let net = NeuralNetwork::Train(&data);

// Setup
// ----------
let mut neural_network = NeuralNetwork::new(784,&[
    Layer::new(100,Activation::Sigmoid),
    Layer::new(10,Activation::Softmax)
],None);

// Setting training and testing data
// `get_mnist_dataset(bool)` simply gets MNIST data in format of `Vec<(Vec<f32>,usize)>` where each entry is an example (tuple.0=input and tuple.1=class).
// The boolean specifies if it is the MNIST testing data (`true`) or training data (`false`).

let training_data:Vec<(Vec<f32>,usize)> = get_mnist_dataset(false);
let testing_data:Vec<(Vec<f32>,usize)> = get_mnist_dataset(true);

// Execution
// ----------
// Trains until no notable accuracy improvements are being made over a number of iterations.
// By default this would end training if 0.5% accuracy improvement was not seen over 6 iterations (often referred to as 'epochs').

neural_network.train(&training_data)
    .evaluation_data(EvaluationData::Actual(&testing_data))
    .go();

// `.evaluation_data(...)` sets the evaluation data. 
// If evaluation data is not set it will simply shuffle and split off a random group from training data to be evaluation data.
// In the case of MNIST where training and evaluation datasets are given seperately, it makes sense to set it as such.

// Evaluation
// ----------
let evaluation:(f32,u32) = neural_network.evaluate(&testing_data); // (cost,example correctly classified)