修改自以下链接：（原文只是做了cpu，没做gpu。我增加了一个布尔变量，可以切换）
用PyTorch实现MNIST手写数字识别(非常详细)_小锋学长生活大爆炸的博客-CSDN博客_pytorch手写数字识别
新代码如下：（关键看第9行）
import torch
import torchvision
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
bFlag_cuda = True #这行就是设置了一个标记，是否用gpu
n_epochs = 3
batch_size_train = 64
batch_size_test = 1000
learning_rate = 0.01
momentum = 0.5
log_interval = 10
random_seed = 1
torch.manual_seed(random_seed)
train_loader = torch.utils.data.DataLoader(
torchvision.datasets.MNIST('./data/', train=True, download=True,
transform=torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(
(0.1307,), (0.3081,))
])),
batch_size=batch_size_train, shuffle=True)
test_loader = torch.utils.data.DataLoader(
torchvision.datasets.MNIST('./data/', train=False, download=True,
transform=torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(
(0.1307,), (0.3081,))
])),
batch_size=batch_size_test, shuffle=True)
examples = enumerate(test_loader)
batch_idx, (example_data, example_targets) = next(examples)
# print(example_targets)
# print(example_data.shape)
fig = plt.figure()
for i in range(6):
plt.subplot(2, 3, i + 1)
plt.tight_layout()
plt.imshow(example_data[i][0], cmap='gray', interpolation='none')
plt.title("Ground Truth: {}".format(example_targets[i]))
plt.xticks([])
plt.yticks([])
plt.show()
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=1)
network = Net()
#1--------------------------------------
if bFlag_cuda:
if torch.cuda.is_available():
network = network.cuda()
#1--------------------------------------
#2--------------------------------------
cross_entropy_loss = nn.CrossEntropyLoss()
if bFlag_cuda:
cross_entropy_loss = cross_entropy_loss.cuda()
#2--------------------------------------
optimizer = optim.SGD(network.parameters(), lr=learning_rate, momentum=momentum)
train_losses = []
train_counter = []
test_losses = []
test_counter = [i * len(train_loader.dataset) for i in range(n_epochs + 1)]
def train(epoch):
network.train()
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
if bFlag_cuda:
data = data.cuda()
target = target.cuda()
output = network(data)
loss = cross_entropy_loss(output, target)
loss.backward()
optimizer.step()
if batch_idx % log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(epoch, batch_idx * len(data),
len(train_loader.dataset),
100. * batch_idx / len(train_loader),
loss.item()))
train_losses.append(loss.item())
train_counter.append((batch_idx * 64) + ((epoch - 1) * len(train_loader.dataset)))
torch.save(network.state_dict(), './model.pth')
torch.save(optimizer.state_dict(), './optimizer.pth')
def test():
network.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
if bFlag_cuda:
data = data.cuda()
target = target.cuda()
output = network(data)
test_loss += cross_entropy_loss(output, target).item()
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).sum()
test_loss /= len(test_loader.dataset)
test_losses.append(test_loss)
print('\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
train(1)
test() # 不加这个，后面画图就会报错：x and y must be the same size
for epoch in range(1, n_epochs + 1):
train(epoch)
test()
fig = plt.figure()
plt.plot(train_counter, train_losses, color='blue')
plt.scatter(test_counter, test_losses, color='red')
plt.legend(['Train Loss', 'Test Loss'], loc='upper right')
plt.xlabel('number of training examples seen')
plt.ylabel('negative log likelihood loss')
examples = enumerate(test_loader)
batch_idx, (example_data, example_targets) = next(examples)
with torch.no_grad():
if bFlag_cuda:
example_data = example_data.cuda()
example_targets = example_targets.cuda()
output = network(example_data)
fig = plt.figure()
for i in range(6):
plt.subplot(2, 3, i + 1)
plt.tight_layout()
plt.imshow(example_data.cpu()[i][0], cmap='gray', interpolation='none')
plt.title("Prediction: {}".format(output.data.max(1, keepdim=True)[1][i].item()))
plt.xticks([])
plt.yticks([])
plt.show()
# ----------------------------------------------------------- #
continued_network = Net()
continued_optimizer = optim.SGD(network.parameters(), lr=learning_rate, momentum=momentum)
network_state_dict = torch.load('model.pth')
continued_network.load_state_dict(network_state_dict)
optimizer_state_dict = torch.load('optimizer.pth')
continued_optimizer.load_state_dict(optimizer_state_dict)
# 注意不要注释前面的“for epoch in range(1, n_epochs + 1):”部分，
# 不然报错：x and y must be the same size
# 为什么是“4”开始呢，因为n_epochs=3，上面用了[1, n_epochs + 1)
for i in range(4, 9):
test_counter.append(i * len(train_loader.dataset))
train(i)
test()
fig = plt.figure()
plt.plot(train_counter, train_losses, color='blue')
plt.scatter(test_counter, test_losses, color='red')
plt.legend(['Train Loss', 'Test Loss'], loc='upper right')
plt.xlabel('number of training examples seen')
plt.ylabel('negative log likelihood loss')
plt.show()
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