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GitHub Repository: y33-j3T/Coursera-Deep-Learning
 Path: blob/master/Neural Networks and Deep Learning/Week 4/Building your Deep Neural Network - Step by Step/testCases_v4.py
Views: 13378
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import numpy as np

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def linear_forward_test_case():

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    np.random.seed(1)

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    """

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    X = np.array([[-1.02387576, 1.12397796],

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 [-1.62328545, 0.64667545],

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 [-1.74314104, -0.59664964]])

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    W = np.array([[ 0.74505627, 1.97611078, -1.24412333]])

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    b = np.array([[1]])

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    """

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    A = np.random.randn(3,2)

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    W = np.random.randn(1,3)

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    b = np.random.randn(1,1)

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    return A, W, b

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def linear_activation_forward_test_case():

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    """

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    X = np.array([[-1.02387576, 1.12397796],

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 [-1.62328545, 0.64667545],

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 [-1.74314104, -0.59664964]])

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    W = np.array([[ 0.74505627, 1.97611078, -1.24412333]])

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    b = 5

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    """

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    np.random.seed(2)

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    A_prev = np.random.randn(3,2)

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    W = np.random.randn(1,3)

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    b = np.random.randn(1,1)

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    return A_prev, W, b

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def L_model_forward_test_case():

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    """

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    X = np.array([[-1.02387576, 1.12397796],

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 [-1.62328545, 0.64667545],

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 [-1.74314104, -0.59664964]])

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    parameters = {'W1': np.array([[ 1.62434536, -0.61175641, -0.52817175],

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        [-1.07296862,  0.86540763, -2.3015387 ]]),

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 'W2': np.array([[ 1.74481176, -0.7612069 ]]),

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 'b1': np.array([[ 0.],

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        [ 0.]]),

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 'b2': np.array([[ 0.]])}

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    """

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    np.random.seed(1)

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    X = np.random.randn(4,2)

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    W1 = np.random.randn(3,4)

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    b1 = np.random.randn(3,1)

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    W2 = np.random.randn(1,3)

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    b2 = np.random.randn(1,1)

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    parameters = {"W1": W1,

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                  "b1": b1,

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                  "W2": W2,

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                  "b2": b2}

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    return X, parameters

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def compute_cost_test_case():

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    Y = np.asarray([[1, 1, 1]])

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    aL = np.array([[.8,.9,0.4]])

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    return Y, aL

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def linear_backward_test_case():

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    """

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    z, linear_cache = (np.array([[-0.8019545 ,  3.85763489]]), (np.array([[-1.02387576,  1.12397796],

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       [-1.62328545,  0.64667545],

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       [-1.74314104, -0.59664964]]), np.array([[ 0.74505627,  1.97611078, -1.24412333]]), np.array([[1]]))

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    """

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    np.random.seed(1)

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    dZ = np.random.randn(1,2)

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    A = np.random.randn(3,2)

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    W = np.random.randn(1,3)

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    b = np.random.randn(1,1)

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    linear_cache = (A, W, b)

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    return dZ, linear_cache

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def linear_activation_backward_test_case():

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    """

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    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]])))

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    """

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    np.random.seed(2)

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    dA = np.random.randn(1,2)

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    A = np.random.randn(3,2)

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    W = np.random.randn(1,3)

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    b = np.random.randn(1,1)

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    Z = np.random.randn(1,2)

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    linear_cache = (A, W, b)

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    activation_cache = Z

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    linear_activation_cache = (linear_cache, activation_cache)

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    return dA, linear_activation_cache

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def L_model_backward_test_case():

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    """

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    X = np.random.rand(3,2)

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    Y = np.array([[1, 1]])

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    parameters = {'W1': np.array([[ 1.78862847,  0.43650985,  0.09649747]]), 'b1': np.array([[ 0.]])}

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    aL, caches = (np.array([[ 0.60298372,  0.87182628]]), [((np.array([[ 0.20445225,  0.87811744],

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           [ 0.02738759,  0.67046751],

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           [ 0.4173048 ,  0.55868983]]),

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    np.array([[ 1.78862847,  0.43650985,  0.09649747]]),

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    np.array([[ 0.]])),

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   np.array([[ 0.41791293,  1.91720367]]))])

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   """

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    np.random.seed(3)

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    AL = np.random.randn(1, 2)

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    Y = np.array([[1, 0]])

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    A1 = np.random.randn(4,2)

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    W1 = np.random.randn(3,4)

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    b1 = np.random.randn(3,1)

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    Z1 = np.random.randn(3,2)

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    linear_cache_activation_1 = ((A1, W1, b1), Z1)

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    A2 = np.random.randn(3,2)

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    W2 = np.random.randn(1,3)

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    b2 = np.random.randn(1,1)

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    Z2 = np.random.randn(1,2)

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    linear_cache_activation_2 = ((A2, W2, b2), Z2)

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    caches = (linear_cache_activation_1, linear_cache_activation_2)

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    return AL, Y, caches

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def update_parameters_test_case():

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    """

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    parameters = {'W1': np.array([[ 1.78862847,  0.43650985,  0.09649747],

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        [-1.8634927 , -0.2773882 , -0.35475898],

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        [-0.08274148, -0.62700068, -0.04381817],

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        [-0.47721803, -1.31386475,  0.88462238]]),

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 'W2': np.array([[ 0.88131804,  1.70957306,  0.05003364, -0.40467741],

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        [-0.54535995, -1.54647732,  0.98236743, -1.10106763],

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        [-1.18504653, -0.2056499 ,  1.48614836,  0.23671627]]),

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 'W3': np.array([[-1.02378514, -0.7129932 ,  0.62524497],

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        [-0.16051336, -0.76883635, -0.23003072]]),

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 'b1': np.array([[ 0.],

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        [ 0.],

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        [ 0.],

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        [ 0.]]),

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 'b2': np.array([[ 0.],

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        [ 0.],

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        [ 0.]]),

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 'b3': np.array([[ 0.],

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        [ 0.]])}

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    grads = {'dW1': np.array([[ 0.63070583,  0.66482653,  0.18308507],

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        [ 0.        ,  0.        ,  0.        ],

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        [ 0.        ,  0.        ,  0.        ],

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        [ 0.        ,  0.        ,  0.        ]]),

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 'dW2': np.array([[ 1.62934255,  0.        ,  0.        ,  0.        ],

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        [ 0.        ,  0.        ,  0.        ,  0.        ],

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        [ 0.        ,  0.        ,  0.        ,  0.        ]]),

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 'dW3': np.array([[-1.40260776,  0.        ,  0.        ]]),

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 'da1': np.array([[ 0.70760786,  0.65063504],

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        [ 0.17268975,  0.15878569],

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        [ 0.03817582,  0.03510211]]),

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 'da2': np.array([[ 0.39561478,  0.36376198],

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        [ 0.7674101 ,  0.70562233],

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        [ 0.0224596 ,  0.02065127],

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        [-0.18165561, -0.16702967]]),

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 'da3': np.array([[ 0.44888991,  0.41274769],

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        [ 0.31261975,  0.28744927],

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        [-0.27414557, -0.25207283]]),

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 'db1': 0.75937676204411464,

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 'db2': 0.86163759922811056,

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 'db3': -0.84161956022334572}

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    """

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    np.random.seed(2)

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    W1 = np.random.randn(3,4)

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    b1 = np.random.randn(3,1)

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    W2 = np.random.randn(1,3)

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    b2 = np.random.randn(1,1)

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    parameters = {"W1": W1,

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                  "b1": b1,

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                  "W2": W2,

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                  "b2": b2}

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    np.random.seed(3)

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    dW1 = np.random.randn(3,4)

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    db1 = np.random.randn(3,1)

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    dW2 = np.random.randn(1,3)

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    db2 = np.random.randn(1,1)

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    grads = {"dW1": dW1,

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             "db1": db1,

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             "dW2": dW2,

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             "db2": db2}

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    return parameters, grads

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def L_model_forward_test_case_2hidden():

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    np.random.seed(6)

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    X = np.random.randn(5,4)

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    W1 = np.random.randn(4,5)

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    b1 = np.random.randn(4,1)

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    W2 = np.random.randn(3,4)

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    b2 = np.random.randn(3,1)

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    W3 = np.random.randn(1,3)

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    b3 = np.random.randn(1,1)

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    parameters = {"W1": W1,

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                  "b1": b1,

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                  "W2": W2,

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                  "b2": b2,

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                  "W3": W3,

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                  "b3": b3}

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    return X, parameters

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def print_grads(grads):

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    print ("dW1 = "+ str(grads["dW1"]))

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    print ("db1 = "+ str(grads["db1"]))

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    print ("dA1 = "+ str(grads["dA1"]))     

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