!20135 update the result of example of Split, AdaptiveAvgPool2D and ReduceMean operators.
Merge pull request !20135 from wangshuide/code_docs_wsd_r1.3
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1cd02eab24
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@ -723,10 +723,10 @@ class SequenceMask(PrimitiveWithCheck):
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>>> sequence_mask = ops.SequenceMask()
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>>> output = sequence_mask(x, 3)
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>>> print(output)
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[[[True, False, False],
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[True, True, True]],
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[[True, True, False],
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[False, False, False]]]
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[[[True False False]
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[True True True]]
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[[True True False]
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[False False False]]]
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"""
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@prim_attr_register
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@ -1040,10 +1040,10 @@ class Split(PrimitiveWithCheck):
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>>> output = split(x)
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>>> print(output)
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(Tensor(shape=[2, 2], dtype=Int32, value=
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[[1 1]
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[2 2]]), Tensor(shape=[2, 2], dtype=Int32, value=
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[[1 1]
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[2 2]]))
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[[1, 1],
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[2, 2]]), Tensor(shape=[2, 2], dtype=Int32, value=
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[[1, 1],
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[2, 2]]))
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>>> split = ops.Split(1, 4)
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>>> output = split(x)
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>>> print(output)
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@ -460,8 +460,8 @@ class ReduceMean(_Reduce):
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[5. ]
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[6. ]]
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[[7.0000005]
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[5. ]
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[6. ]]]
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[8. ]
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[9. ]]]
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"""
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@ -179,15 +179,15 @@ class AdaptiveAvgPool2D(PrimitiveWithInfer):
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>>> adaptive_avg_pool_2d = ops.AdaptiveAvgPool2D((None, 2))
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>>> output = adaptive_avg_pool_2d(input_x)
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>>> print(output)
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[[[2.5 3.5]
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[[[1.5 2.5]
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[4.5 5.5]
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[6.5 7.5]]
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[[2.5 3.5]
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[7.5 8.5]]
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[[1.5 2.5]
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[4.5 5.5]
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[6.5 7.5]]
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[[2.5 3.5]
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[7.5 8.5]]
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[[1.5 2.5]
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[4.5 5.5]
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[6.5 7.5]]]
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[7.5 8.5]]]
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>>> # case 2: output_size=2
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>>> adaptive_avg_pool_2d = ops.AdaptiveAvgPool2D(2)
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>>> output = adaptive_avg_pool_2d(input_x)
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@ -202,9 +202,9 @@ class AdaptiveAvgPool2D(PrimitiveWithInfer):
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>>> adaptive_avg_pool_2d = ops.AdaptiveAvgPool2D((1, 2))
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>>> output = adaptive_avg_pool_2d(input_x)
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>>> print(output)
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[[[3.5 6.5]]
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[[3.5 6.5]]
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[[3.5 6.5]]]
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[[[4.5 5.5]]
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[[4.5 5.5]]
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[[4.5 5.5]]]
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"""
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@prim_attr_register
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@ -6723,7 +6723,7 @@ class ApplyFtrl(PrimitiveWithInfer):
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>>> net = ApplyFtrlNet()
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>>> input_x = Tensor(np.array([[0.3, 0.7], [0.1, 0.8]]).astype(np.float32))
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>>> output = net(input_x)
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>>> print(output)
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>>> print(net.var.asnumpy())
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[[ 0.0390525, 0.11492836]
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[ 0.00066425, 0.15075898]]
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"""
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@ -236,7 +236,7 @@ class Primitive(Primitive_):
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>>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float32)
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>>> output = addn.check_elim((input_x,))
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>>> print(output)
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(True, Tensor(shape = [3], dtype = Float32, value = [1.0000000e+00,2.0000000e+00,3.0000000e+00]))
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(True, Tensor(shape = [3], dtype = Float32, value = [1.0000000e+00,2.0000000e+00,3.0000000e+00]))
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"""
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return (False, None)
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