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); }