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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 close_summary_writer(writer, name=None):
r"""TODO: add doc.
Args:
writer: 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,
"CloseSummaryWriter", name, _ctx._post_execution_callbacks, writer)
return _result
except _core._FallbackException:
try:
return close_summary_writer_eager_fallback(
writer, 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(
"CloseSummaryWriter", writer=writer, name=name)
return _op
_result = None
return _result
def CloseSummaryWriter(writer, name=None):
return close_summary_writer(writer=writer, name=name)
CloseSummaryWriter.__doc__ = close_summary_writer.__doc__
CloseSummaryWriter = _doc_controls.do_not_generate_docs(_kwarg_only(CloseSummaryWriter))
tf_export("raw_ops.CloseSummaryWriter")(CloseSummaryWriter)
def close_summary_writer_eager_fallback(writer, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function close_summary_writer
"""
_ctx = ctx if ctx else _context.context()
writer = _ops.convert_to_tensor(writer, _dtypes.resource)
_inputs_flat = [writer]
_attrs = None
_result = _execute.execute(b"CloseSummaryWriter", 0, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_result = None
return _result
def create_summary_db_writer(writer, db_uri, experiment_name, run_name, user_name, name=None):
r"""TODO: add doc.
Args:
writer: A `Tensor` of type `resource`.
db_uri: A `Tensor` of type `string`.
experiment_name: A `Tensor` of type `string`.
run_name: A `Tensor` of type `string`.
user_name: A `Tensor` of type `string`.
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,
"CreateSummaryDbWriter", name, _ctx._post_execution_callbacks, writer,
db_uri, experiment_name, run_name, user_name)
return _result
except _core._FallbackException:
try:
return create_summary_db_writer_eager_fallback(
writer, db_uri, experiment_name, run_name, user_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.
_, _, _op = _op_def_lib._apply_op_helper(
"CreateSummaryDbWriter", writer=writer, db_uri=db_uri,
experiment_name=experiment_name,
run_name=run_name, user_name=user_name,
name=name)
return _op
_result = None
return _result
def CreateSummaryDbWriter(writer, db_uri, experiment_name, run_name, user_name, name=None):
return create_summary_db_writer(writer=writer, db_uri=db_uri, experiment_name=experiment_name, run_name=run_name, user_name=user_name, name=name)
CreateSummaryDbWriter.__doc__ = create_summary_db_writer.__doc__
CreateSummaryDbWriter = _doc_controls.do_not_generate_docs(_kwarg_only(CreateSummaryDbWriter))
tf_export("raw_ops.CreateSummaryDbWriter")(CreateSummaryDbWriter)
def create_summary_db_writer_eager_fallback(writer, db_uri, experiment_name, run_name, user_name, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function create_summary_db_writer
"""
_ctx = ctx if ctx else _context.context()
writer = _ops.convert_to_tensor(writer, _dtypes.resource)
db_uri = _ops.convert_to_tensor(db_uri, _dtypes.string)
experiment_name = _ops.convert_to_tensor(experiment_name, _dtypes.string)
run_name = _ops.convert_to_tensor(run_name, _dtypes.string)
user_name = _ops.convert_to_tensor(user_name, _dtypes.string)
_inputs_flat = [writer, db_uri, experiment_name, run_name, user_name]
_attrs = None
_result = _execute.execute(b"CreateSummaryDbWriter", 0, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_result = None
return _result
def create_summary_file_writer(writer, logdir, max_queue, flush_millis, filename_suffix, name=None):
r"""TODO: add doc.
Args:
writer: A `Tensor` of type `resource`.
logdir: A `Tensor` of type `string`.
max_queue: A `Tensor` of type `int32`.
flush_millis: A `Tensor` of type `int32`.
filename_suffix: A `Tensor` of type `string`.
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,
"CreateSummaryFileWriter", name, _ctx._post_execution_callbacks,
writer, logdir, max_queue, flush_millis, filename_suffix)
return _result
except _core._FallbackException:
try:
return create_summary_file_writer_eager_fallback(
writer, logdir, max_queue, flush_millis, filename_suffix,
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(
"CreateSummaryFileWriter", writer=writer, logdir=logdir,
max_queue=max_queue,
flush_millis=flush_millis,
filename_suffix=filename_suffix, name=name)
return _op
_result = None
return _result
def CreateSummaryFileWriter(writer, logdir, max_queue, flush_millis, filename_suffix, name=None):
return create_summary_file_writer(writer=writer, logdir=logdir, max_queue=max_queue, flush_millis=flush_millis, filename_suffix=filename_suffix, name=name)
CreateSummaryFileWriter.__doc__ = create_summary_file_writer.__doc__
CreateSummaryFileWriter = _doc_controls.do_not_generate_docs(_kwarg_only(CreateSummaryFileWriter))
tf_export("raw_ops.CreateSummaryFileWriter")(CreateSummaryFileWriter)
def create_summary_file_writer_eager_fallback(writer, logdir, max_queue, flush_millis, filename_suffix, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function create_summary_file_writer
"""
_ctx = ctx if ctx else _context.context()
writer = _ops.convert_to_tensor(writer, _dtypes.resource)
logdir = _ops.convert_to_tensor(logdir, _dtypes.string)
max_queue = _ops.convert_to_tensor(max_queue, _dtypes.int32)
flush_millis = _ops.convert_to_tensor(flush_millis, _dtypes.int32)
filename_suffix = _ops.convert_to_tensor(filename_suffix, _dtypes.string)
_inputs_flat = [writer, logdir, max_queue, flush_millis, filename_suffix]
_attrs = None
_result = _execute.execute(b"CreateSummaryFileWriter", 0,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_result = None
return _result
def flush_summary_writer(writer, name=None):
r"""TODO: add doc.
Args:
writer: 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,
"FlushSummaryWriter", name, _ctx._post_execution_callbacks, writer)
return _result
except _core._FallbackException:
try:
return flush_summary_writer_eager_fallback(
writer, 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(
"FlushSummaryWriter", writer=writer, name=name)
return _op
_result = None
return _result
def FlushSummaryWriter(writer, name=None):
return flush_summary_writer(writer=writer, name=name)
FlushSummaryWriter.__doc__ = flush_summary_writer.__doc__
FlushSummaryWriter = _doc_controls.do_not_generate_docs(_kwarg_only(FlushSummaryWriter))
tf_export("raw_ops.FlushSummaryWriter")(FlushSummaryWriter)
def flush_summary_writer_eager_fallback(writer, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function flush_summary_writer
"""
_ctx = ctx if ctx else _context.context()
writer = _ops.convert_to_tensor(writer, _dtypes.resource)
_inputs_flat = [writer]
_attrs = None
_result = _execute.execute(b"FlushSummaryWriter", 0, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_result = None
return _result
def import_event(writer, event, name=None):
r"""TODO: add doc.
Args:
writer: A `Tensor` of type `resource`.
event: A `Tensor` of type `string`.
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,
"ImportEvent", name, _ctx._post_execution_callbacks, writer, event)
return _result
except _core._FallbackException:
try:
return import_event_eager_fallback(
writer, event, 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(
"ImportEvent", writer=writer, event=event, name=name)
return _op
_result = None
return _result
def ImportEvent(writer, event, name=None):
return import_event(writer=writer, event=event, name=name)
ImportEvent.__doc__ = import_event.__doc__
ImportEvent = _doc_controls.do_not_generate_docs(_kwarg_only(ImportEvent))
tf_export("raw_ops.ImportEvent")(ImportEvent)
def import_event_eager_fallback(writer, event, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function import_event
"""
_ctx = ctx if ctx else _context.context()
writer = _ops.convert_to_tensor(writer, _dtypes.resource)
event = _ops.convert_to_tensor(event, _dtypes.string)
_inputs_flat = [writer, event]
_attrs = None
_result = _execute.execute(b"ImportEvent", 0, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_result = None
return _result
def summary_writer(shared_name="", container="", name=None):
r"""TODO: add doc.
Args:
shared_name: An optional `string`. Defaults to `""`.
container: 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,
"SummaryWriter", name, _ctx._post_execution_callbacks, "shared_name",
shared_name, "container", container)
return _result
except _core._FallbackException:
try:
return summary_writer_eager_fallback(
shared_name=shared_name, container=container, 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 shared_name is None:
shared_name = ""
shared_name = _execute.make_str(shared_name, "shared_name")
if container is None:
container = ""
container = _execute.make_str(container, "container")
_, _, _op = _op_def_lib._apply_op_helper(
"SummaryWriter", shared_name=shared_name, container=container,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("shared_name", _op.get_attr("shared_name"), "container",
_op.get_attr("container"))
_execute.record_gradient(
"SummaryWriter", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def SummaryWriter(shared_name="", container="", name=None):
return summary_writer(shared_name=shared_name, container=container, name=name)
SummaryWriter.__doc__ = summary_writer.__doc__
SummaryWriter = _doc_controls.do_not_generate_docs(_kwarg_only(SummaryWriter))
tf_export("raw_ops.SummaryWriter")(SummaryWriter)
def summary_writer_eager_fallback(shared_name="", container="", name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function summary_writer
"""
_ctx = ctx if ctx else _context.context()
if shared_name is None:
shared_name = ""
shared_name = _execute.make_str(shared_name, "shared_name")
if container is None:
container = ""
container = _execute.make_str(container, "container")
_inputs_flat = []
_attrs = ("shared_name", shared_name, "container", container)
_result = _execute.execute(b"SummaryWriter", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"SummaryWriter", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def write_audio_summary(writer, step, tag, tensor, sample_rate, max_outputs=3, name=None):
r"""TODO: add doc.
Args:
writer: A `Tensor` of type `resource`.
step: A `Tensor` of type `int64`.
tag: A `Tensor` of type `string`.
tensor: A `Tensor` of type `float32`.
sample_rate: A `Tensor` of type `float32`.
max_outputs: An optional `int` that is `>= 1`. Defaults to `3`.
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,
"WriteAudioSummary", name, _ctx._post_execution_callbacks, writer,
step, tag, tensor, sample_rate, "max_outputs", max_outputs)
return _result
except _core._FallbackException:
try:
return write_audio_summary_eager_fallback(
writer, step, tag, tensor, sample_rate, max_outputs=max_outputs,
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 max_outputs is None:
max_outputs = 3
max_outputs = _execute.make_int(max_outputs, "max_outputs")
_, _, _op = _op_def_lib._apply_op_helper(
"WriteAudioSummary", writer=writer, step=step, tag=tag, tensor=tensor,
sample_rate=sample_rate, max_outputs=max_outputs,
name=name)
return _op
_result = None
return _result
def WriteAudioSummary(writer, step, tag, tensor, sample_rate, max_outputs=3, name=None):
return write_audio_summary(writer=writer, step=step, tag=tag, tensor=tensor, sample_rate=sample_rate, max_outputs=max_outputs, name=name)
WriteAudioSummary.__doc__ = write_audio_summary.__doc__
WriteAudioSummary = _doc_controls.do_not_generate_docs(_kwarg_only(WriteAudioSummary))
tf_export("raw_ops.WriteAudioSummary")(WriteAudioSummary)
def write_audio_summary_eager_fallback(writer, step, tag, tensor, sample_rate, max_outputs=3, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function write_audio_summary
"""
_ctx = ctx if ctx else _context.context()
if max_outputs is None:
max_outputs = 3
max_outputs = _execute.make_int(max_outputs, "max_outputs")
writer = _ops.convert_to_tensor(writer, _dtypes.resource)
step = _ops.convert_to_tensor(step, _dtypes.int64)
tag = _ops.convert_to_tensor(tag, _dtypes.string)
tensor = _ops.convert_to_tensor(tensor, _dtypes.float32)
sample_rate = _ops.convert_to_tensor(sample_rate, _dtypes.float32)
_inputs_flat = [writer, step, tag, tensor, sample_rate]
_attrs = ("max_outputs", max_outputs)
_result = _execute.execute(b"WriteAudioSummary", 0, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_result = None
return _result
def write_graph_summary(writer, step, tensor, name=None):
r"""TODO: add doc.
Args:
writer: A `Tensor` of type `resource`.
step: A `Tensor` of type `int64`.
tensor: A `Tensor` of type `string`.
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,
"WriteGraphSummary", name, _ctx._post_execution_callbacks, writer,
step, tensor)
return _result
except _core._FallbackException:
try:
return write_graph_summary_eager_fallback(
writer, step, tensor, 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(
"WriteGraphSummary", writer=writer, step=step, tensor=tensor,
name=name)
return _op
_result = None
return _result
def WriteGraphSummary(writer, step, tensor, name=None):
return write_graph_summary(writer=writer, step=step, tensor=tensor, name=name)
WriteGraphSummary.__doc__ = write_graph_summary.__doc__
WriteGraphSummary = _doc_controls.do_not_generate_docs(_kwarg_only(WriteGraphSummary))
tf_export("raw_ops.WriteGraphSummary")(WriteGraphSummary)
def write_graph_summary_eager_fallback(writer, step, tensor, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function write_graph_summary
"""
_ctx = ctx if ctx else _context.context()
writer = _ops.convert_to_tensor(writer, _dtypes.resource)
step = _ops.convert_to_tensor(step, _dtypes.int64)
tensor = _ops.convert_to_tensor(tensor, _dtypes.string)
_inputs_flat = [writer, step, tensor]
_attrs = None
_result = _execute.execute(b"WriteGraphSummary", 0, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_result = None
return _result
def write_histogram_summary(writer, step, tag, values, name=None):
r"""TODO: add doc.
Args:
writer: A `Tensor` of type `resource`.
step: A `Tensor` of type `int64`.
tag: A `Tensor` of type `string`.
values: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
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,
"WriteHistogramSummary", name, _ctx._post_execution_callbacks, writer,
step, tag, values)
return _result
except _core._FallbackException:
try:
return write_histogram_summary_eager_fallback(
writer, step, tag, values, 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(
"WriteHistogramSummary", writer=writer, step=step, tag=tag,
values=values, name=name)
return _op
_result = None
return _result
def WriteHistogramSummary(writer, step, tag, values, name=None):
return write_histogram_summary(writer=writer, step=step, tag=tag, values=values, name=name)
WriteHistogramSummary.__doc__ = write_histogram_summary.__doc__
WriteHistogramSummary = _doc_controls.do_not_generate_docs(_kwarg_only(WriteHistogramSummary))
tf_export("raw_ops.WriteHistogramSummary")(WriteHistogramSummary)
def write_histogram_summary_eager_fallback(writer, step, tag, values, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function write_histogram_summary
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (values,) = _execute.args_to_matching_eager([values], _ctx, _dtypes.float32)
writer = _ops.convert_to_tensor(writer, _dtypes.resource)
step = _ops.convert_to_tensor(step, _dtypes.int64)
tag = _ops.convert_to_tensor(tag, _dtypes.string)
_inputs_flat = [writer, step, tag, values]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"WriteHistogramSummary", 0, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_result = None
return _result
def write_image_summary(writer, step, tag, tensor, bad_color, max_images=3, name=None):
r"""TODO: add doc.
Args:
writer: A `Tensor` of type `resource`.
step: A `Tensor` of type `int64`.
tag: A `Tensor` of type `string`.
tensor: A `Tensor`. Must be one of the following types: `uint8`, `float32`, `half`.
bad_color: A `Tensor` of type `uint8`.
max_images: An optional `int` that is `>= 1`. Defaults to `3`.
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,
"WriteImageSummary", name, _ctx._post_execution_callbacks, writer,
step, tag, tensor, bad_color, "max_images", max_images)
return _result
except _core._FallbackException:
try:
return write_image_summary_eager_fallback(
writer, step, tag, tensor, bad_color, max_images=max_images,
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 max_images is None:
max_images = 3
max_images = _execute.make_int(max_images, "max_images")
_, _, _op = _op_def_lib._apply_op_helper(
"WriteImageSummary", writer=writer, step=step, tag=tag, tensor=tensor,
bad_color=bad_color, max_images=max_images,
name=name)
return _op
_result = None
return _result
def WriteImageSummary(writer, step, tag, tensor, bad_color, max_images=3, name=None):
return write_image_summary(writer=writer, step=step, tag=tag, tensor=tensor, bad_color=bad_color, max_images=max_images, name=name)
WriteImageSummary.__doc__ = write_image_summary.__doc__
WriteImageSummary = _doc_controls.do_not_generate_docs(_kwarg_only(WriteImageSummary))
tf_export("raw_ops.WriteImageSummary")(WriteImageSummary)
def write_image_summary_eager_fallback(writer, step, tag, tensor, bad_color, max_images=3, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function write_image_summary
"""
_ctx = ctx if ctx else _context.context()
if max_images is None:
max_images = 3
max_images = _execute.make_int(max_images, "max_images")
_attr_T, (tensor,) = _execute.args_to_matching_eager([tensor], _ctx, _dtypes.float32)
writer = _ops.convert_to_tensor(writer, _dtypes.resource)
step = _ops.convert_to_tensor(step, _dtypes.int64)
tag = _ops.convert_to_tensor(tag, _dtypes.string)
bad_color = _ops.convert_to_tensor(bad_color, _dtypes.uint8)
_inputs_flat = [writer, step, tag, tensor, bad_color]
_attrs = ("max_images", max_images, "T", _attr_T)
_result = _execute.execute(b"WriteImageSummary", 0, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_result = None
return _result
def write_raw_proto_summary(writer, step, tensor, name=None):
r"""TODO: add doc.
Args:
writer: A `Tensor` of type `resource`.
step: A `Tensor` of type `int64`.
tensor: A `Tensor` of type `string`.
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,
"WriteRawProtoSummary", name, _ctx._post_execution_callbacks, writer,
step, tensor)
return _result
except _core._FallbackException:
try:
return write_raw_proto_summary_eager_fallback(
writer, step, tensor, 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(
"WriteRawProtoSummary", writer=writer, step=step, tensor=tensor,
name=name)
return _op
_result = None
return _result
def WriteRawProtoSummary(writer, step, tensor, name=None):
return write_raw_proto_summary(writer=writer, step=step, tensor=tensor, name=name)
WriteRawProtoSummary.__doc__ = write_raw_proto_summary.__doc__
WriteRawProtoSummary = _doc_controls.do_not_generate_docs(_kwarg_only(WriteRawProtoSummary))
tf_export("raw_ops.WriteRawProtoSummary")(WriteRawProtoSummary)
def write_raw_proto_summary_eager_fallback(writer, step, tensor, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function write_raw_proto_summary
"""
_ctx = ctx if ctx else _context.context()
writer = _ops.convert_to_tensor(writer, _dtypes.resource)
step = _ops.convert_to_tensor(step, _dtypes.int64)
tensor = _ops.convert_to_tensor(tensor, _dtypes.string)
_inputs_flat = [writer, step, tensor]
_attrs = None
_result = _execute.execute(b"WriteRawProtoSummary", 0, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_result = None
return _result
def write_scalar_summary(writer, step, tag, value, name=None):
r"""TODO: add doc.
Args:
writer: A `Tensor` of type `resource`.
step: A `Tensor` of type `int64`.
tag: A `Tensor` of type `string`.
value: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
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,
"WriteScalarSummary", name, _ctx._post_execution_callbacks, writer,
step, tag, value)
return _result
except _core._FallbackException:
try:
return write_scalar_summary_eager_fallback(
writer, step, tag, value, 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(
"WriteScalarSummary", writer=writer, step=step, tag=tag, value=value,
name=name)
return _op
_result = None
return _result
def WriteScalarSummary(writer, step, tag, value, name=None):
return write_scalar_summary(writer=writer, step=step, tag=tag, value=value, name=name)
WriteScalarSummary.__doc__ = write_scalar_summary.__doc__
WriteScalarSummary = _doc_controls.do_not_generate_docs(_kwarg_only(WriteScalarSummary))
tf_export("raw_ops.WriteScalarSummary")(WriteScalarSummary)
def write_scalar_summary_eager_fallback(writer, step, tag, value, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function write_scalar_summary
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (value,) = _execute.args_to_matching_eager([value], _ctx)
writer = _ops.convert_to_tensor(writer, _dtypes.resource)
step = _ops.convert_to_tensor(step, _dtypes.int64)
tag = _ops.convert_to_tensor(tag, _dtypes.string)
_inputs_flat = [writer, step, tag, value]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"WriteScalarSummary", 0, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_result = None
return _result
def write_summary(writer, step, tensor, tag, summary_metadata, name=None):
r"""TODO: add doc.
Args:
writer: A `Tensor` of type `resource`.
step: A `Tensor` of type `int64`.
tensor: A `Tensor`.
tag: A `Tensor` of type `string`.
summary_metadata: A `Tensor` of type `string`.
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,
"WriteSummary", name, _ctx._post_execution_callbacks, writer, step,
tensor, tag, summary_metadata)
return _result
except _core._FallbackException:
try:
return write_summary_eager_fallback(
writer, step, tensor, tag, summary_metadata, 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(
"WriteSummary", writer=writer, step=step, tensor=tensor, tag=tag,
summary_metadata=summary_metadata, name=name)
return _op
_result = None
return _result
def WriteSummary(writer, step, tensor, tag, summary_metadata, name=None):
return write_summary(writer=writer, step=step, tensor=tensor, tag=tag, summary_metadata=summary_metadata, name=name)
WriteSummary.__doc__ = write_summary.__doc__
WriteSummary = _doc_controls.do_not_generate_docs(_kwarg_only(WriteSummary))
tf_export("raw_ops.WriteSummary")(WriteSummary)
def write_summary_eager_fallback(writer, step, tensor, tag, summary_metadata, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function write_summary
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (tensor,) = _execute.args_to_matching_eager([tensor], _ctx)
writer = _ops.convert_to_tensor(writer, _dtypes.resource)
step = _ops.convert_to_tensor(step, _dtypes.int64)
tag = _ops.convert_to_tensor(tag, _dtypes.string)
summary_metadata = _ops.convert_to_tensor(summary_metadata, _dtypes.string)
_inputs_flat = [writer, step, tensor, tag, summary_metadata]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"WriteSummary", 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
# op {
# name: "CloseSummaryWriter"
# input_arg {
# name: "writer"
# type: DT_RESOURCE
# }
# is_stateful: true
# }
# op {
# name: "CreateSummaryDbWriter"
# input_arg {
# name: "writer"
# type: DT_RESOURCE
# }
# input_arg {
# name: "db_uri"
# type: DT_STRING
# }
# input_arg {
# name: "experiment_name"
# type: DT_STRING
# }
# input_arg {
# name: "run_name"
# type: DT_STRING
# }
# input_arg {
# name: "user_name"
# type: DT_STRING
# }
# is_stateful: true
# }
# op {
# name: "CreateSummaryFileWriter"
# input_arg {
# name: "writer"
# type: DT_RESOURCE
# }
# input_arg {
# name: "logdir"
# type: DT_STRING
# }
# input_arg {
# name: "max_queue"
# type: DT_INT32
# }
# input_arg {
# name: "flush_millis"
# type: DT_INT32
# }
# input_arg {
# name: "filename_suffix"
# type: DT_STRING
# }
# is_stateful: true
# }
# op {
# name: "FlushSummaryWriter"
# input_arg {
# name: "writer"
# type: DT_RESOURCE
# }
# is_stateful: true
# }
# op {
# name: "ImportEvent"
# input_arg {
# name: "writer"
# type: DT_RESOURCE
# }
# input_arg {
# name: "event"
# type: DT_STRING
# }
# is_stateful: true
# }
# op {
# name: "SummaryWriter"
# output_arg {
# name: "writer"
# type: DT_RESOURCE
# }
# attr {
# name: "shared_name"
# type: "string"
# default_value {
# s: ""
# }
# }
# attr {
# name: "container"
# type: "string"
# default_value {
# s: ""
# }
# }
# is_stateful: true
# }
# op {
# name: "WriteAudioSummary"
# input_arg {
# name: "writer"
# type: DT_RESOURCE
# }
# input_arg {
# name: "step"
# type: DT_INT64
# }
# input_arg {
# name: "tag"
# type: DT_STRING
# }
# input_arg {
# name: "tensor"
# type: DT_FLOAT
# }
# input_arg {
# name: "sample_rate"
# type: DT_FLOAT
# }
# attr {
# name: "max_outputs"
# type: "int"
# default_value {
# i: 3
# }
# has_minimum: true
# minimum: 1
# }
# is_stateful: true
# }
# op {
# name: "WriteGraphSummary"
# input_arg {
# name: "writer"
# type: DT_RESOURCE
# }
# input_arg {
# name: "step"
# type: DT_INT64
# }
# input_arg {
# name: "tensor"
# type: DT_STRING
# }
# is_stateful: true
# }
# op {
# name: "WriteHistogramSummary"
# input_arg {
# name: "writer"
# type: DT_RESOURCE
# }
# input_arg {
# name: "step"
# type: DT_INT64
# }
# input_arg {
# name: "tag"
# type: DT_STRING
# }
# input_arg {
# name: "values"
# type_attr: "T"
# }
# attr {
# name: "T"
# type: "type"
# default_value {
# type: DT_FLOAT
# }
# allowed_values {
# list {
# type: DT_FLOAT
# type: DT_DOUBLE
# type: DT_INT32
# type: DT_UINT8
# type: DT_INT16
# type: DT_INT8
# type: DT_INT64
# type: DT_BFLOAT16
# type: DT_UINT16
# type: DT_HALF
# type: DT_UINT32
# type: DT_UINT64
# }
# }
# }
# is_stateful: true
# }
# op {
# name: "WriteImageSummary"
# input_arg {
# name: "writer"
# type: DT_RESOURCE
# }
# input_arg {
# name: "step"
# type: DT_INT64
# }
# input_arg {
# name: "tag"
# type: DT_STRING
# }
# input_arg {
# name: "tensor"
# type_attr: "T"
# }
# input_arg {
# name: "bad_color"
# type: DT_UINT8
# }
# attr {
# name: "max_images"
# type: "int"
# default_value {
# i: 3
# }
# has_minimum: true
# minimum: 1
# }
# attr {
# name: "T"
# type: "type"
# default_value {
# type: DT_FLOAT
# }
# allowed_values {
# list {
# type: DT_UINT8
# type: DT_FLOAT
# type: DT_HALF
# }
# }
# }
# is_stateful: true
# }
# op {
# name: "WriteRawProtoSummary"
# input_arg {
# name: "writer"
# type: DT_RESOURCE
# }
# input_arg {
# name: "step"
# type: DT_INT64
# }
# input_arg {
# name: "tensor"
# type: DT_STRING
# }
# is_stateful: true
# }
# op {
# name: "WriteScalarSummary"
# input_arg {
# name: "writer"
# type: DT_RESOURCE
# }
# input_arg {
# name: "step"
# type: DT_INT64
# }
# input_arg {
# name: "tag"
# type: DT_STRING
# }
# input_arg {
# name: "value"
# type_attr: "T"
# }
# attr {
# name: "T"
# type: "type"
# allowed_values {
# list {
# type: DT_FLOAT
# type: DT_DOUBLE
# type: DT_INT32
# type: DT_UINT8
# type: DT_INT16
# type: DT_INT8
# type: DT_INT64
# type: DT_BFLOAT16
# type: DT_UINT16
# type: DT_HALF
# type: DT_UINT32
# type: DT_UINT64
# }
# }
# }
# is_stateful: true
# }
# op {
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# input_arg {
# name: "writer"
# type: DT_RESOURCE
# }
# input_arg {
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# type: DT_INT64
# }
# input_arg {
# name: "tensor"
# type_attr: "T"
# }
# input_arg {
# name: "tag"
# type: DT_STRING
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# input_arg {
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# type: DT_STRING
# }
# attr {
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# type: "type"
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# is_stateful: true
# }
_op_def_lib = _InitOpDefLibrary(b"\n#\n\022CloseSummaryWriter\022\n\n\006writer\030\024\210\001\001\nd\n\025CreateSummaryDbWriter\022\n\n\006writer\030\024\022\n\n\006db_uri\030\007\022\023\n\017experiment_name\030\007\022\014\n\010run_name\030\007\022\r\n\tuser_name\030\007\210\001\001\nj\n\027CreateSummaryFileWriter\022\n\n\006writer\030\024\022\n\n\006logdir\030\007\022\r\n\tmax_queue\030\003\022\020\n\014flush_millis\030\003\022\023\n\017filename_suffix\030\007\210\001\001\n#\n\022FlushSummaryWriter\022\n\n\006writer\030\024\210\001\001\n\'\n\013ImportEvent\022\n\n\006writer\030\024\022\t\n\005event\030\007\210\001\001\nR\n\rSummaryWriter\032\n\n\006writer\030\024\"\031\n\013shared_name\022\006string\032\002\022\000\"\027\n\tcontainer\022\006string\032\002\022\000\210\001\001\nn\n\021WriteAudioSummary\022\n\n\006writer\030\024\022\010\n\004step\030\t\022\007\n\003tag\030\007\022\n\n\006tensor\030\001\022\017\n\013sample_rate\030\001\"\032\n\013max_outputs\022\003int\032\002\030\003(\0010\001\210\001\001\n8\n\021WriteGraphSummary\022\n\n\006writer\030\024\022\010\n\004step\030\t\022\n\n\006tensor\030\007\210\001\001\ng\n\025WriteHistogramSummary\022\n\n\006writer\030\024\022\010\n\004step\030\t\022\007\n\003tag\030\007\022\013\n\006values\"\001T\"\037\n\001T\022\004type\032\0020\001:\020\n\0162\014\001\002\003\004\005\006\t\016\021\023\026\027\210\001\001\n\204\001\n\021WriteImageSummary\022\n\n\006writer\030\024\022\010\n\004step\030\t\022\007\n\003tag\030\007\022\013\n\006tensor\"\001T\022\r\n\tbad_color\030\004\"\031\n\nmax_images\022\003int\032\002\030\003(\0010\001\"\026\n\001T\022\004type\032\0020\001:\007\n\0052\003\004\001\023\210\001\001\n;\n\024WriteRawProtoSummary\022\n\n\006writer\030\024\022\010\n\004step\030\t\022\n\n\006tensor\030\007\210\001\001\n_\n\022WriteScalarSummary\022\n\n\006writer\030\024\022\010\n\004step\030\t\022\007\n\003tag\030\007\022\n\n\005value\"\001T\"\033\n\001T\022\004type:\020\n\0162\014\001\002\003\004\005\006\t\016\021\023\026\027\210\001\001\n^\n\014WriteSummary\022\n\n\006writer\030\024\022\010\n\004step\030\t\022\013\n\006tensor\"\001T\022\007\n\003tag\030\007\022\024\n\020summary_metadata\030\007\"\t\n\001T\022\004type\210\001\001")