#!/usr/bin/env python2# -*- coding: utf-8 -*-"""Created on Fri Aug 10 16:13:29 2018@author: myhaspl"""from mxnet import ndfrom mxnet.gluon import nnclass MixMLP(nn.Block): ???def __init__(self, **kwargs): ???????# Run `nn.Block`‘s init method ???????super(MixMLP, self).__init__(**kwargs) ???????self.blk = nn.Sequential() ???????self.blk.add(nn.Dense(3, activation=‘relu‘),nn.Dense(4, activation=‘relu‘)) ???????self.dense = nn.Dense(5) ???def forward(self, x): ???????y = nd.relu(self.blk(x)) ???????print(y) ???????return self.dense(y)net = MixMLP()print net1.nn.Sequential中，?MXNet 自动构造前向函数，该函数可以执行增加层，可以自定义一个弹性的前向函数
2.nn.Sequential 和nn.Dense都是?nn.Block的子类
#!/usr/bin/env python2# -*- coding: utf-8 -*-"""Created on Fri Aug 10 16:13:29 2018@author: myhaspl"""from mxnet import ndfrom mxnet.gluon import nnclass MixMLP(nn.Block): ???def __init__(self, **kwargs): ???????# Run `nn.Block`‘s init method ???????super(MixMLP, self).__init__(**kwargs) ???????self.blk = nn.Sequential() ???????self.blk.add(nn.Dense(3, activation=‘relu‘),nn.Dense(4, activation=‘relu‘)) ???????self.dense = nn.Dense(5) ???def forward(self, x): ???????y = nd.relu(self.blk(x)) ???????print(y) ???????return self.dense(y)net = MixMLP()print netnet.initialize()x = nd.random.uniform(shape=(7,2))net(x)print net.blk[0].weight.data()MixMLP(? (dense): Dense(None -> 5, linear)? (blk): Sequential(? ? (0): Dense(None -> 3, Activation(relu))? ? (1): Dense(None -> 4, Activation(relu))? ))[[9.6452924e-05 0.0000000e+00 2.7557719e-04 0.0000000e+00]
?[6.0751504e-04 0.0000000e+00 1.7357409e-03 0.0000000e+00]
?[5.6857511e-04 0.0000000e+00 1.6244850e-03 0.0000000e+00]
?...
?[1.7680142e-04 0.0000000e+00 3.3347241e-03 0.0000000e+00]
?[9.5664361e-04 0.0000000e+00 4.8063148e-04 0.0000000e+00]
?[1.8764728e-04 0.0000000e+00 1.7001196e-03 0.0000000e+00]]
<NDArray 7x4 @cpu(0)>
[[ 0.01617834 -0.04664135]
?[-0.0526652 ? 0.03906714]
?[ 0.04872115 ?0.05109067]]
<NDArray 3x2 @cpu(0)>
mxnet-自定义前向函数
原文地址：http://blog.51cto.com/13959448/2316625