
return handle, outputs
