MNIST手写字体识别（CNN+BP两种实现）-Matlab程序

function cnn = cnn_initialize(cnn) %CNN_INIT initialize the weights and biases, and other parameters % index = 0; num_layer = numel(cnn.layer); for in = 1:num_layer switch cnn.layer{in}{1} case 'input' index = index + 1; height = cnn.layer{in}{3}(1); width = cnn.layer{in}{3}(2); mini_size = cnn.layer{in}{2}; cnn.weights{index} = []; cnn.biases{index} = []; cnn.nabla_w{index} = []; cnn.nabla_b{index} = []; %n*n*m cnn.a{index} = []; cnn.z{index} = []; cnn.delta{index} = []; cnn.mini_size = mini_size; case 'conv' index = index + 1; %kernel height, width, number ker_height = cnn.layer{in}{3}(1); ker_width = cnn.layer{in}{3}(2); ker_num = cnn.layer{in}{2}; cnn.weights{index} = grand(ker_height,ker_width,ker_num) - 0.5; cnn.biases{index} = grand(1,ker_num) - 0.5; cnn.nabla_w{index} = zeros(ker_height,ker_width,ker_num); cnn.nabla_b{index} = zeros(1,ker_num); height = height - ker_height + 1; width = width - ker_width + 1; cnn.a{index} = zeros(height,width,mini_size,ker_num); cnn.z{index} = zeros(height,width,mini_size,ker_num); cnn.delta{index} = zeros(height,width,mini_size,ker_num); case 'pool' index = index + 1; %kernel height, width, number ker_height = cnn.layer{in}{3}(1); ker_width = cnn.layer{in}{3}(2); cnn.weights{index} = []; cnn.biases{index} = []; cnn.nabla_w{index} = []; cnn.nabla_b{index} = []; height = height / ker_height; width = width / ker_width; cnn.a{index} = zeros(height,width,mini_size,ker_num); cnn.z{index} = []; cnn.delta{index} = zeros(height,width,mini_size,ker_num); case 'flat' index = index + 1; cnn.weights{index} = []; cnn.biases{index} = []; cnn.nabla_w{index} = []; cnn.nabla_b{index} = []; cnn.a{index} = zeros(height*width*ker_num,mini_size); cnn.z{index} = []; cnn.delta{index} = zeros(height*width*ker_num,mini_size); case 'full' index = index + 1; %kernel height, width, number neuron_num = cnn.layer{in}{2}; neuron_num0 = size(cnn.a{in-1},1); cnn.weights{index} = grand(neuron_num,neuron_num0) - 0.5; cnn.biases{index} = grand(neuron_num,1) - 0.5; cnn.nabla_w{index} = zeros(neuron_num,neuron_num0); cnn.nabla_b{index} = zeros(neuron_num,1); cnn.a{index} = zeros(neuron_num,mini_size); cnn.z{index} = zeros(neuron_num,mini_size); cnn.delta{index} = zeros(neuron_num,mini_size); case 'output' index = index + 1; %kernel height, width, number neuron_num = cnn.layer{in}{2}; neuron_num0 = size(cnn.a{in-1},1); cnn.weights{index} = grand(neuron_num,neuron_num0) - 0.5; cnn.biases{index} = grand(neuron_num,1); cnn.nabla_w{index} = zeros(neuron_num,neuron_num0); cnn.nabla_b{index} = zeros(neuron_num,1); cnn.a{index} = zeros(neuron_num,mini_size); cnn.z{index} = zeros(neuron_num,mini_size); cnn.delta{index} = zeros(neuron_num,mini_size); otherwise end end end