base.py 文件源码

def features_to_minibatch(self, features, sentences, max_feature_length, max_sentence_length, gpu=True):
        x_batch, x_length_batch, t_batch, t_length_batch, bigram_batch = self.processor.features_to_minibatch(features, sentences, max_feature_length, max_sentence_length, self.token_ids, 
            self.id_blank)

        if self.stats_total > 0:
            for x, length in zip(x_batch, x_length_batch):
                self._update_stats_recursively(x[..., :length])
            x_mean, x_std = self.get_mean_and_std()
            x_batch = (x_batch - x_mean) / x_std

        if gpu:
            x_batch = cuda.to_gpu(x_batch.astype(np.float32))
            t_batch = cuda.to_gpu(t_batch.astype(np.int32))
            bigram_batch = cuda.to_gpu(bigram_batch.astype(np.int32))
            x_length_batch = cuda.to_gpu(np.asarray(x_length_batch).astype(np.int32))
            t_length_batch = cuda.to_gpu(np.asarray(t_length_batch).astype(np.int32))

        return x_batch, x_length_batch, t_batch, t_length_batch, bigram_batch