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Graduate tf.data.experimental.dense_to_ragged_batch to tf.data.Dataset.ragged_batch.
PiperOrigin-RevId: 482614367
This commit is contained in:
Matt Callanan
TensorFlower Gardener
parent
503a749f6c
commit
6beecdaf55
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@ -142,6 +142,10 @@ This release contains contributions from many people at Google, as well as:
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the [RFC](https://github.com/tensorflow/community/pull/415) for more
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details regarding its design and properties.
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* `tf.data`:
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* Graduated experimental APIs:
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* [`tf.data.Dataset.ragged_batch`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset/#ragged_batch), which batches elements of `tf.data.Dataset`s into `tf.RaggedTensor`s.
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## Bug Fixes and Other Changes
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* `tf.image`
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@ -147,22 +147,6 @@ tf_py_test(
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],
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)
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tf_py_test(
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name = "dense_to_ragged_batch_test",
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size = "small",
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srcs = ["dense_to_ragged_batch_test.py"],
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shard_count = 4,
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deps = [
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"//tensorflow/python:array_ops",
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"//tensorflow/python:client_testlib",
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"//tensorflow/python:dtypes",
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"//tensorflow/python:errors",
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"//tensorflow/python/data/kernel_tests:test_base",
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"//tensorflow/python/data/ops:dataset_ops",
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"//third_party/py/numpy",
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],
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)
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tf_py_test(
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name = "dense_to_sparse_batch_test",
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size = "medium",
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@ -13,7 +13,6 @@
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# limitations under the License.
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# ==============================================================================
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"""Batching dataset transformations."""
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from tensorflow.python.data.ops import batch_op
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.ops import structured_function
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from tensorflow.python.data.util import convert
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@ -22,15 +21,14 @@ from tensorflow.python.framework import dtypes
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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_shape
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from tensorflow.python.framework import tensor_spec
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from tensorflow.python.framework import tensor_util
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from tensorflow.python.ops import gen_experimental_dataset_ops as ged_ops
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from tensorflow.python.ops.ragged import ragged_tensor
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from tensorflow.python.util import deprecation
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from tensorflow.python.util.tf_export import tf_export
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@tf_export("data.experimental.dense_to_ragged_batch")
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@deprecation.deprecated(None, "Use `tf.data.Dataset.ragged_batch` instead.")
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def dense_to_ragged_batch(batch_size,
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drop_remainder=False,
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row_splits_dtype=dtypes.int64):
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@ -87,11 +85,8 @@ def dense_to_ragged_batch(batch_size,
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Returns:
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Dataset: A `Dataset`.
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"""
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def _apply_fn(dataset):
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ragged_dataset = _DenseToRaggedDataset(dataset, row_splits_dtype)
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return batch_op.BatchDataset(
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ragged_dataset, batch_size=batch_size, drop_remainder=drop_remainder)
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return dataset.ragged_batch(batch_size, drop_remainder, row_splits_dtype)
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return _apply_fn
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@ -381,78 +376,3 @@ class _MapAndBatchDataset(dataset_ops.UnaryDataset):
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@property
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def element_spec(self):
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return self._element_spec
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class _DenseToRaggedDataset(dataset_ops.UnaryDataset):
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"""A `Dataset` that encodes dense inputs as ragged (w/ ragged_rank=0).
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In particular:
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* Any tf.Tensor elements with rank>0 are encoded as ragged tensors with
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ragged_rank=0. This allows tensors with varying shape to be batched
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together.
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* Any other elements are left as-is.
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"""
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def __init__(self, input_dataset, row_splits_dtype):
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"""Constructs a new _DenseToRaggedDataset.
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Args:
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input_dataset: The dataset whose tf.Tensor elements should be made ragged.
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row_splits_dtype: The dtype that should be used for the `row_splits` of
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any new ragged tensors. Existing `tf.RaggedTensor` elements do *not*
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have their row_splits dtype changed.
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"""
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# Replace each TensorSpec in the input dataset's structure with a
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# corresponding RaggedTensorSpec.
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def to_ragged_spec(spec):
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"""Returns the new spec based on RaggedTensors."""
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if (not isinstance(spec, tensor_spec.TensorSpec) or
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spec.shape.rank is None or
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spec.shape.is_fully_defined()):
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return spec
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else:
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ragged_rank = max([
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axis for (axis, size) in enumerate(spec.shape.as_list())
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if size is None
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])
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return ragged_tensor.RaggedTensorSpec(
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shape=spec.shape,
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dtype=spec.dtype,
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ragged_rank=ragged_rank,
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row_splits_dtype=row_splits_dtype)
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self._structure = nest.map_structure(to_ragged_spec,
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input_dataset.element_spec)
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# Replace each tf.Tensor value in the input dataset with a variant-encoded
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# RaggedTensor. Since we're updating the corresponding structure to be
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# a RaggedTensorSpec, this variant-encoded tensor will be decoded with
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# RaggedTensorSpec._from_tensor_list.
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def to_ragged_variant(value):
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"""Re-encode Tensors as RaggedTensors."""
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if (not isinstance(value, ops.Tensor) or
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value.shape.rank is None or
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value.shape.is_fully_defined()):
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return value
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else:
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spec = to_ragged_spec(tensor_spec.TensorSpec.from_tensor(value))
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if spec._ragged_rank > 0: # pylint: disable=protected-access
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value = ragged_tensor.RaggedTensor.from_tensor(
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value, ragged_rank=spec._ragged_rank) # pylint: disable=protected-access
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return spec._to_tensor_list(value)[0] # pylint: disable=protected-access
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# Tuples are automatically unpacked by `dataset.map` so we repack them.
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if structured_function._should_unpack(input_dataset.element_spec): # pylint: disable=protected-access
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map_fn = lambda *value: nest.map_structure(to_ragged_variant, value)
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else:
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map_fn = lambda value: nest.map_structure(to_ragged_variant, value)
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self._mapped_dataset = input_dataset.map(map_fn)
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variant = self._mapped_dataset._variant_tensor # pylint: disable=protected-access
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super(_DenseToRaggedDataset, self).__init__(input_dataset, variant)
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@property
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def element_spec(self):
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return self._structure
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@ -754,6 +754,22 @@ tf_py_test(
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],
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)
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tf_py_test(
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name = "ragged_batch_test",
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size = "small",
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srcs = ["ragged_batch_test.py"],
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shard_count = 4,
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deps = [
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"//tensorflow/python:array_ops",
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"//tensorflow/python:client_testlib",
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"//tensorflow/python:dtypes",
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"//tensorflow/python:errors",
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"//tensorflow/python/data/kernel_tests:test_base",
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"//tensorflow/python/data/ops:dataset_ops",
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"//third_party/py/numpy",
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],
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)
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tf_py_test(
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name = "random_test",
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size = "small",
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@ -12,11 +12,10 @@
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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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"""Tests for `tf.data.experimental.dense_to_ragged_batch`."""
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"""Tests for `tf.data.Dataset.ragged_batch`."""
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.python.data.experimental.ops import batching
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from tensorflow.python.data.kernel_tests import test_base
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.util import nest
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@ -118,8 +117,7 @@ class RaggedBatchTest(test_base.DatasetTestBase, parameterized.TestCase):
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for _ in range(nrows)]
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# Batch the dataset, and check that batches match slices from `rows`.
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batched_dataset = dataset.apply(
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batching.dense_to_ragged_batch(batch_size, drop_remainder))
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batched_dataset = dataset.ragged_batch(batch_size, drop_remainder)
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get_next = self.getNext(batched_dataset)
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for start_row in range(0, nrows, batch_size):
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end_row = start_row + batch_size
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@ -155,7 +153,7 @@ class RaggedBatchTest(test_base.DatasetTestBase, parameterized.TestCase):
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dataset = dataset_ops.Dataset.from_tensor_slices(np.arange(nrows))
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dataset = dataset.map(make_structure)
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dataset = dataset.apply(batching.dense_to_ragged_batch(batch_size))
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dataset = dataset.ragged_batch(batch_size)
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get_next = self.getNext(dataset)
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for i in range(0, nrows, batch_size):
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@ -53,6 +53,7 @@ py_library(
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":iterator_ops",
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":load_ops",
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":options",
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":ragged_batch_op",
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":rebatch_op",
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":save_ops",
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":structured_function",
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@ -137,6 +138,23 @@ py_library(
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],
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)
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py_library(
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name = "ragged_batch_op",
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srcs = ["ragged_batch_op.py"],
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srcs_version = "PY3",
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deps = [
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"//tensorflow/python:dtypes",
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"//tensorflow/python:experimental_dataset_ops_gen",
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"//tensorflow/python:framework_ops",
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"//tensorflow/python:tensor_shape",
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"//tensorflow/python:tensor_util",
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"//tensorflow/python:util",
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"//tensorflow/python/data/util:convert",
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"//tensorflow/python/data/util:nest",
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"//tensorflow/python/data/util:structure",
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],
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)
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py_library(
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name = "save_ops",
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srcs = ["save_op.py"],
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@ -2126,6 +2126,67 @@ class DatasetV2(
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drop_remainder,
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name=name)
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def ragged_batch(self,
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batch_size,
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drop_remainder=False,
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row_splits_dtype=dtypes.int64,
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name=None):
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"""Combines consecutive elements of this dataset into `tf.RaggedTensor`s.
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Like `tf.data.Dataset.batch`, the components of the resulting element will
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have an additional outer dimension, which will be `batch_size` (or
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`N % batch_size` for the last element if `batch_size` does not divide the
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number of input elements `N` evenly and `drop_remainder` is `False`). If
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your program depends on the batches having the same outer dimension, you
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should set the `drop_remainder` argument to `True` to prevent the smaller
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batch from being produced.
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Unlike `tf.data.Dataset.batch`, the input elements to be batched may have
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different shapes:
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* If an input element is a `tf.Tensor` whose static `tf.TensorShape` is
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fully defined, then it is batched as normal.
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* If an input element is a `tf.Tensor` whose static `tf.TensorShape`
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contains one or more axes with unknown size (i.e., `shape[i]=None`), then
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the output will contain a `tf.RaggedTensor` that is ragged up to any of such
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dimensions.
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* If an input element is a `tf.RaggedTensor` or any other type, then it is
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batched as normal.
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Example:
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>>> dataset = tf.data.Dataset.range(6)
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>>> dataset = dataset.map(lambda x: tf.range(x))
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>>> dataset.element_spec.shape
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TensorShape([None])
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>>> dataset = dataset.ragged_batch(2)
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>>> for batch in dataset:
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... print(batch)
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<tf.RaggedTensor [[], [0]]>
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<tf.RaggedTensor [[0, 1], [0, 1, 2]]>
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<tf.RaggedTensor [[0, 1, 2, 3], [0, 1, 2, 3, 4]]>
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Args:
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batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
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consecutive elements of this dataset to combine in a single batch.
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drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing
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whether the last batch should be dropped in the case it has fewer than
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`batch_size` elements; the default behavior is not to drop the smaller
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batch.
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row_splits_dtype: The dtype that should be used for the `row_splits` of
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any new ragged tensors. Existing `tf.RaggedTensor` elements do not have
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their row_splits dtype changed.
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name: (Optional.) A string indicating a name for the `tf.data` operation.
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Returns:
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A new `Dataset` with the transformation applied as described above.
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"""
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# Loaded lazily due to a circular dependency (dataset_ops ->
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# ragged_batch_op -> dataset_ops).
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from tensorflow.python.data.ops import ragged_batch_op # pylint: disable=g-import-not-at-top
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return ragged_batch_op.ragged_batch(self, batch_size, drop_remainder,
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row_splits_dtype, name)
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def map(self,
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map_func,
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num_parallel_calls=None,
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109
tensorflow/python/data/ops/ragged_batch_op.py
Normal file
109
tensorflow/python/data/ops/ragged_batch_op.py
Normal file
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@ -0,0 +1,109 @@
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# Copyright 2017 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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"""The implementation of `tf.data.Dataset.ragged_batch`."""
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from tensorflow.python.data.ops import batch_op
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.ops import structured_function
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from tensorflow.python.data.util import nest
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor_spec
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from tensorflow.python.ops.ragged import ragged_tensor
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def ragged_batch(self,
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batch_size,
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drop_remainder=False,
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row_splits_dtype=dtypes.int64,
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name=None):
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ragged_dataset = _DenseToRaggedDataset(self, row_splits_dtype, name)
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return batch_op.BatchDataset(
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ragged_dataset, batch_size=batch_size, drop_remainder=drop_remainder)
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class _DenseToRaggedDataset(dataset_ops.UnaryDataset):
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"""A `Dataset` that encodes dense inputs as ragged (w/ ragged_rank=0).
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In particular:
|
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|
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* Any tf.Tensor elements with rank>0 are encoded as ragged tensors with
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ragged_rank=0. This allows tensors with varying shape to be batched
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together.
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* Any other elements are left as-is.
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"""
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def __init__(self, input_dataset, row_splits_dtype, name=None):
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"""Constructs a new _DenseToRaggedDataset.
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Args:
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input_dataset: The dataset whose tf.Tensor elements should be made ragged.
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row_splits_dtype: The dtype that should be used for the `row_splits` of
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any new ragged tensors. Existing `tf.RaggedTensor` elements do *not*
|
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have their row_splits dtype changed.
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name: (Optional.) A string indicating a name for the `tf.data` operation.
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"""
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# Replace each TensorSpec in the input dataset's structure with a
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# corresponding RaggedTensorSpec.
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def to_ragged_spec(spec):
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"""Returns the new spec based on RaggedTensors."""
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if (not isinstance(spec, tensor_spec.TensorSpec) or
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spec.shape.rank is None or
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spec.shape.is_fully_defined()):
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return spec
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else:
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ragged_rank = max([
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axis for (axis, size) in enumerate(spec.shape.as_list())
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if size is None
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])
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return ragged_tensor.RaggedTensorSpec(
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shape=spec.shape,
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dtype=spec.dtype,
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ragged_rank=ragged_rank,
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row_splits_dtype=row_splits_dtype)
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self._structure = nest.map_structure(to_ragged_spec,
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input_dataset.element_spec)
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# Replace each tf.Tensor value in the input dataset with a variant-encoded
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# RaggedTensor. Since we're updating the corresponding structure to be
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# a RaggedTensorSpec, this variant-encoded tensor will be decoded with
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# RaggedTensorSpec._from_tensor_list.
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def to_ragged_variant(value):
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"""Re-encode Tensors as RaggedTensors."""
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if (not isinstance(value, ops.Tensor) or
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value.shape.rank is None or
|
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value.shape.is_fully_defined()):
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return value
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else:
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spec = to_ragged_spec(tensor_spec.TensorSpec.from_tensor(value))
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if spec._ragged_rank > 0: # pylint: disable=protected-access
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value = ragged_tensor.RaggedTensor.from_tensor(
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value, ragged_rank=spec._ragged_rank) # pylint: disable=protected-access
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return spec._to_tensor_list(value)[0] # pylint: disable=protected-access
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# Tuples are automatically unpacked by `dataset.map` so we repack them.
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if structured_function._should_unpack(input_dataset.element_spec): # pylint: disable=protected-access
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map_fn = lambda *value: nest.map_structure(to_ragged_variant, value)
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else:
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map_fn = lambda value: nest.map_structure(to_ragged_variant, value)
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self._mapped_dataset = input_dataset.map(map_fn)
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self._name = name
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variant = self._mapped_dataset._variant_tensor # pylint: disable=protected-access
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super(_DenseToRaggedDataset, self).__init__(input_dataset, variant)
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@property
|
||||
def element_spec(self):
|
||||
return self._structure
|
||||
|
|
@ -143,6 +143,10 @@ tf_class {
|
|||
name: "prefetch"
|
||||
argspec: "args=[\'self\', \'buffer_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ragged_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\', \'row_splits_dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \"<dtype: \'int64\'>\", \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "random"
|
||||
argspec: "args=[\'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
|
|
|
|||
|
|
@ -145,6 +145,10 @@ tf_class {
|
|||
name: "prefetch"
|
||||
argspec: "args=[\'self\', \'buffer_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ragged_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\', \'row_splits_dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \"<dtype: \'int64\'>\", \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "random"
|
||||
argspec: "args=[\'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
|
|
|
|||
|
|
@ -145,6 +145,10 @@ tf_class {
|
|||
name: "prefetch"
|
||||
argspec: "args=[\'self\', \'buffer_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ragged_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\', \'row_splits_dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \"<dtype: \'int64\'>\", \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "random"
|
||||
argspec: "args=[\'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
|
|
|
|||
|
|
@ -145,6 +145,10 @@ tf_class {
|
|||
name: "prefetch"
|
||||
argspec: "args=[\'self\', \'buffer_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ragged_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\', \'row_splits_dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \"<dtype: \'int64\'>\", \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "random"
|
||||
argspec: "args=[\'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
|
|
|
|||
|
|
@ -145,6 +145,10 @@ tf_class {
|
|||
name: "prefetch"
|
||||
argspec: "args=[\'self\', \'buffer_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ragged_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\', \'row_splits_dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \"<dtype: \'int64\'>\", \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "random"
|
||||
argspec: "args=[\'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
|
|
|
|||
|
|
@ -145,6 +145,10 @@ tf_class {
|
|||
name: "prefetch"
|
||||
argspec: "args=[\'self\', \'buffer_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ragged_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\', \'row_splits_dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \"<dtype: \'int64\'>\", \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "random"
|
||||
argspec: "args=[\'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
|
|
|
|||
|
|
@ -145,6 +145,10 @@ tf_class {
|
|||
name: "prefetch"
|
||||
argspec: "args=[\'self\', \'buffer_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ragged_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\', \'row_splits_dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \"<dtype: \'int64\'>\", \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "random"
|
||||
argspec: "args=[\'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
|
|
|
|||
|
|
@ -110,6 +110,10 @@ tf_class {
|
|||
name: "prefetch"
|
||||
argspec: "args=[\'self\', \'buffer_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ragged_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\', \'row_splits_dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \"<dtype: \'int64\'>\", \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "random"
|
||||
argspec: "args=[\'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
|
|
|
|||
|
|
@ -112,6 +112,10 @@ tf_class {
|
|||
name: "prefetch"
|
||||
argspec: "args=[\'self\', \'buffer_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ragged_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\', \'row_splits_dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \"<dtype: \'int64\'>\", \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "random"
|
||||
argspec: "args=[\'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
|
|
|
|||
|
|
@ -111,6 +111,10 @@ tf_class {
|
|||
name: "prefetch"
|
||||
argspec: "args=[\'self\', \'buffer_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ragged_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\', \'row_splits_dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \"<dtype: \'int64\'>\", \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "random"
|
||||
argspec: "args=[\'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
|
|
|
|||
|
|
@ -112,6 +112,10 @@ tf_class {
|
|||
name: "prefetch"
|
||||
argspec: "args=[\'self\', \'buffer_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ragged_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\', \'row_splits_dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \"<dtype: \'int64\'>\", \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "random"
|
||||
argspec: "args=[\'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
|
|
|
|||
|
|
@ -112,6 +112,10 @@ tf_class {
|
|||
name: "prefetch"
|
||||
argspec: "args=[\'self\', \'buffer_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ragged_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\', \'row_splits_dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \"<dtype: \'int64\'>\", \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "random"
|
||||
argspec: "args=[\'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
|
|
|
|||
|
|
@ -113,6 +113,10 @@ tf_class {
|
|||
name: "prefetch"
|
||||
argspec: "args=[\'self\', \'buffer_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ragged_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\', \'row_splits_dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \"<dtype: \'int64\'>\", \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "random"
|
||||
argspec: "args=[\'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
|
|
|
|||
|
|
@ -112,6 +112,10 @@ tf_class {
|
|||
name: "prefetch"
|
||||
argspec: "args=[\'self\', \'buffer_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ragged_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\', \'row_splits_dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \"<dtype: \'int64\'>\", \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "random"
|
||||
argspec: "args=[\'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
|
|
|
|||
|
|
@ -113,6 +113,10 @@ tf_class {
|
|||
name: "prefetch"
|
||||
argspec: "args=[\'self\', \'buffer_size\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "ragged_batch"
|
||||
argspec: "args=[\'self\', \'batch_size\', \'drop_remainder\', \'row_splits_dtype\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \"<dtype: \'int64\'>\", \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "random"
|
||||
argspec: "args=[\'seed\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user