|
1234567891011121314151617181920212223242526272829303132333435363738394041 |
- # Copyright 2019 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.
- # ============================================================================
- from mindspore import Tensor
- from mindspore.ops import operations as P
- import mindspore.nn as nn
- from mindspore.common.api import ms_function
- import numpy as np
- import mindspore.context as context
-
- context.set_context(device_target="Ascend")
-
-
- class Net(nn.Cell):
- def __init__(self):
- super(Net, self).__init__()
- self.matmul = P.MatMul(transpose_b=True)
- self.bias_add = P.BiasAdd()
-
- @ms_function
- def construct(self, x, w, b):
- return self.bias_add(self.matmul(x, w), b)
-
- # def test_net():
- # x = np.random.randn(32, 2048).astype(np.float16)
- # w = np.random.randn(1001, 2048).astype(np.float16)
- # b = np.random.randn(1001).astype(np.float16)
- # FullConnection = Net()
- # output = FullConnection(Tensor(x), Tensor(w), Tensor(b))
- # print(output.asnumpy())
|