🔥 WIP implementation of NeuraNetwork for NeuraSequential

src/gradient_solver/backprop.rs

@@ -17,20 +17,23 @@ impl<Loss> NeuraBackprop<Loss> {
 impl<
         Input,
         Target,
-        Trainable: NeuraOldTrainableNetworkBase<Input>,
+        Trainable: NeuraTrainableLayerBase + NeuraLayer<Input> + NeuraNetworkRec,
         Loss: NeuraLoss<Trainable::Output, Target = Target> + Clone,
     > NeuraGradientSolver<Input, Target, Trainable> for NeuraBackprop<Loss>
 where
     <Loss as NeuraLoss<Trainable::Output>>::Output: ToPrimitive,
-    Trainable: for<'a> NeuraOldTrainableNetwork<Input, (&'a NeuraBackprop<Loss>, &'a Target)>,
+    // Trainable: NeuraOldTrainableNetworkBase<Input, Gradient = <Trainable as NeuraTrainableLayerBase>::Gradient>,
+    // Trainable: for<'a> NeuraOldTrainableNetwork<Input, (&'a NeuraBackprop<Loss>, &'a Target)>,
+    for<'a> (&'a NeuraBackprop<Loss>, &'a Target): BackpropRecurse<Input, Trainable, <Trainable as NeuraTrainableLayerBase>::Gradient>
 {
     fn get_gradient(
         &self,
         trainable: &Trainable,
         input: &Input,
         target: &Target,
-    ) -> Trainable::Gradient {
+    ) -> <Trainable as NeuraTrainableLayerBase>::Gradient {
-        let (_, gradient) = trainable.traverse(input, &(self, target));
+        let (_, gradient) = (self, target).recurse(trainable, input);
+        // let (_, gradient) = trainable.traverse(input, &(self, target));
 
         gradient
     }
@@ -119,14 +122,24 @@ where
     Network::NextNode: NeuraTrainableLayerEval<Network::NodeOutput>,
 {
     fn recurse(&self, network: &Network, input: &Input) -> (Input, Network::Gradient) {
+        let layer = network.get_layer();
+        // Get layer output
         let layer_input = network.map_input(input);
-        let (layer_output, layer_intermediary) =
+        let (layer_output, layer_intermediary) = layer.eval_training(layer_input.as_ref());
-            network.get_layer().eval_training(layer_input.as_ref());
         let output = network.map_output(input, &layer_output);
 
+        // Recurse
         let (epsilon_in, gradient_rec) = self.recurse(network.get_next(), output.as_ref());
 
-        todo!()
+        // Get layer outgoing gradient vector
+        let layer_epsilon_in = network.map_gradient_in(input, &epsilon_in);
+        let layer_epsilon_out = layer.backprop_layer(&layer_input, &layer_intermediary, &layer_epsilon_in);
+        let epsilon_out = network.map_gradient_out(input, &epsilon_in, &layer_epsilon_out);
+
+        // Get layer parameter gradient
+        let gradient = layer.get_gradient(&layer_input, &layer_intermediary, &layer_epsilon_in);
+
+        (epsilon_out.into_owned(), network.merge_gradient(gradient_rec, gradient))
     }
 }
 

src/gradient_solver/forward_forward.rs

@@ -30,17 +30,18 @@ impl<
         F,
         Act: Clone + NeuraDerivable<f64>,
         Input,
-        Trainable: NeuraOldTrainableNetwork<Input, NeuraForwardPair<Act>, Output = DVector<F>>,
+        Trainable: NeuraTrainableLayerBase,
     > NeuraGradientSolver<Input, bool, Trainable> for NeuraForwardForward<Act>
 where
     F: ToPrimitive,
+    Trainable: NeuraOldTrainableNetwork<Input, NeuraForwardPair<Act>, Output = DVector<F>, Gradient = <Trainable as NeuraTrainableLayerBase>::Gradient>
 {
     fn get_gradient(
         &self,
         trainable: &Trainable,
         input: &Input,
         target: &bool,
-    ) -> Trainable::Gradient {
+    ) -> <Trainable as NeuraTrainableLayerBase>::Gradient {
         let target = *target;
 
         trainable.traverse(

src/gradient_solver/mod.rs

@@ -37,7 +37,7 @@ pub trait NeuraGradientSolverTransient<Input, Layer: NeuraTrainableLayerEval<Inp
     ) -> Self::Output<To, Gradient>;
 }
 
-pub trait NeuraGradientSolver<Input, Target, Trainable: NeuraOldTrainableNetworkBase<Input>> {
+pub trait NeuraGradientSolver<Input, Target, Trainable: NeuraTrainableLayerBase> {
     fn get_gradient(
         &self,
         trainable: &Trainable,

src/network/sequential/mod.rs

@@ -1,4 +1,4 @@
-use super::{NeuraOldTrainableNetwork, NeuraOldTrainableNetworkBase};
+use super::*;
 use crate::{
     gradient_solver::NeuraGradientSolverTransient,
     layer::{
@@ -177,6 +177,30 @@ impl<Layer> From<Layer> for NeuraSequential<Layer, ()> {
     }
 }
 
+impl<Layer, ChildNetwork> NeuraNetworkBase for NeuraSequential<Layer, ChildNetwork> {
+    type Layer = Layer;
+
+    fn get_layer(&self) -> &Self::Layer {
+        &self.layer
+    }
+}
+
+impl<Layer: NeuraTrainableLayerBase, ChildNetwork: NeuraTrainableLayerBase> NeuraNetworkRec for NeuraSequential<Layer, ChildNetwork> {
+    type NextNode = ChildNetwork;
+
+    fn get_next(&self) -> &Self::NextNode {
+        &self.child_network
+    }
+
+    fn merge_gradient(
+        &self,
+        rec_gradient: <Self::NextNode as NeuraTrainableLayerBase>::Gradient,
+        layer_gradient: <Self::Layer as NeuraTrainableLayerBase>::Gradient
+    ) -> Self::Gradient {
+        (rec_gradient, Box::new(layer_gradient))
+    }
+}
+
 /// An utility to recursively create a NeuraSequential network, while writing it in a declarative and linear fashion.
 /// Note that this can quickly create big and unwieldly types.
 #[macro_export]

src/network/traits.rs

@@ -1,5 +1,7 @@
 use std::borrow::Cow;
 
+use crate::prelude::NeuraTrainableLayerBase;
+
 use super::*;
 
 /// This trait has to be non-generic, to ensure that no downstream crate can implement it for foreign types,
@@ -46,10 +48,17 @@ where
     ) -> Cow<'a, NodeInput>;
 }
 
-pub trait NeuraNetworkRec: NeuraNetworkBase {
+pub trait NeuraNetworkRec: NeuraNetworkBase + NeuraTrainableLayerBase {
     /// The type of the children network, it does not need to implement `NeuraNetworkBase`,
     /// although many functions will expect it to be either `()` or an implementation of `NeuraNetworkRec`.
-    type NextNode;
+    type NextNode: NeuraTrainableLayerBase;
 
     fn get_next(&self) -> &Self::NextNode;
+
+    fn merge_gradient(
+        &self,
+        rec_gradient: <Self::NextNode as NeuraTrainableLayerBase>::Gradient,
+        layer_gradient: <Self::Layer as NeuraTrainableLayerBase>::Gradient
+    ) -> Self::Gradient
+    where Self::Layer: NeuraTrainableLayerBase;
 }