public void RnnLSTMRandomTest()
{
Python.Initialize();
Chainer.Initialize();
Real[,] input = { { 1.0 }, { 3.0 }, { 5.0 }, { 7.0 }, { 9.0 } };
Real[,] teach = { { 3.0 }, { 5.0 }, { 7.0 }, { 9.0 }, { 11.0 } };
Real[,] input2 = { { 3.0 }, { 5.0 }, { 7.0 }, { 9.0 }, { 11.0 } };
Real[,] teach2 = { { 5.0 }, { 7.0 }, { 9.0 }, { 11.0 }, { 13.0 } };
int outputCount = 1;
int inputCount = 1;
int hiddenCount = 2;
Real[,] upwardInit = (Real[, ])Initializer.GetRealNdArray(new[] { hiddenCount, hiddenCount });
Real[,] lateralInit = (Real[, ])Initializer.GetRealNdArray(new[] { hiddenCount, hiddenCount });
Real[,,] biasInit = (Real[, , ])Initializer.GetRealNdArray(new[] { 1, hiddenCount, 1 });
Real[,,] forgetBiasInit = (Real[, , ])Initializer.GetRealNdArray(new[] { 1, hiddenCount, 1 });
//Chainer
Real[,] w1 = (Real[, ])Initializer.GetRealNdArray(new[] { hiddenCount, inputCount });
Real[] b1 = Initializer.GetRealArray(hiddenCount);
//Chainer
NChainer.Linear <Real> cLinear1 = new NChainer.Linear <Real>(inputCount, hiddenCount, false, Real.ToBaseNdArray(w1), Real.ToBaseArray(b1));
NChainer.LSTM <Real> cLstm = new NChainer.LSTM <Real>(hiddenCount, hiddenCount, Real.ToBaseNdArray(lateralInit), Real.ToBaseNdArray(upwardInit), Real.ToBaseNdArray(biasInit), Real.ToBaseNdArray(forgetBiasInit));
Real[,] w2 = (Real[, ])Initializer.GetRealNdArray(new[] { outputCount, hiddenCount });
Real[] b2 = Initializer.GetRealArray(outputCount);
NChainer.Linear <Real> cLinear2 = new NChainer.Linear <Real>(hiddenCount, outputCount, false, Real.ToBaseNdArray(w2), Real.ToBaseArray(b2));
Variable <Real> cX1 = new Variable <Real>(Real.ToBaseNdArray(input));
Variable <Real> cY11 = cLinear1.Forward(cX1);
Variable <Real> cY12 = cLstm.Forward(cY11);
Variable <Real> cY13 = cLinear2.Forward(cY12);
Variable <Real> cT = new Variable <Real>(Real.ToBaseNdArray(teach));
Variable <Real> cLoss = new NChainer.MeanSquaredError <Real>().Forward(cY13, cT);
cLoss.Backward();
//KelpNet
KelpNet.CL.Linear linear1 = new KelpNet.CL.Linear(inputCount, hiddenCount, false, w1, b1);
KelpNet.LSTM lstm = new KelpNet.LSTM(hiddenCount, hiddenCount, lateralInit, upwardInit, biasInit, forgetBiasInit);
KelpNet.CL.Linear linear2 = new KelpNet.CL.Linear(hiddenCount, outputCount, false, w2, b2);
NdArray x1 = new NdArray(Real.ToRealArray(input), new[] { 1 }, 5);
NdArray y11 = linear1.Forward(x1)[0];
NdArray y12 = lstm.Forward(y11)[0];
NdArray y13 = linear2.Forward(y12)[0];
NdArray t = new NdArray(Real.ToRealArray(teach), new[] { 1 }, 5);
NdArray loss = new KelpNet.MeanSquaredError().Evaluate(y13, t);
y13.Backward();
Real[] cY11data = Real.ToRealArray((Real[, ])cY11.Data);
Real[] cY12data = Real.ToRealArray((Real[, ])cY12.Data);
Real[] cY13data = Real.ToRealArray((Real[, ])cY13.Data);
Real[] cXgrad = Real.ToRealArray((Real[, ])cX1.Grad);
Real[] cupwardWGrad = Real.ToRealArray((Real[, ])cLstm.upward.W.Grad);
Real[] cupwardbGrad = (Real[])cLstm.upward.b.Grad;
//許容範囲を設定
double delta = 0.00001;
//y11
Assert.AreEqual(cY11data.Length, y11.Data.Length);
for (int i = 0; i < cY11data.Length; i++)
{
Assert.AreEqual(cY11data[i], y11.Data[i], delta);
}
//y12
Assert.AreEqual(cY12data.Length, y12.Data.Length);
for (int i = 0; i < cY12data.Length; i++)
{
Assert.AreEqual(cY12data[i], y12.Data[i], delta);
}
//y13
Assert.AreEqual(cY13data.Length, y13.Data.Length);
for (int i = 0; i < cY13data.Length; i++)
{
Assert.AreEqual(cY13data[i], y13.Data[i], delta);
}
//許容範囲を設定
delta = 0.0001;
//loss
Assert.AreEqual(cLoss.Data[0], loss.Data[0], delta);
//x.Grad
Assert.AreEqual(cXgrad.Length, x1.Grad.Length);
for (int i = 0; i < cXgrad.Length; i++)
{
Assert.AreEqual(cXgrad[i], x1.Grad[i], delta);
}
Real[] cWgrad11 = Real.ToRealArray((Real[, ])cLinear1.W.Grad);
Real[] cbgrad11 = (Real[])cLinear1.b.Grad;
//W.grad
Assert.AreEqual(cWgrad11.Length, linear1.Weight.Grad.Length);
for (int i = 0; i < linear1.Weight.Grad.Length; i++)
{
Assert.AreEqual(cWgrad11[i], linear1.Weight.Grad[i], delta);
}
//b.grad
Assert.AreEqual(cbgrad11.Length, linear1.Bias.Grad.Length);
for (int i = 0; i < linear1.Bias.Grad.Length; i++)
{
Assert.AreEqual(cbgrad11[i], linear1.Bias.Grad[i], delta);
}
Real[] cWgrad12 = Real.ToRealArray((Real[, ])cLinear2.W.Grad);
Real[] cbgrad12 = (Real[])cLinear2.b.Grad;
//W.grad
Assert.AreEqual(cWgrad12.Length, linear2.Weight.Grad.Length);
for (int i = 0; i < linear2.Weight.Grad.Length; i++)
{
Assert.AreEqual(cWgrad12[i], linear2.Weight.Grad[i], delta);
}
//b.grad
Assert.AreEqual(cbgrad12.Length, linear2.Bias.Grad.Length);
for (int i = 0; i < linear2.Bias.Grad.Length; i++)
{
Assert.AreEqual(cbgrad12[i], linear2.Bias.Grad[i], delta);
}
//W.grad
int wLen = lstm.upward.Weight.Grad.Length;
Assert.AreEqual(cupwardWGrad.Length, lstm.upward.Weight.Grad.Length);
for (int i = 0; i < wLen; i++)
{
Assert.AreEqual(cupwardWGrad[i + wLen * 0], lstm.upward.Weight.Grad[i], delta);
}
//b.grad
int bLen = lstm.upward.Bias.Length;
Assert.AreEqual(cupwardbGrad.Length, lstm.upward.Bias.Grad.Length);
for (int i = 0; i < bLen; i++)
{
Assert.AreEqual(cupwardbGrad[i + wLen * 0], lstm.upward.Bias.Grad[i], delta);
}
//２周目
Variable <Real> cX2 = new Variable <Real>(Real.ToBaseNdArray(input2));
Variable <Real> cY21 = cLinear1.Forward(cX2);
Variable <Real> cY22 = cLstm.Forward(cY21);
Variable <Real> cY23 = cLinear2.Forward(cY22);
Variable <Real> cT2 = new Variable <Real>(Real.ToBaseNdArray(teach2));
Variable <Real> cLoss2 = new NChainer.MeanSquaredError <Real>().Forward(cY23, cT2);
//KelpNet
NdArray x2 = new NdArray(Real.ToRealArray(input2), new[] { 1 }, 5);
NdArray y21 = linear1.Forward(x2)[0];
NdArray y22 = lstm.Forward(y21)[0];
NdArray y23 = linear2.Forward(y22)[0];
NdArray t2 = new NdArray(Real.ToRealArray(teach2), new[] { 1 }, 5);
NdArray loss2 = new KelpNet.MeanSquaredError().Evaluate(y23, t2);
Assert.AreEqual(cLoss2.Data[0], loss2.Data[0], delta);
//Backwardを実行
cLoss2.Backward();
y23.Backward();
Real[] cYdata21 = Real.ToRealArray((Real[, ])cY21.Data);
Real[] cYdata22 = Real.ToRealArray((Real[, ])cY22.Data);
Real[] cYdata23 = Real.ToRealArray((Real[, ])cY23.Data);
Real[] cXgrad2 = Real.ToRealArray((Real[, ])cX2.Grad);
Real[] cupwardWGrad2 = Real.ToRealArray((Real[, ])cLstm.upward.W.Grad);
Real[] cupwardbGrad2 = (Real[])cLstm.upward.b.Grad;
Real[] clateralWGrad = Real.ToRealArray((Real[, ])cLstm.lateral.W.Grad);
//y21
Assert.AreEqual(cYdata21.Length, y21.Data.Length);
for (int i = 0; i < cYdata21.Length; i++)
{
Assert.AreEqual(cYdata21[i], y21.Data[i], delta);
}
//y22
Assert.AreEqual(cYdata22.Length, y22.Data.Length);
for (int i = 0; i < cYdata22.Length; i++)
{
Assert.AreEqual(cYdata22[i], y22.Data[i], delta);
}
//y23
Assert.AreEqual(cYdata23.Length, y23.Data.Length);
for (int i = 0; i < cYdata23.Length; i++)
{
Assert.AreEqual(cYdata23[i], y23.Data[i], delta);
}
//x.Grad
Assert.AreEqual(cXgrad2.Length, x2.Grad.Length);
for (int i = 0; i < cXgrad2.Length; i++)
{
Assert.AreEqual(cXgrad2[i], x2.Grad[i], delta);
}
//経由が多くかなり誤差が大きい為
delta = 1.0;
Real[] cWgrad22 = Real.ToRealArray((Real[, ])cLinear2.W.Grad);
Real[] cbgrad22 = (Real[])cLinear2.b.Grad;
//W.grad
Assert.AreEqual(cWgrad22.Length, linear2.Weight.Grad.Length);
for (int i = 0; i < linear2.Weight.Grad.Length; i++)
{
Assert.AreEqual(cWgrad22[i], linear2.Weight.Grad[i], delta);
}
//b.grad
Assert.AreEqual(cbgrad22.Length, linear2.Bias.Grad.Length);
for (int i = 0; i < linear2.Bias.Grad.Length; i++)
{
Assert.AreEqual(cbgrad22[i], linear2.Bias.Grad[i], delta);
}
delta = 2.0;
//W.grad
Assert.AreEqual(clateralWGrad.Length, lstm.lateral.Weight.Grad.Length);
for (int i = 0; i < clateralWGrad.Length; i++)
{
Assert.AreEqual(clateralWGrad[i + wLen * 0], lstm.lateral.Weight.Grad[i], delta);
}
for (int i = 0; i < wLen; i++)
{
Assert.AreEqual(cupwardWGrad2[i + wLen * 0], lstm.upward.Weight.Grad[i], delta);
}
//b.grad
for (int i = 0; i < bLen; i++)
{
Assert.AreEqual(cupwardbGrad2[i + wLen * 0], lstm.upward.Bias.Grad[i], delta);
}
delta = 20.0;
Real[] cWgrad21 = Real.ToRealArray((Real[, ])cLinear1.W.Grad);
Real[] cbgrad21 = (Real[])cLinear1.b.Grad;
//W.grad
Assert.AreEqual(cWgrad21.Length, linear1.Weight.Grad.Length);
for (int i = 0; i < linear1.Weight.Grad.Length; i++)
{
Assert.AreEqual(cWgrad21[i], linear1.Weight.Grad[i], delta);
}
//b.grad
Assert.AreEqual(cbgrad21.Length, linear1.Bias.Grad.Length);
for (int i = 0; i < linear1.Bias.Grad.Length; i++)
{
Assert.AreEqual(cbgrad21[i], linear1.Bias.Grad[i], delta);
}
}
public void MeanSquaredRandomTest()
{
Python.Initialize();
Chainer.Initialize();
int batchCount = Mother.Dice.Next(1, 5);
int ch = Mother.Dice.Next(1, 5);
int width = Mother.Dice.Next(1, 16);
int height = Mother.Dice.Next(1, 16);
Real[,,,] inputA = (Real[, , , ])Initializer.GetRealNdArray(new[] { batchCount, ch, height, width });
Real[,,,] inputB = (Real[, , , ])Initializer.GetRealNdArray(new[] { batchCount, ch, height, width });
for (int i = 0; i < inputB.GetLength(0); i++)
{
for (int j = 0; j < inputB.GetLength(1); j++)
{
for (int k = 0; k < inputB.GetLength(2); k++)
{
for (int l = 0; l < inputB.GetLength(3); l++)
{
inputB[i, j, k, l] *= 3.1415;
}
}
}
}
//chainer
NChainer.MeanSquaredError <Real> cMeanSquaredError = new NChainer.MeanSquaredError <Real>();
Variable <Real> cX = new Variable <Real>(Real.ToBaseNdArray(inputA));
Variable <Real> cY = new Variable <Real>(Real.ToBaseNdArray(inputB));
Variable <Real> cZ = cMeanSquaredError.Forward(cX, cY);
cZ.Backward();
Real[] cXgrad = Real.ToRealArray((Real[, , , ])cX.Grad);
//KelpNet
KelpNet.MeanSquaredError meanSquaredError = new KelpNet.MeanSquaredError();
NdArray x = new NdArray(Real.ToRealArray(inputA), new[] { ch, height, width }, batchCount);
NdArray y = new NdArray(Real.ToRealArray(inputB), new[] { ch, height, width }, batchCount);
//KelpNetはBackwaward側のみEvaluateで実行される
NdArray z = meanSquaredError.Evaluate(x, y);
//許容範囲を算出(内部の割引順が違うため誤差が大きい)
double delta = 0.001;
//Loss
Assert.AreEqual(cZ.Data[0], z.Data[0], 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);
}
}
public void MeanSquaredRandomTest()
{
Python.Initialize();
Chainer.Initialize();
int batchCount = Mother.Dice.Next(1, 5);
int ch = Mother.Dice.Next(1, 5);
int width = Mother.Dice.Next(1, 16);
int height = Mother.Dice.Next(1, 16);
Real[,,,] inputA = Initializer.GetRandomValues <Real[, , , ]>(batchCount, ch, height, width);
Real[,,,] inputB = Initializer.GetRandomValues <Real[, , , ]>(batchCount, ch, height, width);
for (int i = 0; i < inputB.GetLength(0); i++)
{
for (int j = 0; j < inputB.GetLength(1); j++)
{
for (int k = 0; k < inputB.GetLength(2); k++)
{
for (int l = 0; l < inputB.GetLength(3); l++)
{
inputB[i, j, k, l] *= (Real)3.1415f;
}
}
}
}
//chainer
NChainer.MeanSquaredError <Real> cMeanSquaredError = new NChainer.MeanSquaredError <Real>();
Variable <Real> cX = new Variable <Real>(inputA);
Variable <Real> cY = new Variable <Real>(inputB);
Variable <Real> cZ = cMeanSquaredError.Forward(cX, cY);
cZ.Backward();
Real[] cXgrad = ((Real[, , , ])cX.Grad).Flatten();
//KelpNet
KelpNet.MeanSquaredError <Real> meanSquaredError = new KelpNet.MeanSquaredError <Real>();
NdArray <Real> x = new NdArray <Real>(inputA, asBatch: true);
NdArray <Real> y = new NdArray <Real>(inputB, asBatch: true);
//KelpNetはBackwaward側のみEvaluateで実行される
NdArray <Real> z = meanSquaredError.Evaluate(x, y);
//許容範囲を算出(内部の割引順が違うため誤差が大きい)
Real delta = 0.001f;
//Loss
Assert.AreEqual(cZ.Data[0], z.Data[0], 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);
}
}