Save and Load Model

from keras.models import Sequential
from keras.layers import Dense
import numpy as np

x_train = np.arange(1, 101)
y_train = x_train
x_test = np.arange(100, 201)
y_test = x_test

model = Sequential()
model.add(Dense( 10, input_shape=(1, ), activation='relu'))
model.add(Dense( 5))
model.add(Dense(1))
model.compile(loss='mse', optimizer='adam', metrics=['mse'])

model.fit(x_train, y_train, epochs=100, batch_size=1, validation_split=0.2, verbose=False)

loss, mse = model.evaluate(x_test, y_test, batch_size=1)
print('loss= %f, mse= %f' % (loss, mse))

model.save("mymodel.hd5")

import numpy as np
import tensorflow as tf

x_test = np.arange(100, 201)
y_test = x_test

model = tf.keras.models.load_model('mymodel.hd5')
loss, mse = model.evaluate(x_test, y_test, batch_size=1)
print('load model loss= %f, mse= %f' % (loss, mse))