How can I generate overfitting on noisy regression data using PyTorch? Despite various attempts, PyTorch tends to generalize the data, making it challenging to memorize (overfit) the data. I modified weight initialization, used batch_size=1, made the learning rate flexible, and increased the number of layers and parameters, but it still generalizes.
Are there any parameters to be changed?