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: 100, verbose: 1);
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");
}
static void Main(string[] args)
{
//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: "sgd", loss: "binary_crossentropy", metrics: new string[] { "accuracy" });
model.Fit(x, y, batch_size: 2, epochs: 1000, verbose: 1);
//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");
var result = loaded_model.Predict(x);
Console.WriteLine("Предсказание для [{0}] = [{1}]", x.ToString(), result.ToString());
}
public static void SmallNetwork(List <Tuple <bool, float[]> > train, List <Tuple <bool, float[]> > test)
{
int vectorSize = train[0].Item2.Length;
//Load train data
var nTrain = ListToNDarrays(train, vectorSize);
var nTest = ListToNDarrays(test, vectorSize);
//Build sequential model
var model = new Sequential();
model.Add(new Dense(8, activation: "relu", input_shape: new Shape(vectorSize)));
model.Add(new Dropout(0.5));
model.Add(new Dense(16, activation: "relu"));
model.Add(new Dropout(0.5));
model.Add(new Dense(1, activation: "sigmoid"));
//Compile and train
//model.Compile(optimizer:"adam", loss:"sparse_categorical_crossentropy", metrics: new string[] { "accuracy" });
model.Compile(optimizer: "adam", loss: "binary_crossentropy", metrics: new string[] { "accuracy" });
model.Fit(
nTrain.Item2,
nTrain.Item1,
batch_size: 8,
epochs: 50,
verbose: 1,
validation_data: new NDarray[] { nTest.Item2, nTest.Item1 });
//Save model and weights
string json = model.ToJson();
File.WriteAllText("./models/sm_model.json", json);
model.SaveWeight("./models/sm_model.h5");
}
public void SaveFiles()
{
if (model == null || string.IsNullOrEmpty(starttime) || string.IsNullOrEmpty(config.Guid))
{
throw new Exception("Model Save Error");
}
config.StartTime = starttime;
config.Model = model.ToJson();
string filename = Utils.GetFileWithoutExtension(model_dir, starttime);
FileIO.SerializeXml(config, filename + config_ext);
model.SaveWeight(filename + model_ext);
}
private static void train()
{
try
{
Console.WriteLine("Начало тренировки нейросети");
var rows = File.ReadAllLines(FILE_PATH).Skip(1).Take(TRAIN_ROWS_COUNT).ToList();
Console.WriteLine("Заполняем датасет данными");
float[,] inputArray = new float[rows.Count, INPUT_LAYER_SIZE];
float[,] outputArray = new float[rows.Count, RESULT_LAYER_SIZE];
for (int i = 0; i < rows.Count; i++)
{
Console.WriteLine("Итерация {0} из {1}", i + 1, TRAIN_ROWS_COUNT);
var values = rows[i].Split(',');
var correctNumber = byte.Parse(values[0]);
byte[] inputValues = values.Skip(1).Select(x => byte.Parse(x)).ToArray();
for (int j = 0; j < inputValues.Length; j++)
{
inputArray[i, j] = inputValues[j];
}
outputArray[i, correctNumber] = 1;
}
var input = new NDarray(inputArray);
input = input.astype(np.float32);
input /= 255;
var output = new NDarray(outputArray);
Console.WriteLine("Запускаем обучение");
model.Fit(input, output, batch_size: batchSize, epochs: epochs, verbose: 2);
Console.WriteLine("Сохраняем модель");
File.WriteAllText("model.json", model.ToJson());
model.SaveWeight("model.h5");
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
throw;
}
}
public static void Run()
{
//Load train data
NDarray dataset = np.loadtxt(fname: "C:/Project/LSTMCoreApp/pima-indians-diabetes.data.csv", delimiter: ",");
var X = dataset[":,0: 8"];
var Y = dataset[":, 8"];
//Build sequential model
var model = new Sequential();
model.Add(new Dense(12, input_dim: 8, kernel_initializer: "uniform", activation: "relu"));
model.Add(new Dense(8, kernel_initializer: "uniform", activation: "relu"));
model.Add(new Dense(1, activation: "sigmoid"));
//Compile and train
model.Compile(optimizer: "adam", loss: "binary_crossentropy", metrics: new string[] { "accuracy" });
model.Fit(X, Y, batch_size: 10, epochs: 150, verbose: 1);
//Evaluate model
var scores = model.Evaluate(X, Y, verbose: 1);
Console.WriteLine("Accuracy: {0}", scores[1] * 100);
//Save model and weights
string json = model.ToJson();
File.WriteAllText("model.json", json);
model.SaveWeight("model.h5");
Console.WriteLine("Saved model to disk");
//Load model and weight
var loaded_model = Sequential.ModelFromJson(File.ReadAllText("model.json"));
loaded_model.LoadWeight("model.h5");
Console.WriteLine("Loaded model from disk");
loaded_model.Compile(optimizer: "rmsprop", loss: "binary_crossentropy", metrics: new string[] { "accuracy" });
scores = model.Evaluate(X, Y, verbose: 1);
Console.WriteLine("Accuracy: {0}", scores[1] * 100);
}
public static void Run()
{
int batch_size = 128;
int num_classes = 10;
int epochs = 100;
// the data, split between train and test sets
var((x_train, y_train), (x_test, y_test)) = Cifar10.LoadData();
Console.WriteLine("x_train shape: " + x_train.shape);
Console.WriteLine(x_train.shape[0] + " train samples");
Console.WriteLine(x_test.shape[0] + " test samples");
// convert class vectors to binary class matrices
y_train = Util.ToCategorical(y_train, num_classes);
y_test = Util.ToCategorical(y_test, num_classes);
// Build CNN model
var model = new Sequential();
model.Add(new Conv2D(32, kernel_size: (3, 3).ToTuple(),
padding: "same",
input_shape: new Shape(32, 32, 3)));
model.Add(new Activation("relu"));
model.Add(new Conv2D(32, (3, 3).ToTuple()));
model.Add(new Activation("relu"));
model.Add(new MaxPooling2D(pool_size: (2, 2).ToTuple()));
model.Add(new Dropout(0.25));
model.Add(new Conv2D(64, kernel_size: (3, 3).ToTuple(),
padding: "same"));
model.Add(new Activation("relu"));
model.Add(new Conv2D(64, (3, 3).ToTuple()));
model.Add(new Activation("relu"));
model.Add(new MaxPooling2D(pool_size: (2, 2).ToTuple()));
model.Add(new Dropout(0.25));
model.Add(new Flatten());
model.Add(new Dense(512));
model.Add(new Activation("relu"));
model.Add(new Dropout(0.5));
model.Add(new Dense(num_classes));
model.Add(new Activation("softmax"));
model.Compile(loss: "categorical_crossentropy",
optimizer: new RMSprop(lr: 0.0001f, decay: 1e-6f), metrics: new string[] { "accuracy" });
x_train = x_train.astype(np.float32);
x_test = x_test.astype(np.float32);
x_train /= 255;
x_test /= 255;
model.Fit(x_train, y_train,
batch_size: batch_size,
epochs: epochs,
verbose: 1,
validation_data: new NDarray[] { x_test, y_test },
shuffle: true);
//Save model and weights
//string model_path = "./model.json";
//string weight_path = "weights.h5";
//string json = model.ToJson();
//File.WriteAllText(model_path, json);
model.SaveWeight("weights.h5");
model.Save("model.h5");
model.SaveTensorflowJSFormat("./");
//Score trained model.
var score = model.Evaluate(x_test, y_test, verbose: 0);
Console.WriteLine("Test loss:" + score[0]);
Console.WriteLine("Test accuracy:" + score[1]);
}