我是 PyTorch 和机器学习的新手，所以我尝试按照这里的教程进行操作：https://medium.com/@nutanbhogendrasharma/pytorch-卷积神经网络-with-mnist-dataset-4e8a4265e118 https://medium.com/@nutanbhogendrasharma/pytorch-convolutional-neural-network-with-mnist-dataset-4e8a4265e118
通过一步步复制代码，我无缘无故地得到了以下错误。我在另一台计算机上尝试了该程序，但出现语法错误。但是，我的 IDE 没有警告我任何有关语法的信息。我真的很困惑如何解决这个问题。任何帮助表示赞赏。
RuntimeError: DataLoader worker exited unexpectedly
这是代码。
import torch
from torchvision import datasets
from torchvision.transforms import ToTensor
import torch.nn as nn
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from torch import optim
from torch.autograd import Variable
train_data = datasets.MNIST(
root='data',
train=True,
transform=ToTensor(),
download=True,
)
test_data = datasets.MNIST(
root='data',
train=False,
transform=ToTensor()
)
print(train_data)
print(test_data)
print(train_data.data.size())
print(train_data.targets.size())
plt.imshow(train_data.data[0], cmap='gray')
plt.title('%i' % train_data.targets[0])
plt.show()
figure = plt.figure(figsize=(10, 8))
cols, rows = 5, 5
for i in range(1, cols * rows + 1):
sample_idx = torch.randint(len(train_data), size=(1,)).item()
img, label = train_data[sample_idx]
figure.add_subplot(rows, cols, i)
plt.title(label)
plt.axis("off")
plt.imshow(img.squeeze(), cmap="gray")
plt.show()
loaders = {
'train': DataLoader(train_data,
batch_size=100,
shuffle=True,
num_workers=1),
'test': DataLoader(test_data,
batch_size=100,
shuffle=True,
num_workers=1),
}
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(
in_channels=1,
out_channels=16,
kernel_size=5,
stride=1,
padding=2,
),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
)
self.conv2 = nn.Sequential(
nn.Conv2d(16, 32, 5, 1, 2),
nn.ReLU(),
nn.MaxPool2d(2),
)
# fully connected layer, output 10 classes
self.out = nn.Linear(32 * 7 * 7, 10)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
# flatten the output of conv2 to (batch_size, 32 * 7 * 7)
x = x.view(x.size(0), -1)
output = self.out(x)
return output, x # return x for visualization
cnn = CNN()
print(cnn)
loss_func = nn.CrossEntropyLoss()
print(loss_func)
optimizer = optim.Adam(cnn.parameters(), lr=0.01)
print(optimizer)
num_epochs = 10
def train(num_epochs, cnn, loaders):
cnn.train()
# Train the model
total_step = len(loaders['train'])
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(loaders['train']):
# gives batch data, normalize x when iterate train_loader
b_x = Variable(images) # batch x
b_y = Variable(labels) # batch y
output = cnn(b_x)[0]
loss = loss_func(output, b_y)
# clear gradients for this training step
optimizer.zero_grad()
# backpropagation, compute gradients
loss.backward()
# apply gradients
optimizer.step()
if (i + 1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))
pass
pass
pass
train(num_epochs, cnn, loaders)
def evalFunc():
# Test the model
cnn.eval()
with torch.no_grad():
correct = 0
total = 0
for images, labels in loaders['test']:
test_output, last_layer = cnn(images)
pred_y = torch.max(test_output, 1)[1].data.squeeze()
accuracy = (pred_y == labels).sum().item() / float(labels.size(0))
pass
print('Test Accuracy of the model on the 10000 test images: %.2f' % accuracy)
pass
evalFunc()
sample = next(iter(loaders['test']))
imgs, lbls = sample
actual_number = lbls[:10].numpy()
test_output, last_layer = cnn(imgs[:10])
pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
print(f'Prediction number: {pred_y}')
print(f'Actual number: {actual_number}')
