execute.py 文件源码

def decode_line(sess, model, enc_vocab, rev_dec_vocab, sentence):
    # Get token-ids for the input sentence.
    token_ids = data_utils.sentence_to_token_ids(tf.compat.as_bytes(sentence), enc_vocab)

    # Which bucket does it belong to?
    bucket_id = min([b for b in xrange(len(_buckets)) if _buckets[b][0] > len(token_ids)])

    # Get a 1-element batch to feed the sentence to the model.
    encoder_inputs, decoder_inputs, target_weights = model.get_batch({bucket_id: [(token_ids, [])]}, bucket_id)

    # Get output logits for the sentence.
    _, _, output_logits = model.step(sess, encoder_inputs, decoder_inputs, target_weights, bucket_id, True)

    # This is a greedy decoder - outputs are just argmaxes of output_logits.
    outputs = [int(np.argmax(logit, axis=1)) for logit in output_logits]

    # If there is an EOS symbol in outputs, cut them at that point.
    if data_utils.EOS_ID in outputs:
        outputs = outputs[:outputs.index(data_utils.EOS_ID)]

    return " ".join([tf.compat.as_str(rev_dec_vocab[output]) for output in outputs])