print(test.a) AttributeError: module 'test' has no attribute 'a'

这个错误提示表明在模块 test 中没有名为 a 的属性。可能的原因是在 test 模块中没有定义 a，或者 a 是一个私有属性，无法从外部访问。

如果想要访问 test 模块中的属性，可以使用以下语法：

import test
print(test.attribute_name)

其中 attribute_name 是要访问的属性名称。

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:

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.

AttributeError: module 'keras' has no attribute 'eval'AttributeError: module 'keras' has no attribute 'eval'

这个错误通常是由于使用了错误的方法或函数名称导致的。根据你提供的错误信息，似乎你正在尝试使用 'eval' 方法，而keras模块中并没有该方法。

如果你想评估模型的性能，可以使用 'evaluate' 方法。示例如下：

model = keras.models.Sequential()
# 构建模型结构和编译模型...

# 使用 evaluate 方法评估模型
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test accuracy:', test_acc)

请确保你的代码中没有拼写错误，并且正确地使用了keras模块中的方法和函数。如果问题仍然存在，请提供更多的代码和错误信息，以便我能够更好地帮助你。