diff --git a/insurance_qa_eval.py b/insurance_qa_eval.py
index f9500e5..e7e16c2 100644
--- a/insurance_qa_eval.py
+++ b/insurance_qa_eval.py
@@ -26,7 +26,7 @@ class Evaluator:
         self.path = data_path
         self.conf = dict() if conf is None else conf
         self.params = conf.get('training_params', dict())
-        self.answers = self.load('answers')  # self.load('generated')
+        self.answers = self.load('answers') # self.load('generated')
         self._vocab = None
         self._reverse_vocab = None
         self._eval_sets = None
@@ -171,7 +171,7 @@ class Evaluator:
                 max_r = np.argmax(r)
                 max_n = np.argmax(r[:n_good])
 
-                # print(' '.join(self.revegrt(d['question'])))
+                # print(' '.join(self.revert(d['question'])))
                 # print(' '.join(self.revert(self.answers[indices[max_r]])))
                 # print(' '.join(self.revert(self.answers[indices[max_n]])))
 
@@ -224,13 +224,13 @@ if __name__ == '__main__':
         'training_params': {
             'save_every': 1,
             'batch_size': 20,
-            'nb_epoch': 100,
+            'nb_epoch': 10,
             'validation_split': 0.2,
             'optimizer': Adam(clipnorm=1e-2),
         },
 
         'model_params': {
-            'n_embed_dims': 100,
+            'n_embed_dims': 1000,
             'n_hidden': 200,
 
             # convolution
@@ -240,12 +240,12 @@ if __name__ == '__main__':
             # recurrent
             'n_lstm_dims': 141,  # * 2
 
-            'initial_embed_weights': np.load('models/word2vec_100_dim.h5'),
+            'initial_embed_weights': np.load('models/word2vec_1000_dim.h5'),
             'similarity_dropout': 0.2,
         },
 
         'similarity_params': {
-            'mode': 'cosine',
+            'mode': 'gesd',
             'gamma': 1,
             'c': 1,
             'd': 2,
diff --git a/keras_models.py b/keras_models.py
index 219d07c..3ab9686 100644
--- a/keras_models.py
+++ b/keras_models.py
@@ -103,12 +103,11 @@ class LanguageModel:
 
         if self._qa_model is None:
             question_output, answer_output = self._models
-
+            dropout = Dropout(self.similarity_params.get('similarity_dropout', 0.2))
             similarity = self.get_similarity()
-            qa_model = merge([question_output, answer_output], mode=similarity, output_shape=lambda _: (None, 1))
-            dropout = Dropout(self.similarity_params.get('similarity_dropout', 0.2))(qa_model)
-
-            self._qa_model = Model(input=[self.question, self.get_answer()], output=[dropout])
+            qa_model = merge([dropout(question_output), dropout(answer_output)],
+                             mode=similarity, output_shape=lambda _: (None, 1))
+            self._qa_model = Model(input=[self.question, self.get_answer()], output=[qa_model])
 
         return self._qa_model