机器学习 (三): 反向传播数学推导 & 神经网络

约定

本小节所使用的神经网络如图所示

其中

• 一共有三层, 序号分别是 0, 1, 2 (第0层为输入层)
• 输入为向量 $x$ 有两个项 $x_1$ 和 $x_2$
• $a^{(i)}$ 表示第 i 层的输出向量, $a_j^{(i)}$ 表示第 i 层输出向量的第 j 项. 注意 $x=a^{(0)}$
• $W^{(i)}$ 表示连接第 i-1 层和第 i 层的边, 即系数, $w_{jk}^{(i)}$ 表示连接第 i 层第 j 个节点和第 i-1 层第 k 个节点的边, 注意 jk 的顺序是从右到左连接
• $z^{(i)}=W^{(i)}\times a^{(i-1)}$
• $a^{(i)}=sigmoid(z^{(i)})$ 也就是说本网络使用的激活函数是 sigmoid
• $sigmoid(x)=\frac{1}{1+e^{-x}}$
• 损失函数为 $J(W,x,y)={\sum }_{i=1}^2[yln(a_i^{(2)})+(1-y)ln(1-a_i^{(2)})]$
• 简便起见不考虑 bias 项

推导

我们的目标是最小化 $J(W,x,y)$, 其中 W 是所有的边, 而 x, y 是测试数据集为常量, 所以我们需要求 $\frac{\delta J}{\delta W}$, 具体地, 我们要求 $\frac{\delta J}{\delta w^{(2)}}$ 和 $\frac{\delta J}{\delta w^{(1)}}$ :

$\frac{\delta J}{\delta w^{(2)}}=\frac{\delta J}{\delta a^{(2)}}\times \frac{\delta a^{(2)}}{\delta z^{(2)}}\times \frac{\delta z^{(2)}}{\delta w^{(2)}}$

$\frac{\delta J}{\delta a^{(2)}}=(yln(a^{(2)})+(1-y)ln(1-a^{(2)}){)}^{{}^{\prime }}=\frac{y}{a^{(2)}}-\frac{1-y}{1-a^{(2)}}$

$\frac{\delta a^{(2)}}{\delta z^{(2)}}=a^{(2)}\cdot (1-a^{(2)})$

由上述二式可得

$\frac{\delta J}{\delta z^{(2)}}=\frac{\delta J}{\delta a^{(2)}}\times \frac{\delta a^{(2)}}{\delta z^{(2)}}=y\cdot (1-a^{(2)})-(1-y)\cdot a^{(2)}$

因为 y 的值只可能是 0 或 1, 当 y = 0 时, 上式等于

$-a^{(2)}=0-a^{(2)}=y-a^{(2)}$

当 y = 1 时, 上式等于

$1-a^{(2)}=y-a^{(2)}$

所以

$\frac{\delta J}{\delta z^{(2)}}=a^{(2)}-y$

令 ${\delta }^{(2)}=\frac{\delta J}{\delta z^{(2)}}=a^{(2)}-y$

因为

$z^{(2)}=W^{(2)}\times a^{(1)}$

所以

$\frac{\delta z^{(2)}}{\delta W^{(2)}}=a^{(1)}$

综上

$\frac{\delta J}{\delta w^{(2)}}=\frac{\delta J}{\delta a^{(2)}}\times \frac{\delta a^{(2)}}{\delta z^{(2)}}\times \frac{\delta z^{(2)}}{\delta W^{(2)}}={\delta }^{(2)}\times a^{(1)}$

---

现在开始求 $\frac{\delta J}{\delta w^{(1)}}$:

$\frac{\delta J}{\delta w^{(1)}}=\frac{\delta J}{\delta a^{(1)}}\times \frac{\delta a^{(1)}}{\delta z^{(1)}}\times \frac{\delta z^{(1)}}{\delta W^{(1)}}$

因为

$z^{(2)}=W^{(2)}\times a^{(1)}$

所以

$\frac{\delta z^{(2)}}{\delta a^{(1)}}=W^{(2)}$

所以

$\frac{\delta J}{\delta a^{(1)}}=\frac{\delta J}{\delta z^{(2)}}\times \frac{\delta z^{(2)}}{\delta a^{(1)}}=W^{(2)T}\times {\delta }^{(2)}$

因为

$\frac{\delta a^{(1)}}{\delta z^{(1)}}=a^{(1)}\cdot (1-a^{(1)})$
所以

$\frac{\delta J}{\delta z^{(1)}}=\frac{\delta J}{\delta a^{(1)}}\times \frac{\delta a^{(1)}}{\delta z^{(1)}}=W^{(2)T}\times {\delta }^{(2)}\cdot a^{(1)}\cdot (1-a^{(1)})$

令 ${\delta }^{(1)}=\frac{\delta J}{\delta z^{(1)}}=W^{(2)T}\times {\delta }^{(2)}\cdot a^{(1)}\cdot (1-a^{(1)})$

又因为

$z^{(1)}=W^{(1)}\times a^{(0)}$

所以

$\frac{\delta z^{(1)}}{\delta W^{(1)}}=a^{(0)}$

综上

$\frac{\delta J}{\delta w^{(1)}}=\frac{\delta J}{\delta a^{(1)}}\times \frac{\delta a^{(1)}}{\delta z^{(1)}}\times \frac{\delta z^{(1)}}{\delta W^{(1)}}={\delta }^{(1)}\times a^{(0)}$

神经网络实现

import numpy as np
import matplotlib.pyplot as plt 

def init_model(X, Y, layers):
	model = {}
	nodes = []
	nodes.append(X.shape[0])
	for l in layers:
		nodes.append(l)
	nodes.append(Y.shape[0])
	model['depth'] = len(layers) + 1
	for n in range(model['depth']):
		model['W' + str(n + 1)] = np.random.rand(nodes[n+1], nodes[n])
		model['B' + str(n + 1)] = np.random.rand(nodes[n+1], 1)
	return model

def sigmoid(x):
	return 1.0 / (1 + np.exp(-x))

def forward_propagation(X, Y, model):
	result = X.copy()
	model['A0'] = result.copy()
	for i in range(model['depth']):
		W = model['W' + str(i+1)]
		B = model['B' + str(i+1)]
		result = np.dot(W, result) + B
		model['Z' + str(i+1)] = result.copy()
		result = sigmoid(result)
		model['A' + str(i+1)] = result.copy()
	return result

def cross_entropy(Y, result):
	return -Y * np.log(result) - (1 - Y) * np.log(1 - result)

def cost(Y, result):
	return np.sum(cross_entropy(Y, result)) / Y.shape[1]

def backward_propagation(Y, model, learning_rate):
	L = model['depth']
	m = Y.shape[1]
	delta = model['A' + str(L)] - Y
	model['dW' + str(L)] = np.dot(delta, model['A' + str(L-1)].T) / m
	model['dB' + str(L)] = np.sum(delta, axis = 1, keepdims=True) / m
	for l in range(L - 1, 0, -1):
		delta = (np.dot(model['W' + str(l+1)].T, delta) * model['A' + str(l)] * (1 - model['A' + str(l)])) 
		model['dW' + str(l)] = np.dot(delta, model['A' + str(l-1)].T) / m
		model['dB' + str(l)] = np.sum(delta, axis=1, keepdims=True) / m

	for l in range(model['depth'], 0, -1):
		model['W' + str(l)] -= learning_rate * model['dW' + str(l)]
		model['B' + str(l)] -= learning_rate * model['dB' + str(l)]

def train(X, Y, model, learning_rate = 0.05, times = 50, show_cost_history = True):
	cost_history = []
	result = 0
	for t in range(times):
		result = forward_propagation(X, Y, model)
		backward_propagation(Y, model, learning_rate)
		cost_history.append(cost(Y, result))
	if show_cost_history:
		plt.plot(cost_history)
		plt.show()

if __name__ == '__main__':
	m = 1000
	X = np.random.rand(2, m)
	Y = np.zeros((1, m))
	for i in range(m):
		if (X[0, i] < 0.5) and (X[1, i] < 0.5):
			Y[0, i] = 1

	model = init_model(X, Y, [2])
	train(X, Y, model)