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modify parameter info

This commit is contained in:
Margaret_wangrui 2021-11-09 19:52:11 +08:00
parent e371440d61
commit 246a828f33
1 changed files with 22 additions and 9 deletions
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31
mindspore/common/parameter.py
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@ -82,23 +82,36 @@ class Parameter(Tensor_):
default_input (Union[Tensor, int, float, numpy.ndarray, list]): Parameter data,
to initialize the parameter data.
name (str): Name of the parameter. Default: None.
1) If the parameter is not given a name, the default name is its variable name. For example, the name of
param_a below is name_a, and the name of param_b is the variable name param_b.
self.param_a = Parameter(Tensor([1], ms.float32), name="name_a")
self.param_b = Parameter(Tensor([2], ms.float32))
.. code-block::
self.param_a = Parameter(Tensor([1], ms.float32), name="name_a")
self.param_b = Parameter(Tensor([2], ms.float32))
2) If parameter in list or tuple is not given a name, will give it a unique name. For example, the names of
parameters below are Parameter$1 and Parameter$2.
self.param_list = [Parameter(Tensor([3], ms.float32)),
Parameter(Tensor([4], ms.float32))]
.. code-block::
self.param_list = [Parameter(Tensor([3], ms.float32)),
Parameter(Tensor([4], ms.float32))]
3) If the parameter is given a name, and the same name exists between different parameters, an exception
will be thrown. For example, "its name 'name_a' already exists." will be thrown.
self.param_a = Parameter(Tensor([1], ms.float32), name="name_a")
self.param_tuple = (Parameter(Tensor([5], ms.float32), name="name_a"),
Parameter(Tensor([6], ms.float32)))
.. code-block::
self.param_a = Parameter(Tensor([1], ms.float32), name="name_a")
self.param_tuple = (Parameter(Tensor([5], ms.float32), name="name_a"),
Parameter(Tensor([6], ms.float32)))
4) If a parameter appear multiple times in list or tuple, check the name of the object only once. For
example, the following example will not throw an exception.
self.param_a = Parameter(Tensor([1], ms.float32), name="name_a")
self.param_tuple = (self.param_a, self.param_a)
.. code-block::
self.param_a = Parameter(Tensor([1], ms.float32), name="name_a")
self.param_tuple = (self.param_a, self.param_a)
requires_grad (bool): True if the parameter requires gradient. Default: True.
layerwise_parallel (bool): When layerwise_parallel is true in data/hybrid parallel mode,
broadcast and gradients communication would not be applied to parameters. Default: False.