public void DoPredict()
{
int batch_size = 16;
Context ctx = new Context(DeviceType.KCpu, 0);
Optimizer optimizer = new CcSgd(momentum: 0.9f, learningRate: 0.001f, wd: 0.00001f, rescaleGrad: 1.0f / batch_size);
var modelload = FeedForward.Load("checkpoint\\tag", ctx: ctx,
numEpoch: 1,
optimizer: optimizer,
initializer: new Xavier(factorType: FactorType.In, magnitude: 2.34f));
}
static void Main(string[] args)
{
NumericsTest test = new NumericsTest();
test.Test();
var log4_net_config = Path.Combine(Path.GetDirectoryName(typeof(Program).Assembly.Location), "log4net.config");
XmlConfigurator.Configure(new FileInfo(log4_net_config));
var model = FeedForward.Load("checkpoint\\cnn");
ReadData rdpredict = new ReadData("data\\train\\", 32, true);
var testOut = model.Predict(rdpredict, 1);
TrainTest();
}
private static void TrainTest()
{
int batch_size = 32;
var pnet = get_ocrnet(batch_size);
//var modelloadtest = FeedForward.Load("checkpoint\\cnn");
ReadData rdtrain = new ReadData("data\\train\\", batch_size);
ReadData rdval = new ReadData("data\\val\\", batch_size);
Context ctx = new Context(DeviceType.KGpu, 0);
Speedometer speed = new Speedometer(batch_size, 50);
DoCheckpoint doCheckpoint = new DoCheckpoint("checkpoint\\cnn");
CustomMetric customMetric = new CustomMetric((l, p) => Accuracy(l, p, batch_size), "Accuracy");
Optimizer optimizer = new CcSgd(momentum: 0.9f, learningRate: 0.01f, wd: 0.00001f, rescaleGrad: 1.0f / batch_size);
FeedForward model = null;
try
{
var modelload = FeedForward.Load("checkpoint\\cnn", ctx: ctx,
numEpoch: 1,
optimizer: optimizer,
initializer: new Xavier(factorType: FactorType.In, magnitude: 2.34f));
model = new FeedForward(pnet, new List <Context> {
ctx
},
numEpoch: 1000,
optimizer: optimizer,
initializer: new Xavier(factorType: FactorType.In, magnitude: 2.34f),
argParams: modelload.ArgParams,
auxParams: modelload.AuxParams
);
}
catch (Exception)
{
// ignored
}
if (model == null)
{
model = new FeedForward(pnet, new List <Context> {
ctx
},
numEpoch: 1,
optimizer: optimizer,
initializer: new Xavier(factorType: FactorType.In, magnitude: 2.34f)
);
}
model.Fit(rdtrain, rdval,
customMetric,
batchEndCallback: new List <BatchEndDelegate> {
speed.Call
},
epochEndCallback: new List <EpochEndDelegate> {
doCheckpoint.Call
});
//model.Save("checkpoint\\cnn");
ReadData rdpredict = new ReadData("data\\train\\", batch_size, true);
// var testOut = model.Predict(rdpredict, 1);
Console.WriteLine("");
}