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# Untitled

a guest Dec 12th, 2019 69 Never
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1. import math
2.
3. ITERS = 1000
4.
5. # pierwsza warstwa
6. w1 = tf.Variable(tf.ones([1, 1]))
7. b1 = tf.Variable(tf.ones([1]))
8.
9. # druga warstwa
10. w2 = tf.Variable(tf.ones([1, 2]))
11. b2 = tf.Variable(tf.ones([2]))
12.
13. # trzecia warstwa
14. w3 = tf.Variable(tf.ones([2, 1]))
15. b3 = tf.Variable(tf.ones([1]))
16.
17. mu = 0.001  # learning speed
18. for i in range(ITERS):
19.     x = tf.random.uniform([16, 1], 0, math.pi * 2)  # dane wejściowe
20.     y_hat = tf.math.sin(x)                          # dane uczące (ground truth)
21.
22.     # propagacja wejścia sieci
23.     z1 = x @ w1 + b1
24.     y1 = tf.nn.sigmoid(z1)
25.
26.     z2 = y1 @ w2 + b2
27.     y2 = tf.nn.sigmoid(z2)
28.
29.     z3 = y2 @ w3 + b3
30.     y3 = z3
31.
32.     if i%100==0:
33.         print(loss(y_hat, y3))
34.
35.     d3 = y_hat-y3
36.     d2 = d3 @ tf.transpose(w3)
37.     d1 = d2 @ tf.transpose(w2)
38.
39.     w1 = w1 + sum(mu*d1*d_sigmoid(z1)*x)
40.     w2 = w2 + sum(mu*d2*d_sigmoid(z2)*y1)
41.     w3 = w3 + sum(mu*d3*d_sigmoid(z3)*y2)
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