public static void Main(string[] args)
{
// Keras speed with the same: 60 ms.
/*ReaderKerasModel reader = new ReaderKerasModel("test_cnn_model.json");
* SequentialModel model = reader.GetSequentialExecutor();
*
* Console.WriteLine((model.GetSummary() as SequentialModelData).GetStringRepresentation());
*
* Console.ReadKey();
* int[] idx = { 1,2,3};
*
* Console.WriteLine(idx[1]);
* Console.ReadKey();*/
Conv2DLayer layer = new Conv2DLayer(0, 0, 1, 1);
Data2D input = new Data2D(6, 5, 3, 1);
input[0, 0, 0, 0] = 1; input[0, 1, 0, 0] = 2; input[0, 2, 0, 0] = 2; input[0, 3, 0, 0] = 1; input[0, 4, 0, 0] = 4;
input[1, 0, 0, 0] = 3; input[1, 1, 0, 0] = 1; input[1, 2, 0, 0] = 0; input[1, 3, 0, 0] = 2; input[1, 4, 0, 0] = 1;
input[2, 0, 0, 0] = 0; input[2, 1, 0, 0] = 2; input[2, 2, 0, 0] = 2; input[2, 3, 0, 0] = 5; input[2, 4, 0, 0] = 2;
input[3, 0, 0, 0] = 6; input[3, 1, 0, 0] = -2; input[3, 2, 0, 0] = -1; input[3, 3, 0, 0] = 3; input[3, 4, 0, 0] = 1;
input[4, 0, 0, 0] = 2; input[4, 1, 0, 0] = 1; input[4, 2, 0, 0] = 2; input[4, 3, 0, 0] = 4; input[4, 4, 0, 0] = 0;
input[5, 0, 0, 0] = 5; input[5, 1, 0, 0] = -3; input[5, 2, 0, 0] = -1; input[5, 3, 0, 0] = -4; input[5, 4, 0, 0] = 0;
input[0, 0, 1, 0] = 2; input[0, 1, 1, 0] = 0; input[0, 2, 1, 0] = 2; input[0, 3, 1, 0] = -1; input[0, 4, 1, 0] = 3;
input[1, 0, 1, 0] = 2; input[1, 1, 1, 0] = 5; input[1, 2, 1, 0] = -1; input[1, 3, 1, 0] = 3; input[1, 4, 1, 0] = 5;
input[2, 0, 1, 0] = 1; input[2, 1, 1, 0] = 1; input[2, 2, 1, 0] = 1; input[2, 3, 1, 0] = 0; input[2, 4, 1, 0] = 1;
input[3, 0, 1, 0] = -3; input[3, 1, 1, 0] = 2; input[3, 2, 1, 0] = -1; input[3, 3, 1, 0] = 4; input[3, 4, 1, 0] = 1;
input[4, 0, 1, 0] = 2; input[4, 1, 1, 0] = 1; input[4, 2, 1, 0] = 2; input[4, 3, 1, 0] = 2; input[4, 4, 1, 0] = 1;
input[5, 0, 1, 0] = 0; input[5, 1, 1, 0] = -3; input[5, 2, 1, 0] = 1; input[5, 3, 1, 0] = -2; input[5, 4, 1, 0] = -1;
input[0, 0, 2, 0] = 4; input[0, 1, 2, 0] = 5; input[0, 2, 2, 0] = 0; input[0, 3, 2, 0] = -1; input[0, 4, 2, 0] = -3;
input[1, 0, 2, 0] = 2; input[1, 1, 2, 0] = 3; input[1, 2, 2, 0] = 1; input[1, 3, 2, 0] = 6; input[1, 4, 2, 0] = 0;
input[2, 0, 2, 0] = 0; input[2, 1, 2, 0] = -4; input[2, 2, 2, 0] = -3; input[2, 3, 2, 0] = -2; input[2, 4, 2, 0] = -4;
input[3, 0, 2, 0] = 4; input[3, 1, 2, 0] = 2; input[3, 2, 2, 0] = 1; input[3, 3, 2, 0] = 0; input[3, 4, 2, 0] = 4;
input[4, 0, 2, 0] = 3; input[4, 1, 2, 0] = 3; input[4, 2, 2, 0] = 0; input[4, 3, 2, 0] = 1; input[4, 4, 2, 0] = 1;
input[5, 0, 2, 0] = -2; input[5, 1, 2, 0] = 1; input[5, 2, 2, 0] = 1; input[5, 3, 2, 0] = 0; input[5, 4, 2, 0] = 5;
Data2D kernel = new Data2D(3, 3, 3, 1);
kernel[0, 0, 0, 0] = 1; kernel[0, 1, 0, 0] = 1; kernel[0, 2, 0, 0] = 0;
kernel[1, 0, 0, 0] = 2; kernel[1, 1, 0, 0] = 0; kernel[1, 2, 0, 0] = 0;
kernel[2, 0, 0, 0] = 1; kernel[2, 1, 0, 0] = 2; kernel[2, 2, 0, 0] = 1;
kernel[0, 0, 1, 0] = 3; kernel[0, 1, 1, 0] = 1; kernel[0, 2, 1, 0] = -1;
kernel[1, 0, 1, 0] = 2; kernel[1, 1, 1, 0] = -1; kernel[1, 2, 1, 0] = -2;
kernel[2, 0, 1, 0] = 0; kernel[2, 1, 1, 0] = 1; kernel[2, 2, 1, 0] = 2;
kernel[0, 0, 2, 0] = 0; kernel[0, 1, 2, 0] = 1; kernel[0, 2, 2, 0] = 1;
kernel[1, 0, 2, 0] = -1; kernel[1, 1, 2, 0] = 2; kernel[1, 2, 2, 0] = 1;
kernel[2, 0, 2, 0] = 3; kernel[2, 1, 2, 0] = 0; kernel[2, 2, 2, 0] = 1;
layer.SetWeights(kernel);
layer.SetInput(input);
layer.Execute();
Data2D output = layer.GetOutput() as Data2D;
}
public void Test_Conv2D_Null_Weights()
{
Data2D weights = null;
Conv2DLayer conv = new Conv2DLayer(1, 1, 1, 1);
conv.SetWeights(weights);
}
public void Test_Conv2D_DifferentData_Weights()
{
DataArray weights = new DataArray(5);
Conv2DLayer conv = new Conv2DLayer(1, 1, 1, 1);
conv.SetWeights(weights);
}
public void Test_Conv2D_Null_Input()
{
Data2D data = null;
Data2D weights = new Data2D(3, 3, 3, 3);
Conv2DLayer conv = new Conv2DLayer(1, 1, 1, 1);
conv.SetWeights(weights);
conv.SetInput(data);
}
public void Test_Conv2D_DifferentData_Input()
{
DataArray data = new DataArray(5);
Data2D weights = new Data2D(3, 3, 3, 3);
Conv2DLayer conv = new Conv2DLayer(1, 1, 1, 1);
conv.SetWeights(weights);
conv.SetInput(data);
}
public void Test_Conv2D_Execute()
{
// Initialize data.
Data2D data = new Data2D(3, 3, 2, 1);
data[0, 0, 0, 0] = 1;
data[1, 0, 0, 0] = 2;
data[2, 0, 0, 0] = 0;
data[0, 1, 0, 0] = 3;
data[1, 1, 0, 0] = 4;
data[2, 1, 0, 0] = 0;
data[0, 2, 0, 0] = 2;
data[1, 2, 0, 0] = 2;
data[2, 2, 0, 0] = 0;
data[0, 0, 1, 0] = 0;
data[1, 0, 1, 0] = 3;
data[2, 0, 1, 0] = 1;
data[0, 1, 1, 0] = 1;
data[1, 1, 1, 0] = 1;
data[2, 1, 1, 0] = 1;
data[0, 2, 1, 0] = 3;
data[1, 2, 1, 0] = 1;
data[2, 2, 1, 0] = 0;
// Initialize weights.
Data2D weights = new Data2D(2, 2, 2, 1);
weights[0, 0, 0, 0] = 1;
weights[1, 0, 0, 0] = 2;
weights[0, 1, 0, 0] = 2;
weights[1, 1, 0, 0] = 3;
weights[0, 0, 1, 0] = 1;
weights[1, 0, 1, 0] = 1;
weights[0, 1, 1, 0] = 1;
weights[1, 1, 1, 0] = 3;
Conv2DLayer conv = new Conv2DLayer(0, 0, 1, 1);
conv.SetWeights(weights);
conv.SetInput(data);
conv.Execute();
Data2D output = conv.GetOutput() as Data2D;
// Checking sizes
Dimension dim = output.GetDimension();
Assert.AreEqual(dim.b, 1);
Assert.AreEqual(dim.c, 1);
Assert.AreEqual(dim.h, 2);
Assert.AreEqual(dim.w, 2);
// Checking calculation
Assert.AreEqual(output[0, 0, 0, 0], 30.0, 0.0000001);
Assert.AreEqual(output[1, 0, 0, 0], 18.0, 0.0000001);
Assert.AreEqual(output[0, 1, 0, 0], 29.0, 0.0000001);
Assert.AreEqual(output[1, 1, 0, 0], 11.0, 0.0000001);
}