我是 PyTorch 和機器學習的新手，所以我嘗試從這里學習教程： https ://medium.com/@nutanbhogendrasharma/pytorch-convolutional-neural-network-with-mnist-dataset-4e8a4265e118
通過逐步復制代碼，我無緣無故地收到以下錯誤。我在另一臺計算機上嘗試了該程式，它給出了語法錯誤。但是，我的 IDE 沒有警告我任何關于語法的事情。我真的很困惑如何解決這個問題。任何幫助表示贊賞。
RuntimeError: DataLoader worker exited unexpectedly
這是代碼。
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}')
uj5u.com熱心網友回復：
如果您正在使用 jupyter 筆記本。問題更可能是num_worker. 你應該設定num_worker=0. 您可以在此處找到一些可遵循的解決方案。因為不幸的是，jupyter notebook 在運行多處理方面存在一些問題。
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