keras 保存模型和加载模型

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]))
说明：

1、保存模型的api:

model.save('my_model.h5')

2、加载模型
model = load_model('my_model.h5')
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作者：BYR_jiandong
来源：CSDN
原文：https://blog.csdn.net/lujiandong1/article/details/55806435
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