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55 lines
1.5 KiB
55 lines
1.5 KiB
#![feature(generic_arg_infer)]
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use std::fs::File;
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use nalgebra::dvector;
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use neuramethyst::derivable::activation::{Relu, Tanh};
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use neuramethyst::derivable::loss::Euclidean;
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use neuramethyst::prelude::*;
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fn main() {
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let mut network = neura_sequential![
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neura_layer!("dense", 4, f64).activation(Relu),
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neura_layer!("dense", 1, f64).activation(Tanh)
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]
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.construct(NeuraShape::Vector(2))
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.unwrap();
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let inputs = [
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(dvector![0.0, 0.0], dvector![0.0]),
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(dvector![0.0, 1.0], dvector![1.0]),
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(dvector![1.0, 0.0], dvector![1.0]),
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(dvector![1.0, 1.0], dvector![0.0]),
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];
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let mut trainer = NeuraBatchedTrainer::new().learning_rate(0.05).iterations(1);
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trainer.batch_size = 1;
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let mut parameters = vec![(
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network.layer.weights.clone(),
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network.layer.bias.clone(),
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network.child_network.layer.weights.clone(),
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network.child_network.layer.bias.clone(),
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)];
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for iteration in 0..4 {
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trainer.train(
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&NeuraBackprop::new(Euclidean),
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&mut network,
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inputs.iter().cloned().skip(iteration).take(1),
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&inputs,
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);
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parameters.push((
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network.layer.weights.clone(),
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network.layer.bias.clone(),
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network.child_network.layer.weights.clone(),
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network.child_network.layer.bias.clone(),
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));
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}
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let mut output = File::create("tests/xor.json").unwrap();
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serde_json::to_writer(&mut output, ¶meters).unwrap();
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}
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