cat_sigmoid_model.py

def model(X_train, Y_train, X_test, Y_test, num_iterations=2000, learning_rate=0.5, print_cost=False):
    #Initialize parameters with 0s
    w, b = initialize_with_zeros(X_train.shape[0])
    
    #Gradient descent
    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)
    
    #Retrive parameters w, b from dictionary
    w = parameters['w']
    b = parameters['b']
    
    #Predict test/train set examples
    Y_prediction_test = predict(w, b, X_test)
    Y_prediction_train = predict(w, b, X_train)
    
    #Print test/train errors
    print("train accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))
    print("test accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))
    
    d = {'costs': costs,
         'Y_prediction_test': Y_prediction_test,
         'Y_prediction_train': Y_prediction_train,
         'w': w,
         'b': b,
         'learning_rate': learning_rate,
         'num_iterations': num_iterations}
    
    return d