李沐笔记（卷积神经网络（LeNet））

LeNet（LeNet-5）由两个部分组成：卷积编码器：由两个卷积层组成; 全连接层密集块：由三个全连接层组成。

 

 

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

# LeNet由两个部分组成：卷积编码器和全连接层密集块
class Reshape(torch.nn.Module):
    def forward(self, X):
        return X.view(-1,1,28,28)   # 批量数不变，通道数为1 28*28


net = torch.nn.Sequential(Reshape(),
                          nn.Conv2d(1, 6,kernel_size=5, padding=2),     # 输入通道1输出通道6
                          nn.Sigmoid(),
                          nn.AvgPool2d(kernel_size=2, stride=2),
                          nn.Conv2d(6, 16, kernel_size=5),
                          nn.Sigmoid(),
                          nn.AvgPool2d(kernel_size=2, stride=2),
                          nn.Flatten(),
                          nn.Linear(16*5*5, 120),
                          nn.Sigmoid(),
                          nn.Linear(120, 84),
                          nn.Sigmoid(),
                          nn.Linear(84, 10))

# 检查模型
X = torch.rand(size=(1, 1, 28, 28),dtype=torch.float32)
for layer in net:
    X = layer(X)
    print(layer.__class__.__name__, 'output shape: \t', X.shape)

# 模型训练
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=batch_size)


def evaluate_accuracy_gpu(net, data_iter, device=None):
    """计算模型在数据集上的精度"""
    if isinstance(net, torch.nn.Module):
        net.eval()  # 设置为评估模式
        if not device:
            device = next(iter(net.parameters())).device
    # 正确预测的数量，总预测的数量
    metric = d2l.Accumulator(2)
    for X, y in data_iter:
        if isinstance(X, list):
            # BERT微调所需的
            X = [x.to(device) for x in X]
        else:
            X = X.to(device)
        y = y.to(device)
        metric.add(d2l.accuracy(net(X), y), y.numel())
    return metric[0] / metric[1]


def train_ch6(net, train_iter, test_iter, num_epochs, lr, device):
    """训练模型"""
    def init_weights(m):
        if type(m) == nn.Linear or type(m) == nn.Conv2d:
            nn.init.xavier_uniform_(m.weight)
    net.apply(init_weights)
    print('training on', device)
    net.to(device)
    optimizer = torch.optim.SGD(net.parameters(), lr=lr)
    loss = nn.CrossEntropyLoss()
    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],
                            legend=['train loss', 'train acc', 'test acc'])
    timer, num_batches = d2l.Timer(), len(train_iter)
    for epoch in range(num_epochs):
        # 训练损失之和，训练准确率之和，范例数
        metric = d2l.Accumulator(3)
        net.train()
        for i, (X, y) in enumerate(train_iter):
            timer.start()
            optimizer.zero_grad()
            X, y = X.to(device), y.to(device)
            y_hat = net(X)
            l = loss(y_hat, y)
            l.backward()
            optimizer.step()
            with torch.no_grad():
                metric.add(l * X.shape[0], d2l.accuracy(y_hat, y), X.shape[0])
            timer.stop()
            train_l = metric[0] / metric[2]
            train_acc = metric[1] / metric[2]
            if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
                animator.add(epoch + (i + 1) / num_batches,
                             (train_l, train_acc, None))
        test_acc = evaluate_accuracy_gpu(net, test_iter)
        animator.add(epoch + 1, (None, None, test_acc))
    print(f'loss {train_l:.3f}, train acc {train_acc:.3f}, '
          f'test acc {test_acc:.3f}')
    print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec '
          f'on {str(device)}')


lr, num_epochs=0.9,10
train_ch6(net,train_iter,test_iter,num_epochs,lr,d2l.try_gpu())