public static List <PredResult> Cifar10Test(Cifar10Model model)
{
string imagePath = string.Format("{0}images\\dog_cls.jpg", AppDomain.CurrentDomain.BaseDirectory);
Cifar10 app = new Cifar10(model);
app.LoadModel();
return(app.Predict(imagePath));
}
public void download_test()
{
string basePath = Path.Combine(NUnit.Framework.TestContext.CurrentContext.WorkDirectory, "cifar10");
var c = new Cifar10(basePath);
Assert.AreEqual(50000, c.Training.Item1.Length);
Assert.AreEqual(50000, c.Training.Item2.Length);
Assert.AreEqual(10000, c.Testing.Item1.Length);
Assert.AreEqual(10000, c.Testing.Item2.Length);
int[] hist;
hist = Vector.Histogram(c.Training.Item2);
Assert.AreEqual(Vector.Create(size: 10, value: 5000), hist);
hist = Vector.Histogram(c.Testing.Item2);
Assert.AreEqual(Vector.Create(size: 10, value: 1000), hist);
}
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(weight_path);
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]);
}