net.py 文件源码

def sample(self, input, temperature=1., hidden=None):
        hidden = self.module_.init_hidden(1) if hidden is None else hidden
        output, hidden = self.module_(input, hidden)
        probas = output.squeeze().data.div(temperature).exp()
        sample = torch.multinomial(probas, 1)[-1]
        if probas.dim() > 1:
            sample = sample[0]
        return sample, self.repackage_hidden(hidden)