我正在使用图书馆https://github.com/cazala/synaptic
我正在尝试预测以下系列中的下一个值(值X):
0 0 0 1 0 0 0 1 0 0 0 X
哪个应该是1
。
以下是代码:
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);
控制台中的输出:
{ error: 0.049765018466871494, iterations: 673, time: 392 }
[ 0.05010961302724895 ]
我期待激活的结果是接近1比0的值(更接近)。我不知道这是否是我对LSTM缺乏知识的库。有人能指出我正确的方向吗?
答案 0 :(得分:0)
我仔细阅读了源代码并弄明白了。
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);
结果:
{ error: 0.004982436660844655, iterations: 2010, time: 384 }
[ [ 0.18288280009908592 ],
[ 0.2948083898027347 ],
[ 0.35061782593064206 ],
[ 0.3900799575806566 ],
[ 0.49454852760556606 ] ]
这是准确的。