How can the backward function in tensor influence the matrix in model
How Backward Functions Influence Model Parameters in PyTorch: An Autograd Postmortem Summary Gradients weren’t updating during training due to improper Tensor detachment during model initialization. The model explicitly detached .weight and .bias tensors during initialization (detach().zero_()), preventing PyTorch’s autograd system from connecting computation graphs to trainable parameters. Root Cause The core issue stems from PyTorch’s … Read more