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。

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planar_utils.py

import matplotlib.pyplot as plt
import numpy as np
import sklearn
import sklearn.datasets
import sklearn.linear_model

def plot_decision_boundary(model, X, y):
    # Set min and max values and give it some padding
    x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1
    y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1
    h = 0.01
    # Generate a grid of points with distance h between them
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    # Predict the function value for the whole grid
    Z = model(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    # Plot the contour and training examples
    plt.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 x

    Arguments:
    x -- A scalar or numpy array of any size.

    Return:
    s -- sigmoid(x)
    """
    s = 1/(1+np.exp(-x))
    return s

def load_planar_dataset():
    np.random.seed(1)
    m = 400 # number of examples
    N = int(m/2) # number of points per class
    D = 2 # dimensionality
    X = np.zeros((m,D)) # data matrix where each row is a single example
    Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue)
    a = 4 # maximum ray of the flower

    for 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 # theta
        r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius
        X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
        Y[ix] = j
        
    X = X.T
    Y = Y.T

    return X, Y
def load_extra_datasets():  
    N = 200
    noisy_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

 

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testCases.py

 

import numpy as np


def 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_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