udc_model.py

import tensorflow as tf
import sys

def get_id_feature(features, key, len_key, max_len):
  ids = features[key]
  ids_len = tf.squeeze(features[len_key], [1])
  ids_len = tf.minimum(ids_len, tf.constant(max_len, dtype=tf.int64))
  return ids, ids_len

def create_train_op(loss, hparams):
  train_op = tf.contrib.layers.optimize_loss(
      loss=loss,
      global_step=tf.contrib.framework.get_global_step(),
      learning_rate=hparams.learning_rate,
      clip_gradients=10.0,
      optimizer=hparams.optimizer)
  return train_op


def create_model_fn(hparams, model_impl):

  def model_fn(features, targets, mode):
    context, context_len = get_id_feature(
        features, "context", "context_len", hparams.max_context_len)

    utterance, utterance_len = get_id_feature(
        features, "utterance", "utterance_len", hparams.max_utterance_len)
		
    #change for TF 1.2, jW 10/13/2017. See https://github.com/dennybritz/chatbot-retrieval/issues/32#issuecomment-265819109
    #batch_size = targets.get_shape().as_list()[0]
    if mode == tf.contrib.learn.ModeKeys.EVAL:
      batch_size = targets.get_shape().as_list()[0]
	  
    if mode == tf.contrib.learn.ModeKeys.TRAIN:
      probs, loss = model_impl(
          hparams,
          mode,
          context,
          context_len,
          utterance,
          utterance_len,
          targets)
      train_op = create_train_op(loss, hparams)
      return probs, loss, train_op

    if mode == tf.contrib.learn.ModeKeys.INFER:
      probs, loss = model_impl(
          hparams,
          mode,
          context,
          context_len,
          utterance,
          utterance_len,
          None)
      return probs, 0.0, None

    if mode == tf.contrib.learn.ModeKeys.EVAL:

      # We have 10 exampels per record, so we accumulate them
      all_contexts = [context]
      all_context_lens = [context_len]
      all_utterances = [utterance]
      all_utterance_lens = [utterance_len]
      all_targets = [tf.ones([batch_size, 1], dtype=tf.int64)]

      for i in range(9):
        distractor, distractor_len = get_id_feature(features,
            "distractor_{}".format(i),
            "distractor_{}_len".format(i),
            hparams.max_utterance_len)
        all_contexts.append(context)
        all_context_lens.append(context_len)
        all_utterances.append(distractor)
        all_utterance_lens.append(distractor_len)
        all_targets.append(
          tf.zeros([batch_size, 1], dtype=tf.int64)
        )
      # change tf.concat and tf.split to 1.0 syntax. jw. 2017/10/09
      probs, loss = model_impl(
          hparams,
          mode,
          tf.concat(all_contexts, 0),
          tf.concat(all_context_lens, 0),
          tf.concat(all_utterances, 0),
          tf.concat(all_utterance_lens, 0),
          tf.concat(all_targets, 0))

      split_probs = tf.split(probs, 10, 0)
      shaped_probs = tf.concat(split_probs, 1)

      # Add summaries. change tf.histogram_summary to tf.summary.histogram, change tf.scalar_summary to jw. 2017/10/10
      tf.summary.histogram("eval_correct_probs_hist", split_probs[0])
      tf.summary.scalar("eval_correct_probs_average", tf.reduce_mean(split_probs[0]))
      tf.summary.histogram("eval_incorrect_probs_hist", split_probs[1])
      tf.summary.scalar("eval_incorrect_probs_average", tf.reduce_mean(split_probs[1]))

      return shaped_probs, loss, None

  return model_fn