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);
}
}