forked from mindspore-Ecosystem/mindspore
49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
# 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.
|
|
# ============================================================================
|
|
import numpy as np
|
|
|
|
import mindspore.common.dtype as mstype
|
|
import mindspore.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.ops import operations as P
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_id=5, device_target="Ascend")
|
|
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.softmax = P.Softmax(axis=1)
|
|
self.add = P.Add()
|
|
self.cast = P.Cast()
|
|
self.relu = P.ReLU()
|
|
self.reduce_mean = P.ReduceMean()
|
|
|
|
def construct(self, x, y):
|
|
x = self.cast(x, mstype.float16)
|
|
y = self.cast(y, mstype.float16)
|
|
x = self.add(x, y)
|
|
x = self.relu(x)
|
|
x = self.reduce_mean(x)
|
|
return x
|
|
|
|
|
|
def test_net():
|
|
x = np.random.randn(32, 10).astype(np.float32)
|
|
relu = Net()
|
|
output = relu(Tensor(x), Tensor(x))
|
|
print(output.asnumpy())
|