As mentioned earlier, the only difference between our networks are the layers that are created and added to the network object. In an LSTM we will add LSTM layers, and in a GRU, unsurprisingly, we will add GRU layers, and so forth. All four types of creation functions are displayed as follows for you to compare:

public static NeuralNetwork MakeLstm(int inputDimension, int hiddenDimension, int hiddenLayers, int outputDimension, INonlinearity decoderUnit, double initParamsStdDev, Random rng)
{
    List<ILayer> layers = new List<ILayer>();
    for (int h = 0; h<hiddenLayers; h++)
    {
        layers.Add(h == 0
         ? new LstmLayer(inputDimension, hiddenDimension, initParamsStdDev, rng)
         : new LstmLayer(hiddenDimension, hiddenDimension, initParamsStdDev, rng));
    }
    layers.Add(new FeedForwardLayer(hiddenDimension, outputDimension, decoderUnit,         initParamsStdDev, rng));
    return new NeuralNetwork(layers);
}


public static NeuralNetwork MakeFeedForward(int inputDimension, int hiddenDimension, inthiddenLayers, int outputDimension, INonlinearity hiddenUnit, INonlinearity decoderUnit, double initParamsStdDev, Random rng)
{
    List<ILayer> layers = new List<ILayer>();
    for (int h = 0; h<hiddenLayers; h++)
    {
        layers.Add(h == 0? new FeedForwardLayer(inputDimension, hiddenDimension,         hiddenUnit, initParamsStdDev, rng): new FeedForwardLayer(hiddenDimension,         hiddenDimension, hiddenUnit, initParamsStdDev, rng));
    }
    layers.Add(new FeedForwardLayer(hiddenDimension, outputDimension,             decoderUnit, initParamsStdDev, rng));
    return new NeuralNetwork(layers);
 }


public static NeuralNetwork MakeGru(int inputDimension, int hiddenDimension, int hiddenLayers, int outputDimension, INonlinearity decoderUnit, double initParamsStdDev, Random rng)
{
    List<ILayer> layers = new List<ILayer>();
    for (int h = 0; h<hiddenLayers; h++)
    {
        layers.Add(h == 0? new GruLayer(inputDimension, hiddenDimension, initParamsStdDev, rng): new GruLayer(hiddenDimension, hiddenDimension, initParamsStdDev, rng));
    }
    layers.Add(new FeedForwardLayer(hiddenDimension, outputDimension, decoderUnit, initParamsStdDev, rng));
    return new NeuralNetwork(layers);
}


public static NeuralNetwork MakeRnn(int inputDimension, int hiddenDimension, int hiddenLayers, int outputDimension, INonlinearity hiddenUnit, INonlinearity decoderUnit, double initParamsStdDev, Random rng)
{
    List<ILayer> layers = new List<ILayer>();
    for (int h = 0; h<hiddenLayers; h++)
    {
        layers.Add(h == 0? new RnnLayer(inputDimension, hiddenDimension, hiddenUnit, initParamsStdDev, rng)
        : new RnnLayer(hiddenDimension, hiddenDimension, hiddenUnit, initParamsStdDev, rng));
    }
    layers.Add(new FeedForwardLayer(hiddenDimension, outputDimension, decoderUnit, initParamsStdDev, rng));
    return new NeuralNetwork(layers);
}