testCases.py
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
def L_model_forward_test_case_2hidden():
np.random.seed(6)
X = np.random.randn(5,4)
W1 = np.random.randn(4,5)
b1 = np.random.randn(4,1)
W2 = np.random.randn(3,4)
b2 = np.random.randn(3,1)
W3 = np.random.randn(1,3)
b3 = np.random.randn(1,1)
parameters = {"W1": W1,
"b1": b1,
"W2": W2,
"b2": b2,
"W3": W3,
"b3": b3}
return X, parameters
def print_grads(grads):
print ("dW1 = "+ str(grads["dW1"]))
print ("db1 = "+ str(grads["db1"]))
print ("dA1 = "+ str(grads["dA2"])) # this is done on purpose to be consistent with lecture where we normally start with A0
# in this implementation we started with A1, hence we bump it up by 1.Last updated