diff --git a/include/NeuralNetwork/ConstructiveAlgorithms/CascadeCorrelation.h b/include/NeuralNetwork/ConstructiveAlgorithms/CascadeCorrelation.h
index 6820947..942ba1a 100644
--- a/include/NeuralNetwork/ConstructiveAlgorithms/CascadeCorrelation.h
+++ b/include/NeuralNetwork/ConstructiveAlgorithms/CascadeCorrelation.h
@@ -39,7 +39,7 @@ namespace NeuralNetwork {
 					} else {
 						error = trainOutputs(network, patterns);
 					}
-					while(_epoch++ < _maxEpochs && _neurons++ < _maxHiddenUnits && error > _errorTreshold) {
+					while(_epoch++ < _maxEpochs && _neurons < _maxHiddenUnits && error > _errorTreshold) {
 						std::vector<std::shared_ptr<Neuron>> candidates = createCandidates(network.getNeuronSize() - outputs);
 
 						std::pair<std::shared_ptr<Neuron>, std::vector<float>> candidate = trainCandidates(network, candidates, patterns);
diff --git a/src/NeuralNetwork/ConstructiveAlgorithms/Cascade2.cpp b/src/NeuralNetwork/ConstructiveAlgorithms/Cascade2.cpp
index df62f07..90b91ac 100644
--- a/src/NeuralNetwork/ConstructiveAlgorithms/Cascade2.cpp
+++ b/src/NeuralNetwork/ConstructiveAlgorithms/Cascade2.cpp
@@ -66,8 +66,6 @@ std::pair<std::shared_ptr<NeuralNetwork::Neuron>, std::vector<float>> Cascade2::
 					float weight = candidateWeights[candidateIndex][output];
 					float diff = activationValue * weight - errors[patternIndex][output];
 
-					float goalDir= pattern.second[output] <0.0? -1.0 :1.0;
-					float diffDir= diff >0.0? -1.0 :1.0;
 					score -= (diff * diff);
 					outSlopes[output] -= 2.0 * diff * activationValue;
 					errSum += diff * weight;
diff --git a/src/NeuralNetwork/ConstructiveAlgorithms/CascadeCorrelation.cpp b/src/NeuralNetwork/ConstructiveAlgorithms/CascadeCorrelation.cpp
index 66661a6..f05de73 100644
--- a/src/NeuralNetwork/ConstructiveAlgorithms/CascadeCorrelation.cpp
+++ b/src/NeuralNetwork/ConstructiveAlgorithms/CascadeCorrelation.cpp
@@ -190,6 +190,10 @@ std::pair <std::shared_ptr<NeuralNetwork::Neuron>, std::vector<float>> CascadeCo
 		}
 	}
 
+	if(sumSquareError < 0.01) {
+		sumSquareError=0.01;
+	}
+
 	std::for_each(meanErrors.begin(), meanErrors.end(), [&patterns](float &n) { n /= patterns.size(); });
 
 	struct CAND {