public static void Run()
{
//Load train data
NDarray x = np.array(new float[, ] {
{ 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 }
});
NDarray y = np.array(new float[] { 0, 1, 1, 0 });
//Build sequential model
var model = new Sequential();
model.Add(new Dense(32, activation: "relu", input_shape: new Shape(2)));
model.Add(new Dense(64, activation: "relu"));
model.Add(new Dense(1, activation: "sigmoid"));
//Compile and train
model.Compile(optimizer: new Adam(), loss: "binary_crossentropy", metrics: new string[] { "accuracy" });
var history = model.Fit(x, y, batch_size: 2, epochs: 10, verbose: 1);
var weights = model.GetWeights();
model.SetWeights(weights);
var logs = history.HistoryLogs;
//Save model and weights
string json = model.ToJson();
File.WriteAllText("model.json", json);
model.SaveWeight("model.h5");
//Load model and weight
var loaded_model = Sequential.ModelFromJson(File.ReadAllText("model.json"));
loaded_model.LoadWeight("model.h5");
}
public void Dense_CustomKRegularizerAndKInitParams()
{
NDarray x = np.array(new float[, ] {
{ 1, 0 }, { 1, 1 }, { 1, 0 }, { 1, 1 }
});
NDarray y = np.array(new float[] { 0, 1, 1, 0 });
var model = new Sequential();
model.Add(new Dense(1, activation: "sigmoid", input_shape: new Shape(x.shape[1]), kernel_regularizer: new L1L2(1000, 2000), kernel_initializer: new Constant(100)));
var modelAsJson = JsonConvert.DeserializeObject <dynamic>(model.ToJson());
Assert.AreEqual("Sequential", modelAsJson.class_name.Value);
int i = 0;
while (modelAsJson.config.layers[i].config.kernel_initializer == null && i < 3)
{
i++;
}
Assert.AreEqual(100, modelAsJson.config.layers[i].config.kernel_initializer.config.value.Value);
Assert.AreEqual("Constant", modelAsJson.config.layers[i].config.kernel_initializer.class_name.Value);
Assert.AreEqual("L1L2", modelAsJson.config.layers[i].config.kernel_regularizer.class_name.Value);
Assert.AreEqual(1000, modelAsJson.config.layers[i].config.kernel_regularizer.config.l1.Value);
Assert.AreEqual(2000, modelAsJson.config.layers[i].config.kernel_regularizer.config.l2.Value);
// Compile and train
model.Compile(optimizer: new Adam(lr: 0.001F), loss: "binary_crossentropy", metrics: new string[] { "accuracy" });
model.Fit(x, y, batch_size: x.shape[0], epochs: 100, verbose: 0);
Assert.AreEqual(2, model.GetWeights().Count);
}
