public static void Run(int epochs = 5)
{
// requires Internet connection
(dynamic train, dynamic test) = tf.keras.datasets.fashion_mnist.load_data();
// will be able to do (trainImages, trainLabels) = train;
ndarray trainImages = np.expand_dims(train.Item1 / 255.0f, axis: 3);
ndarray trainLabels = train.Item2;
ndarray testImages = np.expand_dims(test.Item1 / 255.0f, axis: 3);
ndarray testLabels = test.Item2;
bool loaded = 60000 == trainImages.Length;
Debug.Assert(loaded);
var model = new Sequential(new Layer[] {
// will be able to do: new Flatten(kwargs: new { input_shape = (28, 28) }),
new ResNetBlock(kernelSize: 3, filters: new [] { 1, 2, 3 }),
new ResNetBlock(kernelSize: 3, filters: new [] { 1, 2, 3 }),
new Flatten(),
new Dense(units: 10, activation: tf.nn.softmax_fn),
});
model.compile(
optimizer: new AdamOptimizer(),
loss: "sparse_categorical_crossentropy",
metrics: new dynamic[] { "accuracy" });
model.fit(trainImages, trainLabels, epochs: epochs);
var testEvalResult = model.evaluate(testImages, testLabels);
double testAcc = testEvalResult[1];
Console.WriteLine($"Test accuracy: {testAcc}");
model.summary();
}