name 'each_Data' is not defined

这是一个代码错误，提示“name 'each_Data' is not defined”，意思是代码中使用了一个未定义的变量“each_Data”。这可能是因为变量名拼写错误、变量未被声明或者作用域问题等原因导致的。需要检查代码并修复错误。

另外，引用中的错误提示“Uncaught ReferenceError: set_img_func is not defined at HTMLInputElement.onclick ((index):476)”可能是因为代码中使用了一个未定义的函数“set_img_func”，需要检查代码并确保该函数已经被正确定义。

引用中提到了“Device Name Length”和“NFS V3 Server Optimization”，这可能是一些与设备和网络优化相关的内容。

相关问题

NameError: name 'resample' is not defined

The error message suggests that you are trying to use a variable or function called `resample`, but it has not been defined in your code.

`resample` is a function provided by the `pandas` library that is used for resampling time-series data. To use the `resample` function, you need to first import the `pandas` library using the following code:

import pandas as pd

Then, you can use the `resample` function on a pandas DataFrame object like this:

df_resampled = df.resample('D').sum()

This will resample the data in `df` at a daily frequency and calculate the sum of the values for each day.

Make sure that you have imported the `pandas` library before using any of its functions.

# training model = CNNmodel() print(model) criterion = nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) epochs = 50 model.train() start_time = time.time() for epoch in range(epochs): for seq, y_train in train_data: # The gradient is zeroed and initialized before each parameter update optimizer.zero_grad() # reshape # convert to conv1d input size（batch size, channel, series length） y_pred = model(seq.reshape(1,1,-1)) loss = criterion(y_pred, y_train) loss.backward() optimizer.step() print(f'Epoch: {epoch+1:2} Loss: {loss.item():10.8f}') print(f'\nDuration: {time.time() - start_time:.0f} seconds')报错name 'train_data' is not defined

这个错误说明变量train_data未定义，你需要确认是否已经定义了train_data变量并将其赋值为训练数据。如果没有定义，你需要定义train_data并将其赋值为你的训练数据。例如：

train_data = DataLoader(dataset, batch_size=1, shuffle=True)

请注意，这里的dataset应该是你的训练数据集，batch_size是你要使用的批量大小，shuffle是指是否对数据进行洗牌。你需要根据你的数据集和实验需求进行适当的设置。