mininn 0.1.4

A minimalist deep learnig crate for rust.
Documentation 
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use ndarray::{array, Array1};

use mininn::prelude::*;

fn main() -> NNResult<()> {
    let args = std::env::args().collect::<Vec<String>>();

    let path = args.get(1);

    let train_data = array![[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0],];
    let labels = array![[0.0], [1.0], [1.0], [0.0],];

    // Create the neural network
    let mut nn = NN::new()
        .add(Dense::new(2, 3).apply(Act::Tanh))?
        .add(Dense::new(3, 1).apply(Act::Tanh))?;

    // Set the training configuration
    let train_config = TrainConfig::new()
        .with_epochs(200)
        .with_cost(Cost::MSE)
        .with_learning_rate(0.1)
        .with_batch_size(2)
        .with_verbose(true);

    // Train the neural network
    let loss = nn.train(train_data.view(), labels.view(), train_config)?;

    println!("Predictions:\n");

    let predictions: Array1<f32> = train_data
        .rows()
        .into_iter()
        .map(|input| {
            let pred = nn.predict(input.view()).unwrap();
            let out = if pred[0] >= 0.9 { 1.0 } else { 0.0 };
            println!("{} --> {}", input, out);
            out
        })
        .collect();

    // Calc metrics using MetricsCalculator
    let metrics = MetricsCalculator::new(labels.view(), predictions.view());

    println!("\nConfusion matrix:\n{}\n", metrics.confusion_matrix());

    println!(
        "Accuracy: {}\nRecall: {}\nPrecision: {}\nF1: {}\nLoss: {}",
        metrics.accuracy(),
        metrics.recall(),
        metrics.precision(),
        metrics.f1_score(),
        loss
    );

    if let Some(p) = path {
        match nn.save(p) {
            Ok(_) => println!("Model saved successfully!"),
            Err(e) => println!("Error saving model: {}", e),
        }
    }

    Ok(())
}