40 lines
1.1 KiB
Python
40 lines
1.1 KiB
Python
# -*- coding: utf-8 -*-
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import numpy as np
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import math
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# Create random input and output data
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x = np.linspace(-math.pi, math.pi, 2000) #spaces between -pi and pi with 2000 points equally distributed
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y = np.sin(x)
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# Randomly initialize weights
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a = np.random.randn()
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b = np.random.randn()
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c = np.random.randn()
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d = np.random.randn()
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learning_rate = 1e-6
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for t in range(2000):
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# Forward pass: compute predicted y
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# y = a + b x + c x^2 + d x^3
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y_pred = a + b * x + c * x ** 2 + d * x ** 3
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# Compute and print loss
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loss = np.square(y_pred - y).sum()
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if t % 100 == 99:
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print(t, loss)
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# Backprop to compute gradients of a, b, c, d with respect to loss
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grad_y_pred = 2.0 * (y_pred - y)
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grad_a = grad_y_pred.sum()
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grad_b = (grad_y_pred * x).sum()
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grad_c = (grad_y_pred * x ** 2).sum()
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grad_d = (grad_y_pred * x ** 3).sum()
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# Update weights
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a -= learning_rate * grad_a
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b -= learning_rate * grad_b
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c -= learning_rate * grad_c
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d -= learning_rate * grad_d
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print(f'Result: y = {a} + {b} x + {c} x^2 + {d} x^3') |