diff --git a/6_pytorch/1_NN/2-logistic-regression.ipynb b/6_pytorch/1_NN/2-logistic-regression.ipynb index 1ced160..192c383 100644 --- a/6_pytorch/1_NN/2-logistic-regression.ipynb +++ b/6_pytorch/1_NN/2-logistic-regression.ipynb @@ -4,79 +4,109 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Logistic 回归模型" + "# 逻辑斯蒂回归模型" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "上一节课我们学习了简单的线性回归模型,这一次课中,我们会学习第二个模型,Logistic 回归模型。\n", + "上一节课我们学习了简单的线性回归模型,这一节我们会学习第二个模型:逻辑斯蒂回归模型(Logistic Regression)。\n", "\n", - "Logistic 回归是一种广义的回归模型,其与多元线性回归有着很多相似之处,模型的形式基本相同,虽然也被称为回归,但是其更多的情况使用在分类问题上,同时又以二分类更为常用。" + "逻辑斯蒂回归是一种广义的回归模型,其与多元线性回归有着很多相似之处,模型的形式基本相同,虽然也被称为回归,但是其更多的情况使用在分类问题上,同时又以二分类更为常用。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 模型形式\n", - "Logistic 回归的模型形式和线性回归一样,都是 y = wx + b,其中 x 可以是一个多维的特征,唯一不同的地方在于 Logistic 回归会对 y 作用一个 logistic 函数,将其变为一种概率的结果。 Logistic 函数作为 Logistic 回归的核心,我们下面讲一讲 Logistic 函数,也被称为 Sigmoid 函数。" + "## 1. 模型形式\n", + "\n", + "逻辑斯蒂回归的模型形式和线性回归一样,都是 $y = wx + b$,其中 $x$ 可以是一个多维的特征,唯一不同的地方在于逻辑斯蒂回归会对 $y$ 作用一个 logistic 函数,将其变为一种概率的结果。 \n", + "\n", + "$$\n", + "h_\\theta(x) = g(\\theta^T x) = \\frac{1}{1+e^{-\\theta^T x}}\n", + "$$\n", + "\n", + "Logistic 函数作为 Logistic 回归的核心,我们下面讲一讲 Logistic 函数,也被称为 Sigmoid 函数。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Sigmoid 函数\n", + "### 1.1 Sigmoid 函数\n", "Sigmoid 函数非常简单,其公式如下\n", "\n", "$$\n", "f(x) = \\frac{1}{1 + e^{-x}}\n", "$$\n", "\n", - "Sigmoid 函数的图像如下\n", - "\n", - "![](https://ws2.sinaimg.cn/large/006tKfTcly1fmd3dde091g30du060mx0.gif)\n", + "Sigmoid 函数的图像如下" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "\n", - "可以看到 Sigmoid 函数的范围是在 0 ~ 1 之间,所以任何一个值经过了 Sigmoid 函数的作用,都会变成 0 ~ 1 之间的一个值,这个值可以形象地理解为一个概率,比如对于二分类问题,这个值越小就表示属于第一类,这个值越大就表示属于第二类。" + "plt.figure()\n", + "plt.axis([-10,10,0,1])\n", + "plt.grid(True)\n", + "X=np.arange(-10,10,0.1)\n", + "y=1/(1+np.e**(-X))\n", + "plt.plot(X,y,'b-')\n", + "plt.title(\"Logistic function\")\n", + "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "另外一个 Logistic 回归的前提是确保你的数据具有非常良好的线性可分性,也就是说,你的数据集能够在一定的维度上被分为两个部分,比如\n", "\n", - "![](https://ws1.sinaimg.cn/large/006tKfTcly1fmd3gwdueoj30aw0aewex.jpg)" + "可以看到 Sigmoid 函数的范围是在 0 ~ 1 之间,所以任何一个值经过了 Sigmoid 函数的作用,都会变成 0 ~ 1 之间的一个值,这个值可以形象地理解为一个概率,比如对于二分类问题,这个值越小就表示属于第一类,这个值越大就表示属于第二类。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "可以看到,上面红色的点和蓝色的点能够几乎被一个绿色的平面分割开来" + "另外一个 Logistic 回归的前提是确保你的数据具有非常良好的线性可分性,也就是说,你的数据集能够在一定的维度上被分为两个部分,比如\n", + "\n", + "![linear_sep](imgs/linear_sep.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 回归问题 vs 分类问题\n", - "Logistic 回归处理的是一个分类问题,而上一个模型是回归模型,那么回归问题和分类问题的区别在哪里呢?\n", - "\n", - "从上面的图可以看出,分类问题希望把数据集分到某一类,比如一个 3 分类问题,那么对于任何一个数据点,我们都希望找到其到底属于哪一类,最终的结果只有三种情况,{0, 1, 2},所以这是一个离散的问题。\n", - "\n", - "而回归问题是一个连续的问题,比如曲线的拟合,我们可以拟合任意的函数结果,这个结果是一个连续的值。\n", - "\n", - "分类问题和回归问题是机器学习和深度学习的第一步,拿到任何一个问题,我们都需要先确定其到底是分类还是回归,然后再进行算法设计" + "可以看到,上面绿色的点和蓝色的点能够几乎被一个黑色的平面分割开来" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 损失函数\n", + "### 1.2 损失函数\n", "前一节对于回归问题,我们有一个 loss 去衡量误差,那么对于分类问题,我们如何去衡量这个误差,并设计 loss 函数呢?\n", "\n", "Logistic 回归使用了 Sigmoid 函数将结果变到 0 ~ 1 之间,对于任意输入一个数据,经过 Sigmoid 之后的结果我们记为 $\\hat{y}$,表示这个数据点属于第二类的概率,那么其属于第一类的概率就是 $1-\\hat{y}$。如果这个数据点属于第二类,我们希望 $\\hat{y}$ 越大越好,也就是越靠近 1 越好,如果这个数据属于第一类,那么我们希望 $1-\\hat{y}$ 越大越好,也就是 $\\hat{y}$ 越小越好,越靠近 0 越好,所以我们可以这样设计我们的 loss 函数\n", @@ -108,70 +138,63 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### 1.3 程序示例\n", + "\n", "下面我们通过例子来具体学习 Logistic 回归" ] }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "from torch.autograd import Variable\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 26, + "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 26, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "import torch\n", + "from torch.autograd import Variable\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", "# 设定随机种子\n", - "torch.manual_seed(2017)" + "torch.manual_seed(2021)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "我们从 data.txt 读入数据,感兴趣的同学可以打开 data.txt 文件进行查看\n", - "\n", - "读入数据点之后我们根据不同的 label 将数据点分为了红色和蓝色,并且画图展示出来了" + "我们从 `data.txt` 读入数据。读入数据点之后我们根据不同的 label 将数据点分为了红色和蓝色,并且画图展示出来了" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 27, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -187,6 +210,7 @@ "with open('./data.txt', 'r') as f:\n", " data_list = [i.split('\\n')[0].split(',') for i in f.readlines()]\n", " data = [(float(i[0]), float(i[1]), float(i[2])) for i in data_list]\n", + "\n", "# 标准化\n", "x0_max = max([i[0] for i in data])\n", "x1_max = max([i[1] for i in data])\n", @@ -214,86 +238,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ "np_data = np.array(data, dtype='float32') # 转换成 numpy array\n", - "x_data = torch.from_numpy(np_data[:, 0:2]) # 转换成 Tensor, 大小是 [100, 2]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "下面我们来实现以下 Sigmoid 的函数,Sigmoid 函数的公式为\n", + "x_data = torch.from_numpy(np_data[:, 0:2]) # 转换成 Tensor, 大小是 [100, 2]\n", + "y_data = torch.from_numpy(np_data[:, 2]).unsqueeze(1)\n", "\n", - "$$\n", - "f(x) = \\frac{1}{1 + e^{-x}}\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# 定义 sigmoid 函数\n", - "def sigmoid(x):\n", - " return 1 / (1 + np.exp(-x))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "画出 Sigmoid 函数,可以看到值越大,经过 Sigmoid 函数之后越靠近 1,值越小,越靠近 0" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# 画出 sigmoid 的图像\n", - "\n", - "plot_x = np.arange(-10, 10.01, 0.01)\n", - "plot_y = sigmoid(plot_x)\n", - "\n", - "plt.plot(plot_x, plot_y, 'r')" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ "x_data = Variable(x_data)\n", "y_data = Variable(y_data)" ] @@ -302,23 +254,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "在 PyTorch 当中,不需要我们自己写 Sigmoid 的函数,PyTorch 已经用底层的 C++ 语言为我们写好了一些常用的函数,不仅方便我们使用,同时速度上比我们自己实现的更快,稳定性更好\n", - "\n", - "通过导入 `torch.nn.functional` 来使用,下面就是使用方法" + "在 PyTorch 当中,不需要我们自己写 Sigmoid 的函数,PyTorch 已经用底层的 C++ 语言为我们写好了一些常用的函数,不仅方便我们使用,同时速度上比我们自己实现的更快,稳定性更好。" ] }, { "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "import torch.nn.functional as F" - ] - }, - { - "cell_type": "code", - "execution_count": 33, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ @@ -339,22 +280,22 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 34, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -366,7 +307,7 @@ } ], "source": [ - "# 画出参数更新之前的结果 (FIXME: the plot is wrong)\n", + "# 画出参数更新之前的结果 \n", "w0 = w[0].data[0]\n", "w1 = w[1].data[0]\n", "b0 = b.data[0]\n", @@ -393,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 98, "metadata": {}, "outputs": [], "source": [ @@ -408,27 +349,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "注意到其中使用 `.clamp`,这是[文档](http://pytorch.org/docs/0.3.0/torch.html?highlight=clamp#torch.clamp)的内容,查看一下,并且思考一下这里是否一定要使用这个函数,如果不使用会出现什么样的结果\n", - "\n", - "**提示:查看一个 log 函数的图像**" + "注意到其中使用 `.clamp`,这是[文档](http://pytorch.org/docs/0.3.0/torch.html?highlight=clamp#torch.clamp)的内容,查看一下,并且思考一下这里是否一定要使用这个函数,如果不使用会出现什么样的结果。" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 100, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.6412, grad_fn=)\n" + "tensor(0.6824, grad_fn=)\n" ] } ], "source": [ "y_pred = logistic_regression(x_data)\n", "loss = binary_loss(y_pred, y_data)\n", + "loss.backward()\n", "print(loss)" ] }, @@ -441,56 +381,45 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 112, "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'NoneType' object has no attribute 'data'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# 自动求导并更新参数\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'data'" - ] - } - ], + "outputs": [], "source": [ "# 自动求导并更新参数\n", - "for i in range(10):\n", - " w.grad.data.zero_()\n", - " b.grad.data.zero_()\n", + "for i in range(100):\n", + " # 算出一次更新之后的loss\n", + " y_pred = logistic_regression(x_data)\n", + " loss = binary_loss(y_pred, y_data)\n", " \n", - " # calc grad\n", + " # calc grad & update w,b\n", " loss.backward()\n", " w.data = w.data - 0.1 * w.grad.data\n", " b.data = b.data - 0.1 * b.grad.data\n", "\n", - " # 算出一次更新之后的loss\n", - " y_pred = logistic_regression(x_data)\n", - " loss = binary_loss(y_pred, y_data)\n", - " print(loss)" + " # clear w,b grad\n", + " w.grad.data.zero_()\n", + " b.grad.data.zero_()\n", + " " ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 113, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 38, + "execution_count": 113, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -520,6 +449,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### 1.4 torch.optim\n", "上面的参数更新方式其实是繁琐的重复操作,如果我们的参数很多,比如有 100 个,那么我们需要写 100 行来更新参数,为了方便,我们可以写成一个函数来更新,其实 PyTorch 已经为我们封装了一个函数来做这件事,这就是 PyTorch 中的优化器 `torch.optim`\n", "\n", "使用 `torch.optim` 需要另外一个数据类型,就是 `nn.Parameter`,这个本质上和 Variable 是一样的,只不过 `nn.Parameter` 默认是要求梯度的,而 Variable 默认是不求梯度的\n", @@ -531,12 +461,13 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 114, "metadata": {}, "outputs": [], "source": [ "# 使用 torch.optim 更新参数\n", "from torch import nn\n", + "\n", "w = nn.Parameter(torch.randn(2, 1))\n", "b = nn.Parameter(torch.zeros(1))\n", "\n", @@ -548,20 +479,20 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 115, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 200, Loss: 0.39672, Acc: 0.92000\n", - "epoch: 400, Loss: 0.32435, Acc: 0.92000\n", - "epoch: 600, Loss: 0.29053, Acc: 0.91000\n", - "epoch: 800, Loss: 0.27069, Acc: 0.91000\n", - "epoch: 1000, Loss: 0.25759, Acc: 0.90000\n", + "epoch: 200, Loss: 0.42650, Acc: 0.90000\n", + "epoch: 400, Loss: 0.33615, Acc: 0.92000\n", + "epoch: 600, Loss: 0.29681, Acc: 0.91000\n", + "epoch: 800, Loss: 0.27461, Acc: 0.91000\n", + "epoch: 1000, Loss: 0.26027, Acc: 0.90000\n", "\n", - "During Time: 0.591 s\n" + "During Time: 0.348 s\n" ] } ], @@ -574,10 +505,12 @@ " # 前向传播\n", " y_pred = logistic_regression(x_data)\n", " loss = binary_loss(y_pred, y_data) # 计算 loss\n", + " \n", " # 反向传播\n", " optimizer.zero_grad() # 使用优化器将梯度归 0\n", " loss.backward()\n", " optimizer.step() # 使用优化器来更新参数\n", + " \n", " # 计算正确率\n", " mask = y_pred.ge(0.5).float()\n", " acc = (mask == y_data).sum().item() / y_data.shape[0]\n", @@ -606,22 +539,22 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 43, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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iTBT9mvVja7+tXBF/Bf0W9OO66dex4/AO++L0AKcSqxd61nittuvpC8QCtcXkvuFvRvkG2AEMzn5sCNA++/8E4AtgE5AK3FDYOnXGojysboQNw7Q7Pp9PJq6fKJWeqyTlhpeTl1a/JJlZmW4LMyxlOznhtpvnhfXiROROThQeDHSSaI+w6psZ5m/RT0d+kptn3SwkI1dOulK2H9zuxjBtL9vNidUpXjxp6/aYNaG7ndWZwIE90ufzyfTU6VJlRBUpPbS0jFgxQjKyMtwWpivK9rJQd1W313bz4/ajCk3obmbH3uPgt2jf0X1y+1u3C8lI0utJsmX/FjeG6clE47Si7Kpe/eF089GWJnQ3s2OPd/hb5PP5ZM7WOVJ9VHWJGRIjQ5YNkfTMdFeF6dVE46RA2yw6OnDic3tt14s0obuZHVVFl3yLDhw7IJ3mdRKSkYbjGsqGvRtcE6ZLNpGnBNpVC9uGbq7tepEmdDezq6room/Re9vfkxov1JDoZ6Jl8OLBcirjlCvCdNEm8oRAu6oe5YSXJnQ3K6yqGCFZ5/CJw9L13a5CMpIwJkHW7l7rdEgFyr3Z4+L8N49/BMWW366q5yHCTxO62wVK2hHYLrDgmwUS/994iXomSgZ8OkBOpJ9wOqQzFJa4PP4RFEvuXTU6WmvoobKifqYJ3asi9MzdkVNHpPcHvYVkpN4r9WTFrhVOh3SaYJoWPP4RWCIC6xu2smp7FZTQdcYiN4vQkZcqla7E+HbjWXTPIjJ8GVw95Wr6f9yf4+nHnQ4NCG7zevwjsIRT4797VTiGFNCE7mZWDRDi0sE0rqt7HVv6buGhSx9i9JejuWTcJSz5YYnTYQW1eXXwK7/OnWHnTvD5/H81mQcWjvqZJnQ3s2LkJZcPHVchtgIv3/Qyy+9bTqmoUlw3/Tp6f9Cb3//43bGY8tvsublt8CvlDWEZwC1QW4zdN21DD1Jxz6J4qB3+RPoJeWzhYxL1TJTE/zdePv72Y8diKcm9XCKkY5XrhKMNXRN6pPPgNe5rflojCWMShGSk23vd5PCJw06HdBovJrxgY/bqiU6vfCbay0UVj4dq6LlNnZ4ulc4+LJAlUZV/lH+NWud0SCLizYQXSsxe3F28+JkUhyb0ksyDe3u+/cBjjsnl/V+Rg8cPOhqbFxNeKDEXdHm/W2u+XvxMiqOghK4nRSOdB/uW5de9i4zyrJnWnoQxCczZNsdfG3GAF3uShhJzQSfoXHY+/U9u+UwK6kwWto5mgTK93TetoatAAjf7+yTp9SQhGbn9rdtl39F9YY/Ni7XBUGIO5vJ+t71XN3wmBR0IW32QjDa5KC8p6AuakZUhI1eOlNJDS0uVEVVkeup08fl8YYvNgy1YIcecc+IuUEJ32/l0N3wmBe2zVv/gaEJXnhLMF/Srg1/JlZOuFJKRtrPayk9HfgprfF7oUZFbUWJ2Q803WE5/JgV1JrO6o5kmdOU5wXxBM7My5aXVL0nZYWWl0nOVZML6CWGtrUc6N9R8vUJr6JrQvcPp6k8hvjv0nVwz9RohGbl++vXyw68/OB1S0Fy+aV0fn1toG7omdG/wSDUty5cl49aNkwrPVpDyw8vL6LWjJcuX5XRYBfLIplVBKujHz8ofRk3oqui81JAqIjt/3Sk3zLhBSEaunnK1fHvoW6dDCshjm9YzrEqebj06KSihG//z4ZeUlCQpKSmOlK1CEBXlzzN5GeMfYs+FRISpqVP518J/kZ6VzvBWw+l/WX+io6KdDu00Hty0rpczFl3u6xjKlQv90gur1mMHY8x6EUnK7zm9sCiS2HH1QliGiCu+3G/9/PMNsWn3kfZAGtfXvZ5HP32Uq6ZcxfaD250O8zQe2bSeYtWY4+EYu9wWgarudt+0ycVidjXIeqCht6AQfT6fzNo8S6qOrCqxQ2Pl2eXPSkZWhtMhi4gnNq3nWNVF0M1j2qFt6CWAnQ2ybm1MzBbMW//56M/SYU4HIRlp+lpT2fTzJqfCPY3LN63nWPU1cPP5jYISurahR4oS3CAbyluflzaPfh/147dTvzG4xWAGtRhEbHRseAJVttM2dBUZIq1BNoTzAaG89Q4JHUh7II2OiR1J/jyZpNeTWL93vSUhK+dZNRadB8e08wtUdbf7pk0uFoukBtkQ30tR3/r7X70v575wrkQ/Ey2DFg2SkxknbXgzSlkLbUMvISKlQbYIDZhFfeu/nvxV7nvvPiEZuWj0RbLqx1UWvAGl7FNQQtc2dOU+DpwPWPjdQnp+0JPdv+/mX5f/i6GthlIupoCZopVyiLahK29x4HzAjf93I1v7baVPUh/+u+a/NBjXgM93fm5beUrZQRO6cp/hw/1dCnIrV87/uI0qla7E2LZjWXLvEgThmmnX8OCCBzmWfszyssI2g41yhXB93prQlfvkdDGIi/vrsbJlw1b8tedfy+Y+m3n4socZu24s9cfWZ9H3iyxbf06XuF27/C1Lbp3aTVkjnJ+3JnQ30Wrb6U6e/Ov/Q4fCmvXKx5bnpTYvseK+FZQuVZrWM1rTc35Pjpw6Uux1e/ayclUkYf28A50ttfumvVzyiKRuhwUJtjuKiy7VO5F+Qh7/9HGJeiZKznvxPPnw6w+LtT43X1aurBfOGYuCqqEbY9oYY742xnxnjBkYYJk7jTFpxphtxpg3LP3VKQlKQrUtlGNPC6dyL+6BT9mYsoxsPZI196+hcpnKtHuzHfe+ey+HTx4OORaIvGvAVMHC+nkHyvQ5NyAa2AHUBWKBTUBCnmXqARuBKtn3zy5svVpDz6MkVNtCqXVbVEO3+sDnVMYpeXrJ01JqSCk55/lz5J20d0JeR0k5GFN+rpqxCLgCWJjr/iBgUJ5lRgE9CltX7psm9Dxc1MRgm1B+tCz6Fti1WTfu2yiNxzcWkpE7594p+4/tD+n1kXINmAqOa2YsAjoAE3PdvwcYnWeZ97KT+hfAGqBNgHX1AlKAlFq1ahX9HUWiklBtCzW7WvAtsPPAJz0zXYZ9Pkxih8ZKtVHV5M0tb+ok1cp2BSV0q3q5lMpudrkG6ARMMMZUzruQiLwuIkkiklS9enWLio4Qnh0NKASh9i/v3Bl27vRfHbpzZ5G2hZ3tlzHRMQy+ejAbem2gbpW6dHq7E7e9dRv7ju4r/sqVKoJgEvoeoGau+/HZj+W2G5gvIhki8gPwDf4Er0JhQQJzNQd+tMJxjVLi2Yl80f0Lnm/9PAt3LCRhbALTUqflHJWqEGnv3WIIVHWXv5pJSgHfA+fz10nRxDzLtAGmZf9fDfgJiCtovdqGrsIlnO3VX//ytVw1+SohGblp5k3y428/2ldYBCoJLY/FRXGaXEQkE3gQWAhsB+aIyDZjzBBjTPvsxRYCh4wxacBSYICIHLLsV0e5g0erTuE88Lkw7kI+7/Y5r7R5hc93fU7i2EReX/+61tbzkd/uVBJ679pJR1tUwXHzFC4u9f2v39Pzg54s+WEJrc5vxYR/TKBulbpOh+UKgXanvMk8RwmYeCtoBY22qAldBadOHf+FQHnVru2v9qp8iQgTNkzgsU8fI0uyGHHdCB649AGiTMkedSPQ7hQdDVlZZz6uu9lfdPhcVXwWXrlZkhhj6NW0F9v6bePq2lfT/5P+tJzakm8OfeN0aI4KtNtkZTky0GbE0ISugqPXqxdLzbNqsuCfC5h6y1S2HthKw/ENeWHVC2T58qmOlgCBdpucjk+R3HvXTprQVXAcGqM8khhj6NqoK2n90rjxghsZ8NkArpx8JWkH05wOLewK2p0ivfeunTShq+CUhAufwuTciufy7l3v8uYdb7Lj8A4av9aY4cuHk5GV4XRoYaO7kz30pKhSDjpw/AAPffwQc7bNoXGNxky5ZQoNazR0OizlYnpSVCmXOrv82bzV4S3evvNt9h7dS9KEJP6z9D9Mm5HpxS7/ymGlnA5AKQW3X3w7LWu35JGFjzBk9LeYDzOQdP/XM2fYeNAmCVUwraGr8LDjKlMnrly1scy4cnHMuG0G1ddMRNJPn0NVr5ZUwdAaurJf3ssCrahy2rFOl5T5y75y+T6uXf5VYfSkqLKfHVeZOnHlapjKDFRMxbMPs++n0pSPLW9ZWcp79KSocpYdV5k6ceVqmMrMr492qdJ/cPSqB2kwvgHLdi6ztDwVOTShK/vZcZWpE1euVq0aljLz66M9dVJplr3YG4Ph2mnX0u+jfhz946il5Srv04Su7BfossCbby76CcZwX7k6axb8/vuZj8fG2lJmfldLtqzTks19N/Po5Y8yPmU89cfV59Mdn1petvKwQAOl232LuAkudNbfguXdPn37Fn8mg3Bu80DzocbF2VdmAVb9uEouGn2RkIx0f6+7/HryV0fiUOFHARNc6ElRK+hY4aHz2nC8UVH+FJ6XgwN1n8o8xTPLnuH5Vc9zToVzGN92PP/4+z8ciUWFj54UtZtOsxK6QCcS80vybuDC0SbLlCrDc9c/x5oea4grG0f72e3p8k4XDp3QycJKKk3oVtCxwkMXKBEa487r3F082mTS35JI6ZVCcstk3tr2FgljE3g77W2nwwrIozMZeoImdCu4sPbmesOH+5N3XiLuPLJx+fCAsdGx/Oea/7C+13riK8XTYW4HOs7tyIHjB5wO7TQ5rZO7dvk/6pxrszSpWyRQ47rdt4g6KapTlRdNficZwX+SUxVZRlaGPLv8WYkdGitxI+Nk1uZZ4vP5gn69neeaA51brl3bujIiHQWcFNUauhXsqL2VhOPS2rXzfzwqKrLft81KRZViUItBpPZOpV5cPTq/05lb37qVvUf3Fvpau2vQ2jpps0CZ3u5bRNXQrVZSavz5vc+8t0h83/mxqVqcmZUpL656UcoMKyNnPXeWTN4wWWbO9AUsyu4atNbQi48Cauia0N2oJO31uRNZdHTJed+5heEH/JtfvpGrp1wt3N5JomJPBizKGHtbwUpKXcVOmtC9xu5vlRvNnBm4lh7J71skbD/gWb4sqVLjSIFFhSMUr12D57Z4C0ro2obuRiWt10xOw20gkfq+c4SpYTnKRPHb/koFFhWO3pnhmATaqlNQnuuVEyjT233TGnoBStpxaaBqYaS/7xxhbGILVFSVGkckMytTREKvkbqtBmvl18eNrZ9ok4sH5DfWiZu+JXYK1MQEkf2+c4TxBzy/oqJiTwq3d5IrJ10pXx38yq2hB83KJOzG1k9N6OFS1KqKG78V4eTGalC4hbGae2ZRPpmeOl2qjKgipYeWlpErR0pGVkZQ63LjR2dlEnbj+9OEHg7FScpu3GvCqaT/oLnE3t/3yq2zbxWSkWavN5Mt+7cU+horkqfVv2WBvk7R0aGv2427pib0cAg2Kee397rxuC7c3NYQW0L5fD6ZvWW2VBtVTWKGxMjQz4dKemZ6wOWLWxexI2EWdHlDUdbttl1TE3o4BJOUA+29cXElu4buRuH6FrstW2Q7cOyA3DX3LiEZaTS+kWzYuyHf5YJKyAW8R7sOTmfOjNzLGjShh0Mwe2ZBkyS47biuJAvXcbYbj+fzeHf7u1LjhRoS/Uy0DF48WE5lnDpjmQJ/kwp5j3YenEbqga8m9HAI5stZ0B7m0ppaiRSucxoeOXdy6MQh6fpuVyEZSRiTIGt3rw3+xYW8Rzs3gUc2b8g0oYdLYUk5UvewSBOuqp0TVchiVBw++uYjif9vvEQ9EyUDPh0gJ9JPFP6iQt6jnQcpHjgAKhJN6G4RqXtYpLH7hzcnqeZXhp0/8Bbsf7+d/E16zu8pJCMXvnqhrNy1suAXBLEt7Tw4jcQDX03obhKJe1ikCXe1MVw/8Bb+UH224zOp81IdMclG+i/oL8f+OJb/gjNnisTGnl5ebKzu98VQUELXsVzCLRwDWajisXN2ovzmn81h9yxIFo4Zc33d69nSdwsPXvogr3z5CpeMu4QlPyzJf2GRgu8ryxgJYuMaY9oALwPRwEQRGRFguTuAeUAzEUkpaJ1JSUmSklLgIkpFnqio/BOaMf4feTvVqZP/JNy1a/srF0W0YtcKus/vzneHv6N3096Maj2KSqUr2VpmSWaMWS8iSfk9V2gN3RgTDYwBbgISgE7GmIR8lqsIPAysLV64SlnArTM+OTmSpk1DKbao3YJNfTbx/674f0zYMIH6Y+vzyXef+J/UKYrCqlQQy1wKfCci3wMYY2YDtwBpeZYbCowEBhQ1mIyMDHbv3s2pU6eKugoVgjJlyhAfH09MTIzToVgrZ8zTnKaNnDFPwfkmruHDT48NrB+fNpCc9z54sD+h1qrlL9eCbVIuphwv3PACHRI60P397tw06ya6NerGxPjziP5p95kvcNuQyLNm2bJdwi5Q43rODeiAv5kl5/49wOg8yzQB3s7+fxmQFGBdvYAUIKVWrVpnNPZ///33cvDgwZAmtFVF4/P55ODBg/L99987HYr13N49NMJPjJ/KOCVPLnpSop+Jlr6dzpKMMqXd3bMr1JPgDn9+FKeXS2EJHX+zzTKgjhSS0HPf8uvlkpaWpsk8jHw+n6SlpTkdhvUi9RJBj1m/d700GNdAOt2OHKhWTnxu/QELpQLggq7HBSX0YHq57AFq5rofn/1YjopAfWCZMWYncDkw3xiTb6N9YYwxRXmZKoKI3dYlbcYnl2pybhPW9VzHxf2HcF7/DM4ZWY25Hz2P/POfTod2ulDa+fPrpXTihP9xFwgmoa8D6hljzjfGxAJ3A/NznhSRIyJSTUTqiEgdYA3QXgrp5aKUbcIxj5oKSmx0LP9u+W/W91pP7cq1uXPenXSY24Gfj/3sdGh/CaUC4PKTvIUmdBHJBB4EFgLbgTkiss0YM8QY097uAN1s586dvPHGG3/eT01NZcGCBX/enz9/PiNG5NvDM2TdunVj3rx5APTo0YO0tLznpNWf7OxHrorkknMuYfX9qxlx3Qg++uYjEscmMnPzzJxmW2eFUgFw+9FfoLYYu2+B2tC9ZOnSpdK2bds/70+ZMkUeeOABW8rq2rWrzJ071/L1em2bK+/bfnC7XDHxCiEZafdGO9l9ZLfTIQV/otPlbejBdFt0xCOfPELqz6mWrrNRjUa81OalApeZPn06L7zwAsYYGjRowIwZM+jWrRvt2rWjQ4cOAFSoUIFjx44xcOBAtm/fTqNGjejUqRNjxozh5MmTrFy5kkGDBnHy5ElSUlIYPXo03bp1o1KlSqSkpPDzzz8zatQoOnTogM/n48EHH2TJkiXUrFmTmJgYunfv/mdZ+bnmmmt44YUXSEpKokKFCjz88MN8+OGHlC1blvfff59zzjmHgwcP0qdPH37MPhR86aWXaN68uWXbUqmiuqjaRay4bwWvfvkqTy5+koSxCfz3hv/SvXF3587rdO4c3BGcjV0/raCX/ueybds2hg0bxpIlS9i0aRMvv/xygcuPGDGCFi1akJqayhNPPMGQIUO46667SE1N5a677jpj+X379rFy5Uo+/PBDBg4cCMA777zDzp07SUtLY8aMGaxevTqkmI8fP87ll1/Opk2buPrqq5kwYQIADz/8MP/6179Yt24db7/9Nj169AhpvZ7k1ouJ1Bmio6J55PJH2NJ3C41rNKbHBz24ceaN7Potn6tK3cbFw3e4toZeWE3aDkuWLKFjx45Uq1YNgKpVq1q6/ltvvZWoqCgSEhLYv38/ACtXrqRjx45ERUVRo0YNrr322pDWGRsbS7t27QBo2rQpn332GQCLFi06rZ39999/59ixY1SoUMGid+Mybr6YSAV0QdULWNJ1Ca+lvMbjix6n/rj6jLx+JH2S+hBltL4ZKt1iQShVqhS+7HE2fD4f6enpRVpP6dKl//xfLDoZFBMT8+dhanR0NJmZmYA/zjVr1pCamkpqaip79uyJ3GQOru9OpgKLMlH0bdaXrX23ckX8FTyw4AFaTWvFd4e/czo0z9GEnkurVq2YO3cuhw4dAuDw4cMA1KlTh/Xr1wP+nisZGRkAVKxYkaNHj/75+rz3g9G8eXPefvttfD4f+/fvZ9myZRa8E7jhhht49dVX/7yfmppqyXpdy+XdyVThaleuzcIuC5nUfhKpP6fSYFwD/rf6f2T5spwOzTM0oeeSmJjI4MGDadmyJQ0bNuTRRx8FoGfPnnz++ec0bNiQ1atXU758eQAaNGhAdHQ0DRs25H//+x/XXnstaWlpNGrUiLfeeiuoMu+44w7i4+NJSEigS5cuNGnShLPOOqvY7+WVV14hJSWFBg0akJCQwPjx44u9Tldze3cyFRRjDN0bd2dbv220Or8Vj376KC2mtOCrX75yOjRvCNT9xe5bJHRbtMrRo0dFROSXX36RunXryr59+8JWdsRscxd0J1PW8vl8MnPTTKk6sqqUHlpanlvxnGRkZTgdluPQCS7crV27djRq1IgWLVrw73//mxo1ajgdkvfoxUQRxxhD5wadSeuXRrsL2zFo8SAun3g5m/dvdjo01wpqggs75DfBxfbt27n44osdiaek0m2uvGLutrk8sOABfjv1G4NbDGZQi0HERsc6HVbYFWuCC6WUcoOOiR1JeyCNjokdSf48mWYTmrFh3wanw3IVTehKKc+oVq4as26fxft3v8/B4we5dMKlPLn4SU5l6qQ4oAldKeVB7f/enm39tnFvw3t5buVzNHmtCWt2r3E6LMdpQleqpImQIRKqlK3C5Fsm80nnTziWfozmk5vz2KePcSLjROEvjlDeTugRsmMqFTY5QyTs2uXv3JkzRIKHvzs3/t+NbO23lV5NevHi6hdpOL4hy3ctdzosR3g3oTu8Y06bNo169epRr149pk2bFpYylSq2CB0ioVLpSoxrN47F9y4my5dFy6kteXDBgxxLP+Z0aGHl3W6Lder4k3hetWv7R0Cz0eHDh0lKSiIlJQVjDE2bNmX9+vVUqVLF1nLtoN0WS5ioKH8FKC9j/KMHRoDj6cd5cvGTvPrlq9SuXJsJ/5jA9XWvdzosy0Rmt0Ubxu5Yt24dDRo04NSpUxw/fpzExES2bt16xnILFy6kdevWVK1alSpVqtC6dWs++eSTIperVNiUgCESyseW5+WbXmbFfSuIjY6l9YzW9PqgF0dOHXE6NNt5N6HbsGM2a9aM9u3b89RTT/H444/TpUsX6tevf8Zye/bsoWbNv+bNjo+PZ8+ePWcsp5TrlKD5VpvXak5q71QGXDmASRsnUX9cfRZ8u6DwF3qYdxO6TTvm008/zWeffUZKSgqPP/54sdallOuUsCESysaUZVTrUay+fzWVSlei7Rtt6fpeVw6fPOx0aLbwbkK3acc8dOgQx44d4+jRo5w6lf/FCueddx4//fTTn/d3797NeeedV6xylQobF8+4Y5dLz7uUDb028FSLp5i1eRYJYxJ4d/u7TodlOe+eFLVJ+/btufvuu/nhhx/Yt28fo0ePPmOZw4cP07RpUzZs8F923KRJE9avX2/5DEfh4IZtrlQ4bdy3ke7zu5P6cyp3Jt7J6JtGU718dafDClpknhS1wfTp04mJieGf//wnAwcOZN26dSxZsuSM5apWrcq///1vmjVrRrNmzXj66ac9mcyVKokan9uYL3t8ybBrh/Hu9ndJGJvA7K2zLZtFzElaQy/hdJurkmzbgW3c9/59rNu7jlsvupWxN4/l3IrnOh1WgbSGrpRS+Ug8O5FV969i1PWj+Pjbj0kcm8j0TdM9W1vXhF6ALVu20KhRo9Nul112mdNhKaUsVCqqFAOaD2BTn00kVE+g63tdaftGW3468lPhL3aZUk4H4GaXXHJJ5E+urJQC4O/V/s7y+5Yz+svRDFo8iMSxibx4w4v0aNIDY4zT4QVFa+hKKZUtykTR/7L+bOm7haS/JdHrw160ntGaH379wenQgqIJXSml8qhbpS6L7l3E+Lbj+XLPl1wy7hJGfzkan7h7vBtN6EoplY8oE0XvpN5s7beVq2pdxUMfP0TLqS359tC3TocWkKcTug6HrpSyW62zavFx54+ZcssUth7YSoPxDXhh1Qtk+bKcDu0Mnk3oTo/T36ZNGypXrky7du3CU6BSyjHGGLo16sa2ftu44YIbGPDZAK6cfCVpB9OcDu00nk3oTo/TP2DAAGbMmBGewpRSrvC3in/jvbve443b32DH4R00fq0xz654loysDKdDAzyc0G0YDj3o8dABrrvuOipWrFj0wpRSnmSModMlnUh7II1b/n4Lg5cM5rKJl7Hp501Oh+bdhG7HOP3BjoeulFJnlz+bOR3nMK/jPPYc3UPShCT+s/Q/pGelOxaTZxO6XeP063joSqlQ3JFwB2n90uhUvxNDlg+h6etNSdmbUvgLbeDZhG7XOP3BjIeulFK5xZWLY/pt0/mg0wccPnmYyyZexsBFAzmVGd4c4tmEDvaM09+7d2+GDh1K586deeKJJ4q/QqVUidHuwnZs67eN+xrdx8gvRtJofCNW/bQqbOV7OqFbLdjx0AFatGhBx44dWbx4MfHx8SxcuDDM0Sql3KhymcpMbD+RT7t8ysnMk1w1+SqSlyWHpeygxkM3xrQBXgaigYkiMiLP848CPYBM4CDQXUR2FbROHQ/dHXSbK2Wfo38cZeCigVwefzn3NLzHknUWNB56oaMtGmOigTFAa2A3sM4YM19Ecveo3wgkicgJY0xfYBRwV/FDV0op76pYuiJj2o4JW3nBDJ97KfCdiHwPYIyZDdwC/JnQRWRpruXXAF2sDNIpW7Zs4Z57Tv9VLV26NGvXrnUoIqWUCiyYhH4ekHuk991AQbM83A98nN8TxpheQC+AWgE6jIuIa8YejvTx0L06K4tSKn+WnhQ1xnQBkoDn83teRF4XkSQRSape/cxZtsuUKcOhQ4c00YSBiHDo0CHKlCnjdChKKYsEU0PfA9TMdT8++7HTGGOuBwYDLUXkj6IEEx8fz+7duzl48GBRXq5CVKZMGeLj450OQyllkWAS+jqgnjHmfPyJ/G7gn7kXMMY0Bl4D2ojIgaIGExMTw/nnn1/UlyulVIlWaJOLiGQCDwILge3AHBHZZowZYoxpn73Y80AFYK4xJtUYM9+2iJVSSuUrqEmiRWQBsCDPY0/n+v96i+NSSikVIr1SVCmlIkRQV4raUrAxB4ECryYtQDXgFwvDsYrGFRqNK3RujU3jCk1x4qotImd2E8TBhF4cxpiUQJe+OknjCo3GFTq3xqZxhcauuLTJRSmlIoQmdKWUihBeTeivOx1AABpXaDSu0Lk1No0rNLbE5ck2dKWUUmfyag1dKaVUHprQlVIqQrg6oRtj2hhjvjbGfGeMGZjP848aY9KMMZuNMYuNMbVdElcfY8yW7GEQVhpjEtwQV67l7jDGiDEmLN25gthe3YwxB7O3V6oxpocb4spe5s7sfWybMeYNN8RljPlfrm31jTHmN5fEVcsYs9QYszH7O3mzS+KqnZ0fNhtjlhljwjIinTFmsjHmgDFma4DnjTHmley4NxtjmhS7UBFx5Q3/dHc7gLpALLAJSMizzLVAuez/+wJvuSSuSrn+bw984oa4sperCCzHPxFJkhviAroBo124f9XDPxtXlez7Z7shrjzLPwRMdkNc+E/09c3+PwHY6ZK45gJds/9vBcwI0z52NdAE2Brg+Zvxzx1hgMuBtcUt08019D9nShKRdCBnpqQ/ichSETmRfXcN/qF93RDX77nulgfCcea50LiyDQVGAqfCEFMocYVbMHH1BMaIyK8AUoyRRC2OK7dOwJsuiUuAStn/nwXsdUlcCUDObO9L83neFiKyHDhcwCK3ANPFbw1Q2RhzbnHKdHNCz2+mpPMKWD7gTEkWCyouY8wDxpgd+OdX7e+GuLIP6WqKyEdhiCfouLLdkX3YOc8YUzOf552I60LgQmPMF8aYNdmTpbshLsDflACcz1/Jyum4koEuxpjd+Afze8glcW0Cbs/+/zagojEmLgyxFSbUHFcoNyf0oBU2U5ITRGSMiFwAPAE85XQ8xpgo4L/A/3M6lnx8ANQRkQbAZ8A0h+PJUQp/s8s1+GvCE4wxlZ0MKI+7gXkikuV0INk6AVNFJB5/c8KM7P3OaY8BLY0xG4GW+Od1cMs2s5QbNnYgoc6U1F6KOFOSHXHlMhu41c6AshUWV0WgPrDMGLMTf5vd/DCcGC10e4nIoVyf3USgqc0xBRUX/hrTfBHJEJEfgG/wJ3in48pxN+FpboHg4rofmAMgIquBMvgHoXI0LhHZKyK3i0hj/LkCEfnN5riCEWouKVw4Tg4U8YRCKeB7/IeUOSc7EvMs0xj/CZF6LourXq7//wGkuCGuPMsvIzwnRYPZXufm+v82YI1L4moDTMv+vxr+w+M4p+PKXu4iYCfZFwe6ZHt9DHTL/v9i/G3otsYXZFzVgKjs/4cDQ8KxzbLLq0Pgk6JtOf2k6JfFLi9cb6yIG+Nm/LWiHcDg7MeG4K+NAywC9gOp2bf5LonrZWBbdkxLC0qs4Ywrz7JhSehBbq/nsrfXpuztdZFL4jL4m6nSgC3A3W6IK/t+MjAiHPGEsL0SgC+yP8dU4AaXxNUB+DZ7mYlA6TDF9SawD8jAf7R3P9AH6JNr/xqTHfcWK76Peum/UkpFCDe3oSullAqBJnSllIoQmtCVUipCaEJXSqkIoQldKaUihCZ0pZSKEJrQlVIqQvx/vHDJPHFjoEsAAAAASUVORK5CYII=\n", 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" ] @@ -658,6 +591,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### 1. 5 PyTorch的Loss函数\n", "前面我们使用了自己写的 loss,其实 PyTorch 已经为我们写好了一些常见的 loss,比如线性回归里面的 loss 是 `nn.MSE()`,而 Logistic 回归的二分类 loss 在 PyTorch 中是 `nn.BCEWithLogitsLoss()`,关于更多的 loss,可以查看[文档](http://pytorch.org/docs/0.3.0/nn.html#loss-functions)\n", "\n", "PyTorch 为我们实现的 loss 函数有两个好处,第一是方便我们使用,不需要重复造轮子,第二就是其实现是在底层 C++ 语言上的,所以速度上和稳定性上都要比我们自己实现的要好\n", @@ -667,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 117, "metadata": {}, "outputs": [], "source": [ @@ -685,14 +619,14 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 118, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "tensor(0.6363)\n" + "tensor(0.6314)\n" ] } ], @@ -704,20 +638,20 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 119, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 200, Loss: 0.39427, Acc: 0.88000\n", - "epoch: 400, Loss: 0.32362, Acc: 0.87000\n", - "epoch: 600, Loss: 0.29014, Acc: 0.87000\n", - "epoch: 800, Loss: 0.27045, Acc: 0.87000\n", - "epoch: 1000, Loss: 0.25743, Acc: 0.88000\n", + "epoch: 200, Loss: 0.39446, Acc: 0.88000\n", + "epoch: 400, Loss: 0.32373, Acc: 0.87000\n", + "epoch: 600, Loss: 0.29020, Acc: 0.87000\n", + "epoch: 800, Loss: 0.27049, Acc: 0.87000\n", + "epoch: 1000, Loss: 0.25745, Acc: 0.88000\n", "\n", - "During Time: 0.372 s\n" + "During Time: 0.232 s\n" ] } ], @@ -750,20 +684,6 @@ "source": [ "可以看到,使用了 PyTorch 自带的 loss 之后,速度有了一定的上升,虽然看上去速度的提升并不多,但是这只是一个小网络,对于大网络,使用自带的 loss 不管对于稳定性还是速度而言,都有质的飞跃,同时也避免了重复造轮子的困扰" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "下一节课我们会介绍 PyTorch 中构建模型的模块 `Sequential` 和 `Module`,使用这个可以帮助我们更方便地构建模型" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/6_pytorch/1_NN/3-nn-sequential-module.ipynb b/6_pytorch/1_NN/3-nn-sequential-module.ipynb index 36eb816..91929f1 100644 --- a/6_pytorch/1_NN/3-nn-sequential-module.ipynb +++ b/6_pytorch/1_NN/3-nn-sequential-module.ipynb @@ -4,100 +4,38 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 多层神经网络,Sequential 和 Module\n", - "通过前面的章节,我们了解到了机器学习领域中最常见的两个模型,线性回归模型和 Logistic 回归模型,他们分别是处理机器学习中最常见的两类问题-回归问题和分类问题。\n", + "# 多层神经网络\n", "\n", - "下面我们会讲第一个深度学习的模型,多层神经网络。" + "本节在前面学习线性回归模型的基础上,我们学习如何利用PyTorch实现多层神经网络。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 多层神经网络\n", - "在前面的线性回归中,我们的公式是 $y = w x + b$,而在 Logistic 回归中,我们的公式是 $y = Sigmoid(w x + b)$,其实它们都可以看成单层神经网络,其中 Sigmoid 被称为激活函数,之后我们会详细介绍激活函数以及为什么必须使用激活函数,下面我们从理解神经网络入手。" + "## 1. 多层神经网络\n", + "在前面的线性回归中,我们的公式是 $y = w x + b$,而在 Logistic 回归中,我们的公式是 $y = Sigmoid(w x + b)$,其实它们都可以看成单层神经网络,其中 Sigmoid 被称为激活函数。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 理解神经网络\n", - "神经网络的灵感来自于人脑的神经元系统,下面我们放一张人脑的神经元和神经网络的对比图(来自 cs231n)\n", + "### 1.1 神经网络的结构\n", + "神经网络就是很多个神经元堆在一起形成一层神经网络,那么多个层堆叠在一起就是深层神经网络\n", "\n", - "![](https://ws4.sinaimg.cn/large/006tNc79ly1fmgiz5mqs3j30or0773zg.jpg)\n", + "![nn demo](imgs/nn-forward.gif)\n", "\n", - "左边是一张神经元的图片,神经元通过突触接受输入,然后通过**神经激活**的方式传输给后面的神经元。这对比于右边的神经网络,首先接受数据输入,然后通过计算得到结果,接着经过**激活函数**,再传给第二层的神经元。\n", + "可以看到,神经网络的结构其实非常简单,主要有输入层,隐藏层,输出层构成,输入层需要根据特征数目来决定,输出层根据解决的问题来决定,那么隐藏层的网路层数以及每层的神经元数就是可以调节的参数,而不同的层数和每层的参数对模型的影响非常大,我们看看这个网站的示例 [demo](http://cs.stanford.edu/people/karpathy/convnetjs/demo/classify2d.html)\n", "\n", - "所以前面讲的 logistic 回归模型和线性回归模型都可以看做是一个单层神经网络,而 logistic 回归中使用了激活函数 sigmoid。\n", - "\n", - "神经网络使用的激活函数都是非线性的,每个激活函数都输入一个值,然后做一种特定的数学运算得到一个结果,下面举几个例子\n", - "\n", - "sigmoid 激活函数\n", - "\n", - "$$\\sigma(x) = \\frac{1}{1 + e^{-x}}$$\n", - "\n", - "![](https://ws1.sinaimg.cn/large/006tNc79ly1fmgj7yto7gj308w05oa9w.jpg)\n", - "\n", - "tanh 激活函数\n", - "\n", - "$$tanh(x) = 2 \\sigma(2x) - 1$$\n", - "\n", - "![](https://ws3.sinaimg.cn/large/006tNc79ly1fmgj8yjdnlj308w05mt8j.jpg)\n", - "\n", - "ReLU 激活函数\n", - "\n", - "$$ReLU(x) = max(0, x)$$\n", - "\n", - "![](https://ws1.sinaimg.cn/large/006tNc79ly1fmgj94ky2oj308n05uq2r.jpg)\n", - "\n", - "我们下面重点讲一讲 ReLU 激活函数,因为现在神经网络中 90% 的情况都是使用这个激活函数。一般一个一层的神经网络的公式就是 $y = max(0, w x + b)$,一个两层的神经网络就是 $y = w_2\\ max(0, w_1 x + b_1) + b_2$,非常简单,但是却很有效,使用这个激活函数能够加快梯度下降法的收敛速度,同时对比与其他的激活函数,这个激活函数计算更加简单,所以现在变得非常流行,之后你会发现我们激活在所有的神经网络中都会使用它。" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 神经网络的结构\n", - "神经网络就是很多个神经元堆在一起形成一层神经网络,那么多个层堆叠在一起就是深层神经网络,我们可以通过下面的图展示一个两层的神经网络和三层的神经网络\n", - "\n", - "![](https://ws2.sinaimg.cn/large/006tNc79ly1fmgjiafmmjj30nu07075w.jpg)\n", - "\n", - "可以看到,神经网络的结构其实非常简单,主要有输入层,隐藏层,输出层构成,输入层需要根据特征数目来决定,输出层根据解决的问题来决定,那么隐藏层的网路层数以及每层的神经元数就是可以调节的参数,而不同的层数和每层的参数对模型的影响非常大,我们看看这个网站的 [demo](http://cs.stanford.edu/people/karpathy/convnetjs/demo/classify2d.html)\n", - "\n", - "神经网络向前传播也非常简单,就是一层一层不断做运算就可以了,可以看看下面这个例子\n", - "\n", - "![](https://ws2.sinaimg.cn/large/006tNc79ly1fmgj4q1j78g309u0cc4qq.gif)" + "神经网络向前传播也非常简单,就是一层一层不断做运算即可。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 为什么要使用激活函数\n", - "激活函数在神经网络中非常重要,使用激活函数也是非常必要的,前面我们从人脑神经元的角度理解了激活函数,因为神经元需要通过激活才能往后传播,所以神经网络中需要激活函数,下面我们从数学的角度理解一下激活函数的必要性。\n", - "\n", - "比如一个两层的神经网络,使用 A 表示激活函数,那么\n", - "\n", - "$$\n", - "y = w_2 A(w_1 x)\n", - "$$\n", - "\n", - "如果我们不使用激活函数,那么神经网络的结果就是\n", - "\n", - "$$\n", - "y = w_2 (w_1 x) = (w_2 w_1) x = \\bar{w} x\n", - "$$\n", - "\n", - "可以看到,我们将两层神经网络的参数合在一起,用 $\\bar{w}$ 来表示,两层的神经网络其实就变成了一层神经网络,只不过参数变成了新的 $\\bar{w}$,所以如果不使用激活函数,那么不管多少层的神经网络,$y = w_n \\cdots w_2 w_1 x = \\bar{w} x$,就都变成了单层神经网络,所以在每一层我们都必须使用激活函数。\n", - "\n", - "最后我们看看激活函数对神经网络的影响\n", - "\n", - "![](https://ws1.sinaimg.cn/large/006tNc79ly1fmgkeqjr34g306r065diu.gif)\n", - "\n", - "可以看到使用了激活函数之后,神经网络可以通过改变权重实现任意形状,越是复杂的神经网络能拟合的形状越复杂,这就是著名的神经网络万有逼近定理。\n", - "\n", - "下面我们通过例子来感受一下神经网络的强大之处" + "### 1.2 示例程序" ] }, { @@ -120,72 +58,10 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], - "source": [ - "def plot_decision_boundary(model, x, y):\n", - " # Set min and max values and give it some padding\n", - " x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1\n", - " y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1\n", - " h = 0.01\n", - " # Generate a grid of points with distance h between them\n", - " xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", - " # Predict the function value for the whole grid .c_按行连接两个矩阵,左右相加。\n", - " Z = model(np.c_[xx.ravel(), yy.ravel()])\n", - " Z = Z.reshape(xx.shape)\n", - " # Plot the contour and training examples\n", - " plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n", - " plt.ylabel('x2')\n", - " plt.xlabel('x1')\n", - " plt.scatter(x[:, 0], x[:, 1], c=y.reshape(-1), s=40, cmap=plt.cm.Spectral)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "这次我们仍然处理一个二分类问题,但是比前面的 logistic 回归更加复杂" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(1)\n", - "m = 400 # 样本数量\n", - "N = int(m/2) # 每一类的点的个数\n", - "D = 2 # 维度\n", - "x = np.zeros((m, D))\n", - "y = np.zeros((m, 1), dtype='uint8') # label 向量,0 表示红色,1 表示蓝色\n", - "a = 4\n", - "\n", - "for j in range(2):\n", - " ix = range(N*j,N*(j+1))\n", - " t = np.linspace(j*3.12,(j+1)*3.12,N) + np.random.randn(N)*0.2 # theta\n", - " r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius\n", - " x[ix] = np.c_[r*np.sin(t), r*np.cos(t)]\n", - " y[ix] = j" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", 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" ] @@ -197,39 +73,65 @@ } ], "source": [ - "plt.scatter(x[:, 0], x[:, 1], c=y.reshape(-1), s=40, cmap=plt.cm.Spectral)" + "from sklearn import datasets\n", + "\n", + "# generate sample data\n", + "np.random.seed(0)\n", + "data_x, data_y = datasets.make_moons(200, noise=0.20)\n", + "\n", + "# plot data\n", + "plt.scatter(data_x[:, 0], data_x[:, 1], c=data_y, cmap=plt.cm.Spectral)\n", + "plt.show()" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 3, "metadata": {}, + "outputs": [], "source": [ - "我们可以先尝试用 logistic 回归来解决这个问题" + "def plot_decision_boundary(model, x, y):\n", + " # Set min and max values and give it some padding\n", + " x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1\n", + " y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1\n", + " h = 0.01\n", + " # Generate a grid of points with distance h between them\n", + " xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", + " # Predict the function value for the whole grid .c_按行连接两个矩阵,左右相加。\n", + " Z = model(np.c_[xx.ravel(), yy.ravel()])\n", + " Z = Z.reshape(xx.shape)\n", + " # Plot the contour and training examples\n", + " plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n", + " plt.ylabel('x2')\n", + " plt.xlabel('x1')\n", + " plt.scatter(x[:, 0], x[:, 1], c=y.reshape(-1), s=40, cmap=plt.cm.Spectral)" ] }, { - "cell_type": "code", - "execution_count": 5, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "x = torch.from_numpy(x).float()\n", - "y = torch.from_numpy(y).float()" + "这次我们仍然处理一个二分类问题,但是比前面的 logistic 回归更加复杂。我们可以先尝试用 logistic 回归来解决这个问题" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ + "# 变量\n", + "x = torch.from_numpy(data_x).float()\n", + "y = torch.from_numpy(data_y).float().unsqueeze(1)\n", + "\n", + "# 定义参数\n", "w = nn.Parameter(torch.randn(2, 1))\n", "b = nn.Parameter(torch.zeros(1))\n", "\n", + "# 优化器\n", "optimizer = torch.optim.SGD([w, b], 1e-1)\n", "\n", "def logistic_regression(x):\n", - " #FIXME: sigmod is included in nn.BCEWithLogitsLoss \n", " return torch.mm(x, w) + b\n", " \n", "criterion = nn.BCEWithLogitsLoss()" @@ -237,26 +139,18 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 20, loss: 0.7048085927963257\n", - "epoch: 40, loss: 0.6740389466285706\n", - "epoch: 60, loss: 0.673165500164032\n", - "epoch: 80, loss: 0.6731466054916382\n", - "epoch: 100, loss: 0.6731460690498352\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:8: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n", - " \n" + "epoch: 20, loss: 0.6910861730575562\n", + "epoch: 40, loss: 0.5803939700126648\n", + "epoch: 60, loss: 0.5160364508628845\n", + "epoch: 80, loss: 0.4751732349395752\n", + "epoch: 100, loss: 0.44716107845306396\n" ] } ], @@ -265,16 +159,18 @@ " #更新并自动计算\n", " out = logistic_regression(Variable(x))\n", " loss = criterion(out, Variable(y))\n", + " \n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", + " \n", " if (e + 1) % 20 == 0:\n", - " print('epoch: {}, loss: {}'.format(e+1, loss.data[0]))" + " print('epoch: {}, loss: {}'.format(e+1, loss.item()))" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -287,14 +183,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/torch/nn/functional.py:1006: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n", + "/home/bushuhui/anaconda3/envs/dl/lib/python3.7/site-packages/torch/nn/functional.py:1351: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n", " warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n" ] }, @@ -304,13 +200,13 @@ "Text(0.5, 1.0, 'logistic regression')" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -330,12 +226,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "可以看到,logistic 回归并不能很好的区分开这个复杂的数据集,如果你还记得前面的内容,你就知道 logistic 回归是一个线性分类器,这个时候就该我们的神经网络登场了!" + "### 1.3 多层神经网络示例程序\n", + "\n", + "可以看到,logistic 回归并不能很好的区分开这个复杂的数据集,如果你还记得前面的内容,你就知道 logistic 回归是一个线性分类器。接下来我们用两层神经网络来对同样的数据进行处理,看看效果如何。" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -349,7 +247,7 @@ "# 定义模型\n", "def two_network(x):\n", " x1 = torch.mm(x, w1) + b1\n", - " x1 = F.tanh(x1) # 使用 PyTorch 自带的 tanh 激活函数\n", + " x1 = torch.tanh(x1) # 使用 PyTorch 自带的 tanh 激活函数\n", " x2 = torch.mm(x1, w2) + b2\n", " return x2\n", "\n", @@ -360,51 +258,49 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/torch/nn/functional.py:995: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n", - " warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n", - "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:9: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n", - " if __name__ == '__main__':\n" + "/home/bushuhui/anaconda3/envs/dl/lib/python3.7/site-packages/torch/nn/functional.py:1340: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n", + " warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1000, loss: 0.28478434681892395\n", - "epoch: 2000, loss: 0.2721796929836273\n", - "epoch: 3000, loss: 0.26508721709251404\n", - "epoch: 4000, loss: 0.26026514172554016\n", - "epoch: 5000, loss: 0.2568226456642151\n", - "epoch: 6000, loss: 0.2542745769023895\n", - "epoch: 7000, loss: 0.25232821702957153\n", - "epoch: 8000, loss: 0.2508011758327484\n", - "epoch: 9000, loss: 0.2495756596326828\n", - "epoch: 10000, loss: 0.24857309460639954\n" + "epoch: 100, loss: 0.30548951029777527\n", + "epoch: 200, loss: 0.3037661612033844\n", + "epoch: 300, loss: 0.30283141136169434\n", + "epoch: 400, loss: 0.30222681164741516\n", + "epoch: 500, loss: 0.3017694056034088\n", + "epoch: 600, loss: 0.30137133598327637\n", + "epoch: 700, loss: 0.3009776175022125\n", + "epoch: 800, loss: 0.30053412914276123\n", + "epoch: 900, loss: 0.2999470829963684\n", + "epoch: 1000, loss: 0.29893115162849426\n" ] } ], "source": [ - "# 我们训练 10000 次\n", - "for e in range(10000):\n", + "# 我们训练 1000 次\n", + "for e in range(1000):\n", " out = two_network(Variable(x))\n", " loss = criterion(out, Variable(y))\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", - " if (e + 1) % 1000 == 0:\n", - " print('epoch: {}, loss: {}'.format(e+1, loss.data[0]))" + " if (e + 1) % 100 == 0:\n", + " print('epoch: {}, loss: {}'.format(e+1, loss.item()))" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -420,32 +316,22 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/torch/nn/functional.py:995: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n", - " warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n", - "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/torch/nn/functional.py:1006: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n", - " warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n" - ] - }, - { "data": { "text/plain": [ "Text(0.5, 1.0, '2 layer network')" ] }, - "execution_count": 14, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -472,16 +358,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Sequential 和 Module" + "## 2. Sequential 和 Module" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "我们讲了数据处理,模型构建,loss 函数设计等等内容,但是目前为止我们还没有准备好构建一个完整的机器学习系统,一个完整的机器学习系统需要我们不断地读写模型。在现实应用中,一般我们会将模型在本地进行训练,然后保存模型,接着我们会将模型部署到不同的地方进行应用,所以在这节课我们会教大家如何保存 PyTorch 的模型。\n", - "\n", - "首先我们会讲一下 PyTorch 中的模块,Sequential 和 Module。" + "前面讲了数据处理,模型构建,loss 函数设计等等内容,但是目前为止我们还没有准备好构建一个完整的机器学习系统,一个完整的机器学习系统需要我们不断地读写模型。在现实应用中,一般我们会将模型在本地进行训练,然后保存模型,接着我们会将模型部署到不同的地方进行应用。" ] }, { @@ -489,14 +373,21 @@ "metadata": {}, "source": [ "\n", - "对于前面的线性回归模型、 Logistic回归模型和神经网络,我们在构建的时候定义了需要的参数。这对于比较小的模型是可行的,但是对于大的模型,比如100 层的神经网络,这个时候再去手动定义参数就显得非常麻烦,所以 PyTorch 提供了两个模块来帮助我们构建模型,一个是Sequential,一个是 Module。\n", + "对于前面的线性回归模型、 Logistic回归模型和神经网络,在构建的时候定义了需要的参数。这对于比较小的模型是可行的,但是对于大的模型,比如100 层的神经网络,这个时候再去手动定义参数就显得非常麻烦,所以 PyTorch 提供了两个模块来帮助我们构建模型,一个是Sequential,一个是 Module。\n", "\n", "Sequential 允许我们构建序列化的模块,而 Module 是一种更加灵活的模型定义方式,我们下面分别用 Sequential 和 Module 来定义上面的神经网络。" ] }, { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Sequential" + ] + }, + { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -510,7 +401,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -519,7 +410,7 @@ "Linear(in_features=2, out_features=4, bias=True)" ] }, - "execution_count": 16, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -532,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -540,10 +431,10 @@ "output_type": "stream", "text": [ "Parameter containing:\n", - "tensor([[-0.6538, 0.6585],\n", - " [ 0.3440, 0.4386],\n", - " [ 0.1757, 0.2476],\n", - " [-0.1409, -0.2638]], requires_grad=True)\n" + "tensor([[-0.6391, -0.3023],\n", + " [ 0.4236, -0.2388],\n", + " [ 0.1976, -0.0334],\n", + " [ 0.5111, 0.4610]], requires_grad=True)\n" ] } ], @@ -556,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -569,31 +460,23 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:9: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n", - " if __name__ == '__main__':\n" - ] - }, - { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1000, loss: 0.28410840034484863\n", - "epoch: 2000, loss: 0.2719648480415344\n", - "epoch: 3000, loss: 0.2649618983268738\n", - "epoch: 4000, loss: 0.2594653367996216\n", - "epoch: 5000, loss: 0.23266130685806274\n", - "epoch: 6000, loss: 0.2252696454524994\n", - "epoch: 7000, loss: 0.2217651605606079\n", - "epoch: 8000, loss: 0.2194037288427353\n", - "epoch: 9000, loss: 0.2175876647233963\n", - "epoch: 10000, loss: 0.2160961925983429\n" + "epoch: 1000, loss: 0.07238173484802246\n", + "epoch: 2000, loss: 0.06331096589565277\n", + "epoch: 3000, loss: 0.059657540172338486\n", + "epoch: 4000, loss: 0.057460710406303406\n", + "epoch: 5000, loss: 0.055878859013319016\n", + "epoch: 6000, loss: 0.05458137020468712\n", + "epoch: 7000, loss: 0.053432758897542953\n", + "epoch: 8000, loss: 0.05238530784845352\n", + "epoch: 9000, loss: 0.051434148102998734\n", + "epoch: 10000, loss: 0.05058830603957176\n" ] } ], @@ -606,7 +489,7 @@ " loss.backward()\n", " optim.step()\n", " if (e + 1) % 1000 == 0:\n", - " print('epoch: {}, loss: {}'.format(e+1, loss.data[0]))" + " print('epoch: {}, loss: {}'.format(e+1, loss.item()))" ] }, { @@ -618,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -630,30 +513,22 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/torch/nn/functional.py:1006: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n", - " warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n" - ] - }, - { "data": { "text/plain": [ "Text(0.5, 1.0, 'sequential')" ] }, - "execution_count": 23, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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WnEzrnDkfpbTsBmW3Yxqeo6hv0iJyZMwgmTq8M9nCgkOr5XPtoTdz9/zD+EZg4lmIJShMneLNX3iekWaTRPLeP4OmB4mFVntw9drtiK3I0W6sE/vsD/PzvzI17MsYGqEgDJnnjlHHtlrN6xIDCAYR11FUyi75UXPwmwlEIZM1yGR3911EYxpTJ9YKr1VKLo11K4715Ed1RkYNFudsKuVAgJIpjfFJs6cpz7ColD2sSJw7Fx7pru2kaXhicuWRJzn5+p/tiSAATM1EuH3Dwt+goemMRq06WFjX6/XktEkkcji+v53y3OVn4VeGfRXD5eiPQseA41JRtV7tP/gqBfWaT370YK8nndUpLLk94a+6HpiKNoai1ms+zYbF2YvRnk5uw8D3oTI6ieb7eBvHfJFg29W9M8/EOiGopWWXdkthRoWRUQOr7VOv+319NJGIkBsNggdSab2njtVR4H41D/UjFIRDxFE3I+mb3E3DcN5qmnDmfJSleYdazQMFqXSwCrDaCtvuH4paLLhMTg+vxLMCZuMTXH3TFE0zgRpgMtM8r6/D/V4wI2t+mbXXBJlzetwMIkEkWSZ3NIeRUAh6OZq/5DHnqApDNm+s2v/XE0TFDMeubBhB4bfpDa+XS/bAqKS9iLbZLQr408mnuJ2YwhWj80qvIGiey/nClQMxb2macPJMlLu3rCDhr3Oh+RF9oMP/sBOKQX9CQTjEHDX/QiymMTZhUFjsTvbKjxrE4xqVskut4gVN4HMGyZS2NxEyu8AwNKB/DsIwE6uuJU8GYqCt+Ftk5aLQPA9f09B8j2yjxDPtbx/YdcUTGucuRqlW/DXz0BEs+xEKweYcjZHmPuao+RdGxkzSWYN6NRhsk2kNwxBu3bCw2msmmkbNJp3VmZoxhyIKybQG8/23eW4gCsO4ru9kzq8Tg3UoRaa4QL4wT6a0xGR9AfNi7ECuSSlFYcGhVFzrt1Cresycihz6PI4V7vfooe0SCsIR4SiZkUxTuuL7S0WnSwygM6hUPHJ5nfgelLjYKb43OJRSqUAUjM2DovbnugYVDxAh3qxz5srXATBTB+f0LhVcSsXAFLjyfTUbPndvBXWiDjNver8bTKru8+ih7RIKwhHjKAnDCrXy4OijWtUbiiBslTylDck0fql+k8XYKK7W/Whqnsv47A3gYBPqlFIUCr09swHaLR/b8g9NmO5GQvPQzgkF4Yjy3OVn+ezf/Eu+9N+8MuxL2ZpDaFUwTSEW12g1ux3IIpDKDK895MXaTb6We5iamUR1uttorsNIYZbR4hx0Inv2O6HOsnzqVY9q2R3ofBcJyo9EDtkiIRSC3RMKwhHmnR9/Bi4/c+hXC9mcTrvVu0oI6hQN7xacORXhzs0gFwEBFMTjGlPTQ7AVBafn/5v8HhpGbFUMUD5Zt8G75r+EPhMhkdT2tTSE7ytmb9tdJUQGXq8K8hAOE6EY3BuhIBwDDrsZKZPTqVY8Wq215CYRyOZ1YvHhmRsMI8hTsNoKx1ZEojLUHsE3o5PcSUzhrXcqi0YtkqQwfZqL9dv7fg1LC862xACCzG7zkGQlh0KwN4SCcIw4rMIgIpw8E6Fe86lVPDQtEIl4YviDiYgQiwux+PCuod32mb9r89JDJ/pGGLmayeupM/suCEopysXtlwOfOjGcldR6wuihvSUUhGPIYfQviAjpjE76CJRF9j1FuezSrPsYhpAbMfZtJeM6iu8sRpk7+wjV3Hhgh+kT7ip7WY50ADvtDTHMzmhh9ND+EArCMeWo+Bcc22e54NJu+himMDJm7Fmxtt3guoqbV9t43lqIZbXiMT5l9DSU2Qu+ap7lle97Al8kCG3qY6sxfIcHazf2/NwbKS5vXxAiERlaUmFoHto/QkE45hxWMxKA1fa5dX2tuqZlKZoNm/FJY8vKqPtFYcHBXZdoXc2OUh6bYta1eYs/T0Lbu5abdT3OK6eewF9fBGp9coQIhu9wsjnP2cbdPTvvIFx7e6sQERifOvjfJzQP7T+hINwnHEZhWJxzekotKwVLCy7ZnLFp/4T9otbJsPZF+OYT76Q4Po0SDUHxOvDexS9wujkgxXmH3Eie7L9BBN2xOVG6zaPuHU615g8kcjcaExynvyhoWieqKCqMT5oH3kciLE19MISCcJ9xWPwLSimazcFF5JoNj1RmCLdnZ+S9e/ZhlidmVku4qs5/fzL5ND9x/Q+IDaiDtBM80fr6CwBM2+LdxRcwD6hekPLVQDEwI8K5i9GhmIhC89DBMvwwj5AD550ff+ZQPGiDxheloLDkDqXI3IrT+86FR/rW81ai8xX9/J6c63Rztq+zWDyXC/VbByYGAOWSG+Rj9GFy+uDrTd1Pje0PE6Eg3McM86ETkU0bsVttRaV877PwnTI2YWKYgr1J+u3d1O7t2EoplB8MvHmnxkPVaxi+s7pd912Sfpsnmq/t+hy7oVruLVu+QrNxsL9DKATDIzQZhQzNvzA5ZVKvDh6IykWHXP5gb1HDCMwjEc/G6lfdTgTxd75ycWyf+TmHZqfHczyhMTlj8vTy1zjVmuebmYtYeoRz9Ts8XLtGdJ1IHATDK/i9RigEwycUhJBVDloYdEMQDdSACah/8AsEIIivf7B2g1cij/TatXyf6eVbsIOGap6nuHnNwlv3eVpNn1vXLM5djHGmOceZ5tzeXPwuyeR0Cgu9RexEIL3Pvpy3ffSxIEw6ZOiEJqOQHg5ypraZZToyxDISb669SsSxuvMCfJ+o1eJx/86OjlUtuz3RVAC+CkqDHwZyeYNoVLr0TyQQiv0sL/Lc5WdDMThEDG2FICKngN8CJglWrL+plPq1YV1PSDcHtVqIxTUa9f7RRrn88BLUor7Dh+5+ms9nHuNu9hSgmFq6xTPlV8jEdtZis9Xs7d0MgAq2HQY0TTh9Lkq16nXKiwjZvE4iuT9iEJqHDifDNBm5wC8opV4UkTTwVRH5U6XUt4Z4TX1JVC2yhSaG4+OaOuXxOK30Iav5u0/stzCMTZg0G1bPgGkYQcN731dDK5GQ8tt8oPxlKH957cUdmIpWMCOyWk11IwdZLdR1FMtLDvWaH7QxzeuMjBhI5/sVTcjmDLK5/RsWQiE43AxtTa6UmlNKvdj5uwZ8GzgxrOsZRKrUYnSuTsT20RREbI+x2TrJcnvYl3ag7FdEUiyucfJMhGi0e2B0PViYdbjyanvHNXYOG7kRva9p7CAb3biu4sa1NuWSh+sG1V2XF11u37IPLLz3UIjB+rZvAxBf7Spw4DhwKJzKInIW+C7gr/ts+wjwEYBoZvxArwulyC+10DbcG5qC/GKTRjY6OJh+C6JNh9xSE9P2cEydylicdmoX088DZj9WDImkztmLOrWqy+ztjk1dBTZ2gLu3bM4/EMPYxz4A+4HyFdVOkxnDAGedu0AkqBZ6UOW2S8tOl1MbgnGx3fRp1D1S6aO/KtBdn0TVQvN82skIVtxYez6VIrfUJF1qIwpcU6M0nqCVWVvpG5bH6HydaCsoT2LFDZanUvi6kF1ukajZKA1quRi1fGzXz/5hZuiCICIp4OPAzymlqhu3K6V+E/hNgPT0pQOVbcP2Bs4mRKnAhBTZuZ07XrUYm6uvCo3uuUTu1ihOJmnkBjdON2yP7HKLSMvBNXWqIzGUJigRnKh+oDfofmQ8b1Z6uVbxDmw2vRm25VPrhMqmMjqxAQO67ytuXW9j9VlIigSmolT64Hwk9Zrf12SlFMzdcTj/gI6+x6VCDjJ6KFG1GJ2rQ6fXUXa5jWsIhZk0dsJk7G6NeMNZfeZMx2dsrk5BE1qpCJrrM3WjjKbWAh2iLZepG2XQBM1Tq6/nlprE6zaLpzLHThSG+oSJiEkgBr+tlPqPw7yWfvi6NjAKRgB/Nw+QUowsNPquOkYWm7imRm6pRcT2cA2hOpqgkY2SLLcZnW+sntu0feINZ/UZ93Vh8WQaJ94dO6+5PgJ4xt7PRPe6oqrr9hdfpcB1d+bI3Q8Kiw7Fdf2FiwWXTE7vm8lbKjpY7WAMnj91kRsPvgkrliDWqnP2O19jevY6lbK7LxVU+xEM9v2/X9+HYsFhfHLvVqjPXX42eLIPAM0NzLgbn0bDVUzequIDOr0RbZqC3GKTVipCernZJQZ09tcUsE4MVt4XbblEmy5Wcvg9IfaSYUYZCfB/Ad9WSv3vw7qOzfANjXbcJNZ0um4IBbSSJr6+80FWd320QfZJXzF5u7Z6roitGJ2rk1uoo/u9N2vX/3uK6ZtV2nGD6mgcTxfG5huYtocCXFNneSaFHdv7n3yvzEiJpIZt9a4SRIN4Ql+1dQ+jpk675XeJAQRCVS17pNJ6z2y/3CklffvCG7jx4OP4nSS3djLDa48/hWeYxJaukB85mOvPjxi0mvbA7dWKz/jkvZ9nv8xDhu2RWW4Rbbm4pkZ1NI6VCL7T0dl63/fIhv/6YdrB75SuWP39PAPeJwriDTsUhD3kaeDDwNdF5KXOa88ppZ4f4jX1UJhJMXG7unrjADgRneXp1K6O52uy6apj4zYBjG1MjlfeF2+5RO/UugRDCJzhk7cqzJ7L45n7G0q4W2EYGTOplr2umH0RMM2gecvsbRulgqidiSnzQE0ulXJv0hYEolApuT3X4vvgaTo314nB6jbD5PrD383F4tX9vOQuUhmNWFxotwaYQO/x+LsVAvEVuuvjGRpqQDRZpO0yebOCqJXVsUes4VDLR/EMnVjT3fT6N9vm64Jhe2gDnjG1yfujTWf12o8LQxMEpdRfcu/34b7jGxrzZ7NE2i6m7eNEdOzY7u31StdoJ0xijd5Vx159Gdqg4/lB1FRlIon4injdRnd97JiBY2okqxYRy8eK6TSyUdQuVkCwe/+CaQY9jpcWHBr1TmhkVqfZ8Lv8C44dNII/cTpyYGWYN8ua7pd0ZkaFmpEe6IPyNQ1zIgMcTLSaiDBzMsK1160+2yCd3f33uCsxUIr8YoNUee16arko5Ylkz7M1Ml/vMrGuTHIyJeuenhsfqIzE0DyFkmDWv5HNJm/Rtsf09TJzZ7N45uHvBLgdhu+lOwqIYMdN7D3qu1uYTjF1q4Lu+KtOsL1Wxn7H04CI5RFpu0zcqiJKIWpNPJQE9tFEDXKFFvNns7tymsPu/QuRqMaJ02uRH4VFG6tPFU6lYH7W5sIDB9MMOZXRqdU81IbBXySYfW9kYtKkNtvG1wZ8f5rGSOxgs5TNiMbYhMHy0tpqRyRYcY2O7XwouBfzUH6hQapidQ30mZJFrOmyeDqzZo71FZF2fzW+l+dGAc1MhNpIvK8QbAcBNE+RXW5RnNqdxeCwEQrCEPANjdlzOWJNB9PyyC01d31T7gRFYO6auF1FX+fHWDUvdV7SVFCVc2y2zvzZ7D2d817MSEopioXBU3PXCfY5CJ9CKq0RiwUml42Dab9ErkRSZyZnkyvMUR6bRulrwqD5Lmcbs0T3oKfCThkdN0mkdCpFF89TpNI66ay+o+S/LaOHlELzFL4m0Oe44qseMYCOadPymLpeZu58PjAh7dNPq4TA7CuCEiiNJ8gvNbuuaTurDwGSZYtoy6WZjlDLx3blWzwshIKwD4iviDaD2Z+VMPvbRkVoJyO0k4EtMlF3dnzvr9y7/d7X72ZWEsRWpytbmymEwHaref6e3OC7EQbX3TKHCKutiMX3XxBEhJNnopRL7mqp6ExWJz9iDBxMR8YjvNV6jS/YaZqRJBo+vmhMWMu8femFfb/mQcTjGvETu4so2ip6KFVqBROczkrKMYXqWJJmJrJqCtKdwU4xAXRXkay0qefjIEIzZW7r+VhpYrTVykEB5bFEl2mqPhLHMzWyhRaGE5iGAaLtzf0TsLbyNq0W6WKbuXNZfE2Ca1aKVtI8MialUBD2mNV46JW7SAUzkWZmcKmL8kSSeLMCvhp4820cF5VAO26i+T7RzpJ65b2+BCsBzfPRveCdvq5RmEmheTtYigyIVNRcn2TNQnN97LhJK2kO9KmIFzz8K/6InfgX9EP2DGmaMDJqMrKNfs8VI8Xz099H04ghSqFEmG4t8ZblVxh1etJtDj3bMQ8lSy1GFppd93DUUYzN1XGXNebPZPF1bUsnrAbE604gCEBxKkXkZiWI0FODZ+5eJ/TacHwypTam5a3V70wfAAAgAElEQVRmHK8+G7pQHo1Tz/fm+7TS0a6SNJrnM3GrSsTqfr4GIYDmK2aulrt2zgPVkTiV8cQWRxg+oSDsIYblMrqScLZuIB2dq2NHDdxo/xHOjejMnc2SWW4FmZYb4qEVwY1cmEoSa7mIgmZ6LRNTPJ9YwyZVtUFBIxMNZmSA0ZmNuWbQrlFz/W2bpxxTx9/w8MYaDuN3ggFNFChp40R0Fk5nUevyMszOd7Fi/7ViBsXpJE7U2LZ/QdOEdFanVulvWtG0oA/wQdNqehQLLratiMc1RsYMItG178lH+OTMO2gY8SBmtsNsfJL5+PiREoTf/Y0f5+VP5IAgZDpVbBFtuzgRnVo+vnZPK0V+sdl30BTAsH1yi02K0ymULjSyUVLl/qGeiu68Gd/QmD2fI1GzibTcICO5bneZd3yB4mQSJ27ixOnKQF65vuB+ZdsBIb6uUZxMMnmr2rfGTz9h2mh+XSFTbGHFjUNfjSAUhD1kJS1+I6KCSIl2KkI7YeBrQqpsYbg+7YRBIxvDjegUp1MUp5Jklltkl1urM3Q7ZrB0Ih3kRfQpqqd0jVYmRivTO+vZ6BT2DY3KSJxMca0kx/pLXlkUBDbWZPfBfMX43Vp3xIcKwgBzS01KU8H+muszdbMa1ITp7Bdtu0xfrwDgGUJpPLktM9LUtIlj+z3hkiIwcypy4DkJlZLLwpyzasqyLY9qxePU2SjxRDBs3ImO0xazSwwAXM3g5dxDPFo9uHDTe+G5y8/CJ4K/Tctl6mYVfIUGxJouqYrF0ol0MMgpenwC6xEgWbUodsK1i5NJxPVJ9jEFKaF3Bi9CMxNdXWk363bHvOPhRHTKY4nNcwI6voKdovkqMPnuoLZR34AOBZlSOxSE+wnD8QfOkGItl1jL7ZpVCJCo2YwsNCmPxqmOxkETqmMJaiNxTMvDM2TP7Y+VsThOVCez3EJ3fay4QSMTJd4InNx2TKc2Eu8Rk3jToZ8NSVPBw74iCKlyO5iRbfgOVjDcwIxQcj1qo4lNhUHThTPnYzSbHuWii+tAPCHk8gZm5GCdd76vWJh3uvwazWQaK55CLZd5sGMRmK2aqKn+o09LH1ya5LDQzzw0MtfoEnghmAyMzdW5czG/Ledv12RJhOWTGazlJiNLreCu6hyjNJ7YMoGylYrQOoDB1Y4byB4V/9MOQbb9VoSCsIe0EwaxptN3prQxs3jj67nlFvGmw8LpoD6K0gQ7vk8/z4bZ1go9y+yNb9tklrT+oYm23E1nixB87vxSi9pIfHUJv5l/IZHQSSSG61Rot/3VFZQdifKNJ7+fenYU8T18XWe2foun518gsliABwY4mq3ygV7zTnjq67/AO36x1fO6+Gqgc1WUItJ2seMmVkwn2vYGmoHafWbw9dEEzWyMeD3Iom6lIj1mymHi6xrVDStqCExUtVyUTKm/2avnOATVDQ47h+ebPwbUc51ic+te2+7cYiWqJ14/HB20+tFOmP0LpNH9sDsRfduf27C7Z03v/Pgzh6NMch80Wfttv/Hk91PLj+EbBl4kitINrqZP85WRN5Cql8kuz6N5bvf7XZcnFl7qPfAh4Ln3/33e87M1YnWb7FKT0dk6yYq1talEsSroSyfSKOkTAAEoTShOJDe+GwjMmI1cjEYudqjEYIXKWJziZBI7ouFrQithsHA6Q3kyxfJUEp+1z7ziPlx/VytA6RJMfg454QphD1G6xtzZLPmFBoldDOyagkTdppUySdRsok0Hz9BoZKOHImzNN4IaMpnlbv+D0gKfwAr1fIx0ub8/peeYfQoEiq/4J8/8Xcy2ywdf+0su1G8deNP5fkRjgq5BNZ6hnh1FbUg68zSD74w+wNPqBd7wwme5/tB3MXv2QXzdIFkrc+lbX+bkWGnXWe77wXOXnyVZbnPqSnE1VBRWzJkW2UKQqW/FDaKt3lWC0gS741j2TZ27F/JkC02SVRtRQS5CMx0J6msdgnt4V4isCtZGGrkYTswgs9zEtDycqEE1H6x4Up36SK1UhPJY/FCK3UZCQdhjPFOncDIDdBxx1yvbzi9QBEvR6etlDCcIsfOB7HKLwkyaVnr4DqnKWGDfTRcD/0M7YfY87G5EZ+lkhrHZWmB3Vr2mMkUQJrjxIdEdj6kbFTRfoSn484kn+PPJJ/nQzT9hzB6uuUVEOHE6SqGWRJRHv8fHFZ38RJTyosXFb32FC9/6CiBoohifNAgK/B4Onrv8LLGG3bf6LgQTFHGC6KDl6RRTNyqIUqv3JQJLM+kugfMNjdJUitLUgX2Mg0Ep9I6PcCVibwU7ZlA4kena3U6YVAasiA4zoSDsI07UwIlqRKz+zuaNKAlinw3bX7XlaQAKxmZr3Lk0MrAA2EGyHYdeO2ly52Ie0/JIlttkOjVr1qc2LJ5M97xvdK6Ovq7c8ErW9O+ffi+/+tOv8+rPvbjt6/RcRbvto+lCLCZ7EpEUi2s8Em/xit7/0Yl7bcZGBVMzKC65uC4YJoyNm2Tzh+NxW2+SyxZ6G0CtR4BkzaY4nWL2Qi7Iyu2EndZzh2Plut9EWi5jszX0jlPYMzSWp1Or1VaPE4fjDj3GWHGTqNVbUGwjK6GeydqAjEwJcgAOwyph24jgxAzKU0FiXrbQxHSCUNvyWAJ/w2AinupbuVIAFPzsrz/A7Hue5Ce/8DwtPcakVWDcKvWcVilFYcGhVPQQCbKdDUM4eSbSlS+wW9JYXKrf5ErqNJ627hFSipFWCVuPkh+RA+t1sF36+WYMZxvlM9RacmNtNE5try9s2PiKdLlNqmIFeTzZCLV8HKUFeTuTtytd1VA1x2fidpXZ87ljJ4ihIOwzVsLE71O3pR/6FlFpexX+NgzshMnS6c3rIskmrmghEIwTV8t8ZvopACKey2S7wPvn/gJ9nRuvUnYpFb2u9rmOo7h1w+LCA7E9WSl839JXkHqDV6ceRYkEJgQR7iYm+f2T7+FDdz5DzB/cf+AgGRQ9BGBHDXR3cFkIBQcS3jk0lGLqRoWIvRYdZSy1SFYs5s7mOiHUvW8TFeQdlY+gWWgzQkHYZ5rpCLklDRmQo7DClmnxqn/Y3nHC1zWciEbE7q+Mq3P7zgPqaga3E1P8x5PvpqXHMJTLI5WrpK680rcGkufC7Rs2E9PmwNaX20V5Polrt2DiocAmtPK6btDUdH7v1Pv4nuWXuVS/uSf12TxPUSw4VCvBd5PJaoyMmVu2vXzu8rMwQAwgiKCJN/o77BXga1CaOPwlF3ZLZrnVJQYQ3Gem7ZOqWkE9rwFh5JG227vhiBMKwn4jwvxK5FHNXo282ViaYrMZmpKgGNdRrqK4XYpTKSZvdUpjbNjW73vSgOVofvX1vx59HH7gMfJLs5z/9oukq8Wu/VtNn1vXLGZORe6pwU6z4bM8cQLVz5cgQsuI8xfjT7AQG+V7C9v3e/TD9xW3rlk4zlql1dKyR73qc+ZCtG9xve2G7tpxk8JMirFO17F1JbiwYzqLJzNHIjpmt6RL7YHJpMmyRStldvViXiH4fo7f8Hl8f+lDhK9rLM+kuf3gKLceHKGei+JLMPvazErkCzRTJounMtRGD38M815gJUwW+jibN6PrgRYBTaM0cYIXn3k/1Uxvj0qlYP6uvdqSczeIgOE6SL/uOB1czeDV9Hmqxr2ZFaoVr0sMIPgMjqP61nnaaR5HMxPlzoUc1VwU29Roxw0KM6mgGN1xEwOlgoKLnS9TH1DsUXX+t56L9Z2tKYFanwJ5R53jJ3GHHRGKUynKY4mg37HA5K1qT8z+SrGufrHPxx0rFaEwnWR0vhG8IJ1Cen3CVwcigtINXnz7B5m5/ioXvv0VdG9t8PQ8aDY8YnENfRcrr0RSY/zGDa4/+F2bJuEJPrPxCTK16zs+xwr1mjewfWet5q1GL91LQp9v6pSnUhzePOqdsdIRUPMUVsLANXVG5hska4Hj2DM0ihMJfE26eoOsp5UOsqbnT2cZm62tFor0jKBy8HFzKEMoCEPDNzSszuyrMJ1ibK6zZO9UZGymIzSym5eSOM40szFaqSjxRuCYbSdNsoUmqbLVlRS3qUBIUBl//swlGpkc3/XFT3dtvnMzsJ0bJpw6E91RBJKmCWm3ycVX/oorj31P0Bmtj7NaAPMek+qMdX6CdizB0olzuLrByNIsGVU8tJndwyLadJjoVORdUWulyWpODIDh+ozN1WmmIiRrdt8Ce9XOCsCJGcydz6N3IrI8Q+v7Wx8HQkE4BLQyUe4mg+xk8RXthImzA/tkvGaTW2pi2h6uqVEZja+KSazhEGs6+LrQyBytuHGlS1e9pdJEEjtqkCm20T0f19AwbW/LCC5fN6jlxqhmR8lUlnu2uw5cv2Jx/lJ02wXzlFI4jmLm9hVGlmZ59fGnKI9P92QvA5xuzm3rmOuPbVkKTTod2fIG1YrH3IkLvPb42zrZ4Tp3Lr6BF2JaZ+l0PAeonSK+YuJOtStMFEB5vb1GNBUkj7aSJrGm01Uee+F0Jqivvo6j9OzsllAQDgm+rgX2yh2SrLQZmV/LNDUdn5GFBoYT9EiIWMGA6UuQhFSYTm1ZxO7QsrGEgFKBGaBTImCzIVEh1HP9BWGFG1ctzl2KYuzQbh5rN3njC3/GK299N9X8OL6mYygPRHjP/Bcw1fZbZVYrLguzTjCxVWCYwolTERIzaV57/G3465zYnmGieUF12ZVmMvc76WKrqwTHCoPuDdPxmT+fx2y7RFsuniG0kpG+rT/vB0JBOMp0mpJsnCFrKih3oViLGljZZ2y2zp2kudrB7EgjQnE6RXUkTrLSJtPpR9G32qausTQ5zvTN1wYODr4P116zmDphkslu/miICOmMTq0aDPaa7/P4lz5DNT9OdXyKEyMe5xt3dlSDqdnwmLvTvb9jB/kTrScf6LsI0BSky1YoCAQZxblCa0dhviuNeJyYsaNV+XHlGIwK9y+ap9A2qUbZ78dd6cHQD7PtMnG7yqnXljlxpURmubl1U+NDgBvVqUwkuf3ACLVctG+1TdfQefl7n8TfwrQSRCA5NBtbz+onp03MiKz2wdEE8pUlnnFf4+Ha9R2Jge8r7t7q/7v4HjRsHVfrP2BtVpb8fiK31By4baUK6Xp8gcp9Er23XUJJPMIorU+t4W0Qbbs0Nrxmtl2mblZWZ9ia75MttIi0PAo7DAMdGiKUJpMoTciU2vgiiFJ4hsbiyQxK17n2yMNc/Oa3NjcvKSgsupw+t7nNWDeEcxej1Gs+7ZaHGdHIZHS0LZLF+lGteGwSwcqNeAYlva0ZfYIAhKOMeD6pSpAEFtRI2l0Z7EE9G1bEwI1oQaRQp55JdSR+Xwdu9CMUhCOM0gTX1DCd3pFkpYhcv7pAhtU7+80tNXvMLZqCeMPGtFw0T5Eqt9E8RStl0sjGDkWhvR5EKE8kqY7EibRdfF3Djq1FAH3pfe8hs1xkYn5+U1Gwre11t1oxHSVTGrWqx8Kcg25ALm9sGbXkuYpyycF1FM3mYGX3RKjnUzTTERK1tV7CPkH58OoRqLM/CMP2uquoSmDuXDidwY7vLDPf02Xgirk0maSej2HYHrrrY0eNrh7gIQGhIBxxKiNxRhcafbN6B9LnOYj1qXW/Qm6xuRqFIUCs6ZAutZk/kz20vgjf0Pr2r1W6zp/8xN/msb/8Io9/6a8Q1Rt9AkF0j235VEpBxdJESiOd0ftmBXue4uY1C3dd8li56DE5Y5LN9X/EFuctSstbi44CfF3n5gOX8HWddtIOsmt9n1YqQnXkaNTZ70IpTNvDF2F0ro7md1e3BRi/W+fuhdyOoqdq+Ri5pW6fWvD9CfVcsBJwI3pPa9iQNUJBOOI0slGyxVZXP+eVchf0cbD6Ao0+UUabza5iDafLH6GpoH90ptimMn4069y88sxTXHv0YS7/1m8Tsayuz+caBvGE4sZVC6XAiiVwVYRMoca5c731gwqLDo6julRYKViYdUil9Z795+62qZa3Z+tTInz+b1zGN4JHtZGNHmkzR6JqMTLfWC3UOCgIQPN8TDtoOLNdavlYUG692qkuLMH3t3AqE4blbpNQEI46WlArKbfYJFntdGhKmpTGE+SXWsQa60wMAnZU7zug9JtdbXpaBcmqdWQFAaCez/PJn/wJ3vGHnyS/uBTUihLha888zXf/+V/gReJ864m3U8uNrZaoWLr5Nd7Gja7jVMrewNaijbrXFbHUbnubisFqK0YRfF3n1ccf486li/f4SfcWw/ZIF1tEWy6uGXTR2455J9p0ghXBhhn8QHbqH+tEnVVG40TbLr4uQdvXUAy2TSgIxwBf1yhOpyhOp7peXzqRIlGzO3XeFY1MZ3bZ5wGp5WNE2m5XBJISoZ6NkC5bu3Jei6eCNoq6HNqHspnJ8PyH/zbJapVI26IyOsKp16/gacJLT7+PViIdJCh1rAzfOPtmJgsO5xt3V4+hBll+1pXfXqG8vHX00utvfAOeYXDt0YcpzMzs8pPtDxuDDyKWR7zhUJxK0shunkeTWW71OMUHFnUUcKK7M+14EZ1maBbaFaEgHGckyPRtbicRTYTlmTQV2yPadPB1jVbKRHf8QBA24AvUB5guNMdj/E6NaMd57QssH/KEuEYmQ6PTBVHzfar5SexYoidb1TdMvpp/dFUQtnI+R1Imr6dOUzcSjNolxL05cF8FFCYn+dL733tPn2U/GZ3vnuELgdlnZKFBIx3dNKHL3FBmehAKKE4kD+0k4jgTCkJIFxudbl5EpzwaJ9eZ3QnBAO9EdGr9oluUYuZ6Gc1fm/3pCsZn6yxogrVFsxXD9og1HJQmNFPDSaCbPXuG89+4OnBRVDPWVmKOoxCt/yqhns7xuxffjy8argTZy/Hs4zz6+T8maneL7Epo5GEWA3xFpD1ohSNE2+6mbSXtqN7l69qMI9UZ8BhxxMITQoZBbSzBwukstVyURjrC8lSK+bPZvmGnybLVJQbrGZ2vDz6JUuQW6kxfL5NfbDAyX+fklRKJ6trAKZ6PYXuwz4lYViLB7YtnB26vJ2O87aOP0Wp6FJedvmKggG+89V1YWgRHM1Gi4WgmlUSOV9/0dJfYrPz96R/9EKWJ8d1fuFIYtodpufuTULhF8obaYkZfHUsEwQ7r37bxMEBrSBOBkHCFELJN7LiBHU9tuV+i3j/bVgDDHTxIxes26XJvq9HRuTpW1CC33CRZs1cHkOpInMpYfM2soII8iaDwncKK6ZTHEzuOZV/hte96lJOvF4LG6uvMRn6nWdFP/VKSJ277mAM+UyM7ghuN9Zg9BFieOs2NBy5x+spVAOZPn+KL738vzXT/BECz7WLYHu2EidKFeN0h1qkCayVMmkmTZNUmv9RczVpWugRmur1sfykSNIyp97bc9HUJ8j02wY4ZLJ1IMzrfQPP81WJyal3CjBPVWZ7e+j4L2R9CQQjZUzxj8Cxx4+xwPZlSe2CE08TdamBqWBeimCm2ghLFY0GU08hCUORu5Rjxpkv0VpXFU5keM0a8ZpMpttBdHytuUOn0psgWGuiuTzMVpToaZ/bCKKN3a8RXK2EKmt/kkS9/nUde/Bq671PLjLB44hytZBq300pzZGmWVKWI8tWqM3ojNx56GzcfeAtuxMQ1DcSP9FQt1S2X6ZuVnsqdKwjgly3GNrwGgKcYu1tj/kx2T2v0FCdTTLXLQdmUTiIZwNKJ9LZs/u1UhLsXTHTXR2mCrwnRlovheDgRPehCFvoOhsamd4qIZIBxpdTVDa8/ppR65V5PLiLvA36N4LH5d0qpX77XY4YMl8pYglSld5WggEZm8GxVGzDTFhX0t+1XujhTbFMdiaG7ilTF6olg0RTkFxvMn82tvpZdapIptlaFw3BsElUb3fdWS1dnl5tkSi3aSZtH//olrj3yBEoEER3NM2knT+DLy9x86HHunH8UX+8e9cvjncigAbUogs8ShLgaLhiuS6RdJ1E1WTqZXi2tMHOjMjBOf/UzMrgvhKhAOJdn9q70iGdq3D2fJ1mzibSDsNNGNrqz9q4iXaWkrYSJxfHuF35UGCgIIvK3gF8FFkXEBP5rpdQLnc3/HnjzvZxYRHTgXwM/ANwBXhCRTyilvnUvxw0ZLp6pUx6NkVtuA2slNFxTozQ52BTQSpkYRa/HqaU6B9k42AMYrseP/+q/ojg+w2tvehrP7BWc9U5QzfXJFrtDH4MoGdXVx0DpOvg+M1cXuf7IE119k33DpJnKcPWNb2Fx5vxqwlhfNC2oTLdy7I19C9b9rakgAzzWcGinIkFJ7212iBu0jwCm5SG+z0Nf/RoPvfg1NN/j+sMP8fLTT+GZuxyENTnyCXIh/dlM1p8Dvlsp9SbgJ4GPich/2dm2F2u6twBXlFLXlFI28B+AH9qD44YMmep4ktlzOWrZKI2UydJMitnzuU1rH9VyUTTf65pVKxR2VB94s+meg+E6RK32wOPq7lrF0VjT6W+26ltXWqM0PtO3kqjSDRZPnO9ZGfQ/rpAuLW2riY2m1irRRtru5sfeBgqwYzof/L8/xpOf/RyZSoVUrc4bvvwVPvTrv4Fu9/f3hBwvPvfL2691tZnJSFdKzQEopb4sIu8E/khETrGrNKUeTgC31/37DvDWjTuJyEeAjwBEM/cQgRFyoLhRnVIf52BuqcAjX/kK+cUlWskkVx99hJsPPchTn/40E7dnuXPhDSxPnULzPCZvX+HbTzxC00wSbzhdPgbNdTj1+jcQIFtcQHcdPKM7K1XzXGauf4e5c2nayST+DovxacofGDkzqGXmRkQppm++RjOV7buCWY8CRs9bnPoBF+ubPq1P7ehye89twENTV8kvFbpENUgos3j/q3/M7V96O7/21DRffOO/oN3yWZx3aDV9RCCT0xmf7C3VEXK0+OIO7iNRA8LTROSLwIfX+w9EJA38AfCMUuqe1osi8iPA+5RSf6/z7w8Db1VK/fSg96SnL6nv/q9+7V5Ou2Oe9//lgZ5vhZf++Gj6+1fuJ+kzWNZrXt+a/5o20NxOPKExcz7B58e/m2vJ02j4eD6cuvotzr76tdWBrpHO8dLb3oOvG51BXMgV5njsq5/l7FmTaEzDQ+O3zv4Qtr5hYO4zexfPZerW6yycvtTVpWx1/+BDbvV1oLkOD778RSojE8yeebAn0W09hu/ywdnPMmkVUcBHz/5w0ANh/Xk2XuvKtSi1dmyliHoW7134Ava37tBuDXjGNXjg4WD2aFs+N65Z3SG0AtGocOZ8tO/vuR6lFNWyR7nk4jgKQxfyowaZnL7le0P2n6e/8amvKqWe2Gq/zUadvw9oIvLIil1fKVXrOIJ/bA+u8S5wat2/T3ZeG8iJ8hL/9FO/vgen3j4vhYFY28Jue9y5ZeN0LDS6ATMnIySSgVlFKcXc3QENYDZJ9m23fAzl8f2LX+Zp7Wu09BiFV4t4rW6TSrJW5m1/+vuUxmewo3HS5QKpWhnRgsqlADo+75v/C56f/j5AcEXHVC5Gq4kVTQYhkLqB5jok6hUevvoiUbvNrQtvCESh4+zdaRTM6PxtRhfuUB6dopnuOLhXjtVBVx6PVF5n0ioGm4G/dftP+IOT76Kpry354/UKVjyFr+tonkt+aZaZm69RvnSJYmacuGfxSPUKD9WuI8DgvGi61vmFJbc3n0KBbSuaDZ9karB5zLZ9bl2z8NblrHmuYn7WoV73OHEq9DUcFQaOdkqplwFE5Bsi8jHgnwOxzv8/AXzsHs/9AnBJRM4RCMGPAT9+j8cMGQKu43P9avdg77lw+4bN2QtRojENq612lSu1vmd91HeI+g4yIizM9uZeaUoxurg2pxCB0XGjq2T1dLvA37n5Sa4lT9E0Yky2l5msznNjUePW6FmsaIx8cZEHZIHcaQ13/irL4yep5cfWDrodlEIJ5JeukTQ9dEN48nOfoDB1krnTD9COJYi1GkQ8m9GozYPtO4zbpa5DpL0mH775Sep6nLoRZ8wqU5pvUyp6XYuUWFx4W3EZKfdeWzan027190ckUmurlXazvyorPxDlzQTh7i27SwzWU6/6lIsuuZFwYnUU2M6v9FbgnwFfBNLAbwNP3+uJlVKuiPw08GmCsNOPKqW+ea/HDbl3bMvHdRXRmLYt+3FhcbADdH7W5sz5WDCO7lAQRCDfZyDJZHVsS1EsDD6vYcDYhEk23/v+qO/wcO3a2gumcOmE4pxzDc9SRPKCaAZfMi7wyjNvDi5kp2aPzgd+8e1v48V3PsWP/s6/JVavMj5/m/H52127XXgghr5J/kbKa5HyWgCMT0VIZXwqZRffh3RGJ5XWBpplsnmDYsFdXbmtZ3J6LcrIMCUo4d3nYxibXJtl+Tj25j/swpxDJCq0Gj6Nho9hCPlRnXgiLEB32NiOIDhAC4gTrBCuKzWwvuOOUEo9Dzy/F8cK2TlKKWoVj+WCi+cqIlHB84LG7isWjVhcSCQ1UhmDWKy//bs5YHYJYLWDwSISFXQD3AFthlfG3NWhRUEqrTMy1nuLigjjkyYjowbNpke7pWjUPXwPUhmN0fHdOUINUzBMwUP4TyfezXI0vz0hGGBG0nwf3Vco5RO9W+vZXhqb4u65h/l6JsHZ9jyPVq8Q87eO/IknNOKJ7WUgiwjnLsVYWnColIKVRTKtMTFpYpprv+fImMHsbbtn1SUSiM4gfE9ttH715fYNu2u/WtXDjATmwkhEY3Tc2HQVEnIwbEcQXgD+EHgSGAP+jYj8TaXUh/b1ykL2HNdVFBYdatWg6qRhgm2tPaStdW0c17/WanoUCx7pjM7UCbNnNmoY4AwYx1b8nCLCzMkot29YvYOHBIPcyTMRmo1gdRKPa1u2oNQNIZ0xSGdgfHJvEpsU8EfTb9++GPhe4J3tg/g+nh44uD1dx3DXVjQ3Lj3GrUtvXPVNlOJ5vpm9yHu++cckvBbJlIa+R/V8RISJqQgTU4P3SaV1RscNlpfc1Y8tAidORzftER2Nads2BW7cb+WeaTLSIDYAACAASURBVLk+d2/ZTEwZ5EbCBLVhsh1B+LtKqa90/p4DfqgTERRyhPA8xc2rbdaNSQPtvv1QKpjVJVMamQ1tIccmTW5f768I62f48YTG+Qdiq6Lke4FgZPM6YxOB0AxzllgxUjw/9QzVyDY6bHV8BBG7zOjCMgsnzqEMs2t7ZnkeJBiFX3vsjTzw8isYnocVjXPzgceDBLgOnmbQQvhy9lEefOVLQPDdTEwPbsO514yOm+TyBq2Wj6YFv9dWEUKaJoxOGBQW7i1vQilYWnDJ5Iy+bUpDDoYt77R1YrD+tXt1KIccIL6vWJy3u8RgNygFpaLbIwiJhE5+VKe0oflLIqmRH+3e1zCEqZkIU4er7wtlM83HT/4Armyjlo5SNNIRSpMpvveTX+DU1auUxmew9HXvFaE8cYJo08FKmHz1Hd9HplRi6vYdlqZPI8pHbSx0pOksTZ9ZFQTfh/m7DpWSy8kz0QMZKHVDSKV3JsqjYyYRU2Np0R64UtwutqWIxXs/p1KK0rJLuejh+4pEUmdswthyFRmyM0LX/zGnWnGZn+1fonk3DAoRnZiKMDLqU1p28VUw649tUf3yMPHXI2/ElS2SzTo2j8JUgkY+KKo3efcujXQeJ9Jb2RTRyC01WTiT5f9v782DY8vu+77PuUvvjW7swAPw1uEsXIakSA1nSKlEkTRN8tGiQtpZVHFKYWJZeUnJLjGlSI+VP5JKueISvVTKcUWsFKtcZakkxbQjyoxtasiRLEozwxGHHHLImeGsb8F72NHove9yTv44jQYavWBrdDeA86ki5wF90ff07XvP95zfKh2Hb/6tz5FdWyO7kkdaTvtewm2+qEpZ8erLVcbGnZaoqWEhnbFJZ+LUapJ7d7yGo9mytVmpXNz/BlSqOapsN/fueJSKsskHUSqGXLoWJRIxotArjCCcEcJAsXTfo5jXD54QkMlabOXkkcI92yIgle6SWOVaTM4MZ2MTpRReTREEili8NXrqXny6oy8AtG9BWoJ7V0eRu5yxXjRKcWScTiFUkVrzrik3MUF+dIy51zZb20mGAdN3X6ctCjbWAirlkIXL+yeKDYpo1OLKAzF8T993bkQgQ7j1Zo3A7x56HImItpN7tSKbxGAbKXWE24X54bznTiNGEM4AQaB449Vq0y5AKcht9mhbUMe2YXT89Dn9fE/qpLld0VOjYzYT09pvcfP6DeZe28QJOsTiA17EYulypiXT+Mfv+ymu/uhVRIeZLmzjkJW2xfpMkvGlUqOAnR34xMoFLr3yQtfPUq0oKmXZSPgbVtxdE7vtwOVrUQr5kHJRYtmKYkEShjrPQVj6sl642H5ir5Rlx4jlcukQjjDDvhhBOAOsr/bOJLTN3CUXryr1DkPqcM6xCbdrTPowopTi9lseQT3Gfnve3twI+eP3f4TX3v0ooIvrZdYrLT0ZFFCN26wsZNr2C7577Srvf+rPdC0l22kSDAVsjbVvPF/OxPDiLsmtKtGKx7v/8lmm77yOvc92TilOhSDsxbIEmaxDpp6oPTWjKBUlXk3iRqyuuRSWvVM1t937HgQpdca1AOJJayjNbsOAEYQhRynV9kGpViWVssS2obB1+FVSPCGawky3cSOC2bkI8YQFKRibaPPHp4hySRKGbSqWKnjXs99pCEJ+PE60EhDbboZTP25jOklptP2kDvDO7zyHQHH1pe/y8nt/du9ZSBY8SqPtq00GEZutySSQ5OmRD/PT31QsvK5NRh1LWgtazF1KKaoViecpolGLWHz4bepC1J3XB3Bgp9M2y8JvUQSduNj690rpXZTv6y+yUg7Zyu1aMQmYmXUYyTjIegmoYTXB9RsjCEPK1mbA2mpA4Cu96BQ65N119RbbqzWqKx96d2DbOr48CBQbqwHVqiQSEYxNOGcuezQIVCfzPvFSaecHIVhdGCFSCYhWfKRtUU5FUPskuF165SfYUrJ45ZE27TJ1NzC3FuBHuz9qxWyGpz73iyAlj3/jSR548UdYsrkxUC2WQFkWqZGdcDHfl9x+s9aU8BeNCRYuR89MlVLLFswtRFi8Uw9hqn+fqRG7pSRGtSK5e6vWPaRawdK9gKX7ASidkDg145AeMdOhuQJDyOaGz+pS0DBv7I7s2V2CQCkOVA4ilba0Uw5IpSymZnQmr20LZs+4Qy7aIbsaIDfeuv3RvaMP9lhcfOUnRCu6pERpZLTjcZFquK8gNLAsnvnEx3n2Yx/hsSe/xQMv/ohSZoxX3v0hKskRpG3xYjzkkbs/5tGtV7j1RoVwTzhxraq4f9fjwkKEUlES1h3pp2Hn0IlkyuaBB2MUCyGhhETCavlupVTceavWtVhiE/VnJ/AV9+/6WBcHmwczDBhBGDKUUqytBD2LDJpdcBk5xyufWMxi8eJFpu4u4uxaNgaOw/M/t9fEczje++d/0egw5dYq1BKtrSqdICBwDj8RK8fh2U98nB89/gHG73u6hScCARQrDt8Zf5TvZR/mHfe/xej6Usvfl4qSV19qbhyUTFnMLUQQp9R+btmiJQdmN8V8eORGLUrB2opvBGHQAzA0EwaHNwFtk8laVCo6eiMeF0zNRprq1Zw3bl6/gRVKUrkKC6++weWXf8To6j2K2QzPfeTnWbp08Vjvn97aavz74qsv8vo73o/cna0sJW6tSjK/QS05faRzCBlBWkGLs1sAgR3hxQ98lCe+8Yc4nYpE7aJckqytBj0r8zFsBIE6VnCFV+tVfPbpxQjCkNEpMWc/hICxCddkbqKFACBW8pm8mwegkL3AD564gB+1Wb6Y6drO86CUU6mGKFy49QqVZJp7Vx5GyBAlLKKVEo9898944WcfZ2P2aILg1sIWMdiNAlYvXGb29qv7vpdS2jd1VgUhFrcOVGivE90qzp4XjCAMGZYlSGdsClvhgW/s7YqU510MPvjDL/Dh39Q2faRicjHfNJkKpSfYzFqZ3FTy2Of73gef4MEXXmZ95iICxcyd17n42g8pZsZxa1VS+Q0C1yU/1tm/sB9+1EYKOoqCtGy8SOcoqJbjexyePEzEExbRmOjYIa4bQsB4m8q65w1zBQaAlDpM0LYFjqujUXZXlJyedQmDety02PUQC+pVSgWuC9WqwrYFo2Ot0RbnjZvXb8C2GADxUnsTiqUgtVU7viAoRSkzz8vvm9JhX0qxNnORyfu3ePh730YAoWWRHx1lbaZLmdF9KGWiZNYqOvy43QECUrnVJtt5t3XuaXYs74cQOrpqddkntxk2nMZC6Lpa0ZiF4wiiMcHKko9X20lUHJvQ7T63UUpRrSoCXxGLiaZEu7PM+Z5FBsDGmq8byuwJF41GBdMXdPy/ZQnmL0XxPIlXU7gRgZI0knhicWHiputsm4f2YsnO4aZCHt9WnN6sEisHO+UuhEA6Lquzl5m49xaja/dZXpjnzz/9qcM319mFtC2WL44wuVjA8etlSbZfE+DFIzz12U9z9aWXiFSqJIpFLr72elOp7d1MzeyYi2pVydqKT6UssXYtLE7zvWVZgunZCNOz3Y+7fM3WjaBCRSxqNS3IfE9y95aH7+8IRiptMzvfWvr9rGEEoY8U8+FOBNGeOalW0yFzl69FG6afSMQisisq9Cyv7sqlkNymrmSZHrFJj9j7ZpM2iYFSOqGs/jfVRPtbWwGV5PFt6Jm1ctuVuLRtXn7vB7h3bYpqMnHs8wD4MYd710aJljwy6xWi5QBlC4qZKFsTCZQl+OETjwNghSH2H/9b5t54ExTYYYgSgmRcFyDcvodqVcmtN2uNRUkYKlaXA6pVxezczk2nlKKYl2xuBIShIpk6nRnr7bBsgVeWeNWQZNrGdQVKKe7e8vC85sz2YiFkbUU3ZpJSUcyH+L7uKphMtc+yrlUluc2AMNCh36kD3NODxghCH1lf87v6BZSCjfWAmQtnOzdgL6tLXlOf4HJRV029eKV9yefdQmAFkrGlIomiNhH5UZuN6SS1hEshGyOdqzbs7wotGMc1F9mB1DuQDsaZZL5IIj/B+P1NpC3Ij8YoZaLH2ikA1JIRVpLd7w1p2/zpL36G9MYm48vLVFIplufnQAie+ty3efrzPwBgdbm13IlSOut9fFI2isytLO10WgPwaiH5XMilazFcd7gnt27kNnxWlnbtopZ8xiYcUmm7bStRpSC3ETCSsbldb/K0XYfJcQQXr0SbRHJz3Wd1eSd8vFgMcdcCLl3p3nBo0BhB6CPtbrS9dGp2flap1WSTGIB++LyaYnMjYHxiZzW/1zxk+QEX3tjCUjtTc6QWMnUnz9KlDLmpBF7cYWSjihVKqgmX/HicIHLMWHOpdyOdjPXF7CTpzYquiRDAxP0C0YrPxmxrnsJJURgbpbDHmf3zX/0ZuP4z/IOv/3MqXe6zSkkLgleTTWKwTRjq+lmndeFSq0lWllpzfTbWgp1Wrm0eVSlh8baH3JUFraRuObt8z2PuYhTQiW67xWD3cetrPpPTw3vdjCCcIFIqtjYDSkVto3VcQRh0FwU3Oryrh6OwvW0OfEUiZZHZ0xGrmG8fTaUU5HMh4xMuT3zlUT2Z7cL2Q+Ze1yGfe6+YUJBZr7A2l6Y8EqU8Eu3pZwpdS0f+tHtRKaRlNVdFFRbpXJX8eOL4YtQDbl6/wd+89Tski8WW14SgsYItlTqLRrFwequMbm22T/zcLhzYaRfvOPVSKG0oFiVSKixLdLw2+p6WTB4tArkvGEE4AXxfUSoGrK8EusTvIcJHx05heelObG0GLN/fMZOVipKNtZBLV6MHtkHfvH4Dvtr6+/ElXYeo3bsIIFI9Znu4LlhS1ZeRe15QCktKpNP6WOkEuRK5qZETG9dhePm97+bdTz/b1vmcTGkx62butnaZvwJfsbUVEPqKeNLuWrl0GNhb6mM3UtI27FsIyI47bKx2qCJwwDIyBzxoYJxdL+UAUEqxfN/jzVerLN8LCIKDiYEQekE5fcHVVUZPAUrpkLx2lURBOyl3i4H+Gz15rC3vhISmRuy2pnURs/mLJzqXloiV/K7hlYF7civxRMFr/1wLgeo4ESqShfyJjemw/Oixn+be5UsEjkPgOHgRF8uC+UuRxg4uNdL+GgqhO+IBlIohb7xaZX0lYHMj5P5djzdfq3W8L4aBZNpqf8/VG0CNZFqfwdFxh9Exp+PzHInuhI5vC2rrCbTYDDNmh9BD8lshuY2DbaWFgNl5F9e1UEoRi1lDU2NGKd3ZqlNERKkYsnzP16UCgHjcYnLKIQi1gy0WF5SKYcci9oV8yMyc/nc0qvsub67vrLx812UrM8Yr731P2/PHit0b9yp0OeuTwgpkRzESUiKUartLkOLkdi2HRdk2T332FxldWWFq8R7VeJy7167y5H/+bMPxbNuCmTmXpUUt4EppJ2ospr8zKRX37ngtou97iltvVLnyQGwodwrptM16JMD3mju42bYuonfrjVrLxL+5rh3KkzNOU+FJ0M/yxJTL6rJPIR8SBrpCcShpyoWwHdHkExtGjCAck1pNR8R4NUW1enCHsFJQKkiyYxbFQkixEJLOOMS6VOfsBUoqCvmQQj7UTUtG7UazFd9XLC16lEs7bThHx20mpnbir6tVyeLt5kmgUpbcfstrrLocV7StU9+JyWmXZNriW6m3EanWuPXQg9x66EGk3foe2aUiI7laxwlZAYWRCNUehJZ2wuqSxyAFZDeWyY9NIS0LISUgmH/9x0wtvs4zf/2jLF67emJjOyybU1NsTk01ft7teAYYyTgkEjb5LR12mkjaJJLaJNTNj+B7sL4aMDbhUC5pu3wiqVuXKqWTLmtVnWOTSvV3MSQswaUrUdbXfPI5XRAvPWIzPumS2wjabv6U0qIwMxchErFYXw3w60lrsZjF/bte291DJCpwHEEybZHNOkMdYQRGEI5FsRC2rJAOQ6USkn9zx1a5uR4yOmafWF9iKRV33qxRq+2sjAp5fc7xKZdbb1Sb7KtKwcZaSKUsG318O9pQ2TGP+Z5icz3oaC5N7WmK0im5bC+Rit9RDLZPVYtabM6mDvR+R6WzWQiCWISJpVe5/Mr3WZ29xPLFtyGFxeLVR7jzwDu48MYtlhbmCSPDG2kCO9/JP/j6P8dxBWNtVrZKdjeJrq8FjcgdWbexJ1IWtapsus8sGy5difa19IplCyanIy0O3lpNdrxvt3MTkim7URU18Ovtazv8je8pLl45Pb0pTofBeghRSnF/8ehiIIRucrN3u725EVIpn0wER24jaBKD3efcWAs6OtsqZdUIU6wdcBcUBJAda/YPCKEjNXYXVzuoGABkV8pdX1+fTbJ8OXvseP/98OIOqs0pFFBNuHzzP/0ciUKO1bkrBK6LdF1CN4KyHdZmLzH71sqJjq8jUpHarDB1e4vJO3niBW9fJ9fN6zc6fkeJpNX9z5V+e7nLdFIuypb7TIboJLle1Xw/IkopapXO93c83vqlF/Ldn1UhdNOe04LZIRyRWlUdWQwAHFdvq/eiK1KGHTuXKaXwfYUMoFQKdThn0iY1sn9kRz7XLcSzu327WAhJJG0iUdFYKXVDCIgnbEYyDrmNAD9QJJMWmVEH2xYHEwKlSG7VyK6Vsevhfh0/oYBS5uBF3o5DJekSuDauFzaNR4kd30V+fJrQcXdKW9SRjoMl+79aFFIxfWsL19upnhor+5TTUdZnk/uK6O4dwza2Ixgbt9lYP/4CRoa6PHcv+hFIqSgVJH6gTTrxxMGinnIbOhCkHUJAOuMQBqqpKqqU3ecBBUNvJtqNEYSjcszv2LYFfoe9aa0mWVr0cBxt498urFUshCzd81pWWFtbIe6qONbW1N+nnP62g3lswqVUbHW67UUpXWrDdQUzu0ohxJ76LL/+pYMVextZr7RtfN9yLiDo50MnBCsLKbKrFZL1iCMvpjOktzujLc/Nd/n7/keapHLVJjEAXegvUahRGI3ixQ/mc7l5/UZTxvPkTIRKtUalS87CQSkWwmMLQq0qdde0+u5ECF0nbOHS/hnCWx0WTKDf5/abNRQQiwpm5iNEo1bdn9J5o2XbgljMCMKZJxoV2BYEHZ6DRph6hxtFys4ZkdWqolrRq66N9YCZORc3YnX0Vyip7ZtrKz7Ts51t0yNZ+8jd2Ebq4XLxhMXMnNsIKW1Xl2m7HPfe0gY3r9+ALx3sfEKqA4kBaG2uHrDt5bFQikf+6nne9cyzxCoVKskk3//QE7z66LuaE9GAn7zn7Ywtt4+Gqh20nWYPSeZrba+lUDqM9qCCAK2O54VLEdZWfHIbIVKC6wriSUFhq3OSVzvcY9ZH0nWImvspK6V38yvLB8is7jLW3WXDq1XF7TdrXH1bjFjcIpGyKBdbP6tlwfzFyFBGWnXCCMIREUJwYSHCnVtew1YqhLYQXLwcwaspVpf9jitvGeoIi7ZdmvbY+JcWfRJJsa+9Nr8VMj6pS2tvl/ndfTNmxxzyWyFe7XDmrslpp8nhN5JxSI/YunywBX5NP3BeTWHb+jzjkzu31mH8BABuNWB0paTLQxwACVTTvc1Gbse7/+Jp3vGd53DrdoVEqcRPf+tPcT2fHz/2/qZj8xOjxEubxEpBk1hIAbmp3hS9Owwdimfr1444X+02I01OR5iYqpv1hECGinKx2tEE047jxuhXK6ptv4ftrPfpWdV1ck5nbLwuQRNN7yl14uXYhMvcQoTcRkBuMyQMFZGIbvWZydhDE0p+UIwgHIN4wubqAzG2ctpZG4sLMlkdZnd/sXshu2hMUDtoyz6hVyX7IUN44yfVxs7DcQXzFyONydyqh9vl62GnXk229WOAFraxcZuRrNModNb0utCCAxCJwJV068N8WCEAiJZ8pu7mEepgVjkJBBGbcvpko3Zs3+ed33muJbPXDQLe/fTTvPS+96L2hMkuX8wyslZhJFfFChVezGZzShfe6zelbJTIcmsrTiWgfEwxbedfsGzBpWsxVpZ8Clv7+xjGJ51j9xyQXZLhDjLJj4455Da1X24/tnceoJ+F0XGX0TNQZcAIwjFxXMH45M6NoJQudNXtBhQCkimHSnn/PriN8zj710HS528O/7zzVo2rD+4kCAlLi1Ym61DIhzpSqs2qaiRjMzF19En2KGIAML5cPJCZSKFX26VMjNxk/MQji7Jrax3bjQkpSRRLlDJ7ylIIQX4yQX6y/zuCvRQzURL5GtGKFgVF3Qk+FseP9WYa2OtfcBzBhfkIzOtIm/VVn2pV4bp6QeR72saeHbM7BlEchliic9TT3t1yOzbWgxYxsGyaitltI4TOMThrGEHoMV6t/bZ1G8eBmbnI4cw2SmdCdsx56JARDDpbslP0RiqtO0j5e6KGLOvo7QSPKgSg6/043v7OSQV4UZulK9kjn+swuNUqP/e1r2N3FARFLd6fCKcjIwQrCyPESj6JgocSglImitdj38te/8I2sbjVqAZ6Uti2YGzCYWOtNZN4arb76r1WlWystdq3ZNje16fLd5y96fPsfaIBI/bZ9V66FsVxLKBzaYem9xMwMaXrtF9YiLB0T5ff1aUl9A4llbbYWOuwLVedKzQKoU1IK8t+o5hXImkxNeMeevt+mOihTnRL+FLsXC5lwdqF/pWSftczzxIvFtuasALL4tZDDxEMeaIZAEJQTUWopk5+rO3MSP1gYsolEhVsrOnVfixuMTHl7ttcKp/r7DtwI4IwVI2dguMIZhciZ6JJ0F4GIghCiN8G/gbgAa8D/7VSKjeIsfSaSMTq6CyOxUVdDPTE2251DpDOWPieqpeAcBqlJVJpm2sPxvA9XWbX2RXFUy5VOzYX71YOw3YEs3MRZucO9TEbvOeTAZ+yfu3A0UPdUJagknSJ7ylct23eqEUsaskIhdE4odu/nMqrL72ME7YKrgLyY2M8/dc/drQ33p6BTlEUymEZhDCMZBxGMoeb2rqtyywLLl+LNXowu5Gz28J2UDuEPwF+SykVCCH+IfBbwP80oLH0nAvzEd1VSe4UBLMEzM7vrMyEEFy8HOX+otfIArYsmJxxyWQ7fy1CiLa2y8lpl7u3vJatcjyhm4ufBMcxD7VFqUbEy+4HVAFLl0bwY4Ny2rV/+EPH4ZWfeg+he7hxZVfXeOyb32L6zl2UZfHWQw/x3Ec/TC1+cgX59sPxQpJbNSypqCZd3Wa0h5PeXv/CsJFK2+Q2WvMQRL1C6e4girPMQARBKfWNXT8+A/zNQYzjpIjGLK69LaZDPD1JNGa17RHsuIKFy1GCQCGlwnWPvvJIJG3mLkZYWfIbK5nsqM3EdO8n0Z4LQZ1Y2W/ZHYAWBCdQHNwF31veePvDPPJXz7fuEpTizrVrh3qv5GaOdzz7Im89+AHeePgJJu/fZuHVH/DJ+7/HH33+l1silfpBMldlbLnUiOxK5ap4UYfliyPdmyIckk7+hWEgnrBIpi1KBdm0cXMjguwZ9BV0Yhg+6eeBP+j0ohDiV4BfAZh2B7eCOiyWLciOHezyalvk8R+8ZMrmygN2oybMSWxrT0oMAJJbtba5B1b9tUofbN/t+OHjH2Dh1ddJFgq4vo8UAmnbPP+zP0MlfYhCekoxcyfP4pVHULa+N+5fehtrsxd5z7e/zsXXXufWQw+e0KdojxVIxpZLLRnMkVrAyEaF/ETvI6QG5V/ohhA6IqqQD9na1Al2IxldaqVTGfizyIkJghDiSaCdl/GLSqk/qh/zRSAAfrfT+yilvgx8GeDheHaw1a9OCadNCLbpmnswwMJnfjTKH//y3+bqSy8z9/obVBNxXn30UTZmDtcLMV70kXa0IQYAyrLx3Qgrc9eYWLzXd0FIFNonolgKUlu1ExGEbYZNGIQQR/I/nCVO7JMrpbp62oQQvwx8GvioGnSZQ0NH/uB3fokXvtaf8M7SSJR40WvJQ5CCnvdFPizScXjtXe/ktXe988jvESt5SLv1kVO2w9rMAqFbOs4Qj4To8uh1e62XDLt/4TwxkPLXQohPAL8B/IJSqntNY8PAuHn9Rt/EAKCScqkmXHYXA5UCanH3xDOR+4G0LVSHeBbXr/HmO97e5xGhzXAdqqdYoWJisYB7gv2pt/n5r/5MX3ahhu4Mam/0z4Ao8Cd188YzSqlfHdBYDHsY2IMpBKvzaRIFj+RWDYBSJqrFoI9hfvGCR2a9gu2HeDGHrYlETxK4SpkoIxuVFj+JFQQsL0wOJMoodKy2ZjrBTuG7eMFjdT5FNXXyu7RhMyOdNwYVZfTAIM5r6M4TX3lUR4IMEiEoj0QHZiJKr1fIrpUbZiu75BMrb7EyP0LtmG05g4gukT22rE1D2yaZzckkuemT7fLWiUg1QFkgOlXtrf9v4l6Ru2/rnzAbYRgMpmOaAdAP4MDFYMAIqZrEAPRkaCkak/hxKWVjLM+n8aI2gWNRSkcpZgcXPSctsW+2PIAlda5Cv7l5/Qaxpz7b9/OeV86vO90ADNA8NGREKj5jS51LbrteiJAKdcwQxHjRY2Kx0IiocgoeiaLHynwa6VgoIQgi/ctF8KO2Nhv5ct/AZ7caEAygl8Ovf2kGrt8wu4U+YAThnGKEYIdYyWfybr57lVVx9L4BDZRi/H6xZQciFEzfKTTeP3BtVudS/Zl8hWBlPs3M7TwiVHTLiFHWYA0Kxox08hiT0TnEiEEzo3sSs/YigVIPHNvbu4x2bJumLKWPm7mV73hsrwmiDnevjbI+k+x6nBfvfxZ1O25ev2Hu4RPC7BDOEcP4EMWKHqOrZdxaSOhY5MdiFEZj/Ysqkgq3g218u6heELHZnO4+WR6EbtVcd6N3DYpEvkYp26ey2pagnI1RKnkkC63FBStJF+kMhyBsY3YMvcfsEM4BT3zl0aEUg3jBY3KxQKQWant6IMmulsmu9DFBq8scrYCNqST3L2eQ9vEflcC1CF3rID7cxk6h32zMpvXkj84BkUA14bA2179y44fFOJ57h9khnHFuXr8BXx30KNozutJqqrEUpHM18uMJpNOH9YqC0BbYaEU71gAAE5pJREFUoWpZFYeuRSkb7d1uRQhWL6SZvp1HKNXoXAatuiQFeANw4CpLsLowgu2FuH5I4Np9dXIfFeN47g1GEM4ow7gjaEIpHL998LsSgkgtoOqcfHbyyGYFS7ZvQb82m+q56cqPOSxey5LcquF6IdK2WpLVttuDDjI7O4zYhKdACPZizEjHwwjCGWPYhMD2JdmVEomiLqJWTkXITScJbYEStA3zFEoR9mN3AKRytbYOZSUg4oV4id6XD1e2RXFsJ/eglnAZWypi7wr9tBTMvrXF6nz6VKzQhw0jDEfD+BDOEMMmBiKUzLyVI1nwGhE0yYLHzFs5hFQUs9GmukWgV8dBxMbvk7mka3G3PkX5VJMuuYk4iJ3M4G0fwvTt/EArvZ52TETS4TA7hDPAsN7wqXoHrt1zvkAXTUtt1dicTOL4klhpp/VN6FiszPfPgVlORUjnam1NRpVk/0w2mfVqy05FXyt9ffrRB7lxXqlI5aq6npSA4kiUYjbW02Y5/cZUVD0YRhBOMUNRe6gLsZLf1hxjKd0drTAWZ3V+BKcWEqkFhI5FLe70tZDd1kSCRMHDkqoxVimgmIkSRPtnqnGCDsWEoKOv5USQiulbW7he2LgebrVMOlfFizmIUFJNRiiPRIYuDHU/hrlj27BgBOGUMszRQ9sE9RDLdi0xg10+giBq93Xy3Y10LO5fzZLeqJIoeoS2oDAa63t3Ni9qE6u0LzPtxWxQCjtQSFscu3xGN5L5WpMYgLYru57E9TwEkCgFjK2UqSRd1i+kehKS20+Mf6EzRhBOGcNqHmpHcTRGqk1bTCX0a8OCtC22JhNsTZ5cd7D9yE0mmLrTXD5DCl1ryK0GTN0tIOrmt1I6wsZM6kSEIVFobVAEtJj9AOIln6nbeZYuZ/q6q+sVRhhaMYJwSvjgD7/Ah3+zMuhhHAo/6rAxk2RsqbQziyhYn0n2zWncjenbd3j06WcY2dxkc2KCH3zwCdYuzA5kLLWEy+pcmrHlkjYR1cNOq3GHsZXmCqyJgocdFFi5ONLzcShLtN3VtUMAkVpItBwcuzT4IDH+hR3Eaepe+XA8q77ywPDazE+K07Qr2Euk4jOyXiVSC/AjNptTib5XzExubRErV8hNjBO6euK68uOX+OC//wZOoM00Et0m808/8zdYvHa1r+PbiwgVygKE4MLrm7htfAhSwP3L2Z6b2uL5GpP3igcSBNg2/wnuXR091U7nbc7qbuFDL379u0qp9+933OCXaYaOnGYhABi9XyRd73wm0M7R2K0tli5m8KM2jaXoCZkbEoUCH/5/v8bo6hrSshBK8cIHH+fH738fH3jymw0xAG0nt4KAx7/xJF/91b8zUBOIsnfO3dGhLMD1gp4LQugc7nMLwA4UyYJHKTPYvte94LybkYwgDCHDHj10EFIbZdJbtVbbs4TJxYKO6gkV0hLkx2Lkx+O9nYSV4uO//4ekc1tYu3bB7/6Lp0HpcM52xCplEsUi5fRw1O4JHat9BJLSZbJ7fj7X7pgw2MmUZAGJQu1MCMI251UYTld4wDngTHQuU4rR1UrHXr2OLxu1g2ypyKxXyK6Ue3r+6dt3iRVLKMuikkgRWnrydIOAt73wg44JaUIqfHd47OFb47G2yXt+1MaP9X49F7o2tbgubrcbWT9vu6umqHdeO4PcvH6DJ77y6KCH0TfMDmFIOO3mod04nuzalnHv1KEL2lXZmoijjhPCqBQj6xVGNqrYYZxn/tp/BpZASD29zb35Eldf+h6JcpmtsTFGV1ebdg9SCJYX5vFjwxMBVczGsEP9uRCAglr8ZKuPrs6lmFwsEK0EjXOW0xFCWzCyWWs5XtXHeVY5T/kLRhCGgLMkBgDSFgd2SjYQ4PoS7xiCkF0tk96sZ/wKgXL07a3qu4PFK4+AgszmIv/xFz7NJ37v93F8H8f3CRyHWjzOtz/1ySOf/0QQgq2JBPmxOE69GF7onuzGXtkWKxczOF6I7UuCqI20BPOvbrT9XpUtqJ1Azadh4zyYkYwgDJCzJgTbSMeimnCJlf0DC4NQzclqTShFouDplX8gqSYctiYSTUXfRKh2xKDjuFwWrz7CG/HLFEazfPXv/rcsvPY6I5s5chNj3L12beBtIjuhLNFkInJqHlP37hG4DqsXLpzIuIPITunraNnXPp42pjYrVD3pN31aOMvCYARhAJxVIdjN2oUU03fyOPXmN92mConO1M2ulrGkopyO6NLPdSdz08ofSOY9EgWPpUuZxiTp+GHDvNEN6TgsX7zY+Pethx861uccBA9/93ne92d/jrQsQBE6Lk/9J59hde5C84Fq10R9AIe9CKXOcQgVtbjTVEZEWu3FoHGq86EFTdy8foM//d/j/OW7/tGgh9IzjCD0kdOYXHZUpGNx/3KGSMVn5nah7THbTsrQETrBqarFI170SG/YLF/MYEnFyGa1KepF1P94dKXcSM4KHWtfMQDtJzhtpRZ2M3PrFj/1Z3/eFDKL5/Ox/+er/Ktf/TsN/0diq8roShk7VCgBpUyUjalk+1wBpUhvVBld3XHsK6H7UgipwNLZ0dK2sPZEPCmgknJPZaZyL/jwb1bOVGOe0/tknDJuXr9xbsSggRB48e625a1x7TS11M4uwlI6AzadqxIr+21XnwKIVXaqpErHoppsjY7ZjQQK2dipNm2889nncIPWmkdCSq689AoAiXyN8aUSTj2Sy1KQ3KoxeU8LswglqVyVzGpZJ6LdyTO6Wm7s5Lb/xpJK52dISG55jWii7agnKXS9qvWZVB8++XBzVspsmx3CCXMWbpJjIQSltNu2cfv2xNIu5t1S1Etkd64vJPesStdmU8y/ttl2p6DQReJyU4OrV9QLkvn2uy03CEgU9WvZ1XLb1qSxkk88X2NiqQhKX/dGbmCb99z9OwsglKxPJ7EA2w/xYo4uAnhOdwftOO3+BbNDOEHOvRjU2ZhJ4UftRiy7BKQFy5cz7BePVE26tGtwKQWURiIktmrECzVtK7ctgi4RONUzMHmtzl1oEUIA33VZn5kGOmc3CwUT94pYksaOzOJgdYtA/020FlLMxtiaTFJJ97Df9BnjtOYvmB3CCWCEoBlla39CrOwTqYYErkU5FQFLUB6hpacw1HsSjOgJfHU+zdTd/M6qVujQ1nSuRjq3Exe/ORmnnI7ibFRaVshK0PeS1ifBDx9/jMsvv4Ll72oqZFmU0ynuXrsG6Gtjh63bpONO3VJAYEMyVyVR8FCWoJiNUk2cXx9CN05j/oIpbtdDjBDsjxVIopWA0BF4MR3FMrpUbEzsgnrZ54jN8qVMw94vQkWiUNNOUqXIrlVatrcK2JhO6O5joWxqeFNOR1i/MBzlKI7L2PIyjz35FFOLi0jb5q2HHuS5j/48tXhcZ4kvl3Sv6B6fV6J7Vzj1fgmKeinzTJRN40fYl0EKw0GL2xlB6AHnKXroyNQnqvRWTTuJlY4MWp9JMrlYQEhtvtiOPMpNxClMaHu/7Ye6Tn+9W1dmtUS8ErY9TWgJ7l3NkMrVSBY8pCUoZGOUR06/uaiF7We3/rkiFV/3TVAKsctqdJBP3a3k9fYMUYtaRDzZsvuSgqYQYEN3BiEMptppn7h5/QYYMdiX1Ga10Sxn2zwkfFmfwHYmo20HZ3a9QikbI1byGF8q6dcUjGxUu4aXWlKBEOQnEuQnTrcDeV92CZwIJdN38lh73Af7Lfe2BbibGGynd0Rrsr3zWekeDVtGEA7EMDuezTd4RIx56HBkNto3kUd1nowSBY/RlVLT321HxnSbxNo5Xc86iYLXdvY/7pUQe/5r6B03r9/g/5P/B9//d8MzDZsooyNgxODwdCo33Y1ItX2P4V3N15pQaF/BWWjUcljsQLYN34XmEN+9h+wOOT2q8VgJtEnOcGg+Zf3aUM0nwyNNp4Bh+uJOG17UIdZhgpe0rkxEPZmq09QeWjoMcvcs5kVt1meSPRjt6cOLO237GCignHLxIzapzSrOPruI3Sai/WR126lcGI0NRUvU08ywmJEG+i0KIb4AfAmYVEqtDXIs3TBCcHxyU+2byFfjLk4Q4viyEbkCOyvXTvX3y5kYm1MJYiUfx5f4Ubup9s55o5pw8aM2bi1sXOPtCTs3kWB8uYR9gC3AdpRXORXRjvwOxyl0WHBxNLZvNrrh4AxaGAYmCEKIBeDjwO1BjWE/Yk99ll//0sygh3EmqCVcVuZHGF0pEamFKEtQyEbJTSRAaBt4ZrWM6zc7LveuViW63PJWvcNa9QzkFvQEIVi+mCG7UiKVryEkVBMOm1NJIl5IpBocqvKsE26RylcpJ9NgN08TUtSjwMbPuNN+gAxKGAa5Q/gnwG8AfzTAMXTk5vUbeu9i6Bm1pMvSlawOl9yzki+PRMnW6+m0I3AE0hJUUhHyY3Fkp1LZ5xhlCTZnUi05AZn11kS9bvgRi4/863+DHYSszF1h8cojVJJppO2ACtmYy56pdpnDzM3rN3jqc9/m6c//oC/nG4ggCCE+AywqpV4Q+2zxhRC/AvwKwLQbP/GxmV1BH+jwnUvbgjZlF5SArYnEme7KdZIoIdr6BHab57aRAlxvEwBbhszeeY3ZO681Xi+lU/yr/+7vnuRwDXvoZ8bziQmCEOJJoN3M+kXgJtpctC9KqS8DXwadmNazAbbB7AoGS34sxvj9YtvVbCltTENHpZiNkijUWh3OQpeujpcC3Us6YrM5nWD+9Xsde067nteHERva0Q8z0okJglLqY+1+L4R4F3AF2N4dzAPPCyEeU0otndR4umGcxsNBOR0hUo0xslnVq1eh/29lPn28XsvnnFrcoZiNksrtiEKjR8J0cqcTWn3ntryw0OhDvRsJ3F9Y6OPIDe04SWHou8lIKfVDYGr7ZyHEW8D7BxFlZIRgyBCC3FSS/Fhc90GwBJWEey7zCnqKEGxOpyhlYiTyumZUOR1pjg7aZcYrjGZ54+2PcOWllxu9F6QQBK7L8z/3s30duqEzJ9Gx7dwGDxsxGF6kY1EeMU7LXuPFHF1Q8AA8/YmPs3phlrf/1fNEq1WWLi7wwoeeID82dsKjNByGXndsO3fF7YwQGAyGs0onYTDF7fZgoocMBsNZ57j+hTMvCO/5ZMCnrF8z0UMGg+HccFT/wpkWBGMeMhgM55Wj+BfOpCAYITAYDAbNzes34MWvH+jYMxfcbcTAYDAYjsaZ2SEYITAYDIbjceoF4Q9+55d44WvZQQ/DYDAYTj2nVhAa0UNfG/RIDAaD4WxwKgXBmIcMBoOh95wqp/JidtKIgcFgMJwQp0oQDAaDwXByGEEwGAwGA2AEwWAwGAx1jCAYDAaDATCCYDAYDIY6RhAMBoPBABhBMBgMBkOdU9UxTQixCtwa9Dg6MAH0vS/0kGGugcZcB3MNYLiuwSWl1OR+B50qQRhmhBB/dZAWdWcZcw005jqYawCn8xoYk5HBYDAYACMIBoPBYKhjBKF3fHnQAxgCzDXQmOtgrgGcwmtgfAgGg8FgAMwOwWAwGAx1jCAYDAaDATCCcCIIIb4ghFBCiIlBj6XfCCF+WwjxshDiB0KIfyOEODf9TYUQnxBCvCKEeE0I8ZuDHk+/EUIsCCGeEkL8WAjxIyHE3xv0mAaFEMIWQnxPCPFvBz2Ww2AEoccIIRaAjwO3Bz2WAfEnwDuVUo8CPwF+a8Dj6QtCCBv4P4FPAm8H/gshxNsHO6q+EwBfUEq9HXgc+O/P4TXY5u8BLw16EIfFCELv+SfAbwDn0luvlPqGUiqo//gMMD/I8fSRx4DXlFJvKKU84PeBzwx4TH1FKXVfKfV8/d8F9IQ4N9hR9R8hxDxwHfi/Bz2Ww2IEoYcIIT4DLCqlXhj0WIaEzwP/btCD6BNzwJ1dP9/lHE6G2wghLgPvBZ4d7EgGwj9FLwrloAdyWJxBD+C0IYR4Ephp89IXgZtoc9GZpts1UEr9Uf2YL6JNCL/bz7EZBo8QIgV8Ffj7Sqn8oMfTT4QQnwZWlFLfFUJ8eNDjOSxGEA6JUupj7X4vhHgXcAV4QQgB2lTyvBDiMaXUUh+HeOJ0ugbbCCF+Gfg08FF1fhJdFoGFXT/P1393rhBCuGgx+F2l1L8e9HgGwIeAXxBCfAqIASNCiH+plPovBzyuA2ES004IIcRbwPuVUsNS7bAvCCE+Afxj4OeUUquDHk+/EEI4aCf6R9FC8BzwS0qpHw10YH1E6JXQvwA2lFJ/f9DjGTT1HcL/qJT69KDHclCMD8HQa/4ZkAb+RAjxfSHE/zXoAfWDuiP9fwD+A9qZ+ofnSQzqfAj428BH6t/99+srZcMpwewQDAaDwQCYHYLBYDAY6hhBMBgMBgNgBMFgMBgMdYwgGAwGgwEwgmAwGAyGOkYQDIYeIYT490KI3GmrcGkwbGMEwWDoHb+NjsM3GE4lRhAMhkMihPjper+HmBAiWa/9/06l1DeBwqDHZzAcFVPLyGA4JEqp54QQXwP+NyAO/Eul1IsDHpbBcGyMIBgMR+N/RdcrqgK/NuCxGAw9wZiMDIajMQ6k0HWbYgMei8HQE4wgGAxH43eA/xnd7+EfDngsBkNPMCYjg+GQCCH+K8BXSv1evZfyXwohPgL8L8DDQEoIcRf4b5RS/2GQYzUYDoOpdmowGAwGwJiMDAaDwVDHCILBYDAYACMIBoPBYKhjBMFgMBgMgBEEg8FgMNQxgmAwGAwGwAiCwWAwGOr8/4Gs0tt6TrBSAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] @@ -673,12 +548,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "最后我们讲一讲如何保存模型,保存模型在 PyTorch 中有两种方式,一种是将模型结构和参数都保存在一起,一种是只将参数保存下来,下面我们一一介绍。" + "### 2.2 保存模型参数\n", + "\n", + "保存模型在 PyTorch 中有两种方式,一种是将模型结构和参数都保存在一起,一种是只将参数保存下来。" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -695,7 +572,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -705,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -718,7 +595,7 @@ ")" ] }, - "execution_count": 26, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -729,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -737,10 +614,10 @@ "output_type": "stream", "text": [ "Parameter containing:\n", - "tensor([[-8.8738, 9.7847],\n", - " [10.4652, 12.2881],\n", - " [-9.4986, 2.9617],\n", - " [ 0.1037, -9.5129]], requires_grad=True)\n" + "tensor([[-5.7823, 5.7006],\n", + " [ 5.3129, 3.6949],\n", + " [ 3.5471, -0.7431],\n", + " [ 2.4003, 1.7605]], requires_grad=True)\n" ] } ], @@ -759,10 +636,8 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": true - }, + "execution_count": 23, + "metadata": {}, "outputs": [], "source": [ "# 保存模型参数\n", @@ -778,9 +653,20 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "seq_net2 = nn.Sequential(\n", " nn.Linear(2, 4),\n", @@ -793,20 +679,20 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Sequential(\n", - " (0): Linear(in_features=2, out_features=4)\n", + " (0): Linear(in_features=2, out_features=4, bias=True)\n", " (1): Tanh()\n", - " (2): Linear(in_features=4, out_features=1)\n", + " (2): Linear(in_features=4, out_features=1, bias=True)\n", ")" ] }, - "execution_count": 27, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -817,7 +703,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -825,12 +711,10 @@ "output_type": "stream", "text": [ "Parameter containing:\n", - " -0.5532 -1.9916\n", - " 0.0446 7.9446\n", - " 10.3188 -12.9290\n", - " 10.0688 11.7754\n", - "[torch.FloatTensor of size 4x2]\n", - "\n" + "tensor([[-5.7823, 5.7006],\n", + " [ 5.3129, 3.6949],\n", + " [ 3.5471, -0.7431],\n", + " [ 2.4003, 1.7605]], requires_grad=True)\n" ] } ], @@ -856,13 +740,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "### 2.3 Module\n", "下面我们再用 Module 定义这个模型,下面是使用 Module 的模板\n", "\n", "```\n", @@ -890,10 +768,8 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": true - }, + "execution_count": 27, + "metadata": {}, "outputs": [], "source": [ "class module_net(nn.Module):\n", @@ -914,7 +790,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -923,14 +799,14 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Linear(in_features=2, out_features=4)\n" + "Linear(in_features=2, out_features=4, bias=True)\n" ] } ], @@ -944,7 +820,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -952,12 +828,10 @@ "output_type": "stream", "text": [ "Parameter containing:\n", - " 0.1492 0.4150\n", - " 0.3403 -0.4084\n", - "-0.3114 -0.0584\n", - " 0.5668 0.2063\n", - "[torch.FloatTensor of size 4x2]\n", - "\n" + "tensor([[-0.0458, -0.6043],\n", + " [ 0.0567, -0.6961],\n", + " [ 0.5034, 0.2557],\n", + " [ 0.2466, -0.5245]], requires_grad=True)\n" ] } ], @@ -968,10 +842,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": true - }, + "execution_count": 31, + "metadata": {}, "outputs": [], "source": [ "# 定义优化器\n", @@ -980,23 +852,23 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1000, loss: 0.2618132531642914\n", - "epoch: 2000, loss: 0.2421271800994873\n", - "epoch: 3000, loss: 0.23346386849880219\n", - "epoch: 4000, loss: 0.22809192538261414\n", - "epoch: 5000, loss: 0.224302738904953\n", - "epoch: 6000, loss: 0.2214415818452835\n", - "epoch: 7000, loss: 0.21918588876724243\n", - "epoch: 8000, loss: 0.21736061573028564\n", - "epoch: 9000, loss: 0.21585838496685028\n", - "epoch: 10000, loss: 0.21460506319999695\n" + "epoch: 1000, loss: 0.07277397811412811\n", + "epoch: 2000, loss: 0.06705372780561447\n", + "epoch: 3000, loss: 0.06257135421037674\n", + "epoch: 4000, loss: 0.056195128709077835\n", + "epoch: 5000, loss: 0.050691165030002594\n", + "epoch: 6000, loss: 0.04715902358293533\n", + "epoch: 7000, loss: 0.0447952002286911\n", + "epoch: 8000, loss: 0.04309132695198059\n", + "epoch: 9000, loss: 0.04179977998137474\n", + "epoch: 10000, loss: 0.040784407407045364\n" ] } ], @@ -1009,15 +881,13 @@ " loss.backward()\n", " optim.step()\n", " if (e + 1) % 1000 == 0:\n", - " print('epoch: {}, loss: {}'.format(e+1, loss.data[0]))" + " print('epoch: {}, loss: {}'.format(e+1, loss.item()))" ] }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": true - }, + "execution_count": 33, + "metadata": {}, "outputs": [], "source": [ "# 保存模型\n", @@ -1049,10 +919,8 @@ }, { "cell_type": "code", - "execution_count": 103, - "metadata": { - "collapsed": true - }, + "execution_count": 34, + "metadata": {}, "outputs": [], "source": [ "net = nn.Sequential(\n", @@ -1070,33 +938,33 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1000, loss: 0.3165791928768158\n", - "epoch: 2000, loss: 0.25367119908332825\n", - "epoch: 3000, loss: 0.22129501402378082\n", - "epoch: 4000, loss: 0.20364265143871307\n", - "epoch: 5000, loss: 0.19186729192733765\n", - "epoch: 6000, loss: 0.18199527263641357\n", - "epoch: 7000, loss: 0.173702672123909\n", - "epoch: 8000, loss: 0.16727975010871887\n", - "epoch: 9000, loss: 0.16238373517990112\n", - "epoch: 10000, loss: 0.15855807065963745\n", - "epoch: 11000, loss: 0.15542374551296234\n", - "epoch: 12000, loss: 0.1527201235294342\n", - "epoch: 13000, loss: 0.15030623972415924\n", - "epoch: 14000, loss: 0.14812862873077393\n", - "epoch: 15000, loss: 0.1461697667837143\n", - "epoch: 16000, loss: 0.14440736174583435\n", - "epoch: 17000, loss: 0.14280635118484497\n", - "epoch: 18000, loss: 0.1413293182849884\n", - "epoch: 19000, loss: 0.13908402621746063\n", - "epoch: 20000, loss: 0.13768813014030457\n" + "epoch: 1000, loss: 0.07510872185230255\n", + "epoch: 2000, loss: 0.0662045031785965\n", + "epoch: 3000, loss: 0.062202777713537216\n", + "epoch: 4000, loss: 0.053606368601322174\n", + "epoch: 5000, loss: 0.047997504472732544\n", + "epoch: 6000, loss: 0.045905228704214096\n", + "epoch: 7000, loss: 0.044531650841236115\n", + "epoch: 8000, loss: 0.04245807230472565\n", + "epoch: 9000, loss: 0.0403163880109787\n", + "epoch: 10000, loss: 0.03822056204080582\n", + "epoch: 11000, loss: 0.03605899214744568\n", + "epoch: 12000, loss: 0.033822499215602875\n", + "epoch: 13000, loss: 0.031671419739723206\n", + "epoch: 14000, loss: 0.029688959941267967\n", + "epoch: 15000, loss: 0.02786232717335224\n", + "epoch: 16000, loss: 0.026174388825893402\n", + "epoch: 17000, loss: 0.024574236944317818\n", + "epoch: 18000, loss: 0.022980017587542534\n", + "epoch: 19000, loss: 0.021339748054742813\n", + "epoch: 20000, loss: 0.019654229283332825\n" ] } ], @@ -1109,32 +977,34 @@ " loss.backward()\n", " optim.step()\n", " if (e + 1) % 1000 == 0:\n", - " print('epoch: {}, loss: {}'.format(e+1, loss.data[0]))" + " print('epoch: {}, loss: {}'.format(e+1, loss.item()))" ] }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0.5, 1.0, 'sequential')" ] }, - "execution_count": 105, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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BsivOtgqE7RYE4ili2UpjoVRMhxsmoeRSuU0YwFrHufXCoHl/qP5epRc7h7yu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\n", 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