machinelearning_notebook/5_nn/3-softmax_ce.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Softmax & 交叉熵代价函数\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Softmax`经常被添加在分类任务的神经网络中的输出层，神经网络的反向传播中关键的步骤就是求导，从这个过程也可以更深刻地理解反向传播的过程，还可以对梯度传播的问题有更多的思考。\n",
    "\n",
    "## 1. softmax 函数\n",
    "\n",
    "`softmax`(柔性最大值)函数，一般在神经网络中， `softmax`可以作为分类任务的输出层。其实可以认为`softmax`输出的是几个类别选择的概率，比如有一个分类任务，要分为三个类，softmax函数可以根据它们相对的大小，输出三个类别选取的概率，并且概率和为1。\n",
    "\n",
    "Softmax从字面上来说，可以分成`soft`和`max`两个部分。`max`故名思议就是最大值的意思。Softmax的核心在于`soft`，而`soft`有软的含义，与之相对的是`hard`硬。很多场景中需要找出数组所有元素中值最大的元素，实质上都是求的`hardmax`。下面使用`Numpy`模块实现hardmax。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array([1, 2, 3, 4, 5]) # 创建ndarray数组\n",
    "a_max = np.max(a)\n",
    "print(a_max) # 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "通过上面的例子可以看出hardmax最大的特点就是只选出其中一个最大的值，即非黑即白。但是往往在实际中这种方式是不合情理的，比如对于文本分类来说，一篇文章或多或少包含着各种主题信息，我们更期望得到文章对于每个可能的文本类别的概率值（置信度），可以简单理解成属于对应类别的可信度。所以此时用到了soft的概念，**Softmax的含义就在于不再唯一的确定某一个最大值，而是为每个输出分类的结果都赋予一个概率值，表示属于每个类别的可能性。**\n",
    "\n",
    "softmax函数的公式是这种形式：\n",
    "\n",
    "$$\n",
    "S_i = \\frac{e^{z_i}}{\\sum_k e^{z_k}}\n",
    "$$\n",
    "\n",
    "* $S_i$是经过softmax的类别概率输出\n",
    "* $z_k$是神经元的输出\n",
    "\n",
    "\n",
    "更形象的如下图表示：\n",
    "\n",
    "![softmax_demo](images/softmax_demo.png)\n",
    "\n",
    "softmax直白来说就是将原来输出是$[3,1,-3]$通过softmax函数作用，就映射成为(0,1)的值，而这些值的累和为1（满足概率的性质），那么我们就可以将它理解成概率，在最后选取输出结点的时候，选取概率最大（也就是值对应最大的）结点，作为预测目标！\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "首先是神经元的输出，一个神经元如下图：\n",
    "\n",
    "![softmax_neuron](images/softmax_neuron.png)\n",
    "\n",
    "神经元的输出设为：\n",
    "\n",
    "$$\n",
    "z_i = \\sum_{j} w_{ij} x_{j} + w_b\n",
    "$$\n",
    "\n",
    "其中$W_{ij}$是第$i$个神经元的第$j$个权重，$w_b$是偏置。$z_i$表示该网络的第$i$个输出。**请注意这里没有使用sigmoid等激活函数。**\n",
    "\n",
    "给这个网络输出加上一个softmax函数，那就变成了这样：\n",
    "\n",
    "$$\n",
    "a_i = \\frac{e^{z_i}}{\\sum_k e^{z_k}}\n",
    "$$\n",
    "\n",
    "$a_i$代表softmax的第$i$个输出值，右侧套用了softmax函数。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 交叉熵损失函数\n",
    "\n",
    "在神经网络反向传播中，需要设计一个损失函数，这个损失函数表示真实值与网络的估计值的误差，知道误差了，才能知道怎样去修改网络中的权重。\n",
    "\n",
    "神经网络的设计目的之一是为了使机器可以像人一样学习知识。**人在学习分析新事物时，当发现自己犯的错误越大时，改正的力度就越大**。比如投篮：当运动员发现自己的投篮方向离正确方向越远，那么他调整的投篮角度就应该越大，篮球就更容易投进篮筐。同理，我们希望：ANN在训练时，如果预测值与实际值的误差越大，那么在反向传播训练的过程中，各种参数调整的幅度就要更大，从而使训练更快收敛。然而，**如果使用二次代价函数训练ANN，看到的实际效果是，如果误差越大，参数调整的幅度可能更小，训练更缓慢。**\n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以一个神经元的二类分类训练为例，进行两次实验（神经网络常用的激活函数为`sigmoid`函数，该实验也采用该函数）：输入一个相同的样本数据$x=1.0$（该样本对应的实际分类$y=0$）；两次实验各自随机初始化参数，从而在各自的第一次前向传播后得到不同的输出值，形成不同的代价（误差）：\n",
    "\n",
    "![cross_entropy_loss_1](images/cross_entropy_loss_1.png)\n",
    "实验1：第一次输出值为0.82\n",
    "\n",
    "![cross_entropy_loss_2](images/cross_entropy_loss_2.png)\n",
    "实验2：第一次输出值为0.98\n",
    "\n",
    "\n",
    "在实验1中，随机初始化参数，使得第一次输出值为0.82（该样本对应的实际值为0）；经过300次迭代训练后，输出值由0.82降到0.09，逼近实际值。而在实验2中，第一次输出值为0.98，同样经过300迭代训练，输出值只降到了0.20。\n",
    "\n",
    "\n",
    "神经网络常用的激活函数为sigmoid函数，该函数的曲线如下所示：\n",
    "![cross_entropy_loss_sigmod.png](images/cross_entropy_loss_sigmod.png)\n",
    "\n",
    "如图所示，实验2的初始输出值（0.98）对应的梯度明显小于实验1的输出值（0.82），因此实验2的参数梯度下降得比实验1慢。这就是初始的代价（误差）越大，导致训练越慢的原因。与我们的期望不符，即：不能像人一样，错误越大，改正的幅度越大，从而学习得越快。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "损失函数可以有很多形式，这里用的是交叉熵函数，主要是由于这个求导结果比较简单，易于计算，并且交叉熵解决某些损失函数学习缓慢的问题。**[交叉熵函数](https://blog.csdn.net/u014313009/article/details/51043064)**是这样的：\n",
    "\n",
    "$$\n",
    "C = - \\sum_i y_i ln a_i\n",
    "$$\n",
    "\n",
    "其中$y_i$表示真实的分类结果。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 推导过程\n",
    "\n",
    "首先，我们要明确一下我们要求什么，我们要求的是我们的$loss$对于神经元输出($z_i$)的梯度，即：\n",
    "\n",
    "$$\n",
    "\\frac{\\partial C}{\\partial z_i}\n",
    "$$\n",
    "\n",
    "根据复合函数求导法则：\n",
    "\n",
    "$$\n",
    "\\frac{\\partial C}{\\partial z_i} = \\frac{\\partial C}{\\partial a_j} \\frac{\\partial a_j}{\\partial z_i}\n",
    "$$\n",
    "\n",
    "有个人可能有疑问了，这里为什么是$a_j$而不是$a_i$，这里要看一下$softmax$的公式了，因为$softmax$公式的特性，它的分母包含了所有神经元的输出，所以，对于不等于$i$的其他输出里面，也包含着$z_i$，所有的$a$都要纳入到计算范围中，并且后面的计算可以看到需要分为$i = j$和$i \\ne j$两种情况求导。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 针对$a_j$的偏导\n",
    "\n",
    "$$\n",
    "\\frac{\\partial C}{\\partial a_j} = \\frac{(\\partial -\\sum_j y_j ln a_j)}{\\partial a_j} = -\\sum_j y_j \\frac{1}{a_j}\n",
    "$$\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 针对$z_i$的偏导\n",
    "\n",
    "如果 $i=j$ :\n",
    "\n",
    "\\begin{eqnarray}\n",
    "\\frac{\\partial a_i}{\\partial z_i} & = & \\frac{\\partial (\\frac{e^{z_i}}{\\sum_k e^{z_k}})}{\\partial z_i} \\\\\n",
    "  & = & \\frac{\\sum_k e^{z_k} e^{z_i} - (e^{z_i})^2}{\\sum_k (e^{z_k})^2} \\\\\n",
    "  & = & (\\frac{e^{z_i}}{\\sum_k e^{z_k}} ) (1 - \\frac{e^{z_i}}{\\sum_k e^{z_k}} ) \\\\\n",
    "  & = & a_i (1 - a_i)\n",
    "\\end{eqnarray}\n",
    "\n",
    "如果 $i \\ne j$:\n",
    "\\begin{eqnarray}\n",
    "\\frac{\\partial a_j}{\\partial z_i} & = & \\frac{\\partial (\\frac{e^{z_j}}{\\sum_k e^{z_k}})}{\\partial z_i} \\\\\n",
    "  & = &  \\frac{0 \\cdot \\sum_k e^{z_k} - e^{z_j} \\cdot e^{z_i} }{(\\sum_k e^{z_k})^2} \\\\\n",
    "  & = & - \\frac{e^{z_j}}{\\sum_k e^{z_k}} \\cdot \\frac{e^{z_i}}{\\sum_k e^{z_k}} \\\\\n",
    "  & = & -a_j a_i\n",
    "\\end{eqnarray}\n",
    "\n",
    "当$u$，$v$都是变量的函数时的导数推导公式：\n",
    "$$\n",
    "(\\frac{u}{v})' = \\frac{u'v - uv'}{v^2} \n",
    "$$\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 整体的推导\n",
    "\n",
    "\\begin{eqnarray}\n",
    "\\frac{\\partial C}{\\partial z_i} & = & (-\\sum_j y_j \\frac{1}{a_j} ) \\frac{\\partial a_j}{\\partial z_i} \\\\\n",
    "  & = & - \\frac{y_i}{a_i} a_i ( 1 - a_i) + \\sum_{j \\ne i} \\frac{y_j}{a_j} a_i a_j \\\\\n",
    "  & = & -y_i + y_i a_i + \\sum_{j \\ne i} y_j a_i \\\\\n",
    "  & = & -y_i + a_i \\sum_{j} y_j \\\\\n",
    "  & = & -y_i + a_i\n",
    "\\end{eqnarray}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 参数更新\n",
    "\n",
    "误差与参数的偏导为：\n",
    "$$\n",
    "\\frac{\\partial C}{\\partial w_{ij}} = (-y_i + a_i) x_i\n",
    "$$\n",
    "\n",
    "误差项为：\n",
    "$$\n",
    "\\delta_i = -(-y_i + a_i)\n",
    "$$\n",
    "\n",
    "参数跟新公式为：\n",
    "$$\n",
    "w_{ij} = w_{ij} + \\eta \\delta_i x_i\n",
    "$$\n",
    "\n",
    "\n",
    "其中\n",
    "$$\n",
    "a_i = \\frac{e^{z_i}}{\\sum_k e^{z_k}}\n",
    "$$\n",
    "\n",
    "$$\n",
    "z_i = \\sum_{j} w_{ij} x_{j} + w_b\n",
    "$$\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 二次代价函数的更行方程\n",
    "\n",
    "最为对比，使用二次代价函数的更新方程为：\n",
    "\n",
    "$$\n",
    "\\delta_i = a_i (1-a_i) (y_i - a_i)\n",
    "$$\n",
    "\n",
    "$$\n",
    "w_{ji} = w_{ji} + \\eta \\delta_j x_{ji}\n",
    "$$\n",
    "\n",
    "需要注意这里 $w_{ji}$ 和上面的定义不太一样！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 问题\n",
    "如何将本节所讲的softmax，交叉熵代价函数应用到上节所讲的BP方法中？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参考资料\n",
    "\n",
    "* [一文详解Softmax函数](https://zhuanlan.zhihu.com/p/105722023)\n",
    "* [损失函数：交叉熵详解](https://zhuanlan.zhihu.com/p/115277553)\n",
    "* [交叉熵代价函数（作用及公式推导）](https://blog.csdn.net/u014313009/article/details/51043064)\n",
    "* [手打例子一步一步带你看懂softmax函数以及相关求导过程](https://www.jianshu.com/p/ffa51250ba2e)\n",
    "* [简单易懂的softmax交叉熵损失函数求导](https://www.jianshu.com/p/c02a1fbffad6)"
   ]
  }
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