吴恩达---深度学习编程作业2 带隐藏层的平面数据分类

Planar data classification with a hidden layer

数据集

1.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, 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
2.testCase_v2.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)
    b1 = np.random.randn(4, 1)
    b2 = np.array([[-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': b1,
                  'b2': b2}

    return X_assess, parameters


def compute_cost_test_case():
    np.random.seed(1)
    Y_assess = (np.random.randn(1, 3) > 0)
    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) > 0)
    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) > 0)
    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

代码

import numpy as np
import matplotlib.pyplot as plt
from testCases_v2 import *
import sklearn
import sklearn.datasets
import sklearn.linear_model
from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets

#这里要设置随机种子,保证随机生成的数据与参考答案一致
np.random.seed(1)  # set a seed so that the results are consistent

#返回n[0],n[1],n[2]
def layer_sizes(X, Y):
    n_x = X.shape[0]  # size of input layer
    n_h = 4
    n_y = Y.shape[0]  # size of output layer
    return (n_x, n_h, n_y)

#随机化初始序列
def initialize_parameters(n_x, n_h, n_y):
	#这里要设置随机种子,保证随机生成的数据与参考答案一致
    np.random.seed(2)
    #乘以0.01是为了防止初始参数过大,使得梯度下降得速度变慢
    W1 = np.random.randn(n_h, n_x) * 0.01
    b1 = np.zeros((n_h, 1))
    W2 = np.random.randn(n_y, n_h) * 0.01
    b2 = np.zeros((n_y, 1))
    assert (W1.shape == (n_h, n_x))
    assert (b1.shape == (n_h, 1))
    assert (W2.shape == (n_y, n_h))
    assert (b2.shape == (n_y, 1))
    parameters = {
    
    "W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2": b2}
    return parameters

#正向传播
def forward_propagation(X, parameters):
    W1 = parameters['W1']
    b1 = parameters['b1']
    W2 = parameters['W2']
    b2 = parameters['b2']

    Z1 = np.dot(W1, X) + b1
    A1 = np.tanh(Z1)
    Z2 = np.dot(W2, A1) + b2
    A2 = sigmoid(Z2)  # 二元分类输出层一般使用sigmoid函数
    assert (A2.shape == (1, X.shape[1]))
    cache = {
    
    "Z1": Z1,
             "A1": A1,
             "Z2": Z2,
             "A2": A2}
    return A2, cache

#计算正向传播的损失值
def compute_cost(A2, Y, parameters):
    m = Y.shape[1]  # number of example
    logprobs = np.multiply(Y, np.log(A2)) + np.multiply(1 - Y, np.log(1 - A2))
    cost = (-1 / m) * np.sum(logprobs)
    cost = float(np.squeeze(cost))
    assert (isinstance(cost, float))
    return cost

#反向传播
def backward_propagation(parameters, cache, X, Y):
    m = X.shape[1]
    W1 = parameters['W1']
    W2 = parameters['W2']
    A1 = cache['A1']
    A2 = cache['A2']
    dZ2 = A2 - Y
    dW2 = (1 / m) * np.dot(dZ2, A1.T)
    db2 = (1 / m) * np.sum(dZ2, axis=1, keepdims=True)
    dZ1 = np.dot(W2.T, dZ2) * (1 - np.power(A1, 2))
    dW1 = (1 / m) * np.dot(dZ1, X.T)
    db1 = (1 / m) * np.sum(dZ1, axis=1, keepdims=True)
    grads = {
    
    "dW1": dW1,
             "db1": db1,
             "dW2": dW2,
             "db2": db2}
    return grads

#根据梯度下降来更新参数
def update_parameters(parameters, grads, learning_rate=1.2):
    W1 = parameters['W1']
    b1 = parameters['b1']
    W2 = parameters['W2']
    b2 = parameters['b2']
    dW1 = grads['dW1']
    db1 = grads['db1']
    dW2 = grads['dW2']
    db2 = grads['db2']
    W1 = W1 - learning_rate * dW1
    b1 = b1 - learning_rate * db1
    W2 = W2 - learning_rate * dW2
    b2 = b2 - learning_rate * db2
    parameters = {
    
    "W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2": b2}
    return parameters

#单隐藏层的神经网络的整合模块
def nn_model(X, Y, n_h, num_iterations=10000, print_cost=False):
    np.random.seed(3)
    n_x = layer_sizes(X, Y)[0]
    n_y = layer_sizes(X, Y)[2]
    parameters = initialize_parameters(n_x, n_h, n_y)

    for i in range(0, num_iterations):
        A2, cache = forward_propagation(X, parameters)
        cost = compute_cost(A2, Y, parameters)
        grads = backward_propagation(parameters, cache, X, Y)
        parameters = update_parameters(parameters, grads, learning_rate=1.2)
        if print_cost and i % 1000 == 0:
            print("Cost after iteration %i: %f" % (i, cost))
    return parameters

#预测函数,其实就是A2向量
def predict(parameters, X):
    A2, cache = forward_propagation(X, parameters)
    predictions = np.round(A2)  # 四舍五入
    return predictions

#观察单隐藏层的单元个数与识别准确率的关系
if __name__ =='__main__':
    X, Y = load_planar_dataset()
    plt.figure(figsize=(16, 32))
    hidden_layer_sizes = [1, 2, 3, 4, 5, 20, 50]  # 隐藏层单元个数
    for i, n_h in enumerate(hidden_layer_sizes):
        plt.subplot(5, 2, i + 1)
        plt.title('Hidden Layer of size %d' % n_h)
        parameters = nn_model(X, Y, n_h, num_iterations=5000)
        plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)
        predictions = predict(parameters, X)
        accuracy = float((np.dot(Y, predictions.T) + np.dot(1 - Y, 1 - predictions.T)) / float(Y.size) * 100)
        print("Accuracy for {} hidden units: {} %".format(n_h, accuracy))

4.分析

这是样本点的分布情况
在这里插入图片描述
以下为单隐藏层神经网络对样本点的分类情况
图1~7分别对应隐藏层单元数1, 2, 3, 4, 5, 20, 50
在这里插入图片描述
可以发现隐藏层单元数不是越多越好,过多的话可能会引起过拟合

所谓过拟合,是指模型在训练集中拟合的非常好,但是损失了通用性,此类模型在测试集中表现得往往不是最好

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转载自blog.csdn.net/NP_hard/article/details/113139307