This is an automated email from the ASF dual-hosted git repository. joern pushed a commit to branch master in repository https://gitbox.apache.org/repos/asf/opennlp-sandbox.git
commit f8db1938765eca2f55e61c5ffe1d625dbbe9ce7a Author: Jörn Kottmann <[email protected]> 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) -- To stop receiving notification emails like this one, please contact [email protected].
