public float[] Think(float[] input) // Compute inputs with the network and return outputs
{
//read in all given inputs and set the value of the input neurons
EraseMemory();
for (int i = 0; i < Inputs.Length; i++)
{
Inputs[i].SetValue(Inputs[i], input[i]);
}
//get all computed data from output neurons
float[] output = new float[Outputs.Length];
for (int i = 0; i < Outputs.Length; i++)
{
//output[i] = Neuron.Sigmoid(Outputs[i].Activation);
output[i] = Neuron.ReLU(Outputs[i].Activation);
//output[i] = (float)Math.Tanh(Outputs[i].Activation);
}
return(output);
}
static public float[][][] Backpropagate(Brain brain, float TweakAmount, int outputNum, int expectedNum)
{
Neuron[][] layers = new Neuron[brain.HiddenLayers.Length + 1][];
Array.Copy(brain.AllLayers, 1, layers, 0, brain.AllLayers.Length - 1);
float[] targetOutput = new float[10];
targetOutput[expectedNum] = 1.0F;
float[][][] allChanges = new float[layers.Length][][];
for (int layerNum = layers.Length - 1; layerNum >= 0; layerNum--)
{
Neuron[] layer = layers[layerNum];
float[][] neuronChanges = new float[layer.Length][];
for (int neuronNum = 0; neuronNum < layer.Length; neuronNum++)
{
Neuron neuron = layer[neuronNum];
float[] weightChanges = new float[neuron.Weight.Length];
for (int i = 0; i < neuron.Weight.Length; i++)
{
float deltaW = 0.0F;
if (neuron.Type == Neuron.NeuronType.HiddenNeuron)
{
float delta_i = Delta_i(layers, layerNum, neuronNum, targetOutput);
float activation_j = brain.AllLayers[layerNum][i].Activation;
deltaW = DeltaW(TweakAmount, delta_i, activation_j);
}
else
{
float delta_i = Delta_i(neuron, targetOutput[neuronNum], Neuron.ReLU(neuron.Activation));
float activation_j = brain.AllLayers[layerNum][i].Activation;
deltaW = DeltaW(TweakAmount, delta_i, activation_j);
}
if (!float.IsNaN(deltaW))
{
weightChanges[i] = deltaW;
neuron.Weight[i] += deltaW;
}
}
neuronChanges[neuronNum] = weightChanges;
}
allChanges[layerNum] = neuronChanges;
}
return(allChanges);
}
