RBM Neural Network Handwritten Digit Recognition Based on Matlab GUI
Handwritten digit recognition is an important application of image recognition, which can be applied in many fields, such as automatic bank check recognition, zip code recognition, etc. The RBM neural network model in deep learning can effectively recognize handwritten digits. This article will introduce the use of Matlab GUI to develop a handwritten digit recognition system based on RBM neural network, aiming to help beginners quickly get started in the field of deep learning.
- Dataset preparation
The data set for handwritten digit recognition is a standard MNIST data set, which can be downloaded online. The dataset contains 60,000 training images of 28x28 pixels and 10,000 testing images, each of which has a correct label (a number between 0-9). Importing datasets in Matlab requires installation of toolboxes such as Deep Learning Toolbox.
- Introduction to RBM Neural Networks
The full name of RBM is Restricted Boltzmann Machine (Restricted Boltzmann Machine), which is a bipartite graph model consisting of a visible layer and a hidden layer. Its characteristic is that there is no connection between neurons in the hidden layer, there is no connection between neurons in the visible layer, and there is complete connectivity between the visible layer and the hidden layer. The RBM model can be used for feature extraction, classification, generation and other tasks.
- Algorithm process
The system is implemented using Matlab GUI, and the specific algorithm flow is as follows:
(1) Data read-in: read the training data set into the system, including training pictures and labels.
(2) Data preprocessing: Preprocessing the training images, including binarization, normalization and other operations.
(3) Feature extraction: Use the RBM neural network model to extract features from the image to obtain the output of the hidden layer.
(4) Model training: use the backpropagation algorithm to train the RBM neural network model, and optimize the weights and biases.
(5) Testing and evaluation: use the test data set to verify the model performance.
The following is the interface design of the whole system: