单个感知器使输出值保持接近0.5

Question

我正在尝试仅用一个神经元来创建最简单的感知（神经元接受2个输入值，然后按权重*输入它们的值，然后+偏置并使用（1 /（1 + Math.exp（-x） ））sigmoid函数）并通过反向传播对其进行训练（通过从输出的I增益中减去期望值来获取误差，找到梯度和权重delta，再乘以权重和输入值之间的差），但是在第一次迭代之后，我的权重真正接近0并开始产生一个Sigmoid（0），它是0.5（它总是产生0.48至0.52或接近的值。

class Neuron {constructor(){
    this.inputs = [1,1];
    this.inputWeights = [(Math.random()*2)-1,(Math.random()*2)-1];
    this.bias = 0.1;
    this.activate = () => {
        if(this.inputs.length !== this.inputWeights.length)return "Wrong input length";
        let sum = 0;
        for(var n = 0; n < this.inputs.length;n++){
        sum = sum + (this.inputs[n]*this.inputWeights[n]);      
        }
        sum = sum + this.bias;
        //return sigmoid activated value
        let activated_output = (1 / (1 + Math.exp(-sum)));
        return activated_output;
    };
    this.error = (predicted,desired) => {
        let error = predicted - desired;
        let gradient = predicted * (1-predicted);
        let weights_delta = error * gradient;
        return weights_delta;
    };
    this.changeWeights = (weights_delta) => {
        let info = this.inputWeights[0];
        for(var n = 0; n < this.inputWeights.length; n++){
            this.inputWeights[n] = (this.inputWeights[n] - this.inputs[n])  * weights_delta * learning_rate;
        }
        return "first weight changed from " + info + " to " + this.inputWeights[0];
    }
}}
var testNeuron = new Neuron();
var learning_rate = 0.05;
var dataset = [
{ inputs: [1,0], outputs: [1] },
{ inputs: [0,1], outputs: [0] }, 
{ inputs: [0.5,0.1], outputs: [1] },
{ inputs: [0.1,0.9], outputs: [0] }];
//train
var train = (iterations, data) => {
  for(var i = 0; i < iterations; i++){
    for(var n = 0; n < data.length; n++){
    testNeuron.inputs = data[n].inputs;
    console.log(testNeuron.changeWeights(testNeuron.error(testNeuron.activate() , 
data[n].outputs[0])));
    }
  }
}
train(10,dataset);

这里是所有代码，我在有偏见和无偏见的情况下进行了尝试，但是我觉得我的数学肯定是错误的，但是由于我是菜鸟，所以我不知道在哪里。.halp先生，

Answer 1

最大的错误是我没有使用任何偏差输入，也没有为它调整权重。如果我们不使用偏差，那么像0,0这样的简单输入将始终返回0，并且无法调整权重以更改输出。

[第二，如果我们查看简单的Perceptron，我们应该使用阈值函数而不是Sigmoid。（尽管Sigmoid是可能的，但在此示例中imo较慢）Thresh hold函数是一个简单函数，如果输出为负，则返回0，而1如果是积极的。我的重做和工作代码看起来像这样，增加训练迭代次数可以像应该的那样减少错误，谢谢

class Perceptron{constructor(){
    //bias , input1, input2
    this.inputs = [1,0,0];
    this.inputWeights = [(Math.random()*2)-1,(Math.random()*2)-1,(Math.random()*2)-1];
    this.output = 0;
    this.desiredOutput = 0; 
}//perceptron methods
    activate = () => {
        let sum = 0;
        for(var n = 0; n < this.inputs.length; n++){
            sum += this.inputs[n] * this.inputWeights[n];
        };
        this.output = sum < 0 ? 0 : 1;
        this.desiredOutput == this.output ? console.log("Correct answer") : console.log("Incorrect answer");
    };
    propagate = () => {
        let error = this.desiredOutput - this.output;
        for(var m = 0; m < this.inputs.length; m++){
            let delta = error * this.inputs[m];
            this.inputWeights[m] = this.inputWeights[m] + (delta * learningRate);
        } 
    };
}
let learningRate = 0.1;
var train = (iterations) => {
    for(var x = 0; x < iterations; x++){
        for(var y = 0; y < dataset.length; y++){
        perception.inputs = [1,dataset[y][0],dataset[y][1]];
        perception.desiredOutput = dataset[y][2];   
        perception.activate();
        perception.propagate(); 
        }
    }
}   
var perception = new Perceptron();
//[input1 , input2 , desiredOutput] 
var dataset = [
    [0,0,1],
    [1,1,0],
    [0.1,0.3,1],
    [1.5,1.8,0]
];  
train(100);