mxnet随笔-自定义前向函数

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 10 16:13:29 2018

@author: myhaspl
"""
from mxnet import nd
from mxnet.gluon import nn

class 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 net

1.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 nd
from mxnet.gluon import nn

class 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 net
net.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]]

[[ 0.01617834 -0.04664135]
[-0.0526652 0.03906714]
[ 0.04872115 0.05109067]]