triton_grow 2.2.7

A self sustaining growing neural net that can repair itself until reaching a desired accuracy
Documentation 
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use rand_distr::Distribution;
use triton_grow::{network::{network::Network, activations::Activations, layer::{layers::LayerTypes, conv::Convolutional, distributions::Distributions}, input::*, matrix::Matrix, matrix3d::Matrix3D}, helper::{mnist::MnistEntry, categorical::to_categorical}};

fn main() {
    //Convolutional Example
    //
    /*
    let mut inputs: Vec<&dyn Input> = vec![];
    let mut outputs: Vec<Vec<f32>>;
    let mut true_outputs: Vec<Vec<f32>> = vec![];

    let inputs_undyn: Vec<Matrix>;
    let outputs_uncat: Vec<usize>;

    println!("Generating MNIST....");
    (inputs_undyn, outputs_uncat) = MnistEntry::generate_mnist();
    println!("Done Generating MNIST");

    outputs = to_categorical(outputs_uncat);
    for i in 0..5{
        inputs.push(&inputs_undyn[i]);
        true_outputs.push(outputs[i].clone());
    }

    let mut network = Network::new(5);

    network.add_layer(LayerTypes::DENSE(784, Activations::RELU, 0.1));
    network.add_layer(LayerTypes::DENSE(64, Activations::RELU, 0.1));
    network.add_layer(LayerTypes::DENSE(32, Activations::RELU, 0.1));
    network.add_layer(LayerTypes::DENSE(10, Activations::RELU, 0.1));

    network.compile();

    network.fit(&inputs, &true_outputs, 10);
    for i in 0..inputs.len(){
        println!("predicted: {:?} \n\n actual: {:?}\n", network.predict(inputs[i]), true_outputs[i]);
    }

    network.plot_loss_history("mnist_loss.png");
    */
    //Dense Example
    //
    let mut inputs: Vec<&dyn Input> = vec![];
    let input_1 = vec![1.0,1.0];
    let input_2 = vec![vec![0.0], vec![1.0]];
    let input_3 = Matrix::from(vec![vec![1.0],vec![0.0]]);
    let input_4 = Matrix3D::from(vec![vec![vec![0.0,0.0]]]);
    inputs.push(&input_1);
    inputs.push(&input_2);
    inputs.push(&input_3);
    inputs.push(&input_4);
    
    let outputs: Vec<Vec<f32>> = vec![vec![0.0],vec![1.0],vec![1.0], vec![0.0]];

    let mut new_net = Network::new(4);


    new_net.add_layer(LayerTypes::DENSE(2, Activations::SIGMOID, 0.1));
    new_net.add_layer(LayerTypes::DENSE(3, Activations::SIGMOID, 0.1));
    new_net.add_layer(LayerTypes::DENSE(1, Activations::SOFTMAX, 0.1));

    //new_net.set_seed("teller");
    //new_net.set_seed("I said");
    new_net.compile();

    new_net.fit(&inputs, &outputs, 100);

    println!("1 and 0: {:?}", new_net.predict(&vec![1.0,0.0])[0]);
    println!("0 and 1: {:?}", new_net.predict(&vec![0.0,1.0])[0]);
    println!("1 and 1: {:?}", new_net.predict(&vec![1.0,1.0])[0]);
    println!("0 and 0: {:?}", new_net.predict(&vec![0.0,0.0])[0]);
}