grad_check.py

def gradient_check_n(parameters, gradients, X, Y, epsilon = 1e-7):
   
    parameters_values, _ = dictionary_to_vector(parameters)
    grad = gradients_to_vector(gradients)
    num_parameters = parameters_values.shape[0]
    J_plus = np.zeros((num_parameters, 1))
    J_minus = np.zeros((num_parameters, 1))
    gradapprox = np.zeros((num_parameters, 1))
    
    # Compute gradapprox
    for i in range(num_parameters):        
       
        thetaplus = np.copy(parameters_values)
        thetaplus[i][0] = thetaplus[i][0] + epsilon
        AL, caches = L_model_forward(X,vector_to_dictionary(thetaplus,parameters))
        J_plus[i] = compute_cost(AL, Y)
        
        thetaminus = np.copy(parameters_values)
        thetaminus[i][0] = thetaminus[i][0] - epsilon
        AL, caches = L_model_forward(X,vector_to_dictionary(thetaminus,parameters))
        J_minus[i] = compute_cost(AL,Y)
        
        gradapprox[i] = (J_plus[i]-J_minus[i])/(2*epsilon)
    
    numerator = np.linalg.norm(gradapprox-grad)
    denominator = np.linalg.norm(gradapprox)+np.linalg.norm(grad)
    difference = numerator/denominator

    if difference > 2e-7:
        print ("\033[93m" + "There is a mistake in the backward propagation! difference = " + str(difference) + "\033[0m")
    else:
        print ("\033[92m" + "Your backward propagation works perfectly fine! difference = " + str(difference) + "\033[0m")
    
    return difference