| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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 mxnet as mx |
| from mxnet.gluon import nn |
| |
| |
| def simple_forward(): |
| ctx = mx.gpu() |
| mx.profiler.set_config(profile_all=True) |
| mx.profiler.set_state('run') |
| |
| # define simple gluon network with random weights |
| net = nn.Sequential() |
| with net.name_scope(): |
| net.add(nn.Dense(128, activation='relu')) |
| net.add(nn.Dense(64, activation='relu')) |
| net.add(nn.Dense(10)) |
| net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx) |
| |
| input = mx.nd.zeros((128,), ctx=ctx) |
| predictions = net(input) |
| print('Ran simple NN forward, results:') |
| print(predictions.asnumpy()) |
| |
| |
| if __name__ == '__main__': |
| simple_forward() |