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rustfmt

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This commit is contained in:
lluni 2023-01-21 15:19:55 +01:00
parent d80bd3c5e5
commit 2f3745a31c
Signed by: lluni
GPG key ID: ACEEB468BC325D35
7 changed files with 105 additions and 61 deletions
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40
examples/example_sine.rs
View file

@ -3,20 +3,24 @@ extern crate rust_nn;
use std::error::Error;
use std::f64::consts::PI;
use ndarray_rand::RandomExt;
use ndarray::Array1;
use ndarray_rand::rand_distr::Uniform;
use ndarray_rand::RandomExt;
use plotters::prelude::*;
use rust_nn::Network;
use rust_nn::functions::{activation_functions, loss_functions};
use rust_nn::layers::activation_layer::ActivationLayer;
use rust_nn::layers::fc_layer::{FCLayer, Initializer};
use ndarray::Array1;
use rust_nn::Network;
fn main() -> Result<(), Box<dyn Error>> {
// training data
let training_interval = (0.0f64, 2.0f64 * PI);
let steps = 100000;
let training_values = Array1::random(steps, Uniform::new(training_interval.0, training_interval.1)).to_vec();
let training_values = Array1::random(
steps,
Uniform::new(training_interval.0, training_interval.1),
)
.to_vec();
let mut x_train = Vec::new();
let mut y_train = Vec::new();
for x in training_values {
@ -42,19 +46,23 @@ fn main() -> Result<(), Box<dyn Error>> {
network.add_layer(Box::new(FCLayer::new(
8,
Initializer::GaussianWFactor(0.0, 1.0, 0.1),
Initializer::GaussianWFactor(0.0, 1.0, 0.1)
Initializer::GaussianWFactor(0.0, 1.0, 0.1),
)));
network.add_layer(Box::new(ActivationLayer::new(
activation_functions::Type::LeakyRelu,
)));
network.add_layer(Box::new(ActivationLayer::new(activation_functions::Type::LeakyRelu)));
network.add_layer(Box::new(FCLayer::new(
8,
Initializer::GaussianWFactor(0.0, 1.0, 0.1),
Initializer::GaussianWFactor(0.0, 1.0, 0.1)
Initializer::GaussianWFactor(0.0, 1.0, 0.1),
)));
network.add_layer(Box::new(ActivationLayer::new(
activation_functions::Type::LeakyRelu,
)));
network.add_layer(Box::new(ActivationLayer::new(activation_functions::Type::LeakyRelu)));
network.add_layer(Box::new(FCLayer::new(
1,
Initializer::GaussianWFactor(0.0, 1.0, 0.1),
Initializer::GaussianWFactor(0.0, 1.0, 0.1)
Initializer::GaussianWFactor(0.0, 1.0, 0.1),
)));
// train network on training data
@ -79,20 +87,26 @@ fn main() -> Result<(), Box<dyn Error>> {
.draw()?;
// add the first plot
let data1: Vec<(f64,f64)> = x_test.iter().zip(y_test_true.iter())
let data1: Vec<(f64, f64)> = x_test
.iter()
.zip(y_test_true.iter())
.map(|(x, y)| (x[0], y[0]))
.collect();
chart
.draw_series(LineSeries::new(data1, &RED)).unwrap()
.draw_series(LineSeries::new(data1, &RED))
.unwrap()
.label("true values")
.legend(|(x, y)| PathElement::new(vec![(x, y), (x + 1, y)], &RED));
// add the second plot
let data2: Vec<(f64,f64)> = x_test.iter().zip(y_test_pred.iter())
let data2: Vec<(f64, f64)> = x_test
.iter()
.zip(y_test_pred.iter())
.map(|(x, y)| (x[0], y[0]))
.collect();
chart
.draw_series(LineSeries::new(data2, &BLUE)).unwrap()
.draw_series(LineSeries::new(data2, &BLUE))
.unwrap()
.label("predicted values")
.legend(|(x, y)| PathElement::new(vec![(x, y), (x + 1, y)], &BLUE));

31
examples/example_xor.rs
View file

@ -1,10 +1,10 @@
extern crate rust_nn;
use rust_nn::Network;
use ndarray::array;
use rust_nn::functions::{activation_functions, loss_functions};
use rust_nn::layers::activation_layer::ActivationLayer;
use rust_nn::layers::fc_layer::{FCLayer, Initializer};
use ndarray::array;
use rust_nn::Network;
fn main() {
// training data
@ -12,20 +12,15 @@ fn main() {
array![0.0, 0.0],
array![0.0, 1.0],
array![1.0, 0.0],
array![1.0, 1.0]
];
let y_train = vec![
array![0.0],
array![1.0],
array![1.0],
array![0.0]
array![1.0, 1.0],
];
let y_train = vec![array![0.0], array![1.0], array![1.0], array![0.0]];
// test data
let x_test= vec![
let x_test = vec![
array![0.0, 0.0],
array![0.0, 1.0],
array![1.0, 0.0],
array![1.0, 1.0]
array![1.0, 1.0],
];
// initialize neural network
@ -35,15 +30,19 @@ fn main() {
network.add_layer(Box::new(FCLayer::new(
3,
Initializer::Gaussian(0.0, 1.0),
Initializer::Gaussian(0.0, 1.0)
Initializer::Gaussian(0.0, 1.0),
)));
network.add_layer(Box::new(ActivationLayer::new(
activation_functions::Type::Tanh,
)));
network.add_layer(Box::new(ActivationLayer::new(activation_functions::Type::Tanh)));
network.add_layer(Box::new(FCLayer::new(
1,
Initializer::Gaussian(0.0, 1.0),
Initializer::Gaussian(0.0, 1.0)
Initializer::Gaussian(0.0, 1.0),
)));
network.add_layer(Box::new(ActivationLayer::new(
activation_functions::Type::Tanh,
)));
network.add_layer(Box::new(ActivationLayer::new(activation_functions::Type::Tanh)));
// train network on training data
network.fit(x_train, y_train, 1000, 0.1, false);
@ -58,4 +57,4 @@ fn main() {
prediction.map_mut(|x| *x = x.round());
print!("prediction: {}\n", prediction);
}
}
}