Adding API docs for ones and zeros methods (#7150)

cross_channel_norm
Abhinav Arora 7 years ago committed by GitHub
parent f3812825d0
commit e9a60e4c8e
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23

@ -201,15 +201,47 @@ def fill_constant_batch_size_like(input,
def ones(shape, dtype):
"""
This function performs the same function as fill_constant() declared above
with the constant value being 1.0.
**ones**
This function creates a tensor of specified *shape* and
*dtype*, and initializes this with 1.
It also sets *stop_gradient* to True.
Args:
shape(tuple|list|None): Shape of output tensor
dtype(np.dtype|core.DataType|str): Data type of output tensor
Returns:
Variable: The tensor variable storing the output
Examples:
.. code-block:: python
data = fluid.layers.ones(shape=[1], dtype='int64')
"""
return fill_constant(value=1.0, **locals())
def zeros(shape, dtype):
"""
This function performs the same function as fill_constant() declared above
with the constant value being 0.0.
**zeros**
This function creates a tensor of specified *shape* and
*dtype*, and initializes this with 0.
It also sets *stop_gradient* to True.
Args:
shape(tuple|list|None): Shape of output tensor
dtype(np.dtype|core.DataType|str): Data type of output tensor
Returns:
Variable: The tensor variable storing the output
Examples:
.. code-block:: python
data = fluid.layers.zeros(shape=[1], dtype='int64')
"""
return fill_constant(value=0.0, **locals())

Loading…
Cancel
Save