internal BackPropagationNeuralNetwork(int[] networkShape, int epoch, float learnRate)
{
Epoch = epoch;
LearnRate = learnRate;
Losses = new float[epoch];
for (int i = 0; i < epoch; i++)
{
Losses[i] = 0f;
}
LossFunction = CrossEntropy;
LossFunctionDerivative = CrossEntropyDerivative;
Layers = new FullyConnectedNeuralLayer[networkShape.Length];
Layers[0] = new FullyConnectedNeuralLayer(networkShape[0], 0, this);
for (int i = 1; i < networkShape.Length - 1; i++)
{
Layers[i] = new FullyConnectedNeuralLayer(networkShape[i], networkShape[i - 1], this);
}
Layers[Layers.Length - 1] = new FullyConnectedNeuralLayer(networkShape[Layers.Length - 1], networkShape[Layers.Length - 2], this);
Layers[0].NextLayer = Layers[1];
Layers[Layers.Length - 1].PreviousLayer = Layers[Layers.Length - 2];
for (int i = 1; i < Layers.Length - 1; i++)
{
Layers[i].PreviousLayer = Layers[i - 1];
Layers[i].NextLayer = Layers[i + 1];
}
}
internal void SetInput(float[,,] data, int instanceIndex, FullyConnectedNeuralLayer layer)
{
int sideLength = data.GetLength(1);
for (int i = 0, r = 0, c = 0; i < layer.NumOfNodes; i++)
{
layer.Outputs[i] = data[instanceIndex, r, c];
c++;
if (c == sideLength)
{
c = 0;
r++;
}
}
}
