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1# Copyright 2020 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15""" test Activations """
16import numpy as np
17
18import mindspore.nn as nn
19from mindspore import Tensor
20
21
22# test activation
23def test_relu_default():
24    relu = nn.ReLU()
25    input_data = Tensor(np.random.rand(1, 3, 4, 4).astype(np.float32) - 0.5)
26    output = relu.construct(input_data)
27    output_np = output.asnumpy()
28    assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
29
30
31def test_activation_str():
32    relu = nn.get_activation('relu')
33
34    input_data = Tensor(np.random.rand(1, 3, 4, 4).astype(np.float32) - 0.5)
35    output = relu.construct(input_data)
36    output_np = output.asnumpy()
37    assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
38
39
40def test_activation_param():
41    relu = nn.get_activation('relu')
42
43    input_data = Tensor(np.random.rand(1, 3, 4, 4).astype(np.float32) - 0.5)
44    output = relu.construct(input_data)
45    output_np = output.asnumpy()
46    assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
47
48
49# test softmax
50def test_softmax_axis():
51    layer = nn.Softmax(1)
52    x = Tensor(np.ones([3, 3]).astype(np.float32))
53    assert layer.softmax.axis == (1,)
54    output = layer.construct(x)
55    output_np = output.asnumpy()
56    assert isinstance(output_np[0][0], (np.float32, np.float64))
57
58
59def test_softmax_axis_none():
60    layer = nn.Softmax()
61    x = Tensor(np.ones([3, 2]).astype(np.float32))
62    assert layer.softmax.axis == (-1,)
63    output = layer.construct(x)
64    output_np = output.asnumpy()
65    assert isinstance(output_np[0][0], (np.float32, np.float64))
66