Internet of Things for Architects

CNN training and backpropagation

We have seen the process of feedforward propagation as a CNN executes. Training a CNN relies on the process of backpropagation of errors and gradients, deriving a new result, and correcting errors over and over. The same network including all pooling layers, activation functions, and matrices are used as the backward propagation flows through the network in attempts to optimize or correct the weighting:  

 

CNN forward propagation during training and inference.

Backpropagation is short for "back propagation of errors". Here an error function will calculate the gradient of an error function based on the neural ...