public void ToFlatten()
{
Shape shape = new Shape(2, 2, 2, 2);
float[] data =
{
1, 2,
3, 4,
5, 6,
7, 8,
9, 10,
11, 12,
13, 14,
15, 16
};
Tensor x = CreateTensor(shape, data);
Tensor y = CreateTensor();
x.ToFlatten(y);
Assert.Equal(2, y.Batch);
Assert.Equal(1, y.Channels);
Assert.Equal(1, y.Height);
Assert.Equal(8, y.Width);
Assert.True(FloatComparison.AreEqual(data, y.Storage.Data));
}
public void SoftmaxDx()
{
Shape yShape = new Shape(2, 1, 1, 5);
Shape dyShape = new Shape(2, 1, 1, 5);
float[] yData =
{
1, 4.5f, 0, 2, 3.1f,
0, 1.5f, 2.9f, 1, 0.2f
};
float[] dyData =
{
0.5f, 3, 0.4f, 1, 2.5f,
0.01f, 1.5f, 0, 3.1f, 0.28f
};
float[] dxExp =
{
-23.25f, -93.375f, 0, -45.5f, -65.875f,
0, -5.859f, -15.6774f, -2.306f, -1.0252f
};
Tensor dx = CreateTensor();
Tensor y = CreateTensor(yShape, yData);
Tensor dy = CreateTensor(dyShape, dyData);
y.SoftmaxDx(dy, dx);
Assert.True(FloatComparison.AreEqual(dxExp, dx.Storage.Data));
}
public void Pad()
{
Shape shape = new Shape(2, 2, 1, 1);
float[] data =
{
0.2f,
1,
2.8f,
3
};
float[] res = new float[36];
res[4] = 0.2f;
res[13] = 1;
res[22] = 2.8f;
res[31] = 3;
Tensor t = CreateTensor(shape, data);
Tensor tRes = CreateTensor();
t.Pad(1, tRes);
Assert.Equal(2, tRes.Batch);
Assert.Equal(2, tRes.Channels);
Assert.Equal(3, tRes.Height);
Assert.Equal(3, tRes.Width);
Assert.True(FloatComparison.AreEqual(res, tRes.Storage.Data));
}
public void Transpose2D()
{
Shape shape = new Shape(1, 1, 2, 3);
float[] data =
{
0.2f, 1, 2.8f,
3, 4.1f, 5
};
float[] res =
{
0.2f, 3,
1, 4.1f,
2.8f, 5
};
Tensor t = CreateTensor(shape, data);
Tensor tRes = CreateTensor();
t.Transpose2D(tRes);
Assert.Equal(1, tRes.Batch);
Assert.Equal(1, tRes.Channels);
Assert.Equal(3, tRes.Height);
Assert.Equal(2, tRes.Width);
Assert.True(FloatComparison.AreEqual(res, tRes.Storage.Data));
}
public void Fill()
{
Shape shape = new Shape(1, 2, 2, 2);
float[] data =
{
0, 0,
0, 0,
0, 0,
0, 0
};
float[] res =
{
3, 3,
3, 3,
3, 3,
3, 3
};
Tensor t = CreateTensor(shape, data);
t.Fill(3);
Assert.True(FloatComparison.AreEqual(res, t.Storage.Data));
}
public void MaxPool()
{
Shape shape = new Shape(2, 2, 3, 3);
float[] data =
{
0.2f, 1, 2.8f,
3, 4.1f, 5,
1, 2.2f, 4,
1.8f, 5, 8,
2, 2.1f, 0,
0.4f, 0.9f, 0,
1, 0, 2,
2.1f, 4.5f, 0,
3, 8, 1,
2, 3, 4,
7, 7.2f, 0.1f,
0, 0, 2
};
float[] res =
{
4.1f, 5,
4.1f, 5,
5, 8,
2.1f, 2.1f,
4.5f, 4.5f,
8, 8,
7.2f, 7.2f,
7.2f, 7.2f
};
float[] maxInd =
{
4, 5, 4, 5, 10, 11, 13, 13, 22, 22, 25, 25, 31, 31, 31, 31
};
Tensor t = CreateTensor(shape, data);
Tensor tRes = CreateTensor();
Tensor tMaxInd = CreateTensor();
t.MaxPool(2, 1, tRes, tMaxInd);
Assert.Equal(2, tRes.Batch);
Assert.Equal(2, tRes.Channels);
Assert.Equal(2, tRes.Height);
Assert.Equal(2, tRes.Width);
Assert.True(FloatComparison.AreEqual(res, tRes.Storage.Data));
Assert.True(FloatComparison.AreEqual(maxInd, tMaxInd.Storage.Data));
}
public void FullyConnectedDx()
{
Shape xShape = new Shape(2, 1, 1, 4);
Shape dyShape = new Shape(2, 1, 1, 2);
Shape wShape = new Shape(1, 1, 4, 2);
float[] wData =
{
0, 1.5f,
2, 0.2f,
5, 2.5f,
0.1f, 4
};
float[] xData =
{
2, 1.5f, 0.1f, 0.25f,
2, 1.5f, 0.1f, 0.25f
};
float[] dyData =
{
1, 2.5f,
0, 1.2f
};
float[] dxExp =
{
3.75f, 2.5f, 11.25f,
10.1f, 1.8f, 0.24f,3, 4.8f
};
Tensor x = CreateTensor(xShape, xData);
Tensor w = CreateTensor(wShape, wData);
Tensor dy = CreateTensor(dyShape, dyData);
Tensor dx = CreateTensor();
x.FullyConnectedDx(w, dy, CreateTensor(), dx);
Assert.Equal(2, dx.Batch);
Assert.Equal(1, dx.Channels);
Assert.Equal(1, dx.Height);
Assert.Equal(4, dx.Width);
Assert.True(FloatComparison.AreEqual(dxExp, dx.Storage.Data));
}
public void Im2Col()
{
Shape shape = new Shape(2, 2, 3, 3);
float[] data =
{
//1st batch
0.2f, 1, 2.8f,
3, 4.1f, 5,
1, 2.2f, 4,
1.8f, 5, 8,
2, 2.1f, 0,
0.4f, 0.9f, 0,
//2nd batch
1, 0, 2,
2.1f, 4.5f, 0,
3, 8, 1,
2, 3, 4,
7, 7.2f, 0.1f,
0, 0, 2
};
float[] res =
{
0.2f, 1, 3, 4.1f, 1, 0, 2.1f, 4.5f,
1, 2.8f, 4.1f, 5, 0, 2, 4.5f, 0,
3, 4.1f, 1, 2.2f, 2.1f, 4.5f, 3, 8,
4.1f, 5, 2.2f, 4, 4.5f, 0, 8, 1,
1.8f, 5, 2, 2.1f, 2, 3, 7, 7.2f,
5, 8, 2.1f, 0, 3, 4, 7.2f, 0.1f,
2, 2.1f, 0.4f, 0.9f, 7, 7.2f, 0, 0,
2.1f, 0, 0.9f, 0, 7.2f, 0.1f, 0, 2
};
Tensor t = CreateTensor(shape, data);
Tensor tRes = CreateTensor();
t.Im2Col(2, 2, 1, tRes);
Assert.Equal(1, tRes.Batch);
Assert.Equal(1, tRes.Channels);
Assert.Equal(8, tRes.Height);
Assert.Equal(8, tRes.Width);
Assert.True(FloatComparison.AreEqual(res, tRes.Storage.Data));
}
public void Rotate180()
{
Shape shape = new Shape(2, 2, 2, 2);
float[] data =
{
0f, 1,
2, 3,
4, 5,
6, 7,
8, 9,
10, 11,
12, 13,
14, 15
};
float[] res =
{
3f, 2,
1, 0,
7, 6,
5, 4,
11, 10,
9, 8,
15, 14,
13, 12
};
Tensor t = CreateTensor(shape, data);
Tensor tRes = CreateTensor();
t.Rotate180(tRes);
Assert.Equal(2, tRes.Batch);
Assert.Equal(2, tRes.Channels);
Assert.Equal(2, tRes.Height);
Assert.Equal(2, tRes.Width);
Assert.True(FloatComparison.AreEqual(res, tRes.Storage.Data));
}
public void MeanSquaredError()
{
var oData = new [] { 0.5f, 1, 2, 0.1f, 1.2f, 1.4f, 3, 1, 2.5f, 1.1f };
var tData = new[] { 1, 0.2f, 2.3f, 0.3f, 2, 2, 0.5f, 1, 2.8f, 0.05f };
var shape = new Shape(2, 1, 1, 5);
var o = CreateTensor(shape, oData);
var t = CreateTensor(shape, tData);
var loss = CreateTensor();
o.Loss(t, new MeanSquaredError(), loss);
Assert.Equal(2, loss.Size);
Assert.Equal(2, loss.Batch);
Assert.True(FloatComparison.AreEqual(0.331f, loss[0]));
Assert.True(FloatComparison.AreEqual(1.56f, loss[1]));
}
public void CrossEntropy()
{
var oData = new [] { 0.5f, 1, 2, 0.1f, 1.2f, 1.4f, 3, 1, 2.5f, 1.1f };
var tData = new[] { 1, 0.2f, 2.3f, 0.3f, 2, 2, 0.5f, 1, 2.8f, 0.05f };
var shape = new Shape(2, 1, 1, 5);
var o = CreateTensor(shape, oData);
var t = CreateTensor(shape, tData);
var loss = CreateTensor();
o.Loss(t, new CrossEntropy(), loss);
Assert.Equal(2, loss.Size);
Assert.Equal(2, loss.Batch);
Assert.True(FloatComparison.AreEqual(-0.575f, loss[0]));
Assert.True(FloatComparison.AreEqual(-3.792f, loss[1]));
}
public void Dot2D_ExplicitDimensions()
{
Shape shapeA = new Shape(3, 1, 1, 2);
Shape shapeB = new Shape(1, 1, 2, 4);
Shape shapeC = new Shape(3, 1, 1, 4);
float[] dataA =
{
0.2f, 1,
2.8f, 3,
4.1f, 5
};
float[] dataB =
{
0.1f, 2, 3.5f, 6,
4, 8.8f, 0, 3
};
float[] res =
{
4.02f, 9.2f, 0.7f, 4.2f,
12.28f, 32, 9.8f, 25.8f,
20.41f, 52.2f, 14.35f, 39.6f
};
Tensor a = CreateTensor(shapeA, dataA);
Tensor b = CreateTensor(shapeB, dataB);
Tensor c = CreateTensor();
a.Dot2D(b, a.Batch, a.Width, b.Height, b.Width, shapeC, c);
Assert.Equal(3, c.Batch);
Assert.Equal(1, c.Channels);
Assert.Equal(1, c.Height);
Assert.Equal(4, c.Width);
Assert.True(FloatComparison.AreEqual(res, c.Storage.Data));
}
public void FlattenDx()
{
Shape dyShape = new Shape(1, 1, 1, 16);
Shape xShape = new Shape(2, 2, 2, 2);
float[] dyData = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 };
float[] xData = new float[16];
Tensor dy = CreateTensor(dyShape, dyData);
Tensor x = CreateTensor(xShape, xData);
Tensor dx = CreateTensor();
x.FlattenDx(dy, dx);
Assert.Equal(2, dx.Batch);
Assert.Equal(2, dx.Channels);
Assert.Equal(2, dx.Height);
Assert.Equal(2, dx.Width);
Assert.True(FloatComparison.AreEqual(dyData, dx.Storage.Data));
}
public void FullyConnectedDw()
{
Shape xShape = new Shape(2, 1, 1, 4);
Shape dyShape = new Shape(2, 1, 1, 2);
float[] xData =
{
2, 1.5f, 0.1f, 0.25f,
0, 3.5f, 7.1f, 3.29f
};
float[] dyData =
{
1, 2.5f,
0, 1.2f
};
float[] dwExp =
{
2, 5,
1.5f, 7.95f,
0.1f, 8.77f,
0.25f, 4.573f
};
Tensor x = CreateTensor(xShape, xData);
Tensor dy = CreateTensor(dyShape, dyData);
Tensor dw = CreateTensor();
x.FullyConnectedDw(dy, CreateTensor(), dw);
Assert.Equal(1, dw.Batch);
Assert.Equal(1, dw.Channels);
Assert.Equal(4, dw.Height);
Assert.Equal(2, dw.Width);
Assert.True(FloatComparison.AreEqual(dwExp, dw.Storage.Data));
}
public void Sum()
{
Shape shapeA = new Shape(1, 2, 2, 2);
Shape shapeB = new Shape(1, 2, 2, 2);
float[] dataA =
{
0.2f, 1,
2.8f, 3,
4.1f, 5,
1, 2.2f
};
float[] dataB =
{
0.1f, 2,
3.5f, 6,
4, 8.8f,
1.3f, 4.8f
};
float[] res =
{
0.3f, 3,
6.3f, 9,
8.1f, 13.8f,
2.3f, 7
};
Tensor a = CreateTensor(shapeA, dataA);
Tensor b = CreateTensor(shapeB, dataB);
a.Sum(b);
Assert.True(FloatComparison.AreEqual(res, a.Storage.Data));
}
public void Col2Im()
{
var shape = new Shape(1, 1, 2, 8);
var outShape = new Shape(2, 2, 2, 2);
float[] data =
{
4.1f, 5, 1.1f, 2, 5, 8, 2.1f, 2.1f,
0.1f, 1, 3.5f, 0.05f, 5, 0.8f, 3, 0
};
float[] res =
{
4.1f, 5,
1.1f, 2,
0.1f, 1,
3.5f, 0.05f,
5, 8,
2.1f, 2.1f,
5, 0.8f,
3, 0
};
var t = CreateTensor(shape, data);
var tRes = CreateTensor();
t.Col2Im(outShape, tRes);
Assert.Equal(2, tRes.Batch);
Assert.Equal(2, tRes.Channels);
Assert.Equal(2, tRes.Height);
Assert.Equal(2, tRes.Width);
Assert.True(FloatComparison.AreEqual(res, tRes.Storage.Data));
}
public void MaxPoolDx()
{
Shape xShape = new Shape(1, 2, 3, 3);
Shape dyShape = new Shape(1, 2, 2, 2);
Shape maxIndShape = new Shape(1, 1, 1, 8);
float[] x = new float[18];
float[] dy =
{
4.1f, 5,
4.1f, 5,
5, 8,
2.1f, 2.1f
};
float[] maxInd = { 4, 5, 4, 5, 10, 11, 13, 13 };
float[] dx = new float[18];
dx[4] = 4.1f;
dx[5] = 5;
dx[10] = 5;
dx[11] = 8;
dx[13] = 2.1f;
Tensor xTensor = CreateTensor(xShape, x);
Tensor dyTensor = CreateTensor(dyShape, dy);
Tensor maxTensor = CreateTensor(maxIndShape, maxInd);
Tensor dxTensor = CreateTensor();
xTensor.MaxPoolDx(dyTensor, maxTensor, dxTensor);
Assert.Equal(1, dxTensor.Batch);
Assert.Equal(2, dxTensor.Channels);
Assert.Equal(3, dxTensor.Height);
Assert.Equal(3, dxTensor.Width);
Assert.True(FloatComparison.AreEqual(dx, dxTensor.Storage.Data));
}
public void Softmax()
{
Shape shape = new Shape(2, 1, 1, 5);
float[] xData =
{
1, 4.5f, 0, 2, 3.1f,
0, 3.5f, 0.8f, 0.25f, 2.5f
};
float[] yExp =
{
0.022f, 0.7299f, 0.0081f, 0.0599f, 0.1799f,
0.02f, 0.66486f, 0.044682f, 0.02577f, 0.24459f
};
Tensor x = CreateTensor(shape, xData);
Tensor y = CreateTensor();
Tensor max = CreateTensor();
x.Softmax(y, max);
Assert.True(FloatComparison.AreEqual(yExp, y.Storage.Data));
}
public void ConvolutionDx()
{
Shape xShape = new Shape(2, 2, 3, 3);
Shape wShape = new Shape(2, 2, 2, 2);
Shape dyShape = new Shape(2, 2, 2, 2);
float[] xData =
{
//1st batch
0.2f, 1, 2.8f,
3, 4.1f, 5,
1, 2.2f, 4,
1.8f, 5, 8,
2, 2.1f, 0,
0.4f, 0.9f, 0,
//2nd batch
1, 0, 2,
2.1f, 4.5f, 0,
3, 8, 1,
2, 3, 4,
7, 7.2f, 0.1f,
0, 0, 2
};
float[] wData =
{
0.1f, 2,
3.5f, 6,
4, 8.8f,
1.3f, 4.8f,
1, 0,
5, 0,
0, 3.5f,
4, 0.5f
};
float[] dyData =
{
10, 1.1f,
6.5f, 1.6f,
4.7f, 7.9f,
7.4f, 8.7f,
0, 1.9f,
0.5f, 1.8f,
4.7f, 1,
7.7f, 0.7f
};
float[] dxExp =
{
5.7f, 28.01f, 2.2f,
66.55f, 125.21f, 9.8f,
59.75f, 88.1f, 9.6f,
40f, 108.85f, 37.33f,
57.8f, 172.88f, 53.76f,
38.05f, 71.78f, 12.03f,
4.7f, 1.19f, 3.8f,
31.25f, 13.53f, 15f,
40.25f, 12.8f, 10.8f,
0f, 24.05f, 20.22f,
20.8f, 47.37f, 27.91f,
31.45f, 11.39f, 8.99f
};
var w = CreateTensor(wShape, wData);
var x = CreateTensor(xShape, xData);
var dy = CreateTensor(dyShape, dyData);
var dx = CreateTensor();
x.ConvolutionDx(w, dy, CreateTensor(), CreateTensor(), CreateTensor(), CreateTensor(), CreateTensor(), dx);
Assert.Equal(2, dx.Batch);
Assert.Equal(2, dx.Channels);
Assert.Equal(3, dx.Height);
Assert.Equal(3, dx.Width);
Assert.True(FloatComparison.AreEqual(dxExp, dx.Storage.Data));
}
public void Convolution()
{
Shape xShape = new Shape(2, 2, 3, 3);
Shape wShape = new Shape(2, 2, 2, 2);
float[] xData =
{
//1st batch
0.2f, 1, 2.8f,
3, 4.1f, 5,
1, 2.2f, 4,
1.8f, 5, 8,
2, 2.1f, 0,
0.4f, 0.9f, 0,
//2nd batch
1, 0, 2,
2.1f, 4.5f, 0,
3, 8, 1,
2, 3, 4,
7, 7.2f, 0.1f,
0, 0, 2
};
float[] wData =
{
0.1f, 2,
3.5f, 6,
4, 8.8f,
1.3f, 4.8f,
1, 0,
5, 0,
0, 3.5f,
4, 0.5f
};
float[] res =
{
101, 143.18f,
56.52f, 51.68f,
41.75f, 57.9f,
17.4f, 18.7f,
112.51f, 76.79f,
159.07f, 73.73f,
53.6f, 65.35f,
42.3f, 45.85f
};
Tensor x = CreateTensor(xShape, xData);
Tensor w = CreateTensor(wShape, wData);
Tensor y = CreateTensor();
Tensor im2ColBuffer = CreateTensor();
Tensor dotBuffer = CreateTensor();
x.Convolution(w, 1, im2ColBuffer, dotBuffer, y);
Assert.Equal(2, y.Batch);
Assert.Equal(2, y.Channels);
Assert.Equal(2, y.Height);
Assert.Equal(2, y.Width);
Assert.True(FloatComparison.AreEqual(res, y.Storage.Data));
}
public void ConvolutionDw()
{
Shape xShape = new Shape(2, 2, 3, 3);
Shape im2ColXShape = new Shape(1, 1, 8, 8);
Shape wShape = new Shape(2, 2, 2, 2);
Shape dyShape = new Shape(2, 2, 2, 2);
float[] xData =
{
//1st batch
0.2f, 1, 2.8f,
3, 4.1f, 5,
1, 2.2f, 4,
1.8f, 5, 8,
2, 2.1f, 0,
0.4f, 0.9f, 0,
//2nd batch
1, 0, 2,
2.1f, 4.5f, 0,
3, 8, 1,
2, 3, 4,
7, 7.2f, 0.1f,
0, 0, 2
};
float[] im2ColXData =
{
0.2f, 1, 3, 4.1f, 1, 0, 2.1f, 4.5f,
1, 2.8f, 4.1f, 5, 0, 2, 4.5f, 0,
3, 4.1f, 1, 2.2f, 2.1f, 4.5f, 3, 8,
4.1f, 5, 2.2f, 4, 4.5f, 0, 8, 1,
1.8f, 5, 2, 2.1f, 2, 3, 7, 7.2f,
5, 8, 2.1f, 0, 3, 4, 7.2f, 0.1f,
2, 2.1f, 0.4f, 0.9f, 7, 7.2f, 0, 0,
2.1f, 0, 0.9f, 0, 7.2f, 0.1f, 0, 2
};
float[] wData =
{
0.1f, 2,
3.5f, 6,
4, 8.8f,
1.3f, 4.8f,
1, 0,
5, 0,
0, 3.5f,
4, 0.5f
};
float[] dyData =
{
10, 1.1f,
6.5f, 1.6f,
4.7f, 7.9f,
7.4f, 8.7f,
0, 1.9f,
0.5f, 1.8f,
4.7f, 1,
7.7f, 0.7f
};
float[] dwExp =
{
38.31f, 53.78f,
68.98f, 73f,
62.02f, 83.83f,
40.03f, 30.64f,
90.73f, 137.31f,
116.1f, 193.3f,
152.37f, 175.85f,
76.88f, 51.87f
};
Tensor x = CreateTensor(xShape, xData);
Tensor im2ColX = CreateTensor(im2ColXShape, im2ColXData);
Tensor w = CreateTensor(wShape, wData);
Tensor dy = CreateTensor(dyShape, dyData);
Tensor dw = CreateTensor();
Tensor dy2DBuffer = CreateTensor();
Tensor dot2DBuffer = CreateTensor();
x.ConvolutionDw(w, dy, dy2DBuffer, dot2DBuffer, im2ColX, dw);
Assert.Equal(2, dw.Batch);
Assert.Equal(2, dw.Channels);
Assert.Equal(2, dw.Height);
Assert.Equal(2, dw.Width);
Assert.True(FloatComparison.AreEqual(dwExp, dw.Storage.Data));
}
