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!16395 Remove the random seed from the BatchNorm1d operator example (additional problems) 

From: @dinglinhe123
Reviewed-by: @wuxuejian,@liangchenghui
Signed-off-by: @liangchenghui
This commit is contained in:
mindspore-ci-bot 2021-05-14 14:58:49 +08:00 committed by Gitee
parent 7ccb14330f ac4617f933
commit de0400b477
4 changed files with 35 additions and 26 deletions
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6
mindspore/nn/layer/image.py
View File

@ -231,8 +231,7 @@ class SSIM(Cell):
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore.ops import operations as ops
>>> from mindspore import Tensor
>>> net = nn.SSIM()
>>> img1 = Tensor(np.ones([1, 3, 16, 16]).astype(np.float32))
>>> img2 = Tensor(np.ones([1, 3, 16, 16]).astype(np.float32))
@ -325,8 +324,7 @@ class MSSSIM(Cell):
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore.ops import operations as ops
>>> from mindspore import Tensor
>>> net = nn.MSSSIM(power_factors=(0.033, 0.033, 0.033))
>>> img1 = Tensor(np.ones((1, 3, 128, 128)).astype(np.float32))
>>> img2 = Tensor(np.ones((1, 3, 128, 128)).astype(np.float32))

16
mindspore/nn/layer/normalization.py
View File

@ -311,11 +311,8 @@ class BatchNorm1d(_BatchNorm):
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore.ops import operations as ops
>>> from mindspore import Tensor
>>> net = nn.BatchNorm1d(num_features=4)
>>> grad = Tensor(np.array([[0.7, 0.3, 0.7, 0.6],
... [0.1, 0.1, 0.8, 0.8]]).astype(np.float32))
>>> input = Tensor(np.array([[0.7, 0.5, 0.5, 0.6],
... [0.5, 0.4, 0.6, 0.9]]).astype(np.float32))
>>> output = net(input)
@ -418,7 +415,7 @@ class BatchNorm2d(_BatchNorm):
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore import Tensor
>>> net = nn.BatchNorm2d(num_features=3)
>>> input = Tensor(np.ones([1, 3, 2, 2]).astype(np.float32))
>>> output = net(input)
@ -522,7 +519,7 @@ class BatchNorm3d(Cell):
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore import Tensor
>>> net = nn.BatchNorm3d(num_features=3)
>>> input = Tensor(np.ones([16, 3, 10, 32, 32]).astype(np.float32))
>>> output = net(input)
@ -627,7 +624,8 @@ class GlobalBatchNorm(_BatchNorm):
>>> from mindspore.communication import init
>>> from mindspore import context
>>> from mindspore.context import ParallelMode
>>> from mindspore import nn, Tensor
>>> from mindspore import nn
>>> from mindspore import Tensor
>>> from mindspore.common import dtype as mstype
>>>
>>> context.set_context(mode=context.GRAPH_MODE)
@ -745,7 +743,8 @@ class SyncBatchNorm(_BatchNorm):
>>> from mindspore.communication import init
>>> from mindspore import context
>>> from mindspore.context import ParallelMode
>>> from mindspore import nn, Tensor
>>> from mindspore import Tensor
>>> from mindspore import nn
>>> from mindspore.common import dtype as mstype
>>>
>>> context.set_context(mode=context.GRAPH_MODE)
@ -949,6 +948,7 @@ class InstanceNorm2d(Cell):
class inheriting from `Initializer` not exists.
Examples:
>>> import mindspore
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor

1
mindspore/nn/loss/loss.py
View File

@ -378,6 +378,7 @@ class SoftmaxCrossEntropyWithLogits(_Loss):
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor

38
mindspore/ops/operations/nn_ops.py
View File

@ -5138,7 +5138,8 @@ class ApplyAdaMax(PrimitiveWithInfer):
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore import Tensor
>>> from mindspore import Parameter
>>> from mindspore.ops import operations as ops
>>> import mindspore.common.dtype as mstype
>>> class Net(nn.Cell):
@ -5279,7 +5280,8 @@ class ApplyAdadelta(PrimitiveWithInfer):
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore import Tensor
>>> from mindspore import Parameter
>>> from mindspore.ops import operations as ops
>>> import mindspore.common.dtype as mstype
>>> class Net(nn.Cell):
@ -5400,7 +5402,8 @@ class ApplyAdagrad(PrimitiveWithInfer):
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore import Tensor
>>> from mindspore import Parameter
>>> from mindspore.ops import operations as ops
>>> import mindspore.common.dtype as mstype
>>> class Net(nn.Cell):
@ -5500,7 +5503,8 @@ class ApplyAdagradV2(PrimitiveWithInfer):
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore import Tensor
>>> from mindspore import Parameter
>>> from mindspore.ops import operations as ops
>>> import mindspore.common.dtype as mstype
>>> class Net(nn.Cell):
@ -5604,7 +5608,8 @@ class SparseApplyAdagrad(PrimitiveWithInfer):
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore import Tensor
>>> from mindspore import Parameter
>>> from mindspore.ops import operations as ops
>>> import mindspore.common.dtype as mstype
>>> class Net(nn.Cell):
@ -5706,7 +5711,8 @@ class SparseApplyAdagradV2(PrimitiveWithInfer):
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore import Tensor
>>> from mindspore import Parameter
>>> from mindspore.ops import operations as ops
>>> import mindspore.common.dtype as mstype
>>> class Net(nn.Cell):
@ -5811,7 +5817,8 @@ class ApplyProximalAdagrad(PrimitiveWithInfer):
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore import Tensor
>>> from mindspore import Parameter
>>> from mindspore.ops import operations as ops
>>> class Net(nn.Cell):
... def __init__(self):
@ -6043,7 +6050,8 @@ class ApplyAddSign(PrimitiveWithInfer):
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore import Tensor
>>> from mindspore import Parameter
>>> from mindspore.ops import operations as ops
>>> class Net(nn.Cell):
... def __init__(self):
@ -6171,7 +6179,8 @@ class ApplyPowerSign(PrimitiveWithInfer):
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore import Tensor
>>> from mindspore import Parameter
>>> from mindspore.ops import operations as ops
>>> class Net(nn.Cell):
... def __init__(self):
@ -6364,7 +6373,8 @@ class ApplyProximalGradientDescent(PrimitiveWithInfer):
Examples:
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore import Tensor
>>> from mindspore import Parameter
>>> from mindspore.ops import operations as ops
>>> class Net(nn.Cell):
... def __init__(self):
@ -6555,8 +6565,8 @@ class ApplyFtrl(PrimitiveWithInfer):
>>> import mindspore
>>> import mindspore.nn as nn
>>> import numpy as np
>>> from mindspore import Parameter, Tensor
>>> import mindspore.context as context
>>> from mindspore import Tensor
>>> from mindspore import Parameter
>>> from mindspore.ops import operations as ops
>>> class ApplyFtrlNet(nn.Cell):
... def __init__(self):
@ -7042,9 +7052,9 @@ class CTCLoss(PrimitiveWithInfer):
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter
>>> from mindspore import Tensor
>>> from mindspore.ops import operations as ops
>>> inputs = Tensor(np.array([[[0.3, 0.6, 0.6],
... [0.4, 0.3, 0.9],