Adam implementation

examples/xor_classification.cpp

@@ -16,14 +16,16 @@ int main() {
     auto mlp = SequentialBuilder<Type::float32>::begin()
                 .add(Linear32::create(2, 15))
                 .add(ReLU32::create())
-                .add(Dropout32::create(15, 5, 0.2))
+                .add(Linear32::create(15, 5))
+                .add(Sigmoid32::create())
+                .add(Linear32::create(5, 5))
                 .add(Tanh32::create())
                 .add(Linear32::create(5, 2))
                 .add(Softmax32::create())
                 .build();
     // clang-format on
 
-    Optimizer32 optimizer = Optimizer<Type::float32>(mlp->parameters(), 0.1);
+    Adam32 optimizer = Adam<Type::float32>(mlp->parameters(), 0.05);
     Loss::Function32 lossFunction = Loss::CrossEntropy<Type::float32>;
 
     // ------ TRAINING THE NETWORK ------- //

include/ShkyeraGrad.hpp

@@ -14,9 +14,11 @@
 #include "nn/Loss.hpp"
 #include "nn/Module.hpp"
 #include "nn/Neuron.hpp"
-#include "nn/Optimizer.hpp"
 #include "nn/Sequential.hpp"
 
+#include "nn/optimizers/Adam.hpp"
+#include "nn/optimizers/Optimizer.hpp"
+
 #include "nn/activation/Activation.hpp"
 #include "nn/activation/Exp.hpp"
 #include "nn/activation/ReLU.hpp"

include/core/Value.hpp

@@ -19,6 +19,8 @@
 namespace shkyera {
 
 template <typename T> class Optimizer;
+template <typename T> class Adam;
+
 template <typename T> class Value;
 template <typename T> using ValuePtr = std::shared_ptr<Value<T>>;
 
@@ -39,6 +41,7 @@ template <typename T> class Value : public std::enable_shared_from_this<Value<T>
 
   public:
     friend class Optimizer<T>;
+    friend class Adam<T>;
 
     static ValuePtr<T> create(T data);
 

include/nn/optimizers/Adam.hpp

@@ -0,0 +1,85 @@
+/**
+ * Copyright © 2023 Franciszek Szewczyk. None of the rights reserved.
+ * This code is released under the Beerware License. If you find this code useful or you appreciate the work, you are
+ * encouraged to buy the author a beer in return.
+ * Contact the author at [email protected] for inquiries and support.
+ */
+
+#pragma once
+
+#include <unordered_map>
+#include <vector>
+
+#include "../../core/Type.hpp"
+#include "../../core/Value.hpp"
+#include "../Module.hpp"
+#include "Optimizer.hpp"
+
+namespace shkyera {
+
+template <typename T> class Adam;
+using Adam32 = Adam<Type::float32>;
+using Adam64 = Adam<Type::float32>;
+
+template <typename T> class Adam : public Optimizer<T> {
+  private:
+    T _b1;
+    T _b2;
+    T _eps;
+    size_t _timestep;
+
+    std::unordered_map<Value<T> *, T> _firstMoment;
+    std::unordered_map<Value<T> *, T> _secondMoment;
+
+    T getFirstMoment(const ValuePtr<T> &v);
+    T getSecondMoment(const ValuePtr<T> &v);
+
+  public:
+    Adam(std::vector<ValuePtr<T>> params, T learningRate, T b1 = 0.9, T b2 = 0.999, T eps = 1e-8);
+
+    void step() override;
+};
+
+template <typename T>
+Adam<T>::Adam(std::vector<ValuePtr<T>> params, T learningRate, T b1, T b2, T eps) : Optimizer<T>(params, learningRate) {
+    _timestep = 0;
+    _b1 = b1;
+    _b2 = b2;
+    _eps = eps;
+}
+
+template <typename T> void Adam<T>::step() {
+    _timestep++;
+
+    for (const ValuePtr<T> &param : this->_parameters) {
+        T gradient = param->getGradient();
+
+        T firstMoment = _b1 * getFirstMoment(param) + (1 - _b1) * gradient;
+        T secondMoment = _b2 * getSecondMoment(param) + (1 - _b2) * gradient * gradient;
+
+        T firstMomentHat = firstMoment / (1 - pow(_b1, _timestep));
+        T secondMomentHat = secondMoment / (1 - pow(_b2, _timestep));
+
+        param->_data -= (this->_learningRate * firstMomentHat) / (sqrt(secondMomentHat) + _eps);
+    }
+}
+
+template <typename T> T Adam<T>::getFirstMoment(const ValuePtr<T> &v) {
+    auto moment = _firstMoment.find(v.get());
+    if (moment == _firstMoment.end()) {
+        _firstMoment.insert({v.get(), 0});
+        return 0;
+    }
+    return moment->second;
+}
+
+template <typename T> T Adam<T>::getSecondMoment(const ValuePtr<T> &v) {
+    auto moment = _secondMoment.find(v.get());
+    if (moment == _secondMoment.end()) {
+        _secondMoment.insert({v.get(), 0});
+        return 0;
+    }
+    return moment->second;
+}
+
+} // namespace shkyera

include/nn/Optimizer.hpp → include/nn/optimizers/Optimizer.hpp

@@ -9,25 +9,25 @@
 
 #include <vector>
 
-#include "../core/Type.hpp"
-#include "../core/Value.hpp"
-#include "Module.hpp"
+#include "../../core/Type.hpp"
+#include "../../core/Value.hpp"
+#include "../Module.hpp"
 
 namespace shkyera {
 
 using Optimizer32 = Optimizer<Type::float32>;
 using Optimizer64 = Optimizer<Type::float32>;
 
 template <typename T> class Optimizer {
-  private:
+  protected:
     std::vector<ValuePtr<T>> _parameters;
     T _learningRate;
 
   public:
     Optimizer(std::vector<ValuePtr<T>> params, T learningRate);
 
-    void reset();
-    void step();
+    virtual void reset();
+    virtual void step();
 };
 
 template <typename T>