public static string SaveParams(this NeuralNetwork network)
{
if (network == null)
{
throw new ArgumentNullException(nameof(network));
}
var sb = new StringBuilder();
using (var it = new NeuralIterator())
{
for (var param = it.Begin(network); param != null; param = it.Next())
{
sb.Append($"{it.CurrentParam}:");
var data = param.GetData();
for (var i = 0u; i < data.Length; ++i)
{
sb.Append($" {data[i]}");
}
sb.AppendLine();
}
}
return(sb.ToString());
}
public static void LoadParams(this NeuralNetwork network, string parameters)
{
if (network == null)
{
throw new ArgumentNullException(nameof(network));
}
var lines = parameters.Split(new string[] { Environment.NewLine }, StringSplitOptions.RemoveEmptyEntries);
var vals = new float[lines.Length][];
for (var ln = 0; ln < lines.Length; ++ln)
{
var line = lines[ln];
var s1 = line.Split(':');
var i = Convert.ToUInt32(s1[0]);
var svals = s1[1].Split(new char[] { ' ' }, StringSplitOptions.RemoveEmptyEntries);
vals[i] = new float[svals.Length];
for (var v = 0u; v < svals.Length; ++v)
{
vals[i][v] = Convert.ToSingle(svals[v]);
}
}
using (var it = new NeuralIterator())
{
for (var param = it.Begin(network); param != null; param = it.Next())
{
param.Transfer(vals[it.CurrentParam]);
}
}
}
