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  • deep learning - keras: Smooth L1 loss - Stack Overflow
    I know I'm two years late to the party, but if you are using tensorflow as keras backend you can use tensorflow's Huber loss (which is essentially the same) like so: import tensorflow as tf def smooth_L1_loss(y_true, y_pred): return tf losses huber_loss(y_true, y_pred)
  • How to interpret smooth l1 loss? - Cross Validated
    It tries to mimic the l1-loss (look at the graph), while being smooth The smoothness property allows for treatment as smooth continuous optimization, which is in general easier than non-smooth opt
  • Huber loss (smooth-L1) properties - Cross Validated
    L1- and L2-loss are used in many other problems, and their issues (the robustness issue of L2 and the lack of smoothness of L1, sometimes also the efficiency issue) are relevant in all kinds of setups, so people have started using Huber's loss as a compromise also far beyond the use in the original paper, sometimes with good theoretical
  • How to solve UserWarning: Using a target size (torch. Size ( [])) that . . .
    I am trying to run code from a book I purchased about reinforcement learning in Pytorch The code should work according to the book, but for me the model doesn't converge and the reward remains ne
  • Using a target size (torch. Size([64, 1])) that is different to the . . .
    loss py:536: UserWarning: Using a target size (torch Size([64, 1])) that is different to the input size (torch Size([64, 20, 64])) This will likely lead to incorrect results due to broadcasting Please ensure they have the same size I put some debug print statements and this is what I see right before the warning message
  • UserWarning: Using a target size (torch. Size ( [1])) that is different . . .
    actual_loes_score_g = actual_loes_score_t to(self device, non_blocking=True) predicted_loes_score_g = self model(input_g) loss_func = nn L1Loss() loss_g = loss_func( predicted_loes_score_g, actual_loes_score_g, ) where predicted_loes_score_g is tensor([[-24 9374]], grad_fn=<AddmmBackward0>) and actual_loes_score_g is tensor([20 ], dtype=torch float64) (I am using a batch size of 1 for





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