private static void MNISTTraining()
{
uint batchSize = 32;
var trainIter = new MXDataIter("MNISTIter")
.SetParam("image", "./mnist_data/train-images-idx3-ubyte")
.SetParam("label", "./mnist_data/train-labels-idx1-ubyte")
.SetParam("batch_size", batchSize)
.SetParam("flat", 1)
.CreateDataIter();
var valIter = new MXDataIter("MNISTIter")
.SetParam("image", "./mnist_data/t10k-images-idx3-ubyte")
.SetParam("label", "./mnist_data/t10k-labels-idx1-ubyte")
.SetParam("batch_size", batchSize)
.SetParam("flat", 1)
.CreateDataIter();
var model = new Sequential(new Shape(28 * 28), 10);
model.AddHidden(new Dense(28 * 28, ActivationType.ReLU, new GlorotUniform()));
model.AddHidden(new Dropout(0.25f));
model.AddHidden(new Dense(28 * 28, ActivationType.ReLU, new GlorotUniform()));
model.Compile(OptimizerType.SGD, LossType.CategorialCrossEntropy, "accuracy");
model.Fit(trainIter, 10, batchSize, valIter);
}
private static void ORGate()
{
DataFrame train_x = new DataFrame(4, 2);
DataFrame train_y = new DataFrame(4, 1);
train_x.AddData(0, 0);
train_x.AddData(0, 1);
train_x.AddData(1, 0);
train_x.AddData(1, 1);
train_y.AddData(0);
train_y.AddData(1);
train_y.AddData(1);
train_y.AddData(1);
DataFrameIter train = new DataFrameIter(train_x, train_y);
Sequential model = new Sequential(new Shape(2), 1);
model.AddHidden(new Dense(4, ActivationType.ReLU, new GlorotUniform()));
model.Compile(OptimizerType.SGD, LossType.BinaryCrossEntropy, "accuracy");
model.Fit(train, 100, 2);
model.SaveModel(@"C:\Users\bdkadmin\Desktop\SSHKeys\");
}
