diff --git a/attention_lstm.py b/attention_lstm.py
index ee50807..a37ad16 100644
--- a/attention_lstm.py
+++ b/attention_lstm.py
@@ -26,9 +26,12 @@ class AttentionLSTM(LSTM):
                                    name='{}_U_m'.format(self.name))
         self.b_m = K.zeros((self.output_dim,), name='{}_b_m'.format(self.name))
 
-        self.U_s = self.inner_init((self.output_dim, self.output_dim),
+        # self.U_s = self.inner_init((self.output_dim, self.output_dim),
+        #                            name='{}_U_s'.format(self.name))
+        # self.b_s = K.zeros((self.output_dim,), name='{}_b_s'.format(self.name))
+        self.U_s = self.inner_init((self.output_dim, 1),
                                    name='{}_U_s'.format(self.name))
-        self.b_s = K.zeros((self.output_dim,), name='{}_b_s'.format(self.name))
+        self.b_s = K.zeros((1,), name='{}_b_s'.format(self.name))
 
         self.trainable_weights += [self.U_a, self.U_m, self.U_s, self.b_a, self.b_m, self.b_s]
 
@@ -43,9 +46,10 @@ class AttentionLSTM(LSTM):
         m = K.tanh(K.dot(h, self.U_a) * attention + self.b_a)
         # Intuitively it makes more sense to use a sigmoid (was getting some NaN problems
         # which I think might have been caused by the exponential function -> gradients blow up)
-        s = K.exp(K.dot(m, self.U_s) + self.b_s)
-        # s = K.sigmoid(K.dot(m, self.U_s) + self.b_s)
-        h = h * s
+        # s = K.exp(K.dot(m, self.U_s) + self.b_s)
+        s = K.tanh(K.dot(m, self.U_s) + self.b_s)
+        h = h * K.repeat_elements(s, self.output_dim, axis=1)
+        # h = h * s
 
         return h, [h, c]
 
diff --git a/insurance_qa_eval.py b/insurance_qa_eval.py
index cc399c4..64e7b43 100644
--- a/insurance_qa_eval.py
+++ b/insurance_qa_eval.py
@@ -6,7 +6,7 @@ from time import strftime, gmtime
 
 import pickle
 
-from keras.optimizers import Adam
+from keras.optimizers import Adam, RMSprop
 from scipy.stats import rankdata
 
 from keras_models import *
@@ -98,6 +98,7 @@ class Evaluator:
 
         questions = self.padq(questions)
         good_answers = self.pada(good_answers)
+        # bad_answers = self.pada(random.sample(self.answers.values(), len(good_answers)))
 
         for i in range(nb_epoch):
             # bad_answers = np.roll(good_answers, random.randint(10, len(questions) - 10))
@@ -204,7 +205,7 @@ if __name__ == '__main__':
         'question_len': 20,
         'answer_len': 100,
         'n_words': 22353, # len(vocabulary) + 1
-        'margin': 0.009,
+        'margin': 0.02,
 
         'training_params': {
             'save_every': 1,
@@ -212,12 +213,12 @@ if __name__ == '__main__':
             'batch_size': 128,
             'nb_epoch': 1000,
             'validation_split': 0.2,
-            'optimizer': 'adam',
+            'optimizer': RMSprop(clip_norm=0.1), # Adam(clip_norm=0.1),
             'n_eval': 20,
 
             'evaluate_all_threshold': {
                 'mode': 'all',
-                'top1': 0.55,
+                'top1': 0.5,
             },
         },
 
@@ -264,8 +265,8 @@ if __name__ == '__main__':
 
     # train the model
     # evaluator.load_epoch(model, 25)
-    # evaluator.train(model)
+    evaluator.train(model)
 
     # evaluate mrr for a particular epoch
-    evaluator.load_epoch(model, 115)
-    evaluator.get_mrr(model, evaluate_all=True)
+    # evaluator.load_epoch(model, 53)
+    # evaluator.get_mrr(model, evaluate_all=True)
diff --git a/keras_models.py b/keras_models.py
index 97b9eba..78d97f7 100644
--- a/keras_models.py
+++ b/keras_models.py
@@ -259,6 +259,10 @@ class AttentionModel(LanguageModel):
         question_rnn = merge([f_rnn(question_dropout), b_rnn(question_dropout)], mode='concat', concat_axis=-1)
         question_dropout = dropout(question_rnn)
 
+        # regularize
+        regularize = ActivityRegularization(l2=0.0001)
+        question_dropout = regularize(question_dropout)
+
         # could add convolution layer here (as in paper)
 
         # maxpooling
@@ -272,6 +276,7 @@ class AttentionModel(LanguageModel):
         # b_rnn = LSTM(self.model_params.get('n_lstm_dims', 141), return_sequences=True, go_backwards=True)
         answer_rnn = merge([f_rnn(answer_dropout), b_rnn(answer_dropout)], mode='concat', concat_axis=-1)
         answer_dropout = dropout(answer_rnn)
+        answer_dropout = regularize(answer_dropout)
         answer_pool = maxpool(answer_dropout)
 
         # activation