Comments (3)
1-3: thanks for these.
4. You usually want to do inference on CPU/TPU (optimized for forward pass) so you don't waste a machine with a GPU (which is usually reserved for training with backprop).
5. good catch, I've updated the code on the webpage to look like this now:
# Get conv outputs
interpretable_model.eval()
conv_outputs = []
with torch.inference_mode():
for i, batch in enumerate(dataloader):
# Forward pass w/ inputs
inputs, targets = batch[:-1], batch[-1]
z = interpretable_model(inputs)
# Store conv outputs
conv_outputs.extend(z)
conv_outputs = np.vstack(conv_outputs)
print (conv_outputs.shape) # (len(filter_sizes), num_filters, max_seq_len)
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hey @JellenRoberts , I'm sorry but I'm not entirely sure either but I agree that we don't have to open issues this frequently. @gitgithan I sent you a LinkedIn request so let's chat on there for all future minor issues and clarifications because I think more than a thousand people are watching this repo so they are actively getting emails for every single conversation. We can share any large implications here for the whole community as we come across them.
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Related Issues (20)
- Issue in viewing the experiment in MLflow HOT 2
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- Machine learning
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- Missing some topics after updates HOT 3
- Serving from Old course HOT 3
- TypeError running prediction task -> unexpected keyword argument HOT 1
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