forked from mindspore-Ecosystem/mindspore
66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test Activations """
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor
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# test activation
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def test_relu_default():
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relu = nn.ReLU()
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input_data = Tensor(np.random.rand(1, 3, 4, 4).astype(np.float32) - 0.5)
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output = relu.construct(input_data)
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output_np = output.asnumpy()
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assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
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def test_activation_str():
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relu = nn.get_activation('relu')
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input_data = Tensor(np.random.rand(1, 3, 4, 4).astype(np.float32) - 0.5)
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output = relu.construct(input_data)
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output_np = output.asnumpy()
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assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
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def test_activation_param():
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relu = nn.get_activation('relu')
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input_data = Tensor(np.random.rand(1, 3, 4, 4).astype(np.float32) - 0.5)
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output = relu.construct(input_data)
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output_np = output.asnumpy()
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assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
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# test softmax
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def test_softmax_axis():
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layer = nn.Softmax(1)
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x = Tensor(np.ones([3, 3]))
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assert layer.softmax.axis == (1,)
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output = layer.construct(x)
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output_np = output.asnumpy()
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assert isinstance(output_np[0][0], (np.float32, np.float64))
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def test_softmax_axis_none():
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layer = nn.Softmax()
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x = Tensor(np.ones([3, 2]))
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assert layer.softmax.axis == (-1,)
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output = layer.construct(x)
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output_np = output.asnumpy()
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assert isinstance(output_np[0][0], (np.float32, np.float64))
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