examples/example_sine.rs

@@ -12,7 +12,7 @@ use rust_nn::layers::activation_layer::ActivationLayer;
 use rust_nn::layers::fc_layer::{FCLayer, Initializer};
 use rust_nn::Network;
 
-fn main() {
+fn main() -> Result<(), Box<dyn Error>> {
     // training data
     let training_interval = (0.0f64, 2.0f64 * PI);
     let steps = 100000;
@@ -66,25 +66,11 @@ fn main() {
     )));
 
     // train network on training data
-    network.fit(&x_train, &y_train, 100, 0.05, true);
+    network.fit(x_train, y_train, 100, 0.05, true);
 
     // predict test dataset
-    let y_test_pred = network.predict(&x_test);
+    let y_test_pred = network.predict(x_test.clone());
 
-    // show results
-    if let Ok(()) = draw_results(&training_interval, &x_test, &y_test_true, &y_test_pred) {
-        println!("results can be seen in ./examples/sine.png");
-    } else {
-        println!("failed to draw results");
-    }
-}
-
-fn draw_results(
-    training_interval: &(f64, f64),
-    x_test: &[Array1<f64>],
-    y_test_true: &[Array1<f64>],
-    y_test_pred: &[Array1<f64>],
-) -> Result<(), Box<dyn Error>> {
     // create the chart
     let buf = BitMapBackend::new("./examples/sine.png", (800, 600)).into_drawing_area();
     buf.fill(&WHITE)?;

examples/example_xor.rs

@@ -45,10 +45,10 @@ fn main() {
     )));
 
     // train network on training data
-    network.fit(&x_train, &y_train, 1000, 0.1, false);
+    network.fit(x_train, y_train, 1000, 0.1, false);
 
     // print predictions
-    let y_test = network.predict(&x_test);
+    let y_test = network.predict(x_test.clone());
     println!("{}", x_test.get(0).unwrap());
     for i in 0..y_test.len() {
         print!("input: {}\t\t", x_test.get(i).unwrap());

src/layers/activation_layer.rs

@@ -5,7 +5,7 @@ use crate::functions::activation_functions::*;
 
 pub struct ActivationLayer {
     input: Array1<f64>,
-    // output: Array1<f64>,
+    output: Array1<f64>,
     activation: fn(&Array1<f64>) -> Array1<f64>,
     activation_prime: fn(&Array1<f64>) -> Array1<f64>,
 }
@@ -15,7 +15,7 @@ impl ActivationLayer {
         let (activation, activation_prime) = parse_type(activation_fn);
         ActivationLayer {
             input: arr1(&[]),
-            // output: arr1(&[]),
+            output: arr1(&[]),
             activation,
             activation_prime,
         }
@@ -25,13 +25,15 @@ impl ActivationLayer {
 impl Layer for ActivationLayer {
     fn forward_pass(&mut self, input: ArrayView1<f64>) -> Array1<f64> {
         self.input = input.to_owned();
-        // output isn't needed elsewhere
-        // self.output = (self.activation)(&self.input);
-        // self.output.clone()
-        (self.activation)(&self.input)
+        self.output = (self.activation)(&self.input);
+        self.output.clone()
     }
 
     fn backward_pass(&mut self, output_error: ArrayView1<f64>, _learning_rate: f64) -> Array1<f64> {
-        (self.activation_prime)(&self.input) * output_error
+        // (self.activation_prime)(&self.input).into_shape((1 as usize, output_error.len() as usize)).unwrap().dot(&output_error)
+        // (self.activation_prime)(&self.input) * &output_error
+        let mut temp = (self.activation_prime)(&self.input);
+        temp.zip_mut_with(&output_error, |x, y| *x *= y);
+        temp
     }
 }

src/layers/fc_layer.rs

@@ -40,7 +40,7 @@ pub struct FCLayer {
     weight_initializer: Initializer,
     bias_initializer: Initializer,
     input: Array1<f64>,
-    // output: Array1<f64>,
+    output: Array1<f64>,
     weights: Array2<f64>,
     biases: Array1<f64>,
 }
@@ -57,7 +57,7 @@ impl FCLayer {
             weight_initializer,
             bias_initializer,
             input: arr1(&[]),
-            // output: arr1(&[]),
+            output: arr1(&[]),
             weights: arr2(&[[]]),
             biases: arr1(&[]),
         }
@@ -77,10 +77,8 @@ impl Layer for FCLayer {
         }
 
         self.input = input.to_owned();
-        // output isn't needed elsewhere
-        // self.output = self.input.dot(&self.weights) + &self.biases;
-        // self.output.clone()
-        self.input.dot(&self.weights) + &self.biases
+        self.output = self.input.dot(&self.weights) + &self.biases;
+        self.output.clone()
     }
 
     fn backward_pass(&mut self, output_error: ArrayView1<f64>, learning_rate: f64) -> Array1<f64> {

src/lib.rs

@@ -25,7 +25,7 @@ impl Network {
         self.layers.push(layer);
     }
 
-    pub fn predict(&mut self, inputs: &[Array1<f64>]) -> Vec<Array1<f64>> {
+    pub fn predict(&mut self, inputs: Vec<Array1<f64>>) -> Vec<Array1<f64>> {
         assert!(!inputs.is_empty());
         let mut result = vec![];
 
@@ -35,7 +35,7 @@ impl Network {
             for layer in &mut self.layers {
                 output = layer.forward_pass(output.view());
             }
-            result.push(output);
+            result.push(output.to_owned());
         }
 
         result
@@ -43,8 +43,8 @@ impl Network {
 
     pub fn fit(
         &mut self,
-        x_train: &[Array1<f64>],
-        y_train: &[Array1<f64>],
+        x_train: Vec<Array1<f64>>,
+        y_train: Vec<Array1<f64>>,
         epochs: usize,
         learning_rate: f64,
         trivial_optimize: bool,