public void PrepareData()
{
mnist = MNIST.read_data_sets("mnist", one_hot: true, train_size: TrainSize, validation_size: ValidationSize, test_size: TestSize);
// In this example, we limit mnist data
(Xtr, Ytr) = mnist.train.next_batch(TrainSize == null ? 5000 : TrainSize.Value / 100); // 5000 for training (nn candidates)
(Xte, Yte) = mnist.test.next_batch(TestSize == null ? 200 : TestSize.Value / 100); // 200 for testing
}
public void PrepareData()
{
mnist = MNIST.read_data_sets("mnist", one_hot: true, train_size: train_size, validation_size: validation_size, test_size: test_size);
full_data_x = mnist.train.data;
// download graph meta data
string url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/kmeans.meta";
Web.Download(url, "graph", "kmeans.meta");
}
public void PrepareData()
{
mnist = MNIST.read_data_sets("mnist", one_hot: true);
(x_train, y_train) = (mnist.train.data, mnist.train.labels);
(x_valid, y_valid) = (mnist.validation.data, mnist.validation.labels);
(x_test, y_test) = (mnist.test.data, mnist.test.labels);
print("Size of:");
print($"- Training-set:\t\t{len(mnist.train.data)}");
print($"- Validation-set:\t{len(mnist.validation.data)}");
print($"- Test-set:\t\t{len(mnist.test.data)}");
}
public void PrepareData()
{
mnist = MNIST.read_data_sets("mnist", one_hot: true, train_size: train_size, validation_size: validation_size, test_size: test_size);
}
public void PrepareData()
{
mnist = MNIST.read_data_sets("mnist", one_hot: true);
}
