Struct neuroflow::FeedForward

pub struct FeedForward { /* fields omitted */ }

Feed Forward (multilayer perceptron) neural network that is trained by back propagation algorithm. You can use it for approximation and classification tasks as well.

Examples

In order to create FeedForward instance call its constructor new.

The constructor accepts slice as an argument. This slice determines the architecture of network. First element in slice is amount of neurons in input layer and the last one is amount of neurons in output layer. Denote, that vector of input data must have the equal length as input layer of FeedForward neural network (the same is for expected output vector).

use neuroflow::FeedForward;

let mut nn = FeedForward::new(&[1, 3, 2]);

Then you can train your network simultaneously via fit method:

nn.fit(&[1.2], &[0.2, 0.8]);

Or to use train method with neuroflow::data::DataSet struct:

use neuroflow::data::DataSet;

let mut data = DataSet::new();
data.push(&[1.2], &[1.3, -0.2]);
nn.train(&data, 30_000); // 30_000 is iterations count

It is possible to set parameters of network: ```rust

use neuroflow::FeedForward;

let mut nn = FeedForward::new(&[1, 3, 2]);

nn.learning_rate(0.1) .momentum(0.05) .activation(neuroflow::activators::Type::Tanh); ```

Call method calc in order to calculate value by your(already trained) network:

let d: Vec<f64> = nn.calc(&[1.02]).to_vec();

Methods

impl FeedForward

fn new(architecture: &[i32]) -> FeedForward

The constructor of FeedForward struct

• architecture: &[i32] - the architecture of network where each element in slice represents amount of neurons in this layer. First element in slice is amount of neurons in input layer and the last one is amount of neurons in output layer. Denote, that vector of input data must have the equal length as input layer of FeedForward neural network (the same is for expected output vector).

• return - FeedForward struct

  Example

use neuroflow::FeedForward;
let mut nn = FeedForward::new(&[1, 3, 2]);

fn bind(&mut self, layer: usize, neuron: usize)

Bind a new neuron to layer. It initializes neuron with random weights.

• layer: usize - index of layer. NOTE, layer indexing starts from 1!
• neuron: usize - index of neuron. NOTE, neurons indexing in layer starts from 0!

Examples

nn.bind(2, 0);

fn unbind(&mut self, layer: usize, neuron: usize)

Unbind neuron from layer.

• layer: usize - index of layer. NOTE, layer indexing starts from 1!
• neuron: usize - index of neuron. NOTE, neurons indexing in layer starts from 0!

Examples

nn.unbind(2, 0);

fn train<T>(&mut self, data: &T, iterations: i64) where
    T: Extractable, 

Train neural network by bulked data.

• data: &T - the link on data that implements neuroflow::data::Extractable trait;
• iterations: i64 - iterations count.

Examples

let mut d = neuroflow::data::DataSet::new();
d.push(&[1.2], &[1.3, -0.2]);
nn.train(&d, 30_000);

fn fit(&mut self, X: &[f64], d: &[f64])

Train neural network simultaneously step by step

• X: &[f64] - slice of input data;
• d: &[f64] - expected output.

Examples

nn.fit(&[3.0], &[3.0, 5.0]);

fn calc(&mut self, X: &[f64]) -> &[f64]

Calculate the response by trained neural network.

• X: &[f64] - slice of input data;
• return -> &[f64] - slice of calculated data.

Examples

let v: Vec<f64> = nn.calc(&[1.02]).to_vec();

fn activation(&mut self, func: Type) -> &mut FeedForward

Choose activation function.

• func: neuroflow::activators::Type - enum element that indicates which function to use;
• return -> &mut FeedForward - link on the current struct.

fn learning_rate(&mut self, learning_rate: f64) -> &mut FeedForward

Set the learning rate of network.

• learning_rate: f64 - learning rate;
• return -> &mut FeedForward - link on the current struct.

Examples

nn.learning_rate(0.1);

fn momentum(&mut self, momentum: f64) -> &mut FeedForward

Set the momentum of network.

• momentum: f64 - momentum;
• return -> &mut FeedForward - link on the current struct.

Example

nn.momentum(0.05);

Trait Implementations

impl Display for FeedForward

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

Formats the value using the given formatter.