hello,
i am trying to create a neural network with geometric data as output and i have unrolled them into a vector i would like to share you my file through jovian. i have few doubts .
i am training the data but the training loss does not go below 80. can you please tell me what i am doing wrong?
the main idea is to create a nn with variable hidden layer and pruning value alpha
One is i am using R square score as metric and i am getting a very bad r square score how can i improve it.
how do i overfit the model i have just 441 samples, all of which are of size 2043x3 as output and just 2x1 input
also i am getting issue when i change the hidden layer to 8 or 10 as
"
CUDA out of memory. Tried to allocate 76.00 MiB (GPU 0; 4.00 GiB total capacity; 1.85 GiB already allocated; 37.20 MiB free; 1.88 GiB reserved in total by PyTorch)
"
i have used torch.cuda.empty_cache()
but it still doesnt work
also my next goal is to optimise this whole code to me used in cuda
can you please help me

  
      
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snoozeyouloose/task-3-new-c0fb8 - Jovian 1

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