Vectorflow noob

[Jiyan via Digitalmars-d-learn]

Hey,
wanted to the following simple thing with vectorflow:

I want to develop a simple MLP, which has 2 input neurons and one 
output neuron.
The network should simply add the input values together, so [1,2] 
predicts [3] i guess.
I started in a newbish way to build the following code:

import vectorflow;


struct Obs // The represeneted data
{
	float label; // Did i get that right that label would be the 
DESIRED output (3=1+2)
	float []features; // The features are the input i guess, so 
features = f.e. [1,2]
}

void main()
{
	
	auto net = NeuralNet()
	    .stack(DenseData(2))
	    .stack(Linear(10));   // Is this the right way to construct 
the Net?
	
	// The training data
	Obs []data;
	
	
	data.length = 10;
	
	import std.random;
	import std.algorithm;
	foreach(ref Obs n; data)
	{
		// The features are getting fille with random numbers between 
0.5 and 5
		// The label becomes the sum of feature[0] and feature[1]
		n.features.length = 2;
		n.features[0] = uniform(0.5, 5);
		n.features[1] = uniform(0.5, 5);
		
		n.label = n.features.sum;
		writeln(n.features[0], " ", n.features[1], " ", n.label);
		assert (n.label == n.features[0] + n.features[1]);
	}
	
	net.learn(data, "logistic", AdaGrad(10, 0.1, 500), true, 3);
	
	auto val = net.predict(data[0]); // is this wrong?
	val.writeln;
}

Thanks :)