neuralib 0.0.3

A simple Neural Network Library in Rust
Documentation 
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use crate::activation::Activation;
use crate::neuron::Neuron;

#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};

#[derive(Debug, Serialize, Deserialize)]
pub struct Layer {
    neurons: Vec<Neuron>,
    neuron_count: usize,
    input_size: usize,
}

impl Layer {
    pub fn new(input_size: usize, layer_size: usize, activation: Activation) -> Layer {
        Layer {
            neuron_count: layer_size,
            neurons: (0..layer_size)
                .map(|_| Neuron::new(input_size, activation.clone()))
                .collect(),
            input_size,
        }
    }

    pub fn activate(&mut self, inputs: &[f64]) -> crate::error::Result<Vec<f64>> {
        if inputs.len() != self.input_size {
            return Err(crate::error::InputSizeError {
                inputted: inputs.len(),
                expected: self.input_size,
                chain_depth: "Layer".to_owned(),
            }
            .into());
        }

        Ok(self
            .neurons
            .iter_mut()
            .map(|neuron| {
                neuron
                    .activate(inputs)
                    .expect("Length was already checked. This should not fail. (Layer)")
            })
            .collect())
    }

    pub fn update_gradients_output(&mut self, expected_outputs: &[f64]) {
        for (neuron, output) in self.neurons.iter_mut().zip(expected_outputs) {
            neuron.calculate_deriv_output(output);
            neuron.update_gradients();
        }
    }

    pub fn update_gradients_hidden(&mut self, next_layer: &Layer) {
        for (neuronidx, neuron) in self.neurons.iter_mut().enumerate() {
            neuron.calculate_deriv_hidden(next_layer, neuronidx);
            neuron.update_gradients();
        }
    }

    pub fn get_neuron_count(&self) -> usize {
        self.neuron_count
    }

    pub fn get_neuron(&self, idx: usize) -> Option<&Neuron> {
        self.neurons.get(idx)
    }

    pub fn get_neuron_mut(&mut self, idx: usize) -> Option<&mut Neuron> {
        self.neurons.get_mut(idx)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn layer() {
        let mut layer = Layer::new(1, 2, crate::activation::Activation::Linear);

        assert!(layer.activate(&[]).is_err());
        layer.activate(&[1.0]).unwrap();

        assert_eq!(layer.get_neuron_count(), 2);
    }
}