Method RandomSample

examples/batching/rnnlm-batch.h:164–218

* \brief Samples a string of words/characters from the model * \details This can be used to debug and/or have fun. Try it on * new datasets! * * \param d Dictionary to use (should be same as the one used for training) * \param max_len maximu number of tokens to generate * \param temp Temperature for sampling (the softmax computed is * \f$\frac{e^{\frac{r_t^{(i)}}{T}}}{\sum_{j=

   *  Intuitively lower temperature -> less deviation from the distribution (= more "standard" samples)
   */
  void RandomSample(const dynet::Dict& d, int max_len = 150, float temp = 1.0) {
    // Make some space
    cerr << endl;
    // Initialize computation graph
    ComputationGraph cg;
    // Initialize the RNN for the new computation graph
    rnn.new_graph(cg);
    // Initialize for new sequence (set hidden states, etc..)
    rnn.start_new_sequence();
    // Instantiate embedding parameters in the computation graph
    // output -> word rep parameters (matrix + bias)
    Expression i_R = parameter(cg, p_R);
    Expression i_bias = parameter(cg, p_bias);

    // Start generating
    int len = 0;
    int cur = kSOS;
    while (len < max_len) {
      ++len;
      // Embed current token
      Expression i_x_t = lookup(cg, p_c, cur);
      // Run one step of the rnn
      // y_t = RNN(x_t)
      Expression i_y_t = rnn.add_input(i_x_t);
      // Project into token space
      Expression i_r_t = i_bias + i_R * i_y_t;
      // Get distribution over tokens (with temperature)
      Expression ydist = softmax(i_r_t / temp);

      // Sample token
      unsigned w = 0;
      while (w == 0 || (int)w == kSOS) {
        auto dist = as_vector(cg.incremental_forward(ydist));
        double p = rand01();
        for (; w < dist.size(); ++w) {
          p -= dist[w];
          if (p < 0.0) { break; }
        }
        if (w == dist.size()) w = kEOS;
      }

      if (static_cast<int>(w) == kEOS) {
        // If the sampled token is an EOS, reinitialize network and start generating a new sample
        rnn.start_new_sequence();
        cerr << endl;
        cur = kSOS;
      } else {
        // Otherwise print token and continue
        cerr << (cur == kSOS ? "" : " ") << d.convert(w);
        cur = w;
      }

    }
    cerr << endl;
  }
};

#endif