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# Copyright 2017 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.
# ==============================================================================
"""Ignore_errors dataset transformations."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.util import nest
from tensorflow.python.ops import gen_dataset_ops
def ignore_errors():
"""Creates a `Dataset` from another `Dataset` and silently ignores any errors.
Use this transformation to produce a dataset that contains the same elements
as the input, but silently drops any elements that caused an error. For
example:
```python
dataset = tf.data.Dataset.from_tensor_slices([1., 2., 0., 4.])
# Computing `tf.check_numerics(1. / 0.)` will raise an InvalidArgumentError.
dataset = dataset.map(lambda x: tf.check_numerics(1. / x, "error"))
# Using `ignore_errors()` will drop the element that causes an error.
dataset =
dataset.apply(tf.contrib.data.ignore_errors()) # ==> { 1., 0.5, 0.2 }
```
Returns:
A `Dataset` transformation function, which can be passed to
@{tf.data.Dataset.apply}.
"""
def _apply_fn(dataset):
return IgnoreErrorsDataset(dataset)
return _apply_fn
class IgnoreErrorsDataset(dataset_ops.Dataset):
"""A `Dataset` that silently ignores errors when computing its input."""
def __init__(self, input_dataset):
"""See `Dataset.ignore_errors()` for details."""
super(IgnoreErrorsDataset, self).__init__()
self._input_dataset = input_dataset
def _as_variant_tensor(self):
return gen_dataset_ops.ignore_errors_dataset(
self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access
output_shapes=nest.flatten(self.output_shapes),
output_types=nest.flatten(self.output_types))
@property
def output_shapes(self):
return self._input_dataset.output_shapes
@property
def output_types(self):
return self._input_dataset.output_types
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