动手学习深度学习（5）感知机与多层感知机

本文开始学习感知机以及多层感知机。

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)

'''初始化模型参数'''
# 每个图像由28*28=784个灰度像素值，所有图像共10个类别，
# 我们实现一个包含256个隐藏单元的单层多层感知机
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]
'''激活函数'''
def relu(X):
    a = torch.zeros_like(X)
    return torch.max(X, a)
'''模型'''
def net(X):
    X = X.reshape((-1, num_inputs))
    H = relu(X@W1 + b1)  # 这里“@”代表矩阵乘法
    return (H@W2 + b2)
'''损失函数'''
loss = nn.CrossEntropyLoss(reduction='none')
'''训练'''
# 多层感知机的训练过程与softmax回归的训练过程完全相同
# 可以直接调用d2l包的train_ch3函数（参见 :sec_softmax_scratch ）
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)
'''在一些测试数据上应用这个模型'''
d2l.predict_ch3(net, test_iter)

多层感知机简洁实现

import torch
from torch import nn
from d2l import torch as d2l
'''唯一的区别是我们添加了2个全连接层'''
net = nn.Sequential(nn.Flatten(),
                    nn.Linear(784, 256),
                    nn.ReLU(),
                    nn.Linear(256, 10))
'''训练过程'''
# 训练过程的实现与我们实现softmax回归时完全相同
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(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)