public static void Run()
{
var input = new Input(new Keras.Shape(32, 32));
//var a = new CuDNNLSTM(32).Set(input);
var a = new Dense(32, activation: "sigmoid").Set(input);
//a.Set(input);
var output = new Dense(1, activation: "sigmoid").Set(a);
//output.Set(a);
var model = new Keras.Models.Model(new Input[] { input }, new BaseLayer[] { output });
//Load train data
Numpy.NDarray x = np.array(new float[, ] {
{ 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 }
});
NDarray y = np.array(new float[] { 0, 1, 1, 0 });
var input1 = new Input(new Shape(32, 32, 3));
var conv1 = new Conv2D(32, (4, 4).ToTuple(), activation: "relu").Set(input1);
var pool1 = new MaxPooling2D((2, 2).ToTuple()).Set(conv1);
var flatten1 = new Flatten().Set(pool1);
var input2 = new Input(new Shape(32, 32, 3));
var conv2 = new Conv2D(16, (8, 8).ToTuple(), activation: "relu").Set(input2);
var pool2 = new MaxPooling2D((2, 2).ToTuple()).Set(conv2);
var flatten2 = new Flatten().Set(pool2);
var merge = new Concatenate(flatten1, flatten2);
}
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 functional model
var input = new Input(shape: new Keras.Shape(2));
var hidden1 = new Dense(32, activation: "relu").Set(input);
var hidden2 = new Dense(64, activation: "relu").Set(hidden1);
var output = new Dense(1, activation: "sigmoid").Set(hidden2);
var model = new Keras.Models.Model(new Input[] { input }, new BaseLayer[] { output });
//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");
}
