Types of padding in cnn

Variations of the paddings around the border used in the convolutional layer of the CNN: (a) zero padding, (b) reflection padding, and (c) replication padding.

1. What is padding =' Same in CNN?
2. What are the 4 different layers on CNN?
3. Which layer is padding used in CNN?

What is padding =' Same in CNN?

The padding type is called SAME because the output size is the same as the input size(when stride=1). Using 'SAME' ensures that the filter is applied to all the elements of the input. Normally, padding is set to "SAME" while training the model.

What are the 4 different layers on CNN?

The different layers of a CNN. There are four types of layers for a convolutional neural network: the convolutional layer, the pooling layer, the ReLU correction layer and the fully-connected layer.

Which layer is padding used in CNN?

Padding is simply a process of adding layers of zeros to our input images so as to avoid the problems mentioned above. This prevents shrinking as, if p = number of layers of zeros added to the border of the image, then our (n x n) image becomes (n + 2p) x (n + 2p) image after padding.