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));
int batch_size = 32;
uint w = 60;
uint h = 20;
float learning_rate = 1e-4f;
float weight_decay = 1e-4f;
ReadData rdtrain = new ReadData("data\\train\\", batch_size);
ReadData rdval = new ReadData("data\\val\\", batch_size);
//var first = rdtrain.First();
Context ctx = new Context(DeviceType.KGpu, 0);
//NDArray dataArray = new NDArray(new Shape((uint)batchSize, 3, W, H), ctx, false);
//NDArray labelArray = new NDArray(new Shape((uint)batchSize,4), ctx, false);
//Symbol data1 = Symbol.Variable("data1");
//Symbol data2 = Symbol.Variable("data2");
var pnet = get_ocrnet(batch_size);
Speedometer speed = new Speedometer(batch_size, 50);
CustomMetric custom_metric = new CustomMetric((l, p) => Accuracy(l, p, batch_size));
Optimizer optimizer = new CcSgd(momentum: 0.9f, learning_rate: 0.001f, wd: 0.00001f, rescale_grad: 1.0f / batch_size);
FeedForward model = new FeedForward(pnet, new List <Context> {
ctx
},
num_epoch: 10,
optimizer: optimizer,
initializer: new Xavier(factor_type: FactorType.In, magnitude: 2.34f)
);
model.Fit(rdtrain, rdval,
custom_metric,
batch_end_callback: new List <Action <mxnet.csharp.util.BatchEndParam> > {
speed.Call
});
Console.WriteLine("");
}
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();
}