动手深度学习4月10日

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多层感知机的从零开始实现

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ReLU激活函数表

def relu(X):
    a = torch.zeros_like(X)
    return torch.max(X,a)

实现我们的模型

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

loss = nn.CrossEntropyLoss()

多层感知机的训练过程与softmax回归的训练过程完全相同

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)

多层感知机的简洁实现

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

隐藏层包含256个隐藏单元， 并使用了ReLU激活函数

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);

batch_size, lr, num_epochs = 256, 0.1, 10
loss = nn.CrossEntropyLoss()
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)