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Improvements to the documentation for tf.ragged.map_flat_values.

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PiperOrigin-RevId: 340432201
Change-Id: Id017c6697ae359a452c6d1132e676bc593c08654
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Edward Loper 2020-11-03 06:31:13 -08:00 committed by TensorFlower Gardener
parent 1a774d5539
commit fc864b2b09
1 changed files with 30 additions and 10 deletions
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tensorflow/python/ops/ragged/ragged_functional_ops.py
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@ -32,25 +32,45 @@ from tensorflow.python.util.tf_export import tf_export
@tf_export("ragged.map_flat_values")
@dispatch.add_dispatch_support
def map_flat_values(op, *args, **kwargs):
"""Applies `op` to the values of one or more RaggedTensors.
"""Applies `op` to the `flat_values` of one or more RaggedTensors.
Replaces any `RaggedTensor` in `args` or `kwargs` with its `flat_values`
tensor, and then calls `op`. Returns a `RaggedTensor` that is constructed
from the input `RaggedTensor`s' `nested_row_splits` and the value returned by
the `op`.
tensor (which collapses all ragged dimensions), and then calls `op`. Returns
a `RaggedTensor` that is constructed from the input `RaggedTensor`s'
`nested_row_splits` and the value returned by the `op`.
If the input arguments contain multiple `RaggedTensor`s, then they must have
identical `nested_row_splits`.
This operation is generally used to apply elementwise operations to each value
in a `RaggedTensor`.
Warning: `tf.ragged.map_flat_values` does *not* apply `op` to each row of a
ragged tensor. This difference is important for non-elementwise operations,
such as `tf.reduce_sum`. If you wish to apply a non-elementwise operation to
each row of a ragged tensor, use `tf.map_fn` instead. (You may need to
specify an `output_signature` when using `tf.map_fn` with ragged tensors.)
Examples:
>>> rt = tf.ragged.constant([[1, 2, 3], [], [4, 5], [6]])
>>> map_flat_values(tf.ones_like, rt).to_list()
[[1, 1, 1], [], [1, 1], [1]]
>>> map_flat_values(tf.multiply, rt, rt).to_list()
[[1, 4, 9], [], [16, 25], [36]]
>>> map_flat_values(tf.add, rt, 5).to_list()
[[6, 7, 8], [], [9, 10], [11]]
>>> tf.ragged.map_flat_values(tf.ones_like, rt)
<tf.RaggedTensor [[1, 1, 1], [], [1, 1], [1]]>
>>> tf.ragged.map_flat_values(tf.multiply, rt, rt)
<tf.RaggedTensor [[1, 4, 9], [], [16, 25], [36]]>
>>> tf.ragged.map_flat_values(tf.add, rt, 5)
<tf.RaggedTensor [[6, 7, 8], [], [9, 10], [11]]>
Example with a non-elementwise operation (note that `map_flat_values` and
`map_fn` return different results):
>>> rt = tf.ragged.constant([[1.0, 3.0], [], [3.0, 6.0, 3.0]])
>>> def normalized(x):
... return x / tf.reduce_sum(x)
>>> tf.ragged.map_flat_values(normalized, rt)
<tf.RaggedTensor [[0.0625, 0.1875], [], [0.1875, 0.375, 0.1875]]>
>>> tf.map_fn(normalized, rt)
<tf.RaggedTensor [[0.25, 0.75], [], [0.25, 0.5, 0.25]]>
Args:
op: The operation that should be applied to the RaggedTensor `flat_values`.