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fix: 补充导出JSON的代码 #7

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fix: 补充导出JSON的代码 #7

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54 changes: 18 additions & 36 deletions ...yer-perceptrons-and-keras/ch13-save-your-models-for-later-with-serialization.md
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand All @@ -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：
Expand Down Expand Up @@ -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：
Expand Down