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]))
"""
# 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)
"""
