Comparing LSTM, GRU, Feedforward, and RNN operations

In order to help you see the difference in both the creation and results of all the network objects we have been dealing with, I created the sample code that follows. This sample will allow you to see the difference in training times for all four of the network types we have here. As stated previously, the GRU is the easiest to train and therefore will complete faster (in less iterations) than the other networks. When executing the code, you will see that the GRU achieves the optimal error rate typically in under 10,000 iterations, while a conventional RNN and/or LSTM can take 50,000 or more iterations to converge properly.

Here is what our sample code looks like:

static void Main(string[] args)
{
Console.WriteLine("Running GRU sample", Color.Yellow);
Console.ReadKey();
ExampleGRU.Run();
Console.ReadKey();
Console.WriteLine...