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- {
- "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",
- "\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",
- "\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",
- "\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",
- "\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",
- "\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",
- "\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",
- "\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",
- "\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",
- "\n",
- "\n",
- "代入到前面的一组式子,得到:\n",
- "\n",
- "\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",
- "\n",
- "\n",
- "带入前面的一组式子,得到\n",
- "\n",
- "\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",
- "\n",
- "\n",
- "则每一层的输出向量的计算可以表示为:\n",
- "\n",
- "\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",
- ""
- ]
- },
- {
- "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",
- "\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",
- "\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",
- "\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",
- "\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",
- "\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",
- "\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",
- "\n",
- "\n",
- "考虑上式第一项:\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "考虑上式第二项:\n",
- "\n",
- "\n",
- "\n",
- "将第一项和第二项带入,得到:\n",
- "\n",
- "\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",
- "\n",
- "\n",
- "将上述推导带入随机梯度下降公式,得到:\n",
- "\n",
- "\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",
- "\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",
- "\n",
- "\n",
- "因为$\\delta_j = - \\frac{\\partial E_d}{\\partial net_j}$,带入上式得到:\n",
- "\n",
- "\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",
- "\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",
- "\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",
- "\n",
- "\n",
- "* **对于隐藏层节点**\n",
- "\n",
- "\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",
- "\n",
- "\n",
- "\n",
- "\n",
- "最后,更新每个连接上的权值:\n",
- "\n",
- "\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",
- "\n",
- "\n",
- "类似的,权重$w_{41}$的更新方法如下:\n",
- "\n",
- "\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",
- "\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",
- "\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",
- "\n",
- "\n",
- "### 6.2 tanh 激活函数\n",
- "\n",
- "$$tanh(x) = 2 \\sigma(2x) - 1$$\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 6.3 ReLU 激活函数\n",
- "\n",
- "$$ReLU(x) = max(0, x)$$\n",
- "\n",
- "\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",
- "\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",
- "\n",
- "\n",
- "### 7.2 反向传播:\n",
- "输出层的误差计算:\n",
- "\n",
- "\n",
- "隐层的误差计算:\n",
- "\n",
- "\n",
- "权值更新:\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 8. 示例程序"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "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": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "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_output_dim = 2 # output node size\n",
- "nn.n_hide_dim = 8 # hidden 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": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "epoch [ 0] L = 21.551449, acc = 0.945000\n",
- "epoch [ 100] L = 20.426241, acc = 0.950000\n",
- "epoch [ 200] L = 19.439654, acc = 0.950000\n",
- "epoch [ 300] L = 18.566054, acc = 0.955000\n",
- "epoch [ 400] L = 17.785764, acc = 0.955000\n",
- "epoch [ 500] L = 17.084338, acc = 0.955000\n",
- "epoch [ 600] L = 16.450998, acc = 0.960000\n",
- "epoch [ 700] L = 15.877223, acc = 0.960000\n",
- "epoch [ 800] L = 15.355889, acc = 0.960000\n",
- "epoch [ 900] L = 14.880860, acc = 0.960000\n",
- "epoch [1000] L = 14.446814, acc = 0.960000\n",
- "epoch [1100] L = 14.049131, acc = 0.960000\n",
- "epoch [1200] L = 13.683787, acc = 0.965000\n",
- "epoch [1300] L = 13.347243, acc = 0.965000\n",
- "epoch [1400] L = 13.036353, acc = 0.965000\n",
- "epoch [1500] L = 12.748284, acc = 0.965000\n",
- "epoch [1600] L = 12.480470, acc = 0.965000\n",
- "epoch [1700] L = 12.230590, acc = 0.965000\n",
- "epoch [1800] L = 11.996560, acc = 0.965000\n",
- "epoch [1900] L = 11.776538, acc = 0.965000\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": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "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": 9,
- "metadata": {
- "collapsed": true
- },
- "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": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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0v3MKhz6di/OcEIyQJbwaBxM+uEulxxVC0P7OKbS/c4rbYyPnPM+qq192ZeZorgyfUX+9VK70zKO/LPdcR6Fp7HltFpm74xg554VK262jUx5qy+HPBe4RQvwC9AOyNE1LLuManRqi58u3kLxiB3kJqThzCzD4eiFbTAT3aM2c9jcim420u2Oyq7pULl0rp67wb9WEkX+8yJqb38CRnY+mqAR1a8WIX58tsxAqLzEVZ4EN/9aRFSqaajq+L1elzCF14wGEQSa0X/tyvz7nb2ifi2p3kLhwM1mHEghoVzkRNh2d8lAthVdCiFnAcKARkAI8BxgBNE37rDAt8yNcmTz5wM2appVaVaUXXtUsqlMhYd4G0ncewSc6jL3v/kpu3MmilETZ20LTCX0YWUObrtWBPSuXlLV7sOdYCR/UqcxsoNz4FJZf/jyZe48hJAlTkC9Df3yCiOHda9zW+L/Xsfq6Vz1u/IJr/2DQlw/R4orhNW6LzoWNLo98kWLPyqXgVCa+zcKRTZ6lDwDiflnOutvfdevxKnubuWTDRwR3aYmmaShWe7kEymqD/TP/ZOtjXyCZjWiqijnQj7GL3iCwvefNflVR+L319eQnpBZrXG7wsXDZvm/wja5ZATRN09j84Ccc+OgvjwVqBh8L45e/Q2if9jVqh86Fj15pe5HhtNpZdf2rzGo8g7m97mRW2DQOfPJ3ieefXLXLY0Nv1eFkzxuz2PnaT8wKm8b/+V/C7MjLOfztgpo0v0xObdjH1ie+RLHacWTl4cwpIC8xlcVjHy3mzM/l5Iqd2NNz3B5XHQqHv5pf4zYLIej33t1MWPmem+6QZDQQ0C6KRr3b1bgdOhc3usOvBTRN48j/LeGvHrczO+pK1tz6JrnHzxY5KXYHBacyUJXKtcs7n/V3vkv8H2tQbQ6cuQU4svPZ+ujnHJ+73uP5PtHhSB50/DWHwtFfV7HjqW+wpWWjKSoFJzPYeO9MjsxaXi22VoYDn84tSlMtQtOwZ+VyasN+j9fkn0hD09y/DFS7g7Sdsex58xf2z/yzxiUrwgd1ZuKq9wns3BzJKCOZDDSd1I9xi9+sF3dOOhc2ekinFtj21Nfs//CPovitkCVMgb5M3fkFBz/7h/3vz3FJEHiZ6fnSzXS4a2ql57Jn5/FL+HQUm7u2e6O+7Zm88WO34/nJacxpe0OJ8WVP+LVqwoyYHyttZ1VYPPFxkhZucTtu9Pdh2M9PFRM6O0PWoQT+7nG7m2yCMMhFjlYYXOufId8/TosZwzzOrdgdHPlxCUf+bwmyxUy7Oy4heuqgSjlrW2YustmIwatmpCd0Lk5KC+nUVpbORYstI4d97/1ezNGcqW5deeWLpO88grOwk5JitbPl0c8xBfrS6ppRJY7ptNo59Pk/HPlxCZLRQLvbL6HVDWOQZBlbWrarC5UHh5+f6N6YG8A7IoSxC15n1bWvkpd0GkoIi5xLXsKpMs+pKZpdNoSU1Xvc0hxVu4OwgZ08XhPQLopm04dw/M91RdcJo4zmVM8KoBW+ZGtufIPIMb2KJBeKxlcUFo17jLQth4rGSFm7h9Y3jmXAR/dX+HmUVEtxetth9r49m+zYEzQe2pXOD12Od5NGFR5fR+d89JBODZOx96hHrXjV7uDUxgNFzv4MSr6NnS/+UOJ4qqKwaPTDbHvqa9K2x5C66QAb75vJqmtfBcAnKsxjw3AhSYSVkp8ePrgLlx/7mSajepTrefm3iQQg/8Rpdrz4A6tvep2Ybxe62gTWMK2uH0NA+6izgm1CIHub6fXabaUWpA357jH6vHMnQV1b4tc6kqAuLT2eJwwyiR7uIBLmbSRt26FiXzTOPCsx3ywkKyaxak+qkOP/rGf+sAc4+usq0rYd5sDHf/Fnl9s8ykXo6FQU3eHXMD5NQ93K8QFXiWYJ4bT8E6dLHC9x/ibSd8cV66/qzLOSMG8DaTtjkQwyvd+6o5hCpZAkDD5mer5wU6m2CiE8iqCdj+xtpvfrt5Oyfh9z2t3I7td+5sgPS9h430z+6nobtoycUq+vKgaLiYlrP6Tve3cROa4PLa8eybhFb9Lp/umlXifJMu3vmMylO79kxuEfCO1T0iap5vFvk7RoC85cD2EvSXBy5a5KPJPzZlVV1t/5nutvWzi/anfiyM5jx7PfVnl8HR3d4dcwfi0iCB3QEem8tEjZy4Q52N/jNUGdW5Q4XvJKzxk1mqKSsmYPAO1uncjI354jbGAnfKLDaH6FS46hPEU90VMGEn3pIJfTl4Srm5XJgHdkI2SLiaAuLRnx63M0ndiP1de78srPfKE586zkJZxi92s/lzlPVTFYTLT7zyTGLnidYf/3JOGD3FS5SyUn7gSn1u/z6Ng1p0Lk+L5ux73CAj2qaUqyXC49nbLITTiFPTPX3R5F5cRSXU1cp+roMfxaYNQfL7DmpjdIXLgFSZYw+Hkz4JMHUAqsrLv93WKrddnbTJ83S27V5x0RjGwxuW0+SiYDXuFBRb83ndCPphPcNy/LQkgSQ394gtNbDpG0cDPGAB9aXjUCr/DgYuflHk+hINldIFW1Ozn626pSn0Nd47Ta+XfQfRScynR7TLaYGPz1Ix5DQ61vHMeet34FiuveCINM00n9K23P6a2HWP/f90nbdrjEc8whnhcHOjoVQXf4tYApwJdRf76ELSMHe1YevtFhRUJcpgBfdjz3HTlHTxLUpQW9Xr2N8BI2HgFaXTeGnS+4x/glo4GoKQOrxV4hBKF92xPat+QiINliKrHbk8Gr8o1UaoPjf67FkWd1W91LJiO9Xr2VlleN9HidX4sIhs96mtU3vOY6oGkY/bwZ/c8rGDyktZaHnLgTLBj5kMe7tjMYfCx0evBynFY7adsOY/CxENytlZ7GqVNhdIdfTvKSUombtRxbWjaRY3vTeHj3Cn/gzEF+mIOK3/pHTepPVAVWh7LFxPDZz7L21rdcaZSahiUsiFF/vlBpp1MZvMKCaNSrLambDhSrHDV4mz0Kj9U1WYcT2PzgJySv2IUQeNxcVu0O7Fn5pY4TPWUgV5/6g9SNB5AtJhr1bltpVdCClHTW3vZOic3pDX5eaA6FDvdciuxl4pfwaSAEmqriFR7MmHmv6to7OhVCz8MvBwn/bmTFlS+iKSqqzYHBx0LEyB6M/OOFWhMXyzqUwOobXiN9p6u7UqO+7en6xDX4NQ8noEMzhBA4rXaO/7WW3PgUQvu2r9SXUkXITTjFgmH/w5qWBaqGpmo0ndiX4bOeQTLUH9G1/OQ0/ux0C/asvFKloQ2+Xgz7vyeJrqY7pdI48tNS1v3nHRS7A1R3mwx+XvR49kba3DKe/BNp/NP3LpRzv6SEwLtJCJcf+7neCtzp1A16Hn4VUGx2Vl37iltWTPLyHRz7dRUtr/Z8+1+dOHIL+HfwfdjSc4ocVuqG/az7z9tcHvcT4FrBzh/yAIrVhrPApXkT3K0V4xa/WWOFPb5RYcyI/ZHk5TvIS0ylUZ/2BHVqXiNzVYX9M/90rehLW9wIMDfyp+mkiu97VJT8k+kuZ3/ePsy5KAV2jsxaxoml25CMhuJS1gCahiM7n5Mrd9FkVM8atljnQkF3+GVwav0+j8edeVZif1xcKw7/6K8rXc7hHIelqSqO7Hz+6Xc3mfuOoanFUwmduQWkbT/Mnjd/ocdzN9aYbUKSaDK6V42NXx2c3nLQc2rsuWhgTckkY3ccIT3a1Kg9x/9aBx56FRchBJqmkb49xvWrLHkUXAMN6+msmjFS54JET8ssAyHLUMLCsKywhbPAxtHfVnHoi3lkHzlRaRty4pI9yh4486xk7IlzOQMPq1elwE7s94srPe+FQlCXlggPxWjno1jt7Hq15lNKVafiMYwDrupfYZDgHAfv2dm7hN/CB1csHVXn4kZ3+GUQNrCTx9xrg4+l1L6yqZsPMrvJ5ay97S02PfgJf3W5lU0PfFxiZktpNOrdFoNvCZ2oyhiuJPXIi4mO916G7KHa2Q1NI3Pf0Rq3J3ryAI/HZW8zjYd0RfPQSB1JFKvlMPhY6Hj/dHwi9VagOuVHd/hlIBlkRv35IkY/Lwy+XkhmI7KXmRZXjSB66iCP16iKwtLJT2EvlO5V8m0oVjuHv55P4vxNFbYh6pIB+EaHFZNoEHLZfzrZYqLVtaMrPN+Fhl+LCMYve5uQnm0QkoQwGTy+fkKWCOnZtsbt8W0WTs+Xbkb2Mrl0jyQJ2ctMx3suJaB9tEfbDN4W2v5nIqEDOhI5rg/Dfn6KXq/eWuO26lxY6Fk65cSRk0/8n2uxpefQZHTPUqthU9buYcmkJ3HkuKf4RU8dxKg/X6zw/PasXHa88ANxs5YjJEFgx2acWr/PXSa4EIOvF/5tIpm46n2MJd0dXIQodgeSQWbzw59x6It5xTbjDT4WJm/+hMAOzWrFlsz9x4j7dSWaU6X59CGE9GhDxt6j/NP/7mJ2IQTekSFcflTPyNEpG73jVS1zYtl2lk9/Dke2u8NvMrY34xa+UeU57Fm5/N7mhmJNPYTJgE+TEFpeO5rQvu1pOrEfQpJYtTSWRX8fIC/PRuduTZh+bXdCQn2qbEN9wFlgY+sTXxL77UKcBXYaD+tK/5n3ldj56gyaqrLvvd/Z++5v2NJzCO3bnr7v3kWjXjW/wi+LuF+Ws+6OdxFCoCkqXo2DGfPvqwS0vXBy7nNzbCz+5wA7tybiF2Bh/JSOdOnRpK7NuiDQHX4t4yywMSt8ulv1pMHHwoBPHqD19WOqZZ6cYyfZdN9MkhZtRTIZaHnNKPq+fSdGP++ic37+ZisrFh3GbnPFhSUJvHxMvPrhFAKDGv7Kf9G4Rzm5cheq46zcgTHAh2kHvsO7cXApV9ZvzlTVGn29COra8oKqqs3LtfH0A/PIzrLidLgWKyazzLSruzHh0pKrzHXKh97isJYxeJkZ8u2jyF7mIqlig6+FsAGdqjWN0695Y0bPfYUbbYu4PudfBn3+YDFnn5NtZfmCQ0XOHlxS9zark8X/HKg2O+qKhPkbObFkWzFnD64v3EOfza0jq6oHg8VE+KDOF6SEwtL5h8jJthU5ewC7TeGPn3dRkF9ybUJ9JzOjgB2bEzgam1ap5IzaQM/DryGaTx9KSM82xH6/COvpLJpO7E/T8X0qXYZfGRLjMzEYZRyO4pk6TofKwb0pJVzVcNhw94cej2t2J6e3HUZVFNcm7QXmMBs6u7Ym4bC7ZyLJBon4uAzad67ZhvLVjaZp/PLtNpYuOITRKKOqGo1CfXjkhdEEBXuXPUAtojv8GsSvRQQ9nr+p0terioIjKw9jgE+lNuuCG3njdLqnZQpJ0LhJ1eV865K8xFQKTrqrdZ4hbUcs35vGIZkMtL5hLH3f/S9Gn9oLYeXGp7Dn7dmkbjxAYMdmdH74CoJLaLhysREU7A0Ct5RiRVHxDyi9F0N9ZNPaY6xYFIPToRbdtSQnZTPz9VU8+2bJqdt1ge7w6yGaprH3rdnseu1nlAI7Bm8z3Z+7gY73TavQajU8wp/WbRsRczC1mOM3GiXGT+1YE6ZXO5qmkbJ2D6fW7sWrcTDNZwzF6OeN6lRKvVsqOOFqRq7aHBz5cTE5cScYv+TtWrE580A88wbcg7PAhuZQSN8RS/yc1Yya+wpNRpavo1hVyE9OI/bHxeQnniZiRA+iJg+oV9pG46Z0YPeOpGKhRkkSREQG0CQqoA4t80xuto0tG+LJz7PTqVsEzVuFFHt80T8HsdmKhxVVVeP40QzSUvPqVYKE7vCrgezYJLY99TUnV+7E3CiAzg9fSZubxlU6lLD/wz/Y+dKPRdW1druD7U99g8HHQrvbJlVorPueGM5XM9eza2sSQhL4+Jq45a4BRLeo/xuaqsPJkslPcWrdXhSrSx9o84OfMG7Z24T0aIN3ZAg5sR4qmCVRrJJVsTo4tWE/mQfiayXlcsujn+PIKSiqftZUFWe+jQ3/fY/ph0puX1kdnFy9myWTnkBTVBSrnZjvFhHYIZoJK9+rN83S23YM45pbejPr221IQqAoKpHRgTzw5PC6Ns2NfbuS+eDVlWhoKE6Vv2bvpu/AZtx238Ciz3dBnud9B0kWFBSUIelRy+hZOlUk93gKf3X7D86cgqL0SIO3hY73T6PXK5UrjJkVPh1raqbbce+moVx5/JdKjVmQb6egwElQsFeDiWnv/+hPtj7+ZfGcdMC3eWNmHPk/0rYdZuGoh1GdCkqBDYOvF0LgcrbnYfT3Zsj3j9OshGK56uT/AiZ7rMEQBplr0/4qtrFenWiqyuyoK90a08heJno8fxNdHrmyRuatLHabk4T4DHz9zIRH1L8GLw6Hwr03/kZBfnGnbbYYuPN/g+nZz5Um++sP21n0z4Fim9AAvn5mZn43A6kcRZLViZ6lU4PsefMXnPnWYhIGznwr+977HXuWe7u6stBU1aOzB0qNWZeFl7eJ4BDvBuPsAWK+Wejm7AGsqZlkHUqgUe92XH70J3q/fhudHrqcoT88TvDlo1E97HeodieBHWunoMoU6PkWXjLIyDXYsyDzwHGPtR9KgZ0jPy2tsXkri8lsoFXb0Hrp7AEO7z/lUbrEZnWyZvmRot8nXtaJgEAvTCbX+06SBCazzK33DKh1Z18WekiniqSs2etR+0QyG8k8cJyw/hWLlQtJwrdFBLlHk90eC2jbtNJ2NkhKu/ssfMwc7E/He6cBsGV9PPNOHaS7kBEonPlqE2YjTcb2JqBN7bx+nf43g+1Pf4Pz3NaVFhMtrx1VlKZbE0hGucR0QNlUDi0hHXdKWB+d+zr7+pl55YNLWLUklr07kwkJ9WHMpHY0bRbk+eI6pH59/TRA/NtEgodVs2K1k5dwisPfLCBtZ2yFxuzz9p3I3sXjrbKXmT5v31klW+srNpuzMNPhMKdO5hQdb33TOLfXAVz9XQPOq6TVNI1Z324jTzKzfegk0sMiUSQJh9FERpfujJj9TI0/jzN0vG+ay3aLCWOAD7LFROS4PvT/8N4ande/TVN8moa6vR8NPhba/qdiez860KZDmMc1h9liYPCIVsWOeXmbGD+1Iw8/N4qb7+pfL5096DH8cqNpGgnzNhA3azmS0ZXqFzGyB6e3HmLBiAeLhR4ks9GVFVG4eahpGo2HdGXUXy8im8t3S5+4cDPbn/6G7NgkAtpF0evV2y7IRhcxB0/xzgvL0dBQVQ1Ng9ET23HljT1RHU4Wj3+c01sP4cwrwOBlQRgkxi15i9A+xfvtOhwK/7niZ48fUINR4uvfrq2lZ3QW6+kssg4ex7d5Y5cjrkac+VaELLm9nzL3H2P+iAdRrQ5XQZokiJrUn2E/P6Xr8FSC3duTmPnGKjQNnA4Fk0mme98o7vzfYKTSehrUIbq0QhXRNI1V171Kwtz1RZkzBh8L7e64hL5v/5fjc9ez4a73saXnoGka5iA/rKez0JxnQz2yl4kuj11Fj2cr14xE0zTStseQG59CSM82+DVvXOHrTyzeSswPi0FVaXXdGJfWTh3G9J1Olftu+o283OJZDmazgfueGEbn7k3QNI3k5TtIWbsX74hgWlw5HFOAr9tYmqZx13Wzyc9zz4poFObDO19Mq7HnUZtk7DvG2lvfIm17DEIIIsf3YdCXD+EVdnZFqdjsJPy7iYLkNMIHdyG4W6tSRtQpi6zMAjatPUZBnoNO3SNo1bZRvd4L0x1+FTm5ZjdLJj7h1oRE9jIxdfsXBLSLQtM016aqJPgt+mq3cn+ofJaN9XQWi8Y9SvbhRIQsodqdtLhyOIO+erjcq7b1/32PI/+3tNgXVvPLhzHkm0crbE91sX93Mh+8tgqrh9S1voOacfcjQys03j+/72Xub7uL5XebzDLX396XoaNaV9ne2kDTNGzp2Ri8zBi8ixchWdOymNP6euzZ+UV7GMIg49+6CZft/aZWq7h16i96lk4VSZy/CWe+e8cpTYOkRVsAEELgHRFS6uZYaT1MS2P19a+Rsfcozjwrjux8FKudo7+t4uAnf5fr+vRdR4j9cUmxLyxnnpWjv64kdcvBStlUHTgcakl7Yjg8NQEpg0umd2LSZZ2wWAwYjTLePkYuv65Hg3H2J1fvZk67G5kdeQU/BU9l+RUvFMv0ivluEYrdWbzVpVMhL+k0ySt31YXJOg2MCzpLJ2PfMbIOHiewQzSBHZtXehyjvw+S0ejWSFqSJbecanOQHwEdosnYHVfsuDDKNLu04jng9qxcklfscMsEUvJtHPjor6IMldJIWrTF4x2HYnWQtHCLWzy8tmjfKQzFQ0cus9nAgKEl9xsoCSEEl17VjUtmdCEv14avnxm5nqXFlURWTKLrLvKchUXC3A0sSX6KSWs+cJ1z8DhKgXuaqqao5Bw5ARWs4k1avJWDn/+DM7eAlleNpNV1o2s0i+hiQVVUTibn4O1tJFDX0ql5nPlWlk59hlMb9iEZZFSnQvigzoz666VKVRu2umYUu17+0eNj0ZcNdjs25NtHWTDiQVS7E8Vqx+BjwRziT8+Xb6nEc7EhStgc8lRg5AmjnzeS0YBy3peGbDJ4LAJKWrKVHc99R3bsCYK6tKDnS7cQPrD6ZWvNFiO33TuQrz5cj6JoKIqK2WKgfadw+gwoXc++NAwGiYDAmtPNsZ7OIjsmEd8WEdUmwbz/gzko5y0oVLuDtB0xZOw7RlCn5oT2ac/RX1a4hRaFEBWO02998isOzPyzaKxT6/cR891Cxi97p17JMDQ0tm48zrcfb8ThUFAUldbtQrn74SH41+D7sSJUy/JHCDFeCHFICBErhHjcw+M3CSFShRA7C//dVh3zlsTmRz53lePn21whkHwbJ9fsYevjX1ZqPN9m4Qz59jFkbzNGP2+M/t4Y/bwZ9eeLmAPdNxBDerRh+uEf6P7cDbS+aRx93/kvl+37ptjGWnnxahyMJdyDU5EloqZ47o16Ps1nlBALF9DiyuHFDh37Yw3LLnuW1I0HsJ3O4uSKnSwa8wgnV5UdMog9mMp7r6zg8bv/5qsP15OSnF3mNf0GN+flDyYzaXonRk1sx72PDeOBp0bUu4IVcInZrb/rfX6NvorFE5/g95bXsvKal90cdWXIOpRQbJP/DJJBJjfepWza8tpRmAJ9XW0RC5EtJkJ6t6VRn3blnis34RT73/vdLcSXtj2G43+vq8KzuLjITM8nJ/vsa3j8aDqfv7uW3BwbNqsTp0Ml5sAp3n5xeR1aWZwqr/CFEDLwMTAGSAS2CCHmapq2/7xTZ2uadk9V5ysPsd8vcouXq1Y7sd8tpP8HlTOhxRXDaTqhL8krdiIMMhEje2AopWrSKyyIro9dXam5zkUIQWi/DuTFp7gdL+/4ltBARv72HCuufKnobkFTVIb99BTeEWeFoDRNY/ODn7hVtyoFNrY88jmTN39S4hzbNyfw6dtrsDsU0CAlOYctG+J59s0JREYFlmpfeIQf06/pXq7nUpfsfftXYn9YjGK1F72/jv+9jq2Pf0m/d++q0tjhgzqTsnYvqu38Vb6ToC6u8JbRx4vJWz5l62NfcHzuemSTkdY3jaPH8zdWKGvk5MpdCKMBzpvLmWcl4Z8NNJ9esc3yi42jsWl8/t5aUk/lggbNWgXz3weHsPifgzjOU6dVFI3kpCwSjmUQ1bzuc/OrI6TTF4jVNC0OQAjxCzAVON/h1wqappW4Oeosof9reTH6eRM9ZWCVxqgoeYmpJHhYdUkmAyeWbsMU6Ed+0mlC+7UntH/HEj/4TSf04+qUOSQv3wGa5vrCOi8LRLE5yE887fH69D1xHo+D6zX/4bNN2O3nNlrRsFqd/PrDDv731IjyPNV6z/4P/vDwZWjn0Bfz6Pv2nVXKkulw96Uc+Phv7E4FTXE5DdnbTIsrhuMbFVZ0nnfjYIZ+73YTXSFMgT4e3yfCIGMODUDTNPKOnwJcd7c6Z8nJtvL6M0uKZZYdjUnjlScXEdbYD011z3qUZYnMjIILxuFHAgnn/J4I9PNw3nQhxFDgMPA/TdMSzj9BCHE7cDtAdHTlYrhCCMKHdCFl9Z7ipflC0HhY10qNWZekrNuLZDKinLcaU/JtbLzvI2STAcXmRDLKhA3oxOh5r5SYKWTwMhM1qX+Jc8lmIwY/LxxZeW6PnXsncD65OTZyst03E9Eg5sCpEq9raNgzPWsjKVY7qlNBNlXe4VtCA5my9TNWXf8qp9bvc6l9ShIhvdqgaVq15n1HjuuDZHL/6EtGA+GDOvNHx5vJO+66o/RtFs7wX54huKueyw+wdkUcilJ8Fa+qGgX5dkJCvTGaZLfmLk6HQrOW9UOdtrYCpf8AzTVN6wosAb73dJKmaV9omtZb07TeoaGVr0wc+PH9GP29kcwuxydZTBj9vek/875Kj1lXWEID8ajghEvr3ZFTgGp34MyzkrJuL/s/+KPScwkh6PzwFW5yBgZvM92eLrlS1WwxelKXAMDPv+E1tCiJ0P4dPB4P7NCsWrRq0ncdIW17TJG0s5JbwLbHvmL/zMr/TT0hm4yMW/wmXo2Di/akDN4W+n94D2tveYvsQwkoBXaUAjtZBxNYMOJBHLnlSxC40Dl1Msdjty5V0YhqHoSPrwmD4axbNZllxlzSvt40dqkOh58ERJ3ze9PCY0VompamadqZJeBXQK9qmLdEAjs2Z9qB7+jyyJU0vaQ/XR+9kmkHviOwfeUzPypD/sl0Dn7+Dwc++Zu8xNQKX69pGmk7Y3DkudcAeEIpsHH4mwVlnpe++wibH/mMDXd/wIll24sJQXV74ho6PTADg48F2cuM0d+bHi/cRJubS+7cYzLJDBzWEqOpeHaHySwzadqF05S633t3uySYCzeUhSRh8LYw4OP7q2X8bU997RYycuZb2fnij8XUWKuDkB5tuCLhF8bMf40Rvz3H1afmICTJY/quandy7PdV1Tp/Q6Vt+zDMFg+BEQEdOjfmpXcnMWpiO8Ia+9KidQi33D2AK26oP5Io1RHS2QK0EUK0wOXorwKuOfcEIUSEpmln5B+nADXeQdu7cTA9X7y5pqcpkZgfFrHhzvddejoabHn4M3q98R86lSNv/gx73prNrhd/LNbMA0DyMoFT9fjh9JTpcS77PpzDtie+RrU70BSV2B8WEz11EEN/fAIhBEKS6PXyLXR/5jpsadlYQgPLlZt93e19sVqdbNt0HINBRlVUJkztyJBRF04oILhbK6Zu/5w9b/7C6S2HCOzUnK6PX01Q54rXDHgi9+hJj8cd2fk4820YfasntS/naDJ5CakEdW5O+KDORcfzkk4XU/g8g7PARn5SWrXM3dDpPTCav3/dTWpKblEXOaNJpm2HMFq0doU9r7mlN9fc4rHQtc6pssPXNM0phLgHWATIwDeapu0TQrwIbNU0bS5wnxBiCuAE0oGbqjpvfcCRW8Cx31eRfyKNsAEdaTy8O0II8pPT2HDn+26bx9se+5Km4/oQ0DaqhBHPoioKu1/72WOFb0Dbpij5NrJjit1IIVlMtLp2VIlj5p9MZ9vjXxWzy5ln5fjf60hetp0mo3sVzZ0dewKDt7nchTgmk8xdDw8hO8tKRlo+YRF+eHldeJK8/q0jGfTFQzUytl/rJm4Fe+DaZDX4VD0kYM/OY8WMF0hZuwfJbES1OWh/z6X0eeN2hBCE9e+AwceC87zwjcHbUmI462LAblew5tvx9bdgNMo888YE/vl9D5vWHsNgkBg2pg3jpzSM16daCq80TZsPzD/v2LPn/PwE8ER1zFXTpKzby7YnvyJzf7yrCfmLN9F0fF+389J3HXEVVzmcRe33GvVux9iFrxP/51qPksmqU+HY76vp9mTZyo2O7HyPVZXgWgmOX/YOC0c9hOo42+3Jv3UTOpfS1ejE4q3FcrjP4MyzcmzOapqM7kXSkq2svv41nHmupi7+bZoycs4L+LdqUqbNAP4BlnoTr2xo9H7tNpbPeKHY393gbabnCzdXy6bt2tve5uSa3ag2R9GX/qFP5xLYPpq2t0wgYmQPgru1Im17TJENspeZ4O6tiKiFXrz1DYdD4acvt7B2RRwaGj4+Jq69rQ/9Bjfnqpt6cdVNNRqZrhHqX3VLHXJy1S4WjX2UlDV7sKVlc3rrIZbPeJ6j58UvNU1jxRUvYM/MdTlGRcWZZyV180H2z/zTlVbnSZRO0zyGYTxhCvDB4OP5Fj6gXRSNerXl8rif6P3Gf4q6PU3e/CnGEq4Bl2yzR/EaScLgbSbn2EmWX/Yc1lOZOPOsKAV2MvYeZeGIB1GVimvb6FSMphP6MfyXp/FvF4WQJXyiwug3817a/3dKlcd25OSTMHeDW56/M8/K3nd/A1x7EuOWvEX3Z6/Hv10UAe2i6P7cDYxb/Fa9VoesKb77ZCPrVsbhcCg4HSpZmVa+mrmeg3tTyr64nnJBSitUls0Pf+a2qlbybWx56DOaTx9a9KbPPZpMnod8daXARux3ixg971W2PvaF2+OSyUgzD1IMnhCSRI8XbmTb418W75zkZabXq65CZXOwPx3vuazczy9qYj+3/QBwpWO2un4sh7+ej+o87wtJ1bBn5ZG8fAeRY+pnXPJCInryQKInV3+thz07r0SJDnva2Ypog8VE18eurpaiwYZMXq6NTWvj3UT87DaFv3/bTfvOY+rIsqqhr/DPIXPfMY/H80+cRrE5yI5NYt/7c4j9cUmJ7fc0DfyaN6bHizche5kQBtnVqMLbTMf7p1Uon7njPZfRb+a9+DQLRzIaCOrSglF/vFDpRihGP29GznkBg48Fo58XBl8LssVIz5duJqR7a/LiU1DtHjaCVdWtMbZO/SXn2EkOfvYPsT8uKVLb9I4IwRzs53aukCWajGl4oYmaJjOjANng+Qsy9WTFe1XXF/QV/jl4RQR7zJQw+Hqx581f2PP6LFcKoxCoNveqXdnLTJubxwHQ5eEriZrUn6O/rkRzqjSbPoSQ7hWX6W178wTalpISWVEix/bmqhO/kfDvRhSrnchxfYqKqpqM7kX8n2vdxLk0RSWsBsTTdCqGI68A1aF41G86w47nv2PPm7NBCIQsseG/7zPyjxeIHNubfjPvZdV1r7rCOqqGZDJg8LHQow6z2eoroWG+aB4yYYUkaNWuUe0bVE3oDVDOIea7hWy8Z2axzBjZ20ybm8cT881Cj5uosrfZtWnq40VIj9aMXfRmqRo79Rmn1c4/ve8kJy65aFOvPjRKudixns5izc1vcGLxNgD82zVlyDeP0qh3ccG0Uxv2sXDMI265/LK3mZDurUnd5MqGtoQF4RUaSMSYnnT+3wy8mzRcB1aTzJuzl79/PaehjnBJdz//9kSaNA2oW+NKobQGKPoK/xxa3zgOe2YuO174AdXmQBgkOv1vhkssy+auiHhG68SvRQRhAzoSMapng97cMlhMXLLhI/a9P4ejs1dg8LbQ/q4ptL5hbF2bdtGiaRoLRz1E5sHjRT0RMvceY+Goh5h24Ltizjrm+0UoHvSilHwbpzbsLwpDWlMzkc1Ger/2H10KuRQmTetEcCNv/vl9L9mZBbRuF8rl1/eo186+LHSHfw5CCDo9MIMO91yG9XQW5mA/ZJORzY9+7jFmL4QgfFBn2t46sQ6srRmMft50f+Z6uj9zfV2bUqvExZzmj593kXAsg/Amflx2VTc6dKlY3+Ca4NSG/eQcPeneAMfu5NCX/9LjubM9klWbs8S9pfO7ZNnSskhauJmoS8onsX0xIoRg4LCWDBzWsq5NqTb0TVsPSAYZ78bBRfooLS4fhuzlHqbRFJWoS0oWI6tu8k+cJnnlTvKSKi7T0NAoKHBgt5UvhbWqHD5witeeXsyeHSfIzCjg0L5TvPvScrZtPF4r85dGbtwJj8dVm4Osg8Xta3Hl8HIXaCl2J9mxnsfWuXDRV/jlILRPezrcNZUDH/+NancgJAlhkOj3/t14eWpOUs2oDidrbn6T+DmrkSwmVJuD6KkDGfL94yWKduUlpmLLyCGwfXSDaluXGJ/BVzM3EH80HQF06hbBrfcOJDCo5joGzfp2W7HG5+Cqrvz5m6307BdVp2G64B5tiuSSz0X2NrttpEeO60P01EHE/7XWLY5/PpLRQFDXC2flqlM+Go4nKCfpu46QuukA3k1CiBzft9pilH3evINW143m+N/rkUyGoth9bbDj+e+J/3Mtis1RtJdwfO4Gtj/9LX3evL3YuQUp6Syf8QJp2w4jjDKSLDPg0wdoeWX916TPzbbxypOLyM87u1+yd1cyrz65iNc/nopUQh55VUk4luHxeFpqPg6HislUd3HuoE7NaTKmJ0mLt6GekcSQJUwBvjS9ZABbHvuCxPmbsIQF0vl/Mxj64xNse+pr9r79a4m6SpLZSEC7KCJGdK+9J1KP0DSNpIQsbFYHzVoEYzBePPsYF4zDV50KK658kaSFW0CAJMsY/byYuPp9/FqWTxagLIK7tqoTXfCDn851LwgrsHHo83/cHP6SSU+SvjvO9WEvTDZae+tb+Ldq4pbVUZdomsbieQdZ8Oc+cnPttGgdQvNWwTgd52mNKxpZmQXs351M5+7V83c8H/8AC2mp7j0AzBZDManbuqLjvdNcGTpCQKE2fudHrmDBkPuxns5GtTtgH5zedJBuz1xH/JzVJTp7U5Afra4bTa9Xbm3QCQaVJSU5m/deXkH66TzXnbqAW+4ZQN+BzeratFrhgnH4Bz+bS9KiLUWOUcGVt7z8iheZuvWzujWuipSkRe7ILSjWHCNj71GyDrr3RlWsdvZ9MIdhPz5Z47aWl1+/387SBYeKQimH958i9lAqquK+6agqGqkpNVfsMnlGZ37+ZmuxsI7JLDNucvtK3VXYMnJIWbsHk78PYYM7I8mVX0Has/NYdtmzxQTvNKfC1ke/QMiSy9kX4sy3svOFH6CEfsCSUebyoz9h8veptD0NGVVRef2ZJWSk5RfuYbv+3l++v46mUYE0iSo9+8ZuV9i45ih7dyYT0siH4WPbEB7hXsxWn7lgHP6hz/5xj1uqGln748lLTMWnaeUbqtQ1oX3bu7ognUejPu2KrdIKTqYjPN2eqmdb1tU2h/ef4o9ZuziRmEXT6ACmXd2dyOgAlsw/5NZIQlM1JEmgnif/IIQgukXNtYcbPrYNuTk25v2+F01z3X2MHN+WS6+seIe0fTP/YNtjX7o6SmmuOoaxi94guEvl4uXH/17v8bimKJ6bnpuM+DYP96i6aQkNxOjnXSk7LgQO7T9Ffp7dLZHJ6VRZvugw193WBwCb1cHKJbFs35SAf4CF0RPbEd0ymBcfWUBaah42mxNZFiydf5B7HxtGs5bBHNhzEouXkc7dIup1iOiCcfie8uQBkETJjzUQ+s+8l/nD/oditaM5FYRBRjYbGfBR8Q5eIT3buIljAcgWE5Hj+tSWuUXs2XGCD19fWbRyzsooIObAEq69rTeyLOHgPIfvKmJGlgVK4UrfaJJp0TqElm1qrjhICMHkGV0YP7UjmekFBARaMJkr/tFI3XSAbU98VazJuSMnn8XjHuOKhF8qtdK3Z+V5Ds+UkH2pOpx0e+o61tz8hisnv9C7yd5m+r5310UZxjlDdpbnRkKqqpGZlg+AtcDBC48s4PSpXFePZgE7tybSoXNjUlNyi7R1FEVDURRmvrEKVdVcvbSdrte6ZdsQbr9/EBGR9S9fv+4DlNVEy2tGIpvdM1a8woLwa1k7m6s1RUiPNkzd8QVtb5tIo77taXPLBKbu+MItJm8O9qfzI1cUS82TTEbMIf7VorhYUX76aovH7JfFcw/g9OTEBHTq1phBw1vi42vCP9DCuMnteejZkbXiqIxGmdBw30o5e4CDn/3jsfDJmWclZc2eSo0ZOaaXR4VT2WLyeDdn9Pem+YyhTFz9Pk3H98ErIpiwQZ0Y9eeLtLh8eKVsuFBo0yEMxeme8WQyG+jaOxKAlUtizzp7AM0lmLZ7e5KbkBq4HnM61CJnDxB3OI3nH55PZnp+zTyRKnDBrPC7PHwl8X+sJTf+JM5cq+sDIUsM/b8nL4hVjX+rJgz85IEyz+vx/E0Ed2vNvvd+x5aWRdQlA+jy6FWYg2o31qhpGslJ2R4fS0rMZujo1mxcc7R43NwkM+2a7rRs04jLrs4jIT6TsMaVd8C1jS0j23Phk3D1N6gMAe2iaHvbJGK+WVCkcWTwsRAxuifJy3fgdBTf33Hk5JM4fxNRk/oz5t/XKjXnhUpwiDejJrZjxcIYbIU1HkaTTFi4L/2HuLqWbd90/KyzPxdBiXdVnrDbFZYuOMyMa7tX3fBqpGF8ksqB0c+bKds+I37OGk6u3oVv88a0vnEc3o3rR7f42kIIQfNpQ2g+bUid2+HrZyY3x3M+eHiEH6MntmPZgsPYbU7CI/y4/va+NG8ZzJcfrGPT2ngMRgnFqdKqbSPuf2pEve+g1eyyISQv2+EmPqfanYQP6VLpcfu9fzdNJ/Ql5tuFqHYnra4dhSUsiJPLd7qdq+TbOPzVfKIm1V5BYEPiqpt60aZDGMvmH6Ig30G/Qc0YOaFtUeqtn7/nwjVZlkDGLYusJFRF42iMu4R6XXPBOHwA2WSk5dUjaXn1yLo2RQcYf2kHfv9xp8fHlvx7kA++mcHl1/dEUVSMheGJf//cx+b1Lh3yM7fQMYdS+f7TTdz5YPl6CZwhLuY0G1YfBQ36DW5O6/Y1u3Hf8uqRHPp8Hhl74lxOXwhkLxO9Xrm1SndYQgiaju9brPPaiWXbPTezwdWDVsczQgh694+md/9oj4+PuaQ9u7cnFbvzFAJCQn1o3ymc9SuPIssCBBgMMjar02OoR5YF0S3q32LzgnL4OrXPwb0pzP5+G4nHMwkK8eayq7oyYKgrI2XspJIdfn6uK9YtSQJJOhuLXvrvQbe4v9OhsmV9PLfeO6Doi6Es5vy0g4VzDxRlAq1cEsOIcW1rtLm0bDIyYeW7xM1aTvycNZiD/Wh3xyWEDah+aemwgZ3QPDSzMfhYaHVNyX2NdUqnfadwLr+uB7/+uAODQUJVNQKDvXjkuVGEhvsxeUZnYg6mEhDoRdsOobz3ygr270lx+1sYTTKjJ9afupcz6A5fp9Ic2pfCOy8uK4p5ppzI4ZuPN5Kf52TUhLaYLQYiIv09xvJbt/O82i7I95xRpWkaDrtSLod/MimbBX8fKJb2abcprFh4mMEjWtboyks2GWlz4zja3DiuxuYAMHiZGfLto6y+4XU0p4LqcGLwtRDWvxMtz3P4rl7Kqzj660qMft60vW0ijYd0xZaZS+z3i0jfdYSQHq1pfcNYTAEla+1fLIyd3IEho1px5PBpfHzNNG8VXLQPGBruR2j42bu1h58dxaZ18fwxayepJ3PRNGjVthE33tmPkND6V++gO3ydSvPrjzvcNrjsNoU/ft7JiHFtkCTBjXf2492Xl+OwK2iaa0VvNMlcfYvnLkudukewbWOC24oprLEf3j7l6zOwY0uix9Wv06mwfXNCvbzVrgzNpw8lpGcbYr9fhDU1i6aT+tN0fB+EdDb5TlUUFk98nNQN+4vCTPFzVtP29ks48uMSnAU2lHwbx35dyc6X/o/Jmz/Br3ndq4TWNV7epnJVdkuyxIChLRgwtAWaprlqSUoofKsP1F/LdOo9ScczPR63Wh1FIZsOXRrzzOvj6TuoOaHhvvj6mRECPn9/HVs9qFFeeUNPvL2NGIyut6YkC0xmmZvvKv8mpNEke+zfKkkSJtOFtcbxaxFBj+dvYsDH9xM1sV8xZw+QMHfDWWcPoGk4823s/+APbBk5RcWKznwb9vQcNt77YW0/hQsGIUS9dvagr/B1qkCjMB8SjmW6HTcYZLx8zmbURLcIZvSkdrz1XELRHUFifCafv7eWgtv7MmTU2daPYY39eO2jKSyZd5DYQ6k0aRrA2MkdaNzEv9x29R4QzS/fbXM7LiRB30HumikOh8LyhYdZu/wIQsDQ0a0ZPrZtvdDRqSrH/17nljUEuNJHz7sJ0lS1qKtWfSY+Lp2Vi2PIybLSs38UfQc2q9fVrfUJ3eHrVJpp13Tn07fXFAvrmMwyE6Z2cKWxncOv32/3GP6Z/f12Bo1oVUyzJiDQixnX9ai0XYFBXtx270C+mrm+aFxV1bjxjr40Ciseo1ZVjbdfWEZczOmizeLZ329n55akWiv4qkmMgb4IWfIosewJqZbvgNJS81g6/xAJ8Rm0bBPCqAntCAgsWQp79ZIYfvxyC06niqpq7N5xgqXzD/HEy2PLvaF/MaM7/FLIOpRAXtJpgru2xNKo/pVJ1zU9+0Zx8939mf3ddrKzrZjNBiZe1olLpnd2OzchPtPjGAX5DgryHfj4Vm8f4P5DmtO5ewS7tiahodGtV6THHOv9u5M5GptWLDPIblM4vP8UMQdTadshrFrtqm3a3jqBw1/+66a2esaxq/azTWYks9Ftw7cmOXYkjVefWozTqaI4VQ7uOcmSeYd4/u0JGAwyp0/lEhkdiK+fGXA1xfnxyy3FFg42q5OEYxlsWHWUoaNblzRVnZOelk/MgVP4B1ho1zGszkI/F4XDT1qyle3PfEN2zAkC2kfR6+VbiBhR8grSlp7N0qnPkLY9BslkQLU56HDvZfR+/T8NfsVX3Qwc1pIBQ1tgtzkxmgwlqkuGhHqTdDzL7bjBIGHxqpm3oa+fmUEjWrJrWxKvPb2ElBPZBDfyZto13YpSRw8fOIXN6t5Zy+FUiDlwqsE7/OAuLen3wd1suv8jVyMczeXsh89+lq2PfU7WocSi6uDAjs3p+/adtWbbt59sLPbaOxwqTqedlx5biNXqxGCQcDpURk1oy1U39yL2YCqyQQIPd4qb1h6rlw5f0zR++W4by+YfctkOePuYePylMYRHlD9MWV1c8A4/Yd4GVlz5UtEKJ3XDfpZc8hSj/nihREGxVde/xunNB1EdzqLrDn7yN0GdW9D6+jG1ZntDQQiB2VJ6FexlV3Xjiw/WuUsQT3EP/5SH+Lh0TiRk0SQqgGYtS8662b09iY/eWFW0Kjx1MpdvPt6Iw6YwdEwbAoO8MZllt9x/o1EuNbTQkGh32yRaXD6Mk6t2Y/A203hYNySjgcmbP+XU+n1kHTxOYMdmhPbvWGsLGqdDIT7OvfGMpkFOtuszdyatdvmiwzSO9CcyOhCthJ695c3gqm22bUpgxcIYHA4VR2GVrs3q5L2XV/DaR1NqfQF5wTv8zQ996rF5yOaHP+UyDw7fmpZF8vIdqI7iqz5nnpV97/2mO/xK0mdgM/Ly7Pz2ww6sBQ5kg8S4ye259KpuAByNTePXH7Zz7EgaQSHeTL2iK/0GN3cbx2Z18O7LK4iLOY0kBKqm0bxVCA89MxKLB+mF30pIHf3tp50MGd2afoObMfv77XCecqcsCXoP8FyN2RAxBfgSPWVgsWNCCMIHdSZ8kHsIrqaRJIEsC5zOsgVq7DaFhX8f4LWPpuDlZcRaUPyzaTLLjBzftqZMrRLL5h8q0u05g6ZB2uk8TiRmERkVWKv2XNAOX9M0smOSPD6WdTDB43FHdj6ihBWnLS2n2my7GBk+pg1DR7UmP8+OxctYlAXjiuUuKlpl5+dl8dXM9eRkWRk9qX2xMX75bjtHDqUWrZbAJaEw69ttHlM3T5Yg4JabbcNuc+Lja+bRF0bz0ZuryMuxo6EREOjFvY8N8/gFolM9SLJE/yHN2bj2WLn0afJybUiS4KFnR/Hmc0uxF+49KE6VS6Z3pkOX+lk7UFIhoSRJbl9ctcEF7fCFEFhCA7CmuseOvcI9N9TwbRaO0c/brZmKMMhETujr8Rqd8iNJomgT7gxzftrpLqNsU5jz806GjyueHrluZVwxZw8u6YX1K+M8OvyQMB+SE92dvpe3sUiFs1XbRrz75TTXeQIiIv31vZpa4Prb+3I6NY+4mNPIsitej9Bw2Iv/fYUkihx6VPMg3v9mOgf3ppCXa6ddp7B6HXrrO6gZiccz3Zr9CAHNarCpT0k0/ETjMuj6xLUYvItnZxi8LXR7+jqP5wtJYtDnDyJ7m4uKWCSzEXOQH92f8XyNTtU4diTd43GnQyU7s+C8Y557tTocisf47oxre7g1ITeZZaZe0aWYUxdC0CQqgCZNA3RnX0tYvIw88fJYnntrIv+5byCvfHgJ9z42HJNZ5syfQJYlvLwMxdJ0ZVmiU7cI+g5qVq+dPcCoCW0Jb+xXtLiQJIHJJHPLPQPqpHbggl7hA3S8fxrOfCt73vgF1eFEMhvp9tS1tLtjconXRE8ZyKS1H7Lvvd/JiTtBxMgedLznMiyhgaXOlRN3gt1vzub05oMEdmpGl0evqnRru/LitNrZ/tTXHP5mAUq+jcYjutP/w3sIaBtVo/NWJ43CfErsRnT+3UCHLo3Ztyu5mOy8EK7jnhx17wHR2O39+fWHHWSm5+Pja2bqFV0Yc0l7t3N16oam0YE0jQ4EIDzCn6dfG8/8P/eRkpxDmw6hTJjakeBG9U+XpjyYLUaee3siG9ccZffWJAJDvBk5rm2p/XPzcu2Aho+vucRzKosoade7rundu7e2devWahtPdTixZeRgDvZHMlT/N2v6njjmD74PZ0FhG0JJQrYYGT3vVSKGd6/2+c6weNITnFyx82yTayEwBfgw7cC3eIU3DM2YXduS+OjNVW4ZPMPHtOHa24pvrJ88kc2Ljy7AbldcYmomGaNR4tk3J5TZUs7pUJAN0gW1gnda7dgzc7GEBlSpWbpO+cnKLGDe73vZuTUJXz8T46Z0oN/g5lV+X6Wm5PD5e+uIi00DIKpZIHf8bzBNmlasBkgIsU3TNI+ysBeNw69pFo59hOSl292OB7SPZtr+b2tkzswD8czt/V/3ohqLia6PX0WPZ2+skXlrgnUrjvDLd9vJz7cjSYKR49pyxY09PaZs5mRbWbk4hvgj6US3DGb42Db4B3huXOGJY0fSWLbgMNmZBfToG8XAYS0aTFetM6gOJ5sf+pTDX88vapbe5+07q1Wl05FXwM4XfiD2h8Voikqz6UPp9cotWEIu3iLE3BwbT933Dzk5tqJ2iWazgTGXtOPy63sWnZeelk9aai4RkQFud6mecDgUHr79T7KyrGeF/wT4+Jh458tpFWr+U5rDb1jv8nrMqfX7PB7PjknEWWDD4FX9t2eZ++ORjDJK8TA3qtXO6S2Hqn2+mmTQiFYMGNaS3BxboXhayatVP38Lk2dUroPUmdJ8h1NFUzX27znJkn8P8uybEzA3IKe/8f6PiP1hcVEPXcVqZ8PdH2AJDSRqYr8qj69pGovGPEL6ziNFd4+x3y4kedl2Ltv7NbK5fua91zTLFhwiL9dWrDeuzeZk0dyDjJ/aEbPZwGfvrmX39hMYjK6N6BHj2nD1Lb1LLEoE2LklEavVUVzlVQOnU2XT2mMMH9OmWuyvlk1bIcR4IcQhIUSsEOJxD4+bhRCzCx/fJIRoXh3z1idMgZ51xCWTocb0SQLaRaF6aAYumY0Ed6+eN0htIkkC/wBLjWxmOR0KP3y+ia8/3ojdrhR9sOw2hVPJOaxaHFPtc9YUjrwCYr9b5JZJpuTb2PXSj9UyR8qaPWTsPXo2VIjrrqIgJYNjc9ZUyxwNkb07k92yxAAMRon4uHR+/HJLUcPzgnwHDofCyiUxLJt/sNRxU1Ny3TJ5wFWklXqy+tLBq+zwhRAy8DEwAegIXC2E6HjeabcCGZqmtQbeA96o6rz1jU73T0f2Lr6Kl71MtLl5Qo3FVoM6tyCsf0ckc/HbPdlspMN/S96Urm9omkZ+nr3EDJyKkJdrJyU5B+U8sbBvP9nIqiWxHq+x2xU2rD7Kbz9u583nlvLrD9tJP51XZVuqm+wjJ9j65FesveUtNNVz7nru8ZRqmSttRwyqh7+HM7eAtG2Hq2WOhkhomA+eQvWKouLnb2bDKve0YbtNYeHcA6WO27xVsMeFjsVioEXrRlWy+VyqY+nZF4jVNC0OQAjxCzAV2H/OOVOB5wt//h34SAghtPq6gVAJOj90ObnHThLz7UIksxHV5iBq8gD6vlOz2iSj/n6JzQ99ypEflqDYHYQP7ET/j+/Hu0n1vUlqkj07TvD9Z5tIP52HJAkGj2zFNbf2cUulLAub1cFXH21g+6YEZFnCYJC45uZeDB7VmtxsG5vWHsPpLLnA59iRdI4fzcDpVDm0L4VlCw7x1GvjiW5e8Vzp3duTWDLvILk5Nnr1j2bUxHZVbsAe/9daVl33KqpDQXOUULAjBI16V09bPb+WEUU6Uudi8LHg36ZptcxR1+RkW/n1hx1s3RCPEIKBw1sy/drupf6txk3pyJYNx4slGMiyIDI6kNBwP1QPjXfgTOZNyXTo0pim0YEcP5pe9IVhMEgEh/rQvU/1vd5V3rQVQswAxmuadlvh79cD/TRNu+ecc/YWnpNY+PuRwnNOnzfW7cDtANHR0b3i4+OrZFtdYD2dRdahBPxaNK5Vp6tpGmiaWwOM+szR2OIVtuBqXtKjT1PufmRohcb68PWV7N6WVGx1ZTLLPPDkCHz9zLz29OISqx6FAE8fg7Ydw3jq1Yptgs79dTf/zNlb9JyMJplGoT688M7EMvWGSkKx2ZkVPh1Hdn7JJwmBwdvMpHUfEty1VaXmORfVqfB7m+vJT0w9K60sBKYgXy6P+wmTf8NMkzyDw6Hw5L1zSUvNL7obNBglIqMCeeGdiaVm3GxZH893n27C4VBQFJU27UK565Gh+PmbeeTOv0hNyS12vhDQrVck/3t6ZKk22WxO5v66h7UrjqBpLsXXS6/sWmGdoAazaatp2hfAF+DK0qljcyqFpVFAnUgpCyHweK9Zj5k3Z69b3NJhV9ixOYHMjAICg8pXVJOdZWXXtiS3En27TWHeH3u577FhJa7uhVSCtwdiDpxC07Ryp9vlZtuY+9teHOeEQhx2hbTTeaxaeoSxlcz9P7215BCK7GPBYDHRqE87er16W7U4ewDJIDNp7QesvfktTq7aiQY06tmWwd8+2uCdPcC2jcfJyrQWC/05HSopJ7I5sOckHbtGlHhtn4HN6NkvipTkHLx9TMXepzf9tx8fvLbybEtPWWAyGbjyRs8tPc/FbDZw+fU9uPz6yveCKIvqcPhJwLlVPk0Lj3k6J1EIYQACgLRqmFunAXMyKdujrzUYZdJP5xX7INlsTnKzbQQEebl1osrOLCiS0j2ftFN5eHmbGDOpHUvnHyp2N2EwSjz24hjeeXE51gL31b/JZKhQbvWRw6cxGKViDh9cXzw7tyZW2uHLFpPHHr0AjQd3ZuyCmtkS84kMZdziN3HmW9EUFaOfd43MUxcci033KIvtdKocP5ZRqsMHV7Wvp/z4zt2b8PRr4/n3j32cPJFFq7ahTLysE6Hh9aM5fHU4/C1AGyFEC1yO/SrgmvPOmQvcCGwAZgDLL6T4vU7laNWuEScSs9zink6nWqQV7nSq/Pz1FlYvc7UfNBgkpl/bg9ETz8aqwxr7efzikCRBu44uPfsrbuhJUIg3C/7aT26OjVZtG3H1zb1p1jKYYWNas3zB4WKO2miUGDKqYqtlX3+zxxiuEJT7bsUTIT1aYw7yxZlbPP/W4GMptWK8ujhfmuRCoHFTf8xmg5uSpcEoEdbYr0pjN2sZzF0PD6nSGDVFlQO+mqY5gXuARcAB4FdN0/YJIV4UQkwpPO1rIEQIEQs8CLilbupcfEya1hmTWYZzFtEms8yoCW2LOmDN+nYba5YdwWFXsNsU8vMczP5+G1vWx59zjYFp13RzjVWIEGC2GJhyRZfC3wVjL+nAe19N58vZ1/D4S2OLdPRnXNeDTt0aYzTJeHkbMZpkOnRpzJU3ni2kKQ8t24QQGOTl1kDdaJIZM6ns1X1erp2khEzs5zkhIUmM/ucVzI0CMPp5Y/CxIFtMtLllAtFTB1XIxvKQm3CKhHkbyNh3rNrHri/0H9wc4zmaPeBaIPj4munWK7LuDKth9EpbnTol8Xgms7/fzuH9p/D1MzPh0o6MmtAWIQR2u8Jd1832mJ8c1TyQl98vvrrdtvE48+bsJTO9gPadw7ns6m4VWq2dPJHNicQsmkQG0Diyct2IUlNyeffl5Zw+lYssS6iqxvX/6VOsUfv5OBwK332ykY1rj2EwSGgqTJ7RmUtmdC4WUlLsDpIWbcF2OpvGw7ri17JJpWwsCVVRWPefdzj6ywpXppnDSUiPNoyZ9wqmgPoRkqgKTofCji2JnDqZQ3SLYBqF+fDtxxuJOZgKAjp1jeDWewcQFNywQ1e6tIJOgyQzo4CHb//TLSYOLlG1j3+8og6sKhtN00hKyKIg306zFsFlyjZ89/pi9v+zlXyDhZzARq5sGLPMjXf2Y/CI6tmELQ973/ud7c98U6ygSzIZiZrcn5G/PV/idelp+Rw5lIpfgIW2HcJKrSitK9JS83jp8YUU5Nux2xVMRpmwCD+efGUssiwhJFGuJuiqorJ0/iGWLTyM3eqk14Bopl7RxWO/5LqiwWTp6Oici7+/GbPF4NHht2gdUgcWlQ8hRJH6Y2lomsaG+z5C+XQubSQZNA2blw+7BozFjg/z5uytVYd/YOafbtW7qt1Bwj8bcOZb3WL5nvq1+vqaeeylMVWOg8ccPMWKRTHk5djpMyia/oObV6kC+6uZ68nKKCjaY7EqTk4kZjHn511cd5vnVqfnomkaRw6d5ocvNpF0PKso62vFwsNs35TAqx9ObhANcxpO0rbORYckS1x9U89isXmEK2Zfk6lrtUXcz8uI/XYhkqpicDowKE68crPptHUlANmZniWjawpHTsl5/s7zBPoAtm0826/VWuDEWuAk7XQe77+6okp2LPh7P28+t5T1K+PYuTWRHz7bzOvPLCm1cK407DYnh/aluCcHOFQ2rDpa5vUOh8Kbzy3l9WeXEB+XUcwOp1MlJ9vKupVxlbKtttEdvk69ZvCo1tz9yFBatgnBP9BC916RPPP6uFIblzcU9n/wB0p+cacuoeGblY7ZmkerdrVbLR05rrfH9p6+0eGYg933NJb8e9Bjv9bUlFySk9y7zJWH3Gwbc/5vB3abUpR5ZbM5OX40g83rjlVqTI0SSy1KbIp+Lgv+2k/swVSPe0ngSrs9tK96JC1qGj2ko1Pv6d67Kd17u8rL09PySU7MIjUlh9DwqoUN6hp7tme9Hk0IvITKFddXLEuoqvR69TaSFm3FmWdFsdoRBhnZbGTQVw97rEcorV9rSY+dQVVUlvx7kKXzD2EtcNK9dyTTru3uqmUwyG56NDabky3rjzNwWMUbCpnNBlq3Dy0spDt7XJYFfQc1K/P61UtjsZfg7MGVKlzVEFZtoTt8nQaBoqh8NXM9W9bFYzDKOJ0qnbo25u5HhjY4LfszNLtsMPven+OmVyNbTDz62VVENqvdnqe+0eFM2/8tBz6dS8qaPQR2iKbDvZcRUIJ2Tp+B0ZxIzPLYrzW6Rel3YF/N3MCWDfFFhXBrV8axc1sSN93ZD09rbiHcu59VhNvuHchLjy/AblOwWZ1YvAwEBnmXKzRYkj7OGWRZYsS4tpW2rTZpmJ8UnYuOeXP2snX9cRwOtWj1t2/XSX7+Zis3/de9eXlDoMujV3F09koKUjJQCmwIWUIyGxkx60kim1V/yOpEYhY/fbWFg3tTMFsMDBvTmmnXdC+WnWIJDaTHszeUa7wxk9qzfuVR0lJzsdkUJElgMEjces8ADuw5yS/fbeNkUjZBId5Mu6Zb0eo8NSWXzevii23Gq4pGQb6DE4lZmEyyW+Wz0SQzYlzlJb/DI/x45/PL2LzueGFaZhA9+ka5VW17ov+Q5iz654DHSu7QcF9uv38QIaENQ25CT8vUaRDce+NvHvveGo0yX8y+ul6mApYHR04+Md8uJGnxVnybh9Ph7ksJ7FB2mKGiZKbn8/g9c7EWOIrCGkaTTOduETzw1IhKj2u3OVm/+ii7tiYR3MibkePbkpGWzwevriwWBjGZZa65pTcjxrVl68bjfPXheo9hn669mnD59T156/ml2G1OBAKnonLVjT0ZXY7itZqgIN/OS48t5HRqHjarE5NJRpIFdz8ylC49mtS7lpl6WqZOg8eT1g2A06mgKiqS1DD7uRr9vOl43zQ63jetRudZWigdce76zmFX2LsrmZTk7CIpi4piMhsYPqZNsY5Mn7+3zi3mbbcpzPlpJ8PHtqFRqA+q4r7QlGVB4yb+RDcP4oOvp3No/ykKChy06xheVHldF3h5m3jxvUvYsTmBmAOnaBTuy8BhLasUYqordIev0yBo2zGMvbuSOT/A27RZYI10yCqLvTtP8Mt320lOzCIgyItLr+rK0FKqaeuao7GnPYYkDAaJpONZlXb4njiZlO3xeH6eHavVSeMm/ggPkRRZlhg90bWKl2SJDl0aV5tNVcVgkOgzsBl9Blb/3Vdtoqdl6jQIrr21D14WY1GBjyQJzGYDN91Z+/H7A3tO8sGrK0k45srJTkvN48cvNrN4XuldjeqS6ObBHuPVilOttIxESTQK9xzPNluMmM0Gfvxis8ec+gHDWxAe0TCyXRoqusPXaRA0iQrglQ8nM2ZiO9q0D2X42Da8+N4kWrcPrXVbfv1xh8eQxZ+zdqMqlSsOqmlGT2yHwVj84240SrTpEOpR5rcqTL+mu1vHMpNZZvLlnVFVjY1rj3m829i19XxVdZ3qRg/p6DQYQkJ9uPoWj3tRtUpyoueiIrvNSX6eA1//6ovtappG+ul8ZFkQWAVRr5BQH558ZRw/fL6pKNd90IiWXHNr9b+evfpHc8s9A/j1++1kpOfj42tmyuWdGTu5A3ab02P8HsBaRu5+VUlOymL7pkRkg6DPgGYNJrOmOtEdvk6DJjfHxs/fbGXLung0TaN73yiuu61Pmfrz8XHpzPp2G0cOp+Lr61LpHHNJ+3JlXIQ19iM+Lt3tuNEo4+VTfXoqx46k8ek7a0k7nQeaRmR0IHc9PLTSYY9mLYN55o0JqIqKkESNZpcMGNqCAUNb4HQoyAapaC6zxUjjSH9OJBT/0hSCMpuOVIW/ftnFvD/2oSoaQoLf/28n1/2nT7HN5osBPaSj02BRFZWXH1/IxjXHsNsVHA6VbRuO88Ij80utjExOyuKVJxdxYM9J7DaF9LR8fvu/Hcz+fnu55p1+Tffi+j6A0STRtFkgzz74L+++tJwDe05W6bnl5th4/ZklnDyRjaPwucXHpfPKEwtxehCTqwiSLNVaKqHBKLvNdfNd/TGbDUWptAaDhJe3kStvqpnK4uPHMvj3j3047K4etE6HisOu8H9fbCEzo6DsAS4gdIev02DZsyOZjLR8lHM2AFVVIz/XzrYNx0u87p/f9rhVh9ptCkvnHyI/z17mvN16R/Kfc4ptvLyNSEIQF5tGYnwmu7Yl8e7Ly1m9NLaSzwzWr4or9rzApQdjsznZ2cBj3W07hPHie5MYPrYN7TuFM25KB16dOYXGTap38/gMm9cew+l0/5IUEuzYnFAjc9ZX9JCOToMlKTHTo3Sy1eokMT4DaOHxurjYNI/l8gaDxKmTOTRvVbb0ct+Bzeg7sFlRC8aVi2NQzolN220KP3+zlYHDWlQqbfT0qTyPdylOp0p6Wh6KopKXa8fH14QsS2iaxvpVR1k09wB5uTa6927KlCu6EBBY+daKNUnjJv7ceGe/Sl1rtzn59YftrC7shNauUxjX396XyKjAEq+pp/WltY7u8HUaJKqi4utrxmCUUZTzWgIK2LQuHl8/MyMntMVsKR5Xb9I0gOSkbLecfqdDqfBGnsEgsXdncjFnfwZN0zh5IpumHjRxEuMziItNI6SRDx26NHarFG7TPpSVi2PcGm1LQpCSnMPd1/+Kw6FgNMhMmt6ZvFwbyxYcLmqPuGJRDFvWx/Pqh1OqdRO5PvDh6ys5uDelSGLjwN4UXnpsIa9/NMXjxnbfQc1YNPeA2xeopkKPvlG1YnN9QXf4Og2OdSvj+PnrrdisDteHXlDMeZ+R6J0zaxdrV8bx3FsTi6UJTp7RhT07ThQJdwGYTDJ9BjarVOeigCAvUpJz3I4rThXf88ZTFJWP31rDnu1JCCEQEvj5W3jylbEENzr7ZdOjbxRhjX05mZRd5NhMJpmQUG+XemOh7U6Hyt+/7kZxqsXuWhRFJT/PzrKFh5h6RdcKP6f6yomELA7tO1VcTVNzadYvXXCYGdd2d7smukUwE6d14t8/9hWlzWoatGgTQuzBVHr0bYrsQRb6QuTieJY6Fwz7dyfz3acbyc2xFX3oS9p+dNgVUlNy2bTmWLHjLVqHcP8TwwmP8EOSXO0Eh49rwy13V66Ia+KlHd02cQ0GiXadw92yhZbMO8ieHUnY7Qo2W2HTkNQ8Pn1njdv1T702nvGXdiI0zJfwCD8uvaorBQXOYl9UZ56npxCVw6Gyb1dypZ5TfeVEUpZH5+x0qMTHpZV43WVXdePFdycxYJgrzCcEHN5/ii8+WMerTy7yGBq8ENFX+DoNin9+3+vm8DTNtQFnMhncQiA2q5NdWxMZMqp4q8DO3Zvw5qeXYrM5MRokpCqs8Hr0jWLa1d34Y9YuZFnC6VRp0z6Uux4a4nbu8kWH3exXVY24mDRysq3F7jC8vIzMuLZ7sVXr7/+3s9x2CUkQGt7wm4+fS5PIABQPxW0Go0SzliXvvWiaxqG9KaxbEVcsnm+zOjl+LIPVS2MZNaFdTZhcr9Advk6DIjUl1+Nxgyx53JmTJEFgSMkFS+Zq0tKfcGknRo5vS1JCFgGBXiXuBZTUNUkIUeJj5xIR6U9Sgnvh15k9gHNX+kaDxLjJHcpjfp2jKiq7tiVxYG8KgUFeDBrRkoBAL+x2lzjemX6x/oEWzBaDWzzeaJQZNaFkTfrF/xzgtx93eNy8tdsUNqw6qjt8HZ36Rpv2oaSl5rmFMIQk8PYxY7fnF/tQGwwSI2upOYXZYqRlm5LbEuZm20rMfw8K8SKolC+mM1x1cy9mvr7KPYNHaGiqK1RhNMmYzQZuuXtAmY1I6gN2u8Ibzy4m4VgmNqsTo1Hmz1920axFMHExaWhoNI0O5NZ7BvDNxxvJzytekSskwb2PDSOohEpkRVH5a/Zuty5a53J+SO5CRY/h6zQopl7ZFZNZ5ly/aTLLTLu6G4+/PIbwCH/MZhmLlxEvbyP/eWAQTaKqVyumsrzz0jIy0twbhZvMMnc8MLhcxVBde0byv6dH0Kpto2KZPariusGRDRLDx7blw29n0LNfw8hAWb7wMMfjMorCcQ6Hgt2mEHMwFUVRURWN40czeOXJRSQnZrmFdGRJsL+UQre8HFupd09ms6HBdKyqKvoKvwLYs3JRnQqWkPrhQC5GGjfx57m3JvLHzzs5tP8UgUFeTJ7Rpag36esfTyEpIQub1UGzFsF1Ip3siYRjGSQez/S4udqrX3SFROA6do3gwaeDuf+W393GczpUdmxK4NpyaOQciTnN5++u5dTJHGRZou/gZtx6z8BydYGqTtavPFJqZfQZnA4F1cMi3elU3aQazsXb14wsS55X+AIGjWhB7wHRFTG5waI7/HKQl5TK6hte59S6vQAEtItmyPePEdK9/uqfX8g0aRrAPY8O8/iYEIKm0YG1a1A5cAmgSYC7Y8vKrHh5v6KqJaYnOcuh2Jl4PJOXHl1QFP5yOlXWrzxK/JF0Xp05pcL2VIXypkR6cvZnCG5UcjjMYJCYeFkn5v1RfMNfNkjceGc/ho2+eD7HekinDFRFYf7Q/5Gyejeq3Ylqd5KxJ44Fw/+H9XTJqwodnXOJbhlUYupfVPPACo8XEOhFeGN3ETWDQaLf4LKbdHz78QaPG5hJCVkc2lc1HaCKMmJc23JtnkuleKvQsNKzkaZc0YVLr+pW1DmrUZgPdz00+KJy9qA7/DJJXrod6+lMtPNWTapDIea7hXVklU59QNM0CgocHsM05xMU7E2PPk09PrZ9YyKV6S19x/8G4+VtLCoqM1sMhIb7MuXysgutEuIzS3xs7u97K2xLVRg8oiXdekdiMskYjBIWLwOyLIqFliRJYDTLGI3uLstskYks465OCMGkyzrx8Y9X8PXv1/LOF9PoPaBhd6+qDHpIpwxyjp10c/YASoGN7NgTdWCRTn1g3co4Zn+/ndxsKyazgQmXdmTyjC6lNlM3mQ1uVcEA2dlW4mJO06ptxZq5GE0yfQc3J/bgKXz9zAwZ2YoBQ8un3WOxuNcsnKGk1NeaQpIl7n5kKPFx6Rzefwr/QAtdejRh4d/7WbkkFofdSffeTblkRmdefnwRDqe96DWUZEFgkDedupVPWlkIgcFQv5qO1ya6wy+DkJ5tEB6CpQZfL8IGdKwDi3Tqmu2bEvjuk41FG40F+Q7mzdmLpmpcelW3Eq/Lyihwc/bgWr3mZNsqZMPOLYl8/NZqnIWSCiazTGZ6Ab36R5fL4Y+d0oHfftjh8bGIGlKtLItmLYNp1tKVRpp4PBP/QC+uu60P3fs0LbqLefr1cXz54Xri49IRQKfuEdx6z8BSv2h1zqI7/DII7dOesIEdSVm3F6XAJZ0rmQxYQgNpccXwujVOp074Y9ZOjy0OF/y1n8mXdylxE7J7n6Yc3n/K7VqHQ6FV25Lz989HUVS+/HB9sXHsNoW003ksnLufaVd3L3OMS6Z1ZsGf+8jNKS4HbTAKxtZhsZaqanw1c72roQ0gywJZlnj8pTFEtwgmMiqQ59+aiLXAUSiLUT9c2InELDasPorDrtC7fzSt2jWqtZ4DFUGP4ZeD0f+8QpfHrsYnKhRLeBBtb5vE5M0fY/C6sFQIdcrH6VN5Ho87nSoFeSW36Rs6qhUhoT4YzxFyM5sNTJnRpUKibScSsjw2QXE6VLasL7kPwPm8+O4lRDT1x2iUMFtkjCaZGdf2KHd4pCbYuOYoW9cfdzW0sStYC5zk5dp5/5UVxfY5LF7GeuPsl84/xLMP/su8OXtZ8Nd+3nhuCd99uqlS+zI1Tf14xeo5stlEj2dvoMezN9S1KTr1gMioQGIPpbodt3gZ8S7MAvGE2WLkubcnsnzhYbZuOI6vr4nRk9rTrVdkhea3eBlRStgozs2xoapauUIcIaE+vDZzCgnHMsjJttGidQjePiXbX14yMwqY++tudm1LwsfHxLgpHRk4vEW5VrwrF8dgs7nvLeTm2jl+NKMo5FNfyMos4JdvtxbL8T8j1TBoeEvadgyrQ+vc0R2+jk4FufyGHrzzwrJiIRWTWWbGdd3LdLReXkYmXdaJSZd1qvT8oeG+NGkaQPzRdLc9gbxcGwv/3s/Eco4vhKiU/IKmaTgcKkZj8XaJudk2nv3fPHJzbCiKxmny+O6zTSTEZ3DVTb3KHNfp9JxsLwQeRdPqml3bkpAkCShum83uZNPaY/XO4VcppCOECBZCLBFCxBT+797pwXWeIoTYWfhvblXm1NGpa9p3CuehZ0fRsm0jzGYDjZv4c9u9A2u1PP/eR4d63ABWnBqL/jlQY/Nqmsb8v/Zx9/W/cvuVs3jg1jmsXxVX9PiyBYfIz3Oc1/3LydJ/D5KdZS1z/IHDWnjUtZENUr1b3UNh0ZiH73gBHlNI65qqrvAfB5Zpmva6EOLxwt8f83BegaZp3as4l45OvaF953Cee3NCnc0fEOSFEJ5b9+Xnlt2Xt7L8++c+/p69u6hiNTO9gM/fW8e2jcf574ND2Lc72WOBmcEoEx+XTpceTUodf9iYNmxaG098XDo2qxODUUKSBHc9NKReNinp3rsp36kb3Y4bjTIDhrWsA4tKp6oOfyowvPDn74GVeHb4OjoXNUkJmSyae4CTJ7Jp1ymcMRPb4V+FfrMms4HGkf4kJ2a7PdamQ8Xy+cuLqmrM89CPAGDrhgRmvrGqRB0eRVHLpQZqNMo88dIYdm8/wd6dJwgI8mLwyFYlKmHWNT6+Jv770GA+fWctQhKuIjwNpl7VtV7ekYiq7CQLITI1TQss/FkAGWd+P+88J7ATcAKva5r2Vwnj3Q7cDhAdHd0rPj6+0rbp6NQX9u48wQevrcTpcOXMG4wSFouRF9+dVOEeuudyYM9J3n15OQ6Hila4UWs0yTzz+niimnuMrlaJgnw7d1//q8f+veCKsxsM7iJlsixo1jKY596aWO021Rdyc2xs35yA06HSrVdklf6uVUUIsU3TNI/qeWU6fCHEUqCxh4eeAr4/18ELITI0TXN7pwkhIjVNSxJCtASWA6M0TTtS2ry9e/fWtm7dWqptOjr1HU3TeOj2P0lLLZ7KKQQMHN6S2+8fVKXxE45l8O8f+0hKyKRlmxAmXtaZ8Ah3jZ3qQFU17r3xN3JzKlYk1r5zGPc8OqxS/YJ1Kk5pDr/MkI6maaNLGThFCBGhaVqyECICOFXCGEmF/8cJIVYCPYBSHb6OzoVAVkYB2R7UMDUNNqw+yrW39sbHt/L1HFHNg7jzwcGlnpObbSPlZA6hYT5VCiNJkmDGdd354fNNpSpXnovFYmDMpA66s68nVDWGPxe4EXi98P+/zz+hMHMnX9M0mxCiETAIeLOK8+roNAjMXkaPG6sAqqLx1cwN3P/E8BqZW1VUfvxqC6uXxmI0yjgcCv0GNeeWewZUWvN+xLi2OJ0KP321tdjzKmkDGSEw1MNslYuVqv4lXgfGCCFigNGFvyOE6C2E+KrwnA7AViHELmAFrhj+/irOq6PTIPDyMtK1Z8mFVbu3JXksNKoO/v1zH2uXH8HpUCnId+B0qGxeH8/v/+dZQ6e8jJnUgRfemUSL1iGFYmQSnbtFFOndnIsQroYtOvWDKm3a1iR6DP/iRlVUNq2NZ93KOAxGiWGjW9O9T9N6qU9SFnm5du654VePMsqyQeLDb2fg61f9Mh333vibx9x3s8XA57OuqpbX0uFQkCWBJEv8MWsX8//Yh5AEUmFP+QefHkn7zuFVnken/FQphq+jU9tomsb7r67k4N6UotXv/l0nGTSiJTfe2a+Oras4Pr4m+g9pzobVR93CHmHhvjXi7AHy8zzn49tsTlRVQ5ar7vCN5yhzTru6G8NGt2bPzhNYLAa6926KxctY5Tl0qg89uKZT79i3K5mD+1KKhTpsNidrlh8ptXdpfeby63vg628uEk6TDRJmi4Fb7x1QY3O2LEGBs2l0YI0VMYWE+jB8TBv6D2mhO/t6iL7C16l37N5+wnNzDg327U6mSVTDayIf3MiH1z+aysrFMcQcOEVEpD+jJrYnNLz01nxV4ZpbevPaU4txOBRUVUNIAqNR4obb+9bYnHVBXq6dVUtiOLA3hcYRfoye1I7wiLrR9K/v6A5fp97h62fCYJRweijg8SlFjbK+4+tn5pLpnWttvhatQ3jh3YnMm7OXY0fSaRodyCXTO1dLUdaJxCxOn8olqnlQnVbBZmYU8NyD/5KXZ8dhV9gnC1YuieHBp0fSoYun8qGLG93h69Q7Bg1vxT+/eeirKqBn36jaN6gBExEZwH/uq1px17nk59l5/9UVHI1JQzZIOB0Kg0a04sY7+9VJ16k/Z+0iJ9taVP2rKBqKovDVzPW8/fllDXKTvybRY/g69Y6QUB/++/AQLF4GvLyMWLwM+PqZefi5UXpcuI75+uMNHDl8GrtdoSDfgcOhsn5VHEv/PVgn9uzcmuhR6iEr00pGunvB28WOvsLXqZf07BvFzO+vIObAKWSDRJv2ofVSLfFiwmZ1sHNzoptmvd2msPjfg3XSGtFi8ezCNFXDXE86YtUn9E+QTr3FZJLp1C2C9p3CLwhnn5aax4E9J8nyILXQELB5UMk8Q2mtHWuS0ZPae9TPB/j9px3l0uC/mNC/AnV0qhlN0yjId2A0yRiNMnabk0/eWcPeHckYjBIOh8KQEa244Y6+SDX8RWa3K9V2l+TnbyYoxJvUlNxix4WAzj3qppp21IR2xMels2FVHIqiFdU5OJ0qqxbHsnNLIq/OnIKXHgoEdIevo1OtHNybwjefbOB0Si5CEvQd2AxJEuzd6WoMcqY5yLpVcYRG+FWp1WFZbN+UwOfvr0Ug0NAwGGQeeGo4bdpXru2eEIJb7u7Pe6+sxFmY6umSejZw+XU9qtn68tp09ofzVQMURSU3x8ba5UcYM6l97RtXD9GlFXR0qokTiVk8/9D8YgVjBqOE4lQ9CosFhXjz/tfTa8SWtNQ8Hrv7bxz24mEYi5eRD7+djtlS+RXvicQsVzOXpGzadgqrcjOXqrBlfTxffrC+VD2i3gOiufexYbVoVd2iSyvo6NQCC//e79be7/xagnMpSfqgOli38giaB+0eTdPYvimRAcNaVHrsJk0DuPmu/lUxr9pYtTS2VGdvMEg0blIz/QEaIg1/J0xHp55wIjHLo0Cax1RwAe06VS60Uh5yc+xu2TTgkmTOq8EvmtpG8fAcz0WWpVptLl/f0R2+jk410aZ9mEedeUmWMJqkosIk2SDhZTFy9U0e77qrha49m2D2lLIooFO3C6cCdfCIViWmX4aG+fLwc6NoFFZz8hUNDT2ko6NTTYyd3J6Viw+jKGdj9iazTP8hLRg/tQML/txPUkImrdqFMn5Khxp1RB27utJZz1UcNZsNDBrRkojIhqdFVBL9hzZn09pjLrE9qxODUUIIuPmuAQwc1kKvtD0PfdNWR6caSUnOZvb3O9i/OxlvbyNjJndg3CXtazz90hMXUk+B0tA0jf27T7J35wn8/C0MGNaiTvV96poqNTGvK3SHr6Ojo1NxSnP4egxfR+ciQNM0TiRkkXAsw+PGss7FgR7D19G5wDl+LIMPX1tJVmYBQgi8vIzc/chQ2nasuSwhnfqJvsLX0bmAsdmcvP70YlJTcrHbFGxWJ5kZBbz94jJdZ+YiRHf4OjoXMNs3JXjMVVdVjQ2rjtaBRTp1ie7wdXQuYLIyCzwWYDnsCpkZ+XVgkU5dojt8HZ0LmHYdw5Fk9zRMs8VA+04XTgGWTvnQHb6OzgVMi9YhdOnRBPM5mvEms0x0iyC61JGksU7doWfp6Ohc4NzzyFBWL4tl5ZJYVEVl0IhWjBzftk6KwXTqFt3h6+hc4EiyxPCxbRk+VhcRu9jRv+J1dHR0LhJ0h6+jo6NzkaCHdHR0dGqNtNQ81iw/QmZ6Pp26RdCzX9QF0aC+oaA7fB0dnVph784TfPDaSlRVw+lQWb/qKE2iAnjy5bGYStC016le9K9WHR2dGkdVVD59dy12m1LU9tFmdZIUn8nyhYfr2LqLB93h6+jo1DgJ8Zk4z2uoDmC3K6zXJR5qDd3h6+jo1DgGo0RJvTdMJtnjcZ3qR3f4Ojo6NU6TpgEEBHnDeSoPZrNBbzJei+gOX0dHp8YRQvDAU8Px8zNj8TJgMsuYTDK9B0YzYFiLujbvoqFKW+NCiMuB54EOQF9N0zz2JBRCjAc+AGTgK03TXq/KvDo6Og2PyKhA3v96Oru3nyArs4B2HcNpEnXhNFRvCFQ1F2ovMA34vKQThBAy8DEwBkgEtggh5mqatr+Kc+vo6DQwDEaZnv2i6tqMi5YqOXxN0w6A63atFPoCsZqmxRWe+wswFdAdvo6Ojk4tUhsx/Egg4ZzfEwuPuSGEuF0IsVUIsTU1NbUWTNPR0dG5eChzhS+EWAp46pTwlKZpf1enMZqmfQF8AdC7d2/POVw6Ojo6OpWiTIevadroKs6RBJwbtGtaeExHR0dHpxapjZDOFqCNEKKFEMIEXAXMrYV5dXR0dHTOQZRU/Vaui4W4DJgJhAKZwE5N08YJIZrgSr+cWHjeROB9XGmZ32ia9ko5xk4F4gt/bQScrrShtUtDsVW3s3rR7axeGoqdUP9sbaZpWqinB6rk8GsLIcRWTdN617Ud5aGh2KrbWb3odlYvDcVOaFi26pW2Ojo6OhcJusPX0dHRuUhoKA7/i7o2oAI0FFt1O6sX3c7qpaHYCQ3I1gYRw9fR0dHRqToNZYWvo6Ojo1NFdIevo6Ojc5FQLx2+EOJyIcQ+IYQqhCgx3UkIcUwIsUcIsVMI4VGauaapgK3jhRCHhBCxQojHa9PGwvmDhRBLhBAxhf8HlXCeUvh67hRC1FqBXFmvjxDCLISYXfj4JiFE89qy7Tw7yrLzJiFE6jmv4W11YOM3QohTQoi9JTwuhBAfFj6H3UKInrVtY6EdZdk5XAiRdc5r+Wxt21hoR5QQYoUQYn/hZ/1+D+fUi9e0TDRNq3f/cOnrtwNWAr1LOe8Y0Ki+24qr4OwI0BIwAbuAjrVs55vA44U/Pw68UcJ5uXXwGpb5+gB3AZ8V/nwVMLue2nkT8FFt23aeDUOBnsDeEh6fCCzA1X+qP7Cpnto5HJhXl69loR0RQM/Cn/2Awx7+7vXiNS3rX71c4WuadkDTtEN1bUd5KKetRRLRmqbZgTMS0bXJVOD7wp+/By6t5flLozyvz7n2/w6MEmXoctcA9eHvWCaapq0G0ks5ZSrwg+ZiIxAohIioHevOUg476wWapiVrmra98Occ4ADuir/14jUti3rp8CuABiwWQmwTQtxe18aUQrklomuQcE3Tkgt/PgmEl3CepVCieqMQ4tLaMa1cr0/ROZqmOYEsIKRWrPNgQyEl/R2nF97W/y6EqI/dPurD+7G8DBBC7BJCLBBCdKprYwpDiT2ATec91CBe06p2vKo01SS7PFjTtCQhRBiwRAhxsHDVUK3UpkR0VSjNznN/0TRNE0KUlI/brPA1bQksF0Ls0TTtSHXbegHzDzBL0zSbEOIOXHclI+vYpobKdlzvx9xCPa6/gDZ1ZYwQwheYAzygaVp2XdlRFerM4WtVl11G07Skwv9PCSH+xHXLXe0OvxpsrRWJ6NLsFEKkCCEiNE1LLrzVPFXCGGde0zghxEpcq5madvjleX3OnJMohDAAAUBaDdt1PmXaqWnauTZ9hWvvpL7RICTLz3WqmqbNF0J8IoRopGlarQuVCSGMuJz9T5qm/eHhlAbxmjbYkI4QwkcI4XfmZ2Asrh679ZH6IBE9F7ix8OcbAbc7EyFEkBDCXPhzI2AQtdOKsjyvz7n2zwCWa4W7ZbVImXaeF7edgiveW9+YC9xQmFnSH8g6J9xXbxBCND6zTyOE6IvLX9X2lzyFNnwNHNA07d0STmsQr2md7xp7+gdchisGZgNSgEWFx5sA8wt/bokrS2IXsA9XeKVe2qqd3cU/jGu1XOu24op3LwNigKVAcOHx3rikrAEGAnsKX9M9wK21aJ/b6wO8CEwp/NkC/AbEApuBlnX09y7LztcK34+7gBVA+zqwcRaQDDgK35u3AncCdxY+LoCPC5/DHkrJhKtjO+8557XcCAysIzsH49ov3A3sLPw3sT6+pmX906UVdHR0dC4SGmxIR0dHR0enYugOX0dHR+ciQXf4Ojo6OhcJusPX0dHRuUjQHb6Ojo7ORYLu8HV0dHQuEnSHr6Ojo3OR8P8bHnaTn6HLoQAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "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": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "L = 104.605395, acc = 0.500000\n",
- "L = 49.016934, acc = 0.835000\n",
- "L = 39.837293, acc = 0.855000\n",
- "L = 38.941325, acc = 0.850000\n",
- "L = 38.529515, acc = 0.845000\n",
- "L = 37.909320, acc = 0.860000\n",
- "L = 36.647360, acc = 0.865000\n",
- "L = 33.967945, acc = 0.885000\n",
- "L = 29.328960, acc = 0.910000\n",
- "L = 23.732502, acc = 0.935000\n",
- "L = 18.992955, acc = 0.945000\n",
- "L = 15.867548, acc = 0.955000\n",
- "L = 13.954345, acc = 0.960000\n",
- "L = 12.723923, acc = 0.965000\n",
- "L = 11.862862, acc = 0.965000\n",
- "L = 11.206181, acc = 0.970000\n",
- "L = 10.665492, acc = 0.970000\n",
- "L = 10.200185, acc = 0.970000\n",
- "L = 9.800324, acc = 0.965000\n",
- "L = 9.462811, acc = 0.970000\n"
- ]
- }
- ],
- "source": [
- "# use the NN model and training\n",
- "nn = NN_Model([2, 8, 7, 2])\n",
- "nn.init_weight()\n",
- "nn.backpropagation(X, t, 2000)\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "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": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[0.01103025 0.98838025]\n",
- " [0.13185941 0.86852082]\n",
- " [0.98375489 0.01562161]\n",
- " [0.01149807 0.98788055]\n",
- " [0.00798938 0.99154954]\n",
- " [0.95984579 0.0412429 ]\n",
- " [0.02283659 0.97733521]\n",
- " [0.97419109 0.02667465]\n",
- " [0.0302959 0.96889168]]\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
- }
|