New answers tagged

0

The most usual case of bias=False is in layers before/after Batch Normalization with no activators in between. The BatchNorm layer will re-center the data anyway, removing the bias and making it a useless trainable parameter. Quoting the original BatchNorm paper: Note that, since we normalize $Wu+b$, the bias $b$ can be ignored since its effect will be ...


0

I fixed the problem by implementing a custom dataloader. For more information about custom dataloader: https://debuggercafe.com/custom-dataset-and-dataloader-in-pytorch/ (Links to an external site.) /Oualid


0

Have you tried using a different learning rate? Yours seems to be quite high, you could try 1e-4 or something in that region. Coming from the computer vision side, you could also try to improve image readability with prior grayscaling or blurring. Here are some repos on GTSRB: https://github.com/Junth/Traffic-Sign-Classification-using-ConvNets https://github....


Top 50 recent answers are included