public override void Backward(Tensor <Type> delta, Backpropagation bp)
{
argmax = argmax ?? Op.Argmax(x, Axis, keepDims: true);
var deltaX = UnArgmax <Type> .Create(delta, argmax, Axis, x.Shape);
bp.PushGradientTo(x, deltaX);
}
public ReinforceMul(int N = 7)
{
this.N = N;
int dim0 = N, dim1 = N * N, dim2 = N;
var a = Op.Scalar <int>("a");
var b = Op.Scalar <int>("b");
var c_gold = Op.Scalar <int>("c_gold");
var L = Op.Shared(NN.Random.Uniform(-1f, 1f, N, dim0), "L");
var xa = Blocks.Linear("Wa", L[a], dim1);
var xb = Blocks.Linear("Wb", L[b], dim1);
var xab = Blocks.Linear("Wab", L[a] * L[b], dim1);
var x = Blocks.Linear("Wc", Op.Tanh(xa + xb + xab), dim2);
var y = Op.Softmax(x);
var c = Operators.Scalars.ReinforceCategorical.Create(y, "baseline");
c.Name = nameof(c);
var eq = Op.Eq(c_gold, c);
c.Reward = eq.As <float>();
c.Reward.Name = "reward";
this.baseline = c.Baseline;
var loss = -c.As <float>();
var weights = loss.FindAll <Tensor <float> .Shared>();
foreach (var W in weights)
{
loss += 0.001f * Op.Norm2(W);
}
loss.Name = nameof(loss);
var grad = Op.Grad(loss); // reward isn't differentiable but Reinforce will still backpropagate gradients
var lr = Op.Scalar <float>("lr");
var updates = new OrderedDictionary();
foreach (var W in grad.Keys)
{
updates[W] = W - lr * grad[W];
}
//var dB = (Scalar<float>)loss.Backpropagation.ScalarDerivatives[c.Baseline];
//updates[c.Baseline] = c.Baseline - lr * dB;
updates[c.Baseline] = c.Baseline * 0.9f + 0.1f * c.Reward;
var train_ = Op.Function(new IVar[] { a, b, c_gold, lr }, c.Reward, updates);
Train = lr_ => {
var sample = NextSample();
return((int)train_(sample.Item1, sample.Item2, sample.Item3, lr_));
};
Mul = Op.Function(input: (a, b), output: Op.Argmax(x));
}
