李沐-动手学深度学习-LeNet_李沐深度学习实验

1.LeNet的实现

import torch
from torch import nn
from d2l import torch as d2l
 
net = nn.Sequential(
    nn.Conv2d(1,6,kernel_size=5,padding=2),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))
 
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)
 
 

2.模型训练

LeNet在Fashion-MNIST数据集上的表现,all代码

import torch
from torch import nn
from d2l import torch as d2l
 
net = nn.Sequential(
    nn.Conv2d(1,6,kernel_size=5,padding=2),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))
 
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)
 
batch_size = 256
train_iter,test_iter = d2l.load_data_fashion_mnist(batch_size=batch_size)
 
#对 3.6节中描述的evaluate_accuracy函数进行轻微的修改
def evaluate_accuracy_gpu(net,data_iter,device=None):
    """使用GPU计算模型在数据集上的精度"""
    if isinstance(net,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)
    with torch.no_grad():
        for X,y in data_iter:
            if isinstance(X,list):
                #BERT微调所需的（之后介绍）
                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]
 
#与 3.6节中定义的train_epoch_ch3不同，在进行正向和反向传播之前，将每一小批量数据移动到指定的设备（例如GPU）上
#训练函数train_ch6也类似于 3.6节中定义的train_ch3
#以下训练函数假定从高级API创建的模型作为输入，并进行相应的优化。
# 使用在 4.8.2.2节中介绍的Xavier随机初始化模型参数。与全连接层一样，使用交叉熵损失函数和小批量随机梯度下降。
#@save
def train_ch6(net, train_iter, test_iter, num_epochs, lr, device):
    """用GPU训练模型(在第六章定义)"""
    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，样本数
        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
print(train_ch6(net,train_iter,test_iter,num_epochs,lr,d2l.try_gpu()))
d2l.plt.show()