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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Connects all half, float and double tensors to CheckNumericsOp."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.eager import context
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export
@tf_export("debugging.assert_all_finite", "verify_tensor_all_finite")
@deprecation.deprecated_endpoints("verify_tensor_all_finite")
def verify_tensor_all_finite(t, msg, name=None):
"""Assert that the tensor does not contain any NaN's or Inf's.
Args:
t: Tensor to check.
msg: Message to log on failure.
name: A name for this operation (optional).
Returns:
Same tensor as `t`.
"""
with ops.name_scope(name, "VerifyFinite", [t]) as name:
t = ops.convert_to_tensor(t, name="t")
with ops.colocate_with(t):
verify_input = array_ops.check_numerics(t, message=msg)
out = control_flow_ops.with_dependencies([verify_input], t)
return out
@tf_export("add_check_numerics_ops")
def add_check_numerics_ops():
"""Connect a `check_numerics` to every floating point tensor.
`check_numerics` operations themselves are added for each `half`, `float`,
or `double` tensor in the graph. For all ops in the graph, the
`check_numerics` op for all of its (`half`, `float`, or `double`) inputs
is guaranteed to run before the `check_numerics` op on any of its outputs.
Note: This API is not compatible with the use of `tf.cond` or
`tf.while_loop`, and will raise a `ValueError` if you attempt to call it
in such a graph.
Returns:
A `group` op depending on all `check_numerics` ops added.
Raises:
ValueError: If the graph contains any numeric operations in a control flow
structure.
RuntimeError: If called with eager execution enabled.
@compatibility(eager)
Not compatible with eager execution. To check for `Inf`s and `NaN`s under
eager execution, call tfe.seterr(inf_or_nan='raise') once before executing
the checked operations.
@enc_compatibility
"""
if context.executing_eagerly():
raise RuntimeError(
"add_check_numerics_ops() is not compatible with eager execution. "
"To check for Inf's and NaN's under eager execution, call "
"tfe.seterr(inf_or_nan='raise') once before executing the "
"checked operations.")
check_op = []
# This code relies on the ordering of ops in get_operations().
# The producer of a tensor always comes before that tensor's consumer in
# this list. This is true because get_operations() returns ops in the order
# added, and an op can only be added after its inputs are added.
for op in ops.get_default_graph().get_operations():
for output in op.outputs:
if output.dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
if op._get_control_flow_context() is not None: # pylint: disable=protected-access
raise ValueError("`tf.add_check_numerics_ops() is not compatible "
"with TensorFlow control flow operations such as "
"`tf.cond()` or `tf.while_loop()`.")
message = op.name + ":" + str(output.value_index)
with ops.control_dependencies(check_op):
check_op = [array_ops.check_numerics(output, message=message)]
return control_flow_ops.group(*check_op)
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