Method RandomSample

contrib/csharp/examples/train_rnnlm.cs:211–244

(int maxLen = 200)

                return dy.esum(errs);
            }
            public void RandomSample(int maxLen = 200) {
                // Renew the computation graph
                dy.RenewCG();

                // hidden -> word rep parameter
                Expression R = dy.parameter(p_R);
                // word bias
                Expression bias = dy.parameter(p_bias);

                Random r = new Random();
                // Start with an <s>
                RNNState state = builder.GetInitialState().AddInput(lp[d["<s>"]]);
                int cur = d["<s>"], len = 0;
                while (len < maxLen) {
                    // Regular softmax
                    Expression u_t = dy.affine_transform(bias, R, state.Output());
                    Expression dist_expr = dy.softmax(u_t);
                    float[] dist = dist_expr.VectorValue();
                    // Get a random between 0->1, sample the next item
                    double p = r.NextDouble();
                    for (cur = 0; cur < dist.Length; cur++) {
                        p -= dist[cur];
                        if (p < 0) break;
                    }
                    if (cur == dist.Length) cur = d["</s>"];
                    // Are we at the end?
                    if (cur == d["</s>"])
                        break;
                    len++;
                    // Output the chracter
                    Console.Write((len == 1 ? "" : " ") + di2W[cur]);
                }// next prediction
                Console.WriteLine();
            }
        }
    }
}