function train()
solver_def_file = 'model/lenet_solver.prototxt';
caffe.set_mode_cpu();
caffe.reset_all();
solver = caffe.Solver(solver_def_file);
% solver.solve();%一次性迭代close all;
hold on%画图用的
iter_ = solver.iter();
while iter_<10000solver.step(1);%一步一步迭代iter_ = solver.iter();    loss=solver.net.blobs('loss').get_data();%取训练集的loss  if iter_==1loss_init = loss;elsey_l=[loss_init loss];x_l=[iter_-1, iter_];     plot(x_l, y_l, 'r-');drawnowloss_init = loss;endif mod(iter_, 100) == 0accuracy=solver.test_nets.blobs('accuracy').get_data();%取验证集的accuracy       if iter_/100 == 1accuracy_init = accuracy;else x_l=[iter_-100, iter_];y_a=[accuracy_init accuracy];plot(x_l, y_a,'g-');drawnowaccuracy_init=accuracy;endend
end

function test()
net = init_net();
im_data = 255-caffe.io.load_image('image/00082.png');
res = net.forward({im_data});
[~, idx ]= max(res{1});
disp(idx-1);function net = init_net()
caffe.set_mode_cpu();
caffe.reset_all();
deploy = 'model/lenet_deploy.prototxt';
caffe_model = 'snapshot/lenet_iter_10000.caffemodel';
net = caffe.Net(deploy, caffe_model, 'test');

function retrain()
caffe.set_mode_cpu();
caffe.reset_all();
caffe_model = 'snapshot/lenet_iter_10000.caffemodel';
solver = caffe.Solver('model/lenet_solver.prototxt');
solver.net.copy_from(caffe_model);
% solver.solve();close all;
hold on%画图用的
iter_ = solver.iter();
while iter_<10000solver.step(1);iter_ = solver.iter();    loss=solver.net.blobs('loss').get_data();%取训练集的loss  if iter_==1loss_init = loss;elsey_l=[loss_init loss];x_l=[iter_-1, iter_];     plot(x_l, y_l, 'r-');drawnowloss_init = loss;endif mod(iter_, 100) == 0accuracy=solver.test_nets.blobs('accuracy').get_data();%取验证集的accuracy       if iter_/100 == 1accuracy_init = accuracy;else x_l=[iter_-100, iter_];y_a=[accuracy_init accuracy];plot(x_l, y_a,'g-');drawnowaccuracy_init=accuracy;endend
end