911 lines
41 KiB
Python
911 lines
41 KiB
Python
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"""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 copy(input, tensor_name="", debug_ops_spec=[], name=None):
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r"""Copy a tensor from CPU-to-CPU or GPU-to-GPU.
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Performs CPU-to-CPU or GPU-to-GPU deep-copying of tensor, depending on the
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device on which the tensor is allocated.
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N.B.: If the all downstream attached debug ops are disabled given the current
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gRPC gating status, the output will simply forward the input tensor without
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deep-copying. See the documentation of Debug* ops for more details.
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Unlike the CopyHost Op, this op does not have HostMemory constraint on its
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input or output.
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Args:
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input: A `Tensor`. Input tensor.
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tensor_name: An optional `string`. Defaults to `""`.
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The name of the input tensor.
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debug_ops_spec: An optional list of `strings`. Defaults to `[]`.
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A list of debug op spec (op, url, gated_grpc) for attached debug
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ops. Each element of the list has the format
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<debug_op>;<grpc_url>;<gated_grpc>, wherein gated_grpc is boolean represented
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as 0/1. E.g., "DebugIdentity;grpc://foo:3333;1",
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"DebugIdentity;file:///tmp/tfdbg_1;0".
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name: A name for the operation (optional).
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Returns:
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A `Tensor`. Has the same type as `input`.
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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, "Copy", name, input, "tensor_name", tensor_name,
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"debug_ops_spec", debug_ops_spec)
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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 copy_eager_fallback(
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input, tensor_name=tensor_name, debug_ops_spec=debug_ops_spec,
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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 tensor_name is None:
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tensor_name = ""
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tensor_name = _execute.make_str(tensor_name, "tensor_name")
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if debug_ops_spec is None:
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debug_ops_spec = []
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if not isinstance(debug_ops_spec, (list, tuple)):
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raise TypeError(
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"Expected list for 'debug_ops_spec' argument to "
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"'copy' Op, not %r." % debug_ops_spec)
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debug_ops_spec = [_execute.make_str(_s, "debug_ops_spec") for _s in debug_ops_spec]
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"Copy", input=input, tensor_name=tensor_name,
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debug_ops_spec=debug_ops_spec, name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("T", _op._get_attr_type("T"), "tensor_name",
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_op.get_attr("tensor_name"), "debug_ops_spec",
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_op.get_attr("debug_ops_spec"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"Copy", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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Copy = tf_export("raw_ops.Copy")(_ops.to_raw_op(copy))
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def copy_eager_fallback(input, tensor_name, debug_ops_spec, name, ctx):
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if tensor_name is None:
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tensor_name = ""
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tensor_name = _execute.make_str(tensor_name, "tensor_name")
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if debug_ops_spec is None:
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debug_ops_spec = []
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if not isinstance(debug_ops_spec, (list, tuple)):
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raise TypeError(
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"Expected list for 'debug_ops_spec' argument to "
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"'copy' Op, not %r." % debug_ops_spec)
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debug_ops_spec = [_execute.make_str(_s, "debug_ops_spec") for _s in debug_ops_spec]
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_attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
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_inputs_flat = [input]
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_attrs = ("T", _attr_T, "tensor_name", tensor_name, "debug_ops_spec",
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debug_ops_spec)
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_result = _execute.execute(b"Copy", 1, inputs=_inputs_flat, attrs=_attrs,
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ctx=ctx, name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"Copy", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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def copy_host(input, tensor_name="", debug_ops_spec=[], name=None):
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r"""Copy a tensor to host.
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Performs CPU-to-CPU deep-copying of tensor.
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N.B.: If the all downstream attached debug ops are disabled given the current
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gRPC gating status, the output will simply forward the input tensor without
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deep-copying. See the documentation of Debug* ops for more details.
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Unlike the Copy Op, this op has HostMemory constraint on its input or output.
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Args:
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input: A `Tensor`. Input tensor.
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tensor_name: An optional `string`. Defaults to `""`.
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The name of the input tensor.
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debug_ops_spec: An optional list of `strings`. Defaults to `[]`.
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A list of debug op spec (op, url, gated_grpc) for attached debug
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ops. Each element of the list has the format
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<debug_op>;<grpc_url>;<gated_grpc>, wherein gated_grpc is boolean represented
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as 0/1. E.g., "DebugIdentity;grpc://foo:3333;1",
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"DebugIdentity;file:///tmp/tfdbg_1;0".
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name: A name for the operation (optional).
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Returns:
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A `Tensor`. Has the same type as `input`.
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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, "CopyHost", name, input, "tensor_name", tensor_name,
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"debug_ops_spec", debug_ops_spec)
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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 copy_host_eager_fallback(
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input, tensor_name=tensor_name, debug_ops_spec=debug_ops_spec,
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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 tensor_name is None:
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tensor_name = ""
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tensor_name = _execute.make_str(tensor_name, "tensor_name")
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if debug_ops_spec is None:
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debug_ops_spec = []
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if not isinstance(debug_ops_spec, (list, tuple)):
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raise TypeError(
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"Expected list for 'debug_ops_spec' argument to "
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"'copy_host' Op, not %r." % debug_ops_spec)
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debug_ops_spec = [_execute.make_str(_s, "debug_ops_spec") for _s in debug_ops_spec]
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"CopyHost", input=input, tensor_name=tensor_name,
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debug_ops_spec=debug_ops_spec, name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("T", _op._get_attr_type("T"), "tensor_name",
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_op.get_attr("tensor_name"), "debug_ops_spec",
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_op.get_attr("debug_ops_spec"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"CopyHost", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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CopyHost = tf_export("raw_ops.CopyHost")(_ops.to_raw_op(copy_host))
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def copy_host_eager_fallback(input, tensor_name, debug_ops_spec, name, ctx):
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if tensor_name is None:
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tensor_name = ""
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tensor_name = _execute.make_str(tensor_name, "tensor_name")
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if debug_ops_spec is None:
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debug_ops_spec = []
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if not isinstance(debug_ops_spec, (list, tuple)):
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raise TypeError(
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"Expected list for 'debug_ops_spec' argument to "
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"'copy_host' Op, not %r." % debug_ops_spec)
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debug_ops_spec = [_execute.make_str(_s, "debug_ops_spec") for _s in debug_ops_spec]
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_attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
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_inputs_flat = [input]
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_attrs = ("T", _attr_T, "tensor_name", tensor_name, "debug_ops_spec",
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debug_ops_spec)
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_result = _execute.execute(b"CopyHost", 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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"CopyHost", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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def debug_identity(input, device_name="", tensor_name="", debug_urls=[], gated_grpc=False, name=None):
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r"""Provides an identity mapping of the non-Ref type input tensor for debugging.
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Provides an identity mapping of the non-Ref type input tensor for debugging.
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Args:
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input: A `Tensor`. Input tensor, non-Reference type
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device_name: An optional `string`. Defaults to `""`.
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Name of the device on which the tensor resides.
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tensor_name: An optional `string`. Defaults to `""`.
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Name of the input tensor.
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debug_urls: An optional list of `strings`. Defaults to `[]`.
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List of URLs to debug targets, e.g.,
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file:///foo/tfdbg_dump, grpc:://localhost:11011
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gated_grpc: An optional `bool`. Defaults to `False`.
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Whether this op will be gated. If any of the debug_urls of this
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debug node is of the grpc:// scheme, when the value of this attribute is set
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to True, the data will not actually be sent via the grpc stream unless this
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debug op has been enabled at the debug_url. If all of the debug_urls of this
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debug node are of the grpc:// scheme and the debug op is enabled at none of
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them, the output will be an empty Tensor.
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name: A name for the operation (optional).
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Returns:
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A `Tensor`. Has the same type as `input`.
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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, "DebugIdentity", name, input, "device_name", device_name,
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"tensor_name", tensor_name, "debug_urls", debug_urls, "gated_grpc",
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gated_grpc)
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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 debug_identity_eager_fallback(
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input, device_name=device_name, tensor_name=tensor_name,
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debug_urls=debug_urls, gated_grpc=gated_grpc, 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 device_name is None:
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device_name = ""
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device_name = _execute.make_str(device_name, "device_name")
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if tensor_name is None:
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tensor_name = ""
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tensor_name = _execute.make_str(tensor_name, "tensor_name")
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if debug_urls is None:
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debug_urls = []
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if not isinstance(debug_urls, (list, tuple)):
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raise TypeError(
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"Expected list for 'debug_urls' argument to "
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"'debug_identity' Op, not %r." % debug_urls)
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debug_urls = [_execute.make_str(_s, "debug_urls") for _s in debug_urls]
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if gated_grpc is None:
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gated_grpc = False
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gated_grpc = _execute.make_bool(gated_grpc, "gated_grpc")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"DebugIdentity", input=input, device_name=device_name,
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tensor_name=tensor_name, debug_urls=debug_urls,
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gated_grpc=gated_grpc, name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("T", _op._get_attr_type("T"), "device_name",
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_op.get_attr("device_name"), "tensor_name",
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_op.get_attr("tensor_name"), "debug_urls",
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_op.get_attr("debug_urls"), "gated_grpc",
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_op._get_attr_bool("gated_grpc"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"DebugIdentity", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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DebugIdentity = tf_export("raw_ops.DebugIdentity")(_ops.to_raw_op(debug_identity))
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def debug_identity_eager_fallback(input, device_name, tensor_name, debug_urls, gated_grpc, name, ctx):
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if device_name is None:
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device_name = ""
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device_name = _execute.make_str(device_name, "device_name")
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if tensor_name is None:
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tensor_name = ""
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tensor_name = _execute.make_str(tensor_name, "tensor_name")
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if debug_urls is None:
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debug_urls = []
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if not isinstance(debug_urls, (list, tuple)):
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raise TypeError(
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"Expected list for 'debug_urls' argument to "
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"'debug_identity' Op, not %r." % debug_urls)
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debug_urls = [_execute.make_str(_s, "debug_urls") for _s in debug_urls]
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if gated_grpc is None:
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gated_grpc = False
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gated_grpc = _execute.make_bool(gated_grpc, "gated_grpc")
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_attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
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_inputs_flat = [input]
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_attrs = ("T", _attr_T, "device_name", device_name, "tensor_name",
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tensor_name, "debug_urls", debug_urls, "gated_grpc", gated_grpc)
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_result = _execute.execute(b"DebugIdentity", 1, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"DebugIdentity", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def debug_identity_v2(input, tfdbg_context_id="", op_name="", output_slot=-1, tensor_debug_mode=-1, debug_urls=[], circular_buffer_size=1000, tfdbg_run_id="", name=None):
|
||
|
|
r"""Debug Identity V2 Op.
|
||
|
|
|
||
|
|
Provides an identity mapping from input to output, while writing the content of
|
||
|
|
the input tensor by calling DebugEventsWriter.
|
||
|
|
|
||
|
|
The semantics of the input tensor depends on tensor_debug_mode. In typical
|
||
|
|
usage, the input tensor comes directly from the user computation only when
|
||
|
|
graph_debug_mode is FULL_TENSOR (see protobuf/debug_event.proto for a
|
||
|
|
list of all the possible values of graph_debug_mode). For the other debug modes,
|
||
|
|
the input tensor should be produced by an additional op or subgraph that
|
||
|
|
computes summary information about one or more tensors.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
input: A `Tensor`. Input tensor, non-Reference type
|
||
|
|
tfdbg_context_id: An optional `string`. Defaults to `""`.
|
||
|
|
A tfdbg-generated ID for the context that the op belongs to,
|
||
|
|
e.g., a concrete compiled tf.function.
|
||
|
|
op_name: An optional `string`. Defaults to `""`.
|
||
|
|
Optional. Name of the op that the debug op is concerned with.
|
||
|
|
Used only for single-tensor trace.
|
||
|
|
output_slot: An optional `int`. Defaults to `-1`.
|
||
|
|
Optional. Output slot index of the tensor that the debug op
|
||
|
|
is concerned with. Used only for single-tensor trace.
|
||
|
|
tensor_debug_mode: An optional `int`. Defaults to `-1`.
|
||
|
|
TensorDebugMode enum value. See debug_event.proto for details.
|
||
|
|
debug_urls: An optional list of `strings`. Defaults to `[]`.
|
||
|
|
List of URLs to debug targets, e.g., file:///foo/tfdbg_dump.
|
||
|
|
circular_buffer_size: An optional `int`. Defaults to `1000`.
|
||
|
|
tfdbg_run_id: An optional `string`. Defaults to `""`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `input`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "DebugIdentityV2", name, input, "tfdbg_context_id",
|
||
|
|
tfdbg_context_id, "op_name", op_name, "output_slot", output_slot,
|
||
|
|
"tensor_debug_mode", tensor_debug_mode, "debug_urls", debug_urls,
|
||
|
|
"circular_buffer_size", circular_buffer_size, "tfdbg_run_id",
|
||
|
|
tfdbg_run_id)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return debug_identity_v2_eager_fallback(
|
||
|
|
input, tfdbg_context_id=tfdbg_context_id, op_name=op_name,
|
||
|
|
output_slot=output_slot, tensor_debug_mode=tensor_debug_mode,
|
||
|
|
debug_urls=debug_urls, circular_buffer_size=circular_buffer_size,
|
||
|
|
tfdbg_run_id=tfdbg_run_id, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
if tfdbg_context_id is None:
|
||
|
|
tfdbg_context_id = ""
|
||
|
|
tfdbg_context_id = _execute.make_str(tfdbg_context_id, "tfdbg_context_id")
|
||
|
|
if op_name is None:
|
||
|
|
op_name = ""
|
||
|
|
op_name = _execute.make_str(op_name, "op_name")
|
||
|
|
if output_slot is None:
|
||
|
|
output_slot = -1
|
||
|
|
output_slot = _execute.make_int(output_slot, "output_slot")
|
||
|
|
if tensor_debug_mode is None:
|
||
|
|
tensor_debug_mode = -1
|
||
|
|
tensor_debug_mode = _execute.make_int(tensor_debug_mode, "tensor_debug_mode")
|
||
|
|
if debug_urls is None:
|
||
|
|
debug_urls = []
|
||
|
|
if not isinstance(debug_urls, (list, tuple)):
|
||
|
|
raise TypeError(
|
||
|
|
"Expected list for 'debug_urls' argument to "
|
||
|
|
"'debug_identity_v2' Op, not %r." % debug_urls)
|
||
|
|
debug_urls = [_execute.make_str(_s, "debug_urls") for _s in debug_urls]
|
||
|
|
if circular_buffer_size is None:
|
||
|
|
circular_buffer_size = 1000
|
||
|
|
circular_buffer_size = _execute.make_int(circular_buffer_size, "circular_buffer_size")
|
||
|
|
if tfdbg_run_id is None:
|
||
|
|
tfdbg_run_id = ""
|
||
|
|
tfdbg_run_id = _execute.make_str(tfdbg_run_id, "tfdbg_run_id")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"DebugIdentityV2", input=input, tfdbg_context_id=tfdbg_context_id,
|
||
|
|
op_name=op_name, output_slot=output_slot,
|
||
|
|
tensor_debug_mode=tensor_debug_mode,
|
||
|
|
debug_urls=debug_urls,
|
||
|
|
circular_buffer_size=circular_buffer_size,
|
||
|
|
tfdbg_run_id=tfdbg_run_id, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"), "tfdbg_context_id",
|
||
|
|
_op.get_attr("tfdbg_context_id"), "op_name",
|
||
|
|
_op.get_attr("op_name"), "output_slot",
|
||
|
|
_op._get_attr_int("output_slot"), "tensor_debug_mode",
|
||
|
|
_op._get_attr_int("tensor_debug_mode"), "debug_urls",
|
||
|
|
_op.get_attr("debug_urls"), "circular_buffer_size",
|
||
|
|
_op._get_attr_int("circular_buffer_size"), "tfdbg_run_id",
|
||
|
|
_op.get_attr("tfdbg_run_id"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"DebugIdentityV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
DebugIdentityV2 = tf_export("raw_ops.DebugIdentityV2")(_ops.to_raw_op(debug_identity_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def debug_identity_v2_eager_fallback(input, tfdbg_context_id, op_name, output_slot, tensor_debug_mode, debug_urls, circular_buffer_size, tfdbg_run_id, name, ctx):
|
||
|
|
if tfdbg_context_id is None:
|
||
|
|
tfdbg_context_id = ""
|
||
|
|
tfdbg_context_id = _execute.make_str(tfdbg_context_id, "tfdbg_context_id")
|
||
|
|
if op_name is None:
|
||
|
|
op_name = ""
|
||
|
|
op_name = _execute.make_str(op_name, "op_name")
|
||
|
|
if output_slot is None:
|
||
|
|
output_slot = -1
|
||
|
|
output_slot = _execute.make_int(output_slot, "output_slot")
|
||
|
|
if tensor_debug_mode is None:
|
||
|
|
tensor_debug_mode = -1
|
||
|
|
tensor_debug_mode = _execute.make_int(tensor_debug_mode, "tensor_debug_mode")
|
||
|
|
if debug_urls is None:
|
||
|
|
debug_urls = []
|
||
|
|
if not isinstance(debug_urls, (list, tuple)):
|
||
|
|
raise TypeError(
|
||
|
|
"Expected list for 'debug_urls' argument to "
|
||
|
|
"'debug_identity_v2' Op, not %r." % debug_urls)
|
||
|
|
debug_urls = [_execute.make_str(_s, "debug_urls") for _s in debug_urls]
|
||
|
|
if circular_buffer_size is None:
|
||
|
|
circular_buffer_size = 1000
|
||
|
|
circular_buffer_size = _execute.make_int(circular_buffer_size, "circular_buffer_size")
|
||
|
|
if tfdbg_run_id is None:
|
||
|
|
tfdbg_run_id = ""
|
||
|
|
tfdbg_run_id = _execute.make_str(tfdbg_run_id, "tfdbg_run_id")
|
||
|
|
_attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
|
||
|
|
_inputs_flat = [input]
|
||
|
|
_attrs = ("T", _attr_T, "tfdbg_context_id", tfdbg_context_id, "op_name",
|
||
|
|
op_name, "output_slot", output_slot, "tensor_debug_mode", tensor_debug_mode,
|
||
|
|
"debug_urls", debug_urls, "circular_buffer_size", circular_buffer_size,
|
||
|
|
"tfdbg_run_id", tfdbg_run_id)
|
||
|
|
_result = _execute.execute(b"DebugIdentityV2", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"DebugIdentityV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def debug_nan_count(input, device_name="", tensor_name="", debug_urls=[], gated_grpc=False, name=None):
|
||
|
|
r"""Debug NaN Value Counter Op.
|
||
|
|
|
||
|
|
Counts number of NaNs in the input tensor, for debugging.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
input: A `Tensor`. Input tensor, non-Reference type.
|
||
|
|
device_name: An optional `string`. Defaults to `""`.
|
||
|
|
tensor_name: An optional `string`. Defaults to `""`.
|
||
|
|
Name of the input tensor.
|
||
|
|
debug_urls: An optional list of `strings`. Defaults to `[]`.
|
||
|
|
List of URLs to debug targets, e.g.,
|
||
|
|
file:///foo/tfdbg_dump, grpc:://localhost:11011.
|
||
|
|
gated_grpc: An optional `bool`. Defaults to `False`.
|
||
|
|
Whether this op will be gated. If any of the debug_urls of this
|
||
|
|
debug node is of the grpc:// scheme, when the value of this attribute is set
|
||
|
|
to True, the data will not actually be sent via the grpc stream unless this
|
||
|
|
debug op has been enabled at the debug_url. If all of the debug_urls of this
|
||
|
|
debug node are of the grpc:// scheme and the debug op is enabled at none of
|
||
|
|
them, the output will be an empty Tensor.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `int64`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "DebugNanCount", name, input, "device_name", device_name,
|
||
|
|
"tensor_name", tensor_name, "debug_urls", debug_urls, "gated_grpc",
|
||
|
|
gated_grpc)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return debug_nan_count_eager_fallback(
|
||
|
|
input, device_name=device_name, tensor_name=tensor_name,
|
||
|
|
debug_urls=debug_urls, gated_grpc=gated_grpc, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
if device_name is None:
|
||
|
|
device_name = ""
|
||
|
|
device_name = _execute.make_str(device_name, "device_name")
|
||
|
|
if tensor_name is None:
|
||
|
|
tensor_name = ""
|
||
|
|
tensor_name = _execute.make_str(tensor_name, "tensor_name")
|
||
|
|
if debug_urls is None:
|
||
|
|
debug_urls = []
|
||
|
|
if not isinstance(debug_urls, (list, tuple)):
|
||
|
|
raise TypeError(
|
||
|
|
"Expected list for 'debug_urls' argument to "
|
||
|
|
"'debug_nan_count' Op, not %r." % debug_urls)
|
||
|
|
debug_urls = [_execute.make_str(_s, "debug_urls") for _s in debug_urls]
|
||
|
|
if gated_grpc is None:
|
||
|
|
gated_grpc = False
|
||
|
|
gated_grpc = _execute.make_bool(gated_grpc, "gated_grpc")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"DebugNanCount", input=input, device_name=device_name,
|
||
|
|
tensor_name=tensor_name, debug_urls=debug_urls,
|
||
|
|
gated_grpc=gated_grpc, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"), "device_name",
|
||
|
|
_op.get_attr("device_name"), "tensor_name",
|
||
|
|
_op.get_attr("tensor_name"), "debug_urls",
|
||
|
|
_op.get_attr("debug_urls"), "gated_grpc",
|
||
|
|
_op._get_attr_bool("gated_grpc"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"DebugNanCount", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
DebugNanCount = tf_export("raw_ops.DebugNanCount")(_ops.to_raw_op(debug_nan_count))
|
||
|
|
|
||
|
|
|
||
|
|
def debug_nan_count_eager_fallback(input, device_name, tensor_name, debug_urls, gated_grpc, name, ctx):
|
||
|
|
if device_name is None:
|
||
|
|
device_name = ""
|
||
|
|
device_name = _execute.make_str(device_name, "device_name")
|
||
|
|
if tensor_name is None:
|
||
|
|
tensor_name = ""
|
||
|
|
tensor_name = _execute.make_str(tensor_name, "tensor_name")
|
||
|
|
if debug_urls is None:
|
||
|
|
debug_urls = []
|
||
|
|
if not isinstance(debug_urls, (list, tuple)):
|
||
|
|
raise TypeError(
|
||
|
|
"Expected list for 'debug_urls' argument to "
|
||
|
|
"'debug_nan_count' Op, not %r." % debug_urls)
|
||
|
|
debug_urls = [_execute.make_str(_s, "debug_urls") for _s in debug_urls]
|
||
|
|
if gated_grpc is None:
|
||
|
|
gated_grpc = False
|
||
|
|
gated_grpc = _execute.make_bool(gated_grpc, "gated_grpc")
|
||
|
|
_attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
|
||
|
|
_inputs_flat = [input]
|
||
|
|
_attrs = ("T", _attr_T, "device_name", device_name, "tensor_name",
|
||
|
|
tensor_name, "debug_urls", debug_urls, "gated_grpc", gated_grpc)
|
||
|
|
_result = _execute.execute(b"DebugNanCount", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"DebugNanCount", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def debug_numeric_summary(input, device_name="", tensor_name="", debug_urls=[], lower_bound=float('-inf'), upper_bound=float('inf'), mute_if_healthy=False, gated_grpc=False, name=None):
|
||
|
|
r"""Debug Numeric Summary Op.
|
||
|
|
|
||
|
|
Provide a basic summary of numeric value types, range and distribution.
|
||
|
|
|
||
|
|
output: A double tensor of shape [14 + nDimensions], where nDimensions is the
|
||
|
|
number of dimensions of the tensor's shape. The elements of output are:
|
||
|
|
[0]: is initialized (1.0) or not (0.0).
|
||
|
|
[1]: total number of elements
|
||
|
|
[2]: NaN element count
|
||
|
|
[3]: generalized -inf count: elements <= lower_bound. lower_bound is -inf by
|
||
|
|
default.
|
||
|
|
[4]: negative element count (excluding -inf), if lower_bound is the default
|
||
|
|
-inf. Otherwise, this is the count of elements > lower_bound and < 0.
|
||
|
|
[5]: zero element count
|
||
|
|
[6]: positive element count (excluding +inf), if upper_bound is the default
|
||
|
|
+inf. Otherwise, this is the count of elements < upper_bound and > 0.
|
||
|
|
[7]: generalized +inf count, elements >= upper_bound. upper_bound is +inf by
|
||
|
|
default.
|
||
|
|
Output elements [1:8] are all zero, if the tensor is uninitialized.
|
||
|
|
[8]: minimum of all non-inf and non-NaN elements.
|
||
|
|
If uninitialized or no such element exists: +inf.
|
||
|
|
[9]: maximum of all non-inf and non-NaN elements.
|
||
|
|
If uninitialized or no such element exists: -inf.
|
||
|
|
[10]: mean of all non-inf and non-NaN elements.
|
||
|
|
If uninitialized or no such element exists: NaN.
|
||
|
|
[11]: variance of all non-inf and non-NaN elements.
|
||
|
|
If uninitialized or no such element exists: NaN.
|
||
|
|
[12]: Data type of the tensor encoded as an enum integer. See the DataType
|
||
|
|
proto for more details.
|
||
|
|
[13]: Number of dimensions of the tensor (ndims).
|
||
|
|
[14+]: Sizes of the dimensions.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
input: A `Tensor`. Input tensor, non-Reference type.
|
||
|
|
device_name: An optional `string`. Defaults to `""`.
|
||
|
|
tensor_name: An optional `string`. Defaults to `""`.
|
||
|
|
Name of the input tensor.
|
||
|
|
debug_urls: An optional list of `strings`. Defaults to `[]`.
|
||
|
|
List of URLs to debug targets, e.g.,
|
||
|
|
file:///foo/tfdbg_dump, grpc:://localhost:11011.
|
||
|
|
lower_bound: An optional `float`. Defaults to `float('-inf')`.
|
||
|
|
(float) The lower bound <= which values will be included in the
|
||
|
|
generalized -inf count. Default: -inf.
|
||
|
|
upper_bound: An optional `float`. Defaults to `float('inf')`.
|
||
|
|
(float) The upper bound >= which values will be included in the
|
||
|
|
generalized +inf count. Default: +inf.
|
||
|
|
mute_if_healthy: An optional `bool`. Defaults to `False`.
|
||
|
|
(bool) Do not send data to the debug URLs unless at least one
|
||
|
|
of elements [2], [3] and [7] (i.e., the nan count and the generalized -inf and
|
||
|
|
inf counts) is non-zero.
|
||
|
|
gated_grpc: An optional `bool`. Defaults to `False`.
|
||
|
|
Whether this op will be gated. If any of the debug_urls of this
|
||
|
|
debug node is of the grpc:// scheme, when the value of this attribute is set
|
||
|
|
to True, the data will not actually be sent via the grpc stream unless this
|
||
|
|
debug op has been enabled at the debug_url. If all of the debug_urls of this
|
||
|
|
debug node are of the grpc:// scheme and the debug op is enabled at none of
|
||
|
|
them, the output will be an empty Tensor.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `float64`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "DebugNumericSummary", name, input, "device_name", device_name,
|
||
|
|
"tensor_name", tensor_name, "debug_urls", debug_urls, "lower_bound",
|
||
|
|
lower_bound, "upper_bound", upper_bound, "mute_if_healthy",
|
||
|
|
mute_if_healthy, "gated_grpc", gated_grpc)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return debug_numeric_summary_eager_fallback(
|
||
|
|
input, device_name=device_name, tensor_name=tensor_name,
|
||
|
|
debug_urls=debug_urls, lower_bound=lower_bound,
|
||
|
|
upper_bound=upper_bound, mute_if_healthy=mute_if_healthy,
|
||
|
|
gated_grpc=gated_grpc, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
if device_name is None:
|
||
|
|
device_name = ""
|
||
|
|
device_name = _execute.make_str(device_name, "device_name")
|
||
|
|
if tensor_name is None:
|
||
|
|
tensor_name = ""
|
||
|
|
tensor_name = _execute.make_str(tensor_name, "tensor_name")
|
||
|
|
if debug_urls is None:
|
||
|
|
debug_urls = []
|
||
|
|
if not isinstance(debug_urls, (list, tuple)):
|
||
|
|
raise TypeError(
|
||
|
|
"Expected list for 'debug_urls' argument to "
|
||
|
|
"'debug_numeric_summary' Op, not %r." % debug_urls)
|
||
|
|
debug_urls = [_execute.make_str(_s, "debug_urls") for _s in debug_urls]
|
||
|
|
if lower_bound is None:
|
||
|
|
lower_bound = float('-inf')
|
||
|
|
lower_bound = _execute.make_float(lower_bound, "lower_bound")
|
||
|
|
if upper_bound is None:
|
||
|
|
upper_bound = float('inf')
|
||
|
|
upper_bound = _execute.make_float(upper_bound, "upper_bound")
|
||
|
|
if mute_if_healthy is None:
|
||
|
|
mute_if_healthy = False
|
||
|
|
mute_if_healthy = _execute.make_bool(mute_if_healthy, "mute_if_healthy")
|
||
|
|
if gated_grpc is None:
|
||
|
|
gated_grpc = False
|
||
|
|
gated_grpc = _execute.make_bool(gated_grpc, "gated_grpc")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"DebugNumericSummary", input=input, device_name=device_name,
|
||
|
|
tensor_name=tensor_name, debug_urls=debug_urls,
|
||
|
|
lower_bound=lower_bound,
|
||
|
|
upper_bound=upper_bound,
|
||
|
|
mute_if_healthy=mute_if_healthy,
|
||
|
|
gated_grpc=gated_grpc, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"), "device_name",
|
||
|
|
_op.get_attr("device_name"), "tensor_name",
|
||
|
|
_op.get_attr("tensor_name"), "debug_urls",
|
||
|
|
_op.get_attr("debug_urls"), "lower_bound",
|
||
|
|
_op.get_attr("lower_bound"), "upper_bound",
|
||
|
|
_op.get_attr("upper_bound"), "mute_if_healthy",
|
||
|
|
_op._get_attr_bool("mute_if_healthy"), "gated_grpc",
|
||
|
|
_op._get_attr_bool("gated_grpc"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"DebugNumericSummary", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
DebugNumericSummary = tf_export("raw_ops.DebugNumericSummary")(_ops.to_raw_op(debug_numeric_summary))
|
||
|
|
|
||
|
|
|
||
|
|
def debug_numeric_summary_eager_fallback(input, device_name, tensor_name, debug_urls, lower_bound, upper_bound, mute_if_healthy, gated_grpc, name, ctx):
|
||
|
|
if device_name is None:
|
||
|
|
device_name = ""
|
||
|
|
device_name = _execute.make_str(device_name, "device_name")
|
||
|
|
if tensor_name is None:
|
||
|
|
tensor_name = ""
|
||
|
|
tensor_name = _execute.make_str(tensor_name, "tensor_name")
|
||
|
|
if debug_urls is None:
|
||
|
|
debug_urls = []
|
||
|
|
if not isinstance(debug_urls, (list, tuple)):
|
||
|
|
raise TypeError(
|
||
|
|
"Expected list for 'debug_urls' argument to "
|
||
|
|
"'debug_numeric_summary' Op, not %r." % debug_urls)
|
||
|
|
debug_urls = [_execute.make_str(_s, "debug_urls") for _s in debug_urls]
|
||
|
|
if lower_bound is None:
|
||
|
|
lower_bound = float('-inf')
|
||
|
|
lower_bound = _execute.make_float(lower_bound, "lower_bound")
|
||
|
|
if upper_bound is None:
|
||
|
|
upper_bound = float('inf')
|
||
|
|
upper_bound = _execute.make_float(upper_bound, "upper_bound")
|
||
|
|
if mute_if_healthy is None:
|
||
|
|
mute_if_healthy = False
|
||
|
|
mute_if_healthy = _execute.make_bool(mute_if_healthy, "mute_if_healthy")
|
||
|
|
if gated_grpc is None:
|
||
|
|
gated_grpc = False
|
||
|
|
gated_grpc = _execute.make_bool(gated_grpc, "gated_grpc")
|
||
|
|
_attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
|
||
|
|
_inputs_flat = [input]
|
||
|
|
_attrs = ("T", _attr_T, "device_name", device_name, "tensor_name",
|
||
|
|
tensor_name, "debug_urls", debug_urls, "lower_bound", lower_bound,
|
||
|
|
"upper_bound", upper_bound, "mute_if_healthy", mute_if_healthy,
|
||
|
|
"gated_grpc", gated_grpc)
|
||
|
|
_result = _execute.execute(b"DebugNumericSummary", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"DebugNumericSummary", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def debug_numeric_summary_v2(input, output_dtype=_dtypes.float32, tensor_debug_mode=-1, tensor_id=-1, name=None):
|
||
|
|
r"""Debug Numeric Summary V2 Op.
|
||
|
|
|
||
|
|
Computes a numeric summary of the input tensor. The shape of the output
|
||
|
|
depends on the tensor_debug_mode attribute.
|
||
|
|
This op is used internally by TensorFlow Debugger (tfdbg) v2.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
input: A `Tensor`. Input tensor, to be summarized by the op.
|
||
|
|
output_dtype: An optional `tf.DType` from: `tf.float32, tf.float64`. Defaults to `tf.float32`.
|
||
|
|
Optional. The type of the output. Can be float32 or float64 (default: float32).
|
||
|
|
tensor_debug_mode: An optional `int`. Defaults to `-1`.
|
||
|
|
Tensor debug mode: the mode in which the input tensor is summarized
|
||
|
|
by the op. See the TensorDebugMode enum in
|
||
|
|
tensorflow/core/protobuf/debug_event.proto for details.
|
||
|
|
|
||
|
|
Supported values:
|
||
|
|
2 (CURT_HEALTH): Output a float32/64 tensor of shape [2]. The 1st
|
||
|
|
element is the tensor_id, if provided, and -1 otherwise. The 2nd
|
||
|
|
element is a bit which is set to 1 if the input tensor has an
|
||
|
|
infinity or nan value, or zero otherwise.
|
||
|
|
|
||
|
|
3 (CONCISE_HEALTH): Output a float32/64 tensor of shape [5]. The 1st
|
||
|
|
element is the tensor_id, if provided, and -1 otherwise. The
|
||
|
|
remaining four slots are the total number of elements, -infs,
|
||
|
|
+infs, and nans in the input tensor respectively.
|
||
|
|
|
||
|
|
4 (FULL_HEALTH): Output a float32/64 tensor of shape [11]. The 1st
|
||
|
|
element is the tensor_id, if provided, and -1 otherwise. The 2nd
|
||
|
|
element is the device_id, if provided, and -1 otherwise. The 3rd
|
||
|
|
element holds the datatype value of the input tensor as according
|
||
|
|
to the enumerated type in tensorflow/core/framework/types.proto.
|
||
|
|
The remaining elements hold the total number of elements, -infs,
|
||
|
|
+infs, nans, negative finite numbers, zeros, and positive finite
|
||
|
|
numbers in the input tensor respectively.
|
||
|
|
|
||
|
|
5 (SHAPE): Output a float32/64 tensor of shape [10]. The 1st
|
||
|
|
element is the tensor_id, if provided, and -1 otherwise. The 2nd
|
||
|
|
element holds the datatype value of the input tensor as according
|
||
|
|
to the enumerated type in tensorflow/core/framework/types.proto.
|
||
|
|
The 3rd element holds the rank of the tensor. The 4th element holds
|
||
|
|
the number of elements within the tensor. Finally the remaining 6
|
||
|
|
elements hold the shape of the tensor. If the rank of the tensor
|
||
|
|
is lower than 6, the shape is right padded with zeros. If the rank
|
||
|
|
is greater than 6, the head of the shape is truncated.
|
||
|
|
|
||
|
|
6 (FULL_NUMERICS): Output a float32/64 tensor of shape [22]. The 1st
|
||
|
|
element is the tensor_id, if provided, and -1 otherwise. The 2nd
|
||
|
|
element is the device_id, if provided, and -1 otherwise. The 3rd
|
||
|
|
element holds the datatype value of the input tensor as according
|
||
|
|
to the enumerated type in tensorflow/core/framework/types.proto.
|
||
|
|
The 4th element holds the rank of the tensor. The 5th to 11th
|
||
|
|
elements hold the shape of the tensor. If the rank of the tensor
|
||
|
|
is lower than 6, the shape is right padded with zeros. If the rank
|
||
|
|
is greater than 6, the head of the shape is truncated. The 12th to
|
||
|
|
18th elements hold the number of elements, -infs, +infs, nans,
|
||
|
|
denormal floats, negative finite numbers, zeros, and positive
|
||
|
|
finite numbers in the input tensor respectively. The final four
|
||
|
|
elements hold the min value, max value, mean, and variance of the
|
||
|
|
input tensor.
|
||
|
|
|
||
|
|
8 (REDUCE_INF_NAN_THREE_SLOTS): Output a float32/64 tensor of shape
|
||
|
|
[3]. The 1st element is -inf if any elements of the input tensor
|
||
|
|
is -inf, or zero otherwise. The 2nd element is +inf if any elements
|
||
|
|
of the input tensor is +inf, or zero otherwise. The 3rd element is
|
||
|
|
nan if any element of the input tensor is nan, or zero otherwise.
|
||
|
|
tensor_id: An optional `int`. Defaults to `-1`.
|
||
|
|
Optional. An integer identifier for the tensor being summarized by this op.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `output_dtype`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "DebugNumericSummaryV2", name, input, "output_dtype",
|
||
|
|
output_dtype, "tensor_debug_mode", tensor_debug_mode, "tensor_id",
|
||
|
|
tensor_id)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return debug_numeric_summary_v2_eager_fallback(
|
||
|
|
input, output_dtype=output_dtype,
|
||
|
|
tensor_debug_mode=tensor_debug_mode, tensor_id=tensor_id, name=name,
|
||
|
|
ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
if output_dtype is None:
|
||
|
|
output_dtype = _dtypes.float32
|
||
|
|
output_dtype = _execute.make_type(output_dtype, "output_dtype")
|
||
|
|
if tensor_debug_mode is None:
|
||
|
|
tensor_debug_mode = -1
|
||
|
|
tensor_debug_mode = _execute.make_int(tensor_debug_mode, "tensor_debug_mode")
|
||
|
|
if tensor_id is None:
|
||
|
|
tensor_id = -1
|
||
|
|
tensor_id = _execute.make_int(tensor_id, "tensor_id")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"DebugNumericSummaryV2", input=input, output_dtype=output_dtype,
|
||
|
|
tensor_debug_mode=tensor_debug_mode,
|
||
|
|
tensor_id=tensor_id, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("output_dtype", _op._get_attr_type("output_dtype"), "T",
|
||
|
|
_op._get_attr_type("T"), "tensor_debug_mode",
|
||
|
|
_op._get_attr_int("tensor_debug_mode"), "tensor_id",
|
||
|
|
_op._get_attr_int("tensor_id"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"DebugNumericSummaryV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
DebugNumericSummaryV2 = tf_export("raw_ops.DebugNumericSummaryV2")(_ops.to_raw_op(debug_numeric_summary_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def debug_numeric_summary_v2_eager_fallback(input, output_dtype, tensor_debug_mode, tensor_id, name, ctx):
|
||
|
|
if output_dtype is None:
|
||
|
|
output_dtype = _dtypes.float32
|
||
|
|
output_dtype = _execute.make_type(output_dtype, "output_dtype")
|
||
|
|
if tensor_debug_mode is None:
|
||
|
|
tensor_debug_mode = -1
|
||
|
|
tensor_debug_mode = _execute.make_int(tensor_debug_mode, "tensor_debug_mode")
|
||
|
|
if tensor_id is None:
|
||
|
|
tensor_id = -1
|
||
|
|
tensor_id = _execute.make_int(tensor_id, "tensor_id")
|
||
|
|
_attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
|
||
|
|
_inputs_flat = [input]
|
||
|
|
_attrs = ("output_dtype", output_dtype, "T", _attr_T, "tensor_debug_mode",
|
||
|
|
tensor_debug_mode, "tensor_id", tensor_id)
|
||
|
|
_result = _execute.execute(b"DebugNumericSummaryV2", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"DebugNumericSummaryV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|