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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",
    "## 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，比如我有一个分类任务，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函数一作用，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，我们就可以选取概率最大（也就是值对应最大的）结点，作为我们的预测目标！\n",
    "\n",
    "\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",
    "$$\n",
    "z_i = \\sum_{j} w_{ij} x_{j} + b\n",
    "$$\n",
    "\n",
    "其中$W_{ij}$是第$i$个神经元的第$j$个权重，$b$是偏置。$z_i$表示该网络的第$i$个输出。\n",
    "\n",
    "给这个输出加上一个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",
    "\n",
    "\n",
    "### 损失函数 loss function\n",
    "\n",
    "在神经网络反向传播中，要求一个损失函数，这个损失函数其实表示的是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",
    "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",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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",
    "### 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",
    "### 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",
    "### 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",
    "\\end{eqnarray}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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方法中？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "* Softmax & 交叉熵\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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