[人工智能]model_construction

最近在B站看沐神的动手学深度学习视频，记录一下学习过程
查看本文的jupyter notebook格式,更加清晰美观哦！

模型构造

Block类是nn模块里提供的一个模型构造类，使用它可以用来定义我们想要的模型。下面继承Block类构造多层感知机。这里定义的MLP类重载了Block类的__init__函数和forward函数。它们分别用于创造模型参数和定义前向计算。

from mxnet import gluon, nd
from mxnet.gluon import nn

class MLP(nn.Block):
    def __init__(self, **kwargs):
        super(MLP, self).__init__(**kwargs)
        self.hidden = nn.Dense(256, activation='relu')
        self.output = nn.Dense(10)
    def forward(self, x):
        return self.output(self.hidden(x))

实例化MLP类得到模型变量net。其中net(X)会自动调用MLP继承自Block类的__call__函数，这个函数将调用MLP类定义的forward函数来完成前向计算。

X = nd.random.uniform(shape=(2, 20))
net = MLP()
net.initialize()
net(X)

[[ 0.02394443  0.05150647  0.01035163 -0.06435341 -0.05801985  0.064192
   0.04472664 -0.01852541 -0.03237379  0.07389369]
 [ 0.05207362  0.04186264  0.04021508 -0.06558423 -0.02249499  0.0341314
   0.02135914 -0.06898528  0.02329672  0.0033668 ]]
<NDArray 2x10 @cpu(0)>

定义一个与Sequential类有相同功能的MySequential类。

class MySequential(nn.Block):
    def __init__(self, **kwargs):
        super(MySequential, self).__init__(**kwargs)
    
    def add(self, block):
        """block是Block子类实例，假设它有一个独一无二的名字。我们将它保存在Block类的成员变量_children里，
        其类型是OrderedDict。当MySequential实例调用initialize函数时，
        系统会自动对_children变量里的所有成员初始化"""
        self._children[block.name] = block
    
    def forward(self, x):
        """OrderedDict会保证按照成员添加时的顺序遍历成员"""
        for block in self._children.values():
            x = block(x)
        return x

net = MySequential()
net.add(nn.Dense(256, activation='relu'))
net.add(nn.Dense(10))
net.initialize()
net(X)

[[-0.03358278  0.00098312  0.03334405 -0.00663612  0.07881726 -0.01704565
  -0.01302506 -0.05449733  0.04149391  0.00170795]
 [ 0.01879605 -0.04185785  0.02918838 -0.00970372  0.05835275 -0.031299
  -0.00644606 -0.02542868  0.0442826   0.01446365]]
<NDArray 2x10 @cpu(0)>

构造一个稍微复杂一点的网络FancyMLP。在这个网络中，通过get_constant函数创建训练中不被迭代的参数，即常数参数。

class FancyMLP(nn.Block):
    def __init__(self, **kwargs):
        super(FancyMLP, self).__init__(**kwargs)
        self.rand_weight = self.params.get_constant(
            'rand_weight', nd.random.uniform(shape=(20, 20)))
        self.dense = nn.Dense(20, activation='relu')
        
    def forward(self, x):
        x = self.dense(x)
        x = nd.relu(nd.dot(x, self.rand_weight.data())+1)
        x = self.dense(x)
        while x.norm().asscalar() > 1:
            x/=2
        if x.norm().asscalar() < 0.8:
            x*=10
        return x.sum()

net = FancyMLP()
net.initialize()
net(X)

[26.667124]
<NDArray 1 @cpu(0)>

class NestMLP(nn.Block):
    def __init__(self, **kwargs):
        super(NestMLP, self).__init__(**kwargs)
        self.net = nn.Sequential()
        self.net.add(nn.Dense(64, activation='relu'),
                    nn.Dense(32, activation='relu'))
        self.dense = nn.Dense(16, activation='relu')
        
    def forward(self, x):
        return self.dense(self.net(x))

net = nn.Sequential()
net.add(NestMLP(), nn.Dense(20), FancyMLP())
net.initialize()
net(X)

[26.0023]
<NDArray 1 @cpu(0)>