diff --git a/choose.py b/choose.py
new file mode 100644
index 00000000..4f4a8d1f
--- /dev/null
+++ b/choose.py
@@ -0,0 +1,83 @@
+from collections import namedtuple
+
+from prettyparse import create_parser
+
+from precise.network_runner import Listener
+from precise.params import inject_params
+from precise.train_data import TrainData
+from select import select
+from sys import stdin
+import pygame
+import time
+import os
+import shutil
+from collections import Counter
+
+usage = '''
+    Retag false negatives as wakeword or not wakeword
+    
+    :model str
+        Either Keras (.net) or TensorFlow (.pb) model to test
+    
+    :-t --use-train
+        Evaluate training data instead of test data
+    
+    :-nf --no-filenames
+        Don't print out the names of files that failed
+    
+    :-ap --append
+        Append new tags file to old one
+    ...
+'''
+
+Stats = namedtuple('Stats', 'false_pos false_neg true_pos true_neg')
+
+def calc_stats(filenames, targets, predictions) -> Stats:
+    stats = Stats([], [], [], [])
+    for name, target, prediction in zip(filenames, targets, predictions):
+        {
+            (True, False): stats.false_pos,
+            (True, True): stats.true_pos,
+            (False, True): stats.false_neg,
+            (False, False): stats.true_neg
+        }[prediction[0] > 0.5, target[0] > 0.5].append(name)
+    return stats
+
+def main():
+    args = TrainData.parse_args(create_parser(usage))
+
+    inject_params(args.model)
+
+    data = TrainData.from_both(args.tags_file, args.tags_folder, args.folder)
+    train, test = data.load(args.use_train, not args.use_train)
+    inputs, targets = train if args.use_train else test
+
+    filenames = sum(data.train_files if args.use_train else data.test_files, [])
+    predictions = Listener.find_runner(args.model)(args.model).predict(inputs)
+    stats = calc_stats(filenames, targets, predictions)
+    false_negatives_array = stats.false_neg
+    new_tags = open('tags.txt', 'w')
+    
+
+    changed_tags_array = []
+    for i in range(0, len(false_negatives_array)):
+        pygame.mixer.init(frequency=8000, size=-16, channels=2, buffer=4096)
+        pygame.mixer.music.load(false_negatives_array[i])
+        pygame.mixer.music.play()
+        print('False negative ', (i + 1), ' of ', (len(false_negatives_array)) + 1)
+        user_input = input('Enter y if wakeword, enter n for not wakeword \n')
+        time.sleep(5)
+        false_negatives_array[i] = false_negatives_array[i].lstrip('/Users/madmitrienko/wakewords/files/')
+        false_negatives_array[i] = false_negatives_array[i].rstrip('.wav')
+        if(user_input == 'y'):
+            write_to_tags = '\n' + false_negatives_array[i] + '	wake-word'
+            new_tags.write(write_to_tags)
+
+        elif(user_input == 'n'):
+            write_to_tags = '\n' + false_negatives_array[i] + '	not-wake-word'          
+            new_tags.write(write_to_tags)
+    new_tags.close()
+    tags.close()
+    
+if __name__ == '__main__':
+    main()
diff --git a/precise/scripts/train_optimize.py b/precise/scripts/train_optimize.py
index fb84038e..383061a6 100644
--- a/precise/scripts/train_optimize.py
+++ b/precise/scripts/train_optimize.py
@@ -82,7 +82,7 @@ def run(self):
             print("\n= %d = (example #%d)" % (i + 1, len(self.bb.get_data()["examples"]) + 1))
 
             params = ModelParams(
-                recurrent_units=self.bb.randint("units", 1, 100, guess=50),
+                recurrent_units=self.bb.randint("units", 1, 70, guess=50),
                 dropout=self.bb.uniform("dropout", 0.1, 0.9, guess=0.6),
                 extra_metrics=self.args.extra_metrics,
                 skip_acc=self.args.no_validation,
@@ -119,7 +119,6 @@ def run(self):
                 "fitness": fitness
             })
 
-            print(false_positives)
             print("False positive: ", false_positives * 100, "%")
 
             self.bb.maximize(fitness)
diff --git a/setup.py b/setup.py
index 343c679f..16499e94 100644
--- a/setup.py
+++ b/setup.py
@@ -40,6 +40,7 @@
             'precise-train-optimize=precise.scripts.train_optimize:main',
             'precise-train-sampled=precise.scripts.train_sampled:main',
             'precise-train-incremental=precise.scripts.train_incremental:main',
+            'precise-choose=precise.scripts.choose:main',
         ]
     },
     install_requires=[