DeepSense Forum
Hi DeepSense Team...
Thank you for your greatwork.
I am interested to run radar-aided-beam-prediction-repo. I see that there are functions called train_loop(), test_loop(), and eval_loop() inside network_functions.py. How to call these functions? I am still don't have idea what I should put in this paramaters: criterion and optimizer.
I see in the paper the authors using lookup table mapping as the baseline algorithm, however I can't find the baseline code in the repository. Could you show me where the code is?
Thank you
Hi @avn,
You can use the classical definitions for the loss function (criterion) and optimizer. In this paper, we used Adam optimizer with cross-entropy loss, which can be defined as follows
criterion = torch.nn.CrossEntropyLoss() # Mean for training optimizer = torch.optim.Adam(net.parameters(), lr=1e-3, weight_decay=1e-4)
Note that the variable "net" is the neural network object. For the baseline, we have added the script named baseline.py to the repository to generate the lookup table solution results.
Thanks,
Umut
