private void SaveSnapshot(int epoch, double mse, double initialMse, double accuracy, bool isFinal = false)
{
var final = isFinal ? "final_" : string.Empty;
var fileName = $"{Path.GetDirectoryName(Parameters.Arff)}{Path.GetFileNameWithoutExtension(Parameters.Arff)}_iter_{final}{epoch}.txt";
NeuralNetSave.Save(fileName, this, epoch, mse, initialMse, accuracy);
}
public static void Save(string fileName, NeuralNet neuralNet, int epoch, double mse, double initialMse, double accuracy)
{
var nns = new NeuralNetSave()
{
Rate = neuralNet.Parameters.Rate,
Momentum = neuralNet.Parameters.Momentum,
Activation = neuralNet.Parameters.Activation,
ValidationAccuracy = accuracy,
SnapshotInterval = neuralNet.Parameters.SnapshotInterval,
BatchSize = neuralNet.Parameters.BatchSize,
Mse = mse,
InitialMse = initialMse,
Epoch = epoch,
Layers = new List <Layer>()
};
foreach (var layer in neuralNet.Layers)
{
var newLayer = new Layer()
{
Type = layer.Type,
Nodes = new List <Node>()
};
foreach (var node in layer.Nodes)
{
var newNode = new Node()
{
Weights = node.Weights
};
switch (layer.Type)
{
case NeuralNet.LayerType.Input:
newNode.Feature = ((NeuralNet.InputNode)node).Feature;
break;
case NeuralNet.LayerType.Output:
newNode.IsContinuous = ((NeuralNet.OutputNode)node).IsContinuous;
newNode.LabelCol = ((NeuralNet.OutputNode)node).LabelCol;
newNode.LabelVal = ((NeuralNet.OutputNode)node).LabelVal;
break;
}
newLayer.Nodes.Add(newNode);
}
nns.Layers.Add(newLayer);
}
var json = JsonConvert.SerializeObject(nns, Formatting.Indented);
using (var w = new StreamWriter(fileName))
{
w.Write(json);
}
}
public NeuralNet(Parameters parameters)
{
_rand = Rand.Get();
Parameters = parameters;
Layers = new List <Layer>();
if (!string.IsNullOrEmpty(parameters.SnapshotFileName) && File.Exists(parameters.SnapshotFileName))
{
NeuralNetSave.Load(parameters.SnapshotFileName, this);
}
}
