internal float dropout_rate = 0.2f; //遗忘概率
//创建层
public void Create(int neuron_num, string name = "dense", string activation = "relu", string optimizer = "GD")
{
DenseLayer layer;
if (this.Layers.Count == 0)
{
layer = new DenseLayer(neuron_num, null, name, activation, optimizer);
}
else
{
layer = new DenseLayer(neuron_num, this.Layers.Last(), name, activation, optimizer);
}
Add(layer);
}
//公有字段
public DenseLayer(int neuron_num, dynamic input = null, string name = null, string activation = "relu", string optimizer = "GD")
{
//---- 创建层 ----
this.Name = name == null ? "Undefined" : name;
this.Input = input;
this.Activation = activation;
this.Optimizer = optimizer;
//---- ----
//---- 创建神经元 ----
this.__weights__ = null;
this.__neurons__ = neuron_num;
//---- ----
//判断输入类型
if (input != null)
{
if (input.GetType() == typeof(DenseLayer))//非头层
{
DenseLayer Input = input;
this.__inputNum__ = Input.__neurons__;
this.__weights__ = new float[__inputNum__, __neurons__];
this.__bias__ = new float[__neurons__];
}
if (input.GetType() == typeof(FlattenLayer))
{
FlattenLayer Input = input;
this.__inputNum__ = Input.__outputNum__;
this.__weights__ = new float[__inputNum__, __neurons__];
this.__bias__ = new float[__neurons__];
}
else if (input.GetType() == typeof(List <float>))//头层
{
List <float> Input = input;
this.__inputNum__ = Input.Count;
this.__weights__ = new float[__inputNum__, __neurons__];
this.__bias__ = new float[__neurons__];
}
}
else
{
for (int i = 0; i < neuron_num; i++)
{
DenseLayer Input = input;
this.__inputNum__ = Input.__neurons__;
this.__weights__ = null;
this.__bias__ = new float[__neurons__];
}
}
}
//公有方法
public new List <float> GetOutput()
{
//耗时计算
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
List <float> Output = new List <float>();
//---- 判断输入类型 ----
Type input_type = this.Input.GetType();
if (input_type == typeof(List <float>))
{
List <float> input = this.Input;
var _input = input.ToArray();
var res = Wineforever.Mathematics.client.dense(_input, __weights__, __bias__, Activation);
Output = res.ToList();
}
else if (input_type == typeof(FlattenLayer))
{
FlattenLayer input = this.Input;
var _input = input.GetOutput().ToArray();
var res = Wineforever.Mathematics.client.dense(_input, __weights__, __bias__, Activation);
Output = res.ToList();
}
else if (input_type == typeof(DenseLayer))
{
DenseLayer input = this.Input;
var _input = input.GetOutput().ToArray();
var res = Wineforever.Mathematics.client.dense(_input, __weights__, __bias__, Activation);
Output = res.ToList();
}
if (this.Activation == "softmax")
{
float sum = Output.Sum(i => (float)Math.Pow(Math.E, i - Output.Max()));
Output = Output.Select(i => (float)Math.Pow(Math.E, i - Output.Max()) / sum).ToList();
}
//打印
stopwatch.Stop();
if (this.Father.__debug__)
{
Console.WriteLine("{0}用时(毫秒)：{1}", this.Name, stopwatch.ElapsedMilliseconds);
}
return(Output);
}
//添加层
public void Add(DenseLayer layer)
{
this.Layers.Add(layer);
layer.Father = this;//设置父对象
Update();
}
//私有方法
internal void Update()//更新连接
{
if (input != null)
{
//如果存在平化层，对其后层进行初始化
for (int i = 0; i < Layers.Count; i++)
{
if (Layers[i].GetType() == typeof(FlattenLayer) && Layers[Math.Min(i + 1, Layers.Count - 1)].GetType() == typeof(DenseLayer))
{
int num = 0;
DenseLayer denseLayer = (Layers[Math.Min(i + 1, Layers.Count - 1)] as DenseLayer);
Layer preLayer = Layers[Math.Max(i - 1, 0)];
if (preLayer.GetType() == typeof(ConvLayer))
{
num = 1;
num *= (preLayer as ConvLayer).__kernelNum__;
}
else if (preLayer.GetType() == typeof(DenseLayer))
{
num = 1;
num *= (preLayer as DenseLayer).__neurons__;
}
if (input.GetType() == typeof(List <float>))
{
num *= (input as List <float>).Count;
}
else if (input.GetType() == typeof(float[, ]))
{
num *= (input as float[, ]).GetLength(0) * (input as float[, ]).GetLength(1);
}
else if (input.GetType() == typeof(List <float[, ]>))
{
num *= (input as List <float[, ]>)[0].GetLength(0) * (input as List <float[, ]>)[0].GetLength(1);
}
(Layers[i] as FlattenLayer).__outputNum__ = num;
denseLayer.LoadKernel(denseLayer.__kernelPath__);
denseLayer.LoadBias(denseLayer.__biasPath__);
break;
}
}
if (input.GetType() == typeof(System))
{
if (Layers.First().GetType() == typeof(DenseLayer))
{
var layer = Layers.First() as DenseLayer;
layer.Input = (this.input.Layers[this.input.Layers.Count - 1] as DenseLayer).__neurons__;
}
}
else if (input.GetType() == typeof(List <float>))
{
if (Layers.First().GetType() == typeof(DenseLayer))
{
var layer = Layers.First() as DenseLayer;
layer.Input = input;
}
}
else if (input.GetType() == typeof(List <float[, ]>))
{
if (Layers.First().GetType() == typeof(ConvLayer))
{
var layer = Layers.First() as ConvLayer;
layer.Input = input;
}
}
}
for (int i = 0; i < Layers.Count; i++)
{
if (Layers[i].GetType() == typeof(ConvLayer) && Layers[i].Input != null && (Layers[i] as ConvLayer).__kernelPath__ != "")
{
Layers[i].LoadKernel((Layers[i] as ConvLayer).__kernelPath__);
}
if (Layers[i].GetType() == typeof(ConvLayer) && Layers[i].Input != null && (Layers[i] as ConvLayer).__biasPath__ != "")
{
Layers[i].LoadBias((Layers[i] as ConvLayer).__biasPath__);
}
}
}