Comparative analysis of the results of training a neural network with calculated weights and with random generation of the weights

Neural networks based on metric recognition methods allow, based on the initial conditions of the computer vision task such as the number of images and samples, to determine the structure of the neural network (the number of neurons, layers, connections), and also allow to analytically calculate the values of the weights on the connections of the neural network. As feedforward neural networks, they can also be trained by classical learning algorithms. The possibility of precomputation of the values of the neural network weights allows us to say that the procedure for creating and training a feedforward neural network is accelerated in comparison with the classical scheme for creating and training a neural network where values of the weights are randomly generated. In this work, we conduct two experiments based on the handwritten numbers dataset MNIST that confirm this statement.