p83571920
/
mindspore
forked from mindspore-Ecosystem/mindspore
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

fix the example of Unique, SparseApplyProximalAdagrad, BoundingBoxEncode, SGD and Parameter.

This commit is contained in:
wangshuide2020 2021-02-02 20:42:54 +08:00
parent 07e9c79de8
commit 2e5b22adde
6 changed files with 19 additions and 20 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
mindspore/common/parameter.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -95,17 +95,16 @@ class Parameter(Tensor_):
... def __init__(self):
... super(Net, self).__init__()
... self.matmul = P.MatMul()
... self.weight = Parameter(Tensor(np.ones((1,2))), name="w", requires_grad=True)
... self.weight = Parameter(Tensor(np.ones((1, 2)), mindspore.float32), name="w", requires_grad=True)
...
... def construct(self, x):
... out = self.matmul(self.weight, x)
... return out
>>> net = Net()
>>> x = Tensor(np.ones((2,1)))
>>> x = Tensor(np.ones((2, 1)), mindspore.float32)
>>> print(net(x))
[[2.]]
>>> net.weight.set_data(Tensor(np.zeros((1,2))))
Parameter (name=w, shape=(1, 2), dtype=Float64, requires_grad=True)
>>> _ = net.weight.set_data(Tensor(np.zeros((1, 2)), mindspore.float32))
>>> print(net(x))
[[0.]]
"""

2
mindspore/nn/layer/normalization.py
View File

@ -269,7 +269,7 @@ class BatchNorm1d(_BatchNorm):
Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C_{out})`.
Supported Platforms:
``Ascend`` ``GPU``
``Ascend``
Raises:
TypeError: If `num_features` is not an int.

6
mindspore/ops/_utils/utils.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -33,7 +33,7 @@ def get_broadcast_shape(x_shape, y_shape, prim_name):
List, the shape that broadcast between tensor x and tensor y.
Raises:
ValueError: If tensor x and tensor y are not equal and could't broadcast.
ValueError: If tensor x and tensor y are not equal and couldn't broadcast.
Examples:
>>> x_shape = [1, 2, 3]
@ -66,7 +66,7 @@ def get_broadcast_shape(x_shape, y_shape, prim_name):
def get_concat_offset(x_shp, x_type, axis, prim_name):
"""for concat and concatoffset check args and compute offset"""
validator.check_value_type("shape", x_shp, [tuple], prim_name)
validator.check_value_type("shape", x_shp, [tuple, list], prim_name)
validator.check_positive_int(len(x_shp), "input_x rank", prim_name)
validator.check_subclass("shape0", x_type[0], mstype.tensor, prim_name)
validator.check_positive_int(len(x_shp[0]), "len of x_shp[0]", prim_name)

4
mindspore/ops/operations/array_ops.py
View File

@ -758,14 +758,14 @@ class Unique(Primitive):
>>> class UniqueNet(nn.Cell):
... def __init__(self):
... super(UniqueNet, self).__init__()
... self.unique_op = P.Unique()
... self.unique_op = ops.Unique()
...
... def construct(self, x):
... output, indices = self.unique_op(x)
... return output, indices
...
>>> x = Tensor(np.array([1, 2, 5, 2]), mindspore.int32)
>>> context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
>>> context.set_context(mode=context.GRAPH_MODE)
>>> net = UniqueNet()
>>> output = net(x)
>>> print(output)

12
mindspore/ops/operations/nn_ops.py
View File

@ -2677,7 +2677,7 @@ class SGD(PrimitiveWithCheck):
>>> momentum = Tensor(0.1, mindspore.float32)
>>> stat = Tensor(np.array([1.5, -0.3, 0.2, -0.7]), mindspore.float32)
>>> output = sgd(parameters, gradient, learning_rate, accum, momentum, stat)
>>> print(output[0])
>>> print(output)
(Tensor(shape=[4], dtype=Float32, value= [ 1.98989999e+00, -4.90300000e-01, 1.69520009e+00, 3.98009992e+00]),)
"""
@ -5629,14 +5629,14 @@ class SparseApplyProximalAdagrad(PrimitiveWithCheck):
...
>>> net = Net()
>>> grad = Tensor(np.array([[1, 1], [1, 1]], np.float32))
>>> indices = Tensor(np.array([0], np.int32))
>>> indices = Tensor(np.array([0, 1], np.int32))
>>> output = net(grad, indices)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Float32, value=
[[ 2.97499990e+00, 6.07499981e+00],
[ 0.00000000e+00, 1.87500000e+00]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 6.40000000e+01, 6.40000000e+01],
[ 6.40000000e+01, 6.40000000e+01]]))
[[ 2.09999990e+00, 5.199999981e+00],
[ 0.00000000e+00, 1.000000000e+00]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 1.00000000e+00, 1.000000000e+00],
[ 1.00000000e+00, 1.000000000e+00]]))
"""
__mindspore_signature__ = (

6
mindspore/ops/operations/other_ops.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -133,8 +133,8 @@ class BoundingBoxEncode(PrimitiveWithInfer):
>>> boundingbox_encode = ops.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0))
>>> output = boundingbox_encode(anchor_box, groundtruth_box)
>>> print(output)
[[ －1. 0.25 0. 0.40551758]
[ －1. 0.25 0. 0.40551758]]
[[ -1. 0.25 0. 0.40551758]
[ -1. 0.25 0. 0.40551758]]
"""
@prim_attr_register