private static void TestDense()
{
Tensor tensor = Tensor.FromArray(Global.Device, new float[] { -1, 2, 3, -4, 5, 6, 7, -8, 9 });
tensor = tensor.Reshape(3, -1);
Dense d = new Dense(6, ActivationType.Linear, new Ones(), null, null, true, new Ones());
d.Forward(Variable.Create(tensor));
d.Output.Print();
d.Backward(d.Output);
d.Input.Grad.Print();
d.Params["w"].Grad.Print();
d.Params["b"].Grad.Print();
}
public void ExecuteTest()
{
int inchannels = 4, outchannels = 6, batch = 7;
VariableField x = new Tensor(Shape.Map0D(inchannels, batch));
Layer layer = new Dense(inchannels, outchannels, use_bias: true, "fc");
Field y = layer.Forward(x);
(Flow flow, Parameters parameters) = Flow.Optimize(y);
flow.Execute();
Assert.AreEqual(2, parameters.Count);
Assert.AreEqual(inchannels, layer.InChannels);
Assert.AreEqual(outchannels, layer.OutChannels);
}
public static Field Forward(Field x, int classes)
{
Convolution2D conv1 =
new Convolution2D(
inchannels: 1, outchannels: 4,
kwidth: 3, kheight: 3,
stride: 1, use_bias: true,
pad_mode: PaddingMode.Zero, label: "conv1");
Convolution2D conv2 =
new Convolution2D(
inchannels: 4, outchannels: 8,
kwidth: 3, kheight: 3,
stride: 1, use_bias: true,
pad_mode: PaddingMode.Zero, label: "conv2");
Convolution2D conv3 =
new Convolution2D(
inchannels: 8, outchannels: 16,
kwidth: 3, kheight: 3,
stride: 1, use_bias: true,
pad_mode: PaddingMode.Zero, label: "conv3");
Field h1 = Relu(conv1.Forward(x));
Field h2 = MaxPooling2D(h1, stride: 2);
Field h3 = Relu(conv2.Forward(h2));
Field h4 = MaxPooling2D(h3, stride: 2);
Field h5 = Relu(conv3.Forward(h4));
Dense fc =
new Dense(
inchannels: h5.Shape.DataSize, outchannels: classes,
use_bias: true, label: "fc");
Field y = fc.Forward(h5);
return(y);
}