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Merge pull request #37087 from janosh:patch-1

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PiperOrigin-RevId: 312576759
Change-Id: I555c64cd2a8100a7645d0d288521a5971d2faf7b
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TensorFlower Gardener 2020-05-20 16:30:34 -07:00
commit eb6dddfbb2
1 changed files with 7 additions and 7 deletions
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14
tensorflow/python/ops/array_ops.py
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@ -562,19 +562,16 @@ def shape_v2(input, out_type=dtypes.int32, name=None):
See also `tf.size`, `tf.rank`. See also `tf.size`, `tf.rank`.
This operation returns a 1-D integer tensor representing the shape of `input`. `tf.shape` returns a 1-D integer tensor representing the shape of `input`.
This represents the minimal set of known information at definition time.
For example: For example:
>>> t = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]) >>> t = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]])
>>> tf.shape(t) >>> tf.shape(t)
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([2, 2, 3], dtype=int32)> <tf.Tensor: shape=(3,), dtype=int32, numpy=array([2, 2, 3], dtype=int32)>
>>> tf.shape(t).numpy()
array([2, 2, 3], dtype=int32)
Note: When using symbolic tensors, such as when using the Keras functional Note: When using symbolic tensors, such as when using the Keras API,
API, tf.shape() will return the shape of the symbolic tensor. tf.shape() will return the shape of the symbolic tensor.
>>> a = tf.keras.layers.Input((None, 10)) >>> a = tf.keras.layers.Input((None, 10))
>>> tf.shape(a) >>> tf.shape(a)
@ -585,9 +582,12 @@ def shape_v2(input, out_type=dtypes.int32, name=None):
>>> a.shape >>> a.shape
TensorShape([None, None, 10]) TensorShape([None, None, 10])
(The first `None` represents the as yet unknown batch size.)
`tf.shape` and `Tensor.shape` should be identical in eager mode. Within `tf.shape` and `Tensor.shape` should be identical in eager mode. Within
`tf.function` or within a `compat.v1` context, not all dimensions may be `tf.function` or within a `compat.v1` context, not all dimensions may be
known until execution time. known until execution time. Hence when defining custom layers and models
for graph mode, prefer the dynamic `tf.shape(x)` over the static `x.shape`.
Args: Args:
input: A `Tensor` or `SparseTensor`. input: A `Tensor` or `SparseTensor`.