-
Notifications
You must be signed in to change notification settings - Fork 0
/
testCases_v2.py
186 lines (169 loc) · 5.87 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
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
import numpy as np
def linear_forward_test_case():
np.random.seed(1)
"""
X = np.array([[-1.02387576, 1.12397796],
[-1.62328545, 0.64667545],
[-1.74314104, -0.59664964]])
W = np.array([[ 0.74505627, 1.97611078, -1.24412333]])
b = np.array([[1]])
"""
A = np.random.randn(3,2)
W = np.random.randn(1,3)
b = np.random.randn(1,1)
return A, W, b
def linear_activation_forward_test_case():
"""
X = np.array([[-1.02387576, 1.12397796],
[-1.62328545, 0.64667545],
[-1.74314104, -0.59664964]])
W = np.array([[ 0.74505627, 1.97611078, -1.24412333]])
b = 5
"""
np.random.seed(2)
A_prev = np.random.randn(3,2)
W = np.random.randn(1,3)
b = np.random.randn(1,1)
return A_prev, W, b
def L_model_forward_test_case():
"""
X = np.array([[-1.02387576, 1.12397796],
[-1.62328545, 0.64667545],
[-1.74314104, -0.59664964]])
parameters = {'W1': np.array([[ 1.62434536, -0.61175641, -0.52817175],
[-1.07296862, 0.86540763, -2.3015387 ]]),
'W2': np.array([[ 1.74481176, -0.7612069 ]]),
'b1': np.array([[ 0.],
[ 0.]]),
'b2': np.array([[ 0.]])}
"""
np.random.seed(1)
X = np.random.randn(4,2)
W1 = np.random.randn(3,4)
b1 = np.random.randn(3,1)
W2 = np.random.randn(1,3)
b2 = np.random.randn(1,1)
parameters = {"W1": W1,
"b1": b1,
"W2": W2,
"b2": b2}
return X, parameters
def compute_cost_test_case():
Y = np.asarray([[1, 1, 1]])
aL = np.array([[.8,.9,0.4]])
return Y, aL
def linear_backward_test_case():
"""
z, linear_cache = (np.array([[-0.8019545 , 3.85763489]]), (np.array([[-1.02387576, 1.12397796],
[-1.62328545, 0.64667545],
[-1.74314104, -0.59664964]]), np.array([[ 0.74505627, 1.97611078, -1.24412333]]), np.array([[1]]))
"""
np.random.seed(1)
dZ = np.random.randn(1,2)
A = np.random.randn(3,2)
W = np.random.randn(1,3)
b = np.random.randn(1,1)
linear_cache = (A, W, b)
return dZ, linear_cache
def linear_activation_backward_test_case():
"""
aL, linear_activation_cache = (np.array([[ 3.1980455 , 7.85763489]]), ((np.array([[-1.02387576, 1.12397796], [-1.62328545, 0.64667545], [-1.74314104, -0.59664964]]), np.array([[ 0.74505627, 1.97611078, -1.24412333]]), 5), np.array([[ 3.1980455 , 7.85763489]])))
"""
np.random.seed(2)
dA = np.random.randn(1,2)
A = np.random.randn(3,2)
W = np.random.randn(1,3)
b = np.random.randn(1,1)
Z = np.random.randn(1,2)
linear_cache = (A, W, b)
activation_cache = Z
linear_activation_cache = (linear_cache, activation_cache)
return dA, linear_activation_cache
def L_model_backward_test_case():
"""
X = np.random.rand(3,2)
Y = np.array([[1, 1]])
parameters = {'W1': np.array([[ 1.78862847, 0.43650985, 0.09649747]]), 'b1': np.array([[ 0.]])}
aL, caches = (np.array([[ 0.60298372, 0.87182628]]), [((np.array([[ 0.20445225, 0.87811744],
[ 0.02738759, 0.67046751],
[ 0.4173048 , 0.55868983]]),
np.array([[ 1.78862847, 0.43650985, 0.09649747]]),
np.array([[ 0.]])),
np.array([[ 0.41791293, 1.91720367]]))])
"""
np.random.seed(3)
AL = np.random.randn(1, 2)
Y = np.array([[1, 0]])
A1 = np.random.randn(4,2)
W1 = np.random.randn(3,4)
b1 = np.random.randn(3,1)
Z1 = np.random.randn(3,2)
linear_cache_activation_1 = ((A1, W1, b1), Z1)
A2 = np.random.randn(3,2)
W2 = np.random.randn(1,3)
b2 = np.random.randn(1,1)
Z2 = np.random.randn(1,2)
linear_cache_activation_2 = ( (A2, W2, b2), Z2)
caches = (linear_cache_activation_1, linear_cache_activation_2)
return AL, Y, caches
def update_parameters_test_case():
"""
parameters = {'W1': np.array([[ 1.78862847, 0.43650985, 0.09649747],
[-1.8634927 , -0.2773882 , -0.35475898],
[-0.08274148, -0.62700068, -0.04381817],
[-0.47721803, -1.31386475, 0.88462238]]),
'W2': np.array([[ 0.88131804, 1.70957306, 0.05003364, -0.40467741],
[-0.54535995, -1.54647732, 0.98236743, -1.10106763],
[-1.18504653, -0.2056499 , 1.48614836, 0.23671627]]),
'W3': np.array([[-1.02378514, -0.7129932 , 0.62524497],
[-0.16051336, -0.76883635, -0.23003072]]),
'b1': np.array([[ 0.],
[ 0.],
[ 0.],
[ 0.]]),
'b2': np.array([[ 0.],
[ 0.],
[ 0.]]),
'b3': np.array([[ 0.],
[ 0.]])}
grads = {'dW1': np.array([[ 0.63070583, 0.66482653, 0.18308507],
[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ]]),
'dW2': np.array([[ 1.62934255, 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ]]),
'dW3': np.array([[-1.40260776, 0. , 0. ]]),
'da1': np.array([[ 0.70760786, 0.65063504],
[ 0.17268975, 0.15878569],
[ 0.03817582, 0.03510211]]),
'da2': np.array([[ 0.39561478, 0.36376198],
[ 0.7674101 , 0.70562233],
[ 0.0224596 , 0.02065127],
[-0.18165561, -0.16702967]]),
'da3': np.array([[ 0.44888991, 0.41274769],
[ 0.31261975, 0.28744927],
[-0.27414557, -0.25207283]]),
'db1': 0.75937676204411464,
'db2': 0.86163759922811056,
'db3': -0.84161956022334572}
"""
np.random.seed(2)
W1 = np.random.randn(3,4)
b1 = np.random.randn(3,1)
W2 = np.random.randn(1,3)
b2 = np.random.randn(1,1)
parameters = {"W1": W1,
"b1": b1,
"W2": W2,
"b2": b2}
np.random.seed(3)
dW1 = np.random.randn(3,4)
db1 = np.random.randn(3,1)
dW2 = np.random.randn(1,3)
db2 = np.random.randn(1,1)
grads = {"dW1": dW1,
"db1": db1,
"dW2": dW2,
"db2": db2}
return parameters, grads