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- # Copyright 2020 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.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
-
- import numpy as np
- import pytest
- import mindspore.context as context
- from mindspore import Tensor
- from mindspore.nn import Cell
- import mindspore.ops.operations._grad_ops as G
-
-
- class TanhGradNet(Cell):
- def __init__(self):
- super(TanhGradNet, self).__init__()
- self.tanh_grad = G.TanhGrad()
-
- def construct(self, y, dy):
- return self.tanh_grad(y, dy)
-
-
- def test_tanh_grad():
- np.random.seed(0)
- input_y = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
- input_dy = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
- net = TanhGradNet()
- result = net(Tensor(input_y), Tensor(input_dy))
- expect = input_dy * (1.0 - input_y * input_y)
- res = np.allclose(expect, result.asnumpy(), rtol=1.e-4, atol=1.e-7, equal_nan=True)
- assert res
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_tanh_grad_gpu():
- context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU")
- test_tanh_grad()
-
-
- def test_tanh_grad_ascend():
- context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="Ascend")
- test_tanh_grad()
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