Improved forward_pass

src/layers/activation_layer.rs

@@ -23,8 +23,8 @@ impl ActivationLayer {
 }
 
 impl Layer for ActivationLayer {
-    fn forward_pass(&mut self, input: ArrayView1<f64>) -> Array1<f64> {
-        self.input = input.to_owned();
+    fn forward_pass(&mut self, input: Array1<f64>) -> Array1<f64> {
+        self.input = input;
         // output isn't needed elsewhere
         // self.output = (self.activation)(&self.input);
         // self.output.clone()

src/layers/fc_layer.rs

@@ -71,12 +71,12 @@ impl FCLayer {
 }
 
 impl Layer for FCLayer {
-    fn forward_pass(&mut self, input: ArrayView1<f64>) -> Array1<f64> {
+    fn forward_pass(&mut self, input: Array1<f64>) -> Array1<f64> {
         if !self.is_initialized {
             self.initialize(input.len());
         }
 
-        self.input = input.to_owned();
+        self.input = input;
         // output isn't needed elsewhere
         // self.output = self.input.dot(&self.weights) + &self.biases;
         // self.output.clone()

src/layers/mod.rs

@@ -4,6 +4,6 @@ pub mod activation_layer;
 pub mod fc_layer;
 
 pub trait Layer {
-    fn forward_pass(&mut self, input: ArrayView1<f64>) -> Array1<f64>;
+    fn forward_pass(&mut self, input: Array1<f64>) -> Array1<f64>;
     fn backward_pass(&mut self, output_error: ArrayView1<f64>, learning_rate: f64) -> Array1<f64>;
 }

src/lib.rs

@@ -30,10 +30,9 @@ impl Network {
         let mut result = vec![];
 
         for input in inputs.iter() {
-            let mut output = Array1::default(inputs[0].raw_dim());
-            output.assign(input);
+            let mut output = input.to_owned();
             for layer in &mut self.layers {
-                output = layer.forward_pass(output.view());
+                output = layer.forward_pass(output);
             }
             result.push(output);
         }
@@ -57,10 +56,9 @@ impl Network {
             let mut err = 0.0;
             for j in 0..num_samples {
                 // forward propagation
-                let mut output = Array1::default(x_train[0].raw_dim());
-                output.assign(&x_train[j]);
+                let mut output = x_train[j].to_owned();
                 for layer in self.layers.iter_mut() {
-                    output = layer.forward_pass(output.view());
+                    output = layer.forward_pass(output);
                 }
 
                 // compute loss