use tiny_ml::prelude::*;
fn main() {
let mut net = NeuralNetwork::new().add_layer(1, ActivationFunction::Linear);
let mut inputs = vec![];
let mut outputs = vec![];
for i in -50..50 {
inputs.push([i as f32]);
outputs.push([i as f32 * 3.0]);
}
let data = DataSet { inputs, outputs };
let trainer = BasicTrainer::new(data);
for _ in 0..10 {
trainer.train(&mut net, 10);
println!("{}", trainer.get_total_error(&net))
}
println!("########");
for i in -5..5 {
println!("{}", &net.run(&[i as f32 + 0.5])[0]);
}
}