%?接下來就是LSTM的Matlab代碼，我也進(jìn)行了注釋，用英文注釋的，也比較容易懂：
%?implementation?of?LSTM
clc
clear?all
close?all

%%?training?dataset?generation
binary_dim?????=?8;

largest_number?=?2^binary_dim?-?1;
binary?????????=?cell（largest_number?1）;

for?i?=?1:largest_number?+?1
????binary{i}??????=?dec2bin（i-1?binary_dim）;
????int2binary{i}??=?binary{i};
end

%%?input?variables
alpha??????=?0.1;
input_dim??=?2;
hidden_dim?=?32;
output_dim?=?1;

%%?initialize?neural?network?weights
%?in_gate?????=?sigmoid（X（t）?*?U_i?+?H（t-1）?*?W_i）????-------?（1）
U_i?=?2?*?rand（input_dim?hidden_dim）?-?1;
W_i?=?2?*?rand（hidden_dim?hidden_dim）?-?1;
U_i_update?=?zeros（size（U_i））;
W_i_update?=?zeros（size（W_i））;

%?forget_gate?=?sigmoid（X（t）?*?U_f?+?H（t-1）?*?W_f）????-------?（2）
U_f?=?2?*?rand（input_dim?hidden_dim）?-?1;
W_f?=?2?*?rand（hidden_dim?hidden_dim）?-?1;
U_f_update?=?zeros（size（U_f））;
W_f_update?=?zeros（size（W_f））;

%?out_gate????=?sigmoid（X（t）?*?U_o?+?H（t-1）?*?W_o）????-------?（3）
U_o?=?2?*?rand（input_dim?hidden_dim）?-?1;
W_o?=?2?*?rand（hidden_dim?hidden_dim）?-?1;
U_o_update?=?zeros（size（U_o））;
W_o_update?=?zeros（size（W_o））;

%?g_gate??????=?tanh（X（t）?*?U_g?+?H（t-1）?*?W_g）???????-------?（4）
U_g?=?2?*?rand（input_dim?hidden_dim）?-?1;
W_g?=?2?*?rand（hidden_dim?hidden_dim）?-?1;
U_g_update?=?zeros（size（U_g））;
W_g_update?=?zeros（size（W_g））;

out_para?=?2?*?rand（hidden_dim?output_dim）?-?1;
out_para_update?=?zeros（size（out_para））;
%?C（t）?=?C（t-1）?.*?forget_gate?+?g_gate?.*?in_gate????-------?（5）
%?S（t）?=?tanh（C（t））?.*?out_gate???????????????????????-------?（6）
%?Out??=?sigmoid（S（t）?*?out_para）?????????????????????-------?（7）
%?Note:?Equations?（1）-（6）?are?cores?of?LSTM?in?forward?and?equation?（7）?is
%?used?to?transfer?hiddent?layer?to?predicted?output?i.e.?the?output?layer.
%?（Sometimes?you?can?use?softmax?for?equation?（7））

%%?train?
iter?=?99999;?%?training?iterations
for?j?=?1:iter
????%?generate?a?simple?addition?problem?（a?+?b?=?c）
????a_int?=?randi（round（largest_number/2））;???%?int?version
????a?????=?int2binary{a_int+1};??????????????%?binary?encoding
????
????b_int?=?randi（floor（largest_number/2））;???%?int?version
????b?????=?int2binary{b_int+1};??????????????%?binary?encoding
????
????%?true?answer
????c_int?=?a_int?+?b_int;????????????????????%?int?version
????c?????=?int2binary{c_int+1};??????????????%?binary?encoding
????
????%?where?we‘ll?store?our?best?guess?（binary?encoded）
????d?????=?zeros（size（c））;
????if?length（d）<8
????????pause;
????end
????
????%?total?error
????overallError?=?0;
????
????%?difference?in?output?layer?i.e.?（target?-?out）
????output_deltas?=?[];
????
????%?values?of?hidden?layer?i.e.?S（t）
????hidden_layer_values?=?[];
????cell_gate_values????=?[];
????%?initialize?S（0）?as?a?zero-vector
????hidden_layer_values?=?[hidden_layer_values;?zeros（1?hidden_dim）];
????cell_gate_values????=?[cell_gate_values;?zeros（1?hidden_dim）];
????
????%?initialize?memory?gate

?屬性????????????大小?????日期????時(shí)間???名稱
-----------?---------??----------?-----??----
?????文件???????10107??2018-12-18?11:37??RNN-LSTM\LSTM.m
?????文件????????5821??2018-12-18?11:23??RNN-LSTM\RNN.m
?????文件??????????63??2018-12-18?11:25??RNN-LSTM\sigmoid.m
?????文件??????????81??2018-12-18?11:29??RNN-LSTM\sigmoid_output_to_derivative.m
?????文件??????????68??2018-12-18?12:10??RNN-LSTM\tan_h.m
?????文件??????????72??2018-12-18?12:10??RNN-LSTM\tan_h_output_to_derivative.m
?????文件?????????184??2018-12-18?12:12??RNN-LSTM\參考網(wǎng)址.txt
?????目錄???????????0??2018-12-18?13:40??RNN-LSTM\