diff --git a/segment_data.py b/segment_data.py
index 521fe02..eaceec3 100644
--- a/segment_data.py
+++ b/segment_data.py
@@ -258,7 +258,7 @@ if __name__ == '__main__':
     # plot_segments('story_test_segments')
     # fix_csv('story_words')
     # pass
-    create_segments_tfrecords('story_words.3', sample_count=3,train_test_ratio=0.33)
+    create_segments_tfrecords('story_words.30', sample_count=36,train_test_ratio=0.1)
     # 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 d6c8f37..de21753 100644
--- a/segment_model.py
+++ b/segment_model.py
@@ -7,7 +7,7 @@ from keras.layers import Dense,Conv2D, LSTM, Bidirectional, GRU
 from keras.layers import BatchNormalization,Activation
 from keras.losses import categorical_crossentropy
 from keras.utils import to_categorical
-from keras.optimizers import RMSprop
+from keras.optimizers import RMSprop,Adadelta,Adagrad,Adam,Nadam
 from keras.callbacks import TensorBoard, ModelCheckpoint
 from keras import backend as K
 from keras.utils import plot_model
@@ -50,20 +50,24 @@ def segment_model(input_dim):
 
 def simple_segment_model(input_dim):
     inp = Input(shape=input_dim)
-    b_gr1 = Bidirectional(GRU(256, return_sequences=True),merge_mode='sum')(inp)
+    b_gr1 = Bidirectional(LSTM(32, return_sequences=True))(inp)
+    b_gr1 = Bidirectional(LSTM(16, return_sequences=True),merge_mode='sum')(b_gr1)
+    b_gr1 = LSTM(1, return_sequences=True,activation='softmax')(b_gr1)
+    # b_gr1 = LSTM(4, return_sequences=True)(b_gr1)
+    # b_gr1 = LSTM(2, return_sequences=True)(b_gr1)
     # bn_b_gr1 = BatchNormalization(momentum=0.98)(b_gr1)
-    b_gr2 = Bidirectional(GRU(64, return_sequences=True),merge_mode='sum')(b_gr1)
+    # b_gr2 = GRU(64, return_sequences=True)(b_gr1)
     # bn_b_gr2 = BatchNormalization(momentum=0.98)(b_gr2)
-    d1 = Dense(32)(b_gr2)
-    bn_d1 = BatchNormalization(momentum=0.98)(d1)
-    bn_da1 = Activation('relu')(bn_d1)
-    d2 = Dense(8)(bn_da1)
-    bn_d2 = BatchNormalization(momentum=0.98)(d2)
-    bn_da2 = Activation('relu')(bn_d2)
-    d3 = Dense(1)(bn_da2)
-    bn_d3 = BatchNormalization(momentum=0.98)(d3)
-    bn_da3 = Activation('softmax')(bn_d3)
-    oup = Reshape(target_shape=(input_dim[0],))(bn_da3)
+    # d1 = Dense(32)(b_gr2)
+    # bn_d1 = BatchNormalization(momentum=0.98)(d1)
+    # bn_da1 = Activation('relu')(bn_d1)
+    # d2 = Dense(8)(bn_da1)
+    # bn_d2 = BatchNormalization(momentum=0.98)(d2)
+    # bn_da2 = Activation('relu')(bn_d2)
+    # d3 = Dense(1)(b_gr1)
+    # # bn_d3 = BatchNormalization(momentum=0.98)(d3)
+    # bn_da3 = Activation('softmax')(d3)
+    oup = Reshape(target_shape=(input_dim[0],))(b_gr1)
     return Model(inp, oup)
 
 def write_model_arch(mod,mod_file):
@@ -79,13 +83,13 @@ def load_model_arch(mod_file):
 
 def train_segment(collection_name = 'test',resume_weights='',initial_epoch=0):
     # collection_name = 'story_test'
-    # batch_size = 32
-    batch_size  = 1
+    batch_size = 128
+    # batch_size  = 4
     model_dir = './models/segment/'+collection_name
     create_dir(model_dir)
     log_dir = './logs/segment/'+collection_name
     create_dir(log_dir)
-    tr_gen_fn,te_x,te_y,copy_read_consts = read_segments_tfrecords_generator(collection_name,batch_size,batch_size)
+    tr_gen_fn,te_x,te_y,copy_read_consts = read_segments_tfrecords_generator(collection_name,batch_size,2*batch_size)
     tr_gen = tr_gen_fn()
     n_step,n_features,n_records = copy_read_consts(model_dir)
     input_dim = (n_step, n_features)
@@ -115,8 +119,8 @@ def train_segment(collection_name = 'test',resume_weights='',initial_epoch=0):
         mode='auto',
         period=1)
     # train
-    rms = RMSprop()
-    model.compile(loss=categorical_crossentropy, optimizer=rms, metrics=[accuracy])
+    opt = RMSprop()
+    model.compile(loss=categorical_crossentropy, optimizer=opt, metrics=[accuracy])
     write_model_arch(model,model_dir+'/speech_segment_model_arch.yaml')
     epoch_n_steps = step_count(n_records,batch_size)
     if resume_weights != '':
@@ -137,4 +141,4 @@ def train_segment(collection_name = 'test',resume_weights='',initial_epoch=0):
 
 if __name__ == '__main__':
     # pass
-    train_segment('story_words.3')#,'./models/segment/story_phrases.1000/speech_segment_model-final.h5',1001)
+    train_segment('story_words')#,'./models/segment/story_phrases.1000/speech_segment_model-final.h5',1001)