dnn_utils_v2,lr_utils,planar_utils,testCases,testCases_v2.zip

import numpy as np def layer_sizes_test_case(): np.random.seed(1) """ seed( ) 用于指定随机数生成时所用算法开始的整数值。 1.如果使用相同的seed( )值，则每次生成的随即数都相同； 2.如果不设置这个值，则系统根据时间来自己选择这个值，此时每次生成的随机数因时间差异而不同。 3.设置的seed()值仅一次有效 """ X_assess = np.random.randn(5, 3) Y_assess = np.random.randn(2, 3) return X_assess, Y_assess def initialize_parameters_test_case(): n_x, n_h, n_y = 2, 4, 1 return n_x, n_h, n_y def forward_propagation_test_case(): np.random.seed(1) X_assess = np.random.randn(2, 3) parameters = {'W1': np.array([[-0.00416758, -0.00056267], [-0.02136196, 0.01640271], [-0.01793436, -0.00841747], [ 0.00502881, -0.01245288]]), 'W2': np.array([[-0.01057952, -0.00909008, 0.00551454, 0.02292208]]), 'b1': np.array([[ 0.], [ 0.], [ 0.], [ 0.]]), 'b2': np.array([[ 0.]])} return X_assess, parameters def compute_cost_test_case(): np.random.seed(1) Y_assess = np.random.randn(1, 3) parameters = {'W1': np.array([[-0.00416758, -0.00056267], [-0.02136196, 0.01640271], [-0.01793436, -0.00841747], [ 0.00502881, -0.01245288]]), 'W2': np.array([[-0.01057952, -0.00909008, 0.00551454, 0.02292208]]), 'b1': np.array([[ 0.], [ 0.], [ 0.], [ 0.]]), 'b2': np.array([[ 0.]])} a2 = (np.array([[ 0.5002307 , 0.49985831, 0.50023963]])) return a2, Y_assess, parameters def backward_propagation_test_case(): np.random.seed(1) X_assess = np.random.randn(2, 3) Y_assess = np.random.randn(1, 3) parameters = {'W1': np.array([[-0.00416758, -0.00056267], [-0.02136196, 0.01640271], [-0.01793436, -0.00841747], [ 0.00502881, -0.01245288]]), 'W2': np.array([[-0.01057952, -0.00909008, 0.00551454, 0.02292208]]), 'b1': np.array([[ 0.], [ 0.], [ 0.], [ 0.]]), 'b2': np.array([[ 0.]])} cache = {'A1': np.array([[-0.00616578, 0.0020626 , 0.00349619], [-0.05225116, 0.02725659, -0.02646251], [-0.02009721, 0.0036869 , 0.02883756], [ 0.02152675, -0.01385234, 0.02599885]]), 'A2': np.array([[ 0.5002307 , 0.49985831, 0.50023963]]), 'Z1': np.array([[-0.00616586, 0.0020626 , 0.0034962 ], [-0.05229879, 0.02726335, -0.02646869], [-0.02009991, 0.00368692, 0.02884556], [ 0.02153007, -0.01385322, 0.02600471]]), 'Z2': np.array([[ 0.00092281, -0.00056678, 0.00095853]])} return parameters, cache, X_assess, Y_assess def update_parameters_test_case(): parameters = {'W1': np.array([[-0.00615039, 0.0169021 ], [-0.02311792, 0.03137121], [-0.0169217 , -0.01752545], [ 0.00935436, -0.05018221]]), 'W2': np.array([[-0.0104319 , -0.04019007, 0.01607211, 0.04440255]]), 'b1': np.array([[ -8.97523455e-07], [ 8.15562092e-06], [ 6.04810633e-07], [ -2.54560700e-06]]), 'b2': np.array([[ 9.14954378e-05]])} grads = {'dW1': np.array([[ 0.00023322, -0.00205423], [ 0.00082222, -0.00700776], [-0.00031831, 0.0028636 ], [-0.00092857, 0.00809933]]), 'dW2': np.array([[ -1.75740039e-05, 3.70231337e-03, -1.25683095e-03, -2.55715317e-03]]), 'db1': np.array([[ 1.05570087e-07], [ -3.81814487e-06], [ -1.90155145e-07], [ 5.46467802e-07]]), 'db2': np.array([[ -1.08923140e-05]])} return parameters, grads def nn_model_test_case(): np.random.seed(1) X_assess = np.random.randn(2, 3) Y_assess = np.random.randn(1, 3) return X_assess, Y_assess def predict_test_case(): np.random.seed(1) X_assess = np.random.randn(2, 3) parameters = {'W1': np.array([[-0.00615039, 0.0169021 ], [-0.02311792, 0.03137121], [-0.0169217 , -0.01752545], [ 0.00935436, -0.05018221]]), 'W2': np.array([[-0.0104319 , -0.04019007, 0.01607211, 0.04440255]]), 'b1': np.array([[ -8.97523455e-07], [ 8.15562092e-06], [ 6.04810633e-07], [ -2.54560700e-06]]), 'b2': np.array([[ 9.14954378e-05]])} return parameters, X_assess