private NeuralNetwork CreateNewNetwork()
{
//先创建一个神经网络
var network = new NeuralNetwork();
//创建一个全连接层，输出形状为(1, 10, 1)，激活函数为Sigmoid
//var d1 = new DenseLayer(new Shape(1, 10, 1), ActivationType.Sigmoid);
//network.AddLayer(d1);
//创建一个全连接层，输出形状为(1, 2, 1)，注意这里是最后一层，输入形状要和你需要的输出对应，激活函数为Sigmoid
var d2 = new DenseLayer(new Shape(1, 2, 1), ActivationType.Sigmoid);
network.AddLayer(d2);
//初始化参数
var initArgs = new NetworkInitializeArgs();
//输入形状 你的输入数据形状，这里是小车的demo，输入为射线的数量
initArgs.inputShape = new Shape(1, rayNum, 1);
//权重的初始化范围 weight = Random.Range(-0.1f, 0.1f)
initArgs.initWeightRange = (-0.1f, 0.1f);
//偏执项的初始化范围 bias = Random.Range(-0.1f, 0.1f)
initArgs.initBiasRange = (-0.1f, 0.1f);
//初始化
network.Initialize(initArgs);
return(network);
}
// Start is called before the first frame update
void Start()
{
var network = new NeuralNetwork();
var d1 = new DenseLayer(new Shape(1, 10, 1), ActivationType.Sigmoid);
network.AddLayer(d1);
var initArgs = new NetworkInitializeArgs();
initArgs.inputShape = new Shape(1, 28 * 28, 1);
initArgs.initWeightRange = (-0.1f, 0.1f);
initArgs.initBiasRange = (-0.1f, 0.1f);
network.Initialize(initArgs);
var trainArgs = new NeuralNetworkTrainArgs();
trainArgs.trainingData = ReadTrainingData(); //设置数据
trainArgs.trainingLabels = ReadTrainingLabels(); //设置标签
trainArgs.learningRate = 0.01f; //设置学习速率，越大学习的速度越快，但出现不收敛的可能性也越大
trainArgs.onOnceEpoch = (i) =>
{
var accuracy = GetAccuracy(network, trainArgs.trainingData, trainArgs.trainingLabels);
Debug.Log($"第{i}个训练回合, 准确率:{accuracy}");
};
trainArgs.trainEpoches = 100; //设置训练的回合数
network.Train(trainArgs); //开始训练
TestNetwork(network, trainArgs.trainingData, trainArgs.trainingLabels);
}
public override void Initial(Shape inputShape, INeuralNetwork neuralNetwork, int layerIndex, NetworkInitializeArgs args)
{
this.inputShape = inputShape;
this.neuralNetwork = neuralNetwork;
this.layerIndex = layerIndex;
_weights = new Tensor(1, outputShape.len1, inputShape.len1);
_weights.FillWithRandomValue(args.initWeightRange.Item1, args.initWeightRange.Item2);
_bias = new Tensor(1, outputShape.len1, 1);
_bias.FillWithRandomValue(args.initBiasRange.Item1, args.initBiasRange.Item2);
}
