Struct rusty_machine::learning::nnet::NeuralNet

pub struct NeuralNet<T, A>where
    T: Criterion,
    A: OptimAlgorithm<BaseNeuralNet<T>>,{ /* private fields */ }

Neural Network Model

The Neural Network struct specifies a Criterion and a gradient descent algorithm.

Implementations

impl NeuralNet<BCECriterion, StochasticGD>

pub fn default(layer_sizes: &[usize]) -> NeuralNet<BCECriterion, StochasticGD>

Creates a neural network with the specified layer sizes.

The layer sizes slice should include the input, hidden layers, and output layer sizes. The type of activation function must be specified.

Uses the default settings (stochastic gradient descent and sigmoid activation function).

Examples

use rusty_machine::learning::nnet::NeuralNet;

// Create a neural net with 4 layers, 3 neurons in each.
let layers = &[3; 4];
let mut net = NeuralNet::default(layers);

impl<T, A> NeuralNet<T, A>where
    T: Criterion,
    A: OptimAlgorithm<BaseNeuralNet<T>>,

pub fn new(criterion: T, alg: A) -> NeuralNet<T, A>

Create a new neural network with no layers

Examples

use rusty_machine::learning::nnet::BCECriterion;
use rusty_machine::learning::nnet::NeuralNet;
use rusty_machine::learning::optim::grad_desc::StochasticGD;

// Create a an empty neural net
let mut net = NeuralNet::new(BCECriterion::default(), StochasticGD::default());

pub fn mlp<U>(
    layer_sizes: &[usize],
    criterion: T,
    alg: A,
    activ_fn: U
) -> NeuralNet<T, A>where
    U: ActivationFunc + 'static,

Create a multilayer perceptron with the specified layer sizes.

The layer sizes slice should include the input, hidden layers, and output layer sizes. The type of activation function must be specified.

Currently defaults to simple batch Gradient Descent for optimization.

Examples

use rusty_machine::learning::nnet::BCECriterion;
use rusty_machine::learning::nnet::NeuralNet;
use rusty_machine::learning::toolkit::activ_fn::Sigmoid;
use rusty_machine::learning::optim::grad_desc::StochasticGD;

// Create a neural net with 4 layers, 3 neurons in each.
let layers = &[3; 4];
let mut net = NeuralNet::mlp(layers, BCECriterion::default(), StochasticGD::default(), Sigmoid);

pub fn add<'a>(&'a mut self, layer: Box<dyn NetLayer>) -> &'a mut NeuralNet<T, A>

Adds the specified layer to the end of the network

Examples

use rusty_machine::linalg::BaseMatrix;
use rusty_machine::learning::nnet::BCECriterion;
use rusty_machine::learning::nnet::NeuralNet;
use rusty_machine::learning::nnet::net_layer::Linear;
use rusty_machine::learning::optim::grad_desc::StochasticGD;

// Create a new neural net 
let mut net = NeuralNet::new(BCECriterion::default(), StochasticGD::default());

// Give net an input layer of size 3, hidden layer of size 4, and output layer of size 5
// This net will not apply any activation function to the Linear layer outputs
net.add(Box::new(Linear::new(3, 4)))
   .add(Box::new(Linear::new(4, 5)));

pub fn add_layers<'a, U>(&'a mut self, layers: U) -> &'a mut NeuralNet<T, A>where
    U: IntoIterator<Item = Box<dyn NetLayer>>,

Adds multiple layers to the end of the network

Examples

use rusty_machine::linalg::BaseMatrix;
use rusty_machine::learning::nnet::BCECriterion;
use rusty_machine::learning::nnet::NeuralNet;
use rusty_machine::learning::nnet::net_layer::{NetLayer, Linear};
use rusty_machine::learning::toolkit::activ_fn::Sigmoid;
use rusty_machine::learning::optim::grad_desc::StochasticGD;

// Create a new neural net 
let mut net = NeuralNet::new(BCECriterion::default(), StochasticGD::default());

let linear_sig: Vec<Box<NetLayer>> = vec![Box::new(Linear::new(5, 5)), Box::new(Sigmoid)];

// Give net a layer of size 5, followed by a Sigmoid activation function
net.add_layers(linear_sig);

pub fn get_net_weights(&self, idx: usize) -> MatrixSlice<'_, f64>

Gets matrix of weights between specified layer and forward layer.

Examples

use rusty_machine::linalg::BaseMatrix;
use rusty_machine::learning::nnet::NeuralNet;

// Create a neural net with 4 layers, 3 neurons in each.
let layers = &[3; 4];
let mut net = NeuralNet::default(layers);

let w = &net.get_net_weights(2);

// We add a bias term to the weight matrix
assert_eq!(w.rows(), 4);
assert_eq!(w.cols(), 3);

Trait Implementations

impl<T: Debug, A: Debug> Debug for NeuralNet<T, A>where
    T: Criterion,
    A: OptimAlgorithm<BaseNeuralNet<T>>,

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<T, A> SupModel<Matrix<f64>, Matrix<f64>> for NeuralNet<T, A>where
    T: Criterion,
    A: OptimAlgorithm<BaseNeuralNet<T>>,

Supervised learning for the Neural Network.

The model is trained using back propagation.

fn predict(&self, inputs: &Matrix<f64>) -> LearningResult<Matrix<f64>>

Predict neural network output using forward propagation.

fn train(
    &mut self,
    inputs: &Matrix<f64>,
    targets: &Matrix<f64>
) -> LearningResult<()>

Train the model using gradient optimization and back propagation.