Runtimeerror: работник DataLoader неожиданно вышел ⇐ Python

[Anonymous]

I am new to PyTorch and Machine Learning so I try to follow the tutorial from here:
https://medium.com/@nutanbhogendrasharm ... 8a4265e118
By copying the code step by step I got the following error for no reason. Я попробовал программу на другом компьютере, и это дает синтаксисную ошибку. Тем не менее, мой IDE не предупредил мой о синтаксисе. Я действительно запутался, как я могу решить проблему. Любая помощь ценится. < /P>
RuntimeError: DataLoader worker exited unexpectedly
< /code>
Вот код. < /p>
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}')


Подробнее здесь: https://stackoverflow.com/questions/712 ... expectedly