未验证 提交 d8128930 编写于 作者: Q qingqing01 提交者: GitHub

Refine doc of uniform_random and fix dtype (#15873)

* Refine doc of uniform_random and fix dtype
* Update defaule value in the arguments
上级 44e7fcdd
...@@ -304,7 +304,7 @@ paddle.fluid.layers.reciprocal ArgSpec(args=['x', 'name'], varargs=None, keyword ...@@ -304,7 +304,7 @@ paddle.fluid.layers.reciprocal ArgSpec(args=['x', 'name'], varargs=None, keyword
paddle.fluid.layers.square ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) paddle.fluid.layers.square ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.softplus ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) paddle.fluid.layers.softplus ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.softsign ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)) paddle.fluid.layers.softsign ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.uniform_random ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=(None, None, None, None)) paddle.fluid.layers.uniform_random ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0))
paddle.fluid.layers.hard_shrink ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)) paddle.fluid.layers.hard_shrink ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.cumsum ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None)) paddle.fluid.layers.cumsum ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.thresholded_relu ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,)) paddle.fluid.layers.thresholded_relu ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,))
......
...@@ -60,7 +60,28 @@ __all__ += ["uniform_random"] ...@@ -60,7 +60,28 @@ __all__ += ["uniform_random"]
_uniform_random_ = generate_layer_fn('uniform_random') _uniform_random_ = generate_layer_fn('uniform_random')
def uniform_random(shape, dtype=None, min=None, max=None, seed=None): def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
"""
This operator initializes a variable with random values sampled from a
uniform distribution. The random result is in set [min, max].
Args:
shape (list): The shape of output variable.
dtype(np.dtype|core.VarDesc.VarType|str): The type of data, such as
float32, float64 etc. Default: float32.
min (float): Minimum value of uniform random. Default -1.0.
max (float): Maximun value of uniform random. Default 1.0.
seed (int): Random seed used for generating samples. 0 means use a
seed generated by the system. Note that if seed is not 0, this
operator will always generate the same random numbers every time.
Default 0.
Examples:
.. code-block:: python
result = fluid.layers.uniform_random(shape=[32, 784])
"""
locals_var = locals().keys() locals_var = locals().keys()
if not isinstance(dtype, core.VarDesc.VarType): if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype) dtype = convert_np_dtype_to_dtype_(dtype)
...@@ -72,12 +93,6 @@ def uniform_random(shape, dtype=None, min=None, max=None, seed=None): ...@@ -72,12 +93,6 @@ def uniform_random(shape, dtype=None, min=None, max=None, seed=None):
return _uniform_random_(**kwargs) return _uniform_random_(**kwargs)
uniform_random.__doc__ = _uniform_random_.__doc__ + """
Examples:
>>> result = fluid.layers.uniform_random(shape=[32, 784])
"""
__all__ += ['hard_shrink'] __all__ += ['hard_shrink']
_hard_shrink_ = generate_layer_fn('hard_shrink') _hard_shrink_ = generate_layer_fn('hard_shrink')
......
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