1# Copyright 2019 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# ============================================================================ 15import numpy as np 16 17import mindspore.common.dtype as mstype 18import mindspore.context as context 19import mindspore.nn as nn 20from mindspore import Tensor 21from mindspore.ops import operations as P 22 23context.set_context(mode=context.GRAPH_MODE, device_id=5, device_target="Ascend") 24 25 26class Net(nn.Cell): 27 def __init__(self): 28 super(Net, self).__init__() 29 self.softmax = P.Softmax(axis=1) 30 self.add = P.Add() 31 self.cast = P.Cast() 32 self.relu = P.ReLU() 33 self.reduce_mean = P.ReduceMean() 34 35 def construct(self, x, y): 36 x = self.cast(x, mstype.float16) 37 y = self.cast(y, mstype.float16) 38 x = self.add(x, y) 39 x = self.relu(x) 40 x = self.reduce_mean(x) 41 return x 42 43 44def test_net(): 45 x = np.random.randn(32, 10).astype(np.float32) 46 relu = Net() 47 output = relu(Tensor(x), Tensor(x)) 48 print(output.asnumpy()) 49