吴恩达《机器学习》——第四次作业：BP神经网络

本此作业和上次作业的目标一样，属于多元分类任务。不过这次是使用BP神经网络。准确度比没有使用神经网络的第三次作业要高，但是训练的时间也长了很多。
参考：https://github.com/fengdu78/Coursera-ML-AndrewNg-Notes/tree/master/code

import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
import matplotlib

import scipy.optimize as opt
from sklearn.metrics import classification_report

from sklearn.preprocessing import OneHotEncoder

data = sio.loadmat('ex4data1.mat')
X = data['X']
y = data['y']

encoder = OneHotEncoder(sparse=False)
y_onehot = encoder.fit_transform(y)

def sigmoid(z):
    '''激活函数'''
    return 1 / (1 + np.exp(-z))

def forward_propagate(X, theta1, theta2):
    '''前向传播算法'''
    m = X.shape[0]

    a1 = np.insert(X, 0, values=np.ones(m), axis=1)
    z2 = a1 * theta1.T
    a2 = sigmoid(z2)
    a2 = np.insert(a2, 0, values=np.ones(m), axis=1)
    z3 = a2 * theta2.T
    h = sigmoid(z3)

    return a1, z2, a2, z3, h

def cost(params, input_size, hidden_size, num_labels, X, y, learning_rate):
    '''代价函数'''
    m = X.shape[0]
    X = np.matrix(X)
    y = np.matrix(y)

    theta1 = np.matrix(np.reshape(params[:hidden_size * (input_size + 1)], (hidden_size, (input_size + 1))))
    theta2 = np.matrix(np.reshape(params[hidden_size * (input_size + 1):], (num_labels, (hidden_size + 1))))

    a1, z2, a2, z3, h = forward_propagate(X, theta1, theta2)

    J = 0
    for i in range(m):
        first_term = np.multiply(-y[i, :], np.log(h[i, :]))
        second_term = np.multiply(1 - y[i, :], np.log(1 - h[i, :]))
        J += np.sum(first_term - second_term)

    J = J/m

    J += (float(learning_rate) / (2 * m)) * (np.sum(np.power(theta1[:, 1:], 2)) + np.sum(np.power(theta2[:, 1:], 2)))

    return J
input_size = 400
hidden_size = 25
num_labels = 10
learning_rate = 1
params = (np.random.random(size=hidden_size * (input_size + 1) + num_labels * (hidden_size + 1)) - 0.5) * 0.25
#print(cost(params, input_size, hidden_size, num_labels, X, y_onehot, learning_rate))

def sigmoid_gradient(z):
    return np.multiply(sigmoid(z), (1 - sigmoid(z)))

def back_prop(params, input_size, hidden_size, num_labels, X, y, learning_rate):
    '''反向传播算法'''
    m = X.shape[0]
    X = np.matrix(X)
    y = np.matrix(y)

    theta1 = np.matrix(np.reshape(params[:hidden_size * (input_size + 1)], (hidden_size, (input_size + 1))))
    theta2 = np.matrix(np.reshape(params[hidden_size * (input_size + 1):], (num_labels, (hidden_size + 1))))

    a1, z2, a2, z3, h = forward_propagate(X, theta1, theta2)

    J = 0
    delta1 = np.zeros(theta1.shape)
    delta2 = np.zeros(theta2.shape)

    for i in range(m):
        first_term = np.multiply(-y[i, :], np.log(h[i, :]))
        second_term = np.multiply(1 - y[i, :], np.log(1 - h[i, :]))
        J += np.sum(first_term - second_term)

    J = J / m

    J += (float(learning_rate) / (2 * m)) * (np.sum(np.power(theta1[:, 1:], 2)) + np.sum(np.power(theta2[:, 1:], 2)))

    for t in range(m):
        a1t = a1[t, :]
        z2t = z2[t, :]
        a2t = a2[t, :]
        ht = h[t, :]
        yt = y[t, :]

        d3t = ht - yt
        z2t = np.insert(z2t, 0, values=np.ones(1))
        d2t = np.multiply((theta2.T * d3t.T).T, sigmoid_gradient(z2t))

        delta1 = delta1 + (d2t[:, 1:]).T * a1t
        delta2 = delta2 + d3t.T * a2t

    delta1 /= m
    delta2 /= m

    delta1[:, 1:] = delta1[:, 1:] + (theta1[:, 1:] * learning_rate) / m
    delta2[:, 1:] = delta2[:, 1:] + (theta2[:, 1:] * learning_rate) / m

    grad = np.concatenate((np.ravel(delta1), np.ravel(delta2)))

    return J, grad

fmin = opt.minimize(fun=back_prop, x0=params, args=(input_size, hidden_size, num_labels, X, y_onehot, learning_rate),
                    method='TNC', jac=True, options={'maxiter': 250})

X = np.matrix(X)


X = np.matrix(X)
theta1 = np.matrix(np.reshape(fmin.x[:hidden_size * (input_size + 1)], (hidden_size, (input_size + 1))))
theta2 = np.matrix(np.reshape(fmin.x[hidden_size * (input_size + 1):], (num_labels, (hidden_size + 1))))

a1, z2, a2, z3, h = forward_propagate(X, theta1, theta2)
y_pred = np.array(np.argmax(h, axis=1) + 1)

#准确度
print(classification_report(y, y_pred))