"""
Simple example on how to fine tune models in Keras and how to use them.
Part 2

Source: https://www.guru99.com/keras-tutorial.html
"""

# import tensorflow.keras
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
import matplotlib.pyplot as plt

x = data = np.linspace(1,2,200)
y = x*4 + np.random.randn(*x.shape) * 0.3

model = Sequential()
model.add(Dense(1, input_dim=1, activation='linear'))

model.compile(optimizer='sgd', loss='mse', metrics=['mse'])

weights = model.layers[0].get_weights()
w_init = weights[0][0][0]
b_init = weights[1][0]
print('Linear regression model is initialized with weights w: %.2f, b: %.2f' % (w_init, b_init))

model.fit(x,y, batch_size=1, epochs=30, shuffle=False)

weights = model.layers[0].get_weights()
w_final = weights[0][0][0]
b_final = weights[1][0]
print('Linear regression model is trained to have weight w: %.2f, b: %.2f' % (w_final, b_final))

predict = model.predict(data)

plt.plot(data, predict, 'b', data , y, 'k.')
plt.show()