I'm now implementing seq2seq model based on the example code that tensorflow provides. And I want to get a top-5 decoder outputs to do a reinforcement learning.

However, they implemented translation model with attention decoder so, I should implement beam-search for getting top-k results.

There is a part of code that now implement (this code is added to translate.py).

Reference by https://github.com/tensorflow/tensorflow/issues/654

with tf.Graph().as_default():beam_size = FLAGS.beam_size # Number of hypotheses in beamnum_symbols = FLAGS.tar_vocab_size # Output vocabulary sizeembedding_size = 10num_steps = 5embedding = tf.zeros([num_symbols, embedding_size])output_projection = Nonelog_beam_probs, beam_symbols, beam_path = [], [], []def beam_search(prev, i):if output_projection is not None:prev = tf.nn.xw_plus_b(prev, output_projection[0], output_projection[1])probs = tf.log(tf.nn.softmax(prev))if i > 1:probs = tf.reshape(probs + log_beam_probs[-1], [-1, beam_size * num_symbols])best_probs, indices = tf.nn.top_k(probs, beam_size)indices = tf.stop_gradient(tf.squeeze(tf.reshape(indices, [-1, 1])))best_probs = tf.stop_gradient(tf.reshape(best_probs, [-1, 1]))symbols = indices % num_symbols      # which word in vocabularybeam_parent = indices // num_symbols # which hypothesis it came frombeam_symbols.append(symbols)beam_path.append(beam_parent)log_beam_probs.append(best_probs)return tf.nn.embedding_lookup(embedding, symbols)# Setting up graph.inputs = [tf.placeholder(tf.float32, shape=[None, num_symbols]) for i in range(num_steps)]for i in range(num_steps):beam_search(inputs[i], i+1)input_vals = tf.zeros([1, beam_size], dtype=tf.float32)input_feed = {inputs[i]: input_vals[i][:beam_size, :] for i in xrange(num_steps)}output_feed = beam_symbols + beam_path + log_beam_probssession = tf.InteractiveSession()outputs = session.run(output_feed, feed_dict=input_feed)print("Top_5 Sentences ")for predicted in enumerate(outputs[:5]):print(list(predicted))print("\n")

In input_feed part, there is an error:

ValueError: Shape (1, 12) must have rank 1

Is there any problem on my code to do beam-search?

A tried and true demo:

# -*- coding: utf-8 -*-from __future__ import unicode_literals, print_function
from __future__ import absolute_import
from __future__ import divisionimport tensorflow as tftf.app.flags.DEFINE_integer('beam_size', 4, 'beam size for beam search decoding.')
tf.app.flags.DEFINE_integer('vocab_size', 40, 'vocabulary size.')
tf.app.flags.DEFINE_integer('batch_size', 5, 'the batch size.')
tf.app.flags.DEFINE_integer('num_steps', 10, 'the batch size.')
tf.app.flags.DEFINE_integer('embedding_size', 50, 'the batch size.')FLAGS = tf.app.flags.FLAGSwith tf.Graph().as_default():batch_size = FLAGS.batch_sizebeam_size = FLAGS.beam_size  # Number of hypotheses in beamvocab_size = FLAGS.vocab_size  # Output vocabulary sizenum_steps = FLAGS.num_stepsembedding_size = FLAGS.embedding_sizeembedding = tf.random_normal([vocab_size, embedding_size], -2, 4, dtype=tf.float32, seed=0)output_projection = [tf.random_normal([embedding_size, vocab_size], mean=2, stddev=1, dtype=tf.float32, seed=0),tf.random_normal([vocab_size], mean=0, stddev=1, dtype=tf.float32, seed=0),]index_base = tf.reshape(tf.tile(tf.expand_dims(tf.range(batch_size) * beam_size, axis=1), [1, beam_size]), [-1])log_beam_probs, beam_symbols = [], []def beam_search(prev, i):if output_projection is not None:prev = tf.nn.xw_plus_b(prev, output_projection[0], output_projection[1])# (batch_size*beam_size, embedding_size) -> (batch_size*beam_size, vocab_size)log_probs = tf.nn.log_softmax(prev)if i > 1:# total probabilitylog_probs = tf.reshape(tf.reduce_sum(tf.stack(log_beam_probs, axis=1), axis=1) + log_probs,[-1, beam_size * vocab_size])# (batch_size*beam_size, vocab_size) -> (batch_size, beam_size*vocab_size)best_probs, indices = tf.nn.top_k(log_probs, beam_size)# (batch_size, beam_size)indices = tf.squeeze(tf.reshape(indices, [-1, 1]))best_probs = tf.reshape(best_probs, [-1, 1])# (batch_size*beam_size)symbols = indices % vocab_size       # which word in vocabularybeam_parent = indices // vocab_size  # which hypothesis it came frombeam_symbols.append(symbols)# (batch_size*beam_size, num_steps)real_path = beam_parent + index_base# get rid of the previous probabilityif i > 1:pre_sum = tf.reduce_sum(tf.stack(log_beam_probs, axis=1), axis=1)pre_sum = tf.gather(pre_sum, real_path)else:pre_sum = 0log_beam_probs.append(best_probs-pre_sum)# adapt the previous symbols according to the current symbolif i > 1:for j in range(i)[:0:-1]:beam_symbols[j-1] = tf.gather(beam_symbols[j-1], real_path)log_beam_probs[j-1] = tf.gather(log_beam_probs[j-1], real_path)return tf.nn.embedding_lookup(embedding, symbols)# (batch_size*beam_size, embedding_size)# Setting up graph.init_input = tf.placeholder(tf.float32, shape=[batch_size, embedding_size])next_input = init_inputfor i in range(num_steps):next_input = beam_search(next_input, i+1)seq_rank = tf.stack(values=beam_symbols, axis=1)seq_rank = tf.reshape(seq_rank, [batch_size, beam_size, num_steps])# (batch_size*beam_size, num_steps)init_in = tf.random_uniform([batch_size], minval=0, maxval=vocab_size, dtype=tf.int32, seed=0),init_emb = tf.squeeze(tf.nn.embedding_lookup(embedding, init_in))session = tf.InteractiveSession()init_emb = init_emb.eval()seq_rank = session.run(seq_rank, feed_dict={init_input: init_emb})best_seq = seq_rank[:, 1, :]for i in range(batch_size):print("rank %s" % i, end=": ")print(best_seq[i])

It is simplified from the beam search model in my seq2seq model. Python2.7 and TF1.4