public void AdamRandomTest()
{
Python.Initialize();
Chainer.Initialize();
int inputCount = Mother.Dice.Next(2, 50);
int outputCount = Mother.Dice.Next(2, 50);
int batchCount = Mother.Dice.Next(1, 5);
Real[,] input = Initializer.GetRandomValues <Real[, ]>(batchCount, inputCount);
Real[,] dummyGy = Initializer.GetRandomValues <Real[, ]>(batchCount, outputCount);
Real[,] w = Initializer.GetRandomValues <Real[, ]>(outputCount, inputCount);
Real[] b = Initializer.GetRandomValues <Real[]>(outputCount);
float alpha = (float)Mother.Dice.NextDouble(); //0.001f
float beta1 = (float)Mother.Dice.NextDouble(); //0.9f;
float beta2 = (float)Mother.Dice.NextDouble(); //0.999f;
float eps = (float)Mother.Dice.NextDouble(); //1e-08f;
float eta = (float)Mother.Dice.NextDouble(); //1.0f;
//Chainer
NChainer.Linear <Real> cLinear = new NChainer.Linear <Real>(inputCount, outputCount, false, w, b);
NChainer.Adam <Real> cAdam = new NChainer.Adam <Real>(alpha, beta1, beta2, eps, eta);
cAdam.Setup(cLinear);
Variable <Real> cX = new Variable <Real>(input);
Variable <Real> cY = cLinear.Forward(cX);
cY.Grad = dummyGy;
cY.Backward();
cAdam.Update();
//KelpNet
KelpNet.CL.Linear <Real> linear = new KelpNet.CL.Linear <Real>(inputCount, outputCount, false, w, b);
KelpNet.Adam <Real> adam = new Adam <Real>(alpha, beta1, beta2, eps, eta);
adam.SetUp(linear);
NdArray <Real> x = new NdArray <Real>(input, asBatch: true);
NdArray <Real> y = linear.Forward(x)[0];
y.Grad = dummyGy.Flatten();
y.Backward();
adam.Update();
Real[] cW = ((Real[, ])cLinear.W.Data).Flatten();
Real[] cb = (Real[])cLinear.b.Data;
//許容範囲を算出
Real delta = 0.00001f;
//W.grad
Assert.AreEqual(cW.Length, linear.Weight.Data.Length);
for (int i = 0; i < linear.Weight.Data.Length; i++)
{
Assert.AreEqual(cW[i], linear.Weight.Data[i], delta);
}
//b.grad
Assert.AreEqual(cb.Length, linear.Bias.Data.Length);
for (int i = 0; i < linear.Bias.Data.Length; i++)
{
Assert.AreEqual(cb[i], linear.Bias.Data[i], delta);
}
}
public void AdamRandomTest()
{
Python.Initialize();
Chainer.Initialize();
int inputCount = Mother.Dice.Next(2, 50);
int outputCount = Mother.Dice.Next(2, 50);
int batchCount = Mother.Dice.Next(1, 5);
Real[,] input = (Real[,])Initializer.GetRealNdArray(new[] { batchCount, inputCount });
Real[,] dummyGy = (Real[,])Initializer.GetRealNdArray(new[] { batchCount, outputCount });
Real[,] w = (Real[,])Initializer.GetRealNdArray(new[] { outputCount, inputCount });
Real[] b = Initializer.GetRealArray(outputCount);
float alpha = (float)Mother.Dice.NextDouble(); //0.001f
float beta1 = (float)Mother.Dice.NextDouble(); //0.9f;
float beta2 = (float)Mother.Dice.NextDouble(); //0.999f;
float eps = (float)Mother.Dice.NextDouble(); //1e-08f;
float eta = (float)Mother.Dice.NextDouble(); //1.0f;
//Chainer
NChainer.Linear<Real> cLinear = new NChainer.Linear<Real>(inputCount, outputCount, false, Real.ToBaseNdArray(w), Real.ToBaseArray(b));
NChainer.Adam<Real> cAdam = new NChainer.Adam<Real>(alpha, beta1, beta2, eps, eta);
cAdam.Setup(cLinear);
Variable<Real> cX = new Variable<Real>(Real.ToBaseNdArray(input));
Variable<Real> cY = cLinear.Forward(cX);
cY.Grad = Real.ToBaseNdArray(dummyGy);
cY.Backward();
cAdam.Update();
//KelpNet
KelpNet.CL.Linear linear = new KelpNet.CL.Linear(inputCount, outputCount, false, w, b);
KelpNet.Adam adam = new Adam(alpha, beta1, beta2, eps, eta);
adam.SetUp(linear);
NdArray x = new NdArray(Real.ToRealArray(input), new[] { inputCount }, batchCount);
NdArray y = linear.Forward(x)[0];
y.Grad = Real.ToRealArray(dummyGy);
y.Backward();
adam.Update();
Real[] cW = Real.ToRealArray((Real[,])cLinear.W.Data);
Real[] cb = (Real[])cLinear.b.Data;
//許容範囲を算出
double delta = 0.00001;
//W.grad
Assert.AreEqual(cW.Length, linear.Weight.Data.Length);
for (int i = 0; i < linear.Weight.Data.Length; i++)
{
Assert.AreEqual(cW[i], linear.Weight.Data[i], delta);
}
//b.grad
Assert.AreEqual(cb.Length, linear.Bias.Data.Length);
for (int i = 0; i < linear.Bias.Data.Length; i++)
{
Assert.AreEqual(cb[i], linear.Bias.Data[i], delta);
}
}
