[opennlp-sandbox] 02/02: Name placeholders and variables for use from Java API

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in repository https://gitbox.apache.org/repos/asf/opennlp-sandbox.git

commit f8db1938765eca2f55e61c5ffe1d625dbbe9ce7a
Author: Jörn Kottmann <jo...@apache.org>
AuthorDate: Wed May 30 11:49:09 2018 +0200

    Name placeholders and variables for use from Java API
---
 tf-ner-poc/src/main/python/namefinder.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tf-ner-poc/src/main/python/namefinder.py b/tf-ner-poc/src/main/python/namefinder.py
index e757491..c1220dd 100644
--- a/tf-ner-poc/src/main/python/namefinder.py
+++ b/tf-ner-poc/src/main/python/namefinder.py
@@ -174,10 +174,10 @@ class NameFinder:
 
         with tf.variable_scope("chars"):
             # shape = (batch size, max length of sentence, max length of word)
-            char_ids = tf.placeholder(tf.int32, shape=[None, None, None])
+            char_ids = tf.placeholder(tf.int32, shape=[None, None, None], name="char_ids")
 
             # shape = (batch_size, max_length of sentence)
-            word_lengths_ph = tf.placeholder(tf.int32, shape=[None, None])
+            word_lengths_ph = tf.placeholder(tf.int32, shape=[None, None], name="word_lengths")
 
             dim_char = 100
 
@@ -211,8 +211,8 @@ class NameFinder:
             char_rep = tf.reshape(output, shape=[-1, s[1], 2*char_hidden_size])
 
         with tf.variable_scope("words"):
-            token_ids = tf.placeholder(tf.int32, shape=[None, None])
-            sequence_lengths = tf.placeholder(tf.int32, shape=[None])
+            token_ids = tf.placeholder(tf.int32, shape=[None, None], name="word_ids")
+            sequence_lengths = tf.placeholder(tf.int32, shape=[None], name="sequence_lengths")
 
             # This is a hack to make it load an embedding matrix larger than 2GB
             # Don't hardcode this 300
@@ -252,12 +252,12 @@ class NameFinder:
         ntime_steps = tf.shape(context_rep)[1]
         context_rep_flat = tf.reshape(context_rep, [-1, 2*hidden_size])
         pred = tf.matmul(context_rep_flat, W) + b
-        self.logits = tf.reshape(pred, [-1, ntime_steps, ntags])
+        self.logits = tf.reshape(pred, [-1, ntime_steps, ntags], name="logits")
 
         log_likelihood, transition_params = tf.contrib.crf.crf_log_likelihood(
             self.logits, labels, sequence_lengths)
 
-        self.transition_params = transition_params
+        self.transition_params = tf.identity(transition_params, name="trans_params")
 
         loss = tf.reduce_mean(-log_likelihood)
 

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