There are three main types of machine learning for artificial intelligence: 1) classification, 2) regression, and 3) clustering. Today we focus on the Restricted Boltzmann Machine (RBM) algorithm. Restricted Boltzmann machine RBM has always been an important application in deep learning. It is a probabilistic graph model that can be explained by random neural networks. It was proposed by Smolensky in 1986 based on the Boltzmann machine BM. A special topology of the Boltzmann machine BM. The Boltzmann machine BM principle originated from statistical physics and is a modeling method based on the energy function. It can describe the high-order interactions between variables. The Boltzmann machine BM's learning algorithm is more complex, but the The model and learning algorithm have a relatively complete physical interpretation and strict mathematical statistics theory as the basis. RBM concept: Researchers represented by Hinton and Ackley have proposed BM learning machines from different fields with different motivations. BM is a stochastic recursive neural network and can be viewed as a randomly generated Hopfield network (see the AI ​​artificial Hopfield network). BM is a symmetrically coupled stochastic feedback binary binary neural network consisting of a visible layer and multiple hidden layers. The network nodes are divided into visible units and hidden units. Visible and hidden units are used. To express the learning model of the random network and the random environment, to express the correlation between the units through the weights. Smolensky's proposed RBM consists of a visible neuron layer and a hidden neuron layer. Because the hidden layer neurons are not interconnected and the hidden layer neurons are independent of a given training sample, this makes the direct calculation dependent data. The expectation value becomes easy, visible layer neurons are not connected to each other, and the Markov chain sampling process is performed on the state of the hidden neuron obtained from the training sample to estimate the expected value independent of the data, and all the visible layers are alternately updated in parallel. Neuron and hidden neuron values. RBM introduces: The Restricted Boltzmann Machine RBM simplifies the Boltzmann machine, making the Boltzmann machine BM easier to use. Boltzmann machine BM's hidden elements / explicit elements and hidden elements / hidden elements are all connected, increasing the computational complexity and computational difficulty, difficult to use. The RBM imposes some restrictions on the BM, so that there is no connection between the hidden elements, so that the amount of calculation is greatly reduced, and it is very convenient to use. RBM principle: The RBM parameters are as follows: 1) Direct weight matrix Wij of visible nodes and hidden nodes; 2) The visible node offset b = (b1,b2,...,bn); 3) The offset of the hidden node c = (c1,c2,...,cm); These parameters determine that the RBM network encodes one n-dimensional sample into one m-dimensional sample. Assume that the states of the hidden elements and the explicit elements of the RBM take 1 or 0, and its energy function is: According to Gibbs distribution: p(v,h)=(1/Z)*e[? E(v,h)] and the above energy function establish the joint probability distribution of the model. The visible node state is only affected by m hidden nodes. Similarly, each hidden node is also affected by only n visible nodes. which is: Among them, Z is a normalization factor or a partition function, which represents the sum (energy index) of all possible states of the visible layer and hidden layer node set. The Z computational complexity is very high and cannot be directly calculated, and some mathematical derivation is required to simplify the calculation. Similarly obtain p(h). According to the Bayesian principle, knowing the joint probability and the edge probability, the conditional probability is: Here? Is a sigmoid function. The conditional probability is based on the state of the hidden or explicit element, the weight W, the deviation b or c to determine the status of the explicit or hidden element. Modern Physics Experiment Series Modern physics experiment related equipment for efficient specialized physics laboratory Modern Physics Experiment Instruments,Optical Instruments,Acousto-Optic Modulator Experimental Device,Optical Spectroscopy Experiment Determinator Yuheng Optics Co., Ltd.(Changchun) , https://www.yhencoder.com
April 13, 2023