LSTM: Computing a library?

Question

I am using the https://github.com/cazala/synaptic library

I am trying to predict the following value (X value) in the following series:

0 0 0 1 0 0 0 1 0 0 0 X 

Which should be 1 .

Here is the code:

 const options = { peepholes: Layer.connectionType.ALL_TO_ALL, hiddenToHidden: false, outputToHidden: false, outputToGates: false, inputToOutput: true, }; // 1 input, 3 hidden layers (4 nodes per layer), 1 output const lstm = new Architect.LSTM(1, 4, 4, 4, 1, options); const trainingArray = [ { input: [0], output: [0], }, { input: [0], output: [0], }, { input: [0], output: [1], }, { input: [1], output: [0], }, { input: [0], output: [0], }, { input: [0], output: [0], }, { input: [0], output: [1], }, { input: [1], output: [0], }, ]; const trainingOptions = { rate: 0.1, iterations: 100000, error: 0.05, cost: null, crossValidate: null, }; let results = lstm.trainer.train(trainingArray, trainingOptions); console.log(results); array = [ 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, ]; results = lstm.activate(array); console.log(results); 

Output in the console:

 { error: 0.049765018466871494, iterations: 673, time: 392 } [ 0.05010961302724895 ] 

I expected that the result of activation would be a value close to 1 than 0 (much closer). I do not know if this is the library of my lack of knowledge in LSTM. Can someone point me in the right direction?

Answer 1

I read the source code and understood it.

 const synaptic = require('synaptic'); const Architect = synaptic.Architect; const Layer = synaptic.Layer; const lstmOptions = { peepholes: Layer.connectionType.ALL_TO_ALL, hiddenToHidden: false, outputToHidden: false, outputToGates: false, inputToOutput: true, }; const lstm = new Architect.LSTM(1, 4, 4, 4, 1, lstmOptions); const trainSet = [ { input: [0], output: [0.1] }, { input: [1], output: [0.2] }, { input: [0], output: [0.3] }, { input: [1], output: [0.4] }, { input: [0], output: [0.5] }, ]; const trainOptions = { rate: 0.2, iterations: 10000, error: 0.005, cost: null, crossValidate: null, }; const trainResults = lstm.trainer.train(trainSet, trainOptions); console.log(trainResults); const testResults = []; testResults[0] = lstm.activate([0]); testResults[1] = lstm.activate([1]); testResults[2] = lstm.activate([0]); testResults[3] = lstm.activate([1]); testResults[4] = lstm.activate([0]); console.log(testResults); 

Results in:

 { error: 0.004982436660844655, iterations: 2010, time: 384 } [ [ 0.18288280009908592 ], [ 0.2948083898027347 ], [ 0.35061782593064206 ], [ 0.3900799575806566 ], [ 0.49454852760556606 ] ] 

That's for sure.