public void ForwadBackwardTest()
{
var soft = new MLStudy.Deep.Softmax();
soft.PrepareTrain(new TensorOld(3, 4));
//模拟之前的N次操作
for (int i = 0; i < 3; i++)
{
var noiseIn = TensorOld.Rand(3, 4);
var noiseError = TensorOld.Rand(3, 4);
soft.Forward(noiseIn);
soft.Backward(noiseError);
}
//真正的测试开始，等正常输出说明不受前面影响
var input = new TensorOld(new double[] { 7, 9, 1, -1, 2, -7, 2, 4, 7, 8, 4, -1 }, 3, 4);
var y = new TensorOld(new double[] { 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0 }, 3, 4); //第1个样本正确，第2，3个样本错误
var output = soft.Forward(input);
//用交叉熵计算Loss时反向传回的error
var error = TensorOld.DivideElementWise(y, output) * -1;
//计算出来的结果
var actual = soft.Backward(error);
//推导出来的结果，因为要把softmax和损失函数分离，所以实际应用时要分别计算
var expected = output - y;
//存在精度问题，有些值无法完全相等
MyAssert.ApproximatelyEqual(expected, actual);
}
public void ApplyTest()
{
var t = TensorOld.Rand(20, 30);
var n = TensorOld.Apply(t, a => a * a);
for (int i = 0; i < 20; i++)
{
for (int j = 0; j < 30; j++)
{
Assert.Equal(t[i, j] * t[i, j], n[i, j]);
}
}
}
public void ReshapeTest()
{
var t = TensorOld.Rand(100);
var r = t.Reshape(5, 20);
for (int i = 0; i < 5; i++)
{
for (int j = 0; j < 20; j++)
{
Assert.Equal(t[i * 20 + j], r[i, j]);
}
}
//测试Reshape只是视图
r[0, 0] = 123;
r[0, 1] = 456;
Assert.Equal(123, t[0]);
Assert.Equal(456, t[1]);
}
