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linear_regression.py

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+"""
+Simple linear regression application.
+
+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()