What is flattening in convolutional neural networks?

What is flattening in convolutional neural networks?

Flattening in CNNs is the process of converting a multi-dimensional tensor into a one-dimensional vector.

Why is flattening necessary before feeding data into a fully connected layer?

Flattening is necessary because fully connected layers require a 1D input, allowing the model to learn complex patterns.

Can you give an example of how flattening works in a CNN?

Sure! For instance, if you have a 2D feature map of size 4x4, flattening will convert it into a 16-element vector.

Are there any alternatives to flattening?

Yes, alternatives include global average pooling or global max pooling, which reduce dimensions without flattening.