Fix batch size definition in case of parallel encoders (#279)

OpenNMT · Nov 28, 2018 · 06eba7f · 06eba7f
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -17,6 +17,7 @@ OpenNMT-tf follows [semantic versioning 2.0.0](https://semver.org/). The API cov
 ### Fixes and improvements
 
 * Fix inference error when using parallel inputs and the parameter `bucket_width`
+* Fix size mismatch error when decoding from multi-source models
 
 ## [1.14.0](https://github.com/OpenNMT/OpenNMT-tf/releases/tag/v1.14.0) (2018-11-22)
 

diff --git a/opennmt/models/sequence_to_sequence.py b/opennmt/models/sequence_to_sequence.py
@@ -236,7 +236,7 @@ def _build(self, features, labels, params, mode, config=None):
 
     if mode != tf.estimator.ModeKeys.TRAIN:
       with tf.variable_scope("decoder", reuse=labels is not None):
-        batch_size = tf.shape(encoder_sequence_length)[0]
+        batch_size = tf.shape(tf.contrib.framework.nest.flatten(encoder_outputs)[0])[0]
         beam_width = params.get("beam_width", 1)
         maximum_iterations = params.get("maximum_iterations", 250)
         minimum_length = params.get("minimum_decoding_length", 0)