[opennlp-sandbox] branch master updated: added vector size to NameFinder + only save model if improved + stop training if not improved for 5 iteration

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The following commit(s) were added to refs/heads/master by this push:
     new 8dc9495  added vector size to NameFinder + only save model if improved + stop training if not improved for 5 iteration
8dc9495 is described below

commit 8dc9495d78017eb24cc7f6cf120c14d0c4964a3c
Author: Peter Thygesen <th...@apache.org>
AuthorDate: Thu May 31 11:20:42 2018 +0200

    added vector size to NameFinder + only save model if improved + stop training if not improved for 5 iteration
---
 tf-ner-poc/src/main/python/namefinder.py | 42 +++++++++++++++++++++++++-------
 1 file changed, 33 insertions(+), 9 deletions(-)

diff --git a/tf-ner-poc/src/main/python/namefinder.py b/tf-ner-poc/src/main/python/namefinder.py
index cd5a464..5a15064 100644
--- a/tf-ner-poc/src/main/python/namefinder.py
+++ b/tf-ner-poc/src/main/python/namefinder.py
@@ -14,7 +14,7 @@
 #  "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 #  KIND, either express or implied.  See the License for the
 #  specific language governing permissions and limitations
-#  under the License. 
+#  under the License.
 #
 
 # This poc is based on source code taken from:
@@ -48,10 +48,19 @@ class NameSample:
                 self.tokens.append(parts[i])
                 word_index += 1
 
+class VectorException(Exception):
+    def __init__(self, value):
+        self.value = value
+
+    def __str__(self):
+        return repr(self.value)
+
 class NameFinder:
 
-    def __init__(self):
-        self.label_dict = {}
+    label_dict = {}
+
+    def __init__(self, vector_size=100):
+        self.__vector_size = vector_size
 
     def load_glove(self, glove_file):
         with open(glove_file) as f:
@@ -61,6 +70,10 @@ class NameFinder:
 
             for line in f:
                 parts = line.strip().split(" ")
+                if len(parts) != self.__vector_size + 1:
+                    #print("Bad Vector: ",len(line),len(parts), line)
+                    raise VectorException("Bad Vector in line: {}, size: {} vector: {}".format(len(line),len(parts), line))
+                    continue
                 word_dict[parts[0]] = len(word_dict)
                 embeddings.append(np.array(parts[1:], dtype=np.float32))
 
@@ -218,7 +231,7 @@ class NameFinder:
             # This is a hack to make it load an embedding matrix larger than 2GB
             # Don't hardcode this 300
             embedding_placeholder = tf.placeholder(dtype=tf.float32, name="embedding_placeholder",
-                                                   shape=(len(embedding_dict), 100))
+                                                   shape=(len(embedding_dict), self.__vector_size))
             embedding_matrix = tf.Variable(embedding_placeholder, dtype=tf.float32, trainable=False, name="glove_embeddings")
 
             token_embeddings = tf.nn.embedding_lookup(embedding_matrix, token_ids)
@@ -335,7 +348,7 @@ def main():
         print("Usage namefinder.py embedding_file train_file dev_file test_file")
         return
 
-    name_finder = NameFinder()
+    name_finder = NameFinder(300)
 
     word_dict, rev_word_dict, embeddings = name_finder.load_glove(sys.argv[1])
     sentences, labels, char_set = name_finder.load_data(word_dict, sys.argv[2])
@@ -353,6 +366,8 @@ def main():
     sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
                                             log_device_placement=True))
 
+    best_f1 = 0.0
+    no_improvement = 0
     with sess.as_default():
         init = tf.global_variables_initializer()
         sess.run(init, feed_dict={embedding_ph: embeddings})
@@ -411,10 +426,19 @@ def main():
             print("ACC " + str(acc))
             print("F1  " + str(f1) + "  P " + str(p) + "  R " + str(r))
 
-        saver = tf.train.Saver()
-        builder = tf.saved_model.builder.SavedModelBuilder("./savedmodel")
-        builder.add_meta_graph_and_variables(sess, [tf.saved_model.tag_constants.SERVING])
-        builder.save()
+            if (f1 > best_f1):
+                best_f1 = f1
+                no_improvement = 0
+                saver = tf.train.Saver()
+                builder = tf.saved_model.builder.SavedModelBuilder("./savedmodel/{}".format(epoch))
+                builder.add_meta_graph_and_variables(sess, [tf.saved_model.tag_constants.SERVING])
+                builder.save()
+            else:
+                no_improvement += 1
+
+            if no_improvement > 5:
+                print("No further improvement. Stopping.")
+                break
 
 if __name__ == "__main__":
     main()

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