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update the result of example for Split, Tril, Triu and ApplyFtrl.

tags/v1.4.0
wangshuide2020 4 years ago
parent
8ea0e5b8a1
commit
ce5e320b93
4 changed files with 42 additions and 42 deletions
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  1. +32
    -32
      mindspore/nn/layer/basic.py
  2. +5
    -5
      mindspore/numpy/array_creations.py
  3. +1
    -1
      mindspore/ops/operations/array_ops.py
  4. +4
    -4
      mindspore/ops/operations/nn_ops.py

+ 32
- 32
mindspore/nn/layer/basic.py View File

@@ -1013,10 +1013,10 @@ class Tril(Cell):
>>> tril = nn.Tril()
>>> result = tril(x)
>>> print(result)
[[ 1, 0, 0, 0],
[ 5, 6, 0, 0],
[10, 11, 12, 0],
[14, 15, 16, 17]]))
[[ 1 0 0 0]
[ 5 6 0 0]
[10 11 12 0]
[14 15 16 17]]
>>> x = Tensor(np.array([[ 1, 2, 3, 4],
... [ 5, 6, 7, 8],
... [10, 11, 12, 13],
@@ -1024,10 +1024,10 @@ class Tril(Cell):
>>> tril = nn.Tril()
>>> result = tril(x, 1)
>>> print(result)
[[ 1, 2, 0, 0],
[ 5, 6, 7, 0],
[10, 11, 12, 13],
[14, 15, 16, 17]]))
[[ 1 2 0 0]
[ 5 6 7 0]
[10 11 12 13]
[14 15 16 17]]
>>> x = Tensor(np.array([[ 1, 2, 3, 4],
... [ 5, 6, 7, 8],
... [10, 11, 12, 13],
@@ -1035,10 +1035,10 @@ class Tril(Cell):
>>> tril = nn.Tril()
>>> result = tril(x, 2)
>>> print(result)
[[ 1, 2, 3, 0],
[ 5, 6, 7, 8],
[10, 11, 12, 13],
[14, 15, 16, 17]]))
[[ 1 2 3 0]
[ 5 6 7 8]
[10 11 12 13]
[14 15 16 17]]
>>> x = Tensor(np.array([[ 1, 2, 3, 4],
... [ 5, 6, 7, 8],
... [10, 11, 12, 13],
@@ -1046,10 +1046,10 @@ class Tril(Cell):
>>> tril = nn.Tril()
>>> result = tril(x, -1)
>>> print(result)
[[ 0, 0, 0, 0],
[ 5, 0, 0, 0],
[10, 11, 0, 0],
[14, 15, 16, 0]]))
[[ 0 0 0 0]
[ 5 0 0 0]
[10 11 0 0]
[14 15 16 0]]
"""

def __init__(self):
@@ -1100,10 +1100,10 @@ class Triu(Cell):
>>> triu = nn.Triu()
>>> result = triu(x)
>>> print(result)
[[ 1, 2, 3, 4],
[ 0, 6, 7, 8],
[ 0, 0, 12, 13],
[ 0, 0, 0, 17]]
[[ 1 2 3 4]
[ 0 6 7 8]
[ 0 0 12 13]
[ 0 0 0 17]]
>>> x = Tensor(np.array([[ 1, 2, 3, 4],
... [ 5, 6, 7, 8],
... [10, 11, 12, 13],
@@ -1111,10 +1111,10 @@ class Triu(Cell):
>>> triu = nn.Triu()
>>> result = triu(x, 1)
>>> print(result)
[[ 0, 2, 3, 4],
[ 0, 0, 7, 8],
[ 0, 0, 0, 13],
[ 0, 0, 0, 0]]
[[ 0 2 3 4]
[ 0 0 7 8]
[ 0 0 0 13]
[ 0 0 0 0]]
>>> x = Tensor(np.array([[ 1, 2, 3, 4],
... [ 5, 6, 7, 8],
... [10, 11, 12, 13],
@@ -1122,10 +1122,10 @@ class Triu(Cell):
>>> triu = nn.Triu()
>>> result = triu(x, 2)
>>> print(result)
[[ 0, 0, 3, 4],
[ 0, 0, 0, 8],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0]]
[[ 0 0 3 4]
[ 0 0 0 8]
[ 0 0 0 0]
[ 0 0 0 0]]
>>> x = Tensor(np.array([[ 1, 2, 3, 4],
... [ 5, 6, 7, 8],
... [10, 11, 12, 13],
@@ -1133,10 +1133,10 @@ class Triu(Cell):
>>> triu = nn.Triu()
>>> result = triu(x, -1)
>>> print(result)
[[ 1, 2, 3, 4],
[ 5, 6, 7, 8],
[ 0, 11, 12, 13],
[ 0, 0, 16, 17]]
[[ 1 2 3 4]
[ 5 6 7 8]
[ 0 11 12 13]
[ 0 0 16 17]]
"""

def __init__(self):


+ 5
- 5
mindspore/numpy/array_creations.py View File

@@ -1690,11 +1690,11 @@ def vander(x, N=None, increasing=False):
Examples:
>>> import mindspore.numpy as np
>>> print(np.vander([1., 2., 3., 4., 5.]))
[[ 1 1 1 1 1]
[ 16 8 4 2 1]
[ 81 27 9 3 1]
[256 64 16 4 1]
[625 125 25 5 1]]
[[ 1. 1. 1. 1. 1.]
[ 16. 8. 4. 2. 1.]
[ 81. 27. 9. 3. 1.]
[256. 64. 16. 4. 1.]
[625. 125. 25. 5. 1.]]
"""
if isinstance(x, (list, tuple)):
x = asarray_const(x)


+ 1
- 1
mindspore/ops/operations/array_ops.py View File

@@ -1036,7 +1036,7 @@ class Split(PrimitiveWithCheck):
>>> x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]), mindspore.int32)
>>> print(x)
[[1 1 1 1]
[2 2 2 2]])
[2 2 2 2]]
>>> output = split(x)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Int32, value=


+ 4
- 4
mindspore/ops/operations/nn_ops.py View File

@@ -174,8 +174,8 @@ class AdaptiveAvgPool2D(PrimitiveWithInfer):
Examples:
>>> # case 1: output_size=(None, 2)
>>> input_x = Tensor(np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
>>> [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
>>> [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]), mindspore.float32)
... [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
... [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]), mindspore.float32)
>>> adaptive_avg_pool_2d = ops.AdaptiveAvgPool2D((None, 2))
>>> output = adaptive_avg_pool_2d(input_x)
>>> print(output)
@@ -6748,8 +6748,8 @@ class ApplyFtrl(PrimitiveWithInfer):
>>> input_x = Tensor(np.array([[0.3, 0.7], [0.1, 0.8]]).astype(np.float32))
>>> output = net(input_x)
>>> print(net.var.asnumpy())
[[ 0.0390525, 0.11492836]
[ 0.00066425, 0.15075898]]
[[ 0.0390525 0.11492836]
[ 0.00066425 0.15075898]]
"""

@prim_attr_register


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