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117 lines
3.7 KiB
117 lines
3.7 KiB
#![feature(generic_arg_infer)]
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use std::io::Write;
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#[allow(unused_imports)]
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use neuramethyst::derivable::activation::{LeakyRelu, Linear, Relu, Tanh};
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use neuramethyst::derivable::loss::CrossEntropy;
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use neuramethyst::derivable::regularize::NeuraL1;
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use neuramethyst::prelude::*;
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use rand::Rng;
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fn main() {
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let mut network = neura_sequential![
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neura_layer!("dense", 2, 8; Relu, NeuraL1(0.001)),
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neura_layer!("dropout", 0.25),
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neura_layer!("dense", 2; Linear, NeuraL1(0.001)),
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neura_layer!("softmax"),
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];
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let inputs = (0..1).cycle().map(move |_| {
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let mut rng = rand::thread_rng(); // TODO: move out
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let category = rng.gen_bool(0.5) as usize;
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let (x, y) = if category == 0 {
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let radius: f64 = rng.gen_range(0.0..2.0);
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let angle = rng.gen_range(0.0..std::f64::consts::TAU);
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(angle.cos() * radius, angle.sin() * radius)
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} else {
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let radius: f64 = rng.gen_range(3.0..5.0);
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let angle = rng.gen_range(0.0..std::f64::consts::TAU);
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(angle.cos() * radius, angle.sin() * radius)
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};
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([x, y].into(), neuramethyst::one_hot::<2>(category))
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});
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let test_inputs: Vec<_> = inputs.clone().take(10).collect();
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if std::env::args().any(|arg| arg == "draw") {
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for epoch in 0..200 {
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let mut trainer = NeuraBatchedTrainer::new(0.03, 10);
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trainer.batch_size = 10;
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trainer.train(
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NeuraBackprop::new(CrossEntropy),
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&mut network,
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inputs.clone(),
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&test_inputs,
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);
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let network = network.clone();
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draw_neuron_activation(
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|input| network.eval(&input.into()).into_iter().collect(),
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6.0,
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);
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println!("{}", epoch);
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std::thread::sleep(std::time::Duration::new(0, 50_000_000));
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}
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} else {
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let mut trainer = NeuraBatchedTrainer::new(0.03, 20 * 50);
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trainer.batch_size = 10;
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trainer.log_iterations = 20;
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trainer.train(
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NeuraBackprop::new(CrossEntropy),
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&mut network,
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inputs.clone(),
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&test_inputs,
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);
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// println!("{}", String::from("\n").repeat(64));
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// draw_neuron_activation(|input| network.eval(&input).into_iter().collect(), 6.0);
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}
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let mut file = std::fs::File::create("target/bivariate.csv").unwrap();
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for (input, _target) in test_inputs {
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let guess = neuramethyst::argmax(network.eval(&input).as_ref());
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writeln!(&mut file, "{},{},{}", input[0], input[1], guess).unwrap();
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}
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}
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// TODO: move this to the library?
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fn draw_neuron_activation<F: Fn([f64; 2]) -> Vec<f64>>(callback: F, scale: f64) {
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use viuer::Config;
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const WIDTH: u32 = 64;
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const HEIGHT: u32 = 64;
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let mut image = image::RgbImage::new(WIDTH, HEIGHT);
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fn sigmoid(x: f64) -> f64 {
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1.0 / (1.0 + (-x * 3.0).exp())
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}
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for y in 0..HEIGHT {
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let y2 = 2.0 * y as f64 / HEIGHT as f64 - 1.0;
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for x in 0..WIDTH {
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let x2 = 2.0 * x as f64 / WIDTH as f64 - 1.0;
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let activation = callback([x2 * scale, y2 * scale]);
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let r = (sigmoid(activation.get(0).copied().unwrap_or(-1.0)) * 255.0).floor() as u8;
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let g = (sigmoid(activation.get(1).copied().unwrap_or(-1.0)) * 255.0).floor() as u8;
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let b = (sigmoid(activation.get(2).copied().unwrap_or(-1.0)) * 255.0).floor() as u8;
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*image.get_pixel_mut(x, y) = image::Rgb([r, g, b]);
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}
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}
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let config = Config {
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use_kitty: false,
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// absolute_offset: false,
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..Default::default()
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};
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viuer::print(&image::DynamicImage::ImageRgb8(image), &config).unwrap();
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}
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