# How should I implement the backward pass through a flatten layer of a CNN?

I am making a NN library without any other external NN library, so I am implementing all layers, including the flatten layer, and algorithms (forward and backward pass) from scratch. I know the forward implementation of the flatten layer, but is the backward just reshaping it or not? If yes, can I just call a simple NumPy's reshape function to reshape it?

• Backward pass...would you share your design of backward passing? Mar 1 at 23:14

## 1 Answer

Yes, a simple reshape would do the trick. A flattening layer is just a tool for reshaping data/activations to make them compatible with other layers/functions. The flattening layer doesn't change the activations themselves, so there is no special backpropagation handling needed other than changing back the shape.