diff --git a/4-advanced-multi-layer-perceptrons-and-keras/ch13-save-your-models-for-later-with-serialization.md b/4-advanced-multi-layer-perceptrons-and-keras/ch13-save-your-models-for-later-with-serialization.md
index 6643e5f..df90bab 100644
--- a/4-advanced-multi-layer-perceptrons-and-keras/ch13-save-your-models-for-later-with-serialization.md
+++ b/4-advanced-multi-layer-perceptrons-and-keras/ch13-save-your-models-for-later-with-serialization.md
@@ -52,7 +52,8 @@ model = Sequential()
 model.add(Dense(12, input_dim=8, init='uniform', activation='relu')) model.add(Dense(8, init='uniform', activation='relu'))
 model.add(Dense(1, init='uniform', activation='sigmoid'))
 # Compile model
-model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # Fit the model
+model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) 
+# Fit the model
 model.fit(X, Y, nb_epoch=150, batch_size=10, verbose=0)
 # evaluate the model
 scores = model.evaluate(X, Y, verbose=0)
@@ -71,39 +72,17 @@ print("Saved model to disk")
 ```python
 # later...
 # load json and create model
-# MLP for Pima Indians Dataset serialize to JSON and HDF5
-from keras.models import Sequential
-from keras.layers import Dense
-from keras.models import model_from_json
-import numpy
-import os
-# fix random seed for reproducibility
-seed = 7
-numpy.random.seed(seed)
-# load pima indians dataset
-dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
-# split into input (X) and output (Y) variables
-X = dataset[:,0:8]
-Y = dataset[:,8]
-# create model
-model = Sequential()
-model.add(Dense(12, input_dim=8, init='uniform', activation='relu')) model.add(Dense(8, init='uniform', activation='relu'))
-model.add(Dense(1, init='uniform', activation='sigmoid'))
-# Compile model
-model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # Fit the model
-model.fit(X, Y, nb_epoch=150, batch_size=10, verbose=0)
-# evaluate the model
-scores = model.evaluate(X, Y, verbose=0)
-print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
-# serialize model to JSON
-model_json = model.to_json()
-with open("model.json", "w") as json_file:
-    json_file.write(model_json)
-# serialize weights to HDF5
-model.save_weights("model.h5")
-print("Saved model to disk")
-# later...
-# load json and create model
+json_file = open("model.json", "r")
+loaded_model_json = json_file.read()
+json_file.close()
+loaded_model = model_from_json(loaded_model_json)
+# load weights into new model
+loaded_model.load_weights("model.h5")
+print("Loaded model from disk")
+# evaluate loaded model on test data
+loaded_model.compile(loss="binary_crossentropy", optimizer="rmsprop", metrics=["accuracy"])
+score = loaded_model.evaluate(X, Y, verbose=0)
+print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))
 ```
 
 结果如下。导入的模型和之前导出时一致：
@@ -219,10 +198,13 @@ print("Saved model to disk")
 # later...
 # load YAML and create model
 yaml_file = open('model.yaml', 'r') loaded_model_yaml = yaml_file.read() yaml_file.close()
-loaded_model = model_from_yaml(loaded_model_yaml) # load weights into new model loaded_model.load_weights("model.h5") print("Loaded model from disk")
+loaded_model = model_from_yaml(loaded_model_yaml) 
+# load weights into new model
+loaded_model.load_weights("model.h5")
+print("Loaded model from disk")
 # evaluate loaded model on test data
 loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) score = loaded_model.evaluate(X, Y, verbose=0)
-print "%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100)
+print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))
 ```
 
 结果和之前的一样：