void create_network()
{
Console.WriteLine("Compute Device: " + computeDevice.AsString());
imageVariable = Util.inputVariable(new int[] { 28, 28, 1 }, "image_tensor");
categoricalVariable = Util.inputVariable(new int[] { 10 }, "label_tensor");
network = imageVariable;
network = Layers.Convolution2D(network, 32, new int[] { 3, 3 }, computeDevice, CC.ReLU);
network = CC.Pooling(network, C.PoolingType.Max, new int[] { 2, 2 }, new int[] { 2 });
network = Layers.Convolution2D(network, 64, new int[] { 3, 3 }, computeDevice, CC.ReLU);
network = CC.Pooling(network, C.PoolingType.Max, new int[] { 2, 2 }, new int[] { 2 });
network = Layers.Convolution2D(network, 64, new int[] { 3, 3 }, computeDevice, CC.ReLU);
network = Layers.Dense(network, 64, computeDevice, activation: CC.ReLU);
network = Layers.Dense(network, 10, computeDevice);
Logging.detailed_summary(network);
Logging.log_number_of_parameters(network);
loss_function = CC.CrossEntropyWithSoftmax(network, categoricalVariable);
eval_function = CC.ClassificationError(network, categoricalVariable);
learner = CC.AdamLearner(
new C.ParameterVector(network.Parameters().ToArray()),
new C.TrainingParameterScheduleDouble(0.001 * batch_size, (uint)batch_size),
new C.TrainingParameterScheduleDouble(0.9),
true,
new C.TrainingParameterScheduleDouble(0.99));
trainer = CC.CreateTrainer(network, loss_function, eval_function, new C.LearnerVector(new C.Learner[] { learner }));
evaluator = CC.CreateEvaluator(eval_function);
}
public NDArrayView[] GetParameters()
{
return(Model.Parameters().Select(parameter => parameter.GetValue()).ToArray());
}
