Loss Scaling Free !exclusive! Page

# Compile the model model.compile(optimizer='adam', loss=loss_fn)

# Apply static loss scaling scaled_loss = loss * 1.0 loss scaling free

Loss scaling is a method to rescale the loss value of a neural network during training. The goal of loss scaling is to prevent the loss value from becoming too small or too large, which can cause issues during backpropagation. By rescaling the loss value, we can stabilize the training process and improve the convergence of the model. # Compile the model model

This is primarily achieved through two avenues: # Compile the model model.compile(optimizer='adam'

Finding the right scale factor is annoying.

import tensorflow as tf