MINIST手写字 Matlab BP神经网络程序

% A BP network for MINST data % Author: Guo chengfeng % Version 1.0 % date: 2018-3-15 %神经网络的层次 ntrain = length(y_train); arch = [784,30,10]; nlayer = length(arch); mini_batch_size = 100; max_epochs = 5000; zeta = 2; % mini = 20, zeta = 6 eps= 5000. 可以达到0.946 %神经网络参数初始化 weight = cell(1,nlayer); bias = cell(1,nlayer); nabla_weight = cell(1,nlayer); nabla_bias = cell(1,nlayer); a = cell(1,nlayer); z = cell(1,nlayer); for in = 2:nlayer weight{in} = rand(arch(in),arch(in-1))-0.5; bias{in} = rand(arch(in),1)-0.5; nabla_weight{in} = rand(arch(in),arch(in-1)); nabla_bias{in} = rand(arch(in),1); end for in = 1:nlayer a{in} = zeros(arch(in),mini_batch_size); z{in} = zeros(arch(in),mini_batch_size); end accuracy = zeros(1,max_epochs); for ip = 1:max_epochs pos = randi(ntrain-mini_batch_size); x = x_train(:,pos+1:pos+mini_batch_size); y = y_train(:,pos+1:pos+mini_batch_size); %正向计算 a{1} = x; [a,z]=feedforward(@acti_relu,@acti_sigmoid,weight,bias,nlayer,mini_batch_size,a,z); [weight,bias] = SGD(@acti_relu_prime,@acti_sigmoid_prime,weight,bias,... nabla_weight,nabla_bias,nlayer,mini_batch_size,zeta,a,z,y); accuracy(ip) = evaluateminst(@acti_relu,@acti_sigmoid,x_valid,y_valid,weight,bias,nlayer); plot(accuracy); title(['Accuracy:',num2str(accuracy(ip))]); getframe; end