public void TestTensorVar()
{
var x = Op.Matrix <float>("x");
var f = Op.Function(input: x, output: x);
AssertArray.AreEqual(f(NN.Const(3.0f, 2, 2)), NN.Const(3.0f, 2, 2));
AssertArray.AreNotEqual(f(NN.Const(3.0f, 2, 2)), NN.Const(4.0f, 2, 2));
}
public void TestTensorConst()
{
var x = Op.Const(2.0f, 2, 2);
var f = Op.Function(output: x);
AssertArray.AreEqual(f(), NN.Const(2.0f, 2, 2));
AssertArray.AreNotEqual(f(), NN.Const(3.0f, 2, 2));
}
public void TestTensorUnary()
{
var x = Op.Matrix <float>("x");
var f = Op.Function(input: x, output: Op.Abs(x));
AssertArray.AreEqual(f(NN.Const(5.0f, 2, 2)), NN.Const(5.0f, 2, 2));
AssertArray.AreEqual(f(NN.Const(-5.0f, 2, 2)), NN.Const(5.0f, 2, 2));
AssertArray.AreNotEqual(f(NN.Const(6.0f, 2, 2)), NN.Const(5.0f, 2, 2));
AssertArray.AreNotEqual(f(NN.Const(-6.0f, 2, 2)), NN.Const(5.0f, 2, 2));
}
public void TestTensorMulAdd()
{
var a = Op.Matrix <float>("a");
var b = Op.Matrix <float>("b");
var f = Op.Function(input: (a, b), output: 3 * (a + b));
AssertArray.AreEqual(f(NN.Const(3f, 2, 2), NN.Const(4f, 2, 2)), NN.Const(21f, 2, 2));
AssertArray.AreEqual(f(NN.Const(3f, 2, 2), NN.Const(-4f, 2, 2)), NN.Const(-3f, 2, 2));
AssertArray.AreNotEqual(f(NN.Const(3f, 2, 2), NN.Const(4f, 2, 2)), NN.Const(7f, 2, 2));
AssertArray.AreNotEqual(f(NN.Const(3f, 2, 2), NN.Const(4f, 2, 2)), NN.Const(9f, 2, 2));
}
public void TestTensorAdd()
{
var x = Op.Matrix <float>("x");
var y = Op.Matrix <float>("y");
var f = Op.Function(input: (x, y), output: x + y);
AssertArray.AreEqual(f(NN.Const(3f, 2, 2), NN.Const(4f, 2, 2)), NN.Const(7f, 2, 2));
AssertArray.AreEqual(f(NN.Const(3f, 2, 2), NN.Const(-4f, 2, 2)), NN.Const(-1f, 2, 2));
AssertArray.AreNotEqual(f(NN.Const(3f, 2, 2), NN.Const(4f, 2, 2)), NN.Const(3f, 2, 2));
AssertArray.AreNotEqual(f(NN.Const(3f, 2, 2), NN.Const(-4f, 2, 2)), NN.Const(4f, 2, 2));
}
public static Array <T> Const <T>(T a, int[] shape, Array <T> result = null)
{
if (result != null)
{
result.FillWith(a);
return(result);
}
else
{
return(NN.Const(a, shape));
}
}
// TODO: this test doesn't work due to bugs in Elementwise
public void PushCoherentGradientOnSimpleAbstraction()
{
var x = T.Shared(NN.Range <float>(4), "x");
var b = T.Scalar <float>("b");
var y = T.Apply(x, x_ => x_ + b);
var loss = T.Sum(y);
var dL_db = T.Function(b, T.Grad(loss, b));
//dL_db should be 4;
AssertArray.AreEqual(NN.Const(4f, 10), NN.Range <float>(10).Apply(dL_db));
}
