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43 lines
1012 B
43 lines
1012 B
2 years ago
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#![feature(generic_arg_infer)]
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use neuramethyst::prelude::*;
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use neuramethyst::derivable::activation::{Relu, Tanh};
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use neuramethyst::derivable::loss::Euclidean;
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fn main() {
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let mut network = neura_network![
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neura_layer!("dense", Tanh, 2, 2),
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neura_layer!("dense", Tanh, 3),
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neura_layer!("dense", Relu, 1)
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];
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let inputs = [
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([0.0, 0.0], [0.0]),
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([0.0, 1.0], [1.0]),
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([1.0, 0.0], [1.0]),
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([1.0, 1.0], [0.0])
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];
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// println!("{:#?}", network);
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for (input, target) in inputs {
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println!("Input: {:?}, target: {}, actual: {}", &input, target[0], network.eval(&input)[0]);
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}
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train_batched(
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&mut network,
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inputs.clone(),
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&inputs,
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NeuraBackprop::new(Euclidean),
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0.01,
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1,
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25
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);
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// println!("{:#?}", network);
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for (input, target) in inputs {
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println!("Input: {:?}, target: {}, actual: {}", &input, target[0], network.eval(&input)[0]);
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}
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}
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