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Update docstring for zeros_like:

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- testable examples
- clarify that input can be an array-like

PiperOrigin-RevId: 288333496
Change-Id: I7b6bf1301a314dcb4fb505b386b82219236b43dd
This commit is contained in:
Adam Wood 2020-01-06 10:58:49 -08:00 committed by TensorFlower Gardener
parent 9982d476dd
commit 7f304cc3ef
1 changed files with 33 additions and 25 deletions
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58
tensorflow/python/ops/array_ops.py
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@ -2720,21 +2720,27 @@ def zeros_like(tensor, dtype=None, name=None, optimize=True):
same type and shape as `tensor` with all elements set to zero. Optionally,
you can use `dtype` to specify a new type for the returned tensor.
For example:
Examples:
```python
tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
tf.zeros_like(tensor) # [[0, 0, 0], [0, 0, 0]]
```
>>> tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
>>> tf.zeros_like(tensor)
<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[0, 0, 0],
[0, 0, 0]], dtype=int32)>
>>> tf.zeros_like(tensor, dtype=tf.float32)
<tf.Tensor: shape=(2, 3), dtype=float32, numpy=
array([[0., 0., 0.],
[0., 0., 0.]], dtype=float32)>
Args:
tensor: A `Tensor`.
dtype: A type for the returned `Tensor`. Must be `float16`, `float32`,
`float64`, `int8`, `uint8`, `int16`, `uint16`, `int32`, `int64`,
`complex64`, `complex128`, `bool` or `string`.
`complex64`, `complex128`, `bool` or `string`. (optional)
name: A name for the operation (optional).
optimize: if true, attempt to statically determine the shape of 'tensor' and
encode it as a constant.
optimize: if `True`, attempt to statically determine the shape of `tensor`
and encode it as a constant. (optional, defaults to `True`)
Returns:
A `Tensor` with all elements set to zero.
@ -2750,31 +2756,33 @@ def zeros_like_v2(
name=None):
"""Creates a tensor with all elements set to zero.
Given a single tensor (`tensor`), this operation returns a tensor of the
same type and shape as `tensor` with all elements set to zero. Optionally,
you can use `dtype` to specify a new type for the returned tensor.
Given a single tensor or array-like object (`input`), this operation returns
a tensor of the same type and shape as `input` with all elements set to zero.
Optionally, you can use `dtype` to specify a new type for the returned tensor.
For example:
Examples:
```python
tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
tf.zeros_like(tensor) # [[0, 0, 0], [0, 0, 0]] with dtype=int32
>>> tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
>>> tf.zeros_like(tensor)
<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[0, 0, 0],
[0, 0, 0]], dtype=int32)>
If dtype of input `tensor` is `float32`, then the output is also of `float32`
tensor = tf.constant([[1.0, 2.0, 3.0], [4, 5, 6]])
tf.zeros_like(tensor) # [[0., 0., 0.], [0., 0., 0.]] with dtype=floa32
>>> tf.zeros_like(tensor, dtype=tf.float32)
<tf.Tensor: shape=(2, 3), dtype=float32, numpy=
array([[0., 0., 0.],
[0., 0., 0.]], dtype=float32)>
If you want to specify desired dtype of output `tensor`, then specify it in
the op tensor = tf.constant([[1.0, 2.0, 3.0], [4, 5, 6]])
tf.zeros_like(tensor,dtype=tf.int32) # [[0, 0, 0], [0, 0, 0]] with
dtype=int32
```
>>> tf.zeros_like([[1, 2, 3], [4, 5, 6]])
<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[0, 0, 0],
[0, 0, 0]], dtype=int32)>
Args:
input: A `Tensor`.
input: A `Tensor` or array-like object.
dtype: A type for the returned `Tensor`. Must be `float16`, `float32`,
`float64`, `int8`, `uint8`, `int16`, `uint16`, `int32`, `int64`,
`complex64`, `complex128`, `bool` or `string`.
`complex64`, `complex128`, `bool` or `string` (optional).
name: A name for the operation (optional).
Returns: