Function SupervisedTrain(inputdata As Variant, outputdata As Variant) As Integer '0=unsuccessful and 1 = successful
Dim i, j, k As Integer
If UBound(inputdata) <> Network.Layers(1).NeuronCount Then 'Check if correct amount of input is given
    SupervisedTrain = 0
    Exit Function
End If
If UBound(outputdata) <> Network.Layers(Network.LayerCount).NeuronCount Then 'Check if correct amount of output is given
    SupervisedTrain = 0
    Exit Function
End If
Call Run(inputdata) 'Calculate values of all neurons and set the input
'Calculate delta's
For i = 1 To Network.Layers(Network.LayerCount).NeuronCount
DoEvents
	'Deltas of Output layer
    Network.Layers(Network.LayerCount).Neurons(i).Delta = Network.Layers(Network.LayerCount).Neurons(i).Value * (contd)
	* (1 - Network.Layers(Network.LayerCount).Neurons(i).Value) * (contd)
	* (outputdata(i) - Network.Layers(Network.LayerCount).Neurons(i).Value) 
    For j = Network.LayerCount - 1 To 2 Step -1
    DoEvents
        For k = 1 To Network.Layers(j).NeuronCount
        DoEvents
            Network.Layers(j).Neurons(k).Delta = Network.Layers(j).Neurons(k).Value * (contd)
			* (1 - Network.Layers(j).Neurons(k).Value) * Network.Layers(j + 1).Neurons(i).Dendrites(k).Weight * (contd)
			* Network.Layers(j + 1).Neurons(i).Delta 'Deltas of Hidden Layers
        Next k
    Next j
Next i


For i = Network.LayerCount To 2 Step -1
DoEvents
    For j = 1 To Network.Layers(i).NeuronCount
    DoEvents
        Network.Layers(i).Neurons(j).Bias = Network.Layers(i).Neurons(j).Bias + (contd)
		+ (Network.LearningRate * 1 * Network.Layers(i).Neurons(j).Delta)  'Calculate new bias
        For k = 1 To Network.Layers(i).Neurons(j).DendriteCount
        DoEvents
            'Calculate new weights
            Network.Layers(i).Neurons(j).Dendrites(k).Weight = Network.Layers(i).Neurons(j).Dendrites(k).Weight + (contd)
			(Network.LearningRate * Network.Layers(i - 1).Neurons(k).Value * Network.Layers(i).Neurons(j).Delta) 
        Next k
    Next j
Next i
SupervisedTrain = 1
End Function