【深度学习】RNN循环神经网络Python简单实现

怼烎@ 2022-05-29 00:14 749阅读 0赞

## 前言 ##

代码可在Github上下载:[代码下载][Link 1]  
循环神经网络是一种时间序列预测模型，多应用在自然语言处理上。  
原理网上是有很多的，不展开解释，本文基于一个二进制加法，进行python实现。  
其实python代码并非本人实现  
具体参考两篇博客，第一篇是英语原文，第二篇是译文。  
https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/  
http://blog.csdn.net/zzukun/article/details/49968129

## 原理 ##

![RNN结构图][RNN]

**前向传播**  
 z h = w i h x + w h h a h − 1 a h = σ ( z h ) z k = w h y a h a k = σ ( z k ) \\begin\{array\}\{l\} \{z^h\} = \{w\_\{ih\}\}x + \{w\_\{hh\}\}\{a^\{h - 1\}\}\\\\ \{a^h\} = \\sigma \\left( \{ \{z^h\}\} \\right)\\\\ \{z^k\} = \{w\_\{hy\}\}\{a^h\}\\\\ \{a^k\} = \\sigma \\left( \{ \{z^k\}\} \\right) \\end\{array\} zh=wihx\+whhah−1ah=σ(zh)zk=whyahak=σ(zk)

**反向传播**  
 δ t k = ∂ E ∂ a k ∂ a k ∂ z k = ( y t − a t k ) × ( z k ) ′ = ( y t − a t k ) × ( z k × ( 1 − z k ) ) δ T h = ∂ E ∂ a T k ∂ a T k ∂ z T k ∂ z T k ∂ a T h ∂ a T h ∂ z T h = ( W h y ) T δ T k × ( a h ( 1 − a h ) ) δ t h = ∂ E ∂ a t k ∂ a t k ∂ z t k ∂ z t k ∂ a t h ∂ a t h ∂ z t h + ∂ E ∂ a t + 1 k ∂ a t + 1 k ∂ z t + 1 k ∂ z t + 1 k ∂ a t + 1 h ∂ a t + 1 h ∂ z t + 1 h ∂ z t + 1 h ∂ a t h ∂ a t h ∂ z t h = ( ( W h h ) T δ t + 1 h + ( W h y ) T δ t k ) ∂ a t h ∂ z t h = ( ( W h h ) T δ t + 1 h + ( W h y ) T δ t k ) × ( a h ( 1 − a h ) ) W i h = W i h − η δ t h ( x t ) T W h h = W h h − η δ t h ( a t − 1 h ) T W h y = W h y − η δ t k ( a t h ) T \\begin\{array\}\{l\} \\delta \_t^k = \\frac\{ \{\\partial E\}\}\{ \{\\partial \{a^k\}\}\}\\frac\{ \{\\partial \{a^k\}\}\}\{ \{\\partial \{z^k\}\}\} = (\{y\_t\} - a\_t^k) \\times \\left( \{ \{z^k\}\} \\right)&\#x27; = (\{y\_t\} - a\_t^k) \\times \\left( \{ \{z^k\} \\times \\left( \{1 - \{z^k\}\} \\right)\} \\right)\\\\ \\delta \_T^h = \\frac\{ \{\\partial E\}\}\{ \{\\partial a\_T^k\}\}\\frac\{ \{\\partial a\_T^k\}\}\{ \{\\partial z\_T^k\}\}\\frac\{ \{\\partial z\_T^k\}\}\{ \{\\partial a\_\{\\rm\{T\}\}^h\}\}\\frac\{ \{\\partial a\_T^h\}\}\{ \{\\partial z\_\{\\rm\{T\}\}^h\}\} = \{\\left( \{ \{W\_\{hy\}\}\} \\right)^T\}\\delta \_T^k \\times \\left( \{ \{a^h\}\\left( \{1 - \{a^h\}\} \\right)\} \\right)\\\\ \{\\delta \_t^h = \\frac\{ \{\\partial E\}\}\{ \{\\partial a\_t^k\}\}\\frac\{ \{\\partial a\_t^k\}\}\{ \{\\partial z\_t^k\}\}\\frac\{ \{\\partial z\_t^k\}\}\{ \{\\partial a\_t^h\}\}\\frac\{ \{\\partial a\_t^h\}\}\{ \{\\partial z\_t^h\}\} + \\frac\{ \{\\partial E\}\}\{ \{\\partial a\_\{t + 1\}^k\}\}\\frac\{ \{\\partial a\_\{t + 1\}^k\}\}\{ \{\\partial z\_\{t + 1\}^k\}\}\\frac\{ \{\\partial z\_\{t + 1\}^k\}\}\{ \{\\partial a\_\{t\{\\rm\{ + \}\}1\}^h\}\}\\frac\{ \{\\partial a\_\{t\{\\rm\{ + \}\}1\}^h\}\}\{ \{\\partial z\_\{t + 1\}^h\}\}\\frac\{ \{\\partial z\_\{t + 1\}^h\}\}\{ \{\\partial a\_t^h\}\}\\frac\{ \{\\partial a\_t^h\}\}\{ \{\\partial z\_t^h\}\}\}\\\\ \{\\rm\{ \}\} = \\left( \{ \{ \{\\left( \{ \{W\_\{hh\}\}\} \\right)\}^T\}\\delta \_\{t + 1\}^h + \{ \{\\left( \{ \{W\_\{hy\}\}\} \\right)\}^T\}\\delta \_t^k\} \\right)\\frac\{ \{\\partial a\_t^h\}\}\{ \{\\partial z\_t^h\}\}\\\\ \{\\rm\{ \}\} = \\left( \{ \{ \{\\left( \{ \{W\_\{hh\}\}\} \\right)\}^T\}\\delta \_\{t + 1\}^h + \{ \{\\left( \{ \{W\_\{hy\}\}\} \\right)\}^T\}\\delta \_t^k\} \\right) \\times \\left( \{ \{a^h\}\\left( \{1 - \{a^h\}\} \\right)\} \\right)\\\\ \{W\_\{ih\}\} = \{W\_\{ih\}\} - \\eta \\delta \_t^h\{\\left( \{ \{x\_t\}\} \\right)^T\}\\\\ \{W\_\{hh\}\} = \{W\_\{hh\}\} - \\eta \\delta \_t^h\{\\left( \{a\_\{t - 1\}^h\} \\right)^T\}\\\\ \{W\_\{hy\}\} = \{W\_\{hy\}\} - \\eta \\delta \_t^k\{\\left( \{a\_t^h\} \\right)^T\} \\end\{array\} δtk=∂ak∂E∂zk∂ak=(yt−atk)×(zk)′=(yt−atk)×(zk×(1−zk))δTh=∂aTk∂E∂zTk∂aTk∂aTh∂zTk∂zTh∂aTh=(Why)TδTk×(ah(1−ah))δth=∂atk∂E∂ztk∂atk∂ath∂ztk∂zth∂ath\+∂at\+1k∂E∂zt\+1k∂at\+1k∂at\+1h∂zt\+1k∂zt\+1h∂at\+1h∂ath∂zt\+1h∂zth∂ath=((Whh)Tδt\+1h\+(Why)Tδtk)∂zth∂ath=((Whh)Tδt\+1h\+(Why)Tδtk)×(ah(1−ah))Wih=Wih−ηδth(xt)TWhh=Whh−ηδth(at−1h)TWhy=Why−ηδtk(ath)T

有了前向传播和反向传播的公式，就可以根据这个公式将RNN实现。代码解释在上面的两篇博客中都有所解释，只是他们并没有给出相应的公式，故将公式奉上。

## 代码实现 ##

import copy, numpy as np
    
    np.random.seed(0)
    
    # sigmoid函数
    def sigmoid(x):
        output = 1 / (1 + np.exp(-x))
        return output
    
    # sigmoid导数
    def sigmoid_output_to_derivative(output):
        return output * (1 - output)
    
    
    # 训练数据生成
    int2binary = {}
    binary_dim = 8
    
    largest_number = pow(2, binary_dim)
    binary = np.unpackbits(
        np.array([range(largest_number)], dtype=np.uint8).T, axis=1)
    for i in range(largest_number):
        int2binary[i] = binary[i]
    
    # 初始化一些变量
    alpha = 0.1 #学习率
    input_dim = 2   #输入的大小
    hidden_dim = 8  #隐含层的大小
    output_dim = 1  #输出层的大小
    
    # 随机初始化权重
    synapse_0 = 2 * np.random.random((hidden_dim, input_dim)) - 1   #(8, 2)
    synapse_1 = 2 * np.random.random((output_dim, hidden_dim)) - 1  #(1, 8)
    synapse_h = 2 * np.random.random((hidden_dim, hidden_dim)) - 1  #(8, 8)
    
    synapse_0_update = np.zeros_like(synapse_0) #(8, 2)
    synapse_1_update = np.zeros_like(synapse_1) #(1, 8)
    synapse_h_update = np.zeros_like(synapse_h) #(8, 8)
    
    # 开始训练
    for j in range(100000):
    
        # 二进制相加
        a_int = np.random.randint(largest_number / 2)  # 随机生成相加的数
        a = int2binary[a_int]  # 映射成二进制值
    
        b_int = np.random.randint(largest_number / 2)  # 随机生成相加的数
        b = int2binary[b_int]  # 映射成二进制值
    
        # 真实的答案
        c_int = a_int + b_int   #结果
        c = int2binary[c_int]   #映射成二进制值
    
        # 待存放预测值
        d = np.zeros_like(c)
    
        overallError = 0
    
        layer_2_deltas = list() #输出层的误差
        layer_2_values = list() #第二层的值（输出的结果）
        layer_1_values = list() #第一层的值（隐含状态）
        layer_1_values.append(copy.deepcopy(np.zeros((hidden_dim, 1)))) #第一个隐含状态需要0作为它的上一个隐含状态
    
        #前向传播
        for i in range(binary_dim):
            X = np.array([[a[binary_dim - i - 1], b[binary_dim - i - 1]]]).T    #(2,1)
            y = np.array([[c[binary_dim - i - 1]]]).T   #(1,1)
            layer_1 = sigmoid(np.dot(synapse_h, layer_1_values[-1]) + np.dot(synapse_0, X)) #(1,1)
            layer_1_values.append(copy.deepcopy(layer_1))   #(8,1)
            layer_2 = sigmoid(np.dot(synapse_1, layer_1))   #(1,1)
            error = -(y-layer_2)    #使用平方差作为损失函数
            layer_delta2 = error * sigmoid_output_to_derivative(layer_2)    #(1,1)
            layer_2_deltas.append(copy.deepcopy(layer_delta2))
            d[binary_dim - i - 1] = np.round(layer_2[0][0])
        future_layer_1_delta = np.zeros((hidden_dim, 1))
        #反向传播
        for i in range(binary_dim):
            X = np.array([[a[i], b[i]]]).T
            prev_layer_1 = layer_1_values[-i-2]
            layer_1 = layer_1_values[-i-1]
            layer_delta2 = layer_2_deltas[-i-1]
            layer_delta1 = np.multiply(np.add(np.dot(synapse_h.T, future_layer_1_delta),np.dot(synapse_1.T, layer_delta2)), sigmoid_output_to_derivative(layer_1))
            synapse_0_update += np.dot(layer_delta1, X.T)
            synapse_h_update += np.dot(layer_delta1, prev_layer_1.T)
            synapse_1_update += np.dot(layer_delta2, layer_1.T)
            future_layer_1_delta = layer_delta1
        synapse_0 -= alpha * synapse_0_update
        synapse_h -= alpha * synapse_h_update
        synapse_1 -= alpha * synapse_1_update
        synapse_0_update *= 0
        synapse_1_update *= 0
        synapse_h_update *= 0
        # 验证结果
        if (j % 100 == 0):
            print("Error:" + str(overallError))
            print("Pred:" + str(d))
            print("True:" + str(c))
            out = 0
            for index, x in enumerate(reversed(d)):
                out += x * pow(2, index)
            print(str(a_int) + " + " + str(b_int) + " = " + str(out))
            print("------------")

[Link 1]: https://github.com/FlameCharmander/MachineLearning
[RNN]: /images/20220529/c4ea308cc5d64153bdc2b9ab81c84a07.png