blob: 0ad43c89a6f575890879759ff1f7ad9edb8d6d68 [file] [log] [blame]
# 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()