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.OutputCount = outSize;
List <NdArray <T> > functionParameters = new List <NdArray <T> >();
T[] lateralW = null;
T[] upwardW = null;
T[] upwardb = null;
if (upwardInit != null)
{
upwardW = new T[inSize * outSize * 4];
for (int i = 0; i < 4; i++)
{
Buffer.BlockCopy(upwardInit, 0, upwardW, i * upwardInit.Length * Marshal.SizeOf <T>(), upwardInit.Length * Marshal.SizeOf <T>());
}
}
if (lateralInit != null)
{
lateralW = new T[outSize * outSize * 4];
for (int i = 0; i < 4; i++)
{
Buffer.BlockCopy(lateralInit, 0, lateralW, i * lateralInit.Length * Marshal.SizeOf <T>(), lateralInit.Length * Marshal.SizeOf <T>());
}
}
if (biasInit != null && forgetBiasInit != null)
{
upwardb = new T[outSize * 4];
T[] tmpBiasInit = biasInit.FlattenEx <T>();
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];
}
T[] tmpforgetBiasInit = forgetBiasInit.FlattenEx <T>();
for (int i = 0; i < tmpforgetBiasInit.Length; i++)
{
upwardb[i * 4 + 2] = tmpforgetBiasInit[i];
}
}
this.upward = new Linear <T>(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 <T>(outSize, outSize * 4, noBias: true, initialW: lateralW, name: "lateral");
functionParameters.AddRange(this.lateral.Parameters);
this.Parameters = functionParameters.ToArray();
InitFunc(new StreamingContext());
}
public void RandomTest()
{
Python.Initialize();
Chainer.Initialize();
int inputCount = 1 + Mother.Dice.Next() % 49;
int outputCount = 1 + Mother.Dice.Next() % 49;
Real[,] input = (Real[, ])Initializer.GetRealNdArray(new[] { 1, inputCount });
Real[,] dummyGy = (Real[, ])Initializer.GetRealNdArray(new[] { 1, outputCount });
Real[,] w = (Real[, ])Initializer.GetRealNdArray(new[] { outputCount, inputCount });
Real[] b = Initializer.GetRealArray(outputCount);
//Chainer
NChainer.Linear <Real> cLinear = new NChainer.Linear <Real>(inputCount, outputCount, false, Real.ToBaseNdArray(w), Real.ToBaseArray(b));
Variable <Real> cX = new Variable <Real>(Real.ToBaseNdArray(input));
Variable <Real> cY = cLinear.Forward(cX);
cY.Grad = Real.ToBaseNdArray(dummyGy);
cY.Backward();
//KelpNet
KelpNet.Linear linear = new KelpNet.Linear(inputCount, outputCount, false, w, b);
NdArray x = new NdArray(input);
NdArray y = linear.Forward(x)[0];
y.Grad = Real.ToRealArray(dummyGy);
y.Backward();
Real[] cYdata = Real.ToRealArray((Real[, ])cY.Data);
Real[] cXgrad = Real.ToRealArray((Real[, ])cX.Grad);
Real[] cWgrad = Real.ToRealArray((Real[, ])cLinear.W.Grad);
Real[] cbgrad = (Real[])cLinear.b.Grad;
//許容範囲を算出
double delta = 0.00001;
//y
Assert.AreEqual(cYdata.Length, y.Data.Length);
for (int i = 0; i < y.Data.Length; i++)
{
Assert.AreEqual(cYdata[i], y.Data[i], delta);
}
//x.grad
Assert.AreEqual(cXgrad.Length, x.Grad.Length);
for (int i = 0; i < x.Grad.Length; i++)
{
Assert.AreEqual(cXgrad[i], x.Grad[i], delta);
}
//W.grad
Assert.AreEqual(cWgrad.Length, linear.Weight.Grad.Length);
for (int i = 0; i < linear.Weight.Grad.Length; i++)
{
Assert.AreEqual(cWgrad[i], linear.Weight.Grad[i], delta);
}
//b.grad
Assert.AreEqual(cbgrad.Length, linear.Bias.Grad.Length);
for (int i = 0; i < linear.Bias.Grad.Length; i++)
{
Assert.AreEqual(cbgrad[i], linear.Bias.Grad[i], delta);
}
}
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", gpuEnable: gpuEnable);
functionParameters.AddRange(this.upward.Parameters);
//lateralはBiasは無し
this.lateral = new Linear(outSize, outSize * 4, noBias: true, initialW: lateralW, name: "lateral", gpuEnable: gpuEnable);
functionParameters.AddRange(this.lateral.Parameters);
this.Parameters = functionParameters.ToArray();
SingleInputForward = ForwardCpu;
SingleOutputBackward = BackwardCpu;
}
