generator.py 文件源码

def sample(self, num_samples, start_letter=0):
        """
        Samples the network and returns num_samples samples of length max_seq_len.

        Outputs: samples, hidden
            - samples: num_samples x max_seq_length (a sampled sequence in each row)
        """

        samples = torch.zeros(num_samples, self.max_seq_len).type(torch.LongTensor)

        h = self.init_hidden(num_samples)
        inp = autograd.Variable(torch.LongTensor([start_letter]*num_samples))

        if self.gpu:
            samples = samples.cuda()
            inp = inp.cuda()

        for i in range(self.max_seq_len):
            out, h = self.forward(inp, h)               # out: num_samples x vocab_size
            out = torch.multinomial(torch.exp(out), 1)  # num_samples x 1 (sampling from each row)
            samples[:, i] = out.data

            inp = out.view(-1)

        return samples