Iterating with references

fszewczyk · Nov 8, 2023 · e30b15e · e30b15e
1 parent 71bca9b
commit e30b15e
Show file tree

Hide file tree

Showing 7 changed files with 10 additions and 10 deletions.
diff --git a/examples/xor_classification.cpp b/examples/xor_classification.cpp
@@ -16,7 +16,7 @@ int main() {
     auto mlp = SequentialBuilder<Type::float32>::begin()
                 .add(Linear32::create(2, 15))
                 .add(ReLU32::create())
-                .add(Dropout32::create(15, 5, 0.3))
+                .add(Dropout32::create(15, 5, 0.2))
                 .add(Tanh32::create())
                 .add(Linear32::create(5, 2))
                 .add(Softmax32::create())
@@ -27,7 +27,7 @@ int main() {
     Loss::Function32 lossFunction = Loss::CrossEntropy<Type::float32>;
 
     // ------ TRAINING THE NETWORK ------- //
-    for (size_t epoch = 0; epoch < 500; epoch++) {
+    for (size_t epoch = 0; epoch < 100; epoch++) {
         auto epochLoss = Val32::create(0);
 
         optimizer.reset();

diff --git a/include/nn/Neuron.hpp b/include/nn/Neuron.hpp
@@ -46,8 +46,8 @@ template <typename T> std::vector<ValuePtr<T>> Neuron<T>::parameters() const {
     std::vector<ValuePtr<T>> params;
     params.reserve(_weights.size() + 1);
 
-    for (size_t i = 0; i < _weights.size(); ++i)
-        params.push_back(_weights[i]);
+    for (auto &w : _weights)
+        params.push_back(w);
 
     params.push_back(_bias);
 

diff --git a/include/nn/activation/Exp.hpp b/include/nn/activation/Exp.hpp
@@ -28,7 +28,7 @@ template <typename T> Vector<T> Exp<T>::operator()(const Vector<T> &x) const {
     std::vector<ValuePtr<T>> out;
     out.reserve(x.size());
 
-    for (auto entry : x)
+    for (auto &entry : x)
         out.emplace_back(entry->exp());
 
     return Vector<T>(out);

diff --git a/include/nn/activation/ReLU.hpp b/include/nn/activation/ReLU.hpp
@@ -28,7 +28,7 @@ template <typename T> Vector<T> ReLU<T>::operator()(const Vector<T> &x) const {
     std::vector<ValuePtr<T>> out;
     out.reserve(x.size());
 
-    for (auto entry : x)
+    for (auto &entry : x)
         out.emplace_back(entry->relu());
 
     return Vector<T>(out);

diff --git a/include/nn/activation/Sigmoid.hpp b/include/nn/activation/Sigmoid.hpp
@@ -30,7 +30,7 @@ template <typename T> Vector<T> Sigmoid<T>::operator()(const Vector<T> &x) const
     std::vector<ValuePtr<T>> out;
     out.reserve(x.size());
 
-    for (auto entry : x) {
+    for (auto &entry : x) {
         out.emplace_back(entry->sigmoid());
     }
 

diff --git a/include/nn/activation/Softmax.hpp b/include/nn/activation/Softmax.hpp
@@ -31,12 +31,12 @@ template <typename T> Vector<T> Softmax<T>::operator()(const Vector<T> &x) const
     out.reserve(x.size());
 
     auto maxValue = Value<T>::create(x[0]->getValue());
-    for (auto entry : x)
+    for (auto &entry : x)
         if (entry > maxValue)
             maxValue = entry;
 
     auto sumExponentiated = Value<T>::create(0);
-    for (auto entry : x) {
+    for (auto &entry : x) {
         auto exponentiated = (entry - maxValue)->exp();
         out.emplace_back(exponentiated);
         sumExponentiated = sumExponentiated + exponentiated;

diff --git a/include/nn/activation/Tanh.hpp b/include/nn/activation/Tanh.hpp
@@ -28,7 +28,7 @@ template <typename T> Vector<T> Tanh<T>::operator()(const Vector<T> &x) const {
     std::vector<ValuePtr<T>> out;
     out.reserve(x.size());
 
-    for (auto entry : x)
+    for (auto &entry : x)
         out.emplace_back(entry->tanh());
 
     return Vector<T>(out);