public void Embedding() { var model = new Sequential(); model.add(new Embedding(1000, 64, input_length: 10)); // the model will take as input an integer matrix of size (batch, // input_length). // the largest integer (i.e. word index) in the input should be no larger // than 999 (vocabulary size). // now model.output_shape == (None, 10, 64), where None is the batch // dimension. var input_array = np.random.randint(1000, size: (32, 10)); model.compile("rmsprop", "mse"); }
public void Test1() { var epochs = 200; var batchSize = 128; var classes = 10; var hiddenCount = 128; var validationSplit = 0.2; var flatImageSize = 28 * 28; var mnist = MnistDataset.Read(@"C:\Projects\heightmap_upscale\data\mnist"); var x_train = PrepareImage(mnist.TrainingImages); var x_test = PrepareImage(mnist.TestImages); var y_train = PrepareLabels(mnist.TrainingLabels); var model = new Sequential(); model.add(new Dense(classes, activation: new softmax())); //model.Compile("SGD", loss: "categorical_crossentropy", metrics: new[] { "accuracy" }); model.compile("SGD", "categorical_crossentropy"); }