An Improved Deep Belief Network Prediction Method

Deep belief network applying unsupervised methods of greedy layer training, from the training set automatic feature extraction value, will cause the error by layer transfer, thus affecting the accuracy of the model prediction, in order to solve this problem, proposed using conjugate gradient algorithm in gradient descent can accelerate the convergence of ideas, improvement of the restricted Boltzmann machine network algorithm in the depth of confidence, first from the complexity of the algorithm and the reconstruction error analysis of improved model differences and advantages, and classify the verification on the MNIST data set, and a detailed analysis of the feasibility of the improved model and efficiency, the experimental results shows the feature extraction ability improved deep belief network model has better and classification results.