public void SoftMaxLayer_Backward()
{
var batchSize = 1;
var width = 28;
var height = 28;
var depth = 3;
var numberOfClasses = 10;
var random = new Random(232);
var sut = new SoftMaxLayer(numberOfClasses);
sut.Initialize(width, height, depth, batchSize, Initialization.GlorotUniform, random);
var input = Matrix <float> .Build.Random(batchSize, numberOfClasses, random.Next());
sut.Forward(input);
var delta = Matrix <float> .Build.Random(batchSize, numberOfClasses, random.Next());
var actual = sut.Backward(delta);
Trace.WriteLine(string.Join(", ", actual.ToColumnMajorArray()));
var expected = Matrix <float> .Build.Dense(batchSize, numberOfClasses, new float[] { -0.3891016f, -0.6150756f, 0.0618184f, -0.2334358f, 1.544145f, -1.01483f, 0.6160479f, 0.3225261f, -1.007966f, -0.1111263f });
MatrixAsserts.AreEqual(expected, actual);
}
public void ForwardBackwardTest()
{
Shape shape = new Shape(new[] { 1, SoftMaxLayerTest.weights.Length });
SoftMaxLayer layer = new SoftMaxLayer(shape);
Session session = new Session();
Tensor x = new Tensor(null, shape);
x.Set(SoftMaxLayerTest.weights);
Tensor y = layer.Forward(session, new[] { x })[0];
Helpers.AreArraysEqual(SoftMaxLayerTest.activations, y.Weights);
// unroll the graph
y.Gradient[0] = 1.0f;
session.Unroll();
////float[] expectedDx = SoftMaxLayerTest.activations.Select((w, i) => i == 0 ? w - 1.0f : w).ToArray();
Helpers.AreArraysEqual(new float[] { 1.0f, 0, 0, 0 }, x.Gradient);
}
public void SoftMaxLayer_Forward()
{
var batchSize = 1;
var width = 28;
var height = 28;
var depth = 3;
var numberOfClasses = 10;
var random = new Random(232);
var sut = new SoftMaxLayer(numberOfClasses);
sut.Initialize(width, height, depth, batchSize, Initialization.GlorotUniform, random);
var input = Matrix <float> .Build.Random(batchSize, numberOfClasses, random.Next());
var actual = sut.Forward(input);
Trace.WriteLine(string.Join(", ", actual.ToColumnMajorArray()));
var expected = Matrix <float> .Build.Dense(batchSize, numberOfClasses, new float[] { 0.06976377f, 0.1327717f, 0.02337802f, 0.3784489f, 0.0777365f, 0.05847027f, 0.1072708f, 0.0503228f, 0.0624512f, 0.03938601f });
MatrixAsserts.AreEqual(expected, actual);
}