planar_utils.py和testCases.py源码

这是吴恩达深度学习课程的第一章的第三周的课后作业所需的线下文件，从github上搬运过来，免得花钱下载。希望对各位有所帮助。

planar_utils.py

import matplotlib.pyplot as plt
import numpy as np
import sklearn
import sklearn.datasets
import sklearn.linear_modeldef plot_decision_boundary(model, X, y):# Set min and max values and give it some paddingx_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1h = 0.01# Generate a grid of points with distance h between themxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))# Predict the function value for the whole gridZ = model(np.c_[xx.ravel(), yy.ravel()])Z = Z.reshape(xx.shape)# Plot the contour and training examplesplt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)plt.ylabel('x2')plt.xlabel('x1')plt.scatter(X[0, :], X[1, :], c=y[0], cmap=plt.cm.Spectral)def sigmoid(x):"""Compute the sigmoid of xArguments:x -- A scalar or numpy array of any size.Return:s -- sigmoid(x)"""s = 1/(1+np.exp(-x))return sdef load_planar_dataset():np.random.seed(1)m = 400 # number of examplesN = int(m/2) # number of points per classD = 2 # dimensionalityX = np.zeros((m,D)) # data matrix where each row is a single exampleY = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue)a = 4 # maximum ray of the flowerfor j in range(2):ix = range(N*j,N*(j+1))t = np.linspace(j*3.12,(j+1)*3.12,N) + np.random.randn(N)*0.2 # thetar = a*np.sin(4*t) + np.random.randn(N)*0.2 # radiusX[ix] = np.c_[r*np.sin(t), r*np.cos(t)]Y[ix] = jX = X.TY = Y.Treturn X, Y
def load_extra_datasets():  N = 200noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3)noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2)blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6)gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None)no_structure = np.random.rand(N, 2), np.random.rand(N, 2)return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure

testCases.py

import numpy as npdef layer_sizes_test_case():np.random.seed(1)X_assess = np.random.randn(5, 3)Y_assess = np.random.randn(2, 3)return X_assess, Y_assessdef initialize_parameters_test_case():n_x, n_h, n_y = 2, 4, 1return n_x, n_h, n_ydef 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, parametersdef 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, parametersdef 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_assessdef 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, gradsdef 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_assessdef 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

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