nn.Linear()
class torch.nn.Linear(in_features,out_features,bias = True )
对传入数据应用线性变换:y = A x+ b
参数:
in_features - 每个输入样本的大小
out_features - 每个输出样本的大小
bias - 如果设置为False,则图层不会学习附加偏差。默认值:True
实现代码:
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
batch_size=200
learning_rate=0.01
epochs=10
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True)
class MLP(nn.Module):#自定义类 继承nn.Module
def __init__(self):#初始化函数
super(MLP, self).__init__()#继承父类初始化函数
self.model = nn.Sequential(
nn.Linear(784, 200),
nn.ReLU(inplace=True),
nn.Linear(200, 200),
nn.ReLU(inplace=True),
nn.Linear(200, 10),
nn.ReLU(inplace=True),
)#自定义实例属性 model 传入自定义模型的内部构造 返回类
def forward(self, x):
x = self.model(x)
#x传入自定义的model类 返回经过模型后的输出
return x
net = MLP()#创建实例
optimizer = optim.SGD(net.parameters(), lr=learning_rate)
#优化实例的所有对象
criteon = nn.CrossEntropyLoss()
#创建交叉熵实例
for epoch in range(epochs):
for batch_idx, (data, target) in enumerate(train_loader):
data = data.view(-1, 28*28)
logits = net(data)#得到x经过模型后的输出
loss = criteon(logits, target)
#得到loss
optimizer.zero_grad()
loss.backward()
# print(w1.grad.norm(), w2.grad.norm())
optimizer.step()
if batch_idx % 100 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
test_loss = 0
correct = 0
for data, target in test_loader:
data = data.view(-1, 28 * 28)
logits = net(data)
test_loss += criteon(logits, target).item()
pred = logits.data.max(1)[1]
correct += pred.eq(target.data).sum()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))