Name placeholders and variables for use from Java API
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 @@
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 @@
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 @@
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)
