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check_ops.py
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2184 lines (1774 loc) · 77.1 KB
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# Copyright 2016 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.
# ==============================================================================
# pylint: disable=g-short-docstring-punctuation
"""Asserts and Boolean Checks."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
from tensorflow.python.eager import context
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.util import compat
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export
NUMERIC_TYPES = frozenset(
[dtypes.float32, dtypes.float64, dtypes.int8, dtypes.int16, dtypes.int32,
dtypes.int64, dtypes.uint8, dtypes.qint8, dtypes.qint32, dtypes.quint8,
dtypes.complex64])
__all__ = [
'assert_negative',
'assert_positive',
'assert_proper_iterable',
'assert_non_negative',
'assert_non_positive',
'assert_equal',
'assert_none_equal',
'assert_near',
'assert_integer',
'assert_less',
'assert_less_equal',
'assert_greater',
'assert_greater_equal',
'assert_rank',
'assert_rank_at_least',
'assert_rank_in',
'assert_same_float_dtype',
'assert_scalar',
'assert_type',
'assert_shapes',
'is_non_decreasing',
'is_numeric_tensor',
'is_strictly_increasing',
]
def _maybe_constant_value_string(t):
if not isinstance(t, ops.Tensor):
return str(t)
const_t = tensor_util.constant_value(t)
if const_t is not None:
return str(const_t)
return t
def _assert_static(condition, data):
"""Raises a InvalidArgumentError with as much information as possible."""
if not condition:
data_static = [_maybe_constant_value_string(x) for x in data]
raise errors.InvalidArgumentError(node_def=None, op=None,
message='\n'.join(data_static))
def _shape_and_dtype_str(tensor):
"""Returns a string containing tensor's shape and dtype."""
return 'shape=%s dtype=%s' % (tensor.shape, tensor.dtype.name)
def _unary_assert_doc(sym, sym_name):
"""Common docstring for assert_* ops that evaluate a unary predicate over every element of a tensor.
Args:
sym: Mathematical symbol for the check performed on each element, i.e. "> 0"
sym_name: English-language name for the op described by sym
Returns:
Decorator that adds the appropriate docstring to the function for symbol
`sym`.
"""
def _decorator(func):
"""Generated decorator that adds the appropriate docstring to the function for symbol `sym`.
Args:
func: Function for a TensorFlow op
Returns:
Version of `func` with documentation attached.
"""
opname = func.__name__
cap_sym_name = sym_name.capitalize()
func.__doc__ = """
Assert the condition `x {sym}` holds element-wise.
When running in graph mode, you should add a dependency on this operation
to ensure that it runs. Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.debugging.{opname}(x, y)]):
output = tf.reduce_sum(x)
```
{sym_name} means, for every element `x[i]` of `x`, we have `x[i] {sym}`.
If `x` is empty this is trivially satisfied.
Args:
x: Numeric `Tensor`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "{opname}".
Returns:
Op that raises `InvalidArgumentError` if `x {sym}` is False.
@compatibility(eager)
returns None
@end_compatibility
Raises:
InvalidArgumentError: if the check can be performed immediately and
`x {sym}` is False. The check can be performed immediately during
eager execution or if `x` is statically known.
""".format(
sym=sym, sym_name=cap_sym_name, opname=opname)
return func
return _decorator
def _binary_assert_doc(sym):
"""Common docstring for most of the v1 assert_* ops that compare two tensors element-wise.
Args:
sym: Binary operation symbol, i.e. "=="
Returns:
Decorator that adds the appropriate docstring to the function for
symbol `sym`.
"""
def _decorator(func):
"""Generated decorator that adds the appropriate docstring to the function for symbol `sym`.
Args:
func: Function for a TensorFlow op
Returns:
A version of `func` with documentation attached.
"""
opname = func.__name__
func.__doc__ = """
Assert the condition `x {sym} y` holds element-wise.
This condition holds if for every pair of (possibly broadcast) elements
`x[i]`, `y[i]`, we have `x[i] {sym} y[i]`.
If both `x` and `y` are empty, this is trivially satisfied.
When running in graph mode, you should add a dependency on this operation
to ensure that it runs. Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.compat.v1.{opname}(x, y)]):
output = tf.reduce_sum(x)
```
Args:
x: Numeric `Tensor`.
y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`, `y`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "{opname}".
Returns:
Op that raises `InvalidArgumentError` if `x {sym} y` is False.
@compatibility(eager)
returns None
@end_compatibility
Raises:
InvalidArgumentError: if the check can be performed immediately and
`x {sym} y` is False. The check can be performed immediately during
eager execution or if `x` and `y` are statically known.
""".format(
sym=sym, opname=opname)
return func
return _decorator
def _make_assert_msg_data(sym, x, y, summarize, test_op):
"""Subroutine of _binary_assert that generates the components of the default error message when running in eager mode.
Args:
sym: Mathematical symbol for the test to apply to pairs of tensor elements,
i.e. "=="
x: First input to the assertion after applying `convert_to_tensor()`
y: Second input to the assertion
summarize: Value of the "summarize" parameter to the original assert_* call;
tells how many elements of each tensor to print.
test_op: TensorFlow op that returns a Boolean tensor with True in each
position where the assertion is satisfied.
Returns:
List of tensors and scalars that, when stringified and concatenated,
will produce the error message string.
"""
# Prepare a message with first elements of x and y.
data = []
data.append('Condition x %s y did not hold.' % sym)
if summarize > 0:
if x.shape == y.shape and x.shape.as_list():
# If the shapes of x and y are the same (and not scalars),
# Get the values that actually differed and their indices.
# If shapes are different this information is more confusing
# than useful.
mask = math_ops.logical_not(test_op)
indices = array_ops.where(mask)
indices_np = indices.numpy()
x_vals = array_ops.boolean_mask(x, mask)
y_vals = array_ops.boolean_mask(y, mask)
num_vals = min(summarize, indices_np.shape[0])
data.append('Indices of first %d different values:' % num_vals)
data.append(indices_np[:num_vals])
data.append('Corresponding x values:')
data.append(x_vals.numpy().reshape((-1,))[:num_vals])
data.append('Corresponding y values:')
data.append(y_vals.numpy().reshape((-1,))[:num_vals])
# reshape((-1,)) is the fastest way to get a flat array view.
x_np = x.numpy().reshape((-1,))
y_np = y.numpy().reshape((-1,))
x_sum = min(x_np.size, summarize)
y_sum = min(y_np.size, summarize)
data.append('First %d elements of x:' % x_sum)
data.append(x_np[:x_sum])
data.append('First %d elements of y:' % y_sum)
data.append(y_np[:y_sum])
return data
def _pretty_print(data_item, summarize):
"""Format a data item for use in an error message in eager mode.
Args:
data_item: One of the items in the "data" argument to an assert_* function.
Can be a Tensor or a scalar value.
summarize: How many elements to retain of each tensor-valued entry in data.
Returns:
An appropriate string representation of data_item
"""
if isinstance(data_item, ops.Tensor):
arr = data_item.numpy()
if np.isscalar(arr):
# Tensor.numpy() returns a scalar for zero-dimensional tensors
return str(arr)
else:
flat = arr.reshape((-1,))
lst = [str(x) for x in flat[:summarize]]
if len(lst) < flat.size:
lst.append('...')
return str(lst)
else:
return str(data_item)
def _binary_assert(sym, opname, op_func, static_func, x, y, data, summarize,
message, name):
"""Generic binary elementwise assertion.
Implements the behavior described in _binary_assert_doc() above.
Args:
sym: Mathematical symbol for the test to apply to pairs of tensor elements,
i.e. "=="
opname: Name of the assert op in the public API, i.e. "assert_equal"
op_func: Function that, if passed the two Tensor inputs to the assertion (x
and y), will return the test to be passed to reduce_all() i.e.
static_func: Function that, if passed numpy ndarray versions of the two
inputs to the assertion, will return a Boolean ndarray with containing
True in all positions where the assertion PASSES.
i.e. np.equal for assert_equal()
x: Numeric `Tensor`.
y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`, `y`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to the value of
`opname`.
Returns:
See docstring template in _binary_assert_doc().
"""
with ops.name_scope(name, opname, [x, y, data]):
x = ops.convert_to_tensor(x, name='x')
y = ops.convert_to_tensor(y, name='y')
if context.executing_eagerly():
test_op = op_func(x, y)
condition = math_ops.reduce_all(test_op)
if condition:
return
# If we get here, the assertion has failed.
# Default to printing 3 elements like control_flow_ops.Assert (used
# by graph mode) does. Also treat negative values as "print
# everything" for consistency with Tensor::SummarizeValue().
if summarize is None:
summarize = 3
elif summarize < 0:
summarize = 1e9 # Code below will find exact size of x and y.
if data is None:
data = _make_assert_msg_data(sym, x, y, summarize, test_op)
if message is not None:
data = [message] + list(data)
raise errors.InvalidArgumentError(
node_def=None,
op=None,
message=('\n'.join(_pretty_print(d, summarize) for d in data)))
else: # not context.executing_eagerly()
if data is None:
data = [
'Condition x %s y did not hold element-wise:' % sym,
'x (%s) = ' % x.name, x,
'y (%s) = ' % y.name, y
]
if message is not None:
data = [message] + list(data)
condition = math_ops.reduce_all(op_func(x, y))
x_static = tensor_util.constant_value(x)
y_static = tensor_util.constant_value(y)
if x_static is not None and y_static is not None:
condition_static = np.all(static_func(x_static, y_static))
_assert_static(condition_static, data)
return control_flow_ops.Assert(condition, data, summarize=summarize)
@tf_export(
'debugging.assert_proper_iterable',
v1=['debugging.assert_proper_iterable', 'assert_proper_iterable'])
@deprecation.deprecated_endpoints('assert_proper_iterable')
def assert_proper_iterable(values):
"""Static assert that values is a "proper" iterable.
`Ops` that expect iterables of `Tensor` can call this to validate input.
Useful since `Tensor`, `ndarray`, byte/text type are all iterables themselves.
Args:
values: Object to be checked.
Raises:
TypeError: If `values` is not iterable or is one of
`Tensor`, `SparseTensor`, `np.array`, `tf.compat.bytes_or_text_types`.
"""
unintentional_iterables = (
(ops.Tensor, sparse_tensor.SparseTensor, np.ndarray)
+ compat.bytes_or_text_types
)
if isinstance(values, unintentional_iterables):
raise TypeError(
'Expected argument "values" to be a "proper" iterable. Found: %s' %
type(values))
if not hasattr(values, '__iter__'):
raise TypeError(
'Expected argument "values" to be iterable. Found: %s' % type(values))
@tf_export('debugging.assert_negative', v1=[])
def assert_negative_v2(x, message=None, summarize=None, name=None):
"""Assert the condition `x < 0` holds element-wise.
This Op checks that `x[i] < 0` holds for every element of `x`. If `x` is
empty, this is trivially satisfied.
If `x` is not negative everywhere, `message`, as well as the first `summarize`
entries of `x` are printed, and `InvalidArgumentError` is raised.
Args:
x: Numeric `Tensor`.
message: A string to prefix to the default message.
summarize: Print this many entries of each tensor.
name: A name for this operation (optional). Defaults to "assert_negative".
Returns:
Op raising `InvalidArgumentError` unless `x` is all negative. This can be
used with `tf.control_dependencies` inside of `tf.function`s to block
followup computation until the check has executed.
@compatibility(eager)
returns None
@end_compatibility
Raises:
InvalidArgumentError: if the check can be performed immediately and
`x[i] < 0` is False. The check can be performed immediately during eager
execution or if `x` is statically known.
"""
return assert_negative(x=x, message=message, summarize=summarize, name=name)
@tf_export(v1=['debugging.assert_negative', 'assert_negative'])
@deprecation.deprecated_endpoints('assert_negative')
@_unary_assert_doc('< 0', 'negative')
def assert_negative(x, data=None, summarize=None, message=None, name=None): # pylint: disable=missing-docstring
message = message or ''
with ops.name_scope(name, 'assert_negative', [x, data]):
x = ops.convert_to_tensor(x, name='x')
if data is None:
if context.executing_eagerly():
name = _shape_and_dtype_str(x)
else:
name = x.name
data = [
message,
'Condition x < 0 did not hold element-wise:',
'x (%s) = ' % name, x]
zero = ops.convert_to_tensor(0, dtype=x.dtype)
return assert_less(x, zero, data=data, summarize=summarize)
@tf_export('debugging.assert_positive', v1=[])
def assert_positive_v2(x, message=None, summarize=None, name=None):
"""Assert the condition `x > 0` holds element-wise.
This Op checks that `x[i] > 0` holds for every element of `x`. If `x` is
empty, this is trivially satisfied.
If `x` is not positive everywhere, `message`, as well as the first `summarize`
entries of `x` are printed, and `InvalidArgumentError` is raised.
Args:
x: Numeric `Tensor`.
message: A string to prefix to the default message.
summarize: Print this many entries of each tensor.
name: A name for this operation (optional). Defaults to "assert_positive".
Returns:
Op raising `InvalidArgumentError` unless `x` is all positive. This can be
used with `tf.control_dependencies` inside of `tf.function`s to block
followup computation until the check has executed.
@compatibility(eager)
returns None
@end_compatibility
Raises:
InvalidArgumentError: if the check can be performed immediately and
`x[i] > 0` is False. The check can be performed immediately during eager
execution or if `x` is statically known.
"""
return assert_positive(x=x, summarize=summarize, message=message, name=name)
@tf_export(v1=['debugging.assert_positive', 'assert_positive'])
@deprecation.deprecated_endpoints('assert_positive')
@_unary_assert_doc('> 0', 'positive')
def assert_positive(x, data=None, summarize=None, message=None, name=None): # pylint: disable=missing-docstring
message = message or ''
with ops.name_scope(name, 'assert_positive', [x, data]):
x = ops.convert_to_tensor(x, name='x')
if data is None:
if context.executing_eagerly():
name = _shape_and_dtype_str(x)
else:
name = x.name
data = [
message, 'Condition x > 0 did not hold element-wise:',
'x (%s) = ' % name, x]
zero = ops.convert_to_tensor(0, dtype=x.dtype)
return assert_less(zero, x, data=data, summarize=summarize)
@tf_export('debugging.assert_non_negative', v1=[])
def assert_non_negative_v2(x, message=None, summarize=None, name=None):
"""Assert the condition `x >= 0` holds element-wise.
This Op checks that `x[i] >= 0` holds for every element of `x`. If `x` is
empty, this is trivially satisfied.
If `x` is not >= 0 everywhere, `message`, as well as the first `summarize`
entries of `x` are printed, and `InvalidArgumentError` is raised.
Args:
x: Numeric `Tensor`.
message: A string to prefix to the default message.
summarize: Print this many entries of each tensor.
name: A name for this operation (optional). Defaults to
"assert_non_negative".
Returns:
Op raising `InvalidArgumentError` unless `x` is all non-negative. This can
be used with `tf.control_dependencies` inside of `tf.function`s to block
followup computation until the check has executed.
@compatibility(eager)
returns None
@end_compatibility
Raises:
InvalidArgumentError: if the check can be performed immediately and
`x[i] >= 0` is False. The check can be performed immediately during eager
execution or if `x` is statically known.
"""
return assert_non_negative(x=x, summarize=summarize, message=message,
name=name)
@tf_export(v1=['debugging.assert_non_negative', 'assert_non_negative'])
@deprecation.deprecated_endpoints('assert_non_negative')
@_unary_assert_doc('>= 0', 'non-negative')
def assert_non_negative(x, data=None, summarize=None, message=None, name=None): # pylint: disable=missing-docstring
message = message or ''
with ops.name_scope(name, 'assert_non_negative', [x, data]):
x = ops.convert_to_tensor(x, name='x')
if data is None:
if context.executing_eagerly():
name = _shape_and_dtype_str(x)
else:
name = x.name
data = [
message,
'Condition x >= 0 did not hold element-wise:',
'x (%s) = ' % name, x]
zero = ops.convert_to_tensor(0, dtype=x.dtype)
return assert_less_equal(zero, x, data=data, summarize=summarize)
@tf_export('debugging.assert_non_positive', v1=[])
def assert_non_positive_v2(x, message=None, summarize=None, name=None):
"""Assert the condition `x <= 0` holds element-wise.
This Op checks that `x[i] <= 0` holds for every element of `x`. If `x` is
empty, this is trivially satisfied.
If `x` is not <= 0 everywhere, `message`, as well as the first `summarize`
entries of `x` are printed, and `InvalidArgumentError` is raised.
Args:
x: Numeric `Tensor`.
message: A string to prefix to the default message.
summarize: Print this many entries of each tensor.
name: A name for this operation (optional). Defaults to
"assert_non_positive".
Returns:
Op raising `InvalidArgumentError` unless `x` is all non-positive. This can
be used with `tf.control_dependencies` inside of `tf.function`s to block
followup computation until the check has executed.
@compatibility(eager)
returns None
@end_compatibility
Raises:
InvalidArgumentError: if the check can be performed immediately and
`x[i] <= 0` is False. The check can be performed immediately during eager
execution or if `x` is statically known.
"""
return assert_non_positive(x=x, summarize=summarize, message=message,
name=name)
@tf_export(v1=['debugging.assert_non_positive', 'assert_non_positive'])
@deprecation.deprecated_endpoints('assert_non_positive')
@_unary_assert_doc('<= 0', 'non-positive')
def assert_non_positive(x, data=None, summarize=None, message=None, name=None): # pylint: disable=missing-docstring
message = message or ''
with ops.name_scope(name, 'assert_non_positive', [x, data]):
x = ops.convert_to_tensor(x, name='x')
if data is None:
if context.executing_eagerly():
name = _shape_and_dtype_str(x)
else:
name = x.name
data = [
message,
'Condition x <= 0 did not hold element-wise:'
'x (%s) = ' % name, x]
zero = ops.convert_to_tensor(0, dtype=x.dtype)
return assert_less_equal(x, zero, data=data, summarize=summarize)
@tf_export('debugging.assert_equal', 'assert_equal', v1=[])
def assert_equal_v2(x, y, message=None, summarize=None, name=None):
"""Assert the condition `x == y` holds element-wise.
This Op checks that `x[i] == y[i]` holds for every pair of (possibly
broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is
trivially satisfied.
If `x` and `y` are not equal, `message`, as well as the first `summarize`
entries of `x` and `y` are printed, and `InvalidArgumentError` is raised.
Args:
x: Numeric `Tensor`.
y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
message: A string to prefix to the default message.
summarize: Print this many entries of each tensor.
name: A name for this operation (optional). Defaults to "assert_equal".
Returns:
Op that raises `InvalidArgumentError` if `x == y` is False. This can be
used with `tf.control_dependencies` inside of `tf.function`s to block
followup computation until the check has executed.
@compatibility(eager)
returns None
@end_compatibility
Raises:
InvalidArgumentError: if the check can be performed immediately and
`x == y` is False. The check can be performed immediately during eager
execution or if `x` and `y` are statically known.
"""
return assert_equal(x=x, y=y, summarize=summarize, message=message, name=name)
@tf_export(v1=['debugging.assert_equal', 'assert_equal'])
@_binary_assert_doc('==')
def assert_equal(x, y, data=None, summarize=None, message=None, name=None): # pylint: disable=missing-docstring
with ops.name_scope(name, 'assert_equal', [x, y, data]):
# Short-circuit if x and y are the same tensor.
if x is y:
return None if context.executing_eagerly() else control_flow_ops.no_op()
return _binary_assert('==', 'assert_equal', math_ops.equal, np.equal, x, y,
data, summarize, message, name)
@tf_export('debugging.assert_none_equal', v1=[])
def assert_none_equal_v2(x, y, summarize=None, message=None, name=None):
"""Assert the condition `x != y` holds for all elements.
This Op checks that `x[i] != y[i]` holds for every pair of (possibly
broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is
trivially satisfied.
If any elements of `x` and `y` are equal, `message`, as well as the first
`summarize` entries of `x` and `y` are printed, and `InvalidArgumentError`
is raised.
Args:
x: Numeric `Tensor`.
y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to
"assert_none_equal".
Returns:
Op that raises `InvalidArgumentError` if `x != y` is ever False. This can
be used with `tf.control_dependencies` inside of `tf.function`s to block
followup computation until the check has executed.
@compatibility(eager)
returns None
@end_compatibility
Raises:
InvalidArgumentError: if the check can be performed immediately and
`x != y` is False for any pair of elements in `x` and `y`. The check can
be performed immediately during eager execution or if `x` and `y` are
statically known.
"""
return assert_none_equal(x=x, y=y, summarize=summarize, message=message,
name=name)
@tf_export(v1=['debugging.assert_none_equal', 'assert_none_equal'])
@deprecation.deprecated_endpoints('assert_none_equal')
@_binary_assert_doc('!=')
def assert_none_equal(
x, y, data=None, summarize=None, message=None, name=None):
return _binary_assert('!=', 'assert_none_equal', math_ops.not_equal,
np.not_equal, x, y, data, summarize, message, name)
@tf_export('debugging.assert_near', v1=[])
def assert_near_v2(x, y, rtol=None, atol=None, message=None, summarize=None,
name=None):
"""Assert the condition `x` and `y` are close element-wise.
This Op checks that `x[i] - y[i] < atol + rtol * tf.abs(y[i])` holds for every
pair of (possibly broadcast) elements of `x` and `y`. If both `x` and `y` are
empty, this is trivially satisfied.
If any elements of `x` and `y` are not close, `message`, as well as the first
`summarize` entries of `x` and `y` are printed, and `InvalidArgumentError`
is raised.
The default `atol` and `rtol` is `10 * eps`, where `eps` is the smallest
representable positive number such that `1 + eps != 1`. This is about
`1.2e-6` in `32bit`, `2.22e-15` in `64bit`, and `0.00977` in `16bit`.
See `numpy.finfo`.
Args:
x: Float or complex `Tensor`.
y: Float or complex `Tensor`, same dtype as and broadcastable to `x`.
rtol: `Tensor`. Same `dtype` as, and broadcastable to, `x`.
The relative tolerance. Default is `10 * eps`.
atol: `Tensor`. Same `dtype` as, and broadcastable to, `x`.
The absolute tolerance. Default is `10 * eps`.
message: A string to prefix to the default message.
summarize: Print this many entries of each tensor.
name: A name for this operation (optional). Defaults to "assert_near".
Returns:
Op that raises `InvalidArgumentError` if `x` and `y` are not close enough.
This can be used with `tf.control_dependencies` inside of `tf.function`s
to block followup computation until the check has executed.
@compatibility(eager)
returns None
@end_compatibility
Raises:
InvalidArgumentError: if the check can be performed immediately and
`x != y` is False for any pair of elements in `x` and `y`. The check can
be performed immediately during eager execution or if `x` and `y` are
statically known.
@compatibility(numpy)
Similar to `numpy.assert_allclose`, except tolerance depends on data type.
This is due to the fact that `TensorFlow` is often used with `32bit`, `64bit`,
and even `16bit` data.
@end_compatibility
"""
return assert_near(x=x, y=y, rtol=rtol, atol=atol, summarize=summarize,
message=message, name=name)
@tf_export(v1=['debugging.assert_near', 'assert_near'])
@deprecation.deprecated_endpoints('assert_near')
def assert_near(
x, y, rtol=None, atol=None, data=None, summarize=None, message=None,
name=None):
"""Assert the condition `x` and `y` are close element-wise.
Example of adding a dependency to an operation:
```python
with tf.control_dependencies([tf.compat.v1.assert_near(x, y)]):
output = tf.reduce_sum(x)
```
This condition holds if for every pair of (possibly broadcast) elements
`x[i]`, `y[i]`, we have
```tf.abs(x[i] - y[i]) <= atol + rtol * tf.abs(y[i])```.
If both `x` and `y` are empty, this is trivially satisfied.
The default `atol` and `rtol` is `10 * eps`, where `eps` is the smallest
representable positive number such that `1 + eps != 1`. This is about
`1.2e-6` in `32bit`, `2.22e-15` in `64bit`, and `0.00977` in `16bit`.
See `numpy.finfo`.
Args:
x: Float or complex `Tensor`.
y: Float or complex `Tensor`, same `dtype` as, and broadcastable to, `x`.
rtol: `Tensor`. Same `dtype` as, and broadcastable to, `x`.
The relative tolerance. Default is `10 * eps`.
atol: `Tensor`. Same `dtype` as, and broadcastable to, `x`.
The absolute tolerance. Default is `10 * eps`.
data: The tensors to print out if the condition is False. Defaults to
error message and first few entries of `x`, `y`.
summarize: Print this many entries of each tensor.
message: A string to prefix to the default message.
name: A name for this operation (optional). Defaults to "assert_near".
Returns:
Op that raises `InvalidArgumentError` if `x` and `y` are not close enough.
@compatibility(numpy)
Similar to `numpy.assert_allclose`, except tolerance depends on data type.
This is due to the fact that `TensorFlow` is often used with `32bit`, `64bit`,
and even `16bit` data.
@end_compatibility
"""
message = message or ''
with ops.name_scope(name, 'assert_near', [x, y, rtol, atol, data]):
x = ops.convert_to_tensor(x, name='x')
y = ops.convert_to_tensor(y, name='y', dtype=x.dtype)
eps = np.finfo(x.dtype.as_numpy_dtype).eps
rtol = 10 * eps if rtol is None else rtol
atol = 10 * eps if atol is None else atol
rtol = ops.convert_to_tensor(rtol, name='rtol', dtype=x.dtype)
atol = ops.convert_to_tensor(atol, name='atol', dtype=x.dtype)
if context.executing_eagerly():
x_name = _shape_and_dtype_str(x)
y_name = _shape_and_dtype_str(y)
else:
x_name = x.name
y_name = y.name
if data is None:
data = [
message,
'x and y not equal to tolerance rtol = %s, atol = %s' % (rtol, atol),
'x (%s) = ' % x_name, x, 'y (%s) = ' % y_name, y
]
tol = atol + rtol * math_ops.abs(y)
diff = math_ops.abs(x - y)
condition = math_ops.reduce_all(math_ops.less(diff, tol))
return control_flow_ops.Assert(condition, data, summarize=summarize)
@tf_export('debugging.assert_less', 'assert_less', v1=[])
def assert_less_v2(x, y, message=None, summarize=None, name=None):
"""Assert the condition `x < y` holds element-wise.
This Op checks that `x[i] < y[i]` holds for every pair of (possibly
broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is
trivially satisfied.
If `x` is not less than `y` element-wise, `message`, as well as the first
`summarize` entries of `x` and `y` are printed, and `InvalidArgumentError` is
raised.
Args:
x: Numeric `Tensor`.
y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
message: A string to prefix to the default message.
summarize: Print this many entries of each tensor.
name: A name for this operation (optional). Defaults to "assert_less".
Returns:
Op that raises `InvalidArgumentError` if `x < y` is False.
This can be used with `tf.control_dependencies` inside of `tf.function`s
to block followup computation until the check has executed.
@compatibility(eager)
returns None
@end_compatibility
Raises:
InvalidArgumentError: if the check can be performed immediately and
`x < y` is False. The check can be performed immediately during eager
execution or if `x` and `y` are statically known.
"""
return assert_less(x=x, y=y, summarize=summarize, message=message, name=name)
@tf_export(v1=['debugging.assert_less', 'assert_less'])
@_binary_assert_doc('<')
def assert_less(x, y, data=None, summarize=None, message=None, name=None):
return _binary_assert('<', 'assert_less', math_ops.less, np.less, x, y, data,
summarize, message, name)
@tf_export('debugging.assert_less_equal', v1=[])
def assert_less_equal_v2(x, y, message=None, summarize=None, name=None):
"""Assert the condition `x <= y` holds element-wise.
This Op checks that `x[i] <= y[i]` holds for every pair of (possibly
broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is
trivially satisfied.
If `x` is not less or equal than `y` element-wise, `message`, as well as the
first `summarize` entries of `x` and `y` are printed, and
`InvalidArgumentError` is raised.
Args:
x: Numeric `Tensor`.
y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
message: A string to prefix to the default message.
summarize: Print this many entries of each tensor.
name: A name for this operation (optional). Defaults to "assert_less_equal".
Returns:
Op that raises `InvalidArgumentError` if `x <= y` is False. This can be
used with `tf.control_dependencies` inside of `tf.function`s to block
followup computation until the check has executed.
@compatibility(eager)
returns None
@end_compatibility
Raises:
InvalidArgumentError: if the check can be performed immediately and
`x <= y` is False. The check can be performed immediately during eager
execution or if `x` and `y` are statically known.
"""
return assert_less_equal(x=x, y=y,
summarize=summarize, message=message, name=name)
@tf_export(v1=['debugging.assert_less_equal', 'assert_less_equal'])
@deprecation.deprecated_endpoints('assert_less_equal')
@_binary_assert_doc('<=')
def assert_less_equal(x, y, data=None, summarize=None, message=None, name=None):
return _binary_assert('<=', 'assert_less_equal', math_ops.less_equal,
np.less_equal, x, y, data, summarize, message, name)
@tf_export('debugging.assert_greater', 'assert_greater', v1=[])
def assert_greater_v2(x, y, message=None, summarize=None, name=None):
"""Assert the condition `x > y` holds element-wise.
This Op checks that `x[i] > y[i]` holds for every pair of (possibly
broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is
trivially satisfied.
If `x` is not greater than `y` element-wise, `message`, as well as the first
`summarize` entries of `x` and `y` are printed, and `InvalidArgumentError` is
raised.
Args:
x: Numeric `Tensor`.
y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
message: A string to prefix to the default message.
summarize: Print this many entries of each tensor.
name: A name for this operation (optional). Defaults to "assert_greater".
Returns:
Op that raises `InvalidArgumentError` if `x > y` is False. This can be
used with `tf.control_dependencies` inside of `tf.function`s to block
followup computation until the check has executed.
@compatibility(eager)
returns None
@end_compatibility
Raises:
InvalidArgumentError: if the check can be performed immediately and
`x > y` is False. The check can be performed immediately during eager
execution or if `x` and `y` are statically known.
"""
return assert_greater(x=x, y=y, summarize=summarize, message=message,
name=name)
@tf_export(v1=['debugging.assert_greater', 'assert_greater'])
@_binary_assert_doc('>')
def assert_greater(x, y, data=None, summarize=None, message=None, name=None): # pylint: disable=missing-docstring
return _binary_assert('>', 'assert_greater', math_ops.greater, np.greater, x,
y, data, summarize, message, name)
@tf_export('debugging.assert_greater_equal', v1=[])
def assert_greater_equal_v2(x, y, message=None, summarize=None, name=None):
"""Assert the condition `x >= y` holds element-wise.
This Op checks that `x[i] >= y[i]` holds for every pair of (possibly
broadcast) elements of `x` and `y`. If both `x` and `y` are empty, this is
trivially satisfied.
If `x` is not greater or equal to `y` element-wise, `message`, as well as the
first `summarize` entries of `x` and `y` are printed, and
`InvalidArgumentError` is raised.
Args:
x: Numeric `Tensor`.
y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
message: A string to prefix to the default message.
summarize: Print this many entries of each tensor.
name: A name for this operation (optional). Defaults to
"assert_greater_equal".
Returns:
Op that raises `InvalidArgumentError` if `x >= y` is False. This can be
used with `tf.control_dependencies` inside of `tf.function`s to block
followup computation until the check has executed.
@compatibility(eager)
returns None
@end_compatibility
Raises:
InvalidArgumentError: if the check can be performed immediately and
`x >= y` is False. The check can be performed immediately during eager