PyTorch-Training

• 对对MNIST数据集进行训练

 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
import torchvision
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torch.optim as optim
class CModel(nn.Module):
    def __init__(self):   #初始化
        super(CModel, self).__init__()  #调用父类
        self.fc = nn.Linear(28*28, 10,bias=False)   #线性层
 
    def forward(self, x):
        x = self.fc(x)
        return x
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,labels=batch
inputs=image.reshape(bs,-1)
cemodel=CModel()
cemodel.fc.weight=nn.Parameter(torch.rand(10,784))
out=cemodel(inputs)
loss=F.cross_entropy(out,labels)
print(loss)
loss.backward()
optimizer = torch.optim.SGD(cemodel.parameters(), lr=0.1, momentum=0.9)
optimizer.zero_grad()
#loss_fn(model(input), target).backward()
out=cemodel(inputs)
loss=F.cross_entropy(out,labels)
print(loss)
print(cemodel.fc.weight)
loss.backward()
optimizer.step() 
#相当于updataweight=model.fc.weight-model.fc.weight.grad #权重系数-梯度
#model.fc.weight=nn.Parameter(updataweight)
#下面用循环来实现loss下降 并且所有数据集都训练一遍
cemodel=CModel()
optimizer = torch.optim.SGD(cemodel.parameters(), lr=0.1, momentum=0.9)
total_loss=0

for batch in mnist_loader:
    image,labels=batch
    inputs=image.reshape(bs,-1)
    optimizer.zero_grad()
    out=cemodel(inputs)
    loss=F.cross_entropy(out,labels)
    total_loss+=loss.item() #把数值取出来
    loss.backward()
    optimizer.step() 
print(total_loss)
for epoch in range(10):
    total_loss=0
    for batch in mnist_loader:
        image,labels=batch
        inputs=image.reshape(bs,-1)
        optimizer.zero_grad()
        out=cemodel(inputs)
        loss=F.cross_entropy(out,labels)
        total_loss+=loss.item() #把数值取出来
        loss.backward()
        optimizer.step() 
    print("epoch:",epoch,"loss:",total_loss)
#对迭代器参数进行设置
optimizer = torch.optim.SGD(cemodel.parameters(), lr=0.1)
for epoch in range(10):
    total_loss=0
    for batch in mnist_loader:
        image,labels=batch
        inputs=image.reshape(bs,-1)
        optimizer.zero_grad()
        out=cemodel(inputs)
        loss=F.cross_entropy(out,labels)
        total_loss+=loss.item() #把数值取出来
        loss.backward()
        optimizer.step() 
    print("epoch:",epoch,"loss:",total_loss)