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Does anyone know of code for building an LSTM recurrent neural network?

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I am trying to build a form of recurrent neural network - a Long Short Term Memory RNN. I have not been able to find this architecture available on the web. Any advice will be appreciated.
  1 Commento
Bradley Wright
Bradley Wright il 25 Mag 2016
I also have been on the look for an LTSM network in Matlab that I could adopt and re-purpose. Would really like to see mathworks give more support to neural nets.
In the meantime, I did find this.

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Risposte (8)

oshri il 19 Gen 2017
Hi, I just implemented today LSTM using MATLAB neural network toolbox. Here is the code:
function net1=create_LSTM_network(input_size , before_layers , before_activation,hidden_size, after_layers , after_activations , output_size)
%%this part split the input into two seperate parts the first part
%is the input size and the second part is the memory
real_input_size=input_size ;
N_after=length(after_layers) ;
delays_vec=1 ;
if (N_before>0 ) && (N_after>0)
input_size=before_layers(end) ;
net1=fitnet( [before_layers , input_size+hidden_size , hidden_size*ones(1,9),after_layers]) ;
elseif (N_before>0) && (N_after==0)
input_size=before_layers(end) ;
net1=fitnet([before_layers,input_size+hidden_size , hidden_size*ones(1 , 9)]) ;
elseif (N_before==0)&&(N_after>0)
net1=fitnet([input_size+hidden_ size , hidden_size*ones(1, 9) , after_layers]) ;
net1 =fitnet( [input size+hidden_size, hidden_size*ones(1, 9)]);
net1=configure(net1 ,rand( real_input_size , 200) , rand(output_size,200)) ;
net1.layers{N_before+1}.name='Concatenation Layer';
net1.layers{N_before+2}.name = 'Forget Amount' ;
net1.layers{N_before+3}.name= 'Forget Gate';
net1.layers{N_before+4}.name= 'Remember Amount';
net1.layers{N_before+5}.name= 'tanh Input' ;
net1.layers{N_before+6}.name= 'Forget Gate';
net1.layers{N_before+7}.name= 'Update Memory';
net1.layers {N_before+8}.name= 'tanh Memory';
net1.layers{N_before+9}.name= 'Combine Amount' ;
net1.layers{N_before+10}.name= 'Combine gate' ;
net1.layerConnect(N_before+3 , N_before+7) =1 ;
net1.layerConnect(N_before+1 ,N_before+10)=1 ;
net1.layerConnect(N_before+4 , N_before+3)=0;
net1.layerWeights{N_before+1 , N_before+10}.delays=delays_vec ;
if N_before>0
net1.LW{N_before+1 , N_before} = [eye(input_size) ; zeros(hidden_size, input_size)];
net1.IW{1,1}=[eye( input_size) ;zeros(hidden_size , input_size)];
net1.LW{N_before+1 , N_before+10}=repmat ([zeros(input_size, hidden_size); eye(hidden_size)] , [1 , size(delays_vec,2)] ) ;
net1.layerWeights{N_before+1 ,N_before+10}.learn=false;
if N_before>0
net1.layerWeights{ N_before+1 ,N_before}.learn=false;
net1.inputWeights{ 1, 1}.learn=false ;
net1.biasConnect = [ones(1,N_before) 0 1 0 1 1 0 0 0 1 0 1 ones(1,N_after)]' ;%
%%first gate
net1.layers{N_before+2}.transferFcn= 'logsig' ;
net1.layerWeights{N_before+3, N_before+2}.weightFcn='scalprod' ;
% net1 .layerWeights{3 , 7} .weightFcn= ' scalprod ';
net1.layerWeights{N_before+3, N_before+2}.learn=false;
net1.layerWeights{N_before+3, N_before+7}.learn=false ;
net1.layers{N_before+3}.netinputFcn= 'netprod';
net1.LW{N_before+3, N_before+2}=1;
% net1.LW{3 , 7} =1 ;
%%second gate
net1.layers{N_before+4}.transferFcn='logsig' ;
net1.layerConnect(N_before+5 , N_before+4) =0;
net1.layerConnect( N_before+5 , N_before+1)=1;
%%second gate mult
net1.layerConnect(N_before+6, N_before+4)=1;
net1.layers{N_before+6}.netinputFcn='netprod' ;
net1.layers{N_before+6} .transferFcn= 'purelin';
net1.layerWeights{N_before+6, N_before+5}.weightFcn='scalprod';
net1.layerWeights {N_before+6 , N_before+4}.weightFcn='scalprod';
net1.layerWeights{N_before+6 , N_before+5}.learn=false ;
net1.LW{N_before+6 , N_before+5} =1;
net1.LW{N_before+6 , N_before+4}=1 ;
%%C update
net1.layerConnect(N_before+7,N_before+3)=1 ;
net1.layerWeights{N_before+3,N_before+7} . delays=delays_vec ;
net1.layerWeights{N_before+7,N_before+3}.weightFcn= 'scalprod';
net1.layerWeights{N_before+7,N_before+6}.weightFcn= 'scalprod';
net1 .layers{N_before+7}.transferFcn= 'purelin';
net1.LW{N_before+7 , N_before+3} =1 ;
net1.LW{N_before+7 , N_before+6} =1 ;
net1.LW{N_before+3 , N_before+7}=repmat(eye(hidden_size), [1 , size(delays_vec,2)] );
net1.layerWeights{N_before+3 , N_before+7}.learn=false ;
net1.layerWeights{N_before+7 ,N_before+6}.learn=false;
%%output stage
net1.layerConnect(N_before+9, N_before+8)=0;
net1.layerConnect(N_before+10 , N_before+8) = 1 ;
net1.layerConnect(N_before+9, N_before+1) =1 ;
net1.layerWeights{N_before+10 , N_before+8}.weightFcn='scalprod' ;
net1.layerWeights{N_before+10 , N_before+9}.weightFcn= 'scalprod' ;
net1.LW{N_before +10 ,N_before+9}=1 ;
net1.LW{N_before+10,N_before+8}=1 ;
net1.layers{N_before+10}.netinputFcn= 'netprod' ;
net1.layers{N_before+10}.transferFcn= 'purelin';
net1.layers{N_before+9}.transferFcn= 'logsig';
net1.layers{N_before+8}.transferFcn='tansig' ;
net1.layerWeights{N_before+10 ,N_before+ 9}.learn= false ;
net1.layerWeights{N_before +10,N_before+8 }.learn= false ;
net1.layerWeights{N_before+7 ,N_before+3 }. learn=false ;
for ll=1:N_before
for ll=1:N_after
net1. layers{end-ll}.transferFcn=after_activations ;
net1.layerWeights{N_before+8 , N_before+7}.weightFcn='scalprod' ;
net1.LW{N_before+8 , N_before+7}=1 ;
net1.layerWeights{N_before+8 , N_before+7}.learn=false ;
net1=configure(net1 , rand(real_input_size ,200) , rand(output_size , 200) ) ;
net1.trainFcn= 'trainlm';
  6 Commenti
sujan ghimire
sujan ghimire il 11 Nov 2017
I have tried 25 inputs with 1 output for non linear regression and it is not working.

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Sebastine Hirimeti
Sebastine Hirimeti il 16 Feb 2017
Can someone please help me a bit more on how to run the code, the inputs, etc.,
Also any reference on how this is built like a paper

David Kuske
David Kuske il 26 Ott 2017
Does this code support regressionoutput for LSTMs? Matlab doesnt seem to have implemented that yet. also any help on how to use this code would be highly appreciated.

Shounak Mitra
Shounak Mitra il 31 Ott 2017
As of 17b, MATLAB does support LSTMs. Please check

vinothini v
vinothini v il 9 Ott 2018
pls anyone explain the code i didn't get the code

Shounak Mitra
Shounak Mitra il 9 Ott 2018
Modificato: KSSV il 6 Giu 2019
@Chadi: Yes, it does support regression as well. Here's the doc link: <>
@Vinothini: You do not need to understand the code. you can directly start using LSTMs in your work following the document link I pasted above.

Renaud Jougla
Renaud Jougla il 6 Mag 2019
Hello everybody. I am a relatively new user of matlab. I am trying to use LSTM ANN. The code proposed above runs well. Unfortunetaly I don't understand how to use it then... I mean I have this function called create_LSTM_network but now how can I use with my training data ? For example if I have data called x_train with predictives variables and y_train with data I wnat to predict, how do I have to write my code to train my LSTM ANN with these data ?
Thanks a lot for your help and time.

Kwangwon Seo
Kwangwon Seo il 18 Lug 2019
Hi. I tried to use the code above. And I don't know how to input my data in the code.
Is there anyone to solve this problem?
Thank you.

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