1245 lines
42 KiB
Python
1245 lines
42 KiB
Python
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# pylint: disable=g-short-docstring-punctuation
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"""Asserts and Boolean Checks.
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See the @{$python/check_ops} guide.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.python.eager import context
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import errors
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import sparse_tensor
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from tensorflow.python.framework import tensor_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.util import compat
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from tensorflow.python.util.tf_export import tf_export
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NUMERIC_TYPES = frozenset(
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[dtypes.float32, dtypes.float64, dtypes.int8, dtypes.int16, dtypes.int32,
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dtypes.int64, dtypes.uint8, dtypes.qint8, dtypes.qint32, dtypes.quint8,
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dtypes.complex64])
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__all__ = [
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'assert_negative',
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'assert_positive',
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'assert_proper_iterable',
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'assert_non_negative',
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'assert_non_positive',
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'assert_equal',
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'assert_none_equal',
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'assert_near',
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'assert_integer',
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'assert_less',
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'assert_less_equal',
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'assert_greater',
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'assert_greater_equal',
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'assert_rank',
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'assert_rank_at_least',
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'assert_rank_in',
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'assert_same_float_dtype',
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'assert_scalar',
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'assert_type',
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'is_non_decreasing',
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'is_numeric_tensor',
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'is_strictly_increasing',
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]
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def _maybe_constant_value_string(t):
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if not isinstance(t, ops.Tensor):
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return str(t)
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const_t = tensor_util.constant_value(t)
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if const_t is not None:
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return str(const_t)
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return t
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def _assert_static(condition, data):
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"""Raises a InvalidArgumentError with as much information as possible."""
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if not condition:
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data_static = [_maybe_constant_value_string(x) for x in data]
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raise errors.InvalidArgumentError(node_def=None, op=None,
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message='\n'.join(data_static))
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def _shape_and_dtype_str(tensor):
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"""Returns a string containing tensor's shape and dtype."""
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return 'shape=%s dtype=%s' % (tensor.shape, tensor.dtype.name)
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@tf_export('assert_proper_iterable')
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def assert_proper_iterable(values):
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"""Static assert that values is a "proper" iterable.
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`Ops` that expect iterables of `Tensor` can call this to validate input.
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Useful since `Tensor`, `ndarray`, byte/text type are all iterables themselves.
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Args:
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values: Object to be checked.
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Raises:
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TypeError: If `values` is not iterable or is one of
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`Tensor`, `SparseTensor`, `np.array`, `tf.compat.bytes_or_text_types`.
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"""
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unintentional_iterables = (
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(ops.Tensor, sparse_tensor.SparseTensor, np.ndarray)
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+ compat.bytes_or_text_types
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)
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if isinstance(values, unintentional_iterables):
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raise TypeError(
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'Expected argument "values" to be a "proper" iterable. Found: %s' %
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type(values))
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if not hasattr(values, '__iter__'):
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raise TypeError(
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'Expected argument "values" to be iterable. Found: %s' % type(values))
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@tf_export('assert_negative')
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def assert_negative(x, data=None, summarize=None, message=None, name=None):
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"""Assert the condition `x < 0` holds element-wise.
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Example of adding a dependency to an operation:
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```python
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with tf.control_dependencies([tf.assert_negative(x)]):
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output = tf.reduce_sum(x)
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```
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Negative means, for every element `x[i]` of `x`, we have `x[i] < 0`.
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If `x` is empty this is trivially satisfied.
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Args:
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x: Numeric `Tensor`.
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data: The tensors to print out if the condition is False. Defaults to
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error message and first few entries of `x`.
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summarize: Print this many entries of each tensor.
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message: A string to prefix to the default message.
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name: A name for this operation (optional). Defaults to "assert_negative".
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Returns:
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Op raising `InvalidArgumentError` unless `x` is all negative.
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"""
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message = message or ''
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with ops.name_scope(name, 'assert_negative', [x, data]):
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x = ops.convert_to_tensor(x, name='x')
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if data is None:
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if context.executing_eagerly():
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name = _shape_and_dtype_str(x)
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else:
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name = x.name
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data = [
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message,
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'Condition x < 0 did not hold element-wise:',
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'x (%s) = ' % name, x]
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zero = ops.convert_to_tensor(0, dtype=x.dtype)
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return assert_less(x, zero, data=data, summarize=summarize)
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@tf_export('assert_positive')
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def assert_positive(x, data=None, summarize=None, message=None, name=None):
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"""Assert the condition `x > 0` holds element-wise.
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Example of adding a dependency to an operation:
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```python
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with tf.control_dependencies([tf.assert_positive(x)]):
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output = tf.reduce_sum(x)
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```
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Positive means, for every element `x[i]` of `x`, we have `x[i] > 0`.
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If `x` is empty this is trivially satisfied.
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Args:
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x: Numeric `Tensor`.
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data: The tensors to print out if the condition is False. Defaults to
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error message and first few entries of `x`.
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summarize: Print this many entries of each tensor.
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message: A string to prefix to the default message.
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name: A name for this operation (optional). Defaults to "assert_positive".
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Returns:
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Op raising `InvalidArgumentError` unless `x` is all positive.
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"""
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message = message or ''
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with ops.name_scope(name, 'assert_positive', [x, data]):
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x = ops.convert_to_tensor(x, name='x')
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if data is None:
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if context.executing_eagerly():
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name = _shape_and_dtype_str(x)
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else:
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name = x.name
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data = [
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message, 'Condition x > 0 did not hold element-wise:',
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'x (%s) = ' % name, x]
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zero = ops.convert_to_tensor(0, dtype=x.dtype)
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return assert_less(zero, x, data=data, summarize=summarize)
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@tf_export('assert_non_negative')
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def assert_non_negative(x, data=None, summarize=None, message=None, name=None):
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"""Assert the condition `x >= 0` holds element-wise.
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Example of adding a dependency to an operation:
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```python
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with tf.control_dependencies([tf.assert_non_negative(x)]):
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output = tf.reduce_sum(x)
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```
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Non-negative means, for every element `x[i]` of `x`, we have `x[i] >= 0`.
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If `x` is empty this is trivially satisfied.
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Args:
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x: Numeric `Tensor`.
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data: The tensors to print out if the condition is False. Defaults to
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error message and first few entries of `x`.
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summarize: Print this many entries of each tensor.
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message: A string to prefix to the default message.
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name: A name for this operation (optional).
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Defaults to "assert_non_negative".
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Returns:
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Op raising `InvalidArgumentError` unless `x` is all non-negative.
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"""
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message = message or ''
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with ops.name_scope(name, 'assert_non_negative', [x, data]):
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x = ops.convert_to_tensor(x, name='x')
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if data is None:
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if context.executing_eagerly():
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name = _shape_and_dtype_str(x)
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else:
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name = x.name
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data = [
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message,
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'Condition x >= 0 did not hold element-wise:',
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'x (%s) = ' % name, x]
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zero = ops.convert_to_tensor(0, dtype=x.dtype)
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return assert_less_equal(zero, x, data=data, summarize=summarize)
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@tf_export('assert_non_positive')
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def assert_non_positive(x, data=None, summarize=None, message=None, name=None):
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"""Assert the condition `x <= 0` holds element-wise.
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Example of adding a dependency to an operation:
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```python
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with tf.control_dependencies([tf.assert_non_positive(x)]):
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output = tf.reduce_sum(x)
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```
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Non-positive means, for every element `x[i]` of `x`, we have `x[i] <= 0`.
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If `x` is empty this is trivially satisfied.
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Args:
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x: Numeric `Tensor`.
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data: The tensors to print out if the condition is False. Defaults to
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error message and first few entries of `x`.
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summarize: Print this many entries of each tensor.
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message: A string to prefix to the default message.
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name: A name for this operation (optional).
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Defaults to "assert_non_positive".
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Returns:
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Op raising `InvalidArgumentError` unless `x` is all non-positive.
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"""
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message = message or ''
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with ops.name_scope(name, 'assert_non_positive', [x, data]):
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x = ops.convert_to_tensor(x, name='x')
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if data is None:
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if context.executing_eagerly():
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name = _shape_and_dtype_str(x)
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else:
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name = x.name
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data = [
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message,
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'Condition x <= 0 did not hold element-wise:'
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'x (%s) = ' % name, x]
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zero = ops.convert_to_tensor(0, dtype=x.dtype)
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return assert_less_equal(x, zero, data=data, summarize=summarize)
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@tf_export('assert_equal')
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def assert_equal(x, y, data=None, summarize=None, message=None, name=None):
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"""Assert the condition `x == y` holds element-wise.
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Example of adding a dependency to an operation:
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```python
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with tf.control_dependencies([tf.assert_equal(x, y)]):
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output = tf.reduce_sum(x)
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```
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This condition holds if for every pair of (possibly broadcast) elements
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`x[i]`, `y[i]`, we have `x[i] == y[i]`.
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If both `x` and `y` are empty, this is trivially satisfied.
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Args:
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x: Numeric `Tensor`.
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y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
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data: The tensors to print out if the condition is False. Defaults to
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error message and first few entries of `x`, `y`.
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summarize: Print this many entries of each tensor.
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message: A string to prefix to the default message.
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name: A name for this operation (optional). Defaults to "assert_equal".
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Returns:
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Op that raises `InvalidArgumentError` if `x == y` is False.
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@compatibility{eager} returns None
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Raises:
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InvalidArgumentError: if the check can be performed immediately and
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`x == y` is False. The check can be performed immediately during eager
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execution or if `x` and `y` are statically known.
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"""
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message = message or ''
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with ops.name_scope(name, 'assert_equal', [x, y, data]):
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x = ops.convert_to_tensor(x, name='x')
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y = ops.convert_to_tensor(y, name='y')
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if context.executing_eagerly():
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eq = math_ops.equal(x, y)
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condition = math_ops.reduce_all(eq)
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if not condition:
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# Prepare a message with first elements of x and y.
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summary_msg = ''
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# Default to printing 3 elements like control_flow_ops.Assert (used
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# by graph mode) does.
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summarize = 3 if summarize is None else summarize
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if summarize:
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# reshape((-1,)) is the fastest way to get a flat array view.
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x_np = x.numpy().reshape((-1,))
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y_np = y.numpy().reshape((-1,))
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x_sum = min(x_np.size, summarize)
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y_sum = min(y_np.size, summarize)
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summary_msg = ('First %d elements of x:\n%s\n'
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'First %d elements of y:\n%s\n' %
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(x_sum, x_np[:x_sum],
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y_sum, y_np[:y_sum]))
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index_and_values_str = ''
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if x.shape == y.shape and x.shape.as_list():
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# If the shapes of x and y are the same (and not scalars),
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# Get the values that actually differed and their indices.
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# If shapes are different this information is more confusing
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# than useful.
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mask = math_ops.logical_not(eq)
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indices = array_ops.where(mask)
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indices_np = indices.numpy()
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x_vals = array_ops.boolean_mask(x, mask)
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y_vals = array_ops.boolean_mask(y, mask)
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summarize = min(summarize, indices_np.shape[0])
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index_and_values_str = (
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'Indices of first %s different values:\n%s\n'
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'Corresponding x values:\n%s\n'
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'Corresponding y values:\n%s\n' %
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(summarize, indices_np[:summarize],
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x_vals.numpy().reshape((-1,))[:summarize],
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y_vals.numpy().reshape((-1,))[:summarize]))
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raise errors.InvalidArgumentError(
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node_def=None, op=None,
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message=('%s\nCondition x == y did not hold.\n%s%s' %
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(message or '', index_and_values_str, summary_msg)))
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return
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if data is None:
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data = [
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message,
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'Condition x == y did not hold element-wise:',
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'x (%s) = ' % x.name, x,
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'y (%s) = ' % y.name, y
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]
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condition = math_ops.reduce_all(math_ops.equal(x, y))
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x_static = tensor_util.constant_value(x)
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y_static = tensor_util.constant_value(y)
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if x_static is not None and y_static is not None:
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condition_static = (x_static == y_static).all()
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_assert_static(condition_static, data)
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return control_flow_ops.Assert(condition, data, summarize=summarize)
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@tf_export('assert_none_equal')
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def assert_none_equal(
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x, y, data=None, summarize=None, message=None, name=None):
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"""Assert the condition `x != y` holds for all elements.
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Example of adding a dependency to an operation:
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```python
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with tf.control_dependencies([tf.assert_none_equal(x, y)]):
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output = tf.reduce_sum(x)
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```
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||
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This condition holds if for every pair of (possibly broadcast) elements
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`x[i]`, `y[i]`, we have `x[i] != y[i]`.
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If both `x` and `y` are empty, this is trivially satisfied.
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|
|
||
|
Args:
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x: Numeric `Tensor`.
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y: Numeric `Tensor`, same dtype as and broadcastable to `x`.
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||
|
data: The tensors to print out if the condition is False. Defaults to
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error message and first few entries of `x`, `y`.
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summarize: Print this many entries of each tensor.
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message: A string to prefix to the default message.
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||
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name: A name for this operation (optional).
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Defaults to "assert_none_equal".
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Returns:
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||
|
Op that raises `InvalidArgumentError` if `x != y` is ever False.
|
||
|
"""
|
||
|
message = message or ''
|
||
|
with ops.name_scope(name, 'assert_none_equal', [x, y, data]):
|
||
|
x = ops.convert_to_tensor(x, name='x')
|
||
|
y = ops.convert_to_tensor(y, name='y')
|
||
|
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,
|
||
|
'Condition x != y did not hold for every single element:',
|
||
|
'x (%s) = ' % x_name, x,
|
||
|
'y (%s) = ' % y_name, y
|
||
|
]
|
||
|
condition = math_ops.reduce_all(math_ops.not_equal(x, y))
|
||
|
return control_flow_ops.Assert(condition, data, summarize=summarize)
|
||
|
|
||
|
|
||
|
@tf_export('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.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 != eps`. 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('assert_less')
|
||
|
def assert_less(x, y, data=None, summarize=None, message=None, name=None):
|
||
|
"""Assert the condition `x < y` holds element-wise.
|
||
|
|
||
|
Example of adding a dependency to an operation:
|
||
|
|
||
|
```python
|
||
|
with tf.control_dependencies([tf.assert_less(x, y)]):
|
||
|
output = tf.reduce_sum(x)
|
||
|
```
|
||
|
|
||
|
This condition holds if for every pair of (possibly broadcast) elements
|
||
|
`x[i]`, `y[i]`, we have `x[i] < y[i]`.
|
||
|
If both `x` and `y` are empty, this is trivially satisfied.
|
||
|
|
||
|
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 "assert_less".
|
||
|
|
||
|
Returns:
|
||
|
Op that raises `InvalidArgumentError` if `x < y` is False.
|
||
|
"""
|
||
|
message = message or ''
|
||
|
with ops.name_scope(name, 'assert_less', [x, y, data]):
|
||
|
x = ops.convert_to_tensor(x, name='x')
|
||
|
y = ops.convert_to_tensor(y, name='y')
|
||
|
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,
|
||
|
'Condition x < y did not hold element-wise:',
|
||
|
'x (%s) = ' % x_name, x, 'y (%s) = ' % y_name, y
|
||
|
]
|
||
|
condition = math_ops.reduce_all(math_ops.less(x, y))
|
||
|
return control_flow_ops.Assert(condition, data, summarize=summarize)
|
||
|
|
||
|
|
||
|
@tf_export('assert_less_equal')
|
||
|
def assert_less_equal(x, y, data=None, summarize=None, message=None, name=None):
|
||
|
"""Assert the condition `x <= y` holds element-wise.
|
||
|
|
||
|
Example of adding a dependency to an operation:
|
||
|
|
||
|
```python
|
||
|
with tf.control_dependencies([tf.assert_less_equal(x, y)]):
|
||
|
output = tf.reduce_sum(x)
|
||
|
```
|
||
|
|
||
|
This condition holds if for every pair of (possibly broadcast) elements
|
||
|
`x[i]`, `y[i]`, we have `x[i] <= y[i]`.
|
||
|
If both `x` and `y` are empty, this is trivially satisfied.
|
||
|
|
||
|
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 "assert_less_equal"
|
||
|
|
||
|
Returns:
|
||
|
Op that raises `InvalidArgumentError` if `x <= y` is False.
|
||
|
"""
|
||
|
message = message or ''
|
||
|
with ops.name_scope(name, 'assert_less_equal', [x, y, data]):
|
||
|
x = ops.convert_to_tensor(x, name='x')
|
||
|
y = ops.convert_to_tensor(y, name='y')
|
||
|
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,
|
||
|
'Condition x <= y did not hold element-wise:'
|
||
|
'x (%s) = ' % x_name, x, 'y (%s) = ' % y_name, y
|
||
|
]
|
||
|
condition = math_ops.reduce_all(math_ops.less_equal(x, y))
|
||
|
return control_flow_ops.Assert(condition, data, summarize=summarize)
|
||
|
|
||
|
|
||
|
@tf_export('assert_greater')
|
||
|
def assert_greater(x, y, data=None, summarize=None, message=None, name=None):
|
||
|
"""Assert the condition `x > y` holds element-wise.
|
||
|
|
||
|
Example of adding a dependency to an operation:
|
||
|
|
||
|
```python
|
||
|
with tf.control_dependencies([tf.assert_greater(x, y)]):
|
||
|
output = tf.reduce_sum(x)
|
||
|
```
|
||
|
|
||
|
This condition holds if for every pair of (possibly broadcast) elements
|
||
|
`x[i]`, `y[i]`, we have `x[i] > y[i]`.
|
||
|
If both `x` and `y` are empty, this is trivially satisfied.
|
||
|
|
||
|
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 "assert_greater".
|
||
|
|
||
|
Returns:
|
||
|
Op that raises `InvalidArgumentError` if `x > y` is False.
|
||
|
"""
|
||
|
message = message or ''
|
||
|
with ops.name_scope(name, 'assert_greater', [x, y, data]):
|
||
|
x = ops.convert_to_tensor(x, name='x')
|
||
|
y = ops.convert_to_tensor(y, name='y')
|
||
|
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,
|
||
|
'Condition x > y did not hold element-wise:'
|
||
|
'x (%s) = ' % x_name, x, 'y (%s) = ' % y_name, y
|
||
|
]
|
||
|
condition = math_ops.reduce_all(math_ops.greater(x, y))
|
||
|
return control_flow_ops.Assert(condition, data, summarize=summarize)
|
||
|
|
||
|
|
||
|
@tf_export('assert_greater_equal')
|
||
|
def assert_greater_equal(x, y, data=None, summarize=None, message=None,
|
||
|
name=None):
|
||
|
"""Assert the condition `x >= y` holds element-wise.
|
||
|
|
||
|
Example of adding a dependency to an operation:
|
||
|
|
||
|
```python
|
||
|
with tf.control_dependencies([tf.assert_greater_equal(x, y)]):
|
||
|
output = tf.reduce_sum(x)
|
||
|
```
|
||
|
|
||
|
This condition holds if for every pair of (possibly broadcast) elements
|
||
|
`x[i]`, `y[i]`, we have `x[i] >= y[i]`.
|
||
|
If both `x` and `y` are empty, this is trivially satisfied.
|
||
|
|
||
|
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
|
||
|
"assert_greater_equal"
|
||
|
|
||
|
Returns:
|
||
|
Op that raises `InvalidArgumentError` if `x >= y` is False.
|
||
|
"""
|
||
|
message = message or ''
|
||
|
with ops.name_scope(name, 'assert_greater_equal', [x, y, data]):
|
||
|
x = ops.convert_to_tensor(x, name='x')
|
||
|
y = ops.convert_to_tensor(y, name='y')
|
||
|
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,
|
||
|
'Condition x >= y did not hold element-wise:'
|
||
|
'x (%s) = ' % x_name, x, 'y (%s) = ' % y_name, y
|
||
|
]
|
||
|
condition = math_ops.reduce_all(math_ops.greater_equal(x, y))
|
||
|
return control_flow_ops.Assert(condition, data, summarize=summarize)
|
||
|
|
||
|
|
||
|
def _assert_rank_condition(
|
||
|
x, rank, static_condition, dynamic_condition, data, summarize):
|
||
|
"""Assert `x` has a rank that satisfies a given condition.
|
||
|
|
||
|
Args:
|
||
|
x: Numeric `Tensor`.
|
||
|
rank: Scalar `Tensor`.
|
||
|
static_condition: A python function that takes `[actual_rank, given_rank]`
|
||
|
and returns `True` if the condition is satisfied, `False` otherwise.
|
||
|
dynamic_condition: An `op` that takes [actual_rank, given_rank]
|
||
|
and return `True` if the condition is satisfied, `False` otherwise.
|
||
|
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.
|
||
|
|
||
|
Returns:
|
||
|
Op raising `InvalidArgumentError` if `x` fails dynamic_condition.
|
||
|
|
||
|
Raises:
|
||
|
ValueError: If static checks determine `x` fails static_condition.
|
||
|
"""
|
||
|
assert_type(rank, dtypes.int32)
|
||
|
|
||
|
# Attempt to statically defined rank.
|
||
|
rank_static = tensor_util.constant_value(rank)
|
||
|
if rank_static is not None:
|
||
|
if rank_static.ndim != 0:
|
||
|
raise ValueError('Rank must be a scalar.')
|
||
|
|
||
|
x_rank_static = x.get_shape().ndims
|
||
|
if x_rank_static is not None:
|
||
|
if not static_condition(x_rank_static, rank_static):
|
||
|
raise ValueError(
|
||
|
'Static rank condition failed', x_rank_static, rank_static)
|
||
|
return control_flow_ops.no_op(name='static_checks_determined_all_ok')
|
||
|
|
||
|
condition = dynamic_condition(array_ops.rank(x), rank)
|
||
|
|
||
|
# Add the condition that `rank` must have rank zero. Prevents the bug where
|
||
|
# someone does assert_rank(x, [n]), rather than assert_rank(x, n).
|
||
|
if rank_static is None:
|
||
|
this_data = ['Rank must be a scalar. Received rank: ', rank]
|
||
|
rank_check = assert_rank(rank, 0, data=this_data)
|
||
|
condition = control_flow_ops.with_dependencies([rank_check], condition)
|
||
|
|
||
|
return control_flow_ops.Assert(condition, data, summarize=summarize)
|
||
|
|
||
|
|
||
|
@tf_export('assert_rank')
|
||
|
def assert_rank(x, rank, data=None, summarize=None, message=None, name=None):
|
||
|
"""Assert `x` has rank equal to `rank`.
|
||
|
|
||
|
Example of adding a dependency to an operation:
|
||
|
|
||
|
```python
|
||
|
with tf.control_dependencies([tf.assert_rank(x, 2)]):
|
||
|
output = tf.reduce_sum(x)
|
||
|
```
|
||
|
|
||
|
Args:
|
||
|
x: Numeric `Tensor`.
|
||
|
rank: Scalar integer `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 "assert_rank".
|
||
|
|
||
|
Returns:
|
||
|
Op raising `InvalidArgumentError` unless `x` has specified rank.
|
||
|
If static checks determine `x` has correct rank, a `no_op` is returned.
|
||
|
|
||
|
Raises:
|
||
|
ValueError: If static checks determine `x` has wrong rank.
|
||
|
"""
|
||
|
with ops.name_scope(name, 'assert_rank', (x, rank) + tuple(data or [])):
|
||
|
x = ops.convert_to_tensor(x, name='x')
|
||
|
rank = ops.convert_to_tensor(rank, name='rank')
|
||
|
message = message or ''
|
||
|
|
||
|
static_condition = lambda actual_rank, given_rank: actual_rank == given_rank
|
||
|
dynamic_condition = math_ops.equal
|
||
|
|
||
|
if context.executing_eagerly():
|
||
|
name = ''
|
||
|
else:
|
||
|
name = x.name
|
||
|
|
||
|
if data is None:
|
||
|
data = [
|
||
|
message,
|
||
|
'Tensor %s must have rank' % name, rank, 'Received shape: ',
|
||
|
array_ops.shape(x)
|
||
|
]
|
||
|
|
||
|
try:
|
||
|
assert_op = _assert_rank_condition(x, rank, static_condition,
|
||
|
dynamic_condition, data, summarize)
|
||
|
|
||
|
except ValueError as e:
|
||
|
if e.args[0] == 'Static rank condition failed':
|
||
|
raise ValueError(
|
||
|
'%s. Tensor %s must have rank %d. Received rank %d, shape %s' %
|
||
|
(message, name, e.args[2], e.args[1], x.get_shape()))
|
||
|
else:
|
||
|
raise
|
||
|
|
||
|
return assert_op
|
||
|
|
||
|
|
||
|
@tf_export('assert_rank_at_least')
|
||
|
def assert_rank_at_least(
|
||
|
x, rank, data=None, summarize=None, message=None, name=None):
|
||
|
"""Assert `x` has rank equal to `rank` or higher.
|
||
|
|
||
|
Example of adding a dependency to an operation:
|
||
|
|
||
|
```python
|
||
|
with tf.control_dependencies([tf.assert_rank_at_least(x, 2)]):
|
||
|
output = tf.reduce_sum(x)
|
||
|
```
|
||
|
|
||
|
Args:
|
||
|
x: Numeric `Tensor`.
|
||
|
rank: Scalar `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 "assert_rank_at_least".
|
||
|
|
||
|
Returns:
|
||
|
Op raising `InvalidArgumentError` unless `x` has specified rank or higher.
|
||
|
If static checks determine `x` has correct rank, a `no_op` is returned.
|
||
|
|
||
|
Raises:
|
||
|
ValueError: If static checks determine `x` has wrong rank.
|
||
|
"""
|
||
|
with ops.name_scope(
|
||
|
name, 'assert_rank_at_least', (x, rank) + tuple(data or [])):
|
||
|
x = ops.convert_to_tensor(x, name='x')
|
||
|
rank = ops.convert_to_tensor(rank, name='rank')
|
||
|
message = message or ''
|
||
|
|
||
|
static_condition = lambda actual_rank, given_rank: actual_rank >= given_rank
|
||
|
dynamic_condition = math_ops.greater_equal
|
||
|
|
||
|
if context.executing_eagerly():
|
||
|
name = ''
|
||
|
else:
|
||
|
name = x.name
|
||
|
|
||
|
if data is None:
|
||
|
data = [
|
||
|
message,
|
||
|
'Tensor %s must have rank at least' % name, rank,
|
||
|
'Received shape: ', array_ops.shape(x)
|
||
|
]
|
||
|
|
||
|
try:
|
||
|
assert_op = _assert_rank_condition(x, rank, static_condition,
|
||
|
dynamic_condition, data, summarize)
|
||
|
|
||
|
except ValueError as e:
|
||
|
if e.args[0] == 'Static rank condition failed':
|
||
|
raise ValueError(
|
||
|
'%s. Tensor %s must have rank at least %d. Received rank %d, '
|
||
|
'shape %s' % (message, name, e.args[2], e.args[1], x.get_shape()))
|
||
|
else:
|
||
|
raise
|
||
|
|
||
|
return assert_op
|
||
|
|
||
|
|
||
|
def _static_rank_in(actual_rank, given_ranks):
|
||
|
return actual_rank in given_ranks
|
||
|
|
||
|
|
||
|
def _dynamic_rank_in(actual_rank, given_ranks):
|
||
|
if len(given_ranks) < 1:
|
||
|
return ops.convert_to_tensor(False)
|
||
|
result = math_ops.equal(given_ranks[0], actual_rank)
|
||
|
for given_rank in given_ranks[1:]:
|
||
|
result = math_ops.logical_or(
|
||
|
result, math_ops.equal(given_rank, actual_rank))
|
||
|
return result
|
||
|
|
||
|
|
||
|
def _assert_ranks_condition(
|
||
|
x, ranks, static_condition, dynamic_condition, data, summarize):
|
||
|
"""Assert `x` has a rank that satisfies a given condition.
|
||
|
|
||
|
Args:
|
||
|
x: Numeric `Tensor`.
|
||
|
ranks: Scalar `Tensor`.
|
||
|
static_condition: A python function that takes
|
||
|
`[actual_rank, given_ranks]` and returns `True` if the condition is
|
||
|
satisfied, `False` otherwise.
|
||
|
dynamic_condition: An `op` that takes [actual_rank, given_ranks]
|
||
|
and return `True` if the condition is satisfied, `False` otherwise.
|
||
|
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.
|
||
|
|
||
|
Returns:
|
||
|
Op raising `InvalidArgumentError` if `x` fails dynamic_condition.
|
||
|
|
||
|
Raises:
|
||
|
ValueError: If static checks determine `x` fails static_condition.
|
||
|
"""
|
||
|
for rank in ranks:
|
||
|
assert_type(rank, dtypes.int32)
|
||
|
|
||
|
# Attempt to statically defined rank.
|
||
|
ranks_static = tuple([tensor_util.constant_value(rank) for rank in ranks])
|
||
|
if not any(r is None for r in ranks_static):
|
||
|
for rank_static in ranks_static:
|
||
|
if rank_static.ndim != 0:
|
||
|
raise ValueError('Rank must be a scalar.')
|
||
|
|
||
|
x_rank_static = x.get_shape().ndims
|
||
|
if x_rank_static is not None:
|
||
|
if not static_condition(x_rank_static, ranks_static):
|
||
|
raise ValueError(
|
||
|
'Static rank condition failed', x_rank_static, ranks_static)
|
||
|
return control_flow_ops.no_op(name='static_checks_determined_all_ok')
|
||
|
|
||
|
condition = dynamic_condition(array_ops.rank(x), ranks)
|
||
|
|
||
|
# Add the condition that `rank` must have rank zero. Prevents the bug where
|
||
|
# someone does assert_rank(x, [n]), rather than assert_rank(x, n).
|
||
|
for rank, rank_static in zip(ranks, ranks_static):
|
||
|
if rank_static is None:
|
||
|
this_data = ['Rank must be a scalar. Received rank: ', rank]
|
||
|
rank_check = assert_rank(rank, 0, data=this_data)
|
||
|
condition = control_flow_ops.with_dependencies([rank_check], condition)
|
||
|
|
||
|
return control_flow_ops.Assert(condition, data, summarize=summarize)
|
||
|
|
||
|
|
||
|
@tf_export('assert_rank_in')
|
||
|
def assert_rank_in(
|
||
|
x, ranks, data=None, summarize=None, message=None, name=None):
|
||
|
"""Assert `x` has rank in `ranks`.
|
||
|
|
||
|
Example of adding a dependency to an operation:
|
||
|
|
||
|
```python
|
||
|
with tf.control_dependencies([tf.assert_rank_in(x, (2, 4))]):
|
||
|
output = tf.reduce_sum(x)
|
||
|
```
|
||
|
|
||
|
Args:
|
||
|
x: Numeric `Tensor`.
|
||
|
ranks: Iterable of scalar `Tensor` objects.
|
||
|
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 "assert_rank_in".
|
||
|
|
||
|
Returns:
|
||
|
Op raising `InvalidArgumentError` unless rank of `x` is in `ranks`.
|
||
|
If static checks determine `x` has matching rank, a `no_op` is returned.
|
||
|
|
||
|
Raises:
|
||
|
ValueError: If static checks determine `x` has mismatched rank.
|
||
|
"""
|
||
|
with ops.name_scope(
|
||
|
name, 'assert_rank_in', (x,) + tuple(ranks) + tuple(data or [])):
|
||
|
x = ops.convert_to_tensor(x, name='x')
|
||
|
ranks = tuple([ops.convert_to_tensor(rank, name='rank') for rank in ranks])
|
||
|
message = message or ''
|
||
|
|
||
|
if context.executing_eagerly():
|
||
|
name = ''
|
||
|
else:
|
||
|
name = x.name
|
||
|
|
||
|
if data is None:
|
||
|
data = [
|
||
|
message, 'Tensor %s must have rank in' % name
|
||
|
] + list(ranks) + [
|
||
|
'Received shape: ', array_ops.shape(x)
|
||
|
]
|
||
|
|
||
|
try:
|
||
|
assert_op = _assert_ranks_condition(x, ranks, _static_rank_in,
|
||
|
_dynamic_rank_in, data, summarize)
|
||
|
|
||
|
except ValueError as e:
|
||
|
if e.args[0] == 'Static rank condition failed':
|
||
|
raise ValueError(
|
||
|
'%s. Tensor %s must have rank in %s. Received rank %d, '
|
||
|
'shape %s' % (message, name, e.args[2], e.args[1], x.get_shape()))
|
||
|
else:
|
||
|
raise
|
||
|
|
||
|
return assert_op
|
||
|
|
||
|
|
||
|
@tf_export('assert_integer')
|
||
|
def assert_integer(x, message=None, name=None):
|
||
|
"""Assert that `x` is of integer dtype.
|
||
|
|
||
|
Example of adding a dependency to an operation:
|
||
|
|
||
|
```python
|
||
|
with tf.control_dependencies([tf.assert_integer(x)]):
|
||
|
output = tf.reduce_sum(x)
|
||
|
```
|
||
|
|
||
|
Args:
|
||
|
x: `Tensor` whose basetype is integer and is not quantized.
|
||
|
message: A string to prefix to the default message.
|
||
|
name: A name for this operation (optional). Defaults to "assert_integer".
|
||
|
|
||
|
Raises:
|
||
|
TypeError: If `x.dtype` is anything other than non-quantized integer.
|
||
|
|
||
|
Returns:
|
||
|
A `no_op` that does nothing. Type can be determined statically.
|
||
|
"""
|
||
|
message = message or ''
|
||
|
with ops.name_scope(name, 'assert_integer', [x]):
|
||
|
x = ops.convert_to_tensor(x, name='x')
|
||
|
if not x.dtype.is_integer:
|
||
|
if context.executing_eagerly():
|
||
|
name = 'tensor'
|
||
|
else:
|
||
|
name = x.name
|
||
|
err_msg = (
|
||
|
'%s Expected "x" to be integer type. Found: %s of dtype %s'
|
||
|
% (message, name, x.dtype))
|
||
|
raise TypeError(err_msg)
|
||
|
|
||
|
return control_flow_ops.no_op('statically_determined_was_integer')
|
||
|
|
||
|
|
||
|
@tf_export('assert_type')
|
||
|
def assert_type(tensor, tf_type, message=None, name=None):
|
||
|
"""Statically asserts that the given `Tensor` is of the specified type.
|
||
|
|
||
|
Args:
|
||
|
tensor: A tensorflow `Tensor`.
|
||
|
tf_type: A tensorflow type (`dtypes.float32`, `tf.int64`, `dtypes.bool`,
|
||
|
etc).
|
||
|
message: A string to prefix to the default message.
|
||
|
name: A name to give this `Op`. Defaults to "assert_type"
|
||
|
|
||
|
Raises:
|
||
|
TypeError: If the tensors data type doesn't match `tf_type`.
|
||
|
|
||
|
Returns:
|
||
|
A `no_op` that does nothing. Type can be determined statically.
|
||
|
"""
|
||
|
message = message or ''
|
||
|
with ops.name_scope(name, 'assert_type', [tensor]):
|
||
|
tensor = ops.convert_to_tensor(tensor, name='tensor')
|
||
|
if tensor.dtype != tf_type:
|
||
|
if context.executing_eagerly():
|
||
|
raise TypeError('%s tensor must be of type %s' % (message, tf_type))
|
||
|
else:
|
||
|
raise TypeError('%s %s must be of type %s' % (message, tensor.name,
|
||
|
tf_type))
|
||
|
|
||
|
return control_flow_ops.no_op('statically_determined_correct_type')
|
||
|
|
||
|
|
||
|
# pylint: disable=line-too-long
|
||
|
def _get_diff_for_monotonic_comparison(x):
|
||
|
"""Gets the difference x[1:] - x[:-1]."""
|
||
|
x = array_ops.reshape(x, [-1])
|
||
|
if not is_numeric_tensor(x):
|
||
|
raise TypeError('Expected x to be numeric, instead found: %s' % x)
|
||
|
|
||
|
# If x has less than 2 elements, there is nothing to compare. So return [].
|
||
|
is_shorter_than_two = math_ops.less(array_ops.size(x), 2)
|
||
|
short_result = lambda: ops.convert_to_tensor([], dtype=x.dtype)
|
||
|
|
||
|
# With 2 or more elements, return x[1:] - x[:-1]
|
||
|
s_len = array_ops.shape(x) - 1
|
||
|
diff = lambda: array_ops.strided_slice(x, [1], [1] + s_len)- array_ops.strided_slice(x, [0], s_len)
|
||
|
return control_flow_ops.cond(is_shorter_than_two, short_result, diff)
|
||
|
|
||
|
|
||
|
@tf_export('is_numeric_tensor')
|
||
|
def is_numeric_tensor(tensor):
|
||
|
return isinstance(tensor, ops.Tensor) and tensor.dtype in NUMERIC_TYPES
|
||
|
|
||
|
|
||
|
@tf_export('is_non_decreasing')
|
||
|
def is_non_decreasing(x, name=None):
|
||
|
"""Returns `True` if `x` is non-decreasing.
|
||
|
|
||
|
Elements of `x` are compared in row-major order. The tensor `[x[0],...]`
|
||
|
is non-decreasing if for every adjacent pair we have `x[i] <= x[i+1]`.
|
||
|
If `x` has less than two elements, it is trivially non-decreasing.
|
||
|
|
||
|
See also: `is_strictly_increasing`
|
||
|
|
||
|
Args:
|
||
|
x: Numeric `Tensor`.
|
||
|
name: A name for this operation (optional). Defaults to "is_non_decreasing"
|
||
|
|
||
|
Returns:
|
||
|
Boolean `Tensor`, equal to `True` iff `x` is non-decreasing.
|
||
|
|
||
|
Raises:
|
||
|
TypeError: if `x` is not a numeric tensor.
|
||
|
"""
|
||
|
with ops.name_scope(name, 'is_non_decreasing', [x]):
|
||
|
diff = _get_diff_for_monotonic_comparison(x)
|
||
|
# When len(x) = 1, diff = [], less_equal = [], and reduce_all([]) = True.
|
||
|
zero = ops.convert_to_tensor(0, dtype=diff.dtype)
|
||
|
return math_ops.reduce_all(math_ops.less_equal(zero, diff))
|
||
|
|
||
|
|
||
|
@tf_export('is_strictly_increasing')
|
||
|
def is_strictly_increasing(x, name=None):
|
||
|
"""Returns `True` if `x` is strictly increasing.
|
||
|
|
||
|
Elements of `x` are compared in row-major order. The tensor `[x[0],...]`
|
||
|
is strictly increasing if for every adjacent pair we have `x[i] < x[i+1]`.
|
||
|
If `x` has less than two elements, it is trivially strictly increasing.
|
||
|
|
||
|
See also: `is_non_decreasing`
|
||
|
|
||
|
Args:
|
||
|
x: Numeric `Tensor`.
|
||
|
name: A name for this operation (optional).
|
||
|
Defaults to "is_strictly_increasing"
|
||
|
|
||
|
Returns:
|
||
|
Boolean `Tensor`, equal to `True` iff `x` is strictly increasing.
|
||
|
|
||
|
Raises:
|
||
|
TypeError: if `x` is not a numeric tensor.
|
||
|
"""
|
||
|
with ops.name_scope(name, 'is_strictly_increasing', [x]):
|
||
|
diff = _get_diff_for_monotonic_comparison(x)
|
||
|
# When len(x) = 1, diff = [], less = [], and reduce_all([]) = True.
|
||
|
zero = ops.convert_to_tensor(0, dtype=diff.dtype)
|
||
|
return math_ops.reduce_all(math_ops.less(zero, diff))
|
||
|
|
||
|
|
||
|
def _assert_same_base_type(items, expected_type=None):
|
||
|
r"""Asserts all items are of the same base type.
|
||
|
|
||
|
Args:
|
||
|
items: List of graph items (e.g., `Variable`, `Tensor`, `SparseTensor`,
|
||
|
`Operation`, or `IndexedSlices`). Can include `None` elements, which
|
||
|
will be ignored.
|
||
|
expected_type: Expected type. If not specified, assert all items are
|
||
|
of the same base type.
|
||
|
|
||
|
Returns:
|
||
|
Validated type, or none if neither expected_type nor items provided.
|
||
|
|
||
|
Raises:
|
||
|
ValueError: If any types do not match.
|
||
|
"""
|
||
|
original_expected_type = expected_type
|
||
|
mismatch = False
|
||
|
for item in items:
|
||
|
if item is not None:
|
||
|
item_type = item.dtype.base_dtype
|
||
|
if not expected_type:
|
||
|
expected_type = item_type
|
||
|
elif expected_type != item_type:
|
||
|
mismatch = True
|
||
|
break
|
||
|
if mismatch:
|
||
|
# Loop back through and build up an informative error message (this is very
|
||
|
# slow, so we don't do it unless we found an error above).
|
||
|
expected_type = original_expected_type
|
||
|
original_item_str = None
|
||
|
for item in items:
|
||
|
if item is not None:
|
||
|
item_type = item.dtype.base_dtype
|
||
|
if not expected_type:
|
||
|
expected_type = item_type
|
||
|
original_item_str = item.name if hasattr(item, 'name') else str(item)
|
||
|
elif expected_type != item_type:
|
||
|
raise ValueError('%s, type=%s, must be of the same type (%s)%s.' % (
|
||
|
item.name if hasattr(item, 'name') else str(item),
|
||
|
item_type, expected_type,
|
||
|
(' as %s' % original_item_str) if original_item_str else ''))
|
||
|
return expected_type # Should be unreachable
|
||
|
else:
|
||
|
return expected_type
|
||
|
|
||
|
|
||
|
@tf_export('assert_same_float_dtype')
|
||
|
def assert_same_float_dtype(tensors=None, dtype=None):
|
||
|
"""Validate and return float type based on `tensors` and `dtype`.
|
||
|
|
||
|
For ops such as matrix multiplication, inputs and weights must be of the
|
||
|
same float type. This function validates that all `tensors` are the same type,
|
||
|
validates that type is `dtype` (if supplied), and returns the type. Type must
|
||
|
be a floating point type. If neither `tensors` nor `dtype` is supplied,
|
||
|
the function will return `dtypes.float32`.
|
||
|
|
||
|
Args:
|
||
|
tensors: Tensors of input values. Can include `None` elements, which will be
|
||
|
ignored.
|
||
|
dtype: Expected type.
|
||
|
Returns:
|
||
|
Validated type.
|
||
|
Raises:
|
||
|
ValueError: if neither `tensors` nor `dtype` is supplied, or result is not
|
||
|
float, or the common type of the inputs is not a floating point type.
|
||
|
"""
|
||
|
if tensors:
|
||
|
dtype = _assert_same_base_type(tensors, dtype)
|
||
|
if not dtype:
|
||
|
dtype = dtypes.float32
|
||
|
elif not dtype.is_floating:
|
||
|
raise ValueError('Expected floating point type, got %s.' % dtype)
|
||
|
return dtype
|
||
|
|
||
|
|
||
|
@tf_export('assert_scalar')
|
||
|
def assert_scalar(tensor, name=None):
|
||
|
with ops.name_scope(name, 'assert_scalar', [tensor]) as name_scope:
|
||
|
tensor = ops.convert_to_tensor(tensor, name=name_scope)
|
||
|
shape = tensor.get_shape()
|
||
|
if shape.ndims != 0:
|
||
|
if context.executing_eagerly():
|
||
|
raise ValueError('Expected scalar shape, saw shape: %s.'
|
||
|
% (shape,))
|
||
|
else:
|
||
|
raise ValueError('Expected scalar shape for %s, saw shape: %s.'
|
||
|
% (tensor.name, shape))
|
||
|
return tensor
|