标签:loss self torch batch PyTorch train test 数据 Mnist
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(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)
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)
def train(epoch):
running_loss = 0.0
for batch_idx, data in enumerate(train_loader, 0):
inputs, target = data
optimizer.zero_grad()
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():
for data in 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(10):
train(epoch)
test()
print()
最后结果如下:
Accuracy on test set: 97 %
[7, 300] loss: 0.059
[7, 600] loss: 0.063
[7, 900] loss: 0.066
Accuracy on test set: 97 %
[8, 300] loss: 0.051
[8, 600] loss: 0.052
[8, 900] loss: 0.048
Accuracy on test set: 97 %
[9, 300] loss: 0.042
[9, 600] loss: 0.042
[9, 900] loss: 0.041
Accuracy on test set: 97 %
[10, 300] loss: 0.030
[10, 600] loss: 0.033
[10, 900] loss: 0.038
Accuracy on test set: 97 %
Process finished with exit code 0
学习深度学习时手敲了一下这个代码记录一下。
主要是装环境浪费了好多时间,这个代码不需要提前下载数据集,代码运行可以自己下载数据集
供大家学习参考
标签:loss,self,torch,batch,PyTorch,train,test,数据,Mnist 来源: https://blog.csdn.net/Barry_Qu/article/details/122073803
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