-
Notifications
You must be signed in to change notification settings - Fork 0
/
testCases_v2.py
126 lines (104 loc) · 5.15 KB
/
testCases_v2.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
import numpy as np
def layer_sizes_test_case():
np.random.seed(1)
X_assess = np.random.randn(5, 3)
Y_assess = np.random.randn(2, 3)
return X_assess, Y_assess
def initialize_parameters_test_case():
n_x, n_h, n_y = 2, 4, 1
return n_x, n_h, n_y
def forward_propagation_test_case():
np.random.seed(1)
X_assess = np.random.randn(2, 3)
b1 = np.random.randn(4, 1)
b2 = np.array([[-1.3]])
parameters = {'W1': np.array([[-0.00416758, -0.00056267],
[-0.02136196, 0.01640271],
[-0.01793436, -0.00841747],
[0.00502881, -0.01245288]]),
'W2': np.array([[-0.01057952, -0.00909008, 0.00551454, 0.02292208]]),
'b1': b1,
'b2': b2}
return X_assess, parameters
def compute_cost_test_case():
np.random.seed(1)
Y_assess = (np.random.randn(1, 3) > 0)
parameters = {'W1': np.array([[-0.00416758, -0.00056267],
[-0.02136196, 0.01640271],
[-0.01793436, -0.00841747],
[0.00502881, -0.01245288]]),
'W2': np.array([[-0.01057952, -0.00909008, 0.00551454, 0.02292208]]),
'b1': np.array([[0.],
[0.],
[0.],
[0.]]),
'b2': np.array([[0.]])}
a2 = (np.array([[0.5002307, 0.49985831, 0.50023963]]))
return a2, Y_assess, parameters
def backward_propagation_test_case():
np.random.seed(1)
X_assess = np.random.randn(2, 3)
Y_assess = (np.random.randn(1, 3) > 0)
parameters = {'W1': np.array([[-0.00416758, -0.00056267],
[-0.02136196, 0.01640271],
[-0.01793436, -0.00841747],
[0.00502881, -0.01245288]]),
'W2': np.array([[-0.01057952, -0.00909008, 0.00551454, 0.02292208]]),
'b1': np.array([[0.],
[0.],
[0.],
[0.]]),
'b2': np.array([[0.]])}
cache = {'A1': np.array([[-0.00616578, 0.0020626, 0.00349619],
[-0.05225116, 0.02725659, -0.02646251],
[-0.02009721, 0.0036869, 0.02883756],
[0.02152675, -0.01385234, 0.02599885]]),
'A2': np.array([[0.5002307, 0.49985831, 0.50023963]]),
'Z1': np.array([[-0.00616586, 0.0020626, 0.0034962],
[-0.05229879, 0.02726335, -0.02646869],
[-0.02009991, 0.00368692, 0.02884556],
[0.02153007, -0.01385322, 0.02600471]]),
'Z2': np.array([[0.00092281, -0.00056678, 0.00095853]])}
return parameters, cache, X_assess, Y_assess
def update_parameters_test_case():
parameters = {'W1': np.array([[-0.00615039, 0.0169021],
[-0.02311792, 0.03137121],
[-0.0169217, -0.01752545],
[0.00935436, -0.05018221]]),
'W2': np.array([[-0.0104319, -0.04019007, 0.01607211, 0.04440255]]),
'b1': np.array([[-8.97523455e-07],
[8.15562092e-06],
[6.04810633e-07],
[-2.54560700e-06]]),
'b2': np.array([[9.14954378e-05]])}
grads = {'dW1': np.array([[0.00023322, -0.00205423],
[0.00082222, -0.00700776],
[-0.00031831, 0.0028636],
[-0.00092857, 0.00809933]]),
'dW2': np.array([[-1.75740039e-05, 3.70231337e-03, -1.25683095e-03,
-2.55715317e-03]]),
'db1': np.array([[1.05570087e-07],
[-3.81814487e-06],
[-1.90155145e-07],
[5.46467802e-07]]),
'db2': np.array([[-1.08923140e-05]])}
return parameters, grads
def nn_model_test_case():
np.random.seed(1)
X_assess = np.random.randn(2, 3)
Y_assess = (np.random.randn(1, 3) > 0)
return X_assess, Y_assess
def predict_test_case():
np.random.seed(1)
X_assess = np.random.randn(2, 3)
parameters = {'W1': np.array([[-0.00615039, 0.0169021],
[-0.02311792, 0.03137121],
[-0.0169217, -0.01752545],
[0.00935436, -0.05018221]]),
'W2': np.array([[-0.0104319, -0.04019007, 0.01607211, 0.04440255]]),
'b1': np.array([[-8.97523455e-07],
[8.15562092e-06],
[6.04810633e-07],
[-2.54560700e-06]]),
'b2': np.array([[9.14954378e-05]])}
return parameters, X_assess