Update docstring for zeros_like:
- testable examples
- clarify that input can be an array-like
PiperOrigin-RevId: 288333496
Change-Id: I7b6bf1301a314dcb4fb505b386b82219236b43dd
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 3d712f4..9efa1f0 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -2720,21 +2720,27 @@
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 @@
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:
