1，处理多分类问题：
# 10月27
import torch
# criterion = torch.nn.CrossEntropyLoss()
# Y = torch.LongTensor([2,0,1])
# Y_pred1 = torch.Tensor([
# [0.1,0.2,0.9],
# [1.1,0.1,0.2],
# [0.2,2.1,0.1]
# ])
# Y_pred2 = torch.Tensor([
# [0.8,0.2,0.3],
# [0.2,0.3,0.5],
# [0.2,0.2,0.5]
# ])
# l1 = criterion(Y_pred1,Y)
# l2 = criterion(Y_pred2,Y)
# print(l1.data,l2.data)
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim
batch_size = 64
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ),(0.3081, )) # 均值和标准差
])
train_dataset = datasets.MNIST(
root='../dataset/mnist/',
train=True,
download=True,
transform=transform
)
train_loader = DataLoader(
train_dataset,
shuffle=True,
batch_size=batch_size
)
test_dataset = datasets.MNIST(
root='../dataset/mnist',
train=False,
download=True,
transform=transform
)
test_loader = DataLoader(
test_dataset,
shuffle=False,
batch_size=batch_size
)
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.l1 = torch.nn.Linear(784, 512)
self.l2 = torch.nn.Linear(512, 256)
self.l3 = torch.nn.Linear(256, 128)
self.l4 = torch.nn.Linear(128, 64)
self.l5 = torch.nn.Linear(64, 10)
def forward(self, x):
x = x.view(-1,784) # 改变张量的形状
x = F.relu(self.l1(x))
x = F.relu(self.l2(x))
x = F.relu(self.l3(x))
x = F.relu(self.l4(x))
return self.l5(x)
model = Net()
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr = 0.01, momentum=0.5) # 冲量0.5
def train(epoch):
running_loss = 0.0
for batch_idx, data in enumerate(train_loader, 0):
inputs, target = data # inputs : x target: y
optimizer.zero_grad()
# forward + backward + update
outputs = model(inputs)
loss = criterion(outputs, target)
loss.backward()
optimizer.step()
running_loss += loss.item()
if batch_idx % 300 == 299:
print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
running_loss = 0.0
def test():
correct = 0
total = 0
with torch.no_grad(): # with语句下不执行梯度
for data in test_loader:
images, labels = data
outputs = model(images)
# print(outputs)
_, predicted = torch.max(outputs.data, dim=1) # dim：维度1 两个返回值：最大概率值及其下标
print(predicted)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy on test set: %d %%' % (100 * correct / total))
if __name__ == '__main__':
for epoch in range(10):
train(epoch)
test()
