{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "这是Tensorflow对于回归问题(regression problem)的官方教程,个人感觉代码写的干净漂亮,不仅仅可以学到回归问题的解决方法,也可以学到诸如数据准备、结果展示等技术,因此详细的剖析了这个教程,加上了我个人认为需要补充的资料背景。\n", "\n", "本教程通过[Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg)这个数据集构建了一个模型来预测70年代晚期和80年代早期的汽车燃油经济性,即通常所说的百公里油耗(MPG的本意是Miles Per Gallon)。关于这个数据集的内容,在下面的分析中会给出其结构。\n", "\n", "这个教程既可以运行于Tensorflow 1.x版本,也可以运行于Tensorflow 2.x版本,本人亲测。" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", "id": "1rRo8oNqZ-Rj" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.0.0-beta1\n" ] } ], "source": [ "from __future__ import absolute_import, division, print_function, unicode_literals\n", "\n", "import pathlib\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import seaborn as sns\n", "\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "\n", "print(tf.__version__)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "F_72b0LCNbjx" }, "source": [ "# Auto MPG数据集\n", "\n", "Auto MPG数据集是[UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/)的476个数据集之一,包含了398条记录,结构如下:\n", "\n", "1. mpg: 百公里油耗\n", "2. cylinders: 气缸数\n", "3. displacement: 排气量\n", "4. horsepower: 马力\n", "5. weight: 车重 \n", "6. acceleration: 加速能力\n", "7. model year: 年份\n", "8. origin: 原产地\n", "9. car name: 车型名称\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "gFh9ne3FZ-On" }, "source": [ "## 下载数据集\n", "keras提供了几种下载数据的方式,一种是直接使用keras中已经包含了的数据集,比如mnist数据集。一种是如下的方式下载数据:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": {}, "colab_type": "code", "id": "p9kxxgzvzlyz" }, "outputs": [ { "data": { "text/plain": [ "'/home/subaochen/.keras/datasets/auto-mpg.data'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_path = keras.utils.get_file(\"auto-mpg.data\", \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\")\n", "dataset_path" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "nslsRLh7Zss4" }, "source": [ "使用pandas处理Auto MPG数据集。Auto MPG本质上是csv格式的,因此pandas可以直接打开。由于\"car name\"这一列和MPG并没有关系,因此在实际读取数据集的时候通过column_names限定了用到的数据列。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": {}, "colab_type": "code", "id": "CiX2FI4gZtTt" }, "outputs": [ { "data": { "text/html": [ "
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MPGCylindersDisplacementHorsepowerWeightAccelerationModel YearOrigin
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" ], "text/plain": [ " MPG Cylinders Displacement Horsepower Weight Acceleration \\\n", "393 27.0 4 140.0 86.0 2790.0 15.6 \n", "394 44.0 4 97.0 52.0 2130.0 24.6 \n", "395 32.0 4 135.0 84.0 2295.0 11.6 \n", "396 28.0 4 120.0 79.0 2625.0 18.6 \n", "397 31.0 4 119.0 82.0 2720.0 19.4 \n", "\n", " Model Year Origin \n", "393 82 1 \n", "394 82 2 \n", "395 82 1 \n", "396 82 1 \n", "397 82 1 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',\n", " 'Acceleration', 'Model Year', 'Origin']\n", "raw_dataset = pd.read_csv(dataset_path, names=column_names,\n", " na_values = \"?\", comment='\\t',\n", " sep=\" \", skipinitialspace=True)\n", "\n", "dataset = raw_dataset.copy()\n", "dataset.tail()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "3MWuJTKEDM-f" }, "source": [ "## 清理数据(缺失值)\n", "\n", "这个数据集中有几条没有数据的记录,需要清理掉。通常,新拿到一个数据集,都需要检测数据集是否存在无效记录。" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": {}, "colab_type": "code", "id": "JEJHhN65a2VV" }, "outputs": [ { "data": { "text/plain": [ "MPG 0\n", "Cylinders 0\n", "Displacement 0\n", "Horsepower 6\n", "Weight 0\n", "Acceleration 0\n", "Model Year 0\n", "Origin 0\n", "dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.isna().sum()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "9UPN0KBHa_WI" }, "source": [ "使用dropna从数据集中删除无效记录。" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": {}, "colab_type": "code", "id": "4ZUDosChC1UN" }, "outputs": [], "source": [ "dataset = dataset.dropna()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "8XKitwaH4v8h" }, "source": [ "`\"Origin\"`这个字段需要转换为one-hot编码。这是一种常见的技巧,将类别数字转化为one-hot编码,以便机器学习到原产地对于MPG的影响。如果不进行one-hot编码,`Origin`是用1,2,3等数字来表示的,直接这样使用的话机器学习会把`Origin`当做一个数量而不是**类别**来对待,这显然不是我们的本意。\n", "\n", "这里处理`Origin`字段的方法值得注意:`pop`弹出这一列,然后根据`pop`字段的值重新增加三个列。" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": {}, "colab_type": "code", "id": "gWNTD2QjBWFJ" }, "outputs": [], "source": [ "origin = dataset.pop('Origin')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": {}, "colab_type": "code", "id": "ulXz4J7PAUzk" }, "outputs": [ { "data": { "text/html": [ "
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MPGCylindersDisplacementHorsepowerWeightAccelerationModel YearUSAEuropeJapan
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" ], "text/plain": [ " MPG Cylinders Displacement Horsepower Weight Acceleration \\\n", "393 27.0 4 140.0 86.0 2790.0 15.6 \n", "394 44.0 4 97.0 52.0 2130.0 24.6 \n", "395 32.0 4 135.0 84.0 2295.0 11.6 \n", "396 28.0 4 120.0 79.0 2625.0 18.6 \n", "397 31.0 4 119.0 82.0 2720.0 19.4 \n", "\n", " Model Year USA Europe Japan \n", "393 82 1.0 0.0 0.0 \n", "394 82 0.0 1.0 0.0 \n", "395 82 1.0 0.0 0.0 \n", "396 82 1.0 0.0 0.0 \n", "397 82 1.0 0.0 0.0 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset['USA'] = (origin == 1)*1.0\n", "dataset['Europe'] = (origin == 2)*1.0\n", "dataset['Japan'] = (origin == 3)*1.0\n", "dataset.tail()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Cuym4yvk76vU" }, "source": [ "## 切分训练数据集和测试数据集\n", "\n", "这里切分数据集的方法很巧妙,使用了`DataFrame`的`sample`方法,一次性完成了随机选取,也避免了`shuffle`操作,至少是简洁的做法,是否高效未可知。\n", "\n", "测试数据集的获得也很巧妙,直接从原始数据集中`drop`掉训练数据集即可。\n", "\n", "另外常见的切分数据集的方法是sklearn的train_test_split函数,比如:\n", "\n", "```\n", "from sklearn.model_selection import train_test_split\n", "train_dataset, test_dataset = train_test_split(dataset, train_size=0.8, test_size=0.2, shuffle=True)\n", "```" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": {}, "colab_type": "code", "id": "qn-IGhUE7_1H" }, "outputs": [], "source": [ "train_dataset = dataset.sample(frac=0.8,random_state=0)\n", "test_dataset = dataset.drop(train_dataset.index)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "J4ubs136WLNp" }, "source": [ "## 查看训练数据集\n", "\n", "seaborn的pairplot提供了图形化的方式快速查看数据集,更高效直观,这里使用pairplot来表现两两的特征对比图。比如:\n", "\n", "* 从右上角的图可以看出,车子越重,燃油的经济性越低(百公里油耗越高,即每加仑行驶的里程越少)\n", "* 从第一行第二列可以看出,气缸数越多,燃油经济型越低。\n", "* 同样的,排气量大的车子,燃油经济性也越差。" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": {}, "colab_type": "code", "id": "oRKO_x8gWKv-" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.pairplot(train_dataset[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "gavKO_6DWRMP" }, "source": [ "看一下训练集的统计数据,这个统计数据在后面进行数据的标准化的时候会用到。" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": {}, "colab_type": "code", "id": "yi2FzC3T21jR" }, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
Cylinders314.05.4777071.6997883.04.004.08.008.0
Displacement314.0195.318471104.33158968.0105.50151.0265.75455.0
Horsepower314.0104.86942738.09621446.076.2594.5128.00225.0
Weight314.02990.251592843.8985961649.02256.502822.53608.005140.0
Acceleration314.015.5592362.7892308.013.8015.517.2024.8
Model Year314.075.8980893.67564270.073.0076.079.0082.0
USA314.00.6242040.4851010.00.001.01.001.0
Europe314.00.1783440.3834130.00.000.00.001.0
Japan314.00.1974520.3987120.00.000.00.001.0
\n", "
" ], "text/plain": [ " count mean std min 25% 50% \\\n", "Cylinders 314.0 5.477707 1.699788 3.0 4.00 4.0 \n", "Displacement 314.0 195.318471 104.331589 68.0 105.50 151.0 \n", "Horsepower 314.0 104.869427 38.096214 46.0 76.25 94.5 \n", "Weight 314.0 2990.251592 843.898596 1649.0 2256.50 2822.5 \n", "Acceleration 314.0 15.559236 2.789230 8.0 13.80 15.5 \n", "Model Year 314.0 75.898089 3.675642 70.0 73.00 76.0 \n", "USA 314.0 0.624204 0.485101 0.0 0.00 1.0 \n", "Europe 314.0 0.178344 0.383413 0.0 0.00 0.0 \n", "Japan 314.0 0.197452 0.398712 0.0 0.00 0.0 \n", "\n", " 75% max \n", "Cylinders 8.00 8.0 \n", "Displacement 265.75 455.0 \n", "Horsepower 128.00 225.0 \n", "Weight 3608.00 5140.0 \n", "Acceleration 17.20 24.8 \n", "Model Year 79.00 82.0 \n", "USA 1.00 1.0 \n", "Europe 0.00 1.0 \n", "Japan 0.00 1.0 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_stats = train_dataset.describe()\n", "# 不剔除MPG这一列也没有关系,只不过没有使用而已\n", "train_stats.pop(\"MPG\")\n", "train_stats = train_stats.transpose()\n", "train_stats" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Db7Auq1yXUvh" }, "source": [ "## 获得标签数据集\n", "\n", "标签(label)是训练模型的“指导”或者“准星”,根据label训练模型的目的就是在预测时,能够尽量逼近label。这里直接将train_dataset/test_dataset中的MPG列pop出来即可。" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": {}, "colab_type": "code", "id": "t2sluJdCW7jN" }, "outputs": [], "source": [ "train_labels = train_dataset.pop('MPG')\n", "test_labels = test_dataset.pop('MPG')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "mRklxK5s388r" }, "source": [ "## 数据的标准化处理\n", "\n", "关于为什么要对数据进行标准化处理,数据标准化处理有哪几种方式,感觉这几篇讲的还不错,值得参考:\n", "* [ML入门:归一化、标准化、正则化](https://zhuanlan.zhihu.com/p/29957294)\n", "* [归一化和标准化大全](https://www.jianshu.com/p/c1e112f3f57f)\n", "\n", "什么时候使用标准化,什么时候使用归一化?由于归一化是一维伸缩,标准化二维伸缩,所以基本上如果数据没有太大的噪声可以考虑归一化,处理起来比较简单,如果噪声多可以考虑标准化。下面这张图可以形象的看出标准化的过程(出处见上面第一个链接),先减去均值,然后再除以标准差:\n", "\n", "![数据的标准化过程](https://raw.githubusercontent.com/subaochen/subaochen.github.io/master/images/tensorflow/data-normalization.jpg)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "除了这里的数据标准化方法之外,sklean中已经提供了成熟的数据标准化、归一化的函数,比如:\n", "```\n", "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", "scaler = StandardScaler()\n", "# 标准化\n", "normed_train_dataset = scaler.fit_transform(train_dataset)\n", "# 归一化\n", "mm_train_dataset = MinMaxScaler().fit_transform(train_dataset) \n", "```\n", "\n", "这里自己写了一个norm方法,目的是为了能够演示标准化的步骤,也能够直接使用已经获得统计数据。" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": {}, "colab_type": "code", "id": "JlC5ooJrgjQF" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Cylinders Displacement Horsepower Weight Acceleration Model Year \\\n", "146 -0.869348 -1.009459 -0.784052 -1.025303 -0.379759 -0.516397 \n", "282 -0.869348 -0.530218 -0.442811 -0.118796 0.624102 0.843910 \n", "69 1.483887 1.482595 1.447140 1.736877 -0.738281 -1.060519 \n", "378 -0.869348 -0.865687 -1.099044 -1.025303 -0.308055 1.660094 \n", "331 -0.869348 -0.942365 -0.994047 -1.001603 0.875068 1.115971 \n", "\n", " USA Europe Japan \n", "146 0.774676 -0.465148 -0.495225 \n", "282 0.774676 -0.465148 -0.495225 \n", "69 0.774676 -0.465148 -0.495225 \n", "378 0.774676 -0.465148 -0.495225 \n", "331 -1.286751 -0.465148 2.012852 \n" ] } ], "source": [ "def norm(x):\n", " return (x - train_stats['mean']) / train_stats['std']\n", "normed_train_data = norm(train_dataset)\n", "normed_test_data = norm(test_dataset)\n", "print(normed_train_data[:5])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "BuiClDk45eS4" }, "source": [ "需要特别两点:\n", "\n", "* 当对数据进行了标准操作的时候,所有喂入网络的数据必须根据同一个scaler对象进行标准化操作,即便是one-hot编码的数据也要做标准化处理。这很容易理解:只有数据同步缩放才能消除比较”突出“的数据。\n", "* 测试数据集以及将来进行预测的其他数据也要进行同样的标准化处理。" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "SmjdzxKzEu1-" }, "source": [ "# 模型" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "6SWtkIjhrZwa" }, "source": [ "## 构建模型\n", "\n", "这里使用了高阶的keras API构建模型,这是目前主流的模型构建方法。在这个简单的模型中只包含了两个全连接隐藏层和一个全连接输出层,其中我们的输入层的shape为(len(train_dataset.keys()),),第一个全连接层的输出shape为(None, 64),第二个全连接层的输出为(None, 64)。其中,None的大小由BATCH_SIZE决定。" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": {}, "colab_type": "code", "id": "c26juK7ZG8j-" }, "outputs": [], "source": [ "def build_model():\n", " model = keras.Sequential([\n", " layers.Dense(64, activation=tf.nn.relu, input_shape=[len(train_dataset.keys())]),\n", " layers.Dense(64, activation=tf.nn.relu),\n", " layers.Dense(1)\n", " ])\n", "\n", " optimizer = tf.keras.optimizers.RMSprop(0.001)\n", "\n", " model.compile(loss='mean_squared_error',\n", " optimizer=optimizer,\n", " metrics=['mean_absolute_error', 'mean_squared_error'])\n", " return model" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": {}, "colab_type": "code", "id": "cGbPb-PHGbhs" }, "outputs": [], "source": [ "model = build_model()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Sj49Og4YGULr" }, "source": [ "## 查看模型的结构\n", "\n", "`.summary`函数可以打印出模型的简单描述:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": {}, "colab_type": "code", "id": "ReAD0n6MsFK-" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "dense (Dense) (None, 64) 640 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 64) 4160 \n", "_________________________________________________________________\n", "dense_2 (Dense) (None, 1) 65 \n", "=================================================================\n", "Total params: 4,865\n", "Trainable params: 4,865\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Vt6W50qGsJAL" }, "source": [ "先尝试运行一下模型。这里取批次(batch)大小为10,先喂入10组数据看看结果如何:`model.predict`给出了预测的结果,shape为(10,1),符合模型的定义。" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": {}, "colab_type": "code", "id": "-d-gBaVtGTSC" }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.33291826],\n", " [ 0.20068091],\n", " [-0.2432324 ],\n", " [ 0.2772663 ],\n", " [ 0.25557014],\n", " [ 0.05211536],\n", " [ 0.28773528],\n", " [ 0.38458073],\n", " [ 0.04117467],\n", " [ 0.15005343]], dtype=float32)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example_batch = normed_train_data[:10]\n", "example_result = model.predict(example_batch)\n", "example_result" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "0-qWCsh6DlyH" }, "source": [ "## 训练模型\n", "\n", "这里定义的训练轮次(epochs)为1000。训练和验证的准确率记录在`history`中了,方便通过图形化显示准确率的变动情况。\n", "\n", "注意fit函数的参数validation_split,表示将训练数据自动拿出指定比例的数据作为验证数据集,验证数据集是不参与训练的。因此,使用了validation_split还是很方便的一种手段,不需要手工准备验证数据集。" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": {}, "colab_type": "code", "id": "sD7qHCmNIOY0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "....................................................................................................\n", "....................................................................................................\n", "....................................................................................................\n", "....................................................................................................\n", "....................................................................................................\n", "....................................................................................................\n", "....................................................................................................\n", "....................................................................................................\n", "....................................................................................................\n", "...................................................................................................." ] } ], "source": [ "# Display training progress by printing a single dot for each completed epoch\n", "class PrintDot(keras.callbacks.Callback):\n", " def on_epoch_end(self, epoch, logs):\n", " if epoch % 100 == 0: print('')\n", " print('.', end='')\n", "\n", "EPOCHS = 1000\n", "\n", "history = model.fit(\n", " normed_train_data, train_labels,\n", " epochs=EPOCHS, validation_split = 0.2, verbose=0,\n", " callbacks=[PrintDot()])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "tQm3pc0FYPQB" }, "source": [ "图形化展示`history`对象中存储的训练过程。" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": {}, "colab_type": "code", "id": "4Xj91b-dymEy" }, "outputs": [ { "data": { "text/html": [ "
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lossmean_absolute_errormean_squared_errorval_lossval_mean_absolute_errorval_mean_squared_errorepoch
9952.8567081.0743852.8567088.4997142.2128238.499714995
9962.8160711.0946232.8160718.8837252.3657548.883725996
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9992.6062831.0272122.6062838.4045072.2653478.404507999
\n", "
" ], "text/plain": [ " loss mean_absolute_error mean_squared_error val_loss \\\n", "995 2.856708 1.074385 2.856708 8.499714 \n", "996 2.816071 1.094623 2.816071 8.883725 \n", "997 2.852280 1.078358 2.852280 8.369126 \n", "998 2.754595 1.063068 2.754595 8.362683 \n", "999 2.606283 1.027212 2.606283 8.404507 \n", "\n", " val_mean_absolute_error val_mean_squared_error epoch \n", "995 2.212823 8.499714 995 \n", "996 2.365754 8.883725 996 \n", "997 2.132020 8.369126 997 \n", "998 2.193305 8.362682 998 \n", "999 2.265347 8.404507 999 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hist = pd.DataFrame(history.history)\n", "hist['epoch'] = history.epoch\n", "hist.tail()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": {}, "colab_type": "code", "id": "B6XriGbVPh2t" }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_history(history):\n", " hist = pd.DataFrame(history.history)\n", " hist['epoch'] = history.epoch\n", "\n", " plt.figure()\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Mean Abs Error [MPG]')\n", " plt.plot(hist['epoch'], hist['mean_absolute_error'],\n", " label='Train Error')\n", " plt.plot(hist['epoch'], hist['val_mean_absolute_error'],\n", " label = 'Val Error')\n", " plt.ylim([0,5])\n", " plt.legend()\n", "\n", " plt.figure()\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Mean Square Error [$MPG^2$]')\n", " plt.plot(hist['epoch'], hist['mean_squared_error'],\n", " label='Train Error')\n", " plt.plot(hist['epoch'], hist['val_mean_squared_error'],\n", " label = 'Val Error')\n", " plt.ylim([0,20])\n", " plt.legend()\n", " plt.show()\n", "\n", " # loss\n", " plt.figure()\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Loss [MPG]')\n", " plt.plot(hist['epoch'], hist['loss'],label='Train Loss')\n", " plt.plot(hist['epoch'], hist['val_loss'], label='Val Loss')\n", " plt.legend()\n", " plt.show()\n", "\n", "\n", "plot_history(history)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "AqsuANc11FYv" }, "source": [ "上面的图形显示,训练轮次达到100以后,验证误差不再降低,甚至有些抬升,这意味着继续训练没有益处(过拟合)。Tensorflow提供了EarlyStopping技术可以在验证误差(或者其他指定的指标)不再继续“改进”时停止训练,具体可以参考我的另外一篇博客:[Tensorflow的EarlyStopping技术](https://subaochen.github.io/tensorflow/2019/07/21/tensorflow-earlystopping/)。\n", "\n", "官方的EarlyStopping介绍在 [这里](https://www.tensorflow.org/versions/master/api_docs/python/tf/keras/callbacks/EarlyStopping)." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": {}, "colab_type": "code", "id": "fdMZuhUgzMZ4", "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "...................................................." ] }, { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "model = build_model()\n", "\n", "# The patience parameter is the amount of epochs to check for improvement\n", "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n", "\n", "history = model.fit(normed_train_data, train_labels, epochs=EPOCHS,\n", " validation_split = 0.2, verbose=0, callbacks=[early_stop, PrintDot()])\n", "\n", "plot_history(history)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "3St8-DmrX8P4" }, "source": [ "从上图可以看出,在验证数据集上,平均误差大概在+/-2 MPG。这个误差范围是好还是不好呢?如果平均的MPG是20左右,则误差大概有10%了。平均的MPG可以由统计数据给出。\n", "\n", "下面看一下在测试数据集上模型的表现如何?通常,把在测试数据集上的表现称为模型的“泛化能力”。可以看出,在测试数据集上的表现和验证数据集上是差不多的,训练的这个模型的泛化能力还不错。" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": {}, "colab_type": "code", "id": "jl_yNr5n1kms" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing set Mean Abs Error: 1.84 MPG\n" ] } ], "source": [ "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=0)\n", "\n", "print(\"Testing set Mean Abs Error: {:5.2f} MPG\".format(mae))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ft603OzXuEZC" }, "source": [ "## 进行预测\n", "\n", "用测试数据集进行预测,看一下预测的准确率如何?预测的方法是使用测试数据集调用模型的`predict`方法,将预测结果和测试数据集的label集合对比,即可以看出预测的效果了。这里采取的方法是,将测试集的label和预测值构造为一个(x,y)点显示在二维坐标上,于是所有的(x,y)如果集中在45度角的直线附近的话,证明预测的准确率比较高。" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "colab": {}, "colab_type": "code", "id": "Xe7RXH3N3CWU" }, "outputs": [ { "data": { "image/png": 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\n", 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fTZptoRdQLWqkTfHuvximT/Ab22LsOz5FTltZg9ZPTWd55PgU1164id6Q147QbWxWLdWWS3gj6HnITi3fmpYDs9NiwlA/h2MpU9DnablkCzrewrT6z8BlIHWuXJ8lXzR4/MT0gpfbSe7arzZD37DbFnQbm9VNjeViFRetMR/dnCeaKKqfQ1KaBUbzjNDLUxYZuAyAPcE4DgEPPb+yfPQfPnmaCweibOkN0hfyEE8X0I2lvUKzBd3GphPUs1wAUmtM0M1pRTO6m7N6AiQwBX2eEbpVVCSFEzbuASCYPsGu/ih3PDrE4ZGVUTV6bCLNgeEZ3nihmqPaG/ZiSIinl9ZHtwXdxqYT1FguazVCV5bLtObhgk0R8njQhXvem6KjiRzdJNF9XRBaB54wxJ/nw6/fQb6oc+3nfsm/Pnh82bu1/vDJUwgBr9+tqoR7Q2oPYaltF1vQbWw6QbXlEqrt5zI0leHYRHqJF7bEmBH6lOaiP+Yn6neTdQbnbbmMzKgIXQR7VH/17q0Qf569W7v5yftfwUu39/DRHzzFe/5137J41gCJnMb3nxjmsi3dbIz6geUT9IW0z7WxsbGotlw8AdWYyozQZzIa133hIfq7/Nzxvpcu0yKXANNDny66CXpdbOkJkJoOEFqA5XKxI4Uz2Ktu6N4GowcB6At7+cqNl/GvDx7nb35ymD/698f55k0v7sjLmIu79p/igWfHefzENEfGU0gJ733l9tL9vSH1d2ALuo3NakQvmylqYeaiSyn58zsPMJLIEfA6l2d9S4WZ5ZLBR8jr4qyeINNxPxvmabmMJfL0OtMQeJG6oXsbHP4R6EVwuhBCcOPLtjKd1fg/9z7HeDJPX9jbqVfTYE05/vDfHyfqd3Px5hjXXriJS7Z08ZJtPaVjes01TCRtD93GZvWhF0A4wVEm2KH1kBzl/z0+zI/2nybocZItrKzc6Y5jRugZvKUIPV70YczXcknkVB+XgCmW3dvUQO6ZkxXHXXPBBqSEew6NLmj5rXAirl7j/7lhD1/5vb380avO5qXbexFitkd72OvCswzVorag29h0Ar1AUbh5yz89xM+fNadvdZ2FPv4st33/IJdt6eK3L+4nnS8u7zoXGzNCz0ol6Gf1BJmRAYqZeQr6dJawkagUdID48xXHnbchzGC3n/98avE3oUdGT/GQ9w85N/6zhscIIegLeRm3Bd3GZhWia2g4efh4nHd9+WHe8S+/ZiiwA2d+ms2M8qm37CHsc5NdYdWNHac8Qvc42dIbICkDGNn2i4AMQ5JNxXGig79b3dhA0IUQXLNjAw8emSSZ0xb0EuYi8NxdbBRx1h3+t6bH9YY8S17+bwu6jU0n0AtouNnWG+S2N+zgwPAMv3+vSqX7q0uyDHYHCLidaLqkUFy5rV+b0VKRjJnlkmU2Qk8SwDmP9rnxTEFF5zAboYc3qGlI8WM1x1+zcwMF3eC+ZxZ3PvGWUz8GwPnCf0PiVMPjekNexpe44+Kcgi6E8AkhHhZCPCmEeEoI8Vfm7V8VQhwTQjxh/tuz+Mu1sVmh6AWKwkXI5+Ldv7GVn998Ja965SvQHD4udR0FwO9R/vpq9NEffSHOBR/9KS9MzpF2WUijO7wYOAh5XfQEPeSdQdx6Boz2XvfIjMpBB2YFXQgVpVdF6AAXb+6iN+RZXNtlZohtmSe533cVIOHg9xoeuhzl/61E6HngKinlhcAe4LVCCCs36GYp5R7z3xOLtkobm5WOrqHhwudWoh31u/nT1+7EPXgJYvhRAIJelVSW0Vafj/7Nh0+S0wyOT2aaH6hlKLpULnbQq7JQ3EFzUHabqYtjSZWDDswKOpRy0atxOgSv2bGe+w+PLV7jrgPfBeCBje+BTRfBgW83PLQ3rMr/jSUs/59T0KUiZf7Xbf5bYy3kqjj+35CJL/cqbFYSeqFC0Ev0XwIjB6CYJ2BG6On86orQc5rOTw+qqHdOf7qQQXNYgq5ery9k+t9tpi6OzOTpwpSeQPfsHd3bYOpY3Yj/6gs2kC7oi9YvXR74Dk8YL8K/4UWw63o4/SSMP1v32N6QF92QTGWWzkdvyUMXQjiFEE8AY8B/SSl/bd51uxBivxDi00KIusmfQoibhBD7hBD7xscX19taEnQNvvYmeORLy70Sm5WErlGQLnyuqrfUwKUqpXHkIAGPitBXm+Xys8NjpMzsnFRujqsLLU3BocbPhcwrklBMiXEx097G6EgiR7ejXoS+Tf1ME8M1j3np9h5CXtfi2C5jhxCjB7lTfykDXQHY+TuAgIPfrXv4bLXoChN0KaUupdwDDAB7hRA7gVuB84DLgG7glgaP/aKU8lIp5aV9fX0dWvYyUkiDUYTU2NzH2qwd9AIFnCWfvET/Jerr8L5ShJ4prC7L5c7Hh4n6ze6Jcwl6IUNB+HAI8JtXK11dqspzYrK9gG50Jke/JwMON3jDs3c0yHQB8LqcXHneOv7r6dHOdzo88B2kcHCX/hIGuwJqg3brK2D/t+v2vV+O8v+2slyklNPA/cBrpZSnTTsmD3wF2LsI61t5aFn1da31ubZpjl4wI/QqQY/0q74uQ/tKYp9ZRRH6TEbj/mfGefPF/QjRguWiZcgJH0GPq1Ro09tjCvpEe4I+ksix0Z1RdktZ0U5dQTcM9Q+45oL1TKYLPPpCB9+jUsKB7zDa+2ImiDLQpWwldl2v7J/hx2oe0hde+vL/VrJc+oQQMfN7P/Bq4LAQYqN5mwB+Czi4mAtdMZhpWbag21Sga+SlE5+76i0lhLJdhvcRNC2X1SToPz54moJu8OaLBgh5XCTmjNDTpZRFiw3r1wMwPdXe2LjRRI4+Z6rSbgEIbwKXTwl6cgTu/wR86jz49+sBuOLcdXicjs7aLkOPwPQJnoy9BiFgU8wU9B3XgtMLB75T8xArQl/K1MVWIvSNwH1CiP3AIygP/S7gG0KIA8ABoBf468Vb5grCbA9qC7pNBXqBvOHEV225gLJd4s8T0lW15GqyXO58fJhtvUF2bgrxKs9Bktm5I3RV9j/7c+gxI/TkdHuJBKXhFtWC7nBA11Z4/Bvw6Z1w/8fB3wVH7oFjDxDyurhsaxe/PtbBjdH93waXj/sdl7Mh4sNj7ZX4onDO1XDwDtVfpoyo343bKVaWhy6l3C+lvEhKuVtKuVNK+THz9quklLvM295elglzZmNbLjZ1kHqBfD3LBUo+ejh+AFg9Efqp6SwPH4/zpj39iIN38BntY/Qk5rgQL2RIS29pQxRA+KIAZJOtv2dyms5URiNiJCozXCwGLlF7WXt/H/7oMbjp5ypyv+/jICUXDsR4ZiTZmfRFw4Cn/h+c81qOJhzKPy9n11sgPQbHfl5xsxCCnuDS5qLblaLtotkRuk0tstggbRFUvjIC/9jjwOoR9B8+eQop4U17NsHT3wfAkZvj715LkzYqLRdcXgrCg5Zu/T0zllAiGNBnaiN0gDf+I/zZ8/Daj0PPdnD74OUfhBMPwrGfs6s/iqZLnhlpv0K1htQIZCZg68sZimdm/XOLs68Gb6SUo15Ob9hjC/qKxorQczNtV77ZnLkoQa/joQP4ItB3Hq7TjyHE6rFc7nziFHsGY2yJAEfuBUDm56oUzZA0PJWCDhRcIcgnWs48GUnkEBh4tQaC7nBUtioGuPidahP6vo+zqz8CwP7h+TUFq2BmCAAtPMBIIsdAd1WE7vbB+dfCoR/O6oPJUleL2oLeLgWrUk7OewqLzZmH1AsUcJVS9WoYUBWjAbdjVUToz4+nOHQ6wW/t2aTEvKiEShSaOKuGDnqehO6usFwADE+YoMwwmsi19PwjiRwRMghp1Bf0eri8Kko/+Sv647+iO+jhwFD7TcFqMFv1jjv6MCS1ETrA7uuhkIRn/7Pi5t6Qd0l7otuC3i5aWYRi2y42FrqGJhtYLgD9l0I2zovck6tC0E9NK+HdsSmqIk+XKhYSWpPSfzNhIKF7KjZFAfBGCZPhZHyO1gEmozPmhijMdlpshYveAZEBxP0fZ9emCPuHOhehnyiqddR46ABbXq7SU6uyXXpDXibT+SWbeWoLeruUX1LNoyWozRlKqfS/wVvK3Bi91HVkVVguaXONQacOz/4UznsDAM5iE0HWKsfPleMKxgiLDENT2XqPrGE0kWODywyeWo3QQUXpr/gTGHqEa0OHeG4stfDK3Jkh8EY5nlIfUnUjdIdTVY4+d3dFoNcb8qDpkpm5soM6hC3o7VKwI3SbWkRVc64a1u0Ad4Bd8siSRegnJjPki/N7LutDp3f8V6qp1q7rMXDgldnG5zTfG0ndQ8hTKejeUIwIGU5OtRahD09n2R4y7Zl6WS7N2PN2iPTz8uk70Q3J06fnN/6uxMwQRAcYmsridAg2Rn31j9t1nWpJcOiHpZuscXhL5aPbgt4u5ZectqDbWBjKQ28o6E4XbLyQc+XRJYnQc5rONZ95gG/vG5rX41NmA7HI8Z+AJwzbr6ToChAk17j8vzA7T7Q6Qnf6okQdWU7GW4vQX5jMsC1gimA7ETqAywMDl9GdPQ6wcB995iREBzg5lWFj1IfL2UA2N10E3dtVzrrJbHHR0vjotqC3S4XlYgu6jcJhzBGhA4TWE5WJJYnQEzmNrKYzOtPaJmQ1mXwRBwa+Iz+Fc64BlxfdFSDQTNC12WlF1Zui+KKERWsRupSSk/EMA17zvdauoAP0bMc5c4INIefCM11mhiA2yNBUtr7dYiEE7H6L6sZqDr5Y6n4utqC3SyE9+weWtVvo2gCGjpAGmmyS5QLgixCUmSXptmi16E3Nc4ZpuqCz1/EMIjsJ578RAMMdJChyjfu5WBG69NZE6Hgj+GSekfjceeFTGY1kvsgGd0aV1XuC7b+A7u0IqXPl+iwHFrIxmk+pwC06wMl4pv6GaDk7r6N88EVvaGn7udiC3i5aVnV+80btCH2VoekGjxxfhA9hXV1ON90UBSVqRqa04biYWMOo5+yO2OTxr3fvU9ktL3o1ANIdJEC+cQvdivFzVR9soXUAGInTaHrzEXwnzEyYHofZx6W8MVermA28Lo9Oc3Q8Nf/h3GaGSyHUz1gyr9rmNqP3RRWDL7oCHpwOYQv6ikXLgDsI/pgt6KuM+w6Pcf0/PcQvj7TYJGryKDz4ubqtUSswBb2As7nl4o3glTkK+cV/c1uR+XyFLJPXuFo8DNtfBd4QAMKjIvSGDboKTSyX2CAAG5ng1HRzH90acxeVDcr+W6FnOwA7fBMYkvlvjJqCPu5Qrb8Hu5tYLha73lIafOFwCLqDniXLRbcFvV0KaXD7VTMgW9BXFZNp9ab6j0dOtvaAA9+Fuz/cdBAwoIaeYEbo9Xq5WPhU9aKjWXFOh7CEfL6WSyTxHOuZhPNeX7rN4Q2ZHnoDy0WzLJfaTVFiZwEwIMbn3Bi1ctX9xen5C3qwDzxhBgz1u5t3PrpZVHRSV+uYM0KH2Z/Z8V8AS1stagt6u2hZ8ARsQV+FWFbBfz41wnQrY8GsSuCxp5sfV4rQ3fg8zS0XAFcxuehzJi0hT85T0L05s3e5GekCOH2hObJcZi2Xmgg9OgBAv5hgaI6N0RcmM6wLe3Fm4/PbEAVl0/RsI5B8gY1R3/wzXWaGQDg5mlVXKS1F6LHNypI1/256Q0vXz8UW9HbR0qblYgv6asMSt0LR4M7Ha8eX1ZA3BX30qebHlXnonkYpbVCauhMmS26e+eHlHBlLNixYKW2KzjWQogGiYG5elk0KcvnDBES+pSyXmgjd7UcG+xh0TMyZ6XIinmFzdwAyk/MXdFAphPGj7OqPzj/TZWYIIps4OaPhdgrWhRvkoJcjBKw7H0aVoPeFvEvWQtcW9HYpZGzLZZWSyhUJe13s7I/wrX1Dc5djtxyhm6Lp9JSm9NTFtFzCZBc8KNowJG/+/IP808+P1r0/XfLQ5/c8wiqg84RKtzm8KkJP5RtnuejCjY6zdlMUENFBtrrjc1ouJ+IZzur2qkrshQh6z3aYPsGeTQGeH0/PPW2pHjNDEB3k5FSG/pgfp6PFDdr1O2DsKZCS3rCX8dTSlP/bgt4u1ZaL0XzH3mblkMprhHwu3nrpIIdOJzg43HyjbHJSbZ4aI61F6MLlaX6cabmExcJTF8eSeRK5IhMNpuGkFuihu4pgb0KaAAAgAElEQVSmz18+y9MTIiByjYdcaBkKDj9up8Bbby8hNsiAY7JphJ7TdEYSOc6JGIBceIQuDS6Jqt/zXL/vuphFRSoHvQX/3GLdDhUQJIbpDXkoFI1521/tYAt6u5RbLtJQHdbOZH5yC3zvvcu9io6QyhcJeV1cu6cfr8vBt/adaHq8lla+qxw/PBuF18MSdIe78TGgptsAIbILTl08NpHmpY6DuFL1N2zLN0Xn49e7i2aEXiHoQVwYZLMNIuxChoKjzoaoRXSQPn2MoSYNuoamskgJ24Lmc7TTmKsa0/8/36P2Aw4Mt+mjGzokhpWgxzOt+ecW6y9QX0efni0uWoJRdLagt4uWnbVc4My3XU48BM/fv9yr6AjJXJGQz0XU7+Z1Ozfw/SdONZ1o4y6mKEgnTllETjzX+MSm2M8doZseusgsuFr05Ficr7j/jpdP1s6yBCo+MObz4eEuptGEp7LnuGm/FLINIl0tXRoQXZfYWbhlAVJjDX/uVobLoG+efVzK6VaCHsmcoD/m5+lTbUboqVEwigzLHibTBbb0tFHgtO589XXsqbJq0cX30W1Bbwe9qKIxT3DtCHpyRE1sya/+CYNWhA7wlssGSeaK/OTg6YbHe4opDsqtALxwaF/jE5sRusPlbb4Ay3Jh4ZZLduhJvKKIR6svUqky77xdH11KiddIozmrBMys2NRzDf4WChly9TJcLMxc9GaZLlYO+sb5dFqsJtCtroriz7Mx6mM00WaEbOagf+mARm/Iww2XbW79sf4uiAzA6NNsjPq4YFOkveeeJ3MKuhDCJ4R4WAjxpBDiKSHEX5m3bxVC/FoI8ZwQ4ltCiDnCkzMAqxf6WonQ9SKkzfS1qWPLu5YOkMoVCfuU2Lx4aw+buwN8q0lOus9I8Zz7XIrSwZGDDzc+cUnQ57Bc3D4Mh4ewWLjl4h19Qp1Srz9BqLygqOEmZgPyRYMgWTRXfUGX+QY2o5apXyVqEZ0V9JMN2uieiGcJeJxEpPlBtRBBF0JF6ZNHS33J28LMQf/luJ+PvGEH0cAcv99q1u+A0ac4e32YH/3xy9m7dQFXGy3SSoSeB66SUl4I7AFeK4R4MfC3wKellGcDU8B7Fm+ZKwSrMZc7sDYEPT2u9gkA4s8v71o6QHmE7nAIrr9kgF89H6+fI1zM45YamreHSd9mHGNPN85dNy0Xh3uOCB2QnlBHIvTehNqo9ej1I93yzdB2y/9T+SJBcujuakFXlovR6GqtkCZdr4+LRSlCH2/oo5+Ip9ncHUBkJtUNCxF0UC0A4kfN2Z7tWR4zIyqI2bL9XK69cFP7z71uB0w823z/pcPMKehSYf0G3eY/CVwFWFNR/xX4rUVZ4UqilMq1RiyXZJkdcSYIeq5IyDsbZW1fpwQqnq7zRs+pCLHoCePr38XZnOC7jzZoRWtG6M5WBN0bWXCELqVka/4ZALxGA0HPFYn61WttN9Mlk9cJiyy6O1x5h9Ukq9BgrqiWIS2bWC6+KNIbYbNzskmEXpaD7vKrjLKF0LMdZoZY5xdMZQoU5+gjU86j+w8wI4N85M2XN09HbcT6C8DQoNn+S4dpyUMXQjiFEE8AY8B/AUeBaSml9ZcyBPQ3eOxNQoh9Qoh94+PjnVjz8mH1Ql8rlktyZPb7VS7ohiFJFdSmqIUlPHUj2LwSdN0dJrplD4OOcb73q8P1M0baEHThixAiu6AIfWJygq2o7Ba/zNZtdpUuFNkQUUUw7fZzSReKBMkiq7scmv93FTP1hbGQIVVnQHQ5IraZ7e6puqPopJSzgp6eWHh0DqXUxbOc40gJ8VYqhIGfHhxBnzqJFtrEYPVQ6FZZt0N9nauOoYO0JOhSSl1KuQcYAPYC59c7rMFjvyilvFRKeWlfX9/8V7oSKFkuQdVE3xM6s8fQmRH6MH0QX90eekbTkRLCZWJjiXvdCNYsKpLeSCkFzRd/hgePTtYea15SO91zbyMJX2TBWS6Tzz2MQ0imHV2EyNUV7HS+yLqI+oBp13JJ54uEyJY2cUuYgh4gV/9npqVJGp7GETpAbDMDjvG6o+jGk3lymsFZPQFInoLIPGyOaszUxQFdfQC20iRLSsn/vutptnqm6N60bf7P3XsOOFxzVxp3kLayXKSU08D9wIuBmBDC+s0NAHN0MDoDKFku5if2mV4tmhrFQPCwfg7G5JHlXs2CsPq4lEfolrjXbQdrRuj4oqVI62LfKb7+q+O1x5oRuquFCN3hixJZoOVSeEFl3JzuuoygyNYV7FS+yPWFHzAoRtu2XNIFnZDIIbyhyjtMD131RK89pyxk6g+ILsfMRT8Zr7VtXrBSFrsDqiFaJwTdbKPbpym7rJWN0amMxvB0lgHHJA7T958XLo8S9ZUk6EKIPiFEzPzeD7waOATcB1xnHvYu4PuLtcgVgxmhJw03uiFVC93MGTzkInmaCRnlqLEJR/J0qfnSasQq+w7VjdDrbFqZHrrDF1HNljxhXtMzwc8Oj9WWcJuC7va00OfDFyEiFma5eMee4IRch7vnrLrNsoq6gUdLcu3o53iT48H2LRczQnf4G0foNYJuGIhilrT0EGiUhw4QG8RnZDCy0zWl+C9Mqr+vs0qCXtfFbY9AN/i7iGVVEVkrTbKGpjIEyOHTZkpNxebNuh0rznLZCNwnhNgPPAL8l5TyLuAW4INCiCNAD/Avi7fMFYLpob/zawf56oPHz/gIXZ85zaiM8YJcr26YOr6s61kIVtl1qx56MaMsF1cwVmq2NKgdR9Ml2eqiGNNycXlayNz1hgmRWVAvl97EUxxxnY07EMEriqTSldFuuqArywSIOHNtl5xnsjn8ooDLVyXobnVlGhT52r4oVmOuZpuiUEpdHBATNT1dTsQzOAT0+wrqfJ2I0AG6txNIKUGfbCHTZXgqyyYxUbHeebN+h0p/zC1wDF6LNPnJK6SU+4GL6tz+PMpPXzuYlsto1sEjx+K8x98FY4eXeVGLh544zZjs4rjcoG6IP6/+QDuFlPD9/wkX3gBbX9G589YhlSuyTZxi791vhntMcQf+3S14NPPPNccX0lO4AJdfleuzfgc9o98DJKlcsSIK1bU8TsDtbiFC90YIkiVbmGcqW3qCnuIoo7E3ca5PZaFk0zOouMs8JF8kLJTAdjvzvNCmh65lVZ65K1Al6A6HmitarBOhlzotNin9h5rioh1lBTcnJtNsjPrxZMzsqk4Jes92nC/8Eo/LwXgLEfrwdJZ+Ye6VLDhCN1sAjB2CzS9e2LlawK4UbQfTcsngVRNQzvAI3ZEaqYzQO53pUkjDE9+A5/6rs+etQypf5HLHIYKTB6DrLOg9G+EN81Ln07gTtT1dNDNCdwctQd+JV0uwnqmaiFfTlEh4vHN76PgiODEa53LPgRx+FIBM34W4TcHNpyurRUubmkDU0WADswkF87V7qgUdNYYuSI5ktU1lBjtZ6SXU1ENX1Zb1iotKGS6JDgt693bEzDCbAq1F6ENTWba6zff1QgXdCoCWyEe3Bb0dzErRLF5OxDPk3eZc0SVoi7nk6Bqu7ARjdJEgSMoZ7bygW8UjS3A5msoV2SDiSOGAt34D3vp1uOovAGaLWMooZqZISR8hnynS5sboeY6TNZuoesEUdE8Lgm72c5G5+TV1yx7fhy4Frv6L8JofNvl05c8vVRahRxzZtj10w9w/KF2dlCG8QQIiV7uR3KwXejnBXqTLzxbXZE3q4ol4RmW4JMxe9R2M0EFyQaBBEVkVQ1NZzvFNg3BCeOOcxzclOqiyhWxBX4EUMkgEeVTBxqjmV4UDjQotVjOpMQBGZRfdQQ+nHBsXTdBlbvFTP5P5IhuJI4PrwGkKjpnn7MjVbmzLbIIkgVlxMiOtc8WJmohX1/Lkpav5PFELrzWGbn4zLrUTj3BE9jOwvhd/SAmulq2O0HXCZoQebpCR0gzdtFyEN1xzn/CECJKvnStaNq2oqaALgYgN8iJPvKKfSzpfZCJVmM1wQUBoQ1vrboiZ6XKue6wlQR+ezrLFFVebso4WfqfNsIZdLNHGqC3o7aBlKTr9gKoaO5k1I7Iz0XYxi4pGZRcXbIrwvL6+44JupNTG08joaEfPWw8rQhflmROmoLvrCXpuhqT0z4qTvwstuIHzHCdrs0qKBTRc+D0tvPnNFrrO+bRdlhLf2JPsN7ZxVk8Qt5mFUsxWnktF6ErQQ2TatlxKvVrqCbo3VD9tsTRPdI5NUYDoIANisiIX/YQZrZci9NA6lfbXCUxB3+YYbdFyybBRTC7cbrFYf4GaXmQPuFhhaGk0p+qJHPA4OZIyy8jbFfSZBiXkKwmzqGhUdrFjU4TDhV7kzBBouY49RXZGXQUYmcX/QEzlNTY5phDRsst4szWrp1B7hSDySZIEKsSp2HM+54mTNQJpaPm5B0RbmBG6U5uHhz5zEm8hzn65ncFufymCtiwSC+WhK4EMyGzbgi4sf786Dx0QniBhR50sl0KLlgtAbJA+Y4znx9N8/v4jjCZyJUHf3MkcdAt/DAI9DBinmEwVmk4OSuQ0krkiPcWxzgn6uh1qnGGihbGHC8QW9HbQshSEF5dDcPHmLg5Nm2/gdgR98ih8eie88ODirLFTmIKe9fayuTvAMWM9AgnTL3TsKSxBd2uLPyQklVcRekVus8tLVgTwa7WC7igkSMpAZZHMuvPZLk6RylZGeYYZobdmuSgRdhfn8ZqHHwPgVPB8NRHIFNxqPz5dKBIyI3S/kWnbQxfW78NTK+h4goTqzRXVygZEN8tDB4gOEixOc/mAj7/76TO85OP38r/vUpbEWd1B9bcX7qCgA3RtpU8foaAbtXZRGcNTWdYxRSg/ojbPO0HZsIvFxhb0diioBv6xgJsLNkU4ODkPQU+eBiTMLP6n9YJIjmDggEAv/TE/L5SnLnaIQlL19vHriy/oWnpGRa1VkV/GFSVQrBV0p5Ykib8iQneHuvEKjUyuMjvDKBYo4MLnbuHtZOZ2e4vziNBPPYaGi2Kf2XnDEtxC5blS+WLJQ/ca6bY9dEehzvg5C4+yXGqifnNju+ZDsB4xleny9es2cd+fXsH7rtiOpquS/2jArSLZTkbo5nNG8ypIaeajD01leZ/rBwgE7PkfnXnuDbvhbd+Bwcs6c74mzJmHblOG2e85FvCwY1OEO/WA6j3ZjqBbG6iFFT4wIjnCtKOLaMhPf8zP8UVIXdSTalM0INPKX5xPR7sWcWfNRmNVkV/W3UVIq82ycWspU5xm3yIuM++7kKn8ADKKeTTpbGtT1C+zFIoGHlcbMdXwYzzDWfT3mo3hTMEVVX9L6XyR9WaE7pIasphv67mcWp3xcxaeIH6Zq7VczA3ujCuKyznH85iCzvRJtp59Ljdfcx4ffM25qvo6n1IfDosg6IHsXQgMJlMFtjdoKzV1+hhvc/6M/M634uve2pnn9obgnKs7c645sCP0dtCyZAwPXQE3OzZGmMaMkNoSdPPNt9IzY1IjTIhuugIeNsX8TBMm54p0dmPUFAEnxqJ/wPkyyt6pFoqCp4uIkajxVT3FFBlHEHe5OJml71rVJqRsx3LxhJAIs0FXm+mEY4c5UNzMlh6zl5DTjSbcswJsks7rxJyzVxFB2ktddOspCtXj5yy8IXyyzqZoZpKsI4TX20JxlVV9WWbfOR1CfeBYLZs7UfZfTmwQh1Ggj5mmEfrWw1/EgYH3qj/r7PMvEbagt0NBdZOLBTxs7Q2Cy4fm8M4zQl/hgp5URUVdAdUONRZwM+Hp76igl6cL5hKL2xMnlDczaaoEvejrokskK8v5i3lcskDOWd2cSgl6sTqHXNdMQW/h7eRwoLmCRGiz46JhILKTTBDlrLLZlnlHAFfV1KJUvkhUzG5eh0R7G6PuYoaCo0HLWE8QN1rtoOj0BClndO4NUYDwBtWFcKbOtKhO56BbxJQfPiDGmWwk6DND7Bn/Af/peRWia0tnn3+JsAW9HbQMKcNNzO/G5XRw3oYwCcJnqOVymuFilC5z7NamqJ8hsUFt6nYIT34KTaqodnpqcXvlhzXz/FWFIrqvmy6SlYUyZtaI5qoe8KAEvnqmphWh+1uJ0IGiO0SIbHuCnp9BSINpGaoYVlxwBnEXqyP0IhExK7hh2hN0n5GmUD1+zsL8GRSrK10zkyQckdYE3eFUEfh0PUHvcJWohWnzDDrGGW+UuviLTwGSe/ve2dnnXkJsQW8HLctM0UNXUOXH7tgUYdIIINsQ9HRK+bUzMyu4j3oxD5lJTumx0mvdFPNztLhORVXFzkwv92nTDMleABKLLOhdxXHSrhhU9VuRgR6CIk8qXRZ1m61zteqJPd76I9iEbm2KtibohjtMWGTbs1zMrp5TMqRytU2KrgBeI1thGaXyRYIiA0FlFIfaEHTDkHiNLMWGgm7NFU1X2lSZCaaINC/7Lye2eWkjdDMF8WzPVH3LZfokPPY1vi+uxNe7pbPPvYTYgt4GsmS5qKh1x8YIcSNIIVln6EEDZqaV+CcSK1jQU8qeGEVViQL0x3w8letVM0ana3uftI1hENBnOGE2lUrPtP4ztEjmNN779X2cmq4/zsxCSkmPMUHKs67mPkdQFRflZiZmbzQzNozqtD1rgk++yi4zNLRWK0UBwxsm3K7lYgYNItBd8Ty6K0hAZskXZycIpfNFQjJT8qFDIlu/53sdMpoaP1d010lZhLIWulnS5evPxJmixQgdlI9eN0I/pXokuf2tnadVPEEI9LLVPVnfcvnFPyCBT2XfyEBXh597CbEFvR20LDm8dAVmI/RpGUJLtS5Gpcv1leyhl6pEYyXLpb/Lz+GCmRrQCR89P4MTg2mfipxyyfY99MdPTPOfT43yyPHmj81pBhuIk/Wvr7nPGVJXCIVE2RWCGaEbNRN7lMiJqk1IoWvowoXT0VqWjvRGCLXbE93cQPbHKj+UDE+IoMiSKMs6Sed1/OWC3kaEnsmb4+caCro55IKyTBcpIT3BhBFuXdC7t6oN0KqiqI71Qa9HbDODYqJ2WLShw5P/QfKcN3OKXlvQ1wSGjtDzZKS3JHLnbogwQ7AtD926XK8WhRWFKehjsqv04bUp5u9s10XTQijEVFl2IdV+tag14WYm27wVbTKnsV7EKQRqe4O4w0rQtWR5hG5NK6o/4MFRLehGAd1RJyOkAcIbIUymvalF5s8r0lX1oeQJEaKyWVYul8UjCyXbItzGpmjK7NRoeOqkLMJshF5eXFRIgZ5nXA/OXVRk0X8JIGF4X+Xti5GDbhEbZL0cq43Qp45DMctw5EK1tJgt6Gc+ZkSdMfPQQQ1IkL4uPHXymBshzfM4tMwcRy4jZX1cyj30SSLqUrwTgp5WAlqMqOwDPdO+BXViUv0sE3MIeiqdokck0UO1nfO8EXXVoafLBN2M0J2+qm6Dppg5i5W/O4ehoYvWBd3hb3+uaMHse9PVWynowhuu7a1i9WIxhTFEG5aLOX6uXtk/UPoZVETo5tXDaDHUeoQ+cCkg4OTDlbcnTy+ioG+mWxut9dDH1UyD4w61cTrQNc+h0CsAW9BbxeyFnsNTiloBfJFeFQ1pzX1cC2EKuqu4kiP00xjCRZxw6bUOxFRTshnfQEcEPW9aHK7oetIEYB4dF2dGX+AR7/vwTTZvTZqPq402o045ecCyMNK1Hrqjun2sObHHa2TJF2fF2GFoGG1E6E5/lAhZMm1knqTiY+hSsG5dpeXi8IUJllkqUkocVuOv0HqkcLSVtlgaP1evqAhKlkvFGLq0EvTTxWDrm6K+qCqJP/nr2duKeUiPL6LlchZuWcCfnyRXnqY6dgiAw/om3E7BunALbZBXKLagt0pZNzlrUxQg2q3eYKnp1rI0rMt1l76yI/S0uweJo/Rae0Ne3E7BtKMLMhNznGBu0tNq49UX6SPjDOHIt98TPTb+GH1ihsj0oabHFadVMzRHncgvEO3FkAJHdtaHl1lz/FygStAdTjSHr6YfuENqyDYE3RWI4RUauXxrQQBAJjHOFGEGuisjZ6cvrKJls79MvmgQkObfli+K8IaJOfOte+g5NX6uZp6ohRWhl18VmBF6XLaxKQowuBeG9ikPG2aLihbag7wRpfF345VR+vhhiA7yfMLBxqgfR4t7ISsRW9Bbpazfc7mg961Tl8DHh1rroGhdrrv11t/MnSSZ0zgxOceHSfI0M64ewj5XqVLS4RBsjPqJG+3tGTQiP6M+AAOxdeRdYdxae/3BpZR0p58DwJVtvilqmH1zXN213fPcbg8JgjjLipy07Awp6SPoq23fqrvUxJ5ygXTKIrJeVWUDXKZYGtnWX3MxOcG0DDHYXenvugMRnEKSNtMuy6cV4Q2DN0KXs85AigZY049q5ola1BsUbX7Ax2ljUxRg8HJlb5kRsuqDzqJaLqCmJVVsjI4dhr7zGJ7KrOoNUWhB0IUQg0KI+4QQh4QQTwkh3m/e/pdCiGEhxBPmv99c/OUuI6alYrj8qtOdyaaN6o/vl/ufa9qW08KKzD3G8gj65+47wpu/MEenx9QocUd3KWXRoj/mZ6wY6Iiga8lxctJNLBpFc0fbblY1nsyzXar0SXe+uaALM/LzddUf+DsjIrjzs69Jz0xXDrcow3AHKjcEAaehIR2t9+4WpjevZ9q4KsnEmRFh+kKVdoA1Jq5gCnE6r5emFeGLgDdMpI0xdNawDHed8XPqDstDz9d46HEZnrsXejmDl6uvlu1SEvTFslxmI/TSxqihw8SzsO48NUt0FW+IQmsRehH4Eynl+cCLgf8phLAmBX9aSrnH/PfjRVvlSsC0Spy+ykve7h4VoT/2zDH+4e5n5zyNx4zMvTI/e6m5hIzO5JhI5ZnJNNlITJ5mjK7S5q/Fppif0wWf8pgXuHYjPUmcMN0hn0rjk6m20vheiGc4TyhB92vNBd2VOk1CBghGYnXvTzii+Mpa6OrZBEnprytO1kzNcoF0Uazf96QRVvSbbz1Cd+WnyLmjiKoGZl5L0DPqXKmKCN0U9DY8dC1jCXrt+DkAnC6ky1dpuaQnMBxuUvjbi9C7tkBw3ezG6GJH6N4wuq+r0nKJHwM9j9ZzLqOJPP1neoQupTwtpXzM/D4JHAIW6SO0Q0w8N3sZ1ynMCN3tq6qg86vOd6/Z6uFz9x3hC/c3L433yiy6NN+Uy5CLbvWCPjnVwHbRcpCd4rQepTtQKVL9MR/DebPScoFzQEV2kikZpjvgQfhjRES6pfFgFsOj4ww6lG0TrNP+thxvZoTTsrthW9e0M1rRE13mZlSEXi8FzxMkUJUm6JQaONuYrmNtOOZbbxvs02bQvV01tztLU4vMCL0wO0/UEvR28tB18zzeYIMIHTXkIuYqzJ4zM4nm7QbE3K1zK04klI9eHqF7wrXpoh1ExDYzUG65jCudGPep9NnVnOECbXroQogtwEWAtTX9h0KI/UKILwshav/a1GNuEkLsE0LsGx9f3PLuEj/9ENz5/3X2nKb4ev1Vu//mGLM3n+vl2gs38bc/PczXHjpe/xyGgVfmmCRacc6lxLpMHppqYPmkVMriSS1akc0DKkKfMubRYbIOrtwUU4SJ+F04gzEiZBhvQ9CzQ/sBKAoXYaP5h4s/N8ooPRVWWTkZd4ygPnsOkU/UtM4t3ecNESrvBy4lHoptCnqk9DwtISVhmUCYVa0VWL1VsrMRerjCQzezYFr00A2z8Vi9AdGzzxkk4ijMFjNlJil41NVPW5YLKNtl6piaYZsYhsgibYiaOLo2M+iYmA0exsyURaH2V9aC5QKAECIE3AF8QEqZAL4AbAf2AKeBf6j3OCnlF6WUl0opL+3ra9CEuNNkpzs6WQcoTWTxBKrLwQPg78aRGOYf3nIhrz5/Pbd9/yl+9Xyd6lEtgwPJuFw+QU9k1Rt7qFGEbuagHy9ESjnoFv1d/vm1DK6DpzBF2qksBG+oh5DIMTnTuo8uzCuwkfAuYnJG9dJuQCg/StxZRwxN8u6Y+lAw90DU+Ln6lovDF1IbgpagG+qraEfQfdYYutYi9JnEDF40PGYRVAWeyv4y6byK0A2HR/Wt8YYJyNbnis7OE22Qh24+Z8SZr8hyybhVPNeW5QKw+cXq68lfd370XD1iZ9EvxplMmoI+fghimzmZVlfNZ/ymKIAQwo0S829IKb8HIKUclVLqUkoD+L/A3sVbZptoGbVRU+hgaqBpuQSCdf7QowMwM4Tb6eDvr98NwIGhOlGjKeDj0vRyl6HjYmKuCN3cQDyhRWs2RTfF/ExL8/VnFtbuNlCcIWeKgC+sviamWz9naOZZsiLAVGwnPSRqBy5Y6BqhYpxpV+NgQvN24aZY+n04tSRJGSDkqxUnl08V8pQiXl1dujtcbXjoZoTuanGu6Oio8pb90TqvwRpDl6/McpGWIHsj+Iw2BkVbVw2N8tDBHENXVliUnuBIysvGqE/NBG2HjReqq5uSoC+ymxsdxEeBQsLsjz92GPrOZ3gqi0PAhmgL/dxXMK1kuQjgX4BDUspPld1efm3028DBzi9vnliRbweHshpmQ6ZgPW8xOljqHBf1u3E6BNPZ2o6EuhlFjZuWi1wGQbeiqsYRutmYS8Yq0jNBtdCdxtxDWEiErhcJGkk00xMORlT0nJpuPb99Q+4oY/5tEOzFLwokEw3si9QoDiRJT2NBL/pMt9DM1nBrSRIE6/rBTq+K0FN5U8xMQReuNopRTEF3tyjo46PqqinaXduLptRfxvxbSllZLt5o6X6vkSGTbz4c2aI0/ajePNHScwZVh0rzQ6KYmuC5lJf3vmJb5UCQVnB5YdNFasZuanQJInSVuuhODoFehMnnYN15DE1l2RDxtb/+FUYrq38Z8A7gqqoUxb8TQhwQQuwHrgT+12IutC2ssvp67TnnSSGbxJCCUKhO5GJG6ABCCKJ+N9N1skgySRW1p1xKwKpHmS02Rd0ovQmbRejS4WYKtWFZjt/jxOHvVv9ZiKCbj9VNIXUF1TmzLcwUVnEAACAASURBVDboSmQLvEi+QDp2DiKobIj01EiDg1V0m/bWEUMT6TftmMwkFPM4jQKJBlkuwmuKmfnBaGhWhN6G5eLyUBAe3Hprgp6YVK+tu6+2F40VoVtzQEuVnua4PCvS9hlZcppR+/gqnNaHTNMIPTSbh65ruAoz5Dxd3LB3c0uvp4bBy2H4UZD6kgl6IDusKp71AvSdz9B0dtVviEILM0WllP8N1CudWrlpilaEPtNasU8r5LNpdDw1vjKgBD2fUJkfviixQANBT88QBjR/H2RUEcdSFhmn8kWudjzCy51P8Yn4e5BS1qTBkRyh4O+DjKhJWwQIx3ogTmNBv+evoO9cuPCGxguxKk3NDWXMvOxCi10rT508xnkizal1O3CHVaVubmas/sHmVVrWX0cMTaS5Di05jjtqZosQqD+wwhPCi0Y6pyYCFQo5fIDD3d5vsuAM4tNb20NJTavXFuqqbf9rRdJOs5VEOl8k4sghfKbfbgpzkCzJvIbf0zwLxVlMUxAePM3SMD1BfDJLMlfkwJFj7AIueNHWltsH12Dlo8PiWy5mLnqscBp99GmcgOw7j6F4nMu3Nd5nWS2s7uuLehhGWYTeOUEvZJNkylrnVmA2z7eeL+Z317VccmbxhwytM8/ZXnXkQklki1zj3MfbnPegFXJ1P3SYfoGUT7lp1R46QF80QFI0qRZ97Gtw4DtN11E0G005zNa1+NWeQjHdWj+XxPEnAQgMXog3piJvbWa0wcEqQi8GGwu6s9QTfbzkIRdcodoPO5idK5pRkWzeFHZnOxE6kHeG8LcYoecT6uclAnUEx+Gk4PDhMiuQ1fi57GyEbX4NiSzp/Nx5/p5impyYI1L1BPEaykP/zs+fAODSHee09FrqMli2/bZYZf8Wvih5V5h+JsidegoQ/Oh0mFMzOfZu7V7c514CzjxBL5ZZCfUa6M/3tPk0uao+LiWsobeWoAc8dcXSKv5wR5S4VA8bXmwSOY1eVB/yATFe33aJP8+UT72ermDta+0NeZmRofqCrheVbTHHB2nG7ONidTq0InQj25qgF0fUdk3v9ovxm4KupxukxCZOkcWDw183qxYAV6mF7ngpv75mWpGFKei6uQlZKJiC3maErrlD+GUGo0l2joVuNr+iwWvQnAE8ehrDkGaWS7mgK78+3GLqolvPUHA2mFZk4QnhMTJouuTZ4yqbrO6GbauE1kG3ygNf9AgdyAcH6BcT6CNPo8fO4i9/epzdA1Hecmn9SuLVxJkn6OWpgB300I18uqJ1bgUxS9DV88UaeOgFM6rzdyufsJhd2k3RRE6jVyjB2iJGajdG8ylIjTLmVm+qmL9OhB72EjeC9cfuZSYBqQS9yQZc1mxk5ouavrZPReitNujyxg8zSjehWC8hsz+4TDfYUE2cYkR2E/I3thC8wS6K0oGemihF6MWGgl45V7RQUOlvrjYFXXeFCIts5XDqOkgpEdkpco5gw2rUoktlnaQKRVJ5nRCZkpCXR+jJfPM2wwA+PY3mmitCD+E28jgw6Heb77dAnZTKdhh8sepmGVj8KFmPDDAgxnFOPsOzRj/xdJ6/+e1dLQ8oWcmcwYIuOmq5yEKGLLPDLSoIrgOHuypCr7VciubghEh3HwXprJlNudgkskV6hFrDFjFaWy06dQyAYcdGwl4XHlftn0df2Mu0DFJM19nAtKLkQqppO1yrdW6oy4zq3H504cKnpyrbmjagO3WEYY+K6AKhKHnpxpGp778bM8OcNrqbFryE/GoT2EhPloZbyIbtY62ZmpWWi8vTnuWie8ItDbmYTBeIyBkKdapES+dyh1Q7glyRdE5T3RarLRdas1y8MoPmapLhAhUNul67zXw/1LOD2uGqD8PvflNVjy4yjq7NDIpxvIlj3Bfv4caXbmVnf5NCqlXEmSfopn+eD29WG2LG3Dv7rZ43i5eIr46gOxwQ7S8TdDfpgk6hWPncVlTX29VDBt8yCHqeHpRgne0eq7VczD7nx431xOrYLQB9IS/ThDDq5aGnyzYmm3yY6qkJktJPV8QUHSHQ3BGipBlPzlEtqmv0F19gOvQi9VCHgykRxZ2rL+gycYrTdBOuk1NuEfa6icswIjM529Jgjm6D1qASTVPrbddyMbxqyMVc/WuGprJ0kUI2sYykJ6gi8FwRrZDGhT67/rIIPTVHhK7pBkGZRXfPZbmo+689P8pvbDIFeKGRdXQAtl2xsHO0iKd3KwGRxyWLjHm38sGrF+D/rzDOPEE3i4l+nepVKUmNvNU2EVoGzeFr3Cu5bOit5bNXj0Yz8mk1pKA7ShpfKcpbKvKpKdxCCcjZrjoeuinoR4p9NSmLFr1hL9MyhKNeBF5uezQRdJmZZEqGKjZddW+UiEjPWf5fGHsWD0UKveeXbks4ongKdT5gDANH6jSjco4I3eciLiM4cvGS5SK8DSI2awydJeimh+72tFmQ4o0QIjvn1KKT8QwxkSpt3NZfU9hsGKYhc1alZ2WE3oqHninoBMliNJonWno+df/fvH4bfm1K7YG005xsmfH1nlX6/jVXvLL9dgUrmDNQ0JVIPq2Z+awdsl2ceg7d1aQsuCwX3fLZq20XWUiRwUd30EtG+kodHJcKI6E2I6XTw2ZGOBmvslwmj0Kwj9M5d/29AqwIPYgrP1N79ZMqi9CbbEg7svGKaUigWspGSTMxR4Qef15lVbg37Zx9WmesorlWifQ4wihyei5B97qIE8KVmypZLs6GAx6UQLr0DEXdoGh66G5Pmwmo3jAhcmTys38jn7//CP/+6xMVh52cytBFEl+ksUctvCGCZEnkijgKVqVntPQ8oCyX5BzVoul8kZDIIpsVFUHpQ41CUu2bLNQ/X2JElxJ0Awcvvfwly7yaznLmCbppuRyRlqB3ZmPUpWeRcwl68hToRWLmBtx09azLQpqs8BP0OknjXfK5otK8WhEb99BbHGVkKllZPRg/Bt3biKcLdVMWQXnoMzKEwKhp/2qkxilIJwXcTX/u7nycpCNa4dE7Al1ERKZ2InsV2eEDFKWDrs2zgp5xdxGq13ExqVIWR2R33TJ+i7DPxZQM///tnXl0ZNdd5z/31b6ppJLUklpSa+lur/HeXrLgJA6GxIBtSMiQyQkGQsKBMEMCMyE5zHDCzOHAhCXABBJCCAmQMWBwnIUlcUyAOHGMm3hrt3d3u1tq7UuVVHvVu/PHva+qVKpVrZZK8v2co1NVr56qflLV+77f+93fgi+3AtkESfwE/XUEumJIcjJbLAu6rz1BtwJRLCFLAyXi6Ty/d//zfPSrz2wYb3d2OU3MWscdrp9F4gpE1KJopoArV1UYZLmQniBdVppkE0FP5QqEyZQXVOtREvSkuio73/j5TqOLi0RsHOHd+8VElew/QdchlzOWyg1fnG7czrZVvHYG2Si2GB0BacPaTCnkUp3pYuWTZIQft8siQwDXDnvorpQOPx26EYsiscIcy8kKAV1+CWKTrKbytfPtUc2Xki59wFdlumTjsywSZZbehldG/vwqKffGkIYn1E2EVNMWutb807wkhzh0oBxTzvp66LJrCLq+Sjgne4n4GmS5uC1WRRe+fBzSq3U7LQIbhyRn8xR0DN3XZsjFpbsZ5pIqZv/VE7PkijarqTxfP1m+0plZjqv+5g1i1G5/FyEyJDL5csOvijUA4Yu0NLUomc6q8XP+1kIu5JKqp09ob3no+LvBG0H0X9p83z3GvhN0Z6Hx2lddzroM8OILz2zL6/pkFuFt4qEDxKdKYrhSFXJxFVLkLPUaWSuwaXr8hcad1jHuUdXhbkLMluPouRSsnaPQPcl6tlA7m8dBpxlWC3o+PsuS7OJssRe7QcglWIiT9WwcNuEK9tBjNe+J3pV4lhfFGL0VVxAFX4wA2c3N2PSawBk50NBDF0KQckexsLFXz+iy/zpVjxUZHuvZAkVd+u/1tSfozgCJgs69/+Lj04z3BhmK+vmb4+X/XWJZn4QbLIp6gl0ERZb5eGrj+DkHX4QuK9s05JJZ18Oxm/UjL3no66rqdwdSDbcVIeC2j8Lr3r/blmw7+07Qs7pY52B/HwnfAOvzp1pKhWuIXcRHDlejlqIVxUVRZ1G0ykN3F8pFG1lXEM8OD4r2ZZewsWD4OgDGKlMXV04DkAw5RUX10/CsUO1+LjK5wKKMck72Yq/W8dDzGQIyrQciVOCPEiHFQiJT/w/IrtGTm2E+cHhDFaetY7iyegF8+SUy3h7WCDZd+Eq71QlGLp+qO34OAJcH2/KWOi46Hrq3TQ/dE1LvZ6fjzCcyfPvFJe64epi3XTfCN59fYCaexralql6FhqLp0T36V1aWVVERbAyb+CJEreYeek6PxPPUWz9wcAQ9u74nY+gAXP2fN1ao7hP2naA7Da8C4QiBvjEO2At8+fFz5/WazknC7WsScgGInyXic9fsuOi10xR00UbBFcCzw3NFA7ll1lxRiAwivSFdXKRt0N7sil/9HfVCLgAep2S/StDdqUWWiDJNH67kLBRrpMnpgc4yUC3o3XgosLbWoB3CuUcBWOvZeKnsNOhKV/dzWX6JVV312ihtESDnVR6wtXauccgFsD2hUk90u6AFvc0YulcLejGd4MtPzCAl3H71Qd523Qi2hHu/O83cWoaIrf8f1f+vCoT2xuPxVSI404o2eugRkW6a8+5UMrvrzRMtG69u1+dUJtlei6HvY/afoKfXyEkXkWCQ7qFJRl3LfL4qc6BdEro1qyfQQNC9IXXQxc8ihKA74GGlykP32WlsR9DdIXw7LOjh4gpJdwyEQMQmOeKeL1eLakGfd6sq0Vpl/w5+3e52g6BLiS+3zKKMMi37EMjyjMgKSguz4SoR0OX/2fX6XRztlx/CloLcwes3bLf0gmG6uuPi8ikWvcO4LYGvRpFUJU4LXYFkjQCRBoIuveWOi063RV+bIRdfSK8hZBJ86bFprhiOcrg/zFhviBsnYtxz/CxnllJ0Cx0TbySaWrzXEysb54mWnu9qaWpRIa08dG+9eaIOTgzdGSKz12Lo+5h9J+iFjEoNjAY8iO5RumWCZ87OcWJ66zMwE7rtrbd6/Fw1FamL0aBnU8jFZ6ex9cKq7Q7ik5ntK3xqga7iCmmv9vRik0xY85xdrvDQAzEWiyrGXy/LBSCs+3ZsqBbNxHHLPHl/LzNSi0+NTJe0DiGUvHwHLej5ZH1Bz770LZ6Vowwe2Nhoy9ulmp1lKz30fBoSU8y7DxL2u2s32qqg6C97wAkZajx5x5krWuGhizabc7mDykNfWlrg8ak4d1xdbhv7o8dGOb2U4r7HpukROmulUZxaC2x6PU5EpCm6AuCqsF9PLWoWQ7f1lagvXHuYdtl4r6qMXtGCbjz0jmHfCXpR91zpCrhLce0Jzwp3//vWvfS1hA7jBJsJ+mjdjotFWxIkU7pctb1BLOTGZmIXECklPXKVrE8ffLHDDNpznFvWHuDyixCbLC3kNgq59EZDrMkAmURFIZFTVBTqJxvSDZZqZLqkVpToOiJcQndcdOfitdc8igU85x7hEftirhzZ6EH6nY6LaxUxdC0256yhlgpHZIUoNYyhA5YvrAYvZwrIgj5ptzOCDkoe9NTsPELAD15ZFvTbrhgk5HVxz/GpsqA3CLmUxsXl1gmTwq5uW+CLEJCpph56URcl+ZqFXEB9j1f1MbUXY+j7lH0n6HY2SVr6iAY8pbj2zQMZTs5svVVtSucK+2uNn6ukwkPvqeq4uJ7JEySDcA4+T0Xq1w6QzBXpJUHRGeYQm8RNAXv1rMpFXz4FvYdZ0WmMNbtKavrCXuKEyFf2L9dl/66uAdw95fWEapw4d6i6t7f20LtEqnb5/9wJ3IUUT7ou5dKhjYITjnSTlR7sysKmZZWu+jKtCbrXHyaDEuV6wy0cLF+YoFBZI7KgT9rtCro3RBGLEClumujdMPos6HXzQ1cdpGBLhr1pcPvV7Nq6r6W+S2HSdIk00rtZ0P12kmSz5lzZJkVV1e/pnLAbVbEadpR9J+jkHA+9LOgT7uWm6XCNSCeV5xIKN/miVwy6iFYNuVhLruMWdjlTpjL1awdYS6wSElnskC5Q0e1KD9ozLK4m1MEZm2Q5mSfkdeFz1x9W0K/L/4sVIRd7TYmpPzpAX083K0RreuiOFx3uriqU0amQUeqkLp75DgCF4Rs3dcXrCnhZIgLJihOMXhM4ZQ80XRAFiAQ8rEglhMpDr//3C2+YiI6hSztHEQusNoc7CEGKABFSG8ItDj96TH13D3pTzUMapSEWGcKkS4uklc+7ZJFCLtNwmDatjJ9z8IbUhCEwIZcOYt8JusinSOEn7HWrZvnC4qBYYqlJBWIjMjpzJhxpslhUkYveHdjYcTGlszdcumjDKg333RlBT+kFQ1El6GNijoWzzwESYpPMrWUapixCueNi5aJoakUNlw71HmS4O8CU3Yuskbpor6s+Lr2RqgVmLej1qkWzp77FlOzjyNFLNj0XDXhYll240lWCHuhhLt/Y23YI+9wsOYIum6Q5ekMqbTGbRxZyFJoP/qpJygrRZaV5yyUxePyv4DNvhn/9KADXHurhypEoI75043ALlAQ4JDJERAorEK35fITGmS6iusq04Xvqz8/la+0EYNgR9p2gW7p4x7KEahgUGeKAvUgqVyTVJG2rHjndt9wfaBZyKeeiV3dcTK07Ob7qYHHpmY87NVc0o4dKuCK6B3lkENvtZ1zMsnbuOQAejnfzD0/O8NrDjWOifbrjopUtV2emVmawpaCnb5DhngDTspdireKi9BLLMrI5i0aHXGp2XJQS+fJDPGJfzI01pspE/G6W6MKTrVik1VWv69kCkVodMqsI+9wslzz0QNNFUSXoBUQxR0FssbmTr4s3+Z4l+smr4As/A1OPwCN/Cno04Bd+7rUc7cpDsH5RkXqdcsglTBqrOmSi4/VhkWpY/u/Kr5PF21qjLUfQg7070vLW0BpNBV0IMSqE+IYQ4mkhxFNCiF/Q22NCiPuFEM/r2ybfup3BXUhRcFWkkEVH6CkoMduql553BlE06/tQkYvuVFo6C6PpdSclTAu6PjmkUzszhq6gG3N5u3WGiBDQM8mYmCO/8AIAH7g/zhXDUT5y++UNX8vvcZFyRfDmyplD2fg8K4QZ7Akz3O1XqYs1Bl24MsusEtnsAbvcSG+YPk+a4y9XdU5cOY0/s8BjXMIVI5uvkixLkLCi+HObBX0tk29YJeoQ9rtZQX02aSvcePq7N0RA6iHJdp6i2FqnwYGRSaL5BVXg8q774LbfgvVZWFQnWJclEKnl5h66J4iNRUhkiIr05k6RFSGZRguj7vw6aavF3iaOV27i5x1FKx56AfglKeWlwE3A+4QQlwEfAh6QUh4FHtCPdx13MU3BXXE5Hx0hklXhhmatWeuRz+pcbU+TL3t4QKVzrZ4lqrNEnNRFZ36ok0HgcTz05M546PaaEvTSlCDA6p3ksGuewsILJAhR8HbzqXcdazpIGCDv7SZQSJQEW67NsSijDEb9DHcHOSd71eDiquIjX26VpCtaM41Q+KNc1iP56onZjVdTZx4CIDlwfd3YftLVTdDpuFjIltYE1jKFhjnlDpEKD71YvahYjTeMnyypTBZRzFHcqod+5yfhA0+pwQ6H31juB/7Sv5b3SS01j1ELQcEdVB565fg5B6eFrmjccdFTTJFtWdArPHRDx9BU0KWUM1LK7+r7a8DTwDBwB/A5vdvngDsvlJHt4LUzG7siRkfwp2YR2E1bs9ajFOduJugVgy6qOy46oRV/SAm646nnMjs0KFoX9AR7KnK4ew8zyizulRc5LQf5k7uu35Bt0Qjb142LYmkhzUotskwXfSEfB7WHDmxaGPXnV0l76uQ5+7uZDBdI5orcf7I89Dl36lusyhAHj15d156MN4ZPplX++eoZkDaF6DjZgt1aDF13XAQ2Z4lU4wyKziQR5+GhE+pV3xeHngmIHoJTWtBtW01+aqFXSsEdIkyaIOnNwzkqWug28tC9xSQ5V7uCblIWO4m2YuhCiHHgGuBhYEBKOQNK9IEDdX7nvUKI40KI4wsL2zNsohE+WdUVMTqKZefoI8FScmshF9tp+uRp0Jyr4v02NOjS75nXOb7BsLocdjz1nZoraqUWScggkXDFOkBsEg8FrrVeIDZ6CVeNNikoqUA4cV3tgfuyS6y7Y1iWIOL3EPfqr0OloCcXiRaXSfoGqIk/So+VYrg7wH2PTpc25089xHH7Im6YrN9CNuf0hkkuqr7uQCqs+l63FHLxuTkpx5iTPWT9TUTKqSXIrmPZeWxrm4Y7CAGTN8Ppb4JdVGIu7eYhF1Q7ggNiVdU21PHQ1Ri6+oLua2VAdNVrGg+9s2hZ0IUQYeDvgPdLKVt2K6WUn5JSHpNSHuvvP4/J4K1QLOAlj/BuFHSAg2Jxyx66yKfICX9riz86F73UQld76EUt3D7tmTueejG9MyEXT0b1WdkwJ1RnugRFlpHJxnHzatxVDbpChRVyvvLBLbvKGT8lnvoCLmye6b2l9osGuhEZVTX5b88vqsXR5CKhxIt8V17CtWP1TzgFp9IzuVBKWVwLqb7XrXjoEb+br9vXcWP2D/H6mw9JBpDZ5PYKOsDEG9QYvJnHy+GqFjx06Q0zKPQaQnU/89KiaJpEpn4uekCmKLpbFHTnGDNl/x1FS4IuhPCgxPzzUsp79eY5IcSQfn4ImK/3+zuG7i8ufBtj6ABHfCtb8tALRRurkKLgasE7d95v7RxRvxJ/J4Zemh+qxSAYDFGUgmJ2ZwTdl11m1aoSRC3om+63gDeiDuTc2hLk0wRlimLF5XckNrhp0IV84h6ek6PkYptTDwGV6ZKJ88PXDFO0JV954lwp/3y571qC3gb9VZz3Ti0pQfdFiaOErJU89HBFv/SGGS5QFrP8hRD0m9XtqX9Vfwu05KELX6Wg1/bQB7w5/vI7Z8gXN7ebkFISkGkKzcbPOZRCLnusde4+p5UsFwH8KfC0lPJ3K576EnCXvn8X8MXtN689anZFdATdu7qlRdHF9Rx+ctithFuc95M2kdzCho6LzlDh0oAEv0fPFd2ZStFgfpk1V1UiUuSgyiOGtgU9GFUCmlieR+oKTStSjroN9YSYqRx0sXIaMfUw9xVeU3/Cur8bMqscHYhw+cEu7nt0msLpb5OVHnqO3NjQHuFM9Eku6gyXCdb1vM5wg+EWDpVhmVYFPSgzWHZeLYRvF5EB6L9ULYw6g7hbCGtYvghRoUOD1TF0tw8sD7ddFOLJ6Tgf/+cXNv1+tmATItV8/JyDiaF3JK146K8F3gXcIoR4TP/cBvwmcKsQ4nngVv14V0nq4h13Zb64PwreCGPu5S2FXGbiaTU8odmCqIM+gYj49MaOi7mNC6thr5sU/h0r/Q8Xlkl6qgTdsiA2oe63KeiRHiWgqcQC68sqi8gbLS+4DncHmCpW5KI/+bcAfMl+Da87UkcE/FFVaWsX+eFrhnl8Ks7qs9/kMXmY6yYHa/+OxqWbfeXX5kspi/Nrqrd6ozYGDkGPqxRRCze4EgA2FPJ4KCKtNsv+mzH5enVlsjajjWueEeyqbBxXHXIRAnwRDndJfviaYT7+jRd4YmrjhKdUrkiYTBuCrvczMfSOopUslwellEJKeaWU8mr98w9SyiUp5ZuklEf1bY2x6ztLcl0J+oauiEJAdISDYnFLIZe5RIYgWaxGvdArKRUXnd3QcdHKp8gIvxJRUHNFpb9cnbdFMvkiJ881WdIo5onYa2S8NQ6+2KQafNxmLLQ7przxTGKJ1QW1gBmKDZWeP9gdYFr2Ya+cUamNT97Ds97L8fdP1M+k0Q26yCa4/aqD9IoE0dWnOG5fxPXjjS/tg+FustJNfvWcynKJTfLkVByvy+KigeaVj5YlSkLesodOBo8oILd74v3E61XTtue/ph63EHJxBza2y92ELwzZNT5y++X0h3384t88vqEJWjKdISByrVWJAvQeUVd3vYdb29+wI+yrStH0upPrXfWl7BljoDi7pX4uM/EMAZHF07KgVxYXeUshF1chSdYqh23cLou08J/3oOi/OX6W2z/+4KZWvRvQnRCz/hqCftPPwff977ar/fp6oqSkj0JyieSS8iSjfWVBH+4JcI5e3Kl5NZhi4Rn+Kn1jfe8cStWipFc5EPHxye6/VOeCnltLU6Dq0RX0skwX4tyjqsdIbJLHp1a5dCiycSG4AU7Ype74OQcnbCYyeCiodrLbyfhrQVjwwtdBuMr/lwY4lcdAbVH2dUF2jWjAw0ffdiUvzK/z2199lpcW1vmjf3mB//b5b6nXaaUxF8DIMfiVGeja3IfGsHvsK0HPOF0RqwU9NklfbprVVK7mglAjZhMZguQ2hnEa4Q2p+Y86F30lqYTWXUiRr8rxzYoAVuH8Qi4vzq9TsCXTqw3a8Ooc9GKtdLyJ74FjP9n2+8ZCXlYJIZMrZOIqZ7x3cKT0/LD20AUSvv0H2MLNffkb+J6jLQh6Jg6PfZ7r0w/y24W3M3Dk2qb2RAMelmQXvvnHALB7JjgxneDKkdZTMZ1smKZpjs7CNlm8FBDbGUMH9X84eK2eBhRr7WRbmTtfayaoL1LqpnjzRf2866YxPv3gKW75nX/lo//0bGl03ZGRxqGtDbTbkMxwwdliiVtnktWpgcHqroixSTx2hn5WWU7mGOhqfbrMbDxDxJVDtBpDh1IuejTo4ZlZtVDrKaYp+Da+Rs4K4CmeX8jFEfLZRJrLDtb2ruT6PAKQ4e1LG/W4LNZFBJFZxV6bY00G6O8ui2d/2Me80OJ98oucit7EWjbKjZMNYq7O8Olzj8LX/gfFsdfxTPEu3n/NcP3f0XQFPCzLCFbxNAAvM8B6dmVT7/RGOELeNOSie6eEuEAeOqg4+vTxlsItlTaBAE+Nq0lfpHRiB/jwbZeotPe+EN93+SAH08/DH5eHVxv2JvtK0HNpp81tFAT9igAAGmhJREFU1ZdSL/xNCBV2aUfQZ+IZQiLX+qIoKEFfOUXPIdVxsWhLfHaaortK0F0B3MXzy/Z0ZoLOxuuHk/Jr86rTd6hm7deWSbu6CORWEcLHqogSqeh/YlmCfHgYMoC0+ULhNVxzqLtxTrjjoX/1V8DlxvUjf8yfR0fq719BNODhZZ2miDfMdxeVyLZTLFXy0JsJusuLtNwEdcjFuhCCPvF6+ObvtJ4W6CxS+iKldZoN+CKl/HxQPdf/1x2vKj//krqy4UB79QiGzmJfhVzyGeXthiKbPXSAMWuuZmvWRswlMgREpnljrkqc4qKA6ri4ksoREhls70bPKe8K4S1ufWKRlJJzK0kGWWI2kam7X3ZVZaF4qqcEnSdZbxf+fAJvZpF19+ZMDHePWiCWniB/tnQZrzvS5ArBWRTNJ+EHP1Zej2gBJ+QCQGyCJ6bjBL0uDve33trVyVcPNctyEcoLDulFUeFub0B0S4zeqAZbtOuh11oQBR1yaVDzcOY7KmOl72h7dho6in0l6LYW9E0x9OghpOVmXMy2lboopWQ2nsJvp9vr+awHXfR7lMieXU6p8XNVl8JFT1D1H9kiiXSBtxQe4F98v0h8ebHufoXEHBnpIdBsVmSbFL3dBO0EwfwKWd9m4env6WaGfqaHvpek9PO6RvFzUOLl8sGVPwavemtbtnT5VU90QC+IxnnVcHTTMIxGOJ5505ALgC9EkOyF89A9frj1f8F1P9Ha/k4MvV6WSlNBfwgOvdq0wt3j7KuQi+0U6VSHR1xuZPQQY4tznEu2LuirqTw9hSXc7jx0H2rdEO1ZDqAq/c6upLlWZCvinNped5CATKu0vi0cSGdXUlwuTuMXeYorp4HX1NzPXp9nkaia4rSNyEAPkdV1BEWWAtdsen64J8Bbs7/KG4JHifgSXNUsnu0Nws9+G3rG2rbF67ZYc6nXL3ZPcPKJBHe9ur3XcQqQWmkVILxhuiy1KJq/EIIOcOPPtL6v892qtSAKynPPp6BY2DhAGmBtToVjjv3U1uw0dAz7ykMnl1JzIWusvovew0y0GXKZiWcYt3TXv3YKb3Quer+t4uNnl1OEyJSmFDlITwgXtmr3ugWmVtKMCxVOcSWm6+4n1hdYlF0tDXpoBysYwyfy9IpEuVKzgpHuAOdkL188Geemw724G/UYd+g70tqAhRpkvOoqYdZ1kFzBbivDBSoXRVvI3vCG6HKpwiKX5wKEXNqlMoZeC2d7roaXrtsTc+jV22+XYUfZV4Iu8kmyovaCp4hNMi7mWFyrH2uuZjaRZkxsRdCVh96TU787taJCLhtyhaF8EG6xWnR6tWyfP3Wu7n6u9CJLMko0sL0XZN5IOcziiW7uoHiwW+XdJ3PFxumK28SCf5KM8POoPALAVW0KeqTVRVEAPVfUQwGX5wJ56O3QSgwdaoddznwH3AEYvPLC2GbYMfZVyEUUUuTqNeiPTRIiTTbRelbJbDzLuJhFWh5EGwt0zqALNVhjkumlOD5R2JTLLpy5ork1xBYmv5xbijMiVCpaT2GedK5YcziFJ7PEoryMK7bZQ/d3lUU62DO06fnhnnIhVcOCom0iGR7lJ7ruY2w1RE9wltFYi/13NLddOUSuaNMfacHj9oYIiyk8okiuIzz0JjF0x3moKegPqUKhCxU6MuwY+8pDdxVS5F11UhK1h+1PvNzy683G04yLOegZb6+IwrKg6yCBtPKaF5dV3wxv1WKtE4LJbnFqUWbxNC6hJgYNi8XamS62jS+3zCLbH3IJd5fDLF29mwV9SJf4D3cHmOhrsdL2PIgGPMTTBR6fWuWKke6aU5EaMdwd4H1vPNLa73lDRFBXVp5OEPSmMfQ6Hnp2DWafMOGWfcK+EnRPMUPRXccr04LelWpD0BMZDrvnEVvpV9F9CPfaOVyWYC2uBN0f3Hiwufx6rmhya1OLXCunACi6QxwUS8zGawh6ZhWXLLAqovg92/txOw26ALoPbL6C8XtcTPSFeNOlB9oW163QFfAwn8jw/Px68wXY88UbImLr7p7eDhB0tw9e9bbyGLtqnFBMtaBPPaKGaBy66UJaZ9gh9k3IRUqJ105ju+tccnYfwsZFb24a25ZYLaSzzaymGZWzELutfYOiI4hT36Q74MGbUqmJ7qoYulsLem6Lg6KD66cByBy8kYMvP8bDtTx0XR2YdPduu6h29ZTz2r11ctzv/dnXtDSjdDuIBjylBmztLoi2jTeMX6ecWhciD30rvO1P6z9Xz0M/8x3VN2bk+gtnl2HH2DceeipXJEAGWa+i0+1lPTDEIWYbTm2pJLc6Q4BM261lgdKgi1jAIow68EVVlotHe+zZLUwtSmTyDBRmyLlCeEavY4AV5ldrvI4W9IyveQvWdnHpqUV53OWy/Sp6Ql78np0R9K6KkNJOeOgltrvb4oXAEfT1uY3bzzwEA6+qH6ox7Cn2hKA//NIS/+/hMw33iafzBMhuHD9XRSYyxpiYa7nron9dh2ecnuHtoAddTPrXCAr9flW2OV0h86n2BX1apyymw2N4e8ewhCS1eHbzjnr4RK7ZnMyt4AmSx0PS3dMRBSlRnWc/2OXnQBvtHbbEBkHfA4uJkSEl3N/8HVjXPV2KeZg6DmO16xcMe489Iej/eGKWX//7k0gp6+6TyOQJiUzDvuXF7gnVz6WFatFktsCBvM7t3qqHDoy7lwmhQyF1BL2QaV/Qp1ZUyqKMTZTeq7hSQ9B161w7cAEEXQjc4V66+jqjhaoj6O005NoylZXDe0HQLQt+5E8gk4Av/bwqZpt5QhUbmfj5vmFPCPrh/hDJXJH5BkKcSBcIkN1UvFOJ1TtJl0gRX56ru4/DbCLDmJjDFm6ItlEl6qCLi0Zdy6rsHza1D/DrJmLFTPsdF6eXEoyKBbwHjpTey7U2tXnH9VmKWIjQhZn9KEJ9WF2bM1x2A6cStp2GXFtmr4VcAAYug1t/DZ77Jzj+mXJB0agR9P3CnlgUnehTQvjSQrJup8R4KkeQLBl/fUEPDKrGQ/mFF4HGXeVm4xnGxRzZ8DCB6lLpVuhSLV8PskBI6Jh9lYceDCkPvTRAug3W50/jEUXcA0cgqt7Ln5rZvOPCs0wxSNh/gUIQt/9Be31uLiCHYmr95KbJHRhcvNdCLg43/Aw891XV0bL/YpWS2yEnZMP5syc89Ml+dfC8tFhf+NaTKdzCxtNgEEVo8CIAhE73a8RMPMOYmEX2bCHcAiovONBDf3GhwkPfKOhhn5ek9G1pUHRx8UUAROwweAIkPTGiuTmKdlVYav4kz8hRura5SrTE8HVKGDqAiwcjPPIr38t1Y0bQ62JZcOcnwBOAmcfgkImf7yeaCroQ4jNCiHkhxImKbR8RQkxXDY2+YAx2+fF7LE4t1Be+1HocqDF+rgJXbBwbgS/eXNDn4ipG7T1wHu1Eo6P0FOcJoUNFVd0WQz6XHhTdvofuTZxWd3R8Px0YYojFjQu+uSRy+RQnCyMbMkD2My1VeW4HG2Loe+x/2zWkrqxATawy7Bta8dA/C7y5xvaPVQ6N3l6zNmJZgom+MC8t1hf0jM4UaSToePwsiH7CqcYZMwCJpRm6RBp333kMwY2O0pWdJSgyFCzfpi53bpdFCj8i376HHkmeIWf5IaJGhhUjwxwUixuLixaeQSB5Rh4q9fo2bBN71UN3uPSH4H3/Dlf+p922xLCNNBV0KeW/Acs7YEt9Tj/Ij3se4FQDQc+mlJfrajLMecF7kJ5MjcXDapzpLlvJcHGIjhBMzxAiQ95Vu4I1IwK4Ggl6LglP3aeyEjTJbIHB4jkSgdFSuqCrZ1RXi1b0V587CaBDLnvMi+x09rqggwqVmbmg+4rziaH/vBDiCR2SqVu1IoR4rxDiuBDi+MLCQr3dGvP0l3nr4h9zZjlJrlB7yHMurastG+ShA8QDowwU6ncmdPCtnVZ3zlPQ3fl1BsUKRXdtu7KWH1chVf81vvIBuOcuePGfS5umV1WPmVx0vLTN3zdGWGRYXqr4H889he0OcEYeeMWEXHaMvRxyMexbtironwAOA1cDM8Dv1NtRSvkpKeUxKeWx/v4tDimOTeK1U/TYcc6u1BY/Z/xcM0FPhcboZg3SKw33iyTPYmO1N9iiGp0ffqVvBm+gdigoZwVxF+sI+pN/C0/8dfm+ZmppjVExj9VbPtkE+9Uwh8zi6fLvzz9FMnoUiWU89O1mP3john3HlgRdSjknpSxKKW3gT4AbttesKpyZoGKWl+osjJZyuWtNPK+g0D0OQGb+hbr75Ao2BwrnWPMNnV9LUZ0fPlCc3dRp0SHvCuCtJeirZ5Ff+QDJ/muYPnQH8ukvQ16FU1ZmT+MTBYKD5QVbq1u9l11ZXDR3ktWI2sfE0LcZTxDQ1bFG0A0dwpYEXQhRmbj6w8CJevtuC1rQx8Ucp+qkLpbGzzUZ5ux0Tlyfeb7uPnMJlbKYjpyHdw7lIcfSrpurXXCH8NrluHe2UOQT33iWk594J6lMjrdM/Tj//YXLEbk1VRACZOfUySis0zDVe1UVF63PQ2qRhaAa9mA89G1GiPJnakIuhg6hlbTFu4GHgIuFEFNCiHcDHxVCPCmEeAJ4I/CBC2pl9yEQLi7zLdT10EtTf5qEXPwH1Mkh18BDn02ooqJizxZ6uFSiB100sqvoDuKX5cyUzzx4mtWvf4zLso9z//gv8svveAvJwZtYpJvi4/cAIPSCrVWZgRPqJy885eKiOXWOnfHptsHGQ99+nM/UeOiGDqHpUS6lfEeNzQ36dF4AXB7oPsQl6QW+Wi/TJZ9SV8BNQi6x7m7OyRj9Jz4NZ/++/MT1Pw03vAeApfkZrhdJcv1Hzs9uy1JVnCun63rotidUasOaLRT51oMP8FnPPXDp7dz59g+CEPSFvXzxM6/mJ57/GqRX8K+fJocHb6Sih4oQJLyDdGVmkVIidIbLi9YYllgm5DWCvu0YQTd0GHuiUhSA2CRjzNX00AtFG1dBhy2ahFx6wz7+oPAjzMRuUGlb/ReD2w//+EE48zAAGR3SCA1d1OilWkOHQup56NITwksBCjm+fPwFPpL7GMVAL/zQ75dSEm+c7GVu7IdwyTyJR++lJzPFim9YnTAqSAeHGJCLrGULMH8SGTrAvc9kODYWa6n/u6FNSoJuQi6GzmBPCXp/YZrF9cymfubr2QJBkUEilDg3oDfk5a+Kt/DFo78Bb/9z0nf+Gfff8Gly4RG49z2QSSB1SCM4cB5Vog5OHL2OoDs90u1sEvcDH+GIdQ7vWz8JwY3l6++48w5OyUFmH/wLhoozJMNjm17LjoxwUCwxF8/A3FOsRi7i9FKKd950nmsBhtqUYujGQzd0BntK0H2FdXpY29QCIJ7OEyRLwR1s2pfb73ER8bl59OwqH773SW749a/znr9+jncu/ST26lkKf/9BvPHT2AhEz/j5210S9NohF0fQT9z/We7M/wMvHL4LceSWTftN9Ic5c/AHOJJ8jAkxg10jvu/qGWWAFWaX47DwDP+RGSIW8vLmVw2e/99h2IxzkraMh27oDPaUoIOT6bJR0FXr3Ay2u3G4xaE/4uOfn5nnC49OcetlA3zup27gyLFb+b+FO3A/eTfXrfw9S65+8GxDh8ImHrpT2XrJY7/OC2KM8bf/Zt2XuuYH3oMlJF5RxNO3Ob7v71ODLuxTD0Ihw9cWe/nR60bwuU014AXBGwLLvSn0ZTDsFntnpUynG05Ys7y0sDF1MZ7OExLZ+uPnqvjI7ZczG8/wlisGiegKytdf1M9DV/wGJ+8+wWX28zznu5YtlkFtpJmg6zmjUsKJm36bI776f0PXyKUsdl1OX+IpekYv2fR8ZGAcgO4pVVV6sjjCx28w4ZYLhjdswi2GjmLvuBbdh0BYXBlc3tSkK5FRIZdmKYsON1/Uz9uvHy2JucOrjw4x+d67ybsCxCau2h67e4+qIbyR2j2nRVgNV/4D65183y2bQy3V9L32J5DComt0cz93b+84ACML/0YRiwOTVzHe19r/xLAFuoYgeAEmQRkMW2TveOhuH0RHuDi3wN8sVIdc8oyQwWpR0BvhHzwK/+UR+gLbNPWmZwx+/jjUyWm3B6/mTdnf4gfecDPBVlILr/9pxOE3QleNsW96W29+hhftId7+6m3I0jHU53UfgGM/tdtWGAwl9o6HDhCb5BCznFpMbpgvGk/nCYps006LLdM9Wp6Svh30Hq4bZ71uPMb3v/5m3n1zi216LQv66mTfeALELXUiOu0a502XHtiKtYZW8YZqn1gNhl1izwl6X36adL7IbKJcXZnI5AmRxWowfq5TCXhdfPDNl5QGHJ8vaz6V0RIYuQKPa299vAaD4fzYW0d8bBJfPk6U9Q2pi/F0npCVRWxDyGWvkwsrj/GSq8zgX4PhlcaeE3SAcTHLi3phNFeweXE+qRZFW8xy2c8cGldx89jE1btsicFg2Gn2zqIoQEzFmY96Fji1kOTxs6v88t89wTOza4SDrWe57Gfcl74F1qbqLsIaDIb9y94S9J5xQHBVcJmPPTbNZ799iv6Ij0+/61q892SMoANMvkH9GAyGVxx7S9A9fuga5hKxwPJqjnfcMMqH3nIpUVdOP29CLgaD4ZXL3hJ0gNgEV2eX+cdf+B4uHepS29ZX1a3x0A0GwyuYvbUoChCbxBM/XRZzgFxr80QNBoNhP7MnBZ3UImTi5W15PZPThFwMBsMrmL0n6LpJF7pnOQA5LejGQzcYDK9g9p6g61z0jYKuQy7GQzcYDK9gWhkS/RkhxLwQ4kTFtpgQ4n4hxPP6tufCmlmBM3SiUtDzxkM3GAyGVjz0zwJvrtr2IeABKeVR4AH9eGfwhlQr2uVT5W0m5GIwGAzNBV1K+W/ActXmO4DP6fufA+7cZrsaE5s0IReDwWCoYqsx9AEp5QyAvq3bp1UI8V4hxHEhxPGFhYUtvl0Vg1fC1CNw7jH12IRcDAaD4cIvikopPyWlPCalPNbfvy1D3eD1H4RQP9z7HhVuMSEXg8Fg2LKgzwkhhgD07fz2mdQCwRjc+Uew+Bzc/z8hn1ST111m+rrBYHjlslVB/xJwl75/F/DF7TGnDQ7fAje9Dx75NDz7T+A18XODwfDKppW0xbuBh4CLhRBTQoh3A78J3CqEeB64VT/eed70q3Dgclh4Wk1gNxgMhlcwTZtzSSnfUeepN22zLe3j8cNbPw2feoPJcDEYDK949l63xWoGLlOink3stiUGg8Gwq+x9QQe47PbdtsBgMBh2nb3Xy8VgMBgMNTGCbjAYDPsEI+gGg8GwTzCCbjAYDPsEI+gGg8GwTzCCbjAYDPsEI+gGg8GwTzCCbjAYDPsEIaXcuTcTYgF4eYu/3gcsbqM5200n29fJtkFn29fJtkFn29fJtkFn21dt25iUsmn/8R0V9PNBCHFcSnlst+2oRyfb18m2QWfb18m2QWfb18m2QWfbt1XbTMjFYDAY9glG0A0Gg2GfsJcE/VO7bUATOtm+TrYNOtu+TrYNOtu+TrYNOtu+Ldm2Z2LoBoPBYGjMXvLQDQaDwdCAPSHoQog3CyGeFUK8IIT4UAfY8xkhxLwQ4kTFtpgQ4n4hxPP6tmeXbBsVQnxDCPG0EOIpIcQvdIp9Qgi/EOLfhRCPa9t+TW+fEEI8rG37ayGEd6dtq7DRJYR4VAjxlQ607bQQ4kkhxGNCiON6265/rhX2dQsh/lYI8Yz+/r26E+wTQlys/2fOT0II8f5OsK3Cxg/oY+KEEOJufay0/d3reEEXQriAPwTeAlwGvEMIcdnuWsVngTdXbfsQ8ICU8ijwgH68GxSAX5JSXgrcBLxP/786wb4scIuU8irgauDNQoibgP8DfEzbtgK8exdsc/gF4OmKx51kG8AbpZRXV6S0dcLn6vD7wD9JKS8BrkL9H3fdPinls/p/djVwHZACvtAJtgEIIYaB/wock1K+CnABP8ZWvntSyo7+AV4NfLXi8YeBD3eAXePAiYrHzwJD+v4Q8Oxu26ht+SJqkHdH2QcEge8CN6IKKNy1Pu8dtmkEdWDfAnwFEJ1im37/00Bf1baO+FyBLuAUel2u0+yrsOf7gG91km3AMHAWiKGmyH0F+P6tfPc63kOn/Mc6TOltncaAlHIGQN8e2GV7EEKMA9cAD9Mh9umQxmPAPHA/8CKwKqUs6F128/P9PeCDgK0f99I5tgFI4GtCiP8QQrxXb+uIzxWYBBaAP9Mhq08LIUIdZJ/DjwF36/sdYZuUchr4beAMMAPEgf9gC9+9vSDoosY2k5rTBCFEGPg74P1Syo6ZoC2lLEp16TsC3ABcWmu3nbUKhBA/CMxLKf+jcnONXXfzu/daKeW1qPDj+4QQN++iLdW4gWuBT0gprwGS7G74ZxM6Bn07cM9u21KJjt3fAUwAB4EQ6jOupul3by8I+hQwWvF4BDi3S7Y0Yk4IMQSgb+d3yxAhhAcl5p+XUt7bafYBSClXgX9Bxfm7hRDOwPLd+nxfC9wuhDgN/BUq7PJ7HWIbAFLKc/p2HhUDvoHO+VyngCkp5cP68d+iBL5T7AMlkt+VUs7px51i2/cCp6SUC1LKPHAv8Bq28N3bC4L+CHBUr/h6UZdMX9plm2rxJeAuff8uVOx6xxFCCOBPgaellL9b8dSu2yeE6BdCdOv7AdQX+WngG8DbdtM2KeWHpZQjUspx1Hfsn6WU7+wE2wCEECEhRMS5j4oFn6ADPlcAKeUscFYIcbHe9CbgJB1in+YdlMMt0Dm2nQFuEkIE9fHr/O/a/+7t5gJFG4sGtwHPoeKtv9IB9tyNinXlUZ7Ju1Hx1geA5/VtbJdsex3q0uwJ4DH9c1sn2AdcCTyqbTsB/KrePgn8O/AC6nLYt8uf7xuAr3SSbdqOx/XPU85x0Amfa4WNVwPH9ed7H9DTKfahFuGXgGjFto6wTdvya8Az+rj4C8C3le+eqRQ1GAyGfcJeCLkYDAaDoQWMoBsMBsM+wQi6wWAw7BOMoBsMBsM+wQi6wWAw7BOMoBsMBsM+wQi6wWAw7BOMoBsMBsM+4f8DN/Y9bErUXjwAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "test_predictions = model.predict(normed_test_data).flatten()\n", "plt.scatter(test_labels, test_predictions)\n", "plt.xlabel('True Values [MPG]')\n", "plt.ylabel('Predictions [MPG]')\n", "#plt.axis('equal')\n", "plt.axis('square') # 正方形,即x轴和y轴比例相同\n", "plt.xlim([0,plt.xlim()[1]])\n", "plt.ylim([0,plt.ylim()[1]])\n", "_ = plt.plot([0, 100], [0, 100]) # 参考的对角线\n", "plt.show()\n", "\n", "plt.plot(test_predictions,label='test prediction')\n", "plt.plot(test_labels.values,label='test true')\n", "plt.legend()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "OrkHGKZcusUo" }, "source": [ "It looks like our model predicts reasonably well. Let's take a look at the error distribution." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "下面看一下误差的分布情况:" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "colab": {}, "colab_type": "code", "id": "f-OHX4DiXd8x" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "error = test_predictions - test_labels\n", "plt.hist(error, bins = 25)\n", "plt.xlabel(\"Prediction Error [MPG]\")\n", "plt.ylabel(\"Count\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "r9_kI6MHu1UU" }, "source": [ "理想情况下,误差应该符合正态分布,这里看起来不太正态,和样本的数量少有一定关系。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "vgGQuV-yqYZH" }, "source": [ "# 结论\n", "\n", "这里演示了解决回归问题的一些技术要点:\n", "\n", "* Mean Squared Error (MSE) 是回归问题中常见的损失函数,分类问题通常使用交叉熵作为损失函数,这些在Tensorflow中都有预定义。\n", "* 评估回归问题的常用指标是Mean Absolute Error (MAE)。\n", "* 当输入数据的范围相差比较大时,需要对数据进行标准化处理。\n", "* 当训练数据比较少时,相应的网络层次和节点数要减少,以避免过拟合。\n", "* 早停(EarlyStopping)技术可以有效的阻止过拟合。" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "basic_regression.ipynb", "private_outputs": true, "provenance": [], "toc_visible": true, "version": "0.3.2" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }