I am building a custom architecture for sequential data processing with over 50 layers. I'm noticing that my loss stops decreasing early on, and I suspect it's a vanishing gradient issue. Should I be looking at Batch Normalization, or is switching to Residual Connections (ResNet style) a better fix?
3 answers
Vanishing gradients are the bane of deep architectures! You should definitely implement Residual Connections (skip connections). They allow the gradient to flow through the shortcut paths, bypassing the non-linearities that usually squash the signal. Also, ensure you are using ReLU or Leaky ReLU instead of Sigmoid/Tanh for hidden layers. Batch Normalization is also a must-have as it keeps the activations in a healthy range, preventing them from becoming too small or too large. I’d recommend starting with a standard ResNet block structure as your foundation before adding custom layers.
Have you checked your weight initialization strategy yet? Sometimes using Xavier or He initialization can solve the problem before you even have to change your architecture.
Batch Normalization is usually my go-to. It acts as a regularizer and allows you to use higher learning rates, which can sometimes "push" the model past those early plateaus.
Spot on, Cynthia. Batch Norm essentially smooths the optimization landscape, making it much easier for the gradient descent algorithm to find the global minimum.
That’s a valid point, Matthew. I actually tried He initialization yesterday, and it definitely helped the model start learning faster. However, the skip connections were still necessary to get the deeper layers to converge properly. It seems like a combination of proper initialization and architectural shortcuts is the industry standard for anything over 20 layers deep. I'm seeing much more stable training curves now in my TensorBoard logs.