rustyml 0.13.0

A high-performance machine learning & deep learning library in pure Rust, offering ML algorithms and neural network support
Documentation 
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//! Weight container for the Conv3D layer

use crate::error::Error;
use crate::neural_network::layers::convolution::conv_3d::Conv3D;
use crate::neural_network::traits::ApplyWeights;
use ndarray::{Array2, Array5};
use serde::{Deserialize, Serialize};
use std::borrow::Cow;

/// Weights of a Conv3D layer
///
/// Stored as [`Cow`] so saving borrows the live layer arrays without cloning, while loading
/// deserializes them into owned arrays
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Conv3DLayerWeight<'a> {
    /// 5D convolution kernel with shape \[filters, channels, kernel_depth, kernel_height, kernel_width\]
    pub weight: Cow<'a, Array5<f32>>,
    /// Bias matrix with shape \[1, filters\]
    pub bias: Cow<'a, Array2<f32>>,
}

impl ApplyWeights<Conv3D> for Conv3DLayerWeight<'_> {
    fn apply_to_layer(&self, layer: &mut Conv3D) -> Result<(), Error> {
        layer.set_weights((*self.weight).clone(), (*self.bias).clone())?;
        Ok(())
    }
}