des = [float(x) for x in data.split(',')] AttributeError: 'list' object has no attribute 'split'
时间: 2023-08-01 12:13:34 浏览: 47
这个错误提示表明你尝试对一个列表对象使用了`split()`方法,但是`split()`方法是字符串对象的方法,而不是列表对象的方法。
要解决这个问题,你需要确保你要调用`split()`方法的是一个字符串对象,而不是一个列表对象。你可以检查一下`data`变量的类型,确保它是一个字符串。
如果`data`是一个列表,并且你想将列表中的每个元素拆分成多个项,你可以使用列表推导式来实现。例如,假设每个元素都是一个包含多个项的字符串,你可以这样拆分:
```python
des = [float(x) for item in data for x in item.split(',')]
```
这将遍历列表中的每个元素(字符串),并使用`split()`方法将其拆分成多个项,然后将这些项转换为浮点数,并存储在`des`列表中。
如果以上解决方法不适用于你的情况,请提供更多的代码和详细信息,我将尽力提供更准确的帮助。
相关问题
model=model.module AttributeError: 'list' object has no attribute 'module'
This error occurs when you try to access the 'module' attribute of a list object. It means that you are trying to call a method or attribute that is not defined for a list.
To fix this error, you need to check your code and make sure that you are calling the 'module' attribute on the correct object. It's possible that you are passing a list object to a function that expects a model object.
If you are working with a PyTorch model, make sure that you have defined it correctly and that you are calling the 'module' attribute on the right object. The 'module' attribute is used to access the underlying model when using DataParallel.
Here's an example of how to fix this error when working with a PyTorch model:
```python
import torch.nn as nn
import torch.optim as optim
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(64 * 16 * 16, 10)
def forward(self, x):
x = self.conv1(x)
x = nn.functional.relu(x)
x = self.pool(x)
x = x.view(-1, 64 * 16 * 16)
x = self.fc1(x)
return x
model = MyModel()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# Train the model
for epoch in range(10):
for data in dataloader:
inputs, labels = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# Access the underlying model when using DataParallel
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model.module.training = False
# Test the model
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
```
In this example, we define a simple PyTorch model and train it using an SGD optimizer. After training, we check if there are multiple GPUs available and wrap the model with DataParallel if necessary. Finally, we use the 'module' attribute to access the underlying model when running inference.
n = out.size AttributeError: 'list' object has no attribute 'size'
这个错误提示是因为你使用了一个列表对象,而列表对象没有 `size()` 方法。在 Python 中,列表对象有一个内置的 `len()` 函数来获取列表的长度,你可以尝试将 `size()` 替换为 `len()`,例如:
```
n = len(out)
```
这样就可以获得列表 `out` 的长度了。
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