def testFprint(self):
with self._single_threaded_test_session():
in_data = constant_op.constant(['abc', 'very looooooong string', 'def'])
out_data = gen_sdca_ops._sdca_fprint(in_data)
self.assertAllEqual([[4143508125394299908, -6879828354153669051],
[5849691694103072671, -4874542629849009556],
[603227410218889250, 8762207001949257490]],
out_data.eval())
def testFprint(self):
with self._single_threaded_test_session():
in_data = constant_op.constant(['abc', 'very looooooong string', 'def'])
out_data = gen_sdca_ops._sdca_fprint(in_data)
self.assertAllEqual([[4143508125394299908, -6879828354153669051],
[5849691694103072671, -4874542629849009556],
[603227410218889250, 8762207001949257490]],
out_data.eval())
def minimize(self, global_step=None, name=None):
"""Add operations to train a linear model by minimizing the loss function.
Args:
global_step: Optional `Variable` to increment by one after the
variables have been updated.
name: Optional name for the returned operation.
Returns:
An Operation that updates the variables passed in the constructor.
"""
# Technically, the op depends on a lot more than the variables,
# but we'll keep the list short.
with name_scope(name, 'sdca/minimize'):
sparse_example_indices = []
sparse_feature_indices = []
sparse_features_values = []
for sf in self._examples['sparse_features']:
sparse_example_indices.append(sf.example_indices)
sparse_feature_indices.append(sf.feature_indices)
# If feature values are missing, sdca assumes a value of 1.0f.
if sf.feature_values is not None:
sparse_features_values.append(sf.feature_values)
# pylint: disable=protected-access
example_ids_hashed = gen_sdca_ops._sdca_fprint(
convert_to_tensor(self._examples['example_ids']))
# pylint: enable=protected-access
example_state_data = self._hashtable.lookup(example_ids_hashed)
# Solver returns example_state_update, new delta sparse_feature_weights
# and delta dense_feature_weights.
weights_tensor = self._convert_n_to_tensor(
self._slots['unshrinked_sparse_features_weights'])
sparse_weights = []
sparse_indices = []
for w, i in zip(weights_tensor, sparse_feature_indices):
# Find the feature ids to lookup in the variables.
with ops.device(w.device):
sparse_indices.append(
math_ops.cast(
array_ops.unique(math_ops.cast(i,
dtypes.int32))[0],
dtypes.int64))
sparse_weights.append(
array_ops.gather(w, sparse_indices[-1]))
# pylint: disable=protected-access
esu, sfw, dfw = gen_sdca_ops._sdca_optimizer(
sparse_example_indices,
sparse_feature_indices,
sparse_features_values,
self._convert_n_to_tensor(self._examples['dense_features']),
convert_to_tensor(self._examples['example_weights']),
convert_to_tensor(self._examples['example_labels']),
sparse_indices,
sparse_weights,
self._convert_n_to_tensor(
self._slots['unshrinked_dense_features_weights']),
example_state_data,
loss_type=self._options['loss_type'],
l1=self._options['symmetric_l1_regularization'],
l2=self._symmetric_l2_regularization(),
num_loss_partitions=self._num_loss_partitions(),
num_inner_iterations=1)
# pylint: enable=protected-access
with ops.control_dependencies([esu]):
update_ops = [self._hashtable.insert(example_ids_hashed, esu)]
# Update the weights before the proximal step.
for w, i, u in zip(
self._slots['unshrinked_sparse_features_weights'],
sparse_indices, sfw):
update_ops.append(state_ops.scatter_add(w, i, u))
for w, u in zip(
self._slots['unshrinked_dense_features_weights'], dfw):
update_ops.append(w.assign_add(u))
if not global_step:
return control_flow_ops.group(*update_ops)
with ops.control_dependencies(update_ops):
return state_ops.assign_add(global_step, 1, name=name).op
def minimize(self, global_step=None, name=None):
"""Add operations to train a linear model by minimizing the loss function.
Args:
global_step: Optional `Variable` to increment by one after the
variables have been updated.
name: Optional name for the returned operation.
Returns:
An Operation that updates the variables passed in the constructor.
"""
# Technically, the op depends on a lot more than the variables,
# but we'll keep the list short.
with name_scope(name, 'sdca/minimize'):
sparse_example_indices = []
sparse_feature_indices = []
sparse_features_values = []
for sf in self._examples['sparse_features']:
sparse_example_indices.append(sf.example_indices)
sparse_feature_indices.append(sf.feature_indices)
# If feature values are missing, sdca assumes a value of 1.0f.
if sf.feature_values is not None:
sparse_features_values.append(sf.feature_values)
# pylint: disable=protected-access
example_ids_hashed = gen_sdca_ops._sdca_fprint(
internal_convert_to_tensor(self._examples['example_ids']))
# pylint: enable=protected-access
example_state_data = self._hashtable.lookup(example_ids_hashed)
# Solver returns example_state_update, new delta sparse_feature_weights
# and delta dense_feature_weights.
weights_tensor = self._convert_n_to_tensor(self._slots[
'unshrinked_sparse_features_weights'])
sparse_weights = []
sparse_indices = []
for w, i in zip(weights_tensor, sparse_feature_indices):
# Find the feature ids to lookup in the variables.
with ops.device(w.device):
sparse_indices.append(
math_ops.cast(
array_ops.unique(math_ops.cast(i, dtypes.int32))[0],
dtypes.int64))
sparse_weights.append(array_ops.gather(w, sparse_indices[-1]))
# pylint: disable=protected-access
esu, sfw, dfw = gen_sdca_ops._sdca_optimizer(
sparse_example_indices,
sparse_feature_indices,
sparse_features_values,
self._convert_n_to_tensor(self._examples['dense_features']),
internal_convert_to_tensor(self._examples['example_weights']),
internal_convert_to_tensor(self._examples['example_labels']),
sparse_indices,
sparse_weights,
self._convert_n_to_tensor(self._slots[
'unshrinked_dense_features_weights']),
example_state_data,
loss_type=self._options['loss_type'],
l1=self._options['symmetric_l1_regularization'],
l2=self._symmetric_l2_regularization(),
num_loss_partitions=self._num_loss_partitions(),
num_inner_iterations=1)
# pylint: enable=protected-access
with ops.control_dependencies([esu]):
update_ops = [self._hashtable.insert(example_ids_hashed, esu)]
# Update the weights before the proximal step.
for w, i, u in zip(self._slots['unshrinked_sparse_features_weights'],
sparse_indices, sfw):
update_ops.append(state_ops.scatter_add(w, i, u))
for w, u in zip(self._slots['unshrinked_dense_features_weights'], dfw):
update_ops.append(w.assign_add(u))
if not global_step:
return control_flow_ops.group(*update_ops)
with ops.control_dependencies(update_ops):
return state_ops.assign_add(global_step, 1, name=name).op