public void ForwardBackwardTest()
{
Shape shape = new Shape(new int[] { 2 });
SigmoidLayer layer = new SigmoidLayer(shape);
Session session = new Session();
Tensor source = new Tensor(null, shape);
source.Set(new float[] { 2, -3 });
Tensor x = source.Clone() as Tensor;
Tensor y = layer.Forward(session, new[] { x })[0];
float[] expected = source.Weights.Take(source.Length).Select(w => SigmoidLayerTest.activation(w)).ToArray();
Helpers.AreArraysEqual(x.Length, expected, y.Weights);
// unroll the graph
float[] dy = Enumerable.Range(1, x.Length).Select(w => (float)w).ToArray();
y.SetGradient(dy);
session.Unroll();
Helpers.AreArraysEqual(
expected.Length,
expected.Zip(dy, (w, dw) => SigmoidLayerTest.derivative(w) * dw).ToArray(),
x.Gradient);
}
public void SigmoidLayer_Forward()
{
var layer = new SigmoidLayer();
layer.Setup(bottom, top);
layer.Forward(bottom, top);
Assert.Equal(bottom.Count, top.Count);
using (var topCpu = top.OnCpu())
using (var bottomCpu = bottom.OnCpu())
{
int count = bottom.Count;
for (int i = 0; i < count; i++)
{
Assert.True(MathHelpers.Equality(topCpu.DataAt(i), 1.0d / (1.0d + Math.Exp(-bottomCpu.DataAt(i)))));
// check that we squashed the value between 0 and 1
Assert.True(topCpu.DataAt(i) >= 0.0d);
Assert.True(topCpu.DataAt(i) <= 1.0d);
};
}
}
public void SigmoidLayer_Forward()
{
var layer = new SigmoidLayer();
layer.Setup(bottom, top);
layer.Forward(bottom, top);
Assert.Equal(bottom.Count, top.Count);
using (var topCpu = top.OnCpu())
using (var bottomCpu = bottom.OnCpu())
{
int count = bottom.Count;
for (int i = 0; i < count; i++)
{
Assert.True(MathHelpers.Equality(topCpu.DataAt(i), 1.0d / (1.0d + Math.Exp(-bottomCpu.DataAt(i)))));
// check that we squashed the value between 0 and 1
Assert.True(topCpu.DataAt(i) >= 0.0d);
Assert.True(topCpu.DataAt(i) <= 1.0d);
}
;
}
}
