void Initialize(Array initialW = null, Array initialb = null)
{
this.Weight = new NdArray(OutputCount, InputCount, this._kHeight, this._kWidth);
this.Weight.Name = this.Name + " Weight";
if (initialW == null)
{
Initializer.InitWeight(this.Weight);
}
else
{
this.Weight.Data = Real.ToRealArray(initialW);
}
this.Parameters[0] = this.Weight;
if (!NoBias)
{
this.Bias = new NdArray(OutputCount);
this.Bias.Name = this.Name + " Bias";
if (initialb != null)
{
this.Bias.Data = Real.ToRealArray(initialb);
}
this.Parameters[1] = this.Bias;
}
}
public AddBias(int axis = 0, int[] biasShape = null, Array initialb = null, string name = FUNCTION_NAME, string[] inputNames = null, string[] outputNames = null) : base(name, inputNames, outputNames)
{
this.Axis = axis;
this.Bias = new NdArray(biasShape);
if (initialb != null)
{
Bias.Data = Real.ToRealArray(initialb);
}
this.Parameters = new[] { Bias };
}
public NdArray(Array data, Function parentFunc = null)
{
Real[] resultData = Real.ToRealArray(data);
int[] resultShape = new int[data.Rank];
for (int i = 0; i < data.Rank; i++)
{
resultShape[i] = data.GetLength(i);
}
this.Data = resultData;
this.Shape = resultShape;
this.Length = Data.Length;
this.ParentFunc = parentFunc;
}
public MultiplyScale(int axis = 1, int[] wShape = null, bool biasTerm = false, Array initialW = null, Array initialb = null, string name = FUNCTION_NAME, string[] inputNames = null, string[] outputNames = null) : base(name, inputNames, outputNames)
{
this.Axis = axis;
this.BiasTerm = biasTerm;
#if DEBUG
if (wShape == null)
{
if (biasTerm)
{
throw new Exception("Biasのみでの使用はAddBiasを使用してください");
}
else
{
throw new Exception("パラメータの設定が正しくありません");
}
}
#endif
this.Weight = new NdArray(wShape);
this.Parameters = new NdArray[biasTerm ? 2 : 1];
if (initialW == null)
{
this.Weight.Data = Enumerable.Repeat((Real)1.0, Weight.Data.Length).ToArray();
}
else
{
this.Weight.Data = Real.ToRealArray(initialW);
}
this.Parameters[0] = this.Weight;
if (biasTerm)
{
this.Bias = new NdArray(wShape);
if (initialb != null)
{
this.Bias.Data = Real.ToRealArray(initialb);
}
this.Parameters[1] = this.Bias;
}
SingleInputForward = ForwardCpu;
SingleOutputBackward = BackwardCpu;
}
public BatchNormalization(int channelSize, double decay = 0.9, double eps = 1e-5, Array initialAvgMean = null, Array initialAvgVar = null, bool isTrain = true, string name = FUNCTION_NAME, string[] inputNames = null, string[] outputNames = null) : base(name, inputNames, outputNames)
{
this.ChannelSize = channelSize;
this.Decay = decay;
this.Eps = eps;
this.IsTrain = isTrain;
this.Gamma = new NdArray(channelSize);
this.Gamma.Data = Enumerable.Repeat((Real)1, channelSize).ToArray();
this.Gamma.Name = this.Name + " Gamma";
this.Beta = new NdArray(channelSize);
this.Beta.Name = this.Name + " Beta";
this.Parameters = new NdArray[this.IsTrain ? 2 : 4];
//学習対象のParameterを登録
this.Parameters[0] = this.Gamma;
this.Parameters[1] = this.Beta;
this.AvgMean = new NdArray(channelSize);
this.AvgMean.Name = this.Name + " Mean";
this.AvgVar = new NdArray(channelSize);
this.AvgVar.Name = this.Name + " Variance";
if (initialAvgMean != null)
{
this.AvgMean.Data = Real.ToRealArray(initialAvgMean);
}
if (initialAvgVar != null)
{
this.AvgVar.Data = Real.ToRealArray(initialAvgVar);
}
if (!this.IsTrain)
{
this.Parameters[2] = this.AvgMean;
this.Parameters[3] = this.AvgVar;
}
SingleInputForward = ForwardCpu;
SingleOutputBackward = BackwardCpu;
}
//アレイ配列をバッチとして登録する
public static NdArray FromArrays(Array[] arrays, Function parentFunc = null)
{
int[] resultShape = new int[arrays[0].Rank];
for (int i = 0; i < arrays[0].Rank; i++)
{
resultShape[i] = arrays[0].GetLength(i);
}
int length = arrays[0].Length;
Real[] result = new Real[length * arrays.Length];
for (int i = 0; i < arrays.Length; i++)
{
Array.Copy(Real.ToRealArray(arrays[i]), 0, result, length * i, length);
}
return(new NdArray(result, resultShape, arrays.Length, parentFunc));
}
public EmbedID(int inputCount, int outputCount, Real[,] initialW = null, string name = FUNCTION_NAME, string[] inputNames = null, string[] outputNames = null) : base(name, inputNames, outputNames)
{
this.InputCount = inputCount;
this.OutputCount = outputCount;
this.Weight = new NdArray(inputCount, outputCount);
this.Weight.Name = this.Name + " Weight";
if (initialW == null)
{
Initializer.InitWeight(this.Weight);
}
else
{
//単純に代入しないのはサイズのチェックを兼ねるため
this.Weight.Data = Real.ToRealArray(initialW);
}
this.Parameters = new[] { this.Weight };
}
public Linear(int inputCount, int outputCount, bool noBias = false, Array initialW = null, Array initialb = null, CompressibleActivation activation = null, string name = FUNCTION_NAME, string[] inputNames = null, string[] outputNames = null, bool gpuEnable = false) : base(FUNCTION_NAME, activation, name, inputNames, outputNames, gpuEnable)
{
this.OutputCount = outputCount;
this.InputCount = inputCount;
this.Weight = new NdArray(outputCount, inputCount);
this.Weight.Name = this.Name + " Weight";
this.NoBias = noBias;
this.Parameters = new NdArray[noBias ? 1 : 2];
if (initialW == null)
{
Initializer.InitWeight(this.Weight);
}
else
{
this.Weight.Data = Real.ToRealArray(initialW);
}
this.Parameters[0] = this.Weight;
if (!noBias)
{
this.Bias = new NdArray(outputCount);
this.Bias.Name = this.Name + " Bias";
if (initialb != null)
{
this.Bias.Data = Real.ToRealArray(initialb);
}
this.Parameters[1] = this.Bias;
}
}
public LSTM(int inSize, int outSize, Array lateralInit = null, Array upwardInit = null, Array biasInit = null, Array forgetBiasInit = null, string name = FUNCTION_NAME, string[] inputNames = null, string[] outputNames = null, bool gpuEnable = false) : base(name, inputNames, outputNames)
{
this.InputCount = inSize;
this.OutputCount = outSize;
List <NdArray> functionParameters = new List <NdArray>();
Real[] lateralW = null;
Real[] upwardW = null;
Real[] upwardb = null;
if (upwardInit != null)
{
upwardW = new Real[inSize * outSize * 4];
Real[] tmpUpwardInit = Real.ToRealArray(upwardInit);
for (int i = 0; i < 4; i++)
{
Array.Copy(tmpUpwardInit, 0, upwardW, i * tmpUpwardInit.Length, tmpUpwardInit.Length);
}
}
if (lateralInit != null)
{
lateralW = new Real[outSize * outSize * 4];
Real[] tmpLateralInit = Real.ToRealArray(lateralInit);
for (int i = 0; i < 4; i++)
{
Array.Copy(tmpLateralInit, 0, lateralW, i * tmpLateralInit.Length, tmpLateralInit.Length);
}
}
if (biasInit != null && forgetBiasInit != null)
{
upwardb = new Real[outSize * 4];
Real[] tmpBiasInit = Real.ToRealArray(biasInit);
for (int i = 0; i < biasInit.Length; i++)
{
upwardb[i * 4 + 0] = tmpBiasInit[i];
upwardb[i * 4 + 1] = tmpBiasInit[i];
upwardb[i * 4 + 3] = tmpBiasInit[i];
}
Real[] tmpforgetBiasInit = Real.ToRealArray(forgetBiasInit);
for (int i = 0; i < tmpforgetBiasInit.Length; i++)
{
upwardb[i * 4 + 2] = tmpforgetBiasInit[i];
}
}
this.upward = new Linear(inSize, outSize * 4, noBias: false, initialW: upwardW, initialb: upwardb, name: "upward");
functionParameters.AddRange(this.upward.Parameters);
//lateralはBiasは無し
this.lateral = new Linear(outSize, outSize * 4, noBias: true, initialW: lateralW, name: "lateral");
functionParameters.AddRange(this.lateral.Parameters);
this.Parameters = functionParameters.ToArray();
}
public Deconvolution2D(int inputChannels, int outputChannels, int[] kSize, int[] subSample = null, int[] trim = null, bool noBias = false, Array initialW = null, Array initialb = null, CompressibleActivation activation = null, string name = FUNCTION_NAME, string[] inputNames = null, string[] outputNames = null, bool gpuEnable = false) : base(FUNCTION_NAME, activation, name, inputNames, outputNames, gpuEnable)
{
if (subSample == null)
{
subSample = new[] { 1, 1 }
}
;
if (trim == null)
{
trim = new[] { 0, 0 }
}
;
this._kWidth = kSize[0];
this._kHeight = kSize[1];
this._padX = trim[0];
this._padY = trim[1];
this.NoBias = noBias;
this._strideX = subSample[0];
this._strideY = subSample[1];
this.Parameters = new NdArray[noBias ? 1 : 2];
this.OutputCount = outputChannels;
this.InputCount = inputChannels;
this.Initialize(initialW, initialb);
}
void Initialize(Array initialW = null, Array initialb = null)
{
this.Weight = new NdArray(InputCount, OutputCount, this._kHeight, this._kWidth);
this.Weight.Name = this.Name + " Weight";
if (initialW == null)
{
Initializer.InitWeight(this.Weight);
}
else
{
this.Weight.Data = Real.ToRealArray(initialW);
}
this.Parameters[0] = this.Weight;
if (!NoBias)
{
this.Bias = new NdArray(OutputCount);
this.Bias.Name = this.Name + " Bias";
if (initialb != null)
{
this.Bias.Data = Real.ToRealArray(initialb);
}
this.Parameters[1] = this.Bias;
}
}