public static double[,] GetWeights(
[ExcelArgument(Description = "Name of neural network for which to get weights.")] string name,
[ExcelArgument(Description = "Neural network layer number, starting with 1.")] int layer)
{
var perceptron = ObjectStore.Get <INeuralNetwork>(name);
if (layer > perceptron.Weights.Length || layer <= 0)
{
throw new NNXException($"Layer for neural network {name} must be between " +
$"1 and {perceptron.Weights.Length}; was {layer}.");
}
var result = perceptron.Weights[layer - 1].ToVertical2DArray();
ResizeOutputToArray(result);
return(result);
}
public static string TrainMultilayerPerceptron(
[ExcelArgument(Description = "Name of perceptron object to create.")] string name,
[ExcelArgument(Description = "Name of the trainer that will train this neural network.")] string trainerName,
[ExcelArgument(Description = "Matrix of training inputs.")] object[,] inputs,
[ExcelArgument(Description = "Matrix of training targets.")] object[,] targets,
[ExcelArgument(Description = "Number of nodes in each hidden layer, not including biases. " +
"Must be an array of integers.")] double[] hiddenLayerSizes)
{
var inputTargets = PrepareInputTargetSet(inputs, targets);
var inputWidth = inputs.GetLength(1);
var targedWidth = targets.GetLength(1);
var trainer = ObjectStore.Get <ITrainer>(trainerName);
var intHiddenLayerSizes = hiddenLayerSizes.Select(h => (int)h).ToArray();
var nn = new MultilayerPerceptron(inputWidth, targedWidth, intHiddenLayerSizes);
trainer.Train(inputTargets, nn);
ObjectStore.Add(name, nn);
return(name);
}