A novel method to measure the learning capability of a parameter in artificial neural network with application to network freezing

The artificial neural network is typically trained from initial weight/bias position. As training progresses the network parameters such as weights and biases are updated according to learning algorithm to reduce the performance index. Not all the network parameters are equally learning the input-output mapping. Some parameters would hold more discriminating capability while others are not so effective. We propose a novel method of measuring the learning capability of a network parameter. The learning capability for a parameter we call it as learnability is contribution of that parameter to reduce performance index as the network is training. The proposed method of measuring learnability is applied on network parameters freezing on feedforward neural network. Our method is validated on MNIST handwritten numeral database using backpropagation learning algorithm.