Thanks for your reply. Sorry for the unclear description. Actually I am a beginner of Pytorch and DNN.


In the image classfication problem, I would have 1654*10 images and 1654 labels. Images are fed into the Resnet50 and label would be predicted. The batch size is 64.


Then the problem is changed to replace the label with EEG data (something recording the neural activity and the EEG data corresponding to a image is of size 17 *100, a matrix  which is quite different from that of label in (1).


in 1) the output feature of fc should be something like 1000? But in 2) how to modify the out_feature size of fc layer? I am confused.


p.s the image size is 500 *500 *3 （height *width *channel). And I would incorparate all the images into a matrix 16540 * 500 *500 *3. The corresponding EEG data matrix would be 16540 * 17 *100. Then I do batch on the image with batch size of 64. In my case how to permute the input and output?