Heiko J Schick
37 lines
975 B
Python
37 lines
975 B
Python
"""
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Simple linear regression application.
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Source: https://www.guru99.com/keras-tutorial.html
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"""
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# import tensorflow.keras
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from keras.models import Sequential
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from keras.layers import Dense
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import numpy as np
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import matplotlib.pyplot as plt
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x = data = np.linspace(1,2,200)
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y = x*4 + np.random.randn(*x.shape) * 0.3
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model = Sequential()
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model.add(Dense(1, input_dim=1, activation='linear'))
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model.compile(optimizer='sgd', loss='mse', metrics=['mse'])
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weights = model.layers[0].get_weights()
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w_init = weights[0][0][0]
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b_init = weights[1][0]
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print('Linear regression model is initialized with weights w: %.2f, b: %.2f' % (w_init, b_init))
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model.fit(x,y, batch_size=1, epochs=30, shuffle=False)
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weights = model.layers[0].get_weights()
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w_final = weights[0][0][0]
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b_final = weights[1][0]
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print('Linear regression model is trained to have weight w: %.2f, b: %.2f' % (w_final, b_final))
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predict = model.predict(data)
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plt.plot(data, predict, 'b', data , y, 'k.')
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plt.show()
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