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To?know?more?or?get?code?samples?please?visit?my?website:
https://morvanzhou.github.io/tutorials/
Or?search:?莫煩Python
Thank?you?for?supporting!
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#?please?note?all?tutorial?code?are?running?under?python3.5.
#?If?you?use?the?version?like?python2.7?please?modify?the?code?accordingly

#?10?-?save

import?numpy?as?np
np.random.seed（1337）??#?for?reproducibility

from?keras.models?import?Sequential
from?keras.layers?import?Dense
from?keras.models?import?load_model

#?create?some?data
X?=?np.linspace（-1?1?200）
np.random.shuffle（X）????#?randomize?the?data
Y?=?0.5?*?X?+?2?+?np.random.normal（0?0.05?（200?））
X_train?Y_train?=?X[:160]?Y[:160]?????#?first?160?data?points
X_test?Y_test?=?X[160:]?Y[160:]???????#?last?40?data?points
model?=?Sequential（）
model.add（Dense（output_dim=1?input_dim=1））
model.compile（loss=‘mse‘?optimizer=‘sgd‘）
for?step?in?range（301）:
????cost?=?model.train_on_batch（X_train?Y_train）

#?save
print（‘test?before?save:?‘?model.predict（X_test[0:2]））
model.save（‘my_model.h5‘）???#?HDF5?file?you?have?to?pip3?install?h5py?if?don‘t?have?it
del?model??#?deletes?the?existing?model

#?load
model?=?load_model（‘my_model.h5‘）
print（‘test?after?load:?‘?model.predict（X_test[0:2]））
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#?save?and?load?weights
model.save_weights（‘my_model_weights.h5‘）
model.load_weights（‘my_model_weights.h5‘）

#?save?and?load?fresh?network?without?trained?weights
from?keras.models?import?model_from_json
json_string?=?model.to_json（）
model?=?model_from_json（json_string）
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