724 lines
28 KiB
Python
724 lines
28 KiB
Python
"""Python wrappers around TensorFlow ops.
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This file is MACHINE GENERATED! Do not edit.
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"""
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import collections
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from tensorflow.python import pywrap_tfe as pywrap_tfe
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from tensorflow.python.eager import context as _context
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from tensorflow.python.eager import core as _core
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from tensorflow.python.eager import execute as _execute
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from tensorflow.python.framework import dtypes as _dtypes
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from tensorflow.python.framework import op_def_registry as _op_def_registry
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from tensorflow.python.framework import ops as _ops
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from tensorflow.python.framework import op_def_library as _op_def_library
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from tensorflow.python.util.deprecation import deprecated_endpoints
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from tensorflow.python.util import dispatch as _dispatch
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from tensorflow.python.util.tf_export import tf_export
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from typing import TypeVar
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def close_summary_writer(writer, name=None):
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r"""TODO: add doc.
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Args:
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writer: A `Tensor` of type `resource`.
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name: A name for the operation (optional).
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Returns:
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The created Operation.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "CloseSummaryWriter", name, writer)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return close_summary_writer_eager_fallback(
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writer, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"CloseSummaryWriter", writer=writer, name=name)
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return _op
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CloseSummaryWriter = tf_export("raw_ops.CloseSummaryWriter")(_ops.to_raw_op(close_summary_writer))
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def close_summary_writer_eager_fallback(writer, name, ctx):
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writer = _ops.convert_to_tensor(writer, _dtypes.resource)
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_inputs_flat = [writer]
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_attrs = None
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_result = _execute.execute(b"CloseSummaryWriter", 0, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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_result = None
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return _result
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def create_summary_db_writer(writer, db_uri, experiment_name, run_name, user_name, name=None):
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r"""TODO: add doc.
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Args:
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writer: A `Tensor` of type `resource`.
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db_uri: A `Tensor` of type `string`.
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experiment_name: A `Tensor` of type `string`.
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run_name: A `Tensor` of type `string`.
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user_name: A `Tensor` of type `string`.
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name: A name for the operation (optional).
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Returns:
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The created Operation.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "CreateSummaryDbWriter", name, writer, db_uri, experiment_name,
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run_name, user_name)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return create_summary_db_writer_eager_fallback(
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writer, db_uri, experiment_name, run_name, user_name, name=name,
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ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"CreateSummaryDbWriter", writer=writer, db_uri=db_uri,
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experiment_name=experiment_name,
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run_name=run_name, user_name=user_name,
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name=name)
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return _op
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CreateSummaryDbWriter = tf_export("raw_ops.CreateSummaryDbWriter")(_ops.to_raw_op(create_summary_db_writer))
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def create_summary_db_writer_eager_fallback(writer, db_uri, experiment_name, run_name, user_name, name, ctx):
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writer = _ops.convert_to_tensor(writer, _dtypes.resource)
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db_uri = _ops.convert_to_tensor(db_uri, _dtypes.string)
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experiment_name = _ops.convert_to_tensor(experiment_name, _dtypes.string)
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run_name = _ops.convert_to_tensor(run_name, _dtypes.string)
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user_name = _ops.convert_to_tensor(user_name, _dtypes.string)
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_inputs_flat = [writer, db_uri, experiment_name, run_name, user_name]
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_attrs = None
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_result = _execute.execute(b"CreateSummaryDbWriter", 0, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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_result = None
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return _result
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def create_summary_file_writer(writer, logdir, max_queue, flush_millis, filename_suffix, name=None):
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r"""TODO: add doc.
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Args:
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writer: A `Tensor` of type `resource`.
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logdir: A `Tensor` of type `string`.
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max_queue: A `Tensor` of type `int32`.
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flush_millis: A `Tensor` of type `int32`.
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filename_suffix: A `Tensor` of type `string`.
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name: A name for the operation (optional).
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Returns:
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The created Operation.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "CreateSummaryFileWriter", name, writer, logdir, max_queue,
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flush_millis, filename_suffix)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return create_summary_file_writer_eager_fallback(
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writer, logdir, max_queue, flush_millis, filename_suffix, name=name,
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ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"CreateSummaryFileWriter", writer=writer, logdir=logdir,
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max_queue=max_queue,
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flush_millis=flush_millis,
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filename_suffix=filename_suffix, name=name)
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return _op
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CreateSummaryFileWriter = tf_export("raw_ops.CreateSummaryFileWriter")(_ops.to_raw_op(create_summary_file_writer))
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def create_summary_file_writer_eager_fallback(writer, logdir, max_queue, flush_millis, filename_suffix, name, ctx):
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writer = _ops.convert_to_tensor(writer, _dtypes.resource)
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logdir = _ops.convert_to_tensor(logdir, _dtypes.string)
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max_queue = _ops.convert_to_tensor(max_queue, _dtypes.int32)
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flush_millis = _ops.convert_to_tensor(flush_millis, _dtypes.int32)
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filename_suffix = _ops.convert_to_tensor(filename_suffix, _dtypes.string)
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_inputs_flat = [writer, logdir, max_queue, flush_millis, filename_suffix]
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_attrs = None
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_result = _execute.execute(b"CreateSummaryFileWriter", 0,
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inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
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name=name)
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_result = None
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return _result
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def flush_summary_writer(writer, name=None):
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r"""TODO: add doc.
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Args:
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writer: A `Tensor` of type `resource`.
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name: A name for the operation (optional).
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Returns:
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The created Operation.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "FlushSummaryWriter", name, writer)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return flush_summary_writer_eager_fallback(
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writer, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"FlushSummaryWriter", writer=writer, name=name)
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return _op
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FlushSummaryWriter = tf_export("raw_ops.FlushSummaryWriter")(_ops.to_raw_op(flush_summary_writer))
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def flush_summary_writer_eager_fallback(writer, name, ctx):
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writer = _ops.convert_to_tensor(writer, _dtypes.resource)
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_inputs_flat = [writer]
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_attrs = None
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_result = _execute.execute(b"FlushSummaryWriter", 0, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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_result = None
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return _result
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def import_event(writer, event, name=None):
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r"""TODO: add doc.
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Args:
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writer: A `Tensor` of type `resource`.
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event: A `Tensor` of type `string`.
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name: A name for the operation (optional).
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Returns:
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The created Operation.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "ImportEvent", name, writer, event)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return import_event_eager_fallback(
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writer, event, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"ImportEvent", writer=writer, event=event, name=name)
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return _op
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ImportEvent = tf_export("raw_ops.ImportEvent")(_ops.to_raw_op(import_event))
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def import_event_eager_fallback(writer, event, name, ctx):
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writer = _ops.convert_to_tensor(writer, _dtypes.resource)
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event = _ops.convert_to_tensor(event, _dtypes.string)
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_inputs_flat = [writer, event]
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_attrs = None
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_result = _execute.execute(b"ImportEvent", 0, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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_result = None
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return _result
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def summary_writer(shared_name="", container="", name=None):
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r"""TODO: add doc.
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Args:
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shared_name: An optional `string`. Defaults to `""`.
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container: An optional `string`. Defaults to `""`.
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name: A name for the operation (optional).
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Returns:
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A `Tensor` of type `resource`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "SummaryWriter", name, "shared_name", shared_name, "container",
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container)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return summary_writer_eager_fallback(
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shared_name=shared_name, container=container, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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if shared_name is None:
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shared_name = ""
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shared_name = _execute.make_str(shared_name, "shared_name")
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if container is None:
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container = ""
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container = _execute.make_str(container, "container")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"SummaryWriter", shared_name=shared_name, container=container,
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name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("shared_name", _op.get_attr("shared_name"), "container",
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_op.get_attr("container"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"SummaryWriter", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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SummaryWriter = tf_export("raw_ops.SummaryWriter")(_ops.to_raw_op(summary_writer))
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def summary_writer_eager_fallback(shared_name, container, name, ctx):
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if shared_name is None:
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shared_name = ""
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shared_name = _execute.make_str(shared_name, "shared_name")
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if container is None:
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container = ""
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container = _execute.make_str(container, "container")
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_inputs_flat = []
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_attrs = ("shared_name", shared_name, "container", container)
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_result = _execute.execute(b"SummaryWriter", 1, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"SummaryWriter", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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def write_audio_summary(writer, step, tag, tensor, sample_rate, max_outputs=3, name=None):
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r"""Writes an audio summary.
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Writes encoded audio summary `tensor` at `step` with `tag` using summary `writer`.
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`sample_rate` is the audio sample rate is Hz.
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Args:
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writer: A `Tensor` of type `resource`.
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step: A `Tensor` of type `int64`.
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tag: A `Tensor` of type `string`.
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tensor: A `Tensor` of type `float32`.
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sample_rate: A `Tensor` of type `float32`.
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max_outputs: An optional `int` that is `>= 1`. Defaults to `3`.
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name: A name for the operation (optional).
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Returns:
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The created Operation.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "WriteAudioSummary", name, writer, step, tag, tensor,
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sample_rate, "max_outputs", max_outputs)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return write_audio_summary_eager_fallback(
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writer, step, tag, tensor, sample_rate, max_outputs=max_outputs,
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name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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if max_outputs is None:
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max_outputs = 3
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max_outputs = _execute.make_int(max_outputs, "max_outputs")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"WriteAudioSummary", writer=writer, step=step, tag=tag, tensor=tensor,
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sample_rate=sample_rate, max_outputs=max_outputs,
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name=name)
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return _op
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WriteAudioSummary = tf_export("raw_ops.WriteAudioSummary")(_ops.to_raw_op(write_audio_summary))
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def write_audio_summary_eager_fallback(writer, step, tag, tensor, sample_rate, max_outputs, name, ctx):
|
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if max_outputs is None:
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max_outputs = 3
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max_outputs = _execute.make_int(max_outputs, "max_outputs")
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writer = _ops.convert_to_tensor(writer, _dtypes.resource)
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step = _ops.convert_to_tensor(step, _dtypes.int64)
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tag = _ops.convert_to_tensor(tag, _dtypes.string)
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tensor = _ops.convert_to_tensor(tensor, _dtypes.float32)
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sample_rate = _ops.convert_to_tensor(sample_rate, _dtypes.float32)
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_inputs_flat = [writer, step, tag, tensor, sample_rate]
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_attrs = ("max_outputs", max_outputs)
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_result = _execute.execute(b"WriteAudioSummary", 0, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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_result = None
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return _result
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def write_graph_summary(writer, step, tensor, name=None):
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r"""Writes a graph summary.
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Writes TensorFlow graph `tensor` at `step` using summary `writer`.
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Args:
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writer: A `Tensor` of type `resource`.
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step: A `Tensor` of type `int64`.
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tensor: A `Tensor` of type `string`.
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name: A name for the operation (optional).
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|
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Returns:
|
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The created Operation.
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|
"""
|
|
_ctx = _context._context or _context.context()
|
|
tld = _ctx._thread_local_data
|
|
if tld.is_eager:
|
|
try:
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
|
_ctx, "WriteGraphSummary", name, writer, step, tensor)
|
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return _result
|
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except _core._NotOkStatusException as e:
|
|
_ops.raise_from_not_ok_status(e, name)
|
|
except _core._FallbackException:
|
|
pass
|
|
try:
|
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return write_graph_summary_eager_fallback(
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writer, step, tensor, name=name, ctx=_ctx)
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except _core._SymbolicException:
|
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"WriteGraphSummary", writer=writer, step=step, tensor=tensor,
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name=name)
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return _op
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WriteGraphSummary = tf_export("raw_ops.WriteGraphSummary")(_ops.to_raw_op(write_graph_summary))
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|
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def write_graph_summary_eager_fallback(writer, step, tensor, name, ctx):
|
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writer = _ops.convert_to_tensor(writer, _dtypes.resource)
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step = _ops.convert_to_tensor(step, _dtypes.int64)
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tensor = _ops.convert_to_tensor(tensor, _dtypes.string)
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_inputs_flat = [writer, step, tensor]
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_attrs = None
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_result = _execute.execute(b"WriteGraphSummary", 0, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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_result = None
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return _result
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def write_histogram_summary(writer, step, tag, values, name=None):
|
|
r"""Writes a histogram summary.
|
|
|
|
Writes histogram `values` at `step` with `tag` using summary `writer`.
|
|
|
|
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`, `bool`.
|
|
name: A name for the operation (optional).
|
|
|
|
Returns:
|
|
The created Operation.
|
|
"""
|
|
_ctx = _context._context or _context.context()
|
|
tld = _ctx._thread_local_data
|
|
if tld.is_eager:
|
|
try:
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
|
_ctx, "WriteHistogramSummary", name, writer, step, tag, values)
|
|
return _result
|
|
except _core._NotOkStatusException as e:
|
|
_ops.raise_from_not_ok_status(e, name)
|
|
except _core._FallbackException:
|
|
pass
|
|
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.
|
|
# Add nodes to the TensorFlow graph.
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
|
"WriteHistogramSummary", writer=writer, step=step, tag=tag,
|
|
values=values, name=name)
|
|
return _op
|
|
WriteHistogramSummary = tf_export("raw_ops.WriteHistogramSummary")(_ops.to_raw_op(write_histogram_summary))
|
|
|
|
|
|
def write_histogram_summary_eager_fallback(writer, step, tag, values, name, ctx):
|
|
_attr_T, (values,) = _execute.args_to_matching_eager([values], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.int64, _dtypes.bfloat16, _dtypes.uint16, _dtypes.half, _dtypes.uint32, _dtypes.uint64, _dtypes.bool, ], _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"""Writes an image summary.
|
|
|
|
Writes image `tensor` at `step` with `tag` using summary `writer`.
|
|
|
|
`tensor` is image with shape [height, width, channels].
|
|
|
|
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`, `float64`, `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()
|
|
tld = _ctx._thread_local_data
|
|
if tld.is_eager:
|
|
try:
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
|
_ctx, "WriteImageSummary", name, writer, step, tag, tensor, bad_color,
|
|
"max_images", max_images)
|
|
return _result
|
|
except _core._NotOkStatusException as e:
|
|
_ops.raise_from_not_ok_status(e, name)
|
|
except _core._FallbackException:
|
|
pass
|
|
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.
|
|
# Add nodes to the TensorFlow graph.
|
|
if max_images is None:
|
|
max_images = 3
|
|
max_images = _execute.make_int(max_images, "max_images")
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
|
"WriteImageSummary", writer=writer, step=step, tag=tag, tensor=tensor,
|
|
bad_color=bad_color, max_images=max_images,
|
|
name=name)
|
|
return _op
|
|
WriteImageSummary = tf_export("raw_ops.WriteImageSummary")(_ops.to_raw_op(write_image_summary))
|
|
|
|
|
|
def write_image_summary_eager_fallback(writer, step, tag, tensor, bad_color, max_images, name, ctx):
|
|
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.uint8, _dtypes.float64, _dtypes.float32, _dtypes.half, ], _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"""Writes a serialized proto summary.
|
|
|
|
Writes `tensor`, a serialized proto at `step` using summary `writer`.
|
|
|
|
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()
|
|
tld = _ctx._thread_local_data
|
|
if tld.is_eager:
|
|
try:
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
|
_ctx, "WriteRawProtoSummary", name, writer, step, tensor)
|
|
return _result
|
|
except _core._NotOkStatusException as e:
|
|
_ops.raise_from_not_ok_status(e, name)
|
|
except _core._FallbackException:
|
|
pass
|
|
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.
|
|
# Add nodes to the TensorFlow graph.
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
|
"WriteRawProtoSummary", writer=writer, step=step, tensor=tensor,
|
|
name=name)
|
|
return _op
|
|
WriteRawProtoSummary = tf_export("raw_ops.WriteRawProtoSummary")(_ops.to_raw_op(write_raw_proto_summary))
|
|
|
|
|
|
def write_raw_proto_summary_eager_fallback(writer, step, tensor, name, ctx):
|
|
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"""Writes a scalar summary.
|
|
|
|
Writes scalar `value` at `step` with `tag` using summary `writer`.
|
|
|
|
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()
|
|
tld = _ctx._thread_local_data
|
|
if tld.is_eager:
|
|
try:
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
|
_ctx, "WriteScalarSummary", name, writer, step, tag, value)
|
|
return _result
|
|
except _core._NotOkStatusException as e:
|
|
_ops.raise_from_not_ok_status(e, name)
|
|
except _core._FallbackException:
|
|
pass
|
|
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.
|
|
# Add nodes to the TensorFlow graph.
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
|
"WriteScalarSummary", writer=writer, step=step, tag=tag, value=value,
|
|
name=name)
|
|
return _op
|
|
WriteScalarSummary = tf_export("raw_ops.WriteScalarSummary")(_ops.to_raw_op(write_scalar_summary))
|
|
|
|
|
|
def write_scalar_summary_eager_fallback(writer, step, tag, value, name, ctx):
|
|
_attr_T, (value,) = _execute.args_to_matching_eager([value], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.int64, _dtypes.bfloat16, _dtypes.uint16, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
|
|
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"""Writes a tensor summary.
|
|
|
|
Writes `tensor` at `step` with `tag` using summary `writer`.
|
|
|
|
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()
|
|
tld = _ctx._thread_local_data
|
|
if tld.is_eager:
|
|
try:
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
|
_ctx, "WriteSummary", name, writer, step, tensor, tag,
|
|
summary_metadata)
|
|
return _result
|
|
except _core._NotOkStatusException as e:
|
|
_ops.raise_from_not_ok_status(e, name)
|
|
except _core._FallbackException:
|
|
pass
|
|
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.
|
|
# Add nodes to the TensorFlow graph.
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
|
"WriteSummary", writer=writer, step=step, tensor=tensor, tag=tag,
|
|
summary_metadata=summary_metadata, name=name)
|
|
return _op
|
|
WriteSummary = tf_export("raw_ops.WriteSummary")(_ops.to_raw_op(write_summary))
|
|
|
|
|
|
def write_summary_eager_fallback(writer, step, tensor, tag, summary_metadata, name, ctx):
|
|
_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
|
|
|