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,))
#mean,Std
])
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=True,
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()
# 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 = 0.0
def test():
correct = 0
total = 0
# 不计算梯度
with torch.no_grad():
for data in test_loader:
images, labels = data
outputs = model(images)
_, predicted = torch.max(outputs.data, dim=1)
#max,index of max
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()
===============================================================================================================================================
[1, 300] loss: 2.204
1654784it [00:44, 43276.74it/s][1, 600] loss: 0.904
[1, 900] loss: 0.420
Accuracy on test set: 90 %
[2, 300] loss: 0.305
[2, 600] loss: 0.263
[2, 900] loss: 0.222
Accuracy on test set: 93 %
[3, 300] loss: 0.183
[3, 600] loss: 0.168
[3, 900] loss: 0.148
Accuracy on test set: 95 %
[4, 300] loss: 0.132
[4, 600] loss: 0.118
[4, 900] loss: 0.116
Accuracy on test set: 96 %
[5, 300] loss: 0.097
[5, 600] loss: 0.095
[5, 900] loss: 0.092
Accuracy on test set: 96 %
[6, 300] loss: 0.074
[6, 600] loss: 0.079
[6, 900] loss: 0.078
Accuracy on test set: 97 %
[7, 300] loss: 0.065
[7, 600] loss: 0.061
[7, 900] loss: 0.061
Accuracy on test set: 97 %
[8, 300] loss: 0.050
[8, 600] loss: 0.050
[8, 900] loss: 0.052
Accuracy on test set: 97 %
[9, 300] loss: 0.038
[9, 600] loss: 0.047
[9, 900] loss: 0.041
Accuracy on test set: 97 %
[10, 300] loss: 0.031
[10, 600] loss: 0.038
[10, 900] loss: 0.034
Accuracy on test set: 97 %
9920512it [09:12, 17950.01it/s]
1654784it [05:37, 4904.61it/s]
Process finished with exit code 0