模型构造
回顾一下感知机。
nn.Sequential()：定义了一种特殊的module。
torch.rand()：用于生成具有均匀分布的随机数，这些随机数的范围在[0, 1)之间。它接受一个形状参数（shape），返回一个指定形状的张量（Tensor）。
import torch
from torch import nn
from torch.nn import functional as F
# 定义网络结构
net = nn.Sequential(nn.Linear(20, 256), nn.ReLU(), nn.Linear(256, 10))
# 生成随机数[0，1)，大小为2x20
X = torch.rand(2, 20)
# 输入网络
net(X)torch.nn.functional.relu()：ReLU激活函数
# 自定义MLP模块
class MLP(nn.Module):
def __init__(self): #初始化类与参数
super().__init__() #父类Module的init
self.hidden = nn.Linear(20, 256) #隐藏层
self.out = nn.Linear(256, 10) #输出层
def forward(self, X): #进行前向传播
return self.out(F.relu(self.hidden(X)))
# 实例化MLP类,然后在每次调用前向传播函数的时候调用这些层
net = MLP() # 实例化的时候，()里面的参数是init中的参数
net(X) # 调用的时候，()里面的参数是forward中的参数.values(): 返回一个字典中所有值
# 自定义顺序块
class MySequential(nn.Module):
def __init__(self, *args):
super().__init__()
for block in args:
self._modules[block] = block
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, 100))
net (X)torch.mm：矩阵乘法
# 隐藏层固定的MLP
class FixedHiddenMLP(nn.Module):
def __init__(self):
super().__init__()
self.rand_weight = torch.rand((20, 20), requires_grad = False) #权重固定无需进行训练
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)# 混合嵌套MLP
class NestMLP(nn.Module):
def __init__(self):
super().__init__()
self.net = nn.Sequential(nn.Linear(20, 64), nn.ReLU(),
nn.Linear(64, 32), nn.ReLU())
self.linear = nn.Linear(32, 16)
def forward(self, X):
return self.linear(self.net(X))
chimera = nn.Sequential(NestMLP(), nn.Linear(16, 20), FixedHiddenMLP())
chimera(X)参数管理
state_dict：一个Python字典，将每一层的参数映射成Tensor张量，只包含具有可学习参数的层，例如卷积层（Conv2d）和全连接层（Linear）。权重和偏置就是Linear层的状态。
import torch
from torch import nn
# 关注具有隐藏层的多感知机
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()) #net[2]拿到的是最后的输出层
bias：偏置，包括参数值和梯度值
bias.data：访问参数值
bias.grad：访问梯度值
named_parameters()：返回一个包含元组的迭代器，每个元组包含两个元素，参数的名称和参数的值。
# 目标函数
print(type(net[2].bias))
print(net[2].bias)
print(net[2].bias.data) # 参数值
net[2].weight.grad == None # 梯度值
# 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()])
# ReLU没有参数所以拿不出
net.state_dict()['2.bias'].data
# 通过名字获取想要的参数
net.add_module(name, module)： 添加每一层，并且为每一层增加了一个单独的名字。
# 从嵌套块收集函数
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)
print(rgnet)
torch.nn.init模块中的所有函数都用于初始化神经网络参数，因此它们都在torch.no_grad()模式下运行，autograd不会将其考虑在内。
nn.init.normal_(tensor, mean=0.0, std=1.0): 将tensor变成指定mean和std正态分布
torch.nn.init.zeros_(tensor): 将tensor置零
torch.nn.init.constant_(tensor, val): 用val数值填充tensor
# 内置初始化
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)
net[0].weight.data[0], net[0].bias.data[0]
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]
torch.nn.ini.xavier_uniform_(tensor, gain=1.0
)：
# 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)
nn.init.uniform_(tensor, val1, val2): 将tensor变成均匀分布
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]
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
# 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]))
net = nn.Sequential(nn.Linear(8, 128), CenteredLayer())
Y = net(torch.rand(4, 8))
Y.mean()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))
net = nn.Sequential(MyLinear(64, 8), MyLinear(8, 1))
net(torch.rand(2, 64))读写文件
import torch
from torch import nn
from torch.nn import funcational as F
# 加载和保存张量
x = torch.arange(4)
torch.save(x, 'x-file')
x2 = torch.load("x-file")# 存储一个张量列表,读回内存
y = torch.zeros(4)
torch.save([x, y], 'x-files')
x2, y2 = torch.load('x-files')
(x2, y2)
# 写入或读取字符串映射到张量的字典
mydict = {'x':x, 'y':y}
torch.save(mydict, 'mydict')
mydict2 = torch.load('mydict')# 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)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
clone = MLP()
clone.load_state_dict(torch.load("mlp.params"))
clone.eval()Y_clone = clone(X)
Y_clone == Y
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