先使用卷积层来学习图片空间信息。
然后使用全连接层来转换到类别空间。
import torch
from torch import nn
from d2l import torch as d2l
class Reshape(torch.nn.Module):
def forward(self,x):
return x.view(-1,1,28,28)
net=torch.nn.Sequential(
Reshape(),nn.Conv2d(1,6,kernel_size=5,padding=2),nn.Sigmoid(),
nn.AvgPool2d(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)
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,torch.nn.Module):
net.eval()
if not device:
device=next(iter(net.parameters())).device
metric=d2l.Accumulator(2)
for X,y in data_iter:
if isinstance(X,list):
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):
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.Accumulator(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):
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(1 * 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())