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56 lines
1.4 KiB
56 lines
1.4 KiB
#![feature(generic_arg_infer)]
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use nalgebra::dvector;
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use neuramethyst::cycle_shuffling;
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use neuramethyst::derivable::activation::Relu;
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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).activation(Relu),
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neura_layer!("dense", 3).activation(Relu),
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neura_layer!("dense", 1).activation(Relu)
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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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for (input, target) in &inputs {
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println!(
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"Input: {:?}, target: {}, actual: {:.3}",
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&input,
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target[0],
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network.eval(&input)[0]
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);
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}
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let mut trainer = NeuraBatchedTrainer::new().learning_rate(0.05).iterations(0);
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trainer.batch_size = 6;
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trainer.log_iterations = 250;
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trainer.learning_momentum = 0.01;
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trainer.train(
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&NeuraBackprop::new(Euclidean),
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&mut network,
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cycle_shuffling(inputs.iter().cloned(), rand::thread_rng()),
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&inputs,
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);
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for (input, target) in inputs {
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println!(
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"Input: {:?}, target: {}, actual: {:.3}",
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&input,
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target[0],
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network.eval(&input)[0]
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);
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}
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}
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