def get_single_element(dataset):
"""Returns the single element in `dataset` as a nested structure of tensors.
This function enables you to use a @{tf.data.Dataset} in a stateless
"tensor-in tensor-out" expression, without creating a @{tf.data.Iterator}.
This can be useful when your preprocessing transformations are expressed
as a `Dataset`, and you want to use the transformation at serving time.
For example:
```python
input_batch = tf.placeholder(tf.string, shape=[BATCH_SIZE])
def preprocessing_fn(input_str):
# ...
return image, label
dataset = (tf.data.Dataset.from_tensor_slices(input_batch)
.map(preprocessing_fn, num_parallel_calls=BATCH_SIZE)
.batch(BATCH_SIZE))
image_batch, label_batch = tf.contrib.data.get_single_element(dataset)
```
Args:
dataset: A @{tf.data.Dataset} object containing a single element.
Returns:
A nested structure of @{tf.Tensor} objects, corresponding to the single
element of `dataset`.
Raises:
TypeError: if `dataset` is not a `tf.data.Dataset` object.
InvalidArgumentError (at runtime): if `dataset` does not contain exactly
one element.
"""
if not isinstance(dataset, dataset_ops.Dataset):
raise TypeError("`dataset` must be a `tf.data.Dataset` object.")
nested_ret = nest.pack_sequence_as(
dataset.output_types, gen_dataset_ops.dataset_to_single_element(
dataset._as_variant_tensor(), # pylint: disable=protected-access
output_types=nest.flatten(sparse.as_dense_types(
dataset.output_types, dataset.output_classes)),
output_shapes=nest.flatten(sparse.as_dense_shapes(
dataset.output_shapes, dataset.output_classes))))
return sparse.deserialize_sparse_tensors(
nested_ret, dataset.output_types, dataset.output_shapes,
dataset.output_classes)
def get_single_element(dataset):
"""Returns the single element in `dataset` as a nested structure of tensors.
This function enables you to use a `tf.data.Dataset` in a stateless
"tensor-in tensor-out" expression, without creating a
`tf.compat.v1.data.Iterator`.
This can be useful when your preprocessing transformations are expressed
as a `Dataset`, and you want to use the transformation at serving time.
For example:
```python
input_batch = tf.compat.v1.placeholder(tf.string, shape=[BATCH_SIZE])
def preprocessing_fn(input_str):
# ...
return image, label
dataset = (tf.data.Dataset.from_tensor_slices(input_batch)
.map(preprocessing_fn, num_parallel_calls=BATCH_SIZE)
.batch(BATCH_SIZE))
image_batch, label_batch = tf.data.experimental.get_single_element(dataset)
```
Args:
dataset: A `tf.data.Dataset` object containing a single element.
Returns:
A nested structure of `tf.Tensor` objects, corresponding to the single
element of `dataset`.
Raises:
TypeError: if `dataset` is not a `tf.data.Dataset` object.
InvalidArgumentError (at runtime): if `dataset` does not contain exactly
one element.
"""
if not isinstance(dataset, dataset_ops.DatasetV2):
raise TypeError("`dataset` must be a `tf.data.Dataset` object.")
# pylint: disable=protected-access
return structure.from_compatible_tensor_list(
dataset.element_spec,
gen_dataset_ops.dataset_to_single_element(dataset._variant_tensor,
**dataset._flat_structure)) # pylint: disable=protected-access
def get_single_element(dataset):
"""Returns the single element in `dataset` as a nested structure of tensors.
This function enables you to use a `tf.data.Dataset` in a stateless
"tensor-in tensor-out" expression, without creating a `tf.data.Iterator`.
This can be useful when your preprocessing transformations are expressed
as a `Dataset`, and you want to use the transformation at serving time.
For example:
```python
input_batch = tf.placeholder(tf.string, shape=[BATCH_SIZE])
def preprocessing_fn(input_str):
# ...
return image, label
dataset = (tf.data.Dataset.from_tensor_slices(input_batch)
.map(preprocessing_fn, num_parallel_calls=BATCH_SIZE)
.batch(BATCH_SIZE))
image_batch, label_batch = tf.data.experimental.get_single_element(dataset)
```
Args:
dataset: A `tf.data.Dataset` object containing a single element.
Returns:
A nested structure of `tf.Tensor` objects, corresponding to the single
element of `dataset`.
Raises:
TypeError: if `dataset` is not a `tf.data.Dataset` object.
InvalidArgumentError (at runtime): if `dataset` does not contain exactly
one element.
"""
if not isinstance(dataset, dataset_ops.DatasetV2):
raise TypeError("`dataset` must be a `tf.data.Dataset` object.")
# pylint: disable=protected-access
return dataset._element_structure._from_compatible_tensor_list(
gen_dataset_ops.dataset_to_single_element(
dataset._variant_tensor, **dataset_ops.flat_structure(dataset)))
