matlab里面stacked的意思,GitHub - gilgarmish/Stacked_Autoencoder: 用 MATLAB 实现深度学习网络中的 stacked auto-encoder...

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