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GitHub Repository: y33-j3T/Coursera-Deep-Learning
 Path: blob/master/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/week5/Gradient Checking/testCases.py
Views: 13377
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import numpy as np

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def gradient_check_n_test_case(): 

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    np.random.seed(1)

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

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

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

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    b1 = np.random.randn(5,1) 

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    W2 = np.random.randn(3,5) 

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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, y, parameters

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