rustyml 0.13.0

A high-performance machine learning & deep learning library in pure Rust, offering ML algorithms and neural network support
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33

//! Shared input/weight validation for the layer module

use crate::error::{Error, NnError};

/// Validates that a weight array assigned to a layer has the shape the layer expects
///
/// Layers initialize their weight arrays with the correct shape in `new()`, so loaded or
/// user-supplied weights must match that shape. This turns a silent corruption (and a later
/// opaque panic deep inside `dot`) into a clear, recoverable error
///
/// # Parameters
///
/// - `name` - Name of the parameter being set, used in the error message, e.g. "weight" or "bias"
/// - `expected` - Shape the layer currently has for this parameter
/// - `found` - Shape of the array being assigned
///
/// # Errors
///
/// - `Error::NeuralNetwork(NnError::WeightShape)` if the shapes differ
pub(super) fn validate_weight_shape(
    name: &str,
    expected: &[usize],
    found: &[usize],
) -> crate::error::RustymlResult<()> {
    if expected != found {
        return Err(Error::NeuralNetwork(NnError::WeightShape {
            name: name.to_string(),
            expected: expected.to_vec(),
            found: found.to_vec(),
        }));
    }
    Ok(())
}