On Thursday, 10 August 2017 at 19:10:05 UTC, Jiyan wrote:
Hey,
wanted to the following simple thing with vectorflow:
...
You'll want to end your stack with your wanted output size (1 -
being the sum).
Training it with the "square" function seems to give the best
result for simple additions.
Hope you can use it:
import std.stdio;
import std.random;
import std.algorithm;
import std.range;
import std.math;
import vectorflow;
struct Obs
{
float label;
float[] features;
}
void main()
{
const
learning_rate = 0.01,
epochs = 200,
verbose = true,
cores = 3;
auto net = NeuralNet()
.stack(DenseData(2))
.stack(Linear(1));
net.initialize(0.1);
Obs [] data;
foreach(i; iota(2))
{
auto features = [uniform(0.0f, 100.0f), uniform(0.0f,
100.0f)];
data ~= Obs(features.sum, features);
}
net.learn(data, "square", AdaGrad(epochs, learning_rate),
verbose, cores);
auto val = net.predict([50.0f, 200.0f]);
val.writeln;
assert(fabs(250.0f - val[0]) < 0.1);
}
