Intelegentny_Pszczelarz/.venv/Lib/site-packages/tensorflow/python/ops/numerics.py
2023-06-19 00:49:18 +02:00

132 lines
5.0 KiB
Python

# 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 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 import dispatch
from tensorflow.python.util.tf_export import tf_export
@tf_export(v1=["debugging.assert_all_finite", "verify_tensor_all_finite"])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints("verify_tensor_all_finite")
def verify_tensor_all_finite(t=None, msg=None, name=None, x=None, message=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).
x: Alias for t.
message: Alias for msg.
Returns:
Same tensor as `t`.
"""
x = deprecation.deprecated_argument_lookup("x", x, "t", t)
message = deprecation.deprecated_argument_lookup(
"message", message, "msg", msg)
return verify_tensor_all_finite_v2(x, message, name)
@tf_export("debugging.assert_all_finite", v1=[])
@dispatch.add_dispatch_support
def verify_tensor_all_finite_v2(x, message, name=None):
"""Assert that the tensor does not contain any NaN's or Inf's.
>>> @tf.function
... def f(x):
... x = tf.debugging.assert_all_finite(x, 'Input x must be all finite')
... return x + 1
>>> f(tf.constant([np.inf, 1, 2]))
Traceback (most recent call last):
...
InvalidArgumentError: ...
Args:
x: Tensor to check.
message: Message to log on failure.
name: A name for this operation (optional).
Returns:
Same tensor as `x`.
"""
with ops.name_scope(name, "VerifyFinite", [x]) as name:
x = ops.convert_to_tensor(x, name="x")
with ops.colocate_with(x):
verify_input = array_ops.check_numerics(x, message=message)
out = control_flow_ops.with_dependencies([verify_input], x)
return out
@tf_export(v1=["add_check_numerics_ops"])
def add_check_numerics_ops():
"""Connect a `tf.debugging.check_numerics` to every floating point tensor.
`check_numerics` operations themselves are added for each `half`, `float`,
or `double` tensor in the current default 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 `tf.debugging.enable_check_numerics()` once before
executing the checked operations.
@end_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 "
"tf.debugging.enable_check_numerics() 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)