Lesson13.2拟合度与欠拟合.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesson 13.2 模型拟合度与欠拟合模型的结构调整策略\n",
    "\n",
    "## 一、模型拟合度概念介绍与实验\n",
    "\n",
    "### 1.测试集的“不可知”悖论\n",
    "\n",
    "- 根据测试集调整模型的话，测试集就变成了训练集\n",
    "- 测试集是严格不能带入训练的数据集，在实际建模过程中我们可以先把测试集切分出来，然后“假装这个数据集不存在”，在剩余的数据集中划分**训练集和验证集**，把训练集带入模型进行运算，再把验证集放在训练好的模型中进行运行，观测运行结果，再进行模型调整。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.模型拟合度概念与实验\n",
    "\n",
    "&emsp;&emsp;模型如果能很好的捕捉总体规律，就能够有较好的优化问题中，最基础的也是最核心的问题，就是关于模型拟合程度的探讨与未知数据的预测效果。但限制模型捕捉总体规律的原因主要有两点：\n",
    "- 其一，样本数据能否很好的反应总体规律      \n",
    "&emsp;&emsp;如果样本数据本身无法很好的反应总体规律，那建模的过程就算捕捉到了规律可能也无法适用于未知数据。 \n",
    "- 其二，样本数据能反应总体规律，但模型没有捕捉到     \n",
    "&emsp;&emsp;如果数据能反应总体规律而模型效果不佳，则核心原因就在模型本身了。导致模型在测试集上效果不好的原因其实也主要有两点\n",
    "  - 一是模型没捕捉到训练集上数据的规律，\n",
    "  - 二则是模型过分捕捉训练集上的数据规律，导致模型捕获了大量训练集独有的、无法适用于总体的规律（局部规律），而测试集也是从总体中来，这些规律也不适用于测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设计随机数种子\n",
    "np.random.seed(123)\n",
    "\n",
    "# 创建数据\n",
    "n_dots = 20\n",
    "x = np.linspace(0, 1, n_dots)                        # 从0到1，等宽排布的20个数\n",
    "y = np.sqrt(x) + 0.2*np.random.rand(n_dots) - 0.1    \n",
    "\n",
    "# 给定 X,y的关系，在加以干扰"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.63440096, 0.84943179, 0.72445532, 0.61102351, 0.72244338,\n",
       "       0.32295891, 0.36178866, 0.22826323, 0.29371405, 0.63097612])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其中，x是一个0到1之间等距分布20个点组成的ndarray，$y=\\sqrt{x}+r$，其中r是人为制造的随机噪声，在[-0.1,0.1]之间服从均匀分布。\n",
    "\n",
    "然后我们借助numpy的polyfit函数来进行多项式拟合，polyfit函数会根据设置的多项式阶数，在给定数据的基础上利用最小二乘法进行拟合，并返回拟合后各阶系数。\n",
    "- `polyfit(x,y,最高次)`\n",
    "-  `np.poly1d(np.polyfit(x, y0, 2))`逆向构造多项式方程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.00000000e+00, -2.00019564e-18, -3.29090297e-17])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y0 = x ** 2\n",
    "np.polyfit(x, y0, 2)\n",
    "\n",
    "# polyfit(x,y,最高次)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   2\n",
      "1 x - 2e-18 x - 3.291e-17\n"
     ]
    }
   ],
   "source": [
    "# 该系数可用ploy1d逆向构造多项式方程\n",
    "p = np.poly1d(np.polyfit(x, y0, 2))\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "能够看到多项式结构基本和原多项式保持一致，此时生成的p对象相当于是一个多项式方程，可通计算输入参数的多项式输出结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来，进行多项式拟合。分别利用1阶x多项式、3阶x多项式和10阶x多项式来拟合y。并利用图形观察多项式的拟合度，首先我们可定义一个辅助画图函数，方便后续我们将图形画于一张画布中，进而方便观察"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \n",
    "def plot_polynomial_fit(x, y, deg):\n",
    "  \n",
    "    p = np.poly1d(np.polyfit(x, y, deg))\n",
    "    t = np.linspace(0, 1, 200)\n",
    "    plt.plot(x, y, 'r.', t, p(t), '-', t, np.sqrt(t), 'r--')\n",
    "\n",
    "#   其中，t为[0,1]中等距分布的100个点，\n",
    "# 而p是deg参数决定的多项式回归拟合方程，p(t)即为拟合方程x输入t值时多项式输出结果\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_polynomial_fit(x, y, 3)\n",
    "\n",
    "# 点的分布就是真实值\n",
    "# 拟合结果是蓝色，红色虚线是X和y的真实关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 3600x800 with 0 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Under Fitting')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fitting')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Over Fitting')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3600x800 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#————————————————————————将1阶拟合、3阶拟合和10阶拟合绘制在一张图中————————————————————————————\n",
    "plt.figure(figsize=(18, 4), dpi=200)\n",
    "titles = ['Under Fitting', 'Fitting', 'Over Fitting']\n",
    "\n",
    "for index, deg in enumerate([1, 3, 10]):\n",
    "    plt.subplot(1, 3, index + 1)\n",
    "    plot_polynomial_fit(x, y, deg)\n",
    "    plt.title(titles[index], fontsize=20)\n",
    "\n",
    "\n",
    "# 一阶: 欠拟合 训练误差和泛化误差都很大\n",
    "# 10阶：过拟合 训练误差很小，但是泛化误差很大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因此，我们有基本结论如下：      \n",
    "- 模型欠拟合：训练集上误差较大\n",
    "- 模型过拟合：训练集上误差较小，但测试集上误差较大\n",
    "\n",
    "模型越复杂，越有能力捕捉训练集上的规律，因此如果模型欠拟合，我们可以通过提高模型复杂度来进一步捕捉规律，但同时也会面临模型过于复杂而导致过拟合的风险。\n",
    "\n",
    "<img src=\"https://i.loli.net/2021/02/06/kSiX5adUTybOePf.jpg\" alt=\"44\" style=\"zoom:50%;\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、模型欠拟合实例\n",
    "\n",
    "首先我们来讨论模型欠拟合时，通过提升模型复杂度提升模型效果的基本方法。当然，从神经网络整体模型结构来看，提升复杂度只有两种办法\n",
    "- 一是修改激活函数，在神经元内部对加权求和汇总之后的值进行更加复杂的处\n",
    "- 另一种方法则是添加隐藏层，包括隐藏层层数和每一层隐藏层的神经元个数。\n",
    "  接下来我们通过一些列实验来查看相关效果。\n",
    "\n",
    "&emsp;&emsp;还是多元线性回归的例子，如果是高次项方程，通过简单的线性网络拟合就会出现欠拟合的情\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 构建数据集  \n",
    "创建一个数据满足$y = 2x_1^2 - x_2^2$方程的分布数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1c0c05fefd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c0ce7cccd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c0ce8b0ac0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置随机数种子\n",
    "torch.manual_seed(420)  \n",
    "\n",
    "# 创建最高项为2的多项式回归数据集\n",
    "features, labels = tensorGenReg(w=[2, -1], bias=False, deg=2)\n",
    "\n",
    "# 绘制图像查看数据分布\n",
    "plt.subplot(121)\n",
    "plt.scatter(features[:, 0], labels)\n",
    "plt.subplot(122)\n",
    "plt.scatter(features[:, 1], labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.0070,  0.5044],\n",
       "        [ 0.6704, -0.3829],\n",
       "        [ 0.0302,  0.3826],\n",
       "        ...,\n",
       "        [-0.9164, -0.6087],\n",
       "        [ 0.7815,  1.2865],\n",
       "        [ 1.4819,  1.1390]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 进行数据集切分与加载\n",
    "train_loader, test_loader = split_loader(features, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 训练模型  \n",
    "  定义简单线性回归模型,没有设置激活函数, y = X*w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LR_class(nn.Module):                                         # 没有激活函数\n",
    "    def __init__(self, in_features=2, out_features=1):       # 定义模型的点线结构\n",
    "        super(LR_class, self).__init__()\n",
    "        self.linear = nn.Linear(in_features, out_features)\n",
    "        \n",
    "    def forward(self, x):                                    # 定义模型的正向传播规则\n",
    "        out = self.linear(x)             \n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1c0c05fefd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0cec28e50>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0cec290f0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c0ce97a1d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#————————————————————————————训练模型————————————————————————————\n",
    "# 设置随机数种子\n",
    "torch.manual_seed(420)  \n",
    "\n",
    "# 实例化模型\n",
    "LR = LR_class()\n",
    "\n",
    "train_l = []           # 列表容器，存储训练误差\n",
    "test_l = []            # 列表容器，存储测试误差\n",
    "\n",
    "num_epochs = 20\n",
    "\n",
    "# 执行循环\n",
    "for epochs in range(num_epochs):\n",
    "    fit(net = LR,\n",
    "        criterion = nn.MSELoss(),\n",
    "        optimizer = optim.SGD(LR.parameters(), lr = 0.03),\n",
    "        batchdata = train_loader,\n",
    "        epochs = epochs)\n",
    "    train_l.append(mse_cal(train_loader, LR).detach().numpy())\n",
    "    test_l.append(mse_cal(test_loader, LR).detach().numpy())\n",
    "    \n",
    "# 绘制图像，查看MSE变化情况\n",
    "plt.plot(list(range(num_epochs)), train_l, label='train_mse')\n",
    "plt.plot(list(range(num_epochs)), test_l, label='test_mse')\n",
    "plt.legend(loc = 1)\n",
    "\n",
    "# 对比此前线性回归的MSE，上述模型效果较差，且训练误差和测试误差均较大（此前是0.0001），\n",
    "# 模型存在欠拟合情况，接下来考虑增加模型复杂程度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以根据上述过程定义一个记录、对比模型训练过程训练集、测试集误差变化情况的函数，并写入模块文件。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_train_test(model, \n",
    "                     train_data,\n",
    "                     test_data,\n",
    "                     num_epochs = 20, \n",
    "                     criterion = nn.MSELoss(), \n",
    "                     optimizer = optim.SGD, \n",
    "                     lr = 0.03, \n",
    "                     cla = False, \n",
    "                     eva = mse_cal):\n",
    "    \"\"\"模型误差测试函数：\n",
    "    \n",
    "    :param model_l：模型\n",
    "    :param train_data：训练数据\n",
    "    :param test_data: 测试数据   \n",
    "    :param num_epochs：迭代轮数\n",
    "    :param criterion: 损失函数\n",
    "    :param lr: 学习率\n",
    "    :param cla: 是否是分类模型\n",
    "    :return：MSE列表\n",
    "    \"\"\"\n",
    "    \n",
    "    # 模型评估指标列表\n",
    "    train_l = []\n",
    "    test_l = []\n",
    "    # 模型训练过程\n",
    "    for epochs in range(num_epochs):\n",
    "        fit(net = model, \n",
    "            criterion = criterion, \n",
    "            optimizer = optimizer(model.parameters(), lr = lr), \n",
    "            batchdata = train_data, \n",
    "            epochs = epochs, \n",
    "            cla = cla)\n",
    "        train_l.append(eva(train_data, model).detach())\n",
    "        test_l.append(eva(test_data, model).detach())\n",
    "    return train_l, test_l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1c0c05fefd0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0ce441120>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0ce443f40>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c0ce441240>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#————————————————————————————测试模型效果————————————————————————————\n",
    "\n",
    "# 设置随机数种子\n",
    "torch.manual_seed(420)  \n",
    "\n",
    "# 实例化模型\n",
    "LR = LR_class()\n",
    "\n",
    "# 模型训练\n",
    "train_l, test_l = model_train_test(LR, \n",
    "                                   train_loader,\n",
    "                                   test_loader,\n",
    "                                   num_epochs = 20, \n",
    "                                   criterion = nn.MSELoss(), \n",
    "                                   optimizer = optim.SGD, \n",
    "                                   lr = 0.03, \n",
    "                                   cla = False, \n",
    "                                   eva = mse_cal)\n",
    "\n",
    "# 绘制图像，查看MSE变化情况\n",
    "plt.plot(list(range(num_epochs)), train_l, label='train_mse')\n",
    "plt.plot(list(range(num_epochs)), test_l, label='test_mse')\n",
    "plt.legend(loc = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#————————————————————提升模型复杂度：增加神经网络的层数——————————————————\n",
    "class LR_class1(nn.Module):\n",
    "    def __init__(self, in_features=2, n_hidden=4, out_features=1):       # 此处隐藏层设置四个激活函数\n",
    "        super(LR_class1, self).__init__()\n",
    "        self.linear1 = nn.Linear(in_features, n_hidden)\n",
    "        self.linear2 = nn.Linear(n_hidden,out_features)\n",
    "\n",
    "    def forward(self, x):                                    \n",
    "        z1 = self.linear1(x)\n",
    "        out = self.linear2(z1)             \n",
    "        return out    \n",
    "\n",
    "#这里我们构建了一个两层都是线性层的神经网络，\n",
    "# 并且没有加入激活函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1c0c05fefd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 设置随机数种子\n",
    "torch.manual_seed(420)  \n",
    "\n",
    "# 实例化模型\n",
    "LR1 = LR_class1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1c0c05fefd0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0ceed6770>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0ceed65c0>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c0ce3529e0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置随机数种子\n",
    "torch.manual_seed(420)  \n",
    "\n",
    "# 实例化模型\n",
    "LR1 = LR_class1()\n",
    "\n",
    "# 模型训练\n",
    "train_l, test_l = model_train_test(LR1, \n",
    "                                   train_loader,\n",
    "                                   test_loader,\n",
    "                                   num_epochs = 20, \n",
    "                                   criterion = nn.MSELoss(), \n",
    "                                   optimizer = optim.SGD, \n",
    "                                   lr = 0.03, \n",
    "                                   cla = False, \n",
    "                                   eva = mse_cal)\n",
    "\n",
    "# 绘制图像，查看MSE变化情况\n",
    "plt.plot(list(range(num_epochs)), train_l, label='train_mse')\n",
    "plt.plot(list(range(num_epochs)), test_l, label='test_mse')\n",
    "plt.legend(loc = 1)\n",
    "\n",
    "\n",
    "# 我们发现，结果没有显著提升，但模型稳定性却有所提升\n",
    "# 平稳性比较好的话，可信度比较高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于叠加线性层的神经网络模型来说，由于模型只是对数据仿射变换，**因此并不能满足拟合高次项的目的**。\n",
    "\n",
    "也就是说，在增加模型复杂度的过程中，首先需要激活函数的配合，然后再是增加模型的层数和每层的神经元个数\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、激活函数性能比较\n",
    "\n",
    "### 1.常用激活函数对比\n",
    "\n",
    "输出层的激活函数和隐藏层的激活函数应该分开对待\n",
    "- 隐藏层的激活函数是为了对数据进行非线性变换\n",
    "- 输出层的激活函数一般都是为了满足某种特定的输出结果所设计的，\n",
    "  - 如softmax、Sigmoid等\n",
    "  - 进行回归类问题实验，在输出层加入激活函数反而会影响输出结果。\n",
    "\n",
    "- 创建模型类\n",
    " <center><img src=\"https://i.loli.net/2021/02/26/fOKePkACV54Fya9.jpg\" alt=\"66\" style=\"zoom:40%;\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#————————————————————————————3个激活函数+不加激活函数————————————————————————————\n",
    "# 只有隐藏层的激活函数有区别\n",
    "\n",
    "# Sigmoid激活函数\n",
    "class Sigmoid_class1(nn.Module):                                   \n",
    "    def __init__(self, in_features=2, n_hidden=4, out_features=1, bias = True):       \n",
    "        super(Sigmoid_class1, self).__init__()\n",
    "        self.linear1 = nn.Linear(in_features, n_hidden, bias=bias)\n",
    "        self.linear2 = nn.Linear(n_hidden, out_features, bias=bias)\n",
    "        \n",
    "    def forward(self, x):                                   \n",
    "        z1 = self.linear1(x)\n",
    "        p1 = torch.sigmoid(z1)       # 区别              \n",
    "        out = self.linear2(p1)\n",
    "        return out\n",
    "\n",
    "# tanh激活函数\n",
    "class tanh_class1(nn.Module):                                   \n",
    "    def __init__(self, in_features=2, n_hidden=4, out_features=1, bias = True):       \n",
    "        super(tanh_class1, self).__init__()\n",
    "        self.linear1 = nn.Linear(in_features, n_hidden, bias=bias)\n",
    "        self.linear2 = nn.Linear(n_hidden, out_features, bias=bias)\n",
    "        \n",
    "    def forward(self, x):                                   \n",
    "        z1 = self.linear1(x)\n",
    "        p1 = torch.tanh(z1)                   \n",
    "        out = self.linear2(p1)\n",
    "        return out\n",
    "\n",
    "# ReLU激活函数\n",
    "class ReLU_class1(nn.Module):                                   \n",
    "    def __init__(self, in_features=2, n_hidden=4, out_features=1, bias = True):       \n",
    "        super(ReLU_class1, self).__init__()\n",
    "        self.linear1 = nn.Linear(in_features, n_hidden, bias=bias)\n",
    "        self.linear2 = nn.Linear(n_hidden, out_features, bias=bias)\n",
    "        \n",
    "    def forward(self, x):                                   \n",
    "        z1 = self.linear1(x)\n",
    "        p1 = torch.relu(z1)                  \n",
    "        out = self.linear2(p1)\n",
    "        return out\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 上述三个激活函数还能使用F.方法，但推荐使用torch.方法执行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-1.3191, -1.2474,  1.8058,  1.4594, -0.1661])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = torch.randn(5)\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.0000, 0.0000, 1.8058, 1.4594, 0.0000])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "tensor([0.0000, 0.0000, 1.8058, 1.4594, 0.0000])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.relu(t)\n",
    "\n",
    "torch.relu(t)\n",
    "\n",
    "# 推荐使用torch.方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1c0c05fefd0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#————————————————————————————实例化模型————————————————————————————\n",
    "torch.manual_seed(420)  \n",
    "\n",
    "LR1 = LR_class1()\n",
    "sigmoid_model1 = Sigmoid_class1()\n",
    "tanh_model1 = tanh_class1()\n",
    "relu_model1 = ReLU_class1()\n",
    "\n",
    "model_l = [LR1, sigmoid_model1, tanh_model1, relu_model1]           # 将实例化后模型放在一个列表容器中\n",
    "name_l = ['LR1', 'sigmoid_model1', 'tanh_model1', 'relu_model1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#————————————————————————————定义核心参数————————————————————————————\n",
    "num_epochs = 30\n",
    "lr = 0.03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#——————————————————————定义训练集、测试集MSE存储张量——————————————————————\n",
    "mse_train = torch.zeros(len(model_l), num_epochs)\n",
    "mse_test = torch.zeros(len(model_l), num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#————————————————————————————训练模型————————————————————————————\n",
    "# [LR1, sigmoid_model1, tanh_model1, relu_model1]  对4种激活函数都进行训练 \n",
    "\n",
    "for epochs in range(num_epochs):\n",
    "    for i, model in enumerate(model_l):\n",
    "        fit(net = model, \n",
    "            criterion = nn.MSELoss(), \n",
    "            optimizer = optim.SGD(model.parameters(), lr = lr), \n",
    "            batchdata = train_loader, \n",
    "            epochs = epochs)\n",
    "        mse_train[i][epochs] = mse_cal(train_loader, model).detach()\n",
    "        mse_test[i][epochs] = mse_cal(test_loader, model).detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[10.8300,  9.6960,  9.5622,  9.5223,  9.5458,  9.5954,  9.5359,  9.6136,\n",
       "          9.5801,  9.5964,  9.6088,  9.5391,  9.5435,  9.6457,  9.5339,  9.6280,\n",
       "          9.5463,  9.5790,  9.5291,  9.6231,  9.6323,  9.5592,  9.7235,  9.6189,\n",
       "          9.6135,  9.5363,  9.6436,  9.5781,  9.5489,  9.5466],\n",
       "        [10.7485,  9.6246,  9.2736,  7.1681,  4.2412,  3.0897,  2.8030,  2.6019,\n",
       "          2.5310,  2.4331,  2.4021,  2.4125,  2.4407,  2.3779,  2.3087,  2.5533,\n",
       "          2.3416,  2.3221,  2.6079,  2.3055,  2.3146,  2.3081,  2.3664,  3.1914,\n",
       "          2.2998,  2.4680,  2.2942,  2.6507,  2.3046,  2.4291],\n",
       "        [10.7273,  9.0404,  6.0416,  5.0775,  4.6106,  4.4453,  4.3490,  4.7661,\n",
       "          4.3492,  4.2997,  4.2669,  4.3732,  4.2767,  4.2490,  4.4358,  4.3139,\n",
       "          4.2618,  4.4504,  4.3120,  4.3259,  4.2744,  4.4678,  4.2618,  4.3907,\n",
       "          4.2713,  4.2294,  4.2711,  4.2295,  4.2537,  4.2408],\n",
       "        [10.2269,  1.5506,  0.6582,  0.6659,  0.3920,  0.3590,  0.3089,  0.3818,\n",
       "          0.3591,  0.2564,  0.3204,  0.5053,  0.3461,  0.2730,  0.2482,  0.2833,\n",
       "          0.3408,  0.2560,  0.2641,  0.2606,  0.2738,  0.2501,  0.2472,  0.2950,\n",
       "          0.2495,  0.4986,  0.2589,  0.2819,  0.2655,  0.2433]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "tensor([[13.2264, 11.4769, 11.3752, 11.3732, 11.2853, 11.2702, 11.2457, 11.5075,\n",
       "         11.2162, 11.7994, 11.5239, 11.4593, 11.3358, 11.6676, 11.3245, 11.5025,\n",
       "         11.3631, 11.2606, 11.2952, 11.7086, 11.7071, 11.2788, 11.7602, 11.4373,\n",
       "         11.1840, 11.3217, 11.4141, 11.3705, 11.3440, 11.3679],\n",
       "        [13.1832, 11.2691, 10.6894,  8.7440,  5.0175,  3.4692,  2.7936,  2.4461,\n",
       "          2.3718,  2.0881,  2.0349,  2.0134,  2.0608,  1.9349,  1.8626,  2.1943,\n",
       "          1.7982,  1.8370,  2.1317,  1.8005,  1.8485,  1.8082,  1.9161,  2.7955,\n",
       "          1.8118,  2.0475,  1.7516,  2.2262,  1.7782,  2.0306],\n",
       "        [12.6266, 10.7718,  7.4650,  6.6652,  5.8065,  5.9088,  5.7766,  6.0403,\n",
       "          5.8673,  5.8220,  5.7869,  5.9660,  5.6660,  5.7603,  5.7065,  5.9467,\n",
       "          5.6148,  5.8969,  5.6322,  5.8451,  5.7456,  5.6308,  5.6584,  6.0047,\n",
       "          5.5412,  5.6194,  5.8680,  5.6615,  5.8319,  5.6611],\n",
       "        [12.2868,  1.6156,  0.9103,  0.8671,  0.5498,  0.4631,  0.4497,  0.5996,\n",
       "          0.5352,  0.3907,  0.4913,  0.5368,  0.4611,  0.3942,  0.3699,  0.3813,\n",
       "          0.4859,  0.3613,  0.3952,  0.3489,  0.4129,  0.3535,  0.3765,  0.3923,\n",
       "          0.4167,  0.7108,  0.3665,  0.3850,  0.3884,  0.3655]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_train\n",
    "mse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0cf1af2b0>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0cf1dc1f0>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0cf1dc6d0>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0cf1dc9a0>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c0cf1afca0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mse_train')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#————————————————————————————绘制图像观测结果————————————————————————————\n",
    "\n",
    "# 训练误差\n",
    "for i, name in enumerate(name_l):\n",
    "    plt.plot(list(range(num_epochs)), mse_train[i], label=name)\n",
    "plt.legend(loc = 1)\n",
    "plt.title('mse_train')\n",
    "\n",
    "\n",
    "# ReLU激活函数效果明显更好\n",
    "# ReLU的收敛速度最快"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 激活函数性能简介与应用场景\n",
    "\n",
    "ReLU激活函数是目前使用面最广、效果也相对更好的一种激活函数，但这并不代表tanh和Sigmoid激活函数就没有应用场景（比如RNN、LSTM模型仍然偏爱tanh和Siamoid）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#————————————————————————————封装上述函数————————————————————————————\n",
    "def model_comparison(model_l, \n",
    "                     name_l, \n",
    "                     train_data,\n",
    "                     test_data,\n",
    "                     num_epochs = 20, \n",
    "                     criterion = nn.MSELoss(), \n",
    "                     optimizer = optim.SGD, \n",
    "                     lr = 0.03, \n",
    "                     cla = False, \n",
    "                     eva = mse_cal):\n",
    "    \"\"\"模型对比函数：\n",
    "    \n",
    "    :param model_l：模型序列\n",
    "    :param name_l：模型名称序列\n",
    "    :param train_data：训练数据\n",
    "    :param test_data：测试数据    \n",
    "    :param num_epochs：迭代轮数\n",
    "    :param criterion: 损失函数\n",
    "    :param lr: 学习率\n",
    "    :param cla: 是否是分类模型\n",
    "    :return：MSE张量矩阵 \n",
    "    \"\"\"\n",
    "    # 模型评估指标矩阵\n",
    "    train_l = torch.zeros(len(model_l), num_epochs)\n",
    "    test_l = torch.zeros(len(model_l), num_epochs)\n",
    "    # 模型训练过程\n",
    "    for epochs in range(num_epochs):\n",
    "        for i, model in enumerate(model_l):\n",
    "            fit(net = model, \n",
    "                criterion = criterion, \n",
    "                optimizer = optimizer(model.parameters(), lr = lr), \n",
    "                batchdata = train_data, \n",
    "                epochs = epochs, \n",
    "                cla = cla)\n",
    "            train_l[i][epochs] = eva(train_data, model).detach()\n",
    "            test_l[i][epochs] = eva(test_data, model).detach()\n",
    "    return train_l, test_l\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、构建复杂神经网络\n",
    "\n",
    "我们尝试增加模型复杂度，也就是添加隐藏层来构建更加复杂的神经网络模型。\n",
    "\n",
    "### 1.ReLU激活函数叠加\n",
    "\n",
    "&emsp;&emsp;首先是ReLU激活函数的叠加，那么我们考虑添加几层隐藏层并考虑在隐藏层中使用ReLU函数，也就是所谓的添加ReLU层。此处我们在ReLU_class1的基础上创建ReLU_class2结构如下：\n",
    "\n",
    "- 输入层2应该为隐藏层2\n",
    "<img src=\"https://i.loli.net/2021/02/08/qBxbuJfQlURjG8N.jpg\" alt=\"51\" style=\"zoom:40%;\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ReLU_class2(nn.Module):                                   \n",
    "    def __init__(self, in_features=2, n_hidden_1=4, n_hidden_2=4, out_features=1, bias=True):       \n",
    "        super(ReLU_class2, self).__init__()\n",
    "        self.linear1 = nn.Linear(in_features, n_hidden_1, bias=bias)\n",
    "        self.linear2 = nn.Linear(n_hidden_1, n_hidden_2, bias=bias)\n",
    "        self.linear3 = nn.Linear(n_hidden_2, out_features, bias=bias)\n",
    "        \n",
    "    def forward(self, x):                                   \n",
    "        z1 = self.linear1(x)\n",
    "        p1 = torch.relu(z1)\n",
    "        z2 = self.linear2(p1)\n",
    "        p2 = torch.relu(z2)\n",
    "        out = self.linear3(p2)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1c0c05fefd0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#————————————————————借助model_comparison函数进行模型性能测试—————————————————————\n",
    "\n",
    "# 创建随机数种子\n",
    "torch.manual_seed(24)  \n",
    "\n",
    "# 实例化模型\n",
    "relu_model1 = ReLU_class1()\n",
    "relu_model2 = ReLU_class2()\n",
    "\n",
    "# 模型列表容器\n",
    "model_l = [relu_model1, relu_model2]           \n",
    "name_l = ['relu_model1', 'relu_model2']\n",
    "\n",
    "# 核心参数\n",
    "num_epochs = 20\n",
    "lr = 0.03\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train_l, test_l = model_comparison(model_l = model_l, \n",
    "                                   name_l = name_l, \n",
    "                                   train_data = train_loader, \n",
    "                                   test_data = test_loader,\n",
    "                                   num_epochs = num_epochs, \n",
    "                                   criterion = nn.MSELoss(), \n",
    "                                   optimizer = optim.SGD, \n",
    "                                   lr = 0.03, \n",
    "                                   cla = False, \n",
    "                                   eva = mse_cal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0cf2461d0>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0cf246620>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c0cf1fa290>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mse_train')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/5B7SwdVUxNjV1Us8mRrdE8xgzuug6bmxDUxEpMCyTuDOuW3AV4EtwHagzTl3/9D9zOwDZrbazFbnY7bruqoYzkHzwSZ2GGryHGj/hyZ5EJGSkksJZTLwdmAuMAMoM7N/Hrqfc+5G51yDc66huro6+0jTBmbmyaQOPrkecNC6Nefji4gUi1xKKG8GXnbOtTjn4sAdwGvzE9bIairTV2Nm0hNl0hxvqQt6RKSE5JLAtwCnmdlEMzPgTcD6/IQ1sozGQ+k3ud5btm7OezwiIuMllxr4E8By4Gng2fRr3ZinuEY0pSxCOGiZ9UQpr4VgFPa8MnaBiYgUWCiXJzvnPgt8Nk+xjIqZUVMxiqnVBgsEvK6EKqGISAnx3ZWYkJ6ZJ5MaOHhllFa1wEWkdPgzgVfGRj8eSr/Jc1RCEZGS4ssEXlMZzawXCng9UXpaobt1LEISESk4XybwusoYXX1JOnpGMbVav4GeKGqFi0hp8GcCr+q/mCfDqzFBZRQRKRm+TOAZz8wDuphHREqOLxN4fws8o54oEyZBbJJKKCJSMnyZwGv7L6fPqifK5vwHJCIyDnyZwCdGQlTEQtn1RFENXERKhC8TOHg9UTIaDwXSF/NsgdQoxxIXESli/k3gmc7MA14JJdkLnTvGJigRkQLybQLPeDwU2NcXXGUUESkBvk3gdVVRmjt6Sabc6J80qd5bqieKiJQA3ybw2soYyZRjV1cGZZRJswBTTxQRKQm+TuAATW0ZJPBQFCpnqIQiIiXBtwk8q5l5wOtKqBKKiJQA3ybw2mwmNwZdzCMiJcO3CXxaeYSAZZPA66H9H5DIsAuiiEiR8W0CDwUDVFdEM5+ZZ9IcwEHr1jGJS0SkUHybwMErozR1ZHExD2iGehHxPf8n8GzmxgT1RBER3/N1As9qPJTyOghGdSJTRHzP1wm8tjJKW3ecnnhy9E8KBGDSbHUlFBHf83kCz6UroRK4iPibrxN4VjPzQHpc8M35D0hEpIB8ncAHWuAZ90Sph55W6G7Nd0giIgVTGgk8454o/V0JVUYREf/ydQKvjIWYEA5mNx4KqA4uIr6WUwI3s0lmttzMNpjZejNblK/ARnl8aiuj2U/soBa4iPhYKMfnfxP4g3PuQjOLABPzEFNGaiuzmJlnwiSIVelEpoj4WtYtcDOrAhYDPwZwzvU551rzFNeoZTU3JmiGehHxvVxKKHOBFuAnZvZXM7vJzMqG7mRmHzCz1Wa2uqWlJYfDDa82fTWmcxlMrQbpGeqVwEXEv3JJ4CHgJOD7zrkTgS7gk0N3cs7d6JxrcM41VFdX53C44dVWxuhLpGjdG8/sif0X86RSeY9JRKQQckngjUCjc+6J9P3leAm9oOoG+oJncSIz2QudTfkPSkSkALJO4M65HcBWMzs6velNwLq8RJWB2sookM3VmPXeUmUUEfGpXPuBXwv8wsyeAU4A/jvniDKU03gooJ4oIuJbOXUjdM6tBRryE0p2atIt8Ix7olTNAkw9UUTEt3x9JSZANBRkSlkk86sxwzGomK4WuIj4lu8TOGQ5Mw94ZRTVwEXEp0oigddVRjPvhQJeTxSVUETEp0oigddWxtjRluXVmO3bIJHFc0VExlnJJPBdXb3EkxlelDN5DuCgrXFM4hIRGUslkcDrqmI4By3ZTOwAOpEpIr5UEgl84GKerMcF35zfgERECqBEEniWM/NUTIdgRD1RRMSXSiKB12V7NWYgAJNmqyeKiPhSSSTwyRMjhIPGjqzHBd+c95hERMZaSSTwQMCoqchiZh7QuOAi4lslkcChf2aeLK/G7N4DPW35D0pEZAyVTAKvrYxm3gsFNEO9iPhWCSXwbMdDqfeWKqOIiM+UTAKvq4zR1ZekszeR2RM1LriI+FTJJPD+vuAZz8wzYTJEq1RCERHfKbkEnvWJTJVQRMRnSiaB11XlmMBVQhERnymZBJ71eCjg9URp3QLO5TkqEZGxUzIJfGIkREUslH1PlEQPdDblPS4RkbFSMgkcvJ4oGU9uDBpWVkR8qaQSeG1lLLsSykAC14lMEfGPkkvgWZ3ErJrlLdUCFxEfKakEXlcVpbmjl1Qqw5OR4Zg3Nri6EoqIj5RUAq+tjJFMOXZ2ZVkHVwlFRHyk5BI4QFO2M9SrhCIiPlKaCTzbi3nat0GiL89RiYiMjZJK4P1Tq2XfE8VB29a8xiQiMlZyTuBmFjSzv5rZinwElItp5REClmULXDPUi4jP5KMF/mFgfR5eJ2ehYIBp5dHsSyignigi4hs5JXAzmwmcB9yUn3ByV1cVy25y44rpEIyoJ4qI+EauLfD/B3wcSI20g5l9wMxWm9nqlpaWHA93aFnPzBMIehf0qIQiIj6RdQI3syVAs3NuzcH2c87d6JxrcM41VFdXZ3u4UautjNLUkUUCB40LLiK+kksL/HTgfDPbDPwKONPMfp6XqHJQVxmjdW+cnngy8ydPrlcLXER8I+sE7pz7lHNupnOuHrgIeMg59895iyxLOfUFnzQHuvdAT3ueoxIRyb+S6gcOgxN4DsPKqowiIj6QlwTunPuTc25JPl4rV/1Tq2V3MY/6gouIf5RuCzybnigDF/OoBS4ixa/kEnhlLEQsHMiuBj5hMkSrVEIREV8ouQRuZtRlOzOPGUyerRKKiPhCySVwyGFmHkgPK6sWuIgUvxJO4Fn0QgGvJ0rrK+AynNVHRKTASjKBe+Oh9OCyScKT6yHRA51NeY9LRCSfSjKB11bG6EukaN0bz/zJ6okiIj5Rogk8CpDdmCj9F/PoRKaIFLmSTOADM/Nk1Rd8trdUV0IRKXIlmcD7L+ZpzuZEZjjmjQ2uEoqIFLmSTOA16RJKVn3BQTPUi4gvlGQCj4aCTCmLZJ/ANS64iPhASSZw8MoozVkn8Hpo3waJvrzGJCKSTyWcwKO5lVBcCtq25jcoEZE8KtkEXlcZY0dbtldjaoZ6ESl+JZvAaytj7OrqJZ4ccb7lkakvuIj4QEkncOegpSOLVnjFdAiE1ZVQRIpaySbwuqocuhIGgjBplkooIlLUSjaB77uYJ4eeKCqhiEgRK/kEntXl9KBxwUWk6JVsAp8yMUI4aOzIelzwOdC9G3ra8xuYiEielGwCDwSMmoocL+YB1cFFpGiVbAKHPFzMAyqjiEjRKukE3j8zT1bUAheRIlfSCdwbDyXLGviEyRCtVE8UESlaJZ/AO3sTdPYmMn+ymXqiiEhRK+kEntPMPOD1RFELXESKVEkn8LxczNO6BbKZ3V5EZIxlncDNbJaZPWxm68zseTP7cD4Dy4fafMzMk+iGzuY8RiUikh+5tMATwP9xzs0DTgOuNrN5+QkrP+qq0iWUXHuiqIwiIkUo6wTunNvunHs6vd4BrAdela/A8mFiJERFLJR9TxSNCy4iRSwvNXAzqwdOBJ4Y5rEPmNlqM1vd0tKSj8NlpLYylsN4KLO9pXqiiEgRyjmBm1k58L/Adc65AwYOcc7d6JxrcM41VFdX53q4jNVV5nAxT3gClNephCIiRSmnBG5mYbzk/Qvn3B35CSm/cprcGDRDvYgUrVx6oRjwY2C9c+7r+Qspv2orozR39JJKZdkVcHK9SigiUpRyaYGfDrwXONPM1qZv5+Yprrypq4qRSDl2dmV5InPSHGhvhGQ8v4GJiOQolO0TnXOPAJbHWMZETUX/xTy9A+sZmTwHXAratsKUI/IcnYhI9kr6SkwY1Bc868vp672lTmSKSJEp/QRemePFPBoXXESKVMkn8GnlEQKWw3golTMgEFZPFBEpOiWfwEPBANPKc5iZJxCESbNUQhGRopP1SUw/8WbmybIXCnhllBfvg5vOgvIaKJsGZTVQVg3l1d6yLL19wmRvLHERkTF2WCTwudPK+MNzO/j9M9s57/jpmb/A666Dp38GXS2w+++w9Qno2gkM07c8EEon9PRtcMKvqIPy2n3LWJWSvYhk7bBI4P93yTwa93Rz9S+f5u8tr+GaM4/EMkmcR7zBuw2WSsLe3dDV7CX2zpYh6+n7O1/0hqNNDvMLIDQBKmq9y/VHXNbBhCkQKPlql4hk6LBI4NPKo/zi/afyyf99hq898CIvtXTyxQuOJxYOZv+igaBXPikfxfguzkFPG3Q2QccObzl4vWMHNK+Hl/4EvW3DHCsEtfPhdR+BY89XMhcR4DBJ4ACxcJBvvPsEjqqt4Cv3vcCW3Xv54XsbqK6Ijv3BzWDCJO9WffTB9+3bO0yC3w7rV8BvLoPqY2Hxx+C4pd6XiIgctswVcLqwhoYGt3r16oIdbyT3PLudj/56LVPLovz48gaOqasc75AOLZWE5++ElV+Blg0w9ShYfD3MvwCCh833sMhhyczWOOcahm4/LH+Ln7tgOr/+4CISqRQXfO8xHtrQNN4hHVogCAsuhH9bBe+6FYIRuPMD8N2T4a+/0FgtIoehwzKBAxw/cxJ3Xf065laX8f5bV3PTX/5OIX+NZC0QgOPeAVc+Au/+OUTK4K6r4DsN8PRPIdE33hGKSIEctgkcvP7hv/7gIs6eV8d//n49/37ns/QlUuMd1ugEAnDs2+CDf4GLf+X1P7/7Wvj2P8HqmyGRQ793EfGFwzqBgzdv5vcuOYmr3/hqbntyK5fd/CSte33UijWDo98K//owXLLc63e+4iPwrRPhyR9BPIfJLESkqB32CRwgEDCuP+cYvr5sIWte2cPS7z3G31s6xzuszJjBUWfB+x+E994JVbPgno/BNxfC49+HePd4RygieaYEPsg7T5rJL//1VNq747zju4/y6Kad4x1S5szg1WfCFX+Ay34HU4+EP3wSvjEfHv4f7yIjESkJSuBDNNRP4bdXn05dVYxLb36SXzzh01EIzWDuYnjf7+Hye2BmA/z5i/CN4+Cua6Bp3XhHKCI5Oiz7gY9GR0+ca2/7K396oYX3nV7Pp8+bRzDg83FLdm6Ex78Ha2+DRLfXUl90Nbz6TRqTRWQs9XZAtCLrp6sfeIYqYmFuurSBK06fy08e3cyFP3iMO55uZG9fYrxDy960o2DJN+Cj6+DM/+u1wn9+AXzvNFhzq+rkRaqtO86GHe3+6OYq+4t3w0P/5f3yHYMhqdUCH4XfrN7Ktx/axJbdeymLBDnv+OlccNJMTpk7JbNBsYpNog+evwNWfQd2PAsTp8HJ/wInv9/rzVIsetq8P4SKuvGOpKA2NnVw66rN3PH0Nvb2JZk3vZIrXjeXty2cTjSkYRSK3qYH4fcfgz0vw4JlcM5/j27spGGM1AJXAh8l5xxPbd7D8jVb+f0z2+nqSzJ7ykQuOGkm7zzpVcyaMnG8Q8yec7D5EVj1XXjxDxAMw/HL4LSroXZe4eOJd8OWx+HllfDyn+Eff/VifM1b4LR/82r7fv7iPIhkyvHwhmZueWwzj2zaSSQU4PyFM5g/o5JfPrmFF5s6mVYe5Z9Pm80lp84pzFg+kpn27XDfp7yhL6YeCed97cDRTDOkBJ5He/sS3Pf8DpavaeSxl3bhHCw6YioX/tNM3rqgjokRH49NsnMTPPF97/L8RDcc8UZYdA0cOYZ18mTcS9J//7OXsLc+6Q2/a0Hv5OvcxV4CX/MT2LsLao7zEvmCd0E4NjYxFVhbd5zfrN7Kras2s3V3N3WVMd67aA4XnTyLqeVeknbO8cimndz8yMs8/EILkWCAt58wg/edPpd5M3wwnk+pSybgqR95JZNknzfo3OkfhlDuX7JK4GOkcc9e7nx6G8ufbuSVXV6J5a0LpnPhP83klPopBPx64nPvblhzCzx5ozcaYnktTJ4LVTMH3WZ5y0mzvMkpRiuVgubn0wl7JbzyGPR1eI/VLYC5r/ducxbtf+In3gPPLff6tTc9BxOnQsMVXsnHp+WVjU0d3PKYVybpjic5uX4yl792LmcfV0s4OPIpqpdaOrnl0c0sX9NIdzzJoiOmcsXr5nLmMTX+P9nuR41rYMV1sOMZOPLNcO5XYMoReXt5JfAx5pxjzSt7WL6mkRXPbKezN8GsKRO44KSZXHDSTP+WWBJ9sO638NLD0LYV2hqhfZvXwhgsWjkkuQ9K8FWzvEv7N6/0kvbmv3gtafB+Ys5d7CXs+jOgbOqhY3LOe43Hvw8v3OuNlz7/nV6rfMaJA7v1xJPs7upjV2cf3fEkEyNByqMhymMhyqMhoqHAuJzDSKYcD21o5tZBZZK3L5zBZa+tZ/6rMvgiBNr2xvnVU1v46apX2NbazZypE7n8tfW8q2EW5VEf/xL0i+5W+OMXvOErKurgLf8D896R91+rSuAF1N2XHCixPPrSTpyDo2srqKmMUl2RvpXvW05LLydNDBfdSVHnHF19SfoSKcJBIxIKEAmAde30knnb1n2JfeB+474EPVTlq9It7MXerepVo46lL5Fid1cfOzt7vcTc1Uu8eRNHvPwLFjT/jmiqm3Xh47jNzuPu3hNp6z34ZzsUMMqiXjIvj4YoiwYpj4UpjwbT9/c9Vh4LURkLUzkhTGUslF6GqZwQOvCEYm+H1+Ng98veCaw9m6Gnjd6JM3iqrYI7NwdZ215JsmIm71r0mv3KJNlKJFPcv66Jmx95mdWv7KEiGmLZybO4/LX1/m08FDPn4Jlfw/03eJ/1Uz4Ib/x3iI1NKUsJfJxsa+3mzqcbWbu1lZbOPnZ29NLS0Utf8sBBs8JBY2rZviQ/rTwykOQrJ4QJBoxQIJBeGsH0bfD6fvsEjYB5jydSjo6eOB09CdrTy4H73f3bD3ysoydOapiPSDhoRIIBIqEA4fQyEgoMbCsP9FHHLqa7FmpcC0GDddGF7AjOIOEgmUqRSDqSKUci5Ug5N+h+imTKkXSOZNJ7vDuepKNn+C6coYAxuyzOu0N/YWnf76hJ7qA1UsvzMy9i+6uXUTlpGhMiQbp6k3T1JugcdBu435Ogq89betuT3rIvwdA/ESNFDa3MtmbmBJqYG2zh1cEWZlsTr3JNVLn9Z1XqCVXRFSijoreZiA35N5TVwOQ5MGn2kFu99+slixr/37a28pNHX2bFM9tJOcdZ82p550kziYQCA+/n4Pc5kXKkUoO3O+//J7Xv/XfOEQwECAX3feZCASMUDOy3DAaMcNAG9g0N+jymnHeclIOk8/7PnXMkUxywPnBLefvi8F4vGCC833G91w8H920LB71jh/tjSP8d9CZS9MST6VuK3niSnoS33hNPDno8vUwk6U2vx5OOaDjAhHCQGYktnPPyV5jZtpqdVfNZu/Bz9FXPJxYOEAsHiYWDTEjf+tdjEe9vI9sGmhJ4EXHO0d6ToCWdzFs6e73E3und35letnT0squrj+RwGTRPzKAiGqIi3bqsiIW8FmbMW69ItzIjwQDxpKMvmaIvkaIvmSKeXvYl9m0beGy/7V5C6P9DHvylE+r/Yw/s+7IJBgc9HvAej4YCTC2LMLU8ypSyCNPKI0xJ36+Mhfb9YaSSXlnl8e/DK49AuAxOeI838YUFIBX3TpqmEull+v7g9UGPpRJxEvE+4ntbcbtfJtj2CpGOLQQHzXGaIkBruJaWUB3/CNSxxdXycqqGjfFprO+Zwu7kRCKhAEsX1nHFCRM5OroHWrdA6yuw55X0+hbvl0tqyLju5XVeQq+amZ4ge/Bk2YMmz44c2Mre0dbDzx7fzC+f2MKevYffePFBkkyhnWpro5xuOpjIHlfOHiroJXLI50dCAWIhLymHgwGId/PexHKu4C66ifLlxEXcljyT1Cgvp3ngI4s5qja7i3mUwH0qlXLs2dtHe0/Ca5WmW06pFPu1oJKDbvu1oNK3gBmVE9IJeSA5hyiLhPx7ovVQtj8DT/wAnv3NgTX7TIXLYHI9TJnrLQfW53o1/tDwCcE5R296iOJDzsGaSnrT6LUOSur9622N0LUTetuHf26kHMqmeS36svRcrWXVUFZDb2wqr+yNQjBMMBjGQmECoQiBYIhget1bhgmFIgTCEYLBEMFQhFAoTDDdckwlUySSCRLxHhLxPpJ9vSQTvSTjfaTivSQTcVKJXlLxPlKJPm890YdL9OFSSVwoigtGsXDM65kRikEwioViEE4vQzGCoRAWCBA070vdzGtoJJKOZN9eUp0tBLq8icMDe71baO9OQj07CXXvJNKzi0jvTqJ9rSO+1clgjER0MqnYZFITJsOEKdjEKdjEyYTKphIsn0pg4lSYOMWbVHzXJvjDJ7xy2PHvJv6mL9AdnUpPX5LudKu9O56kuy/dqh+yvSee5JJTZzNp4qG/OIYzJgnczN4CfBMIAjc55754sP2VwGVcdDbDtqe9qecCYa+feyA85H7Im+VopMeK5dxEvNtL5F3N3rKzGbpaBm1r8QYs62qBvTvB5WF8ewvu+/VSCBbwknt/kg9FAfP+jf29lYaKVKS/tGq8L7Lymv3Xo5XeBWHdu70eVt17vNve3Qduc8nhjzH1KFjyde/cTYGNlMCzPk1tZkHgu8BZQCPwlJnd7ZzTKElSXMpr4Oi3jHcU+RGe4HXbnDTr0Pumkl5C6mz2lqn+8lBiULkoOWg9fX+gnJTw9u/fHozs+5IbWIYP3B4YZrsFvL79iV5I9Hi9mxI9g+4PWe63b693/LJpQ0pINft+aYQn5Of9TaW8Xzndu2FvOqF37wYM5p2flz7d+ZRLP6NTgE3Oub8DmNmvgLcDSuAixSAQTCe9aeMdiX8EAjBhknebMt7BHFoug1m9Ctg66H5jett+zOwDZrbazFa3tGgsahGRfBnz0Qidczc65xqccw3V1dkN5CIiIgfKJYFvAwYX4mamt4mISAHkksCfAo4ys7lmFgEuAu7OT1giInIoWZ/EdM4lzOwa4D68boQ3O+eez1tkIiJyUDmNduOcuwe4J0+xiIhIBjSlmoiITymBi4j4VEHHQjGzFuCVLJ8+DdiZx3DyTfHlRvHlRvHlptjjm+OcO6AfdkETeC7MbPVwYwEUC8WXG8WXG8WXm2KPbyQqoYiI+JQSuIiIT/kpgd843gEcguLLjeLLjeLLTbHHNyzf1MBFRGR/fmqBi4jIIErgIiI+VXQJ3MzeYmYvmNkmM/vkMI9Hzez29ONPmFl9AWObZWYPm9k6M3vezD48zD5vMLM2M1ubvn2mUPGlj7/ZzJ5NH/uA+evM8630+/eMmZ1UwNiOHvS+rDWzdjO7bsg+BX3/zOxmM2s2s+cGbZtiZg+Y2cb0cvIIz70svc9GM7usgPF9xcw2pP//7jSzSSM896CfhTGM73Nmtm3Q/+G5Izz3oH/rYxjf7YNi22xma0d47pi/fzlzzhXNDW9QrJeAI4AI8Ddg3pB9rgJ+kF6/CLi9gPFNB05Kr1cALw4T3xuAFeP4Hm4Gph3k8XOBewEDTgOeGMf/6x14FyiM2/sHLAZOAp4btO3LwCfT658EvjTM86YAf08vJ6fXJxcovrOBUHr9S8PFN5rPwhjG9zngY6P4/z/o3/pYxTfk8a8Bnxmv9y/XW7G1wAemaXPO9QH907QN9nbg1vT6cuBNZoWZcdY5t90593R6vQNYzzCzEBW5twM/dZ7HgUlmNn0c4ngT8JJzLtsrc/PCObcS2D1k8+DP2K3AO4Z56jnAA8653c65PcADQN4n3hwuPufc/c65RPru43hj8Y+LEd6/0RjN33rODhZfOm8sA27L93ELpdgS+GimaRvYJ/0hbgOmFiS6QdKlmxOBJ4Z5eJGZ/c3M7jWz4wobGQ6438zWmNkHhnl8VFPhFcBFjPyHM57vH0Ctc257en0HUDvMPsXyPl6B94tqOIf6LIyla9IlnptHKEEVw/t3BtDknNs4wuPj+f6NSrElcF8ws3Lgf4HrnHPtQx5+Gq8ssBD4NvDbAof3OufcScBbgavNbHGBj39I6QlAzgd+M8zD4/3+7cd5v6WLsq+tmd0AJIBfjLDLeH0Wvg+8GjgB2I5XpihGF3Pw1nfR/y0VWwIfzTRtA/uYWQioAnYVJDrvmGG85P0L59wdQx93zrU75zrT6/cAYTMr2LTgzrlt6WUzcCfeT9XBimEqvLcCTzvnmoY+MN7vX1pTf1kpvWweZp9xfR/N7HJgCXBJ+kvmAKP4LIwJ51yTcy7pnEsBPxrhuOP9/oWAdwK3j7TPeL1/mSi2BD6aadruBvrP+F8IPDTSBzjf0jWzHwPrnXNfH2Gfuv6avJmdgvceF+QLxszKzKyifx3vZNdzQ3a7G7g03RvlNKBtULmgUEZs+Yzn+zfI4M/YZcBdw+xzH3C2mU1OlwjOTm8bc2b2FuDjwPnOub0j7DOaz8JYxTf4nMrSEY473lMyvhnY4JxrHO7B8Xz/MjLeZ1GH3vB6SbyId4b6hvS2L+B9WAFieD+9NwFPAkcUMLbX4f2cfgZYm76dC1wJXJne5xrgebyz6o8Dry1gfEekj/u3dAz979/g+Az4bvr9fRZoKPD/bxleQq4atG3c3j+8L5LtQByvDvsveOdU/ghsBB4EpqT3bQBuGvTcK9Kfw03A+woY3ya8+nH/Z7C/V9YM4J6DfRYKFN/P0p+tZ/CS8vSh8aXvH/C3Xoj40ttv6f/MDdq34O9frjddSi8i4lPFVkIREZFRUgIXEfEpJXAREZ9SAhcR8SklcBERn1ICFxHxKSVwERGf+v+h+5NMYL3qmAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 两个模型的训练误差\n",
    "for i, name in enumerate(name_l):\n",
    "    plt.plot(list(range(num_epochs)), train_l[i], label=name)\n",
    "plt.legend(loc = 1)\n",
    "plt.title('mse_train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0cec76e30>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0cec77340>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c0cf1ae710>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mse_train')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 测试误差\n",
    "for i, name in enumerate(name_l):\n",
    "    plt.plot(list(range(num_epochs)), test_l[i], label=name)\n",
    "plt.legend(loc = 1)\n",
    "plt.title('mse_train')\n",
    "\n",
    "# 我们发现，模型效果并没有明显提升，\n",
    "# 反而出现了更多的波动，迭代收敛速度也有所下降。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1c0c05fefd0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#————————————————————————————继续添加隐藏层————————————————————————————\n",
    "# 设置随机数种子\n",
    "torch.manual_seed(420)  \n",
    "\n",
    "# 创建最高项为2的多项式回归数据集\n",
    "features, labels = tensorGenReg(w=[2, -1], bias=False, deg=2)\n",
    "\n",
    "# 进行数据集切分与加载\n",
    "train_loader, test_loader = split_loader(features, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#————————————————————————————构建三、四个隐藏层的神经网络————————————————————————————\n",
    "# 构建三个隐藏层的神经网络\n",
    "class ReLU_class3(nn.Module):                                   \n",
    "    def __init__(self, in_features=2, n_hidden1=4, n_hidden2=4, n_hidden3=4, out_features=1, bias=True):       \n",
    "        super(ReLU_class3, self).__init__()\n",
    "        self.linear1 = nn.Linear(in_features, n_hidden1, bias=bias)\n",
    "        self.linear2 = nn.Linear(n_hidden1, n_hidden2, bias=bias)\n",
    "        self.linear3 = nn.Linear(n_hidden2, n_hidden3, bias=bias)\n",
    "        self.linear4 = nn.Linear(n_hidden3, out_features, bias=bias) \n",
    "        \n",
    "    def forward(self, x):                                    \n",
    "        z1 = self.linear1(x)\n",
    "        p1 = torch.relu(z1)\n",
    "        z2 = self.linear2(p1)\n",
    "        p2 = torch.relu(z2)\n",
    "        z3 = self.linear3(p2)\n",
    "        p3 = torch.relu(z3)\n",
    "        out = self.linear4(p3)\n",
    "        return out\n",
    "\n",
    "\n",
    "# 构建四个隐藏层的神经网络\n",
    "class ReLU_class4(nn.Module):                                   \n",
    "    def __init__(self, in_features=2, n_hidden1=4, n_hidden2=4, n_hidden3=4, n_hidden4=4, out_features=1, bias=True):       \n",
    "        super(ReLU_class4, self).__init__()\n",
    "        self.linear1 = nn.Linear(in_features, n_hidden1, bias=bias)\n",
    "        self.linear2 = nn.Linear(n_hidden1, n_hidden2, bias=bias)\n",
    "        self.linear3 = nn.Linear(n_hidden2, n_hidden3, bias=bias)\n",
    "        self.linear4 = nn.Linear(n_hidden3, n_hidden4, bias=bias)\n",
    "        self.linear5 = nn.Linear(n_hidden4, out_features, bias=bias) \n",
    "        \n",
    "    def forward(self, x):                                    \n",
    "        z1 = self.linear1(x)\n",
    "        p1 = torch.relu(z1)\n",
    "        z2 = self.linear2(p1)\n",
    "        p2 = torch.relu(z2)\n",
    "        z3 = self.linear3(p2)\n",
    "        p3 = torch.relu(z3)\n",
    "        z4 = self.linear4(p3)\n",
    "        p4 = torch.relu(z4)\n",
    "        out = self.linear5(p4)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1c0c05fefd0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建随机数种子\n",
    "torch.manual_seed(24)  \n",
    "\n",
    "# 实例化模型\n",
    "relu_model1 = ReLU_class1()\n",
    "relu_model2 = ReLU_class2()\n",
    "relu_model3 = ReLU_class3()\n",
    "relu_model4 = ReLU_class4()\n",
    "\n",
    "# 模型列表容器\n",
    "model_l = [relu_model1, relu_model2, relu_model3, relu_model4]           \n",
    "name_l = ['relu_model1', 'relu_model2', 'relu_model3', 'relu_model4']\n",
    "\n",
    "# 核心参数\n",
    "num_epochs = 20\n",
    "lr = 0.03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_l, test_l = model_comparison(model_l = model_l, \n",
    "                                   name_l = name_l, \n",
    "                                   train_data = train_loader, \n",
    "                                   test_data = test_loader,\n",
    "                                   num_epochs = num_epochs, \n",
    "                                   criterion = nn.MSELoss(), \n",
    "                                   optimizer = optim.SGD, \n",
    "                                   lr = lr, \n",
    "                                   cla = False, \n",
    "                                   eva = mse_cal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0cecec820>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0cececcd0>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0ceced060>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0ceced2d0>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c0cecbc940>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mse_train')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 训练误差\n",
    "for i, name in enumerate(name_l):\n",
    "    plt.plot(list(range(num_epochs)), train_l[i], label=name)\n",
    "plt.legend(loc = 4)\n",
    "plt.title('mse_train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0ced2ead0>]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0ced2ee90>]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0ced2f1f0>]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0ced2f460>]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c0ceca5630>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mse_test')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 测试误差\n",
    "for i, name in enumerate(name_l):\n",
    "    plt.plot(list(range(num_epochs)), test_l[i], label=name)\n",
    "plt.legend(loc = 4)\n",
    "plt.title('mse_test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在堆叠ReLU激活函数的过程中，模型效果并没有朝向预想的方向发展，MSE不仅没有越来越低，model3和model4甚至出现了模型失效的情况\n",
    "\n",
    "- 伴随模型复杂度增加，模型收敛速度变慢、收敛过程波动增加、甚至有可能出现模型失效的情况\n",
    "\n",
    "> 此处ReLU激活函数叠加后出现的模型失效问题，也就是Dead ReLU Problem。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.Sigmoid激活函数叠加\n",
    "\n",
    "&emsp;&emsp;同样，我们再构建拥有两个隐藏层、三个隐藏层和四个隐藏层的神经网络。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Sigmoid_class2(nn.Module):                                   \n",
    "    def __init__(self, in_features=2, n_hidden1=4, n_hidden2=4, out_features=1):       \n",
    "        super(Sigmoid_class2, self).__init__()\n",
    "        self.linear1 = nn.Linear(in_features, n_hidden1)\n",
    "        self.linear2 = nn.Linear(n_hidden1, n_hidden2)\n",
    "        self.linear3 = nn.Linear(n_hidden2, out_features) \n",
    "        \n",
    "    def forward(self, x):                                    \n",
    "        z1 = self.linear1(x)\n",
    "        p1 = torch.sigmoid(z1)\n",
    "        z2 = self.linear2(p1)\n",
    "        p2 = torch.sigmoid(z2)\n",
    "        out = self.linear3(p2)\n",
    "        return out\n",
    "\n",
    "\n",
    "class Sigmoid_class3(nn.Module):                                   \n",
    "    def __init__(self, in_features=2, n_hidden1=4, n_hidden2=4, n_hidden3=4, out_features=1):       \n",
    "        super(Sigmoid_class3, self).__init__()\n",
    "        self.linear1 = nn.Linear(in_features, n_hidden1)\n",
    "        self.linear2 = nn.Linear(n_hidden1, n_hidden2)\n",
    "        self.linear3 = nn.Linear(n_hidden2, n_hidden3)\n",
    "        self.linear4 = nn.Linear(n_hidden3, out_features) \n",
    "        \n",
    "    def forward(self, x):                                    \n",
    "        z1 = self.linear1(x)\n",
    "        p1 = torch.sigmoid(z1)\n",
    "        z2 = self.linear2(p1)\n",
    "        p2 = torch.sigmoid(z2)\n",
    "        z3 = self.linear3(p2)\n",
    "        p3 = torch.sigmoid(z3)\n",
    "        out = self.linear4(p3)\n",
    "        return out\n",
    "\n",
    "    \n",
    "class Sigmoid_class4(nn.Module):                                   \n",
    "    def __init__(self, in_features=2, n_hidden1=4, n_hidden2=4, n_hidden3=4, n_hidden4=4, out_features=1):       \n",
    "        super(Sigmoid_class4, self).__init__()\n",
    "        self.linear1 = nn.Linear(in_features, n_hidden1)\n",
    "        self.linear2 = nn.Linear(n_hidden1, n_hidden2)\n",
    "        self.linear3 = nn.Linear(n_hidden2, n_hidden3)\n",
    "        self.linear4 = nn.Linear(n_hidden3, n_hidden4)\n",
    "        self.linear5 = nn.Linear(n_hidden4, out_features) \n",
    "        \n",
    "    def forward(self, x):                                    \n",
    "        z1 = self.linear1(x)\n",
    "        p1 = torch.sigmoid(z1)\n",
    "        z2 = self.linear2(p1)\n",
    "        p2 = torch.sigmoid(z2)\n",
    "        z3 = self.linear3(p2)\n",
    "        p3 = torch.sigmoid(z3)\n",
    "        z4 = self.linear4(p3)\n",
    "        p4 = torch.sigmoid(z4)\n",
    "        out = self.linear5(p4)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1c0c05fefd0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#————————————————————————————测试模型效果————————————————————————————\n",
    "# 创建随机数种子\n",
    "torch.manual_seed(24)  \n",
    "\n",
    "# 实例化模型\n",
    "sigmoid_model1 = Sigmoid_class1()\n",
    "sigmoid_model2 = Sigmoid_class2()\n",
    "sigmoid_model3 = Sigmoid_class3()\n",
    "sigmoid_model4 = Sigmoid_class4()\n",
    "\n",
    "# 模型列表容器\n",
    "model_l = [sigmoid_model1, sigmoid_model2, sigmoid_model3, sigmoid_model4]           \n",
    "name_l = ['sigmoid_model1', 'sigmoid_model2', 'sigmoid_model3', 'sigmoid_model4']\n",
    "\n",
    "# 核心参数\n",
    "num_epochs = 50\n",
    "lr = 0.03\n",
    "\n",
    "\n",
    "train_l, test_l = model_comparison(model_l = model_l, \n",
    "                                   name_l = name_l, \n",
    "                                   train_data = train_loader,\n",
    "                                   test_data = test_loader,\n",
    "                                   num_epochs = num_epochs, \n",
    "                                   criterion = nn.MSELoss(), \n",
    "                                   optimizer = optim.SGD, \n",
    "                                   lr = lr, \n",
    "                                   cla = False, \n",
    "                                   eva = mse_cal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d128dfc0>]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d128ed70>]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d128efe0>]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d128f8e0>]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c0ced141c0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mse_train')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xp6OlpyOsFmQohAyF0INB6NyGI0aqBE1DCNF1HyGMD4puN4kEXRJpaCBcU4Pe3m7K7wjAkp0dDZheLLke0PWu+spg8ID7QfRgEBkIGo+DQYTVapxzejpauts474wMhMNunNeBfxcdAYK7dxPctWu/89DcbvSODohEup4L33cvHZ1lCAGIaHYJAQLjvbNawWpFWIyGhrBYEU4HlvT0mN8blQ+8d/nAH3nkEV566SXeeuutQ/7u+yIhptJPz5/O9PzpXY/l8ZKbn9/EhtK3+cZ3/seE405FjliIdHq7/qn0QBC93UdNQzllNTuo3LeB6qptNLbWkxOwkB90kht24w3aKfbpWJvaIRwmIsNEIh2E9QBhQoREmA6HlfJh6TRMcVOXY6EmS1CdLWlw6WhSoOlg0UHTJRYdLBGJVViwYcUmLFjQsGHBpguGBJ0UtqeR16aR3RKhqCnAmLpW8IWIWDUiVkHYanyDCGo6OmDTBRbdCO4WXaKFJJoUyAwXssANGemQmU4wMx3pduL3t+JvaSDQ1kK4rZVIexu6z0dFoAJbUxVeu4chdi9OzY6uRyBstDy7glH0BmDJzMSSlYUlz0va6NHG49wcHBMn4Zg8GduQw19/CDc20rFxI/6NGwnX1qL72tHbjLqEa2vR9+xBhsOItLTPPzg677vSkFIan0a6bgTqSBj0aA4cAQIjsAuhgUVgnzCB9PnzsObnG7e8fKz5eQibDb293Th+ezt6uw+9vR0ZCiGsNiOw2awQDXLoOuH6esK1tYTr6ojU1RGuqSVYvg8hxP71tdnQXE5EVpbxfFoawm5HpBnnQjhCpK0Vvc2H3tZGuLaWyJ7dSP+h5zAIq5W0ESVknXsu9tGjSBs9Gvvo0Vhyc0HXiTQ2GvWqraXU7caan//5hxnst5W6bnyjCIXROzqM+9EcQo5Jk2IOIhUVFeTm5nLJJZeQnZ3Ngw8+2PWz2bNnc/3119PY2EhGRgbPPfdcVyu7J+bMmcMPfvAD6urqyMnJ4cknn+Taa6/db5958+Zx2WWX8bOf/YxwOMyLL77I1VdfHdM5zZ07l8suu4wbb7wRKSVLlizhX//6F1LKQx6rez7wCy64ACklGzZsOCid7Kuvvspdd93F8uXLu1rxZkiIAH4gIQS/PWsKPw5GOHXtGB5cMIsveYYgAA54c4oZRzEn9e1AzfvglZ/CZ2/BzzcYLcAEtqV+C09vf5qXd71MR2QH0/KmcdH4SzhlxCn7ddeYxZqTQ/q8eaSbvBJ3yrJYsHq9WL1emDgRbetWbPk9v4AvpSRSX0+oqgrCYYixlavygRt6kg/8+9//PoFAgEWLFgHGhcy///3vMdc3sfKBHyAQjnDu3z6gosnPsuvmUpjVDxcrVz8CL14H122AnBLzy4+D5kAzL+58kf9s/w97WvawqGQRf1jwh3hXS+mlvuQDj7S0ENy7F/vo0WjO/r24r/KB903y5gM/gN1q4a9fn0EgrHP9U+uI6P3wYeQdb2zrdhx5vwSSZc/ikkmX8MLZL3DisBPZ1bQr3lVSBorV+NIt+9Df21sqH3j/S8gulO5G5aUb3SnPrOfetz/jui+NNfcA3nHGtnY7jF1kbtlxJoRgWPowNtVtOvrOSlIQAxjAVT7w/pfwARzgvGOHs/KzOv781g6OH5XLcaM85hXu9oDLk1Qt8O48Dg9NgSZCegib1j9X/pXBoyuAh/o/gMeLygeegH5z9hRKPG6ue2odjb6guYV7xydvAHcaH3YN/gPzlSnJSGjRoaPhULyropggaQJ4ut3KX78+gwZfkMXPrsfUi7N544wulCTUGcDrOuriXBNlwNisA9KFovS/pAngAFOGZXHjVybw5tYaHv1gj3kFe8eBvwF8yRfkvE4vAPX++jjXRBkowqoCeLJIqgAOcPkXR3DyhHxuW7aN8kaTZuQl4UiUTh6H0QJXATx1qACePJIugAshWHzqeIIRnVV7Gs0pNK/bSJQk09mFUt+hAniq6M8ArvKBH3qfX/ziF0ybNo3p06dzyimnUFFRYUo9ki6AA4zOSyfNqrG1ssWcAjOHg82VlC1wp9WJ2+ZWLfAU0pkuQHbLq2IWlQ/80BYvXsyGDRtYt24dZ5xxBr/5zW9MKTcphhEeyGbRGDcknS1mBXBNA8+YpGyBg9GNogJ4Yrvz4zvZ1rCtR/vKcBgZCKDtdR4xPcSE3AncMOeGw/5c5QPveT7wzMzM/d63lEpm1ReTCjN5a2sNUkpz3qy88bD3o9jLGYS8Tq8ahZJKov8PUkpi+c9Q+cB7lw/8pptu4rHHHiMrK4t33nknhnf+c0kdwJ9eVU5ta4D8TEfsBXrHw8ZnIOiDtIHLbT0QPE4PO5t2xrsaSgyO1FI+kN7RQeCzz0grKsLSiwRTB1L5wHuXD/zWW2/l1ltv5fbbb+fee+/l17/+9VHrezRJ2QcOMLHQ+Mqy2axulM4LmXWfmlPeIJLryKXOr1rgqeLz2ZixTebpzAc+depUbr755l7165qdlztex+2eD7zztnXr1iO+5uKLL+a5557r9bEOJXkD+FAjgG+pMCmAd+ZEScILmV6nl5ZgC6GImp2XEiwWECLmkSgVFRW4XC4uueQSFi9ezJo1a7p+Nnv2bJYvX05jYyPhcLjXAWvOnDksX76curo6IpEITz75JPPnz99vn3nz5vH888/j9/tpbW3lxRdfjOl8wMgH/vzzz9Pe3o7P52PJkiXMnTv3sMfqng8cjG6p9evXH1Tup59+3vBbunTpQX3kfZW0XSiZDhtFuU7zRqLkjgZhScoA3n0oYYG7IM61UfqbEMKUoYQqH7ihJ/nAb7zxRrZv346maZSUlJiSCxwSPB/40Vz12Co+q23j7R8vMKfAvx4L+ZPgwn+ZU94g8fbet7nunet46vSnmOydHO/qKD3Ul3zgnQI7d4LFgn3ECHMr1Y3KB943KZMP/GgmDc1kd52P9qBJfWpJmtSqazq9msyTMoTVaqzK049UPvD+l7RdKGCMRJEStle1MqM4J/YCvWPh09chEgZL8rx1XV0oaix46rBakX5/vx5C5QPvf8kThQ6hcyTKlsoWcwJ43njQQ9C4B7xjYi9vkOjMh6JGoqSOzj5w0+ZJDCIqH3iSGJ7jJMNhNe9CZldSq+SakemwOki3pasulBQiogsaq6RWiS2pA7gQgomFmSYOJYwu15aEU+o9To9qgaeQzrHg/d0PrvSvpA7gYPSDb6tqRTdjwWNHJmQUJuWFTJUPJbUM5NqYSv9J/gA+NJP2YITSBrNyg49LzgDu9KgulFRi0mxMJb6SP4AXmjwjM2881O6AARw/PxA8DtWFkkr6qwWu8oEfeZ977rkHIQR1deb8r8UUwIUQPxRCbBZCbBJCPCmEMCFrlLnG5Kdj1QRbKpvNKdA7DoKt0FppTnmDhNfppTXYSjBi8oLQyqAkNA1hsZjeB67ygR9eWVkZr7/+OsXFxaaV2edhhEKIYcAPgElSSr8Q4mngIuARk+pmCofNwpj8dLZWtppToLfb6jyZQ80pcxDoWp2+o0FNp09AVbfdRmBrz/KBd9L9ftA0tG5JnbqzT5xAwc9/ftjXq3zgPc8HDvDDH/6Qu+6666CUALGIdRy4FXAKIUKACzBnnSCTTSzM5MOdJvXv5nUOJfwURi80p8xBoHM2Zp2/TgXwVCFETF2BKh94z/OBL126lGHDhh2UIyVWfQ7gUsp9Qoi7gb2AH3hdSvn6gfsJIa4CrgJM/erQG5MKM1mydh8NviC57rTYCksfAvaspBsLrhY3TmxHaikfTrCsDL3dj2P8uD4dU+UD71k+8Pb2dm677TZef/2g8BizWLpQcoCzgJFAE/CMEOISKeW/u+8npXwAeACMZFZ9r2rfdc7I3FrZwhfHeGMrTAgjN3iSjQXv7EJRFzJTh7DakOHWPs/G7MwHvmzZMm6++WZOPvnkHr/2aHm5bdGJRmbrz3zgh7Nz5052797d1fouLy9n5syZfPzxxxQUxPZtN5aLmF8Cdkspa6WUIeC/wBdiqk0/mViYAZicGzzJhhKq1elTj7BaQeqg6316vcoH3rN84FOnTqWmpoY9e/awZ88ehg8fzpo1a2IO3hBbH/he4HghhAujC+VkYOByxfaCJ91OQabDxCn142Dd4+BvAme2OWXGmd1iJ8OWobpQUont86GEwmLp9ctVPnBDT/KB95eY8oELIX4NXAiEgbXAt6WUh14QjoHPB97d5f/8mMrmDl69fl7shW1/BZ68CK54E4pi/6MZLM5ccibjcsZxz4J74l0VpQdiyQcOEGlrI7hnD2kjR2Jxm7/Oq8oH3jcDlg9cSvkrKeUEKeUUKeWlRwre8TZpaCaf1bQRCEdiL6xrebXk6wdXXSipw6y1MQ9H5QPvf0mdTra7SYVZhHXJp9VtTBnW95W4AcguAUta8l3IdHjY0ZhcffvK4fV3QiuVD7z/pUwA77qQWdkSewC3WMEzJulWqPc6vXxY8WG8q6H0Qkz5vE1a3HiwSeR84L3t0k76XCidSjxuXGkWk0eiJFkL3OmhNdRKIDJoe8KUbhwOB/X19b3+p+9k1uLGijmklNTX1+Nw9DwjScq0wC2aYEJBhnkjUfLGw9YXINQBtkGXAqZPuk/mGZqePGkCktXw4cMpLy+ntra2z2WEa2tB07C2mpRqQomJw+Fg+PDhPd4/ZQI4GBN6XlhfYc4yUvmTjDG0NVtg2ExzKhhnXYsbqwCeEGw2GyNHjoypjLJ77yNUVsaoF5aaVCtlIKVMFwoYI1FaO8KUN5qwmOvQ6ca2cv0Rd0skajJP6rF6vUYrXElIKRXAu0+pj1l2CTiykiqAd09opaQGa14ekcZGtbBDgkqpAD5+iDES5dOattgLEwIKj0mqAJ7rMBIPqdmYqcPqNT60w/Xqd56IUiqAu+1W8jLslNb7zCmw8Bio3gyR5Gi9pFnSyEjLUF0oKcSab2T7C9eqb12JKKUCOEBJrovSepPWxyycDpFAUk3o8Tq9qgslhXS1wOtUP3giSrkAXuwxM4BHE9YkUTeKWp0+tVjzOlvgKoAnopQL4CM8bqpaOugImZATJXc0pKUnVQD3Or2qCyWFWD3GyKOwSYvsKgMr5QJ4icdYUaOswYRWuKZBwdSkCuAep2qBpxKRloYlO1u1wBNUygXw4lwjgJvajVK1EXQTWvSDgMfhoS3URke4I95VUQaINc9LRLXAE1LKBfASj5H3eI+ZI1FCPqjfaU55cdY1G1N1o6QMa14e4RrVAk9EKRfAc1w2MhxW9prRhQJJdyGzazam6kZJGRavV/WBJ6iUC+BCCErMHIniHQ9WB1SuM6e8OFOr06cea14e4draPmc1VOIn5QI4QEmu27wWuMUKQyYnXQu8rkO1yFKF1ZuHDAbRVUbChJOSAbzY46KsoZ1wpG+rcR+k8Bio3ABJ0IJRLfDU0zWZR41ESTgpGcBHeFyEdUlls0kjLQqPgUAzNO4xp7w4sllsZNmz1GzMFPL5ZB71O080KRnAi3ONkShqRuaheRweGjoa4l0NZYBY8zqn06sAnmhSMoB3TuYpbTBpKGH+JNCsyXMhU03mSSlqOn3iSskAXpDpIM2qmdcCt9ohf2LStMC9DpXQKpVoGRmItDSV0CoBpWQA1zRBca7LvLSy8Hlu8GS4kOn0qIk8KUQI0TWUUEksKRnAweS0smCklm2vh5Z95pUZJx6nB1/Ihz9swtJzSkKwetV0+kSUsgG82ONib0O7eZMXkuhCphpKmHqs+aoFnohSNoCX5LpoD0aobQuYU+CQKSC05AjganHjlGPxetUwwgQUUwAXQmQLIZ4VQmwTQmwVQpxgVsX6W2dSq71mdaOkuYxp9UkQwNXixqnHmpdHpKkJGQzGuypKL8TaAv8z8KqUcgJwDLA19ioNjK6hhKb2gyfHIseqCyX1qMWNE1OfA7gQIguYBzwEIKUMSimbTKpXvxue40ITmD8SpbUSWqvNKzMOcp3R1elVF0rKSCsuAaBt+XtxronSG7G0wEcCtcA/hRBrhRAPCiHcB+4khLhKCLFKCLGqdhBdJEmzahRmOSk1K6kVfH4hs2qDeWXGgU2zkW3PVi3wFOI6bg6u2bOp/eMfCTc2xrs6Sg/FEsCtwEzgfinlDMAH3HjgTlLKB6SUs6SUs/KiM74GC1PTyoKxvBokxYxMtbhxahFCUPDLXxDx+ai55554V0fpoVgCeDlQLqX8KPr4WYyAnjBKPCamlQVwZBoLHSdBP7ha3Dj12MeOJfdb36T52edoX7M23tVReqDPAVxKWQWUCSHGR586GdhiSq0GSInHRYMvSEtHyLxCk+RCZq4zV41CSUF53/se1oICqn7zG2Q4HO/qKEcR6yiUa4HHhRAbgOnAbTHXaACVRBc4Nm0oIRgBvGkvtCd2Nr8CVwEVbRX8/pPfU+Wrind1lAGiud0M+dnPCGzbRuMTT8S1LlJK2lauVH3yRxBTAJdSrov2b0+TUp4tpUyod7q4X4YSTjO21ZvNKzMOvjX5W5w68lQe3/o4X3nuK9z0/k181vhZvKulDICMUxbhnjuX2j//hVBNTVzqEGlqYt8PrqPsim+z57zz6di2LS71GOxSdiYmfD6Zx7S0sgCZw4xtW2IPJfQ4Pdwx9w6WnbuMiyZcxBulb3DOC+fw/be+z8eVHxOImDSDVRl0hBAU3HwTMhSi5s67Bvz47atWseucc2l99108V16JjETY8/Vv0PL66wNel8HOGu8KxFO63Yo3PY3SOhNb4O7oSBtfcvQfD00fyg1zbuDqaVfz1PaneGLrE1zx+hVYhIWRWSMZlzOO8bnjGZ8znvG547tmcSqJLa2kBM+VV1J3331kX3A+7uOP7/djynCYur//H3V/+xu2ouGMeOIJnFOnkHPpJZRfey37fnAdgWu/j/e730VoKd327CIGciXqWbNmyVWrVg3Y8Xri3L+tJM2q8dRVJmUB0HX4rRfm/ghOutmcMgcRf9jP+/veZ2v9VnY07mB74/b9+si9Ti8TcicwMXeisfVMZHj6cIQQcay10hd6Rwe7zvwqwmplxJNPoGVl9dvvMVRZyb7Fi/GvWk3mV8+k4Je/wpL++bQSPRCg6pe/onnpUjK+/GWG3n4bmsvVL3UZjIQQq6WUsw58PqVb4GB0o3y0y8ThcpoGrtykaYEfyGl1sqhkEYtKFnU91xxoNoJ5w3a2Nmxla8NWPqz4kIiMAJBhy+Cnc37K2WPOjlOtlb7QHA4KfnEzZVddzY7jT0BzubAWFGArGIJ1SAHWgiFYc3LQ3G40d7qxTXcbW5cLYUtDpNnQ0tIQaWlgtUIoRLB8H8HSPQRLSwmWlhIqLcW/YSPoOkPvvIOss846uC52O4V33I59/Hhq7r6bPXv3kveDaxHWaAjr3hC1WLC4o/Xoduvat4dkJGKUa7Ec8oNLhkLo7e2f33w+ZDiC5nSgOZ0IpwvN5TTu9/LYPaUCuMfF8+v20RGK4LBZzCnU5QXf4Jl12t+y7FnMLpjN7ILZXc8FIgE+a/yMLQ1buHftvawoX6ECeAJKnzeP4kceoWPLFsLVVYSqqglXVeH74AMj/ayu97ywziDYLdhqGRmklZSQcfLJeL/7HdJGjDjCywWe/3c59jGj2fejH1P+3e/16lyEzYZISzO23W7YrMhQCBkIIgMBZCCAHgxC92GUmmYEck1DWCzIcLhXib+EzcbI55dgHz26V3U+GhXAPS6khPLGdsbkZ5hTqNtrLO6QwuwWO5O9k5nsnczLu15WY8oTmPv443Aff9xBz8twGL2tDd3nI9LmQ/d13trQ/R3IYPDzWyiIHgggNAtpxUXYiotJGzECS3Z2r7tl0ufNY/RrrxIqK9v/B9FyZDjcrS7GLeLzIdvbjUDdeQtGt+GwEcztdoQ9Dc3uMO6n2YwyIzoyEja2egTCEeObhctlfNNwubC43QiXC2Gxonf4kX4/ert/v/uW3Nw+/w4OJ+UDePcV6k0N4FWbzCkrCeQ589hSn1BzvJQeEFYrluxsLNnZ2Ab42FaPB6vHM8BHHXxS/lJuv6SVdXmhXbU4O3mdapFkRekPKR/APe400u1Wc3OiuL3gb4SIiVP0E5jX6aU93E57yMT3WFEUFcCFMFao32NmXnBX9Ktdgk+nN0ueyxgbX+tPnQu7ijIQUj6Ag9GNYmo+lM7JPKobBQCvQy3Rpij9QQVwjJwoZY3tRHSTJjW5o7MRk3QseG95Xcb7oVrgimIuFcCBER43oYikoslvToHRgKVa4IY8p/GNpE69H4piKhXA6ZZW1qwLmaoFvp8sexZWYVVdKIpiMhXA6Ye0ss4cEJoK4FGa0PA4PaoLRVFMpgI4UJjlxGYR5qWV1SzgzFVdKN3kOfNUC1xRTKYCOGDRBCM8bj6tbjOvULdXtcC7UZN5FMV8KoBHTS/KZl1ZE6al13WpAN6d16UCuKKYTQXwqBnFOTT4gub1g7s9qgulmzxnHo0djYR0NTtVUcyiAnjUjOJsANaWmbSspztPtcC78Tq9SCQNfjU7VVHMogJ41LghGbjTLKwpbTKnQFdnPpTw0fdNAZ1LraluFEUxjwrgURZNcExRtoktcC8gQbU4gW6TeVQAVxTTqADezYzibLZWtuIPRmIvrDOhlepGAT5vgaux4IpiHhXAu5lZnENEl2zc1xx7YSqh1X48TuMDTQVwRTGPCuDdTC/KBmDNXhO6UdR0+v2kWdLIsmdR70/tpeYUxUwqgHfjSbdT4nGx1owA7lIB/EB5zjxq21ULXFHMogL4AWYW57BmrwkTely5gFBdKN2o2ZiKYq6YA7gQwiKEWCuEeMmMCsXbjOJsalsD7Is1taxmMZJaqRZ4FxXAFcVcZrTArwO2mlDOoDCjKAeAtXubYi/Mnada4N3kOfOo9deal65AUVJcTAFcCDEcOB140JzqxN+EwgwcNs2kAO4Fn7po18nr9BLSQ7QEW+JdFUVJCrG2wP8E/BTQY6/K4GCzaEwbZtKEHpcHfOqiXSc1G1NRzNXnAC6EOAOokVKuPsp+VwkhVgkhVtXWJkYwm1GczeZ9LQTCMU7ocXtVF0o3anV6RTFXLC3wLwJfFULsAZ4CThJC/PvAnaSUD0gpZ0kpZ+Xl5cVwuIEzozibYERnc0WMX/XdedDeALoJMzuTgGqBK4q5+hzApZQ/k1IOl1KOAC4C3pZSXmJazeJoRrFJFzJdnflQTMqvkuC6Arj6VqIoplDjwA9hSKaDYdnO2GdkujvzoaguA4B0WzoOi0N1oSiKSaxmFCKlfBd414yyBovpxdmsM6UFjhoLHiWEUGPBFcVEqgV+GDOLc9jX5Ke6paPvhXTmQ1FdBl1UAFcU86gAfhhdK/TE0o3SmZFQtcC75LnyVBeKophEBfDDmDw0kzRLjBN6nLnGVgXwLh6HR7XAFcUkKoAfht1qYfKwzNgCuMVq5ENRXShd8lx5tAZb6QjH0DWlKAqgAvgRzSjKYcO+JkKRGCaauryqBd5N59Jq9R0qxYCixEoF8COYUZxNR0hnW2Vr3wtx50G7CladulbmUXnBFSVmKoAfwcyS6ISeWPKiuD2qBd6NWtxYUcyjAvgRDM1ykJ9hj60f3OVVE3m66cyHogK4osROBfAjEEIwa0QOb2+roayhvW+FuL3gbwA9aRI2xiTHnoMmNDWUUFFMoAL4USz+8gSklFz52CraAuHeF+DygtRVPpQoi2Yh15GrWuCKYgIVwI9ipNfNfRfP5NOaNn74n3Xoei9Xk1GzMQ+S58xTAVxRTKACeA/MHZvHzadP5I0t1fzhjR29e3FnAFf94F08To8ahaIoJlABvIcu+8IIvj6niHvf+Yyl6/b1/IUqodVBVAtcUcyhAngPCSH49VenMGdkLj99dgPry5p69kLVhXIQr9NLQ0cDEbXQhaLERAXwXkizatx/8UzyMuxc+diqnmUqdHXmBFeTeTp5nV4iMkJjQF3YVZRYqADeS550O//45izaAmGuePQTdtW2HfkFFhs4slUfeDdqLLiimEMF8D6YWJjJX78+g921Pk7543v89qUtNLeHDv8CtbjxftTamIpiDhXA++jkiUN4Z/ECzj92OA+v3M2Cu9/hsQ/3ED5U4iuV0Go/nQFcjURRlNioAB6D/AwHd5w3jZeuPZEJBZn8culmvvLnFby5pZrSeh/lje1UNXcQsOcQaaujtSOElL0cR56EOgO4ykioKLExZU3MVDd5aBZPXHkcb2yp5tZlW/n2Y6v2+/lt1gCLLBXMvuV1hmU7mTcuj/njvJww2kuW0xanWseP0+ok3ZauWuCKEiMVwE0ihOCUyQUsGJ/PO9tr8AXChHVJRJdM2TYaz+7l3HjqONbsbebF9RU8+fFeLJpgelE288bmMbMkm1F56RRmOtA0Ee/T6Xdep1flQ1GUGKkAbrI0q8aXJxfs/6Q+GnZF+M4cDywYSyiis3ZvEys+reW9HbX86a0ddPas2K0aIzxuRnrdjMxzc/KEfGaNyB34E+lnea486v2qC0VRYqEC+EDoPhvTlYvNojFnZC5zRuby41PG0+gLsq2qld11PnbXtbG7zseOmlbe3FrNQyt28/4NC8nPdMT3HEzmdXjZVL8p3tVQlISmAvhA2G825riDfpzjTuOE0R5OGO3Z7/nSeh8L736Xh1bu5mdfmTgAFR04XpeXuvI6pJQIkfxdRorSH9QolIHQx4RWJR43p08byuP/20uz/wjjzBNQnjMPf9hPe7iPedYVRVEBfEDEkNDqO/NH0RYI8+//lZpcqfhSY8EVJXYqgA+EznwofVjcePLQLBaMz+Ph93fTEUqe5E9dAVyNRFGUPlMBfCBY08CR1efZmN+dP5p6X5BnVpWZXLH46VzcWI1EUZS+63MAF0IUCSHeEUJsEUJsFkJcZ2bFkk4MixvPGZnLzOJs/u+9XYeeqp+AVAtcUWIXSws8DPxYSjkJOB64RggxyZxqJaEYEloJIfjegjGUN/p5aUOlyRWLjyx7FjbNphJaKUoM+hzApZSVUso10futwFZgmFkVSzoub0w5wU+akM+4Ienc/+7OpMinIoTA6/SqAK4oMTClD1wIMQKYAXx0iJ9dJYRYJYRYVVubwl+X3Z6YUspqmuC7C0azvbqVt7fVmFix+PE6vZS2lFLtq06KDyVFGWgxT+QRQqQDzwHXSylbDvy5lPIB4AGAWbNmpe5/qTvPGIWi66D17XPzjGlDufu1Hdz/7k5OnjjE5AoOvKKMIpbtXsaXnv0STquT4oxiijOLKcksYXredOYXzY93FRVlUIspgAshbBjB+3Ep5X/NqVKScnlBD0NHE7j6ltvEZtG4at4ofvXCZj7Z08DsBM+R8qsTfsXZY86mtKW067ajcQfv7H2HsAxz/czruWLqFfGupqIMWn0O4MKY//wQsFVK+QfzqpSkuqbT1/c5gAN8bVYRf3nrU/72zmf88/I5JlUuPlw2FycMPYEThp6w3/OhSIibVt7En9b8CZtm45uTvxmnGirK4BZLH/gXgUuBk4QQ66K300yqV/LpWtw4tot2zjQLl39xBO9sr2X5juS8pmCz2LjtxNtYVLKI36/6PU9teyreVVKUQanPLXAp5fuAykLUU25j4ooZa2N+e+4oXlxfyY/+s45XrpubdJkKAayalTvn3kkoEuLWj27Fptk4b9x58a6WogwqaibmQOljQqtDcdgs3PuNGfiCYa7/zzoienJeG7ZZbNyz4B6+OPSL/PrDX/PCzhfiXSVFGVRUAB8oLi8IDZrLTSlu7JAMfvPVKXyws5773vnMlDIHozRLGn9a+CfmFMzhFyt/wau7X413lRRl0BADOf521qxZctWqVUffMVk9+CWQEq58y5TipJT88D/reGF9BU9eeTzHjfIc/UUJqj3Uznff/C5ratbgcXgY4h7CEJdxK3AXUOAuYO7wuWSmZca7qopiOiHEainlrIOeVwF8AL17h3H76a6YRqJ01xYIc+Zf38cfjLDsurnkutNMKXcw8oV8PLntScpby6lqr6LaV011ezWtwVYAch25/HjWjzlz1JlqkQglqagAPhiUfQIPfQnOewimnm9asZv2NXPu3z7gxLFeHvrWrJQLXu2hdrY3bufuT+5mQ90Gjh1yLDcddxNjc8bGu2qKYorDBXDVBz6Qhs0ERzZ8Zk4XSqcpw7L4+WkTeHtbDQ+9v9vUshOBy+ZiRv4M/nXav/jVCb/is6bPuODFC7j7k7vxhXzxrp6i9Bu1JuZA0iww+iTY+ZbRF25iS/lbXxjBBzvrufPVbVS3dPCVqYVMH56NpqVOa1wTGuePO5+Ti0/mz2v+zKNbHuWVPa+wsGghYT1MSA91bTvvR/QIYbn/1qpZGZM9hgm5E5iQO4FxOeNw2VzxPj1FOYjqQhloax+Hpd+D77wPBVNNLbq5PcRPnl3Pu9trCEUkhVkOvjy5gNOmFnJsSQ6WFArmAOtq1vH7T37P3ta92DQbVs2KTbMZN4sNi7Bg1axYhAWbZsOiWbAICx2RDrY3bKclaKT2EQhKMkuYkDuBBUULWFi0sEcBPaJHEEKgCfVFV4mN6gMfLFoq4Q8T4Eu3wIk/7JdDNPtDvLW1mlc2VbF8Ry3BsI433c6Vc0dyxYkjsVpUQDkaKSXV7dVsa9jG1oatbG/YzobaDdT6a3FYHCwoWsBXRn6FE4edSJrFuHAc1sNsa9jGx1Uf83Hlx6ypWUNGWgbnjzuf88aeR74r3/R6+kI+nFan+pBIciqADyb3fxGcOXDZS/1+qLZAmHe21fDs6nKW76hl2vAs7jp/GhMK1HC73tKlzprqNbyy+xVeL32dpkATGWkZLCxaSHOgmdXVq2kLtQEwOms0swpmUd5Wzsp9K7EKKwuLF3LR+IuYXTD7qBeapZT4w35agi00BZpo7Gikoq2CstYyytvKjW1rOS3BFooyirh44sWcPeZs3Db3QLwVygBTAXwweeOX8OF9cMMesGcMyCGllCzbWMUvl26ipSPENQvH8L0FY0izqpZbX4T0EB9VfsQru1/h7b1vk+vIZU7hHOYUzGF2weyuJeMA9rbs5Zkdz7DksyU0B5oZlTWK+cPnE9SD+EI+fCEf7aF2fCEfbaE2WgJG0A7qwYOOaxVWhqYPpSijiOEZw8l35fNe+Xusr11Pui2dc8eeyzcmfoNh6T1fW8Uf9rO8bDmb6zdz1bSryEgbmL9JpedUAB9Mdr8Hj54JFz0JEwY2/1eDL8ivX9zM0nUVTCjI4PfnH8PU4VkDWodU1RHu4LU9r/H09qfZXL8Zl9WF0+bEbXPjtrqNrc1Nlj2LbHs2Wfas/e4PSx/GENcQLJrloLI31G7g31v/zRt73kBH5+Tikzlt5GmMzBpJUUZRVzdPp5Ae4sOKD3l518u8U/YO/rAfgIVFC/nTwj+pLplBRgXwwSQchDtHwDEXwRnxycT7xpZqblqykXpfkK/NGs7Z04cxe0TuEUetbK9qZcnafWyuaOb35x9DQVbyJdEaKFLKfhmvX+Wr4qltT/HMjmf2uwg7NH0oJZkllGSWEIwEeXPvmzQHmslMy2RRySJOH3U62xq2cdcnd3HtjGu5atpVptdN6TsVwAebJy6Cms1w3QZThxP2RrM/xJ2vbuO/a8rpCOkMybRz2tRCzpg2lJnF2QghqGz288K6Cpas3ce2qlYsmsAiBMeP9vDo5Ufvy1XioyPcwc6mnexp2UNpS2nXtrSlFF3qLChawOkjT+cLQ7+AzWIDjA+Vn73/M5btWsbfvvQ3Thx2YpzPQumkAvhg8/E/YNlP4PurwTsmrlXxBcK8ta2GF9dXsHx7LcGIzrBsJ8OynXxS2oCUMKM4m3NmDOP0qYW8vLGSXy7dzO/OnsIlx5fEte5K70gp0aV+yG4YMPrDL112KZW+Sp464ymKMooGuIbKoagAPtg07Ia/TIev3AXHXR3v2nRp6QjxxuZqXtpQQVVLgFMnF3DW9KGM8H4+ukHXJd98+GPW7G3klevmUuJRIx+SSVlrGRe+dCFD3UP512n/wml1xrtKKU8F8MHoLzPBMxoufibeNem1iiY/X/7Te4wfksF/rj4h5SYJJbsV5Su45q1rOG3Uadx+4u0p2VW2rWEbxRnFg2IWrsqFMhiNORl2r4BQR7xr0mtDs53ccuZkVpU28uCKXfGujmKyucPncs30a3h518s8se2JeFdnwH3a+CkXvnQhN664Md5VOSIVwONpzJcg7Ie9H8S7Jn1y7sxhnDJpCPe8voPtVa3xro5isiunXcnCooXc/cnd/GXNX/i08VMG8ht7PN2z+h50qfNO2Tusq1kX7+oclgrg8TTiRLCkmZ6dcKAIIbjt3KlkOKz86Ol1BMN6vKukmEgTGreeeCsnDD2BhzY9xLkvnMvZS8/mb+v+xs6mnT0qo6mjiZX7VvLAhge47u3ruOK1K3h518uE9XA/177vPtj3ASv3reSa6dfgdXr54+o/DtoPLtUHHm+PnQWt1XDN/+Jdkz57dVMV3/n3an5w0hh+dMr4eFdH6Qd1/jreKn2L10pfY1XVKiSSMdljGJs9tisJWGdiMItmod5fz+b6zexr29dVxojMEUgkpS2lDHUP5dJJl3Lu2HMHRR9zp4ge4YKXLsAf8rP07KUs+XQJv/vod9x38n3MGz4vbvVSFzEHqw/+Cq/fDD/cDFnD412bPvvRf9axdH0Ff75oOqdPLUzJi16pos5fxxulb/Bm6ZvUtNcQkZH9U/PKCBm2DCZ7JzPZY9wmeiaSkZaBLnXeK3+Pf276J2tq1pCZlsmF4y/kGxO/sV/6gXh5bsdz3PLhLdwz/x5OGXEKIT3E2c+fjd1q55kznjns8Mv+pgL4YFW9Be4/Ac78Cxz7rXjXps+a/SEufvB/bNrXwqJJQ/jNWZMpzFLDz5TDW1+7nkc2PcJbe9/CqllZULSAs0afxReGfQGbZjP3YLoObdWQWXjYXdpD7Zy+5HSKMop49NRHuxohr+5+lcXvLea2E2/jzNFnmluvHlIBfLCSEv442Vix/uRfwpTzjIUfBis9Aq2Vh/y2EIroPPz+bv745g6smsbiL4/nkuNL1BBD5YhKW0p5attTvLzrZRoDjeQ6cjlt5GmcNeYsxueMj/3bXCQEz10BW16As/8G079xyN3uXXsv/7fh/3j8tMeZljet63ld6lz00kU0B5p58ZwXD8orMxBUAB/MSj+AZT+F6o2QNwEW/hwmnAnaILrG3LQX1vwL1v4bWitg7Cnw5dvAe/C6k3vr27np+Y2s+LSO6UXZ3HHeVJW+VjmqkB7i/fL3eXHXi7xb9i4hPcTYnLEsGL6A2QWzmZ4/vfeTisJBePZy2PYSeMZC/Wdw7gMw7Wv77Vblq+LMJWeysGghd82/66BiPqj4gKvfuJqfzv4pl066NIaz7BsVwAc7XYetS+Gd26Buh7Faz8KbYdyX45YrhXAQti+DNY/BzreN58acDIXHGKkAQu0w52qY/1NwZu/3UiklS9dV8NuXttDsD/GliUOYWZLNjOIcpg7LwmEbxN8ylLhrDjTz6u5XeWnXS2ys20hEGkvdTfVOZdaQWcwumM2M/Bk4rEdIqBYOwNPfgh2vwKl3wsxvwhNfg9KVcN6DxrfdqJvev4lXd7/KC+e8cNhUvFe+fiXbG7az7NxlpKel9+p8Yk1epgJ4otAjsPFZePd2aNwN6QVgsYHUjZseMbaaBTKHQlaRccuObrOGgc1tvMaSFr1F71sdh2/V6zo07YGarVCzxdjufg98tZA5DGZcYtyyi43922rh7d8awd2VCwtvgmMv27/7J+SnuXovzyxfxRtl8FFTFiCwaoJJQzOZWZzD2CHpeNxp5Lrt5Lpt5LrtZDltydftIiXUfWqsh7rzbSOVgiMT7JnG1pFl3E/Ph5HzoOCYwfUNLI58IR9ra9bySdUnrKpaxeb6zURkBIfFwfGFxzO/aD7zh88nz5X3+YtCHfD0pfDp63Da3TDnSuP5oA/+fT6UfQTnPwyTz2ZL/RYufOlCLp9yOT869keHrcfm+s1c9NJFXD3tar4/4/tHrbeUkm0N23hp10u8UfoG/znjP+Q4cvr0HvRLABdCnAr8GbAAD0op7zjS/iqA90IkBOufMloLQjNa4UIDYTG2kSC07IPmcmgqMyYEHZUwFpCwZ0Ba+uf3O5qgdrvRou6UVQzDZsL0i41W9+H65Ss3wKs/g9L3je6fjEJorTK6WTqa9z+ljGFU585inWUqr7SN4c1KJ/5QZL99HATIFW0MdwQYYg+QbwvgtXXgsXSQpfnJ0IIEbRl0pOVGbx46HB5CadlkyRY84SpygpVkdVSS3lGBq30f0p5FuGA6+tBj0YbNwJ6eg8Nm+fxDIhKC2m1QsRYq1hndRe4844JXRvSWWQjufJARo2UX8qOHOtBDfmSoA4tmQbN1fmDaox+aNuN93fkWcufbiOZyAPwZI2jLHo+LDhwRH5ZgC3S0QKDl899B+hAYswjGnQKjFhgBHmgPhtm0r4UN5U2sL29mc0UzWU4bxwzPZnpRNtOGZzHC4+73xax1XdLQHsSqCTIdtgFdPNsX8rGmeg0r9q1gedlyKnwVAEz2TGZ+0XwmZ43B/95dtNdspn3qebQXTsMX8hHUgzitTtzCinvN47ga9+A+4Voebd7C7ubdvHzuy0ddzOIny3/Ce+XvsezcZYcdNVPZVsnLu1/m5V0v81nTZ1g1K/OGzeMns35CUWbfkoOZHsCFEBZgB7AIKAc+Ab4updxyuNeoAN5PpIT2emgug5YKCPmNoBQJRm8hiAQg2A7BNiNQBFo/v6W5IX8S5E80tnnje7dSkJSw9QV4/4/GB0xGgfHtIKPACH7p+UaLc88K2PO+UVdAZhURzChCtjcg/I1YA01Y9MARDxWWGlbRswlDVTKHfdJLDq2M0qq6nt+pF7JOjqYdJ1O13UykFLsIAdCGi3IxhGzZgpdGrMQ+OakVFx/oU1gemcp7+lTK5f5rY2Y6rAzNdlKY5WCEs50JbR8zofVDxrZ+jEtvI4KF3c4p7A7nUuZ30CTdNONGc2aT682nJSDZW9dGOBLCgk56mmBkrpNCRwhXqB53sJ6McAMZ4XoyI41YZYhmWx4t9kLaHIV0uIcRTB+O7s7HogfRQu1Ywu1Ywz4skXa0cDv+jiBtHSF8AWPrD4TQdUkEjYiwYk2z47DbSbM7cDqcpNmdCEc6mj0di92NzZmBzZmBNc2BFg4gIx0Q6kBEOhDhICLSgaZpaJowPgw1DatFQ9MsSARSQgSBRKBL0KUgKAXtIcmejio2+zezPbiZ6she4OCYJrBgETbCMnDIn58/4lrm5Z+F0INYIn6s4XasET9SD6NHdCK6TkSPUBWo4daKO/FYvWTZvGiaA4vmxKKlYRU2GoNVlLZvAGBUxhS+MOQUvlBwEh5nDqPy3H3uOuyPAH4CcIuU8svRxz8DkFLefrjXqACuIKXR2t29Ava8Z3TFuHLBmQuunOg2FxzZRquz+82eabRqg23gq0VvrUX31SJbq5Ht9YTScvCnD8fnHEpL2hDadRvtoQiBUISIrwF3/UYyGzaS27SRvOZN2CJ+qtwTqHCNZ59rPGWOCdTahhKRAiEEVhEhM9JMVriOzHAdGeFG0KxELHakxU7E4kBa7OgWO6FwhGAgQDDYQTjYQTgUIBLsoMmWT6tnGp5MN/kZDvIy7ORl2LFqgqqWDiqaOqhs9ndt69oCSGl84bJKnSns4Av6ambLjeSLZjLwYY/4evWWt5BOo5ZDk5ZDkyWHMDZywjXkRWookLXYROTohSSIek2jzJbGQ/rXeCvyBcIhG6GwDSmt0T10EGGEFiBda+K39r9TolUyKqDhItCj9+KZDDevut0EhSAgBEEh6BAaHULg0gVfbJPMb9UpCIPo9mFhufQ5Royd3Kfz6o8Afj5wqpTy29HHlwLHSSm/f8B+VwFXARQXFx9bWlrap+MpihIVCRndU/4mo/tLjxhdXEKLbi3GNs1tdMVY7YcvS9fRWyoJ1JcSbK4EqxNhdyPS0o1ttAXtSLPv35WHMO5L/YBvetFtyA+hdiIdbQT8LQTbWwn624gE/ca1GJsDrA6EzYmwOsBiQwLhiI6u60QiEWOrR0CCJiSaAM1oj6MhsWrg0HTsmiRNRIxvZnrY6MormrP/W6ZLQhGdYEQnEOrcRgi3N5C96l5EJIhucxGxuohYnUSsLnSrE6HZot8MNDSLhiY0LJpA00NogRa0YCtasAURaDG6wkJ+IhJ0KdElRHSMrZRknXUnLm9xn37lhwvg1kPtbCYp5QPAA2C0wPv7eIqS9Cw2cHuNW6w0DS17GM7sYfRt2pXFqA+HzglvAVzRWzxZol0zDpsF9hu4kg4lBw8bTBSxXObeB3TvkR8efU5RFEUZALEE8E+AsUKIkUKINOAi4AVzqqUoiqIcTZ+7UKSUYSHE94HXML4pPSyl3GxazRRFUZQjiqkPXEq5DFhmUl0URVGUXlBTvRRFURKUCuCKoigJSgVwRVGUBKUCuKIoSoIa0GyEQohaoK9TMb1AnYnVSRTqvFNLqp43pO659+S8S6SUeQc+OaABPBZCiFWHmkqa7NR5p5ZUPW9I3XOP5bxVF4qiKEqCUgFcURQlQSVSAH8g3hWIE3XeqSVVzxtS99z7fN4J0weuKIqi7C+RWuCKoihKNyqAK4qiJKiECOBCiFOFENuFEJ8JIW6Md336ixDiYSFEjRBiU7fncoUQbwghPo1u+7as9SAmhCgSQrwjhNgihNgshLgu+nxSn7sQwiGE+FgIsT563r+OPj9SCPFR9O/9P9F0zUlHCGERQqwVQrwUfZz05y2E2COE2CiEWCeEWBV9rs9/54M+gEcXT74P+AowCfi6EGJSfGvVbx4BTj3guRuBt6SUY4G3oo+TTRj4sZRyEnA8cE30d5zs5x4ATpJSHgNMB04VQhwP3An8UUo5BmgErohfFfvVdcDWbo9T5bwXSimndxv73ee/80EfwIE5wGdSyl1SyiDwFHBWnOvUL6SU7wENBzx9FvBo9P6jwNkDWaeBIKWslFKuid5vxfinHkaSn7s0tEUf2qI3CZwEPBt9PunOG0AIMRw4HXgw+liQAud9GH3+O0+EAD4MKOv2uDz6XKoYIqWsjN6vAobEszL9TQgxApgBfEQKnHu0G2EdUAO8AewEmqSU4eguyfr3/ifgp4AefewhNc5bAq8LIVZHF3yHGP7O+31RY8U8UkophEjacZ9CiHTgOeB6KWWL0SgzJOu5SykjwHQhRDawBJgQ3xr1PyHEGUCNlHK1EGJBnKsz0E6UUu4TQuQDbwghtnX/YW//zhOhBZ7qiydXCyEKAaLbmjjXp18IIWwYwftxKeV/o0+nxLkDSCmbgHeAE4BsIURn4yoZ/96/CHxVCLEHo0v0JODPJP95I6XcF93WYHxgzyGGv/NECOCpvnjyC8C3ove/BSyNY136RbT/8yFgq5TyD91+lNTnLoTIi7a8EUI4gUUY/f/vAOdHd0u685ZS/kxKOVxKOQLj//ltKeXFJPl5CyHcQoiMzvvAKcAmYvg7T4iZmEKI0zD6zDoXT741vjXqH0KIJ4EFGOklq4FfAc8DTwPFGKl4vyalPPBCZ0ITQpwIrAA28nmf6M8x+sGT9tyFENMwLlpZMBpTT0spfyOEGIXRMs0F1gKXSCkD8atp/4l2ofxESnlGsp939PyWRB9agSeklLcKITz08e88IQK4oiiKcrBE6EJRFEVRDkEFcEVRlASlAriiKEqCUgFcURQlQakAriiKkqBUAFcURUlQKoAriqIkqP8Pjpxa2nO7OfsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 训练误差\n",
    "for i, name in enumerate(name_l):\n",
    "    plt.plot(list(range(num_epochs)), train_l[i], label=name)\n",
    "plt.legend(loc = 1)\n",
    "plt.title('mse_train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d12f6d10>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d12f7190>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d12f7400>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d12f76d0>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c0d12c92d0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mse_test')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 测试误差\n",
    "for i, name in enumerate(name_l):\n",
    "    plt.plot(list(range(num_epochs)), test_l[i], label=name)\n",
    "plt.legend(loc = 1)\n",
    "plt.title('mse_test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "伴随模型复杂增加，模型效果没有提升，但收敛速度却下降的很严重，而model4更是没有收敛到其他几个模型的MSE，问题不小。\n",
    "\n",
    "> 此处Sigmoid激活函数堆叠后出现的问题，本质上就是梯度消失所导致的问题。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.tanh激活函数叠加\n",
    "\n",
    "&emsp;&emsp;最后，我们再来看下tanh激活函数叠加后的模型效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "class tanh_class2(nn.Module):                                   \n",
    "    def __init__(self, in_features=2, n_hidden1=4, n_hidden2=4, out_features=1):       \n",
    "        super(tanh_class2, self).__init__()\n",
    "        self.linear1 = nn.Linear(in_features, n_hidden1)\n",
    "        self.linear2 = nn.Linear(n_hidden1, n_hidden2)\n",
    "        self.linear3 = nn.Linear(n_hidden2, out_features) \n",
    "        \n",
    "    def forward(self, x):                                    \n",
    "        z1 = self.linear1(x)\n",
    "        p1 = torch.tanh(z1)\n",
    "        z2 = self.linear2(p1)\n",
    "        p2 = torch.tanh(z2)\n",
    "        out = self.linear3(p2)\n",
    "        return out\n",
    "\n",
    "    \n",
    "class tanh_class3(nn.Module):                                   \n",
    "    def __init__(self, in_features=2, n_hidden1=4, n_hidden2=4, n_hidden3=4, out_features=1):       \n",
    "        super(tanh_class3, self).__init__()\n",
    "        self.linear1 = nn.Linear(in_features, n_hidden1)\n",
    "        self.linear2 = nn.Linear(n_hidden1, n_hidden2)\n",
    "        self.linear3 = nn.Linear(n_hidden2, n_hidden3)\n",
    "        self.linear4 = nn.Linear(n_hidden3, out_features) \n",
    "        \n",
    "    def forward(self, x):                                    \n",
    "        z1 = self.linear1(x)\n",
    "        p1 = torch.tanh(z1)\n",
    "        z2 = self.linear2(p1)\n",
    "        p2 = torch.tanh(z2)\n",
    "        z3 = self.linear3(p2)\n",
    "        p3 = torch.tanh(z3)\n",
    "        out = self.linear4(p3)\n",
    "        return out\n",
    "\n",
    "    \n",
    "class tanh_class4(nn.Module):                                   \n",
    "    def __init__(self, in_features=2, n_hidden1=4, n_hidden2=4, n_hidden3=4, n_hidden4=4, out_features=1):       \n",
    "        super(tanh_class4, self).__init__()\n",
    "        self.linear1 = nn.Linear(in_features, n_hidden1)\n",
    "        self.linear2 = nn.Linear(n_hidden1, n_hidden2)\n",
    "        self.linear3 = nn.Linear(n_hidden2, n_hidden3)\n",
    "        self.linear4 = nn.Linear(n_hidden3, n_hidden4)\n",
    "        self.linear5 = nn.Linear(n_hidden4, out_features) \n",
    "        \n",
    "    def forward(self, x):          \n",
    "        z1 = self.linear1(x)\n",
    "        p1 = torch.tanh(z1)\n",
    "        z2 = self.linear2(p1)\n",
    "        p2 = torch.tanh(z2)\n",
    "        z3 = self.linear3(p2)\n",
    "        p3 = torch.tanh(z3)\n",
    "        z4 = self.linear4(p3)\n",
    "        p4 = torch.tanh(z4)\n",
    "        out = self.linear5(p4)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1c0c05fefd0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#————————————————————————————tahn训练模型————————————————————————————\n",
    "\n",
    "# 创建随机数种子\n",
    "torch.manual_seed(42)  \n",
    "\n",
    "# 实例化模型\n",
    "tanh_model1 = tanh_class1()\n",
    "tanh_model2 = tanh_class2()\n",
    "tanh_model3 = tanh_class3()\n",
    "tanh_model4 = tanh_class4()\n",
    "\n",
    "# 模型列表容器\n",
    "model_l = [tanh_model1, tanh_model2, tanh_model3, tanh_model4]           \n",
    "name_l = ['tanh_model1', 'tanh_model2', 'tanh_model3', 'tanh_model4']\n",
    "\n",
    "# 核心参数\n",
    "num_epochs = 50\n",
    "lr = 0.03  \n",
    "\n",
    "train_l, test_l = model_comparison(model_l = model_l, \n",
    "                                   name_l = name_l, \n",
    "                                   train_data = train_loader,\n",
    "                                   test_data = test_loader,\n",
    "                                   num_epochs = num_epochs, \n",
    "                                   criterion = nn.MSELoss(), \n",
    "                                   optimizer = optim.SGD, \n",
    "                                   lr = lr, \n",
    "                                   cla = False, \n",
    "                                   eva = mse_cal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d1361120>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d1361570>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d1361840>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d1361b10>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c0d13496c0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mse_train')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 训练误差\n",
    "for i, name in enumerate(name_l):\n",
    "    plt.plot(list(range(num_epochs)), train_l[i], label=name)\n",
    "plt.legend(loc = 1)\n",
    "plt.title('mse_train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d1432560>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d14329b0>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d1432950>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0d1432f50>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c0d1409210>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'mse_test')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 测试误差\n",
    "for i, name in enumerate(name_l):\n",
    "    plt.plot(list(range(num_epochs)), test_l[i], label=name)\n",
    "plt.legend(loc = 1)\n",
    "plt.title('mse_test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tanh激活函数叠加效果中规中矩，在model1到model2的过程效果明显向好，MSE基本一致、收敛速度基本一致、但收敛过程稳定性较好，也证明模型结果较为可信。\n",
    "\n",
    "而model3、model4则表现出了和前面两种激活函数在叠加过程中所出现的类似的问题，当然对于tanh来说，最明显的问题是出现了剧烈波动，甚至出现了“跳跃点”。\n",
    "\n",
    "> 此处tanh激活函数堆叠所导致的迭代过程剧烈波动的问题，也被称为迭代不平稳，需要优化迭代过程来解决。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 五、神经网络结构选择策略\n",
    "\n",
    "### 1.参数和超参数\n",
    "\n",
    "神经网络的模型结构是影响建模结果至关重要的因素。但对于一个数据集，构建几层神经网络、每一层设置多少个神经元，却不是一个存在唯一最优解的参数。\n",
    "- 首先根据经验设置模型结构、然后再根据实际建模效果不断调整的过程。\n",
    "\n",
    "> 关于参数和超参数的说明：      \n",
    "&emsp;&emsp;在机器学习中，参数其实分为两类，其一是参数，其二则是超参数，一个影响模型的变量是参数还是超参数，核心区别就在于这个**变量的取值能否通过一个严谨的数学过程求出**，如果可以，我们就称其为参数，如果不行，我们就称其为超参数。  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.神经网络模型结构选择策略\n",
    "\n",
    "**层数选择方面**   \n",
    "- 三层以内：模型效果会随着层数增加而增加；\n",
    "  \n",
    "- 三层至六层：随着层数的增加，模型的稳定性迭代的稳定性会受到影响，并且这种影响是随着层数增加“指数级”增加的，此时我们就需要采用一些优化方法对输入数据、激活函数、损失函数和迭代过程进行优化，一般来说在六层以内的神经网络在通用的优化算法配合下，是能够收敛至一个较好的结果的；\n",
    "- 六层以上：在模型超过六层之后，优化方法在一定程度上仍然能够辅助模型训练，但此时保障模型正常训练的更为核心的影响因素，就变成了数据量本身和算力。神经网络模型要迭代收敛至一个稳定的结果，所需的epoch是随着神经网络层数增加而增加的，也就是说神经网络模型越复杂，收敛所需迭代的轮数就越多，此时所需的算力也就越多。而另一方面，伴随着模型复杂度增加，训练所需的数据量也会增加，如果是复杂模型应用于小量样本数据，则极有可能会出现“过拟合”的问题从而影响模型的泛化能力。当然，伴随着模型复杂度提升、所需训练数据增加，对模型优化所采用的优化算法也会更加复杂。也就是说，六层以内神经网络应对六层以上的神经网络模型，我们需要更多的算力支持、更多的数据量、以及更加复杂的优化手段支持。\n",
    "\n",
    "**每一层神经元个数选择方面：**\n",
    "- 输入层的神经元个数就是特征个数，\n",
    "- 输出层神经元个数，\n",
    "  - 如果是回归类问题或者是逻辑回归解决二分类问题，输出层就只有一个神经元\n",
    "  - 如果是多分类问题，输出层神经元个数就是类别总数。\n",
    "- 隐藏层神经元个数，可以按照最多不超过输入特征的2-4倍进行设置\n",
    "  - 当然默认连接方式是全连接，每一个隐藏层可以设置相同数量的神经元\n",
    "\n",
    "&emsp;&emsp;其实对于神经元个数设置来说，后期是有调整空间的，哪怕模型创建过程神经元数量有些“饱和”，可以通过丢弃法（优化方法的一种）对隐藏层神经元个数和连接方式进行修改。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.激活函数使用的单一性\n",
    "\n",
    "&emsp;&emsp;同时，对于激活函数的交叉使用，**通常来说是不会出现多种激活函数应用于一个神经网络中**的情况的。\n",
    "\n",
    "主要原因并不是因为模型效果就一定会变差，而是如果几种激活函数效果类似，那么交叉使用几种激活函数其实效果和使用一种激活函数区别不大，而如果几种激活函数效果差异非常明显，那么这几种激活函数的堆加就会使得模型变得非常不可控。\n",
    "\n",
    "目前工业界针对激活函数的优化算法都是针对某一种激活函数来使用的，激活函数的交叉使用会令这些优化算法失效。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 六、【加餐】损失函数的随机创建现象\n",
    "\n",
    "&emsp;&emsp;接下来，我们通过手动创建一个实例，来观察在小批梯度下降过程中，损失函数是如何根据数据数据变化而变化的，这里既是作为本节内容的一个补充，同时也是指出一个常见的误区、以及解决一个常见的问题。\n",
    "\n",
    "<center><img src=\"https://i.loli.net/2021/01/23/caNAtMFI5dqhfH6.jpg\" alt=\"11\" style=\"zoom:30%;\" />\n",
    "\n",
    "- 误区：损失函数从始至终都不变，梯度下降迭代过程就是“从山顶走到山脚”，一步步找到最小值；\n",
    "- 问题：为何迭代过程中损失函数下降，但模型评估指标却出现波动。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们通过手动实例进行说明。首先创建一组数据如下：\n",
    "\n",
    "|x|y|\n",
    "| :------: | :------: |\n",
    "| 1 | 2 |\n",
    "| 3 | 5 |\n",
    "| 6 | 4 |\n",
    "| 8 | 3 |\n",
    "\n",
    "们使用$y=wx$方程对其进行拟合，并以SSE作为损失函数。假设我们使用小批量梯度下降，每次带入两条数据进行损失函数计算，第一次带入前两条数据、第二次带入后两条数据，则第一轮epoch的第一次迭代过程中损失函数如下：\n",
    "$$ \\begin{align} SSE_{(1)}\n",
    "& = (2-1*w)^2 + (5-3*w)^2 \\\\\n",
    "& = w^2-4w+4+9w^2-30w+25 \\\\\n",
    "& = 10w^2-34w+29  \\end{align} $$\n",
    "\n",
    "据此可以算得当前梯度公式如下：\n",
    "$ \\begin{align} \n",
    "grad_{(1)}\n",
    "& = \\frac{\\partial{SSE_{(1)}}}{\\partial{(w)}}  \\\\\n",
    "& = 20w-34 \\\\ \\end{align} $\n",
    "\n",
    "\n",
    "然后再进行迭代。然而再第一轮epoch的第二次迭代时，由于我们带入了另外两条数据，此时的损失函数就有所不同了：\n",
    "\n",
    "$$ \\begin{align} SSE_{(2)}\n",
    "& = (4-6*w)^2 + (3-8*w)^2 \\\\\n",
    "& = 36w^2-48w+16+64w^2-48w+9 \\\\\n",
    "& = 100w^2-96w+25  \\end{align} $$\n",
    "\n",
    "对应的梯度计算公式也会有所不同\n",
    "$ \\begin{align} \n",
    "grad_{(2)}\n",
    "& = \\frac{\\partial{SSE_{(1)}}}{\\partial{(w)}}  \\\\\n",
    "& = 200w-96 \\\\ \\end{align} $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "而此时如果我们以SSE作为模型评估指标，则模型评估指标其实是每一个“小批”数据对应损失函数之和：\n",
    "\n",
    "$$ \\begin{align} SSE\n",
    "& = (2-1*w)^2 + (5-3*w)^2 + (4-6w)^2 + (3-8w)^2 \\\\\n",
    "& = SSE_{(1)}+SSE_{(2)} \\\\\n",
    "& = 110w^2-100w+54 \\end{align} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "迭代过程我们要求每一次迭代损失函数都降低，但通过观察上述过程不难发现，第一轮epoch迭代第一次时，$SSE_1$能够顺利降低，但$SSE_2$和$SSE$并不一定，同理，第一轮epoch迭代第二次时，$SSE_2$能够顺利降低，但$SSE_1$和$SSE$并不一定。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SSE1损失函数计算函数\n",
    "def loss_sse1(x):\n",
    "    loss1 = 10 * x * x - 34 * x + 29\n",
    "    return loss1\n",
    "\n",
    "# SSE2损失函数计算函数\n",
    "def loss_sse2(x):\n",
    "    loss2 = 100 * x * x - 96 * x + 25\n",
    "    return loss2\n",
    "\n",
    "# SSE损失函数计算函数\n",
    "def loss_sse(x):\n",
    "    loss = 110 * x * x - 100 * x + 54\n",
    "    return loss\n",
    "\n",
    "# SSE1的梯度计算函数\n",
    "def g1(x):\n",
    "    grad1 = 20 * x - 34\n",
    "    return grad1\n",
    "\n",
    "# SSE2的梯度计算函数\n",
    "def g2(x):\n",
    "    grad2 = 200 * x - 96\n",
    "    return grad2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
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     "data": {
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     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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     "execution_count": 71,
     "metadata": {},
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     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 定义核心参数\n",
    "w = 0                      # w的初始取值\n",
    "lr = 0.01                  # 模型学习率\n",
    "num_epochs = 20            # 迭代轮数\n",
    "\n",
    "# 创建空列表容器\n",
    "w_l = [0]                  # w迭代过程取值\n",
    "loss_l = []                # 整体SSE取值\n",
    "loss1_l = []               # SSE1取值\n",
    "loss2_l = []               # SSE2取值\n",
    "\n",
    "\n",
    "for i in range(num_epochs):\n",
    "    loss_l.append(loss_sse(w))         # 每一轮迭代开始前计算整体SSE\n",
    "    loss1_l.append(loss_sse1(w))       # 每一轮第一次迭代开始前计算SSE1\n",
    "    w -= lr * g1(w)                    # 每一轮的第一次迭代\n",
    "    w_l.append(w)                      # 每一轮第一次迭代后储存w\n",
    "    loss1_l.append(loss_sse1(w))       # 每一轮第一次迭代后计算SSE1\n",
    "    loss2_l.append(loss_sse2(w))       # 每一轮第二次迭代开始前计算SSE2\n",
    "    w -= lr * g2(w)                    # 每一轮第二次迭代\n",
    "    w_l.append(w)                      # 每一轮第二次迭代后储存w\n",
    "    loss2_l.append(loss_sse2(w))       # 每一轮第二次迭代后计算SSE2\n",
    "\n",
    "loss_l.append(loss_sse(w))             # 训练完成后存储最终w\n",
    "\n",
    "# 绘图部分\n",
    "# 查看整体SSE变化情况\n",
    "plt.subplot(311)\n",
    "plt.plot(list(range(num_epochs+1)), loss_l)\n",
    "# 查看SSE1变化情况\n",
    "plt.subplot(312)\n",
    "plt.plot(list(range(num_epochs*2)), loss1_l)\n",
    "# 查看SSE2变化情况\n",
    "plt.subplot(313)\n",
    "plt.plot(list(range(num_epochs*2)), loss2_l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于SSE1和SSE2来说都是降低的，但每一轮之间SSE1和SSE2都会出现波动，原因也是因为其他的损失函数计算出来的w不一定能有效帮助当前损失函数降低取值，当然整体SSE也是在波动中趋于稳定的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 关于小批量梯度下降算法捕捉局部规律的讨论:      \n",
    "&emsp;&emsp;其实带入部分数据还是带入全部数据，其实都是相对的。在小批量梯度下降中，某一次迭代虽然是带入了全部训练数据集的一部分，但如果我们把这部分数据看成全部，就相当于是带入了全部数据进行训练。所有的训练都是为了捕捉规律，因此我们可以把某一次迭代看成是模型在捕捉这一部分数据的“全部规律”，但由于这部分数据只是所有训练数据的一部分，因此模型还只是捕捉了局部规律。从这个角度来说，小批量梯度下降是希望通过捕捉一个个“小批”数据的局部规律最终构成整个数据集的整体规律。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 关于小批量梯度下降算法有效性的讨论:      \n",
    "&emsp;&emsp;而为何通过捕捉局部规律最后能够更好的把握整体规律呢？从实际运算结果来看是因为小批量梯度下降能够跨越局部最小值点，而根本原因则是对于某些“小批”数据来说，由于局部规律和整体规律存在差异性，整体的局部最小值点对于当前“小批”数据来说根本就不是局部最小值点（毕竟不同数据的损失函数不同），因此带入该“小批”数据时候就有可能直接跨越该点，也就是说，小批量梯度下降无法跨越小批数据对应的损失函数的最小值点，但在下降的过程中却有可能偶然帮助整体损失函数跨越最小值点，这就是借助随机性解决问题的典型。"
   ]
  }
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