From 2ea55025758107a31e9e28c46be7467221b98a8c Mon Sep 17 00:00:00 2001
From: Adrien Burgun <adrien.burgun@orange.fr>
Date: Sat, 22 Apr 2023 14:13:58 +0200
Subject: [PATCH] :art: Small cleanup

---
 src/layer/dense.rs                   | 2 +-
 src/layer/normalize.rs               | 8 ++++----
 src/layer/softmax.rs                 | 6 +++---
 src/network/sequential/layer_impl.rs | 6 +++---
 4 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/src/layer/dense.rs b/src/layer/dense.rs
index 0df187a..3b81686 100644
--- a/src/layer/dense.rs
+++ b/src/layer/dense.rs
@@ -242,7 +242,7 @@ impl<
 {
     fn backprop_layer(
         &self,
-        input: &DVector<F>,
+        _input: &DVector<F>,
         evaluated: &Self::IntermediaryRepr,
         epsilon: &Self::Output,
     ) -> DVector<F> {
diff --git a/src/layer/normalize.rs b/src/layer/normalize.rs
index 2cb81a1..427dec6 100644
--- a/src/layer/normalize.rs
+++ b/src/layer/normalize.rs
@@ -89,9 +89,9 @@ impl<F: Float + Scalar + NumAssignOps> NeuraTrainableLayerSelf<DVector<F>> for N
 
     fn get_gradient(
         &self,
-        input: &DVector<F>,
-        intermediary: &Self::IntermediaryRepr,
-        epsilon: &Self::Output,
+        _input: &DVector<F>,
+        _intermediary: &Self::IntermediaryRepr,
+        _epsilon: &Self::Output,
     ) -> Self::Gradient {
         ()
     }
@@ -102,7 +102,7 @@ impl<F: Float + Scalar + NumAssignOps> NeuraTrainableLayerBackprop<DVector<F>>
 {
     fn backprop_layer(
         &self,
-        input: &DVector<F>,
+        _input: &DVector<F>,
         (jacobian_partial, stddev): &Self::IntermediaryRepr,
         epsilon: &Self::Output,
     ) -> DVector<F> {
diff --git a/src/layer/softmax.rs b/src/layer/softmax.rs
index 6f97472..c9956f5 100644
--- a/src/layer/softmax.rs
+++ b/src/layer/softmax.rs
@@ -81,9 +81,9 @@ impl<F: Float + Scalar + NumAssignOps> NeuraTrainableLayerSelf<DVector<F>> for N
     #[inline(always)]
     fn get_gradient(
         &self,
-        input: &DVector<F>,
-        intermediary: &Self::IntermediaryRepr,
-        epsilon: &Self::Output,
+        _input: &DVector<F>,
+        _intermediary: &Self::IntermediaryRepr,
+        _epsilon: &Self::Output,
     ) -> Self::Gradient {
         ()
     }
diff --git a/src/network/sequential/layer_impl.rs b/src/network/sequential/layer_impl.rs
index e454b84..088b725 100644
--- a/src/network/sequential/layer_impl.rs
+++ b/src/network/sequential/layer_impl.rs
@@ -63,9 +63,9 @@ impl<
 
     fn get_gradient(
         &self,
-        input: &Input,
-        intermediary: &Self::IntermediaryRepr,
-        epsilon: &Self::Output,
+        _input: &Input,
+        _intermediary: &Self::IntermediaryRepr,
+        _epsilon: &Self::Output,
     ) -> Self::Gradient {
         unimplemented!("NeuraSequential::get_gradient is not yet implemented, sorry");
     }