tests fixed, removed unused broadcast add

src/lib.rs

@@ -1,3 +1,2 @@
 pub mod net;
 pub mod dataloader;
-mod mat;

src/mat.rs

@@ -1,209 +0,0 @@
-use core::ops::Add;
-use std::fmt::Debug;
-use std::ops::AddAssign;
-use nalgebra::DMatrix;
-
-/// matrix add with broadcasting
-/// like the numpy add operation
-/// not sure I even need it anymore, after fixing inconsistencies with the matrix shapes
-/// TODO see if it's still needed, or that standard matrix addition suffices
-pub fn add<T>(v1: DMatrix<T>, v2: DMatrix<T>) -> Result<DMatrix<T>, String>
-    where T: PartialEq + Copy + Clone + Debug + Add + Add<Output=T> + AddAssign + 'static
-{
-    let (r1, c1) = v1.shape();
-    let (r2, c2) = v2.shape();
-
-    if r1 == r2 && c1 == c2 {
-        // same size, no broadcasting needed
-        Ok(v1 + v2)
-    } else if r1 == 1 && c2 == 1 {
-        Ok(DMatrix::from_fn(r2, c1, |r, c| *v1.get(c).unwrap() + *v2.get(r).unwrap()))
-    } else if c1 == 1 && r2 == 1 {
-        Ok(DMatrix::from_fn(r1, c2, |r, c| *v1.get(r).unwrap() + *v2.get(c).unwrap()))
-    } else if r1 == 1 && c1 == c2 {
-        Ok(DMatrix::from_fn(r2, c1, |r, c| *v1.get(c).unwrap() + *v2.get(c * r2 + r).unwrap()))
-    } else if r2 == 1 && c1 == c2 {
-        Ok(DMatrix::from_fn(r1, c2, |r, c| *v2.get(c).unwrap() + *v1.get(c * r1 + r).unwrap()))
-    } else if c1 == 1 && r1 == r2 {
-        Ok(DMatrix::from_fn(r1, c2, |r, c| *v1.get(r).unwrap() + *v2.get(c * r2 + r).unwrap()))
-    } else if c2 == 1 && r1 == r2 {
-        Ok(DMatrix::from_fn(r2, c1, |r, c| *v2.get(r).unwrap() + *v1.get(c * r2 + r).unwrap()))
-    } else {
-        Err(format!("ValueError: operands could not be broadcast together ({},{}), ({},{})", r1,c1, r2, c2))
-    }
-}
-
-#[cfg(test)]
-mod test {
-    use nalgebra::dmatrix;
-    use super::*;
-
-    #[test]
-    fn stretch_row_column_to_square() {
-        let v1: DMatrix<u32> = dmatrix![1,2,3];
-        let v2: DMatrix<u32> = dmatrix![1;2;3];
-
-        let sum = add(v1, v2).unwrap();
-        assert_eq!(sum.shape(), (3, 3));
-        assert_eq!(sum, dmatrix![2,3,4;3,4,5;4,5,6]);
-    }
-
-    #[test]
-    fn stretch_row_column_to_rect() {
-        let v1: DMatrix<u32> = dmatrix![1,2,3];
-        let v2: DMatrix<u32> = dmatrix![1;2];
-
-        let sum = add(v1, v2).unwrap();
-        assert_eq!(sum.shape(), (2, 3));
-        assert_eq!(sum, dmatrix![2,3,4;3,4,5]);
-    }
-
-    #[test]
-    fn stretch_column_row_to_square() {
-        let v1: DMatrix<u32> = dmatrix![1;2;3];
-        let v2: DMatrix<u32> = dmatrix![1,2,3];
-
-        let sum = add(v1, v2).unwrap();
-        assert_eq!(sum.shape(), (3, 3));
-        assert_eq!(sum, dmatrix![2,3,4;3,4,5;4,5,6]);
-    }
-
-    #[test]
-    fn stretch_column_row_to_rect() {
-        let v1: DMatrix<u32> = dmatrix![1;2;3];
-        let v2: DMatrix<u32> = dmatrix![1,2];
-
-        let sum = add(v1, v2).unwrap();
-        assert_eq!(sum.shape(), (3, 2));
-        assert_eq!(sum, dmatrix![2,3;3,4;4,5]);
-    }
-
-    #[test]
-    fn stretch_row() {
-        let v1: DMatrix<u32> = dmatrix![1,2,3];
-        let v2: DMatrix<u32> = dmatrix![1,2,3;4,5,6];
-
-        let sum = add(v1, v2).unwrap();
-        assert_eq!(sum.shape(), (2, 3));
-        assert_eq!(sum, dmatrix![2,4,6;5,7,9]);
-    }
-
-    #[test]
-    fn stretch_row_commute() {
-        let v1: DMatrix<u32> = dmatrix![1,2,3;4,5,6];
-        let v2: DMatrix<u32> = dmatrix![1,2,3];
-
-        let sum = add(v1, v2).unwrap();
-        assert_eq!(sum.shape(), (2, 3));
-        assert_eq!(sum, dmatrix![2,4,6;5,7,9]);
-    }
-
-    #[test]
-    fn stretch_column() {
-        let v1: DMatrix<u32> = dmatrix![1;2];
-        let v2: DMatrix<u32> = dmatrix![1,2,3;4,5,6];
-
-        let sum = add(v1, v2).unwrap();
-        assert_eq!(sum.shape(), (2, 3));
-        assert_eq!(sum, dmatrix![2,3,4;6,7,8]);
-    }
-
-    #[test]
-    fn stretch_column_commute() {
-        let v1: DMatrix<u32> = dmatrix![1,2,3;4,5,6];
-        let v2: DMatrix<u32> = dmatrix![1;2];
-
-        let sum = add(v1, v2).unwrap();
-        assert_eq!(sum.shape(), (2, 3));
-        assert_eq!(sum, dmatrix![2,3,4;6,7,8]);
-    }
-
-    #[test]
-    fn test_broadcast_2dims() {
-        let v1: DMatrix<u32> = dmatrix![1,2,3];
-        let v2: DMatrix<u32> = dmatrix![1;2;3];
-
-        let sum = add(v1, v2).unwrap();
-        assert_eq!(sum.shape(), (3, 3));
-        assert_eq!(sum, dmatrix![2,3,4;3,4,5;4,5,6]);
-    }
-
-    #[test]
-    fn test_add_commutative() {
-        let v1 = dmatrix![1,2,3];
-        let v2 = dmatrix![1;2;3];
-
-        let sum = add(v2, v1).unwrap();
-        assert_eq!(sum.shape(), (3, 3));
-        assert_eq!(sum, dmatrix![2,3,4;3,4,5;4,5,6]);
-    }
-
-    #[test]
-    fn test_add_same_size() {
-        let v1 = dmatrix![1,2;3,4];
-        let v2 = dmatrix![3,4;5,6];
-
-        let sum = add(v2, v1).unwrap();
-        assert_eq!(sum.shape(), (2, 2));
-        assert_eq!(sum, dmatrix![4,6;8,10]);
-    }
-
-    #[test]
-    fn test_add_row_broadcast() {//
-        let v1 = dmatrix![1,2;3,4];
-        let v2 = dmatrix![3,4];
-
-        let sum = add(v1, v2).unwrap();
-        assert_eq!(sum, dmatrix![4,6;6,8]);
-    }
-
-    #[test]
-    fn test_add_row_broadcast2() {
-        let v1 = dmatrix![1,1];
-        let v2 = dmatrix![1,2;3,4];
-
-        let sum = add(v1, v2).unwrap();
-        assert_eq!(sum.shape(), (2, 2));
-        assert_eq!(sum, dmatrix![2,3;4,5]);
-    }
-
-    #[test]
-    fn test_column_broadcast() {
-        let v1 = dmatrix![1;1];
-        let v2 = dmatrix![1,2;3,4];
-
-        let sum = add(v1, v2).unwrap();
-        assert_eq!(sum.shape(), (2, 2));
-        assert_eq!(sum, dmatrix![2,3;4,5]);
-    }
-
-    #[test]
-    fn test_column_broadcast2() {
-        let v1 = dmatrix![1,2;3,4];
-        let v2 = dmatrix![1;1];
-
-        let sum = add(v1, v2).unwrap();
-        assert_eq!(sum.shape(), (2, 2));
-        assert_eq!(sum, dmatrix![2,3;4,5]);
-    }
-
-    #[test]
-    fn column_too_long() {
-        let v1 = dmatrix![1;1;1];
-        let v2 = dmatrix![1,2;3,4];
-
-        let result = add(v1, v2);
-        assert_eq!(result, Err("ValueError: operands could not be broadcast together".to_owned()));
-    }
-
-    #[test]
-    fn row_too_long() {
-        let v1 = dmatrix![1,1,1];
-        let v2 = dmatrix![1,2;3,4];
-
-        let result = add(v1, v2);
-        assert_eq!(result, Err("ValueError: operands could not be broadcast together".to_owned()));
-    }
-
-
-}

src/net.rs

@@ -6,7 +6,6 @@ use rand::prelude::*;
 use rand_distr::Normal;
 
 use crate::dataloader::{Data, DataLine, OneHotVector};
-use crate::mat::add;
 
 #[derive(Debug)]
 pub struct Network {
@@ -56,7 +55,7 @@ impl Network {
     fn feed_forward_activation(&self, input: Vec<f64>, activation: fn(&mut f64)) -> Vec<f64> {
         let mut a = DMatrix::from_vec(input.len(), 1, input);
         for (b, w) in zip(&self.biases, &self.weights) {
-            a = add(b.clone(), w * a).unwrap();
+            a = b.clone()+ w * a;
             a.apply(activation);
         }
         a.column(0).iter().copied().collect()
@@ -83,14 +82,7 @@ impl Network {
     /// The ``mini_batch`` is a list of tuples ``(x, y)``, and ``eta``
     /// is the learning rate.
     fn update_mini_batch(&mut self, mini_batch: &[DataLine<f64, OneHotVector>], eta: f64) {
-        let mut nabla_b: Vec<DMatrix<f64>> = self.biases.iter()
+        let  (mut nabla_b, mut nabla_w) = self.zero_gradient();
-            .map(|b| b.shape())
-            .map(|s| DMatrix::zeros(s.0, s.1))
-            .collect();
-        let mut nabla_w: Vec<DMatrix<f64>> = self.weights.iter()
-            .map(|w| w.shape())
-            .map(|s| DMatrix::zeros(s.0, s.1))
-            .collect();
         for line in mini_batch.iter() {
             let (delta_nabla_b, delta_nabla_w) = self.backprop(line.inputs.to_vec(), &line.label);
 
@@ -124,15 +116,7 @@ impl Network {
     /// `nabla_w` are layer-by-layer lists of matrices, similar
     /// to `self.biases` and `self.weights`.
     fn backprop(&self, x: Vec<f64>, y: &OneHotVector) -> (Vec<DMatrix<f64>>, Vec<DMatrix<f64>>) {
-        // zero_grad ie. set gradient to zero
+        let  (mut nabla_b, mut nabla_w) = self.zero_gradient();
-        let mut nabla_b: Vec<DMatrix<f64>> = self.biases.iter()
-            .map(|b| b.shape())
-            .map(|s| DMatrix::zeros(s.0, s.1))
-            .collect();
-        let mut nabla_w: Vec<DMatrix<f64>> = self.weights.iter()
-            .map(|w| w.shape())
-            .map(|s| DMatrix::zeros(s.0, s.1))
-            .collect();
 
         // feedforward
         let mut activation = DMatrix::from_vec(x.len(), 1, x);
@@ -168,6 +152,19 @@ impl Network {
 
         (nabla_b, nabla_w)
     }
+
+    fn zero_gradient(&self) -> (Vec<DMatrix<f64>>, Vec<DMatrix<f64>>) {
+        let nabla_b: Vec<DMatrix<f64>> = self.biases.iter()
+            .map(|b| b.shape())
+            .map(|s| DMatrix::zeros(s.0, s.1))
+            .collect();
+        let nabla_w: Vec<DMatrix<f64>> = self.weights.iter()
+            .map(|w| w.shape())
+            .map(|s| DMatrix::zeros(s.0, s.1))
+            .collect();
+        (nabla_b, nabla_w)
+    }
+
 }
 
 fn cost_derivative(output_activations: &DMatrix<f64>, y: &OneHotVector) -> DMatrix<f64> {
@@ -225,7 +222,6 @@ fn sigmoid_prime(val: f64) -> f64 {
 
 #[cfg(test)]
 mod test {
-    use std::convert::identity;
     use nalgebra::DMatrix;
 
     use super::*;
@@ -241,7 +237,7 @@ mod test {
     fn test_sigmoid_inplace() {
         let mut v = 10.0;
         sigmoid_inplace(&mut v);
-        assert_eq!(0.9999546, v);
+        assert_eq!(0.9999546021312976, v);
     }
 
     #[test]