From 370451c2e6f5e4a73b75123f6e1a4294fb2c7914 Mon Sep 17 00:00:00 2001
From: Michael Mandl <mandlm@molez.net>
Date: Fri, 16 Oct 2015 22:59:04 +0200
Subject: [PATCH] Calculation of hidden neuron gradients (partial)

---
 Layer.cpp  |  4 ++++
 Layer.h    |  2 ++
 Net.cpp    | 19 ++++++++++++++++---
 Neuron.cpp | 15 +++++++++++++++
 Neuron.h   | 12 +++++++++---
 5 files changed, 46 insertions(+), 6 deletions(-)

diff --git a/Layer.cpp b/Layer.cpp
index 259f68e..466dd84 100644
--- a/Layer.cpp
+++ b/Layer.cpp
@@ -50,3 +50,7 @@ void Layer::connectTo(const Layer & nextLayer)
 		neuron.createRandomOutputWeights(nextLayer.size());
 	}
 }
+
+void Layer::updateInputWeights(const Layer & prevLayer)
+{
+}
diff --git a/Layer.h b/Layer.h
index 3524e04..8251a20 100644
--- a/Layer.h
+++ b/Layer.h
@@ -13,4 +13,6 @@ public:
 	void feedForward(const Layer &inputLayer);
 	double getWeightedSum(int outputNeuron) const;
 	void connectTo(const Layer & nextLayer);
+
+	void updateInputWeights(const Layer &prevLayer);
 };
diff --git a/Net.cpp b/Net.cpp
index 5488ac0..fbbda5b 100644
--- a/Net.cpp
+++ b/Net.cpp
@@ -68,6 +68,8 @@ void Net::backProp(const std::vector<double> &targetValues)
 
 	std::vector<double> resultValues = getOutput();
 	size_t numResultValues = resultValues.size();
+
+	// calculate rms error
 	double rmsError = 0.0;
 
 	for (unsigned int i = 0; i < numResultValues; ++i)
@@ -78,19 +80,30 @@ void Net::backProp(const std::vector<double> &targetValues)
 
 	rmsError = sqrt(rmsError / numResultValues);
 
+	// calculate output neuron gradients 
 	for (unsigned int i = 0; i < numResultValues; ++i)
 	{
 		outputLayer[i].calcOutputGradients(targetValues[i]);
 	}
 
+	// calculate hidden neuron gradients
 	for (auto it = end() - 1; it != begin(); --it)
 	{
-		Layer &hiddenLayer = *it;
-		Layer &prevLayer = *(it - 1);
+		Layer &hiddenLayer = *(it - 1);
+		Layer &nextLayer = *it;
 
 		for (auto neuron : hiddenLayer)
 		{
-			//neuron.calcHiddenGradients(prevLayer);
+			neuron.calcHiddenGradients(nextLayer);
 		}
 	}
+
+	// update the input weights
+	for (auto it = end() - 1; it != begin(); --it)
+	{
+		Layer &currentLayer = *it;
+		Layer &prevLayer = *(it - 1);
+
+		currentLayer.updateInputWeights(prevLayer);
+	}
 }
diff --git a/Neuron.cpp b/Neuron.cpp
index c10133c..5fc8944 100644
--- a/Neuron.cpp
+++ b/Neuron.cpp
@@ -60,3 +60,18 @@ void Neuron::calcOutputGradients(double targetValue)
 	gradient = delta * transferFunctionDerivative(outputValue);
 }
 
+double Neuron::sumDOW(const Layer & nextLayer) const
+{
+	double sum = 0;
+
+	// sum it up
+
+	return sum;
+}
+
+void Neuron::calcHiddenGradients(const Layer &nextLayer)
+{
+	double dow = sumDOW(nextLayer);
+	gradient = dow * transferFunctionDerivative(outputValue);
+}
+
diff --git a/Neuron.h b/Neuron.h
index acd8af8..51d68ed 100644
--- a/Neuron.h
+++ b/Neuron.h
@@ -2,6 +2,8 @@
 
 #include <vector>
 
+class Layer;
+
 class Neuron
 {
 private:
@@ -13,13 +15,17 @@ public:
 	Neuron(double value = 1.0);
 
 	void setOutputValue(double value);
-	static double transferFunction(double inputValue);
-	static double transferFunctionDerivative(double inputValue);
 	void feedForward(double inputValue);
 	double getWeightedOutputValue(unsigned int outputNeuron) const;
 	void createRandomOutputWeights(size_t numberOfWeights);
 	double getOutputValue() const;
 
 	void calcOutputGradients(double targetValue);
-	//void calcHiddenGradients(const Layer &prevLayer);
+	void calcHiddenGradients(const Layer &nextLayer);
+
+private:
+	static double transferFunction(double inputValue);
+	static double transferFunctionDerivative(double inputValue);
+	double sumDOW(const Layer &nextLayer) const;
+
 };