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seanyen
seanyen / tensorflow python
Repository URL to install this package:
|
Version:
1.14.0 ▾
|
"""Python wrappers around TensorFlow ops.
This file is MACHINE GENERATED! Do not edit.
"""
import collections as _collections
import six as _six
from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
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 errors as _errors
from tensorflow.python.framework import tensor_shape as _tensor_shape
from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
# Needed to trigger the call to _set_call_cpp_shape_fn.
from tensorflow.python.framework import common_shapes as _common_shapes
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 tensorflow.python.util.tf_export import kwarg_only as _kwarg_only
from tensorflow.tools.docs import doc_controls as _doc_controls
def choose_fastest_branch_dataset(input_dataset, ratio_numerator, ratio_denominator, other_arguments, num_elements_per_branch, branches, other_arguments_lengths, output_types, output_shapes, name=None):
r"""TODO: add doc.
Args:
input_dataset: A `Tensor` of type `variant`.
ratio_numerator: A `Tensor` of type `int64`.
ratio_denominator: A `Tensor` of type `int64`.
other_arguments: A list of `Tensor` objects.
num_elements_per_branch: An `int` that is `>= 1`.
branches: A list of functions decorated with @Defun that has length `>= 1`.
other_arguments_lengths: A list of `ints` that has length `>= 1`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ChooseFastestBranchDataset", name, _ctx._post_execution_callbacks,
input_dataset, ratio_numerator, ratio_denominator, other_arguments,
"num_elements_per_branch", num_elements_per_branch, "branches",
branches, "other_arguments_lengths", other_arguments_lengths,
"output_types", output_types, "output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return choose_fastest_branch_dataset_eager_fallback(
input_dataset, ratio_numerator, ratio_denominator,
other_arguments, num_elements_per_branch=num_elements_per_branch,
branches=branches,
other_arguments_lengths=other_arguments_lengths,
output_types=output_types, output_shapes=output_shapes, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
num_elements_per_branch = _execute.make_int(num_elements_per_branch, "num_elements_per_branch")
if not isinstance(branches, (list, tuple)):
raise TypeError(
"Expected list for 'branches' argument to "
"'choose_fastest_branch_dataset' Op, not %r." % branches)
if not isinstance(other_arguments_lengths, (list, tuple)):
raise TypeError(
"Expected list for 'other_arguments_lengths' argument to "
"'choose_fastest_branch_dataset' Op, not %r." % other_arguments_lengths)
other_arguments_lengths = [_execute.make_int(_i, "other_arguments_lengths") for _i in other_arguments_lengths]
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'choose_fastest_branch_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'choose_fastest_branch_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ChooseFastestBranchDataset", input_dataset=input_dataset,
ratio_numerator=ratio_numerator,
ratio_denominator=ratio_denominator,
other_arguments=other_arguments,
num_elements_per_branch=num_elements_per_branch,
branches=branches,
other_arguments_lengths=other_arguments_lengths,
output_types=output_types,
output_shapes=output_shapes, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("Targuments", _op.get_attr("Targuments"),
"num_elements_per_branch",
_op.get_attr("num_elements_per_branch"), "branches",
_op.get_attr("branches"), "other_arguments_lengths",
_op.get_attr("other_arguments_lengths"), "output_types",
_op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ChooseFastestBranchDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ChooseFastestBranchDataset(input_dataset, ratio_numerator, ratio_denominator, other_arguments, num_elements_per_branch, branches, other_arguments_lengths, output_types, output_shapes, name=None):
return choose_fastest_branch_dataset(input_dataset=input_dataset, ratio_numerator=ratio_numerator, ratio_denominator=ratio_denominator, other_arguments=other_arguments, num_elements_per_branch=num_elements_per_branch, branches=branches, other_arguments_lengths=other_arguments_lengths, output_types=output_types, output_shapes=output_shapes, name=name)
ChooseFastestBranchDataset.__doc__ = choose_fastest_branch_dataset.__doc__
ChooseFastestBranchDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ChooseFastestBranchDataset))
tf_export("raw_ops.ChooseFastestBranchDataset")(ChooseFastestBranchDataset)
def choose_fastest_branch_dataset_eager_fallback(input_dataset, ratio_numerator, ratio_denominator, other_arguments, num_elements_per_branch, branches, other_arguments_lengths, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function choose_fastest_branch_dataset
"""
_ctx = ctx if ctx else _context.context()
num_elements_per_branch = _execute.make_int(num_elements_per_branch, "num_elements_per_branch")
if not isinstance(branches, (list, tuple)):
raise TypeError(
"Expected list for 'branches' argument to "
"'choose_fastest_branch_dataset' Op, not %r." % branches)
if not isinstance(other_arguments_lengths, (list, tuple)):
raise TypeError(
"Expected list for 'other_arguments_lengths' argument to "
"'choose_fastest_branch_dataset' Op, not %r." % other_arguments_lengths)
other_arguments_lengths = [_execute.make_int(_i, "other_arguments_lengths") for _i in other_arguments_lengths]
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'choose_fastest_branch_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'choose_fastest_branch_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
ratio_numerator = _ops.convert_to_tensor(ratio_numerator, _dtypes.int64)
ratio_denominator = _ops.convert_to_tensor(ratio_denominator, _dtypes.int64)
_inputs_flat = [input_dataset, ratio_numerator, ratio_denominator] + list(other_arguments)
_attrs = ("Targuments", _attr_Targuments, "num_elements_per_branch",
num_elements_per_branch, "branches", branches, "other_arguments_lengths",
other_arguments_lengths, "output_types", output_types, "output_shapes",
output_shapes)
_result = _execute.execute(b"ChooseFastestBranchDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ChooseFastestBranchDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_assert_next_dataset(input_dataset, transformations, output_types, output_shapes, name=None):
r"""TODO: add doc.
Args:
input_dataset: A `Tensor` of type `variant`.
transformations: A `Tensor` of type `string`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalAssertNextDataset", name, _ctx._post_execution_callbacks,
input_dataset, transformations, "output_types", output_types,
"output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_assert_next_dataset_eager_fallback(
input_dataset, transformations, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_assert_next_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_assert_next_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalAssertNextDataset", input_dataset=input_dataset,
transformations=transformations,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalAssertNextDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalAssertNextDataset(input_dataset, transformations, output_types, output_shapes, name=None):
return experimental_assert_next_dataset(input_dataset=input_dataset, transformations=transformations, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalAssertNextDataset.__doc__ = experimental_assert_next_dataset.__doc__
ExperimentalAssertNextDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalAssertNextDataset))
tf_export("raw_ops.ExperimentalAssertNextDataset")(ExperimentalAssertNextDataset)
def experimental_assert_next_dataset_eager_fallback(input_dataset, transformations, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_assert_next_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_assert_next_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_assert_next_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
transformations = _ops.convert_to_tensor(transformations, _dtypes.string)
_inputs_flat = [input_dataset, transformations]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalAssertNextDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalAssertNextDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_auto_shard_dataset(input_dataset, num_workers, index, output_types, output_shapes, name=None):
r"""Creates a dataset that shards the input dataset.
Creates a dataset that shards the input dataset by num_workers, returning a
sharded dataset for the index-th worker. This attempts to automatically shard
a dataset by examining the Dataset graph and inserting a shard op before the
inputs to a reader Dataset (e.g. CSVDataset, TFRecordDataset).
This dataset will throw a NotFound error if we cannot shard the dataset
automatically.
Args:
input_dataset: A `Tensor` of type `variant`.
A variant tensor representing the input dataset.
num_workers: A `Tensor` of type `int64`.
A scalar representing the number of workers to distribute this dataset across.
index: A `Tensor` of type `int64`.
A scalar representing the index of the current worker out of num_workers.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalAutoShardDataset", name, _ctx._post_execution_callbacks,
input_dataset, num_workers, index, "output_types", output_types,
"output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_auto_shard_dataset_eager_fallback(
input_dataset, num_workers, index, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_auto_shard_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_auto_shard_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalAutoShardDataset", input_dataset=input_dataset,
num_workers=num_workers, index=index,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalAutoShardDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalAutoShardDataset(input_dataset, num_workers, index, output_types, output_shapes, name=None):
return experimental_auto_shard_dataset(input_dataset=input_dataset, num_workers=num_workers, index=index, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalAutoShardDataset.__doc__ = experimental_auto_shard_dataset.__doc__
ExperimentalAutoShardDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalAutoShardDataset))
tf_export("raw_ops.ExperimentalAutoShardDataset")(ExperimentalAutoShardDataset)
def experimental_auto_shard_dataset_eager_fallback(input_dataset, num_workers, index, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_auto_shard_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_auto_shard_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_auto_shard_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
num_workers = _ops.convert_to_tensor(num_workers, _dtypes.int64)
index = _ops.convert_to_tensor(index, _dtypes.int64)
_inputs_flat = [input_dataset, num_workers, index]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalAutoShardDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalAutoShardDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_bytes_produced_stats_dataset(input_dataset, tag, output_types, output_shapes, name=None):
r"""Records the bytes size of each element of `input_dataset` in a StatsAggregator.
Args:
input_dataset: A `Tensor` of type `variant`.
tag: A `Tensor` of type `string`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalBytesProducedStatsDataset", name,
_ctx._post_execution_callbacks, input_dataset, tag, "output_types",
output_types, "output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_bytes_produced_stats_dataset_eager_fallback(
input_dataset, tag, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_bytes_produced_stats_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_bytes_produced_stats_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalBytesProducedStatsDataset", input_dataset=input_dataset,
tag=tag,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalBytesProducedStatsDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalBytesProducedStatsDataset(input_dataset, tag, output_types, output_shapes, name=None):
return experimental_bytes_produced_stats_dataset(input_dataset=input_dataset, tag=tag, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalBytesProducedStatsDataset.__doc__ = experimental_bytes_produced_stats_dataset.__doc__
ExperimentalBytesProducedStatsDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalBytesProducedStatsDataset))
tf_export("raw_ops.ExperimentalBytesProducedStatsDataset")(ExperimentalBytesProducedStatsDataset)
def experimental_bytes_produced_stats_dataset_eager_fallback(input_dataset, tag, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_bytes_produced_stats_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_bytes_produced_stats_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_bytes_produced_stats_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
tag = _ops.convert_to_tensor(tag, _dtypes.string)
_inputs_flat = [input_dataset, tag]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalBytesProducedStatsDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalBytesProducedStatsDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_csv_dataset(filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols, record_defaults, output_shapes, name=None):
r"""TODO: add doc.
Args:
filenames: A `Tensor` of type `string`.
compression_type: A `Tensor` of type `string`.
buffer_size: A `Tensor` of type `int64`.
header: A `Tensor` of type `bool`.
field_delim: A `Tensor` of type `string`.
use_quote_delim: A `Tensor` of type `bool`.
na_value: A `Tensor` of type `string`.
select_cols: A `Tensor` of type `int64`.
record_defaults: A list of `Tensor` objects with types from: `float32`, `float64`, `int32`, `int64`, `string`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalCSVDataset", name, _ctx._post_execution_callbacks,
filenames, compression_type, buffer_size, header, field_delim,
use_quote_delim, na_value, select_cols, record_defaults,
"output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_csv_dataset_eager_fallback(
filenames, compression_type, buffer_size, header, field_delim,
use_quote_delim, na_value, select_cols, record_defaults,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_csv_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalCSVDataset", filenames=filenames,
compression_type=compression_type,
buffer_size=buffer_size, header=header,
field_delim=field_delim,
use_quote_delim=use_quote_delim,
na_value=na_value, select_cols=select_cols,
record_defaults=record_defaults,
output_shapes=output_shapes, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalCSVDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalCSVDataset(filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols, record_defaults, output_shapes, name=None):
return experimental_csv_dataset(filenames=filenames, compression_type=compression_type, buffer_size=buffer_size, header=header, field_delim=field_delim, use_quote_delim=use_quote_delim, na_value=na_value, select_cols=select_cols, record_defaults=record_defaults, output_shapes=output_shapes, name=name)
ExperimentalCSVDataset.__doc__ = experimental_csv_dataset.__doc__
ExperimentalCSVDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalCSVDataset))
tf_export("raw_ops.ExperimentalCSVDataset")(ExperimentalCSVDataset)
def experimental_csv_dataset_eager_fallback(filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols, record_defaults, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_csv_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_csv_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_attr_output_types, record_defaults = _execute.convert_to_mixed_eager_tensors(record_defaults, _ctx)
filenames = _ops.convert_to_tensor(filenames, _dtypes.string)
compression_type = _ops.convert_to_tensor(compression_type, _dtypes.string)
buffer_size = _ops.convert_to_tensor(buffer_size, _dtypes.int64)
header = _ops.convert_to_tensor(header, _dtypes.bool)
field_delim = _ops.convert_to_tensor(field_delim, _dtypes.string)
use_quote_delim = _ops.convert_to_tensor(use_quote_delim, _dtypes.bool)
na_value = _ops.convert_to_tensor(na_value, _dtypes.string)
select_cols = _ops.convert_to_tensor(select_cols, _dtypes.int64)
_inputs_flat = [filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols] + list(record_defaults)
_attrs = ("output_types", _attr_output_types, "output_shapes",
output_shapes)
_result = _execute.execute(b"ExperimentalCSVDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalCSVDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_choose_fastest_dataset(input_datasets, num_experiments, output_types, output_shapes, name=None):
r"""TODO: add doc.
Args:
input_datasets: A list of at least 2 `Tensor` objects with type `variant`.
num_experiments: An `int`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalChooseFastestDataset", name,
_ctx._post_execution_callbacks, input_datasets, "num_experiments",
num_experiments, "output_types", output_types, "output_shapes",
output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_choose_fastest_dataset_eager_fallback(
input_datasets, num_experiments=num_experiments,
output_types=output_types, output_shapes=output_shapes, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(input_datasets, (list, tuple)):
raise TypeError(
"Expected list for 'input_datasets' argument to "
"'experimental_choose_fastest_dataset' Op, not %r." % input_datasets)
_attr_N = len(input_datasets)
num_experiments = _execute.make_int(num_experiments, "num_experiments")
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_choose_fastest_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_choose_fastest_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalChooseFastestDataset", input_datasets=input_datasets,
num_experiments=num_experiments,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("N", _op.get_attr("N"), "num_experiments",
_op.get_attr("num_experiments"), "output_types",
_op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalChooseFastestDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalChooseFastestDataset(input_datasets, num_experiments, output_types, output_shapes, name=None):
return experimental_choose_fastest_dataset(input_datasets=input_datasets, num_experiments=num_experiments, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalChooseFastestDataset.__doc__ = experimental_choose_fastest_dataset.__doc__
ExperimentalChooseFastestDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalChooseFastestDataset))
tf_export("raw_ops.ExperimentalChooseFastestDataset")(ExperimentalChooseFastestDataset)
def experimental_choose_fastest_dataset_eager_fallback(input_datasets, num_experiments, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_choose_fastest_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(input_datasets, (list, tuple)):
raise TypeError(
"Expected list for 'input_datasets' argument to "
"'experimental_choose_fastest_dataset' Op, not %r." % input_datasets)
_attr_N = len(input_datasets)
num_experiments = _execute.make_int(num_experiments, "num_experiments")
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_choose_fastest_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_choose_fastest_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_datasets = _ops.convert_n_to_tensor(input_datasets, _dtypes.variant)
_inputs_flat = list(input_datasets)
_attrs = ("N", _attr_N, "num_experiments", num_experiments, "output_types",
output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalChooseFastestDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalChooseFastestDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_dataset_cardinality(input_dataset, name=None):
r"""Returns the cardinality of `input_dataset`.
Returns the cardinality of `input_dataset`.
Args:
input_dataset: A `Tensor` of type `variant`.
A variant tensor representing the dataset to return cardinality for.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalDatasetCardinality", name,
_ctx._post_execution_callbacks, input_dataset)
return _result
except _core._FallbackException:
try:
return experimental_dataset_cardinality_eager_fallback(
input_dataset, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalDatasetCardinality", input_dataset=input_dataset,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = None
_execute.record_gradient(
"ExperimentalDatasetCardinality", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalDatasetCardinality(input_dataset, name=None):
return experimental_dataset_cardinality(input_dataset=input_dataset, name=name)
ExperimentalDatasetCardinality.__doc__ = experimental_dataset_cardinality.__doc__
ExperimentalDatasetCardinality = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalDatasetCardinality))
tf_export("raw_ops.ExperimentalDatasetCardinality")(ExperimentalDatasetCardinality)
def experimental_dataset_cardinality_eager_fallback(input_dataset, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_dataset_cardinality
"""
_ctx = ctx if ctx else _context.context()
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
_inputs_flat = [input_dataset]
_attrs = None
_result = _execute.execute(b"ExperimentalDatasetCardinality", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalDatasetCardinality", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_dataset_to_tf_record(input_dataset, filename, compression_type, name=None):
r"""Writes the given dataset to the given file using the TFRecord format.
Args:
input_dataset: A `Tensor` of type `variant`.
A variant tensor representing the dataset to write.
filename: A `Tensor` of type `string`.
A scalar string tensor representing the filename to use.
compression_type: A `Tensor` of type `string`.
A scalar string tensor containing either (i) the empty string (no
compression), (ii) "ZLIB", or (iii) "GZIP".
name: A name for the operation (optional).
Returns:
The created Operation.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalDatasetToTFRecord", name, _ctx._post_execution_callbacks,
input_dataset, filename, compression_type)
return _result
except _core._FallbackException:
try:
return experimental_dataset_to_tf_record_eager_fallback(
input_dataset, filename, compression_type, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalDatasetToTFRecord", input_dataset=input_dataset,
filename=filename,
compression_type=compression_type,
name=name)
return _op
_result = None
return _result
def ExperimentalDatasetToTFRecord(input_dataset, filename, compression_type, name=None):
return experimental_dataset_to_tf_record(input_dataset=input_dataset, filename=filename, compression_type=compression_type, name=name)
ExperimentalDatasetToTFRecord.__doc__ = experimental_dataset_to_tf_record.__doc__
ExperimentalDatasetToTFRecord = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalDatasetToTFRecord))
tf_export("raw_ops.ExperimentalDatasetToTFRecord")(ExperimentalDatasetToTFRecord)
def experimental_dataset_to_tf_record_eager_fallback(input_dataset, filename, compression_type, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_dataset_to_tf_record
"""
_ctx = ctx if ctx else _context.context()
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
filename = _ops.convert_to_tensor(filename, _dtypes.string)
compression_type = _ops.convert_to_tensor(compression_type, _dtypes.string)
_inputs_flat = [input_dataset, filename, compression_type]
_attrs = None
_result = _execute.execute(b"ExperimentalDatasetToTFRecord", 0,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_result = None
return _result
def experimental_dense_to_sparse_batch_dataset(input_dataset, batch_size, row_shape, output_types, output_shapes, name=None):
r"""Creates a dataset that batches input elements into a SparseTensor.
Args:
input_dataset: A `Tensor` of type `variant`.
A handle to an input dataset. Must have a single component.
batch_size: A `Tensor` of type `int64`.
A scalar representing the number of elements to accumulate in a
batch.
row_shape: A `Tensor` of type `int64`.
A vector representing the dense shape of each row in the produced
SparseTensor. The shape may be partially specified, using `-1` to indicate
that a particular dimension should use the maximum size of all batch elements.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalDenseToSparseBatchDataset", name,
_ctx._post_execution_callbacks, input_dataset, batch_size, row_shape,
"output_types", output_types, "output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_dense_to_sparse_batch_dataset_eager_fallback(
input_dataset, batch_size, row_shape, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_dense_to_sparse_batch_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_dense_to_sparse_batch_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalDenseToSparseBatchDataset", input_dataset=input_dataset,
batch_size=batch_size,
row_shape=row_shape,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalDenseToSparseBatchDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalDenseToSparseBatchDataset(input_dataset, batch_size, row_shape, output_types, output_shapes, name=None):
return experimental_dense_to_sparse_batch_dataset(input_dataset=input_dataset, batch_size=batch_size, row_shape=row_shape, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalDenseToSparseBatchDataset.__doc__ = experimental_dense_to_sparse_batch_dataset.__doc__
ExperimentalDenseToSparseBatchDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalDenseToSparseBatchDataset))
tf_export("raw_ops.ExperimentalDenseToSparseBatchDataset")(ExperimentalDenseToSparseBatchDataset)
def experimental_dense_to_sparse_batch_dataset_eager_fallback(input_dataset, batch_size, row_shape, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_dense_to_sparse_batch_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_dense_to_sparse_batch_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_dense_to_sparse_batch_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
batch_size = _ops.convert_to_tensor(batch_size, _dtypes.int64)
row_shape = _ops.convert_to_tensor(row_shape, _dtypes.int64)
_inputs_flat = [input_dataset, batch_size, row_shape]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalDenseToSparseBatchDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalDenseToSparseBatchDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_directed_interleave_dataset(selector_input_dataset, data_input_datasets, output_types, output_shapes, name=None):
r"""A substitute for `InterleaveDataset` on a fixed list of `N` datasets.
Args:
selector_input_dataset: A `Tensor` of type `variant`.
A dataset of scalar `DT_INT64` elements that determines which of the
`N` data inputs should produce the next output element.
data_input_datasets: A list of at least 1 `Tensor` objects with type `variant`.
`N` datasets with the same type that will be interleaved according to
the values of `selector_input_dataset`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalDirectedInterleaveDataset", name,
_ctx._post_execution_callbacks, selector_input_dataset,
data_input_datasets, "output_types", output_types, "output_shapes",
output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_directed_interleave_dataset_eager_fallback(
selector_input_dataset, data_input_datasets,
output_types=output_types, output_shapes=output_shapes, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(data_input_datasets, (list, tuple)):
raise TypeError(
"Expected list for 'data_input_datasets' argument to "
"'experimental_directed_interleave_dataset' Op, not %r." % data_input_datasets)
_attr_N = len(data_input_datasets)
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_directed_interleave_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_directed_interleave_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalDirectedInterleaveDataset", selector_input_dataset=selector_input_dataset,
data_input_datasets=data_input_datasets,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"), "N", _op.get_attr("N"))
_execute.record_gradient(
"ExperimentalDirectedInterleaveDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalDirectedInterleaveDataset(selector_input_dataset, data_input_datasets, output_types, output_shapes, name=None):
return experimental_directed_interleave_dataset(selector_input_dataset=selector_input_dataset, data_input_datasets=data_input_datasets, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalDirectedInterleaveDataset.__doc__ = experimental_directed_interleave_dataset.__doc__
ExperimentalDirectedInterleaveDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalDirectedInterleaveDataset))
tf_export("raw_ops.ExperimentalDirectedInterleaveDataset")(ExperimentalDirectedInterleaveDataset)
def experimental_directed_interleave_dataset_eager_fallback(selector_input_dataset, data_input_datasets, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_directed_interleave_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(data_input_datasets, (list, tuple)):
raise TypeError(
"Expected list for 'data_input_datasets' argument to "
"'experimental_directed_interleave_dataset' Op, not %r." % data_input_datasets)
_attr_N = len(data_input_datasets)
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_directed_interleave_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_directed_interleave_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
selector_input_dataset = _ops.convert_to_tensor(selector_input_dataset, _dtypes.variant)
data_input_datasets = _ops.convert_n_to_tensor(data_input_datasets, _dtypes.variant)
_inputs_flat = [selector_input_dataset] + list(data_input_datasets)
_attrs = ("output_types", output_types, "output_shapes", output_shapes, "N",
_attr_N)
_result = _execute.execute(b"ExperimentalDirectedInterleaveDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalDirectedInterleaveDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_group_by_reducer_dataset(input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments, key_func, init_func, reduce_func, finalize_func, output_types, output_shapes, name=None):
r"""Creates a dataset that computes a group-by on `input_dataset`.
Creates a dataset that computes a group-by on `input_dataset`.
Args:
input_dataset: A `Tensor` of type `variant`.
A variant tensor representing the input dataset.
key_func_other_arguments: A list of `Tensor` objects.
A list of tensors, typically values that were captured when
building a closure for `key_func`.
init_func_other_arguments: A list of `Tensor` objects.
A list of tensors, typically values that were captured when
building a closure for `init_func`.
reduce_func_other_arguments: A list of `Tensor` objects.
A list of tensors, typically values that were captured when
building a closure for `reduce_func`.
finalize_func_other_arguments: A list of `Tensor` objects.
A list of tensors, typically values that were captured when
building a closure for `finalize_func`.
key_func: A function decorated with @Defun.
A function mapping an element of `input_dataset`, concatenated
with `key_func_other_arguments` to a scalar value of type DT_INT64.
init_func: A function decorated with @Defun.
A function mapping a key of type DT_INT64, concatenated with
`init_func_other_arguments` to the initial reducer state.
reduce_func: A function decorated with @Defun.
A function mapping the current reducer state and an element of `input_dataset`,
concatenated with `reduce_func_other_arguments` to a new reducer state.
finalize_func: A function decorated with @Defun.
A function mapping the final reducer state to an output element.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalGroupByReducerDataset", name,
_ctx._post_execution_callbacks, input_dataset,
key_func_other_arguments, init_func_other_arguments,
reduce_func_other_arguments, finalize_func_other_arguments,
"key_func", key_func, "init_func", init_func, "reduce_func",
reduce_func, "finalize_func", finalize_func, "output_types",
output_types, "output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_group_by_reducer_dataset_eager_fallback(
input_dataset, key_func_other_arguments,
init_func_other_arguments, reduce_func_other_arguments,
finalize_func_other_arguments, key_func=key_func,
init_func=init_func, reduce_func=reduce_func,
finalize_func=finalize_func, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_group_by_reducer_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_group_by_reducer_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalGroupByReducerDataset", input_dataset=input_dataset,
key_func_other_arguments=key_func_other_arguments,
init_func_other_arguments=init_func_other_arguments,
reduce_func_other_arguments=reduce_func_other_arguments,
finalize_func_other_arguments=finalize_func_other_arguments,
key_func=key_func,
init_func=init_func,
reduce_func=reduce_func,
finalize_func=finalize_func,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("key_func", _op.get_attr("key_func"), "init_func",
_op.get_attr("init_func"), "reduce_func",
_op.get_attr("reduce_func"), "finalize_func",
_op.get_attr("finalize_func"), "Tkey_func_other_arguments",
_op.get_attr("Tkey_func_other_arguments"),
"Tinit_func_other_arguments",
_op.get_attr("Tinit_func_other_arguments"),
"Treduce_func_other_arguments",
_op.get_attr("Treduce_func_other_arguments"),
"Tfinalize_func_other_arguments",
_op.get_attr("Tfinalize_func_other_arguments"), "output_types",
_op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalGroupByReducerDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalGroupByReducerDataset(input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments, key_func, init_func, reduce_func, finalize_func, output_types, output_shapes, name=None):
return experimental_group_by_reducer_dataset(input_dataset=input_dataset, key_func_other_arguments=key_func_other_arguments, init_func_other_arguments=init_func_other_arguments, reduce_func_other_arguments=reduce_func_other_arguments, finalize_func_other_arguments=finalize_func_other_arguments, key_func=key_func, init_func=init_func, reduce_func=reduce_func, finalize_func=finalize_func, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalGroupByReducerDataset.__doc__ = experimental_group_by_reducer_dataset.__doc__
ExperimentalGroupByReducerDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalGroupByReducerDataset))
tf_export("raw_ops.ExperimentalGroupByReducerDataset")(ExperimentalGroupByReducerDataset)
def experimental_group_by_reducer_dataset_eager_fallback(input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments, key_func, init_func, reduce_func, finalize_func, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_group_by_reducer_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_group_by_reducer_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_group_by_reducer_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_attr_Tkey_func_other_arguments, key_func_other_arguments = _execute.convert_to_mixed_eager_tensors(key_func_other_arguments, _ctx)
_attr_Tinit_func_other_arguments, init_func_other_arguments = _execute.convert_to_mixed_eager_tensors(init_func_other_arguments, _ctx)
_attr_Treduce_func_other_arguments, reduce_func_other_arguments = _execute.convert_to_mixed_eager_tensors(reduce_func_other_arguments, _ctx)
_attr_Tfinalize_func_other_arguments, finalize_func_other_arguments = _execute.convert_to_mixed_eager_tensors(finalize_func_other_arguments, _ctx)
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
_inputs_flat = [input_dataset] + list(key_func_other_arguments) + list(init_func_other_arguments) + list(reduce_func_other_arguments) + list(finalize_func_other_arguments)
_attrs = ("key_func", key_func, "init_func", init_func, "reduce_func",
reduce_func, "finalize_func", finalize_func, "Tkey_func_other_arguments",
_attr_Tkey_func_other_arguments, "Tinit_func_other_arguments",
_attr_Tinit_func_other_arguments, "Treduce_func_other_arguments",
_attr_Treduce_func_other_arguments, "Tfinalize_func_other_arguments",
_attr_Tfinalize_func_other_arguments, "output_types", output_types,
"output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalGroupByReducerDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalGroupByReducerDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_group_by_window_dataset(input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments, key_func, reduce_func, window_size_func, output_types, output_shapes, name=None):
r"""Creates a dataset that computes a windowed group-by on `input_dataset`.
// TODO(mrry): Support non-int64 keys.
Args:
input_dataset: A `Tensor` of type `variant`.
key_func_other_arguments: A list of `Tensor` objects.
reduce_func_other_arguments: A list of `Tensor` objects.
window_size_func_other_arguments: A list of `Tensor` objects.
key_func: A function decorated with @Defun.
A function mapping an element of `input_dataset`, concatenated
with `key_func_other_arguments` to a scalar value of type DT_INT64.
reduce_func: A function decorated with @Defun.
window_size_func: A function decorated with @Defun.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalGroupByWindowDataset", name,
_ctx._post_execution_callbacks, input_dataset,
key_func_other_arguments, reduce_func_other_arguments,
window_size_func_other_arguments, "key_func", key_func, "reduce_func",
reduce_func, "window_size_func", window_size_func, "output_types",
output_types, "output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_group_by_window_dataset_eager_fallback(
input_dataset, key_func_other_arguments,
reduce_func_other_arguments, window_size_func_other_arguments,
key_func=key_func, reduce_func=reduce_func,
window_size_func=window_size_func, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_group_by_window_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_group_by_window_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalGroupByWindowDataset", input_dataset=input_dataset,
key_func_other_arguments=key_func_other_arguments,
reduce_func_other_arguments=reduce_func_other_arguments,
window_size_func_other_arguments=window_size_func_other_arguments,
key_func=key_func,
reduce_func=reduce_func,
window_size_func=window_size_func,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("key_func", _op.get_attr("key_func"), "reduce_func",
_op.get_attr("reduce_func"), "window_size_func",
_op.get_attr("window_size_func"), "Tkey_func_other_arguments",
_op.get_attr("Tkey_func_other_arguments"),
"Treduce_func_other_arguments",
_op.get_attr("Treduce_func_other_arguments"),
"Twindow_size_func_other_arguments",
_op.get_attr("Twindow_size_func_other_arguments"), "output_types",
_op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalGroupByWindowDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalGroupByWindowDataset(input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments, key_func, reduce_func, window_size_func, output_types, output_shapes, name=None):
return experimental_group_by_window_dataset(input_dataset=input_dataset, key_func_other_arguments=key_func_other_arguments, reduce_func_other_arguments=reduce_func_other_arguments, window_size_func_other_arguments=window_size_func_other_arguments, key_func=key_func, reduce_func=reduce_func, window_size_func=window_size_func, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalGroupByWindowDataset.__doc__ = experimental_group_by_window_dataset.__doc__
ExperimentalGroupByWindowDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalGroupByWindowDataset))
tf_export("raw_ops.ExperimentalGroupByWindowDataset")(ExperimentalGroupByWindowDataset)
def experimental_group_by_window_dataset_eager_fallback(input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments, key_func, reduce_func, window_size_func, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_group_by_window_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_group_by_window_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_group_by_window_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_attr_Tkey_func_other_arguments, key_func_other_arguments = _execute.convert_to_mixed_eager_tensors(key_func_other_arguments, _ctx)
_attr_Treduce_func_other_arguments, reduce_func_other_arguments = _execute.convert_to_mixed_eager_tensors(reduce_func_other_arguments, _ctx)
_attr_Twindow_size_func_other_arguments, window_size_func_other_arguments = _execute.convert_to_mixed_eager_tensors(window_size_func_other_arguments, _ctx)
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
_inputs_flat = [input_dataset] + list(key_func_other_arguments) + list(reduce_func_other_arguments) + list(window_size_func_other_arguments)
_attrs = ("key_func", key_func, "reduce_func", reduce_func,
"window_size_func", window_size_func, "Tkey_func_other_arguments",
_attr_Tkey_func_other_arguments, "Treduce_func_other_arguments",
_attr_Treduce_func_other_arguments, "Twindow_size_func_other_arguments",
_attr_Twindow_size_func_other_arguments, "output_types", output_types,
"output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalGroupByWindowDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalGroupByWindowDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_identity_indexed_dataset(size, name=None):
r"""TODO: add doc.
Args:
size: A `Tensor` of type `uint64`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalIdentityIndexedDataset", name,
_ctx._post_execution_callbacks, size)
return _result
except _core._FallbackException:
try:
return experimental_identity_indexed_dataset_eager_fallback(
size, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalIdentityIndexedDataset", size=size, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = None
_execute.record_gradient(
"ExperimentalIdentityIndexedDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalIdentityIndexedDataset(size, name=None):
return experimental_identity_indexed_dataset(size=size, name=name)
ExperimentalIdentityIndexedDataset.__doc__ = experimental_identity_indexed_dataset.__doc__
ExperimentalIdentityIndexedDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalIdentityIndexedDataset))
tf_export("raw_ops.ExperimentalIdentityIndexedDataset")(ExperimentalIdentityIndexedDataset)
def experimental_identity_indexed_dataset_eager_fallback(size, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_identity_indexed_dataset
"""
_ctx = ctx if ctx else _context.context()
size = _ops.convert_to_tensor(size, _dtypes.uint64)
_inputs_flat = [size]
_attrs = None
_result = _execute.execute(b"ExperimentalIdentityIndexedDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalIdentityIndexedDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_ignore_errors_dataset(input_dataset, output_types, output_shapes, name=None):
r"""Creates a dataset that contains the elements of `input_dataset` ignoring errors.
Args:
input_dataset: A `Tensor` of type `variant`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalIgnoreErrorsDataset", name,
_ctx._post_execution_callbacks, input_dataset, "output_types",
output_types, "output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_ignore_errors_dataset_eager_fallback(
input_dataset, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_ignore_errors_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_ignore_errors_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalIgnoreErrorsDataset", input_dataset=input_dataset,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalIgnoreErrorsDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalIgnoreErrorsDataset(input_dataset, output_types, output_shapes, name=None):
return experimental_ignore_errors_dataset(input_dataset=input_dataset, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalIgnoreErrorsDataset.__doc__ = experimental_ignore_errors_dataset.__doc__
ExperimentalIgnoreErrorsDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalIgnoreErrorsDataset))
tf_export("raw_ops.ExperimentalIgnoreErrorsDataset")(ExperimentalIgnoreErrorsDataset)
def experimental_ignore_errors_dataset_eager_fallback(input_dataset, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_ignore_errors_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_ignore_errors_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_ignore_errors_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
_inputs_flat = [input_dataset]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalIgnoreErrorsDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalIgnoreErrorsDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_indexed_dataset_get(materialized, index, output_types, output_shapes, name=None):
r"""TODO: add doc.
Args:
materialized: A `Tensor` of type `resource`.
index: A `Tensor` of type `uint64`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A list of `Tensor` objects of type `output_types`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalIndexedDatasetGet", name, _ctx._post_execution_callbacks,
materialized, index, "output_types", output_types, "output_shapes",
output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_indexed_dataset_get_eager_fallback(
materialized, index, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_indexed_dataset_get' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_indexed_dataset_get' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalIndexedDatasetGet", materialized=materialized,
index=index,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
if not _result:
return _op
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalIndexedDatasetGet", _inputs_flat, _attrs, _result, name)
return _result
def ExperimentalIndexedDatasetGet(materialized, index, output_types, output_shapes, name=None):
return experimental_indexed_dataset_get(materialized=materialized, index=index, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalIndexedDatasetGet.__doc__ = experimental_indexed_dataset_get.__doc__
ExperimentalIndexedDatasetGet = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalIndexedDatasetGet))
tf_export("raw_ops.ExperimentalIndexedDatasetGet")(ExperimentalIndexedDatasetGet)
def experimental_indexed_dataset_get_eager_fallback(materialized, index, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_indexed_dataset_get
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_indexed_dataset_get' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_indexed_dataset_get' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
materialized = _ops.convert_to_tensor(materialized, _dtypes.resource)
index = _ops.convert_to_tensor(index, _dtypes.uint64)
_inputs_flat = [materialized, index]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalIndexedDatasetGet",
len(output_types), inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ExperimentalIndexedDatasetGet", _inputs_flat, _attrs, _result, name)
return _result
def experimental_indexed_dataset_materialize(dataset, materialized, name=None):
r"""TODO: add doc.
Args:
dataset: A `Tensor` of type `variant`.
materialized: A `Tensor` of type `resource`.
name: A name for the operation (optional).
Returns:
The created Operation.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalIndexedDatasetMaterialize", name,
_ctx._post_execution_callbacks, dataset, materialized)
return _result
except _core._FallbackException:
try:
return experimental_indexed_dataset_materialize_eager_fallback(
dataset, materialized, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalIndexedDatasetMaterialize", dataset=dataset,
materialized=materialized,
name=name)
return _op
_result = None
return _result
def ExperimentalIndexedDatasetMaterialize(dataset, materialized, name=None):
return experimental_indexed_dataset_materialize(dataset=dataset, materialized=materialized, name=name)
ExperimentalIndexedDatasetMaterialize.__doc__ = experimental_indexed_dataset_materialize.__doc__
ExperimentalIndexedDatasetMaterialize = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalIndexedDatasetMaterialize))
tf_export("raw_ops.ExperimentalIndexedDatasetMaterialize")(ExperimentalIndexedDatasetMaterialize)
def experimental_indexed_dataset_materialize_eager_fallback(dataset, materialized, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_indexed_dataset_materialize
"""
_ctx = ctx if ctx else _context.context()
dataset = _ops.convert_to_tensor(dataset, _dtypes.variant)
materialized = _ops.convert_to_tensor(materialized, _dtypes.resource)
_inputs_flat = [dataset, materialized]
_attrs = None
_result = _execute.execute(b"ExperimentalIndexedDatasetMaterialize", 0,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_result = None
return _result
def experimental_iterator_get_device(resource, name=None):
r"""Returns the name of the device on which `resource` has been placed.
Args:
resource: A `Tensor` of type `resource`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `string`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalIteratorGetDevice", name, _ctx._post_execution_callbacks,
resource)
return _result
except _core._FallbackException:
try:
return experimental_iterator_get_device_eager_fallback(
resource, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalIteratorGetDevice", resource=resource, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = None
_execute.record_gradient(
"ExperimentalIteratorGetDevice", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalIteratorGetDevice(resource, name=None):
return experimental_iterator_get_device(resource=resource, name=name)
ExperimentalIteratorGetDevice.__doc__ = experimental_iterator_get_device.__doc__
ExperimentalIteratorGetDevice = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalIteratorGetDevice))
tf_export("raw_ops.ExperimentalIteratorGetDevice")(ExperimentalIteratorGetDevice)
def experimental_iterator_get_device_eager_fallback(resource, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_iterator_get_device
"""
_ctx = ctx if ctx else _context.context()
resource = _ops.convert_to_tensor(resource, _dtypes.resource)
_inputs_flat = [resource]
_attrs = None
_result = _execute.execute(b"ExperimentalIteratorGetDevice", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalIteratorGetDevice", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_lmdb_dataset(filenames, output_types, output_shapes, name=None):
r"""TODO: add doc.
Args:
filenames: A `Tensor` of type `string`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalLMDBDataset", name, _ctx._post_execution_callbacks,
filenames, "output_types", output_types, "output_shapes",
output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_lmdb_dataset_eager_fallback(
filenames, output_types=output_types, output_shapes=output_shapes,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_lmdb_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_lmdb_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalLMDBDataset", filenames=filenames,
output_types=output_types,
output_shapes=output_shapes, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalLMDBDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalLMDBDataset(filenames, output_types, output_shapes, name=None):
return experimental_lmdb_dataset(filenames=filenames, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalLMDBDataset.__doc__ = experimental_lmdb_dataset.__doc__
ExperimentalLMDBDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalLMDBDataset))
tf_export("raw_ops.ExperimentalLMDBDataset")(ExperimentalLMDBDataset)
def experimental_lmdb_dataset_eager_fallback(filenames, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_lmdb_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_lmdb_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_lmdb_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
filenames = _ops.convert_to_tensor(filenames, _dtypes.string)
_inputs_flat = [filenames]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalLMDBDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalLMDBDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_latency_stats_dataset(input_dataset, tag, output_types, output_shapes, name=None):
r"""Records the latency of producing `input_dataset` elements in a StatsAggregator.
Args:
input_dataset: A `Tensor` of type `variant`.
tag: A `Tensor` of type `string`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalLatencyStatsDataset", name,
_ctx._post_execution_callbacks, input_dataset, tag, "output_types",
output_types, "output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_latency_stats_dataset_eager_fallback(
input_dataset, tag, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_latency_stats_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_latency_stats_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalLatencyStatsDataset", input_dataset=input_dataset,
tag=tag, output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalLatencyStatsDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalLatencyStatsDataset(input_dataset, tag, output_types, output_shapes, name=None):
return experimental_latency_stats_dataset(input_dataset=input_dataset, tag=tag, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalLatencyStatsDataset.__doc__ = experimental_latency_stats_dataset.__doc__
ExperimentalLatencyStatsDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalLatencyStatsDataset))
tf_export("raw_ops.ExperimentalLatencyStatsDataset")(ExperimentalLatencyStatsDataset)
def experimental_latency_stats_dataset_eager_fallback(input_dataset, tag, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_latency_stats_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_latency_stats_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_latency_stats_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
tag = _ops.convert_to_tensor(tag, _dtypes.string)
_inputs_flat = [input_dataset, tag]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalLatencyStatsDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalLatencyStatsDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_map_and_batch_dataset(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f, output_types, output_shapes, preserve_cardinality=False, name=None):
r"""Creates a dataset that fuses mapping with batching.
Creates a dataset that applies `f` to the outputs of `input_dataset` and then
batches `batch_size` of them.
Unlike a "MapDataset", which applies `f` sequentially, this dataset invokes up
to `batch_size * num_parallel_batches` copies of `f` in parallel.
Args:
input_dataset: A `Tensor` of type `variant`.
A variant tensor representing the input dataset.
other_arguments: A list of `Tensor` objects.
A list of tensors, typically values that were captured when building a closure
for `f`.
batch_size: A `Tensor` of type `int64`.
A scalar representing the number of elements to accumulate in a
batch. It determines the number of concurrent invocations of `f` that process
elements from `input_dataset` in parallel.
num_parallel_calls: A `Tensor` of type `int64`.
A scalar representing the maximum number of parallel invocations of the `map_fn`
function. Applying the `map_fn` on consecutive input elements in parallel has
the potential to improve input pipeline throughput.
drop_remainder: A `Tensor` of type `bool`.
A scalar representing whether the last batch should be dropped in case its size
is smaller than desired.
f: A function decorated with @Defun.
A function to apply to the outputs of `input_dataset`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
preserve_cardinality: An optional `bool`. Defaults to `False`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalMapAndBatchDataset", name,
_ctx._post_execution_callbacks, input_dataset, other_arguments,
batch_size, num_parallel_calls, drop_remainder, "f", f,
"output_types", output_types, "output_shapes", output_shapes,
"preserve_cardinality", preserve_cardinality)
return _result
except _core._FallbackException:
try:
return experimental_map_and_batch_dataset_eager_fallback(
input_dataset, other_arguments, batch_size, num_parallel_calls,
drop_remainder, f=f, output_types=output_types,
output_shapes=output_shapes,
preserve_cardinality=preserve_cardinality, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_map_and_batch_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_map_and_batch_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
if preserve_cardinality is None:
preserve_cardinality = False
preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality")
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalMapAndBatchDataset", input_dataset=input_dataset,
other_arguments=other_arguments,
batch_size=batch_size,
num_parallel_calls=num_parallel_calls,
drop_remainder=drop_remainder, f=f,
output_types=output_types,
output_shapes=output_shapes,
preserve_cardinality=preserve_cardinality,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("f", _op.get_attr("f"), "Targuments", _op.get_attr("Targuments"),
"output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"), "preserve_cardinality",
_op.get_attr("preserve_cardinality"))
_execute.record_gradient(
"ExperimentalMapAndBatchDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalMapAndBatchDataset(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f, output_types, output_shapes, preserve_cardinality=False, name=None):
return experimental_map_and_batch_dataset(input_dataset=input_dataset, other_arguments=other_arguments, batch_size=batch_size, num_parallel_calls=num_parallel_calls, drop_remainder=drop_remainder, f=f, output_types=output_types, output_shapes=output_shapes, preserve_cardinality=preserve_cardinality, name=name)
ExperimentalMapAndBatchDataset.__doc__ = experimental_map_and_batch_dataset.__doc__
ExperimentalMapAndBatchDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalMapAndBatchDataset))
tf_export("raw_ops.ExperimentalMapAndBatchDataset")(ExperimentalMapAndBatchDataset)
def experimental_map_and_batch_dataset_eager_fallback(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f, output_types, output_shapes, preserve_cardinality=False, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_map_and_batch_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_map_and_batch_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_map_and_batch_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
if preserve_cardinality is None:
preserve_cardinality = False
preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality")
_attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
batch_size = _ops.convert_to_tensor(batch_size, _dtypes.int64)
num_parallel_calls = _ops.convert_to_tensor(num_parallel_calls, _dtypes.int64)
drop_remainder = _ops.convert_to_tensor(drop_remainder, _dtypes.bool)
_inputs_flat = [input_dataset] + list(other_arguments) + [batch_size, num_parallel_calls, drop_remainder]
_attrs = ("f", f, "Targuments", _attr_Targuments, "output_types",
output_types, "output_shapes", output_shapes, "preserve_cardinality",
preserve_cardinality)
_result = _execute.execute(b"ExperimentalMapAndBatchDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalMapAndBatchDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_map_dataset(input_dataset, other_arguments, f, output_types, output_shapes, use_inter_op_parallelism=True, preserve_cardinality=False, name=None):
r"""Creates a dataset that applies `f` to the outputs of `input_dataset`.
Args:
input_dataset: A `Tensor` of type `variant`.
other_arguments: A list of `Tensor` objects.
f: A function decorated with @Defun.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
use_inter_op_parallelism: An optional `bool`. Defaults to `True`.
preserve_cardinality: An optional `bool`. Defaults to `False`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalMapDataset", name, _ctx._post_execution_callbacks,
input_dataset, other_arguments, "f", f, "output_types", output_types,
"output_shapes", output_shapes, "use_inter_op_parallelism",
use_inter_op_parallelism, "preserve_cardinality",
preserve_cardinality)
return _result
except _core._FallbackException:
try:
return experimental_map_dataset_eager_fallback(
input_dataset, other_arguments, f=f, output_types=output_types,
output_shapes=output_shapes,
use_inter_op_parallelism=use_inter_op_parallelism,
preserve_cardinality=preserve_cardinality, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_map_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_map_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
if use_inter_op_parallelism is None:
use_inter_op_parallelism = True
use_inter_op_parallelism = _execute.make_bool(use_inter_op_parallelism, "use_inter_op_parallelism")
if preserve_cardinality is None:
preserve_cardinality = False
preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality")
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalMapDataset", input_dataset=input_dataset,
other_arguments=other_arguments, f=f,
output_types=output_types,
output_shapes=output_shapes,
use_inter_op_parallelism=use_inter_op_parallelism,
preserve_cardinality=preserve_cardinality,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("f", _op.get_attr("f"), "Targuments", _op.get_attr("Targuments"),
"output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"), "use_inter_op_parallelism",
_op.get_attr("use_inter_op_parallelism"), "preserve_cardinality",
_op.get_attr("preserve_cardinality"))
_execute.record_gradient(
"ExperimentalMapDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalMapDataset(input_dataset, other_arguments, f, output_types, output_shapes, use_inter_op_parallelism=True, preserve_cardinality=False, name=None):
return experimental_map_dataset(input_dataset=input_dataset, other_arguments=other_arguments, f=f, output_types=output_types, output_shapes=output_shapes, use_inter_op_parallelism=use_inter_op_parallelism, preserve_cardinality=preserve_cardinality, name=name)
ExperimentalMapDataset.__doc__ = experimental_map_dataset.__doc__
ExperimentalMapDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalMapDataset))
tf_export("raw_ops.ExperimentalMapDataset")(ExperimentalMapDataset)
def experimental_map_dataset_eager_fallback(input_dataset, other_arguments, f, output_types, output_shapes, use_inter_op_parallelism=True, preserve_cardinality=False, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_map_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_map_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_map_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
if use_inter_op_parallelism is None:
use_inter_op_parallelism = True
use_inter_op_parallelism = _execute.make_bool(use_inter_op_parallelism, "use_inter_op_parallelism")
if preserve_cardinality is None:
preserve_cardinality = False
preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality")
_attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
_inputs_flat = [input_dataset] + list(other_arguments)
_attrs = ("f", f, "Targuments", _attr_Targuments, "output_types",
output_types, "output_shapes", output_shapes, "use_inter_op_parallelism",
use_inter_op_parallelism, "preserve_cardinality", preserve_cardinality)
_result = _execute.execute(b"ExperimentalMapDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalMapDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_matching_files_dataset(patterns, name=None):
r"""TODO: add doc.
Args:
patterns: A `Tensor` of type `string`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalMatchingFilesDataset", name,
_ctx._post_execution_callbacks, patterns)
return _result
except _core._FallbackException:
try:
return experimental_matching_files_dataset_eager_fallback(
patterns, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalMatchingFilesDataset", patterns=patterns, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = None
_execute.record_gradient(
"ExperimentalMatchingFilesDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalMatchingFilesDataset(patterns, name=None):
return experimental_matching_files_dataset(patterns=patterns, name=name)
ExperimentalMatchingFilesDataset.__doc__ = experimental_matching_files_dataset.__doc__
ExperimentalMatchingFilesDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalMatchingFilesDataset))
tf_export("raw_ops.ExperimentalMatchingFilesDataset")(ExperimentalMatchingFilesDataset)
def experimental_matching_files_dataset_eager_fallback(patterns, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_matching_files_dataset
"""
_ctx = ctx if ctx else _context.context()
patterns = _ops.convert_to_tensor(patterns, _dtypes.string)
_inputs_flat = [patterns]
_attrs = None
_result = _execute.execute(b"ExperimentalMatchingFilesDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalMatchingFilesDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_materialized_index_dataset_handle(container, shared_name, output_types, output_shapes, name=None):
r"""TODO: add doc.
Args:
container: A `string`.
shared_name: A `string`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `resource`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalMaterializedIndexDatasetHandle", name,
_ctx._post_execution_callbacks, "container", container, "shared_name",
shared_name, "output_types", output_types, "output_shapes",
output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_materialized_index_dataset_handle_eager_fallback(
container=container, shared_name=shared_name,
output_types=output_types, output_shapes=output_shapes, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
container = _execute.make_str(container, "container")
shared_name = _execute.make_str(shared_name, "shared_name")
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_materialized_index_dataset_handle' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_materialized_index_dataset_handle' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalMaterializedIndexDatasetHandle", container=container,
shared_name=shared_name,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("container", _op.get_attr("container"), "shared_name",
_op.get_attr("shared_name"), "output_types",
_op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalMaterializedIndexDatasetHandle", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalMaterializedIndexDatasetHandle(container, shared_name, output_types, output_shapes, name=None):
return experimental_materialized_index_dataset_handle(container=container, shared_name=shared_name, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalMaterializedIndexDatasetHandle.__doc__ = experimental_materialized_index_dataset_handle.__doc__
ExperimentalMaterializedIndexDatasetHandle = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalMaterializedIndexDatasetHandle))
tf_export("raw_ops.ExperimentalMaterializedIndexDatasetHandle")(ExperimentalMaterializedIndexDatasetHandle)
def experimental_materialized_index_dataset_handle_eager_fallback(container, shared_name, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_materialized_index_dataset_handle
"""
_ctx = ctx if ctx else _context.context()
container = _execute.make_str(container, "container")
shared_name = _execute.make_str(shared_name, "shared_name")
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_materialized_index_dataset_handle' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_materialized_index_dataset_handle' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_inputs_flat = []
_attrs = ("container", container, "shared_name", shared_name,
"output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalMaterializedIndexDatasetHandle", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalMaterializedIndexDatasetHandle", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_max_intra_op_parallelism_dataset(input_dataset, max_intra_op_parallelism, output_types, output_shapes, name=None):
r"""Creates a dataset that overrides the maximum intra-op parallelism.
Args:
input_dataset: A `Tensor` of type `variant`.
max_intra_op_parallelism: A `Tensor` of type `int64`.
Identifies the maximum intra-op parallelism to use.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalMaxIntraOpParallelismDataset", name,
_ctx._post_execution_callbacks, input_dataset,
max_intra_op_parallelism, "output_types", output_types,
"output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_max_intra_op_parallelism_dataset_eager_fallback(
input_dataset, max_intra_op_parallelism,
output_types=output_types, output_shapes=output_shapes, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_max_intra_op_parallelism_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_max_intra_op_parallelism_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalMaxIntraOpParallelismDataset", input_dataset=input_dataset,
max_intra_op_parallelism=max_intra_op_parallelism,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalMaxIntraOpParallelismDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalMaxIntraOpParallelismDataset(input_dataset, max_intra_op_parallelism, output_types, output_shapes, name=None):
return experimental_max_intra_op_parallelism_dataset(input_dataset=input_dataset, max_intra_op_parallelism=max_intra_op_parallelism, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalMaxIntraOpParallelismDataset.__doc__ = experimental_max_intra_op_parallelism_dataset.__doc__
ExperimentalMaxIntraOpParallelismDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalMaxIntraOpParallelismDataset))
tf_export("raw_ops.ExperimentalMaxIntraOpParallelismDataset")(ExperimentalMaxIntraOpParallelismDataset)
def experimental_max_intra_op_parallelism_dataset_eager_fallback(input_dataset, max_intra_op_parallelism, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_max_intra_op_parallelism_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_max_intra_op_parallelism_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_max_intra_op_parallelism_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
max_intra_op_parallelism = _ops.convert_to_tensor(max_intra_op_parallelism, _dtypes.int64)
_inputs_flat = [input_dataset, max_intra_op_parallelism]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalMaxIntraOpParallelismDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalMaxIntraOpParallelismDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_non_serializable_dataset(input_dataset, output_types, output_shapes, name=None):
r"""TODO: add doc.
Args:
input_dataset: A `Tensor` of type `variant`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalNonSerializableDataset", name,
_ctx._post_execution_callbacks, input_dataset, "output_types",
output_types, "output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_non_serializable_dataset_eager_fallback(
input_dataset, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_non_serializable_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_non_serializable_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalNonSerializableDataset", input_dataset=input_dataset,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalNonSerializableDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalNonSerializableDataset(input_dataset, output_types, output_shapes, name=None):
return experimental_non_serializable_dataset(input_dataset=input_dataset, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalNonSerializableDataset.__doc__ = experimental_non_serializable_dataset.__doc__
ExperimentalNonSerializableDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalNonSerializableDataset))
tf_export("raw_ops.ExperimentalNonSerializableDataset")(ExperimentalNonSerializableDataset)
def experimental_non_serializable_dataset_eager_fallback(input_dataset, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_non_serializable_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_non_serializable_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_non_serializable_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
_inputs_flat = [input_dataset]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalNonSerializableDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalNonSerializableDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_numa_map_and_batch_dataset(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f, output_types, output_shapes, preserve_cardinality=False, name=None):
r"""Creates a dataset that fuses mapping with batching.
Creates a dataset that applies `f` to the outputs of `input_dataset` and then
batches `batch_size` of them.
Unlike a "MapDataset", which applies `f` sequentially, this dataset invokes up
to `batch_size * num_parallel_batches` copies of `f` in parallel.
Unlike "MapAndBatchDatasetV2", this dataset uses a NUMA-aware thread scheduling
policy. Because it uses the single-threaded executor, it only supports the
function-based control flow ops.
Args:
input_dataset: A `Tensor` of type `variant`.
A variant tensor representing the input dataset.
other_arguments: A list of `Tensor` objects.
A list of tensors, typically values that were captured when building a closure
for `f`.
batch_size: A `Tensor` of type `int64`.
A scalar representing the number of elements to accumulate in a
batch. It determines the number of concurrent invocations of `f` that process
elements from `input_dataset` in parallel.
num_parallel_calls: A `Tensor` of type `int64`.
A scalar representing the maximum number of parallel invocations of the `map_fn`
function. Applying the `map_fn` on consecutive input elements in parallel has
the potential to improve input pipeline throughput.
drop_remainder: A `Tensor` of type `bool`.
A scalar representing whether the last batch should be dropped in case its size
is smaller than desired.
f: A function decorated with @Defun.
A function to apply to the outputs of `input_dataset`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
preserve_cardinality: An optional `bool`. Defaults to `False`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalNumaMapAndBatchDataset", name,
_ctx._post_execution_callbacks, input_dataset, other_arguments,
batch_size, num_parallel_calls, drop_remainder, "f", f,
"output_types", output_types, "output_shapes", output_shapes,
"preserve_cardinality", preserve_cardinality)
return _result
except _core._FallbackException:
try:
return experimental_numa_map_and_batch_dataset_eager_fallback(
input_dataset, other_arguments, batch_size, num_parallel_calls,
drop_remainder, f=f, output_types=output_types,
output_shapes=output_shapes,
preserve_cardinality=preserve_cardinality, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_numa_map_and_batch_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_numa_map_and_batch_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
if preserve_cardinality is None:
preserve_cardinality = False
preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality")
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalNumaMapAndBatchDataset", input_dataset=input_dataset,
other_arguments=other_arguments,
batch_size=batch_size,
num_parallel_calls=num_parallel_calls,
drop_remainder=drop_remainder,
f=f, output_types=output_types,
output_shapes=output_shapes,
preserve_cardinality=preserve_cardinality,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("f", _op.get_attr("f"), "Targuments", _op.get_attr("Targuments"),
"output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"), "preserve_cardinality",
_op.get_attr("preserve_cardinality"))
_execute.record_gradient(
"ExperimentalNumaMapAndBatchDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalNumaMapAndBatchDataset(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f, output_types, output_shapes, preserve_cardinality=False, name=None):
return experimental_numa_map_and_batch_dataset(input_dataset=input_dataset, other_arguments=other_arguments, batch_size=batch_size, num_parallel_calls=num_parallel_calls, drop_remainder=drop_remainder, f=f, output_types=output_types, output_shapes=output_shapes, preserve_cardinality=preserve_cardinality, name=name)
ExperimentalNumaMapAndBatchDataset.__doc__ = experimental_numa_map_and_batch_dataset.__doc__
ExperimentalNumaMapAndBatchDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalNumaMapAndBatchDataset))
tf_export("raw_ops.ExperimentalNumaMapAndBatchDataset")(ExperimentalNumaMapAndBatchDataset)
def experimental_numa_map_and_batch_dataset_eager_fallback(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f, output_types, output_shapes, preserve_cardinality=False, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_numa_map_and_batch_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_numa_map_and_batch_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_numa_map_and_batch_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
if preserve_cardinality is None:
preserve_cardinality = False
preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality")
_attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
batch_size = _ops.convert_to_tensor(batch_size, _dtypes.int64)
num_parallel_calls = _ops.convert_to_tensor(num_parallel_calls, _dtypes.int64)
drop_remainder = _ops.convert_to_tensor(drop_remainder, _dtypes.bool)
_inputs_flat = [input_dataset] + list(other_arguments) + [batch_size, num_parallel_calls, drop_remainder]
_attrs = ("f", f, "Targuments", _attr_Targuments, "output_types",
output_types, "output_shapes", output_shapes, "preserve_cardinality",
preserve_cardinality)
_result = _execute.execute(b"ExperimentalNumaMapAndBatchDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalNumaMapAndBatchDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_parallel_interleave_dataset(input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements, f, output_types, output_shapes, name=None):
r"""Creates a dataset that applies `f` to the outputs of `input_dataset`.
The resulting dataset is similar to the `InterleaveDataset`, with the exception
that if retrieving the next value from a dataset would cause the requester to
block, it will skip that input dataset. This dataset is especially useful
when loading data from a variable-latency datastores (e.g. HDFS, GCS), as it
allows the training step to proceed so long as some data is available.
!! WARNING !! This dataset is not deterministic!
Args:
input_dataset: A `Tensor` of type `variant`.
other_arguments: A list of `Tensor` objects.
cycle_length: A `Tensor` of type `int64`.
block_length: A `Tensor` of type `int64`.
sloppy: A `Tensor` of type `bool`.
buffer_output_elements: A `Tensor` of type `int64`.
prefetch_input_elements: A `Tensor` of type `int64`.
f: A function decorated with @Defun.
A function mapping elements of `input_dataset`, concatenated with
`other_arguments`, to a Dataset variant that contains elements matching
`output_types` and `output_shapes`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalParallelInterleaveDataset", name,
_ctx._post_execution_callbacks, input_dataset, other_arguments,
cycle_length, block_length, sloppy, buffer_output_elements,
prefetch_input_elements, "f", f, "output_types", output_types,
"output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_parallel_interleave_dataset_eager_fallback(
input_dataset, other_arguments, cycle_length, block_length,
sloppy, buffer_output_elements, prefetch_input_elements, f=f,
output_types=output_types, output_shapes=output_shapes, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_parallel_interleave_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_parallel_interleave_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalParallelInterleaveDataset", input_dataset=input_dataset,
other_arguments=other_arguments,
cycle_length=cycle_length,
block_length=block_length,
sloppy=sloppy,
buffer_output_elements=buffer_output_elements,
prefetch_input_elements=prefetch_input_elements,
f=f,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("f", _op.get_attr("f"), "Targuments", _op.get_attr("Targuments"),
"output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalParallelInterleaveDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalParallelInterleaveDataset(input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements, f, output_types, output_shapes, name=None):
return experimental_parallel_interleave_dataset(input_dataset=input_dataset, other_arguments=other_arguments, cycle_length=cycle_length, block_length=block_length, sloppy=sloppy, buffer_output_elements=buffer_output_elements, prefetch_input_elements=prefetch_input_elements, f=f, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalParallelInterleaveDataset.__doc__ = experimental_parallel_interleave_dataset.__doc__
ExperimentalParallelInterleaveDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalParallelInterleaveDataset))
tf_export("raw_ops.ExperimentalParallelInterleaveDataset")(ExperimentalParallelInterleaveDataset)
def experimental_parallel_interleave_dataset_eager_fallback(input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements, f, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_parallel_interleave_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_parallel_interleave_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_parallel_interleave_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
cycle_length = _ops.convert_to_tensor(cycle_length, _dtypes.int64)
block_length = _ops.convert_to_tensor(block_length, _dtypes.int64)
sloppy = _ops.convert_to_tensor(sloppy, _dtypes.bool)
buffer_output_elements = _ops.convert_to_tensor(buffer_output_elements, _dtypes.int64)
prefetch_input_elements = _ops.convert_to_tensor(prefetch_input_elements, _dtypes.int64)
_inputs_flat = [input_dataset] + list(other_arguments) + [cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements]
_attrs = ("f", f, "Targuments", _attr_Targuments, "output_types",
output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalParallelInterleaveDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalParallelInterleaveDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_parse_example_dataset(input_dataset, num_parallel_calls, dense_defaults, sparse_keys, dense_keys, sparse_types, dense_shapes, output_types, output_shapes, sloppy=False, name=None):
r"""Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features.
Args:
input_dataset: A `Tensor` of type `variant`.
num_parallel_calls: A `Tensor` of type `int64`.
dense_defaults: A list of `Tensor` objects with types from: `float32`, `int64`, `string`.
A dict mapping string keys to `Tensor`s.
The keys of the dict must match the dense_keys of the feature.
sparse_keys: A list of `strings`.
A list of string keys in the examples features.
The results for these keys will be returned as `SparseTensor` objects.
dense_keys: A list of `strings`.
A list of Ndense string Tensors (scalars).
The keys expected in the Examples features associated with dense values.
sparse_types: A list of `tf.DTypes` from: `tf.float32, tf.int64, tf.string`.
A list of `DTypes` of the same length as `sparse_keys`.
Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`),
and `tf.string` (`BytesList`) are supported.
dense_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`).
List of tuples with the same length as `dense_keys`.
The shape of the data for each dense feature referenced by `dense_keys`.
Required for any input tensors identified by `dense_keys`. Must be
either fully defined, or may contain an unknown first dimension.
An unknown first dimension means the feature is treated as having
a variable number of blocks, and the output shape along this dimension
is considered unknown at graph build time. Padding is applied for
minibatch elements smaller than the maximum number of blocks for the
given feature along this dimension.
output_types: A list of `tf.DTypes` that has length `>= 1`.
The type list for the return values.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
The list of shapes being produced.
sloppy: An optional `bool`. Defaults to `False`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalParseExampleDataset", name,
_ctx._post_execution_callbacks, input_dataset, num_parallel_calls,
dense_defaults, "sparse_keys", sparse_keys, "dense_keys", dense_keys,
"sparse_types", sparse_types, "dense_shapes", dense_shapes,
"output_types", output_types, "output_shapes", output_shapes,
"sloppy", sloppy)
return _result
except _core._FallbackException:
try:
return experimental_parse_example_dataset_eager_fallback(
input_dataset, num_parallel_calls, dense_defaults,
sparse_keys=sparse_keys, dense_keys=dense_keys,
sparse_types=sparse_types, dense_shapes=dense_shapes,
output_types=output_types, output_shapes=output_shapes,
sloppy=sloppy, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(sparse_keys, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_keys' argument to "
"'experimental_parse_example_dataset' Op, not %r." % sparse_keys)
sparse_keys = [_execute.make_str(_s, "sparse_keys") for _s in sparse_keys]
if not isinstance(dense_keys, (list, tuple)):
raise TypeError(
"Expected list for 'dense_keys' argument to "
"'experimental_parse_example_dataset' Op, not %r." % dense_keys)
dense_keys = [_execute.make_str(_s, "dense_keys") for _s in dense_keys]
if not isinstance(sparse_types, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_types' argument to "
"'experimental_parse_example_dataset' Op, not %r." % sparse_types)
sparse_types = [_execute.make_type(_t, "sparse_types") for _t in sparse_types]
if not isinstance(dense_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'dense_shapes' argument to "
"'experimental_parse_example_dataset' Op, not %r." % dense_shapes)
dense_shapes = [_execute.make_shape(_s, "dense_shapes") for _s in dense_shapes]
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_parse_example_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_parse_example_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
if sloppy is None:
sloppy = False
sloppy = _execute.make_bool(sloppy, "sloppy")
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalParseExampleDataset", input_dataset=input_dataset,
num_parallel_calls=num_parallel_calls,
dense_defaults=dense_defaults,
sparse_keys=sparse_keys,
dense_keys=dense_keys,
sparse_types=sparse_types,
dense_shapes=dense_shapes,
output_types=output_types,
output_shapes=output_shapes,
sloppy=sloppy, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("sparse_keys", _op.get_attr("sparse_keys"), "dense_keys",
_op.get_attr("dense_keys"), "sparse_types",
_op.get_attr("sparse_types"), "Tdense", _op.get_attr("Tdense"),
"dense_shapes", _op.get_attr("dense_shapes"), "output_types",
_op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"), "sloppy", _op.get_attr("sloppy"))
_execute.record_gradient(
"ExperimentalParseExampleDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalParseExampleDataset(input_dataset, num_parallel_calls, dense_defaults, sparse_keys, dense_keys, sparse_types, dense_shapes, output_types, output_shapes, sloppy=False, name=None):
return experimental_parse_example_dataset(input_dataset=input_dataset, num_parallel_calls=num_parallel_calls, dense_defaults=dense_defaults, sparse_keys=sparse_keys, dense_keys=dense_keys, sparse_types=sparse_types, dense_shapes=dense_shapes, output_types=output_types, output_shapes=output_shapes, sloppy=sloppy, name=name)
ExperimentalParseExampleDataset.__doc__ = experimental_parse_example_dataset.__doc__
ExperimentalParseExampleDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalParseExampleDataset))
tf_export("raw_ops.ExperimentalParseExampleDataset")(ExperimentalParseExampleDataset)
def experimental_parse_example_dataset_eager_fallback(input_dataset, num_parallel_calls, dense_defaults, sparse_keys, dense_keys, sparse_types, dense_shapes, output_types, output_shapes, sloppy=False, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_parse_example_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(sparse_keys, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_keys' argument to "
"'experimental_parse_example_dataset' Op, not %r." % sparse_keys)
sparse_keys = [_execute.make_str(_s, "sparse_keys") for _s in sparse_keys]
if not isinstance(dense_keys, (list, tuple)):
raise TypeError(
"Expected list for 'dense_keys' argument to "
"'experimental_parse_example_dataset' Op, not %r." % dense_keys)
dense_keys = [_execute.make_str(_s, "dense_keys") for _s in dense_keys]
if not isinstance(sparse_types, (list, tuple)):
raise TypeError(
"Expected list for 'sparse_types' argument to "
"'experimental_parse_example_dataset' Op, not %r." % sparse_types)
sparse_types = [_execute.make_type(_t, "sparse_types") for _t in sparse_types]
if not isinstance(dense_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'dense_shapes' argument to "
"'experimental_parse_example_dataset' Op, not %r." % dense_shapes)
dense_shapes = [_execute.make_shape(_s, "dense_shapes") for _s in dense_shapes]
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_parse_example_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_parse_example_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
if sloppy is None:
sloppy = False
sloppy = _execute.make_bool(sloppy, "sloppy")
_attr_Tdense, dense_defaults = _execute.convert_to_mixed_eager_tensors(dense_defaults, _ctx)
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
num_parallel_calls = _ops.convert_to_tensor(num_parallel_calls, _dtypes.int64)
_inputs_flat = [input_dataset, num_parallel_calls] + list(dense_defaults)
_attrs = ("sparse_keys", sparse_keys, "dense_keys", dense_keys,
"sparse_types", sparse_types, "Tdense", _attr_Tdense, "dense_shapes",
dense_shapes, "output_types", output_types, "output_shapes", output_shapes,
"sloppy", sloppy)
_result = _execute.execute(b"ExperimentalParseExampleDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalParseExampleDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_private_thread_pool_dataset(input_dataset, num_threads, output_types, output_shapes, name=None):
r"""Creates a dataset that uses a custom thread pool to compute `input_dataset`.
Args:
input_dataset: A `Tensor` of type `variant`.
num_threads: A `Tensor` of type `int64`.
Identifies the number of threads to use for the private threadpool.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalPrivateThreadPoolDataset", name,
_ctx._post_execution_callbacks, input_dataset, num_threads,
"output_types", output_types, "output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_private_thread_pool_dataset_eager_fallback(
input_dataset, num_threads, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_private_thread_pool_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_private_thread_pool_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalPrivateThreadPoolDataset", input_dataset=input_dataset,
num_threads=num_threads,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalPrivateThreadPoolDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalPrivateThreadPoolDataset(input_dataset, num_threads, output_types, output_shapes, name=None):
return experimental_private_thread_pool_dataset(input_dataset=input_dataset, num_threads=num_threads, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalPrivateThreadPoolDataset.__doc__ = experimental_private_thread_pool_dataset.__doc__
ExperimentalPrivateThreadPoolDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalPrivateThreadPoolDataset))
tf_export("raw_ops.ExperimentalPrivateThreadPoolDataset")(ExperimentalPrivateThreadPoolDataset)
def experimental_private_thread_pool_dataset_eager_fallback(input_dataset, num_threads, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_private_thread_pool_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_private_thread_pool_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_private_thread_pool_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
num_threads = _ops.convert_to_tensor(num_threads, _dtypes.int64)
_inputs_flat = [input_dataset, num_threads]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalPrivateThreadPoolDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalPrivateThreadPoolDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_random_dataset(seed, seed2, output_types, output_shapes, name=None):
r"""Creates a Dataset that returns pseudorandom numbers.
Args:
seed: A `Tensor` of type `int64`.
A scalar seed for the random number generator. If either seed or
seed2 is set to be non-zero, the random number generator is seeded
by the given seed. Otherwise, a random seed is used.
seed2: A `Tensor` of type `int64`.
A second scalar seed to avoid seed collision.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalRandomDataset", name, _ctx._post_execution_callbacks,
seed, seed2, "output_types", output_types, "output_shapes",
output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_random_dataset_eager_fallback(
seed, seed2, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_random_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_random_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalRandomDataset", seed=seed, seed2=seed2,
output_types=output_types,
output_shapes=output_shapes, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalRandomDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalRandomDataset(seed, seed2, output_types, output_shapes, name=None):
return experimental_random_dataset(seed=seed, seed2=seed2, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalRandomDataset.__doc__ = experimental_random_dataset.__doc__
ExperimentalRandomDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalRandomDataset))
tf_export("raw_ops.ExperimentalRandomDataset")(ExperimentalRandomDataset)
def experimental_random_dataset_eager_fallback(seed, seed2, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_random_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_random_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_random_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
seed = _ops.convert_to_tensor(seed, _dtypes.int64)
seed2 = _ops.convert_to_tensor(seed2, _dtypes.int64)
_inputs_flat = [seed, seed2]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalRandomDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalRandomDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_rebatch_dataset(input_dataset, num_workers, output_types, output_shapes, name=None):
r"""Creates a dataset that changes the batch size.
Creates a dataset that changes the batch size of the dataset to current batch
size // num_workers.
Args:
input_dataset: A `Tensor` of type `variant`.
A variant tensor representing the input dataset.
num_workers: A `Tensor` of type `int64`.
A scalar representing the number of workers to distribute this batch across. As
a result of this transformation the current batch size would end up being
divided by this parameter.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalRebatchDataset", name, _ctx._post_execution_callbacks,
input_dataset, num_workers, "output_types", output_types,
"output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_rebatch_dataset_eager_fallback(
input_dataset, num_workers, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_rebatch_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_rebatch_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalRebatchDataset", input_dataset=input_dataset,
num_workers=num_workers,
output_types=output_types,
output_shapes=output_shapes, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalRebatchDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalRebatchDataset(input_dataset, num_workers, output_types, output_shapes, name=None):
return experimental_rebatch_dataset(input_dataset=input_dataset, num_workers=num_workers, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalRebatchDataset.__doc__ = experimental_rebatch_dataset.__doc__
ExperimentalRebatchDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalRebatchDataset))
tf_export("raw_ops.ExperimentalRebatchDataset")(ExperimentalRebatchDataset)
def experimental_rebatch_dataset_eager_fallback(input_dataset, num_workers, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_rebatch_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_rebatch_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_rebatch_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
num_workers = _ops.convert_to_tensor(num_workers, _dtypes.int64)
_inputs_flat = [input_dataset, num_workers]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalRebatchDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalRebatchDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_scan_dataset(input_dataset, initial_state, other_arguments, f, output_types, output_shapes, preserve_cardinality=False, name=None):
r"""Creates a dataset successively reduces `f` over the elements of `input_dataset`.
Args:
input_dataset: A `Tensor` of type `variant`.
initial_state: A list of `Tensor` objects.
other_arguments: A list of `Tensor` objects.
f: A function decorated with @Defun.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
preserve_cardinality: An optional `bool`. Defaults to `False`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalScanDataset", name, _ctx._post_execution_callbacks,
input_dataset, initial_state, other_arguments, "f", f, "output_types",
output_types, "output_shapes", output_shapes, "preserve_cardinality",
preserve_cardinality)
return _result
except _core._FallbackException:
try:
return experimental_scan_dataset_eager_fallback(
input_dataset, initial_state, other_arguments, f=f,
output_types=output_types, output_shapes=output_shapes,
preserve_cardinality=preserve_cardinality, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_scan_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_scan_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
if preserve_cardinality is None:
preserve_cardinality = False
preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality")
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalScanDataset", input_dataset=input_dataset,
initial_state=initial_state,
other_arguments=other_arguments, f=f,
output_types=output_types,
output_shapes=output_shapes,
preserve_cardinality=preserve_cardinality,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("f", _op.get_attr("f"), "Tstate", _op.get_attr("Tstate"),
"Targuments", _op.get_attr("Targuments"), "output_types",
_op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"), "preserve_cardinality",
_op.get_attr("preserve_cardinality"))
_execute.record_gradient(
"ExperimentalScanDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalScanDataset(input_dataset, initial_state, other_arguments, f, output_types, output_shapes, preserve_cardinality=False, name=None):
return experimental_scan_dataset(input_dataset=input_dataset, initial_state=initial_state, other_arguments=other_arguments, f=f, output_types=output_types, output_shapes=output_shapes, preserve_cardinality=preserve_cardinality, name=name)
ExperimentalScanDataset.__doc__ = experimental_scan_dataset.__doc__
ExperimentalScanDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalScanDataset))
tf_export("raw_ops.ExperimentalScanDataset")(ExperimentalScanDataset)
def experimental_scan_dataset_eager_fallback(input_dataset, initial_state, other_arguments, f, output_types, output_shapes, preserve_cardinality=False, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_scan_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_scan_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_scan_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
if preserve_cardinality is None:
preserve_cardinality = False
preserve_cardinality = _execute.make_bool(preserve_cardinality, "preserve_cardinality")
_attr_Tstate, initial_state = _execute.convert_to_mixed_eager_tensors(initial_state, _ctx)
_attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
_inputs_flat = [input_dataset] + list(initial_state) + list(other_arguments)
_attrs = ("f", f, "Tstate", _attr_Tstate, "Targuments", _attr_Targuments,
"output_types", output_types, "output_shapes", output_shapes,
"preserve_cardinality", preserve_cardinality)
_result = _execute.execute(b"ExperimentalScanDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalScanDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_set_stats_aggregator_dataset(input_dataset, stats_aggregator, tag, counter_prefix, output_types, output_shapes, name=None):
r"""TODO: add doc.
Args:
input_dataset: A `Tensor` of type `variant`.
stats_aggregator: A `Tensor` of type `resource`.
tag: A `Tensor` of type `string`.
counter_prefix: A `Tensor` of type `string`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalSetStatsAggregatorDataset", name,
_ctx._post_execution_callbacks, input_dataset, stats_aggregator, tag,
counter_prefix, "output_types", output_types, "output_shapes",
output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_set_stats_aggregator_dataset_eager_fallback(
input_dataset, stats_aggregator, tag, counter_prefix,
output_types=output_types, output_shapes=output_shapes, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_set_stats_aggregator_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_set_stats_aggregator_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalSetStatsAggregatorDataset", input_dataset=input_dataset,
stats_aggregator=stats_aggregator,
tag=tag,
counter_prefix=counter_prefix,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalSetStatsAggregatorDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalSetStatsAggregatorDataset(input_dataset, stats_aggregator, tag, counter_prefix, output_types, output_shapes, name=None):
return experimental_set_stats_aggregator_dataset(input_dataset=input_dataset, stats_aggregator=stats_aggregator, tag=tag, counter_prefix=counter_prefix, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalSetStatsAggregatorDataset.__doc__ = experimental_set_stats_aggregator_dataset.__doc__
ExperimentalSetStatsAggregatorDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalSetStatsAggregatorDataset))
tf_export("raw_ops.ExperimentalSetStatsAggregatorDataset")(ExperimentalSetStatsAggregatorDataset)
def experimental_set_stats_aggregator_dataset_eager_fallback(input_dataset, stats_aggregator, tag, counter_prefix, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_set_stats_aggregator_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_set_stats_aggregator_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_set_stats_aggregator_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
stats_aggregator = _ops.convert_to_tensor(stats_aggregator, _dtypes.resource)
tag = _ops.convert_to_tensor(tag, _dtypes.string)
counter_prefix = _ops.convert_to_tensor(counter_prefix, _dtypes.string)
_inputs_flat = [input_dataset, stats_aggregator, tag, counter_prefix]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalSetStatsAggregatorDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalSetStatsAggregatorDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_sleep_dataset(input_dataset, sleep_microseconds, output_types, output_shapes, name=None):
r"""TODO: add doc.
Args:
input_dataset: A `Tensor` of type `variant`.
sleep_microseconds: A `Tensor` of type `int64`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalSleepDataset", name, _ctx._post_execution_callbacks,
input_dataset, sleep_microseconds, "output_types", output_types,
"output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_sleep_dataset_eager_fallback(
input_dataset, sleep_microseconds, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_sleep_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_sleep_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalSleepDataset", input_dataset=input_dataset,
sleep_microseconds=sleep_microseconds,
output_types=output_types,
output_shapes=output_shapes, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalSleepDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalSleepDataset(input_dataset, sleep_microseconds, output_types, output_shapes, name=None):
return experimental_sleep_dataset(input_dataset=input_dataset, sleep_microseconds=sleep_microseconds, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalSleepDataset.__doc__ = experimental_sleep_dataset.__doc__
ExperimentalSleepDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalSleepDataset))
tf_export("raw_ops.ExperimentalSleepDataset")(ExperimentalSleepDataset)
def experimental_sleep_dataset_eager_fallback(input_dataset, sleep_microseconds, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_sleep_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_sleep_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_sleep_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
sleep_microseconds = _ops.convert_to_tensor(sleep_microseconds, _dtypes.int64)
_inputs_flat = [input_dataset, sleep_microseconds]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalSleepDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalSleepDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_sliding_window_dataset(input_dataset, window_size, window_shift, window_stride, output_types, output_shapes, name=None):
r"""Creates a dataset that passes a sliding window over `input_dataset`.
Args:
input_dataset: A `Tensor` of type `variant`.
window_size: A `Tensor` of type `int64`.
A scalar representing the number of elements in the
sliding window.
window_shift: A `Tensor` of type `int64`.
A scalar representing the steps moving the sliding window
forward in one iteration. It must be positive.
window_stride: A `Tensor` of type `int64`.
A scalar representing the stride of the input elements of the sliding window.
It must be positive.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalSlidingWindowDataset", name,
_ctx._post_execution_callbacks, input_dataset, window_size,
window_shift, window_stride, "output_types", output_types,
"output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_sliding_window_dataset_eager_fallback(
input_dataset, window_size, window_shift, window_stride,
output_types=output_types, output_shapes=output_shapes, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_sliding_window_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_sliding_window_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalSlidingWindowDataset", input_dataset=input_dataset,
window_size=window_size,
window_shift=window_shift,
window_stride=window_stride,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalSlidingWindowDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalSlidingWindowDataset(input_dataset, window_size, window_shift, window_stride, output_types, output_shapes, name=None):
return experimental_sliding_window_dataset(input_dataset=input_dataset, window_size=window_size, window_shift=window_shift, window_stride=window_stride, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalSlidingWindowDataset.__doc__ = experimental_sliding_window_dataset.__doc__
ExperimentalSlidingWindowDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalSlidingWindowDataset))
tf_export("raw_ops.ExperimentalSlidingWindowDataset")(ExperimentalSlidingWindowDataset)
def experimental_sliding_window_dataset_eager_fallback(input_dataset, window_size, window_shift, window_stride, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_sliding_window_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_sliding_window_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_sliding_window_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
window_size = _ops.convert_to_tensor(window_size, _dtypes.int64)
window_shift = _ops.convert_to_tensor(window_shift, _dtypes.int64)
window_stride = _ops.convert_to_tensor(window_stride, _dtypes.int64)
_inputs_flat = [input_dataset, window_size, window_shift, window_stride]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalSlidingWindowDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalSlidingWindowDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_sql_dataset(driver_name, data_source_name, query, output_types, output_shapes, name=None):
r"""Creates a dataset that executes a SQL query and emits rows of the result set.
Args:
driver_name: A `Tensor` of type `string`.
The database type. Currently, the only supported type is 'sqlite'.
data_source_name: A `Tensor` of type `string`.
A connection string to connect to the database.
query: A `Tensor` of type `string`. A SQL query to execute.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalSqlDataset", name, _ctx._post_execution_callbacks,
driver_name, data_source_name, query, "output_types", output_types,
"output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_sql_dataset_eager_fallback(
driver_name, data_source_name, query, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_sql_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_sql_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalSqlDataset", driver_name=driver_name,
data_source_name=data_source_name,
query=query, output_types=output_types,
output_shapes=output_shapes, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalSqlDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalSqlDataset(driver_name, data_source_name, query, output_types, output_shapes, name=None):
return experimental_sql_dataset(driver_name=driver_name, data_source_name=data_source_name, query=query, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalSqlDataset.__doc__ = experimental_sql_dataset.__doc__
ExperimentalSqlDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalSqlDataset))
tf_export("raw_ops.ExperimentalSqlDataset")(ExperimentalSqlDataset)
def experimental_sql_dataset_eager_fallback(driver_name, data_source_name, query, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_sql_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_sql_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_sql_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
driver_name = _ops.convert_to_tensor(driver_name, _dtypes.string)
data_source_name = _ops.convert_to_tensor(data_source_name, _dtypes.string)
query = _ops.convert_to_tensor(query, _dtypes.string)
_inputs_flat = [driver_name, data_source_name, query]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalSqlDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalSqlDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_stats_aggregator_handle(container="", shared_name="", name=None):
r"""Creates a statistics manager resource.
Args:
container: An optional `string`. Defaults to `""`.
shared_name: An optional `string`. Defaults to `""`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `resource`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalStatsAggregatorHandle", name,
_ctx._post_execution_callbacks, "container", container, "shared_name",
shared_name)
return _result
except _core._FallbackException:
try:
return experimental_stats_aggregator_handle_eager_fallback(
container=container, shared_name=shared_name, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if container is None:
container = ""
container = _execute.make_str(container, "container")
if shared_name is None:
shared_name = ""
shared_name = _execute.make_str(shared_name, "shared_name")
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalStatsAggregatorHandle", container=container,
shared_name=shared_name,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("container", _op.get_attr("container"), "shared_name",
_op.get_attr("shared_name"))
_execute.record_gradient(
"ExperimentalStatsAggregatorHandle", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalStatsAggregatorHandle(container="", shared_name="", name=None):
return experimental_stats_aggregator_handle(container=container, shared_name=shared_name, name=name)
ExperimentalStatsAggregatorHandle.__doc__ = experimental_stats_aggregator_handle.__doc__
ExperimentalStatsAggregatorHandle = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalStatsAggregatorHandle))
tf_export("raw_ops.ExperimentalStatsAggregatorHandle")(ExperimentalStatsAggregatorHandle)
def experimental_stats_aggregator_handle_eager_fallback(container="", shared_name="", name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_stats_aggregator_handle
"""
_ctx = ctx if ctx else _context.context()
if container is None:
container = ""
container = _execute.make_str(container, "container")
if shared_name is None:
shared_name = ""
shared_name = _execute.make_str(shared_name, "shared_name")
_inputs_flat = []
_attrs = ("container", container, "shared_name", shared_name)
_result = _execute.execute(b"ExperimentalStatsAggregatorHandle", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalStatsAggregatorHandle", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_stats_aggregator_summary(iterator, name=None):
r"""Produces a summary of any statistics recorded by the given statistics manager.
Args:
iterator: A `Tensor` of type `resource`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `string`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalStatsAggregatorSummary", name,
_ctx._post_execution_callbacks, iterator)
return _result
except _core._FallbackException:
try:
return experimental_stats_aggregator_summary_eager_fallback(
iterator, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalStatsAggregatorSummary", iterator=iterator, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = None
_execute.record_gradient(
"ExperimentalStatsAggregatorSummary", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalStatsAggregatorSummary(iterator, name=None):
return experimental_stats_aggregator_summary(iterator=iterator, name=name)
ExperimentalStatsAggregatorSummary.__doc__ = experimental_stats_aggregator_summary.__doc__
ExperimentalStatsAggregatorSummary = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalStatsAggregatorSummary))
tf_export("raw_ops.ExperimentalStatsAggregatorSummary")(ExperimentalStatsAggregatorSummary)
def experimental_stats_aggregator_summary_eager_fallback(iterator, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_stats_aggregator_summary
"""
_ctx = ctx if ctx else _context.context()
iterator = _ops.convert_to_tensor(iterator, _dtypes.resource)
_inputs_flat = [iterator]
_attrs = None
_result = _execute.execute(b"ExperimentalStatsAggregatorSummary", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalStatsAggregatorSummary", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_take_while_dataset(input_dataset, other_arguments, predicate, output_types, output_shapes, name=None):
r"""Creates a dataset that stops iteration when predicate` is false.
The `predicate` function must return a scalar boolean and accept the
following arguments:
* One tensor for each component of an element of `input_dataset`.
* One tensor for each value in `other_arguments`.
Args:
input_dataset: A `Tensor` of type `variant`.
other_arguments: A list of `Tensor` objects.
A list of tensors, typically values that were captured when
building a closure for `predicate`.
predicate: A function decorated with @Defun.
A function returning a scalar boolean.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalTakeWhileDataset", name, _ctx._post_execution_callbacks,
input_dataset, other_arguments, "predicate", predicate,
"output_types", output_types, "output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_take_while_dataset_eager_fallback(
input_dataset, other_arguments, predicate=predicate,
output_types=output_types, output_shapes=output_shapes, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_take_while_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_take_while_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalTakeWhileDataset", input_dataset=input_dataset,
other_arguments=other_arguments,
predicate=predicate,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("predicate", _op.get_attr("predicate"), "Targuments",
_op.get_attr("Targuments"), "output_types",
_op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalTakeWhileDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalTakeWhileDataset(input_dataset, other_arguments, predicate, output_types, output_shapes, name=None):
return experimental_take_while_dataset(input_dataset=input_dataset, other_arguments=other_arguments, predicate=predicate, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalTakeWhileDataset.__doc__ = experimental_take_while_dataset.__doc__
ExperimentalTakeWhileDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalTakeWhileDataset))
tf_export("raw_ops.ExperimentalTakeWhileDataset")(ExperimentalTakeWhileDataset)
def experimental_take_while_dataset_eager_fallback(input_dataset, other_arguments, predicate, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_take_while_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_take_while_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_take_while_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_attr_Targuments, other_arguments = _execute.convert_to_mixed_eager_tensors(other_arguments, _ctx)
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
_inputs_flat = [input_dataset] + list(other_arguments)
_attrs = ("predicate", predicate, "Targuments", _attr_Targuments,
"output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalTakeWhileDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalTakeWhileDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_thread_pool_dataset(input_dataset, thread_pool, output_types, output_shapes, name=None):
r"""Creates a dataset that uses a custom thread pool to compute `input_dataset`.
Args:
input_dataset: A `Tensor` of type `variant`.
thread_pool: A `Tensor` of type `resource`.
A resource produced by the ThreadPoolHandle op.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalThreadPoolDataset", name, _ctx._post_execution_callbacks,
input_dataset, thread_pool, "output_types", output_types,
"output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_thread_pool_dataset_eager_fallback(
input_dataset, thread_pool, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_thread_pool_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_thread_pool_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalThreadPoolDataset", input_dataset=input_dataset,
thread_pool=thread_pool,
output_types=output_types,
output_shapes=output_shapes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalThreadPoolDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalThreadPoolDataset(input_dataset, thread_pool, output_types, output_shapes, name=None):
return experimental_thread_pool_dataset(input_dataset=input_dataset, thread_pool=thread_pool, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalThreadPoolDataset.__doc__ = experimental_thread_pool_dataset.__doc__
ExperimentalThreadPoolDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalThreadPoolDataset))
tf_export("raw_ops.ExperimentalThreadPoolDataset")(ExperimentalThreadPoolDataset)
def experimental_thread_pool_dataset_eager_fallback(input_dataset, thread_pool, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_thread_pool_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_thread_pool_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_thread_pool_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
thread_pool = _ops.convert_to_tensor(thread_pool, _dtypes.resource)
_inputs_flat = [input_dataset, thread_pool]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalThreadPoolDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalThreadPoolDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_thread_pool_handle(num_threads, display_name, max_intra_op_parallelism=1, container="", shared_name="", name=None):
r"""Creates a dataset that uses a custom thread pool to compute `input_dataset`.
Args:
num_threads: An `int`. The number of threads in the thread pool.
display_name: A `string`.
A human-readable name for the threads that may be visible in some
visualizations.
threadpool.
max_intra_op_parallelism: An optional `int`. Defaults to `1`.
The maximum degree of parallelism to use within operations that execute on this
threadpool.
container: An optional `string`. Defaults to `""`.
shared_name: An optional `string`. Defaults to `""`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `resource`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalThreadPoolHandle", name, _ctx._post_execution_callbacks,
"num_threads", num_threads, "max_intra_op_parallelism",
max_intra_op_parallelism, "display_name", display_name, "container",
container, "shared_name", shared_name)
return _result
except _core._FallbackException:
try:
return experimental_thread_pool_handle_eager_fallback(
num_threads=num_threads,
max_intra_op_parallelism=max_intra_op_parallelism,
display_name=display_name, container=container,
shared_name=shared_name, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
num_threads = _execute.make_int(num_threads, "num_threads")
display_name = _execute.make_str(display_name, "display_name")
if max_intra_op_parallelism is None:
max_intra_op_parallelism = 1
max_intra_op_parallelism = _execute.make_int(max_intra_op_parallelism, "max_intra_op_parallelism")
if container is None:
container = ""
container = _execute.make_str(container, "container")
if shared_name is None:
shared_name = ""
shared_name = _execute.make_str(shared_name, "shared_name")
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalThreadPoolHandle", num_threads=num_threads,
display_name=display_name,
max_intra_op_parallelism=max_intra_op_parallelism,
container=container,
shared_name=shared_name, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("num_threads", _op.get_attr("num_threads"),
"max_intra_op_parallelism",
_op.get_attr("max_intra_op_parallelism"), "display_name",
_op.get_attr("display_name"), "container",
_op.get_attr("container"), "shared_name",
_op.get_attr("shared_name"))
_execute.record_gradient(
"ExperimentalThreadPoolHandle", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalThreadPoolHandle(num_threads, display_name, max_intra_op_parallelism=1, container="", shared_name="", name=None):
return experimental_thread_pool_handle(num_threads=num_threads, display_name=display_name, max_intra_op_parallelism=max_intra_op_parallelism, container=container, shared_name=shared_name, name=name)
ExperimentalThreadPoolHandle.__doc__ = experimental_thread_pool_handle.__doc__
ExperimentalThreadPoolHandle = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalThreadPoolHandle))
tf_export("raw_ops.ExperimentalThreadPoolHandle")(ExperimentalThreadPoolHandle)
def experimental_thread_pool_handle_eager_fallback(num_threads, display_name, max_intra_op_parallelism=1, container="", shared_name="", name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_thread_pool_handle
"""
_ctx = ctx if ctx else _context.context()
num_threads = _execute.make_int(num_threads, "num_threads")
display_name = _execute.make_str(display_name, "display_name")
if max_intra_op_parallelism is None:
max_intra_op_parallelism = 1
max_intra_op_parallelism = _execute.make_int(max_intra_op_parallelism, "max_intra_op_parallelism")
if container is None:
container = ""
container = _execute.make_str(container, "container")
if shared_name is None:
shared_name = ""
shared_name = _execute.make_str(shared_name, "shared_name")
_inputs_flat = []
_attrs = ("num_threads", num_threads, "max_intra_op_parallelism",
max_intra_op_parallelism, "display_name", display_name, "container",
container, "shared_name", shared_name)
_result = _execute.execute(b"ExperimentalThreadPoolHandle", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalThreadPoolHandle", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_unbatch_dataset(input_dataset, output_types, output_shapes, name=None):
r"""A dataset that splits the elements of its input into multiple elements.
Args:
input_dataset: A `Tensor` of type `variant`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalUnbatchDataset", name, _ctx._post_execution_callbacks,
input_dataset, "output_types", output_types, "output_shapes",
output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_unbatch_dataset_eager_fallback(
input_dataset, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_unbatch_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_unbatch_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalUnbatchDataset", input_dataset=input_dataset,
output_types=output_types,
output_shapes=output_shapes, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalUnbatchDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalUnbatchDataset(input_dataset, output_types, output_shapes, name=None):
return experimental_unbatch_dataset(input_dataset=input_dataset, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalUnbatchDataset.__doc__ = experimental_unbatch_dataset.__doc__
ExperimentalUnbatchDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalUnbatchDataset))
tf_export("raw_ops.ExperimentalUnbatchDataset")(ExperimentalUnbatchDataset)
def experimental_unbatch_dataset_eager_fallback(input_dataset, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_unbatch_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_unbatch_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_unbatch_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
_inputs_flat = [input_dataset]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalUnbatchDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalUnbatchDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def experimental_unique_dataset(input_dataset, output_types, output_shapes, name=None):
r"""Creates a dataset that contains the unique elements of `input_dataset`.
Args:
input_dataset: A `Tensor` of type `variant`.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ExperimentalUniqueDataset", name, _ctx._post_execution_callbacks,
input_dataset, "output_types", output_types, "output_shapes",
output_shapes)
return _result
except _core._FallbackException:
try:
return experimental_unique_dataset_eager_fallback(
input_dataset, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_unique_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_unique_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"ExperimentalUniqueDataset", input_dataset=input_dataset,
output_types=output_types,
output_shapes=output_shapes, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"ExperimentalUniqueDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ExperimentalUniqueDataset(input_dataset, output_types, output_shapes, name=None):
return experimental_unique_dataset(input_dataset=input_dataset, output_types=output_types, output_shapes=output_shapes, name=name)
ExperimentalUniqueDataset.__doc__ = experimental_unique_dataset.__doc__
ExperimentalUniqueDataset = _doc_controls.do_not_generate_docs(_kwarg_only(ExperimentalUniqueDataset))
tf_export("raw_ops.ExperimentalUniqueDataset")(ExperimentalUniqueDataset)
def experimental_unique_dataset_eager_fallback(input_dataset, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function experimental_unique_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'experimental_unique_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'experimental_unique_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
_inputs_flat = [input_dataset]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"ExperimentalUniqueDataset", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ExperimentalUniqueDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def sampling_dataset(input_dataset, rate, seed, seed2, output_types, output_shapes, name=None):
r"""Creates a dataset that contains `rate` elements from the `input_dataset`.
Args:
input_dataset: A `Tensor` of type `variant`.
rate: A `Tensor` of type `float32`.
A scalar representing the sample rate of elements from the `input_dataset`
that should be taken.
seed: A `Tensor` of type `int64`.
A scalar representing seed of random number generator.
seed2: A `Tensor` of type `int64`.
A scalar representing seed2 of random number generator.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"SamplingDataset", name, _ctx._post_execution_callbacks,
input_dataset, rate, seed, seed2, "output_types", output_types,
"output_shapes", output_shapes)
return _result
except _core._FallbackException:
try:
return sampling_dataset_eager_fallback(
input_dataset, rate, seed, seed2, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'sampling_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'sampling_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"SamplingDataset", input_dataset=input_dataset, rate=rate, seed=seed,
seed2=seed2, output_types=output_types,
output_shapes=output_shapes, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"SamplingDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def SamplingDataset(input_dataset, rate, seed, seed2, output_types, output_shapes, name=None):
return sampling_dataset(input_dataset=input_dataset, rate=rate, seed=seed, seed2=seed2, output_types=output_types, output_shapes=output_shapes, name=name)
SamplingDataset.__doc__ = sampling_dataset.__doc__
SamplingDataset = _doc_controls.do_not_generate_docs(_kwarg_only(SamplingDataset))
tf_export("raw_ops.SamplingDataset")(SamplingDataset)
def sampling_dataset_eager_fallback(input_dataset, rate, seed, seed2, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function sampling_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'sampling_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'sampling_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
rate = _ops.convert_to_tensor(rate, _dtypes.float32)
seed = _ops.convert_to_tensor(seed, _dtypes.int64)
seed2 = _ops.convert_to_tensor(seed2, _dtypes.int64)
_inputs_flat = [input_dataset, rate, seed, seed2]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"SamplingDataset", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"SamplingDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def snapshot_dataset(input_dataset, path, output_types, output_shapes, name=None):
r"""Creates a dataset that will write to / read from a snapshot.
This dataset attempts to determine whether a valid snapshot exists at the
`snapshot_path`, and reads from the snapshot in lieu of using `input_dataset`.
If not, it will run the preprocessing pipeline as usual, and write out a
snapshot of the data processed for future use.
Args:
input_dataset: A `Tensor` of type `variant`.
A variant tensor representing the input dataset.
path: A `Tensor` of type `string`.
The path we should write snapshots to / read snapshots from.
output_types: A list of `tf.DTypes` that has length `>= 1`.
output_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`) that has length `>= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `variant`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"SnapshotDataset", name, _ctx._post_execution_callbacks,
input_dataset, path, "output_types", output_types, "output_shapes",
output_shapes)
return _result
except _core._FallbackException:
try:
return snapshot_dataset_eager_fallback(
input_dataset, path, output_types=output_types,
output_shapes=output_shapes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'snapshot_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'snapshot_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
_, _, _op = _op_def_lib._apply_op_helper(
"SnapshotDataset", input_dataset=input_dataset, path=path,
output_types=output_types,
output_shapes=output_shapes, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_types", _op.get_attr("output_types"), "output_shapes",
_op.get_attr("output_shapes"))
_execute.record_gradient(
"SnapshotDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def SnapshotDataset(input_dataset, path, output_types, output_shapes, name=None):
return snapshot_dataset(input_dataset=input_dataset, path=path, output_types=output_types, output_shapes=output_shapes, name=name)
SnapshotDataset.__doc__ = snapshot_dataset.__doc__
SnapshotDataset = _doc_controls.do_not_generate_docs(_kwarg_only(SnapshotDataset))
tf_export("raw_ops.SnapshotDataset")(SnapshotDataset)
def snapshot_dataset_eager_fallback(input_dataset, path, output_types, output_shapes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function snapshot_dataset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(output_types, (list, tuple)):
raise TypeError(
"Expected list for 'output_types' argument to "
"'snapshot_dataset' Op, not %r." % output_types)
output_types = [_execute.make_type(_t, "output_types") for _t in output_types]
if not isinstance(output_shapes, (list, tuple)):
raise TypeError(
"Expected list for 'output_shapes' argument to "
"'snapshot_dataset' Op, not %r." % output_shapes)
output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
input_dataset = _ops.convert_to_tensor(input_dataset, _dtypes.variant)
path = _ops.convert_to_tensor(path, _dtypes.string)
_inputs_flat = [input_dataset, path]
_attrs = ("output_types", output_types, "output_shapes", output_shapes)
_result = _execute.execute(b"SnapshotDataset", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"SnapshotDataset", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def stats_aggregator_handle_v2(container="", shared_name="", name=None):
r"""TODO: add doc.
Args:
container: An optional `string`. Defaults to `""`.
shared_name: An optional `string`. Defaults to `""`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `resource`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"StatsAggregatorHandleV2", name, _ctx._post_execution_callbacks,
"container", container, "shared_name", shared_name)
return _result
except _core._FallbackException:
try:
return stats_aggregator_handle_v2_eager_fallback(
container=container, shared_name=shared_name, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if container is None:
container = ""
container = _execute.make_str(container, "container")
if shared_name is None:
shared_name = ""
shared_name = _execute.make_str(shared_name, "shared_name")
_, _, _op = _op_def_lib._apply_op_helper(
"StatsAggregatorHandleV2", container=container,
shared_name=shared_name, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("container", _op.get_attr("container"), "shared_name",
_op.get_attr("shared_name"))
_execute.record_gradient(
"StatsAggregatorHandleV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def StatsAggregatorHandleV2(container="", shared_name="", name=None):
return stats_aggregator_handle_v2(container=container, shared_name=shared_name, name=name)
StatsAggregatorHandleV2.__doc__ = stats_aggregator_handle_v2.__doc__
StatsAggregatorHandleV2 = _doc_controls.do_not_generate_docs(_kwarg_only(StatsAggregatorHandleV2))
tf_export("raw_ops.StatsAggregatorHandleV2")(StatsAggregatorHandleV2)
def stats_aggregator_handle_v2_eager_fallback(container="", shared_name="", name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function stats_aggregator_handle_v2
"""
_ctx = ctx if ctx else _context.context()
if container is None:
container = ""
container = _execute.make_str(container, "container")
if shared_name is None:
shared_name = ""
shared_name = _execute.make_str(shared_name, "shared_name")
_inputs_flat = []
_attrs = ("container", container, "shared_name", shared_name)
_result = _execute.execute(b"StatsAggregatorHandleV2", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"StatsAggregatorHandleV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def stats_aggregator_set_summary_writer(stats_aggregator, summary, name=None):
r"""Set a summary_writer_interface to record statistics using given stats_aggregator.
Args:
stats_aggregator: A `Tensor` of type `resource`.
summary: A `Tensor` of type `resource`.
name: A name for the operation (optional).
Returns:
The created Operation.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"StatsAggregatorSetSummaryWriter", name,
_ctx._post_execution_callbacks, stats_aggregator, summary)
return _result
except _core._FallbackException:
try:
return stats_aggregator_set_summary_writer_eager_fallback(
stats_aggregator, summary, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"StatsAggregatorSetSummaryWriter", stats_aggregator=stats_aggregator,
summary=summary, name=name)
return _op
_result = None
return _result
def StatsAggregatorSetSummaryWriter(stats_aggregator, summary, name=None):
return stats_aggregator_set_summary_writer(stats_aggregator=stats_aggregator, summary=summary, name=name)
StatsAggregatorSetSummaryWriter.__doc__ = stats_aggregator_set_summary_writer.__doc__
StatsAggregatorSetSummaryWriter = _doc_controls.do_not_generate_docs(_kwarg_only(StatsAggregatorSetSummaryWriter))
tf_export("raw_ops.StatsAggregatorSetSummaryWriter")(StatsAggregatorSetSummaryWriter)
def stats_aggregator_set_summary_writer_eager_fallback(stats_aggregator, summary, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function stats_aggregator_set_summary_writer
"""
_ctx = ctx if ctx else _context.context()
stats_aggregator = _ops.convert_to_tensor(stats_aggregator, _dtypes.resource)
summary = _ops.convert_to_tensor(summary, _dtypes.resource)
_inputs_flat = [stats_aggregator, summary]
_attrs = None
_result = _execute.execute(b"StatsAggregatorSetSummaryWriter", 0,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_result = None
return _result
def _InitOpDefLibrary(op_list_proto_bytes):
op_list = _op_def_pb2.OpList()
op_list.ParseFromString(op_list_proto_bytes)
_op_def_registry.register_op_list(op_list)
op_def_lib = _op_def_library.OpDefLibrary()
op_def_lib.add_op_list(op_list)
return op_def_lib
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\n\routput_shapes\022\013list(shape)(\0010\001\n\354\002\n\037ExperimentalParseExampleDataset\022\021\n\rinput_dataset\030\025\022\026\n\022num_parallel_calls\030\t\022\030\n\016dense_defaults2\006Tdense\032\n\n\006handle\030\025\"\035\n\013sparse_keys\022\014list(string)(\001\"\034\n\ndense_keys\022\014list(string)(\001\"%\n\014sparse_types\022\nlist(type)(\001:\007\n\0052\003\001\t\007\"\037\n\006Tdense\022\nlist(type)(\001:\007\n\0052\003\001\t\007\"\035\n\014dense_shapes\022\013list(shape)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\"\022\n\006sloppy\022\004bool\032\002(\000\n\230\001\n$ExperimentalPrivateThreadPoolDataset\022\021\n\rinput_dataset\030\025\022\017\n\013num_threads\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\201\001\n\031ExperimentalRandomDataset\022\010\n\004seed\030\t\022\t\n\005seed2\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\216\001\n\032ExperimentalRebatchDataset\022\021\n\rinput_dataset\030\025\022\017\n\013num_workers\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\225\002\n\027ExperimentalScanDataset\022\021\n\rinput_dataset\030\025\022\027\n\rinitial_state2\006Tstate\022\035\n\017other_arguments2\nTarguments\032\n\n\006handle\030\025\"\t\n\001f\022\004func\"\030\n\006Tstate\022\nlist(type)(\0010\001\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\" \n\024preserve_cardinality\022\004bool\032\002(\000\n\276\001\n%ExperimentalSetStatsAggregatorDataset\022\021\n\rinput_dataset\030\025\022\024\n\020stats_aggregator\030\024\022\007\n\003tag\030\007\022\022\n\016counter_prefix\030\007\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\223\001\n\030ExperimentalSleepDataset\022\021\n\rinput_dataset\030\025\022\026\n\022sleep_microseconds\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\271\001\n ExperimentalSlidingWindowDataset\022\021\n\rinput_dataset\030\025\022\017\n\013window_size\030\t\022\020\n\014window_shift\030\t\022\021\n\rwindow_stride\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\233\001\n\026ExperimentalSqlDataset\022\017\n\013driver_name\030\007\022\024\n\020data_source_name\030\007\022\t\n\005query\030\007\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\nf\n!ExperimentalStatsAggregatorHandle\032\n\n\006handle\030\024\"\027\n\tcontainer\022\006string\032\002\022\000\"\031\n\013shared_name\022\006string\032\002\022\000\210\001\001\nB\n\"ExperimentalStatsAggregatorSummary\022\014\n\010iterator\030\024\032\013\n\007summary\030\007\210\001\001\n\315\001\n\034ExperimentalTakeWhileDataset\022\021\n\rinput_dataset\030\025\022\035\n\017other_arguments2\nTarguments\032\n\n\006handle\030\025\"\021\n\tpredicate\022\004func\"\032\n\nTarguments\022\nlist(type)(\001\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\224\001\n\035ExperimentalThreadPoolDataset\022\021\n\rinput_dataset\030\025\022\017\n\013thread_pool\030\024\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\210\001\001\n\262\001\n\034ExperimentalThreadPoolHandle\032\n\n\006handle\030\024\"\022\n\013num_threads\022\003int\"#\n\030max_intra_op_parallelism\022\003int\032\002\030\001\"\026\n\014display_name\022\006string\"\027\n\tcontainer\022\006string\032\002\022\000\"\031\n\013shared_name\022\006string\032\002\022\000\210\001\001\n}\n\032ExperimentalUnbatchDataset\022\021\n\rinput_dataset\030\025\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n|\n\031ExperimentalUniqueDataset\022\021\n\rinput_dataset\030\025\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\221\001\n\017SamplingDataset\022\021\n\rinput_dataset\030\025\022\010\n\004rate\030\001\022\010\n\004seed\030\t\022\t\n\005seed2\030\t\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n|\n\017SnapshotDataset\022\021\n\rinput_dataset\030\025\022\010\n\004path\030\007\032\n\n\006handle\030\025\"\036\n\014output_types\022\nlist(type)(\0010\001\" \n\routput_shapes\022\013list(shape)(\0010\001\n\\\n\027StatsAggregatorHandleV2\032\n\n\006handle\030\024\"\027\n\tcontainer\022\006string\032\002\022\000\"\031\n\013shared_name\022\006string\032\002\022\000\210\001\001\nG\n\037StatsAggregatorSetSummaryWriter\022\024\n\020stats_aggregator\030\024\022\013\n\007summary\030\024\210\001\001")