public void Add(List <float> trainLoss, List <float> trainMetric, List <float> valLoss, List <float> valMetric)
{
TrainLoss.Add(trainLoss.Average());
TrainMetric.Add(trainMetric.Average());
if (valLoss.Count > 0)
{
ValLoss.Add(valLoss.Average());
}
if (valMetric.Count > 0)
{
ValMetric.Add(valMetric.Average());
}
}
public void Fit(DataIter train, uint epochs = 1, uint batchSize = 32, DataIter validation = null, bool shuffle = false)
{
string labelName = "label";
var label = Symbol.Variable(labelName);
List <uint> inputShape = new List <uint>();
inputShape.Add(batchSize);
inputShape.AddRange(InputShape);
args["X"] = new NDArray(new Shape(inputShape.ToArray()));
args[labelName] = new NDArray(new Shape(batchSize));
Model.InferArgsMap(mx.Device, args, args);
var defaultInitializer = new Initializers.GlorotUniform();
foreach (var arg in args)
{
if (ParamInitializers.ContainsKey(arg.Key))
{
ParamInitializers[arg.Key].Generate(arg.Value);
}
else
{
defaultInitializer.Generate(arg.Value);
}
}
using (var exec = Model.SimpleBind(mx.Device, args))
{
var argNames = Model.ListArguments();
// Start training
var sw = new Stopwatch();
for (var iter = 1; iter <= epochs; iter++)
{
uint samples = 0;
train.BatchSize = batchSize;
train.Reset();
Metric.Reset();
TrainMetric.Reset();
sw.Restart();
while (train.IterNext())
{
samples += batchSize;
var dataBatch = train.Next();
// Set data and label
dataBatch.Data[0].CopyTo(args["X"]);
dataBatch.Label[0].CopyTo(args[labelName]);
// Compute gradients
exec.Forward(true);
exec.Backward();
TrainMetric.Update(args[labelName], exec.Output);
// Update parameters
for (var i = 0; i < argNames.Count; ++i)
{
if (argNames[i] == "X" || argNames[i] == labelName)
{
continue;
}
ModelOptimizer.Update(i, exec.ArgmentArrays[i], exec.GradientArrays[i], null);
}
}
sw.Stop();
if (validation != null)
{
validation.BatchSize = batchSize;
validation.Reset();
while (validation.IterNext())
{
var dataBatch = validation.Next();
dataBatch.Data[0].CopyTo(args["X"]);
dataBatch.Label[0].CopyTo(args[labelName]);
NDArray.WaitAll();
// Forward pass is enough as no gradient is needed when evaluating
exec.Forward(false);
Metric.Update(args[labelName], exec.Output);
}
}
var duration = sw.ElapsedMilliseconds == 0 ? 1 : sw.ElapsedMilliseconds;
if (validation == null)
{
Logging.LG($"Epoch: {iter} {Convert.ToInt32(samples * 1000 / duration)} samples/sec Train_Metric: {TrainMetric.Get()}");
}
else
{
Logging.LG($"Epoch: {iter} {Convert.ToInt32(samples * 1000 / duration)} samples/sec, Train_Metric: {TrainMetric.Get()}, Val_Metric: {Metric.Get()}");
}
}
}
//MXNet.MXNotifyShutdown();
}
