{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 多层神经网络\n", "\n", "基于生物神经元模型可得到多层感知器(Multi-layer Perceptron, MLP)的基本结构,最典型的MLP包括包括三层:**输入层(Input Layer)**、**隐层(Hidden Layer)**和**输出层(Output Layer)**,MLP神经网络不同层之间是全连接的(全连接的意思就是:上一层的任何一个神经元与下一层的所有神经元都有连接)。\n", "\n", "![mlp_theory](images/mlp_theory.gif)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. 神经元\n", "\n", "神经元和感知器本质上是一样的,感知器的激活函数是阶跃函数;而神经元的激活函数往往选择为sigmoid函数或tanh函数。如下图所示:\n", "\n", "![neuron](images/neuron.gif)\n", "\n", "计算一个神经元的输出的方法和计算一个感知器的输出是一样的。假设神经元的输入是向量$\\vec{x}$,权重向量是$\\vec{w}$(偏置项是$w_0$),激活函数是sigmoid函数,则其输出$y$:\n", "$$\n", "y = sigmoid(\\vec{w}^T \\cdot \\vec{x})\n", "$$\n", "\n", "sigmoid函数的定义如下:\n", "$$\n", "sigmoid(x) = \\frac{1}{1+e^{-x}}\n", "$$\n", "将其带入前面的式子,得到\n", "$$\n", "y = \\frac{1}{1+e^{-\\vec{w}^T \\cdot \\vec{x}}}\n", "$$\n", "\n", "sigmoid函数是一个非线性函数,值域是(0,1)。函数图像如下图所示\n", "\n", "![sigmod_function](images/sigmod.jpg)\n", "\n", "sigmoid函数的导数是:\n", "\\begin{eqnarray}\n", "y & = & sigmod(x) \\tag{1} \\\\\n", "y' & = & y(1-y)\n", "\\end{eqnarray}\n", "\n", "可以看到,sigmoid函数的导数可以用sigmoid函数自身来表示。这样,一旦计算出sigmoid函数的值,计算它的导数的值就非常方便。\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. 神经网络的结构\n", "\n", "![nn1](images/nn1.jpeg)\n", "\n", "神经网络就是按照一定规则连接起来的多个神经元。上图展示了一个全连接(Full Connected, FC)神经网络,通过观察上面的图,可以发现它的规则包括:\n", "\n", "* 神经元按照层来布局\n", " - 最左边的层叫做输入层,负责接收输入数据;\n", " - 最右边的层叫输出层,可以从这层获取神经网络输出数据;\n", " - 输入层和输出层之间的层叫做隐藏层,因为它们对于外部来说是不可见的。\n", "* 同一层的神经元之间没有连接\n", "* 第N层的每个神经元和第N-1层的所有神经元相连(这就是full connected的含义),第N-1层神经元的输出就是第N层神经元的输入\n", "* 每个连接都有一个权值\n", "\n", "\n", "由此可知,神经网络主要有三个基本要素:权重、偏置和激活函数\n", "* 权重:神经元之间的连接强度由权重控制,权重的大小表示可能性的大小\n", "* 偏置:偏置的设置是为了正确分类样本,是模型中一个重要的参数,即保证通过输入算出的输出值不能随便激活。\n", "* 激活函数:起非线性映射的作用,其可将神经元的输出幅度限制在一定范围内,一般限制在(-1\\~1)或(0\\~1)之间。最常用的激活函数是Sigmoid函数,可将(-∞,+∞)的数映射到(0~1)的范围内。\n", "\n", "上面这些规则定义了全连接神经网络的结构。事实上还存在很多其它结构的神经网络,比如卷积神经网络(CNN)、循环神经网络(RNN),他们都具有不同的连接规则。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. 计算神经网络的输出\n", "\n", "神经网络实际上就是一个输入向量$\\vec{x}$到输出向量$\\vec{y}$的函数,即:\n", "\n", "$$\n", "\\vec{y} = f_{network}(\\vec{x})\n", "$$\n", "根据输入计算神经网络的输出\n", "* 首先将输入向量$\\vec{x}$的每个元素的值$x_i$赋给神经网络的输入层的对应神经元\n", "* 然后根据式(1)依次向前计算每一层的每个神经元的值,直到最后一层输出层的所有神经元的值计算完毕\n", "* 最后,将输出层每个神经元的值串在一起就得到了输出向量$\\vec{y}$。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "接下来举一个例子来说明这个过程,我们先给神经网络的每个单元写上编号。\n", "\n", "![nn2](images/nn2.png)\n", "\n", "* 输入层有三个节点,我们将其依次编号为1、2、3;\n", "* 隐藏层的4个节点,编号依次为4、5、6、7;\n", "* 最后输出层的两个节点编号为8、9。\n", "\n", "因为这个神经网络是全连接网络,所以可以看到每个节点都和上一层的所有节点有连接。比如,隐藏层的节点4,它和输入层的三个节点1、2、3之间都有连接,其连接上的权重分别为$w_{41}$,$w_{42}$,$w_{43}$。那么,怎样计算节点4的输出值$a_4$呢?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "为了计算节点4的输出值,必须先得到其所有上游节点(也就是节点1、2、3)的输出值。节点1、2、3是输入层的节点,所以,他们的输出值就是输入向量$\\vec{x}$本身。按照上图画出的对应关系,可以看到节点1、2、3的输出值分别是$x_1$,$x_2$,$x_3$。要求输入向量的维度和输入层神经元个数相同,而输入向量的某个元素对应到哪个输入节点是可以自由决定的。\n", "\n", "一旦有了节点1、2、3的输出值,就可以根据式1计算节点4的输出值$a_4$:\n", "\n", "![eqn_3_4](images/eqn_3_4.png)\n", "\n", "上式的$w_{4b}$是节点4的偏置项,图中没有画出来。而$w_{41}$,$w_{42}$,$w_{43}$分别为节点1、2、3到节点4连接的权重,在给权重$w_{ji}$编号时,目标节点的编号$j$放在前面,把源节点的编号$i$放在后面。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "同样,可以继续计算出节点5、6、7的输出值$a_5$,$a_6$,$a_7$。这样,隐藏层的4个节点的输出值就计算完成了,就可以接着计算输出层的节点8的输出值$y_1$:\n", "\n", "![eqn_5_6](images/eqn_5_6.png)\n", "\n", "同理,我们还可以计算出$y_2$的值。这样输出层所有节点的输出值计算完毕,就得到了在输入向量$\\vec{x} = (x_1, x_2, x_3)^T$时,神经网络的输出向量$\\vec{y} = (y_1, y_2)^T$。可以看出:输出向量的维度和输出层神经元个数相同。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. 神经网络的矩阵表示\n", "\n", "神经网络的计算如果用矩阵来表示会很方便,此外可以用优化加速算法提高计算速度。\n", "\n", "隐藏层4个节点的计算依次排列出来:\n", "\n", "![eqn_hidden_units](images/eqn_hidden_units.png)\n", "\n", "接着,定义网络的输入向量$\\vec{x}$和隐藏层每个节点的权重向量$\\vec{w}$。令\n", "\n", "![eqn_7_12](images/eqn_7_12.png)\n", "\n", "代入到前面的一组式子,得到:\n", "\n", "![eqn_13_16](images/eqn_13_16.png)\n", "\n", "现在,把上述计算$a_4$, $a_5$,$a_6$,$a_7$的四个式子写到一个矩阵里面,每个式子作为矩阵的一行,就可以利用矩阵来表示它们的计算了。令\n", "\n", "![eqn_matrix1](images/eqn_matrix1.png)\n", "\n", "带入前面的一组式子,得到\n", "\n", "![formular_2](images/formular_2.png)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在(式2)中,\n", "* $f$是激活函数,在本例中是$sigmod$函数;\n", "* $W$是某一层的权重矩阵;\n", "* $\\vec{x}$是某层的输入向量;\n", "* $\\vec{a}$是某层的输出向量。\n", "\n", "(式2)说明神经网络的每一层的作用实际上就是先将输入向量左乘一个数组进行线性变换,得到一个新的向量,然后再对这个向量逐元素应用一个激活函数。\n", "\n", "每一层的算法都是一样的。比如,对于包含一个输入层,一个输出层和三个隐藏层的神经网络,我们假设其权重矩阵分别为$W_1$,$W_2$,$W_3$,$W_4$,每个隐藏层的输出分别是$\\vec{a}_1$,$\\vec{a}_2$,$\\vec{a}_3$,神经网络的输入为$\\vec{x}$,神经网络的输出为$\\vec{y}$,如下图所示:\n", "\n", "![nn_parameters_demo](images/nn_parameters_demo.png)\n", "\n", "则每一层的输出向量的计算可以表示为:\n", "\n", "![eqn_17_20](images/eqn_17_20.png)\n", "\n", "\n", "这就是神经网络输出值的矩阵计算方法。\n", "\n", "如果写成一个公式:\n", "$$\n", "\\vec{y} = f(W4 \\cdot f(W3 \\cdot f(W2 \\cdot f(W1 \\cdot \\vec{x}))))\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "神经网络正向计算的过程比较简单,就是一层一层不断做运算,动态的演示如下图所示:\n", "![](images/neural_network_demo.gif)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. 神经网络的训练 - 反向传播算法\n", "\n", "神经网络的每个连接上的权值如果知道,那么就可以将输入数据代入得到希望的结果。神经网络是一个模型,那么这些权值就是**模型的参数**,也就是模型要学习的东西。然而,一个神经网络的连接方式、网络的层数、每层的节点数这些参数,则不是学习出来的,而是人为事先设置的。对于这些人为设置的参数,我们称之为**超参数(Hyper-Parameters)**。\n", "\n", "前面课程中所学的最小二乘、逻辑回归等可以直接优化损失函数来求解模型参数的更新值。在多层神经网络中:\n", "* 最后一层的参数可以用这样的方式求解得到;\n", "* 隐层节点没有输出的真值,因此无法直接构建损失函数来求解\n", "\n", "如何化解这个难题?\n", "\n", "反向传播算法其实就是链式求导法则的应用。然而,这个如此简单且显而易见的方法,却是在Roseblatt提出感知器算法将近30年之后才被发明和普及的。对此,Bengio这样回应道:\n", "\n", "> 很多看似显而易见的想法只有在事后才变得显而易见。\n", "\n", "按照机器学习的通用套路,我们先确定神经网络的目标函数,然后用随机梯度下降优化算法去求目标函数最小值时的参数值。\n", "\n", "我们取网络所有输出层节点的误差平方和作为目标函数:\n", "\n", "![bp_loss](images/bp_loss.png)\n", "\n", "其中,$E_d$表示是样本$d$的误差, **t是样本的标签值**,**y是神经网络的输出值**。\n", "\n", "然后,使用随机梯度下降算法对目标函数进行优化:\n", "\n", "![bp_weight_update](images/bp_weight_update.png)\n", "\n", "随机梯度下降算法也就是需要求出误差$E_d$对于每个权重$w_{ji}$的偏导数(也就是梯度),如何求解?\n", "\n", "![nn3](images/nn3.png)\n", "\n", "观察上图,我们发现权重$w_{ji}$仅能通过影响节点$j$的输入值影响网络的其它部分,设$net_j$是节点$j$的加权输入,即\n", "\n", "![eqn_21_22](images/eqn_21_22.png)\n", "\n", "$E_d$是$net_j$的函数,而$net_j$是$w_{ji}$的函数。根据链式求导法则,可以得到:\n", "\n", "![eqn_23_25](images/eqn_23_25.png)\n", "\n", "\n", "上式中,$x_{ji}$是节点传递给节点$j$的输入值,也就是节点$i$的输出值。\n", "\n", "对于的$\\frac{\\partial E_d}{\\partial net_j}$推导,需要区分输出层和隐藏层两种情况。\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.1 输出层权值训练\n", "\n", "![nn3](images/nn3.png)\n", "\n", "对于输出层来说,$net_j$仅能通过节点$j$的输出值$y_j$来影响网络其它部分,也就是说$E_d$是$y_j$的函数,而$y_j$是$net_j$的函数,其中$y_j = sigmod(net_j)$。所以我们可以再次使用链式求导法则:\n", "\n", "![eqn_26](images/eqn_26.png)\n", "\n", "考虑上式第一项:\n", "\n", "![eqn_27_29](images/eqn_27_29.png)\n", "\n", "\n", "考虑上式第二项:\n", "\n", "![eqn_30_31](images/eqn_30_31.png)\n", "\n", "将第一项和第二项带入,得到:\n", "\n", "![eqn_ed_net_j.png](images/eqn_ed_net_j.png)\n", "\n", "如果令$\\delta_j = - \\frac{\\partial E_d}{\\partial net_j}$,也就是一个节点的误差项$\\delta$是网络误差对这个节点输入的偏导数的相反数。带入上式,得到:\n", "\n", "![eqn_delta_j.png](images/eqn_delta_j.png)\n", "\n", "将上述推导带入随机梯度下降公式,得到:\n", "\n", "![eqn_32_34.png](images/eqn_32_34.png)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.2 隐藏层权值训练\n", "\n", "现在我们要推导出隐藏层的$\\frac{\\partial E_d}{\\partial net_j}$。\n", "\n", "![nn3](images/nn3.png)\n", "\n", "首先,我们需要定义节点$j$的所有直接下游节点的集合$Downstream(j)$。例如,对于节点4来说,它的直接下游节点是节点8、节点9。可以看到$net_j$只能通过影响$Downstream(j)$再影响$E_d$。设$net_k$是节点$j$的下游节点的输入,则$E_d$是$net_k$的函数,而$net_k$是$net_j$的函数。因为$net_k$有多个,我们应用全导数公式,可以做出如下推导:\n", "\n", "![eqn_35_40](images/eqn_35_40.png)\n", "\n", "因为$\\delta_j = - \\frac{\\partial E_d}{\\partial net_j}$,带入上式得到:\n", "\n", "![eqn_delta_hidden.png](images/eqn_delta_hidden.png)\n", "\n", "\n", "至此,我们已经推导出了反向传播算法。需要注意的是,我们刚刚推导出的训练规则是根据激活函数是sigmoid函数、平方和误差、全连接网络、随机梯度下降优化算法。如果激活函数不同、误差计算方式不同、网络连接结构不同、优化算法不同,则具体的训练规则也会不一样。但是无论怎样,训练规则的推导方式都是一样的,应用链式求导法则进行推导即可。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.3 具体解释\n", "\n", "假设每个训练样本为$(\\vec{x}, \\vec{t})$,其中向量$\\vec{x}$是训练样本的特征,而$\\vec{t}$是样本的目标值。\n", "\n", "![nn3](images/nn3.png)\n", "\n", "首先,根据上一节介绍的算法,用样本的特征$\\vec{x}$,计算出神经网络中每个隐藏层节点的输出$a_i$,以及输出层每个节点的输出$y_i$。\n", "\n", "然后,按照下面的方法计算出每个节点的误差项$\\delta_i$:\n", "\n", "* **对于输出层节点$i$**\n", "\n", "![formular_3.png](images/formular_3.png)\n", "\n", "其中,$\\delta_i$是节点$i$的误差项,$y_i$是节点$i$的输出值,$t_i$是样本对应于节点$i$的目标值。举个例子,根据上图,对于输出层节点8来说,它的输出值是$y_1$,而样本的目标值是$t_1$,带入上面的公式得到节点8的误差项应该是:\n", "\n", "![forumlar_delta8.png](images/forumlar_delta8.png)\n", "\n", "* **对于隐藏层节点**\n", "\n", "![formular_4.png](images/formular_4.png)\n", "\n", "其中,$a_i$是节点$i$的输出值,$w_{ki}$是节点$i$到它的下一层节点$k$的连接的权重,$\\delta_k$是节点$i$的下一层节点$k$的误差项。例如,对于隐藏层节点4来说,计算方法如下:\n", "\n", "![forumlar_delta4.png](images/forumlar_delta4.png)\n", "\n", "\n", "\n", "最后,更新每个连接上的权值:\n", "\n", "![formular_5.png](images/formular_5.png)\n", "\n", "其中,$w_{ji}$是节点$i$到节点$j$的权重,$\\eta$是一个成为学习速率的常数,$\\delta_j$是节点$j$的误差项,$x_{ji}$是节点$i$传递给节点$j$的输入。例如,权重$w_{84}$的更新方法如下:\n", "\n", "![eqn_w84_update.png](images/eqn_w84_update.png)\n", "\n", "类似的,权重$w_{41}$的更新方法如下:\n", "\n", "![eqn_w41_update.png](images/eqn_w41_update.png)\n", "\n", "\n", "偏置项的输入值永远为1。例如,节点4的偏置项$w_{4b}$应该按照下面的方法计算:\n", "\n", "![eqn_w4b_update.png](images/eqn_w4b_update.png)\n", "\n", "计算一个节点的误差项,需要先计算每个与其相连的下一层节点的误差项,这就要求误差项的计算顺序必须是从输出层开始,然后反向依次计算每个隐藏层的误差项,直到与输入层相连的那个隐藏层,这就是反向传播算法的名字的含义。当所有节点的误差项计算完毕后,就可以根据式5来更新所有的权重。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. 为什么要使用激活函数\n", "激活函数在神经网络中非常重要,使用激活函数也是非常必要的,前面我们从人脑神经元的角度理解了激活函数,因为神经元需要通过激活才能往后传播,所以神经网络中需要激活函数,下面我们从数学的角度理解一下激活函数的必要性。\n", "\n", "比如一个两层的神经网络,使用 f 表示激活函数,那么\n", "\n", "$$\n", "y = f( w_2 f(w_1 x) )\n", "$$\n", "\n", "如果不使用激活函数,那么神经网络的结果就是\n", "\n", "$$\n", "y = w_2 (w_1 x) = (w_2 w_1) x = \\bar{w} x\n", "$$\n", "\n", "可以看到,将两层神经网络的参数合在一起,用 $\\bar{w}$ 来表示,两层的神经网络其实就变成了一层神经网络,只不过参数变成了新的 $\\bar{w}$,所以如果不使用激活函数,那么不管多少层的神经网络,$y = w_n \\cdots w_2 w_1 x = \\bar{w} x$,就都变成了单层神经网络,所以在每一层都必须使用激活函数。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "最后看看激活函数对神经网络的影响\n", "\n", "![](images/nn-activation-function.gif)\n", "\n", "可以看到使用了激活函数之后,神经网络可以通过改变权重实现任意形状,越是复杂的神经网络能拟合的形状越复杂,这就是著名的神经网络万有逼近定理。神经网络使用的激活函数都是非线性的,每个激活函数都输入一个值,然后做一种特定的数学运算得到一个结果。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.1 sigmoid 激活函数\n", "\n", "$$\\sigma(x) = \\frac{1}{1 + e^{-x}}$$\n", "\n", "![](images/act-sigmoid.jpg)\n", "\n", "### 6.2 tanh 激活函数\n", "\n", "$$tanh(x) = 2 \\sigma(2x) - 1$$\n", "\n", "![](images/act-tanh.jpg)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.3 ReLU 激活函数\n", "\n", "$$ReLU(x) = max(0, x)$$\n", "\n", "![](images/act-relu.jpg)\n", "\n", "当输入 $x<0$ 时,输出为 $0$,当 $x> 0$ 时,输出为 $x$。该激活函数使网络更快速地收敛。它不会饱和,即它可以对抗梯度消失问题,至少在正区域($x> 0$ 时)可以这样,因此神经元至少在一半区域中不会把所有零进行反向传播。由于使用了简单的阈值化(thresholding),ReLU 计算效率很高。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在网络中,不同的输入可能包含着大小不同关键特征,使用大小可变的数据结构去做容器,则更加灵活。假如神经元激活具有稀疏性,那么不同激活路径上:不同数量(选择性不激活)、不同功能(分布式激活)。两种可优化的结构生成的激活路径,可以更好地从有效的数据的维度上,学习到相对稀疏的特征,起到自动化解离效果。\n", "\n", "![](images/nn-sparse.png)\n", "\n", "稀疏特征并不需要网络具有很强的处理线性不可分机制,因此在深度学习模型中,使用简单、速度快的线性激活函数可能更为合适。如图,一旦神经元与神经元之间改为线性激活,网络的非线性部分仅仅来自于神经元部分选择性激活。\n", "\n", "\n", "更倾向于使用线性神经激活函数的另外一个原因是,减轻梯度法训练深度网络时的Vanishing Gradient Problem。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "看过BP推导的人都知道,误差从输出层反向传播算梯度时,在各层都要乘当前层的输入神经元值,激活函数的一阶导数。\n", "$$\n", "grad = error ⋅ sigmoid'(x) ⋅ x\n", "$$\n", "\n", "使用双端饱和(即值域被限制)Sigmoid系函数会有两个问题:\n", "\n", "1. sigmoid'(x) ∈ (0,1) 导数缩放\n", "2. x∈(0,1)或x∈(-1,1) 饱和值缩放\n", "\n", "这样,经过每一层时,误差都是成倍的衰减,一旦进行递推式的多层的反向传播,梯度就会不停的衰减,消失,使得网络学习变慢。而校正激活函数的梯度是1,且只有一端饱和,梯度很好的在反向传播中流动,训练速度得到了很大的提高。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. 算法与处理步骤\n", "\n", "```\n", "W = random\n", "\n", "# 每次训练\n", "for k in range(epoch)\n", " # 正向计算\n", " for j in range(NN_depth):\n", " # 式2 ( a = xxx)\n", " X_j = f( W_{j, j-1} X_{j-1})\n", "\n", " # 反向误差计算\n", " for j in range(NN_depth, 0, -1):\n", " # 式3, 式4\n", " delta = y_i(1-y_i)(t_i-y_i)\n", " or \n", " delta = a_i(1-a_i) \\sum w_ki delta_k\n", "\n", " # 式5\n", " w_ji = w_j + epsilon delta_j x_ji\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.1 正向计算:\n", "![formular_2](images/formular_2.png)\n", "\n", "### 7.2 反向传播:\n", "输出层的误差计算:\n", "![formular_3.png](images/formular_3.png)\n", "\n", "隐层的误差计算:\n", "![formular_4.png](images/formular_4.png)\n", "\n", "权值更新:\n", "![formular_5.png](images/formular_5.png)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8. 示例程序" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "from sklearn import datasets, linear_model\n", "import matplotlib.pyplot as plt\n", "from sklearn.metrics import accuracy_score\n", "\n", "# generate sample data\n", "np.random.seed(0)\n", "x, y = datasets.make_moons(200, noise=0.20)\n", "\n", "y_true = np.array(y).astype(float)\n", "\n", "\n", "# generate nn output target\n", "t = np.zeros((x.shape[0], 2))\n", "t[np.where(y==0), 0] = 1\n", "t[np.where(y==1), 1] = 1\n", "\n", "\n", "# plot data\n", "plt.scatter(x[:, 0], x[:, 1], c=y, cmap=plt.cm.Spectral)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# generate the NN model\n", "class NN_Model:\n", " epsilon = 0.01 # learning rate\n", " n_epoch = 1000 # iterative number\n", " \n", "nn = NN_Model()\n", "nn.n_input_dim = x.shape[1] # input size\n", "nn.n_hide_dim = 8 # hidden node size\n", "nn.n_output_dim = 2 # output node size\n", "\n", "# initial weight array\n", "nn.W1 = np.random.randn(nn.n_input_dim, nn.n_hide_dim) / np.sqrt(nn.n_input_dim)\n", "nn.b1 = np.zeros((1, nn.n_hide_dim))\n", "nn.W2 = np.random.randn(nn.n_hide_dim, nn.n_output_dim) / np.sqrt(nn.n_hide_dim)\n", "nn.b2 = np.zeros((1, nn.n_output_dim))\n", "\n", "# define sigmod & its derivate function\n", "def sigmod(x):\n", " return 1.0/(1+np.exp(-x))\n", "\n", "# network forward calculation\n", "def forward(n, x):\n", " n.z1 = sigmod(x.dot(n.W1) + n.b1)\n", " n.z2 = sigmod(n.z1.dot(n.W2) + n.b2)\n", " return n\n", "\n", "\n", "# use random weight to perdict\n", "forward(nn, x)\n", "y_pred = np.argmax(nn.z2, axis=1)\n", "\n", "# plot data\n", "plt.scatter(x[:, 0], x[:, 1], c=y_pred, cmap=plt.cm.Spectral)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "epoch [ 0] L = 104.423202, acc = 0.500000\n", "epoch [ 100] L = 40.777627, acc = 0.835000\n", "epoch [ 200] L = 38.952112, acc = 0.855000\n", "epoch [ 300] L = 38.646741, acc = 0.845000\n", "epoch [ 400] L = 38.542146, acc = 0.845000\n", "epoch [ 500] L = 38.476230, acc = 0.850000\n", "epoch [ 600] L = 38.412223, acc = 0.850000\n", "epoch [ 700] L = 38.332965, acc = 0.850000\n", "epoch [ 800] L = 38.218330, acc = 0.850000\n", "epoch [ 900] L = 38.031606, acc = 0.850000\n", "epoch [1000] L = 37.700218, acc = 0.855000\n", "epoch [1100] L = 37.093093, acc = 0.865000\n", "epoch [1200] L = 36.035521, acc = 0.865000\n", "epoch [1300] L = 34.426662, acc = 0.875000\n", "epoch [1400] L = 32.378129, acc = 0.895000\n", "epoch [1500] L = 30.159633, acc = 0.910000\n", "epoch [1600] L = 28.011835, acc = 0.915000\n", "epoch [1700] L = 26.059731, acc = 0.930000\n", "epoch [1800] L = 24.339998, acc = 0.930000\n", "epoch [1900] L = 22.845633, acc = 0.935000\n" ] } ], "source": [ "\n", "# back-propagation\n", "def backpropagation(n, x, t):\n", " for i in range(n.n_epoch):\n", " # forward to calculate each node's output\n", " forward(n, x)\n", " \n", " # print loss, accuracy\n", " L = np.sum((n.z2 - t)**2)\n", " \n", " y_pred = np.argmax(nn.z2, axis=1)\n", " acc = accuracy_score(y_true, y_pred)\n", " \n", " if i % 100 == 0:\n", " print(\"epoch [%4d] L = %f, acc = %f\" % (i, L, acc))\n", " \n", " # calc weights update\n", " d2 = n.z2*(1-n.z2)*(t - n.z2)\n", " d1 = n.z1*(1-n.z1)*(np.dot(d2, n.W2.T))\n", " \n", " # update weights\n", " n.W2 += n.epsilon * np.dot(n.z1.T, d2)\n", " n.b2 += n.epsilon * np.sum(d2, axis=0)\n", " n.W1 += n.epsilon * np.dot(x.T, d1)\n", " n.b1 += n.epsilon * np.sum(d1, axis=0)\n", "\n", "nn.n_epoch = 2000\n", "backpropagation(nn, x, t)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot data\n", "y_pred = np.argmax(nn.z2, axis=1)\n", "\n", "plt.scatter(x[:, 0], x[:, 1], c=y, cmap=plt.cm.Spectral)\n", "plt.title(\"ground truth\")\n", "plt.show()\n", "\n", "plt.scatter(x[:, 0], x[:, 1], c=y_pred, cmap=plt.cm.Spectral)\n", "plt.title(\"predicted\")\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 9. 如何使用类的方法封装多层神经网络?" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from sklearn import datasets, linear_model\n", "from sklearn.metrics import accuracy_score\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "# define sigmod\n", "def sigmoid(X):\n", " return 1.0/(1+np.exp(-X))\n", "\n", "\n", "# generate the NN model\n", "class NN_Model:\n", " def __init__(self, nodes=None):\n", " self.epsilon = 0.01 # learning rate\n", " self.n_epoch = 1000 # iterative number\n", " \n", " if not nodes:\n", " self.nodes = [2, 8, 2] # default nodes size (from input -> output)\n", " else:\n", " self.nodes = nodes\n", " \n", " def init_weight(self):\n", " W = []\n", " B = []\n", " \n", " n_layer = len(self.nodes)\n", " for i in range(n_layer-1):\n", " w = np.random.randn(self.nodes[i], self.nodes[i+1]) / np.sqrt(self.nodes[i])\n", " b = np.random.randn(1, self.nodes[i+1])\n", " \n", " W.append(w)\n", " B.append(b)\n", " \n", " self.W = W\n", " self.B = B\n", " \n", " def forward(self, X):\n", " Z = []\n", " x0 = X\n", " for i in range(len(self.nodes)-1):\n", " z = sigmoid(np.dot(x0, self.W[i]) + self.B[i])\n", " x0 = z\n", " \n", " Z.append(z)\n", " \n", " self.Z = Z\n", " return Z[-1]\n", " \n", " # back-propagation\n", " def backpropagation(self, X, y, n_epoch=None, epsilon=None):\n", " if not n_epoch: n_epoch = self.n_epoch\n", " if not epsilon: epsilon = self.epsilon\n", " \n", " self.X = X\n", " self.Y = y\n", " \n", " for i in range(n_epoch):\n", " # forward to calculate each node's output\n", " self.forward(X)\n", "\n", " self.epoch = i\n", " self.evaluate()\n", " \n", " # calc weights update\n", " W = self.W\n", " B = self.B\n", " Z = self.Z\n", " \n", " D = []\n", " d0 = y\n", " n_layer = len(self.nodes)\n", " for j in range(n_layer-1, 0, -1):\n", " jj = j - 1\n", " z = self.Z[jj]\n", " \n", " if j == n_layer - 1:\n", " d = z*(1-z)*(d0 - z)\n", " else:\n", " d = z*(1-z)*np.dot(d0, W[j].T)\n", " \n", " d0 = d\n", " D.insert(0, d)\n", " \n", " # update weights\n", " for j in range(n_layer-1, 0, -1):\n", " jj = j - 1\n", " \n", " if jj != 0:\n", " W[jj] += epsilon * np.dot(Z[jj-1].T, D[jj])\n", " else:\n", " W[jj] += epsilon * np.dot(X.T, D[jj])\n", " \n", " B[jj] += epsilon * np.sum(D[jj], axis=0)\n", " \n", " def evaluate(self):\n", " z = self.Z[-1]\n", " \n", " # print loss, accuracy\n", " L = np.sum((z - self.Y)**2)\n", " \n", " y_pred = np.argmax(z, axis=1)\n", " y_true = np.argmax(self.Y, axis=1)\n", " acc = accuracy_score(y_true, y_pred)\n", " \n", " if self.epoch % 100 == 0:\n", " print(\"L = %f, acc = %f\" % (L, acc))\n", " " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# generate sample data\n", "np.random.seed(0)\n", "X, y = datasets.make_moons(200, noise=0.20)\n", "\n", "# generate nn output target\n", "t = np.zeros((X.shape[0], 2))\n", "t[np.where(y==0), 0] = 1\n", "t[np.where(y==1), 1] = 1\n", "\n", "# plot data\n", "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "L = 129.614605, acc = 0.500000\n", "L = 97.925025, acc = 0.800000\n", "L = 62.698146, acc = 0.815000\n", "L = 40.227408, acc = 0.850000\n", "L = 39.131957, acc = 0.845000\n", "L = 38.935737, acc = 0.845000\n", "L = 38.777492, acc = 0.845000\n", "L = 38.606838, acc = 0.845000\n", "L = 38.395635, acc = 0.855000\n", "L = 38.059367, acc = 0.860000\n", "L = 37.331248, acc = 0.865000\n", "L = 35.114319, acc = 0.880000\n", "L = 26.769351, acc = 0.920000\n", "L = 16.234740, acc = 0.950000\n", "L = 12.006890, acc = 0.970000\n", "L = 10.224714, acc = 0.975000\n", "L = 9.160757, acc = 0.980000\n", "L = 8.486381, acc = 0.980000\n", "L = 8.054299, acc = 0.980000\n", "L = 7.768036, acc = 0.980000\n" ] } ], "source": [ "# use the NN model and training\n", "nn = NN_Model([2, 8, 8, 8, 2])\n", "nn.init_weight()\n", "nn.backpropagation(X, t, 2000)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# predict results & plot results\n", "y_res = nn.forward(X)\n", "y_pred = np.argmax(y_res, axis=1)\n", "\n", "# plot data\n", "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)\n", "plt.title(\"ground truth\")\n", "plt.show()\n", "\n", "plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap=plt.cm.Spectral)\n", "plt.title(\"predicted\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 10. 深入分析与问题" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0.004756 0.99540845]\n", " [0.03952755 0.96007114]\n", " [0.99242233 0.00735218]\n", " [0.00492591 0.9952462 ]\n", " [0.00339531 0.99675505]\n", " [0.98196349 0.01799577]\n", " [0.01231621 0.98777358]\n", " [0.98536952 0.01453796]\n", " [0.01023822 0.98993045]]\n" ] } ], "source": [ "# print some results\n", "\n", "print(y_res[1:10, :])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**问题**\n", "1. 我们希望得到的每个类别的概率,如何实现?\n", "2. 如何做多分类问题?\n", "3. 如何能让神经网络更快的训练好?\n", "4. 如何更好的构建网络的类定义和接口设计,从而让神经网络的类支持更多的类型的处理层?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 参考资料\n", "\n", "* [零基础入门深度学习(3) - 神经网络和反向传播算法](https://www.zybuluo.com/hanbingtao/note/476663)\n", "* [Neural Network Using Python and Numpy](https://www.python-course.eu/neural_networks_with_python_numpy.php)\n", "* http://www.cedar.buffalo.edu/%7Esrihari/CSE574/Chap5/Chap5.3-BackProp.pdf\n", "* https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 2 }