rust-lstm 0.8.0

A complete LSTM neural network library with training capabilities, multiple optimizers, and peephole variants.
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
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171

use ndarray::{Array1, Array2};

/// Loss function trait for training neural networks
pub trait LossFunction {
    /// Compute the loss between predictions and targets
    fn compute_loss(&self, predictions: &Array2<f64>, targets: &Array2<f64>) -> f64;
    
    /// Compute the gradient of the loss with respect to predictions
    fn compute_gradient(&self, predictions: &Array2<f64>, targets: &Array2<f64>) -> Array2<f64>;

    /// Compute batch loss for multiple predictions and targets
    /// Default implementation computes average loss across batch
    fn compute_batch_loss(&self, predictions: &Array2<f64>, targets: &Array2<f64>) -> f64 {
        let batch_size = predictions.ncols();
        let mut total_loss = 0.0;

        for i in 0..batch_size {
            let pred_col = predictions.column(i).to_owned().insert_axis(ndarray::Axis(1));
            let target_col = targets.column(i).to_owned().insert_axis(ndarray::Axis(1));
            total_loss += self.compute_loss(&pred_col, &target_col);
        }

        total_loss / batch_size as f64
    }

    /// Compute batch gradients for multiple predictions and targets
    /// Default implementation computes gradients for each sample and concatenates
    fn compute_batch_gradient(&self, predictions: &Array2<f64>, targets: &Array2<f64>) -> Array2<f64> {
        let batch_size = predictions.ncols();
        let mut batch_gradients = Array2::zeros(predictions.raw_dim());

        for i in 0..batch_size {
            let pred_col = predictions.column(i).to_owned().insert_axis(ndarray::Axis(1));
            let target_col = targets.column(i).to_owned().insert_axis(ndarray::Axis(1));
            let grad = self.compute_gradient(&pred_col, &target_col);
            batch_gradients.column_mut(i).assign(&grad.column(0));
        }

        batch_gradients
    }
}

/// Mean Squared Error loss function
pub struct MSELoss;

impl LossFunction for MSELoss {
    fn compute_loss(&self, predictions: &Array2<f64>, targets: &Array2<f64>) -> f64 {
        let diff = predictions - targets;
        let squared_diff = &diff * &diff;
        squared_diff.sum() / (predictions.len() as f64)
    }
    
    fn compute_gradient(&self, predictions: &Array2<f64>, targets: &Array2<f64>) -> Array2<f64> {
        let diff = predictions - targets;
        2.0 * diff / (predictions.len() as f64)
    }

    fn compute_batch_loss(&self, predictions: &Array2<f64>, targets: &Array2<f64>) -> f64 {
        let diff = predictions - targets;
        let squared_diff = &diff * &diff;
        squared_diff.sum() / (predictions.len() as f64)
    }

    fn compute_batch_gradient(&self, predictions: &Array2<f64>, targets: &Array2<f64>) -> Array2<f64> {
        let diff = predictions - targets;
        2.0 * diff / (predictions.len() as f64)
    }
}

/// Mean Absolute Error loss function
pub struct MAELoss;

impl LossFunction for MAELoss {
    fn compute_loss(&self, predictions: &Array2<f64>, targets: &Array2<f64>) -> f64 {
        let diff = predictions - targets;
        diff.map(|x| x.abs()).sum() / (predictions.len() as f64)
    }
    
    fn compute_gradient(&self, predictions: &Array2<f64>, targets: &Array2<f64>) -> Array2<f64> {
        let diff = predictions - targets;
        diff.map(|x| if *x > 0.0 { 1.0 } else if *x < 0.0 { -1.0 } else { 0.0 }) / (predictions.len() as f64)
    }

    fn compute_batch_loss(&self, predictions: &Array2<f64>, targets: &Array2<f64>) -> f64 {
        let diff = predictions - targets;
        diff.map(|x| x.abs()).sum() / (predictions.len() as f64)
    }

    fn compute_batch_gradient(&self, predictions: &Array2<f64>, targets: &Array2<f64>) -> Array2<f64> {
        let diff = predictions - targets;
        diff.map(|x| if *x > 0.0 { 1.0 } else if *x < 0.0 { -1.0 } else { 0.0 }) / (predictions.len() as f64)
    }
}

/// Cross-Entropy Loss with softmax
pub struct CrossEntropyLoss;

impl LossFunction for CrossEntropyLoss {
    fn compute_loss(&self, predictions: &Array2<f64>, targets: &Array2<f64>) -> f64 {
        let softmax_preds = softmax(predictions);
        let epsilon = 1e-15;
        let log_preds = softmax_preds.map(|x| (x + epsilon).ln());
        -(targets * log_preds).sum() / (predictions.shape()[1] as f64)
    }
    
    fn compute_gradient(&self, predictions: &Array2<f64>, targets: &Array2<f64>) -> Array2<f64> {
        let softmax_preds = softmax(predictions);
        (softmax_preds - targets) / (predictions.shape()[1] as f64)
    }
}

/// Numerically stable softmax function
pub fn softmax(x: &Array2<f64>) -> Array2<f64> {
    let mut result = Array2::zeros(x.raw_dim());
    
    for (i, col) in x.axis_iter(ndarray::Axis(1)).enumerate() {
        let max_val = col.iter().max_by(|a, b| a.partial_cmp(b).unwrap()).unwrap();
        let exp_vals: Array1<f64> = col.map(|&val| (val - max_val).exp());
        let sum_exp = exp_vals.sum();
        
        for (j, &exp_val) in exp_vals.iter().enumerate() {
            result[[j, i]] = exp_val / sum_exp;
        }
    }
    
    result
}

#[cfg(test)]
mod tests {
    use super::*;
    use ndarray::arr2;

    #[test]
    fn test_mse_loss() {
        let loss_fn = MSELoss;
        let predictions = arr2(&[[1.0, 2.0], [3.0, 4.0]]);
        let targets = arr2(&[[1.5, 2.5], [2.5, 3.5]]);
        
        let loss = loss_fn.compute_loss(&predictions, &targets);
        assert!((loss - 0.25).abs() < 1e-6);
        
        let gradient = loss_fn.compute_gradient(&predictions, &targets);
        assert_eq!(gradient.shape(), predictions.shape());
    }

    #[test]
    fn test_mae_loss() {
        let loss_fn = MAELoss;
        let predictions = arr2(&[[1.0, 2.0], [3.0, 4.0]]);
        let targets = arr2(&[[1.5, 2.5], [2.5, 3.5]]);
        
        let loss = loss_fn.compute_loss(&predictions, &targets);
        assert!((loss - 0.5).abs() < 1e-6);
        
        let gradient = loss_fn.compute_gradient(&predictions, &targets);
        assert_eq!(gradient.shape(), predictions.shape());
    }

    #[test]
    fn test_softmax() {
        let input = arr2(&[[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]]);
        let output = softmax(&input);
        
        // Each column should sum to 1
        for col in output.axis_iter(ndarray::Axis(1)) {
            let sum: f64 = col.sum();
            assert!((sum - 1.0).abs() < 1e-6);
        }
    }
}