public static void Run()
{
//Logistic Regression is one of the first models newcomers to Deep Learning are implementing.
//The focus of this tutorial is to show how to do logistic regression using Gluon API.
var ctx = mx.Cpu();
int train_data_size = 1000;
int val_data_size = 100;
var(train_x, train_ground_truth_class) = GetRandomData(train_data_size, ctx);
var train_dataset = new ArrayDataset((train_x, train_ground_truth_class));
train_dataloader = new DataLoader(train_dataset, batch_size: batch_size, shuffle: true);
var(val_x, val_ground_truth_class) = GetRandomData(val_data_size, ctx);
var val_dataset = new ArrayDataset((val_x, val_ground_truth_class));
val_dataloader = new DataLoader(val_dataset, batch_size: batch_size, shuffle: true);
net = new HybridSequential();
net.Add(new Dense(units: 10, activation: ActivationType.Relu));
net.Add(new Dense(units: 10, activation: ActivationType.Relu));
net.Add(new Dense(units: 10, activation: ActivationType.Relu));
net.Add(new Dense(units: 1));
net.Initialize(new Xavier());
loss = new SigmoidBinaryCrossEntropyLoss();
trainer = new Trainer(net.CollectParams(), new SGD(learning_rate: 0.1f));
accuracy = new Accuracy();
f1 = new F1();
int epochs = 10;
float threshold = 0.5f;
foreach (var e in Enumerable.Range(0, epochs))
{
var avg_train_loss = TrainModel() / train_data_size;
var avg_val_loss = ValidateModel(threshold) / val_data_size;
Console.WriteLine($"Epoch: {e}, Training loss: {avg_train_loss}, Validation loss: {avg_val_loss}, Validation accuracy: {accuracy.Get().Item2}, F1 score: {f1.Get().Item2}");
accuracy.Reset();
}
}
public static void Run()
{
//Logistic Regression is one of the first models newcomers to Deep Learning are implementing.
//The focus of this tutorial is to show how to do logistic regression using Gluon API.
var ctx = mx.Cpu();
int train_data_size = 1000;
int val_data_size = 100;
int batch_size = 10;
var(train_x, train_ground_truth_class) = GetRandomState(train_data_size, ctx);
var train_dataset = new ArrayDataset(train_x, train_ground_truth_class);
var train_dataloader = new DataLoader(train_dataset, batch_size: batch_size, shuffle: true);
var(val_x, val_ground_truth_class) = GetRandomState(val_data_size, ctx);
var val_dataset = new ArrayDataset(val_x, val_ground_truth_class);
var val_dataloader = new DataLoader(val_dataset, batch_size: batch_size, shuffle: true);
var net = new HybridSequential();
net.Add(new Dense(units: 10, activation: ActivationType.Relu));
net.Add(new Dense(units: 10, activation: ActivationType.Relu));
net.Add(new Dense(units: 10, activation: ActivationType.Relu));
net.Add(new Dense(units: 1));
net.Initialize(new Xavier());
var loss = new SigmoidBinaryCrossEntropyLoss();
var trainer = new Trainer(net.CollectParams(), new SGD(learning_rate: 0.1f));
var accuracy = new Accuracy();
var f1 = new F1();
float cumulative_train_loss = 0;
int i = 0;
foreach (var item in train_dataloader)
{
var data = item[0];
var label = item[1];
}
}