Hey guys. I am currently working on a 12 leads ECG prediction project, which inputs a 5000 x 12 tensor to output 55 different classes of heart diseases. To make the rest of the work easier, I am thinking maybe there is a way that I can put my model( constructed using pytorch) into a fastai Learner, so that I can use
lr_find() and other convenient APIs just like
I already found that there a way to modify the resnet by cutting layers and adding new layers, but the problem is that the kernel size doesn’t fit well in this condition, so I need to be able to change the kernel size and other settings for Conv layers.
If anyone had done something like this before, please help me out.