PyTorch-Loss Function

• softmax中dim维度这个参数的取值
• 准则是dim等于多少就是，该索引维度数据可以变化，dim=0, 二维 索引 [0,0]+[1,0]+[2,0]，三维度 [0,0,0]+[1,0,0]+[2,0,0]，dim=1,[0,0]+[0,1]+[0,2]

 代码：

import torch
import numpy as np
import torchvision #torch的视觉包
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data.dataloader import DataLoader
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt
import PIL.Image as Image
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
    def __init__(self):   #初始化
        super(Model, self).__init__()  #调用父类
        self.fc = nn.Linear(28*28, 10)   #线性层

    def forward(self, x):
        x = self.fc(x)
        x=F.softmax(x,dim=1)
        return x
model=Model()
print(model)
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
root='D:\Project_Encyclopedia'
mnist=torchvision.datasets.MNIST(root,train=True,transform=ToTensor(),target_transform=None,download=False)
bs=8
mnist_loader=torch.utils.data.DataLoader(dataset=mnist,batch_size=bs,shuffle=True)
batch=next(iter(mnist_loader))
image,label=batch
len(image)
len(label)
label
grid=torchvision.utils.make_grid(batch[0],nrow=8)#创建一个网络
plt.figure(figsize=(15,15))
plt.imshow(np.transpose(grid,(1,2,0)))
print("labels",batch[1])
image.shape
images=image.reshape((-1,784)) #转为8×784 这样就可以8×784 与784×10
out=model(images)
print(out)
out.shape
out.argmax()
out.argmax(dim=1) #dim=0的时候 是选择矩阵之间的列的最大值 而dim=1是比较每一个行矩阵内部的最大值
label
#onehot
onehot_labels=torch.zeros(bs,10)
print(onehot_labels)
for i in range(bs):
    onehot_labels[i][label[i]]=1
print(onehot_labels)
#平分误差
loss=((onehot_labels-out)*(onehot_labels-out)).sum()
print(loss)
#绝对值误差
loss=(abs(onehot_labels-out)).sum()
print(loss)
#pytorch也提供了one_hot函数
a=F.one_hot(label)
print(a)
eloss=F.cross_entropy(out,label)
print(eloss)