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use std::io::Write;
use nalgebra::{dvector, DVector};
use neuramethyst::derivable::activation::Linear;
use neuramethyst::derivable::loss::CrossEntropy;
use neuramethyst::derivable::regularize::NeuraL1;
use neuramethyst::{one_hot, plot_losses, prelude::*};
use rand::Rng;
fn main() {
let mut network = neura_residual![
<= 0, 2;
neura_layer!("dense", 4).regularization(NeuraL1(0.001));
neura_layer!("dropout", 0.25);
neura_layer!("dense", 2)
.activation(Linear)
.regularization(NeuraL1(0.001));
neura_layer!("softmax");
]
.construct(NeuraShape::Vector(2))
.unwrap();
let inputs = (0..1).cycle().map(move |_| {
let mut rng = rand::thread_rng();
let category = rng.gen_bool(0.5) as usize;
let (x, y) = if category == 0 {
let radius: f32 = rng.gen_range(0.0..2.0);
let angle = rng.gen_range(0.0..std::f32::consts::TAU);
(angle.cos() * radius, angle.sin() * radius)
} else {
let radius: f32 = rng.gen_range(3.0..5.0);
let angle = rng.gen_range(0.0..std::f32::consts::TAU);
(angle.cos() * radius, angle.sin() * radius)
};
(dvector![x, y], one_hot(category, 2))
});
let test_inputs: Vec<_> = inputs.clone().take(10).collect();
if std::env::args().any(|arg| arg == "draw") {
for epoch in 0..200 {
let mut trainer = NeuraBatchedTrainer::new().learning_rate(0.03).iterations(0);
trainer.batch_size = 10;
trainer.train(
&NeuraBackprop::new(CrossEntropy),
&mut network,
inputs.clone(),
&test_inputs,
);
neuramethyst::draw_neuron_activation(
|input| {
let output = network.eval(&dvector![input[0], input[1]]);
let estimation = output[0] / (output[0] + output[1]);
let color = network.eval(&dvector![input[0], input[1]]);
(&color / color.map(|x| x * x).sum() * estimation)
.into_iter()
.map(|x| x.abs() as f32)
.collect::<Vec<_>>()
},
6.0,
);
println!("{}", epoch);
std::thread::sleep(std::time::Duration::new(0, 50_000_000));
}
} else {
let mut trainer = NeuraBatchedTrainer::new(0.03, 20 * 50);
trainer.batch_size = 10;
trainer.log_iterations = 20;
plot_losses(
trainer.train(
&NeuraBackprop::new(CrossEntropy),
&mut network,
inputs.clone(),
&test_inputs,
),
128,
48,
);
// println!("{}", String::from("\n").repeat(64));
// draw_neuron_activation(|input| network.eval(&input).into_iter().collect(), 6.0);
}
let mut file = std::fs::File::create("target/bivariate.csv").unwrap();
for (input, _target) in test_inputs {
let guess = neuramethyst::argmax(network.eval(&input).as_slice());
writeln!(&mut file, "{},{},{}", input[0], input[1], guess).unwrap();
}
}