[Error] When using plot top losses with custom model

[FCLayer]

I get the error: heatmap=True requires a model like cnn_learner produces.'

When I try to call

interp.plot_top_losses(9, figsize = (8,6), heatmap = True, largest=top).

I understand this is because I initialized my own custom Learner instead of cnn_learner(I used my own model)

learn = Learner(data, get_cifar_model(), loss_func=nn.CrossEntropyLoss(), metrics=[accuracy])

But when I initialize it with cnn_learner I get the exact error described here:
“Exception: No weight layer”

Error with custom CNN, Exception: No weight layer

Hi all, I’m still very much a beginner and working through the first part of the fastai course. Apologies in advance if this is very basic or is covered in a later class. I wanted to create the simplest CNN I could for image recognition and then incrementally add layers to see what happened to the accuracy. I copied the alexnet.py code from torchvision/models and edited it to be a two-layer CNN. This is the main body of the file: class MoonNet2L(nn.Module): def __init__(self, num_classes=2…

Is there anyway to get heatmaps from plot_top_losses with a custom model?

Thank you in advance