动手学深度学习v2 - 神经网络基础

层和块

一个简单的MLP构建代码：

import torch
from torch import nn
from torch.nn import functional as F

net = nn.Sequential(nn.Linear(20, 256), nn.ReLU(), nn.Linear(256, 10))

X = torch.rand(2, 20)
net(X)

nn.Sequential定义了一种特殊的Module

自定义块

class MLP(nn.Module):
    def __init__(self):
        super().__init__()
        self.hidden = nn.Linear(20, 256)
        self.out = nn.Linear(256, 10)

    def forward(self, X):
        return self.out(F.relu(self.hidden(X)))

使用这个类，先实例化MLP的层，然后在每次调用正向传播函数时调用这些层，

net = MLP()
net(X)

顺序块

class MySequential(nn.Module):
    def __init__(self, *args):
        super().__init__()
        for block in args:
            self._modules[block] = block #层的字典

    def forward(self, X):
        for block in self._modules.values():
            X = block(X)
        return X

net = MySequential(nn.Linear(20, 256), nn.ReLU(), nn.Linear(256, 10))
net(X)

在正向传播函数中执行代码

class FixedHiddenMLP(nn.Module):
    def __init__(self):
        super().__init__()
        self.rand_weight = torch.rand((20, 20), requires_grad=False)
        self.linear = nn.Linear(20, 20)

    def forward(self, X):
        X = self.linear(X)
        X = F.relu(torch.mm(X, self.rand_weight) + 1)
        X = self.linear(X)
        while X.abs().sum() > 1:
            X /= 2
        return X.sum()

net = FixedHiddenMLP()
net(X)

可以随意混搭嵌套使用

class FixedHiddenMLP(nn.Module):
    def __init__(self):
        super().__init__()
        self.rand_weight = torch.rand((20, 20), requires_grad=False)
        self.linear = nn.Linear(20, 20)

    def forward(self, X):
        X = self.linear(X)
        X = F.relu(torch.mm(X, self.rand_weight) + 1)
        X = self.linear(X)
        while X.abs().sum() > 1:
            X /= 2
        return X.sum()

net = FixedHiddenMLP()
net(X)

参数管理

单隐藏层的MLP

import torch
from torch import nn

net = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 1))
X = torch.rand(size=(2, 4))
net(X)

参数访问

print(net[2].state_dict())

# OrderedDict([('weight', tensor([[ 0.3334, -0.1448, -0.0597, -0.0028,  0.3032,  0.2327,  0.3047,  0.3079]])), ('bias', tensor([0.3206]))])

索引访问最后的输出层的参数

访问具体的参数

print(type(net[2].bias))
print(net[2].bias)
print(net[2].weight) # 获取信息
print(net[2].bias.data) # 获取值
print(net[2].weight.grad) # 获取梯度，需要进行反向计算

一次性访问所有参数

print(*[(name, param.shape) for name, param in net[0].named_parameters()])
print(*[(name, param.shape) for name, param in net.named_parameters()])

或者这样访问参数

net.state_dict()['2.bias'].data

从嵌套块收集参数

def block1():
    return nn.Sequential(nn.Linear(4, 8), nn.ReLU(), nn.Linear(8, 4),
                         nn.ReLU())

def block2():
    net = nn.Sequential()
    for i in range(4):
        net.add_module(f'block {i}', block1())
    return net

rgnet = nn.Sequential(block2(), nn.Linear(4, 1))
rgnet(X)

内置初始化

# 参数初始化为正态分布
def init_normal(m):
    if type(m) == nn.Linear:
        nn.init.normal_(m.weight, mean=0, std=0.01)
        nn.init.zeros_(m.bias)

net.apply(init_normal) # 初始化这个神经网络
net[0].weight.data[0], net[0].bias.data[0]

# 甚至可以初始化为常数，不过没有意义
def init_constant(m):
    if type(m) == nn.Linear:
        nn.init.constant_(m.weight, 1)
        nn.init.zeros_(m.bias)

net.apply(init_constant)
net[0].weight.data[0], net[0].bias.data[0]

对某些块应用不同的初始化方法

def xavier(m):
    if type(m) == nn.Linear:
        nn.init.xavier_uniform_(m.weight)

def init_42(m):
    if type(m) == nn.Linear:
        nn.init.constant_(m.weight, 42)

net[0].apply(xavier)
net[2].apply(init_42)
print(net[0].weight.data[0])
print(net[2].weight.data)

自定义初始化

def my_init(m):
    if type(m) == nn.Linear:
        print(
            "Init",
            *[(name, param.shape) for name, param in m.named_parameters()][0])
        nn.init.uniform_(m.weight, -10, 10)
        m.weight.data *= m.weight.data.abs() >= 5

net.apply(my_init)
net[0].weight[:2]

更暴力的方法初始化参数

net[0].weight.data[:] += 1
net[0].weight.data[0, 0] = 42
net[0].weight.data[0]

参数绑定

shared = nn.Linear(8, 8)
net = nn.Sequential(nn.Linear(4, 8), nn.ReLU(), shared, nn.ReLU(), shared,
                    nn.ReLU(), nn.Linear(8, 1))
net(X)
print(net[2].weight.data[0] == net[4].weight.data[0])
net[2].weight.data[0, 0] = 100
print(net[2].weight.data[0] == net[4].weight.data[0])

自定义层

构造一个没有任何参数的自定义层

import torch
import torch.nn.functional as F
from torch import nn

class CenteredLayer(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, X):
        return X - X.mean()

layer = CenteredLayer()
layer(torch.FloatTensor([1, 2, 3, 4, 5]))

将层作为组件合并到构建更复杂的模型中

net = nn.Sequential(nn.Linear(8, 128), CenteredLayer())

Y = net(torch.rand(4, 8))
Y.mean()

带参数的图层

class MyLinear(nn.Module):
    def __init__(self, in_units, units):
        super().__init__()
        self.weight = nn.Parameter(torch.randn(in_units, units))
        self.bias = nn.Parameter(torch.randn(units,))

    def forward(self, X):
        linear = torch.matmul(X, self.weight.data) + self.bias.data
        return F.relu(linear)

dense = MyLinear(5, 3)
dense.weight

使用自定义层直接执行正向传播计算

dense(torch.rand(2, 5))

使用自定义层构建模型

net = nn.Sequential(MyLinear(64, 8), MyLinear(8, 1))
net(torch.rand(2, 64))

读写文件

加载和保存张量

import torch
from torch import nn
from torch.nn import functional as F

x = torch.arange(4)
torch.save(x, 'x-file')

x2 = torch.load("x-file")
x2

存储一个张量列表，然后把它们读回内存

y = torch.zeros(4)
torch.save([x, y], 'x-files')
x2, y2 = torch.load('x-files')
(x2, y2)

写入或读取从字符串映射到张量的字典

mydict = {'x': x, 'y': y}
torch.save(mydict, 'mydict')
mydict2 = torch.load('mydict')
mydict2

加载和保存模型参数

class MLP(nn.Module):
    def __init__(self):
        super().__init__()
        self.hidden = nn.Linear(20, 256)
        self.output = nn.Linear(256, 10)

    def forward(self, x):
        return self.output(F.relu(self.hidden(x)))

net = MLP()
X = torch.randn(size=(2, 20))
Y = net(X)

将模型的参数存储为一个叫做“mlp.params”的文件

torch.save(net.state_dict(), 'mlp.params')

实例化了原始多层感知机模型的一个备份。 直接读取文件中存储的参数

clone = MLP()
clone.load_state_dict(torch.load("mlp.params"))
clone.eval()