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# Untitled

a guest Sep 20th, 2018 62 Never
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1. def initialize_parameters_zeros(layers_dims):
2.     np.random.seed(1)
3.     parameters = {}
4.     L = len(layers_dims)
5.
6.     for l in range(1, L):
7.         parameters["W" + str(l)] = np.zeros(
8.             (layers_dims[l], layers_dims[l - 1]))
9.         parameters["b" + str(l)] = np.zeros((layers_dims[l], 1))
10.
11.     return parameters
12.
13.
14. def initialize_parameters_random(layers_dims):
15.     np.random.seed(1)
16.     parameters = {}
17.     L = len(layers_dims)
18.
19.     for l in range(1, L):
20.         parameters["W" + str(l)] = np.random.randn(
21.             layers_dims[l], layers_dims[l - 1]) * 10
22.         parameters["b" + str(l)] = np.zeros((layers_dims[l], 1))
23.
24.     return parameters
25.
26.
27. def initialize_parameters_he_xavier(layers_dims, initialization_method="he"):
28.     np.random.seed(1)
29.     parameters = {}
30.     L = len(layers_dims)
31.
32.     if initialization_method == "he":
33.         for l in range(1, L):
34.             parameters["W" + str(l)] = np.random.randn(
35.                 layers_dims[l],
36.                 layers_dims[l - 1]) * np.sqrt(2 / layers_dims[l - 1])
37.             parameters["b" + str(l)] = np.zeros((layers_dims[l], 1))
38.     elif initialization_method == "xavier":
39.         for l in range(1, L):
40.             parameters["W" + str(l)] = np.random.randn(
41.                 layers_dims[l],
42.                 layers_dims[l - 1]) * np.sqrt(1 / layers_dims[l - 1])
43.             parameters["b" + str(l)] = np.zeros((layers_dims[l], 1))
44.
45.     return parameters
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