diff --git a/examples/task_conditional_language_model.py b/examples/task_conditional_language_model.py
index 5729748e..a72ce2ed 100644
--- a/examples/task_conditional_language_model.py
+++ b/examples/task_conditional_language_model.py
@@ -143,7 +143,7 @@ def just_show():
     print(random_sentiment.generate(0, 5, 5), '\n')
 
 
-class Evaluate(keras.callbacks.Callback):
+class Evaluator(keras.callbacks.Callback):
     def __init__(self):
         self.lowest = 1e10
 
@@ -158,7 +158,7 @@ def on_epoch_end(self, epoch, logs=None):
 
 if __name__ == '__main__':
 
-    evaluator = Evaluate()
+    evaluator = Evaluator()
     train_generator = data_generator(data, batch_size)
 
     model.fit_generator(
diff --git a/examples/task_image_caption.py b/examples/task_image_caption.py
index b3e3a9b7..09fdd66e 100644
--- a/examples/task_image_caption.py
+++ b/examples/task_image_caption.py
@@ -194,7 +194,7 @@ def just_show():
         print()
 
 
-class Evaluate(keras.callbacks.Callback):
+class Evaluator(keras.callbacks.Callback):
     def __init__(self):
         self.lowest = 1e10
 
@@ -209,7 +209,7 @@ def on_epoch_end(self, epoch, logs=None):
 
 if __name__ == '__main__':
 
-    evaluator = Evaluate()
+    evaluator = Evaluator()
     train_generator = data_generator(train_data, batch_size)
 
     model.fit_generator(
diff --git a/examples/task_language_model.py b/examples/task_language_model.py
index f43a655c..a2e2e803 100644
--- a/examples/task_language_model.py
+++ b/examples/task_language_model.py
@@ -147,7 +147,7 @@ def just_show():
         print(u'结果: %s\n' % ('\n'.join(t)))
 
 
-class Evaluate(keras.callbacks.Callback):
+class Evaluator(keras.callbacks.Callback):
     def __init__(self):
         self.lowest = 1e10
 
@@ -162,7 +162,7 @@ def on_epoch_end(self, epoch, logs=None):
 
 if __name__ == '__main__':
 
-    evaluator = Evaluate()
+    evaluator = Evaluator()
     train_generator = data_generator(data, batch_size)
 
     model.fit_generator(
diff --git a/examples/task_reading_comprehension_by_mlm.py b/examples/task_reading_comprehension_by_mlm.py
index 3d410a95..5080f941 100644
--- a/examples/task_reading_comprehension_by_mlm.py
+++ b/examples/task_reading_comprehension_by_mlm.py
@@ -199,7 +199,7 @@ def predict_to_file(data, filename):
             f.flush()
 
 
-class Evaluate(keras.callbacks.Callback):
+class Evaluator(keras.callbacks.Callback):
     def __init__(self):
         self.lowest = 1e10
 
@@ -212,7 +212,7 @@ def on_epoch_end(self, epoch, logs=None):
 
 if __name__ == '__main__':
 
-    evaluator = Evaluate()
+    evaluator = Evaluator()
     train_generator = data_generator(train_data, batch_size)
 
     model.fit_generator(
diff --git a/examples/task_reading_comprehension_by_seq2seq.py b/examples/task_reading_comprehension_by_seq2seq.py
index c38097dd..a2bb1e54 100644
--- a/examples/task_reading_comprehension_by_seq2seq.py
+++ b/examples/task_reading_comprehension_by_seq2seq.py
@@ -229,7 +229,7 @@ def predict_to_file(data, filename, topk=1):
             f.flush()
 
 
-class Evaluate(keras.callbacks.Callback):
+class Evaluator(keras.callbacks.Callback):
     def __init__(self):
         self.lowest = 1e10
 
@@ -242,7 +242,7 @@ def on_epoch_end(self, epoch, logs=None):
 
 if __name__ == '__main__':
 
-    evaluator = Evaluate()
+    evaluator = Evaluator()
     train_generator = data_generator(train_data, batch_size)
 
     model.fit_generator(
diff --git a/examples/task_seq2seq_autotitle.py b/examples/task_seq2seq_autotitle.py
index a5beb713..9b44471b 100644
--- a/examples/task_seq2seq_autotitle.py
+++ b/examples/task_seq2seq_autotitle.py
@@ -116,7 +116,7 @@ def just_show():
     print()
 
 
-class Evaluate(keras.callbacks.Callback):
+class Evaluator(keras.callbacks.Callback):
     def __init__(self):
         self.lowest = 1e10
 
@@ -131,7 +131,7 @@ def on_epoch_end(self, epoch, logs=None):
 
 if __name__ == '__main__':
 
-    evaluator = Evaluate()
+    evaluator = Evaluator()
     train_generator = data_generator(txts, batch_size)
 
     model.fit_generator(
diff --git a/examples/task_seq2seq_autotitle_csl.py b/examples/task_seq2seq_autotitle_csl.py
index 51a35ec0..1942e673 100644
--- a/examples/task_seq2seq_autotitle_csl.py
+++ b/examples/task_seq2seq_autotitle_csl.py
@@ -118,7 +118,7 @@ def generate(self, text, topk=1):
 autotitle = AutoTitle(start_id=None, end_id=tokenizer._token_end_id, maxlen=32)
 
 
-class Evaluate(keras.callbacks.Callback):
+class Evaluator(keras.callbacks.Callback):
     def __init__(self):
         self.rouge = Rouge()
         self.smooth = SmoothingFunction().method1
@@ -163,7 +163,7 @@ def evaluate(self, data, topk=1):
 
 if __name__ == '__main__':
 
-    evaluator = Evaluate()
+    evaluator = Evaluator()
     train_generator = data_generator(train_data, batch_size)
 
     model.fit_generator(
diff --git a/examples/task_sequence_labeling_cws_crf.py b/examples/task_sequence_labeling_cws_crf.py
index a9c750ea..183b623d 100644
--- a/examples/task_sequence_labeling_cws_crf.py
+++ b/examples/task_sequence_labeling_cws_crf.py
@@ -184,7 +184,7 @@ def predict_to_file(in_file, out_file):
     fw.close()
 
 
-class Evaluate(keras.callbacks.Callback):
+class Evaluator(keras.callbacks.Callback):
     def __init__(self):
         self.best_val_acc = 0
 
@@ -202,7 +202,7 @@ def on_epoch_end(self, epoch, logs=None):
 
 if __name__ == '__main__':
 
-    evaluator = Evaluate()
+    evaluator = Evaluator()
     train_generator = data_generator(train_data, batch_size)
 
     model.fit_generator(
diff --git a/examples/task_sequence_labeling_ner_crf.py b/examples/task_sequence_labeling_ner_crf.py
index 8d266c50..56d5d96d 100644
--- a/examples/task_sequence_labeling_ner_crf.py
+++ b/examples/task_sequence_labeling_ner_crf.py
@@ -181,7 +181,7 @@ def evaluate(data):
     return f1, precision, recall
 
 
-class Evaluate(keras.callbacks.Callback):
+class Evaluator(keras.callbacks.Callback):
     def __init__(self):
         self.best_val_f1 = 0
 
@@ -207,7 +207,7 @@ def on_epoch_end(self, epoch, logs=None):
 
 if __name__ == '__main__':
 
-    evaluator = Evaluate()
+    evaluator = Evaluator()
     train_generator = data_generator(train_data, batch_size)
 
     model.fit_generator(