【代码pycharm】动手学深度学习v2-10 多层感知机 + 代码实现

课程链接

1.多层感知机从0开始实现

import torch
from torch import nn
from d2l import torch as d2l
batch_size=256
train_iter,test_iter=d2l.load_data_fashion_mnist(batch_size)

#实现一个具有单隐藏层的多层感知机，它包含256个隐藏单元
num_inputs,num_outputs,num_hiddens=784,10,256
W1=nn.Parameter(torch.randn(num_inputs,num_hiddens,requires_grad=True)*0.01)
b1=nn.Parameter(torch.zeros(num_hiddens,requires_grad=True))

W2=nn.Parameter(torch.randn(num_hiddens,num_outputs,requires_grad=True)*0.01)
b2=nn.Parameter(torch.zeros(num_outputs,requires_grad=True))
params=[W1,b1,W2,b2]

# 实现ReL激活函数
def relu(X):
    a=torch.zeros_like(X)
    return torch.max(X,a)

# 模型的实现
def net(X):
    X=X.reshape(-1,num_inputs)
    H=relu(X@W1+b1)
    return (H@W2+b2)

loss=nn.CrossEntropyLoss(reduction='none')

#训练
num_epochs,lr=10,0.1
updater=torch.optim.SGD(params,lr=lr)
d2l.train_ch3(net,train_iter,test_iter,loss,num_epochs,updater)

d2l.plt.show()

运行结果

2.多层感知机简洁实现

import torch
from torch import nn
from d2l import torch as d2l

#模型
net=nn.Sequential(nn.Flatten(),
                  nn.Linear(784,256),
                  nn.ReLU(),
                  nn.Linear(256,10))
def init_weights(m):
    if type(m)==nn.Linear:
        nn.init.normal_(m.weight,std=0.01)
net.apply(init_weights);

#训练
batch_size,lr,num_epochs=256,0.1,10
loss=nn.CrossEntropyLoss(reduction='none')
trainer=torch.optim.SGD(net.parameters(),lr=lr)
train_iter,test_iter=d2l.load_data_fashion_mnist(batch_size)
d2l.train_ch3(net,train_iter,test_iter,loss,num_epochs,trainer)

d2l.plt.show()

运行结果