Struct rusty_machine::learning::nnet::NeuralNet

pub struct NeuralNet<'a, T, A> where T: Criterion, A: OptimAlgorithm<BaseNeuralNet<'a, T>> { /* fields omitted */ }

Neural Network Model

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

Methods

impl<'a> NeuralNet<'a, BCECriterion, StochasticGD>

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<'a, T, A> NeuralNet<'a, T, A> where T: Criterion, A: OptimAlgorithm<BaseNeuralNet<'a, T>>

fn new(layer_sizes: &'a [usize], criterion: T, alg: A) -> NeuralNet<'a, T, A>

Create a new 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.

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::optim::grad_desc::StochasticGD;

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

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<'a, T: Debug, A: Debug> Debug for NeuralNet<'a, T, A> where T: Criterion, A: OptimAlgorithm<BaseNeuralNet<'a, T>>

fn fmt(&self, __arg_0: &mut Formatter) -> Result

Formats the value using the given formatter.

impl<'a, T, A> SupModel<Matrix<f64>, Matrix<f64>> for NeuralNet<'a, T, A> where T: Criterion, A: OptimAlgorithm<BaseNeuralNet<'a, 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.