"""Python wrappers around TensorFlow ops.
This file is MACHINE GENERATED! Do not edit.
Original C++ source file: sdca_ops.cc
"""
import collections
from tensorflow.python import pywrap_tfe as pywrap_tfe
from tensorflow.python.eager import context as _context
from tensorflow.python.eager import core as _core
from tensorflow.python.eager import execute as _execute
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.framework import op_def_registry as _op_def_registry
from tensorflow.python.framework import ops as _ops
from tensorflow.python.framework import op_def_library as _op_def_library
from tensorflow.python.util.deprecation import deprecated_endpoints
from tensorflow.python.util import dispatch as _dispatch
from tensorflow.python.util.tf_export import tf_export
from typing import TypeVar
@_dispatch.add_fallback_dispatch_list
@_dispatch.add_type_based_api_dispatcher
@tf_export(v1=['train.sdca_fprint'])
@deprecated_endpoints('train.sdca_fprint')
def sdca_fprint(input, name=None):
r"""Computes fingerprints of the input strings.
Args:
input: A `Tensor` of type `string`.
vector of strings to compute fingerprints on.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SdcaFprint", name, input)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
_result = _dispatcher_for_sdca_fprint(
(input, name,), None)
if _result is not NotImplemented:
return _result
return sdca_fprint_eager_fallback(
input, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
_result = _dispatch.dispatch(
sdca_fprint, (), dict(input=input, name=name)
)
if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return _result
raise
else:
_result = _dispatcher_for_sdca_fprint(
(input, name,), None)
if _result is not NotImplemented:
return _result
# Add nodes to the TensorFlow graph.
try:
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SdcaFprint", input=input, name=name)
except (TypeError, ValueError):
_result = _dispatch.dispatch(
sdca_fprint, (), dict(input=input, name=name)
)
if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return _result
raise
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ()
_inputs_flat = _op.inputs
_execute.record_gradient(
"SdcaFprint", _inputs_flat, _attrs, _result)
_result, = _result
return _result
SdcaFprint = tf_export("raw_ops.SdcaFprint")(_ops.to_raw_op(sdca_fprint))
_dispatcher_for_sdca_fprint = sdca_fprint._tf_type_based_dispatcher.Dispatch
def sdca_fprint_eager_fallback(input, name, ctx):
input = _ops.convert_to_tensor(input, _dtypes.string)
_inputs_flat = [input]
_attrs = None
_result = _execute.execute(b"SdcaFprint", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SdcaFprint", _inputs_flat, _attrs, _result)
_result, = _result
return _result
_SdcaOptimizerOutput = collections.namedtuple(
"SdcaOptimizer",
["out_example_state_data", "out_delta_sparse_weights", "out_delta_dense_weights"])
@_dispatch.add_fallback_dispatch_list
@_dispatch.add_type_based_api_dispatcher
@tf_export(v1=['train.sdca_optimizer'])
@deprecated_endpoints('train.sdca_optimizer')
def sdca_optimizer(sparse_example_indices, sparse_feature_indices, sparse_feature_values, dense_features, example_weights, example_labels, sparse_indices, sparse_weights, dense_weights, example_state_data, loss_type, l1, l2, num_loss_partitions, num_inner_iterations, adaptative=True, name=None):
r"""Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
linear models with L1 + L2 regularization. As global optimization objective is
strongly-convex, the optimizer optimizes the dual objective at each step. The
optimizer applies each update one example at a time. Examples are sampled
uniformly, and the optimizer is learning rate free and enjoys linear convergence
rate.
[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012
$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$
[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan,
Peter Richtarik, Martin Takac. 2015
[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
Args:
sparse_example_indices: A list of `Tensor` objects with type `int64`.
a list of vectors which contain example indices.
sparse_feature_indices: A list with the same length as `sparse_example_indices` of `Tensor` objects with type `int64`.
a list of vectors which contain feature indices.
sparse_feature_values: A list of `Tensor` objects with type `float32`.
a list of vectors which contains feature value
associated with each feature group.
dense_features: A list of `Tensor` objects with type `float32`.
a list of matrices which contains the dense feature values.
example_weights: A `Tensor` of type `float32`.
a vector which contains the weight associated with each
example.
example_labels: A `Tensor` of type `float32`.
a vector which contains the label/target associated with each
example.
sparse_indices: A list with the same length as `sparse_example_indices` of `Tensor` objects with type `int64`.
a list of vectors where each value is the indices which has
corresponding weights in sparse_weights. This field maybe omitted for the
dense approach.
sparse_weights: A list with the same length as `sparse_example_indices` of `Tensor` objects with type `float32`.
a list of vectors where each value is the weight associated with
a sparse feature group.
dense_weights: A list with the same length as `dense_features` of `Tensor` objects with type `float32`.
a list of vectors where the values are the weights associated
with a dense feature group.
example_state_data: A `Tensor` of type `float32`.
a list of vectors containing the example state data.
loss_type: A `string` from: `"logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss"`.
Type of the primal loss. Currently SdcaSolver supports logistic,
squared and hinge losses.
l1: A `float`. Symmetric l1 regularization strength.
l2: A `float`. Symmetric l2 regularization strength.
num_loss_partitions: An `int` that is `>= 1`.
Number of partitions of the global loss function.
num_inner_iterations: An `int` that is `>= 1`.
Number of iterations per mini-batch.
adaptative: An optional `bool`. Defaults to `True`.
Whether to use Adaptive SDCA for the inner loop.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).
out_example_state_data: A `Tensor` of type `float32`.
out_delta_sparse_weights: A list with the same length as `sparse_example_indices` of `Tensor` objects with type `float32`.
out_delta_dense_weights: A list with the same length as `dense_features` of `Tensor` objects with type `float32`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SdcaOptimizer", name, sparse_example_indices,
sparse_feature_indices, sparse_feature_values, dense_features,
example_weights, example_labels, sparse_indices, sparse_weights,
dense_weights, example_state_data, "loss_type", loss_type,
"adaptative", adaptative, "l1", l1, "l2", l2, "num_loss_partitions",
num_loss_partitions, "num_inner_iterations", num_inner_iterations)
_result = _SdcaOptimizerOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
_result = _dispatcher_for_sdca_optimizer(
(sparse_example_indices, sparse_feature_indices,
sparse_feature_values, dense_features, example_weights,
example_labels, sparse_indices, sparse_weights, dense_weights,
example_state_data, loss_type, l1, l2, num_loss_partitions,
num_inner_iterations, adaptative, name,), None)
if _result is not NotImplemented:
return _result
return sdca_optimizer_eager_fallback(
sparse_example_indices, sparse_feature_indices,
sparse_feature_values, dense_features, example_weights,
example_labels, sparse_indices, sparse_weights, dense_weights,
example_state_data, loss_type=loss_type, adaptative=adaptative,
l1=l1, l2=l2, num_loss_partitions=num_loss_partitions,
num_inner_iterations=num_inner_iterations, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
_result = _dispatch.dispatch(
sdca_optimizer, (), dict(sparse_example_indices=sparse_example_indices,
sparse_feature_indices=sparse_feature_indices,
sparse_feature_values=sparse_feature_values,
dense_features=dense_features,
example_weights=example_weights,
example_labels=example_labels,
sparse_indices=sparse_indices,
sparse_weights=sparse_weights,
dense_weights=dense_weights,
example_state_data=example_state_data,
loss_type=loss_type, l1=l1, l2=l2,
num_loss_partitions=num_loss_partitions,
num_inner_iterations=num_inner_iterations,
adaptative=adaptative, name=name)
)
if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return _result
raise
else:
_result = _dispatcher_for_sdca_optimizer(
(sparse_example_indices, sparse_feature_indices,
sparse_feature_values, dense_features, example_weights,
example_labels, sparse_indices, sparse_weights, dense_weights,
example_state_data, loss_type, l1, l2, num_loss_partitions,
num_inner_iterations, adaptative, name,), None)
if _result is not NotImplemented:
return _result
# Add nodes to the TensorFlow graph.
if not isinstance(sparse_example_indices, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_example_indices' argument to "
"'sdca_optimizer' Op, not %r." % sparse_example_indices)
_attr_num_sparse_features = len(sparse_example_indices)
if not isinstance(sparse_feature_indices, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_feature_indices' argument to "
"'sdca_optimizer' Op, not %r." % sparse_feature_indices)
if len(sparse_feature_indices) != _attr_num_sparse_features:
raise ValueError(
"List argument 'sparse_feature_indices' to 'sdca_optimizer' Op with length %d "
"must match length %d of argument 'sparse_example_indices'." %
(len(sparse_feature_indices), _attr_num_sparse_features))
if not isinstance(sparse_indices, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_indices' argument to "
"'sdca_optimizer' Op, not %r." % sparse_indices)
if len(sparse_indices) != _attr_num_sparse_features:
raise ValueError(
"List argument 'sparse_indices' to 'sdca_optimizer' Op with length %d "
"must match length %d of argument 'sparse_example_indices'." %
(len(sparse_indices), _attr_num_sparse_features))
if not isinstance(sparse_weights, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_weights' argument to "
"'sdca_optimizer' Op, not %r." % sparse_weights)
if len(sparse_weights) != _attr_num_sparse_features:
raise ValueError(
"List argument 'sparse_weights' to 'sdca_optimizer' Op with length %d "
"must match length %d of argument 'sparse_example_indices'." %
(len(sparse_weights), _attr_num_sparse_features))
if not isinstance(sparse_feature_values, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_feature_values' argument to "
"'sdca_optimizer' Op, not %r." % sparse_feature_values)
_attr_num_sparse_features_with_values = len(sparse_feature_values)
if not isinstance(dense_features, (list, tuple)):
raise TypeError(
"Expected list for 'dense_features' argument to "
"'sdca_optimizer' Op, not %r." % dense_features)
_attr_num_dense_features = len(dense_features)
if not isinstance(dense_weights, (list, tuple)):
raise TypeError(
"Expected list for 'dense_weights' argument to "
"'sdca_optimizer' Op, not %r." % dense_weights)
if len(dense_weights) != _attr_num_dense_features:
raise ValueError(
"List argument 'dense_weights' to 'sdca_optimizer' Op with length %d "
"must match length %d of argument 'dense_features'." %
(len(dense_weights), _attr_num_dense_features))
loss_type = _execute.make_str(loss_type, "loss_type")
l1 = _execute.make_float(l1, "l1")
l2 = _execute.make_float(l2, "l2")
num_loss_partitions = _execute.make_int(num_loss_partitions, "num_loss_partitions")
num_inner_iterations = _execute.make_int(num_inner_iterations, "num_inner_iterations")
if adaptative is None:
adaptative = True
adaptative = _execute.make_bool(adaptative, "adaptative")
try:
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SdcaOptimizer", sparse_example_indices=sparse_example_indices,
sparse_feature_indices=sparse_feature_indices,
sparse_feature_values=sparse_feature_values,
dense_features=dense_features,
example_weights=example_weights,
example_labels=example_labels,
sparse_indices=sparse_indices,
sparse_weights=sparse_weights,
dense_weights=dense_weights,
example_state_data=example_state_data,
loss_type=loss_type, l1=l1, l2=l2,
num_loss_partitions=num_loss_partitions,
num_inner_iterations=num_inner_iterations,
adaptative=adaptative, name=name)
except (TypeError, ValueError):
_result = _dispatch.dispatch(
sdca_optimizer, (), dict(sparse_example_indices=sparse_example_indices,
sparse_feature_indices=sparse_feature_indices,
sparse_feature_values=sparse_feature_values,
dense_features=dense_features,
example_weights=example_weights,
example_labels=example_labels,
sparse_indices=sparse_indices,
sparse_weights=sparse_weights,
dense_weights=dense_weights,
example_state_data=example_state_data,
loss_type=loss_type, l1=l1, l2=l2,
num_loss_partitions=num_loss_partitions,
num_inner_iterations=num_inner_iterations,
adaptative=adaptative, name=name)
)
if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return _result
raise
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("loss_type", _op.get_attr("loss_type"), "adaptative",
_op._get_attr_bool("adaptative"), "num_sparse_features",
_op._get_attr_int("num_sparse_features"),
"num_sparse_features_with_values",
_op._get_attr_int("num_sparse_features_with_values"),
"num_dense_features", _op._get_attr_int("num_dense_features"),
"l1", _op.get_attr("l1"), "l2", _op.get_attr("l2"),
"num_loss_partitions", _op._get_attr_int("num_loss_partitions"),
"num_inner_iterations",
_op._get_attr_int("num_inner_iterations"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SdcaOptimizer", _inputs_flat, _attrs, _result)
_result = _result[:1] + [_result[1:1 + _attr_num_sparse_features]] + _result[1 + _attr_num_sparse_features:]
_result = _result[:2] + [_result[2:]]
_result = _SdcaOptimizerOutput._make(_result)
return _result
SdcaOptimizer = tf_export("raw_ops.SdcaOptimizer")(_ops.to_raw_op(sdca_optimizer))
_dispatcher_for_sdca_optimizer = sdca_optimizer._tf_type_based_dispatcher.Dispatch
def sdca_optimizer_eager_fallback(sparse_example_indices, sparse_feature_indices, sparse_feature_values, dense_features, example_weights, example_labels, sparse_indices, sparse_weights, dense_weights, example_state_data, loss_type, l1, l2, num_loss_partitions, num_inner_iterations, adaptative, name, ctx):
if not isinstance(sparse_example_indices, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_example_indices' argument to "
"'sdca_optimizer' Op, not %r." % sparse_example_indices)
_attr_num_sparse_features = len(sparse_example_indices)
if not isinstance(sparse_feature_indices, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_feature_indices' argument to "
"'sdca_optimizer' Op, not %r." % sparse_feature_indices)
if len(sparse_feature_indices) != _attr_num_sparse_features:
raise ValueError(
"List argument 'sparse_feature_indices' to 'sdca_optimizer' Op with length %d "
"must match length %d of argument 'sparse_example_indices'." %
(len(sparse_feature_indices), _attr_num_sparse_features))
if not isinstance(sparse_indices, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_indices' argument to "
"'sdca_optimizer' Op, not %r." % sparse_indices)
if len(sparse_indices) != _attr_num_sparse_features:
raise ValueError(
"List argument 'sparse_indices' to 'sdca_optimizer' Op with length %d "
"must match length %d of argument 'sparse_example_indices'." %
(len(sparse_indices), _attr_num_sparse_features))
if not isinstance(sparse_weights, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_weights' argument to "
"'sdca_optimizer' Op, not %r." % sparse_weights)
if len(sparse_weights) != _attr_num_sparse_features:
raise ValueError(
"List argument 'sparse_weights' to 'sdca_optimizer' Op with length %d "
"must match length %d of argument 'sparse_example_indices'." %
(len(sparse_weights), _attr_num_sparse_features))
if not isinstance(sparse_feature_values, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_feature_values' argument to "
"'sdca_optimizer' Op, not %r." % sparse_feature_values)
_attr_num_sparse_features_with_values = len(sparse_feature_values)
if not isinstance(dense_features, (list, tuple)):
raise TypeError(
"Expected list for 'dense_features' argument to "
"'sdca_optimizer' Op, not %r." % dense_features)
_attr_num_dense_features = len(dense_features)
if not isinstance(dense_weights, (list, tuple)):
raise TypeError(
"Expected list for 'dense_weights' argument to "
"'sdca_optimizer' Op, not %r." % dense_weights)
if len(dense_weights) != _attr_num_dense_features:
raise ValueError(
"List argument 'dense_weights' to 'sdca_optimizer' Op with length %d "
"must match length %d of argument 'dense_features'." %
(len(dense_weights), _attr_num_dense_features))
loss_type = _execute.make_str(loss_type, "loss_type")
l1 = _execute.make_float(l1, "l1")
l2 = _execute.make_float(l2, "l2")
num_loss_partitions = _execute.make_int(num_loss_partitions, "num_loss_partitions")
num_inner_iterations = _execute.make_int(num_inner_iterations, "num_inner_iterations")
if adaptative is None:
adaptative = True
adaptative = _execute.make_bool(adaptative, "adaptative")
sparse_example_indices = _ops.convert_n_to_tensor(sparse_example_indices, _dtypes.int64)
sparse_feature_indices = _ops.convert_n_to_tensor(sparse_feature_indices, _dtypes.int64)
sparse_feature_values = _ops.convert_n_to_tensor(sparse_feature_values, _dtypes.float32)
dense_features = _ops.convert_n_to_tensor(dense_features, _dtypes.float32)
example_weights = _ops.convert_to_tensor(example_weights, _dtypes.float32)
example_labels = _ops.convert_to_tensor(example_labels, _dtypes.float32)
sparse_indices = _ops.convert_n_to_tensor(sparse_indices, _dtypes.int64)
sparse_weights = _ops.convert_n_to_tensor(sparse_weights, _dtypes.float32)
dense_weights = _ops.convert_n_to_tensor(dense_weights, _dtypes.float32)
example_state_data = _ops.convert_to_tensor(example_state_data, _dtypes.float32)
_inputs_flat = list(sparse_example_indices) + list(sparse_feature_indices) + list(sparse_feature_values) + list(dense_features) + [example_weights, example_labels] + list(sparse_indices) + list(sparse_weights) + list(dense_weights) + [example_state_data]
_attrs = ("loss_type", loss_type, "adaptative", adaptative,
"num_sparse_features", _attr_num_sparse_features,
"num_sparse_features_with_values", _attr_num_sparse_features_with_values,
"num_dense_features", _attr_num_dense_features, "l1", l1, "l2", l2,
"num_loss_partitions", num_loss_partitions, "num_inner_iterations",
num_inner_iterations)
_result = _execute.execute(b"SdcaOptimizer", _attr_num_sparse_features +
_attr_num_dense_features + 1,
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SdcaOptimizer", _inputs_flat, _attrs, _result)
_result = _result[:1] + [_result[1:1 + _attr_num_sparse_features]] + _result[1 + _attr_num_sparse_features:]
_result = _result[:2] + [_result[2:]]
_result = _SdcaOptimizerOutput._make(_result)
return _result
_SdcaOptimizerV2Output = collections.namedtuple(
"SdcaOptimizerV2",
["out_example_state_data", "out_delta_sparse_weights", "out_delta_dense_weights"])
def sdca_optimizer_v2(sparse_example_indices, sparse_feature_indices, sparse_feature_values, dense_features, example_weights, example_labels, sparse_indices, sparse_weights, dense_weights, example_state_data, loss_type, l1, l2, num_loss_partitions, num_inner_iterations, adaptive=True, name=None):
r"""Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
linear models with L1 + L2 regularization. As global optimization objective is
strongly-convex, the optimizer optimizes the dual objective at each step. The
optimizer applies each update one example at a time. Examples are sampled
uniformly, and the optimizer is learning rate free and enjoys linear convergence
rate.
[Proximal Stochastic Dual Coordinate Ascent](http://arxiv.org/pdf/1211.2717v1.pdf).
Shai Shalev-Shwartz, Tong Zhang. 2012
$$Loss Objective = \sum f_{i} (wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|$$
[Adding vs. Averaging in Distributed Primal-Dual Optimization](http://arxiv.org/abs/1502.03508).
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan,
Peter Richtarik, Martin Takac. 2015
[Stochastic Dual Coordinate Ascent with Adaptive Probabilities](https://arxiv.org/abs/1502.08053).
Dominik Csiba, Zheng Qu, Peter Richtarik. 2015
Args:
sparse_example_indices: A list of `Tensor` objects with type `int64`.
a list of vectors which contain example indices.
sparse_feature_indices: A list with the same length as `sparse_example_indices` of `Tensor` objects with type `int64`.
a list of vectors which contain feature indices.
sparse_feature_values: A list of `Tensor` objects with type `float32`.
a list of vectors which contains feature value
associated with each feature group.
dense_features: A list of `Tensor` objects with type `float32`.
a list of matrices which contains the dense feature values.
example_weights: A `Tensor` of type `float32`.
a vector which contains the weight associated with each
example.
example_labels: A `Tensor` of type `float32`.
a vector which contains the label/target associated with each
example.
sparse_indices: A list with the same length as `sparse_example_indices` of `Tensor` objects with type `int64`.
a list of vectors where each value is the indices which has
corresponding weights in sparse_weights. This field maybe omitted for the
dense approach.
sparse_weights: A list with the same length as `sparse_example_indices` of `Tensor` objects with type `float32`.
a list of vectors where each value is the weight associated with
a sparse feature group.
dense_weights: A list with the same length as `dense_features` of `Tensor` objects with type `float32`.
a list of vectors where the values are the weights associated
with a dense feature group.
example_state_data: A `Tensor` of type `float32`.
a list of vectors containing the example state data.
loss_type: A `string` from: `"logistic_loss", "squared_loss", "hinge_loss", "smooth_hinge_loss", "poisson_loss"`.
Type of the primal loss. Currently SdcaSolver supports logistic,
squared and hinge losses.
l1: A `float`. Symmetric l1 regularization strength.
l2: A `float`. Symmetric l2 regularization strength.
num_loss_partitions: An `int` that is `>= 1`.
Number of partitions of the global loss function.
num_inner_iterations: An `int` that is `>= 1`.
Number of iterations per mini-batch.
adaptive: An optional `bool`. Defaults to `True`.
Whether to use Adaptive SDCA for the inner loop.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (out_example_state_data, out_delta_sparse_weights, out_delta_dense_weights).
out_example_state_data: A `Tensor` of type `float32`.
out_delta_sparse_weights: A list with the same length as `sparse_example_indices` of `Tensor` objects with type `float32`.
out_delta_dense_weights: A list with the same length as `dense_features` of `Tensor` objects with type `float32`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SdcaOptimizerV2", name, sparse_example_indices,
sparse_feature_indices, sparse_feature_values, dense_features,
example_weights, example_labels, sparse_indices, sparse_weights,
dense_weights, example_state_data, "loss_type", loss_type, "adaptive",
adaptive, "l1", l1, "l2", l2, "num_loss_partitions",
num_loss_partitions, "num_inner_iterations", num_inner_iterations)
_result = _SdcaOptimizerV2Output._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sdca_optimizer_v2_eager_fallback(
sparse_example_indices, sparse_feature_indices,
sparse_feature_values, dense_features, example_weights,
example_labels, sparse_indices, sparse_weights, dense_weights,
example_state_data, loss_type=loss_type, adaptive=adaptive, l1=l1,
l2=l2, num_loss_partitions=num_loss_partitions,
num_inner_iterations=num_inner_iterations, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
if not isinstance(sparse_example_indices, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_example_indices' argument to "
"'sdca_optimizer_v2' Op, not %r." % sparse_example_indices)
_attr_num_sparse_features = len(sparse_example_indices)
if not isinstance(sparse_feature_indices, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_feature_indices' argument to "
"'sdca_optimizer_v2' Op, not %r." % sparse_feature_indices)
if len(sparse_feature_indices) != _attr_num_sparse_features:
raise ValueError(
"List argument 'sparse_feature_indices' to 'sdca_optimizer_v2' Op with length %d "
"must match length %d of argument 'sparse_example_indices'." %
(len(sparse_feature_indices), _attr_num_sparse_features))
if not isinstance(sparse_indices, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_indices' argument to "
"'sdca_optimizer_v2' Op, not %r." % sparse_indices)
if len(sparse_indices) != _attr_num_sparse_features:
raise ValueError(
"List argument 'sparse_indices' to 'sdca_optimizer_v2' Op with length %d "
"must match length %d of argument 'sparse_example_indices'." %
(len(sparse_indices), _attr_num_sparse_features))
if not isinstance(sparse_weights, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_weights' argument to "
"'sdca_optimizer_v2' Op, not %r." % sparse_weights)
if len(sparse_weights) != _attr_num_sparse_features:
raise ValueError(
"List argument 'sparse_weights' to 'sdca_optimizer_v2' Op with length %d "
"must match length %d of argument 'sparse_example_indices'." %
(len(sparse_weights), _attr_num_sparse_features))
if not isinstance(sparse_feature_values, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_feature_values' argument to "
"'sdca_optimizer_v2' Op, not %r." % sparse_feature_values)
_attr_num_sparse_features_with_values = len(sparse_feature_values)
if not isinstance(dense_features, (list, tuple)):
raise TypeError(
"Expected list for 'dense_features' argument to "
"'sdca_optimizer_v2' Op, not %r." % dense_features)
_attr_num_dense_features = len(dense_features)
if not isinstance(dense_weights, (list, tuple)):
raise TypeError(
"Expected list for 'dense_weights' argument to "
"'sdca_optimizer_v2' Op, not %r." % dense_weights)
if len(dense_weights) != _attr_num_dense_features:
raise ValueError(
"List argument 'dense_weights' to 'sdca_optimizer_v2' Op with length %d "
"must match length %d of argument 'dense_features'." %
(len(dense_weights), _attr_num_dense_features))
loss_type = _execute.make_str(loss_type, "loss_type")
l1 = _execute.make_float(l1, "l1")
l2 = _execute.make_float(l2, "l2")
num_loss_partitions = _execute.make_int(num_loss_partitions, "num_loss_partitions")
num_inner_iterations = _execute.make_int(num_inner_iterations, "num_inner_iterations")
if adaptive is None:
adaptive = True
adaptive = _execute.make_bool(adaptive, "adaptive")
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SdcaOptimizerV2", sparse_example_indices=sparse_example_indices,
sparse_feature_indices=sparse_feature_indices,
sparse_feature_values=sparse_feature_values,
dense_features=dense_features,
example_weights=example_weights,
example_labels=example_labels,
sparse_indices=sparse_indices,
sparse_weights=sparse_weights,
dense_weights=dense_weights,
example_state_data=example_state_data,
loss_type=loss_type, l1=l1, l2=l2,
num_loss_partitions=num_loss_partitions,
num_inner_iterations=num_inner_iterations,
adaptive=adaptive, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("loss_type", _op.get_attr("loss_type"), "adaptive",
_op._get_attr_bool("adaptive"), "num_sparse_features",
_op._get_attr_int("num_sparse_features"),
"num_sparse_features_with_values",
_op._get_attr_int("num_sparse_features_with_values"),
"num_dense_features", _op._get_attr_int("num_dense_features"),
"l1", _op.get_attr("l1"), "l2", _op.get_attr("l2"),
"num_loss_partitions", _op._get_attr_int("num_loss_partitions"),
"num_inner_iterations",
_op._get_attr_int("num_inner_iterations"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SdcaOptimizerV2", _inputs_flat, _attrs, _result)
_result = _result[:1] + [_result[1:1 + _attr_num_sparse_features]] + _result[1 + _attr_num_sparse_features:]
_result = _result[:2] + [_result[2:]]
_result = _SdcaOptimizerV2Output._make(_result)
return _result
SdcaOptimizerV2 = tf_export("raw_ops.SdcaOptimizerV2")(_ops.to_raw_op(sdca_optimizer_v2))
def sdca_optimizer_v2_eager_fallback(sparse_example_indices, sparse_feature_indices, sparse_feature_values, dense_features, example_weights, example_labels, sparse_indices, sparse_weights, dense_weights, example_state_data, loss_type, l1, l2, num_loss_partitions, num_inner_iterations, adaptive, name, ctx):
if not isinstance(sparse_example_indices, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_example_indices' argument to "
"'sdca_optimizer_v2' Op, not %r." % sparse_example_indices)
_attr_num_sparse_features = len(sparse_example_indices)
if not isinstance(sparse_feature_indices, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_feature_indices' argument to "
"'sdca_optimizer_v2' Op, not %r." % sparse_feature_indices)
if len(sparse_feature_indices) != _attr_num_sparse_features:
raise ValueError(
"List argument 'sparse_feature_indices' to 'sdca_optimizer_v2' Op with length %d "
"must match length %d of argument 'sparse_example_indices'." %
(len(sparse_feature_indices), _attr_num_sparse_features))
if not isinstance(sparse_indices, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_indices' argument to "
"'sdca_optimizer_v2' Op, not %r." % sparse_indices)
if len(sparse_indices) != _attr_num_sparse_features:
raise ValueError(
"List argument 'sparse_indices' to 'sdca_optimizer_v2' Op with length %d "
"must match length %d of argument 'sparse_example_indices'." %
(len(sparse_indices), _attr_num_sparse_features))
if not isinstance(sparse_weights, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_weights' argument to "
"'sdca_optimizer_v2' Op, not %r." % sparse_weights)
if len(sparse_weights) != _attr_num_sparse_features:
raise ValueError(
"List argument 'sparse_weights' to 'sdca_optimizer_v2' Op with length %d "
"must match length %d of argument 'sparse_example_indices'." %
(len(sparse_weights), _attr_num_sparse_features))
if not isinstance(sparse_feature_values, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_feature_values' argument to "
"'sdca_optimizer_v2' Op, not %r." % sparse_feature_values)
_attr_num_sparse_features_with_values = len(sparse_feature_values)
if not isinstance(dense_features, (list, tuple)):
raise TypeError(
"Expected list for 'dense_features' argument to "
"'sdca_optimizer_v2' Op, not %r." % dense_features)
_attr_num_dense_features = len(dense_features)
if not isinstance(dense_weights, (list, tuple)):
raise TypeError(
"Expected list for 'dense_weights' argument to "
"'sdca_optimizer_v2' Op, not %r." % dense_weights)
if len(dense_weights) != _attr_num_dense_features:
raise ValueError(
"List argument 'dense_weights' to 'sdca_optimizer_v2' Op with length %d "
"must match length %d of argument 'dense_features'." %
(len(dense_weights), _attr_num_dense_features))
loss_type = _execute.make_str(loss_type, "loss_type")
l1 = _execute.make_float(l1, "l1")
l2 = _execute.make_float(l2, "l2")
num_loss_partitions = _execute.make_int(num_loss_partitions, "num_loss_partitions")
num_inner_iterations = _execute.make_int(num_inner_iterations, "num_inner_iterations")
if adaptive is None:
adaptive = True
adaptive = _execute.make_bool(adaptive, "adaptive")
sparse_example_indices = _ops.convert_n_to_tensor(sparse_example_indices, _dtypes.int64)
sparse_feature_indices = _ops.convert_n_to_tensor(sparse_feature_indices, _dtypes.int64)
sparse_feature_values = _ops.convert_n_to_tensor(sparse_feature_values, _dtypes.float32)
dense_features = _ops.convert_n_to_tensor(dense_features, _dtypes.float32)
example_weights = _ops.convert_to_tensor(example_weights, _dtypes.float32)
example_labels = _ops.convert_to_tensor(example_labels, _dtypes.float32)
sparse_indices = _ops.convert_n_to_tensor(sparse_indices, _dtypes.int64)
sparse_weights = _ops.convert_n_to_tensor(sparse_weights, _dtypes.float32)
dense_weights = _ops.convert_n_to_tensor(dense_weights, _dtypes.float32)
example_state_data = _ops.convert_to_tensor(example_state_data, _dtypes.float32)
_inputs_flat = list(sparse_example_indices) + list(sparse_feature_indices) + list(sparse_feature_values) + list(dense_features) + [example_weights, example_labels] + list(sparse_indices) + list(sparse_weights) + list(dense_weights) + [example_state_data]
_attrs = ("loss_type", loss_type, "adaptive", adaptive,
"num_sparse_features", _attr_num_sparse_features,
"num_sparse_features_with_values", _attr_num_sparse_features_with_values,
"num_dense_features", _attr_num_dense_features, "l1", l1, "l2", l2,
"num_loss_partitions", num_loss_partitions, "num_inner_iterations",
num_inner_iterations)
_result = _execute.execute(b"SdcaOptimizerV2", _attr_num_sparse_features +
_attr_num_dense_features + 1,
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SdcaOptimizerV2", _inputs_flat, _attrs, _result)
_result = _result[:1] + [_result[1:1 + _attr_num_sparse_features]] + _result[1 + _attr_num_sparse_features:]
_result = _result[:2] + [_result[2:]]
_result = _SdcaOptimizerV2Output._make(_result)
return _result
@_dispatch.add_fallback_dispatch_list
@_dispatch.add_type_based_api_dispatcher
@tf_export(v1=['train.sdca_shrink_l1'])
@deprecated_endpoints('train.sdca_shrink_l1')
def sdca_shrink_l1(weights, l1, l2, name=None):
r"""Applies L1 regularization shrink step on the parameters.
Args:
weights: A list of `Tensor` objects with type mutable `float32`.
a list of vectors where each value is the weight associated with a
feature group.
l1: A `float`. Symmetric l1 regularization strength.
l2: A `float`.
Symmetric l2 regularization strength. Should be a positive float.
name: A name for the operation (optional).
Returns:
The created Operation.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
raise RuntimeError("sdca_shrink_l1 op does not support eager execution. Arg 'weights' is a ref.")
else:
_result = _dispatcher_for_sdca_shrink_l1(
(weights, l1, l2, name,), None)
if _result is not NotImplemented:
return _result
# Add nodes to the TensorFlow graph.
if not isinstance(weights, (list, tuple)):
raise TypeError(
"Expected list for 'weights' argument to "
"'sdca_shrink_l1' Op, not %r." % weights)
_attr_num_features = len(weights)
l1 = _execute.make_float(l1, "l1")
l2 = _execute.make_float(l2, "l2")
try:
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SdcaShrinkL1", weights=weights, l1=l1, l2=l2, name=name)
except (TypeError, ValueError):
_result = _dispatch.dispatch(
sdca_shrink_l1, (), dict(weights=weights, l1=l1, l2=l2, name=name)
)
if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return _result
raise
return _op
SdcaShrinkL1 = tf_export("raw_ops.SdcaShrinkL1")(_ops.to_raw_op(sdca_shrink_l1))
_dispatcher_for_sdca_shrink_l1 = sdca_shrink_l1._tf_type_based_dispatcher.Dispatch
def sdca_shrink_l1_eager_fallback(weights, l1, l2, name, ctx):
raise RuntimeError("sdca_shrink_l1 op does not support eager execution. Arg 'weights' is a ref.")