diff --git a/segment_data.py b/segment_data.py
index de86d61..9d95f57 100644
--- a/segment_data.py
+++ b/segment_data.py
@@ -257,7 +257,7 @@ if __name__ == '__main__':
     # plot_segments('story_test_segments')
     # fix_csv('story_phrases')
     # pass
-    create_segments_tfrecords('story_phrases', sample_count=1000)
+    create_segments_tfrecords('story_phrases', sample_count=100)
     # record_generator,input_data,output_data,copy_read_consts = read_segments_tfrecords_generator('story_test')
     # tr_gen = record_generator()
     # for i in tr_gen:
diff --git a/segment_model.py b/segment_model.py
index f470d68..cd3aecf 100644
--- a/segment_model.py
+++ b/segment_model.py
@@ -36,7 +36,7 @@ def ctc_lambda_func(args):
     return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
 
 def segment_model(input_dim):
-    input_dim = (100,100,1)
+    # input_dim = (100,100,1)
     inp = Input(shape=input_dim)
     cnv1 = Conv2D(filters=32, kernel_size=(5,9))(inp)
     cnv2 = Conv2D(filters=1, kernel_size=(5,9))(cnv1)
@@ -55,7 +55,7 @@ def segment_model(input_dim):
 
 def simple_segment_model(input_dim):
     # input_dim = (100,100)
-    input_dim = (506,743)
+    # input_dim = (506,743)
     inp = Input(shape=input_dim)
     b_gr1 = Bidirectional(GRU(256, return_sequences=True),merge_mode='sum')(inp)
     # b_gr1
@@ -77,7 +77,7 @@ def load_model_arch(mod_file):
 
 def train_segment(collection_name = 'test'):
     # collection_name = 'story_test'
-    batch_size = 128
+    batch_size = 64
     # batch_size  = 4
     model_dir = './models/segment/'+collection_name
     create_dir(model_dir)
@@ -133,4 +133,4 @@ def train_segment(collection_name = 'test'):
 
 if __name__ == '__main__':
     # pass
-    train_segment('test')
+    train_segment('story_phrases')