pytorch训练MNIST数据集
本帖最后由 luoshiyong123 于 2020-9-11 22:26 编辑本文采用全连接网络对MNIST数据集进行训练，训练模型主要由五个线性单元和relu激活函数组成
import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional asF
import torch.optim as optim
import os
import sys
batch_size = 64
transform = transforms.Compose(
[
transforms.ToTensor(), #将0-255变成0-1
transforms.Normalize((0.1307,),(0.3081,)) #正则化
]
)
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)
classNet(torch.nn.Module):
def __init__(self):
super(Net,self).__init__()
self.f1 = torch.nn.Linear(784,512)
self.f2 = torch.nn.Linear(512,256)
self.f3 = torch.nn.Linear(256,128)
self.f4 = torch.nn.Linear(128,64)
self.f5 = torch.nn.Linear(64,10)
def forward(self,x):
#这里将
x = x.view(-1,784)#展成1*784
x = F.relu(self.f1(x))
x = F.relu(self.f2(x))
x = F.relu(self.f3(x))
x = F.relu(self.f4(x))
return self.f5(x)
model = Net()
#loss--交叉熵
criterion = torch.nn.CrossEntropyLoss()
#带冲量
optimzer = optim.SGD(model.parameters(),lr=0.01,momentum = 0.5)
#训练
def train(epoch):
running_loss =0.0
for batch_idx,data in enumerate(train_loader):
inputs,target = data
optimzer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs,target)
loss.backward()
optimzer.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():
for batch_idx,data in enumerate(test_loader):
images,labels = data
outputs = model(images)
_,predicted = torch.max(outputs.data,dim=1)
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(7):
train(epoch)
test()
#保存网络参数
结果： 经过7论训练测试集可以达到97%
五层全连接模型的参数多点，显卡好就可以硬怼
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