NLLLoss（Negative Log Likelihood Loss）,最大似然函数.
把Label对应的输出log_result值拿出来,求和取平均.
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CrossEntropyLoss交叉熵损失函数.
一步执行完：softmax+log+NLLLoss合并起来了.

NLLLoss

CrossEntropyLoss

softmax：

CrossEntropyLoss示例：

#torch.LongTensor是64位整型
#torch.Tensor默认torch.FloatTensor,是32位浮点类型数据.
#torch.tensor是一个类,Used to generate a single-precision floating-point tensor.

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("Loss1 = ",l1.data.item(),"\nLoss2 = ",l2.data.item())

 mnist数据集实践

#minstThe mean of the dataset is0.1307,标准差是0.3081
import torch
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))
])

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(train_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)
        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)   #最后一层不做激活,不进行非线性变换

model = Net()

criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr=0.01,momentum=0.5)

def train(epoch):
    running_loss = 0.0
    for batch_idx,data in enumerate(train_loader,0):
        inputs, target = data
        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

def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images,labels = data
            outputs = model(images)
            _,predicted = torch.max(outputs.data,dim=1)  #dim=1维度1,行是第0个维度,列是第1个维度
            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()

结果：

[1,  300]loss:2.223
[1,  600]loss:0.923
[1,  900]loss:0.435
Accuracy on test set:89 %
[2,  300]loss:0.328
[2,  600]loss:0.272
[2,  900]loss:0.239
Accuracy on test set:94 %
[3,  300]loss:0.188
[3,  600]loss:0.175
[3,  900]loss:0.158
Accuracy on test set:96 %
[4,  300]loss:0.126
[4,  600]loss:0.130
[4,  900]loss:0.121
Accuracy on test set:97 %
[5,  300]loss:0.098
[5,  600]loss:0.099
[5,  900]loss:0.097
Accuracy on test set:97 %
[6,  300]loss:0.078
[6,  600]loss:0.079
[6,  900]loss:0.081
Accuracy on test set:97 %
[7,  300]loss:0.066
[7,  600]loss:0.063
[7,  900]loss:0.064
Accuracy on test set:98 %
[8,  300]loss:0.050
[8,  600]loss:0.056
[8,  900]loss:0.051
Accuracy on test set:98 %
[9,  300]loss:0.041
[9,  600]loss:0.043
[9,  900]loss:0.043
Accuracy on test set:99 %
[10,  300]loss:0.034
[10,  600]loss:0.034
[10,  900]loss:0.037
Accuracy on test set:99 %

Process finished with exit code 0