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47 lines
1.6 KiB
47 lines
1.6 KiB
4 years ago
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# 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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import numpy as np
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import pytest
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import mindspore.context as context
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from mindspore import Tensor
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from mindspore.nn import Cell
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import mindspore.ops.operations._grad_ops as G
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context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU")
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class TanhGradNet(Cell):
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def __init__(self):
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super(TanhGradNet, self).__init__()
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self.tanh_grad = G.TanhGrad()
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def construct(self, y, dy):
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return self.tanh_grad(y, dy)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_tanh_grad():
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np.random.seed(0)
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input_y = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
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input_dy = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
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net = TanhGradNet()
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result = net(Tensor(input_y), Tensor(input_dy))
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expect = input_dy * (1.0 - input_y * input_y)
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res = np.allclose(expect, result.asnumpy(), rtol=1.e-4, atol=1.e-7, equal_nan=True)
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assert res
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