forked from mindspore-Ecosystem/mindspore
update the example of Unique and EditDistance and registration info of BiasAddGrad
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@ -767,7 +767,6 @@ class Unique(Primitive):
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... return output, indices
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...
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>>> x = Tensor(np.array([1, 2, 5, 2]), mindspore.int32)
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>>> context.set_context(mode=context.GRAPH_MODE)
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>>> net = UniqueNet()
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>>> output = net(x)
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>>> print(output)
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@ -4625,11 +4624,10 @@ class EditDistance(PrimitiveWithInfer):
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>>> from mindspore import Tensor
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>>> import mindspore.nn as nn
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>>> import mindspore.ops.operations as ops
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>>> context.set_context(mode=context.GRAPH_MODE)
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>>> class EditDistance(nn.Cell):
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... def __init__(self, hypothesis_shape, truth_shape, normalize=True):
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... super(EditDistance, self).__init__()
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... self.edit_distance = P.EditDistance(normalize)
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... self.edit_distance = ops.EditDistance(normalize)
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... self.hypothesis_shape = hypothesis_shape
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... self.truth_shape = truth_shape
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...
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@ -4887,9 +4887,9 @@ class BinaryCrossEntropy(PrimitiveWithInfer):
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Otherwise, the output is a scalar.
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Raises:
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TypeError: If dtype of `input_x`, `input_y` or `weight`(if given) is neither float16 not float32.
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TypeError: If dtype of `input_x`, `input_y` or `weight` (if given) is neither float16 not float32.
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ValueError: If `reduction` is not one of 'none', 'mean', 'sum'.
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ValueError: If shape of `input_y` is not the same as `input_x` or `weight`(if given).
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ValueError: If shape of `input_y` is not the same as `input_x` or `weight` (if given).
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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