public void TrainTest(bool isTtrain, bool finetune)
{
Python.Initialize();
Chainer.Initialize();
int batchCount = Mother.Dice.Next(1, 50);
int ioCount = Mother.Dice.Next(1, 50);
Real[,] input = (Real[, ])Initializer.GetRealNdArray(new[] { batchCount, ioCount });
Real[,] dummyGy = (Real[, ])Initializer.GetRealNdArray(new[] { batchCount, ioCount });
Real[] gamma = Initializer.GetRealArray(ioCount);
Real[] beta = Initializer.GetRealArray(ioCount);
Real[] avgMean = Initializer.GetRealArray(ioCount);
Real[] avgVar = Initializer.GetRealArray(ioCount);
//Chainer
Chainer.Config["train"] = isTtrain;
NChainer.BatchNormalization <Real> cBatchNormalization = new NChainer.BatchNormalization <Real>(ioCount, dtype: Real.Type);
cBatchNormalization.gamma = new Variable <Real>(Real.ToBaseNdArray(gamma));
cBatchNormalization.beta = new Variable <Real>(Real.ToBaseNdArray(beta));
cBatchNormalization.avgMean = Real.ToBaseNdArray(avgMean);
cBatchNormalization.avgVar = Real.ToBaseNdArray(avgVar);
Variable <Real> cX = new Variable <Real>(Real.ToBaseNdArray(input));
Variable <Real> cY = cBatchNormalization.Forward(cX, finetune);
cY.Grad = Real.ToBaseNdArray(dummyGy);
cY.Backward();
//KelpNet
KelpNet.BatchNormalization batchNormalization = new BatchNormalization(ioCount, train: isTtrain, finetune: finetune);
batchNormalization.Gamma.Data = Real.ToRealArray(gamma);
batchNormalization.Beta.Data = Real.ToRealArray(beta);
batchNormalization.AvgMean.Data = Real.ToRealArray(avgMean);
batchNormalization.AvgVar.Data = Real.ToRealArray(avgVar);
NdArray x = new NdArray(Real.ToRealArray(input), new[] { ioCount }, batchCount);
NdArray y = batchNormalization.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[] cGammaGrad = Real.ToRealArray((Real[])cBatchNormalization.gamma.Grad);
Real[] cBetaGrad = Real.ToRealArray((Real[])cBatchNormalization.beta.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);
}
//gamma.grad
Assert.AreEqual(cGammaGrad.Length, batchNormalization.Gamma.Grad.Length);
for (int i = 0; i < batchNormalization.Gamma.Grad.Length; i++)
{
Assert.AreEqual(cGammaGrad[i], batchNormalization.Gamma.Grad[i], delta);
}
//beta.grad
Assert.AreEqual(cBetaGrad.Length, batchNormalization.Beta.Grad.Length);
for (int i = 0; i < batchNormalization.Beta.Grad.Length; i++)
{
Assert.AreEqual(cBetaGrad[i], batchNormalization.Beta.Grad[i], delta);
}
}
public void TrainTest(bool isTtrain, bool finetune)
{
Python.Initialize();
Chainer.Initialize();
int batchCount = Mother.Dice.Next(1, 50);
int ioCount = Mother.Dice.Next(1, 50);
Real[,] input = Initializer.GetRandomValues <Real[, ]>(batchCount, ioCount);
Real[,] dummyGy = Initializer.GetRandomValues <Real[, ]>(batchCount, ioCount);
Real[] gamma = Initializer.GetRandomValues <Real[]>(ioCount);
Real[] beta = Initializer.GetRandomValues <Real[]>(ioCount);
Real[] avgMean = Initializer.GetRandomValues <Real[]>(ioCount);
Real[] avgVar = Initializer.GetRandomValues <Real[]>(ioCount);
//Chainer
Chainer.Config["train"] = isTtrain;
NChainer.BatchNormalization <Real> cBatchNormalization = new NChainer.BatchNormalization <Real>(ioCount, dtype: typeof(Real));
cBatchNormalization.gamma = new Variable <Real>(gamma);
cBatchNormalization.beta = new Variable <Real>(beta);
cBatchNormalization.avgMean = avgMean;
cBatchNormalization.avgVar = avgVar;
Variable <Real> cX = new Variable <Real>(input);
Variable <Real> cY = cBatchNormalization.Forward(cX, finetune);
cY.Grad = dummyGy;
cY.Backward();
//KelpNet
KelpNet.BatchNormalization <Real> batchNormalization = new BatchNormalization <Real>(ioCount, train: isTtrain, finetune: finetune);
batchNormalization.Gamma.Data = gamma;
batchNormalization.Beta.Data = beta;
batchNormalization.AvgMean.Data = avgMean;
batchNormalization.AvgVar.Data = avgVar;
NdArray <Real> x = new NdArray <Real>(input, asBatch: true);
NdArray <Real> y = batchNormalization.Forward(x)[0];
y.Grad = dummyGy.Flatten();
y.Backward();
Real[] cYdata = ((Real[, ])cY.Data).Flatten();
Real[] cXgrad = ((Real[, ])cX.Grad).Flatten();
Real[] cGammaGrad = (Real[])cBatchNormalization.gamma.Grad;
Real[] cBetaGrad = (Real[])cBatchNormalization.beta.Grad;
//許容範囲を算出
Real delta = 0.00005f;
//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);
}
//gamma.grad
Assert.AreEqual(cGammaGrad.Length, batchNormalization.Gamma.Grad.Length);
for (int i = 0; i < batchNormalization.Gamma.Grad.Length; i++)
{
Assert.AreEqual(cGammaGrad[i], batchNormalization.Gamma.Grad[i], delta);
}
//beta.grad
Assert.AreEqual(cBetaGrad.Length, batchNormalization.Beta.Grad.Length);
for (int i = 0; i < batchNormalization.Beta.Grad.Length; i++)
{
Assert.AreEqual(cBetaGrad[i], batchNormalization.Beta.Grad[i], delta);
}
}