public static KeyValuePair <double, double> Action(Network network, Optimizer optimizer, IDataEnumerator data, IDataEnumerator val, int count, int batch = 32)
{
DateTime start;
start = DateTime.Now;
int layers = network.layers.Count;
for (int i = 0; i < layers - 1; i++)
{
Network background;
Network autoencoder;
if (!network.layers[i].ITrained)
{
continue;
}
background = new Network();
for (int j = 0; j < i; j++)
{
background.layers.Add(network.layers[j]);
}
autoencoder = new Network();
autoencoder.layers.Add(network.layers[i]);
autoencoder.layers.Add(network.layers[i].Mirror);
background.CompileOnlyError();
autoencoder.CompileOnlyError();
NetworkEnumerator nEm = new NetworkEnumerator(background, data);
MirrorEnumerator Mem = new MirrorEnumerator(nEm);
optimizer.network = autoencoder;
double err = optimizer.TrainBatch(Mem, batch, 1).Last();
for (int k = 1; k < count; k++)
{
Console.Write("{0} ", k);
optimizer.TrainBatchContinue(Mem, batch, 1);
if ((DateTime.Now - start).TotalMinutes >= 10)
{
System.IO.File.WriteAllText("autosave_" + i + "_" + k + ".neural", network.SaveJSON());
Console.WriteLine("Save to " + "autosave_" + i + "_" + k + ".neural");
start = DateTime.Now;
}
}
Console.WriteLine("Trained {0} with error {1}", i, (float)err);
}
return(network.GetError(val));
}
public double TrainContinue(Optimizer optimizer, IDataEnumerator data, int batch)
{
NetworkEnumerator nEm = new NetworkEnumerator(background, data);
MirrorEnumerator Mem = new MirrorEnumerator(nEm);
optimizer.network = autoencoder;
double err = optimizer.TrainBatchContinue(Mem, batch, 1).Last();
return(err);
}
public double TrainContinue(Optimizer optimizer, IDataEnumerator data, int batch)
{
optimizer.network = autoencoder;
if (background.layers.Count != 0)
{
NetworkEnumerator nEm = new NetworkEnumerator(background, data);
double err = optimizer.TrainBatchContinue(nEm, batch, 1).Last();
return(err);
}
else
{
double err = optimizer.TrainBatchContinue(data, batch, 1).Last();
return(err);
}
}
public KeyValuePair <double, double> TrainBatch(Optimizer optimizer, IDataEnumerator data, IDataEnumerator val, int count, int batch = 32)
{
DateTime start = DateTime.Now;
NetworkEnumerator nEm = new NetworkEnumerator(background, data);
optimizer.network = autoencoder;
optimizer.TrainBatch(nEm, batch, 1).Last();
for (int k = 1; k < count; k++)
{
optimizer.TrainBatchContinue(nEm, batch, 1);
if ((DateTime.Now - start).TotalMinutes >= 10)
{
System.IO.File.WriteAllText("autosave_" + k + ".neural", network.SaveJSON());
start = DateTime.Now;
}
}
return(autoencoder.GetError(val));
}
public KeyValuePair <double, double> Validation(IDataEnumerator data)
{
NetworkEnumerator nEm = new NetworkEnumerator(background, data);
return(autoencoder.GetError(nEm));
}