60 lines
1.7 KiB
Rust
60 lines
1.7 KiB
Rust
extern crate rust_nn;
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use ndarray::array;
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use rust_nn::functions::{activation_functions, loss_functions};
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use rust_nn::layers::activation_layer::ActivationLayer;
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use rust_nn::layers::fc_layer::{FCLayer, Initializer};
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use rust_nn::Network;
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fn main() {
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// training data
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let x_train = vec![
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array![0.0, 0.0],
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array![0.0, 1.0],
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array![1.0, 0.0],
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array![1.0, 1.0],
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];
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let y_train = vec![array![0.0], array![1.0], array![1.0], array![0.0]];
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// test data
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let x_test = vec![
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array![0.0, 0.0],
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array![0.0, 1.0],
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array![1.0, 0.0],
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array![1.0, 1.0],
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];
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// initialize neural network
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let mut network = Network::new(loss_functions::Type::MSE);
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// add layers
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network.add_layer(Box::new(FCLayer::new(
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3,
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Initializer::Gaussian(0.0, 1.0),
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Initializer::Gaussian(0.0, 1.0),
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)));
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network.add_layer(Box::new(ActivationLayer::new(
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activation_functions::Type::Tanh,
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)));
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network.add_layer(Box::new(FCLayer::new(
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1,
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Initializer::Gaussian(0.0, 1.0),
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Initializer::Gaussian(0.0, 1.0),
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)));
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network.add_layer(Box::new(ActivationLayer::new(
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activation_functions::Type::Tanh,
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)));
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// train network on training data
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network.fit(x_train, y_train, 1000, 0.1, false);
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// print predictions
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let y_test = network.predict(x_test.clone());
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println!("{}", x_test.get(0).unwrap());
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for i in 0..y_test.len() {
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print!("input: {}\t\t", x_test.get(i).unwrap());
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let mut prediction = y_test.get(i).unwrap().to_owned();
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// comment the following line to see the exact predictions
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prediction.map_mut(|x| *x = x.round());
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print!("prediction: {}\n", prediction);
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}
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}
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