text_RHL.py 文件源码

def gather_nd(params, indices, shape):
    rank = len(shape)
    flat_params = tf.reshape(params, [-1])
    multipliers = [reduce(lambda x, y: x*y, shape[i+1:], 1) for i in range(0, rank)]
    indices_unpacked = tf.unstack(tf.transpose(indices, [rank - 1] + list(range(0, rank - 1))))
    flat_indices = sum([a*b for a,b in zip(multipliers, indices_unpacked)])
    return tf.gather(flat_params, flat_indices)

# ctc_label_dense_to_sparse is taken from https://github.com/tensorflow/tensorflow/issues/1742#issuecomment-205291527
#
# The CTC implementation in TensorFlow needs labels in a sparse representation,
# but sparse data and queues don't mix well, so we store padded tensors in the
# queue and convert to a sparse representation after dequeuing a batch.
#