version 1.0 基本完整版run_SAE_once(sparse + de-noising + 各种activation function )这个是基本版，都是别人的工作。接下来版本应该是自己改进main()|---load_MNIST_data(images_file, labels_file, preprocess, is_show_images) // for train|---load_MNIST_data(images_file, labels_file, preprocess, is_show_images) // for test|       |---load_MNIST_images(images_file, preprocess, is_show_images, varargin )|       |       |---whitening(data)|       |---load_MNIST_labels(labels_file)||---get_SAE_option(preOption_SAE, varargin)||---get_AE_option(preOption_AE)||---get_BP_option(preOption_BP)|---get_BPNN_option(preOption_BPNN)||---run_SAE_once(images_train, labels_train, images_test, labels_test, architecture, option_SAE, option_BPNN, is_disp_network, is_disp_info )|       |---train_SAE(input, output, architecture, preOption_SAE) // SAE|       |       |---init_parameters(architecture_AE) <----------------------------------------------------------------------+|       ||---train_AE(input, theta_AE, architecture_AE, option_AE)                              ||       |||---denoising_switch(input, count_AE, option_AE)                                    ||       |||---minFunc(fun, theta_AE, options)                                               ||       ||||---calc_AE_batch(input, theta_AE, architecture_AE, option_AE, (input_corrupted,) ~)|||||---predict_NN(input, architecture_AE(1:2), theta_AE(W1,b1), option_AE)||||||||------------------------------------- until train all stacked AE ------------------------------------------+||||       ||---init_parameters(architecture_BP, last_active_is_softmax, varargin)|       ||---train_BPNN(input, output, theta_BP, architecture_BP, option_BP)|       |||---fun = @(x) calcBPBatch(input, output, x, architecture, option_BP)|       |||---minFunc(fun, theta_BP, options)||||---display_network(W)|||       |---predict_NN(input, architecture, theta_SAE, preOption_BPNN)|       |---get_accuracy(predicted_labels, labels)|       ||       |---train_BPNN(input, output, theta_SAE, architecture, preOption_BPNN) // fine-tune|       ||       |---predict_NN(input, architecture, theta_SAE, preOption_BPNN)|       |---get_accuracy(predicted_labels, labels)||end[784 400 200 10] + ReLu + sparse(rho = 0.1, beta = 0.3) + de-noising( mode = 'On_Off', rate = 0.15 ): 98+%, 1900s ;by 郑煜伟 Ewing 2016-04