Improved backward_pass

src/layers/activation_layer.rs

@@ -1,4 +1,4 @@
-use ndarray::{arr1, Array1, ArrayView1};
+use ndarray::{arr1, Array1};
 
 use super::Layer;
 use crate::functions::activation_functions::*;
@@ -31,7 +31,7 @@ impl Layer for ActivationLayer {
         (self.activation)(&self.input)
     }
 
-    fn backward_pass(&mut self, output_error: ArrayView1<f64>, _learning_rate: f64) -> Array1<f64> {
+    fn backward_pass(&mut self, output_error: Array1<f64>, _learning_rate: f64) -> Array1<f64> {
         (self.activation_prime)(&self.input) * output_error
     }
 }

src/layers/fc_layer.rs

@@ -1,6 +1,6 @@
 extern crate ndarray;
 
-use ndarray::{arr1, arr2, Array, Array1, Array2, ArrayView1, ShapeBuilder};
+use ndarray::{arr1, arr2, Array, Array1, Array2, ShapeBuilder};
 use ndarray_rand::rand_distr::{Normal, Uniform};
 use ndarray_rand::RandomExt;
 
@@ -83,18 +83,13 @@ impl Layer for FCLayer {
         self.input.dot(&self.weights) + &self.biases
     }
 
-    fn backward_pass(&mut self, output_error: ArrayView1<f64>, learning_rate: f64) -> Array1<f64> {
+    fn backward_pass(&mut self, output_error: Array1<f64>, learning_rate: f64) -> Array1<f64> {
         let input_error = output_error.dot(&self.weights.t());
         let delta_weights = self
             .input
-            .to_owned()
+            .to_shape((self.input.len(), 1usize))
-            .into_shape((self.input.len(), 1usize))
             .unwrap()
-            .dot(
+            .dot(&output_error.to_shape((1usize, output_error.len())).unwrap());
-                &output_error
-                    .into_shape((1usize, output_error.len()))
-                    .unwrap(),
-            );
         self.weights = &self.weights + learning_rate * &delta_weights;
         self.biases = &self.biases + learning_rate * &output_error;
         input_error

src/layers/mod.rs

@@ -1,9 +1,9 @@
-use ndarray::{Array1, ArrayView1};
+use ndarray::Array1;
 
 pub mod activation_layer;
 pub mod fc_layer;
 
 pub trait Layer {
     fn forward_pass(&mut self, input: Array1<f64>) -> Array1<f64>;
-    fn backward_pass(&mut self, output_error: ArrayView1<f64>, learning_rate: f64) -> Array1<f64>;
+    fn backward_pass(&mut self, output_error: Array1<f64>, learning_rate: f64) -> Array1<f64>;
 }

src/lib.rs

@@ -68,9 +68,9 @@ impl Network {
                 let mut error = (self.loss_prime)(y_train[j].view(), output.view());
                 for layer in self.layers.iter_mut().rev() {
                     if trivial_optimize {
-                        error = layer.backward_pass(error.view(), learning_rate / (i + 1) as f64);
+                        error = layer.backward_pass(error, learning_rate / (i + 1) as f64);
                     } else {
-                        error = layer.backward_pass(error.view(), learning_rate);
+                        error = layer.backward_pass(error, learning_rate);
                     }
                 }
             }