public void AddBiasTest1()
{
foreach (int length in new[] { 24, 128 })
{
Session session = new Session();
Tensor x = new Tensor(null, 1, length);
x.Randomize(random);
Tensor b = new Tensor(null, new[] { length });
b.Randomize(random);
Tensor y1 = NeuralOperations.AddBias(session, x, b);
Tensor y2 = NeuralOperations.AddBias(session, x, b);
Tensor expected = new Tensor(null, 1, length);
expected.Set(x.Weights.Zip(b.Weights, (xw, bw) => xw + bw).ToArray());
Helpers.AreTensorsEqual(expected, y1);
Helpers.AreTensorsEqual(expected, y2);
Tensor dy1 = session.GetGradient(y1);
Tensor dy2 = session.GetGradient(y2);
dy1.Randomize(random);
dy2.Randomize(random);
session.Unroll();
Tensor expectedDX = new Tensor(null, 1, length);
expectedDX.Set(dy1.Weights.Zip(dy2.Weights, (dyw1, dyw2) => dyw1 + dyw2).ToArray());
Helpers.AreTensorsEqual(expectedDX, session.GetGradient(x));
Tensor expectedDB = new Tensor(null, new[] { length });
expectedDB.Set(dy1.Weights.Zip(dy2.Weights, (dyw1, dyw2) => dyw1 + dyw2).ToArray());
Helpers.AreTensorsEqual(expectedDB, session.GetGradient(b));
}
}
public void DropoutTest1()
{
const float P = 0.6f;
Session session = new Session(false);
Tensor x = new Tensor(null, 1024);
x.Randomize(random);
float xsum = x.Weights.Sum();
Tensor y1 = NeuralOperations.Dropout(session, x, this.random, P);
Tensor y2 = NeuralOperations.Dropout(session, x, this.random, P);
Assert.AreEqual(P * xsum, y1.Weights.Sum(), 0.01f * xsum);
Assert.AreEqual(P * xsum, y2.Weights.Sum(), 0.01f * xsum);
Tensor dy1 = session.GetGradient(y1);
Tensor dy2 = session.GetGradient(y2);
dy1.Randomize(random);
dy2.Randomize(random);
session.Unroll();
Assert.IsTrue(session.GetGradient(x).Weights.All(w => w == 0.0f));
}
public void DropoutTest2()
{
const float P = 0.6f;
Session session = new Session();
Tensor x = new Tensor(null, new[] { 1024 });
x.Randomize(this.random);
float xsum = x.Weights.Sum();
Tensor y1 = NeuralOperations.Dropout(session, x, this.random, P);
Tensor y2 = NeuralOperations.Dropout(session, x, this.random, P);
Assert.AreEqual(P * xsum, y1.Weights.Sum(), 0.02f * xsum);
Assert.AreEqual(P * xsum, y2.Weights.Sum(), 0.02f * xsum);
y1.RandomizeGradient(this.random);
y2.RandomizeGradient(this.random);
session.Unroll();
Helpers.AreArraysEqual(
y1.Length,
y1.Weights.Take(y1.Length)
.Zip(y1.Gradient, (yw, dyw) => yw == 0.0f ? 0.0f : dyw)
.Zip(y2.Weights.Zip(y2.Gradient, (yw, dyw) => yw == 0.0f ? 0.0f : dyw), (a, b) => a + b)
.ToArray(),
x.Gradient);
}
