[
  {
    "path": ".gitignore",
    "content": ".idea\ntutorial-contents/*pkl"
  },
  {
    "path": "LICENCE",
    "content": "MIT License\n\nCopyright (c) 2017\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE."
  },
  {
    "path": "README.md",
    "content": "<p align=\"center\">\n    <a href=\"http://pytorch.org/\" target=\"_blank\">\n    <img width=\"40%\" src=\"logo.png\" style=\"max-width:100%;\">\n    </a>\n</p>\n\n\n<br>\n\n### If you'd like to use **Tensorflow**, no worries, I made a new **Tensorflow Tutorial** just like PyTorch. Here is the link: [https://github.com/MorvanZhou/Tensorflow-Tutorial](https://github.com/MorvanZhou/Tensorflow-Tutorial)\n\n# pyTorch Tutorials\n\nIn these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years.\n\nThanks for [liufuyang's](https://github.com/liufuyang) [**notebook files**](tutorial-contents-notebooks)\nwhich is a great contribution to this tutorial.\n\n* pyTorch basic\n  * [torch and numpy](tutorial-contents/201_torch_numpy.py)\n  * [Variable](tutorial-contents/202_variable.py)\n  * [Activation](tutorial-contents/203_activation.py)\n* Build your first network\n  * [Regression](tutorial-contents/301_regression.py)\n  * [Classification](tutorial-contents/302_classification.py)\n  * [An easy way](tutorial-contents/303_build_nn_quickly.py)\n  * [Save and reload](tutorial-contents/304_save_reload.py)\n  * [Train on batch](tutorial-contents/305_batch_train.py)\n  * [Optimizers](tutorial-contents/306_optimizer.py)\n* Advanced neural network\n  * [CNN](tutorial-contents/401_CNN.py)\n  * [RNN-Classification](tutorial-contents/402_RNN_classifier.py)\n  * [RNN-Regression](tutorial-contents/403_RNN_regressor.py)\n  * [AutoEncoder](tutorial-contents/404_autoencoder.py)\n  * [DQN Reinforcement Learning](tutorial-contents/405_DQN_Reinforcement_learning.py)\n  * [A3C Reinforcement Learning](https://github.com/MorvanZhou/pytorch-A3C)\n  * [GAN (Generative Adversarial Nets)](tutorial-contents/406_GAN.py) / [Conditional GAN](tutorial-contents/406_conditional_GAN.py)\n* Others (WIP)\n  * [Why torch dynamic](tutorial-contents/501_why_torch_dynamic_graph.py)\n  * [Train on GPU](tutorial-contents/502_GPU.py)\n  * [Dropout](tutorial-contents/503_dropout.py)\n  * [Batch Normalization](tutorial-contents/504_batch_normalization.py)\n\n**For Chinese speakers: All methods mentioned below have their video and text tutorial in Chinese.\nVisit [莫烦 Python](https://mofanpy.com/tutorials/) for more.\nYou can watch my [Youtube channel](https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg) as well.**\n\n\n### [Regression](tutorial-contents/301_regression.py)\n\n<a href=\"tutorial-contents/301_regression.py\">\n    <img class=\"course-image\" src=\"https://mofanpy.com/static/results/torch/1-1-2.gif\">\n</a>\n\n### [Classification](tutorial-contents/302_classification.py)\n\n<a href=\"tutorial-contents/302_classification.py\">\n    <img class=\"course-image\" src=\"https://mofanpy.com/static/results/torch/1-1-3.gif\">\n</a>\n\n### [CNN](tutorial-contents/401_CNN.py)\n<a href=\"tutorial-contents/401_CNN.py\">\n    <img class=\"course-image\" src=\"https://mofanpy.com/static/results/torch/4-1-2.gif\" >\n</a>\n\n### [RNN](tutorial-contents/403_RNN_regressor.py)\n\n<a href=\"tutorial-contents/403_RNN_regressor.py\">\n    <img class=\"course-image\" src=\"https://mofanpy.com/static/results/torch/4-3-1.gif\" >\n</a>\n\n### [Autoencoder](tutorial-contents/404_autoencoder.py)\n\n<a href=\"tutorial-contents/403_RNN_regressor.py\">\n    <img class=\"course-image\" src=\"https://mofanpy.com/static/results/torch/4-4-1.gif\" >\n</a>\n\n<a href=\"tutorial-contents/403_RNN_regressor.py\">\n    <img class=\"course-image\" src=\"https://mofanpy.com/static/results/torch/4-4-2.gif\" >\n</a>\n\n### [GAN (Generative Adversarial Nets)](tutorial-contents/406_GAN.py)\n<a href=\"tutorial-contents/406_GAN.py\">\n    <img class=\"course-image\" src=\"https://mofanpy.com/static/results/torch/4-6-1.gif\" >\n</a>\n\n### [Dropout](tutorial-contents/503_dropout.py)\n<a href=\"tutorial-contents/503_dropout.py\">\n    <img class=\"course-image\" src=\"https://mofanpy.com/static/results/torch/5-3-1.gif\" >\n</a>\n\n### [Batch Normalization](tutorial-contents/504_batch_normalization.py)\n<a href=\"tutorial-contents/504_batch_normalization.py\">\n    <img class=\"course-image\" src=\"https://mofanpy.com/static/results/torch/5-4-2.gif\" >\n</a>\n\n# Donation\n\n*If this does help you, please consider donating to support me for better tutorials. Any contribution is greatly appreciated!*\n\n<div >\n  <a href=\"https://www.paypal.com/cgi-bin/webscr?cmd=_donations&amp;business=morvanzhou%40gmail%2ecom&amp;lc=C2&amp;item_name=MorvanPython&amp;currency_code=AUD&amp;bn=PP%2dDonationsBF%3abtn_donateCC_LG%2egif%3aNonHosted\">\n    <img style=\"border-radius: 20px;  box-shadow: 0px 0px 10px 1px  #888888;\"\n         src=\"https://www.paypalobjects.com/webstatic/en_US/i/btn/png/silver-pill-paypal-44px.png\"\n         alt=\"Paypal\"\n         height=\"auto\" ></a>\n</div>\n\n<div>\n  <a href=\"https://www.patreon.com/morvan\">\n    <img src=\"https://mofanpy.com/static/img/support/patreon.jpg\"\n         alt=\"Patreon\"\n         height=120></a>\n</div>"
  },
  {
    "path": "tutorial-contents/201_torch_numpy.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.1.11\nnumpy\n\"\"\"\nimport torch\nimport numpy as np\n\n# details about math operation in torch can be found in: http://pytorch.org/docs/torch.html#math-operations\n\n# convert numpy to tensor or vise versa\nnp_data = np.arange(6).reshape((2, 3))\ntorch_data = torch.from_numpy(np_data)\ntensor2array = torch_data.numpy()\nprint(\n    '\\nnumpy array:', np_data,          # [[0 1 2], [3 4 5]]\n    '\\ntorch tensor:', torch_data,      #  0  1  2 \\n 3  4  5    [torch.LongTensor of size 2x3]\n    '\\ntensor to array:', tensor2array, # [[0 1 2], [3 4 5]]\n)\n\n\n# abs\ndata = [-1, -2, 1, 2]\ntensor = torch.FloatTensor(data)  # 32-bit floating point\nprint(\n    '\\nabs',\n    '\\nnumpy: ', np.abs(data),          # [1 2 1 2]\n    '\\ntorch: ', torch.abs(tensor)      # [1 2 1 2]\n)\n\n# sin\nprint(\n    '\\nsin',\n    '\\nnumpy: ', np.sin(data),      # [-0.84147098 -0.90929743  0.84147098  0.90929743]\n    '\\ntorch: ', torch.sin(tensor)  # [-0.8415 -0.9093  0.8415  0.9093]\n)\n\n# mean\nprint(\n    '\\nmean',\n    '\\nnumpy: ', np.mean(data),         # 0.0\n    '\\ntorch: ', torch.mean(tensor)     # 0.0\n)\n\n# matrix multiplication\ndata = [[1,2], [3,4]]\ntensor = torch.FloatTensor(data)  # 32-bit floating point\n# correct method\nprint(\n    '\\nmatrix multiplication (matmul)',\n    '\\nnumpy: ', np.matmul(data, data),     # [[7, 10], [15, 22]]\n    '\\ntorch: ', torch.mm(tensor, tensor)   # [[7, 10], [15, 22]]\n)\n# incorrect method\ndata = np.array(data)\nprint(\n    '\\nmatrix multiplication (dot)',\n    '\\nnumpy: ', data.dot(data),        # [[7, 10], [15, 22]]\n    '\\ntorch: ', tensor.dot(tensor)     # this will convert tensor to [1,2,3,4], you'll get 30.0\n)"
  },
  {
    "path": "tutorial-contents/202_variable.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.1.11\n\"\"\"\nimport torch\nfrom torch.autograd import Variable\n\n# Variable in torch is to build a computational graph,\n# but this graph is dynamic compared with a static graph in Tensorflow or Theano.\n# So torch does not have placeholder, torch can just pass variable to the computational graph.\n\ntensor = torch.FloatTensor([[1,2],[3,4]])            # build a tensor\nvariable = Variable(tensor, requires_grad=True)      # build a variable, usually for compute gradients\n\nprint(tensor)       # [torch.FloatTensor of size 2x2]\nprint(variable)     # [torch.FloatTensor of size 2x2]\n\n# till now the tensor and variable seem the same.\n# However, the variable is a part of the graph, it's a part of the auto-gradient.\n\nt_out = torch.mean(tensor*tensor)       # x^2\nv_out = torch.mean(variable*variable)   # x^2\nprint(t_out)\nprint(v_out)    # 7.5\n\nv_out.backward()    # backpropagation from v_out\n# v_out = 1/4 * sum(variable*variable)\n# the gradients w.r.t the variable, d(v_out)/d(variable) = 1/4*2*variable = variable/2\nprint(variable.grad)\n'''\n 0.5000  1.0000\n 1.5000  2.0000\n'''\n\nprint(variable)     # this is data in variable format\n\"\"\"\nVariable containing:\n 1  2\n 3  4\n[torch.FloatTensor of size 2x2]\n\"\"\"\n\nprint(variable.data)    # this is data in tensor format\n\"\"\"\n 1  2\n 3  4\n[torch.FloatTensor of size 2x2]\n\"\"\"\n\nprint(variable.data.numpy())    # numpy format\n\"\"\"\n[[ 1.  2.]\n [ 3.  4.]]\n\"\"\""
  },
  {
    "path": "tutorial-contents/203_activation.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.4\nmatplotlib\n\"\"\"\nimport torch\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nimport matplotlib.pyplot as plt\n\n# fake data\nx = torch.linspace(-5, 5, 200)  # x data (tensor), shape=(100, 1)\nx = Variable(x)\nx_np = x.data.numpy()   # numpy array for plotting\n\n# following are popular activation functions\ny_relu = torch.relu(x).data.numpy()\ny_sigmoid = torch.sigmoid(x).data.numpy()\ny_tanh = torch.tanh(x).data.numpy()\ny_softplus = F.softplus(x).data.numpy() # there's no softplus in torch\n# y_softmax = torch.softmax(x, dim=0).data.numpy() softmax is a special kind of activation function, it is about probability\n\n# plt to visualize these activation function\nplt.figure(1, figsize=(8, 6))\nplt.subplot(221)\nplt.plot(x_np, y_relu, c='red', label='relu')\nplt.ylim((-1, 5))\nplt.legend(loc='best')\n\nplt.subplot(222)\nplt.plot(x_np, y_sigmoid, c='red', label='sigmoid')\nplt.ylim((-0.2, 1.2))\nplt.legend(loc='best')\n\nplt.subplot(223)\nplt.plot(x_np, y_tanh, c='red', label='tanh')\nplt.ylim((-1.2, 1.2))\nplt.legend(loc='best')\n\nplt.subplot(224)\nplt.plot(x_np, y_softplus, c='red', label='softplus')\nplt.ylim((-0.2, 6))\nplt.legend(loc='best')\n\nplt.show()\n"
  },
  {
    "path": "tutorial-contents/301_regression.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.4\nmatplotlib\n\"\"\"\nimport torch\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\n\n# torch.manual_seed(1)    # reproducible\n\nx = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)  # x data (tensor), shape=(100, 1)\ny = x.pow(2) + 0.2*torch.rand(x.size())                 # noisy y data (tensor), shape=(100, 1)\n\n# torch can only train on Variable, so convert them to Variable\n# The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors\n# x, y = Variable(x), Variable(y)\n\n# plt.scatter(x.data.numpy(), y.data.numpy())\n# plt.show()\n\n\nclass Net(torch.nn.Module):\n    def __init__(self, n_feature, n_hidden, n_output):\n        super(Net, self).__init__()\n        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer\n        self.predict = torch.nn.Linear(n_hidden, n_output)   # output layer\n\n    def forward(self, x):\n        x = F.relu(self.hidden(x))      # activation function for hidden layer\n        x = self.predict(x)             # linear output\n        return x\n\nnet = Net(n_feature=1, n_hidden=10, n_output=1)     # define the network\nprint(net)  # net architecture\n\noptimizer = torch.optim.SGD(net.parameters(), lr=0.2)\nloss_func = torch.nn.MSELoss()  # this is for regression mean squared loss\n\nplt.ion()   # something about plotting\n\nfor t in range(200):\n    prediction = net(x)     # input x and predict based on x\n\n    loss = loss_func(prediction, y)     # must be (1. nn output, 2. target)\n\n    optimizer.zero_grad()   # clear gradients for next train\n    loss.backward()         # backpropagation, compute gradients\n    optimizer.step()        # apply gradients\n\n    if t % 5 == 0:\n        # plot and show learning process\n        plt.cla()\n        plt.scatter(x.data.numpy(), y.data.numpy())\n        plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)\n        plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color':  'red'})\n        plt.pause(0.1)\n\nplt.ioff()\nplt.show()\n"
  },
  {
    "path": "tutorial-contents/302_classification.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.4\nmatplotlib\n\"\"\"\nimport torch\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\n\n# torch.manual_seed(1)    # reproducible\n\n# make fake data\nn_data = torch.ones(100, 2)\nx0 = torch.normal(2*n_data, 1)      # class0 x data (tensor), shape=(100, 2)\ny0 = torch.zeros(100)               # class0 y data (tensor), shape=(100, 1)\nx1 = torch.normal(-2*n_data, 1)     # class1 x data (tensor), shape=(100, 2)\ny1 = torch.ones(100)                # class1 y data (tensor), shape=(100, 1)\nx = torch.cat((x0, x1), 0).type(torch.FloatTensor)  # shape (200, 2) FloatTensor = 32-bit floating\ny = torch.cat((y0, y1), ).type(torch.LongTensor)    # shape (200,) LongTensor = 64-bit integer\n\n# The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors\n# x, y = Variable(x), Variable(y)\n\n# plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=y.data.numpy(), s=100, lw=0, cmap='RdYlGn')\n# plt.show()\n\n\nclass Net(torch.nn.Module):\n    def __init__(self, n_feature, n_hidden, n_output):\n        super(Net, self).__init__()\n        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer\n        self.out = torch.nn.Linear(n_hidden, n_output)   # output layer\n\n    def forward(self, x):\n        x = F.relu(self.hidden(x))      # activation function for hidden layer\n        x = self.out(x)\n        return x\n\nnet = Net(n_feature=2, n_hidden=10, n_output=2)     # define the network\nprint(net)  # net architecture\n\noptimizer = torch.optim.SGD(net.parameters(), lr=0.02)\nloss_func = torch.nn.CrossEntropyLoss()  # the target label is NOT an one-hotted\n\nplt.ion()   # something about plotting\n\nfor t in range(100):\n    out = net(x)                 # input x and predict based on x\n    loss = loss_func(out, y)     # must be (1. nn output, 2. target), the target label is NOT one-hotted\n\n    optimizer.zero_grad()   # clear gradients for next train\n    loss.backward()         # backpropagation, compute gradients\n    optimizer.step()        # apply gradients\n\n    if t % 2 == 0:\n        # plot and show learning process\n        plt.cla()\n        prediction = torch.max(out, 1)[1]\n        pred_y = prediction.data.numpy()\n        target_y = y.data.numpy()\n        plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=pred_y, s=100, lw=0, cmap='RdYlGn')\n        accuracy = float((pred_y == target_y).astype(int).sum()) / float(target_y.size)\n        plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 20, 'color':  'red'})\n        plt.pause(0.1)\n\nplt.ioff()\nplt.show()\n"
  },
  {
    "path": "tutorial-contents/303_build_nn_quickly.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.1.11\n\"\"\"\nimport torch\nimport torch.nn.functional as F\n\n\n# replace following class code with an easy sequential network\nclass Net(torch.nn.Module):\n    def __init__(self, n_feature, n_hidden, n_output):\n        super(Net, self).__init__()\n        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer\n        self.predict = torch.nn.Linear(n_hidden, n_output)   # output layer\n\n    def forward(self, x):\n        x = F.relu(self.hidden(x))      # activation function for hidden layer\n        x = self.predict(x)             # linear output\n        return x\n\nnet1 = Net(1, 10, 1)\n\n# easy and fast way to build your network\nnet2 = torch.nn.Sequential(\n    torch.nn.Linear(1, 10),\n    torch.nn.ReLU(),\n    torch.nn.Linear(10, 1)\n)\n\n\nprint(net1)     # net1 architecture\n\"\"\"\nNet (\n  (hidden): Linear (1 -> 10)\n  (predict): Linear (10 -> 1)\n)\n\"\"\"\n\nprint(net2)     # net2 architecture\n\"\"\"\nSequential (\n  (0): Linear (1 -> 10)\n  (1): ReLU ()\n  (2): Linear (10 -> 1)\n)\n\"\"\""
  },
  {
    "path": "tutorial-contents/304_save_reload.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.4\nmatplotlib\n\"\"\"\nimport torch\nimport matplotlib.pyplot as plt\n\n# torch.manual_seed(1)    # reproducible\n\n# fake data\nx = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)  # x data (tensor), shape=(100, 1)\ny = x.pow(2) + 0.2*torch.rand(x.size())  # noisy y data (tensor), shape=(100, 1)\n\n# The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors\n# x, y = Variable(x, requires_grad=False), Variable(y, requires_grad=False)\n\n\ndef save():\n    # save net1\n    net1 = torch.nn.Sequential(\n        torch.nn.Linear(1, 10),\n        torch.nn.ReLU(),\n        torch.nn.Linear(10, 1)\n    )\n    optimizer = torch.optim.SGD(net1.parameters(), lr=0.5)\n    loss_func = torch.nn.MSELoss()\n\n    for t in range(100):\n        prediction = net1(x)\n        loss = loss_func(prediction, y)\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n    # plot result\n    plt.figure(1, figsize=(10, 3))\n    plt.subplot(131)\n    plt.title('Net1')\n    plt.scatter(x.data.numpy(), y.data.numpy())\n    plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)\n\n    # 2 ways to save the net\n    torch.save(net1, 'net.pkl')  # save entire net\n    torch.save(net1.state_dict(), 'net_params.pkl')   # save only the parameters\n\n\ndef restore_net():\n    # restore entire net1 to net2\n    net2 = torch.load('net.pkl')\n    prediction = net2(x)\n\n    # plot result\n    plt.subplot(132)\n    plt.title('Net2')\n    plt.scatter(x.data.numpy(), y.data.numpy())\n    plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)\n\n\ndef restore_params():\n    # restore only the parameters in net1 to net3\n    net3 = torch.nn.Sequential(\n        torch.nn.Linear(1, 10),\n        torch.nn.ReLU(),\n        torch.nn.Linear(10, 1)\n    )\n\n    # copy net1's parameters into net3\n    net3.load_state_dict(torch.load('net_params.pkl'))\n    prediction = net3(x)\n\n    # plot result\n    plt.subplot(133)\n    plt.title('Net3')\n    plt.scatter(x.data.numpy(), y.data.numpy())\n    plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)\n    plt.show()\n\n# save net1\nsave()\n\n# restore entire net (may slow)\nrestore_net()\n\n# restore only the net parameters\nrestore_params()\n"
  },
  {
    "path": "tutorial-contents/305_batch_train.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.1.11\n\"\"\"\nimport torch\nimport torch.utils.data as Data\n\ntorch.manual_seed(1)    # reproducible\n\nBATCH_SIZE = 5\n# BATCH_SIZE = 8\n\nx = torch.linspace(1, 10, 10)       # this is x data (torch tensor)\ny = torch.linspace(10, 1, 10)       # this is y data (torch tensor)\n\ntorch_dataset = Data.TensorDataset(x, y)\nloader = Data.DataLoader(\n    dataset=torch_dataset,      # torch TensorDataset format\n    batch_size=BATCH_SIZE,      # mini batch size\n    shuffle=True,               # random shuffle for training\n    num_workers=2,              # subprocesses for loading data\n)\n\n\ndef show_batch():\n    for epoch in range(3):   # train entire dataset 3 times\n        for step, (batch_x, batch_y) in enumerate(loader):  # for each training step\n            # train your data...\n            print('Epoch: ', epoch, '| Step: ', step, '| batch x: ',\n                  batch_x.numpy(), '| batch y: ', batch_y.numpy())\n\n\nif __name__ == '__main__':\n    show_batch()\n\n"
  },
  {
    "path": "tutorial-contents/306_optimizer.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.4\nmatplotlib\n\"\"\"\nimport torch\nimport torch.utils.data as Data\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\n\n# torch.manual_seed(1)    # reproducible\n\nLR = 0.01\nBATCH_SIZE = 32\nEPOCH = 12\n\n# fake dataset\nx = torch.unsqueeze(torch.linspace(-1, 1, 1000), dim=1)\ny = x.pow(2) + 0.1*torch.normal(torch.zeros(*x.size()))\n\n# plot dataset\nplt.scatter(x.numpy(), y.numpy())\nplt.show()\n\n# put dateset into torch dataset\ntorch_dataset = Data.TensorDataset(x, y)\nloader = Data.DataLoader(dataset=torch_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,)\n\n\n# default network\nclass Net(torch.nn.Module):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.hidden = torch.nn.Linear(1, 20)   # hidden layer\n        self.predict = torch.nn.Linear(20, 1)   # output layer\n\n    def forward(self, x):\n        x = F.relu(self.hidden(x))      # activation function for hidden layer\n        x = self.predict(x)             # linear output\n        return x\n\nif __name__ == '__main__':\n    # different nets\n    net_SGD         = Net()\n    net_Momentum    = Net()\n    net_RMSprop     = Net()\n    net_Adam        = Net()\n    nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam]\n\n    # different optimizers\n    opt_SGD         = torch.optim.SGD(net_SGD.parameters(), lr=LR)\n    opt_Momentum    = torch.optim.SGD(net_Momentum.parameters(), lr=LR, momentum=0.8)\n    opt_RMSprop     = torch.optim.RMSprop(net_RMSprop.parameters(), lr=LR, alpha=0.9)\n    opt_Adam        = torch.optim.Adam(net_Adam.parameters(), lr=LR, betas=(0.9, 0.99))\n    optimizers = [opt_SGD, opt_Momentum, opt_RMSprop, opt_Adam]\n\n    loss_func = torch.nn.MSELoss()\n    losses_his = [[], [], [], []]   # record loss\n\n    # training\n    for epoch in range(EPOCH):\n        print('Epoch: ', epoch)\n        for step, (b_x, b_y) in enumerate(loader):          # for each training step\n            for net, opt, l_his in zip(nets, optimizers, losses_his):\n                output = net(b_x)              # get output for every net\n                loss = loss_func(output, b_y)  # compute loss for every net\n                opt.zero_grad()                # clear gradients for next train\n                loss.backward()                # backpropagation, compute gradients\n                opt.step()                     # apply gradients\n                l_his.append(loss.data.numpy())     # loss recoder\n\n    labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']\n    for i, l_his in enumerate(losses_his):\n        plt.plot(l_his, label=labels[i])\n    plt.legend(loc='best')\n    plt.xlabel('Steps')\n    plt.ylabel('Loss')\n    plt.ylim((0, 0.2))\n    plt.show()\n"
  },
  {
    "path": "tutorial-contents/401_CNN.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.4\ntorchvision\nmatplotlib\n\"\"\"\n# library\n# standard library\nimport os\n\n# third-party library\nimport torch\nimport torch.nn as nn\nimport torch.utils.data as Data\nimport torchvision\nimport matplotlib.pyplot as plt\n\n# torch.manual_seed(1)    # reproducible\n\n# Hyper Parameters\nEPOCH = 1               # train the training data n times, to save time, we just train 1 epoch\nBATCH_SIZE = 50\nLR = 0.001              # learning rate\nDOWNLOAD_MNIST = False\n\n\n# Mnist digits dataset\nif not(os.path.exists('./mnist/')) or not os.listdir('./mnist/'):\n    # not mnist dir or mnist is empyt dir\n    DOWNLOAD_MNIST = True\n\ntrain_data = torchvision.datasets.MNIST(\n    root='./mnist/',\n    train=True,                                     # this is training data\n    transform=torchvision.transforms.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to\n                                                    # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]\n    download=DOWNLOAD_MNIST,\n)\n\n# plot one example\nprint(train_data.train_data.size())                 # (60000, 28, 28)\nprint(train_data.train_labels.size())               # (60000)\nplt.imshow(train_data.train_data[0].numpy(), cmap='gray')\nplt.title('%i' % train_data.train_labels[0])\nplt.show()\n\n# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)\ntrain_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)\n\n# pick 2000 samples to speed up testing\ntest_data = torchvision.datasets.MNIST(root='./mnist/', train=False)\ntest_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[:2000]/255.   # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)\ntest_y = test_data.test_labels[:2000]\n\n\nclass CNN(nn.Module):\n    def __init__(self):\n        super(CNN, self).__init__()\n        self.conv1 = nn.Sequential(         # input shape (1, 28, 28)\n            nn.Conv2d(\n                in_channels=1,              # input height\n                out_channels=16,            # n_filters\n                kernel_size=5,              # filter size\n                stride=1,                   # filter movement/step\n                padding=2,                  # if want same width and length of this image after Conv2d, padding=(kernel_size-1)/2 if stride=1\n            ),                              # output shape (16, 28, 28)\n            nn.ReLU(),                      # activation\n            nn.MaxPool2d(kernel_size=2),    # choose max value in 2x2 area, output shape (16, 14, 14)\n        )\n        self.conv2 = nn.Sequential(         # input shape (16, 14, 14)\n            nn.Conv2d(16, 32, 5, 1, 2),     # output shape (32, 14, 14)\n            nn.ReLU(),                      # activation\n            nn.MaxPool2d(2),                # output shape (32, 7, 7)\n        )\n        self.out = nn.Linear(32 * 7 * 7, 10)   # fully connected layer, output 10 classes\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.conv2(x)\n        x = x.view(x.size(0), -1)           # flatten the output of conv2 to (batch_size, 32 * 7 * 7)\n        output = self.out(x)\n        return output, x    # return x for visualization\n\n\ncnn = CNN()\nprint(cnn)  # net architecture\n\noptimizer = torch.optim.Adam(cnn.parameters(), lr=LR)   # optimize all cnn parameters\nloss_func = nn.CrossEntropyLoss()                       # the target label is not one-hotted\n\n# following function (plot_with_labels) is for visualization, can be ignored if not interested\nfrom matplotlib import cm\ntry: from sklearn.manifold import TSNE; HAS_SK = True\nexcept: HAS_SK = False; print('Please install sklearn for layer visualization')\ndef plot_with_labels(lowDWeights, labels):\n    plt.cla()\n    X, Y = lowDWeights[:, 0], lowDWeights[:, 1]\n    for x, y, s in zip(X, Y, labels):\n        c = cm.rainbow(int(255 * s / 9)); plt.text(x, y, s, backgroundcolor=c, fontsize=9)\n    plt.xlim(X.min(), X.max()); plt.ylim(Y.min(), Y.max()); plt.title('Visualize last layer'); plt.show(); plt.pause(0.01)\n\nplt.ion()\n# training and testing\nfor epoch in range(EPOCH):\n    for step, (b_x, b_y) in enumerate(train_loader):   # gives batch data, normalize x when iterate train_loader\n\n        output = cnn(b_x)[0]               # cnn output\n        loss = loss_func(output, b_y)   # cross entropy loss\n        optimizer.zero_grad()           # clear gradients for this training step\n        loss.backward()                 # backpropagation, compute gradients\n        optimizer.step()                # apply gradients\n\n        if step % 50 == 0:\n            test_output, last_layer = cnn(test_x)\n            pred_y = torch.max(test_output, 1)[1].data.numpy()\n            accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0))\n            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)\n            if HAS_SK:\n                # Visualization of trained flatten layer (T-SNE)\n                tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n                plot_only = 500\n                low_dim_embs = tsne.fit_transform(last_layer.data.numpy()[:plot_only, :])\n                labels = test_y.numpy()[:plot_only]\n                plot_with_labels(low_dim_embs, labels)\nplt.ioff()\n\n# print 10 predictions from test data\ntest_output, _ = cnn(test_x[:10])\npred_y = torch.max(test_output, 1)[1].data.numpy()\nprint(pred_y, 'prediction number')\nprint(test_y[:10].numpy(), 'real number')\n"
  },
  {
    "path": "tutorial-contents/402_RNN_classifier.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.4\nmatplotlib\ntorchvision\n\"\"\"\nimport torch\nfrom torch import nn\nimport torchvision.datasets as dsets\nimport torchvision.transforms as transforms\nimport matplotlib.pyplot as plt\n\n\n# torch.manual_seed(1)    # reproducible\n\n# Hyper Parameters\nEPOCH = 1               # train the training data n times, to save time, we just train 1 epoch\nBATCH_SIZE = 64\nTIME_STEP = 28          # rnn time step / image height\nINPUT_SIZE = 28         # rnn input size / image width\nLR = 0.01               # learning rate\nDOWNLOAD_MNIST = True   # set to True if haven't download the data\n\n\n# Mnist digital dataset\ntrain_data = dsets.MNIST(\n    root='./mnist/',\n    train=True,                         # this is training data\n    transform=transforms.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to\n                                        # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]\n    download=DOWNLOAD_MNIST,            # download it if you don't have it\n)\n\n# plot one example\nprint(train_data.train_data.size())     # (60000, 28, 28)\nprint(train_data.train_labels.size())   # (60000)\nplt.imshow(train_data.train_data[0].numpy(), cmap='gray')\nplt.title('%i' % train_data.train_labels[0])\nplt.show()\n\n# Data Loader for easy mini-batch return in training\ntrain_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)\n\n# convert test data into Variable, pick 2000 samples to speed up testing\ntest_data = dsets.MNIST(root='./mnist/', train=False, transform=transforms.ToTensor())\ntest_x = test_data.test_data.type(torch.FloatTensor)[:2000]/255.   # shape (2000, 28, 28) value in range(0,1)\ntest_y = test_data.test_labels.numpy()[:2000]    # covert to numpy array\n\n\nclass RNN(nn.Module):\n    def __init__(self):\n        super(RNN, self).__init__()\n\n        self.rnn = nn.LSTM(         # if use nn.RNN(), it hardly learns\n            input_size=INPUT_SIZE,\n            hidden_size=64,         # rnn hidden unit\n            num_layers=1,           # number of rnn layer\n            batch_first=True,       # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)\n        )\n\n        self.out = nn.Linear(64, 10)\n\n    def forward(self, x):\n        # x shape (batch, time_step, input_size)\n        # r_out shape (batch, time_step, output_size)\n        # h_n shape (n_layers, batch, hidden_size)\n        # h_c shape (n_layers, batch, hidden_size)\n        r_out, (h_n, h_c) = self.rnn(x, None)   # None represents zero initial hidden state\n\n        # choose r_out at the last time step\n        out = self.out(r_out[:, -1, :])\n        return out\n\n\nrnn = RNN()\nprint(rnn)\n\noptimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all cnn parameters\nloss_func = nn.CrossEntropyLoss()                       # the target label is not one-hotted\n\n# training and testing\nfor epoch in range(EPOCH):\n    for step, (b_x, b_y) in enumerate(train_loader):        # gives batch data\n        b_x = b_x.view(-1, 28, 28)              # reshape x to (batch, time_step, input_size)\n\n        output = rnn(b_x)                               # rnn output\n        loss = loss_func(output, b_y)                   # cross entropy loss\n        optimizer.zero_grad()                           # clear gradients for this training step\n        loss.backward()                                 # backpropagation, compute gradients\n        optimizer.step()                                # apply gradients\n\n        if step % 50 == 0:\n            test_output = rnn(test_x)                   # (samples, time_step, input_size)\n            pred_y = torch.max(test_output, 1)[1].data.numpy()\n            accuracy = float((pred_y == test_y).astype(int).sum()) / float(test_y.size)\n            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)\n\n# print 10 predictions from test data\ntest_output = rnn(test_x[:10].view(-1, 28, 28))\npred_y = torch.max(test_output, 1)[1].data.numpy()\nprint(pred_y, 'prediction number')\nprint(test_y[:10], 'real number')\n\n"
  },
  {
    "path": "tutorial-contents/403_RNN_regressor.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.4\nmatplotlib\nnumpy\n\"\"\"\nimport torch\nfrom torch import nn\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# torch.manual_seed(1)    # reproducible\n\n# Hyper Parameters\nTIME_STEP = 10      # rnn time step\nINPUT_SIZE = 1      # rnn input size\nLR = 0.02           # learning rate\n\n# show data\nsteps = np.linspace(0, np.pi*2, 100, dtype=np.float32)  # float32 for converting torch FloatTensor\nx_np = np.sin(steps)\ny_np = np.cos(steps)\nplt.plot(steps, y_np, 'r-', label='target (cos)')\nplt.plot(steps, x_np, 'b-', label='input (sin)')\nplt.legend(loc='best')\nplt.show()\n\n\nclass RNN(nn.Module):\n    def __init__(self):\n        super(RNN, self).__init__()\n\n        self.rnn = nn.RNN(\n            input_size=INPUT_SIZE,\n            hidden_size=32,     # rnn hidden unit\n            num_layers=1,       # number of rnn layer\n            batch_first=True,   # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)\n        )\n        self.out = nn.Linear(32, 1)\n\n    def forward(self, x, h_state):\n        # x (batch, time_step, input_size)\n        # h_state (n_layers, batch, hidden_size)\n        # r_out (batch, time_step, hidden_size)\n        r_out, h_state = self.rnn(x, h_state)\n\n        outs = []    # save all predictions\n        for time_step in range(r_out.size(1)):    # calculate output for each time step\n            outs.append(self.out(r_out[:, time_step, :]))\n        return torch.stack(outs, dim=1), h_state\n\n        # instead, for simplicity, you can replace above codes by follows\n        # r_out = r_out.view(-1, 32)\n        # outs = self.out(r_out)\n        # outs = outs.view(-1, TIME_STEP, 1)\n        # return outs, h_state\n        \n        # or even simpler, since nn.Linear can accept inputs of any dimension \n        # and returns outputs with same dimension except for the last\n        # outs = self.out(r_out)\n        # return outs\n\nrnn = RNN()\nprint(rnn)\n\noptimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all cnn parameters\nloss_func = nn.MSELoss()\n\nh_state = None      # for initial hidden state\n\nplt.figure(1, figsize=(12, 5))\nplt.ion()           # continuously plot\n\nfor step in range(100):\n    start, end = step * np.pi, (step+1)*np.pi   # time range\n    # use sin predicts cos\n    steps = np.linspace(start, end, TIME_STEP, dtype=np.float32, endpoint=False)  # float32 for converting torch FloatTensor\n    x_np = np.sin(steps)\n    y_np = np.cos(steps)\n\n    x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis])    # shape (batch, time_step, input_size)\n    y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis])\n\n    prediction, h_state = rnn(x, h_state)   # rnn output\n    # !! next step is important !!\n    h_state = h_state.data        # repack the hidden state, break the connection from last iteration\n\n    loss = loss_func(prediction, y)         # calculate loss\n    optimizer.zero_grad()                   # clear gradients for this training step\n    loss.backward()                         # backpropagation, compute gradients\n    optimizer.step()                        # apply gradients\n\n    # plotting\n    plt.plot(steps, y_np.flatten(), 'r-')\n    plt.plot(steps, prediction.data.numpy().flatten(), 'b-')\n    plt.draw(); plt.pause(0.05)\n\nplt.ioff()\nplt.show()\n"
  },
  {
    "path": "tutorial-contents/404_autoencoder.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.4\nmatplotlib\nnumpy\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.utils.data as Data\nimport torchvision\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import cm\nimport numpy as np\n\n\n# torch.manual_seed(1)    # reproducible\n\n# Hyper Parameters\nEPOCH = 10\nBATCH_SIZE = 64\nLR = 0.005         # learning rate\nDOWNLOAD_MNIST = False\nN_TEST_IMG = 5\n\n# Mnist digits dataset\ntrain_data = torchvision.datasets.MNIST(\n    root='./mnist/',\n    train=True,                                     # this is training data\n    transform=torchvision.transforms.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to\n                                                    # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]\n    download=DOWNLOAD_MNIST,                        # download it if you don't have it\n)\n\n# plot one example\nprint(train_data.train_data.size())     # (60000, 28, 28)\nprint(train_data.train_labels.size())   # (60000)\nplt.imshow(train_data.train_data[2].numpy(), cmap='gray')\nplt.title('%i' % train_data.train_labels[2])\nplt.show()\n\n# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)\ntrain_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)\n\n\nclass AutoEncoder(nn.Module):\n    def __init__(self):\n        super(AutoEncoder, self).__init__()\n\n        self.encoder = nn.Sequential(\n            nn.Linear(28*28, 128),\n            nn.Tanh(),\n            nn.Linear(128, 64),\n            nn.Tanh(),\n            nn.Linear(64, 12),\n            nn.Tanh(),\n            nn.Linear(12, 3),   # compress to 3 features which can be visualized in plt\n        )\n        self.decoder = nn.Sequential(\n            nn.Linear(3, 12),\n            nn.Tanh(),\n            nn.Linear(12, 64),\n            nn.Tanh(),\n            nn.Linear(64, 128),\n            nn.Tanh(),\n            nn.Linear(128, 28*28),\n            nn.Sigmoid(),       # compress to a range (0, 1)\n        )\n\n    def forward(self, x):\n        encoded = self.encoder(x)\n        decoded = self.decoder(encoded)\n        return encoded, decoded\n\n\nautoencoder = AutoEncoder()\n\noptimizer = torch.optim.Adam(autoencoder.parameters(), lr=LR)\nloss_func = nn.MSELoss()\n\n# initialize figure\nf, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))\nplt.ion()   # continuously plot\n\n# original data (first row) for viewing\nview_data = train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.\nfor i in range(N_TEST_IMG):\n    a[0][i].imshow(np.reshape(view_data.data.numpy()[i], (28, 28)), cmap='gray'); a[0][i].set_xticks(()); a[0][i].set_yticks(())\n\nfor epoch in range(EPOCH):\n    for step, (x, b_label) in enumerate(train_loader):\n        b_x = x.view(-1, 28*28)   # batch x, shape (batch, 28*28)\n        b_y = x.view(-1, 28*28)   # batch y, shape (batch, 28*28)\n\n        encoded, decoded = autoencoder(b_x)\n\n        loss = loss_func(decoded, b_y)      # mean square error\n        optimizer.zero_grad()               # clear gradients for this training step\n        loss.backward()                     # backpropagation, compute gradients\n        optimizer.step()                    # apply gradients\n\n        if step % 100 == 0:\n            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy())\n\n            # plotting decoded image (second row)\n            _, decoded_data = autoencoder(view_data)\n            for i in range(N_TEST_IMG):\n                a[1][i].clear()\n                a[1][i].imshow(np.reshape(decoded_data.data.numpy()[i], (28, 28)), cmap='gray')\n                a[1][i].set_xticks(()); a[1][i].set_yticks(())\n            plt.draw(); plt.pause(0.05)\n\nplt.ioff()\nplt.show()\n\n# visualize in 3D plot\nview_data = train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.\nencoded_data, _ = autoencoder(view_data)\nfig = plt.figure(2); ax = Axes3D(fig)\nX, Y, Z = encoded_data.data[:, 0].numpy(), encoded_data.data[:, 1].numpy(), encoded_data.data[:, 2].numpy()\nvalues = train_data.train_labels[:200].numpy()\nfor x, y, z, s in zip(X, Y, Z, values):\n    c = cm.rainbow(int(255*s/9)); ax.text(x, y, z, s, backgroundcolor=c)\nax.set_xlim(X.min(), X.max()); ax.set_ylim(Y.min(), Y.max()); ax.set_zlim(Z.min(), Z.max())\nplt.show()\n"
  },
  {
    "path": "tutorial-contents/405_DQN_Reinforcement_learning.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\nMore about Reinforcement learning: https://mofanpy.com/tutorials/machine-learning/reinforcement-learning/\n\nDependencies:\ntorch: 0.4\ngym: 0.8.1\nnumpy\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport gym\n\n# Hyper Parameters\nBATCH_SIZE = 32\nLR = 0.01                   # learning rate\nEPSILON = 0.9               # greedy policy\nGAMMA = 0.9                 # reward discount\nTARGET_REPLACE_ITER = 100   # target update frequency\nMEMORY_CAPACITY = 2000\nenv = gym.make('CartPole-v0')\nenv = env.unwrapped\nN_ACTIONS = env.action_space.n\nN_STATES = env.observation_space.shape[0]\nENV_A_SHAPE = 0 if isinstance(env.action_space.sample(), int) else env.action_space.sample().shape     # to confirm the shape\n\n\nclass Net(nn.Module):\n    def __init__(self, ):\n        super(Net, self).__init__()\n        self.fc1 = nn.Linear(N_STATES, 50)\n        self.fc1.weight.data.normal_(0, 0.1)   # initialization\n        self.out = nn.Linear(50, N_ACTIONS)\n        self.out.weight.data.normal_(0, 0.1)   # initialization\n\n    def forward(self, x):\n        x = self.fc1(x)\n        x = F.relu(x)\n        actions_value = self.out(x)\n        return actions_value\n\n\nclass DQN(object):\n    def __init__(self):\n        self.eval_net, self.target_net = Net(), Net()\n\n        self.learn_step_counter = 0                                     # for target updating\n        self.memory_counter = 0                                         # for storing memory\n        self.memory = np.zeros((MEMORY_CAPACITY, N_STATES * 2 + 2))     # initialize memory\n        self.optimizer = torch.optim.Adam(self.eval_net.parameters(), lr=LR)\n        self.loss_func = nn.MSELoss()\n\n    def choose_action(self, x):\n        x = torch.unsqueeze(torch.FloatTensor(x), 0)\n        # input only one sample\n        if np.random.uniform() < EPSILON:   # greedy\n            actions_value = self.eval_net.forward(x)\n            action = torch.max(actions_value, 1)[1].data.numpy()\n            action = action[0] if ENV_A_SHAPE == 0 else action.reshape(ENV_A_SHAPE)  # return the argmax index\n        else:   # random\n            action = np.random.randint(0, N_ACTIONS)\n            action = action if ENV_A_SHAPE == 0 else action.reshape(ENV_A_SHAPE)\n        return action\n\n    def store_transition(self, s, a, r, s_):\n        transition = np.hstack((s, [a, r], s_))\n        # replace the old memory with new memory\n        index = self.memory_counter % MEMORY_CAPACITY\n        self.memory[index, :] = transition\n        self.memory_counter += 1\n\n    def learn(self):\n        # target parameter update\n        if self.learn_step_counter % TARGET_REPLACE_ITER == 0:\n            self.target_net.load_state_dict(self.eval_net.state_dict())\n        self.learn_step_counter += 1\n\n        # sample batch transitions\n        sample_index = np.random.choice(MEMORY_CAPACITY, BATCH_SIZE)\n        b_memory = self.memory[sample_index, :]\n        b_s = torch.FloatTensor(b_memory[:, :N_STATES])\n        b_a = torch.LongTensor(b_memory[:, N_STATES:N_STATES+1].astype(int))\n        b_r = torch.FloatTensor(b_memory[:, N_STATES+1:N_STATES+2])\n        b_s_ = torch.FloatTensor(b_memory[:, -N_STATES:])\n\n        # q_eval w.r.t the action in experience\n        q_eval = self.eval_net(b_s).gather(1, b_a)  # shape (batch, 1)\n        q_next = self.target_net(b_s_).detach()     # detach from graph, don't backpropagate\n        q_target = b_r + GAMMA * q_next.max(1)[0].view(BATCH_SIZE, 1)   # shape (batch, 1)\n        loss = self.loss_func(q_eval, q_target)\n\n        self.optimizer.zero_grad()\n        loss.backward()\n        self.optimizer.step()\n\ndqn = DQN()\n\nprint('\\nCollecting experience...')\nfor i_episode in range(400):\n    s = env.reset()\n    ep_r = 0\n    while True:\n        env.render()\n        a = dqn.choose_action(s)\n\n        # take action\n        s_, r, done, info = env.step(a)\n\n        # modify the reward\n        x, x_dot, theta, theta_dot = s_\n        r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8\n        r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5\n        r = r1 + r2\n\n        dqn.store_transition(s, a, r, s_)\n\n        ep_r += r\n        if dqn.memory_counter > MEMORY_CAPACITY:\n            dqn.learn()\n            if done:\n                print('Ep: ', i_episode,\n                      '| Ep_r: ', round(ep_r, 2))\n\n        if done:\n            break\n        s = s_"
  },
  {
    "path": "tutorial-contents/406_GAN.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.4\nnumpy\nmatplotlib\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# torch.manual_seed(1)    # reproducible\n# np.random.seed(1)\n\n# Hyper Parameters\nBATCH_SIZE = 64\nLR_G = 0.0001           # learning rate for generator\nLR_D = 0.0001           # learning rate for discriminator\nN_IDEAS = 5             # think of this as number of ideas for generating an art work (Generator)\nART_COMPONENTS = 15     # it could be total point G can draw in the canvas\nPAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])\n\n# show our beautiful painting range\n# plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')\n# plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')\n# plt.legend(loc='upper right')\n# plt.show()\n\n\ndef artist_works():     # painting from the famous artist (real target)\n    a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis]\n    paintings = a * np.power(PAINT_POINTS, 2) + (a-1)\n    paintings = torch.from_numpy(paintings).float()\n    return paintings\n\nG = nn.Sequential(                      # Generator\n    nn.Linear(N_IDEAS, 128),            # random ideas (could from normal distribution)\n    nn.ReLU(),\n    nn.Linear(128, ART_COMPONENTS),     # making a painting from these random ideas\n)\n\nD = nn.Sequential(                      # Discriminator\n    nn.Linear(ART_COMPONENTS, 128),     # receive art work either from the famous artist or a newbie like G\n    nn.ReLU(),\n    nn.Linear(128, 1),\n    nn.Sigmoid(),                       # tell the probability that the art work is made by artist\n)\n\nopt_D = torch.optim.Adam(D.parameters(), lr=LR_D)\nopt_G = torch.optim.Adam(G.parameters(), lr=LR_G)\n\nplt.ion()   # something about continuous plotting\n\nfor step in range(10000):\n    artist_paintings = artist_works()  # real painting from artist\n    G_ideas = torch.randn(BATCH_SIZE, N_IDEAS, requires_grad=True)  # random ideas\\n\n    G_paintings = G(G_ideas)                    # fake painting from G (random ideas)\n    prob_artist1 = D(G_paintings)               # D try to reduce this prob\n    G_loss = torch.mean(torch.log(1. - prob_artist1))  \n    opt_G.zero_grad()\n    G_loss.backward()\n    opt_G.step()\n     \n    prob_artist0 = D(artist_paintings)          # D try to increase this prob\n    prob_artist1 = D(G_paintings.detach())  # D try to reduce this prob\n    D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1))\n    opt_D.zero_grad()\n    D_loss.backward(retain_graph=True)      # reusing computational graph\n    opt_D.step()\n\n    if step % 50 == 0:  # plotting\n        plt.cla()\n        plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)\n        plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')\n        plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')\n        plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 13})\n        plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 13})\n        plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=10);plt.draw();plt.pause(0.01)\n\nplt.ioff()\nplt.show()"
  },
  {
    "path": "tutorial-contents/406_conditional_GAN.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.4\nnumpy\nmatplotlib\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# torch.manual_seed(1)    # reproducible\n# np.random.seed(1)\n\n# Hyper Parameters\nBATCH_SIZE = 64\nLR_G = 0.0001           # learning rate for generator\nLR_D = 0.0001           # learning rate for discriminator\nN_IDEAS = 5             # think of this as number of ideas for generating an art work (Generator)\nART_COMPONENTS = 15     # it could be total point G can draw in the canvas\nPAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])\n\n# show our beautiful painting range\n# plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')\n# plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')\n# plt.legend(loc='upper right')\n# plt.show()\n\n\ndef artist_works_with_labels():     # painting from the famous artist (real target)\n    a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis]\n    paintings = a * np.power(PAINT_POINTS, 2) + (a-1)\n    labels = (a-1) > 0.5            # upper paintings (1), lower paintings (0), two classes\n    paintings = torch.from_numpy(paintings).float()\n    labels = torch.from_numpy(labels.astype(np.float32))\n    return paintings, labels\n\n\nG = nn.Sequential(                      # Generator\n    nn.Linear(N_IDEAS+1, 128),          # random ideas (could from normal distribution) + class label\n    nn.ReLU(),\n    nn.Linear(128, ART_COMPONENTS),     # making a painting from these random ideas\n)\n\nD = nn.Sequential(                      # Discriminator\n    nn.Linear(ART_COMPONENTS+1, 128),   # receive art work either from the famous artist or a newbie like G with label\n    nn.ReLU(),\n    nn.Linear(128, 1),\n    nn.Sigmoid(),                       # tell the probability that the art work is made by artist\n)\n\nopt_D = torch.optim.Adam(D.parameters(), lr=LR_D)\nopt_G = torch.optim.Adam(G.parameters(), lr=LR_G)\n\nplt.ion()   # something about continuous plotting\n\nfor step in range(10000):\n    artist_paintings, labels = artist_works_with_labels()           # real painting, label from artist\n    G_ideas = torch.randn(BATCH_SIZE, N_IDEAS)                      # random ideas\n    G_inputs = torch.cat((G_ideas, labels), 1)                      # ideas with labels\n    G_paintings = G(G_inputs)                                       # fake painting w.r.t label from G\n\n    D_inputs0 = torch.cat((artist_paintings, labels), 1)            # all have their labels\n    D_inputs1 = torch.cat((G_paintings, labels), 1)\n    prob_artist0 = D(D_inputs0)                 # D try to increase this prob\n    prob_artist1 = D(D_inputs1)                 # D try to reduce this prob\n\n    D_score0 = torch.log(prob_artist0)          # maximise this for D\n    D_score1 = torch.log(1. - prob_artist1)     # maximise this for D\n    D_loss = - torch.mean(D_score0 + D_score1)  # minimise the negative of both two above for D\n    G_loss = torch.mean(D_score1)               # minimise D score w.r.t G\n\n    opt_D.zero_grad()\n    D_loss.backward(retain_graph=True)      # reusing computational graph\n    opt_D.step()\n\n    opt_G.zero_grad()\n    G_loss.backward()\n    opt_G.step()\n\n    if step % 200 == 0:  # plotting\n        plt.cla()\n        plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)\n        bound = [0, 0.5] if labels.data[0, 0] == 0 else [0.5, 1]\n        plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + bound[1], c='#74BCFF', lw=3, label='upper bound')\n        plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + bound[0], c='#FF9359', lw=3, label='lower bound')\n        plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 13})\n        plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 13})\n        plt.text(-.5, 1.7, 'Class = %i' % int(labels.data[0, 0]), fontdict={'size': 13})\n        plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=10);plt.draw();plt.pause(0.1)\n\nplt.ioff()\nplt.show()\n\n# plot a generated painting for upper class\nz = torch.randn(1, N_IDEAS)\nlabel = torch.FloatTensor([[1.]])     # for upper class\nG_inputs = torch.cat((z, label), 1)\nG_paintings = G(G_inputs)\nplt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='G painting for upper class',)\nplt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + bound[1], c='#74BCFF', lw=3, label='upper bound (class 1)')\nplt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + bound[0], c='#FF9359', lw=3, label='lower bound (class 1)')\nplt.ylim((0, 3));plt.legend(loc='upper right', fontsize=10);plt.show()"
  },
  {
    "path": "tutorial-contents/501_why_torch_dynamic_graph.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.4\nmatplotlib\nnumpy\n\"\"\"\nimport torch\nfrom torch import nn\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# torch.manual_seed(1)    # reproducible\n\n# Hyper Parameters\nINPUT_SIZE = 1          # rnn input size / image width\nLR = 0.02               # learning rate\n\n\nclass RNN(nn.Module):\n    def __init__(self):\n        super(RNN, self).__init__()\n\n        self.rnn = nn.RNN(\n            input_size=1,\n            hidden_size=32,     # rnn hidden unit\n            num_layers=1,       # number of rnn layer\n            batch_first=True,   # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)\n        )\n        self.out = nn.Linear(32, 1)\n\n    def forward(self, x, h_state):\n        # x (batch, time_step, input_size)\n        # h_state (n_layers, batch, hidden_size)\n        # r_out (batch, time_step, output_size)\n        r_out, h_state = self.rnn(x, h_state)\n\n        outs = []                                   # this is where you can find torch is dynamic\n        for time_step in range(r_out.size(1)):      # calculate output for each time step\n            outs.append(self.out(r_out[:, time_step, :]))\n        return torch.stack(outs, dim=1), h_state\n\n\nrnn = RNN()\nprint(rnn)\n\noptimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all cnn parameters\nloss_func = nn.MSELoss()                                # the target label is not one-hotted\n\nh_state = None   # for initial hidden state\n\nplt.figure(1, figsize=(12, 5))\nplt.ion()   # continuously plot\n\n########################  Below is different #########################\n\n################ static time steps ##########\n# for step in range(60):\n#     start, end = step * np.pi, (step+1)*np.pi   # time steps\n#     # use sin predicts cos\n#     steps = np.linspace(start, end, 10, dtype=np.float32)\n\n################ dynamic time steps #########\nstep = 0\nfor i in range(60):\n    dynamic_steps = np.random.randint(1, 4)  # has random time steps\n    start, end = step * np.pi, (step + dynamic_steps) * np.pi  # different time steps length\n    step += dynamic_steps\n\n    # use sin predicts cos\n    steps = np.linspace(start, end, 10 * dynamic_steps, dtype=np.float32)\n\n#######################  Above is different ###########################\n\n    print(len(steps))       # print how many time step feed to RNN\n\n    x_np = np.sin(steps)    # float32 for converting torch FloatTensor\n    y_np = np.cos(steps)\n\n    x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis])    # shape (batch, time_step, input_size)\n    y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis])\n\n    prediction, h_state = rnn(x, h_state)   # rnn output\n    # !! next step is important !!\n    h_state = h_state.data        # repack the hidden state, break the connection from last iteration\n\n    loss = loss_func(prediction, y)         # cross entropy loss\n    optimizer.zero_grad()                   # clear gradients for this training step\n    loss.backward()                         # backpropagation, compute gradients\n    optimizer.step()                        # apply gradients\n\n    # plotting\n    plt.plot(steps, y_np.flatten(), 'r-')\n    plt.plot(steps, prediction.data.numpy().flatten(), 'b-')\n    plt.draw()\n    plt.pause(0.05)\n\nplt.ioff()\nplt.show()\n"
  },
  {
    "path": "tutorial-contents/502_GPU.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.4\ntorchvision\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.utils.data as Data\nimport torchvision\n\n# torch.manual_seed(1)\n\nEPOCH = 1\nBATCH_SIZE = 50\nLR = 0.001\nDOWNLOAD_MNIST = False\n\ntrain_data = torchvision.datasets.MNIST(root='./mnist/', train=True, transform=torchvision.transforms.ToTensor(), download=DOWNLOAD_MNIST,)\ntrain_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)\n\ntest_data = torchvision.datasets.MNIST(root='./mnist/', train=False)\n\n# !!!!!!!! Change in here !!!!!!!!! #\ntest_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[:2000].cuda()/255.   # Tensor on GPU\ntest_y = test_data.test_labels[:2000].cuda()\n\n\nclass CNN(nn.Module):\n    def __init__(self):\n        super(CNN, self).__init__()\n        self.conv1 = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2,),\n                                   nn.ReLU(), nn.MaxPool2d(kernel_size=2),)\n        self.conv2 = nn.Sequential(nn.Conv2d(16, 32, 5, 1, 2), nn.ReLU(), nn.MaxPool2d(2),)\n        self.out = nn.Linear(32 * 7 * 7, 10)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.conv2(x)\n        x = x.view(x.size(0), -1)\n        output = self.out(x)\n        return output\n\ncnn = CNN()\n\n# !!!!!!!! Change in here !!!!!!!!! #\ncnn.cuda()      # Moves all model parameters and buffers to the GPU.\n\noptimizer = torch.optim.Adam(cnn.parameters(), lr=LR)\nloss_func = nn.CrossEntropyLoss()\n\nfor epoch in range(EPOCH):\n    for step, (x, y) in enumerate(train_loader):\n\n        # !!!!!!!! Change in here !!!!!!!!! #\n        b_x = x.cuda()    # Tensor on GPU\n        b_y = y.cuda()    # Tensor on GPU\n\n        output = cnn(b_x)\n        loss = loss_func(output, b_y)\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n        if step % 50 == 0:\n            test_output = cnn(test_x)\n\n            # !!!!!!!! Change in here !!!!!!!!! #\n            pred_y = torch.max(test_output, 1)[1].cuda().data  # move the computation in GPU\n\n            accuracy = torch.sum(pred_y == test_y).type(torch.FloatTensor) / test_y.size(0)\n            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.cpu().numpy(), '| test accuracy: %.2f' % accuracy)\n\n\ntest_output = cnn(test_x[:10])\n\n# !!!!!!!! Change in here !!!!!!!!! #\npred_y = torch.max(test_output, 1)[1].cuda().data # move the computation in GPU\n\nprint(pred_y, 'prediction number')\nprint(test_y[:10], 'real number')\n"
  },
  {
    "path": "tutorial-contents/503_dropout.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.4\nmatplotlib\n\"\"\"\nimport torch\nimport matplotlib.pyplot as plt\n\n# torch.manual_seed(1)    # reproducible\n\nN_SAMPLES = 20\nN_HIDDEN = 300\n\n# training data\nx = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)\ny = x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))\n\n# test data\ntest_x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)\ntest_y = test_x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))\n\n# show data\nplt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=50, alpha=0.5, label='train')\nplt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.5, label='test')\nplt.legend(loc='upper left')\nplt.ylim((-2.5, 2.5))\nplt.show()\n\nnet_overfitting = torch.nn.Sequential(\n    torch.nn.Linear(1, N_HIDDEN),\n    torch.nn.ReLU(),\n    torch.nn.Linear(N_HIDDEN, N_HIDDEN),\n    torch.nn.ReLU(),\n    torch.nn.Linear(N_HIDDEN, 1),\n)\n\nnet_dropped = torch.nn.Sequential(\n    torch.nn.Linear(1, N_HIDDEN),\n    torch.nn.Dropout(0.5),  # drop 50% of the neuron\n    torch.nn.ReLU(),\n    torch.nn.Linear(N_HIDDEN, N_HIDDEN),\n    torch.nn.Dropout(0.5),  # drop 50% of the neuron\n    torch.nn.ReLU(),\n    torch.nn.Linear(N_HIDDEN, 1),\n)\n\nprint(net_overfitting)  # net architecture\nprint(net_dropped)\n\noptimizer_ofit = torch.optim.Adam(net_overfitting.parameters(), lr=0.01)\noptimizer_drop = torch.optim.Adam(net_dropped.parameters(), lr=0.01)\nloss_func = torch.nn.MSELoss()\n\nplt.ion()   # something about plotting\n\nfor t in range(500):\n    pred_ofit = net_overfitting(x)\n    pred_drop = net_dropped(x)\n    loss_ofit = loss_func(pred_ofit, y)\n    loss_drop = loss_func(pred_drop, y)\n\n    optimizer_ofit.zero_grad()\n    optimizer_drop.zero_grad()\n    loss_ofit.backward()\n    loss_drop.backward()\n    optimizer_ofit.step()\n    optimizer_drop.step()\n\n    if t % 10 == 0:\n        # change to eval mode in order to fix drop out effect\n        net_overfitting.eval()\n        net_dropped.eval()  # parameters for dropout differ from train mode\n\n        # plotting\n        plt.cla()\n        test_pred_ofit = net_overfitting(test_x)\n        test_pred_drop = net_dropped(test_x)\n        plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=50, alpha=0.3, label='train')\n        plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.3, label='test')\n        plt.plot(test_x.data.numpy(), test_pred_ofit.data.numpy(), 'r-', lw=3, label='overfitting')\n        plt.plot(test_x.data.numpy(), test_pred_drop.data.numpy(), 'b--', lw=3, label='dropout(50%)')\n        plt.text(0, -1.2, 'overfitting loss=%.4f' % loss_func(test_pred_ofit, test_y).data.numpy(), fontdict={'size': 20, 'color':  'red'})\n        plt.text(0, -1.5, 'dropout loss=%.4f' % loss_func(test_pred_drop, test_y).data.numpy(), fontdict={'size': 20, 'color': 'blue'})\n        plt.legend(loc='upper left'); plt.ylim((-2.5, 2.5));plt.pause(0.1)\n\n        # change back to train mode\n        net_overfitting.train()\n        net_dropped.train()\n\nplt.ioff()\nplt.show()"
  },
  {
    "path": "tutorial-contents/504_batch_normalization.py",
    "content": "\"\"\"\nView more, visit my tutorial page: https://mofanpy.com/tutorials/\nMy Youtube Channel: https://www.youtube.com/user/MorvanZhou\n\nDependencies:\ntorch: 0.4\nmatplotlib\nnumpy\n\"\"\"\nimport torch\nfrom torch import nn\nfrom torch.nn import init\nimport torch.utils.data as Data\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# torch.manual_seed(1)    # reproducible\n# np.random.seed(1)\n\n# Hyper parameters\nN_SAMPLES = 2000\nBATCH_SIZE = 64\nEPOCH = 12\nLR = 0.03\nN_HIDDEN = 8\nACTIVATION = torch.tanh\nB_INIT = -0.2   # use a bad bias constant initializer\n\n# training data\nx = np.linspace(-7, 10, N_SAMPLES)[:, np.newaxis]\nnoise = np.random.normal(0, 2, x.shape)\ny = np.square(x) - 5 + noise\n\n# test data\ntest_x = np.linspace(-7, 10, 200)[:, np.newaxis]\nnoise = np.random.normal(0, 2, test_x.shape)\ntest_y = np.square(test_x) - 5 + noise\n\ntrain_x, train_y = torch.from_numpy(x).float(), torch.from_numpy(y).float()\ntest_x = torch.from_numpy(test_x).float()\ntest_y = torch.from_numpy(test_y).float()\n\ntrain_dataset = Data.TensorDataset(train_x, train_y)\ntrain_loader = Data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,)\n\n# show data\nplt.scatter(train_x.numpy(), train_y.numpy(), c='#FF9359', s=50, alpha=0.2, label='train')\nplt.legend(loc='upper left')\n\n\nclass Net(nn.Module):\n    def __init__(self, batch_normalization=False):\n        super(Net, self).__init__()\n        self.do_bn = batch_normalization\n        self.fcs = []\n        self.bns = []\n        self.bn_input = nn.BatchNorm1d(1, momentum=0.5)   # for input data\n\n        for i in range(N_HIDDEN):               # build hidden layers and BN layers\n            input_size = 1 if i == 0 else 10\n            fc = nn.Linear(input_size, 10)\n            setattr(self, 'fc%i' % i, fc)       # IMPORTANT set layer to the Module\n            self._set_init(fc)                  # parameters initialization\n            self.fcs.append(fc)\n            if self.do_bn:\n                bn = nn.BatchNorm1d(10, momentum=0.5)\n                setattr(self, 'bn%i' % i, bn)   # IMPORTANT set layer to the Module\n                self.bns.append(bn)\n\n        self.predict = nn.Linear(10, 1)         # output layer\n        self._set_init(self.predict)            # parameters initialization\n\n    def _set_init(self, layer):\n        init.normal_(layer.weight, mean=0., std=.1)\n        init.constant_(layer.bias, B_INIT)\n\n    def forward(self, x):\n        pre_activation = [x]\n        if self.do_bn: x = self.bn_input(x)     # input batch normalization\n        layer_input = [x]\n        for i in range(N_HIDDEN):\n            x = self.fcs[i](x)\n            pre_activation.append(x)\n            if self.do_bn: x = self.bns[i](x)   # batch normalization\n            x = ACTIVATION(x)\n            layer_input.append(x)\n        out = self.predict(x)\n        return out, layer_input, pre_activation\n\nnets = [Net(batch_normalization=False), Net(batch_normalization=True)]\n\n# print(*nets)    # print net architecture\n\nopts = [torch.optim.Adam(net.parameters(), lr=LR) for net in nets]\n\nloss_func = torch.nn.MSELoss()\n\n\ndef plot_histogram(l_in, l_in_bn, pre_ac, pre_ac_bn):\n    for i, (ax_pa, ax_pa_bn, ax, ax_bn) in enumerate(zip(axs[0, :], axs[1, :], axs[2, :], axs[3, :])):\n        [a.clear() for a in [ax_pa, ax_pa_bn, ax, ax_bn]]\n        if i == 0:\n            p_range = (-7, 10);the_range = (-7, 10)\n        else:\n            p_range = (-4, 4);the_range = (-1, 1)\n        ax_pa.set_title('L' + str(i))\n        ax_pa.hist(pre_ac[i].data.numpy().ravel(), bins=10, range=p_range, color='#FF9359', alpha=0.5);ax_pa_bn.hist(pre_ac_bn[i].data.numpy().ravel(), bins=10, range=p_range, color='#74BCFF', alpha=0.5)\n        ax.hist(l_in[i].data.numpy().ravel(), bins=10, range=the_range, color='#FF9359');ax_bn.hist(l_in_bn[i].data.numpy().ravel(), bins=10, range=the_range, color='#74BCFF')\n        for a in [ax_pa, ax, ax_pa_bn, ax_bn]: a.set_yticks(());a.set_xticks(())\n        ax_pa_bn.set_xticks(p_range);ax_bn.set_xticks(the_range)\n        axs[0, 0].set_ylabel('PreAct');axs[1, 0].set_ylabel('BN PreAct');axs[2, 0].set_ylabel('Act');axs[3, 0].set_ylabel('BN Act')\n    plt.pause(0.01)\n\n\nif __name__ == \"__main__\":\n    f, axs = plt.subplots(4, N_HIDDEN + 1, figsize=(10, 5))\n    plt.ion()  # something about plotting\n    plt.show()\n\n    # training\n    losses = [[], []]  # recode loss for two networks\n\n    for epoch in range(EPOCH):\n        print('Epoch: ', epoch)\n        layer_inputs, pre_acts = [], []\n        for net, l in zip(nets, losses):\n            net.eval()              # set eval mode to fix moving_mean and moving_var\n            pred, layer_input, pre_act = net(test_x)\n            l.append(loss_func(pred, test_y).data.item())\n            layer_inputs.append(layer_input)\n            pre_acts.append(pre_act)\n            net.train()             # free moving_mean and moving_var\n        plot_histogram(*layer_inputs, *pre_acts)     # plot histogram\n\n        for step, (b_x, b_y) in enumerate(train_loader):\n            for net, opt in zip(nets, opts):     # train for each network\n                pred, _, _ = net(b_x)\n                loss = loss_func(pred, b_y)\n                opt.zero_grad()\n                loss.backward()\n                opt.step()    # it will also learns the parameters in Batch Normalization\n\n    plt.ioff()\n\n    # plot training loss\n    plt.figure(2)\n    plt.plot(losses[0], c='#FF9359', lw=3, label='Original')\n    plt.plot(losses[1], c='#74BCFF', lw=3, label='Batch Normalization')\n    plt.xlabel('step');plt.ylabel('test loss');plt.ylim((0, 2000));plt.legend(loc='best')\n\n    # evaluation\n    # set net to eval mode to freeze the parameters in batch normalization layers\n    [net.eval() for net in nets]    # set eval mode to fix moving_mean and moving_var\n    preds = [net(test_x)[0] for net in nets]\n    plt.figure(3)\n    plt.plot(test_x.data.numpy(), preds[0].data.numpy(), c='#FF9359', lw=4, label='Original')\n    plt.plot(test_x.data.numpy(), preds[1].data.numpy(), c='#74BCFF', lw=4, label='Batch Normalization')\n    plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='r', s=50, alpha=0.2, label='train')\n    plt.legend(loc='best')\n    plt.show()\n"
  },
  {
    "path": "tutorial-contents-notebooks/.ipynb_checkpoints/401_CNN-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 401 CNN\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* torchvision\\n\",\n    \"* matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import torch.utils.data as Data\\n\",\n    \"import torchvision\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<torch._C.Generator at 0x7f33c006fe50>\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"torch.manual_seed(1)    # reproducible\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Hyper Parameters\\n\",\n    \"EPOCH = 1               # train the training data n times, to save time, we just train 1 epoch\\n\",\n    \"BATCH_SIZE = 50\\n\",\n    \"LR = 0.001              # learning rate\\n\",\n    \"DOWNLOAD_MNIST = True   # set to False if you have downloaded\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\\n\",\n      \"Processing...\\n\",\n      \"Done!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Mnist digits dataset\\n\",\n    \"train_data = torchvision.datasets.MNIST(\\n\",\n    \"    root='./mnist/',\\n\",\n    \"    train=True,                                     # this is training data\\n\",\n    \"    transform=torchvision.transforms.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to\\n\",\n    \"                                                    # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]\\n\",\n    \"    download=DOWNLOAD_MNIST,                        # download it if you don't have it\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([60000, 28, 28])\\n\",\n      \"torch.Size([60000])\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33a14e6518>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot one example\\n\",\n    \"print(train_data.train_data.size())                 # (60000, 28, 28)\\n\",\n    \"print(train_data.train_labels.size())               # (60000)\\n\",\n    \"plt.imshow(train_data.train_data[0].numpy(), cmap='gray')\\n\",\n    \"plt.title('%i' % train_data.train_labels[0])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)\\n\",\n    \"train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# convert test data into Variable, pick 2000 samples to speed up testing\\n\",\n    \"test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)\\n\",\n    \"test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1)).type(torch.FloatTensor)[:2000]/255.   # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)\\n\",\n    \"test_y = test_data.test_labels[:2000]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class CNN(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(CNN, self).__init__()\\n\",\n    \"        self.conv1 = nn.Sequential(         # input shape (1, 28, 28)\\n\",\n    \"            nn.Conv2d(\\n\",\n    \"                in_channels=1,              # input height\\n\",\n    \"                out_channels=16,            # n_filters\\n\",\n    \"                kernel_size=5,              # filter size\\n\",\n    \"                stride=1,                   # filter movement/step\\n\",\n    \"                padding=2,                  # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1\\n\",\n    \"            ),                              # output shape (16, 28, 28)\\n\",\n    \"            nn.ReLU(),                      # activation\\n\",\n    \"            nn.MaxPool2d(kernel_size=2),    # choose max value in 2x2 area, output shape (16, 14, 14)\\n\",\n    \"        )\\n\",\n    \"        self.conv2 = nn.Sequential(         # input shape (1, 28, 28)\\n\",\n    \"            nn.Conv2d(16, 32, 5, 1, 2),     # output shape (32, 14, 14)\\n\",\n    \"            nn.ReLU(),                      # activation\\n\",\n    \"            nn.MaxPool2d(2),                # output shape (32, 7, 7)\\n\",\n    \"        )\\n\",\n    \"        self.out = nn.Linear(32 * 7 * 7, 10)   # fully connected layer, output 10 classes\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.conv1(x)\\n\",\n    \"        x = self.conv2(x)\\n\",\n    \"        x = x.view(x.size(0), -1)           # flatten the output of conv2 to (batch_size, 32 * 7 * 7)\\n\",\n    \"        output = self.out(x)\\n\",\n    \"        return output, x    # return x for visualization\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"CNN(\\n\",\n      \"  (conv1): Sequential(\\n\",\n      \"    (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\\n\",\n      \"    (1): ReLU()\\n\",\n      \"    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\\n\",\n      \"  )\\n\",\n      \"  (conv2): Sequential(\\n\",\n      \"    (0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\\n\",\n      \"    (1): ReLU()\\n\",\n      \"    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\\n\",\n      \"  )\\n\",\n      \"  (out): Linear(in_features=1568, out_features=10, bias=True)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"cnn = CNN()\\n\",\n    \"print(cnn)  # net architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)   # optimize all cnn parameters\\n\",\n    \"loss_func = nn.CrossEntropyLoss()                       # the target label is not one-hotted\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/yoshitaka-i/.conda/envs/tensor/lib/python3.5/site-packages/ipykernel_launcher.py:29: 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     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 2.3101 | test accuracy: 0.20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3344d2df60>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.6699 | test accuracy: 0.88\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3344d2f1d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.0701 | test accuracy: 0.94\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJztnXmc1VXdx9/f2ZiBgWFTRhYFE1HceBTQJ3FPRTMpSxMKTVEyU8oll7THrExJw7Q0NTXQxDX3NEFNTQ0BFRNQEFFwgBlkGwacfc7zx70X79z53Xt/+3bP+/WaF/fe33LOb+byOed8z3cRpRQajUajKRyKgu6ARqPRaPxFC79Go9EUGFr4NRqNpsDQwq/RaDQFhhZ+jUajKTC08Gs0Gk2BoYVf4xsicoeI/MLjNl4RkXOSr78nInNcvv8vReRvbt4zSzszReQ3XrejKUy08GtcQUReEJFfGXw+QURqRaREKXWeUurXfvVJKfWAUuo4v9ozi4gcKSI1QfdDU7ho4de4xUxgsohIxueTgQeUUm3+d0ljBREpCboPGn/Qwq9xiyeBvsBhqQ9EpA9wEnBf8v0O84WI9BeRZ0Vki4hsEpF/i0hR8pgSkT3S7pN+XZ/kdZ+LyObk68FGHRKRH4jI68nXl4nItrSfVhGZmTxWJSL3iMg6EVkjIr8RkWIzDy0ijyZXNPUi8pqI7JN27EQRWSoiDcn7XioiPYDngYFpfRmYp42szywip4rI2xnnXyIiTyZfdxORm0RktYjUJc1tFcljR4pIjYhcLiK1wF/NPLMm+ugRPkRMaXujFhhg49K6e0oOrXa7P1ZQSjWKyCPAGcBryY9PAz5USr1ncMklQA2wU/L9IYCZ/CFFJATqNKAYuBf4E/DNPP37HfA7ABEZArwFPJI8PAuoA/YAegDPAp8Bd5roz/PA2UALMB14ABiVPHYPcJpS6t/JQXCYUmq7iJwA/E0pZThgGZDrmZ8G7hSRvZVSHyTP/z6Q2h+YDuye7FMrMBv4P+DK5PFqEgP2buiJYMGg/9Dhwo7oO7nObWYBp6ZmlCQGgVlZzm0FdgF2U0q1KqX+rUwkjlJKbVRK/V0p9YVSqgG4DjjCbAeTfXsSuEUp9ZyIDABOAH6qlNqulFoP3AycbuZ+Sql7lVINSqlm4JfAASJSlfaMI0Wkl1Jqs1LqHbP9zGgj6zMn232YhNiTXHEMBZ5Nmt3OBS5SSm1KXvvbjGfrAK5RSjUrpRrt9E8TPbTwa1xDKfU68DkwQUR2B8aQmGEacSOwApgjIitF5AozbYhIdxG5U0RWichWEquL3mZNMyRm4cuUUtOT73cDSoF1SbPTFhIz/Z1N9KVYRG4QkY+Tffk0eah/8t9vAycCq0TkVRH5X5N9zGwn3zPPAiYlhX4y8EhyQNgJ6A68nfZs/+TLVRbA50qpJjv90kQXbeoJAQ5MPOn3UITA5EPCnn8GMAKYo5SqMzopOfu8BLgkOUv9l4gsUEq9BHxBQrBSVJMwC5G8ZgRwsFKqVkRGAe8CmZvKXUgOLiOAcWkffwY0A/1tbEBPAiYAXyMh+lXA5lRflFILSAyCpcAFJExLQzBn0kon5zMrpeaJSAuJ/ZVJyR+ADUAjsI9Sak2We+v0vAWInvGHA7dMNWEw+dxHQgjPJbuZBxE5SUT2SM5StwLtyR+ARSRmsMUiMp7OppyeJMRsi4j0Ba4x06mkXX0a8M10k4ZSah0wB/i9iPQSkSIR+YqImDEf9SQxaGwkMVD9Nq29MknEEVQppVrTnhES+wn90kxCZtrJ98z3kbD7tyVXXiilOoC/ADeLyM7Jfg0SkeNNtquJKVr4Y8aUtjdU2k+t3+0rpT4F3iSxSfp0jlOHAy8C24D/ALcrpV5JHvsJ8A1gC/A9Ejb5FH8AKkjMZueRMF2Y4bskTBwfpHnT3JE8dgZQBiwlMWN/jMT+Qz7uA1YBa5LXzss4Phn4NGmeOY+kHV4p9SHwILAyaYLJ6dWDuWe+H9g3+W86l5Mwqc1L9uNFEqsHTQEjuhBL8CTNNJ5wT8mheU0gmuiT3LReDxyolPoo6P5owo2e8Ws08eBHwAIt+hozaOGPGPXLV/PXiiOofd3INV5TiIjIpyTMY5cE3BVNRNBePRFj0XWzqD58VP4TNQWDUmpo0H3QRAst/D7ghrsmwOfzl1JR3Rcp1gs1jUZjH8fCLyLlJAJKuiXv95hS6hoRGQY8RCIc/B1gslKqJde9+vfvr4YOHeq0S4Ez8h+/prRfL9fvu+j6WRx298+Z/7M/mb5m9OjRevdeo4k5b7/99gal1E75z0zgxoy/GThaKbUtGajyuog8D1wM3KyUeijpNjcF+HOuGw0dOpSFCxe60KVgmdL2huv3/Oy5N+l/0F6U9zPr+p0gDr9PjUaTGxFZZeV8xzYDlWBb8m1p8kcBR5Pwh4ZEIE/OJFqa3Gx87yNqX32XF75+MWtfWsCCy29j2yrf3fQ1Gk0McMXGn8wZ8jaJ7Ia3AR8DW9JC4GuAQVmunQpMBdh1113d6I6rXNQ2n620Bt0NRl15JqOuPBOA186+jj3PPonK3YLOzqDRaKKIK8KvlGoHRolIb+AJYG+j07JcexdwF4TTHh0G0c/k8HuvCroLGo0mwrjqHqKU2gK8QiK3em/5sqLPYGCtm21FEe2Dr9FowoBj4ReRnZIz/VTY+NeAD4B/Ad9JnnYm8JTTtqKO9sHXaDRhwA1Tzy7ArKSdv4hELvBnRWQp8JAkSua9SyIPesGiffA1Gk1YcMOr579Kqf9RSu2vlNpXKfWr5OcrlVJjlVJ7KKVOTRaGiA1WzTaLrp/F/pd93+NeaTQaTX709NMmVs02R95/jWUffFeprgYRaz/VJryG7NzX7L01Go0naOG3Qcps02NQ3up8Oyit7G7aB9+TTeA6w0JYzq+xc18n12k0Gsdo4beBXbPN8f+YwcBjxnDiv27L6YNfteeunNX4KtXjDnDSTXpR6uh6jUYTT3SSNovYTZ2Qwg8f/F6UcnPJWM/b0Wg00UQLv0V2pE74z/tsXryS+mWrOGr2r0IVRetq0JnoAl4aTdzQwm+ROKVOuAaYS6LY7K3A/hG5t0ajcYYWfgdEOXXCImA+iaron5GoNv6vCNxbo9E4R2/uhgy/0jqMAp5Pvh6Cu8K8HDgo7d6fkMjdrdFowoEW/pBhJT4grHl/9iWRsKkFeI9EatbNQXZIo9F0oqBNPWFJuZzCaloHv/L+fBVrtvqRwCTgWOArwD6A6dJAGo3Gcwp6xh8m0Qfr8QFGAWQzBp/MPaXjukbKOuBN4H7gJxauOR94lUQZtv2AYkc90Gg0blLQM/4w4TQ+IEVV3SaXetSZdFt9NxPnHwe0Af1IVObRaDThQQt/SAgqPsCs22W6rd5Mj+a40juNRuMFBW3qyUYQBVNGXXkmJ8y9dUdahzHTf5xX9J3W3k13u8xnyrkFbavXaOKCnvEbEHTBFLPxAQOPGeMogMyKS+edwFloW71GEwe08GcQpYIpfgaQlQIzfGtNEzU+XHUT7e3bLV9XXNyDvXa71IMeaXIRfnXzmTgWTFnk0n3MJ6HWFBp2RN/JdRpn6Bl/Gm551oSN5STMOqFiwICge6BxgN0ZviYcaOFPw4pnzfzLb6Pp8y3sefZJjvPme82+QTauVJCtazzCTdFfsvJaQJt9/EQLfxpWMm/uf9n3mf+zP/nZPduMNHGOzqapyYUfM3y9gvAPLfxZyLdxGidzkN/ZNO2mytAFZoLDrigveX8tf7jpRdpaO9h3/4FccsVxLvdMYwct/A5Y+9ICQ3PQjAsvp6p+q6l7TLnvz7bbv6d0nO1r08mWTdNMhK4d7KbKCFuKjTjjxgy/taWNm298kVtu/y49Kr36NmnsoIXfAdn86M2K/kW33mCpvRmDT/YkJcO+JMw7LcAHmI/Q1eah+OKG2WXRuzV0717GZRf9ncYvWvjxT4/ioDG75bzmw1U3aTu/D2jhd4BTP/qtva2Zi7zKw2Mnm6YutqLJx5iDhzLm4KF0dCg+X9/AOZNn8fScC5AcSQO1nd8ftPBrgEQ2zfOBxcAN5I/Q9ds8pHEfv1wyi4qEAdW96N2nO5s2bqdf/0rP29TkRgu/BrCeTdOueSgX9ctX8/gBkzlh7q2hd5GNA37Orrdva2bTxu307tM977kp9047aJdQcxSU8AdZeOWChWtYuKmRdgUXj+jPxKG9A+lHNqxm0/Si2ErQOZI03nHOGfdx0WXHUuxxKhRtKjJHQaVssCL6n89fyvzLb+O1s69znKVz8ZYmltQ3M++4PXj56GFc/X5dzvOtZge9hkSVrCOB/zrqqTXcLLaSypFkVFxGE30efPxcvnb83kF3Q5OkoITfCm7m7BlYUUJZkdDaoWho7aBvWW6JtDLzXUQinfKbJOrc+ulZcxxwNHAtzhO4xTFHUqGw5P21nHvmfZw1aSa/v0FXYogCBWXqMYudnD25XC37Ai8APLyYgcACgAff554Hx9FRJBR1ZElr8PLbedsNMg+Pmf/iU9reMHWvI++/htLK/PZfjfdYCbry21dfB4S5g2PhF5EhwH0k9vU6gLuUUreISF/gYWAo8ClwmlJqs9P2TPP4edBU3+mjewxOm3LaBV0+M5uzp/b193ZsQtp1tcwq+iYJNA+PCVJBZvUD+nJxzdNZzyut7M4LX7/Y1+pjmq5YFXI7vvp+9U2THTdm/G3AJUqpd0SkJ/C2iMwFfgC8pJS6QUSuAK4ALnehPXNkiL4VrOTsCRozeXjCgJmB8fh/zAj97zvuWBXyz+saWP5hLY89cx7bt7eY8tW3wzMHX0LzhkrG82uez+Pw9Vja6x4D4FJ7BepijWPhV0qtA9YlXzeIyAfAIGACif1GgFkkTND+Cb9L+FnsRKN/30FjVcireldwwIFDqOxZTmXPcs989Zs32Lvf9tx+FAWLq5u7IjIU+B/gLWBAclBIDQ6G7hoiMlVEForIws8//9zN7gROUN42YWlfEz3ShTw96Cob+40axKpPNtLW1m7JV18TLK5t7opIJfB34KdKqa1ml3pKqbuAuwBGjx4dqeTtfzvlCvpsbjA8lpnSoIeP/TJqPwwpFf5acQRnNb4acC80ufjfcV/h0MP32PH+/kem5Dy/V68KJp1xMGdNmklbW4cvvvoa57gi/CJSSkL0H1BKPZ78uE5EdlFKrRORXYD1brTlhKU19Zw/cyEAza0dLK9t4JsGm7tmySb60DWlgd+EMaWCDs4KP0VF1m3zJ3/rAE7+lvNIa6WU63sDGmMcD82S+EvdA3yglEp3534aODP5+kzgKadtOWXk4CpeufoYXrn6GC46YQSnjvVOkvclsanRAjgL/3Kn/VRKBSP8Mgnp4CyNERNP+QsvvvCBLdF/l5nczVe5h0NZyzse9C6euLEmOxSYDBwtIouSPyeSyPV1rIh8RCKy31oOYo/52xuf8v1xQ+nV6E2Id3pKg1ssXOeWCGe2ny2lQrpJ6H4SwWBBYCZauRelPvZI4xd2o3ob2cxb3MoPeIVT+BvPM82D3sUTN7x6XgeyDdXHOL2/F2xsaObDtQ0cumd/xj3z1y7HjXz77ZCe8dIMbtvlzWTcDItJyEy0sq6+pUmnhrfYjcMooYw+DKOFbbTRTInOEZuXgozcfXjeak49eIjn9sT0jJePmjj/zs+eYmB1P1LbabsD6VtrVguxmMm46VaWTTNFWda+tMDw2lSeHsmzKZgrCliXZfSWJe+vZZ/9BgbdjU40soly+ux4X04VjWyiJ7sE2KtoUJDC/8Abq7j73DGet2M1a0lLdb+cx7NFvmYrwWimfTeybKbyBeVLprt97QaMfEQWXT+Lw+7+uaPi9boso3ekImbvvu+MoLvSiQr60sSWHe+bqKeCvgH2KDoUnN/VyvXbaG5rZ+9B8SmW7hSnWTaXQ/b/bt/cCybu9+VPBnbyImncobjYnJNxKpo3k+eefp/xR/6BbQ1N1NVu5RvH/hGlunpke5W4bTAHs5rXaaeVLaymjEpt5jFJwc34d9+5koW/OT7oboQKq0VYMsmZL6gi94as2bxIGvcxKlhiVAQlFc2bidmoXa8SqVXQhzGcz185AkEYb8mNorApOOG3SiHYjs3Mxz6cdyl7HXITF332FDcPmdDpmJN8QVHKi1SopAQ+k/1GDeKPN79MW1s7zU1tWaN2316wKme+n312vyZvHx7L8vmBnM2BnJ33ek1nCs7UY4Z7Sg7d8RMW0bdanMVt2ndKzOK25tmHcMLh916Vs+Rivt/BRW3zvepaQZNKy5BJetRurgpbv/z504YmIE1w6Bl/RMjl7lg/oK/ttNBecg35N3ytkM/lU2/wmsdKofWUwBthJmr3mbkXWu6fxlu08JvFIL+/X+Rzd7y45umsnj1uMqX1ddPnpmIS3OSIWb9w+Y7xxIqom8WNlAya8BBfU0+5TS+RbNcFJPoQzbKE6YFhGn/RBcc1+YjvjP+UO4LugSuYdXf00tzT2t96btFUYFgUOPH0NqyaoEXguYfi+9/HL8y6lHYMaKGorqtLaT56DLB8SUGgv7khx6y7Y2Zw16tn/povajfwP784O+uG6cWrXwQSATo/Ome2qyXtUoFhmfTaUs/W3uHy2bez76j3Kp1jxpsnxbW11kVfkx0t/DZZWlPvS9lDO+6OZlMgpPCqbur5Bp/dPO0K5qxr4M8rNvHYobtS39rOYS+u5J3xe9Ato79T7vtzl+vrl6/m8QMmc8LcW3N6AGnCwfZtzbo+bgjRwm+TkYOroKoc6pt8a9NsWcKdxo5kp7H5h6U7Bh3GeWv+bbnc3uZ+VfTZmH/P4ziMYwSefPhP9O3bm6nJ94dgPEgYYSaZmyY4MlePWvTDiRZ+J9z+TQBO/v1rXHbS3owbkZbl5vwnfR0U7PBFceI/pdW6qWOOP6SLGcnIqyhbYFhz3962+mt1JRNXHmh7nEZyfLd2HW74cWl7G6PXfOKo7SXvr+UPN71IW2sHf539gy7HvVo9atylsP8HuUB6iudO3P5NeOD0YDplESt1U1PiG0RRFT+9mxa8eiIvPbULHy/9rS/tWSGn6OegtbjrPG/J+2s598z7OGvSzLw5dVLJ2v5w23cNRR++TO8wfcYpXP/7U3TwVkjRM36H+JXi2Uus1E3tNXwIY6f/2OceJvAzmdt+Y+5iQ91LNDeu8aW9IEgJudlN/czZ/L0P/KDLOVZXj5pg0MLvEL9SPNvF7GaomQjMz557kyEnftXtLprGz2Ru5d0He3LfMGHVLJO5F2SE2fw9mmDRph4H+JXi2UmeHjc3Qze+95Er98lGvuc8Ye6tHP+PGQw8Zgxjpv9YJ3NziFWzTPpsfkB1L8NzzObv0QSLnvE7wFSK5wEDoK7OUTt2xdvtzdCUW6kRbgSQmX1Os95Nhcb5PX/AsLF7AHDI98Zx2NlH5jzfqlkmczafzTxkZvUYJuykuCgu7mGY1joqREf47ebKKa8KNoq3tmsecwCqq00NCE7E243KVmbJVh1sxoWXU1W/Ne/12mPHOb0H9eVnL11t+nyrZpnMvaAHHz/XjW4Hjp0UF1FPixEd4bebKyfAHDs5MbkKsCvefle2yraXcPEfp+94fU/JoYkXBrVz/RykzLB4wQ/ZsnEeHR3N1G96mwPH/T3oLuVla+0Wbjz61/ToV8lpN36f/kNzF9G0sqmfImqzeassHDTM0PvJiP+0zd7xuoJyvldyilfdcp3oCH8B4kS83a5stb0od8h8PjNNL7JX4gpj+cV9x9wZdBcsc/2KW+jZvyeL5/yXWVP/wiVzfp73mrgLuVXMin4mdl1sg6IwhN+OmShoExHmxdtUSua1G2CP7wAJe3zKNDPujZ92ObVjSx3f+dHnpvtpxkyTq6CNLr9ojYNqVlLW0W58cHUt/7tXBefefSqs/nIzvqWomLcH7+5TDzVhpzCE3465JwQmIq/KEubbhC3qPQAwL/xOzTS6/KI1soq+y9do4kukhX9pTT3nz1wIQHNrB8trG9h4p0d2tvIqe6sGl/Dbk6WpsZjyivxi4baZxslz5jInRY2bqmF7lm2g73zsb1+skp7WYd/9B3pWbF1jn0gL/8jBVbxy9TEAPDJvNS8vceY22YXZExP/hsDs4zf/eGwPvj15Wd7z/DTT3FM6rmDyIWcT/bBjNRo4Klh1lQ07kRb+dP72xqdcdtLe3tw8BGYfP1EdHXxxx1SYfEnec/000zzw2W18z4P7ilgfT4wydORNnpaFMHuEWE2rHNckbVZdZcNObIT/6UsOD7oLsaF14TO0vjeHxg1Tqejf0/R1XpujGqvtZfXMh1uVtOx6drjhEeK2eSUl+FZn7VZTfEcFq66yYSc2wq+xTq/ajYafl42dQNnYCTw/t+uxUyYu9rhX+YnjzNoJXphX7N4nrkna7LjKhhlXhF9E7gVOAtYrpfZNftYXeBgYCnwKnKaU2uxGe27i6wZxHnrVbmRrdT/L19gh5QI6874ttq53QinttFJs+RqAu9OCZqzSSJPt68MyaLzLTL6T8VmYzCtmo4HN1toNCz2TK999j9uf2dNmBtsZF3Brxj8T+BNwX9pnVwAvKaVuEJErku8vt3X3x89z2r+seL5BbIGbh0wIrO1snPjND0x591hhNDWu3s8PnJhj3NoYbGQzb3Er0Pl7Yta8UtreZqtdK5iNBo5SnpumbU2UVZRRVFxEzX9XU9nP3Ooll2eWU3oMgEuzZIMxgyvCr5R6TUSGZnw8ATgy+XoW8AqZwj97Yi0wIPV24cXDv/SkMYmbM3ZPN4g95BpgLlAG3Ars7+K93Rb9QsStjcEa3mI3DuvyuVnzitPqW2aJWzTwuqVruP/8eyjvWY6IMPn2KYbnXSudBdlLzyyn9/bSxj9AKbUOQCm1TkSMSjYNMPjMEm7N2LNW0go5i4D5wJvAZ8AZwL8C7VFX4uRfbwe3NgYb2UQ5fbp8rnPge8uwsV/h/xaaq8QWFTfcWG3uOpmxR7WS1nLgoOTrIcAnQDNgdmtu5y11rO/tePztwo6EbD7ixKTipZ+2WxuDFfSliS2QIf52kq1pChsvhb9ORHZJzvZ3AdZ72JbjGXvYK2llY18S5p0W4AOgBtgMmPWkXzZtRM7jUya+7qR7vuLEpOKln7ZbG4ODOZiXuRr4RpdjVs0rOrrWGesGf4uOugrDY9dGYO7opfA/DZwJ3JD89ykP23I0Y/erklYnXCjQAjASmAQcC3wF2Afww8PYbElHt0ifkWcTaCcmFbPX2nElVUohIpY2Bo2ooA9jOJ+EUc8+cY2utUtxcQ/L+fWziX4+3mUmb3MXgnACf2QgB9q6j1Pccud8kMRGbn8RqSGx33gD8IiITAFWA6e60VY2nMzYzVTS2vDouRxx0JV579WvuJjXdjPhSpdRoGVK2xvMGHyyrSpW5yd/FpP4pZtxlPwvzjaB3SzpaAYzM3InJhWz19rx7hERfnfUr3JuDJrlQM4GrnV0D7vun05XCXve8Cyl25qT70zWN6jqB392f9eqs8fNlx5Gg1rtuwvnI+WVdQ7zaGANjzOZKdhfUaevLH6JOuhawUz8ed01imq3vHqyueIc4+S+Zj12/Jix92/dZuq8je05vGBypYc+7YKsVazSMUrBfBzQBvQDbjPVS/gJ9jeBg6iWlT4jP//RiwzPcWJS8dpP+7J//Z/r97SL3ehaM6uEfa52uWBNvb04lXxk24Rtry2nuNqb3Popr6wSyujDMFrYRhvNlJjekXOFARDyzV2zHjumat+GAY9y/syxcY3VTeB0gqiWlT4jN8Kur7XTa6OI3ejasASJeUntEO+C9DK9ssqpopFN9GQXz9rMRqiFP52o+tiHFaubwCmCqpaVPiM3wqyvtdvXBkHT5z0o38l+zVe77p9hzcHjZaCUFfLZ77/0ykrQRD0V9LV8HzeIhPBH1cc+zNjdBA6iWlbmjHzw/rt2OceKr7Wb14I7rqB3Z9Rvhewzz2cPyR31+p2PE3sAzz39PrfOeIl/vtK5yppd988gc/CERdyzYcZ+n/LKaqeVBtZRRqWhmcfNfYBsREL4w+Jj397QwOqzzkLKyuhobGTnSy+l8lB3/NV7NW5na4V/+Uv2w3gTOJ/5J4hqWZkz8p+9/AtP27OK266gjTTRY4A9oevW/8u9qJRJxwg70bWrPtloeZVwwcI1LNzUSLuCBcfvkfP4xSP6M3GocQbWMIs+GNvvM0l5Zf2VIxCE8dxieC8/9gEiIfxh8bEv6tGDoQ89hJSU0LJ6NTXTpjkW/h0b2L95ydckcTOyfG7F/ONXVTCnM3Kv8SJlr5k8LEtW5vbuSZl03MLqKmHxliaW1Dcz77g9aGjt6vSQeXzUP1dkFf4gsGJyMbLfG3EgZyc9s7Ljxz5A6IXfbx/7k3//GpedtDfjRnT9zytFRVCU+LJ3bNtG+V57OW7PcsoJl/z/jfJngD8xABWUu5KDPiyENWVvyqTjFlZXCQMrSigrElo7FA2tHfQsLc55vG+ZtYytXmLV9dLIfm+Xr3Jxp/sY7QOkY2dPIPTC76fHjpm9hNbaWmqmTaPlk08YOH26q+2b2sBO9/+vrnZlEEjHj/96memNnaRaDgNeuIIa/U7spIYOMllan7JihvcsY89nl7G9rYP1p4zMefwvYwcH1NOuWHW9NLLf26UbvfLuA6SwGxsQeuE3Q8mFcxg4fTo9jz66y7El88ybI/r17Mby33896/Ed97p4OJ8X7c+gS39p2KbVDKMAHR3KXBWx9HvPOMq4HnAIPC3CSFtLG01bG6m0UFUsRWLDtSt+uoJGbZU0t3YbaxrbWHHSCOoNTD2Zxw97cSXjd6mkWwjyDFl1vUzZ75vZSm92dbwhm28fIL3d83gHgD4MM91uLIR/2BNPsGrSJGMR9oidOhop6uHeZmxRkU2xbqrvPBiU++tm6QZ+mX5KykpsiT50XaWkiJorqJ8ooE9pMcVF0sXMY3S8pUPRbqH2cSObqTDIVuoGZl0v08lnu7fCObzp2r2MiIXwF1dW7hDhV9++3nSUrVMGXB3C4ssuB4nZqgpmMQ1zuqjaLasYFGHfeA6SY6sreXDVFsbN/ZjmDtXFqyfz+IV79qN7ifk84JNSAAAgAElEQVTZfg1vMZzxbncbgOGM73TvH7HIk3aCImjhr8OFnPyrp07dIcJ+iT7gmiun67i0AQwZVcGUhemYTazasKO+PxAG9tn9mi6ffbjqJsuJy4obOg/YRSLMPMTYndTM8Xx4JfqFQLDCP+nBamZPdKQmG0orGfbII271KB7UZvEF1Lb/QLES6PXve19xtSZANrLVvrVVGnGSm7XfNGaJpVdPLvfKfQ65zpM2w1SA/cjfvBSKfmicYyXQK5vo390225XC70azfI03eJmCIZZePUGlaghTAXZX++GiCSgsRCkmwL0SjInntZNHPnWdxh/cTsWcid2Mn6EW/nypGsy6ajqZwYclOZwr/aitjZ25JzXz9cvW72SgcTvQy5Y5xkdueuJltjd9OWm7ZqL3piAns2svZuZep2K2m/Ez1MLvVqoGuzN4OyuOfi0Nps6zMhi5uvKxO+sf4H5dXtv86Kiuedrvu8KXprMNNOn+/NlID/TKlmXULFYGOjdMQ6ao6tfp75Iu+gDbGvtRWWExv36VeY8yJ7Nrr2bmXqdituF2WgchFn6vUjVYmTnbWXEsrannSBOCbmUwcjVJXbaNX6cYibEZUhWW7F7vA9mCt9JJ9+cPU9EV8DHwK7NS1oOd3/7+SXOlf66x6e7hZHbt1czcTjyAFcxm/CRZeSv1JrTC70WqBqszZzsrDjuri3yDUViS1OXErminrgtI9M8pmeTKfdz257/xmN8A9tM8FyJOZtdezcwtCHMn7uFQU+Ymsxk/00UfQiz8ZrBqu7cyc3ZjxWFmdZFvMAqkEHwEqdiyjcbe1tIlmJnJB4WbaZ7DhJceLk5m117NzM0Kcyan8DfT5iYzGT8zibTwW51dW5k5O11xmF1d5BuMIlNWMmC+N81EGcjZncs2Hr5qFb9tX2mpnX7Fxby2m72Sg2b9+LfW1XPnxFtNe//U19VTNSD3xMCtpG9OcGJHN1OfwO7s2um1+bAjzF7X5I208KeTb3bt2cx5zofQ0jUBVT9g+VFD4R9Lja8rK4bj9gqPGceOjT1lnw8BZgt6pLOxvevfzYtrUpj145fiIn728i9Me//kE/1s+O0G68SOnlmf4FqDeZLd2bWVa9fyjielEI3wsiZvLITfzOzas5mzgeibvS5UZhw7Nvb6jXmjNe0Ish3+NHpQ/pN+dFSgA5VZP34v0jxn44G2x32b9ftRbNzO7NrKtc8zzZNSiEa4vRGcTvD5T5PuRU4IS2lGq8TdjJNeYenlo4dx9ftZ/tR+hfq7uIFsZ3/g+hW38LOXf8Hh5x7DrKl/MTynaVsTHe0dAJ6neQZ/Z/1ee7j4QWqV4iUKxRZWu2puyiT4Gf+kB3fsNo8ePVotvHj4jkMj2qezPlnCbHPxeVlvERpzSViYPdE4T7/PhLnCklOMZsn5fOvNzOTdTPNsuQi8k8I+AwbkdRX20o7uF16aXwDe4V7e4W7LpiqrBC/8OVifpW5lOqEyl4QJl9Mz2yHMFZaMaG9oYPVZZyFlZXQ0NrLzpZe6loHVbMEWq26hTduaKK80Xn1YLgLvJJ2HiWud2ODDgterFCemqhx0+eOEWvjN4La5pE/7l7PkXKuMfPhl2z78wCvYWJaluMhKY48VJ54pVghzhSUjinr0YOhDDyElJbSsXk3NtGmuCb9XBVvWLV3DsLFfMTzmRRF4p3gkbL5hdpViJgjNaIPaZeoy/fdTRF74/SYzduA/YwZ2OSfdtt3Q2s6of67wTPizin6ua2x4ptgZyJxWWLLTh0PmrLA92EpRERQlBqWObdso32sv+53NwKuCLdlEH8JbBN4NzLh3us0XbGQ8f/C30TykBhgReVspNdrsdeET/vIqz8wUt8/9iDWbG7nuNPubiZmxA2zompsnbrZtuwOZ0wpLdvrgdLBtra2lZto0Wj75hIHTp1u6NmyZQk17B6VceSful3j/4Pved84hme6dRrg9o+5OP7qTP3dQjxCltcpG+IQ/tSH5+HlgPeNsTtzeBP7bG59y2oiuXwQ/bNupPP08601NgnTsDmROKyzZ6UPePqY8iKr6wc/u6XK4tLqaYY88QktNjeU6zukbvkFXBrNUBN6Et9M1wFygDLgV0CVXOmM3v1BQhE/4U5xyB9zv3szf7U3gVOwABsLvh207teogT2rqDaWVHHHQlY7aCsMmrdk+lBYJA7uXdqnv2gUDsetobqaoW8J+m17H2U9uPOY3ruTncXNPYREwH3gT+Aw4A8gVDZGqmzyo1Xwb7bXl1A4prCJDQZirUngu/CIyHrgFKAbuVkrd4HWbRri9CZyKHTDCbdu2E9yoQRyGTVo/+tC8fDl1110HRUWotrYddZz9JJcXzu+O+pXpzJ9u7iksBw5Kvh4CfAI0Q9YtTjvmruLqxDVem0k+4p+s4J+ckLTV/5lRnMtbgbiVpsxVN1XbGwCc/K48FX4RKQZuA44FaoAFIvK0UipLHoPs1KleDJCtbnfRNjvMRu/WdDnmpm07DIRhIPOiD0uu/mbXD9MXCs/fmPjJh0HqCjv2/u2btjHrh3/J6oETVLrnfUmYd1qAD0j8R94Mxu4iDvDDXBLGWAIz+xVu4/WMfyywQim1EkBEHgImAJaFf6+O3+14rdpa2X7L9yk7cjJlB51keL4TV8x8dDIbGQi/m7btMBCGgez4XXpy/C6JzcrSIuGjb4zwtf2cGJiNrFYGa9jQQM/+PXdE9YbJA2ckMInE7O0rwD5A8I6h9vAiliAKm7mZeC38g0iYBVPUAAennyAiU4GpALvuumveG6qODr64YyqlB309q+h7TdxTLWQSt4EsjPiZn8cO5yd/FgM3kLDbZpJrkLMcRewhZmIJorZZaxWvhd/IoarTr1QpdRdwFyRSNuS7YevCZ2h9bw4dW9fT8ubDFA8eSfczTCzHY4iTWsKekJH22LccPFlw4tPvJ5Y8cALiOKCNRNbZ22xcbzmKWOMpXgt/DYn9oBSDgbVOblg2dgJlYyc46lRYSLlk2hVtu7WEI0HKlzzlW24DtwLovI7C9iqq1wzn9/wB53wrEaj2wK0X0Ni7knMe/F6X8+Y4bCeMUcSFjNfCvwAYLiLDgDXA6STMhd4zKaPg5+PnhSJ/TTpuiraVWsJxwIwY5/PpN7Mi8CMK26uoXjP0HvRl3hmrFcysEOco4ijiqfArpdpE5ALgBRJmwXuVUku8bDMrZjNVWo0dKCu2l5M/TYycirbVWsKGVPULbbHzTMyKcT6ffzNiHrco7Ey21m4Bdva8nbDvYRQanvvxK6WeA57zuh0vMOVCelyWfC6ZK47ZEw1Pc0O0XalHkHJHtFOJCxIDh5PrLWBWjPP5/JsR8zAEr3nJn7bcy90etxHUHobdAKkoeulYJbyRuyEg3YXUDJvP6A0PnG7pGjdE29VUFPkqVGUTdhPVuEzTmDvk06wY5/P5NyPmYQhes0tYPGmC2sMIwj8+KoRa+HcuF9Y3Wfer2rk8OpW4nIq27/UI3J7NZ0kIlsoN86bBMStinMvnP/36CYN7GZ4ThuC1fGTLyW/Xk+aLAVV0r7Nm8vwiR91f03sY6ROHENVzjiOhFv5lpxr/Z/QS24PNlrQ1Zbk5EXZDtOMYU5CeG8YIt8Q4/fpshCF4LR/ZcvLb9aSZXXN7p/c/3+tifvvhDFf6apqI7DdFlVALfxAsO7UXGJhdbgceJhG5+DbwDsZBLF1s+zkISrT7FYdog9LAXXMU8EhjKzz5oeElborxV+d+zIV7Zk+1G1Tw2o1H/5rTbz6DIQfkL5iTLSe/W5402hUzfmjhN4mZyMWwsmT33YPugmV6VpTSQiINcCZuinHeLJ4B8bOXf+H4HmY9aVJ7AdnMQtoVM36Ea80aYo4DjgauBXxe9EaCCxau4ZA5K1y957Gu3q2waNrWREd7B0BeT5p8ewHpA8jG1Rvc7agmEPSM3yROIxfjTLpfvZu86urdCgsrnjQpU47RKiMK6SQ01om/8FdXQ12MUhlYxeRGsxPS/epLi9zzqDoaeNm1u4UHP9wsrUQDp0w5RthxxWxraaOkzJq0VGxxXjdCY574C39YRN/DWsI5MRux7IB0v/pPTjZXoNxMyoU4ij6EL2FZNtEHe+kk0kX/nDO61l3a1NzGxDc/49kjhvJ5UxsT/r0qtHstcSX+wh8WnAhwlqjfsJDuV5/JC+saduTRT2Ep/01jK1SUWu6TUspZJLMVqrJ7Bd1ZcyBfdHTeot5pzpvcvDr3LbsXtfDDwe/kbTp1/4t2nWeqq5mkm3L8Iu7R0FFAb+5GAbvmmi2N7vYjC+l+9ZkcW93VJmwp/82THyaCvFI/BrywroGz5yUK4rR2KIY/s4xGA8f+5uRmp+vkCDTKFH2zfNFRxs2rDzF1nhPWLV3Dbw6+mt8d9StH97FC+kThw6/vyc/fq/Xub6MxpGBn/KnI0DISZeVcSTYwwKMkH6fcYRhbYIrz3e2KEel+9a8f29mnvMig327P+Mz69Yct4tYMZsQ/dZ7ZVUI6QWQGjUI0dNyJlfBPmtrG5gwz+vMG56VHhn4GnAEYztlUiL6NAwZY36/waiDKwKpfvdv5b8y2H7aIW7exMvs3MkGZwc7gkkkUoqHjTqyEP1P0s7EcOCj5egjwCdAMAZdczkNtfDJOxWrGl8O+HwRmVwh2cWpaAl3KMwzESvjNsi8J804L8AGJMmGbgeqM8/ZZudLyvfsVF/PabvnD7AsZ0zO+ifslyjkGXMKxCw4TiLU3NLD6rLOQsjI6GhvZ+dJLqTz0UBc76C3nD72M4a+91uXz1AZz+uBzjm+90lihIIV/JIkyYMeSyL2zD+BW9pGN7TaKshQYkZrxeZAlsqhHD4Y+9BBSUkLL6tXUTJuWV/jDNFgU9egRSLsa94iF8BvZ9lOccFr23O6DGmu5+Jkhkcu9o3GRzALxPiBFRVCUWOF0bNtG+V75Yx/sDBZeMeBq4xiEm1cfQmttLaWZS2dN6IiF8Ju17Xe5rqKaa4HbXO1NzIlQicYw01pbS820abR88gkDp0/Pe76dwcIqZlcVuQac0mqt+lEgFsLvhEeD7kDUSDd7hM32bpUAN2ZLq6sZ9sgjtNTUsGrSJHoefXTea6wOFpmkC/vQ2bO7HHe6quhobqaoW2cXiQ2Vvem/bYvlvoZt0zxuFLzwWyFMdlaNDQIw6xiRLpDFlZWmbeZ2Bot00oXdCKeriubly6m77jqGPvTQjs+OuGJml/NS/TfaII5iCvEoooXfAmGys2qiS0ogKSpCtbVltZlno2zwYEPRzEe6sGfDyaqiYr/9Ool+OnYHO403xFb4F7x6Ilu3vMvQ4RfylZHuFI7ww86qScPN5X5VPx5oe5xGmixfur29lL/Xjs3ppnv4qlWmPbpyCaTXpIR92COPGB53uqrIhtPBTuMusRX+/cbcxYa6l2huXOPqfZ3aWWOFnY1ev4top5l3Gtu62rXN0KO4Na+oR8WNNyXsRng5Kw9ysNN0JbbCX97dWf6XDf37G37u1Ywokvgp4CHATkBfmDDafE1Hz8oLh9gKv1my+fmvvr5r3lxtp9REmXRhN/Lq0bPywqHghf+d17/NgeP+3uXzXa/cNc+VuzJs1BLar2tnzVXumpM0/uJHRSw3setdpoVdk6Lghd9I9K1QvE3H/HqO3aAxk5vDXlXEam9oQLW1UdKnj6v31d5lGqfEVvgXL/ghWzbOo6OjmfpNbzsWeE2AeLyXkCo23qNfJafd+H36D3Unc1NRjx7QkSgwkhLo3Z980vF94+pd1q9YT6L8IrbCv++YO4PugiYipIqNL57zX2ZN/QuXzHHf/bds111Ni76ZspFR9C7TwVnhwVH1AxE5VUSWiEiHiIzOOHaliKwQkWUicryzbmo03pEqNr7vcfuzcfWGgHuDqVrBKe+yYU88Qe0vf+l9pzSxwmnZm8XAKUCnMEIRGQmcTiLj8XjgdhHxZB03aWqbF7fVFAhN25roSNZ7rfnvair7da0RHDY6mpt3vHbbu6xfcbGemRcAjkw9SqkPwHCGMgF4SCnVDHwiIiuAscB/nLRnhN3MnF6h7ZTRYt3SNdx//j2U9yxHRJh8+5Sgu5QXL/3tN7a3Rz5eQZMfr2z8g4B5ae9rkp91QUSmAlMBdt01nwtlONEzpOjid7FxNxL9WXXLzJeVU1N45BV+EXmRrlUJAa5SSj2V7TKDzwyrqiql7gLuAhg9enSoKq+azfczaWobs++K7T65xkWCcMXMl5XTCjpDbTzI+01QSn3Nxn1rSNQxTzEYWGvjPoFiNt9P2MxNGvepnd+H6rGbHQtfEK6YZrJymsXuwKVNoOHCq2nq08BsEZkBDASGA/M9assznOb70cSHjtaEcLoxYw/CFTNfVk6zWB24tBk0nDgSfhH5FvBHErXK/yEii5RSxyullojII8BSoA34sVLKUvrCXHV0NZqgMCt8qZXBsMce63IsiER/2bJy2lnBRDGGQNMZp149TwBPZDl2HXCd3XtHTfRP+G5Xt9I+VWjbf4iooNxWPv6tjWWd3psRvtTKIJMgEv3lysppZwWjM9RGn4JVJS8KtWSyuR6YPTH/ieVVcModnvRB8yXfKzmFPvc7n1GYEb5sdnW3XDFzlS/M1WamV49V043OUBsPClb4zWzc+pbvpyliy5uY0fbpezTefxkUFSFFJVRM+SPFOw81PNeK8LXW1lJa3dkhzkmGTLuim69NK6YbKwOX3tANL5ESfjdn6WY2bnW+n8KgqHc1lZc+hlT0pPW9OTQ9/lt6nHeX4blWhC9T9J3iVeCWFdON2YFLb+qGm0gJv1flFDWFTVHvAV++KS5DirP/tzArfPmqXdnBi3z62nRTmERK+LV7ZbwY8ehW1jdZj9nbuVxYdmov1/ujmrfT9Niv6X7ubY7vlZqdh73wiS63WJiEUvjjlnhtaU09589cCEBzawfLaxvYeOcpAfcqeOyIfuq6zE1ap4OBamtl+5/Opts3LqJ4kPOgqqhUu4pKPzXuEkrh98OV06+N2xOeup8+3bbwytUXAvDIvNW8vKTOk7YKmfRBxM5KQjV/QelBX6fsoJPc7ppGEzpCKfx+4OfG7ebm3jte/+2NT7nspL2t3eBHR1kvPVjVz/PKVWawa85xgp32inpU0fLmw7S8+TDFg0fS/YwbPeiZP6iODujo6OSb70blL7Nob57wE7jwn/DdtlpgAMBOX5lnGAiVIozlFNvatlFSYi6H+8aGZj5c28Che/a31oiderN2rvEAv0XfCT1//o+gu+AK6b75G2v2ofxn/2btG4ljAw919r3Q3jrxIHDhJyn6Zgibe2XNJ7Noblxj2rX04XmrOfXgIaYqLGnCRzbR8zN/vVEfjNpP+eZ3+8lLfnRLEzHCIPyR5dNlMyytPh54YxV3nzvGh57FCysBVnHGigkl5ZufmumnaG8RisuiswrTeIMWfgeMG/+e6XNXrt9Gc1s7ew+q8rBH8cRKgJURYR84+hUX89puu7l2v1wxBHUL+pq6h1OTkCbcaOH3id13rmThb9yrOX/BwjUs3NRIu4KLR/Rn4tDe+S8KEVbE2EqAlfH1zgYOOzS+/34n//idfvITw+Rndm3mnTfN+znoqaYQ0cIfQRZvaWJJfTPzjtuDhtZ2Rv1zReSE344Y2w2wcjpwQCJOwApe+8fb3TQP++pH4w+xFn4/MnAGwcCKEsqKhNYORUNrB33Louc+Z1WM3QiwyjdwuBkRXDu/z47iLbno88aXQSteRSSn42T1o90040OshT+uuX36lBUzvGcZez67jO1tHfxlbHRTWZiZxauODr64Y6qjACszA4ebrqdmRN/L9rNhdsDVbpvxJtbCH9fcPnNrt7GmsY0VJ42gvrWdw15cyfhdKulW7E5dVb8wO4tvXfgMre/NoWPrelsBVm4MHHHDzbxEmugRa+H3ElfNSOXWPH0U0Ke0mOIioWdpMS0divaIeehZEeOysRMoGzvBdltWBg6jQi1+mGDcwKz93u28RJrooYXfJlbNSCc8dT8Azz/s/Fd+bHUlD67awri5H9Pcobhwz350L4nWbN/pLN6IbNW1nA4cRknhOpPwqikq7aB67Gbb7TjFjP1er340EFHhtzLbHjzsTE/uHaQZqUiEmYcMCax9N3AqxmGko7Voh208fdMW/PGmMWO/92LA1USPSAq/l5u2Vu5tdVDpo2O3Yk+2lUHJ0APo+YsX8l4/4tGtjs1Kuez3cRxwNdaJpPB7Odv26t5umHg08cepZ4/b9vu+99djp0dR2RcpVLQaaTQmiELgkxf2e7vDUJSyshYiWvgNsGrCMUN6uuk+VTD7Lv2rN8Pmycb2sUyTSrow9/jJbIoq+7jaDz/TPuTeSM6Ott9rzBJr9Qlr5O7menLWHegyMFT1s1eIJQTsXC6uzv6yFXbJFObG/zzmqjC7kfbBa7T9XmOWMHx767CQkx/MF2TxK3LX7QGmS+nJEFTSskvKzmunEpdRfpxs9/BLmPMFPm27/htZzUBRMBeB9bxEmugRuPA//3BJder1Cd9tM6UMZguy+OVyGdfUEG7i10aflxGpZjZOK698Juv1Zj17vCab+cwMURm8NLkJXPjjQFxTQ0QNLyNSdeBTgiBSXGvcRwt/BmHdF9Dkxqww252xBrVxGrYZdhT2OjT5cfRXE5EbgW8ALcDHwFlKqS3JY1cCU4B2YJpSKvg1rgm02cZf7Nj+jTArzHZnrEFtnLo9w3bLfq+TvEUbp8P1XOBKpVSbiEwHrgQuF5GRwOnAPsBA4EUR2VMp1e6wPUuY3QROR5tt7HFR23y20mrpml6Usr5phCvtmxXmqM1YzfbXid3eKjrJW/Rx9K1XSs1JezsP+E7y9QTgIaVUM/CJiKwAxgL/cdKeVcxuAjvFzgATN6yKvt1r3MKvGatbppqwzLD1Xkc8cHO6czbwcPL1IBIDQYqa5GeRwGoAl18DjMYd/JyxumGqUW2tqLZWev7ypaznZAv6cjt1gg4Siwd5hV9EXgSqDQ5dpZR6KnnOVUAb8EDqMoPzDQ25IjIVmAow/rTgZoBhIxXgpaN83cXvGatT01Kqvz0u+Kut9o1SSjsZDHSQWDzI+y1USn0t13ERORM4CThGKZUS9xogPW/wYGBtlvvfBdwFcPy361RRSTgiTsNCZpRvIQwEXnqyBDVjtWuqSfXXTXQeHY1Tr57xwOXAEUqpL9IOPQ3MFpEZJDZ3hwPz891v46qvs3Dhwpzn5Ep1UAh0ieoNOfXLV/P4AZM5Ye6tVI87wNQ1XvqKBzFjdWJa0jNsjRc4Ldv0J6AnMFdEFonIHQBKqSXAI8BS4J/Aj/326HGDBa+eyEtP7cLHS38bdFciy6LrZlF9+ChL1xT1HoBU9Ey8iYDnTS7Cuhk64tGtQXdBEyBOvXr2yHHsOuA6J/cPGu3T74zP5y+lorovYrMIfFg8WRzR1mLbPu8l2txT2ESrUKvPOPHpX7zgh7z+zwN47bm9eOf1b2c9L84rikXXz2L/y75v69q4+IpLWXnQXXCE3XAvnegt3ERuDd2nKhp2brMuniP2vyGWK4rPnnuT/gftRXk/64FFTs0jqnk7264/Oaf7o8Ycm3wMDNP4R+SE365HS6FvCvvNxvc+ovbVd3nhP++zefFK6pet4qjZv6JyNyPP4M448bxJXyloNBpjIif8mmgw6sozGXVlIhDutbOvY8+zTzIl+uDMk0VKSqm85OH8J2o0BYwW/hzoVAzucPi9VwXdhVCRilNwmps/bJk7NdFBC38OdCoGjRek4hScklnYpeOLCGx+aUKBFv4AaW2t59NlM/SKosDolMYhAyez+KLueiNWYw4t/AFSWlrFuPHvBd0NTYjQFa40fqCFXxMLMmfKuWrfuonq6OCL26dQsu9RdDvyDPPXtbUiJaVdPo9avQBNNCmYb1VU/P+jSktTCWXl1lxmmxqLbWWKNEpBnDlTzsSrjVA7rqdmMm7GImpZE1oKRvjz+f9rP39n3F95sPWLegKnutN+5ky563FvTCh2XE/zZdzMF7W87fpvaA8ejSN0yoYkffS+WGTIlQ4gNVPOJEyJ38rGTqD3X4yjtc1ELXc78UKaHo9nmg+NPxTMjD8f6SsCPfsPN8tO7WVo7jETtRt2E4op05G2/Wscor89EUOvTIwxM1OOQuI3M6ajMA9cmmighT9EPP+w/nPYJXOm3PPn/+h0PKx58e0Q5oFLEw200mhiQb6ZslXvm53LJbQ5690YuHTa5MJGC39I0CYcb7HqfbPs1F6MeHRrKMW/4bdft5SxdLNOrazJQL6sjx48IvI5sMrnZvsDG9I/OHrCugPKuvV3dVBsad7Q9vJTuwQVptvlGaNO7/u2HOTl/bec0fttL+8P3j9DCj+exWVi9301wO1n3E0ptZPZk0M147fScbcQkYVKqdHet1QNBDPI+veM/tHn/npPf5l+/L763F9fC2RP3OMSUfvbx/H7mknQzxgq4ddoQkKdH41snlyVtUCB1wObprDRwq+JKnV4MFvePLlK73pqYo8WfiiE1Iexe0aj2bKITFVK3QV6xpyGL6sXl4nd99WAQJ8xVJu7Go1b2BX+sMz4bdr/63KZjzSaFHrGr4krdkxBoZkdawHXeIme8Ws0Gk2BUbDZOUXkRhH5UET+KyJPiEjvtGNXisgKEVkmIscH2U8niMipIrJERDpEZHTGsVg8I4CIjE8+xwoRuSLo/riFiNwrIutFZHHaZ31FZK6IfJT8t0+QfXSCiAwRkX+JyAfJ7+lPkp/H5hkBRKRcROaLyHvJ57w2+fkwEXkr+ZwPi0jXfOIeUbDCD8wF9lVK7Q8sB64EEJGRwOnAPsB44HYRKQ6sl85YDJwCvJb+YZyeMdnv24ATgJHAxOTzxYGZJP4+6VwBvKSUGg68lHwfVdqAS5RSewOHAD9O/u3i9IwAzcDRSqkDgFHAeBE5BJgO3PwZQjQAAAKFSURBVJx8zs3AFL86VLDCr5Sao5RK5V+eBwxOvp4APKSUalZKfQKsAMYG0UenKKU+UEotMzgUm2ck0e8VSqmVSqkW4CESzxd5lFKvAZsyPp4AzEq+ngV809dOuYhSap1S6p3k6wbgA2AQMXpGAJVgW/JtafJHAUcDjyU/9/U5C1b4MzgbeD75ehDwWdqxmuRncSJOzxinZzHDAKXUOkgIJ7BzwP1xBREZCvwP8BYxfEYRKRaRRcB6EtaGj4EtaZNPX7+3sfbqEZEXSeRKyOQqpdRTyXOuIrHkfCB1mcH5od0BN/OMRpcZfBbaZ8xDnJ6lIBGRSuDvwE+VUltFQuFR6ypKqXZgVHIv8Qlgb6PT/OpPrIVfKfW1XMdF5EzgJOAY9aV7Uw0wJO20wcBab3ronHzPmIVIPWMe4vQsZqgTkV2UUutEZBcSM8jIIiKlJET/AaXU48mPY/WM6SiltojIKyT2NHqLSEly1u/r97ZgTT0iMh64HDhZKfVF2qGngdNFpJuIDAOGA/OD6KOHxOkZFwDDkx4SZSQ2rZ8OuE9e8jRwZvL1mUC2VV3okcTU/h7gA6XUjLRDsXlGABHZKeU1KCIVwNdI7Gf8C/hO8jR/n1MpVZA/JDY0PwMWJX/uSDt2FQkb3DLghKD76uAZv0ViRtxMIjjphbg9Y/JZTiThmfUxCRNX4H1y6bkeBNYBrcm/4xSgHwlPl4+S//YNup8Onm8cCfPGf9P+H54Yp2dMPuf+wLvJ51wM/F/y891JTLhWAI8C3fzqkw7g0mg0mgKjYE09Go1GU6ho4ddoNJoCQwu/RqPRFBha+DUajabA0MKv0Wg0BYYWfo1GoykwtPBrNBpNgfH/3muC0bx+Cf8AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3340189c18>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.1329 | test accuracy: 0.94\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33401d2e10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.0395 | test accuracy: 0.96\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33504f0160>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.0199 | test accuracy: 0.96\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3350563320>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.1077 | test accuracy: 0.97\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3340435668>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.0391 | test accuracy: 0.97\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33503aa240>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.0789 | test accuracy: 0.97\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33504d67b8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.1067 | test accuracy: 0.97\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33402252e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.0277 | test accuracy: 0.97\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f334ff549b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.0251 | test accuracy: 0.98\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJztnXl4VdW5/z9vThISkjAXoqCC1hEVriJ6r7NWFMfq72orVami1Kq1tQ51ukWrVK2tXFvr7cWhoHVCq5baegtqrVWLDBUVnIoICBJQhpBASHKS9fvjnAOH5Ax7PHvvc97P8+QxZw9rr+0J71rrHb5LjDEoiqIopUNZ0B1QFEVRCosafkVRlBJDDb+iKEqJoYZfURSlxFDDryiKUmKo4VcURSkx1PArBUNEfiMi/+XzM14VkYuTv39LRGZ53P4tIvI7L9vM8pxpInK7389RShM1/IoniMhfROQnGY6fISINIlJujLnUGHNbofpkjHnMGDOmUM+ziogcIyIrg+6HUrqo4Ve8YhpwvohIl+PnA48ZY+KF75JiBxEpD7oPSmFQw694xfNAP+DI1AER6QucCjyS/LzNfSEiA0TkBRHZKCLrReTvIlKWPGdE5Ktp7aTf1zd53xcisiH5+5BMHRKRb4vI68nfrxOR5rSfdhGZljzXW0QeEpHVIrJKRG4XkZiVlxaRp5MrmkYReU1EhqedO1lE3heRpmS714hIDfAisHNaX3bO84ys7ywiZ4vIgi7XXy0izyd/7yEiPxeRFSKyJuluq06eO0ZEVorIj0SkAfitlXdWoo+O8EXIhPgbDcAgm7eteaj88HqnzzTGtIjIDOAC4LXk4XOAD40x72S45WpgJfCV5OfDACv6IWUkDNQ5QAx4GLgP+Hqe/v0M+BmAiOwCvAXMSJ6eDqwBvgrUAC8AnwH/a6E/LwIXAW3AXcBjwMjkuYeAc4wxf08OgsOMMZtFZCzwO2NMxgErA7neeSbwvyKyrzHmg+T15wGp+MBdwO7JPrUDjwM/Bm5Inq8nMWDvhk4ESwb9oosTu0bf6T1dmQ6cnZpRkhgEpme5th3YCdjNGNNujPm7sSAcZYxZZ4z5vTFmizGmCZgMHG21g8m+PQ/ca4z5s4gMAsYCPzDGbDbGrAWmAN+00p4x5mFjTJMxphW4BRghIr3T3nE/EelljNlgjPmn1X52eUbWd04+9ykSxp7kimMo8ELS7XYJcJUxZn3y3p92ebdOYJIxptUY0+Kkf0r00Bl/EeBwhu85xpjXReQL4AwRmQscApyV5fK7SRjKWcmwwFRjzJ35niEiPUkY5pOAvsnDdSISM8Z0WOjmQ8BHxpi7kp93AyqA1WnhiTISM/58fYmRMMJnk1i5dCZPDQAagf8H3AzcKSLvAtcbY/5hoY9dn5PvnacDT4jIzSRiKjOMMa0iMhDoCSxIezchsWpI8YUxZqvdPinRRg2/hwThYkkSuNFP4xESM/29gVnGmDWZLkrOPq8Grk7OUv8qIvOMMS8DW0gYrBT1JNxCJO/ZGzjUGNMgIiOBt0kYtJyIyPXJe49IO/wZ0AoMcBCAHgecAXwNWAb0Bjak+mKMmUdiEKwAriDhWtoFay6tdHK+szFmjoi0kYivjEv+AHwJtADDjTGrsrSt8rwliLp6vMWRi2VC/A2T/GnwvEeF5xEShvASsrt5EJFTReSrSXfEJqAj+QOwEBgnIjEROYkdXTl1JIzZRhHpB0yy0qmkX/1K4OvpLg1jzGpgFvALEeklImUisoeIWHEf1ZEYNNaRGKh+mva8SknUEfQ2xrSnvSMk4gn901xCVp6T750fIeH3jxtjXk++WyfwADAlOftHRAaLyIkWn6sUKWr4w0WYZu6OMMYsA94kESSdmePSPYGXgGbgH8D9xphXk+e+D5wGbAS+RcInn+K/gWoSs9k5wP9Z7No3SLhjPkjLpvlN8twFQCXwPokZ+zMk4g/5eARYDqxK3juny/nzgWUisgm4lKQf3hjzIfAEsDSZ1ZQzqwdr7/wosH/yv+n8CFgCzEn24yUSqwelhBHdiMU7JsTf8OJ/5hqCHQC8cD0pBSYZtF4LHGSM+VfQ/VHCjc74w0fQs/6gn68447vAPDX6ihVcG34RqRKRuSLyjogsFpFbk8eHichbIvIvEXlKRCrddze85PLPN368gt9WH03D65nS2RXFHSKyjIR77OqAu6JEBC9m/K3AccaYESSKRE4SkcNIFI5MMcbsScJvOsGDZ4WZrDPlhZOnU3/UyGynC4IOPsWLMWaoMWY3Y8zbQfdFiQauDb9J0Jz8WJH8McBxJIJkkMjuyFlZWax8Mfd9quv7UTN4YKD9CMPgoyhKOPAkjz9ZyLKARMn7r4FPgI1pedErgcFZ7p0ITASoqak5eJ999vGiSwVnxJx7Mx5feMd0jnzwRuZee1+Be7Sd1OAjMWvj/KhRozTirygRYsGCBV8aY76S/8oEnhj+ZPXgSBHpAzwH7Jvpsiz3TgWmQsLgzJ8/34suFZwJ8Te6Hfvsz28y4OB9qOqfP1278eMV9N5rVz+6Znvwiep3oCiliogst3O9p1k9xpiNwKskBLf6yHaZ1yHA514+Kwqse+dfNPztbf5yyg/5/OV5zPvRr2lenjkGvHBy1lonV9gZfBRFKQ1cz/hF5CtAuzFmYzKX+GskArt/Bf4TeBIYD/zB7bOixsgbxjPyhvEAvHbRZPa66FRqd+ueIt/ZFufo6f5sTLVt8PnHe2xYtJTGj5Zz7OM/ydgPRVFKA9cFXCJyIIngbYzECmKGMeYnIrI7CaPfj4SuyHlJJcGsFJurJ2ykBp/6I0bkvO6h8sML1CNFUbxARBYYY0ZZvd71jN8Y8y7wbxmOLwVGu21f8Y6jHr4p6C4oihICtHJXURSlxFBZZhdcFZ/LJtqD7oZ/1NfDmoyqyrkZNAgaikFoVFGKE53xu6BQRj+wilsnRj91n0jip16DyIoSNtTwRwCrFbehDMqmDwLpPzogKEpgqOGPAHbkHnpR4WNPPMTpakJRFNeo4Q8BXgqoTSkf7XrmP3zpUoYvXeq6L4qihBM1/D6QMuRW8UJAretM3+nMv63D3p/EJOA/gGOAdx09UVGUQqNZPT5gx5BbEVD7/OV5OStuM83wp5R3L6Hweha/EJhLYp/Fz0jsX/hXT5+gKIofqOH3GLtKmFYE1HY+/pCscg/ZyJRqelgeDbi2jjL+ucr6BlwfAwcnf98F+JTE5gw9ulz3LnCg5VYVRfEbdfV4zMI7pnPgdedZutaqgNpRD9+UV2ahK05STStjnTS/YV16Yn8SinxtwDsktLc3ZLju+7Z7oiiKn6jh9xC7Sph21Duz4XUWz9q777Z87X7AOOAE4F5gOJBJEDyr+0dTPBUlENTV4yFdlTDzYVW9MxebaPdUIK7K5kY4lyV/FgF3klDqc4ymeCpKQVDD7yFdDbkdwiKgVjdmjK3rxwBxoD+JrdcURQk/6urxibAY8q7kqxlouOUWW+3NAl4BngaC3VVYURSrqOEvMfKlmpbV1PjyXM33V5TwoIY/wtit+E2lmuaSgBh0881edW8b6fn+j6JZPooSNGr4I4zdil8rqaa1h3sv9DYSeDH5+y7ADBL5/oqiBIMafp/wUn8nE1Zm7+mEadP1r9C9yEtRlMKhht8nvNDfydm+jUIxcFYz8OWAAVnPqc9eUaKLpnO6oBcVGStk7co22KWjtc327N1JzcDRb73F4j326HZcNXoUJdrojN8FmYTQwP5s3C6xHpWuKn6dSECkk02jR1GUaKAzfo+x40tva9pMZZ2z9Mmxs38JOK/4dcP+wC9JaPR8wHaNHhVcUJRooIbfY7rKNuSSU3Zq9NPxslDsniGn03vN+rzX7Qe8nvx9BNAA9M1zzyRgNlBJYtBQtU5FCQ41/B7jhf5OUFgx+pnIHgJOoDEBRQkX6uP3ETe+dL/TQQuJrZiAqnUqiu+o4Q8pfqeDFhKruv3dWLOmu3SzDgiK4hp19YQQL9NBH7rgu5lPPPGe67atkq7bvwfZdfsto/LNiuIKnfGHEN/SQZ/7IGHwbRp9L4q1LgP+BvwQOACXuv2KorjC9YxfRHYBHiGRzdcJTDXG3Csi/YCngKHAMuAcY4ylFX4p46u0wta47Vu8Csyqbr+ihAcvZvxx4GpjzL7AYcDlIrIfcD3wsjFmT+Dl5GclD3alFfwOAntVrKW6/YoSHlzP+I0xq4HVyd+bROQDYDBwBgnvAMB0EvG9H7l9XtjIJtvglPR0UCvkDQI/8R7vAxPZnntvhyCKtTTnX1H8xdPgrogMBf4NeAsYlBwUMMasFpGinOilZBu67nu78I7prH5lAWWV5WxYtJSaIQM57Y2pnj7bahA4veDKLp4HZvOgOf+K4j+eBXdFpBb4PfADY8wmG/dNFJH5IjL/iy++8Ko7gTPyhvGMnf1LTvzTPex8/CEcctflnj/Db02gFIUMzKoOkKL4jyczfhGpIGH0HzPGPJs8vEZEdkrO9ncC1ma61xgzFZgKMGrUKONFf8KGH/vvFlJfv5CBWaeupavic2273HpRkVVoT1GKGS+yegR4CPjAGHNP2qmZwHjgzuR//+D2Wcp28mkCWdXdscIsT1qxhlPXkpM4i5exGUWJEl64eg4HzgeOE5GFyZ+TSRj8E0TkXyT+Hd/pwbOKFrvZOZlcSemaQHaMfipPPyxozr+i+IsXWT2vA5Ll9PFu2y8V3Eg0uHElpQdTvcJtVo7m/CuKv6hkQwjwe8euXKQHU73Ai6ycQrqWFKUUUcmGEFCo7JxMpAuoeUHYsnKKSeVUUbxCZ/wBU8jsnEykB1NnAm57UaiCr651E9novdeuXNjyN4+frijRRg1/wNjZscsvLkv+LAIuB37noq1CF3wp7nks/iwtbLV9XzVVfKv8LB96pPiNGv6ACcOOXV4HU9MHkjvRrByv8dpQO2nLzX1K8Kjh94JnL+WhrY1MOOcKV804yc55qOKIbsfG2GzD62Cq31k5jYP6+dBqdFBDrbhFDb8XbG0EoFfLZjZVu99A3S1BZ8X4/fwfrpzp8xMUpbhRw+8hU/7425zn3a4IlMRsv/HjFTw74nzGzv5l1j2NrVwTFE5dNYriFWr4lcgwoT2hMbpw/G15i93CvGdxVIz+ZXXfZtjorwJw2LeO4MiLjgm0P4p3qOGPCM3LGwoe9A0LjYP67eDeOXr6f+W8PsiCuCCxYqgfjD8OWMvI6TO4H9e+fLPX3VRCgBp+n3h/ZSOXTZsPQGt7Jx83NHHBaRc6igF0tLWXrNEHe7pDkCiIO/LBG5l77X0+9Sic3N80zfK1VlYdmxo2cvdxt1HTv5Zz7j6PAUM1MbdYUMPvE/sN6c2rNyekimbMWcEri9dkjAFY8fvHKiuynxw0CNascdzPYiOIgrhi9dnfseRe6gbUsWjWu0yf+ABXz7ox6C4pHqGGvwD87o1lXHfqvv403pDcj1ey6eQVhlTOvpviLy8IoiDOC6MfRn963YA6APYfcyD7j9ENMIsJNfw+s66plQ8/b+LwvQYE3RVf2Z/gjT7YK4jrRY6VVIEJwp+e8vcXPfX1zlbFgwZtn1gVGWr4feapOSs4+9BdkIBn5MWE1VTNXAVxD5Uf7kfXHBNVf3r64BFaCQenrtAidqGq4feZx95YzoOXHBLY87tW0RbDjvdhTtV0ilt/+t8ffjVw91ALW22tIkI7UJQApZXvVmCWrm2mNd7BvoOzBxp7UeHY5WDlvlnAK8DThMPop3b7OgZ4N8s1uWScU6maNYPD8DbWuazu2znPp/vT16340nb7QRt9JxRjQDwq6IzfR3YfWMv820/Mec2UmQ9vk3ywRVVvOOs3DnsWDFY3ackl4xzVVM0+g7PrC21t3kpldSVlsTJWvruC2v61ttv/ctkXkXEPpaMrhGDQGX/QODH6bu4LEKubtGQzX0HvXeCGTQ0bs55b/f4qbj/0Zn527E944gfTOf/+CRmvu/v42/n7w69mPDd94gOO+pVvJRImbK8Q6usDz3YLKzrjt8Kzl0bS0IYNq5u0ZJNxzpSqecpr/0NZzJ7wcxDZPHcsuTfruWGj9+DH83+at41cWT9O3EOQeyXSlTCmnOakiIOzblHDbwU1+p7gdpOW9FRNu/Siginlox3d6wUpH74b7j97StaMHyfuIci9EumK3ZRTPwYKO5ITSnbU8BcLEangDWqTlk20F+hJ3Un34bvhqEuOz5rxk809lI9cK5Gu2E059bM2QQPD7lAff7HQ0ADGWPsJkDHAccCtwD2B9qRwpHz4bsmV8bPLiN0ctWlnJXLHknu59pX/2jYA5SM1UNx/9hS+XPaFo/4p/qAzfr8Z90T3Y4+fW/h+eMgkYDZQScJnb6eYP9smLVd99gc21fd327VQYtWHnw+nGT+56OzotLwSSU85ffzKaXmv91vr58H44+rycYjO+B3y/spGjrn9ZY65/WX+fdJs+n/n2fw3PXtp5I1+ekrmo8D3PWrXC6Pf+PEKflt9NA2vv+NBj8JF8/rmnBk/TrG6EtnavJXOjk7A+gDktjbBCk5cPlZqSYodnfE7JJP6Zl6KIEicLSWzR2A92k4xVvSmqO1Xy7Wv5N6HwAlWVyJVtVXbfh9y4K7c+MZPcl5vOg3GGMe1CX5lEFmtJSl21PB7wDmH7co5h+2a+WRqhl8VvdzzTFhNySw0pbr5SliRMuG2UTdRVVeFiNheqdgJDD8Wf9ayuyfME5dCooa/UBRotn/U8uWs6+jIec1iF+27Tcn0i6hW9BYzbuIadjKI7Lh7wjpxKTRq+IuMfEbfCwqdkplPjTPoit5qqjS90GPsBoYzST9cnOG6sE5cCo0nhl9EHgZOBdYaY/ZPHusHPAUMBZYB5xhjNnjxvGIi0xaN6/7X3yyFLwcMYMCXzoNtXRU/nWAni6f3XrtyYcvfsp4PYvOVdLzMKvFKIz9yVbZdsJtBZIegaknChFcz/mnAfcAjaceuB142xtwpItcnP//Io+cVDelBYoDGLf4XGh391lvdjv3t0EMtDwbZUjLt4GXqpp3NV8KOV6sHvzd28XNg8UK0LheWJy6DBnn63DDhieE3xrwmIkO7HD6DRMYUwHTgVaJo+J+9NO8lXs7ae/cMZleoo996i8W7777jwQgKXOXafCUK2F09ZFshWPWRX1w+ztEqw8+BZfX7q3j0soccB4bzYWniEnCho9/46eMfZIxZDWCMWS0iGQXURWQiMBFg112zZMYEiYWgrKPUTi9IzxjKIdHc0dTEigsvRCor6WxpYeA111B7eLh2oFK8xe/iKT93DHNS8Hb38bcD0XRrBUHgwV1jzFRgKsCoUaMiP8z6urF6NvIMTmU1NQx98kmkvJy2FStYeeWVaviLHD995OD/wGKXQu9XHHX8NPxrRGSn5Gx/J2Ctj88KBWHdWF3KyqAskd/e2dxM1T77BNyj3FjdU9dzvnssNK6zf1/v/vA/4SkD8ttHDs4Hlp8d+xMu//0PqennfZ+2bNjMlsYtnqw+il0Owk/DPxMYTyJwPh74g4/P8gaXuvv5NlYPIoMnRXtDAyuvvJK2Tz9l57vusnZTQIqfgVXgOjH6bu7zCb995G4Gluv++mNP+5JOz7419Oxb41l7xZyi61U65xMkArkDRGQlCTmMO4EZIjIBWAGc7cWzfMVlkdU3/n1X1jZm/2MJLBYAVNTXM2zGDNpWrmT5uHHUHXdc/psaGrb/XqBAr18VuEFsvhIUdn3kdjOJ/B5YvGLLoN70XGP/3/SWQcVRZZ8Lr7J6simPHZ/leFHSv7YH/WutFX8XMhbQ2dpKWY9Ev2K1tZTVOJgVFWj276QC96HyEMQrxmXQKA2ZC6gr1ST0dzK5M3Jl+nilNuo3j6+8P+guhJbAg7uliC+xgFSGz2GTu51q/fhj1kyeDGVlmHicQTdnCYTlUg6959juxza2wOXeefCCrsD1nJC5gLqSy3+t1cjFjRr+AMgXC0jhVUyg+oADGPrkk476mpM+1Z42F3QFbjaumL+K+etb6DDww70HcO7QPtZvzrQSSBHiFUFqUPCqkjiqZHr/Ygj6quEPgMfeWM6DlxyS97ogYwJB4KQCt1fDOhhi80E2s3fuGzUYgKb2Dkb+3xJ7hj8XIV8RhAknlcJ+VRcXw0pIDb9FvJp9L13bTGu8g30H23NnBFIfECC5KnAfqjhixwN2qywdGtym9k76VYZL2cWpSybl3w+Kcd/7FT0bN/PgI9dbut5JpbDfshVRRg2/RezOvt9f2ch+Q7ob990H1jL/9hNtPTuo+oB/nzS7oCmnYWfk//2LB0bbXV7kIZcrKJ0sbqGouhx6Nm7OeHxr89YdNn1J4aRS2M/q4qiju1Y44HdvLOO8I4bmvCaT0XdK/7oefPyLU/LGBAD+tuAOz5571di9OXv0LrkvCsEG7oXiw1P24sZ3GmhNbkFYUErELbT6/VUZj9vd6N3pPaWCzvhtEtbq3BQD2psd3de/ranbMVvupYCKvWwpKDqtzE1SVxGjrdPQYXGccxUUzkS21UGIg8R2GTZ6j4zHnVQKO60ujrqktRXU8NvEakZOkCyek1uhMlO8YnEGd46tAa6hoXBqnk5XGDmMvhUj/R+zP+F7e/WnZ7m1hXIqKOw7jeu6DwpFNBg4qRR2U11sJTaQL9sp7Jk/avhtYjUjJ8x03QMgG7YHOBuz/l4N6xxp8vtRgbto41YWN7YyZ8xXc2buzDvxq54/O4Xnq4OQuoaat1Zy63MncO3YUwC4mz/lvcdJpbCb6mIvYgNhz/xRw28Dpxk5UeW8w3ezd4MNiYcpu5yR/WSBYwY7V5dTWSa0d5rAMnesDDzFQG1Vm+17nFQKu6kuDpvyqB+o4beBk4wcLym0yJvtAS5d5O6xb1q/z+MKYLv0rYyxZ10le73wEZvjnd5n7ljAl4EnosVjQeO3pHUYUMMfIUJZ0JVL5sEqHlcA22V2QzOrWuIsOXVvGts7OPKlpZy0Uy09PBaKy0XBB56QuII2tVTSq9r+KsAvrMYGoh4AVsMfAo65/WXA3iy+1Aq6/MQAfStixMrEduaOFaz47+0OPJ7HBALitudP6Hbs7nPz+/39wmpsIOrFYWr4Q4DdWbyblNIg9wQIKyfU1/LE8o0cMfsTWjtNzswduwbXauD4uSMT8ZR+PcpZfMpenrQZBHZnwvFl79Dy6HVQVoaUlVM94Vf+dzIHVmMDUS8OU8OfTlVv15r8brA6i3eTUto1o6dxS7vtNrxm+GGT4ZM0VdGlS7f92j8W47XdbAaZbVImwrTD8hSq4czg+hE4ttrmYbOWuF4R2JWEsDsTLutTT+01zyDVdbS/M4udjoXNHRXUxIL/u8xF1APAavjTSd+w3OVuXGBvdm1nFu9lSmnvnuHeoGRdR0fQXdiGEyPuR+DYapterAiy5aJny2O3MhPe+fD0+EI50EZH23pWLqqkvE8dU1cdbLufV+06Z9vvU1Yclvf6xTd/3bJOUCaiHgBWw5+N9EEghc1App1grNVZfNhSSktJz8eJEc8UOD5jSC9X/RAR7hs1mPStaowx3f52gkhPdToTjlUatj5zG3xnhs899IbOjk5f9zT2GzX8HrB0bTO7D8z95edz41idxQedUtqVq8buHY7sogLgJPvn6IE1jNkpMTu04r93SqYJg50B6qjly22trq7aNfNxNzPhvWdHw+gD3H7ozaHfejIXavg9IJ/Rz+fG8WsW70Ugd11TK/9+y0t89POTMxqXXAOa1ecvGzeOzpYWBl5zDbWHh2AbxSw4yf4pZEpoV+wMUF641NzIJPhNR1MTKy68EKmspLOlBYZD9cZmWvo462MUtp7MhRr+ApDPjePXLN6LvP98fc81oFl9/tDHH6dtxQpWXnllqA2/neyfMOBXemo2gtyEPeXXT/f1pxOrq2PYM89s+zwcYFPyJ0n5+i+pG3ssrZ2G84f24cq9B7iKA4QZNfwFIAz6Pk7z/vP13Wp2Ub7ndzY3U7XPPrb7V0isZv8A8MR7if+ee4B/HcpDoQcoq6mQzW+84fkAv/TMMxl4zTVsHux8g5l4vwG8fkJmddBiQw2/z4QhGOs0799K363o+eR7/qfnnEPbp5+y81132eqfkhurRmzvpzexdqtNwbwsPn4rrL37bs8N/5B772XllVcy9fDnPW23WFHD7zNhCMY6zfu30ncrA1q+5w+bMYO2lStZPm4cdccdZ6uPUcRKEVghK3PXbi2sKJ4fKzuvVozDb+8ycKzY/mvPsja+M+Sfrp8RBtTw2yHgAq+czPoQ2jIH6C4D6BmDFxZ3P1kZgzH+ulisuLpitbWU1dT42g969w9co8ZqEVhYK3O9oG7MGM/bXD5+vO8rxi2dlb62X0jU8NshPbffC3EyL8li9H27zyJW3EXLxo3DxOMMutln7ROrapRW98HNwbtAplasFoEFLRPtJw233JJ1Zdc1+8Zqptew557LuWJ02q5Tgt7MPh9q+J0S5tl/iLDiLhr6eO7djKJGR0s73wcyDTNWi8CClonOh1NVzc3tsZwru7KaGoY++SRSXm4r06tyyBD2fO01z9vtyvcOupPdn+8eR1i8++622woSNfxO6VrZG7YVgOIvqaydJDNIzPJvB2LAp0Ar0KPLbVaLwIKWic4knrbzKb2IVSbiAQ99cZDltlree481kydDWVnelZ2UlUFZ4j29zPTq2u5ujz7qqB2n94UNNfxeoSsA3/lw+c/p6Nhs655YrIZ9drsGsF6dmiESkpf9gV8CbcAHwEpgA1Df5TqrRWB+ykQDiXhHDrqKp2199qfEzrzD0aOqDziAoU8+afn69oYGVl55peeZXunt7j1vnqM2YnV1nvUnSHyfQojISSLykYgsEZHirIaAxApg3BOJASBEXDF/FYfNWsIhf1nCE8s22r7//tn/4qYZ7/rQM/vYNfpd7/FT8G0/YBxwAnAviQKhTEK9J9TX0onhiNmf5Ny83co1mVi0cSvHvpxQN21q72CPP36U+cI88Y6yPoOQ6qSRi1UiscLNESvq6xk2YwbDnnuOhltu8aXdUsfXb1NEYsCvSfx7WAnME5GZxpj3/XxuoHgg7pYNuxIMXui2h6H4LCrzefHTAAAd1UlEQVRclvxZBNxJwuXTFatFYJYLxbrgtQy0ad3M1mduo+clv3bVjlU6W1sp65FwkHmZ6dW13VLH72F8NLDEGLMUQESeBM4Aitfw+4hdCQbLRmDcExkHp0IUn31ZUTz/CMcAcaA/idlOEHgpA23i7Wy+7yJ6nHYVscH7AP6nwqaMM0CsTx/2ePFFT9pt/fjjHeIMw2ZERxDOD/w2/IOBz9I+rwQO9fmZ4cMH/78VCQa3RsDL4rOjDrqedZX2/aP9Y9FJZZwVdAfwbv9g09nJlt9MpOLgU6g8+NSM1xQ6RdIKHU1NGf3wduMMxY7fhj9TqeYOYSoRmQhMBNh1Vxd14GHGY/ePVQmGMGwinuK1f965/UNV78z/Txyw+L3P+e+fv0S8vZP9D9yZq6+3XhyUzXB9WduHAc324yFhwKv9g9vn/5H2d2bRuWktbW8+RWzIfnD4jiE6r1IkvcSNayiMA5lf+G34VwLpzsohwOfpFxhjpgJTAUaNGlXY2vGIYlWCwe9NxIGEmygg2tviTLn7Je69/xvU1HZNnMxPNsN19PXTdrguY472E/a3vSwEXiiIDjpkPbHDj4Crcgf1/Uq9dIOUOZ/UhHEg8wu/Df88YE8RGQasAr5JIvmhdKmvhzUW5ZEf+2bmwxYDrn7LCB910PWsS9sf1ype7aO78O2V9OxZyXVX/Z6WLW1c/oNjOfgQ6+0WxHC1tEN14ba3tBo8Hr7D97ZjamcqV98KfqVeBkEYBzK/8NXwG2PiInIF8BcSSQ4PG2OcpEkXD1aNfhbsBFxtyQg7wInPHnZMq0woQ1ozNK93mXx9saaJjz9s4Jk/XsrmzW1cfP50Zs66wpYYne+G6/kPd/ycRaZ5fWucc9/8jBeOHsoXW+Oc8fflzDvxq973x2NSKZJei+wF5XYppoEsF74n5xpj/gz82e/nlAq+qX0GVIDmRhmyd59qRhy0C7V1VdTWVdGnb0/Wr9tM/wHWM4X8Mlx28WNTdr/xK/USnLtd0vvkBCd/D1FKQEihlbtKAifBVgduHi85YORgfjXlFeLxDlq3xlm/bjN9+va0fL8rwzVokP3VW1X2f25hCsRbpWuKZDYpBiezd6dul659+sr3v295pWD17yFqujyZUMOv0PdR6zP9gVXCR2f38rE31unVq5pxFxzKheOmEY93ctV1JxCzYSitGq6MNDQk/ttFydOpy6YggfgslFV00tluf4CxmiLpdPbuxO3iJm3T1d9DxFDDHxImAbOBShKaL+6Fgf0hn2vGC99sJoGw2MChGa89/cwRnH7mCFvtp/Ajt9upy6aQ+/l+Wbtj9Xb96A07fG8c7q1aqtPZe6HdcKWU66+GPwQsBOYCb5KodruAzJK+OamMOdLWX1PZz/Y9ufAiJS6TQFjNpVM97adfOHXZeBaIfzy/rtLRGVx06d+bH9idvfsZP1BCZvj77/Ynxn4jbuuevr3h8amheg3bfAwcnPx9F9IkfTe2QJ9qa40kd9FaY3qxT+fPdji14fzuGUB23Dt28CIlrqzPoO0fCiwQ5pYgXTb5FDdzkf69+YHd2XspuV2CINB/UWO/EW8Atv0rLyu3/4e7oZEdBosoDgRZJX0v/8O2a/o+Ep1KUiuzO6sDT8X+x1Cx/zEe9s5ffHfZWJjROyX1vXmtY+Nk9l5KbpcgCNpCDsp/iT02RFASP13Sdw+yS/r6jR3fei7CkiIZBH7XTvhJ6nvzGp29h4+gDb8vRHEFYEXS12+88K1bmd01zO3rqH/r2nrSv3KLrXtise3P7x+L2dbkj2KOthOs5r+nX9excSPLzj03r4JmULN3P4rAiuXvIfwW0SVRWQHkk/QduHENa/vYXyANrLJexeqFb93K7M5J6iDAGfMm7vA5U+wiF17IRASKCx9+PtK/t1x7IEdp9m410aAY8vLtEnrDP+9vJ7Np49sM3fN77LHfjUF3xzfySfp+dOXeWbV7AE/F0uxuvtEwt2+aMT+Gyu8ds+3cJmDTG551LbTsfdzzrO1h3zAPbF3HR6983drFeXbNcoPVWXmUfO+lpL1jl9Ab/kOOVrWHQtJ98438OJ3BFxNOjL6b+9yy42CdmZ0P93/jFb8pFe0du4Te8HtBrhTRqMQACoGVzTeKATvCcOkEWrXssZunVAbrUk40yEXJW7yoxABy4tEG75k23+h5wd2etJ0Lp9lEuVJCcxlpp8Jw6fdtGzzG/t1RWzvgY4pmKaNFYNkpecMfKXze9KRy9BlUjj7D12dkwo9K3bVbDXs/vcm3GbobVVGlMEQpEF1oQm/4CxHcHfuNeOFcPk5UHVP3hYxc6ZF2ZvF+Vequ3Wp8q1COMv1jsR23wUvS9TvjhWm22544eAE1sXbb923uqGDqqoPzX5iBbH+HUQpEF5rQG/4DDpnKl2teprVlla/P6eryGTcx7sgNlHcASak6FgGp9Mi+b3T/H+VkFm83m6hYSA1OhYohvLbbbvQl/3cW39hEeR97m+04Mfq57rO6W9vwgCXCo0boDX9Vz2A2pHDq+y+KmIEH2J3FO8km8hKvqpbdELT7qOt3tuwHP6PnJfd3u86vbJ9SzKcPitAbfiXaWJnFhyGbyG2cIQwDh1eU6sqrlFDD7wPjJsZDnyI6sEocpzRaxeos3kk2kdeGNn22WzFiDBUjxti8P7pS0ukEvfJSCkO4rZNHWA0Q25WEzkYU3D1++5LtzOKdZBPZNbRNk8dSd1NuTRk3RFlKOoWfK6/L6r7NsNGJncgO+9YRHHnRMZ62r9gj9H+di+Z9h43r5tDZ2Urj+gUcdMTvbbdRqABxKdHv0UZyrRf8rgmwa2j9NPrpuHWTFDrQm46f31mfwf249mX/0imdCPCl7itFQm/49z/kf123EVSAuJjJ5yQqVE1AmPzRXrpJggj0+vmdbWrYyN3H3UZN/1rOufs8Bgz1Vng88gJ8BSZow78GHzT5ldLAjqE1rZuRHv5VboYhQF0o0rNvrKZR3rHkXuoG1LFo1rtMn/gAV88qXsHFKBCoYMeLT5XXB/n8TMz728m8/Ied+OT9n+a8Lt95xV/sGtrN913U7Vh82Ts03XYiTZPH0nzHaXSsXZa3nb6PNmYsCku5SdrefIqmn57ClkeutfQeUceqq6RuQKIeYP8xB7JuxZd+dkmxQNAz/tCh8YBoYNcfXXv1U92OlQ8dQe3NLyJS5joTJyi5i6Dp6mJ5MD6n2zVbm7dSWV1JWayMle+uoLZ/baG6p2ShJAy/nQBxVc8hDBk2Pm+bxbw3QBTwytCKJBe9Ec3EiQKr31/Fo5c9RFVdFSLC+fdPCLpLJU9J/KV7ESBWipcwBYiLkWGj9+DH89U1GiZKQ5RbKTjti15lywOXZTxnd8tEvynFgiU7hXhe3KeEi5KY8SuFxekM2g/Zg6bbTszbnpUAcde+1d7wR1f9CprANpRRQoErwy8iZwO3APsCo40x89PO3QBMADqAK40xf3HzLCUauMll90P2wEp7PY65wHbfOrc0UtYzXCsXxXt+Xg+bHaio1wyCa0IsxOvW1bMIOAt4Lf2giOwHfBMYDpwE3C8ikS2Rs5riWeq4zWUv6zMIqU7KAHsUbPWqvfS+VYwYo0a/RHBi9N3cVyhc/csyxnwAINLN73cG8KQxphX4VESWAKOBf7h5XlBoiqc1vCr59zrYqsHbwlFNFS1sdXSfUjj88vEPBtITelcmjwVCejpnba/htvV+VPLBGl6kWHqtDqlqk4XlW+VnBd0FxQJ5Db+IvARkqrC9yRjzh2y3ZTiWTXzEd9kGTeeMBl7LHpSSjIKi2CGv4TfGfM1BuyuBXdI+D4GM23zuINswatQoM3/+/EyXeSaZrIQXr9UhrbbXfMdpkd44RSk8bzONBUxFEMbyK3bmoKC7ZAu/XD0zgcdF5B5gZ2BPYK5PzyoIVqp5U3QPeShWsOoqar7jNEvplFbb63Hy9/JmEBXTDluW+e6x0Ohgm8Xe/eF//up9fwhHlk0LG3iLX3Ixc2hiFc9yPhN43ZvGC4TbdM4zgV8BXwH+JCILjTEnGmMWi8gM4H0gDlxujLEvlh1RjNlxhZJ3A3bFFj1O/p63DVrI+CmWHbZs4cTou7nPAm6ybG7NMiGzOyhU05dL+ScAfRmW1ejfKuFN63Sb1fMc8FyWc5OByW7aLxaisCOXXYT8mvzZ7nNNrNKLVrZhJeOn0DtsaYVs4cg1KHjRdhjRaajiiPUByi7Ehh7oaXt2Mn4KkRoaxO5bSmmhhl/xlb2f3uT5blJltf08bc9qxo+fqaFh0y9Sihs1/A6wunl72LkqPpdNtNu+b2tLjD8/v68PPcqPaWtBKqs9a6+zeQObf3le3gwiP1NDo+TWuWL+Kuavb6HDwA/3HsC5Q/vYb8Rm0Pjnz73C5q0Dcl4T9SybQqOG3wHFUsnrxOgDVFV3cNa5i/Je5+UA0Tb3D2x54DJiw0ZSd+OfPGkToKy2r6X2nKSaFtssftHGrSxubGXOmK/S1N7ByP9b4szw2wz+5jP6QWbZRHXAibThT1XkHnHSOwV9rlbyWqOq2rtErlypmYVItSzVHbbSif30LO7rVc3i5OeZd7Ht924svXX7fbEa9tntGt/6tZK32I0jKaeSvgyjjWbitFJOD9+eCdYHnFtjQKf99v3MCIq04deKXAVKNNUyADp7OXOxdXRs9rgnO9LCeqrou+1zFb1pYT117OTrcy0POA6MPvibERRpw69EDz9m54VOtVTsszhtBcDt/y/ntbGmrexzl3V3XjX92MrGbZ+30khtRpUZbwlqwPEC/ReiZKTx4xU8O+J8xs7+JfVHjPCsXT9n56rCmZ8Pl//c8gw89qNT6KgrvGpmR10VTyzbaDl+MIRDeYWb6aCdJlZTSS3iTcVITjINONV4m3HmF2r4HWBn8/aosnDydOqPGpn3OrsDhF+zc1XhtIYdt4tVo7/4vc/575+/RLy9k/0P3Jmrrx9jq0973fkCFc2tOxwbnmb0J52buW6juaU/v3j+r1TTl0O4jN9yNIJwEvfaen4mrARtMw04duIKQQaG1fA7oNhjC1/MfZ/q+n5ILP8+PVYHiK7km50PrBLL+f92Uy1N62aa7zidnpf8WgcJl7S3xZly90vce/83qKl1FkztavStUlu9PTvoIC7iIC5y1E5XrAZt3Qw4Qev9RGaz9b7FlRkXahbeMZ0Drzsv73WpAaJm8EBb7VuZnX90di82nN/bUkpkKtWy7c2naPrpKWx55FpXz7b6Ds2/+AZtC15w3EYxsPDtlfTsWcl1V/2ei741jQXzlgfdpZy8zTQe5D94iMP5PKm305WUFk8qaJvLIB/ERVzMm0zgDQYzynI/sgWGC0VkZvz5RM5UttkbPvvzmww4eB+q+uc3uAvvmM6RD97I3Gvvs9y+H4VQVlMtvXq26vxv54s1TXz8YQPP/PFSNm9u4+LzpzNz1hWZduXrxi/unGXbLeSGoGfZO/Yl2MBwZAy/UhjWvfMvGv72Nn/5x3tsWLSUxo+Wc+zjP6F2tx2zJOwMEOl4rbkfxLODfIew0btPNSMO2oXauipq66ro07cn69dtpv+A2rz3FtLoQ3D5/pmeEXRgWA2/sgMjbxjPyBsSew+8dtFk9rro1G5GH6wPEF0JshDKq2cXezFXKlj7wPQL8l57wMjB/GrKK8TjHbRujbN+3Wb69O1p6TkL5i3n4EN2y3reE3mINIKaZWd6RnpgWCjjuyzMeG8u1VA3BV5Fb/jDoqszbmI8cpr8Rz18U9ZzVgcIJVqkB2ut0KtXNeMuOJQLx00jHu/kqutOIGYhKQDglhtnMnPWFRnPeSYPkUZQs+xMz6imL98h826DVnFT4BUtS+SAsOjqbGjMHIcohk1acg0QSrRID9b++oFxlu45/cwRnH5m7lReY0w3v3/KLZSJnavLqSwT2jsNTe2d9KuMWXuBHDhNv3SbdumnKym1IrgFc/Ctkn1f80lmx4q2aFscC1jV1QlqZVCMm7Qo0SU9WOslmfz+udxCfStj7FlXyV4vfMTmeCcPjHavj+Uk/TJMAWEXDEobFNZMMtQXjeHv29udEQ3LykCxjom3s/ne86g85vySz67xivRgrZdkMvC53EKzG5pZ1RJnyal709jewZEvLeWknWrpYdGNlA27+f5BBYR9ZBBEKI8/H27dJaq4GR6s6NOHPaUyShr76RwwcjDLP11HPO7tFtmZDPzXTswu2W2AvhUxYmVCXUWMtk5Dh7f7+VgiW0DYb6zUG7ihaGb8in16UeFYk98Ofmwl6EdKpensZMv9E6i54reu2omyDn96sPbRGRMC68cJ9bU8sXwjR8z+hNZOw/f26k/P8sLPU4MICBfCvVT0hr8UdHWcMqV8tOVrnezW1YsK34ygHymVqcGk1LESrPWbMhGmHbZL3usmZVkFeLV5uls9HifYdS85CT4XveEvdl2dQmFnkIgqxZ6fr9jHDwG4fNipN3C6Oih6w2+V9JVBlPfRVYIlqr79YqNmkHcbmXgpAGcFO+4lp8FnNfxJdGWguMGPOIbinEwVrV65f/zGjnvJaTWyGn6HeJn3n0tgrhgKvIqNKAdvY7Ea37dCVDJj1Rdvx73kNPisFsUhhcr71wIve9r8Sm66bnpuZ0euTGza1MLE8Y/yu6cnUF7urLq2vbaHM03+3v0dPS8I7PrirbqXnAafi8rwuy3isoPm/RcOP1woez+9ydFgUmw+/PSBYId9cS3iRfrnx9dnrsMYvvskR+2FEb8KwdI1f/qwq+W0z6Iy/F1dIuMmxnXGrGRE/fHeYSX90+32jFEnaP39rrgy/CJyN3Aa0AZ8AlxojNmYPHcDMAHoAK40xvzFZV9tk803HsYB4ZP3f6rZREpR4sX2jF7gZaaPXYLW3++K2xn/bOAGY0xcRO4CbgB+JCL7Ad8EhgM7Ay+JyF7GGG/rwB3SdUDwc/cuq0HgXOc0+KtEmXTFz5YtbVz+g2Nz6vD7hV3tei+zgIIoBMuFK4thjEkvc5wD/Gfy9zOAJ40xrcCnIrIEGA38w83zwoTVimC/g8BhW7koSlfcbM9YLARRCJYLL6eKFwFPJX8fTGIgSLEyeaxosJr3r0FgpdRxsz1jMeFXIZgTyYa8qkci8pKILMrwc0baNTcBceCx1KEMTWVMoRCRiSIyX0Tmf/HFF3k77Ad9o5uWrSi+svi9z7lk/CNcOG4av7jTmY5RuuLn5uZWW9szKrlJpYl+m1c5i9/xIldaui/vjN8Y87Vc50VkPHAqcLwxJmXcVwLpCktDgM+ztD8VmAowatSoQJK1033kfvr7rRCWrSIVxaugrJvtGYPESTC40AFkB2mia8B9Vs9JwI+Ao40xW9JOzQQeF5F7SAR39wTmunlWqaAbwihhwWpQ9pLxj+TdmN2J4mfXuoJYrKZbAZqfON3IvJDSEHbSRCeZ7Z4Ytz7++4AewOxkoGaOMeZSY8xiEZkBvE/CBXR5WDJ6Co1dWWiNCShhwWpQ9r9/bW1jdreo1ER3ApFsMMZ8Nce5ycBkN+2Hhfb2RioqnAUCVPxNiSpWg7J2Nmb3E6fyE4VeSWRjC+t4gtNtbeiukg0+4tToK0qUOWDkYH415RXi8Q5at8azBmXvuuesgvUpk6xEynA7XREEvZJoYQPTOZ6LmcNZ/M7Wjls20kR3iDyo4VcUJSNWg7Jeb8xul6ANt1vc6vhYSROdZKhP/6yGP2ToVpFKmLASlI3HOxwrcyrB6Pio4Q8ZGhNQokY2Zc4/z3yPk08/IIAe2ceqMqkf8YAgdHzU8CuK4opscsyFNPq5DLeXyqB+uJWC0PFRw9+FQmr6K4qSHWNMt9TRX9w5y5bhDkIZ1G4RVxA6Pmr4u5BJ6TKMMs6KElWszsAzpY7ana0HoQyaXvhltZjLSoDWRVVwt7vU8Fvg8anlgUs5KIrfFGI/Xjsz8Ewibu3tHVRUWA8k21EGDftmMbkqiUVkgTFmlNW21PArigJ03483hZMtGbORPgN3UvRVURHL6ALKhtUitNSAdPqHC2lbVwdvwTMPZG7zmQzHagY5l3gIgvArJSk5UWVRJYykVD27kpqBuyn6sqPjb1UZNDUgta2rc9SnoHb2corO+C0SVND3xaf0K1KCxa4LKN2d05X0GXghsFqElhqQjipIr4JHrYpFcm1vqP5/pZjJlbeeyQ2Uy52TLgNRqKIvK0VoqQGJzwrSJdvUDPK2PXX1eIC6WxRlO7ncOekz8Ex4sfGLE1IuIa/xwmBPMt7HD3TG7wFWNjt3khKqA4oSRfK5c7LNwIPIuU+RGpDasixunGxvCDsa7J/XO9vYxQ/U8BcIK4ODohQDTt05dnLu/Ui9PP3METyTwfCntje8mDk0scqWemY6Ycr6UWukKIqnpLtzssk5ZMJqzn2hVwZu1TPDiPr4FUXxnNPPHGHL6MOOLqJB9b225dx3JX1lcNG3prFg3nKvup2RbOqZUUYNv6IoocBqzn168PiOX5zFLTfOxBiTsc3mpq2sadjEaSf8Kus1+QhCPdNv1NWjKEoosJpzb7UaF7B0TT6CUM/0GzX8iqKEBis591a3hAQsXZOPINQz/UYNv6IokcLqygDIe83w3Sft8DmTDg9YU8+MEmr4FUVxjFNFz02NLfTqXe34uVZWBpB9k5hSRw2/oiiOsSvn8OeZ7/HLe17mmT9e6me3lDxoVo+iKAWj0CJtSmbU8CuK4guxWE23Y+kpm52dztIrFfeEytWzYMGCL0XE32oM5wwAvgy6Ez6g7xUtIvVeiz655eD0z10DsxO+cwRfO3HfoLqHiCxI//xfg1oPjq2ptN1Ox6A2RHosyHCqUN+Xrf0kxWlRQ6khIvPtbG0WFfS9okXU3mvx0lvDbGDWDN99Un36gQnxNxoAJ9Joax4qP7y+68Gwfl+hmvEriqJ4wfDdJ1nfpiuNTMa7GFEfv6IoSomhht86U4PugE/oe0WLYn2vYiWU35cafosYY0L5BbpF3ytaFOt7ZSLprnGyjXlotj4P6/elPn5FUfxkDQ6DpQBdg6+KN2hWj6IoSomhrp48iMjdIvKhiLwrIs+JSJ+0czeIyBIR+UhETgyyn3YRkbNFZLGIdIrIqC7nIvteACJyUrLvS0Tk+qD74xQReVhE1orIorRj/URktoj8K/nfvrnaCBsisouI/FVEPkj+/X0/eTzq71UlInNF5J3ke92aPD5MRN5KvtdTImK/SMAH1PDnZzawvzHmQOBj4AYAEdkP+CYwHDgJuF9ErG8wGjyLgLOA19IPRv29kn39NTAW2A84N/lOUWQaie8gneuBl40xewIvJz9HiThwtTFmX+Aw4PLk9xP192oFjjPGjABGAieJyGHAXcCU5HttAEKhGqeGPw/GmFnGmHjy4xxgSPL3M4AnjTGtxphPgSXA6CD66ARjzAfGmI8ynIr0e5Ho6xJjzFJjTBvwJIl3ihzGmNeg2x5/ZwDTk79PB75e0E65xBiz2hjzz+TvTcAHwGCi/17GGNOc/FiR/DHAcWxXew7Ne6nht8dFwIvJ3wcDn6WdW5k8FnWi/l5R738+BhljVkPCiAIDA+6PY0RkKPBvwFsUwXuJSExEFgJrSXgKPgE2pk0cQ/O3qFk9gIi8BGTKHrjJGPOH5DU3kVimPpa6LcP1oYqUW3mvTLdlOBaq98pD1PtfEohILfB74AfGmE0ijgptQ4UxpgMYmYwDPgdkEiEKxd+iGn7AGPO1XOdFZDxwKnC82Z4GtRLYJe2yIcDn/vTQGfneKwuhf688RL3/+VgjIjsZY1aLyE4kZpeRQkQqSBj9x4wxzyYPR/69UhhjNorIqyRiGH1EpDw56w/N36K6evIgIicBPwJON8ZsSTs1E/imiPQQkWHAnsDcIProMVF/r3nAnslsikoSgeqZAffJS2YC45O/jweyrdxCiSSm9g8BHxhj7kk7FfX3+koq409EqoGvkYhf/BX4z+Rl4XkvY4z+5PghEdz8DFiY/PlN2rmbSPjxPgLGBt1Xm+91JonZcSuJYpm/FMN7Jft/MokMrE9IuLUC75PD93gCWA20J7+rCUB/Elkv/0r+t1/Q/bT5TkeQcHe8m/Zv6uQieK8DgbeT77UI+HHy+O4kJk5LgKeBHkH31RijBVyKoiilhrp6FEVRSgw1/IqiKCWGGn5FUZQSQw2/oihKiaGGX1EUpcRQw68oilJiqOFXFEUpMf4/XVWmXA3CFnwAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33504c8470>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# following function (plot_with_labels) is for visualization, can be ignored if not interested\\n\",\n    \"from matplotlib import cm\\n\",\n    \"try: from sklearn.manifold import TSNE; HAS_SK = True\\n\",\n    \"except: HAS_SK = False; print('Please install sklearn for layer visualization')\\n\",\n    \"def plot_with_labels(lowDWeights, labels):\\n\",\n    \"    plt.cla()\\n\",\n    \"    X, Y = lowDWeights[:, 0], lowDWeights[:, 1]\\n\",\n    \"    for x, y, s in zip(X, Y, labels):\\n\",\n    \"        c = cm.rainbow(int(255 * s / 9)); plt.text(x, y, s, backgroundcolor=c, fontsize=9)\\n\",\n    \"    plt.xlim(X.min(), X.max()); plt.ylim(Y.min(), Y.max()); plt.title('Visualize last layer'); plt.show(); plt.pause(0.01)\\n\",\n    \"\\n\",\n    \"plt.ion()\\n\",\n    \"# training and testing\\n\",\n    \"for epoch in range(EPOCH):\\n\",\n    \"    for step, (x, y) in enumerate(train_loader):   # gives batch data, normalize x when iterate train_loader\\n\",\n    \"        b_x = Variable(x)   # batch x\\n\",\n    \"        b_y = Variable(y)   # batch y\\n\",\n    \"\\n\",\n    \"        output = cnn(b_x)[0]               # cnn output\\n\",\n    \"        loss = loss_func(output, b_y)   # cross entropy loss\\n\",\n    \"        optimizer.zero_grad()           # clear gradients for this training step\\n\",\n    \"        loss.backward()                 # backpropagation, compute gradients\\n\",\n    \"        optimizer.step()                # apply gradients\\n\",\n    \"\\n\",\n    \"        if step % 100 == 0:\\n\",\n    \"            test_output, last_layer = cnn(test_x)\\n\",\n    \"            pred_y = torch.max(test_output, 1)[1].data.squeeze()\\n\",\n    \"            accuracy = (pred_y == test_y).sum().item() / float(test_y.size(0))\\n\",\n    \"            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)\\n\",\n    \"            if HAS_SK:\\n\",\n    \"                # Visualization of trained flatten layer (T-SNE)\\n\",\n    \"                tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\\n\",\n    \"                plot_only = 500\\n\",\n    \"                low_dim_embs = tsne.fit_transform(last_layer.data.numpy()[:plot_only, :])\\n\",\n    \"                labels = test_y.numpy()[:plot_only]\\n\",\n    \"                plot_with_labels(low_dim_embs, labels)\\n\",\n    \"plt.ioff()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[7 2 1 0 4 1 4 9 5 9] prediction number\\n\",\n      \"[7 2 1 0 4 1 4 9 5 9] real number\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# print 10 predictions from test data\\n\",\n    \"test_output, _ = cnn(test_x[:10])\\n\",\n    \"pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()\\n\",\n    \"print(pred_y, 'prediction number')\\n\",\n    \"print(test_y[:10].numpy(), 'real number')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"tensor\",\n   \"language\": \"python\",\n   \"name\": \"tensor\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/.ipynb_checkpoints/406_GAN-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 406 GAN\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* numpy\\n\",\n    \"* matplotlib\\n\",\n    \"\\n\",\n    \"Note: Below cells only work for pytorch version lower than 1.5, if you are using pytorch 1.5 or higher, then you will get in place error when you run For loop for Step(10000). Fixed version has been added below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1)    # reproducible\\n\",\n    \"np.random.seed(1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Hyper Parameters\\n\",\n    \"BATCH_SIZE = 64\\n\",\n    \"LR_G = 0.0001           # learning rate for generator\\n\",\n    \"LR_D = 0.0001           # learning rate for discriminator\\n\",\n    \"N_IDEAS = 5             # think of this as number of ideas for generating an art work (Generator)\\n\",\n    \"ART_COMPONENTS = 15     # it could be total point G can draw in the canvas\\n\",\n    \"PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8lOW5+P/PlT0QCEvCDknY9zUgLiCgImoPuFBF6966\\ntLan7bft91tfPVaPv55fe2p72tPq97Ral1qtYl2pdasiRSyKRPZ9CxAMW8JO9tzfP65nmEnIRjKT\\n2a736zUv5nnmmZmLJ5Mr99zPfV+3OOcwxhgTWxLCHYAxxpjgs+RujDExyJK7McbEIEvuxhgTgyy5\\nG2NMDLLkbowxMciSuzHGxCBL7sYYE4MsuRtjTAxKCtcbZ2Vludzc3HC9vTHGRKWCgoLDzrns5o4L\\nW3LPzc1l5cqV4Xp7Y4yJSiKyuyXHWbeMMcbEIEvuxhgTgyy5G2NMDApbn7sxJvJUVVVRVFREeXl5\\nuEOJe2lpafTr14/k5ORWPd+SuzHmjKKiIjp16kRubi4iEu5w4pZzjpKSEoqKisjLy2vVazTbLSMi\\naSKyQkTWiMgGEfn3Bo5JFZGFIrJdRD4VkdxWRWOMCavy8nK6d+9uiT3MRITu3bu36RtUS/rcK4BZ\\nzrlxwHhgjohMrXfMV4EjzrnBwK+A/2x1RM0oq4Y3tsDh06F6B2PimyX20Kl1UFoG1bXNH9vWn0Oz\\nyd2pk95msnervzbfPOCP3v2XgUskBJ+QrSXwi+WwrAhe2wK2QqAxJpqcqIBTVbD/JJysDO17tWi0\\njIgkishq4CDwd+fcp/UO6QvsBXDOVQPHgO4NvM7dIrJSRFYeOnTonINNT4YT3gnZWgprDp7zSxhj\\nTJNyc3M5fPhw0F+3qsafvxzga/0uWbKEL33pS0F/vxYld+dcjXNuPNAPmCIio1vzZs65x51z+c65\\n/OzsZmfPnqV/Z7ign3/7ja1QVtWaSIwxRi9c1ta2oI+kze8DR8r9XR6pidChdYNgWuycxrk7544C\\nHwJz6j20D+gPICJJQCZQEowA65szCDqn6v2TlfD2jlC8izEmHAoLCxk92t92/MUvfsFDDz0EwIwZ\\nM/j2t7/N+PHjGT16NCtWrADgoYce4pZbbuH8889nyJAhPPHEE2ee/8gjjzB58mTGjh3Lgw8+eOY9\\nhg0bxq233sro0aPZu3fvWXH8/Oc/Z8yYMUyZMoXt27efed6sWbMYO3Ysl1xyCXv27AHg9ttv5+WX\\nXz7z3IyMDEBb5DNmzGD+/PkMGz6ce+74Cs45BFix5B1GjBjOxIkTefXVV4N3AgM0OxRSRLKBKufc\\nURFJBy7j7Aumi4DbgOXAfGCxc6HpEU9LgnlD4E/rdfuTfZDfGwZkhuLdjIlfP/ggdK/9yCWte97p\\n06dZvXo1S5cu5c4772T9ek0Ea9eu5ZNPPuHUqVNMmDCBq666ivXr17Nt2zZWrFiBc465c+eydOlS\\nBgwYwLZt2/jjH//I1Kn1x4aozMxM1q1bx7PPPst3vvMd3nzzTb71rW9x2223cdttt/HUU0/xr//6\\nr7z++utNxrtq1SrWrttAQmYfrr7sQj775GMuOi+fr997F4sXL2bw4MHccMMNrTsZzWhJy7038KGI\\nrAU+Q/vc3xSRh0VkrnfMk0B3EdkO/C/ghyGJ1jOmBwz3evQd8PJmqAn9NytjTJjdeOONAEyfPp3j\\nx49z9OhRAObNm0d6ejpZWVnMnDmTFStW8N577/Hee+8xYcIEJk6cyObNm9m2bRsAOTk5jSb2wPe5\\n8cYbWb58OQDLly/npptuAuCWW25h2bJlzcY7ZcoUMrL7gSQwcsx4ivcWsm/XZvLy8hgyZAgiws03\\n39z6E9KEZlvuzrm1wIQG9v844H458OXghtY4EbhmGPziE6iqheKTsGwvXJzTXhEYY0IhKSmpTh94\\n/XHe9Qfh+bYb2u+c4/777+eee+6p81hhYSEdO3ZsMo7A12tu4F9gzLW1tVRW+ofBJKekctq7LpiY\\nmEiKVJPQTiNNo3aGard0uGwgvKXdYby7E8b2gK7p4Y3LmFjR2q6TtujZsycHDx6kpKSEjIwM3nzz\\nTebM8V/iW7hwITNnzmTZsmVkZmaSman9sW+88Qb3338/p06dYsmSJfzsZz8jPT2dBx54gK985Stk\\nZGSwb9++Fk/lX7hwIT/84Q9ZuHAh559/PgAXXHABL774IrfccgvPP/8806ZNA3R0TUFBAddffz2L\\nFi2iqkqzea3TETI+SQmQkgjDhw+nsLCQHTt2MGjQIF544YVgnLqzRG1yB5jeHz7fr2NGq2rh9a1w\\n+1ht2Rtjok9ycjI//vGPmTJlCn379mX48OF1Hk9LS2PChAlUVVXx1FNPndk/duxYZs6cyeHDh3ng\\ngQfo06cPffr0YdOmTWeSc0ZGBs899xyJiYnNxnHkyBHGjh1LamrqmeT729/+ljvuuINHHnmE7Oxs\\nnn76aQDuuusu5s2bx7hx45gzZ86ZbwWnq8D3HSRBdISM7//w+OOPc9VVV9GhQwemTZvGiRMn2nLa\\nGiQhuu7ZrPz8fBeMxToKj8JjBf7tW8don7wx5txt2rSJESNGhDuMBs2YMYNf/OIX5Ofn19n/0EMP\\nkZGRwfe///0wRXa2qho4cMo/9LFrGmSknPvrNPTzEJEC51x+I085I+pL/uZ2gfP6+Lff2Arl1eGL\\nxxgT35yDowFj2lMSoWOIx7Q3JKq7ZXyuHAwbDsHJKjhWAe/thLlDwx2VMSaYlixZ0uB+3zj4SHG6\\nCsq9vnZBW+3h6CqO+pY76EyvwGS+bC8UHQ9fPMaY+FRTqw1Mn4wUbbmHQ0wkd4DxPWFIN73vgFc2\\n69VqY4xpL8cqoMbLO0nin00fDjGT3EXg2mE63Aig6AT8syi8MRlj4kdFtVZ89OmSRruNaW9IzCR3\\ngKwOMCvXv/3ODjhmq4UZY0LMVxjMJz1Jq9iGU0wld4CZOZDdQe9X1OjoGWNM9PAV3mpvM2bMoLXD\\ns09U6lwb0NZ6l7Tmn1O/SFqwxVxyT0qA6wLmPaw7BJuCX5rZGBPlampqmj+oBapq4HjARdTOKf7u\\n4XCKgBCCb1BXrRTp89oWqAzOz9EY006cc/zgBz9g9OjRjBkzhoULFwJw3333sWjRIgCuueYa7rzz\\nTgCeeuopfvSjHwHw3HPPMWXKFMaPH88999xzJpFnZGTwve99j3Hjxp0pCBboT3/601klhUtLS7n6\\n6qsZO3YsU6dOZe3atYAOwXzkkV9wtEIHcVw6dTT7iwo5/EUhI0aM4K677mLUqFHMnj2bsrIyAAoK\\nChg3bhzjxo3jscceC93JI0bGuTfkS4Nh42Edc3qkHP6+C64aHO6ojIkij94Uutf+5p+bPeTVV19l\\n9erVrFmzhsOHDzN58mSmT5/OtGnT+Oijj5g7dy779u2juLgYgI8++ogFCxawadMmFi5cyMcff0xy\\ncjLf+MY3eP7557n11ls5deoU5513Hr/85S8bfM+GSgo/+OCDTJgwgddff53Fixdz6623snr1akC7\\nYgInTWam6uCObdu28cILL/DEE09w/fXX88orr3DzzTdzxx138OijjzJ9+nR+8IMftP08NiEmW+4A\\nHVM0wfss3aPVI40x0WHZsmXceOONJCYm0rNnTy6++GI+++yzM8l948aNjBw5kp49e1JcXMzy5cu5\\n4IIL+OCDDygoKGDy5MmMHz+eDz74gJ07dwJamfG6665r9D0bKim8bNkybrnlFgBmzZpFSUkJx48f\\nx7m6iT1B/GPa8/LyGD9+PACTJk2isLCQo0ePcvToUaZPnw5w5jVDJWZb7qBdMyuLYedRHfP+8ia4\\nLz+8w5OMMW3Tt29fjh49yjvvvMP06dMpLS3lpZdeIiMjg06dOuGc47bbbuOnP/3pWc9NS0trsnBY\\nYyWFG1JNEs5bSCJRoKrCP1wmNdU/wD0xMfFMt0x7iunkLgLXDodffaoTC/Ych0/3wfn9mn+uMXGv\\nBV0noTRt2jR+//vfc9ttt1FaWsrSpUt55JFHAJg6dSq//vWvWbx4MSUlJcyfP5/58+cDcMkllzBv\\n3jy++93v0qNHD0pLSzlx4gQ5Oc0v+NBQSeFp06bx/PPP88ADD7BkyRKysrJI7dCZnv1yef+dNwHY\\ntelzdu3a1eRrd+nShS5durBs2TIuuuginn/++TaeoabFdHIH6NkRZuTAB4W6/fYOGJ0NncI4c8wY\\n07xrrrmG5cuXM27cOESEn//85/Tq1QvQxP/ee+8xePBgcnJyKC0tPVNffeTIkfzkJz9h9uzZ1NbW\\nkpyczGOPPdai5N5QSeGHHnqIO++8k7Fjx9KhQweeeeaPHCmHK+Zex8svPMtlU0dx/nnnMXRo8wWt\\nnn76ae68805EhNmzZ7fh7DQv6kv+tkRVDfzyUyjxvhlN6Ak3hW54qTFRK5JL/kaKExVw1Bv6KECv\\njNANfYzrkr8tkZyopQl8Vh2ALSXhi8cYE52q6xUG65waGWPaGxKhYQXf0O5aXMzntS11l8Ayxpim\\n1K/TnpwAnVqxAEd7iZvkDvAvQyDNu8pQUubvhzfG+IWrqzbSlVXrzSfUddrb+nOIq+TeORWuHOTf\\nXrJbl8Iyxqi0tDRKSkoswddT67XafTomQ2oIh6M45ygpKSEtrQVFahoR86Nl6juvLxTsh93HdHjk\\nq5vh3om2qLYxAP369aOoqIhDhw6FO5SIcrpKCxGCXkTtnAoHQpwz0tLS6Nev9eO24y65J3h13//7\\nM/1rvPOoTnSa3Kf55xoT65KTk8nLywt3GBGl6Dg8/Zm/r/3GUTCqV1hDapFmu2VEpL+IfCgiG0Vk\\ng4h8u4FjZojIMRFZ7d1+HJpwg6NPJ5jW37/95nY4VRm+eIwxkanW6apuvsQ+pJsOpY4GLelzrwa+\\n55wbCUwF7hORkQ0c95Fzbrx3ezioUYbA7IF6QQT0K9eb28MbjzEm8vyzSFd1Ax3yeM2w6OnCbTa5\\nO+eKnXOfe/dPAJuAvqEOLNRSEuHqgLHvK4thx5HwxWOMiSzHynU1N59ZAQsBRYNzGi0jIrnABODT\\nBh4+X0TWiMjbIjKqkeffLSIrRWRlJFywGZkFY7L9269u1kkKxhizaJv/Imp2B5iZG85ozl2Lk7uI\\nZACvAN9xzh2v9/DnQI5zbhzwW+D1hl7DOfe4cy7fOZefnZ3d0CHtbt5QSPWKxB08De83XfvHGBMH\\n1h+CtQf929cNj9yZqI1pUbgikowm9uedc6/Wf9w5d9w5d9K7/xaQLCJZQY00RDLTYE7A2PfFhdY9\\nY0w8O1oOL230b0/qrau7RZuWjJYR4Elgk3Puvxo5ppd3HCIyxXvdqKneckE/GNRF7zvgzxts9Iwx\\n8aimFv683j8TtUsqzB0S3phaqyUt9wuBW4BZAUMdrxSRe0XkXu+Y+cB6EVkD/AZY4KJoiluCwI2j\\nddYZ6GK3CzdqLQljTPz4+y7YdUzvJ4hWj+2QHN6YWqvZSUzOuWXopKymjnkUeDRYQYVDZircMBKe\\nWqPbm0rgo70wfUB44zLGtI/tpdot6zM7D/K6hC2cNouySwShNSKrbjJ/azvsrX/p2BgTc05Wanes\\n78v64K7RNzqmPkvu9VwxCPp10vs1Dp5fX3cRXGNMbKl12g17wrvO1jEZFoyK/rWWLbnXk5QAXxnt\\nHx5ZUuZNP7b+d2Ni0kd7YHPA8I8FI7WbNtpZcm9AVgcd1+qz+oDOYDXGxJa9x+GtgFmoFw+A4VEx\\niLt5ltwbMaEXTAmoFPnaFqv9bkwsKauG59ZrtwxA/85157xEO0vuTZg3FHp4tSSqarX/3ZbmMyb6\\nOQevbILSMt1OS9Tu2GibhdqUGPqvBF9KItw8xv8DLz4Jf90W3piMMW33WTGsCSwvMAK6p4cvnlCw\\n5N6M3hl1Z6gt3wfrDjZ+vDEmsh04Ca9v8W+f1wfGR0mN9nNhyb0FpvatWz3yL5vgSFn44jHGtE5V\\njfazV3nVX3t2hLlDwxtTqFhybwERmD/Cv7hHWTU8v0HrUBhjoseibbDfGxiRlAA3j9bu11hkyb2F\\nOiRrnQnfxIbdx+C9neGNyRjTcmsOwCf7/NvzhkKvjPDFE2qW3M9BbibMGejf/nA3bI2a2pfGxK/S\\nMnh5s397bA/ta49lltzP0cU5ukguaB2KFzbCiYqwhmSMaUJNbd0yIl3TYP7w6FkLtbUsuZ+jBIEb\\nR0JGim6frIQXN/onQhhjIsu7O2GPVwAwQXQ8e3qUlvE9F5bcW6FTqiZ4n62l8I894YvHGNOwLSXa\\nfeozZxDkZIYvnvZkyb2VhnaHmTn+7Xd26EVWY0xkOF4BL27wbw/tprVj4oUl9za4fCAM6Kz3a73y\\nwGVV4Y3JGKO/jy9uhJPe72OnFLgxBsr4ngtL7m2Q6JUHTvfWszpSrlfkrTywMeG1ZDdsK9X7gtZn\\n910nixeW3NuoW7pOcPJZexA+/SJ88RgT7wqP6UVUn5m52iUTbyy5B8HYHnB+X//2G1th/8nwxWNM\\nvDpdBX8OKOObk6lrocYjS+5B8i9D/LPdqmu1fkWllQc2pt0459V9Ktft9CT4yijtPo1HcfrfDr7k\\nRK1Tkeyd0QOntAVvjGkfy/fB+kP+7S+PgK4xVsb3XFhyD6KeHeHqYf7tFV/A6v3hi8eYePHFibpr\\nLZzfF8b0CF88kaDZ5C4i/UXkQxHZKCIbROTbDRwjIvIbEdkuImtFZGJowo18k3vXrQ398mZdZNsY\\nExqVNToMudqr0to7Q7tJ411LWu7VwPeccyOBqcB9IjKy3jFXAEO8293A/wQ1yigiootrd/O+DlbU\\n++AZY4LHOV144+Bp3U72yvgmx2gZ33PRbHJ3zhU75z737p8ANgF96x02D3jWqU+ALiLSO+jRRom0\\nJP2AJXoTJvYeh5es/owxQfePPbpkns81w6BHx/DFE0nOqc9dRHKBCcCn9R7qC+wN2C7i7D8AcaV/\\nZ7hqsH971QH42/bwxWNMrCkorvs7NbEX5Mdtk/JsLU7uIpIBvAJ8xzl3vDVvJiJ3i8hKEVl56NCh\\n5p8Q5S7qX3f8+9I98I/djR9vjGmZLSXw0ib/9sAu8VHG91y0KLmLSDKa2J93zr3awCH7gP4B2/28\\nfXU45x53zuU75/Kzs7PrPxxzRHT0zOiA/+qb22GVjaAxptX2Hodn1/m7OXt1hNvHWj97fS0ZLSPA\\nk8Am59x/NXLYIuBWb9TMVOCYc664kWPjSoLATaMgr4t/38KNtoKTMa1x+DQ8udo/QbBLKnxtfHzU\\nZz9XLWm5XwjcAswSkdXe7UoRuVdE7vWOeQvYCWwHngC+EZpwo1NyorYsenoXemqctjyKWtW5ZUx8\\nOlEBf1gNp7xKj+lJ8LUJkJkW3rgiVVJzBzjnlqGF1Zo6xgH3BSuoWNQhWVsYj66EYxU6RPLJ1XBf\\nPmR1CHd0xkS28mp4ao1/zkhSAtw5zt9gMmezGartqEsa3DXeXyL4ZJUm+JOV4Y3LmEhWXQt/WgdF\\nJ3Rb0KHGuV2afFrcs+TeznpmwB3jtOUBcLhME3xFdXjjMiYS1TodFbO11L/vuuEwKvbHY7SZJfcw\\nyOuii3z4+rqKTmgffI3NYjWmjrfqjS6bnQfnxfUMmpaz5B4mo7N1Np3P1lJtodgqTsaopXvqLjw/\\ntS9cGqe12VvDknsYnd8PLgv4sH6+H97eEb54jIkUq/fXrfI4ymsM2SSllrPkHmaX5cF5ffzbH+6G\\nj/Y0frwxsW5bqS5u7ZObqYtuxNPi1sFgyT3MRLRFMjLLv++v22D1gfDFZEy47DsBf1yrc0FAhzre\\nMc5mn7aGJfcIkJigF1hzMnXbAS9ugO2lTT7NmJhSWqaTlCq82aeZ3uzTDjb7tFUsuUeIlESdlNHD\\nm9BU4+CZtdqSMSbWnayEJ1b553ykJ2li72KzT1vNknsE6ZCs06k7p+q2bxZrqa3kZGJYhTf79HDA\\n7NPbx/oXnDetY8k9wnRN0xZLmjeL9USlV0/DZrGaGFRTC8+t10qPoHM/bhoFA7uGNayYYMk9AvXO\\n0JaLbxbrodPasvFVwjMmFjgHf9kMmwMqpF4zzBa2DhZL7hFqUFe4cZR/Fuue41pfw2axmljxzg5d\\nTcnn0lyd+2GCw5J7BBvbQxf78NlcAq9stlmsJvot2wuLA1Ylm9IHZg8MXzyxyJJ7hLugH1yS69/+\\nrBje3Rm2cIxpszUHYNFW//aILLjWZp8GnSX3KHD5QJgcsPDvB4Xw8d5GDzcmYu04Ai9s0LkcAAM6\\na/neRMtEQWenNAqIaJnT4d39+97YCmttFquJIl+cgGfW+GefZnfQuR0pNvs0JCy5R4nEBLhlDPTv\\nrNsOeH4DrPgirGEZ0yI7j8DvPodyb8RX5xQd8tsxJbxxxTJL7lEkJRG+Ok5bPKALGfxlk446sIus\\nJlKt2g+Pr4Iyb0GatET46njolh7euGKdJfco0zEF7pmgY+F9PijUfsxqGyZpIohz+tn88wZ/V0xG\\nMtw9Efp0CmtoccGSexTKTINvTIJhAX3wqw5obY7TVeGLyxifmlp4ebN+q/Tp0QG+NdnftWhCy5J7\\nlEpLgjvG6uo0PjuPwqMr4fDp8MVlTFk1PLmm7vWgQV3hm/nWFdOeLLlHscQEHR981WD/vkOnNcHv\\nPha+uEz8OlIO/3elLrjhM6mXXjxNt9K97cqSe5QTgRk5OlbYV4vmVJWOTFh7MLyxmfhSdBwe/Qz2\\nn/Lvm50HN4z0fzZN+2n2lIvIUyJyUETWN/L4DBE5JiKrvduPgx+mac64nnqhtaPXOqquhefWwT92\\n20gaE3obD8P/fA7HveqliQILRsJlA23mabi05O/pM8CcZo75yDk33rs93PawTGvkdtF+zSyvX9MB\\nb26H17ZYwTETOv8s0slJvqql6Ulw1wSY1Lvp55nQaja5O+eWArbgW5TI6gDfnAx5mf59y/fpqk4V\\n1eGLy8SeWqfr/b62xV9OoGsa3JevF1BNeAWrJ+x8EVkjIm+LyKggvaZppY7J2nIa39O/b3OJfm0+\\nVhG+uEzsqKzREtRL9/j39e8M38rXRa1N+AUjuX8O5DjnxgG/BV5v7EARuVtEVorIykOHDgXhrU1j\\nkhO1HnxgRcl9J+C3n0HxybCFZWLAyUr4/eewPuBXeHQ23DsROqWGLy5TV5uTu3PuuHPupHf/LSBZ\\nRLIaOfZx51y+cy4/Ozu7rW9tmpEgMGcQfHmE3gdtuT+2EraUNP1cYxpy8JQ2EPYc9++b1l/rHlkB\\nsMjS5uQuIr1E9Hq4iEzxXtNSRwSZ0kdr0qR5v3wVNbps36f7whuXiS47jugcitJy3Rbg6qEwd6i/\\n8WAiR1JzB4jIC8AMIEtEioAHgWQA59zvgPnA10WkGigDFjhng+8izdDu8I18eGo1HK3Qi2Evb4bS\\nMrh8kP1ymqYVFGuROl+NmOQEuHkMjGzwO7qJBBKuPJyfn+9WrlwZlveOZ8cq4Ok12v/uM74nXD9C\\n++mNCeQcvF8I7wWs/tUpReuw97MaMWEhIgXOufzmjrN5Y3EmMxW+PhFGBBQdW+0VHTtlRcdMgOpa\\neGlT3cTes6MW/7LEHvksuceh1CS4bSycH1B0bNcxnTpuRccMQFkVPLkaVhb79w3ppmPYu6aFLy7T\\ncpbc41RiAlwzDL40RC+MARwu0wtm22zKWlw7eAoeK4DtR/z7JvfWi/LpzV6lM5HCflRxTAQuHgDd\\n0nRBhepa7Zp5fJVW8vvSEMiwZdDiRlWNLq6xZLf/winAnIEwK9dqxEQbS+6GMT3g66k6PNLX716w\\nX4tBXTUYJvex0TSxbkuJlhEoKfPvSxSt6DihV/jiMq1nyd0AMCATvnse/HUrrPFKBZdV63DJz4rh\\nuuF1l/YzseF4BSwK+Jn7DOisP3NbDi96WXI3Z2Sm6tjlfK8VV+q14nYfg1+v0JmIswfaTMRYUOtg\\neZEug1de49+fngRXDtaJb/ZtLbpZcjdnGd4dvn9e3f7XWgf/2KMtvGuGwkirHhG1io7Dq1tg7/G6\\n+yf2gi8NtvowscKSu2lQcqLWpZnQC17drOuzAhwth6fXwqhsnXrexYbFRY3yanh3J3y811+iFyC7\\ngy7XOLhb2EIzIWDJ3TSpZ0et9lewH97c5r/guuGQDpmcPRAu6qdDK01kcg7WHYQ3tmkfu09SAszK\\ngZm5tgxeLLLkbpolAvm9YUQWvLXdv6p9ZY0m/ALvgmtOZtOvY9pfaZleP9lcr5TfkG46zyG7Q3ji\\nMqFnyd20WMdkLR+c3xte2QwHvIWQi09qGeHz+sIVg6CDrXIfdtW1upDG+7ugKmCJxYwUmDtE6wnZ\\nuPXYZsndnLO8LvDdKbB0L/x9pyYPB3yyD9Yf1BKwljzCZ+cRvWDq++MLOgt5qvfHN93++MYFS+6m\\nVRITYGYOjOsBr2+BTd7X/pNVOtt1xRdw7XD72t+eTlXC37brvIRAfTK022yAdZvFFUvupk26pcMd\\n43TJtTe2+tdo3X4EfvmJTlufmWPlhEPJOS3w9eZ2OB1Q2TMlES4fCBfaBe+4ZMndtJmIljAY0k3L\\nwy7zhtrVOPj7Lli1Xy/eDelmXTXBtv+kXjD1DVX1GZ0N82yoalyz5G6CJi1J+9sneRdcfZNkDpfB\\nE6uhV0d9bGIv6GwTZVqtvFqHNq4sPjupd02Dq4fZCknGVmIyIVLr9ALr2zs0GQUSYFh3TfSjsqzL\\npiVqnXZ1FRRrYg8cAQNaKuDiAXBpnpWHiHUtXYnJWu4mJBIELuin3QPv7NDVnnwJyaHjrjeXaGt/\\nXA8dXpmTad029R08pQm9YL//ekYgQctFXDHYCruZuqzlbtpFeTWsPaiJqn5Xgk9WurbmJ/WCrunt\\nG18kOV2lfwwLimHP8YaP8XVxTeilBd9M/LCWu4koaUlaaXBKH60Z7muNlgbUDz9cprVP3t0Jg7pq\\na35Mti4LGOtqamFLqZ6XDYfqLpbh0zEZJvTUpN63k33LMU2Lg18bE2m6p2tNmsvyYNdRTfJrDkBF\\nQOnZHUf09lqiJvj83jCwa+yVof3ihF4YXbVf5wjUlyha9mFSb+1+sRowpqUsuZuwEdGEPbCrDtvb\\ncEgT3baEhkd6AAAY70lEQVRSf9XCyhpN/gX7dVjfpF6a6KJ5ctSJClh1QP+vxScbPqZfJ/2DNr4n\\ndLSlDk0rWHI3ESElUfuPJ/SCY+Xw+X5NfgdP+485Wq415j8o1Iuvk3rpkL9OqZHdoncOTlfDjlJY\\nuV+XtKttoNulc6oOE53UC3rZxVHTRs1eUBWRp4AvAQedc6MbeFyA/wauBE4DtzvnPm/uje2CqmmO\\nc1DkdVus3q8JsiGJouO7u6brv90C7ndN06QZyuTvnJZCPlKu1xB8/x4th9Jy3a6safi5SQk6oii/\\nt07yiuQ/UiYyBPOC6jPAo8CzjTx+BTDEu50H/I/3b+i4WhDrfIx1ItC/s97+ZQhsOqyJfnO9lm+N\\n04uxh8safp1EgUxf0m/gj0DnlKan5zsHJyo1SR8phyNl/qR9xEvm9cedNycvU7uXxvbUpe1MHPE1\\nqEN8RbzZj5VzbqmI5DZxyDzgWadfAT4RkS4i0ts5V9zEc1qvcBWseAWu+A50sml48SIpQUscjOkB\\nJyv1AuSag3DoVOMtep8apy3p0kaSf4LocMJuadAlXe+fCkzm5VpCty1SE7UOzyjv4mhWFF8zMG1Q\\nWQ4f/A6ycyH/6pC+VTDaDH2BvQHbRd6+4Cf3I1/Ae49CZRm89G8w59vQd0TQ38ZEtowUmDZAb6Bj\\n6M90gZT5E7Kvi+RUA6NQAtU6/3NoZAx+c9IS634j6JJe95tChyQbuhj3jh2Av/0XlO6FHZ9BVg7k\\nTgjZ27XrF0IRuRu4G2DAgAHn/gKHCqG6Uu+XHYc3/n+46GYYM9t+c+JYWpJegGzsImRljT/p1+9O\\nKS3XbwLNSU/SlneXRrp3rEa6adLuNdowrfAV2XdQvDXik/s+oH/Adj9v31mcc48Dj4NeUD3ndxp6\\nAWR0g7d/rcm9tgaW/hEO7YaLb4ckGzNmzpaSCD0z9NaQqpq6rf3jlTphqFuaJvOu6dYvblrJOVj1\\nJix/0d/XnpgMM+6EEReH9K2D8ZFdBHxTRF5EL6QeC1l/O0Cf4XD9f8Dbv4KDO3XfpiX6VeeK72ry\\nN+YcJCdCj456MyZoqsph8ROwbbl/X8ducOV3oeegkL99s0NOROQFYDkwTESKROSrInKviNzrHfIW\\nsBPYDjwBfCNk0fp06g7X/hiGT/fvO7ADXvoRFG8J+dsbY0yTjh+CV/69bmLvPQxu+I92SewQ7YXD\\nnIO178Ky53R4JEBCIky/HUZf0uYYjTHmnBVtgHf+G8oDph+PvhSm3QqJbe8siY/CYSIwbg507w/v\\n/AbKT2g//JIn9eLr9NuCcjKNMaZZzsGad+Dj5+s2Ni++A0bNavdwYmMmUL9RcP1PdOyoz4YP4LWf\\nwKkjYQvLGBMnqivh/f+BZX/yJ/YOXeCaB8KS2CFWkjtA52y49kEYcoF/3/6tOh5+//bwxWWMiW0n\\nDmv/+pZl/n09B+vAj95DwxZW7CR3gORUmH0fXPgV/7j3U0fg1Ydh45KwhmaMiUH7NmkD8tAu/76R\\nM+DaByCja9jCgmjvc2+ICEy4CroPgHd/o5MGaqth8ePaD3/RzdYPb4xpG+dg3d+1G6bWqwqXkKgX\\nTUdfGhGTKmOr5R5owBjth+8eML9q3Xs6q/X0sfDFZYyJbtWVOn596TP+xJ7eGa7+EYy5LCISO8Ry\\ncgfI7AnX/TsMmuLf98Vm/RrlmwBljDEtdbJUB2psWuLf12Og9q/3GR62sBoS28kdICVNC4ydvwBd\\nKx44WaIXQDZ/FNbQjDFRpHiLNgwPBAzQGDZNJ1R26h6+uBoRH53PIjBpLmQNgHcfhcrTUFOlQ5cO\\nFcKFN2l/mTHGNGT9B3W7YSQBLvoKjJ0TMd0w9cV+yz1Qzni4/v+Drn39+9a8DYt+poXIjDEmUE01\\nfPikToz0Jfa0DJh3P4y7ImITO8Rbcgfo0hu+/DAMDJi9W7QBXnoADu8OX1zGmMhy6ii8/hOdEOmT\\nlaP96/1GhS+uFoq/5A6Qkq4rOU2Z79934hC8/CBsWOwvzWmMiU9713mFCLf69w25AK57SCdMRoH4\\n6HNviCTAlGshOwfe+79QVaZDnD78g66SMusuKx9sTLypKod/vqBj2H1E4IKbYPyVEd0NU198ttwD\\n5U2C6x+Grn38+/asgRf+t04ntla8MfGheAu8eH/dxJ6WAf/yf3RiZBQldoj2kr/BVF0JyxdqVTcC\\nzsmgKbpqSnrnsIVmjAmh6kr49GVY9Tfq/O7nTYKZX4MOmWELrSHxUfI3mJJSYNoteqH1/d9pHzzA\\njhU68WnmV2Hg5PDGaIwJroM7dUh0acDKoCnpWi582LSoa60HspZ7QyrL4OM/171KDjDsIv2hp9p6\\nbMZEtZpqWPm63nwlegH6j4FZd0fkpCQfa7m3RUq611LP14JjvprwW5ZB0Ua92JozLrwxGmNap2Sv\\nfwKjT1KqTmaMkKJfwWAt9+aUn4SPnq1bqxlg1CVaWjglLTxxGWPOTW0trP4bfPIXrRTr03sYXHqv\\n1qKKAtZyD5a0DLjsG9qKX/KUfybrhg9g71q45F7oOyK8MRpjmnZ0v15L2x8wbj0xGaZerzNNE2Jv\\n4KAl95YaNEX/wi95CnZ+pvuOH9IKceOv0A9JUkp4YzTG1OVqYd37Ona9usK/PzsPLvs6dOsXvthC\\nzJL7ueiQqTNbt34M/3hGC5DhYPVbsHs1XPp16Dko3FEaY0CXv/vg91pexCchEfKv0UKCMb5oT2z/\\n70JBREfN9B2hBfv3rNX9R77Q8gWT5sLka2P+g2NMxHIONv1DV0mqLPPv79ZPG2A98sIXWzuyDNRa\\nGd115tqGxfDxc1BVoV8BV74Ohav0Q5Q1INxRGhNfTh3VEiKFn/v3icCEL8F587WfPU606CqCiMwR\\nkS0isl1EftjA47eLyCERWe3dvhb8UCOQCIy+BBb8rO4qLId3a9GhgkX+MqHGmNDa9gn8+X/XTeyZ\\nPeHaB+GCG+MqsUMLWu4ikgg8BlwGFAGficgi59zGeocudM59MwQxRr7MnnDNv2npguULdSGQ2hpY\\n/iLsXKmt+K69wx2lMbGp7AQsfVqTe6Axs+GCBZAcn8OVW9ItMwXY7pzbCSAiLwLzgPrJPb5JglaN\\nGzBOJ0j41mg9sB1e/CGMmwOT5kFqh/DGaUysqKnWIckrXoXyE/79Gd3hknug/+jwxRYBWpLc+wJ7\\nA7aLgPMaOO46EZkObAW+65zb28Axsa9bX5j/7/D5X2HFK9qCr6nS7Y1LtN9v5Ey74GpMazkHuwp0\\neOPR4rqPjZgBF91sjSiCd0H1r8ALzrkKEbkH+CMwq/5BInI3cDfAgAExfLExIRHyr9Zl/T78g78V\\nX34C/vG0dt9ceBPkToyZqc7GtIsDO+Hj5+GLTXX3d8rSuk95k8ITVwRqtvyAiJwPPOScu9zbvh/A\\nOffTRo5PBEqdc03WyYya8gNt5Wph63L4ZKGOuw3Ud6Qm+R4DwxObMdHiRIn+DtUvA5KSrg2psZfH\\nzSTCYJYf+AwYIiJ5wD5gAXBTvTfr7ZzzfT+aC9T7sxrHJAGGXQiDJmuLveAN/9jbfRvhpX/TcfNT\\nb4joSnTGhEXlaR11tvpt7d70SUjU+k5TrrW1FhrRbHJ3zlWLyDeBd4FE4Cnn3AYReRhY6ZxbBPyr\\niMwFqoFS4PYQxhydklJ0gtPIGdoXv/4Df6nRLctg+6d6QXbSXG2NGBPPamt0DsmKV/z1nHzyJunQ\\nxsDV08xZrCpkuBz5Qi8I7Sqouz+9s/+ia0JieGIzJlyc00mA//yz/o4Eys7Ti6VxXqivpd0yltzD\\nrWijznANrC0N0LWv9sfnjLeLriY+HCrUi6WBtWBAhzaefwMMvUC7OeOcJfdo4mph6z91AtTJkrqP\\n9RuldeOzc8MSmjEhd7JEa6xv/og6a5gmp0P+PJ0jEicXS1vCkns0qq7UC0cFi6AqoOARAsOnaVnh\\njG5hC8+YoKos0/kfq9/Sz76PJGhZj8nXRtzi1JHAFuuIRkkp2lLxXXTdsNi76Opg81LY/glMuEqL\\nINlFVxOtamt0Qt+Kl+H0sbqP5U6EC2/UbknTJtZyj2Sl+/TCUuGquvs7ZMJ5X4YRF9tFVxM9nIM9\\na3Tx+dKiuo9l52r3Y79RYQktmli3TCwp2qAXmupfdM3sqZM3RkyHFJtubSJUTTXsWKHzPA5sr/tY\\nRjed4zHsQrtY2kKW3GONq9Xx8MtfglOldR9LToeRF2uij5JFfk0cKDuuXYvr3m/gM5umczrGXQHJ\\nqeGJL0pZco9VVRV6AWr136DidL0HBXLH6+iCfqNtCKUJj8N7tJW+9eO6s0oBEpK0ITJlvl0sbSW7\\noBqrklNh8jW6KPfmj2DtuwGTPbwJIIWrdEmxsZdraQNrGZlQq62FwgJY866W1aivQxcYfamOgrGk\\n3i6s5R7tXC3sXa8tpd2rz348tSOMmgVjLtPKecYEU8UpHfmy7j04fujsx3sM1G+Sg6damesgsZZ7\\nvJAEGDBWb0eKtSW/eSlUlevjFad0LPGqv8HAfP1F6z3MumxM2xz5IuCzVlH3MUmAQVP0s9ZriH3W\\nwsRa7rGo4jRsWqK/fA21prJz9RdvyPlxt66kaQNXC3vW6bfEPWvOfjwtw/8tMcMqnIaKXVA12g+6\\ne5X+Mtav1wHa9znqEu0L7dil/eMz0aGyXFvoa989e+UjgG79tbEw7EIrE9AOLLmbug7v0V/OLcsa\\nGMGQqK34sXOgpy0cYjzHD8La97RPvbKBkVl5EzWp9x1pXS/tyJK7aVjZCdj4oV4AO1l69uO9hmoL\\nLG+S1bGJRxWnoHC1lroo/FxnlQZKSdd1SsfOtjkVYWLJ3TStphp2rtQum/1bGz6mx0BN8nmToHt/\\na53FqhOHdV2BXQWwb5PWfqkvsxeMuxyGT7e6RmFmyd203IGdsPYd2La84V9sgM49NMkPzIfeQ62m\\nTTRzDg7v9if0+mUtAvUfo10vOeOsPECEsORuzt2po1oDZNfKxltwoKMicibAwEk6BDM5rX3jNOeu\\nphqKt+i3tV0FZy/WHqjHQP0jPmiKLWUXgSy5m7apOAW712iiL1xTr758gMRk6D8a8vL1ApvNPowc\\nlWU6ZHFngY6aOqtchSchUasx+rrg7FpLRLPkboKnpkqXA9xVALs+P7sI1BkCvQZ73TeTrCZ3OJw6\\noj+jXSth7waorW74uJQOWocoLx9yxlpV0Shiyd2EhnNwcJcmj10FULK38WO79Pb30/ccDAnWZxt0\\nzmndf9/P48COxo/N6O7/w9tnhJUDiFKW3E37OHZAk8rOAijefPbQOZ/UDMjOgayAW9c+lmDOhauF\\nYwf1YmjgraEhrT5ZOf4/sFk5NuIpBlhyN+2v7IT27e4sgD1robqi6eMTkqB7P+g+QBN/9xzIGqAX\\nbONdVYV+KzqTxPdAyR5/zaDGSAL0HeHvP++c3T7xmnZjhcNM+0vvpOOgh0/XBY/3rtdWfeHnZ6+V\\nCdoffKhQb5sD9nfK8lr3A/yt/M7ZsTkUzzk4fdSfwA8X6r9Hixv/FlRfcpqOWhqYDznj7Y+jAVqY\\n3EVkDvDfQCLwB+fcz+o9ngo8C0wCSoAbnHOFwQ3VRJWkFB09kzdRuxOOH/KSV0B3QmPD8U4c9k+s\\n8UlOh6z+XrLP1cTfvX901TKpqdakHdgaP7xbVyxqqfTOAV1bA/RcdO1t8w7MWZpN7iKSCDwGXAYU\\nAZ+JyCLnXGBF/q8CR5xzg0VkAfCfwA2hCNhEIUnQqeqZPWHQZP/+8pNe10OhP9GVFDU8wqOqDIq3\\n6u3M6wp06Aqp6ZDSEVI7eLeOOvojtYP3b8BjgdutqYhZU611VirLdLhoxWm9VXr3K73tinrblaf1\\n20v9uj6NnjPRC9KB1yiyBuiiF9ZvblqgJS33KcB259xOABF5EZgHBCb3ecBD3v2XgUdFRFy4OvRN\\ndEjL0P7hviP8+wJbt4d2Q4n3b/mJs5/vnA7LPNXK909M9id63x+D1A76LaG6ol6y9hJ4/drlwZCc\\n5r/u4Evi3frbClqmTVqS3PsCgePdioDzGjvGOVctIseA7kAT0+CMaUBikna3dO+vSwSCl8SP1m3h\\nH94NR/cDbWg/1FRpf/fpo8GIvGUyutftUskaAJk9YvN6ggmrdr2gKiJ3A3cDDBgwoD3f2kQzEcjo\\nqrfcCf791ZU6QqfilNfCrt810kx3SWPlFZqLJaWl3T8dArqLOkJaR5ssZNpNS5L7PqB/wHY/b19D\\nxxSJSBKQiV5YrcM59zjwOOhQyNYEbMwZSSnQqbvezpVzXtdLmf8PgC/xV5Xra9dP4KkdtAvFWtkm\\nCrQkuX8GDBGRPDSJLwBuqnfMIuA2YDkwH1hs/e0moolook5OA7qGOxpjgq7Z5O71oX8TeBcdCvmU\\nc26DiDwMrHTOLQKeBP4kItuBUvQPgDHGmDBpUZ+7c+4t4K16+34ccL8c+HJwQzPGGNNa1nlojDEx\\nyJK7McbEIEvuxhgTgyy5G2NMDLLkbowxMShs9dxF5BCwu5VPzyIySxtEalwQubFZXOfG4jo3sRhX\\njnOu2UL9YUvubSEiK1tSrL69RWpcELmxWVznxuI6N/Ecl3XLGGNMDLLkbowxMShak/vj4Q6gEZEa\\nF0RubBbXubG4zk3cxhWVfe7GGGOaFq0td2OMMU2I2OQuIl8WkQ0iUisijV5VFpE5IrJFRLaLyA8D\\n9ueJyKfe/oUiEpSVlEWkm4j8XUS2ef+eVS9WRGaKyOqAW7mIXO099oyI7Ap4bHx7xeUdVxPw3osC\\n9ofzfI0XkeXez3utiNwQ8FhQz1djn5eAx1O9//9273zkBjx2v7d/i4hc3pY4WhHX/xKRjd75+UBE\\ncgIea/Bn2k5x3S4ihwLe/2sBj93m/dy3icht7RzXrwJi2ioiRwMeC+X5ekpEDorI+kYeFxH5jRf3\\nWhGZGPBYcM+Xcy4ib8AIYBiwBMhv5JhEYAcwEEgB1gAjvcdeAhZ4938HfD1Icf0c+KF3/4fAfzZz\\nfDe0DHIHb/sZYH4IzleL4gJONrI/bOcLGAoM8e73AYqBLsE+X019XgKO+QbwO+/+AmChd3+kd3wq\\nkOe9TmI7xjUz4DP0dV9cTf1M2ymu24FHG3huN2Cn929X737X9oqr3vHfQkuVh/R8ea89HZgIrG/k\\n8SuBtwEBpgKfhup8RWzL3Tm3yTm3pZnDzize7ZyrBF4E5omIALPQxboB/ghcHaTQ5nmv19LXnQ+8\\n7Zw7HaT3b8y5xnVGuM+Xc26rc26bd/8L4CDQ7CSNVmjw89JEvC8Dl3jnZx7wonOuwjm3C9juvV67\\nxOWc+zDgM/QJuiJaqLXkfDXmcuDvzrlS59wR4O/AnDDFdSPwQpDeu0nOuaVoY64x84BnnfoE6CIi\\nvQnB+YrY5N5CDS3e3RddnPuoc6663v5g6OmcK/bu7wd6NnP8As7+YP2H95XsVyISrCXuWxpXmois\\nFJFPfF1FRND5EpEpaGtsR8DuYJ2vxj4vDR7jnQ/fYu8teW4o4wr0VbT159PQz7Q947rO+/m8LCK+\\nJTkj4nx53Vd5wOKA3aE6Xy3RWOxBP1/tukB2fSLyPtCrgYd+5Jx7o73j8WkqrsAN55wTkUaHG3l/\\nkcegq1j53I8muRR0ONT/AR5ux7hynHP7RGQgsFhE1qEJrNWCfL7+BNzmnKv1drf6fMUiEbkZyAcu\\nDth91s/UObej4VcIur8CLzjnKkTkHvRbz6x2eu+WWAC87JwLXA09nOer3YQ1uTvnLm3jSzS2eHcJ\\n+nUnyWt9NbSod6viEpEDItLbOVfsJaODTbzU9cBrzrmqgNf2tWIrRORp4PvtGZdzbp/3704RWQJM\\nAF4hzOdLRDoDf0P/sH8S8NqtPl8NaMti7y15bijjQkQuRf9gXuycq/Dtb+RnGoxk1WxczrmSgM0/\\noNdYfM+dUe+5S4IQU4viCrAAuC9wRwjPV0s0FnvQz1e0d8ucWbxbdHTHAmCR0ysUH6L93aCLdwfr\\nm4BvMfCWvO5ZfX1egvP1c18NNHhVPRRxiUhXX7eGiGQBFwIbw32+vJ/da2hf5Mv1Hgvm+Wrw89JE\\nvIGLvS8CFoiOpskDhgAr2hDLOcUlIhOA3wNznXMHA/Y3+DNtx7h6B2zOBTZ5998FZnvxdQVmU/cb\\nbEjj8mIbjl6cXB6wL5TnqyUWAbd6o2amAse8Bkzwz1ewrxYH6wZcg/Y7VQAHgHe9/X2AtwKOuxLY\\niv7l/VHA/oHoL9924C9AapDi6g58AGwD3ge6efvzgT8EHJeL/jVOqPf8xcA6NEk9B2S0V1zABd57\\nr/H+/WoknC/gZqAKWB1wGx+K89XQ5wXt5pnr3U/z/v/bvfMxMOC5P/KetwW4Isif9+biet/7PfCd\\nn0XN/UzbKa6fAhu89/8QGB7w3Du987gduKM94/K2HwJ+Vu95oT5fL6CjvarQ/PVV4F7gXu9xAR7z\\n4l5HwEjAYJ8vm6FqjDExKNq7ZYwxxjTAkrsxxsQgS+7GGBODLLkbY0wMsuRujDExyJK7McbEIEvu\\nxhgTgyy5G2NMDPp/MA9tfAK2AEYAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f383fd3e278>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# show our beautiful painting range\\n\",\n    \"plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')\\n\",\n    \"plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')\\n\",\n    \"plt.legend(loc='upper right')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def artist_works():     # painting from the famous artist (real target)\\n\",\n    \"    a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis]\\n\",\n    \"    paintings = a * np.power(PAINT_POINTS, 2) + (a-1)\\n\",\n    \"    paintings = torch.from_numpy(paintings).float()\\n\",\n    \"    return Variable(paintings)\\n\",\n    \"\\n\",\n    \"G = nn.Sequential(                      # Generator\\n\",\n    \"    nn.Linear(N_IDEAS, 128),            # random ideas (could from normal distribution)\\n\",\n    \"    nn.ReLU(),\\n\",\n    \"    nn.Linear(128, ART_COMPONENTS),     # making a painting from these random ideas\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"D = nn.Sequential(                      # Discriminator\\n\",\n    \"    nn.Linear(ART_COMPONENTS, 128),     # receive art work either from the famous artist or a newbie like G\\n\",\n    \"    nn.ReLU(),\\n\",\n    \"    nn.Linear(128, 1),\\n\",\n    \"    nn.Sigmoid(),                       # tell the probability that the art work is made by artist\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"opt_D = torch.optim.Adam(D.parameters(), lr=LR_D)\\n\",\n    \"opt_G = torch.optim.Adam(G.parameters(), lr=LR_G)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3825b915c0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3825b08b70>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f38432e2a90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nrkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f38380db128>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nEEIeETNBEAQh5Pl/WXhCtKyEW6cAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f383fc88da0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n2p0AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f38380689b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nPFrMNBqNRhPy/D9TK2HEl34cegAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f38380b3dd8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n9qiiu5MaAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f383801a978>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n5BExEwRBEEKe/wdydEUjSCJJkQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f383db88f28>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3825bf7da0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for step in range(10000):\\n\",\n    \"    artist_paintings = artist_works()           # real painting from artist\\n\",\n    \"    G_ideas = Variable(torch.randn(BATCH_SIZE, N_IDEAS))    # random ideas\\n\",\n    \"    G_paintings = G(G_ideas)                    # fake painting from G (random ideas)\\n\",\n    \"\\n\",\n    \"    prob_artist0 = D(artist_paintings)          # D try to increase this prob\\n\",\n    \"    prob_artist1 = D(G_paintings)               # D try to reduce this prob\\n\",\n    \"\\n\",\n    \"    D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1))\\n\",\n    \"    G_loss = torch.mean(torch.log(1. - prob_artist1))\\n\",\n    \"\\n\",\n    \"    opt_D.zero_grad()\\n\",\n    \"    D_loss.backward(retain_variables=True)      # retain_variables for reusing computational graph\\n\",\n    \"    opt_D.step()\\n\",\n    \"\\n\",\n    \"    opt_G.zero_grad()\\n\",\n    \"    G_loss.backward()\\n\",\n    \"    opt_G.step()\\n\",\n    \"\\n\",\n    \"    if step % 1000 == 0:  # plotting\\n\",\n    \"        plt.cla()\\n\",\n    \"        plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)\\n\",\n    \"        plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')\\n\",\n    \"        plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')\\n\",\n    \"        plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 15})\\n\",\n    \"        plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 15})\\n\",\n    \"        plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=12);plt.draw();plt.pause(0.01)\\n\",\n    \"        plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# below is for pytorch version => 1.5\\n\",\n    \"# this fix is done by albanD - https://github.com/pytorch/pytorch/issues/39141\\n\",\n    \"\\n\",\n    \"for step in range(10000):\\n\",\n    \"    \\n\",\n    \"    artist_paintings = artist_works()           # real painting from artist\\n\",\n    \"    G_ideas = torch.randn(BATCH_SIZE, N_IDEAS, requires_grad=True)  # random ideas\\n\",\n    \"    G_paintings = G(G_ideas)                    # fake painting from G (random ideas)\\n\",\n    \"\\n\",\n    \"    prob_artist1 = D(G_paintings)               # D try to reduce this prob\\n\",\n    \"    \\n\",\n    \"    G_loss = torch.mean(torch.log(1. - prob_artist1))  \\n\",\n    \"    opt_G.zero_grad()\\n\",\n    \"    G_loss.backward()\\n\",\n    \"    opt_G.step()\\n\",\n    \"     \\n\",\n    \"    prob_artist0 = D(artist_paintings)          # D try to increase this prob\\n\",\n    \"    prob_artist1 = D(G_paintings.detach())  # D try to reduce this prob\\n\",\n    \"    D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1))      \\n\",\n    \"\\n\",\n    \"    opt_D.zero_grad()\\n\",\n    \"    D_loss.backward(retain_graph=True)      # reusing computational graph\\n\",\n    \"    opt_D.step()\\n\",\n    \"\\n\",\n    \"    if step % 1000 == 0:  # plotting\\n\",\n    \"        plt.cla()\\n\",\n    \"        plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)\\n\",\n    \"        plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')\\n\",\n    \"        plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')\\n\",\n    \"        plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 15})\\n\",\n    \"        plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 15})\\n\",\n    \"        plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=12);plt.draw();plt.pause(0.01)\\n\",\n    \"        plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/201_torch_numpy.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 201 Torch and Numpy\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* numpy\\n\",\n    \"\\n\",\n    \"Details about math operation in torch can be found in: http://pytorch.org/docs/torch.html#math-operations\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\nnumpy array: [[0 1 2]\\n [3 4 5]] \\ntorch tensor: tensor([[ 0,  1,  2],\\n        [ 3,  4,  5]], dtype=torch.int32) \\ntensor to array: [[0 1 2]\\n [3 4 5]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# convert numpy to tensor or vise versa\\n\",\n    \"np_data = np.arange(6).reshape((2, 3))\\n\",\n    \"torch_data = torch.from_numpy(np_data)\\n\",\n    \"tensor2array = torch_data.numpy()\\n\",\n    \"print(\\n\",\n    \"    '\\\\nnumpy array:', np_data,          # [[0 1 2], [3 4 5]]\\n\",\n    \"    '\\\\ntorch tensor:', torch_data,      #  0  1  2 \\\\n 3  4  5    [torch.LongTensor of size 2x3]\\n\",\n    \"    '\\\\ntensor to array:', tensor2array, # [[0 1 2], [3 4 5]]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\nabs \\nnumpy:  [1 2 1 2] \\ntorch:  tensor([ 1.,  2.,  1.,  2.])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# abs\\n\",\n    \"data = [-1, -2, 1, 2]\\n\",\n    \"tensor = torch.FloatTensor(data)  # 32-bit floating point\\n\",\n    \"print(\\n\",\n    \"    '\\\\nabs',\\n\",\n    \"    '\\\\nnumpy: ', np.abs(data),          # [1 2 1 2]\\n\",\n    \"    '\\\\ntorch: ', torch.abs(tensor)      # [1 2 1 2]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([ 1.,  2.,  1.,  2.])\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tensor.abs()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\nsin \\nnumpy:  [-0.84147098 -0.90929743  0.84147098  0.90929743] \\ntorch:  tensor([-0.8415, -0.9093,  0.8415,  0.9093])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# sin\\n\",\n    \"print(\\n\",\n    \"    '\\\\nsin',\\n\",\n    \"    '\\\\nnumpy: ', np.sin(data),      # [-0.84147098 -0.90929743  0.84147098  0.90929743]\\n\",\n    \"    '\\\\ntorch: ', torch.sin(tensor)  # [-0.8415 -0.9093  0.8415  0.9093]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([ 0.2689,  0.1192,  0.7311,  0.8808])\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tensor.sigmoid()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([ 0.3679,  0.1353,  2.7183,  7.3891])\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tensor.exp()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\nmean \\nnumpy:  0.0 \\ntorch:  tensor(0.)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# mean\\n\",\n    \"print(\\n\",\n    \"    '\\\\nmean',\\n\",\n    \"    '\\\\nnumpy: ', np.mean(data),         # 0.0\\n\",\n    \"    '\\\\ntorch: ', torch.mean(tensor)     # 0.0\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\nmatrix multiplication (matmul) \\nnumpy:  [[ 7 10]\\n [15 22]] \\ntorch:  tensor([[ 7., 10.],\\n        [15., 22.]])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# matrix multiplication\\n\",\n    \"data = [[1,2], [3,4]]\\n\",\n    \"tensor = torch.FloatTensor(data)  # 32-bit floating point\\n\",\n    \"# correct method\\n\",\n    \"print(\\n\",\n    \"    '\\\\nmatrix multiplication (matmul)',\\n\",\n    \"    '\\\\nnumpy: ', np.matmul(data, data),     # [[7, 10], [15, 22]]\\n\",\n    \"    '\\\\ntorch: ', torch.mm(tensor, tensor)   # [[7, 10], [15, 22]]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"ename\": \"RuntimeError\",\n     \"evalue\": \"dot: Expected 1-D argument self, but got 2-D\",\n     \"traceback\": [\n      \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[0;31mRuntimeError\\u001b[0m                              Traceback (most recent call last)\",\n      \"\\u001b[0;32m<ipython-input-3-a29f9258176b>\\u001b[0m in \\u001b[0;36m<module>\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m      5\\u001b[0m     \\u001b[0;34m'\\\\nmatrix multiplication (dot)'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m      6\\u001b[0m     \\u001b[0;34m'\\\\nnumpy: '\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdata\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdot\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdata\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m        \\u001b[0;31m# [[7, 10], [15, 22]]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 7\\u001b[0;31m     \\u001b[0;34m'\\\\ntorch: '\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mtorch\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdot\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensor\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdot\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtensor\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m     \\u001b[0;31m# 30.0. Beware that torch.dot does not broadcast, only works for 1-dimensional tensor\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m      8\\u001b[0m )\\n\",\n      \"\\u001b[0;31mRuntimeError\\u001b[0m: dot: Expected 1-D argument self, but got 2-D\"\n     ],\n     \"output_type\": \"error\"\n    }\n   ],\n   \"source\": [\n    \"# incorrect method\\n\",\n    \"data = np.array(data)\\n\",\n    \"tensor = torch.Tensor(data)\\n\",\n    \"print(\\n\",\n    \"    '\\\\nmatrix multiplication (dot)',\\n\",\n    \"    '\\\\nnumpy: ', data.dot(data),        # [[7, 10], [15, 22]]\\n\",\n    \"    '\\\\ntorch: ', torch.dot(tensor.dot(tensor))     # NOT WORKING! Beware that torch.dot does not broadcast, only works for 1-dimensional tensor\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that:\\n\",\n    \"\\n\",\n    \"torch.dot(tensor1, tensor2) → float\\n\",\n    \"\\n\",\n    \"Computes the dot product (inner product) of two tensors. Both tensors are treated as 1-D vectors.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([[  7.,  10.],\\n        [ 15.,  22.]])\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tensor.mm(tensor)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([[  1.,   4.],\\n        [  9.,  16.]])\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tensor * tensor\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor(7.)\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"torch.dot(torch.Tensor([2, 3]), torch.Tensor([2, 1]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/202_variable.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 202 Variable\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"\\n\",\n    \"Variable in torch is to build a computational graph,\\n\",\n    \"but this graph is dynamic compared with a static graph in Tensorflow or Theano.\\n\",\n    \"So torch does not have placeholder, torch can just pass variable to the computational graph.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"from torch.autograd import Variable\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \" 1  2\\n\",\n      \" 3  4\\n\",\n      \"[torch.FloatTensor of size 2x2]\\n\",\n      \"\\n\",\n      \"Variable containing:\\n\",\n      \" 1  2\\n\",\n      \" 3  4\\n\",\n      \"[torch.FloatTensor of size 2x2]\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"tensor = torch.FloatTensor([[1,2],[3,4]])            # build a tensor\\n\",\n    \"variable = Variable(tensor, requires_grad=True)      # build a variable, usually for compute gradients\\n\",\n    \"\\n\",\n    \"print(tensor)       # [torch.FloatTensor of size 2x2]\\n\",\n    \"print(variable)     # [torch.FloatTensor of size 2x2]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Till now the tensor and variable seem the same.\\n\",\n    \"\\n\",\n    \"However, the variable is a part of the graph, it's a part of the auto-gradient.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"7.5\\n\",\n      \"Variable containing:\\n\",\n      \" 7.5000\\n\",\n      \"[torch.FloatTensor of size 1]\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"t_out = torch.mean(tensor*tensor)       # x^2\\n\",\n    \"v_out = torch.mean(variable*variable)   # x^2\\n\",\n    \"print(t_out)\\n\",\n    \"print(v_out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"v_out.backward()    # backpropagation from v_out\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"$$ v_{out} = {{1} \\\\over {4}} sum(variable^2) $$\\n\",\n    \"\\n\",\n    \"the gradients w.r.t the variable, \\n\",\n    \"\\n\",\n    \"$$ {d(v_{out}) \\\\over d(variable)} = {{1} \\\\over {4}} 2 variable = {variable \\\\over 2}$$\\n\",\n    \"\\n\",\n    \"let's check the result pytorch calculated for us below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Variable containing:\\n\",\n       \" 0.5000  1.0000\\n\",\n       \" 1.5000  2.0000\\n\",\n       \"[torch.FloatTensor of size 2x2]\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"variable.grad\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Variable containing:\\n\",\n       \" 1  2\\n\",\n       \" 3  4\\n\",\n       \"[torch.FloatTensor of size 2x2]\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"variable # this is data in variable format\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"\\n\",\n       \" 1  2\\n\",\n       \" 3  4\\n\",\n       \"[torch.FloatTensor of size 2x2]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"variable.data # this is data in tensor format\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 1.,  2.],\\n\",\n       \"       [ 3.,  4.]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"variable.data.numpy() # numpy format\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that we did `.backward()` on `v_out` but `variable` has been assigned new values on it's `grad`.\\n\",\n    \"\\n\",\n    \"As this line \\n\",\n    \"```\\n\",\n    \"v_out = torch.mean(variable*variable)\\n\",\n    \"``` \\n\",\n    \"will make a new variable `v_out` and connect it with `variable` in computation graph.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"torch.autograd.variable.Variable\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"type(v_out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"torch.FloatTensor\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"type(v_out.data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/203_activation.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 203 Activation\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* matplotlib\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Firstly generate some fake data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x = torch.linspace(-5, 5, 200)  # x data (tensor), shape=(200, 1)\\n\",\n    \"x = Variable(x)\\n\",\n    \"x_np = x.data.numpy()   # numpy array for plotting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Following are popular activation functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"C:\\\\Users\\\\morvanzhou\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python36\\\\lib\\\\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.\\\")\\nC:\\\\Users\\\\morvanzhou\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python36\\\\lib\\\\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\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"y_relu = F.relu(x).data.numpy()\\n\",\n    \"y_sigmoid = torch.sigmoid(x).data.numpy()\\n\",\n    \"y_tanh = F.tanh(x).data.numpy()\\n\",\n    \"y_softplus = F.softplus(x).data.numpy()\\n\",\n    \"\\n\",\n    \"# y_softmax = F.softmax(x)\\n\",\n    \"# softmax is a special kind of activation function, it is about probability\\n\",\n    \"# and will make the sum as 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Plot to visualize these activation function\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAe8AAAFpCAYAAAC1YKAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n1jJu3mZmZi3zH58alggIp0WVAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f5f99d757b8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(1, figsize=(8, 6))\\n\",\n    \"plt.subplot(221)\\n\",\n    \"plt.plot(x_np, y_relu, c='red', label='relu')\\n\",\n    \"plt.ylim((-1, 5))\\n\",\n    \"plt.legend(loc='best')\\n\",\n    \"\\n\",\n    \"plt.subplot(222)\\n\",\n    \"plt.plot(x_np, y_sigmoid, c='red', label='sigmoid')\\n\",\n    \"plt.ylim((-0.2, 1.2))\\n\",\n    \"plt.legend(loc='best')\\n\",\n    \"\\n\",\n    \"plt.subplot(223)\\n\",\n    \"plt.plot(x_np, y_tanh, c='red', label='tanh')\\n\",\n    \"plt.ylim((-1.2, 1.2))\\n\",\n    \"plt.legend(loc='best')\\n\",\n    \"\\n\",\n    \"plt.subplot(224)\\n\",\n    \"plt.plot(x_np, y_softplus, c='red', label='softplus')\\n\",\n    \"plt.ylim((-0.2, 6))\\n\",\n    \"plt.legend(loc='best')\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/301_regression.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 301 Regression\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<torch._C.Generator at 0x27b2aa59f70>\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"torch.manual_seed(1)    # reproducible\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)  # x data (tensor), shape=(100, 1)\\n\",\n    \"y = x.pow(2) + 0.2*torch.rand(x.size())                 # noisy y data (tensor), shape=(100, 1)\\n\",\n    \"\\n\",\n    \"plt.scatter(x.data.numpy(), y.data.numpy())\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class Net(torch.nn.Module):\\n\",\n    \"    def __init__(self, n_feature, n_hidden, n_output):\\n\",\n    \"        super(Net, self).__init__()\\n\",\n    \"        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer\\n\",\n    \"        self.predict = torch.nn.Linear(n_hidden, n_output)   # output layer\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = F.relu(self.hidden(x))      # activation function for hidden layer\\n\",\n    \"        x = self.predict(x)             # linear output\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Net(\\n  (hidden): Linear(in_features=1, out_features=10, bias=True)\\n  (predict): Linear(in_features=10, out_features=1, bias=True)\\n)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"net = Net(n_feature=1, n_hidden=10, n_output=1)     # define the network\\n\",\n    \"print(net)  # net architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = torch.optim.SGD(net.parameters(), lr=0.2)\\n\",\n    \"loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.ion()   # something about plotting\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAZsAAAD8CAYAAAChHgmuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzt3XucHGWd7/HPL8kkGW6ZBMIt5AaEm6AJGS5LdkXxAuoucBAF9iDi6nLUxcuqrGF1lcWzL6J4Xqzu4oX1cDjqEbygGAE3KoFlZU3ChIBAJJgAkhmCRGASIUMySX7nj6c7U91T1V3dUzV9me/79erXTFdVVz9T3VO/ep7n9zxl7o6IiEiexjW6ACIi0v4UbEREJHcKNiIikjsFGxERyZ2CjYiI5E7BRkREcqdgIyIiuVOwERGR3CnYiIhI7iY06o0POOAAnzNnTqPeXkSkJa1evfoP7j690eWoVdVgY2Y3An8OPOfux8es/+/AJwtPXwI+4O4PVdvvnDlz6OnpqbG4IiJjm5n9rtFlqEeaZrSbgLMqrH8SON3dXw18Drghg3KJiEgbqVqzcfd7zWxOhfX/FXm6Ajhs5MUSEZF2knWCwHuBnyatNLPLzKzHzHo2b96c8VuLiEizyizYmNnrCcHmk0nbuPsN7t7t7t3Tp7dc/5aIiNQpk2w0M3s18A3gLe7+fBb7FBGR9jHimo2ZzQJ+CLzL3R8feZFERKTdpEl9vhl4HXCAmfUCnwU6ANz9a8BngP2Br5gZwE53786rwCIi0nrSZKNdVGX9+4D3ZVYiERFpOw2bQWAkblvTx7XL1vFM/wCHdnVyxZlHc+6CGY0uloiIJGi5YHPbmj6u/OHDDAzuAqCvf4Arf/gwgAKOiEiTarmJOK9dtm5PoCkaGNzFtcvWNahEIiJSTcsFm2f6B2paLiIijddywebQrs6alouISOO1XLC54syj6ewYX7Kss2M8V5x5dINKJCIi1bRcgkAxCUDZaCIiraPlgg2EgKPgIiLSOlquGU1ERFqPgo2IiOROwUZERHKnYCMiIrlTsBERkdy1ZDaaiEgr0eTBCjYiIrnS5MGBmtFERHKkyYMDBRsRkRxp8uBAwUZEJEeaPDhQsBERyZEmDw6UICAikpFKWWfKRmtTSjUUkdFULetsrJ9/2rIZrfih9/UP4Ax96Let6Wt00USkTSnrrLKWDza3relj0ZLlzF18B4uWLN9To9GHLiKjoXgO6kvILuvrH9hzbhrLWroZLanaWh5oisZaqqGI5Kv8HJRkrA7kjKpaszGzG83sOTN7JGG9mdmXzWy9mf3azE7Mvpjxkmow481itx9rqYYikq+4c1CSsd66kqYZ7SbgrArr3wLMKzwuA7468mKlk1RT2eVOebgZi6mGIpKvWltLxnLrStVg4+73Ai9U2OQc4JserAC6zOyQrApYSaWaisOegDOjq5NrzjthzFZfRSQfSecgta4Ml0WCwAxgY+R5b2FZ7uIGS0U5IdDct/gMBRoRyVzSgM2LTpmpgZxlskgQiAvhHruh2WWEpjZmzZo14jeODpZKygQZy9VWEclXpQGb3bOnaaxfhLnHxoXSjczmALe7+/Ex674O3OPuNxeerwNe5+6bKu2zu7vbe3p66ilzrKTUw2LNRkSkHZjZanfvbnQ5apVFM9pS4JJCVtqpwJZqgSYPaecfihuXIyIi+arajGZmNwOvAw4ws17gs0AHgLt/DbgTeCuwHtgGvCevwlaSZv4h3cRIRKQxUjWj5SHrZrQ01NQmIq1uLDejtQzdxEhEpDHGVLDRTYxERBpjTAUb3cRIRKQxWnoizlrpJkYiIo0xpoIN6CZGIiKNMKaa0UREpDEUbEREJHcKNiIikjsFGxERyd2YSxAQEUnrtjV9sdmrScslmYKNiEiMpLkUe373Areu7tMcizVSsCnQlYqIRF27bN2egFI0MLiLm1duZFfZnJIDg7u4dtk6nTMqULBBs0GLyHBJcyaWB5pq20ugBAGSr2CuXbauQSUSkUZLmjNxvMXdnFhzLFajYINmgxaR4ZLmUrzolJmaY7EOakYjXJHE3edGVyoiY1eluRS7Z09TH2+NFGwIVzDRPhvQlYrIWJUmWUhzLNZuTASbNLnyUzo7mNwxjv5tg7pSERmjlCyUn7YPNmlz5fsHBunsGM91F8zXl0pkjKqULKTzwsi0fbBRrryIpFUpWUhj8Uam7bPRlCsvImklJQVN6ezgyh8+TF//AM5QC8lta/pGt4AtrO2DjXLlRSStpHRnMzQWb4TaPtgoV15E0jp3wQyuOe8EZnR1YsCMrk6uOe8E+rcNxm6vlpD02r7Ppp5cebXNioxdcWnN1y5bp7F4I2Se0HeRt+7ubu/p6WnIe1dSnr0GocZzzXknKOCIjFHNdF4ws9Xu3j2qb5qBVM1oZnaWma0zs/Vmtjhm/Swzu9vM1pjZr83srdkXdXRonjSRseG2NX0sWrKcuYvvYNGS5RU7+5Oa13QBml7VZjQzGw9cD7wJ6AXuN7Ol7r42stmnge+5+1fN7DjgTmBODuXNneZJE2l/9Qze1KwBI5OmZnMysN7dn3D3HcAtwDll2ziwX+H3KcAz2RVxdCW1waptVqT1FWszH/3ug2rBGGVpgs0MYGPkeW9hWdRVwMVm1kuo1Xwok9I1QFL2mrLURFpbsTYT19FfpBaM/KQJNnEDUsqzCi4CbnL3w4C3At8ys2H7NrPLzKzHzHo2b95ce2lHgdpmRdpTXH9sObVg5CdN6nMvMDPy/DCGN5O9FzgLwN1/ZWaTgQOA56IbufsNwA0QstHqLHPu1DYr0n6q1VrUgpGvNDWb+4F5ZjbXzCYCFwJLy7Z5GngDgJkdC0wGmrPqIiJjUqVai1ow8le1ZuPuO83scmAZMB640d0fNbOrgR53Xwp8HPg3M/tbQhPbpd6oATw50mBPkdaVdN8qBZnRkWoGAXe/k9DxH132mcjva4FF2RatORQDTF//AMZQZ5XucyHSWirNJiL5a/vpakaiPBe/vKqmWxKItBb1xzaOgk0FabJXlCop0nzU5N18FGwqSBNIlCop0lx0a+fm1Pa3GBiJaoFEqZIizUfzGzYnBZsK4mYTKI5wVaqkSHOqZ37DWibllPqoGa0CZa+ItJ5DuzpruveMmt1Gh4JNFcpeEWktSeNpkpq8KzW76X8/Owo2dYpmu0zp7MAM+rcNqvYj0mC1tkjotiKjQ8GmDuXV7v6BofuTqwou0ni1tEjU2uwm9VGCQB2qjb9R5otI69BtRUaHajZ1SFO9VhVcpDlUG+CpRKDRoWBTh6Rqd/k2ItJYaTPNlAiUPzWj1SGu2h2lKrhIc9AAz+ahmk0dyqvdykYTaU7KNGseCjZ1UrVbpPkp06x5qBlNRNqWMs2ah2o2ItK2lGnWPBRsRKStqcm7OSjYiEhL0Y3RWpOCjYi0DM3Q3LqUICAiLUPjZlqXgo2ItAyNm2ldCjYi0jKSxsdo3EzzU7ARkZahcTOtSwkCOVLWjEi2NG6mdaUKNmZ2FvAlYDzwDXdfErPNO4GrAAcecve/zLCcLaMYYPr6BzDCwQBlzYhkReNmWlPVZjQzGw9cD7wFOA64yMyOK9tmHnAlsMjdXwV8NIeyNr1iWmZxLiYvW6+sGREZq9LUbE4G1rv7EwBmdgtwDrA2ss1fA9e7+4sA7v5c1gVtBdXu4AnVs2bU9CYi7ShNgsAMYGPkeW9hWdRRwFFmdp+ZrSg0uw1jZpeZWY+Z9WzevLm+EjexNOmXlbJmojUjZ6jp7bY1fRmWUkRk9KUJNhazrLyFaAIwD3gdcBHwDTPrGvYi9xvcvdvdu6dPn15rWZtetfRLIwSQRUuWxwYQDVgTkXaVphmtF5gZeX4Y8EzMNivcfRB40szWEYLP/ZmUskVccebRJVNpAHuSBMqTBa74/kP8408eLbnhmgasiUi7SlOzuR+YZ2ZzzWwicCGwtGyb24DXA5jZAYRmtSeyLGgrOHfBDK457wRmdHViwIyuTq67YD4zujqHVQUHdzsvbhssaS7r2qsjdr8asCYira5qzcbdd5rZ5cAyQurzje7+qJldDfS4+9LCujeb2VpgF3CFuz+fZ8GbVVxa5t9+98GqrxsY3MWkCePo7BhfUjPSgDURaQepxtm4+53AnWXLPhP53YGPFR5SJunWtOW2DAxy3QXzlY0mklI0e3NKZwdmlDRN63+neWgGgVEQ15cT59CuTg1YE0mp/HYD/QODe9ZpEHXzUbAZBeVTbEzp7ODlHTsZ3DXUk6PmMpFkcePPqo1rK2ZyKtg0BwWbUVJeY9HgTZHKqk39VK2lAJook3P3btiwAVavhgcegFNPhfPOa3SpRpWCTYNEg0/xn+pvv/ugAo8Iw5vI4qZ+Gm/GLi9fU6ohmZy7dsHjj4egUgwua9bA1q1D21x8sYKNjC7d5lZkuDRTP+1yH5a9GTUqTdM7d8Jjjw0FltWr4cEH4eWXK79u9ep8y9WEWi/Y/Nu/wR13wCGHxD8OPBAmtM6fVWnWAAUbGavSNH/NiPTdjEo22uAgrF07VFtZvRoeeggG6miqe+yxEJD23ju78jW51jkrF61aBT/+cfJ6sxBwkoJR9DFp0uiVO0GlWQPUryNjVbXhAsVaS27Zm9u3wyOPlAaWhx8Oy7PgHmpAixZls78W0HrBZtOmyuvd4fe/D48HqwymnDp1KPAcemhyUNpnn+zKXybpn2pKZ4ea16QtpbmIqjT104ysL7wGBuDXvy7tY3nkkVCTydI++8CCBbBwIZx4Ihx1VLb7b3LtF2xq8eKL4bF2beXt9tlnKPAcfHBpcJoxA445JvxucXOWVhb3T9XZMR4z1LwmbSdtH2Vud+TcuTMEklWr4P77w89HHw2d+lmaMiUElOJj4UKYNw/GpZkhrD2N7WCT1ksvwW9/Gx5Jurrg+OOhuxtOOSWkNs6eXTUAJf1TJU1x0zSpnCJ1qKWPcsRNZO6wcSOsXDn0WL26vj6WSqZNC8GkWGtZuBDmzh3TgSVO6wWb730P+vpC0Il7vPBCY8rV3w+//GV4FB144FDgOeUUOOkk2G+/YS+N+6cqji8op0k5pZXlOrP51q3Q0zMUWFatyv7idPr0oYBSrLWkuKiUVgw2f/qnlddv3w7PPjs8CD3zTOnzzZvDQKs8Pfcc/OQn4QHhC3nssUPB59RT4VWvgvHjh700qXlNswxIK0vqo6z5IqrYHBattfzmN6E2k5WDDy4NLAsXhmZzBZa6tF6wqWbSpHClMXt25e127gwBJ6mGVAxOzz6bXUehe+gfWrsWbrwxLNt771DjOeWUocehh+bXZi3SQHVdRLlDb+/w5rBt27Ir2MyZpf0rJ54Y+mElM+ZZXgnUoLu723t6ehry3jXZvTs0zSUFpSeeCMEjy3bgmTOHAs+pp4Yv/l57Zbd/kQaqmo32xz+GzvtocHn22ewKMG0anHzyUNP2SSeFJu8WYWar3b270eWolYJNFnbtCkHngQdgxYrwWLMmu5z88ePh1a8u7f856ih1QErr27kzZIMV+1hWrgzPszovTZwI8+eH/5ligDnyyJZuClOwqVFbBZs4O3aE0cUrVgxdna1fn93+u7qG/nmKjwMOyG7/IiMUW4OZ7qU1lp6ebJvDjjyy9P9i/vymGLydJQWbGrV9sInzhz+E5oFi7WfVqpDFlpUjjiit/cyfH67sREbZbWv6+NzNKzlq4zpes+lx5m9ax4JNj3PQHzO8ge/UqaU1lpNPHhMXXAo2NRqTwabc7t1h7E7xKm/FijCSeefObPY/aVLI/Y/2/8yZ09JNCNKkdu0aag5buZINty9nznO/Y7xnlPHZ0THUHFZ8tHhzWL0UbGqkYJNg27bQ9xMNQBs3Zrf/6dOHaj7FDtIpU7Lbv4wNvb1DfSzF5rBqMx3X4vDDSwPL/PkweXJ2+29hCjY1UrCpwaZNQ4Fn5crQFJfVP3Zx7E+09vOqV7XUzNmSs5deKh0suXJlGBqQlalTS/tZxkhzWL0UbGqkYDMC0SaLYgBauza7DJ699ho+9meGxveMCbt2he9SNLA8+mhmA6B3T+hg3PzXlH635s3b0xymmc6rU7CpkYJNxopTdRSDz4oVYQaDrBx2WGntZ+FCjf1pB888U3rRknFz2Maph7Dm4Hk8efjxzD//TE6/8M2JzWHlk3RCGPB5zXknKOBEKNjUSMGmNjVf8bnD735X2vfzwAP5jP0pBiCN/WluL70URt5Hay19fZntfse+U1g1/QhWH3wUDx56NA8dchQDU6alDhaLliyPncpmRlcn9y0+I7NytjoFmxop2KSX2RXfjh0h2y1a+8ly7M+UKaG9vZiAcPzxYTYEBaDRt2tXmCsserGRYXMYEybAa0qbw/701o30bh1+MVMtWBQvpJJulmbAk0velk2520CrBptUvcBmdhbwJWA88A13X5Kw3fnA94GT3F2RJCOZ3Tp64sRwC4Tubrj88rDs+edDVlExAK1aFe7xU48tW+DnPw+PosmTQ4rq3LlDc9YVH7NmwUEHjcn01cwVm8OKn+H994eaTFbmzi3twF+wADpLJ8/s2xp/4VJpRue4C6lymum8PVQNNmY2HrgeeBPQC9xvZkvdfW3ZdvsCHwZW5lHQsSzXadn33x/e8pbwgOFjf1auDDMh1Dv255VXwuy8jzwSv37SpBB04gLR7Nmhr6ijo773blcvvzy8Oay3N7v9F2uoxVrqySenmjusnhmd4y6kojTTeftIU7M5GVjv7k8AmNktwDlA+e0tPwd8AfhEpiWUiv/EmWfvjBsHRx8dHpdcEpYNDAyN/SnWgJ5+uv73iNq+vfKN6caNC3dFLQ9G0Uc7JypEm8OK41oeeSS7O0tGm8OK6cd19r3VM6NzpQumzG//LA2VJtjMAKKjCnuBU6IbmNkCYKa7325mCjYZS/onfv0x01PdYnfEOjth0aLwKCqO/Sk+sm62Kdq9O1y19/bCfffFb3PAAaW1ofLHtGmt01RXflx7esIsyFmZM6c0sJx44rDmsFpFL3imdHYwuWMc/dsGU138JF1IKSmg/aQJNnH/pXuyCsxsHHAdcGnVHZldBlwGMGvWrHQllMR729TTl5NZTeiQQ+Dcc8MDSsdnrFgBDz8M69aFfpy8/eEP4bF6dfz6vfdODkSzZ4e/JeYGdrl7+eXS2SJWrsx2toj99hs+WPKgg7LbP8P7XPoHBunsGM91F8xP9b3STQLHjqrZaGb2J8BV7n5m4fmVAO5+TeH5FGADULysPRh4ATi7UpKAstFGbu7iO4j79JKyd0Z9HIN7uEHd+vUhDfvpp8PP6COP2lCtJkwIfUNx/UZz5oSMupFOlbJ7Nzz2WGlTZJbNYeWp6KecEppCc8oErJZBVkvNRAM5a9PO2Wj3A/PMbC7QB1wI/GVxpbtvAfbMLWFm9wCfUDZafor/nEmXCUkdsplltaVlFjqWDzwQTjtt+Hr3kPlWDDxxwWjz5uzLVW7nTnjqqfBIcvDBlZvqyueXe/bZ4c2MWTaHzZ5dGlgWLBi1vqs0GWS1JK+cu2CGgssYUDXYuPtOM7scWEZIfb7R3R81s6uBHndfmnchZUi1f/RKTRC5ZrXVwyz0p0ybFk6WcbZtC01L5UGoGJx6e7OrHVTy7LNDASTOlCkhqL70UrhtRJZ3bt1vv+HTB2XcHFaLahlkoHRlGS7VOBt3vxO4s2zZZxK2fd3IiyVJKv2jV8veSeqMdcLo7aZsvthrr6HsuDg7d4YxJsUA9NRTw2tIr7ySfzm3bMmmf2qUm8OqiWviqnZxoj4XiaOpfVtM0j+6QWIbebR93SC2+S23TLa8TZgQmrZmzYI/+7Ph64v9RpWa6uodxJqFWbNKA8uJJzZNKnd5Lbr4Henaq4MXtw3GvkbpypJEwabF1DrmBig5YTgkBpxc+28aJdpvdNJJ8dv88Y+Vg9GmTdnMqL3vvsOzww4+eOT7zUlSH9+kCePo7BivCTOlJgo2LabWMTeTO8YNO2FUOm02rP+mkfbdN8zjdvzx8et37Aj9RnGB6Omnw2PHjtLXTJgQ9lcMKqeeCscc01LzxCV9F7YMDHLdBfNryiBTxpko2LSYWsfcVOvILVdpVoIxe8KYOBGOOCI84uzeDb//fWiO23df6OqCffZpnYGkCSrVomvJIEtqjoMWa7KVEdGsz20iacxNkq7ODrbv3D2shvT2hTO4dXVf6uVqOmkPaZpgYagJtpa+Gd06IFutOs6mder0UlEtqaadHeO56uxXcc15JzCjqxMj/ONfc94J3P3Y5tga0s0rNyaO0ZHWVqx59PUP4JTWPIrfESjt6ytuc9ua6vfDabqUe2kIBZs2ccWZR9PZUX3KlWJQKTaD3Lf4DJ5c8jbuW3wG5y6YkXgC2JVQA9YJo/VVG+x73+IzmNHVOazmnPZiI+lCSGNxxhYFmzZx7oIZJVehcYrNFtUmRowzPqH/QSeM1pem5jGS2knchZDG4ow9CjZtpHgV+s8XzK/7nzvpxHDRKTN1wmhTaWoeI6mdRC+Eok226usbW5SN1oaSMtbS/HNXem337GljMxutzaWZeXmkszNr/jNRNprUZcymQbepNJ+nPvPm0KrZaAo2UrNRv1WBiOzRqsFGzWhSUdzV7KjfqkBEWp6CjSRKGvmdNCuB0qBFJImy0SRRUg1GadAiUivVbCRRpQGecbP+VspMUudydvI+lvqsJA+q2UiipJpKcZxE2nETSdOhpJnqRErlfSz1WUleVLORRJXGVtQybkIJBdnJ+1jqs5K8KNhIopEMDo3SRIzZyftY6rOSvCjYSEVZjPyudF8UqU09x7KWPhh9VpIX9dlI7uqdiPG2NX0sWrKcuYvvYNGS5eo3oPZjWWsfjCbNlLyoZiO5q6c5Tnd3jFfrsay1DyarplORcgo2MipqbY5TR3Vp89eUzg7MoH/bYE0BoJ4+GE2aKXlQsJGmNNY7qstrdv0Dg3vW1VLLUx+MNAv12UhTaqW7O+bRtxRXs4uqdpfMYpn6+gcon+9BfTDSCKmCjZmdZWbrzGy9mS2OWf8xM1trZr82s7vMbHb2RZWxpFU6qvMaBJmmBpe0TbRMAA57Ao5uXCaNUrUZzczGA9cDbwJ6gfvNbKm7r41stgbodvdtZvYB4AvABXkUWNpPpdTcuOXNNJ1KXn1LSc1f5dukLZMzdFtwkUZI02dzMrDe3Z8AMLNbgHOAPcHG3e+ObL8CuDjLQkr+GnUCr5Z1FncDr2bKUkuqXfT1D7BoyfLE41jteMfN3hBVqZY31vu7pDmlaUabAWyMPO8tLEvyXuCnIymUjK5GzodVqWaQxfZ5q9SHlHQc0xzvcxfMKJl/rquzg6l7daSaiy5tf5fGMcloSlOziZtPPvb2nmZ2MdANnJ6w/jLgMoBZs2alLKLkrZFpxrVehTfbVXu1GkjccUx7vGtNQS7WlopJAdF/0vKaULPVEKX9panZ9AIzI88PA54p38jM3gh8Cjjb3bfH7cjdb3D3bnfvnj59ej3llRw08gRea9ZZs2WpRWsgScqPYx7Hu9akgGarIUr7SxNs7gfmmdlcM5sIXAgsjW5gZguArxMCzXPZF1Py1MgTeK1ZZ82YpXbughnct/iMxIBTfhyTjqtD3c1Z1ZICymsrzVZDlPZXNdi4+07gcmAZ8Bvge+7+qJldbWZnFza7FtgH+L6ZPWhmSxN2J02okSfw8r6Jav0RabdvRH9E2uMYt11Rvf1laYNH8bjEtoPTnOOYpD2Ye9LXLl/d3d3e09PTkPeW4ZopnXikyvsjIJz0Rzq+JM0xSnsco/0rcWpNUy4O4Ky0n7jjEpXFMZL8mdlqd+9udDlqpWAjbSfNibdWeQWwuYvviK1lGPDkkrdlWr6k4wLh2LTyBcZY0qrBRnOjSUPkWZPKoz9itAdv1tqclWa25qS/30CDPSV3CjaSm6SAknfabR6TT+bVoV7p1tu1qpYqrUk5pZE0EafkotLAxbzTbit11NebOJBXxl6tCRLlavl7mjGTT8YO1WwkF5UCSt5pt0lNSkDdNaosayBx5a2nRldrDVE3RpNGUrCREYtrLqsUULJszklqqos7gS9asrzufpdmPFHX04+kG6NJoyjYyIgkXV137dXBi9sGh21fPElnUUuo9cp+pDWqeqePySs4aWCmtBL12ciIJF1du5PYPzDSfopq753U9zOaMyWMxuSmzTZ1j0glqtnIiCRdRfcPDNLV2cHkjnH0bxscdmWfRXNOrVf2efa7lBuNyU1H8+8RGSkFGxmRSjf56h8YpLNjPNddML+m7Kq0TU+19v2MZr/LaDRxNWM/kkgSBRsZkXqm2E9Sax9MPVf2STWqrPtXRmtMizr8pVUo2MiIRK+uk2o4aa/mq/XB1HLr6FrkMchUTVwipRRsZMSKV9dJc2+lvZqvdIvlWm4dXat6+leq1YTqDYTtNCGqSJSCjWRmpFfzSU1P481y6WyvNvNyUvBLWxOqJ1Vad8+UdqXUZ8nMSFOak6ZT2ZUwM3mWd7aMk1Qjy2u6Hd09U9qZgo1kqnjXyieXvC32DpHVXhsXrNLeAbOWecLiTuxRlWpkeWWatfwgTbPwGGvWroV3vhMOPBAmT4ajj4bPfhYG6vjcenvhr/4KDj0UJk2COXPgox+FF1+s/DqzszH7KWabMduO2UbMlmJ2asL2HZh9GLOVmG3B7GXMHsfsm5hNT3jNAZhdi9ljmA1g1o/ZGsw+n+ZPUzOaNJWkpqdqzXNZzSYApfd2ietDqSfTLE1fjGZlbkErV8IZZ8DgIJx/PsycCcuXw9VXw113hcekSen2tWEDnHYaPPccnHMOHHMMrFoFX/oS/Pu/w333wf77l77GbBzwNeCvgY3AD4HngYOAU4GFwIqy10wDfgqcDDwA3AjsAGYCbyy8dnPZaxYQ7ta8P/Az4DZgMnA48E7gk9X+PAUbaXppOttrzWRLOrFXurNlMYC9feEMbl3dl7pvKm0gVAZbi9m1C97zHti2DX78Yzj77LB89+5Q07n1VrjuOli8ON3+PvjBEGi+/GX40IeGln/sY2E/n/oUfO1r5a/6OCHQfAt4H+47StaadcS80zcJgeaDuH+1bHt0mC34AAAPz0lEQVSjvMXLbCrwE2AisAj38uAV9x7D6E6d0haS7ngJ4YRdfgJPChhp7mxZrPmkzRqr5c6hLZ2NVmxCS3tOuesuuPbacPW+bRvMmgXnnQdXXglTppRu+8QTsGRJqDX09UFnJ8yYAYsWwT/909AV/44d4YR8003w5JOwfXto3nrNa8IJ/I1vzOzPZflyeMMb4LWvhf/4j+HlPeIImD07lKNa82Jx+zlzQg1nXOR8/8c/wiGHhOP63HPYPvuEO3Wa7Qf0Af3Akbhvr1pmszOAu4Af4P6OVH+n2dXAPwCX4359qtfEUM1G2kKtmWx3P7aZa847oa47Wz7TP1BTplktfTFjZpDm178OH/gA7L03vOMdISDccw98/vPwk5+EJqOurrDtpk1w0kmwdSu89a3w9rfDK6+Ek/i3vgWXXz4UbC69FG6+GY4/Hi65JASlZ56BX/4yNEVlHWwAzjpr+LrDD4ejjoLHHx8KJGn29eY3lwYagH33DUH1Zz+DFSWVirOBfQjNaOMwOx84Evgj8EvcH4p5p78s/LwJs4OAPwcOBJ4FfoZ7XEfnXwK7gG9hdhzwBmAvYAPw77i/VPmPCxRspC0kNUElJQGkCRhZ9aGoL6bM734HH/4w7LNPqNUcc8zQug9+EL76Vfi7v4MbbgjLfvADeOEF+Od/ho98pHRfL788dHLesgVuuQUWLgx9KeNLMxt5/vnS5zfdBE89lb7cc+aEYFa0rpAleNRR8dvPmxeCzeOPVw82afb1s5+FfQ05qfBzEPgNMLvkNWa3Apfgvi3mNUcB3yMEjaJBzK7G/X9G9jEVOAJ4HLgK+CjhTuJFz2N2Ce53Vv4DFWykTST16ySNo0lzos+qD0V9MWW+/e3Q3PXxj5cGGghNYt/+dqix/Mu/lHaud8Z8ZnvvPfS7WWhqmjRpeO0Ahneu33TT8OavSk4/vTTYbNkSfpY3+RUVl/f3V993ffs6sPDz74A1hI76tcBxwPXA24GXgEtjXnMt8B3gakIywBmEGtLnMOvF/aay7Y8APkRIBPgmIeBcDFwD3IrZibj/ptKfqGAjbaPeTLZK+4ORT4ejCTPLPPBA+HnGGcPXTZ0KCxbAvffCY4+Fvpazz4a//3v4m7+BZcvgzDNDs9Jxx5X2hey3H/zFX4RmuPnzQ3Pbn/0ZnHIK7LXX8Pe6555c/rw9in1XWaSDx++rWHUbAP4C92cLz1dhdjahNvIuzD4VaR4rvmYN8G6GOu1/hNlOYClwJXBT2fbjgf+F+7WR9/8iZocAHyPUeP5HpT9BwUba2khP9Fn1oYyZvpg0ilfxhxwSv764vHgVP3t2aG676qrQ7/LDH4blM2fCJz4RmuSKvvvd0O/zne+EsS4Qxr6cfz588Ytw0EHZ/R3F2kbx7ym3dWvpdtnvqzj4ZkUk0ATumzBbSehf6SYkEhRfMx24jeHZYXcQUqCPwmwK7lsi7wHwo5iS/YgQbE6OL/gQBRtpezrRN5niCfPZZ+FVrxq+ftOm0u0Ajj02BJKdO+Ghh+AXvwjNbB/5SGhKe+97w3adnSEoXXUVbNwYakg33RSa5p56Cv7zP4f2OdI+m6MLtePSfpQhv/1t+JnUDxNV376KU0sktdMVA0W0/XEdob9m+Gvcd2O2FTig8JothaC1Fdgv4X3i3iOeu1d9AGcVCrkeWByzfhLw3cL6lcCcavtcuHChizSjHz3Q66ddc5fP+eTtfto1d/mPHuhtdJFaQ2jsqb7d5z4Xtvv0p4eve/FF9/32c5882f2VVyrv5957w37+/M8rb7drl/u8eWHbP/xhaPnppw+VOc3j9NNL93vXXWH5a187/D03bAjrZs923727cvnc3devD9vPmRPKG7V1q/vee7t3drq/9JIDPe6Ow2sKZVvrcedZWFtYf2pk2UcKy74as/1BhXUvOUyILP9RYfkFMa+5oLDu32PLEHlUna7GzMYTOpveQuh4ushC+lvUe4EX3f1I4Dog1fQFIs1mNG7nPOZdfDF0dISayfr1pev+4R9Ck9HFFw8lB6xaBb///fD9FJcV+2M2bw5ZaOVefjmMVZkwASZOHFp+zz21hJrhfTynnx5qXPfeC0uXDi3fvRs+WRhQ//73l/azDA6GvqgNG0r3dcQRIe35qafg+rKhLJ/9bPgbLrmkNCEipDbfBxyL2ftKXhOeH0tIT74/sub/EWool2J2QmT7ccAXCs9+gPvOyGuKBfo0Zl2R13QRxt8A3EIVVQd1mtmfAFe5+5mF51cCuPs1kW2WFbb5lZlNIORsT/cKO9egTmlGtQzAlDLFk+q73528zVe+EoLDV74SOvz33TeMtp8+PWSG/epXIUPtvvtg2rTwmo9+NJyATz8djjwyJBFs2BASAdzh7rvhT/4EHnwwJBcceyyceGLo09m6FW6/HZ5+OvTtfOlL2f7N5dPVzJoVBqv29IQkhvLpap56CubODf1Q5U145dPVHHts2P/dd4fms//6L9h/f8wsDOoEMDsa+CWh6eunwKOESsFbgW3Ambj/suR9zN5BCA7bgVsJ2WinAycSWqdOw718upovE7LR+gizCUAYo3MYYeqat+O+u+Kxqlb1Ac4HvhF5/i7gX8u2eQQ4LPJ8A3BAzL4uA3qAnlmzZlWvWoqMsjmfvN1nxzzmfPL2Rhet+aWpH7z44tD2y5a5v+lN7l1d7hMnuh9xhPsVV5Ru4+6+YoX7+9/v/upXu0+dGprYjjjC/dJL3R9+eGi7F190/8d/dH/9690PPTTs8+CDQ/PXd76TrjmrHo8+6n7++e777x/ec9489898xn3btuHbPvnkUPNanKefDn/XwQe7d3S4z5rl/uEPuz///J5NKDajDTVlzXT4hkOfww6HZx2+43CsJ53b4TSH2x2eL7xmg8MXHaZWeM0lDisKzWwDDmscPuQwPvE1kUeams07gDPd/X2F5+8CTnb3D0W2ebSwTW/h+YbCNs/H7RNUs5HmpJqNNLuSmk0LSXOLgV7CbKBFhwHPJG1TaEabAryQRQFFRlPSPXXG7ABMkYykCTb3A/PMbK6ZTQQuJAz8iVoKFBtqzweWe7Uqk0gTGukN4EQkXtVxNu6+08wuJ9zLYDxwo7s/amEm0B53Xwr8b+BbZraeUKO5MM9Ci+RJ43JEspdqUKeHSdbuLFv2mcjvrwDppqsWEZExR7eFFhGR3CnYiIhI7hRsREQkdwo2IiKSOwUbERHJnYKNiIjkrup0Nbm9sdlm4Hcj3M0BwB8yKE6WmrFMoHLVqhnL1YxlApWrFlmUaba7T8+iMKOpYcEmC2bW02xzBDVjmUDlqlUzlqsZywQqVy2asUyjRc1oIiKSOwUbERHJXasHmxsaXYAYzVgmULlq1YzlasYygcpVi2Ys06ho6T4bERFpDa1esxERkRbQ9MHGzN5hZo+a2W4zS8ziMLOzzGydma03s8WR5XPNbKWZ/dbMvlu4J89IyzTNzH5e2OfPzWxqzDavN7MHI49XzOzcwrqbzOzJyLr5Iy1T2nIVttsVee+lkeWZH6u05TKz+Wb2q8Jn/WszuyCyLrPjlfQ9iayfVPjb1xeOxZzIuisLy9eZ2Zn1lqHOcn3MzNYWjs1dZjY7si728xylcl1qZpsj7/++yLp3Fz7z35rZu8tfm2OZrouU53Ez64+sy+VYmdmNZvacmT2SsN7M7MuFMv/azE6MrMvlODWdNPeObuQDOBY4GrgH6E7YZjywATgcmAg8BBxXWPc94MLC718DPpBBmb4ALC78vhj4fJXtpxHu87NX4flNwPk5HKtU5QJeSlie+bFKWy7gKGBe4fdDgU1AV5bHq9L3JLLNB4GvFX6/EPhu4ffjCttPAuYW9pPq3usZlev1ke/PB4rlqvR5jlK5LgX+Nea104AnCj+nFn5Pvr99hmUq2/5DhHtw5X2sXgucCDySsP6twE8BA04FVuZ5nJrx0fQ1G3f/jbuvq7LZycB6d3/C3XcAtwDnmJkBZwA/KGz3f4FzMyjWOYV9pd3n+cBP3X1bBu9dSa3l2iPHY5WqXO7+uLv/tvD7M8BzQNYD12K/JxXK+gPgDYVjcw5wi7tvd/cngfWF/Y1Kudz97sj3ZwXh9ux5S3O8kpwJ/NzdX3D3F4GfA2c1oEwXATdn8L4Vufu9hAvKJOcA3/RgBdBlZoeQ33FqOk0fbFKaAWyMPO8tLNsf6Hf3nWXLR+ogd98EUPh5YJXtL2T4F/6fCtXp68xsUgZlqqVck82sx8xWFJv2yO9Y1VIuAMzsZMJV64bI4iyOV9L3JHabwrHYQjg2aV5br1r3/V7CVXJR3Oc5muV6e+Gz+YGZzazxtXmViUJT41xgeWRxXseqmqRy5/m9aiqp7tSZNzP7BXBwzKpPufuP0+wiZplXWD6iMqV5fWQ/hwAnEG6rXXQl8CzhhHoD8Eng6lEs1yx3f8bMDgeWm9nDwNaY7VKnKmZ8vL4FvNvddxcW1328yncfs6z8b8z8u5RC6n2b2cVAN3B6ZPGwz9PdN8S9Pody/QS42d23m9n7CbXCM1K+Nq8yFV0I/MDdd0WW5XWsqmnE96qpNEWwcfc3jnAXvcDMyPPDgGcIcxB1mdmEwlVqcfmIymRmvzezQ9x9U+Hk+FyFXb0T+JG7D0b2vanw63Yz+z/AJ9KUKatyFZqpcPcnzOweYAFwK3Ueq6zKZWb7AXcAny40NRT3XffxKpP0PYnbptfMJgBTCM0jaV5br1T7NrM3EoL36e6+vbg84fPM4gRatVzu/nzk6b8Bn4+89nVlr71nNMoUcSHwN9EFOR6rapLKnddxajrt0ox2PzDPQjbVRMKXbKmHHri7CX0mAO8G0tSUqlla2FeafQ5rMy6ccIv9JOcCsRkseZTLzKYWm6HM7ABgEbA2x2OVtlwTgR8R2rW/X7Yuq+MV+z2pUNbzgeWFY7MUuNBCttpcYB6wqs5y1FwuM1sAfB04292fiyyP/TxHsVyHRJ6eDfym8Psy4M2F8k0F3kxp7T63MhXKdTShw/1XkWV5HqtqlgKXFLLSTgW2FC6i8jpOzafRGQrVHsB/I0T/7cDvgWWF5YcCd0a2eyvwOOEq5VOR5YcTTgrrge8DkzIo0/7AXcBvCz+nFZZ3A9+IbDcH6APGlb1+OfAw4aT5bWCfjI5V1XIBpxXe+6HCz/fmeaxqKNfFwCDwYOQxP+vjFfc9ITTJnV34fXLhb19fOBaHR177qcLr1gFvyfh7Xq1cvyh8/4vHZmm1z3OUynUN8Gjh/e8Gjom89q8Kx3E98J7RKlPh+VXAkrLX5XasCBeUmwrf4V5Cv9r7gfcX1htwfaHMDxPJrM3rODXbQzMIiIhI7tqlGU1ERJqYgo2IiOROwUZERHKnYCMiIrlTsBERkdwp2IiISO4UbEREJHcKNiIikrv/D26PV5dTCGvqAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for t in range(100):\\n\",\n    \"    prediction = net(x)     # input x and predict based on x\\n\",\n    \"\\n\",\n    \"    loss = loss_func(prediction, y)     # must be (1. nn output, 2. target)\\n\",\n    \"\\n\",\n    \"    optimizer.zero_grad()   # clear gradients for next train\\n\",\n    \"    loss.backward()         # backpropagation, compute gradients\\n\",\n    \"    optimizer.step()        # apply gradients\\n\",\n    \"\\n\",\n    \"    if t % 10 == 0:\\n\",\n    \"        # plot and show learning process\\n\",\n    \"        plt.cla()\\n\",\n    \"        plt.scatter(x.data.numpy(), y.data.numpy())\\n\",\n    \"        plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)\\n\",\n    \"        plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color':  'red'})\\n\",\n    \"        plt.show()\\n\",\n    \"        plt.pause(0.1)\\n\",\n    \"\\n\",\n    \"plt.ioff()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.1\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/302_classification.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 302 Classification\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<torch._C.Generator at 0x10725f2a0>\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1)    # reproducible\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nSUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x106c34e10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# make fake data\\n\",\n    \"n_data = torch.ones(100, 2)\\n\",\n    \"x0 = torch.normal(2*n_data, 1)      # class0 x data (tensor), shape=(100, 2)\\n\",\n    \"y0 = torch.zeros(100)               # class0 y data (tensor), shape=(100, 1)\\n\",\n    \"x1 = torch.normal(-2*n_data, 1)     # class1 x data (tensor), shape=(100, 2)\\n\",\n    \"y1 = torch.ones(100)                # class1 y data (tensor), shape=(100, 1)\\n\",\n    \"x = torch.cat((x0, x1), 0).type(torch.FloatTensor)  # shape (200, 2) FloatTensor = 32-bit floating\\n\",\n    \"y = torch.cat((y0, y1), ).type(torch.LongTensor)    # shape (200,) LongTensor = 64-bit integer\\n\",\n    \"\\n\",\n    \"# torch can only train on Variable, so convert them to Variable\\n\",\n    \"x, y = Variable(x), Variable(y)\\n\",\n    \"\\n\",\n    \"plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=y.data.numpy(), s=100, lw=0, cmap='RdYlGn')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class Net(torch.nn.Module):\\n\",\n    \"    def __init__(self, n_feature, n_hidden, n_output):\\n\",\n    \"        super(Net, self).__init__()\\n\",\n    \"        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer\\n\",\n    \"        self.out = torch.nn.Linear(n_hidden, n_output)   # output layer\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = F.relu(self.hidden(x))      # activation function for hidden layer\\n\",\n    \"        x = self.out(x)\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Net (\\n\",\n      \"  (hidden): Linear (2 -> 10)\\n\",\n      \"  (out): Linear (10 -> 2)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"net = Net(n_feature=2, n_hidden=10, n_output=2)     # define the network\\n\",\n    \"print(net)  # net architecture\\n\",\n    \"\\n\",\n    \"# Loss and Optimizer\\n\",\n    \"# Softmax is internally computed.\\n\",\n    \"# Set parameters to be updated.\\n\",\n    \"optimizer = torch.optim.SGD(net.parameters(), lr=0.02)\\n\",\n    \"loss_func = torch.nn.CrossEntropyLoss()  # the target label is NOT an one-hotted\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.ion()   # something about plotting\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAakAAAD8CAYAAADNGFurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nRK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10979b550>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAakAAAD8CAYAAADNGFurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x109773a90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAakAAAD8CAYAAADNGFurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nwAblEaVSfAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1099ec908>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAakAAAD8CAYAAADNGFurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nPGwyAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1098f1668>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAagAAAD8CAYAAAAi2jCVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n180XgVpTAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x109aef668>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAakAAAD8CAYAAADNGFurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1099d6438>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAakAAAD8CAYAAADNGFurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x104bce320>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAagAAAD8CAYAAAAi2jCVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\noLuEAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x109c06eb8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAagAAAD8CAYAAAAi2jCVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\noLuEAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x109d37518>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAagAAAD8CAYAAAAi2jCVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\noLuEAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x109d58da0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAagAAAD8CAYAAAAi2jCVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x109d0bcf8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAagAAAD8CAYAAAAi2jCVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x109def4a8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for t in range(100):\\n\",\n    \"    out = net(x)                 # input x and predict based on x\\n\",\n    \"    loss = loss_func(out, y)     # must be (1. nn output, 2. target), the target label is NOT one-hotted\\n\",\n    \"\\n\",\n    \"    optimizer.zero_grad()   # clear gradients for next train\\n\",\n    \"    loss.backward()         # backpropagation, compute gradients\\n\",\n    \"    optimizer.step()        # apply gradients\\n\",\n    \"    \\n\",\n    \"    if t % 10 == 0 or t in [3, 6]:\\n\",\n    \"        # plot and show learning process\\n\",\n    \"        plt.cla()\\n\",\n    \"        _, prediction = torch.max(F.softmax(out), 1)\\n\",\n    \"        pred_y = prediction.data.numpy().squeeze()\\n\",\n    \"        target_y = y.data.numpy()\\n\",\n    \"        plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=pred_y, s=100, lw=0, cmap='RdYlGn')\\n\",\n    \"        accuracy = sum(pred_y == target_y)/200.\\n\",\n    \"        plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 20, 'color':  'red'})\\n\",\n    \"        plt.show()\\n\",\n    \"        plt.pause(0.1)\\n\",\n    \"\\n\",\n    \"plt.ioff()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.1\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/303_build_nn_quickly.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 303 Build NN Quickly\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# replace following class code with an easy sequential network\\n\",\n    \"class Net(torch.nn.Module):\\n\",\n    \"    def __init__(self, n_feature, n_hidden, n_output):\\n\",\n    \"        super(Net, self).__init__()\\n\",\n    \"        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer\\n\",\n    \"        self.predict = torch.nn.Linear(n_hidden, n_output)   # output layer\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = F.relu(self.hidden(x))      # activation function for hidden layer\\n\",\n    \"        x = self.predict(x)             # linear output\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"net1 = Net(1, 10, 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# easy and fast way to build your network\\n\",\n    \"net2 = torch.nn.Sequential(\\n\",\n    \"    torch.nn.Linear(1, 10),\\n\",\n    \"    torch.nn.ReLU(),\\n\",\n    \"    torch.nn.Linear(10, 1)\\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Net (\\n\",\n      \"  (hidden): Linear (1 -> 10)\\n\",\n      \"  (predict): Linear (10 -> 1)\\n\",\n      \")\\n\",\n      \"Sequential (\\n\",\n      \"  (0): Linear (1 -> 10)\\n\",\n      \"  (1): ReLU ()\\n\",\n      \"  (2): Linear (10 -> 1)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(net1)     # net1 architecture\\n\",\n    \"print(net2)     # net2 architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/304_save_reload.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 304 Save and Reload\\n\",\n    \"\\n\",\n    \"\\\"\\\"\\\"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<torch._C.Generator at 0x7f931012a918>\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1)    # reproducible\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Generate some fake data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)  # x data (tensor), shape=(100, 1)\\n\",\n    \"y = x.pow(2) + 0.2*torch.rand(x.size())  # noisy y data (tensor), shape=(100, 1)\\n\",\n    \"x, y = Variable(x, requires_grad=False), Variable(y, requires_grad=False)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def save():\\n\",\n    \"    # save net1\\n\",\n    \"    net1 = torch.nn.Sequential(\\n\",\n    \"        torch.nn.Linear(1, 10),\\n\",\n    \"        torch.nn.ReLU(),\\n\",\n    \"        torch.nn.Linear(10, 1)\\n\",\n    \"    )\\n\",\n    \"    optimizer = torch.optim.SGD(net1.parameters(), lr=0.5)\\n\",\n    \"    loss_func = torch.nn.MSELoss()\\n\",\n    \"\\n\",\n    \"    for t in range(100):\\n\",\n    \"        prediction = net1(x)\\n\",\n    \"        loss = loss_func(prediction, y)\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        loss.backward()\\n\",\n    \"        optimizer.step()\\n\",\n    \"\\n\",\n    \"    # plot result\\n\",\n    \"    plt.figure(1, figsize=(10, 3))\\n\",\n    \"    plt.subplot(131)\\n\",\n    \"    plt.title('Net1')\\n\",\n    \"    plt.scatter(x.data.numpy(), y.data.numpy())\\n\",\n    \"    plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)\\n\",\n    \"\\n\",\n    \"    # 2 ways to save the net\\n\",\n    \"    torch.save(net1, 'net.pkl')  # save entire net\\n\",\n    \"    torch.save(net1.state_dict(), 'net_params.pkl')   # save only the parameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def restore_net():\\n\",\n    \"    # restore entire net1 to net2\\n\",\n    \"    net2 = torch.load('net.pkl')\\n\",\n    \"    prediction = net2(x)\\n\",\n    \"\\n\",\n    \"    # plot result\\n\",\n    \"    plt.subplot(132)\\n\",\n    \"    plt.title('Net2')\\n\",\n    \"    plt.scatter(x.data.numpy(), y.data.numpy())\\n\",\n    \"    plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def restore_params():\\n\",\n    \"    # restore only the parameters in net1 to net3\\n\",\n    \"    net3 = torch.nn.Sequential(\\n\",\n    \"        torch.nn.Linear(1, 10),\\n\",\n    \"        torch.nn.ReLU(),\\n\",\n    \"        torch.nn.Linear(10, 1)\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    # copy net1's parameters into net3\\n\",\n    \"    net3.load_state_dict(torch.load('net_params.pkl'))\\n\",\n    \"    prediction = net3(x)\\n\",\n    \"\\n\",\n    \"    # plot result\\n\",\n    \"    plt.subplot(133)\\n\",\n    \"    plt.title('Net3')\\n\",\n    \"    plt.scatter(x.data.numpy(), y.data.numpy())\\n\",\n    \"    plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)\\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAlYAAADSCAYAAACIG474AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\ngg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f92d43e7cc0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# save net1\\n\",\n    \"save()\\n\",\n    \"# restore entire net (may slow)\\n\",\n    \"restore_net()\\n\",\n    \"# restore only the net parameters\\n\",\n    \"restore_params()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/305_batch_train.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 305 Batch Train\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<torch._C.Generator at 0x7faffc159918>\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.utils.data as Data\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1)    # reproducible\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 5\\n\",\n    \"# BATCH_SIZE = 8\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = torch.linspace(1, 10, 10)       # this is x data (torch tensor)\\n\",\n    \"y = torch.linspace(10, 1, 10)       # this is y data (torch tensor)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)\\n\",\n    \"loader = Data.DataLoader(\\n\",\n    \"    dataset=torch_dataset,      # torch TensorDataset format\\n\",\n    \"    batch_size=BATCH_SIZE,      # mini batch size\\n\",\n    \"    shuffle=True,               # random shuffle for training\\n\",\n    \"    num_workers=2,              # subprocesses for loading data\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | Step:  0 | batch x:  [ 6.  7.  2.  3.  1.] | batch y:  [  5.   4.   9.   8.  10.]\\n\",\n      \"Epoch:  0 | Step:  1 | batch x:  [  9.  10.   4.   8.   5.] | batch y:  [ 2.  1.  7.  3.  6.]\\n\",\n      \"Epoch:  1 | Step:  0 | batch x:  [  3.   4.   2.   9.  10.] | batch y:  [ 8.  7.  9.  2.  1.]\\n\",\n      \"Epoch:  1 | Step:  1 | batch x:  [ 1.  7.  8.  5.  6.] | batch y:  [ 10.   4.   3.   6.   5.]\\n\",\n      \"Epoch:  2 | Step:  0 | batch x:  [ 3.  9.  2.  6.  7.] | batch y:  [ 8.  2.  9.  5.  4.]\\n\",\n      \"Epoch:  2 | Step:  1 | batch x:  [ 10.   4.   8.   1.   5.] | batch y:  [  1.   7.   3.  10.   6.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for epoch in range(3):   # train entire dataset 3 times\\n\",\n    \"    for step, (batch_x, batch_y) in enumerate(loader):  # for each training step\\n\",\n    \"        # train your data...\\n\",\n    \"        print('Epoch: ', epoch, '| Step: ', step, '| batch x: ',\\n\",\n    \"              batch_x.numpy(), '| batch y: ', batch_y.numpy())\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Suppose a different batch size that cannot be fully divided by the number of data entreis:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | Step:  0 | batch x:  [  3.  10.   9.   4.   7.   8.   2.   1.] | batch y:  [  8.   1.   2.   7.   4.   3.   9.  10.]\\n\",\n      \"Epoch:  0 | Step:  1 | batch x:  [ 5.  6.] | batch y:  [ 6.  5.]\\n\",\n      \"Epoch:  1 | Step:  0 | batch x:  [  4.   8.   3.   2.   1.  10.   5.   6.] | batch y:  [  7.   3.   8.   9.  10.   1.   6.   5.]\\n\",\n      \"Epoch:  1 | Step:  1 | batch x:  [ 7.  9.] | batch y:  [ 4.  2.]\\n\",\n      \"Epoch:  2 | Step:  0 | batch x:  [  6.   2.   4.  10.   9.   3.   8.   5.] | batch y:  [ 5.  9.  7.  1.  2.  8.  3.  6.]\\n\",\n      \"Epoch:  2 | Step:  1 | batch x:  [ 7.  1.] | batch y:  [  4.  10.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"BATCH_SIZE = 8\\n\",\n    \"loader = Data.DataLoader(\\n\",\n    \"    dataset=torch_dataset,      # torch TensorDataset format\\n\",\n    \"    batch_size=BATCH_SIZE,      # mini batch size\\n\",\n    \"    shuffle=True,               # random shuffle for training\\n\",\n    \"    num_workers=2,              # subprocesses for loading data\\n\",\n    \")\\n\",\n    \"for epoch in range(3):   # train entire dataset 3 times\\n\",\n    \"    for step, (batch_x, batch_y) in enumerate(loader):  # for each training step\\n\",\n    \"        # train your data...\\n\",\n    \"        print('Epoch: ', epoch, '| Step: ', step, '| batch x: ',\\n\",\n    \"              batch_x.numpy(), '| batch y: ', batch_y.numpy())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/306_optimizer.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 206 Optimizers\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.utils.data as Data\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<torch._C.Generator at 0x7f502d43b930>\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"torch.manual_seed(1)    # reproducible\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"LR = 0.01\\n\",\n    \"BATCH_SIZE = 32\\n\",\n    \"EPOCH = 12\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Generate some fake data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f4ffd5388d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# fake dataset\\n\",\n    \"x = torch.unsqueeze(torch.linspace(-1, 1, 1000), dim=1)\\n\",\n    \"y = x.pow(2) + 0.1*torch.normal(torch.zeros(*x.size()))\\n\",\n    \"\\n\",\n    \"# plot dataset\\n\",\n    \"plt.scatter(x.numpy(), y.numpy())\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Put dataset into torch dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)\\n\",\n    \"loader = Data.DataLoader(\\n\",\n    \"    dataset=torch_dataset, \\n\",\n    \"    batch_size=BATCH_SIZE, \\n\",\n    \"    shuffle=True, num_workers=2,)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Default network\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class Net(torch.nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(Net, self).__init__()\\n\",\n    \"        self.hidden = torch.nn.Linear(1, 20)   # hidden layer\\n\",\n    \"        self.predict = torch.nn.Linear(20, 1)   # output layer\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = F.relu(self.hidden(x))      # activation function for hidden layer\\n\",\n    \"        x = self.predict(x)             # linear output\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Different nets\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"net_SGD         = Net()\\n\",\n    \"net_Momentum    = Net()\\n\",\n    \"net_RMSprop     = Net()\\n\",\n    \"net_Adam        = Net()\\n\",\n    \"nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Different optimizers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"opt_SGD         = torch.optim.SGD(net_SGD.parameters(), lr=LR)\\n\",\n    \"opt_Momentum    = torch.optim.SGD(net_Momentum.parameters(), lr=LR, momentum=0.8)\\n\",\n    \"opt_RMSprop     = torch.optim.RMSprop(net_RMSprop.parameters(), lr=LR, alpha=0.9)\\n\",\n    \"opt_Adam        = torch.optim.Adam(net_Adam.parameters(), lr=LR, betas=(0.9, 0.99))\\n\",\n    \"optimizers = [opt_SGD, opt_Momentum, opt_RMSprop, opt_Adam]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"loss_func = torch.nn.MSELoss()\\n\",\n    \"losses_his = [[], [], [], []]   # record loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0\\n\",\n      \"Epoch:  1\\n\",\n      \"Epoch:  2\\n\",\n      \"Epoch:  3\\n\",\n      \"Epoch:  4\\n\",\n      \"Epoch:  5\\n\",\n      \"Epoch:  6\\n\",\n      \"Epoch:  7\\n\",\n      \"Epoch:  8\\n\",\n      \"Epoch:  9\\n\",\n      \"Epoch:  10\\n\",\n      \"Epoch:  11\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAZYAAAEKCAYAAAAxXHOuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nMBjcimlYDAaDweBWTMNiMBgMBrfy/yB4amTjla2eAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f4ffb683470>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# training\\n\",\n    \"for epoch in range(EPOCH):\\n\",\n    \"    print('Epoch: ', epoch)\\n\",\n    \"    for step, (batch_x, batch_y) in enumerate(loader):          # for each training step\\n\",\n    \"        b_x = Variable(batch_x)\\n\",\n    \"        b_y = Variable(batch_y)\\n\",\n    \"\\n\",\n    \"        for net, opt, l_his in zip(nets, optimizers, losses_his):\\n\",\n    \"            output = net(b_x)              # get output for every net\\n\",\n    \"            loss = loss_func(output, b_y)  # compute loss for every net\\n\",\n    \"            opt.zero_grad()                # clear gradients for next train\\n\",\n    \"            loss.backward()                # backpropagation, compute gradients\\n\",\n    \"            opt.step()                     # apply gradients\\n\",\n    \"            l_his.append(loss.item())     # loss recoder\\n\",\n    \"\\n\",\n    \"labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']\\n\",\n    \"for i, l_his in enumerate(losses_his):\\n\",\n    \"    plt.plot(l_his, label=labels[i])\\n\",\n    \"plt.legend(loc='best')\\n\",\n    \"plt.xlabel('Steps')\\n\",\n    \"plt.ylabel('Loss')\\n\",\n    \"plt.ylim((0, 0.2))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/401_CNN.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 401 CNN\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* torchvision\\n\",\n    \"* matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import torch.utils.data as Data\\n\",\n    \"import torchvision\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<torch._C.Generator at 0x7f33c006fe50>\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"torch.manual_seed(1)    # reproducible\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Hyper Parameters\\n\",\n    \"EPOCH = 1               # train the training data n times, to save time, we just train 1 epoch\\n\",\n    \"BATCH_SIZE = 50\\n\",\n    \"LR = 0.001              # learning rate\\n\",\n    \"DOWNLOAD_MNIST = True   # set to False if you have downloaded\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\\n\",\n      \"Processing...\\n\",\n      \"Done!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Mnist digits dataset\\n\",\n    \"train_data = torchvision.datasets.MNIST(\\n\",\n    \"    root='./mnist/',\\n\",\n    \"    train=True,                                     # this is training data\\n\",\n    \"    transform=torchvision.transforms.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to\\n\",\n    \"                                                    # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]\\n\",\n    \"    download=DOWNLOAD_MNIST,                        # download it if you don't have it\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([60000, 28, 28])\\n\",\n      \"torch.Size([60000])\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33a14e6518>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot one example\\n\",\n    \"print(train_data.train_data.size())                 # (60000, 28, 28)\\n\",\n    \"print(train_data.train_labels.size())               # (60000)\\n\",\n    \"plt.imshow(train_data.train_data[0].numpy(), cmap='gray')\\n\",\n    \"plt.title('%i' % train_data.train_labels[0])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)\\n\",\n    \"train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# convert test data into Variable, pick 2000 samples to speed up testing\\n\",\n    \"test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)\\n\",\n    \"test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1)).type(torch.FloatTensor)[:2000]/255.   # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)\\n\",\n    \"test_y = test_data.test_labels[:2000]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class CNN(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(CNN, self).__init__()\\n\",\n    \"        self.conv1 = nn.Sequential(         # input shape (1, 28, 28)\\n\",\n    \"            nn.Conv2d(\\n\",\n    \"                in_channels=1,              # input height\\n\",\n    \"                out_channels=16,            # n_filters\\n\",\n    \"                kernel_size=5,              # filter size\\n\",\n    \"                stride=1,                   # filter movement/step\\n\",\n    \"                padding=2,                  # if want same width and length of this image after con2d, padding=(kernel_size-1)/2 if stride=1\\n\",\n    \"            ),                              # output shape (16, 28, 28)\\n\",\n    \"            nn.ReLU(),                      # activation\\n\",\n    \"            nn.MaxPool2d(kernel_size=2),    # choose max value in 2x2 area, output shape (16, 14, 14)\\n\",\n    \"        )\\n\",\n    \"        self.conv2 = nn.Sequential(         # input shape (1, 28, 28)\\n\",\n    \"            nn.Conv2d(16, 32, 5, 1, 2),     # output shape (32, 14, 14)\\n\",\n    \"            nn.ReLU(),                      # activation\\n\",\n    \"            nn.MaxPool2d(2),                # output shape (32, 7, 7)\\n\",\n    \"        )\\n\",\n    \"        self.out = nn.Linear(32 * 7 * 7, 10)   # fully connected layer, output 10 classes\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.conv1(x)\\n\",\n    \"        x = self.conv2(x)\\n\",\n    \"        x = x.view(x.size(0), -1)           # flatten the output of conv2 to (batch_size, 32 * 7 * 7)\\n\",\n    \"        output = self.out(x)\\n\",\n    \"        return output, x    # return x for visualization\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"CNN(\\n\",\n      \"  (conv1): Sequential(\\n\",\n      \"    (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\\n\",\n      \"    (1): ReLU()\\n\",\n      \"    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\\n\",\n      \"  )\\n\",\n      \"  (conv2): Sequential(\\n\",\n      \"    (0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\\n\",\n      \"    (1): ReLU()\\n\",\n      \"    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\\n\",\n      \"  )\\n\",\n      \"  (out): Linear(in_features=1568, out_features=10, bias=True)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"cnn = CNN()\\n\",\n    \"print(cnn)  # net architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)   # optimize all cnn parameters\\n\",\n    \"loss_func = nn.CrossEntropyLoss()                       # the target label is not one-hotted\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/yoshitaka-i/.conda/envs/tensor/lib/python3.5/site-packages/ipykernel_launcher.py:29: 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     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 2.3101 | test accuracy: 0.20\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3344d2df60>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.6699 | test accuracy: 0.88\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3344d2f1d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.0701 | test accuracy: 0.94\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3340189c18>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.1329 | test accuracy: 0.94\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33401d2e10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.0395 | test accuracy: 0.96\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33504f0160>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.0199 | test accuracy: 0.96\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3350563320>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.1077 | test accuracy: 0.97\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3340435668>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.0391 | test accuracy: 0.97\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33503aa240>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.0789 | test accuracy: 0.97\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33504d67b8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.1067 | test accuracy: 0.97\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33402252e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.0277 | test accuracy: 0.97\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f334ff549b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.0251 | test accuracy: 0.98\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33504c8470>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# following function (plot_with_labels) is for visualization, can be ignored if not interested\\n\",\n    \"from matplotlib import cm\\n\",\n    \"try: from sklearn.manifold import TSNE; HAS_SK = True\\n\",\n    \"except: HAS_SK = False; print('Please install sklearn for layer visualization')\\n\",\n    \"def plot_with_labels(lowDWeights, labels):\\n\",\n    \"    plt.cla()\\n\",\n    \"    X, Y = lowDWeights[:, 0], lowDWeights[:, 1]\\n\",\n    \"    for x, y, s in zip(X, Y, labels):\\n\",\n    \"        c = cm.rainbow(int(255 * s / 9)); plt.text(x, y, s, backgroundcolor=c, fontsize=9)\\n\",\n    \"    plt.xlim(X.min(), X.max()); plt.ylim(Y.min(), Y.max()); plt.title('Visualize last layer'); plt.show(); plt.pause(0.01)\\n\",\n    \"\\n\",\n    \"plt.ion()\\n\",\n    \"# training and testing\\n\",\n    \"for epoch in range(EPOCH):\\n\",\n    \"    for step, (x, y) in enumerate(train_loader):   # gives batch data, normalize x when iterate train_loader\\n\",\n    \"        b_x = Variable(x)   # batch x\\n\",\n    \"        b_y = Variable(y)   # batch y\\n\",\n    \"\\n\",\n    \"        output = cnn(b_x)[0]               # cnn output\\n\",\n    \"        loss = loss_func(output, b_y)   # cross entropy loss\\n\",\n    \"        optimizer.zero_grad()           # clear gradients for this training step\\n\",\n    \"        loss.backward()                 # backpropagation, compute gradients\\n\",\n    \"        optimizer.step()                # apply gradients\\n\",\n    \"\\n\",\n    \"        if step % 100 == 0:\\n\",\n    \"            test_output, last_layer = cnn(test_x)\\n\",\n    \"            pred_y = torch.max(test_output, 1)[1].data.squeeze()\\n\",\n    \"            accuracy = (pred_y == test_y).sum().item() / float(test_y.size(0))\\n\",\n    \"            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data, '| test accuracy: %.2f' % accuracy)\\n\",\n    \"            if HAS_SK:\\n\",\n    \"                # Visualization of trained flatten layer (T-SNE)\\n\",\n    \"                tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\\n\",\n    \"                plot_only = 500\\n\",\n    \"                low_dim_embs = tsne.fit_transform(last_layer.data.numpy()[:plot_only, :])\\n\",\n    \"                labels = test_y.numpy()[:plot_only]\\n\",\n    \"                plot_with_labels(low_dim_embs, labels)\\n\",\n    \"plt.ioff()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[7 2 1 0 4 1 4 9 5 9] prediction number\\n\",\n      \"[7 2 1 0 4 1 4 9 5 9] real number\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# print 10 predictions from test data\\n\",\n    \"test_output, _ = cnn(test_x[:10])\\n\",\n    \"pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()\\n\",\n    \"print(pred_y, 'prediction number')\\n\",\n    \"print(test_y[:10].numpy(), 'real number')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"tensor\",\n   \"language\": \"python\",\n   \"name\": \"tensor\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.4\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/402_RNN.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 402 RNN\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* matplotlib\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"from torch import nn\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import torchvision.datasets as dsets\\n\",\n    \"import torchvision.transforms as transforms\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<torch._C.Generator at 0x7f5864059930>\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"torch.manual_seed(1)    # reproducible\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Hyper Parameters\\n\",\n    \"EPOCH = 1               # train the training data n times, to save time, we just train 1 epoch\\n\",\n    \"BATCH_SIZE = 64\\n\",\n    \"TIME_STEP = 28          # rnn time step / image height\\n\",\n    \"INPUT_SIZE = 28         # rnn input size / image width\\n\",\n    \"LR = 0.01               # learning rate\\n\",\n    \"DOWNLOAD_MNIST = True   # set to True if haven't download the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Mnist digital dataset\\n\",\n    \"train_data = dsets.MNIST(\\n\",\n    \"    root='./mnist/',\\n\",\n    \"    train=True,                         # this is training data\\n\",\n    \"    transform=transforms.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to\\n\",\n    \"                                        # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]\\n\",\n    \"    download=DOWNLOAD_MNIST,            # download it if you don't have it\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([60000, 28, 28])\\n\",\n      \"torch.Size([60000])\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAP8AAAEICAYAAACQ6CLfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f580f9626a0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot one example\\n\",\n    \"print(train_data.train_data.size())     # (60000, 28, 28)\\n\",\n    \"print(train_data.train_labels.size())   # (60000)\\n\",\n    \"plt.imshow(train_data.train_data[0].numpy(), cmap='gray')\\n\",\n    \"plt.title('%i' % train_data.train_labels[0])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Data Loader for easy mini-batch return in training\\n\",\n    \"train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# convert test data into Variable, pick 2000 samples to speed up testing\\n\",\n    \"test_data = dsets.MNIST(root='./mnist/', train=False, transform=transforms.ToTensor())\\n\",\n    \"test_x = Variable(test_data.test_data, volatile=True).type(torch.FloatTensor)[:2000]/255.   # shape (2000, 28, 28) value in range(0,1)\\n\",\n    \"test_y = test_data.test_labels.numpy().squeeze()[:2000]    # covert to numpy array\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class RNN(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(RNN, self).__init__()\\n\",\n    \"\\n\",\n    \"        self.rnn = nn.LSTM(         # if use nn.RNN(), it hardly learns\\n\",\n    \"            input_size=INPUT_SIZE,\\n\",\n    \"            hidden_size=64,         # rnn hidden unit\\n\",\n    \"            num_layers=1,           # number of rnn layer\\n\",\n    \"            batch_first=True,       # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.out = nn.Linear(64, 10)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        # x shape (batch, time_step, input_size)\\n\",\n    \"        # r_out shape (batch, time_step, output_size)\\n\",\n    \"        # h_n shape (n_layers, batch, hidden_size)\\n\",\n    \"        # h_c shape (n_layers, batch, hidden_size)\\n\",\n    \"        r_out, (h_n, h_c) = self.rnn(x, None)   # None represents zero initial hidden state\\n\",\n    \"\\n\",\n    \"        # choose r_out at the last time step\\n\",\n    \"        out = self.out(r_out[:, -1, :])\\n\",\n    \"        return out\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"RNN (\\n\",\n      \"  (rnn): LSTM(28, 64, batch_first=True)\\n\",\n      \"  (out): Linear (64 -> 10)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"rnn = RNN()\\n\",\n    \"print(rnn)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all cnn parameters\\n\",\n    \"loss_func = nn.CrossEntropyLoss()                       # the target label is not one-hotted\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 2.3127 | test accuracy: 0.14\\n\",\n      \"Epoch:  0 | train loss: 1.1182 | test accuracy: 0.56\\n\",\n      \"Epoch:  0 | train loss: 0.9374 | test accuracy: 0.68\\n\",\n      \"Epoch:  0 | train loss: 0.6279 | test accuracy: 0.75\\n\",\n      \"Epoch:  0 | train loss: 0.8004 | test accuracy: 0.75\\n\",\n      \"Epoch:  0 | train loss: 0.3407 | test accuracy: 0.88\\n\",\n      \"Epoch:  0 | train loss: 0.3342 | test accuracy: 0.89\\n\",\n      \"Epoch:  0 | train loss: 0.3200 | test accuracy: 0.91\\n\",\n      \"Epoch:  0 | train loss: 0.3430 | test accuracy: 0.92\\n\",\n      \"Epoch:  0 | train loss: 0.1234 | test accuracy: 0.93\\n\",\n      \"Epoch:  0 | train loss: 0.2714 | test accuracy: 0.92\\n\",\n      \"Epoch:  0 | train loss: 0.1043 | test accuracy: 0.93\\n\",\n      \"Epoch:  0 | train loss: 0.2893 | test accuracy: 0.93\\n\",\n      \"Epoch:  0 | train loss: 0.0635 | test accuracy: 0.94\\n\",\n      \"Epoch:  0 | train loss: 0.2412 | test accuracy: 0.94\\n\",\n      \"Epoch:  0 | train loss: 0.1203 | test accuracy: 0.92\\n\",\n      \"Epoch:  0 | train loss: 0.0807 | test accuracy: 0.94\\n\",\n      \"Epoch:  0 | train loss: 0.1307 | test accuracy: 0.94\\n\",\n      \"Epoch:  0 | train loss: 0.1166 | test accuracy: 0.95\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# training and testing\\n\",\n    \"for epoch in range(EPOCH):\\n\",\n    \"    for step, (x, y) in enumerate(train_loader):        # gives batch data\\n\",\n    \"        b_x = Variable(x.view(-1, 28, 28))              # reshape x to (batch, time_step, input_size)\\n\",\n    \"        b_y = Variable(y)                               # batch y\\n\",\n    \"\\n\",\n    \"        output = rnn(b_x)                               # rnn output\\n\",\n    \"        loss = loss_func(output, b_y)                   # cross entropy loss\\n\",\n    \"        optimizer.zero_grad()                           # clear gradients for this training step\\n\",\n    \"        loss.backward()                                 # backpropagation, compute gradients\\n\",\n    \"        optimizer.step()                                # apply gradients\\n\",\n    \"\\n\",\n    \"        if step % 50 == 0:\\n\",\n    \"            test_output = rnn(test_x)                   # (samples, time_step, input_size)\\n\",\n    \"            pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()\\n\",\n    \"            accuracy = sum(pred_y == test_y) / float(test_y.size)\\n\",\n    \"            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[7 2 1 0 4 1 4 9 5 9] prediction number\\n\",\n      \"[7 2 1 0 4 1 4 9 5 9] real number\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# print 10 predictions from test data\\n\",\n    \"test_output = rnn(test_x[:10].view(-1, 28, 28))\\n\",\n    \"pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()\\n\",\n    \"print(pred_y, 'prediction number')\\n\",\n    \"print(test_y[:10], 'real number')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/403_RNN_regressor.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 403 RNN Regressor\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* matplotlib\\n\",\n    \"* numpy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"from torch import nn\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<torch._C.Generator at 0x7f9888178930>\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"torch.manual_seed(1)    # reproducible\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Hyper Parameters\\n\",\n    \"TIME_STEP = 10      # rnn time step\\n\",\n    \"INPUT_SIZE = 1      # rnn input size\\n\",\n    \"LR = 0.02           # learning rate\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAYYAAAD8CAYAAABzTgP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\noihJ+H8Lckc7eouA8QAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f9831f53208>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# show data\\n\",\n    \"steps = np.linspace(0, np.pi*2, 100, dtype=np.float32)\\n\",\n    \"x_np = np.sin(steps)    # float32 for converting torch FloatTensor\\n\",\n    \"y_np = np.cos(steps)\\n\",\n    \"plt.plot(steps, y_np, 'r-', label='target (cos)')\\n\",\n    \"plt.plot(steps, x_np, 'b-', label='input (sin)')\\n\",\n    \"plt.legend(loc='best')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class RNN(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(RNN, self).__init__()\\n\",\n    \"\\n\",\n    \"        self.rnn = nn.RNN(\\n\",\n    \"            input_size=INPUT_SIZE,\\n\",\n    \"            hidden_size=32,     # rnn hidden unit\\n\",\n    \"            num_layers=1,       # number of rnn layer\\n\",\n    \"            batch_first=True,   # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)\\n\",\n    \"        )\\n\",\n    \"        self.out = nn.Linear(32, 1)\\n\",\n    \"\\n\",\n    \"    def forward(self, x, h_state):\\n\",\n    \"        # x (batch, time_step, input_size)\\n\",\n    \"        # h_state (n_layers, batch, hidden_size)\\n\",\n    \"        # r_out (batch, time_step, hidden_size)\\n\",\n    \"        r_out, h_state = self.rnn(x, h_state)\\n\",\n    \"\\n\",\n    \"        outs = []    # save all predictions\\n\",\n    \"        for time_step in range(r_out.size(1)):    # calculate output for each time step\\n\",\n    \"            outs.append(self.out(r_out[:, time_step, :]))\\n\",\n    \"        return torch.stack(outs, dim=1), h_state\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"RNN (\\n\",\n      \"  (rnn): RNN(1, 32, batch_first=True)\\n\",\n      \"  (out): Linear (32 -> 1)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"rnn = RNN()\\n\",\n    \"print(rnn)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all cnn parameters\\n\",\n    \"loss_func = nn.MSELoss()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"h_state = None      # for initial hidden state\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f982fc30c50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(1, figsize=(12, 5))\\n\",\n    \"plt.ion()           # continuously plot\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f982fbd9240>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for step in range(60):\\n\",\n    \"    start, end = step * np.pi, (step+1)*np.pi   # time range\\n\",\n    \"    # use sin predicts cos\\n\",\n    \"    steps = np.linspace(start, end, TIME_STEP, dtype=np.float32)\\n\",\n    \"    x_np = np.sin(steps)    # float32 for converting torch FloatTensor\\n\",\n    \"    y_np = np.cos(steps)\\n\",\n    \"\\n\",\n    \"    x = Variable(torch.from_numpy(x_np[np.newaxis, :, np.newaxis]))    # shape (batch, time_step, input_size)\\n\",\n    \"    y = Variable(torch.from_numpy(y_np[np.newaxis, :, np.newaxis]))\\n\",\n    \"\\n\",\n    \"    prediction, h_state = rnn(x, h_state)   # rnn output\\n\",\n    \"    # !! next step is important !!\\n\",\n    \"    h_state = Variable(h_state.data)        # repack the hidden state, break the connection from last iteration\\n\",\n    \"\\n\",\n    \"    loss = loss_func(prediction, y)         # cross entropy loss\\n\",\n    \"    optimizer.zero_grad()                   # clear gradients for this training step\\n\",\n    \"    loss.backward()                         # backpropagation, compute gradients\\n\",\n    \"    optimizer.step()                        # apply gradients\\n\",\n    \"\\n\",\n    \"    # plotting\\n\",\n    \"    plt.plot(steps, y_np.flatten(), 'r-')\\n\",\n    \"    plt.plot(steps, prediction.data.numpy().flatten(), 'b-')\\n\",\n    \"    plt.draw(); plt.pause(0.05)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/404_autoencoder.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 404 Autoencoder\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"Dependencies:\\n\",\n    \"\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* matplotlib\\n\",\n    \"* numpy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import torch.utils.data as Data\\n\",\n    \"import torchvision\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n    \"from matplotlib import cm\\n\",\n    \"import numpy as np\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<torch._C.Generator at 0x7f54e0186918>\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"torch.manual_seed(1)    # reproducible\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Hyper Parameters\\n\",\n    \"EPOCH = 10\\n\",\n    \"BATCH_SIZE = 64\\n\",\n    \"LR = 0.005         # learning rate\\n\",\n    \"DOWNLOAD_MNIST = False\\n\",\n    \"N_TEST_IMG = 5\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Mnist digits dataset\\n\",\n    \"train_data = torchvision.datasets.MNIST(\\n\",\n    \"    root='./mnist/',\\n\",\n    \"    train=True,                                     # this is training data\\n\",\n    \"    transform=torchvision.transforms.ToTensor(),    # Converts a PIL.Image or numpy.ndarray to\\n\",\n    \"                                                    # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]\\n\",\n    \"    download=DOWNLOAD_MNIST,                        # download it if you don't have it\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([60000, 28, 28])\\n\",\n      \"torch.Size([60000])\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAP8AAAEICAYAAACQ6CLfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\ngg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f548b738940>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot one example\\n\",\n    \"print(train_data.train_data.size())     # (60000, 28, 28)\\n\",\n    \"print(train_data.train_labels.size())   # (60000)\\n\",\n    \"plt.imshow(train_data.train_data[2].numpy(), cmap='gray')\\n\",\n    \"plt.title('%i' % train_data.train_labels[2])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)\\n\",\n    \"train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class AutoEncoder(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(AutoEncoder, self).__init__()\\n\",\n    \"\\n\",\n    \"        self.encoder = nn.Sequential(\\n\",\n    \"            nn.Linear(28*28, 128),\\n\",\n    \"            nn.Tanh(),\\n\",\n    \"            nn.Linear(128, 64),\\n\",\n    \"            nn.Tanh(),\\n\",\n    \"            nn.Linear(64, 12),\\n\",\n    \"            nn.Tanh(),\\n\",\n    \"            nn.Linear(12, 3),   # compress to 3 features which can be visualized in plt\\n\",\n    \"        )\\n\",\n    \"        self.decoder = nn.Sequential(\\n\",\n    \"            nn.Linear(3, 12),\\n\",\n    \"            nn.Tanh(),\\n\",\n    \"            nn.Linear(12, 64),\\n\",\n    \"            nn.Tanh(),\\n\",\n    \"            nn.Linear(64, 128),\\n\",\n    \"            nn.Tanh(),\\n\",\n    \"            nn.Linear(128, 28*28),\\n\",\n    \"            nn.Sigmoid(),       # compress to a range (0, 1)\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        encoded = self.encoder(x)\\n\",\n    \"        decoded = self.decoder(encoded)\\n\",\n    \"        return encoded, decoded\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"AutoEncoder (\\n\",\n      \"  (encoder): Sequential (\\n\",\n      \"    (0): Linear (784 -> 128)\\n\",\n      \"    (1): Tanh ()\\n\",\n      \"    (2): Linear (128 -> 64)\\n\",\n      \"    (3): Tanh ()\\n\",\n      \"    (4): Linear (64 -> 12)\\n\",\n      \"    (5): Tanh ()\\n\",\n      \"    (6): Linear (12 -> 3)\\n\",\n      \"  )\\n\",\n      \"  (decoder): Sequential (\\n\",\n      \"    (0): Linear (3 -> 12)\\n\",\n      \"    (1): Tanh ()\\n\",\n      \"    (2): Linear (12 -> 64)\\n\",\n      \"    (3): Tanh ()\\n\",\n      \"    (4): Linear (64 -> 128)\\n\",\n      \"    (5): Tanh ()\\n\",\n      \"    (6): Linear (128 -> 784)\\n\",\n      \"    (7): Sigmoid ()\\n\",\n      \"  )\\n\",\n      \")\\n\",\n      \"Epoch:  0 | train loss: 0.2323\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAASwAAACACAYAAACx1FRUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f547c47d4e0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 0.0559\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAASwAAACACAYAAACx1FRUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXmQXFX1x789W8+SZSYJsyQxkz0TshIgLCpERaVARdzQ\\nEhC1wFIByxLKci0tNa5YJQhqmRKXWKWUiigWFSxBXEIC2SiyACGEDJNgJjOZTGZfut/vj/f7nnf7\\n9uue7pn3uucN5/NPz3S/7nfvu+/d+z3nnntuzHEcKIqiRIGSYhdAURQlV7TDUhQlMmiHpShKZNAO\\nS1GUyKAdlqIokUE7LEVRIoN2WIqiRAbtsBRFiQzaYSmKEhnK8jk4FotFPizecZxYts+nQh0BdDiO\\nc062A6ZCPcdqS0DrGSVyqacqrKnJsWIXQFHCQDssRVEig3ZYiqJEBu2wFEWJDNphKYoSGfKaJVSC\\n4/zzzwcA3HrrrQCAG2+8Eb/+9a8BAPfccw8AYM+ePcUpnKJMUlRhKYoSGWL5ZBwNI9ajtLQUADBz\\n5sy0z6g+qqursWLFCgDApz/9aQDAD37wAwDAhz70IQwODgIAvvOd7wAAvv71r2c8X7HjsNavXw8A\\neOyxxwAAM2bMSDumu7sbADB79uzxnma34zgXZDtgMsXtvOUtbwEA/Pa3v8Xll18OAHj++efH/N5k\\nj0/68pe/DMC7H0tKXH2wadMmPPHEEzn/zmSvZ1DkUs+CmIQLFiwAAFRUVODSSy8FALzhDW8AANTW\\n1gIA3vve92b9jba2NgDA3XffDQC49tprAQA9PT145plnACCvm6AYbNy4EX/84x8BeB00B4yenh4M\\nDw8D8Dqqiy++GIBrGvKzQnDZZZdJOR588MHQz3fhhRcCAJ5++unQz1UobrrpJnz+858HACSTyZTP\\nNC35+FGTUFGUyBCqwrLNHz+zLxeSyaTI697eXgCu+QAAr776Krq6ugDkZkYUkurqagDAhg0bAABb\\nt25FU1OT77GHDx/G9773PQDA7373OwDAf//7XwCuafHtb3877OIKmzZtAgAsW7YsVIVFE2nRokUA\\ngObmZsRiY1oFkaC5uRmVlZXFLsaEuOiiiwAA119/vZjqq1atSjnmjjvuwIkTJwB4VtPWrVsBADt3\\n7gy8TKqwFEWJDKEqrNbWVgBAZ2cngLEVFnvkM2fOAADe9KY3AQCGh4fxm9/8JqxihsbPfvYzAO7E\\nwFhs2LAB06ZNA+D54qh01q5dG04BM3DjjTcCAJ588slQz0O1efPNNwNwR+bnnnsu1HOGzRVXXAEA\\nuO222+Q91ukd73gHAODkyZOFL1geXHfddQCAH/3oRwCAOXPmiPL95z//CQA45xx3bf33v/99+R6P\\n4Wcf/OAHAy+bKixFUSJDqArr9OnTAIA777wTgDvC7N27F4A320f27duHt771rQCAvr4+AJ69/JnP\\nfCbMYgYOg0KvvvpqAEjxy1A9/fWvfwXghWecOHFCrg19cm9+85vTvl8I6FsKmy1btqT8f/jw4YKc\\nNwzov7n//vsBpFoTVCHHjk3eJBplZWW44AI3EubnP/85AM8H+69//Qvf+MY3AAD/+c9/AADxeBwA\\n8MADD+Btb3tbym/t2rUrvHKG9ssGf/7znwG4zveenh4AwLp16wAAH//4xwG4Dy47KnLgwAEAwC23\\n3FKIYgbC+vXr8fe//x2AF2PFaexHHnlEzEM6MTmZsGXLFpw6dQoAJEyD0+FXX321OO7DjH6n6dnQ\\n0BDaOUxsFwGvWxT5yEc+AgCYO3euvEfziSsYJjPXX3992gDC9rjuuutw9uzZlM9oNpqdFUOPfvWr\\nX4VWTjUJFUWJDAVdS2j20ozmJjfffDN+//vfA0gPtIsCy5cvB+Cav1QOHR0dANzQC8AdeRiW8be/\\n/S3lNRtVVVX43Oc+BwD48Ic/HGzBDa666io5X5hQwTGcgRw/fjzU84bBnDlzAAAf+9jHAHj37pkz\\nZ/DNb36zaOXKFZp6X/ziF8USuO+++wB46t9WVwDwpS99Ke2922+/HQDEUggDVViKokSGomVr+NrX\\nvgbAc1BffvnlMiX86KOPFqtYeUPnI53nV111lfjpGB5AJ+RElAuXN4UJ12sS+hCDhteKSuuFF14A\\nALluUWHhwoWy1MrmnnvuweOPP17gEuXOV7/6VQCusgLc0KFt27YBgCwpGhgYkOMZBEufFe/HWCwm\\nSvKhhx4KvdyqsBRFiQxFU1icEWTQ4J49e2Q6lSMTlcm99947aReMnnfeeQA8/w8AXHPNNQAm/2Ls\\nsQhiMTJnSq+88koA7myUPQ1OPwoDhqPClVdemRbU+49//AOAF3Q52WCygU996lMAvBnsbdu24d3v\\nfrfvd5YuXSpL4WgRkT/84Q+ypKwQFD2B35EjRwC4q9sZw3LDDTekvNbU1MjUMB3Yk4Uf/vCHALxY\\nqSeeeCKwjorxUMWahJg1a1bGz9atWyd1pik/f/58AG5WDk4OsA40L3bu3ImhoSEAbuwPAOzevTuE\\n0ocHH2ymMwK8+CSGN9iTSpOFiooKAN5kAbn99ttRX18PAPjoRz8KAHjXu94FAFi9erWswmAHx9et\\nW7emhSOFiZqEiqJEhqIrLPLggw9KpDNVCxO7bd68Gc3NzQCAb33rWwCKPwXOdWHMSMER5y9/+Utg\\n56CychwH+/btC+x3M0EVxLr89Kc/Faeszdq1a0VhjY6OAgD6+/sBAAcPHsQvfvELAJ5ZT9V58uRJ\\nCTDkJERU1g8uXLgQAHwd7S+99BKAyb9OkHnVGHrAdX9Hjx7N6HY5ceKEhDZw/SdDdrhio1CowlIU\\nJTJMGoUFAPv37wcAfOADHwAAvPOd7wTgrs/6xCc+AcDN0QRA1h0WC6oD+gTa29sBQIJfxwNDJBjy\\nQR577DF84QtfGPfv5godsVzzxuywfrS2tsqSq0OHDgEAduzYMeY5brnlFhnVqUqiQqYMokCqP2sy\\nw4kN+uEefvhhAK6/kv5khif88pe/BOCuCWaONios/l9oVGEpihIZJpXCIhwFmANry5YtMqPEfOPM\\nFcUFpsWGM1/jncWMx+OyFILZLejrueuuu2RJTyH47ne/G9pv0y8J+PuCJiP0U9rhGICnRiZbttux\\nYO45qt1sXHbZZbJYn+qyWOp4UnVYjGl53/veB8DbnICdFeA6dAE35cVkYrzOdj4Md955p6yA50Mw\\n1sYcUacQG1wEAVde1NXVpby/Y8cO3HTTTUUoUWGpqqpKmQAC1CRUFEUZk6IrLK5fu/XWW/Ge97wH\\nANDY2Jh2XCKRAOCZXMXO6MApfb7SiZlrssHPfvazAICvfOUrANzcUIwm5hpEZXLAbdfse+6+++4r\\nqKleLLjGcDKgCktRlMhQcIVF9cTMm9zdmUF5fuzatUsCRoMMzJwI9hIF1uvuu++WoEluvsENUW+4\\n4QbJtMplLNyoY9u2bZKHaKpDVcocYrmEQxSL+++/P2PK6O3btxe4NMXh7W9/e7GLIKjCUhQlMhRE\\nYTHv0bnnnosf//jHAICWlpaMx3PKlcn7H3rooaL7rMaitLQUgBt8ydk9LmdgsKsJR2dmpmB+otcC\\nVKWF2uxiPHD29oorrpB7j8ta7r33XgCTfxlOUCxevLjYRRBC6bC4yp/78rHxs1V8+/btuOuuuwB4\\nTj4zgdhkg3v2MQULQzAAzzy0N3Po7OyU6eCo7QQUBpdccgkAL6J6MsE0LOYEENev3nHHHUUpU7H4\\n97//XfTMIWTyDnGKoigWgSmsiy66CIAbALlx40YAwLx58zIez5X93J9w8+bNBc2rM1EYhc5QDK51\\nZLS6CZO5/eQnP8GLL75YoBJOXgq9z6IyMfbv3y+ZVGglLVmyBEC4G074oQpLUZTIEJjCuvbaa1Ne\\nTbic5uGHH5bcSfRXRS0trg0DWZlhwc60oKTyyCOP4P3vf3+xizEmzNG1fft22dX5tczmzZsBeLt1\\nM8zotttuk+e7EKjCUhQlMsTy2dwhFotNzp0g8sBxnKwOlKlQRwC7Hce5INsBU6GeY7UloPUMCm4m\\n8sADDwDw8vj/6U9/khzwE/VB51RP7bBSmQp1hHZYgtYzWNhx0ST85Cc/KVlWJmoa5lJPNQkVRYkM\\nqrAspkIdoQpL0HpGB1VYiqJMKfINa+gAcCyMghSI5hyOiXodgddGPXOpI6D1jAo51TMvk1BRFKWY\\nqEmoKEpk0A5LUZTIoB2WoiiRQTssRVEig3ZYiqJEBu2wFEWJDNphKYoSGbTDUhQlMmiHpShKZMhr\\nac5rYYHlVKgjgA7Hcc7JdsBUqKcuCvZ4rdSz4Ds/R4GSkhI4jpO2WQKXMfltouD3Wbb3woDnSSaT\\nOa0pi8ViKfXMpbz2sfbxhWIyLCnj9YsK+ZY3271s/p/tubC/P96ykEnVYeVT4TDhQ5zLw5oNv/pw\\nw9Vs2Hu/ZetUzLKM5xqZZfQrb6Y6+22CymNjsZh87tfps378Tb+97nJ5CPIhjM4l2+9lG9TCJNcO\\nxDw+F9ie9m/n2z4TbU/1YSmKEhkKrrDYw9ojcElJSdp75eXl8tnIyAgAYHBwEADSRulClDmXkSsW\\ni6XV0dyaPdPIVFZWJnViXc062vX0q3e+CnAsTGXn90q1GI/H0165O5LdTmVlZbKjN9sykUikvJrn\\nCYqJXJN8ysI2LykpQVmZ+3hxi3tTSRbivs33HsnXpANciyGTUs5WR9vlkuv1UIWlKEpkKIjCYk9a\\nUVGBadOmAQDq6uoAAK973esAAI2NjWhudnN4zZ07FwDQ3d0NADh9+rTsmHz06FEAQHt7OwDg7Nmz\\nGBoaAhDeqJVNWZkjKuDWsaqqCgBkhGXi/unTp2P+/PkAPPVIldLb24ve3l4Abp0Ab1fds2fPiiqh\\ncvFzegddf1Mt2qqxpqYGs2fPBgBpU9alqqoKtbW1Ke9xp+8TJ05I/eyReXR0NKMyKxR+SpJ1Zl0q\\nKioAuPWkqpwzZw4Ar81nzZqFnp4eAJC2q6ysBAAcOXIEHR0dAIKvp5+/049s9bTvc9NfyfpRDSeT\\nSamD/SyYlhHLMtF6qsJSFCUyhKqw2NNWV1cDAFavXo0lS5YAABYsWAAAuOACd6+ElpYWUVbssfl6\\n5MgR7N27FwDw6KOPAnBHeMBVXNwPjSOZ36zTRMg228dXjp61tbWiLpqamgBA6rxixQpRlCw/1VdX\\nV5eMyC+99BIA4MknnwTgbp/EHbL5SiUCZJ85HAu/EdkcUVk/jqxUU4sWLZL6TZ8+HYCrKgCgoaFB\\nlEdraysATxEPDg6K2qJSoY+ntLRUzseR2fRvhRk+kUlJVldXS1l4DdjWTU1NYhUsX75c3uOxrPv+\\n/fsBeCqzrq5O6kVVHYSVkGkmNNvsr6keWT/+Bq2AyspKsRp4LUzFb/tczfuH9wHry2PN8+RD4B2W\\n6YxlJdetWwcAmD17tlSGEpoPbnV1tRzPStJ0qKysRH19PQDv4edNPjQ0JA84L7gtQ8dTh2yUlJSk\\nmQc0j2pqasTcbWxsTCnzhg0bpKNm/dnx9Pf3y03AuvKGqa2txVNPPQXAqzevTS7lzYbfNLjpIGeb\\nsGNlXZqamuQ9PrTsuBzHkfKxffk706dPR0NDg/w+4D2sw8PD8lDzPjEfgrDMQz+zz7yXbLONHdaM\\nGTOkzqbZz9/h37w32L5nz56VduR52dbDw8PjjtuzzS77vvAz+3j/xmKxtA555syZANx7lYMwr8Xp\\n06cBuB0uf4udLzun4eFhmVixyzgyMqJOd0VRpjaBKyxzOpdK6X//+x+A1KBBOpSPHz8OwB1h2tra\\n3EJZSun06dNiUtiO7GPHjo07iC0TmRSHOTrxPduEqaioENOIZsLq1aulzKbz2aSiokLON2/evJTz\\nJZNJOZ4mIUezZDIZqMPWrFNlZaWYgFQJNO+bmppwzjnu6h8eQ4aGhqSeVJActWfPni1qggqEdeno\\n6JARuRDR8+Z9w/uK75mmC6+HHaZiqmIeQ3UxODgobfXKK68A8CaMBgYGUn4f8JTO6OhoYC4N+z4u\\nLS2Va8/6stwzZ86UZ4pWAM3blpYWaWvWl3Xr6uqSNmY78vXw4cNiFlNF030z3kBeVViKokSGwBWW\\n4zgyyrBXpZoaHR2V92gTs3c+evSo+Dv4nhl4xxH75MmTADzV1tvbm+YsDGqaP9PvOY4joy3Pzf/r\\n6upkZFq1alVKXROJhChFjlAmrD+VKWloaBC1xvCOIEMZMjmczaBAtin/j8fjKf4PwGuTvr4+CUmh\\nsuL3pk2bJvWkH4ff6+rqknObQaRB4zelb07Tm5SUlKQF9PIe5j3J4wBPNZ48eVJ8qy+//DIAz+84\\nOjoq57aVXRBkCksoLy+XNuN5qZiXLVsm9yt9zmboEdUXwzGoGufMmSMKm9eJn/X19Ymi4vd47WyF\\nmSuqsBRFiQyhhDWYMwGANwMyMDAgU/fscUlFRYX00BwF+L1EIiG+jVdffRWAF1Q6MjKSNhpPVHXY\\nWQwyHQN4CoIzgosXL8aKFSsAeCMUy9rV1ZXmi6L6dBxH/AQLFy5M+c2ZM2dKwCln2/wWLQdRb5PS\\n0lIZnTmKsmzz5s0TJcy2ZN36+vpkJOUITpVZUVGRtmSF5zCnyLMprKB8dmZ9M53P9BH63Q9UHmvX\\nrk0p27Fjx9DZ2QnA89uYS3ToO+LxQQY/2z5X0xqwnzHOSC9duhTLli0D4IWncCZ7ZGREnju+sq1r\\na2uljfmM0s/lOE7KzCiQGvhc1LAGvylKO57KcRwxB9hgfBAaGxvR1dXlFur/b2i+Dg4OSoXZ6LwJ\\nksmkSPPxykybXKaH2ZmyPuxQ5s2bJyahHTvV2dmJ559/HoBnBpGBgQG5CdjB8fwrV66Ujp430bFj\\nbgaZoOpsYkbt08Thednx1NTUSHvZnfDIyIiYvuxg+YDMmjVLOmLevLwW8XhcHlx2ILajGwgu80Iu\\nmS4cx0mLueN9uXLlSrz+9a8HAJx77rkAvGuRSCTSzCDWN5FIyHtmnbKVIx/87ldiuzLM0Avb7KMp\\nO336dOzbtw+A12Hxub3kkkvEhcFnk5Nn7e3tMtHAPmCiA62ahIqiRIbAFJafGrED7uLxuAQZtrS0\\nAECKDKWy4EjN3zp16pSYTn7S3W993UTJlrysrKxMFANHGiqsRYsWiUTmSMXQjf7+flGKVCfmNDrl\\ns+2Urq6uFvMyWzknit96L5q8vMasN+CpXH7G/0+fPi1tyO9TgSxatEgCR23VWF5enhLBD/iPzEFN\\npuSCqaapLnnPrlq1Ctdccw0Ar55UFK2trSluCyB94mK8ZcpWVj/VaK7VtM1xlq2kpERMOqp3Os87\\nOjrkPX7v/PPPB+CalLwuprICXLWZKYPDeO9bVViKokSGwH1Y2dYtNTQ0YP369QCANWvWAPCyNSST\\nSVkKQB8H1ciZM2fScgpxNOjv7w9lyYbfb9L+j8fjMqIyyJNlrq+vF3XBaW2GYgAQRyzxy1TAurHO\\nvb298hnVm7leK6xMmqZzmPU1l29Qedhrz6qrq6XsdOouWrQIgLuMh9eKCpS/nUgk0sIaMt1PQfuw\\nskHFS2c0fZTr16+XgFoqFaqL3bt3py1VCSpjQSY4UZQpd5oZTmG+xzJyuQ19V3w9c+aMqGFODK1c\\nuRKAe014D9OSOHDgAADP35WprCSf9lSFpShKZAjch2X+bS4JANzemD4Qe9V3aWmp+DI4g8LR3Fy2\\nYo9S5ir+sHxYNqWlpaIGmWGCqmFoaEgySzDIk4rpzJkzUl87H5a5JMOuz8jISMpSCPMzv7KO9zrY\\n3xsdHU1bKsNZpcrKSmlL+jBMlU3lQV+lmZWDs0qskzn9bi9xMevo588aD/ncL2aZ+Mo23LBhgxxH\\nNbVnzx4Arj+Hx2XzXQWJrVT86mmXgc9Yb2+vPJO0AqiSKysrRQXTf3fxxRcDcJfxcAZx+/btALxQ\\nnWwz2GZZ87kugZuE5snN6XHArQCdd3TQ8rWxsVEuCjssOi27u7vlAefUq22OBEmmlMjm+is+kDRt\\neTMfOHAATz/9NACv4XkdzFX6vCbs+Lq7u8X0oCOf5t/Zs2clHMJe72e+FxTmYMCHzc66YMZo8bqY\\nmSjYidEUpAnR2Ngo5oWdCaK2tlbqbod2mKsLCul0dxwnzVSnmVtbWyv3Bs0pZtXo7u4uShLCscwr\\nOysF63T8+HGpC+vLiZ7q6mqZKLnwwgsBeO2aSCRkgKaTnvd4NveQ/beahIqiTDlCiXS3Az/NSHc6\\noDn60MSIx+PS49JUMNUYTS4qEzr4zM0rMjkb8yXT92jK1NfXi/ORSoum2t69e2V611yZDrgjD0cv\\nOxQjkUiI4uBoZprI5npMwBsh/UaxoEb0oaEhUT88D9Xv0NCQtC9DVVju0tJS+R5fWe6BgQEZgfl9\\n3gvxeFyO5zE0q4KsV77QtOE1p6o2V2fQFKSjOZlMpk1EhV3+bMGvgFsPO6EeTdlTp06lPbecXJgz\\nZ46sL3zjG98IwJuA2Ldvn6h/v0mGfDa2yAVVWIqiRIZQFJY5/Q+k+j/s96i+BgcHxUlNZUWlUldX\\nJwrL9ouZvpygnM9+vwV4I09NTY2sqaOfiQrr1KlTaama+WpmkzTXzwGpG1SwrvTptbW1ye9zFDPX\\nok1USWZSAmaoBetkpjO225DlHRkZkZAU+kj4mwMDA1J2M7sD4W/YSsBeylIozPxuXCN63nnnyWcs\\n++OPPw7Auw/CzJA6FtmeA7P9AC94t6SkRKwd3tO0dOrq6sRvx1e271NPPZW2NCtbPi/zHlMflqIo\\nU5pQcrrbGSWpHFasWCGjMgNG2bO2tbVJoJo9gzhv3jzxj9gZHoH0bdPDythIH1NlZSU2btwIwPPR\\ncEawpqZG6m/6rlhO+zNeozVr1ohq4/Xi+V988UUJSDTzZZvHjIdM/gVTBVIhmUG8gBuwy+tM1cfP\\nBgYGpJ3o66DvcsmSJTITSNVkhr/YfpRCZB71wywTldWmTZsAeL6dRCKBgwcPAvBmCc1MCeOZJZxo\\nUKxfjn4/HxrvI/N5YntQ3fKzBQsWiK+Wz8Du3bsBAIcOHZJ70y/jRC7+u6KENZhT3LwhebMuXboU\\ngNtJcaqUF4MN3drampbcizEfy5cvl+l/PhzszExpaa+TGi9+Cd4Az4ytra2VhmMd2WE1NTXJ9C6n\\n5s0blw3PV27qsHLlSoma5w3DdWkHDx6Ua2OnZA4i6jtT3FxJSYl0QjQTaKbPmDEjLQSBnVJXV5dc\\nF/42Jyl6enqknRjpzs6Q7wP+YSsTXYeWD2abs10YtmGm+ebORrwGpjskn/sw6KSTfr9tJiqkqW9O\\nptiDoXk/sP14H1JctLa2SvvZHbT5bGabHNJId0VRpiSBB45WVlaKmUOFxGjn5uZmGUUpIzkNXFZW\\nJtP4HLmZFrixsVGS2R85cgSA58ytqKhI29ZroiZhpulYvjdjxgwZaaiUeO6Wlpa0/QWpBqdNmyaj\\nNJUV09LOnTtXRrZDhw4BgEwXl5aWSh3p1PUbvYOePi8rK5Pz8LxsN0b4A+kprc3tr+xkcuXl5aKS\\n7QwAiUQiLezDDP8IOlGhn2Kz1042NTWJo5lqk0qyv79fJg5YTjOEJ9dIeiAYN0Ym5Wm2gb3NF8tr\\nmrB2rrqWlhZRzGz/5557DoCrMvksZluJ4Jf6WvNhKYoypQnch1VdXS02P53HDCxsbm6WXpg9thmm\\nYG4FBaTuiky1Ygcdmmu9zOnxiZBpBOZIYu5eTF8WHeabNm0SpcjRl/6tmTNniqJavHgxAG806+3t\\nxa5duwB4fj36RTo7O+U3bH+BX3lzhaolU4bKZDIp56FqpLo4fvy4nI9tSZVRX1+ftiaQPrDR0VG5\\nLlSNVFzDw8PSvrb/z15LOBGVZas+09nPctLXunz58rTNUejHM1Ne26Es9kSQTRjBpGNZBmaIhr2W\\n1dz4hP5ic1kV1eWzzz4LAHjhhRcAuG2WybfolxvPLm++qMJSFCUyBKawbPsX8FQQe++ysjLpvalQ\\nuOq7qqpK1ApHXI7Er7zyioxqfI8jcH9/f5oNHQR+s1NUGb29vTLi0KY3t6Pn0g17ic60adNEPXK0\\npmJ5+eWXJXCWs4Nmwn8qLL+V//YIl+vIlcnnYbalnVud5R0eHpZrQEVNX09VVZWM4PR98RrE4/G0\\nnOG8hh0dHXK87Zc0Fz8HHbZSXl4uior3IFXyjBkzpM1MNczv0c/D60QrYWBgIG3JWL7T/fmSy+yp\\nXRYzqNneSPXSSy8F4F4LPmO8R/m9ysrKnGYCxypvrtchsA6LDdbX1yeynjc3natA5nWGpaWl8gAw\\ntoWOvWeffVb+ZidgrmkLcw87E96o7e3t0pns2LEDgFef+fPnSxQ+TWEzGR1NByY527lzJwA3roUd\\nlb13YX9/f8Z984JwRtvfNyP0zbWOAFKc8Hb6HA5CppnAgYUddGdnp5j3vC/4/fb29pTNRcwy2fFF\\nQT7oyWRS2o/mLTuwWbNmiZnITpTHdnV1SRv57fzsVwcyntQqYzHWwOXX0Zt7LrIOXGlhrus8fPgw\\ngPTUMX19ffJ3pvRIfmXxK28uqEmoKEpkCDyBX3d3t6ghjjbslVtaWiRPEqU0/3ccR6bx6dCj6XD0\\n6FFxNpt7nvG1UKvgqRIOHDggf3PkoTNyzZo1YhpxxKIa7Ovrk+OoImnqtbW1yWhNJWeOiJlGZL/8\\nQuNxvpvfNxWhaTaZdTLPS2XFyYLq6mppL9bdjAS3twczj81k7pntHHTg6PDwcEobAZ5pODAwIG4I\\nKg4qwra2Nrm3qSTpNjBVZqHI5vQmbFsztxvg1o1mMOvOeh49elTuVz6bbLuurq60zCPZlNVEr4kq\\nLEVRIkMsz3VOeXWP7MXNzTTplOR7JJlMpm2Sam6QavfeE1ATWYfnWCzmZFqDZf5vZ42gYozH4/Ke\\nndtpdHRUFCLryP+HhoZyWgOWoU4pxyeTyd2O41yQaz1zmZa2J1DKy8vTsqayTc0gRL8AUCpIqhJz\\nuUguW2FR+Y3VlqxnDsekBUuaAaR2lk5iKkLeq2xPc7uy8WYRybeemVIkm7AufA655KaxsVECvbkl\\nG+vb0dHWpqOwAAAB40lEQVQhfkf6LenD7enpybgprOkDHYtc66kKS1GUyBBKPizCUYZ2/UQIMtfV\\neM5nn5N/0843M2OO93zZpsGzlWUiZAo2tM9hbgdF+D36pDgi+wW12gurAf8NRnOdBg8Sx3HSMnBS\\nLY6Ojqb57cwZU5bXrosZkBvGjGCmepjl9FPO5qwg68Bj2cacpabafOaZZ2RGl75IPtOJREL8m/y+\\nX5aKbIuy8yHUDitICunAHGva3O/zXM23bI2Uy64whejEzN/LdPObZfQLuchUh9HR0bTwiWwPdFiO\\n9kzYaVdoOgFIM3Mdx0mbJDDDGybaUYX5PTPMBHDNW6YdNxMxAm6nZrpngNS2s9/zK0euoQ5joSah\\noiiRITIKq5DYZlI+U7PZdmQeb8BjvmovKLJNi+eqeIIKtQgbu3xUIDQR/fBLlpeLSVsod4bfRJX9\\nHh3m2dYEjkW27wVdZ1VYiqJEhnwVVgeAY2MeNXlpzuGYDsdxjoXhP8hnineC58u5nvmeb4zzjvu4\\ncXw/lzoCId6z+fpjxnkt8q5npvPkem8Gee/n8Vs51TOvOCxFUZRioiahoiiRQTssRVEig3ZYiqJE\\nBu2wFEWJDNphKYoSGbTDUhQlMmiHpShKZNAOS1GUyKAdlqIokeH/ALN9RSZ3dn+lAAAAAElFTkSu\\nQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f547c8bc898>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  5 | train loss: 0.0374\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAASwAAACACAYAAACx1FRUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f547c21ee80>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  5 | train loss: 0.0349\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAASwAAACACAYAAACx1FRUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f547c180630>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  9 | train loss: 0.0367\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAASwAAACACAYAAACx1FRUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f547c45fac8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  9 | train loss: 0.0351\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAASwAAACACAYAAACx1FRUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\njU64AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f547c253208>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"autoencoder = AutoEncoder()\\n\",\n    \"print(autoencoder)\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(autoencoder.parameters(), lr=LR)\\n\",\n    \"loss_func = nn.MSELoss()\\n\",\n    \"\\n\",\n    \"# original data (first row) for viewing\\n\",\n    \"view_data = Variable(train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.)\\n\",\n    \"\\n\",\n    \"for epoch in range(EPOCH):\\n\",\n    \"    for step, (x, y) in enumerate(train_loader):\\n\",\n    \"        b_x = Variable(x.view(-1, 28*28))   # batch x, shape (batch, 28*28)\\n\",\n    \"        b_y = Variable(x.view(-1, 28*28))   # batch y, shape (batch, 28*28)\\n\",\n    \"        b_label = Variable(y)               # batch label\\n\",\n    \"\\n\",\n    \"        encoded, decoded = autoencoder(b_x)\\n\",\n    \"\\n\",\n    \"        loss = loss_func(decoded, b_y)      # mean square error\\n\",\n    \"        optimizer.zero_grad()               # clear gradients for this training step\\n\",\n    \"        loss.backward()                     # backpropagation, compute gradients\\n\",\n    \"        optimizer.step()                    # apply gradients\\n\",\n    \"\\n\",\n    \"        if step % 500 == 0 and epoch in [0, 5, EPOCH-1]:\\n\",\n    \"            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0])\\n\",\n    \"\\n\",\n    \"            # plotting decoded image (second row)\\n\",\n    \"            _, decoded_data = autoencoder(view_data)\\n\",\n    \"            \\n\",\n    \"            # initialize figure\\n\",\n    \"            f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))\\n\",\n    \"            \\n\",\n    \"            for i in range(N_TEST_IMG):\\n\",\n    \"                a[0][i].imshow(np.reshape(view_data.data.numpy()[i], (28, 28)), cmap='gray'); a[0][i].set_xticks(()); a[0][i].set_yticks(())\\n\",\n    \"    \\n\",\n    \"            for i in range(N_TEST_IMG):\\n\",\n    \"                a[1][i].clear()\\n\",\n    \"                a[1][i].imshow(np.reshape(decoded_data.data.numpy()[i], (28, 28)), cmap='gray')\\n\",\n    \"                a[1][i].set_xticks(()); a[1][i].set_yticks(())\\n\",\n    \"            plt.show(); plt.pause(0.05)\\n\",\n    \"            \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAcUAAAE1CAYAAACWU/udAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXmcHHWZ/z/V9z2TuTLJTDKTzJHJ5L4gQYTVReQQEMFF\\nXuyyrHiwCj88F1SUBY3BY4VF3Zf3euxvRdl1DbDCLvBTFDAQA0gCJHP03PfZ91lVvz8mVVR3V3fX\\n3d3T3/frxYtMd3VVzXR3fep5vs/zeSiWZUEgEAgEAgEwlfoECAQCgUAoF4goEggEAoFwFiKKBAKB\\nQCCchYgigUAgEAhnIaJIIBAIBMJZiCgSCAQCgXAWIooEAoFAIJyFiCKBQCAQCGchokggEAgEwlks\\nMrcn9jcEAoFAqEQoKRuRSJFAIBAIhLMQUSQQCAQC4SxEFAkEAoFAOAsRRQKBQCAQzkJEkUAgEAiE\\nsxBRJBAIBALhLEQUCQQCgUA4CxFFAoFAIBDOQkSRQCAQCISzEFEkEAgEAuEsRBQJBAKBQDgLEUUC\\ngUAgEM5CRJFAIBAIhLMQUSQQCAQC4SxEFAkEAoFAOAsRRQKBQCAQzkJEkUAgEAiEs1hKfQIEgpGw\\nLAuapkFRFEwmEyhK0jBuAoFQJRBRJFQFnBim02kkEgmwLAsAMJlMMJlMsFgsMJvN/M9ELAmE6oTi\\nLg4SkbUxgVBqODGcmJhAU1MTKIpCKpWC8HPPsiyyvwcmkwlms5n/j4glgVDxSPrykkiRsCoRRoYs\\ny8Lv96O5uTljG07gsoWOE8h0Oo1UKpXxHBFLAmF1Q0SRsKpgWRbpdBo0TYNlWX7tUA5KxHJxcRFr\\n164lYkkgVDhEFAmrAk4M0+k0ACgSw2IUEsuhoSHU19eTyJJAqHCIKBIqGjExLCY4FEXlrCGqgTtm\\ntggLI8tkMplxXkQsCYTyhIgioSJRIoZGI4wshYLJFfakUikilgRCmUFEkVBRMAzDF9AA5SmGxch3\\nzsXE0mKx8KJJxJJA0AciioSKQAsx5ApvypViYplMJnN+h+yo0mw2V+SNAoFQLhBRJJQ1DMPw1aSA\\n8siQW0esRLEoJJbczUIhseQEk4glgVAcIoqEskQrMeTQurimHJAjlkNDQ9i0aRMRSwKhCEQUCWWD\\ncE2NYRgA2q0ZrkZRzIfY32xpaQkdHR28WGZDxJJAWIGIIqHk6CmGHCaTqWpEMR9SIstsOJHkinyI\\nWBJWO0QUCSWDuxin02ndxJCjmiJFuUgRy2QymbE9V9RDxJKw2iCiSDAc7mIbj8cxNDSEzs5O3S+o\\nFEVlCC+hOEQsCdUIEUWCYWRHhizLYnFx0ZALJokUtUOpWIqN5yJiSSg3iCgSdCd7YgV3MTSbzXz0\\npjdEFPWnmFgmEomc7cXce7T2rCUQ5EBEkaAbYmKYzx9Ub7JFsVJ7FisRMbHk3gvu8xGNRjE3N4e2\\ntjZRseRMCQgEvSGiSNAcKWIIGLu2p3f1KYlC5ZE9cYRhGKRSKf5zInQvEr4me72SiCVBa4goEjRD\\nqhiWApI+LW+EaXUg/yzLbLFkWTajuIf4whLUQkSRoBotBvvqDVd9mkgk4Pf7kUql4PV64Xa74Xa7\\nYbVaS32KulEJNwMMwxQUMaliKUyLCyNLIpYEqRBRJCjGiMG+WsEwDIaHhxGJRLBx40ZYrVZEo1HM\\nzs4iHA4jnU7DZrPB7XbD4/HwYmk2m0t96lUBF/HJRYlYkvFchEIQUSTIphJmGXJwkeH8/Dw2btyI\\nnTt38utXa9as4bfjplBEIhFEIhFMTEwgEomAYRg4HA5eKD0eD5xOZ9mKvxiVEClqXfhUTCzT6TRS\\nqVTGc0QsCQARRYIMKlEMl5aWsGnTJrAsi7q6urznS1EU7HY77HY76urq+MdZlkU8HkckEkE4HMb8\\n/Dyi0SgAwOVy8REl13dZrn+Pcj0vjmLpU60gYkkoBhFFQlEqSQw5lxxODHt6ekBRFILBoKKIiaIo\\nOJ1OOJ1ONDQ08I8zDINYLIZwOIxQKIRkMonjx4/DZDLxQslFlzabrWz/XuWC0vSpVkgVyzNnzqCj\\noyNj4DMRy9UFEUVCXoyacq9FhJVPDDmENm9aIBQ/AFhYWMCBAwdA0zSi0SjC4TAWFxcxOjqKZDIJ\\ni8WSs15pVHFPOUewHOV6jtliGYvFeEEESGS5GiGiSMhB61mGhTCZTGAYRnFBi1AMN2/enCOGHEa1\\nZJjNZni9Xni93ozHU6kUv145MzODSCTCF/cIhbJai3sYhqmIdVruPItFlqlUCslkMuN5IpaVARFF\\nAg+XEpyamkJLS4shaVKlosiJ4fLysmhkmI0Rolgo2rFaraitrUVtbW3G9lxxTzgczijucTqdGWlY\\nNcU91VhooxfFzpN7LvvzLByPRsSyvCGiWOVkzzJMp9OYnp7Ghg0bDDk+J4pSkSuGHOXYvK+muIeL\\nLh0Oh+Tfv5wp9Zqi3hTyhS0klsIeS869p9zfy0qHiGKVkm+wr8ViMcykG5AuikrFUHgcPUWRE10t\\nLlhSinuCwSAmJycRj8dXRXEPwzCwWMr/cqT137SYWCaTyZzPVT5f2Ep6v8uZ8v8UEjSl2JR7s9ks\\nOoFdL4qJYjweh9/vRyAQUCSGHMJCm0q9eGQX93DQNM2vV4oV9zidTtA0jVQqVbbOPZWSPjUKKeO5\\nCoklJ5hELOVDRLFKyJ5lyH1Zsr8wRn+B8qU1s8Vw69atqs5N7/RpKdOzZrMZPp8PPp8v43GuuCcY\\nDCKZTOLkyZNlW9xTKYU2pUaqWE5NTcHn88Hj8RCxlAkRxVWOVDEsFdmRotZiyFGOa4p6wxX3uFwu\\nLC4uYvfu3YYV98ilEiJFowwGlJD9nY5Go/D5fPz3Syz7w4mkcOpIOV0bSgURxVVKvsG+5faB5760\\nQjHcvHmzZmLIsZojRTkUK+4Jh8OIRCKaFPfIoVJEsVKi2XQ6DYvFIimyTCaTGc9Vu1gSUVxllPP4\\nJjEYhoHf70cikdBFDDm0bt6vNIr9TYXFPY2NjfzjDMMgGo3yadjs4h5hGlZNcU8lCA5N0yVPM0ul\\n2LnKFUvuOlINYklEcZVQaWIYi8UwNDSE+fl5tLa2Ys+ePbp+uUwmU87QWi0p50hRzXmZTCZ4PB54\\nPJ6Mx4XFPQsLCxnFPdnrlVKKe0ikqC3pdFqRgMsRy7GxMXzrW9/Cd7/7XdXnW04QUaxw9BJDvS5S\\nnBhyaVKLxYKamhrdL4jVHilqTbHinkLOPR6PBy6XK+OiXQl9ipUkilpHtWJiuby8jFgsptkxygUi\\nihWKnoN9ubYMLfvGYrEY/H4/gsFgRpo0HA4bIlbZkZwcEf6F41Gg2ObnAX0Yz3yMBa6LXyHjLPXB\\nyChMinPP+Ph4TnFPJBJBPB6Hx+MpW+Ghabpsz00Mvc81EAjk3BStBogoVhhGDPaV6zJTiGwx7O3t\\nzXHtMCLtqCq9qVRPyjsbaBhSinvm5+cxOTmJkZERAMYU98hFjUfvaiQYDGbc/KwWiChWCEaOb9Ki\\ngV8ohh0dHTliyGFUWrNUa36/cD7K/9vB2nFV/GLDz6Fc1+uExT0zMzPYvHkzXC6XYcU9cqm0SFFv\\nAoEAampqSn0amkNEscwpxSxDk8mkWBSliqHwWKtZFIXEqURJj1/OCIXbqOIeuVRKpGjU5zwQCGTY\\nEK4WiCiWKZwYJhIJvPLKK9i/f79hd8Rms1m2UMkVQw41AiwHTnxZlsX8/Dzm5+fzFn1I5c7O2+Hw\\nOECZTTBbzLjrhS/pcObqKfXNgBSkFNoUK+4Jh8MZxT12uz3DjEDp+8xRKYU2RrWOhEIhdHR06H4c\\noyGiWGZkD/Y1m81IpVKGpr/kpE+ViiGHyWTKGdKqF9FoFC+++CJcLhfq6uoQi8UwNjaGaDQKhmEy\\n1rE8Ho+kdaxPPnUXvA3egtuUA+WYPhWixi2mUHEPZ0YgVtzDRZdSnXsqJX1qlCgGAgGypkjQDyMH\\n+xZDSkozFothcHAQ4XBYtIBGy2OpZXFxEadPnwZN09i3bx9cLlfOmB5uAkUkEkEoFMLU1BTi8fjK\\nxeV8XU+PAO3XPYXFPfX19RnHETr3zM7O8m0FxYp7KiV9apQoBoNBsqZI0J5yEkOOQpFithhu27ZN\\n1fnqKYrLy8vo7++H1WrF5s2bsbi4CLfbLZpOFE6gaGpq4h9Pp9MYwKT4ASgKD1x6H0ABF37wL3HB\\nB9+uy++hlnIttBFiVGpSinNPIBAQLe6JRCLweDxl//fkLN70hkSKBE1hGCbv+KZSIyZU0WgUfr9f\\nMzHk0KP6NBAIYGBgABRFoaenB0/UPIOT1CDQCryE0ysbSfjkF6sWveN3X8CaljoEZwO4/5L70Nyz\\nDt1v3arRb1FdlFpohMU9a9eu5R8XFvcI51gKi3u4/5fLPEgjI0UiigRVFJtlWOy1RhbacJGiXmLI\\noWWfYigUQn9/P1iWRWdnJ5/aUVr1Wex1a1pWeu58TTXY8+79GDruLyqKy8vLulVH5qPUgiOFcj1H\\nYXFPNBpFbW0t6uvrM4p7pqendSvuUQJJn6qDiKIBqBFD4M3Izagvl8lkQjwex6lTpxAOh9HR0aG5\\nGAqPpTZSDIfDGBgYQCqVQmdnJ9asWaPR2WX2GQpJROJgGRYOrxOJSByvP3kS77rr6qL7y66OzLY+\\nq4RCDr0oR1EUIkzxGlXcowSj0qecfd9qg4iijmTPMgSUpUm5yM0IUYxGo3yRSW9vr25iyKFGFCOR\\nCAYHBxGPx9HZ2ZnhlqI3wZkg/uXa+wGs3Jmf+77zsP2du4q+bsuWLQBWPhuJRIK/gC4sLGSMahKK\\npd1uL3vBqAakTJ7IV9zDFXHJLe7R4zy1oBLafJRCRFEHtB7sa7FYdL8rE6ZJ6+rqYDKZMgoR9EKJ\\nKHLFPpFIBB0dHaivr1f0t1XTZ9i4uQl3v3RE9jE5KIqCw+GAw+HIaIDmCj7C4TACgQAmJiaQSCRg\\nsVgy2kXkrGGVa2qy0lBaDERRFFwuF1wul6TiHrPZnHNjZLVaJb+HXBbCCFbj54qIooboNdhXC9u1\\nfAjFkEuTzs3NIRAI6HK8bOSIYjwex+DgIN8T2djYqPpvW259hvncXLLXsMLhMGiahsPhyBBLPdNy\\n1Y7WfYrFinvC4XBe555CN0Zam/mLEY/H4XA4dD1GqSCiqAF6T7m3WCyai2I0Gs2ItoRpUj1FOBsp\\n9muJRAJ+vx9LS0uKDAJWA/nWsOLxOH8BnZub49NywmKP1ZzqMhKj1vULOfdw6XbhjVF2cU8qldL9\\nPJeXl1dlkQ1ARFEVRg321VKkODGMRqPYvHkzGhoacgTGKD/SYsdKJpMYGhrCwsICNm3ahJ6eHm3F\\nsEL6DPMh7LnLTsFy61dLS0tYXl5GNBpFLBYr2zaCSqDUNm9WqxVr1qzJKCTLLu4ZGxvDwsIClpaW\\nMoRS6+Ke1Vp5ChBRVITRU+7NZrPqqfFSxFB4PKMiRTFRTKVSGB4exuzsLNrb29HV1ZX37/uw6THF\\nLRertc/QZDLB6/XC611JCweDQUxNTWHz5s38xXNqagqRSIRPwQrXKkkKVpxytHkTK+45deoUNm3a\\nBIqi+CyC1sU9q3WWIkBEURZGiyGHGpGSI4YcpYoU0+k0RkZGMD09jY0bN+LQoUNF/75qJk8o6TOs\\nRLj0ab5Ig7M9E148ueIQoVgaOaapHJFiWl4O0DQNq9UKm80mubgneyyXx+MpWNi3Wt1sACKKkhBO\\nuf/Tn/6E/fv3G/rlULKmGIlE4Pf7EY1GZVdoGh0p0jQNv9+PqakptLa2ShJDLZHTZ7jayGd7RtM0\\nXwUrLPawWq05VbCV4AeqBZVSxZtOp/O+J/mKe9LpdMb7PTIyglQqlbe4R0s3m7GxMdx4442YmZkB\\nRVH40Ic+hNtvv12TfSuBiGIBxGYZGj2xAlgRqURCWkSkRgw5jIoUaZrG6OgowuEwTCYTDh48WJIL\\n7OHzviC5z1DII/f+J+weB975ict1OjNtUHIxN5vNGSlYjmQyyafkJiYmRJvTuSpYqcckhUDaomTt\\n02KxSCruGRgYwOc+9zl4vV40NDSgvb0d27dvR09PD5xOp6LztVgs+Kd/+ifs3bsXoVAI+/btwzve\\n8Q709vYq2p9aiCiKUIrBvoWQErlxjeyxWExV757U46mBYRiMj49jbGwM69atg9vtRnt7u27HK8a9\\nf/6qpO2UuthwOFhjesf0xGazwWaz5aRgueZ0bqZhLBaTnJJTMzbKSCrhHDm0OtfslPuWLVtw6aWX\\n4siRI6BpGrOzs3jwwQexuLiIo0ePKjrGunXrsG7dOgCA1+vF1q1bMTExQUSxHJAihpyBdbmkT7UU\\nQw69RJFhGExOTmJkZARr167FueeeC4vFgunpac2PpQdyXGyui11h5KkVRO+0X77m9Ox+Oy4lZ7Va\\nM9Kvdrtd0vfpg59wIBBU9nvU+Fh8/xtxRa8lZGIymZBOp3HBBRfgyiuv1HTfw8PDePnll3Huuedq\\nul85EFFE7mDfQpGhEe4y2YiJlB5iyKH1BZRlWUxOTmJ4eBiNjY0455xzDDPEVuNak41aF5tqI1+/\\nnbCFYGJiAuFwGNFoFCdPnsypghV+FpUKotrXAiTFm40eLRnhcBjXXHMNHnjggZJWtla1KMoRQ45S\\niSJ3jnqKodawLIvp6WkMDQ2hvr4eBw4cKImBcLm51hhNuV3QbTYb6urqeK/aRCKBN954Ax0dHQiH\\nwwiFQpiens5JwQLdJTvnSimyMSoVHQgENBXFVCqFa665BjfccAPe8573aLZfJVSlKKoZ7GuxWJBK\\npfQ8vRy4QptXX30VsViMN78u1y8py7KYnZ2F3+9HbW0t9u3bZ5gXI0Gccv2sAG+2OnApWOGQZ2EK\\ntpSUY4+iGEaOjdJqGg3Lsrj55puxdetWfOITn9Bkn2qoKlHUYso9FykaRTgcRn9/PwKBAPbs2VP2\\nYjg/P4+BgQH4fD7s2bOn9P6IFe5aUw0Uim7ypWCFnHzxA5ib+g1s9iacf8krup1jJbSeFGrH0BIt\\nWzKee+45/OxnP8OOHTuwe/duAMCXv/xlXHbZZZrsXy5VI4rcqB5AXTWpHj6kYoTDYfj9fsTjcWza\\ntAnxeDxjHE05wbIsFhYWMDg4CJfLhV27dsHlcpX6tACsXtcaOZR76k9tU3zLpr/Fxq6P4OQL7y+4\\n3fj4OJ+K/cgdXpnrjE4AB8u+YMcIM3BgZRkn27ReKeeff35ZpfirRhS1Mum2Wq26pk/D4TAGBweR\\nSCTQ0dHBr7v09/frdsx8SLmYLi4uYmBgAA6HA9u3b4fb7ZZ9HD0reqW41ggrRbkikCdbntP8XAji\\nqBXtusa3IhoZLrodRVGYm5vD0NAQAsG3KDqW2oIdvTFqlmKluPsooWpEEVgpJVZ7R6JX+lRMDEt5\\nd8818Of7gi0vL6O/vx9WqxW9vb2q7hq1eF/EUNJXyBWBOFi7Ygu5cutHLOdIcaXNwglA/9mdH7+7\\nU/djlBqj0qdAea9Tq6GqRFELLBaLZHcZKZSbGHJwbSDZX7BAIICBgQFQFIUtW7ZoUjrNRYpaf5nl\\n9BVmc1X84qLbCN1dpqam+NSV0+nEsGcYHo8HXq+36j1DC1HukZcYy8vL8Hg8ZTlhxIj0aamnhehN\\n+b2rOiJldl8xtIoUs8Ww3NYLzWZzhtVbKBTCwMAAGIZBZ2enpuXYetnK6d1XKHR3SafT8Hg8qK+v\\nRywWQzgcRiAQwMTEBBKJBO8hWY2eoauNmZkZ+P1+fsK90LHH5XKVVDCMSJ+GQqEc+7/VRFWJohao\\nFUU1YmhkGowz6g6HwxgYGEAqlUJnZ6dmZdjZxzJqKodecDdcXF+d2+3OMFzmPCSFnqEsy8LpdGaI\\npd1u18U8gUSq2vH5r+2WtX2Nl8H3vhE35D1Ip9O6R4qreWwUQERRNkpFUW1kyKUzjUrZsCyL06dP\\nI51Oo7OzU9dIVqkoplIpoEKCLbGxTQzDkKhSBLktFq/88a+xNPcMkol5/PbRdnRt+wJaNxeuRDWS\\nQMiE6z6Yvxpby4pWmqZ17wkOBoNEFFcLWtypyRVFLtJKJpOq0qRGiWIsFsPg4CCWlpawefNmtLW1\\n6X6HK1cUaZrGyMgIpqamgAt0PDEZKEnNy4kqGYbJmG8oJ6qstEhRaosFx+5D/yZ530b0NMolEKQw\\nODiYMWFEaQrWiPTp8vLyqp2lCFSZKGqBVFEUph2FrRVK0XtyRTweh9/vRyAQQEdHBz86yIiLqVRR\\nFE7XaGlpwcGDBzHJPq5q0HA5km8YMDfvbrVHlVJbLJQgV3CNoqamBuFwGHNzc4jFYgAgOmGk2PfR\\niPSpHr6n5URViaIWF/hi4hQKhTA4OKiZGHLoZRqQSCTg9/v5yHDr1q2gKArLy8uGrfMVE0Whobhw\\nugYAvJd5FwDgYdNjisRRq/YJLYq4iu1faVSZTCbLqjm6lGgtuFpFng0NDWhoaOB/ZhgGkUgEkUgE\\nS0tLGBsbQzKZzLgJ4j4PQhE0IlIMBAIkUiS8ST5h1UsMOYSm4FqQTCYxNDSEhYUFtLe3o6enJ+N3\\n0zsyFZJPUDgP1cHBQdTV1RU0FOfEsRDHjx/H7t27eQOGSpnjV4hCUWUkEkEgEMDi4iISiQSWlpZW\\nXVRZavSKPE0mk+iQZ+HQ36mpKUQiEdA0DYfDAY/Hg0gkwt8E6fXZJpHiKkKPD4neYsihlUilUikM\\nDw9jdnYW7e3t6OrqEl2/MLIiVOxYCwsLGBgYgMfjwd69ezXxUNUzmtM7UpSDMKpsamqCx+NBPB7H\\n+vXr+b7KyclJhMNhPqp0u93wer1wu91wOBwVf7OgFqkFO1pFnn/1gTen1hcqvMl3ExSPxxEOhzE9\\nPY3x8XEMDg7ycy6FN0JaTKkJBALYuHGj6v2UK1UliloSDAbh9/t1F0MOtenTdDqNkZERTE9PY+PG\\njTh06FDBxXwjI0WhKHJOOTabTbFtXD44k4BsPu94HSFKfhTuZS34Yrw008HlwEUNVqsVtbW1Gakv\\nlmX5CthgMIjJyUnE43GYzeaMi6nH46mqqPJtVwyX7NiBIJUhkkBhoaQoCk6nE06nEyMjI9i+fTvf\\nUsWtQy8sLGB0dBTJZJIf8ixcs5Tz3pJIkZBBKBRCLBbD6dOn0dXVpUvfnhhK06fpdBqjo6OYnJzE\\nhg0bioqh8HhGjcgymUyIRCJ46aWXAAA9PT26NAfni+aUCGL268opUpQDF01kj2xKp9P8WuXU1BQf\\nVWb3VeoRVZZ7i0UpkOr8I/Qk5Yrlsr9LQicm4To0995yYpk95JmDiOIqQs2XV5gmdbvd2Llzp6Fj\\nkeRGbjRNY2xsDOPj42hpacGhQ4dk3Q0alT6NRqOYmZkBwzDYtm2brjcZegjXx5yvrvyji3tkXlUE\\n6f7fT8GUCCp6LWP3IXLx13MeV7K+ZLFYCkaVoVAIU1NTukSVUlssfvmDWMHns6MtgAgukOnExMG9\\nt5xYzszM5Ax5jsfjcLvdmo6Nev/734/HHnsMTU1NOHXqlCb7VEtViaISOHszrol9zZo1eOWVVwyd\\nqQi8OWi4GMK2hXXr1uHgwYOKSrSNagHh7jp9Pp/uUbdR0VyISuNjzleLiqMaARRDy32JITWq5Io/\\njIgq5SKnp7GaEL63jY1vmrMLhzw/99xz+MlPfoLx8XHccsst2L9/P3bs2IHzzz8fnZ3KzNZvuukm\\n3Hrrrbjxxhu1+lVUU1WiKOcLKSaGHFar1XBRtFgsiEQieZ9nGAaTk5MYGRnJaVtQgl6RorDqlWsB\\nGRsbM0SshNM4pHwWGJqGSUXEUywtq7eIcejdvK8mqlyZU1i5GBl5cpFvvvVFPb5DwiHPN910E266\\n6Sa8/e1vx09+8hMMDAzg5MmTeO211xSL4gUXXIDh4WFtT1olVSWKUuDEkKZpdHR0iEYveo2PKkS+\\nyE3Yw9fY2FiwbUGL4ylFWOjT3t6O7u5u/kLNFQXojdxI8fUHH8b2j79PxzNavUiNKoHm0p2kBpQi\\n8swuxOFE0qjpFclkEm1tbWhvb8dFF12k+/GMpupEMW+xhQQx5CgHUWRZFtPT0xgaGiraw6cErSJF\\nhmEwOjqKiYkJtLa2ihb6mEwmQ4p68lWfihEZn8XY43+sOFH0PvqhnMe2cP94Of+6o1GIRZXVgJ5R\\nJFeEY4QNZCUWk8ml6kQxm2AwiMHBQUliyFEKUeRaMriGdr/fj9raWs16+LJRGykK07nNzc0F07lG\\nFfUIb4gCgQDS6XTeKtcXPvkgDhz5e93PyWiMStkawdzcHNxud94qyXLCiBYPowYMUxRV9n9vNVSd\\nKHIXRiViyGGxWAxrV+Dg2hZeeOEFeL1e7N69G06nfusxSoWKZVl+3lxDQwPOOeccWK1WXY4lF4qi\\nEIlEMDg4CGBlbdjv9wPn557f23/xJUn7jM8H8IebD+MdR7+q+vzOTAZx3Tef53/2z4Zx77U78LFL\\ntxR4VWVT42MVDRr2emi+WT0Wi+ED07uwzGS+j3ttDGxJ+enEZGIeNntD8Q01Rq1lnBEWb1yf42qm\\n6kQxGAyiv78fNE2js7NTURrHYrHwpr16w7IsFhYW0N/fj3g8jnPPPRcuV/4xNFohN1JkWRbz8/MY\\nHByEz+fDvn37JI+wMUIUY7EYFhYWsLy8jN7eXng8HqTTaZhMJvwMryrer6NBu36tLet9eOXIJQAA\\nmmHQcusjuHp/q2b7L0e+/404Tp8+jfXr1ysYR7SJ/9fyr3M/Py9dLO07evCxNw0iopFhvPSHd5dk\\ngoZay7hKnKV4/fXX43e/+x3m5+fR2tqKe+65BzfffLNm+1dC1YliKBTCpk2bVK1pGJU+XVxcxMDA\\nAOx2O7Zv347XXnvNEEEE5Ini0tIS+vv74XA4sHPnTtnnqKcoJpNJ3vDc4/Fgw4YNqK2tNSz97e7s\\nhGl2VvzJ/5t/vfLpUzPoaPKgrVE7R59ypdJGW+mFWss4IyJFrRv3f/7zn2u2L62oOlFsbW1VXemo\\ntyguLy9jYGAAFouFj2pYljV0Or0UoeKibpPJxJ+nEvToHxTOXGxvb8eWLVvQ399veKFAXkEswkPH\\nRnH9edI2R1hoAAAgAElEQVT9JeWkXt3/+6mSFttkY1TV5GpmpRp1Q8FttBhmHAgEVrWbDVCFoqgF\\neoliIBDAwMAAKIpCd3d3RprC6DtpYU9fNpFIBP39/Uin0+jq6lL9JdEyUmQYBhMTExgdHeVnLnJ3\\nz0LxlfP3ZGgaj5z7AbhbGmWvHX587Cju33CVrNck0zQeOTGBI9ftkvwaOanXciu2KadI0eVuL5vh\\nw1qjZO02Zx9EFFcfWnz5tBZFYTuIFiKjF/F4HAMDA4hEIujs7ER9fb0m+9VCFIVjpvIV+CiNSF9/\\n8GHUbm1DKhgVfX7ytyfwzI334sKffiHnuWCz/L/R469MYW/7GqytUVZVXGmpV71EkVkYR/R7t4AJ\\nzAEUBftf/C3s71x9FcVGstpnKQJVKIpaoJUohsNhDAwMIJVK5bjmlBPCdbmOjg40NjZqehFTK4qL\\ni4vo7+8vOmZKKIqftZ5EyFb8PeT6FXd95ka89sAvRLdZ/7Z9ooJYiH8GcLvYEx/5Na4KxHEVANzw\\nkKx9osYB/Mu7ZadeS41u6VOzBY7rvwRL+26wsRBCX/gLWLa/DeaWHk12/+0jo7wRAecTyjn1fOIf\\nu4rvoAJZ7WbgQBWKohYXc7PZrOoiHo1GMTAwgFgsJjviMjLVxLIs+vv7MTc3x6/L6XFspaIYCoXQ\\n19cHk8mEbdu2FV3TFKaEpU7G4PoVU2HxKFEJpwB8H3lEMaBizefsa3/0oXOV76ME6PWZNtU2w1S7\\n4phDOb0wre8GszSlmSg2NjaK+oSGQiFF+1NjGae2nUMqgUAALS0tuu2/HKg6USwlsVgMg4ODCIfD\\n6OjoQENDg6yLAVcRqnfZNU3TGB0dRSQSgd1ux8GDB3UthJArirFYjG9R6e7ulpzOkZs+Hf3v5+Bo\\nrEXDvh5MPfNS3u3krjNux4owyo4EVylGFNrQcyOgR07C0rFPt2MIfUKVoMYyTm07h1SCwSC2bt2q\\n6zFKDRFFA+AmQgQCAXR0dGDbtm2K7ow5Vxu9RFFYpLJ+/Xq43W5s2LBB98hUqigK07idnZ2ybyrk\\n2LwBwOzzJ7Hn7pWeqXUX7sW6C/dKfi1BOnpnP9h4GNFv3gjnDV8G5dSux64QSk0JlKK2nUMqWo6N\\nKleqThS1/PIV+zInEgn+Is5NhFBzfG7QsNSmeKmwLIupqSneVJwrUuHmHOrd+1QsgqNpGsPDw7yZ\\nuNI0rtxIcf/hW2QfQxPe3QM4FbiGxKS7LHkLRDNMUxMiAwPyj68QPUWRTacQefBGWA+9F7YDV+py\\nDDHEWh/E5jtWGqTQhpAXrthGzPJIOB6pvb0dPT09mq1lajlNgmVZzM3NYXBwELW1tTkuNNzx9BbF\\nfJFidnuFmJm4HIyap6gaJYKo5nVZmGZnc0RTT6HUSxRZlkX0h7fCtL4bjktv1Xz/1QiJFFchWn35\\nxEQxlUpheHgYs7OzaGtrQ1dXl6ZrJVz6VAu4ik23253XR9UoT9Lsnkgp7RVKkJs+NYp/xkrhDQvg\\nNZX72v2ZJ3TxTFVqQiAVPUSR7juG1HO/gGlDL4J3nQ8AcL73C7DuuljzY1ULJFJcpWgRMQjbMoSz\\nAjdu3Kg6oskHlz5VQyAQQH9/PywWS9GKTa0jUylIba9Qgslk0qy/VE1DvxCuEvVFAFoM/jpx+GJp\\nnqlCi7l4CniqL/+2sRTw69NIJBKw2Wxl02hfDMuWQ6j96XKpT4On0DqjUdWjMzMz8Hg8cDqdiq5R\\n4XBYU+/TcqQqRVELLBYLEokE5ufnMTExgQ0bNmS4p+iBGpEKh8Po7+8HwzA5bjn5MCpSBFbWDU+c\\nOAGTyYTt27fD7da+8VzL9Gmxhn6pvAHgXABS3GLvPz2PHwwugqKAHTUO/OvBVjjMmRc2RY37jiJR\\n+Nm07OnTK8JotVrh8Xjg9Xrh8Xjgcrmq0qZNyzVCtdWjUts5YrEY5ubmEIvFQFEU3G4331vp8XiK\\nZmRYljVkPFUpIaKoAK4f6bXXXuMjQyM+KEpEMRaLZfRE1tXV6Xo8uQjbK3bs2KFrakar9KmUhn6p\\nbAfwOQALAJzIL44T0RQe7JvH65d1w2kx4a+eHcVDIwHctDnT8EHPxv1du1Zs55LJJN+0PjIygmg0\\nqugCS3gTtdWjUts52tvb+X9z17FwOIy5uTkMDQ3xhXzC95K76amI9XgNqEpRVBoxMAyD8fFxjI2N\\nwW63o6OjA62txo32kbOmyFW+Li8vK2pfAFYiRb1EMbu9IhQK6b5WoVWkqGVD/1YAdwC4GIAbwO8L\\nbJtmgRjNwGqiEKUZrHfmfn3leqYqwWazoa6uLuMGK98F1uFwZESVDoej7NKvchvlVwtifZUsy/I3\\nPaFQCAsLC4hGo/j2t7+NRCKBRCKB3/72t9i1a5dqm8cnnngCt99+O2iaxgc+8AHceeedan8lTahK\\nUZSLcIr82rVrcc4552BqasrwOyez2YxkMllwm1QqhaGhIczPz2PTpk2qKl/VOveIIWyv2LRpE99e\\n0d/fr+lxxNBCFKU29Mvh5rP/FaLFZcWnehqw8ZEzcJopXNzswcXrvDnbFfNMfeDxM7oMLc53gY3H\\n4/wFdmpqCvF4HGazmRdJbgJMKXnbFcMlPX45QVEU7HY77HZ7huh9+9vfxrFjx/D5z38ejz76KA4f\\nPoyFhQX85Cc/4TMIcqBpGh/96Efx5JNPorW1FQcOHMCVV16J3t5eLX8dRVSlKEoVCa5/b2hoCI2N\\njThw4ABstpVyCG5N0UgKpTOFo5La2to0caHRMn2qdXuFErQQxdnnT2L0secw/sQx0PEkksFIXjNw\\njptTz8I3vZB3WsYsgCYAowDyJT6XkjSOjgcxdMUW1NrMeO+zo/i3oSX89abM9On157UVPP98BTjb\\nf9OXd51SKRRFwel0wul0ZtihpVIpPv06MTGBaDSK48ePw+VyZUSV3HdNCo12YE7B19GqbpJSBkYV\\ny5QCt9uNLVu2oLW1Fffffz8AqPouvfjii+js7MTmzZsBAO973/tw9OhRIorlCsuymJmZgd/vR11d\\nHfbv35/TMG+xWBCJRAw9LzEjcmFKN3tUklq0ml7B/S2FxgD5ttUztabFusj+w7fwTf1Tz7yEU994\\nSJIZeKFpGddgZU3RCuDPebZ5ajqMTR4bGh0rX9n3bPDh+flojii+50DhdH6+ApxTl3XnXafUGqvV\\nijVr1vAG+OFwGPv27UM0GkU4HMbi4iJGRkaQSqVgs9kyokqXyyX6GTlxaa6QG90sb5TVWqnIbsdQ\\n813lihM5Wltb8cILL6g6P60goiiA64/z+/2oqakp2BKg96BhMYSRG8uymJycxPDwMNauXYtzzz1X\\nc/s3tZGinPYKLorTUxQpigJN05icnCw2j9VQ/iBhm40uK47NRxFNM3CaKTw9Hcb++tyLfo1LWWNH\\nmmHzrlPqCXeTIpwwIXwumUwiFArxa5XRaDRjW6/XC7fbrbsfsBTUFMuoMQM3imAwuOrbMYAqFcXs\\nCy/Lspifn8fg4CA8Hk/eZnYhpRLFdDqNmZkZDA4Oor6+PiOlqzVKC2246RVms1lyewUXleqZUg0G\\ng5ienl55/zUQRSP9UM9tcOHajTXY+8QALCZgzxonPtQhvZK4GOt+/UbedUo9KXQjJFzfamho4B+n\\naZpPv05PTyMcDoOmaTidzoz060o9b2WgxgxcKjU+dVkSLRv3W1paMDY2xv88Pj5eNtM3qlIUOViW\\nxeLiIgYGBuByubBz5064XFI6xkojilw1mNVq1byxXQwphT1CuPaKRCKBrq4uWV8gPXsiw+Ewzpw5\\nA5qmUV9ff9bl/0+6HEtP7tmxFvfsWKvLviffvTXvOqWeKMkOmM1m1NTUZMz1Y1kWsVgM4XAYgUAA\\nExMTAA5qfLblwy9/EMv4eW5uDuFwGJs2bdLtmIFAQLNZigcOHEB/fz+GhobQ0tKChx56CP/+7/+u\\nyb7VUpWiSFEUL4Z2u11Rs7iRori8vIz+/n5YrVa4XC5s27bNkONKrT5NJpMYHBxU3f6htSgmEgn0\\n9/cjEomgu7sbJpMp4+7UcK7fAfz8ZOmOXwCricq7TqknWqXMKYqCy+WCy+VCU1MTAOMnVZSSdDqt\\newpZS99Ti8WCb33rW3jnO98Jmqbx/ve/37DrWjGqUhQDgQBGRkawdetWeL3K0kVGiGIoFOJbFXp6\\neuB2uw1djC6WPhXa26lt/5Aqig+bHkOcklhm6AJwtlp8lJ3DJYEL+TUsL2uRPGi4GmBZNu86pZ7o\\nmTIXm1QBqC/A+eUPYmU38cII4/5AIIDm5mbN9nfZZZfhsssu02x/WlGVolhTU4M9e/ao2oeeDg/R\\naBQDAwNIJBLo7Ozkq/QAdWXQcslXaKNHe4XUdgnJgijyOuEx7qN3IZlMgqIofMz5qqJ9riZ2PN5f\\neJ3y+h3wfvgtAADGV4fI1x7V5Lh6F1eJoSaCrPHlv3ErZbFMOp2WvPSjlGAwqFn6tJypSlEsN0cN\\njng8jsHBQYRCIXR2dqK+vr6k55odvWW3V2hZ8ZovUpQVGRZBrs0bQ9MwaX337bAA8fKLUE9d1i15\\nW1NwUbPj6l1cJUa+CFKMdDqNP/3pT1i/fj0ikQgikQiOH2cBXJCzrRHFMoB4wYxRkeJqn5ABVKko\\nlhvC+YsdHR3o7e0tC+EWRor/vMGC2JwZQNvZ/6ThXsvitpHixTpiojg09U3EW9fLOeWCyG3ef/3B\\nh9H9d++CrTb/JBE5xGkGjqu35j5RDuuMF3UXNwYX4H30QwAAxu5D5OKvKz5sKSJFOVgsFphMJmzY\\nsIE/z1KNH6vxsXkF3QhRJJHiKkarLyEXeSi90xWuybW3t6O7u7usLhAmkwmJRAInTpxAbO48RfuI\\nzEj7fcREkWa0NUeQI4qc6Xfd7i689sAvJI2H4pr5820ruTE+llI2MNim4qIoQxCFmBJBPq2ajZQ0\\na7mLIofwHLWIbLOrR9ViVKGNcClntVKVoghoO1NRbp8gTdMYGxvDxMQEWltbZa3JqRViqUSjUZw5\\ncwahUAj79+/Hk7oezZgxVXLecy1NvzkkN8b/+rT8nQvnI5YJUtKspUiflgMnT57M6Km02+2qbg5I\\n+lQ7qlYUtUCuKAqNxZubmxWtyXEpTb0uJML2is2bNyOZTBqSMpGz3ndn5+1weBygzCaYLWbc9cKX\\nJL1OanGUUtPvYs38oo3x//WG5P2vRiohUtTj/Do6OhAOhxEMBjExMYFEIgGLxZJhaed2uyV/z9Pp\\ntO6imEgkipqarAaIKKpAaluGsECloaGhoP9nMThRLPb6b7bZJKcuhVjXANefqEFPTw9YloXf71d0\\nnnKRW837yafugrehcDvN/aOZzdv3Yw6+pib8rsi+lZh+S0G0MV6ropvlGFCr7QXrzGQQ133zef5n\\n/2wY9167Q9MpG5UginqQ3VMJvGmUHgqFMDY2xnsrC2cber1e0e8+TdO6pk+572Y1vFdVK4papk/z\\nwdnHDQwMoKamBvv27csxFpeLVD9SJYIIAKklO9avXylukfI36scTeAK3gwGNvfgA3gplM9GMSJ8C\\nQFDC3bRS0+9i6NoY/9GjK//PSqPe/Fe34oe//JaiXW5Z78MrRy4BANAMg5ZbHxGdsnH9c6OKp2uU\\ne/pUjxaofHZr2UbpwMrfh5tTubCwgOHhYX4QsDD9asTfsVpuYKpWFLWgkCguLS2hv78fDocDu3bt\\n0qyHqBT2cvlgQOM3+Cj+Bk/Ch1Z8HwewBVeiCfLHvwhF0T/5IBi2wFoeReGBS+8DKODCD/4lLvjg\\n25X+CrryjdYrUTOTta42oF07gxjbDh7OfGAUuGDvnfj9S/ep2u/Tp2bQ0eQRnbJBszKKiLIo9wst\\nwzCan5+clhCTyQSv15thMsKyLBKJBB9VzszMIBqN4sSJEzlG6VqlVI1Iz5YLVSuKWnzQxQQqGAyi\\nv78fJpMJvb29Ga7/WqDljEO1TOBF1KETdViZibYd78MZHFUsislkEq+//jpsNYWLW+743RewpqUO\\nwdkA7r/kPjT3rEP3W3NbHb6Hh/EhvDfn8b0TExk/H8w3xBDqTL9zBLFELNjUm3w/dGwU158n/odS\\nM12j3CNFhmHKTgwoioLD4YDD4eCN0o8fP45du3bxRumTk5MIh8NgGEZ0TqXc61+1TMgAqlgUtUAo\\nipFIBP39/Uin0+jq6tKtOCVbFJWuHSYRhg3qBDuICfgE4yZ8aMU45NvQ0TSNhYUFBINBbNmyBZEi\\nWdQ1LSuuK76mGux5934MHfeLimItNJwgWwHMCyZJaEkyTeORExM4cp34hPUaq0nxdI1KiBTLWbSB\\nN1O8FosFtbW1GRWiDMPwRulLS0sYGxtDMpmE1WrNmVNZ6PeslspTgIiiKiwWC4LBIE6dOoVIJMK7\\n0Oh9TKEoKl07tMGDWbyuKKrTCpZlMTExgZGREXg8HrR2vowIcyxjm0Oj/QX3ceimnYim2bzDeUvF\\nN1qvVPzahwE8AeCHZ3/+IoCHBgc1OCtlPP7KFPa2r8HaGvGpLJE0m3e6RigUKlhFWe6iaESrg1oK\\npXhNJhPcbjfcbjfWrn1zwopwTuXCwgKi0SgoiuKLejjB5Ip3tJyQUe5UrSiq/SImk0lMTU0hEAhg\\n27ZtaGxsNOTLzc1U1IJ8qc77HMJioLfh93levw3XYhuu5X/eiqsRxHjBYw5NfTOzKZ8C1rZLP2cx\\nXBbt/+4nxpuQYpRfDNWkTrcD+ByABaxMBPxNnu2YRALD73sf2GQSoGl4L7kETR/7mOLj5uPnfxzF\\n9efldzEqNF0ju4qSu9h6vV5YLJayj8TynZ9S/1S1Mw3FUFJ5arPZUF9fn3ETT9M0X9QzOzuLwcFB\\npFIpfOUrX0FTUxMikQiGh4fR1tamybXu4Ycfxj/+4z/ijTfewIsvvoj9+/er3qcWVK0oKiWdTmNo\\naAizs7NoamqC3W7PKKvWGy3XFIOYKL6RDGzwYAsKR0hau9QU45f4Gf/vv8Lf5DzPJMR9VdUIYjH+\\nGcD3AbAAXhN5fiuAOwBcDMANYDcgemNC2Wxo/7d/g8ntBptKYei66+C58EK4VJrdC4nE03jy1DS+\\ne3P+C1ah6Rq9vSs3XVwVZSgUwtzcHPx+P2iaBkVRsNlscLlc8Hq9ita79CSfKMopltEbraJZs9kM\\nn8+XsXbIMAy+/OUv46GHHsKJEydw++23Y3h4GOvXr8dvfvMbVe/V9u3b8atf/Qof/vCHVZ+7llSt\\nKMp9M2maxujoKCYnJ7FhwwYcOnQI0WgUgwantSwWi6zBv0bThJWZaO869nU4GlcEcEBb7dUUSsR4\\n4b//awsScfl9pE2JBZz5f+8uuM0prAjiiwAKWT7cfPY/APhsnm0oigJ1dg4om04D6TSgsaC4HRYs\\nfPc9BbdhgPzTNc6Sr4pyZGQEsVgMgUAA4+PjSCQSOe0GLperZEJZCelTPS3eTCYTtm7diq6uLrS0\\ntOCTn/wkgJW0uNr3ZGXYd/lRtaIoFeGYpPXr1+PgwYP8l6QU7RFapk8vh7L+NSlwgqiWYDCGuz/z\\nCAb6ZgGKwhfvuwq7924o/kKJ7GudzXns8qvPFH1dPGbGb36d+aWetRdfT34DwLlYGfVYiFkATQBG\\nAfwKQL7LMkvT8F91FZIjI6j767+Ga/fuouegNT87pOz9oCgKFosFNTU1fG8ssOKcwq13zc7OIhaL\\nwWw2Zwilx+MxJO1a7uldwBjhXl5ezsiIKZ1DWwlUrSjmu8sRr+bsPPtfNnYA5+FJSJ8GoZZStWRo\\n1aSfj2AwBp8vNwV3371P4C0XdOL+b1+HVDKNWDyVdx/7xv2wMfn/NsJUKsc3zBcpOl+HU9l7kL1e\\nmE8crzm7jRXAtwH8nzzbUWYzOh57DHQwiLFbbkH8zBk4tmjnOKMW74ffUtAYXKzQxm63w2638+0G\\nwEo0xPXljY+Pi65TejwexU5R+dDTUlErjOgh5MbZyeWiiy7C9PR0zuOHDx/GVVddpcWpaU7VimI+\\nlFZzKn2dXDhRDAaD6OvrA5A5oYABDVPeuEIZWjbp5+O+e5/Al79+dcZjoVAcJ46P4PDXVlKSVpsF\\nVlv+j2whQSwlFxw7hoXGRv5nK8Sm8eXSMDeH3x88WHxDAGafD+5DhxD+/e/LShSBwsbgUiOxfO0G\\nYuuUTqeTjyrVrlOWY59iNnpbvAEr1adKJmQ89dRTOpyNvhBRFLC4uAhgXalPoyDpdBrz8/OIxWLo\\n7u5G9kduAi9iAw5pekw5TfotqX8HAPwRXTnPmVMpnDM1nPM4J345xx1bwpo6F+76h1/jzOkZ9G5f\\nhzs/fylcLnlTSUqNUBC1fF16YQGU1QqzzwcmHkf42WfRUGZFC8VQ05KRb52S68sLBAK82TbXlyd3\\nnZKkT1eollmKQBWLovALEQgE0N/ff/ZuqzxFkZtesbi4CLvdjv3794t+qfNVlKpJf2rWpJ8ntcWJ\\nXzbpNIM3XpvCZ+++DDt3t+LIvY/jh995Frd9Qn9bN4am8ci5H4C7pVHSLEUh95+ex8d1Oi8h6bk5\\nTH7602BpGmAY+C6/HN63l6flXT607lOkKErUbFtoizY3N4doNMqvUxayRauUQhut08bZ6DFL8b/+\\n679w2223YW5uDpdffjl2796N//mf/9H0GEqoWlEEVlxo+vr6wDAMuru74fP58ITIdnqvp3EUdqex\\nY6U4f4VnZOzXiPSnGjjxy6Z5nQ9rm33YuXvFhPriS3vxg+88K7qPQk3+Sop1Xn/wYdRubUMqmN9y\\njp44DXNLT8ZjE9EUHuwzRhQdPT3Y/GjhIb56E4kkcc5/94GigB01DtnG4EZFYtw6pbAvT7hOOTEx\\ngXA4DAAZDeypVEr2vFSj4VLGeqJH8/7VV1+Nq6++uviGBlO1osiyLN+IWleXv5xcqaAotV9Tiw8t\\nOY+p9Sj1oQVBjPE/BzEuehylcOKXTUOjF83rajDkn8emzQ049rwfHZ3yU5FyinUAIDI+i7HH/4hd\\nn7kRrz3wi7zbMUtTOaIIAOki/dlGNd1rymO5HZUT0RTOf2oQr1/WDafFhL96dlS2MXgpHW0KrVOG\\nw2HMzc1hbm4OFEVhZmYmI/2qdiiwlhgRzYZCIZI+Xe1QFIXt27cXHQ2jVFBKIYgAsB4Hch5Tm/58\\n/MFeRGt/iQcQOPvIRwAAJ/ifBfz8cv6fHkccd1/9dNH9c+InxmfvvhR3fPw/kUrR2LBhDb741cJ9\\ngNnILdYBgBc++SAOHPl7pMKFjcktHftyHmtxWfGpngbgpdzIl0NO0/02AB8seBalJc0CMZqB1UQp\\nMgYvN5s34TrlunXrYDKZsGbNGjidzrzrlFxUWap+Sj37FDmkzHBdLVStKALS5gVqtZ5mFGYd3tKo\\nQh/gcFzcK1OMz959qejjPb3r8MujyotH5BbrjP73c3A01qJhXw+mnnmp4L4pZ250u5SkcXQ8iNsL\\nvU5G0/2fAVxS8CxKB3cDsPGRM3CaKVzc7JFtDF7uhSxc9anYOiXnHxoKhTA/P49oNMqLqh7jm/Kh\\nd6TIsqwucyXLlaoWxWpB7/SnFvT06lPgJLdYZ/b5kxh97DmMP3EMdDyJZDCCZ268V/KQ4aemw9jk\\nsQGzhc0LpDbdWwBcCOAhSUfXjtAV38v42fvYW3K24W4Ahq7YglqbGe99djSvMXg+yi1SzKZQn6KY\\nfyi3ThkOhzExMYFIJAKWZXkbO04stYy6jBBFQJtxe5VAVYuilDe5EgSlGOtxAAvoxxKG4EULTuEh\\nXIN/L/VpGYKcYh0A2H/4Fuw/fAsAYOqZl3DqGw9JFkQA2Oiy4th84bQrIL3pPor8huClhrsBaHSs\\nXEYKGYPno1IiRankW6eMRqN8RDk8PIx0Og2Hw5EhlErXKfVOn8bjcd0LecqJqhZFKWgtKHIrWbVo\\nxjfDgsvwLfwM7wQLGnvwft6jVCnMwjii37sFTGAOoCjY/+JvYX/n36vapx5oVawjRurP/wvrrosz\\nHju3wYVrN9YAp3Lt48Qo1nR/CVZqjo/NzSGpsN+xPhlS9LpicDcA0TQDp5kqaAyej3KPFLUQbZPJ\\nxLd+rFu3khFhWRbxeJxPv05OTvLrlELjAafTWfT4ekeK1TQ2CqhyUZTyZdRSUJRUsmrVjN+Ny9CN\\nyxS99mM3in0hagD8AZEaBt/76iRCX/gLWLa/TbQaUw5/atmElFn+x9JKp7F/Ykj0OaXFOusu3It1\\nF+7Nf8wsQeS4Z8fagqKY3XT/au84mt0W4Njncjf+v+9b+f/gA0C29/xyDPjoUf7HUDCY8bT30Q/l\\nPQct4G4A9j4xAIsJ2LPGWdQYPJtyF0W9BIeiKDidTjidTtF1ynA4nLFOme37mn1Oev4NiSgSclAj\\nKEKUVLIWGu/UjyfQVeIyDHfABMrphWl9d94WBTkoEcRir1NbrKM12U33zf+YW8UqiVp5UdmZySCu\\n++bz/M/+2TDuvXYHPnZpboTq9/v5aMVut+c8z3HPjrUrNwEKqYT0qZHnl2+dkrOzm5ycRCQSAcMw\\nvO9rOp1GKpXSrTq0mtxsACKKhqJlJSsXdd6GMzCV+G2k50ZAj5wUbVFYjXADiNe/ZSHnuW1v+fXK\\nP76U+zo6SWHmeB2At8Bx5/MwWRk0n7MkHiGqxP3pK4Asz9EtAF55S5ZpAZ3O7UG0meE96M1I6/2l\\n5me4QrlHiuUg2twkEaEwCdcp0+k0Tp48ya9TCqNKh8Oh+u+7vLxMRLFaKOcvI0e+oh4u6uQE8Q84\\nAgB4Kz5j2LlxRL95I5w3fFm0RWE1onQAsdmWWdbOpPS72BYy4S5KkkZjYyMahWuYP1R/TgDg/t9P\\nwZR4M817PrAyH6sIM6wPPUym3V6jHThxqb6CVQ6iKAaXUnW73RgfH8fevXtF1ynj8bio76uc3ykY\\nDGYUDq12qloU9SDf9ApAWSWrWDP+ymv175+UWhRkPfRe2A5cqemxqxk5aU4ORrAuVc4IBVEOa6nc\\n12/n/48AACAASURBVM0l1J5N5SNc8yy0TsnZ2S0sLCASiWQU/3BimW/tlESKVYTWkeKf//xnJJNJ\\n0ekVgLJKVj2a8aUgpyjIcemtup7LnZ23w+FxgDKbYLaYcdcLIrnJVcSW9T68cmRlrZhmGLTc+giu\\n3t8qum12cU258sYbb8Dr9aI8Z63np9yzSVIKgWw2G+rq6jLsLGma5oVyamoK4XCYX6cUGg/Y7XaE\\nQiFs2rRJ0/P+9Kc/jUcffRQ2mw0dHR3413/917KJRqtaFLWmpaUlYzBqNlpWsurdPymnKCh41/kA\\nAOd7v5C3IpPj60f+F5/6TOFtxPjkU3fB26D9tG8XnUDUnL+QRCp6+Zk+fWoGHU0etDW6Ve8LWJng\\n8YPBRcUG3kppaWlBKKRPa0g1o7RH0Ww2F1ynXFhYwNNPP43Dhw/D4/Ggt7cXXq8Xu3fvRltbm+qb\\nhXe84x04cuQILBYL7rjjDhw5cgRf+cpXVO1TK4goZuFeyyryLXWvZQsKIodWlaxSo84rX3oZHR0d\\nBSsIAeA+R+bzctKzvi/lb4bP5oMfeWve506/PgVszJ3DKJVlOFCLuKzX3DLxB/61H8J7RbfJbqEw\\nibjXyfEzlcNDx0Zx/XkbVe2Dg5vgocTAm/HVKV6nZHx18Pl88Pl8QJZznpJUMeFNtGwZEaZUAaCr\\nqwvXXHMNbrvtNmzZsgUvv/wyfvSjH6G3txf33XefqmNdfPGbN8YHDx7Ef/zHf6jan5ZUtSiK3e3c\\nNpIs+BqGYTA+Po7R0VFs2LABra2teOGFF3DeeefpdZqiSI06e3vLYzwUR01N/jaCnt51+KPYExSF\\nBy69D6CACz/4l7jgg+IWbflE7Zf4Gf/vD+JaBCCvlSG7haLjidwBY3L8TIVMPlePfN4MyTSNR05M\\n4Mh1u2SdbyGUGnhHvqbPiCo5qWKjqQS/z3Q6rWvjvslkQiKRwBVXXIGdO3fqcowf/ehHuO6663TZ\\ntxKqWhQBaabgwMoXZHp6GkNDQ2hsbMTBgwd1d6YvhlZRpxh6pGetdFrR6+743RewpqUOwdkA7r/k\\nPjT3rEP3W6WtTtUglvHz9yF+R/qNjRcBAI6N5nqwSp1bKNXPVCqPvzKFve1rsLYmv7F6KpUCy7Iw\\nmUwrwkxReSsLtTDw1hMlqeLZ2Vl4PB44nU7N1//KtfJUCE3Tul+HAoGAovW+iy66CNPT0zmPHz58\\nGFdddRX/b4vFghtuuEH1eWpF1YuiFBYWFtDf3w+fz4d9+/YVTUWuBrSytys0/Fcqa1pWCgR8TTXY\\n8+79GDruFxXF483NAICh6a+pPqYSpPqZSuXnfxzF9ee1FdzGarWCYRiwLAuGYQCsXCjF0MLAW0+U\\npIojkQhmZmYQi8VgsVj41gMtRjlViijqPYUjGAxizRr5n5GnnhIrN3yTH//4x3jsscfw9NNPl1VB\\nU9WLYqFIkWuvsFgs2LlzJ1wuV979lHsTslz08EtVQiISB8uwcHidSETieP3Jk3jXXYWndZtMLjBM\\ncVNuvSjmZyqFSDyNJ09N47s37y+43djYGLxeL3w+HxwOBy+QYmhh4K0XSlPFwqrIVCqVM8rJbDZn\\neIm63W7JQmeE4KhF7/QpAESj0YLXPiU88cQT+OpXv4pnnnlG832rpepFUYxoNIr+/n6+vaJYj47Z\\nbM5JY6gp2Mle18wugpHDfQ676D6loGd6VirBmSD+5dr7AaxcpM5933nY/s7CF862po8CAA6IpG7y\\nsZeegc3MKD7P7GKc8LPPouHDudZywTvPlWSg7nZYsPDd9xQ9rt1ux/z8PIaGhpBKpeByueDz+URv\\nX7Qw8NYLKaniYlit1pzWA26UUygUwtjYGCKRCCiK4i3SCvXoVUqk6HAo/5sVg7vB0vrvcOuttyKR\\nSOAd73gHgJVim+985zuaHkMpRBQFJJNJDA4OYnl5GV1dXZKqSYEVG6bs0mglIqQXSsRZC+qshWcK\\nSqFxcxPufumI5O3/3vxnWM6K20GZRZtJWvkXP7sYx3f55fC+PbcgyHffC2BjIc0M1NetW5cxeSEU\\nCmFwMNs5fAU5Bt6pVIpfpzRCGKSkipUgNsqJpmneS5Tr0WNZlhdKLrKsBFE0IlIEtO/XHBgY0HR/\\nWlL1okhRFNLpNIaHhzEzM4PNmzejp6dH1oeAE8VvttkUC5DSaK4c+NiNNbju2KdBN3oMPe6+cT9s\\nzJvrZ0P4Gv5BwusiJhu+23pBzuNqIkWpxTgANDVQ52BZFrOzs/D7/WhtzV+9KdXA22Qy8euU3Bol\\ny7Iwm838d0MrwZCaKtYKs9n8ZovIWYQ9enNzc/D7/UgmV76PIyMjvFDabDZDzlEqehfaMAyzqpaF\\npFD1ojg9PY0zZ85gw4YNOHTokKIvutVqRSqVUhWRib02nU7D7/fDuqYbqaXyLu7pOfh10cc/C6AV\\nwEdEnkvE3+wpZFkWr7/+OrDxtORjCgVRDm5G2s3Hxs9sxOiRTGNOJpGASWWhlRQD9S30VzALCdZa\\n/5kS/FC38t8ksKTqDFcuhna7nU+fMQwjWtCjhVBKTRXridjMw8XFRUxPT8Nut2NpaQmjo6NIpVL8\\ncGDuP5vNVjLh0HvdMxQKwestnwplI6h6UaypqVHdXmE2m5FOK2s3EINhGIyNjWFsbAwbN27ExycY\\nfDXPWrTcocVGMAugCSs+z78CcKzAtizL8hfajRs34iVIF8VSQKmMFNh4WJKBuiRB1JE33ngD8Xgc\\ndrudL+ThinmkCKXJZOIFstxTkPngbgyaz1Y1A5nDgQOBACYmJpBIJGCz2TKEUovpFFLQO31abb6n\\nABFFuFwu1YJmtVrzlsHLgUuBDQ4OSuqFVDK02AiuAbAAwArg2wDydThxF1MuRVNuVWi1PjZngIPa\\nC13kwRsrwkD97ya245V3WZFIJBAMBhEMBjE1NYVYLAabzQafz8eLJdcjmC2S3HeC+3+lpeEYhhEd\\n5itmup1IJPjK1+npacTjcc1bRMTQO31abbMUASKKmmCxWJBKpYpvWITjx4/D5XJh7969kirKlAwt\\nFqJmDTSbe8DCjWl8CuvwB4mv4W5GuKZzYKXBX+mgYa354T+ncEB6AaskTOu7cwzUZ1if6BSIUsJN\\noLDb7TljpJLJJC+UMzMziEajsFqtGULJCYBQJLloslKQU2hjt9tht9szivPytYgIi3nktIiIoXf6\\nlESKVYgWd24WiwWJRP45NlJTnFu3bpWVv1c7PkrrqtQImotvJEAohhz7J4ZUnUMwGMPdn3kEA32z\\nAEXhi/ddhd17NxR/oUGk3/h9joF69pzA9W9ZAJ7LfS2zMI7o924BE5iT1NahFzabDQ0NDRkCkEwm\\nEQqFEAwGMTc3xwsAl3blBKCSoGlalWDJaRGROsYpG70rZKttliJARFETLBYLIhHx9gM5Kc5qW9DW\\nI512371P4C0XdOL+b1+HVDKNWFxZBF+nw4WGTlLwHX6+6HaTz9WLP2G2wHH9l2Bp3120rWPG7MVa\\nWtlUihnbykW85T8z/3aNduCVd1lFX2Oz2VBfX4/6+jfPnYuUhEIp7lpbnjAMo3lqUk2LSL5z0TMt\\nTdKnVYhWkWK+dUm1Kc5C6D0+qhSocaMJheI4cXwEh7/2bgCA1WaB1absI/4/eYb2Zjfpj/zt36Lh\\nwx/O6EnMK2oqMdU2w1S7Eo0Xa+voufg3/L/D918P+zs+BOv2t2VsI9VIgEPuUF9hpMR5BydmnLCz\\nseIvzmKGzS1KarCxoGlat15KmqYNacHI1yLCCSXXIkLTNFwuV4bpgN4EAgEiigT5FBJFtSnOQmjl\\nTyoFo6pcOTcaKWR7nE6MLWFNnQt3/cOvceb0DHq3r8Odn78ULlfuhe2ZG+/FhT/9Qt59DwwMZFyk\\nOKQ26euNlLaOYttpbSSQj2g0itOnT8PhcKDhsvtBWzOjzXQ6za+9BYNBhMNhvkWCEwuPx4MJkylr\\njZIFw6xUwgqrXosZo0tFrNDGKEwmEy9+HCzLZsw7HB4eRiQSwauvvqpbi0gwGER7e7sm+6oUiChq\\nQCFR1BM1/qT5/DH78QS6cEnGY3pUubJ5IjE1pNMM3nhtCp+9+zLs3N2KI/c+jh9+51nc9olc0con\\niK8d+1zmz2IbfW5lhM681YML992m9rRlI7WtQ8p2ehgJcDAMg+HhYczNzWHLli1516YsFgvWrFmT\\nYTpN0zQvlGNjYwiHwwDAX/g5oTSbzRlCKWaMzomkXKEsN0cbzp7O7XajubkZDMPgxIkT6Orq4ltE\\nxsfHkUwmYbfbMzxflbaIkEixCtE7fSonxanE41SJP+nS0hLOnDkD4MKMxznxux2ZNmFyUsCRs+sh\\nYkU0WsK5jQhpXufD2mYfdu5ecXS5+NJe/OA70gcgy6UhFdZt3/lg0ylJbR1St5MaccplcXERfX19\\naG5uxoEDB2SLi9lsFl17C4fDCAaDmJiYQCi0smbKXfy5gh6bzaaJUJabKGbDtWNkt4iwLMsXPqlt\\nEQmFQqTQphqROlMxH4VE0cgUZzGi0Sj6+vpA0zS2b9+O32U9z4lfNnJTwHpeSDhjg8nJSTRvynyu\\nodGL5nU1GPLPY9PmBhx73o+OzkbxHVUgLMsi+sNbRds6spGyndSIUw7JZBJ9fX1IpVLYtWsXnE7t\\nDMfNZjNqamoyIheGYXihnJqaQl9fHxiG4VOvwpRiMaEEkOH3Wu5TMrL9ljkoihJtEUkmk3zlq9QW\\nERIpEhTB+USKUS4jmM6cOYPFxUV0d3dnVAgKyRY/JegZHc7Pz2NgYABNTU0455xzMDqXGwV+9u5L\\nccfH/xOpFI0NG9bgi199t27nYzR03zGknvsFTBt6c9o6spGynZZGAizLYnJyEqOjo9i8eTOampoM\\nadY3mUx5i1S4PsqBgQHQNA23250hlMJRW9z/uX+nUinEYjHehMAoY3Q5yBVtm80m2iISCoUQDocz\\nWkT8fj/GxsYQDAY1n8Lx+c9/HkePHoXJZEJTUxN+/OMfY/369ZoeQw1EFKE+UmQYRjSdx1EOI5jc\\nbje6u7sVXahKXeUaiUT4uZa7d+8u+CXt6V2HXx7NHdm0GrBsOYTany5L2lbKdlIiTimEw2GcPn0a\\nXq8XBw4c0H0SfDHEilQ4w+9gMMi7RnHVnELTAZvNhmAwiNOnT6OmpgZut7ugMbreywSFyBcpyiHf\\neq7NZsPo6CjGxsZw3XXXgaZpbNu2DR/96EdxzjnnqDrmpz/9aXzxi18EADz44IO49957y2ZsFEBE\\nURUsy2Jubg4DAwOauXXoVeVZaHICR7b4cZQqBcwZoi8vL6O7u9vQtY0zk0Fc9803ewr9s2Hce+0O\\nfOxSZUODyxExIwE50DSNoaEhLC4uoqenR7Rat1wQGn5zUQnLsnzbw/z8PPx+P6LRKFiWRXNzMxoa\\nGkBRFJ96BcT9Xrl96TFBpBB6pXfNZjN27tyJnTt34ujRozh27BhSqRRef/31vFkmOQg/J1xkWk4Q\\nUVRIIBBAX18f7HY79uzZg1deeUX1PvX0Ms0u4nGvzY2MOfHLxugUsDAVt3HjRnR1dRn+xdmy3odX\\njqxU4dIMg5ZbH8HV+4vfWFQSUowE8sGlstevX48DBw6U3YVNCkInGYfDgaWlJbS1taGhoQHhcBgL\\nCwsZw5uFxuh2u72gUGo1QaQQeq95CrNnNpsNu3fv1mzfn/vc5/DTn/4UNTU1+O1vf6vZfrWAiCLk\\nrYPFYjH09fUhmUxiy5Yt/F2PFikjPRv9sxGzeOPETwyjUsDLy8vo6+tDTU0N9u/fD6tV3EHFSJ4+\\nNYOOJg/aGnNtyg5unMr4+Vc/327UaZWERCJxtnIZRVPZlUA6nUZ/fz9isVhGYZDH4+GnY7Asi1gs\\nhlAohKWlJYyMjCCZTMLpdCoSSq0miGiRPpWCkhueiy66CNPTucbBhw8fxlVXXYXDhw/j8OHDOHLk\\nCL71rW/hnnvu0eJUNYGIokRSqRT8fj8WFxfR2dmZYZAMaCOKcqo8/25kFOFwGA9v01YwS7X2GY/H\\nMTAwgFQqhW3btpXEJ9MXE7fqe+jYKK4/b6Pi/bIsi+j3bgHlXgPXX9+neD/lwMsvv4zOzs6MqsZK\\nhVv6aGtrKzhYnJvg4nK5sHbtyoBmboRUMBhEIBDA2NgYEokEHA5Hxhql3W7PO0GEa13ijiFXKPWe\\nkMGNxFLCU089JWm7G264AZdddhkRxXKj0J0QwzAYHR3FxMQE2tra8harWCwWOBsZxOaMqVAzmUxY\\nXpZWdGEkJ06c4O+cufl7hf6+NE1jdHQUMzMz6OjoyLnZKIRSSziTyYVvJvfwP7t+fXPebZNpGo+c\\nmMCR63bJPg6HnKrRcufAgQNl3aYghWQyidOnV+Z27t27F3YFQ6OFI6SEQikctTUxMcHPpBQKJfed\\nyK58FRu1VaiXMp1OKzp3qQQCAV3Wifv7+9HV1QUAOHr0KHp69HFSUgoRxTxwXo1+vx/Nzc04ePBg\\nwYuBxWLB3/x5JqPcuRjZ63xyqjz7+voKutKUavDwjh07EAgEEAwGMTk5iXg8zt89+3w+1NTUwGaz\\n8UVK3N/3nHPOkZ1KkmMJp5THX5nC3vY1WFujPE0op2q03KlkQWRZFlNTUxgZGRHN9qiFoig4HA44\\nHI6cWYv5ZlJyYimcSSnVdMCIWYp6FLfdeeedOHPmDEwmE9ra2sqq8hQgoijK0tIS+vr64PV6sX//\\nfkl3Y1pYvcmp8uzs7MTs7GzO46UePGyz2TLm73F3z4FAAMvLyxgdHUU8Hufvctvb21FfX1+yHrDP\\nWk/igQLP//yPo7j+vLb/396ZR0dVn///nWWy7xtLAglmyAYBs8LxCIJLFcV6QI9VodLiWhNBOVi0\\nFAs/K4Via6SoIEv1q1VUVGq1RQWqGBsSQbZAkgnZdxJCZs/MnZn7+wM+lzuTyTKTO/feCZ/XOTmn\\njWTu52Ym9/15ns/zfh7R1kPxDEajEVVVVQgODhbdNuJsJiUZSqzRaNDZ2Qmj0QiFQmHXmcfZTEr+\\nbEqtVouYmBhYLBbB+r3y0Wg0HokUP/nkE8FfU0ioKOJq+lSv13NFBNOmTXOpC70QouhKlWd4eDja\\n29sHfF/MYp2RwN89x8TEcP4wpVIJm83GncfwzdWRkZEIDw8XJSrR+gz+nun7LfimshM7Hsn3+Doo\\nnoFlWTQ3N6OjowPp6el2fjwpGazjDBHKrq4uGI1GrjUbfyalTqfDuXPnEB8fj8jISEH7vfLp6+u7\\n5lq8AVQUAVz+MFZVVUGj0SAtLc2lFChBqKbgI63y9PPzs2tPRXClWCckwQbDBeF2liEJztO5LMui\\ntbUVra2tSElJQXp6OrcRmTBhAgD7LiSkXRfLsly7rsjISISFhYkaUYYG+ePijsWiXc8baGtrQ0RE\\nxKgnxouBVqtFdXU1oqOjveIsdLCZlBqNBlqtFhcuXEBfXx+sVivi4+MRFBQEs9mMkJAQ+A6YIGJz\\nWvXqilBeiwOGASqKAC7vqqKjo5GZmem238rf3x9Go+tz4txlMFF0hafqjR73l/X29qK2thaxsbFD\\npq34XUgSEy+fo5IUkUaj4SYl+Pj4IDw8HJGRkdzD2Rs9ct6K1WpFU1MTN96Jb0mQi1CSpgKXLl1C\\nRkaGVw/vVigUiI2NhUKhQFdXF5KTkzFx4kSuh2lDQwP0ej3X7o68H+S9GE1jdE8V2sgdKoq4vEMj\\nEYu7iD0+ajBRdKVYx5NiQvycLMsiOzsbISEhLr+Gr6/vgAbQpFejRqPhHghkSCsRSlK0cK1hu9gK\\nw1tPwqbudml48EiJDwQmT75qTeG/F2S2H2kwzRdKMd8LMgFmwoQJyM/P9/rPgc1m47oG8Y90Buth\\nqtVqB2xa+KO2RiKUwOW/PbVaPaJOWGMNKooCIbYoDvbHLvVUDqvVisbGRvT09ECpVArSFoqPs16N\\nJMVEzmIMBgMCAgI4keT7xTxNYBADU797DQfiA4GTC0f+s4mfMPbf8PNH0IN/hH/K9SMaHrxv4jGk\\npKRg/Pjxbv1unL0XFouFS/c5blr4UYzQ78VgJnxvRqPRoKqqCuPGjRtW4IeaSemYaeFPEAkLC3M6\\nQaSjowPvvfceHnvsMTFuVVZQUYTnZyqKiVRTOViWRVdXFxoaGpCYmOjWDD13ISkmvgA784sRawgR\\nS3eNyUNx16KaEf9bvldSCHyjxsM36nIXlpEMD/ZExyB/f/8BUQzDMNzDmfQXJUJJvkYy228wRmrC\\n9xYGiw5dxdWZlOXl5UhOTkZXVxe2bt2KLVu2YOHChYLckzdBRfEKnpyp6IzRXGs4xJ7KodFooFKp\\nEBoairy8PI+Ijas4lsHzO5DwW3XhpiCoA4MRaXL9PJgNlO95y0iGB4vVQk+hUDgVSrJp6e7uhl6v\\nh0KhsPPuDSeUpOWcj4+PbD53o8WV6NAdhppJ+dVXX+G9995DQ0MDJkyYgH379qGhoQFFRUWyL1IS\\nEiqKAuGKKKrVatTU1CAgZjbMva4/mJw185YCs9mM8+fPw2g0Ij09XdYFDYN1IHkfJ7HqnqsdbZq/\\n/AGt/ynDDdtWo+O7n1D517247Z9/dvqalyO9Ex5d9/VfMOg2ufYznhgeLDTOontHS4LBYLATSnJe\\nDMCjJnwpsNlsqK+vx6VLl0YVHbqDj48PfvjhB3z00UdYu3Yt7r//fjAMg7Nnz+LMmTPXlCACVBQ5\\nxIgU+/v7oVKpYDKZkJmZicJ2GwAXn3g8pKr0s9lsaGlpQXt7+7ADZV+fEgzDBfd2uyEJLIoaPFfR\\n62zNF/53Bs1f/IDWA0dh7TfDrNHju4f/H276vxedvkY46z+k13EwwtmR/em5LIgWRtDhwWLizJJg\\nNpu5iLKzsxMGgwFmsxlBQUFITk5GWFiYXQ9Rb8TT0eFw137++edx6dIlfP3111wT9ICAAOTk5CAn\\nR9gUvzdARVEghvogWywWNDQ0oLu7m9vZCvHBl2IHR0YGJSQkoLCwcNg1uCuIo/1Zd8l/+Unkv/wk\\nAHCR4mCCWF1djeKIWNlYQ1iWhWF3sWDDg+VAQEAA4uLiEBsby3VDmjbt8hm5VqtFTU0NjEbjoP1F\\n5Qw/Opw+fbqoTfBZlsWRI0fw/PPP45lnnsGyZctkYaeRA1QUPQjLsmhra0NTUxOSkpIwe/ZswT54\\nLMuKOlZJr9dDpVLB399/TIwMAi7fE0ZxDDVu3Dio1WqueMTf339Aqk/MB7MrjcfjPddHWnC0Wi2q\\nqqoQExNj1yPXsW0aiShJz10ilGJXII8EKaNDg8GAF198EbW1tfj888+RnEzbGPKhongFoT+UFy9e\\nhEqlQnR0NAoLCwUTMH7HioyMDBwQ5FUHh0S5ly5dwtSpU2XTJms08O8JNw7+vky4KRcTbsod9L8P\\nZQ0h/SwdH8xCbCaIHzHshS/svu9K43FXrB9SwTfhZ2ZmDnlm7aywaqgKZBJVir25kzI6BIDy8nKs\\nWrUKy5cvx7Zt22h06AQqigLi6+sLrVaL2tpa+Pj4YObMmW6Z1p3BF0PgsogrFAqEJLBupRkHa8nG\\nvx4pZpg0aRKUSqWgGwcpJnmwLIsLFy6gvr4eSUlJKCgowLs4KdjrD2UNUavVaG1thclkQnBwsJ1Q\\nulw1ecWPOJYhJvyJEye6FUk5m1jBF0pnMxD5EaUnkDI6NJlM2LhxI8rLy/Hhhx8iLS1NtGt7G1QU\\nrzDaDyjDMOjv78eZM2eQkZHhVv9UZ7Asa2eqJS2ZCMMVohArglqt5kY6Wa1WhIWFobn5avNt/o5R\\nrVZDpVIhIiLCI142KSZ5kGbvQUFBopbvD2UN6e3tRWNjIxiG4Zqhk6+hpjjw/YhjDU+a8AcTSvJ+\\nkCkuZrN59BsXHlJHh6dPn8bTTz+N++67D4cPHxZ1Qog3Qn87o4Q/hDgwMBDZ2dmClVOTwaNEDN1J\\ndfCtCKSyjDTfVqvVnIGXTBc3GC4P7R0uXTUaxJzkwU+Vpqen2/mzpGAwa4jBYIBarcaFCxe4SSKX\\nP0fpkq5XTMi9i2nCH+z9cOZpDQkJsSvmGYlQShkdMgyDV199FQcOHMDu3bsxY8YM0a7tzVBRvIKr\\nH1YyJJdUYs6ePRtVVVWjbtJNXtsxVSrkHxO/JyJwtTVbR0cHoqOjYbVacfbsWc4jRjrACHX+4sok\\nD3dxliqVS5GFIz4+PggNDUVoaCgmTpwI4OrGBSNvkOO1yM2EP5hQGo3GARE+EUryRbIqUkeH1dXV\\nKC4uxi233IIjR45I/jv1JqgouoFGo+FScbm5uZxYjLbVm6fF0Nn1uru7UV9fj/Hjx+OGG26wi0bN\\nZjOXcuWfh/F7iopZATtSpEqVCgnZuACM0//OWhj4+Mvvd+8K/HNruZvwSSYlJCSEy7iQCF+j0aCn\\npwcNDQ1gGAYBAQHQ6/WIi4vDzJkzRf38Wa1WbN++HXv37sWbb76JwsJC0a49VqCi6AL9/f12HVwc\\nx6q4K4rDnRt6Ap1OB5VKhcDAQOTk5DgtLggICBhwHkZ2yz09Paivr+fSfPzhwMOleV2Z5OEK7qRK\\nPW2+9wTEjxjy8CvwCXY9xS0HO4bBYEB1dTVCQkKGHCkmZ/gR/oQJE2Cz2VBXV4eLFy9i8uTJMJlM\\nOHXqFKxW64CI0hP329TUhKKiIlx//fUoLS31eFP0/v5+zJ07FyaTCRaLBffddx82bNjg0WuKgfd9\\nEj3EUCJE0otdXV1ITU0dtIOLO6IoxLmhKzAMg7q6Omi1WqSlpbl0xuZstzzY+SSZeQgoB7yO0JM8\\nRpMq3chku31dT3N5PufAP1HiR9Q2nwF8Ln9enPkRv89pgUajGWANkfJclZzBd3Z2IiMjY8wMseWf\\nHc6aNcvu88eyLDdAm39mzC+uCg8Pd1sobTYb3nnnHezcuROvvfYabrrpJqFua0gCAwNx+PBhhIWF\\ngWEY3HjjjViwYAFmz54tyvU9BRXFISDpHTL5YTjzvSuiKEWqtLW1Fa2trUhOTkZ6erog13M8X9dZ\\n7gAAHkBJREFUnwQubyJI2bszhJzkMRZSpY7whQMY2NB7pH7E6667jvvfpHDEMRXOPzP2dCqcmPBj\\nY2PtTPjezEjODsm4prCwMLszY5J67erqQm1tLWw2m91Yp4iIiGE7RnV0dKC4uBjJycn4/vvvRe0/\\nTO4LuLzZZhhGtuf2ruDjYr9PeXSi9hAm09VGk8QnFRkZidTU1BE9bDs7O6HX65GamjrovxFbDAGg\\nt7cX58+fR3R0NKZMmSJqqmpL6Oh8ms/pDU6/L7eqUqHo6+tDTU0N4uPjkZKSgkmfuV+41Xbv4CLH\\nT4WTL4vFwqX5SCpciM+KowlfzGbXnoQfHSYnJ4/675hkXcj7odVq7YSSDAr29/cHy7L46KOP8Oqr\\nr2LTpk1YsGCBJIJktVqRl5eH8+fPo6ioCJs3bxZ9DS4wol8QjRQdMBgMUKlUsNlsyM7OdqlqbKhI\\nUYpzQ6PRyO1Ap0+fLlgjASnxpqpSV2AYhjuvto84Rl/N7IzBCkfIQ9kxeiHRZFhYmEs9d0drwpcj\\nnqos5WddEhMTuWuR44n29nbs2bMHhw4dQkBAAIKDg/HKK69g7ty5kv1e/fz8cPLkSfT19WHRokWo\\nrKzE9OnTJVmLUFBRvALLslCpVLh48SLS0tLcmhg/mCiKfW5IzkB7enqgVCrduhe5UFFRYVfp2tzc\\njODg4DGTKmVZFp2dnWhsbERKSoqkQ3IHS/M5DqUl/44IZWho6IDPNF/khTbhS4nYvkO+ULIsi3nz\\n5qG0tBRLlixBZGQk9u3bh3Xr1mHjxo245ZZbPLqWoYiKisL8+fNx4MABKopjBR8fH8THx2Pq1Klu\\nf9AdRVGKc8Ouri7uDLSgoMDrz22+mz/P4Tv2zYtDEmwoaugXbT1CQiowg4KCPNI5SAh8fX25TQnB\\narVycw+bmpqg0+ng5+fH/TuGYdDa2oopU6ZIKvJCIrXvsK+vD2vWrIFer8c333zD+SelpLu7GwqF\\nAlFRUTAajfjmm2+wZs0aqZc1aqgo8oiNjeUEzB2IKEpxbqjRaKBSqRAaGjpmoqiRYLjgi7KyMq/w\\nTxJsNhsaGxvR3d2N9PR0r6vA9PPzQ1RUlN26LRYLLl68iPr6elgsFvj7+6O9vR06nc6uGbo3CqRa\\nrUZ1dbUkXWlYlsV///tf/O53v8Pq1auxdOlS2Wx0Ozo6sGzZMlitVthsNtx///1YuHCh1MsaNVQU\\nBcTf3x9ms5mLFsUQQ7PZbOedFLP6TC7Mnj1bEP+kGPT29kKlUmH8+PEjiuTjA10fNEx+TixIhqK5\\nudnOhM8fEMwf58TfvHiq+bYQSB0d6nQ6rFu3Do2Njfjyyy8xadKk4X9IRGbMmIETJ05IvQzBodWn\\nPCwWi9tt2liWhcViQWVlJfR6PRe5kAeA0BWfNpsNLS0taG9vx3XXXTeod1JqXp8S7PFhwc4qVPkF\\nCmq1Glqt1i4VGBkZiZCQENF+Z2azGSqVChaLBenp6WPmjM1gMKCqqgqhoaFQKpVDfs4dxzmp1Wq7\\nnqJyivL50aEQlaWu8r///Q/PPfccHn/8cTzxxBOy2NCNAUb0JlJR5OGOKDpLlQKXKz9JizQymYIY\\n2iMjI50WJ4yUixcvora2livbd6UaUK6MxroxmG3DEYvFAq1Wy70ver0eAQEBHunvSmBZFu3t7Whu\\nbkZqairi4+NluXlxFeKl7OrqGlUKmG8N4U9xcZwaItZnnB8dZmVliR4d9vf3449//CN++ukn7Nq1\\nC0rlwOYXFLehougqroiiq+eGNpuNeyCr1Wro9Xr4+/tzD+PIyMhhH8jELuLr64upU6eOmWhDr9fj\\njYSBfS9HOnNxpKLoDH5/V7VaLWh/V51Oh+rqaoSHhyM1NdUrW5k5g2/CnzJliuBRDN8aQqJ8lmXt\\n/HqeSIeT6HD8+PGYPHmy6JuXEydOYOXKlXjggQfw7LPPjonNrsygougqVqt12I40fK/haP2G5MyF\\nCKXJZEJISIidUPr5+dkZ1adOnWo37d2b4d+XY5WpDVb8DWl2MxfvxQdOx0uNRhQdIZGLY5TPtyAM\\n90C2Wq2or69HX1+f0x653gr/vsQ24RNrCHlfdDod106QCKW72Repo0OGYbBlyxYcPnwYO3fuxLRp\\n7nV3ogwLFUVXGU4UHf2GQu8k+XP1yAPZbDaDYRjEx8cjOTkZYWFhXp9+czTgJyUl4ZUw+wdRC8rw\\nLdbjl/gKAPA9/gQAmIMXBryekKLoDMfzSfJAdnY+2dPTg/PnzyMxMRFJSUle/14R+Cb8SZMmyeK+\\n+NYQkn3hW0MiIiKGPTeWOjo8d+4ciouLcccdd2Dt2rWyOE8dw9CONq4y2B+EWBYLftf90NBQaLVa\\nxMXFIS4uDnq9Hg0NDQPOwSIjI2VdwefISHuVijFzcaTwDdRJSUkA7M8n6+rqoNPpwDAM/P39kZyc\\njLi4OFkIx2hhGAa1tbUwmUyyM+E7s4YwDMO9LxcuXIDBYOD+XvjWEJZlJa0stVqt2LZtGz755BPs\\n2LEDeXkDe9xSpIGK4hBI4Tc0mUyora2F2Wy2S1HFxcVx/4acg6nVarS0tMBsNiM0NJQTyfDwcNmd\\nR4y1XqX+/v6Ijo5GVFQUWlpaYDAYkJaWBn9/f2g0Gpw7d85r5k8OBpnokJKSgvHjx3uFyCsUCsTE\\nxCAmJob7Hv/cuL29HXq9HgzDIDIyEikpKaK/J/X19SgqKsKsWbNQWloqeHEXZXRQUXSCkOeGI8Vm\\ns6GpqYkbTzVUpOFsziEpTGhvb4dWqwUAu2hSTPsBH3d7lXpq5qKQkGHTUVFRKCgo4DYijvMn1Wr1\\nAP/kSM8npcBkMqG6uhq+vr5johEE+XuJjY3lmgtkZWWBYRj09fWhublZFGuIzWbD7t278fbbb2Pr\\n1q2YM2eOoK8/GC0tLXj44YfR1dUFHx8fPP7441i5cqUo1/ZG6JkiD+Kj8uS5obNrdnd3o76+njvX\\nEOIhyR/fpFarYTAYuJl6RCg9/bDjp0qVSuWQ13O0ZFhhwd+QhmU4hHAkXim0ed/piClPnyk6YrFY\\nuJmUGRkZLhWc8HuJDnc+KTZ8+wjfhD8WGO7skJzn86eG8K0ho83AtLW1oaioCEqlElu2bBE1XdvR\\n0YGOjg7k5uZCq9UiLy8P+/fvR1bWwKK1MQ4ttHGVDz/8EPv370d+fj4KCwuRnZ3tUeHQ6XRQqVQI\\nDAyEUqn0+NmgyWTiRFKtVoNhGC5qEbLrizupUmc+RRX+jQN4hpu5OBdrnf6sWKJINjB1dXWYPHky\\nJk6cKIh4SeGfdMQVE743YbPZUFdXh76+PpcrS52NcuJbQyIjIxEWFjbk34zNZsPevXuxdetWvPLK\\nK7jtttskT0Pfc889KC4uxm233SbpOiSAiqKrMAyDU6dO4ejRoygvL0dlZSVCQ0ORn5+PgoICFBYW\\nCvIgZBgG9fX10Gg0SEtLk+x8jWVZuzJ3MgGBH00GBweP+H6dVZWO9GfFMO+PBqPRiOrqaigUCqSl\\npXk8yiadX8h7Q+w6/KhFiPSeUCZ8OeKJylLiNyZCybeGkA1McHAw/Pz8cOHCBaxcuRJRUVEoKSmR\\nhZWqsbERc+fORWVl5ZixCrkAFcXRwrIsent7UV5ejrKyMpSXl6OjowNKpRIFBQUoKChATk7OiIWD\\nZVm0tbWhpaUFycnJmDBhguS7RkcsFotd2tVoNCIoKMhOKJ09jF1JlTpDrqJIRKOzsxNpaWl2BRxi\\nIoR/0hFPm/ClYjTRoTuQowqNRoOLFy9i+fLlUCgUuHjxIpYsWYInn3wSSqVS8r91nU6Hm266CWvX\\nrsXixYslXYtEUFH0BFarFTU1NSgrK0NFRQV++uknAEBubi6Xdk1NTR3wgLl06RJqa2sRHR2NKVOm\\neE16ipyz8tOu/IdxaGgoenp6OKO6u1GvHEWxr68PNTU1XDs9uYmG4/nkSPu7SmnC9zRS+w4vXbqE\\n1atXw2Kx4P7778f58+fx448/oq+vD4cPHxZ1LXwYhsHChQtx++23Y9WqVZKtQ2KoKIoBSUEeO3aM\\niybr6+u5eYbJycl4//33sXTpUvz85z9HSIj7D3+5QFJIbW1t6Orqgp+fH4KDg+2iSVfHBMlJFIk3\\nr7+/H+np6aJ72EYDifTJl+P5JBlbJScTvhCQ6FCtViMzM1P094xlWRw8eBDr1q3DmjVr8NBDD8nm\\nd8uyLJYtW4aYmBiUlJRIvRwpoaIoFTabDTU1Nfj973+PH374AZmZmejr60N2djaXds3KyvKaaNER\\nZ6lShmHs0q79/f0ICgriRDIyMnLI+5WDKLIsi87OTjQ2NnqVN284TCYTent70dTUhP7+figUigFj\\ntbzJP+mI1NGhVqvF2rVr0d7ejp07dyIxUV7WodLSUsyZMwfZ2dlctmPjxo248847JV6Z6FBRlJIV\\nK1ZAqVTiN7/5DRQKBUwmE06cOIGjR4/i6NGjqKqq4vxt5GvcuHGyfgi7UlXKsiz6+/s5kRxuUoi7\\nI6ZCElgUNRjdvicCEfrg4GAolUqvFglHHE34gPMpLnL3Tzoih+iwtLQUv/3tb1FUVIRHH31U9r+z\\naxwqinKGVGoePXqUO5/s6elBWloaV+k6c+ZMWXS7GE1VKR9+5R7x6Pn5+dlFk2Lfr81mQ0NDA3p6\\nesZc9SXfhJ+enj5k8ZO755NSIXV0aDQasWHDBlRWVmL37t2YMmWKqNenuAUVRW/DYrHg3LlzKCsr\\nw9GjR3H69GkoFArk5eVx0WRycrKou9HRVpUOB8MwdkU8jtYDTwxoJvT29kKlUgnaNEEOEBN+S0sL\\nlEqlXYtAV+CfT5IGEAEBAXZt68TexJAiIamiQwA4duwYnn32WSxduhQrVqyQXUtFyqBQUfR2WJaF\\nWq3Gjz/+yBXxNDU1ISUlhYsmc3NzPTI5Q6pepXzrAUnvsSw7IO06mvs1m81QqVSwWCxIT0+XVZPr\\n0eJpE/5w/klPbmKkjg7NZjM2bdqE0tJS7Ny5E5mZmaJenzJqqCiORcg5Cokmf/rpJzAMg5kzZ3LR\\nZHp6utu7V6FSpULibESQQqFweVIIv41Zamoq4uPjJb83oZDKhO8J/6QjcogOz549i+LiYtx9991Y\\ns2bNmDpzvoagonitYDAYcPz4ca4Tj0qlQkJCAuebzM/PR2xs7LAC4OlUqZDwJ4Wo1WpuUgg/YuFv\\nDHQ6HaqrqxEeHo7U1FSvrfx1htxM+I4DgUdzPil1dGixWLB161Z8/vnn2LFjB3JyckS9PkVQqChe\\nq5CIiEST5eXl0Gg0yMrK4tKu06dP50RPrVajra0NOp3Oa8c68Qc0k0IRknY1mUwwmUzIzMz0ynsb\\nDG8y4Q93PukY7cshOqytrUVxcTFuvPFGrF+/3qvmllKcQkWRchWGYXD69GnubLKyshLBwcFISEjA\\nyZMn8dJLL2HRokWSRxlCcuHCBahUKu7Mlf8gFmtSiKcgRULebMIf7HwyICAAvb29mDhxIlJSUkS/\\nN6vVil27duHdd9/Ftm3bcMMNN4h6fYrHoKJIGZyqqioUFRVBoVBg5syZOHny5Kj6usqJ/v5+qFQq\\nAEBaWppdhSS/ZZ1Go+HSrlFRUdz5l5yrCUm3HZPJhIyMjDFVJGSxWKBSqdDX14eIiAgYjUbR/ZMt\\nLS146qmnkJWVhc2bN4vWgWr58uX44osvkJCQgMrKSlGueQ1CRZEyONu3b0dOTg5mzZrFfY904iHR\\n5Ej7usoFlmXR0tKC9vb2EVsRyIBmftqVPykkIiJCNv48RxO+HNYkFGq1GlVVVQMiXyHPJ4fCZrPh\\nvffew5tvvom//OUvuOWWW0T9/R45cgRhYWF4+OGHqSh6DiqKlNHh2Ne1oqICdXV1mDhxIgoLC1FQ\\nUID8/HxERkZK/oDWaDSorq5GTEwMpkyZMqpoz9n5V2BgoF3aVczqQ2LC9/PzE2VslZi4c3bo6vnk\\ncHR2dmLFihVISEjAq6++Ktm5c2NjIxYuXEhF0XNQUaQIDyn9J0U8P/74I4xGI7Kzs7loUsy+rhaL\\nBXV1ddBqtcjIyPBYsYljyzqLxTKgf6jQEbRQJny5Mlh06A7u+CdZlsWnn36KP//5z9i4cSMWLlwo\\n6eaOiqLHoaJIEQeTyYSTJ09yQilGX1e+n3Ly5MmCDH92BTKVnZ929fPzG9WkED6eNuFLiRiVpaQa\\nmS+UNpsNfX19OHXqFLKzs7F3714oFAr87W9/k8WGg4qix6GiKCbPPfcc/vWvfyEgIACpqan4+9//\\nPqb6aLoCv68rsYQI2dfVaDSiuroaCoVCVulEi8XCPYD5A5qJSEZERAybdpXKhC8WQkaHrmKz2VBf\\nX4/XX38dpaWlMBqNGD9+PPLz83H77bfjrrvuEm0tzqCi6HGoKIrJ119/jZtvvhn+/v5Ys2YNAGDz\\n5s0Sr0o+DNXXlaRdh+vrSgSjs7MTaWlpiImJEfEOXGeoSSEkogwLC+PumZyLysWELyT86DArK0uS\\nuaIajQYvvPACenp68NZbb2HChAnQarU4fvw49Ho9FcWxDxVFqfjss8+wb98+/OMf/5B6KbLF1b6u\\npJ1dfHw8UlJSvFYw+NWUZFKIr68vWJaFxWJBRkYGYmJiJC9cEhIpo0Pg8mft+++/x5o1a7By5Ur8\\n6le/kt3n58EHH8S3336Lnp4ejBs3Dhs2bMAjjzwi9bLGGlQUpeLuu+/GL37xCyxdulTqpXgVpK8r\\nSbseP34cBoMBCoUCVqsVW7duRU5Ojqx9hK7S29uLmpoaREVFISAgABqNBv39/QgODrZLu3rjmaIc\\nokODwYA//OEPqKmpwe7du5GcnCz6GiiygYqi0Nx6663o7Owc8P2XX34Z99xzD/e/jx07hk8//XRM\\n7fbFhmVZvP/++9i0aRMWL16M0NBQVFRUcH1d8/LyOFvISPq6yo2hTPjOJoXYbDa7SSGemIwiJFJH\\nhwBQUVGBVatW4de//jWKiopkFx1SRIeKoti8/fbb2LFjBw4dOiTJrngswbIs3njjDSxZssSu2MTV\\nvq5yxB0TPhnQTIRSr9fD39/fLpqUw0BqOUSHJpMJGzduRHl5OXbu3In09HTR10CRJVQUxeTAgQNY\\ntWoVvvvuO8THx0u9nGsK0teVpF0rKysREhLCFfAUFhaKbtlwhtAmfDIphFS7Em8eXyjFTDXLITo8\\nffo0nn76aSxevBjPPfecV6adKR6DiqKYKJVKmEwmxMbGAgBmz56N7du3S7yqaxOWZdHb24vy8nKu\\nEw9p/Zafn4+CggLk5uaK1teVZVm0tbWhpaUFU6dO9ZgnbqhJIUINaHaG1WpFXV0dF7FLER0yDIOS\\nkhL85z//wVtvvYUZM2aIvgaK7KGiSLnKxx9/jPXr16OqqgoVFRXIz8+XekmiMlxf14KCAiiVSsHP\\nnYgJPywsTJI5jmRAMz/tKuSkkL6+PlRXV0saHdbU1KC4uBjz58/Hiy++KOvUOUVSqChSrlJVVQVf\\nX1888cQTeOWVV645UXSENAInfV3Ly8tx/vx5JCYmCtLXlW/Cz8jIkNUcRzIphKRdyaQQIpIjmRQi\\nh+jQarVi+/bt2Lt3L958800UFhaKvgaKV0FFkTKQefPmUVEcBMe+rseOHYPBYHC5rysx4cfFxXmF\\np3KoSSHOJlHIITpsampCUVERZs6ciY0bN46pEVoUj0FFkTIQKoqu4UpfV51Oh7Nnz8LX1xeZmZke\\na04uBlarlYsk+ZMorFYrrFYrsrKyJIl+bTYb3nnnHbz11lsoKSnB/PnzRV8DxWsZkSjS0qwxxEh8\\nlBTXCAwMxKxZs7i5k6SvKyni2bFjB7q7uxEbG4umpibOE+ftkYufnx+io6MRHR0N4HJ0eO7cOW5s\\nVm1tLSwWy4C0qyej4o6ODhQXF2PSpEkoLS1FeHi4x67lyIEDB7By5UpYrVY8+uijeP7550W7NkVc\\naKR4jUEjRWG5dOkSVq9ejfPnz2PBggVQqVQ4deoUFAoFcnNzOe/kcH1d5cpQZ4fOJoX4+vrazTUU\\nosKXZVl8/PHH+Otf/4pNmzZhwYIFoqZsrVYr0tLS8M033yApKQkFBQX44IMPkJWVJdoaKIJAI0UK\\nxdOcPXsWt956K3bt2sU9qB37uu7bt8+ur2tBQQHy8vJk35WGf3Y4derUAWv19fVFeHg4wsPDkZSU\\nBODqAGC1Wo2uri63JoXw6e7uxqpVqxAcHIxvv/1WkibwFRUVUCqVuO666wAADzzwAP75z39SURyj\\n0EjxGuGzzz7D008/je7ubkRFReH666/HV199JfWyrhnI2CJyNnn8+HGYzWZcf/31nFCmp6fLoq+r\\nkJWlZFKIRqNBX18fNykkLCzMrmWdYxTNsiy+/PJLvPTSS/jDH/6Ae++9V7INxL59+3DgwAHs2rUL\\nAPDuu++ivLwc27Ztk2Q9FLehkSLlKosWLcKiRYukXsY1i6+vL5RKJZRKJX75y18CuOxhPH78OMrL\\ny7F582bU1NQgPj6eq3SVoq/rcNGhq/j4+CA4OBjBwcEYN24cAPtJIc3NzdDpdPDz80Nvby9aW1uR\\nk5OD7du3Q6fT4eDBg9zPUShiQEWRQpGIkJAQzJkzB3PmzAFg39e1rKwMr732GtdDlEST2dnZHjGn\\n86PDGTNmeNR36Ovry9k9Jk2aBOBqq75PPvkEJSUlYBgGOTk52LNnD26++Wau0EkKEhMT0dLSwv3/\\n1tZWJCYmSrYeimeh6VOKpNCqvqFhGAZnzpzh0q5nzpxBaGiooH1d5eA71Ov1WLduHRoaGrBr1y4k\\nJSWhoaEB5eXlYFkWDz30kOhrIlgsFqSlpeHQoUNITExEQUEB3n//fUybNk2yNVHcgvoUKfKGVvW5\\nDunrWlFRwXXiaW9vR2pqKhdNjrSvK4kOtVotMjMzJZvsUlZWhtWrV+Oxxx7Dk08+Kcsq3X//+994\\n5plnYLVasXz5cqxdu1bqJVFch4oiRd6UlZVh/fr1XMHPn/70JwDACy+8IOWyvA7Hvq4nTpwAy7JD\\n9nXt7u5GXV2dpNFhf38/N390165dmDp1quhroFxT0EIbirxpa2vjzpQAICkpCeXl5RKuyDshHXQy\\nMzOxfPnyAX1d169fzwlgTk4Oampq4O/vjzfeeAOhoaGSrPnEiRNYsWIFHnjgARw+fFgWVbcUCkBF\\nkUIZc/j4+CAsLAzz5s3DvHnzAFyOJvfv34/Vq1cjLS0NGo0GP/vZzzB9+nSuwcBI+rqOFoZhsGXL\\nFhw6dAjvvPMOpk+f7tHrUSiuQkWRIhm0qk88ent7sWvXLhw8eJAzofP7upaUlKC6uhqRkZHchJDC\\nwkKur6sQnDt3DsXFxbjjjjtw5MgRl0z8FIpY0DNFimTQqj554djXtby8HN3d3UhPT+eiyZkzZyIo\\nKMil17VarXj99dexb98+bN++nbYYpEgFLbShyB9a1SdvLBYLzp07h6NHj+Lo0aMu93VtaGjAU089\\nhcLCQrz00ksuCyqFIiBUFCkUirCwLAuNRsNZQioqKtDU1ITk5GQu5ZqXl4fQ0FDs2bMHe/bswdat\\nWzF37lypl06hUFGkUIRg+fLl+OKLL5CQkIDKykqplyM7nPV1raurw913342tW7d69VxJypiCiiKF\\nIgRHjhxBWFgYHn74YSqKI6SnpwcxMTGyNOJTrllGJIr0E0uhDMPcuXMlGVnkzcTFxUkiiB9//DGm\\nTZsGX19fHDt2TPTrU7wfKooUCmXMMH36dHz66af0DJPiNtSnSKFQxgyZmZlSL4Hi5dBIkUKhUCiU\\nK9BIkUKheBW33norOjs7B3z/5Zdfxj333CPBiihjCSqKFMowPPjgg/j222/R09ODpKQkbNiwAY88\\n8ojUy7pmOXjwoNRLoIxhqChSKMPwwQcfSL0ECoUiEvRMkUIZI7S0tGD+/PnIysrCtGnT8Nprr0m9\\nJNH57LPPkJSUhLKyMtx11124/fbbpV4Sxcug5n0KZYzQ0dGBjo4O5ObmQqvVIi8vD/v370dWVpbU\\nS6NQ5AA171Mo1xITJkxAbm4uACA8PByZmZloa2uTeFUUindBRZFCGYM0NjbixIkTmDVrltRLoVC8\\nCiqKFMoYQ6fT4d5770VJSQkiIiKkXg6F4lVQUaRQxhAMw+Dee+/FkiVLsHjxYqmXQ6F4HbTQhkIZ\\nI7Asi2XLliEmJgYlJSVSL4dCkRt0dBSFci1RWlqKOXPmIDs7m5tQsXHjRtx5550Sr4xCkQVUFCkU\\nCoVCucKIRNHVjjYjelEKhUKhULwRWmhDoVAoFMoVqChSKBQKhXIFKooUCoVCoVyBiiKFQqFQKFeg\\nokihUCgUyhWoKFIoFAqFcgUqihQKhUKhXIGKIoVCoVAoV6CiSKFQKBTKFagoUigUCoVyhf8Pe8N0\\nUYr8fEEAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f547c4ed7b8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# visualize in 3D plot\\n\",\n    \"view_data = Variable(train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.)\\n\",\n    \"encoded_data, _ = autoencoder(view_data)\\n\",\n    \"fig = plt.figure(2); ax = Axes3D(fig)\\n\",\n    \"X, Y, Z = encoded_data.data[:, 0].numpy(), encoded_data.data[:, 1].numpy(), encoded_data.data[:, 2].numpy()\\n\",\n    \"values = train_data.train_labels[:200].numpy()\\n\",\n    \"for x, y, z, s in zip(X, Y, Z, values):\\n\",\n    \"    c = cm.rainbow(int(255*s/9)); ax.text(x, y, z, s, backgroundcolor=c)\\n\",\n    \"ax.set_xlim(X.min(), X.max()); ax.set_ylim(Y.min(), Y.max()); ax.set_zlim(Z.min(), Z.max())\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/405_DQN_Reinforcement_learning.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 405 DQN Reinforcement Learning\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"More about Reinforcement learning: https://mofanpy.com/tutorials/machine-learning/reinforcement-learning/\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* gym: 0.8.1\\n\",\n    \"* numpy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import numpy as np\\n\",\n    \"import gym\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[2017-06-20 22:23:40,418] Making new env: CartPole-v0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Hyper Parameters\\n\",\n    \"BATCH_SIZE = 32\\n\",\n    \"LR = 0.01                   # learning rate\\n\",\n    \"EPSILON = 0.9               # greedy policy\\n\",\n    \"GAMMA = 0.9                 # reward discount\\n\",\n    \"TARGET_REPLACE_ITER = 100   # target update frequency\\n\",\n    \"MEMORY_CAPACITY = 2000\\n\",\n    \"env = gym.make('CartPole-v0')\\n\",\n    \"env = env.unwrapped\\n\",\n    \"N_ACTIONS = env.action_space.n\\n\",\n    \"N_STATES = env.observation_space.shape[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class Net(nn.Module):\\n\",\n    \"    def __init__(self, ):\\n\",\n    \"        super(Net, self).__init__()\\n\",\n    \"        self.fc1 = nn.Linear(N_STATES, 10)\\n\",\n    \"        self.fc1.weight.data.normal_(0, 0.1)   # initialization\\n\",\n    \"        self.out = nn.Linear(10, N_ACTIONS)\\n\",\n    \"        self.out.weight.data.normal_(0, 0.1)   # initialization\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.fc1(x)\\n\",\n    \"        x = F.relu(x)\\n\",\n    \"        actions_value = self.out(x)\\n\",\n    \"        return actions_value\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class DQN(object):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        self.eval_net, self.target_net = Net(), Net()\\n\",\n    \"\\n\",\n    \"        self.learn_step_counter = 0                                     # for target updating\\n\",\n    \"        self.memory_counter = 0                                         # for storing memory\\n\",\n    \"        self.memory = np.zeros((MEMORY_CAPACITY, N_STATES * 2 + 2))     # initialize memory\\n\",\n    \"        self.optimizer = torch.optim.Adam(self.eval_net.parameters(), lr=LR)\\n\",\n    \"        self.loss_func = nn.MSELoss()\\n\",\n    \"\\n\",\n    \"    def choose_action(self, x):\\n\",\n    \"        x = Variable(torch.unsqueeze(torch.FloatTensor(x), 0))\\n\",\n    \"        # input only one sample\\n\",\n    \"        if np.random.uniform() < EPSILON:   # greedy\\n\",\n    \"            actions_value = self.eval_net.forward(x)\\n\",\n    \"            action = torch.max(actions_value, 1)[1].data.numpy()[0, 0]     # return the argmax\\n\",\n    \"        else:   # random\\n\",\n    \"            action = np.random.randint(0, N_ACTIONS)\\n\",\n    \"        return action\\n\",\n    \"\\n\",\n    \"    def store_transition(self, s, a, r, s_):\\n\",\n    \"        transition = np.hstack((s, [a, r], s_))\\n\",\n    \"        # replace the old memory with new memory\\n\",\n    \"        index = self.memory_counter % MEMORY_CAPACITY\\n\",\n    \"        self.memory[index, :] = transition\\n\",\n    \"        self.memory_counter += 1\\n\",\n    \"\\n\",\n    \"    def learn(self):\\n\",\n    \"        # target parameter update\\n\",\n    \"        if self.learn_step_counter % TARGET_REPLACE_ITER == 0:\\n\",\n    \"            self.target_net.load_state_dict(self.eval_net.state_dict())\\n\",\n    \"        self.learn_step_counter += 1\\n\",\n    \"\\n\",\n    \"        # sample batch transitions\\n\",\n    \"        sample_index = np.random.choice(MEMORY_CAPACITY, BATCH_SIZE)\\n\",\n    \"        b_memory = self.memory[sample_index, :]\\n\",\n    \"        b_s = Variable(torch.FloatTensor(b_memory[:, :N_STATES]))\\n\",\n    \"        b_a = Variable(torch.LongTensor(b_memory[:, N_STATES:N_STATES+1].astype(int)))\\n\",\n    \"        b_r = Variable(torch.FloatTensor(b_memory[:, N_STATES+1:N_STATES+2]))\\n\",\n    \"        b_s_ = Variable(torch.FloatTensor(b_memory[:, -N_STATES:]))\\n\",\n    \"\\n\",\n    \"        # q_eval w.r.t the action in experience\\n\",\n    \"        q_eval = self.eval_net(b_s).gather(1, b_a)  # shape (batch, 1)\\n\",\n    \"        q_next = self.target_net(b_s_).detach()     # detach from graph, don't backpropagate\\n\",\n    \"        q_target = b_r + GAMMA * q_next.max(1)[0]   # shape (batch, 1)\\n\",\n    \"        loss = self.loss_func(q_eval, q_target)\\n\",\n    \"\\n\",\n    \"        self.optimizer.zero_grad()\\n\",\n    \"        loss.backward()\\n\",\n    \"        self.optimizer.step()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"dqn = DQN()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Collecting experience...\\n\",\n      \"Ep:  201 | Ep_r:  1.59\\n\",\n      \"Ep:  202 | Ep_r:  4.18\\n\",\n      \"Ep:  203 | Ep_r:  2.73\\n\",\n      \"Ep:  204 | Ep_r:  1.97\\n\",\n      \"Ep:  205 | Ep_r:  1.18\\n\",\n      \"Ep:  206 | Ep_r:  0.86\\n\",\n      \"Ep:  207 | Ep_r:  2.88\\n\",\n      \"Ep:  208 | Ep_r:  1.63\\n\",\n      \"Ep:  209 | Ep_r:  3.91\\n\",\n      \"Ep:  210 | Ep_r:  3.6\\n\",\n      \"Ep:  211 | Ep_r:  0.98\\n\",\n      \"Ep:  212 | Ep_r:  3.85\\n\",\n      \"Ep:  213 | Ep_r:  1.81\\n\",\n      \"Ep:  214 | Ep_r:  2.32\\n\",\n      \"Ep:  215 | Ep_r:  3.75\\n\",\n      \"Ep:  216 | Ep_r:  3.53\\n\",\n      \"Ep:  217 | Ep_r:  4.75\\n\",\n      \"Ep:  218 | Ep_r:  2.4\\n\",\n      \"Ep:  219 | Ep_r:  0.64\\n\",\n      \"Ep:  220 | Ep_r:  1.15\\n\",\n      \"Ep:  221 | Ep_r:  2.3\\n\",\n      \"Ep:  222 | Ep_r:  7.37\\n\",\n      \"Ep:  223 | Ep_r:  1.25\\n\",\n      \"Ep:  224 | Ep_r:  5.02\\n\",\n      \"Ep:  225 | Ep_r:  10.29\\n\",\n      \"Ep:  226 | Ep_r:  17.54\\n\",\n      \"Ep:  227 | Ep_r:  36.2\\n\",\n      \"Ep:  228 | Ep_r:  6.61\\n\",\n      \"Ep:  229 | Ep_r:  10.04\\n\",\n      \"Ep:  230 | Ep_r:  55.19\\n\",\n      \"Ep:  231 | Ep_r:  10.03\\n\",\n      \"Ep:  232 | Ep_r:  13.25\\n\",\n      \"Ep:  233 | Ep_r:  8.75\\n\",\n      \"Ep:  234 | Ep_r:  3.83\\n\",\n      \"Ep:  235 | Ep_r:  -0.92\\n\",\n      \"Ep:  236 | Ep_r:  5.12\\n\",\n      \"Ep:  237 | Ep_r:  3.56\\n\",\n      \"Ep:  238 | Ep_r:  5.69\\n\",\n      \"Ep:  239 | Ep_r:  8.43\\n\",\n      \"Ep:  240 | Ep_r:  29.27\\n\",\n      \"Ep:  241 | Ep_r:  17.95\\n\",\n      \"Ep:  242 | Ep_r:  44.77\\n\",\n      \"Ep:  243 | Ep_r:  98.0\\n\",\n      \"Ep:  244 | Ep_r:  38.78\\n\",\n      \"Ep:  245 | Ep_r:  45.02\\n\",\n      \"Ep:  246 | Ep_r:  27.73\\n\",\n      \"Ep:  247 | Ep_r:  36.96\\n\",\n      \"Ep:  248 | Ep_r:  48.98\\n\",\n      \"Ep:  249 | Ep_r:  111.36\\n\",\n      \"Ep:  250 | Ep_r:  95.61\\n\",\n      \"Ep:  251 | Ep_r:  149.77\\n\",\n      \"Ep:  252 | Ep_r:  29.96\\n\",\n      \"Ep:  253 | Ep_r:  2.79\\n\",\n      \"Ep:  254 | Ep_r:  20.1\\n\",\n      \"Ep:  255 | Ep_r:  24.25\\n\",\n      \"Ep:  256 | Ep_r:  3074.75\\n\",\n      \"Ep:  257 | Ep_r:  1258.49\\n\",\n      \"Ep:  258 | Ep_r:  127.39\\n\",\n      \"Ep:  259 | Ep_r:  283.46\\n\",\n      \"Ep:  260 | Ep_r:  166.96\\n\",\n      \"Ep:  261 | Ep_r:  101.71\\n\",\n      \"Ep:  262 | Ep_r:  63.45\\n\",\n      \"Ep:  263 | Ep_r:  288.94\\n\",\n      \"Ep:  264 | Ep_r:  130.49\\n\",\n      \"Ep:  265 | Ep_r:  207.05\\n\",\n      \"Ep:  266 | Ep_r:  183.71\\n\",\n      \"Ep:  267 | Ep_r:  142.75\\n\",\n      \"Ep:  268 | Ep_r:  126.53\\n\",\n      \"Ep:  269 | Ep_r:  310.79\\n\",\n      \"Ep:  270 | Ep_r:  863.2\\n\",\n      \"Ep:  271 | Ep_r:  365.12\\n\",\n      \"Ep:  272 | Ep_r:  659.52\\n\",\n      \"Ep:  273 | Ep_r:  103.98\\n\",\n      \"Ep:  274 | Ep_r:  554.83\\n\",\n      \"Ep:  275 | Ep_r:  246.01\\n\",\n      \"Ep:  276 | Ep_r:  332.23\\n\",\n      \"Ep:  277 | Ep_r:  323.35\\n\",\n      \"Ep:  278 | Ep_r:  278.71\\n\",\n      \"Ep:  279 | Ep_r:  613.6\\n\",\n      \"Ep:  280 | Ep_r:  152.21\\n\",\n      \"Ep:  281 | Ep_r:  402.02\\n\",\n      \"Ep:  282 | Ep_r:  351.4\\n\",\n      \"Ep:  283 | Ep_r:  115.87\\n\",\n      \"Ep:  284 | Ep_r:  163.26\\n\",\n      \"Ep:  285 | Ep_r:  631.0\\n\",\n      \"Ep:  286 | Ep_r:  263.47\\n\",\n      \"Ep:  287 | Ep_r:  511.21\\n\",\n      \"Ep:  288 | Ep_r:  337.18\\n\",\n      \"Ep:  289 | Ep_r:  819.76\\n\",\n      \"Ep:  290 | Ep_r:  190.83\\n\",\n      \"Ep:  291 | Ep_r:  442.98\\n\",\n      \"Ep:  292 | Ep_r:  537.24\\n\",\n      \"Ep:  293 | Ep_r:  1101.12\\n\",\n      \"Ep:  294 | Ep_r:  178.42\\n\",\n      \"Ep:  295 | Ep_r:  225.61\\n\",\n      \"Ep:  296 | Ep_r:  252.62\\n\",\n      \"Ep:  297 | Ep_r:  617.5\\n\",\n      \"Ep:  298 | Ep_r:  617.8\\n\",\n      \"Ep:  299 | Ep_r:  244.01\\n\",\n      \"Ep:  300 | Ep_r:  687.91\\n\",\n      \"Ep:  301 | Ep_r:  618.51\\n\",\n      \"Ep:  302 | Ep_r:  1405.07\\n\",\n      \"Ep:  303 | Ep_r:  456.95\\n\",\n      \"Ep:  304 | Ep_r:  340.33\\n\",\n      \"Ep:  305 | Ep_r:  502.91\\n\",\n      \"Ep:  306 | Ep_r:  441.21\\n\",\n      \"Ep:  307 | Ep_r:  255.81\\n\",\n      \"Ep:  308 | Ep_r:  403.03\\n\",\n      \"Ep:  309 | Ep_r:  229.1\\n\",\n      \"Ep:  310 | Ep_r:  308.49\\n\",\n      \"Ep:  311 | Ep_r:  165.37\\n\",\n      \"Ep:  312 | Ep_r:  153.76\\n\",\n      \"Ep:  313 | Ep_r:  442.05\\n\",\n      \"Ep:  314 | Ep_r:  229.23\\n\",\n      \"Ep:  315 | Ep_r:  128.52\\n\",\n      \"Ep:  316 | Ep_r:  358.18\\n\",\n      \"Ep:  317 | Ep_r:  319.03\\n\",\n      \"Ep:  318 | Ep_r:  381.76\\n\",\n      \"Ep:  319 | Ep_r:  199.19\\n\",\n      \"Ep:  320 | Ep_r:  418.63\\n\",\n      \"Ep:  321 | Ep_r:  223.95\\n\",\n      \"Ep:  322 | Ep_r:  222.37\\n\",\n      \"Ep:  323 | Ep_r:  405.4\\n\",\n      \"Ep:  324 | Ep_r:  311.32\\n\",\n      \"Ep:  325 | Ep_r:  184.85\\n\",\n      \"Ep:  326 | Ep_r:  1026.71\\n\",\n      \"Ep:  327 | Ep_r:  252.41\\n\",\n      \"Ep:  328 | Ep_r:  224.93\\n\",\n      \"Ep:  329 | Ep_r:  620.02\\n\",\n      \"Ep:  330 | Ep_r:  174.54\\n\",\n      \"Ep:  331 | Ep_r:  782.45\\n\",\n      \"Ep:  332 | Ep_r:  263.79\\n\",\n      \"Ep:  333 | Ep_r:  178.63\\n\",\n      \"Ep:  334 | Ep_r:  242.84\\n\",\n      \"Ep:  335 | Ep_r:  635.43\\n\",\n      \"Ep:  336 | Ep_r:  668.89\\n\",\n      \"Ep:  337 | Ep_r:  265.42\\n\",\n      \"Ep:  338 | Ep_r:  207.81\\n\",\n      \"Ep:  339 | Ep_r:  293.09\\n\",\n      \"Ep:  340 | Ep_r:  530.23\\n\",\n      \"Ep:  341 | Ep_r:  479.26\\n\",\n      \"Ep:  342 | Ep_r:  559.77\\n\",\n      \"Ep:  343 | Ep_r:  241.39\\n\",\n      \"Ep:  344 | Ep_r:  158.83\\n\",\n      \"Ep:  345 | Ep_r:  1510.69\\n\",\n      \"Ep:  346 | Ep_r:  425.17\\n\",\n      \"Ep:  347 | Ep_r:  266.94\\n\",\n      \"Ep:  348 | Ep_r:  166.08\\n\",\n      \"Ep:  349 | Ep_r:  630.52\\n\",\n      \"Ep:  350 | Ep_r:  250.95\\n\",\n      \"Ep:  351 | Ep_r:  625.88\\n\",\n      \"Ep:  352 | Ep_r:  417.7\\n\",\n      \"Ep:  353 | Ep_r:  867.81\\n\",\n      \"Ep:  354 | Ep_r:  150.62\\n\",\n      \"Ep:  355 | Ep_r:  230.89\\n\",\n      \"Ep:  356 | Ep_r:  1017.52\\n\",\n      \"Ep:  357 | Ep_r:  190.28\\n\",\n      \"Ep:  358 | Ep_r:  396.91\\n\",\n      \"Ep:  359 | Ep_r:  305.53\\n\",\n      \"Ep:  360 | Ep_r:  131.61\\n\",\n      \"Ep:  361 | Ep_r:  387.54\\n\",\n      \"Ep:  362 | Ep_r:  298.82\\n\",\n      \"Ep:  363 | Ep_r:  207.56\\n\",\n      \"Ep:  364 | Ep_r:  248.56\\n\",\n      \"Ep:  365 | Ep_r:  589.12\\n\",\n      \"Ep:  366 | Ep_r:  179.52\\n\",\n      \"Ep:  367 | Ep_r:  130.19\\n\",\n      \"Ep:  368 | Ep_r:  1220.84\\n\",\n      \"Ep:  369 | Ep_r:  126.35\\n\",\n      \"Ep:  370 | Ep_r:  133.31\\n\",\n      \"Ep:  371 | Ep_r:  485.81\\n\",\n      \"Ep:  372 | Ep_r:  823.4\\n\",\n      \"Ep:  373 | Ep_r:  253.26\\n\",\n      \"Ep:  374 | Ep_r:  466.06\\n\",\n      \"Ep:  375 | Ep_r:  203.27\\n\",\n      \"Ep:  376 | Ep_r:  386.5\\n\",\n      \"Ep:  377 | Ep_r:  491.02\\n\",\n      \"Ep:  378 | Ep_r:  239.45\\n\",\n      \"Ep:  379 | Ep_r:  276.93\\n\",\n      \"Ep:  380 | Ep_r:  331.98\\n\",\n      \"Ep:  381 | Ep_r:  764.79\\n\",\n      \"Ep:  382 | Ep_r:  198.29\\n\",\n      \"Ep:  383 | Ep_r:  717.18\\n\",\n      \"Ep:  384 | Ep_r:  562.15\\n\",\n      \"Ep:  385 | Ep_r:  29.44\\n\",\n      \"Ep:  386 | Ep_r:  344.95\\n\",\n      \"Ep:  387 | Ep_r:  671.87\\n\",\n      \"Ep:  388 | Ep_r:  299.81\\n\",\n      \"Ep:  389 | Ep_r:  899.76\\n\",\n      \"Ep:  390 | Ep_r:  319.04\\n\",\n      \"Ep:  391 | Ep_r:  252.11\\n\",\n      \"Ep:  392 | Ep_r:  865.62\\n\",\n      \"Ep:  393 | Ep_r:  255.64\\n\",\n      \"Ep:  394 | Ep_r:  81.74\\n\",\n      \"Ep:  395 | Ep_r:  213.13\\n\",\n      \"Ep:  396 | Ep_r:  422.33\\n\",\n      \"Ep:  397 | Ep_r:  167.47\\n\",\n      \"Ep:  398 | Ep_r:  507.34\\n\",\n      \"Ep:  399 | Ep_r:  614.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"\\n\",\n    \"print('\\\\nCollecting experience...')\\n\",\n    \"for i_episode in range(400):\\n\",\n    \"    s = env.reset()\\n\",\n    \"    ep_r = 0\\n\",\n    \"    while True:\\n\",\n    \"        env.render()\\n\",\n    \"        a = dqn.choose_action(s)\\n\",\n    \"\\n\",\n    \"        # take action\\n\",\n    \"        s_, r, done, info = env.step(a)\\n\",\n    \"\\n\",\n    \"        # modify the reward\\n\",\n    \"        x, x_dot, theta, theta_dot = s_\\n\",\n    \"        r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8\\n\",\n    \"        r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5\\n\",\n    \"        r = r1 + r2\\n\",\n    \"\\n\",\n    \"        dqn.store_transition(s, a, r, s_)\\n\",\n    \"\\n\",\n    \"        ep_r += r\\n\",\n    \"        if dqn.memory_counter > MEMORY_CAPACITY:\\n\",\n    \"            dqn.learn()\\n\",\n    \"            if done:\\n\",\n    \"                print('Ep: ', i_episode,\\n\",\n    \"                      '| Ep_r: ', round(ep_r, 2))\\n\",\n    \"\\n\",\n    \"        if done:\\n\",\n    \"            break\\n\",\n    \"        s = s_\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/406_GAN.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 406 GAN\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* numpy\\n\",\n    \"* matplotlib\\n\",\n    \"\\n\",\n    \"Note: Below cells only work for pytorch version lower than 1.5, if you are using pytorch 1.5 or higher, then you will get in place error when you run For loop for Step(10000). Fixed version has been added below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1)    # reproducible\\n\",\n    \"np.random.seed(1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Hyper Parameters\\n\",\n    \"BATCH_SIZE = 64\\n\",\n    \"LR_G = 0.0001           # learning rate for generator\\n\",\n    \"LR_D = 0.0001           # learning rate for discriminator\\n\",\n    \"N_IDEAS = 5             # think of this as number of ideas for generating an art work (Generator)\\n\",\n    \"ART_COMPONENTS = 15     # it could be total point G can draw in the canvas\\n\",\n    \"PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nxhgTgyy5G2NMDPp/MA9tfAK2AEYAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f383fd3e278>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# show our beautiful painting range\\n\",\n    \"plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')\\n\",\n    \"plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')\\n\",\n    \"plt.legend(loc='upper right')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def artist_works():     # painting from the famous artist (real target)\\n\",\n    \"    a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis]\\n\",\n    \"    paintings = a * np.power(PAINT_POINTS, 2) + (a-1)\\n\",\n    \"    paintings = torch.from_numpy(paintings).float()\\n\",\n    \"    return Variable(paintings)\\n\",\n    \"\\n\",\n    \"G = nn.Sequential(                      # Generator\\n\",\n    \"    nn.Linear(N_IDEAS, 128),            # random ideas (could from normal distribution)\\n\",\n    \"    nn.ReLU(),\\n\",\n    \"    nn.Linear(128, ART_COMPONENTS),     # making a painting from these random ideas\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"D = nn.Sequential(                      # Discriminator\\n\",\n    \"    nn.Linear(ART_COMPONENTS, 128),     # receive art work either from the famous artist or a newbie like G\\n\",\n    \"    nn.ReLU(),\\n\",\n    \"    nn.Linear(128, 1),\\n\",\n    \"    nn.Sigmoid(),                       # tell the probability that the art work is made by artist\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"opt_D = torch.optim.Adam(D.parameters(), lr=LR_D)\\n\",\n    \"opt_G = torch.optim.Adam(G.parameters(), lr=LR_G)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3825b915c0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3825b08b70>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f38432e2a90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nrkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f38380db128>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nEEIeETNBEAQh5Pl/WXhCtKyEW6cAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f383fc88da0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n2p0AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f38380689b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8FNX2wL832dRN7wmBhBqUoCJIER6JSqRLEStI8fds\\nD3yioshDAbE9sGNvDywoAj4LVXkKogJCQAQhEBJ6GumkbrK79/fH7E52N9kUDIaE+X4+88nemTv3\\nnpns7Jl77rnnCCklGhoaGhoarRmXlhZAQ0NDQ0Pjz6IpMw0NDQ2NVo+mzDQ0NDQ0Wj2aMtPQ0NDQ\\naPVoykxDQ0NDo9WjKTMNDQ0NjVZPg8pMCOEphNgphPhdCHFACPFkHXU8hBCfCyHShBC/CiFiz4ew\\nGhoaGhoaddGYkZkBuFZKeTlwBTBMCNHfoc7/AYVSyi7Ay8Ci5hVTQ0NDQ0PDOQ0qM6lQaim6WTbH\\nldZjgA8tn1cD1wkhRLNJqaGhoaGhUQ+6xlQSQrgCu4EuwBtSyl8dqrQDTgFIKY1CiGIgGMhzaOdu\\n4G4AvV7fu3v37k0S1mCC3PKacpg3uLs2qQkNDQ2NFsEsIbsUzJZygAf4uDe9nd27d+dJKUObVbg2\\nQKOUmZTSBFwhhAgAvhRCxEsp/2hqZ1LKd4F3Afr06SOTk5Ob2gQf74d9Z5TPHfxgeh9w0caAGhoa\\nFzhfHIIdGcrnMG94qB+4noMLnhDiRPNK1jZo0q2UUhYBm4FhDocygPYAQggd4A/kN4eAjozsAq4W\\n5XXyLPyecz560dDQ0Gg+skvh14ya8qiu56bINJzTGG/GUMuIDCGEF5AEHHKo9g0wxfJ5AvCDPE8R\\njIO8YHCHmvL6NKg2nY+eNDQ0NJqHNUdqHA26BkH34BYVp03SmHeDSGCzEGIfsAvYJKVcK4RYKIS4\\nwVLnAyBYCJEGPAQ8dn7EVbg2FvRuyuciA2w9eT5709DQ0Dh3DuVBaoHyWQCju4LmHtf8NDhnJqXc\\nB/SqY/88m8+VwE3NK5pzPHUwtBP897BS/uEEXBUFfh5/lQQaGhoaDWMyK6MyK32jINKn5eRpyzTK\\nAeRCpG8UbDsN2WVQZYKN6XDzpS0tlUZbwmw2c/r0acrKylpaFI1WisEIAz0BT2VU5ichJaXh8/R6\\nPdHR0bi4aBNrjaXVKjNXF2US9f29Sjk5Cwa2h3a+LSuXRtshLy8PIQRxcXHaj4pGkzGbIatMcckH\\n8PdonPXIbDaTkZFBXl4eYWFh51fINkSrfkLjgmsmUiWWSVYtcbZGM1FUVER4eLimyDTOibNVNYpM\\n59L4NWUuLi6Eh4dTXFx8/oRrg7T6p3RU15p1ZumFcCCv/voaGo3FZDLh5ubW0mJotEKqTVBaVVP2\\n92jaelg3NzeMRmPzC9aGafXKLFwPA9rVlNcdAaPZeX0NjaagRWXTOBeKDTWu+B6u4NXECR3te9d0\\nWr0yA0jqqHg4AuRVwPbTLSuPhobGxUulESpsBlX+Hpor/l9Bm1BmencY0rGmvOkYlFW3nDwaGhrN\\ny7Jlyxg0aNB5advHx4ejR4+e07k//fQTcXFxallKZVRmxdsNPFqtm13rok0oM4CB0RDipXyuMMKm\\nc/tuami0GlasWEG/fv3Q6/WEhYXRr18/3nzzTc5T8J0/RWJiIu+//35Li1EnpaWldOrUqVF1hRCk\\npaWp5b/97W8cPnxYLZdXK0uFQHHF99fWvv5ltBllpnOBEV1qytsz4Iy2PEijjfLiiy/ywAMP8Mgj\\nj5CdnU1OTg5vv/02v/zyC1VVVQ030IxojgoKZodRma+78ruk8dfQpm51fCh0ClA+myWsTau/voZG\\na6S4uJh58+bx5ptvMmHCBHx9fRFC0KtXL5YvX46HhzIcMBgMzJo1iw4dOhAeHs69995LRUUFAFu2\\nbCE6OpoXX3yRsLAwIiMjWbp0qdpHY85dtGgRERERTJs2jcLCQkaNGkVoaCiBgYGMGjWK06eVyeu5\\nc+fy008/MWPGDHx8fJgxYwYAhw4dIikpiaCgIOLi4li5cqXaf35+PjfccAN+fn707duX9PR0p/fj\\n+PHjCCF49913iYqKIjIykhdeeEE9vnPnTgYMGEBAQACRkZHMmDHDTuHbjramTp3K9OnTGTlyJL6+\\nvvTr10/te/DgwQBcfvnl+Pj48Pnnn6v3AqCkCvrFx/LOkhcYevVldAj355ZbbqGyslLta/HixURG\\nRhIVFcX7779fa6Snce60KWuuEErcsyW7FE+iFEtMtG5BLS2ZRmtnZMqVf1lf6y7ZU+/x7du3YzAY\\nGDNmTL31HnvsMdLT09m7dy9ubm7cfvvtLFy4kOeeew6A7OxsiouLycjIYNOmTUyYMIGxY8cSGBjY\\nqHMLCgo4ceIEZrOZ8vJypk2bxsqVKzGZTNx5553MmDGDr776imeeeYZffvmFSZMm8fe//x2AsrIy\\nkpKSWLhwIRs2bGD//v0kJSURHx/PpZdeyvTp0/H09CQrK4tjx44xdOhQOnbs6PRaATZv3syRI0c4\\nevQo1157LVdccQVDhgzB1dWVl19+mT59+nD69GmGDx/Om2++ycyZM+tsZ8WKFWzYsIErr7ySKVOm\\nMHfuXFasWMHWrVsRQvD777/TpYtiBtqyZQugeFCXWEZla79cyTdrNxLk58nAgQNZtmwZ9957Lxs3\\nbuSll17i+++/p2PHjtx99931Xo9G02hTIzOAaD/oHVlTXnOkZuGihkZbIC8vj5CQEHS6mnfRq6++\\nmoCAALy8vNi6dStSSt59911efvllgoKC8PX15V//+hcrVqxQz3Fzc2PevHm4ubkxYsQIfHx8OHz4\\ncKPOdXFx4cknn8TDwwMvLy+Cg4O58cYb8fb2xtfXl7lz5/Ljjz86vYa1a9cSGxvLtGnT0Ol09OrV\\nixtvvJFVq1ZhMpn44osvWLhwIXq9nvj4eKZMmeK0LSvz589Hr9fTs2dPpk2bxmeffQZA79696d+/\\nPzqdjtjYWO655556ZRs3bhx9+/ZFp9MxceJE9u7d22Dftq74d933TzrHRBEUFMTo0aPV81euXMm0\\nadPo0aMH3t7eLFiwoMF2NRpPmxqZWRnWWUngWWVS8gjtyoR+7Ro+T0OjNRAcHExeXh5Go1FVaNu2\\nbQMgOjoas9lMbm4u5eXl9O7dWz1PSonJZLJrx1Yhent7U1pa2qhzQ0ND8fT0VMvl5eU8+OCDbNy4\\nkcLCQgBKSkowmUy4utZOB3/ixAl+/fVXAgIC1H1Go5E77riD3NxcjEYj7du3V4/FxMQ0eF8c6+/f\\nvx+A1NRUHnroIZKTkykvL8doNNpdmyMRERG17klDlNt4T3dsH6G64nt7e5OZmQlAZmYmffr0qVNe\\njT9Pm1Rm/h6QGAPfWTwaN6bD5eE1a9E0NJpKQ6a/v5IBAwbg4eHB119/zY033lhnnZCQELy8vDhw\\n4ADt2jXtTa4x5zou6n3xxRc5fPgwv/76KxEREezdu5devXqpnpWO9du3b09CQgKbNm2q1bbJZEKn\\n03Hq1Cm6d+8OwMmTDed5cqwfFRUFwH333UevXr347LPP8PX15ZVXXmH16tUNttcYpLS3/AjAvbbu\\nBiAyMlKdR7TKq9F8tDkzo5WEDjVusaXV8MPxFhVHQ6PZCAgIYP78+fzjH/9g9erVlJSUYDab2bt3\\nrxrh38XFhbvuuosHH3yQM2fOAJCRkcG3337bYPvncm5JSQleXl4EBARQUFDAk08+aXc8PDzcbi3X\\nqFGjSE1N5eOPP6a6uprq6mp27dpFSkoKrq6ujB8/ngULFlBeXs7Bgwf58MMPG5T7qaeeory8nAMH\\nDrB06VJuueUWVTY/Pz98fHw4dOgQb731VoNtOcPxOgymGvOioP6QVTfffDNLly4lJSWF8vJynnrq\\nqXOWQ6M2bVaZubvC8M415Z9OQUFFy8mjodGcPProo7z00kssXryY8PBwwsPDueeee1i0aBFXX301\\nAIsWLaJLly70798fPz8/hgwZYrcmqj6aeu7MmTOpqKggJCSE/v37M2zYMLvjDzzwAKtXryYwMJB/\\n/vOf+Pr68t1337FixQqioqKIiIhg9uzZGAyKF8Xrr79OaWkpERERTJ06lWnTpjUoc0JCAl26dOG6\\n665j1qxZXH/99QC88MILfPrpp/j6+nLXXXepSu5cWLBgAVOmTCEgIIAVn6+0C87QUCDh4cOH889/\\n/pNrrrlGvbeA6n2q8ecQLbXAsk+fPjI5Ofm89mGW8HoynDqrlC8Pg0k9z2uXGm2IlJQULrnkkpYW\\nQ6MBjh8/TseOHamurrabAzzfnDXUrCtzERChV1JTNZaUlBTi4+MxGAx1yu3s+yeE2C2l7FPrwEVO\\nmx2ZgfIFG921pvz7GThe1HLyaGhotA1MZmVdmRV/j8Ypsi+//BKDwUBhYSGzZ89m9OjRf6kCbsu0\\naWUG0DEALrPJb/eN5qqvoaHxJzlrqPkdcXMBfSMzBb3zzjuEhYXRuXNnXF1d/9T8nYY9F8Urwcgu\\ncCAXTFIxOf6eA70iGj5PQ0Pjwic2NvYvjUdZbbIPZN6UqPgbN248P0JptP2RGUCQFwzuUFNen1YT\\nDFRDQ0OjsUgJRTYLpD112pKfC4WLQpkBXBtbYwooMsDWhpetaGhoaNhRaVQ2qImKr+UquzC4aJSZ\\npw6G2mR52HxCsXtraGhoNIa6cpU5WyCt8ddz0SgzgL5RivssKGbGjc4DcWtoaGjYUVYN1Wbls4vQ\\ncpVdaFxUyszVxd5VPzkLMkpaTh4NDY3WgclcO1dZU9aUaZx/Lrp/R7dg6B6sfJbAmlTFfKChodE8\\nXChZpZctW8agQYOapa2SqhpXfJ1Lw9E+NP56LjplBjCqa00MtfQiOJDXsvJoaGhcuFSboNRhgXR9\\nMRg1WoYGlZkQor0QYrMQ4qAQ4oAQ4oE66iQKIYqFEHst27zzI27zEK6HATbBwNcdUZLraWhoNA3b\\ntDBtFdtcZR6u4KW54l+QNGZkZgQellJeCvQHpgshLq2j3k9Syiss28JmlfI8kNSxZn1IXgVsO11/\\nfQ2NCwkhBGlpaWp56tSpPP7444CS/Tg6Oppnn32WkJAQYmNjWb58uV3de++9l6SkJHx9fUlISODE\\niRPq8UOHDpGUlERQUBBxcXGsXLnS7tz77ruPESNGoNfr2bx5c53ypaen07dvX/z8/BgzZgwFBQXq\\nsW+++YYePXoQEBBAYmIiKSkpTbquF198kbCwMCIjI1m6dKlaNz8/nxtuuAE/Pz/69u1Levqf9/Cq\\nNEKFsaasueJfuDT4jiGlzAKyLJ9LhBApQDvg4HmW7byid4chHWHtEaX8v2NKhurGhqXRuLh45Pu/\\nrq/nr/vzbWRnZ5OXl0dGRgY7duxgxIgR9OnTh7i4OACWL1/OunXr6NevH48++igTJ07k559/pqys\\njKSkJBYuXMiGDRvYv38/SUlJxMfHc+mlyjvsp59+yvr161m7di1VVVV19v/RRx/x7bff0rFjRyZP\\nnsw///lPPvnkE1JTU7ntttv46quvSExM5OWXX2b06NEcPHgQd/eGJ6Kys7MpLi4mIyODTZs2MWHC\\nBMaOHUtgYCDTp0/H09OTrKwsjh07xtChQ+nYseM538O6XPE9tFHZBUuT5syEELFAL+DXOg4PEEL8\\nLoTYIITo0QyynXcGRkOIl/K5wgibjtZfX0OjNfHUU0/h4eFBQkICI0eOtBthjRw5ksGDB+Ph4cEz\\nzzzD9u3bOXXqFGvXriU2NpZp06ah0+no1asXN954I6tWrVLPHTNmDAMHDsTFxcUu27Qtd9xxB/Hx\\n8ej1ep566ilWrlyJyWTi888/Z+TIkSQlJeHm5sasWbOoqKhQM2U3hJubG/PmzcPNzY0RI0bg4+PD\\n4cOHMZlMfPHFFyxcuBC9Xk98fDxTpkz5U/evvLomUpB1gbTGhUujlZkQwgf4ApgppTzrcHgPECOl\\nvBx4DfjKSRt3CyGShRDJubm55ypzs6FzgRFdasrbM+BMWcvJo6HRXAQGBqLX69VyTEwMmZmZarl9\\n+/bqZx8fH4KCgsjMzOTEiRP8+uuvBAQEqNvy5cvJzs6u81xn2NaJiYmhurqavLw8MjMziYmJUY+5\\nuLjQvn17MjIyGnVdwcHBdlHmvb29KS0tJTc3F6PRWKvfc8Usa7vi6y5Kd7nWQ6MGzUIINxRFtlxK\\n+V/H47bKTUq5XgjxphAiREqZ51DvXeBdUPKZ/SnJm4n4UOgUAEeLlC/wfw/B3Vdq3koa9jSH6a85\\n8fb2pry8XC1nZ2cTHR2tlgsLCykrK1MV2smTJ4mPj1ePnzp1Sv1cWlpKQUEBUVFRtG/fnoSEBDZt\\n2uS0b9GISSPb9k+ePImbmxshISFERUWxf/9+9ZiUklOnTtGuXbtGXZczQkND0el0nDp1iu7du6v9\\nnitnDUpgcgBXAb7aqOyCpzHejAL4AEiRUr7kpE6EpR5CiL6WdvObU9DzhRBwQzfFjACKq/52zRlE\\n4wLniiuu4NNPP8VkMrFx40Z+/PHHWnXmz59PVVUVP/30E2vXruWmm25Sj61fv56ff/6Zqqoqnnji\\nCfr370/79u0ZNWoUqampfPzxx1RXV1NdXc2uXbvsnDQawyeffMLBgwcpLy9n3rx5TJgwAVdXV26+\\n+WbWrVvH999/T3V1NS+++CIeHh5qduzGXFdduLq6Mn78eBYsWEB5eTkHDx7kww8/bJLMVgxGzRW/\\nNdKYgfNA4A7gWhvX+xFCiHuFEPda6kwA/hBC/A4sAW6VLZXC+hxo5wuJNhaJdWmQX9Fy8pwvFixY\\ngBACIQQuLi4EBgZy1VVXMXfuXDszksb5paioiAMHDrB7927++OMPO08/Z+Tl5ZGcnKxu99xzDytX\\nrsTf35/ly5czduxYAM6cOcPp06cJDg6mvLycyMhIJk6cyNtvv62OWABuv/12nnzySYKCgti9ezef\\nfPIJVVVVHDlyhDVr1rBixQqioqKIiIjgwQcfZP/+/ezevZvi4mKqq6udiakyevRobrrpJsLCwsjO\\nzubOO+8kOTmZDh068Mknn3D//fcTEhLCmjVrWLNmjer88eqrr/Lll1/i5+fHkiVLGDJkSKPv6+uv\\nv05paSkRERFMnTqVadOmOa27b98+9V7u3r2b33//nSNHjpCXl09BhbSLiu9dh1NYbm4uhYWFjZZN\\n49wQQswSQjTK/Uq0lM7p06ePTE5ObpG+68Johld2Qo5lzqxTANzTxsyNCxYs4JVXXlFzKhUXF7Nn\\nzx7eeustKioq2LhxI717925hKS8cnKWt/zOUlJRw+PBhwsLCCAgIoLi4mJycHLp27Yq/v7/T8/Ly\\n8jh+/DjdunXDxaXmHdTDwwM3t5pf25SUFHbu3Mljjz3GmjVriIuLw9fX166tqVOnEh0dzdNPP223\\n/8SJE5hMJjp1qonInZWVRWZmJu3bt8fT05OcnBzKysro0aOHXb+OHDt2jLKyMmJjY+32e3t728nv\\nSFlZGSkpKbRr1w5fX190Op1TJ5M/w759+/Dx8SEsTMncW1VVxdmzZ8nPz8fNy5fAdl1wcXEhXF/3\\nXNnBgwfx8vL6U96SDeHs+yeE2C2l7HPeOr6AEEL4AieBcVLKLfXV1RxNLehc4JZL4fVkZe7sqMXc\\nOLDhue5WhU6no3///mp56NCh3HfffQwePJhbb72VQ4cO4eraOkOBV1RU4OXl1dJi1EtWVha+vr50\\n6KAk2PPz86OyspKsrKx6lZkVvV5f7/+ne/fu5OTk1Ksw6sJkMpGfn0+XLjUeUWazmezsbCIjI9Uf\\nfb1ez/79+zlz5ow6z+UMFxcXfHx8miRHZWUlAGFhYX/6e2g2m+u9D25ubnby6f2CMHkGUXg6lbKC\\nbNq3i9KcPpqAEMIVcJVS1r1e4xywLAf7Argf2FJfXe1fZUN7P0i0SeK5Lg3yyp3XbysEBASwePFi\\n0tLS6p34z8rK4s4776RTp054eXnRrVs3Hn/88VprjSoqKnj00UeJiYnBw8ODjh07MmfOHLs67733\\nHj179sTT05Pw8HAmTJhAcXExoMT2mzBhgl39LVu2IITgjz/+AOD48eMIIVi+fDmTJ08mICCA0aNH\\nA8oap0GDBhEUFERgYCDXXHMNdVkBtm7dyjXXXIOPjw/+/v4kJiby22+/UVBQgKenJ6WlpXb1pZTs\\n37/fzrmhKZjNZkpKSggMDLTbHxgYSGlpKUaj0cmZjacxzhl1UVBQgIuLi90orrS0FJPJZCevq6ur\\nOqJsbo4dO8axY8cA+O2330hOTqakRIkEbjAYSEtLY8+ePezZs4cjR46ois9KcnIy2dnZnDx5kr17\\n93LgwIFG922WUFAJ7t5+ePoGUlGcW6d5EeDw4cOUl5eTn5+vmirz8hRfNyklmZmZ7Nu3TzUj5+c3\\nzn0gNzdXNT/v3buX3Nxcu/u8cuVKevbsCXClEOKUEOIZIYQ6IBFCTBVCSCFETyHEJiFEmRDikBBi\\nvE2dBUKIbCGE3W+/EGKk5dwuNvv+bon6ZBBCnBBCPOpwzjKLd/pYIcQBoBLoZzmWKITYJ4SoFELs\\nEkL0FULkCSEWOLQxxtJGpUWuxRaHQ1u+AEYJIYLqu3/ayMyBpE5KrMacMiXdw6qUtmdurIvExER0\\nOh07duxg2LBhddbJy8sjKCiIl156icDAQFJTU1mwYAG5ubm88847gPIwjxkzhu3bt/PEE0/Qu3dv\\nMjIy+Omnn9R2nn76aebNm8c//vEPnn/+ecrLy1m3bh2lpaWNGp3YMmvWLMaPH8+qVavUN/njx48z\\nefJkOnfuTFVVFZ999hl/+9vfOHDggGpC27JlC0lJSVxzzTV8+OGH6PV6fvnlFzIyMujVqxfjxo2r\\npcxKSkowGAwEBwer19oYrArGYDAgpaw1erSWDQaDndt5Xezfvx+j0ai+BISGhtaqk5iYSFpamtMf\\n82XLltXaV1JSgre3t50ytCoLRzOfp6dno+b5Kisr2bNnD1JK9Hq9ajp0RmRkJO7u7mRlZanmVC8v\\nL8xmM6mpqQghiI2NRQhBZmYmhw8fpkePHnb3LCcnBx8fnyab/84aakLaeXj7UVlSSFWVAQ+P2m6M\\nHTp0ID09HQ8PDyIjI5VzLPUyMzPV0axer6ewsFBV0NbvTV1kZmaSmZlJWFgY0dHRmM1mDhw4oD4T\\n3333HbfccguTJ0/mjz/+SAPeB54CgoF7HZr7FMVr/HmUEc0KIUQnKeVp4HNgPpAA2IZvuQXYLaVM\\nAxBCPAI8CyxGGRH1Bp4SQpRLKV+3OS/WUmchkA0cE0K0A9YD24B/ARHAcsDuiy+EuBn4DHjHUq8z\\n8BzKIGuWTdXtgBvwN+BrZ/dQU2YO1GVu3HYaBrUxc6Mjnp6ehISEkJOT47ROz549eeGFF9TywIED\\n0ev13Hnnnbz22mu4u7vz3XffsWnTJr7++mtuuOEGte7kyZMBxfnh2WefZebMmbz0Uo1z7Pjx4zkX\\n+vfvzxtvvGG3b968mtCgZrOZpKQkdu7cySeffKIemzNnDpdffjnffvut+gNuq8T/7//+D4PBgMFQ\\n84OWn5+Pt7c33t7egDJyOXz4cIMy9uzZEw8PDzWOoaP5zFqub2Tm5uZGVFSU6mpfUFDAiRMnMJvN\\nhIeHNyhDQ5SVlREQEGC3z2Qy4erqWmu05+rqitlsrteM5+3tjV6vx8vLi+rqanJyckhNTaV79+52\\n699s8fT0VO+1rTn1zJkzGAwG9T5aj+/fv5/c3FxVoYBynzp37tyka3f0XvTzdqcYqK6urlOZeXl5\\n4eLigk6nszNTGo1GcnJyiIyMJCoqCgB/f3+qq6vJyspyqsyMRiPZ2dmEh4fbrZMLDg5WTbnz5s0j\\nMTGRDz/8kI8++uislHKx5f/ynBDiaYuisvKylPI/oMyvATnAKOBtKWWKEGIfivLabKnjAYxBUY4I\\nIfxQFN7TUsonLW1uEkJ4A48LId6SUlqDcgYDQ6SUe62dCyGeB8qB0VLKCsu+syiK1FpHoCjbj6SU\\n/7DZbwDeEEI8J6XMB5BSFgkhTgJ9qUeZaWbGOmjvZ+/duP4iMTc2NNKQUvLKK69w6aWX4uXlhZub\\nGxMnTsRgMKhren744QeCgoLsFJkt27dvp6Kiol5Ps6YwcuTIWvtSUlIYN24c4eHhuLq64ubmxuHD\\nh0lNTQWUH+5ff/2VKVOmODXLXXfddeh0OtV8ZDKZKCwsJCQkRK3j7e3NJZdc0uBWn6NEY/H39ycq\\nKgp/f3/8/f3p2LEjgYGBZGVlNXqEWB/V1dUNjgqbQnh4OGFhYfj6+hIUFES3bt1wc3MjKyuryW2V\\nl5ej1+vtFIu7uzs+Pj61Rs9NHdlbzYu23oue53gbKioqMJvNdZqRKysrnXqBlpWVYTabnSo7k8nE\\nnj177JZWWPgc5Td8gMP+76wfLArhDBDtcN6NNibK4YAvYA0RMwDQA6uEEDrrBvwAhDu0lWGryCxc\\nBWyyKjIL3zjU6QZ0AFbW0YcnEO9QPw9lhOcUTZk5IaljTVZqq7nR3GoWGzSdyspK8vPz633Lf+WV\\nV5g1axbjxo3j66+/ZufOneqoyGqSys/Pt3tTdsQ6f1BfnabgKG9JSQnXX389p06d4qWXXuKnn35i\\n165dXH755aqMhYWFSCnrlUEIgV6vJz8/HyklBQUFSCkJCqox27u4uKgjtfo26+jFOtJwjDRvLTdV\\nmQQGBmI0Gp3GR2wKUspaoyxXV1dMJlMtZWkymXBxcWmSk4mrqyv+/v52C6IbS1VVVZ33RqfT1RrN\\nNvUe2poXXQQEeqLez6a+hFiVleN51rKzDAPWa3DWX15eHtXV1XU9m1YziuNcUpFDuQpFQVj5HAgB\\nrrWUbwG2Symtq8ytb2wHgGqbzWqWtLVT1WXKiQDsQjxJKSsB2zcPax/rHfo4VkcfAAaHa6iFZmZ0\\ngtXc+NpFYm7cvHkzRqORAQMcX/JqWLVqFRMmTOCZZ55R9x08aB9vOjg4uN63b+vbZ1ZWlt0oxxZP\\nT89aP9DO1vQ4jqy2b9/O6dOn2bRpk926KtuJ9MDAQFxcXBocJfj4+GAwGCgpKSE/P5+AgAC7H8um\\nmhk9PDzQZ31BAAAgAElEQVQQQlBZWWk3d2RVsnWZtP4qdDpdrR9b61yZwWCwmzerrKw8J3f5c3VO\\ncXd3p6Ki9sJPo9FYS3k1pQ+TVJJuWgnwUJ77s2fP4ubm1uT/h1UZOY5yrUrOmXemtW51dXWdCi0k\\nJAQ3NzfOnDnjeMiq3RqewLRBSpkuhEgGbhFC/AyMRpmzsmJtbxR1KyvbL31dr/jZgN1krhDCE7B1\\nbbX2cTfwWx1tHHMoB9DAdWojs3qI9oNrLgJzY1FREbNnz6ZLly71LlKtqKio9YDbphYBxTxXUFDA\\n2rVr62xjwIABeHl51RudITo6mkOHDtnt++6775zUri0j2CuGbdu2cfz4cbWs1+vp168fH330Ub0m\\nOp1Oh5+fH5mZmZSWltZSvk01M1q9BR2dJwoKCvDx8WnyqKKwsBCdTteoaPMN4eHhgcFgsNvn4+OD\\nq6urnbwmk4mioqKmm/PMZoqKitT5xqag1+spKyuzk6+qqorS0tImu/7bUmkzqLMujj579iyFhYV1\\nOtbYIoTAbLZPgmidS3N88SosLMTT09PpyEuv1+Pi4uLU69HV1ZXevXvbBXu2cDNgRnGQaCorgHGW\\nzQuwbXw7UAFESSmT69hKGmh7F5AkhLB1+HCcdzgMZACxTvpQb4bF87IDkFpfp9rIrAGGdIQDuZBt\\n8W5cmQL3tmLvRqPRyI4dOwDFJLd7927eeustysvL2bhxY71re5KSkliyZAn9+vWjc+fOLF++3C73\\nlLXO0KFDuf3225k3bx5XXnklWVlZbN26lXfeeYeAgACeeOIJ5s6dS1VVFSNGjMBgMLBu3Trmz59P\\nu3btGDduHB988AEPPvggI0eOZPPmzepC74bo378/Pj4+3HXXXTz66KOcPn2aBQsW1FoT9e9//5sh\\nQ4YwfPhw7r77bvR6Pdu3b6dPnz6MGjVKrRcSEsLRo0dxd3fHz8/Prg1XV1enzgzOiIyM5PDhw5w8\\neZLAwECKi4spLi6ma9euah2DwcD+/fuJjY1VFWhaWhp6vR5vb2+klMTHxzNnzhwmTJhgNxqx/uhb\\nRwMlJSWqI0N9svr4+NRyt3dxcSEiIoKsrCx18bLVQci67gwUM9i2bdsYM2aM2m9aWhrBwcF4eHio\\njhHV1dXnZF4ODg4mOzubI0eOEBUVpXoz6nQ6p96ckyZN4u9//7vTNs0SjNXVVFeUIgQI12pOnCkm\\nPz8fPz8/IiLqnZ7By8tL/d/pdDo8PDzQ6XSEh4eTlZWFEAJvb2+KioooLi62W4juiE6nIzIykoyM\\nDKSU+Pv7I6UkPz+fjIwM2rVrx5NPPsnQoUOtc81+QohZKA4b7zk4fzSWlSgOGM8DWy2pvgDV4WIB\\n8KoQIgbYijLw6QZcI6Uc10DbrwDTgTVCiJdRzI6PoTiFmC19mIUQDwMfWxxONqCYQzsBY4EJUkrr\\n0CEOZVT3S32dasqsARzNjceK4JdT8LcODZ97IVJcXMyAAQMQQuDn50eXLl2YNGkS999/f4MP8Lx5\\n88jNzVWTJY4fP54lS5ao67tAeWP98ssveeKJJ3jllVfIzc0lKiqK22+/Xa0zZ84cgoKCePXVV3nn\\nnXcIDAxk8ODBqult5MiRPPvss7z55pu8//77jBkzhldffZUxY8Y0eH3h4eGsWrWKWbNmMWbMGLp2\\n7crbb7/N4sWL7eoNHjyYTZs28cQTTzBp0iTc3d3p1auXGhbKSkBAAEIIgoODz9lMZouvry+dO3cm\\nMzOT3NxcPDw86NSpU4MjHU9PT/Lz81WHD+ucn+M8ypkzZ+ze8K2R8oODg+t1Vw8MDCQ7O9vOexNQ\\nvxNZWVkYjUb0er3qzOEMq6dfVlYW1dXVuLi4oNfriYuLa7Lyt7bXrVs3Tp06pY6wrffxXJxWKo2K\\nbayypIDKkgKEEJzV6fDy8iI2NpagoKAG/9eRkZEYDAaOHj2KyWRSXzysXoy5ubnqS0THjh3t5lqd\\ntafT6cjJySE3NxedTofZbFafieuvv54VK1ZYo7Z0AWYCL6J4HTYZKeUpIcQ2lHCFT9ZxfLEQIhN4\\nEHgYZQ1ZKjYeifW0nSGEGAm8CvwXSAHuBDYBtkHpP7d4Of7LctwEHAXWoig2K8Ms++syR6po4awa\\nycZ0+P648tnNBR7sB6FNt5hotCJSUlKIioriyJEjxMfHn5ewSudKbGws77//fpNiFzbEgQMHCA4O\\nbvClpq65quPHj9OxY8dm94o8F+obmZmlsobU6vThpYNgrwsze3RbCmclhBgE/ARcK6WsOz2583O3\\nA+uklE/XV0+bM2skQzpChMU8X22GVQfbtnfjxU5mZiaVlZWcPn0af3//C0qROWIwGJg5cyZRUVFE\\nRUUxc+ZMdX4pISGBL774AoBffvkFIQTr1q0D4Pvvv+eKK65Q2/nhhx+4+uqrCQwMZOjQoZw4cUI9\\nJoTgjTfeoGvXrnYmUUf+85//EBUVRWRkpN2axPpkXLZsGYMGDbJrRwihmrCnTp3K9OnTGTlyJL6+\\nvvTr14/09HS1rtXZx9/fnxkzZtQ7D1rs4L0Y4HlhKrLWjhBikRDiVkskkHtQ5uj2AY1Lg1DTTj+g\\nO/B6Q3U1M2Mj0bnALZfYmBuLW7e5UaN+3n33Xfr3709MTIwSR/H12xs+qbmY8WmTqj/zzDPs2LGD\\nvXv3IoRgzJgxPP300zz11FMkJCSwZcsWbrzxRn788Uc6derE1q1bGTlyJD/++CMJCQkAfP3117z6\\n6qssW7aM3r178/LLL3PbbbfZZYD+6quv+PXXX+uNf7l582aOHDnC0aNHufbaa7niiisYMmRIvTI2\\nhhUrVrBhwwauvPJKpkyZwty5c1mxYgV5eXmMHz+epUuXMmbMGF5//XXefvtt7rjjjlptVDosjrZ6\\nL2qcFzxQ5uPCgRKUtW8PSSnN9Z5VmyBgipTScblBLbR/ZROI9oNrY2vKG9Ihtw16N2ooGQZiYmK4\\n5JJLWtRlvjEsX76cefPmERYWRmhoKPPnz+fjjz8GlJGZNSfY1q1bmTNnjlq2VWZvv/02c+bMYfDg\\nwej1ev71r3+xd+9eu9GZda6zPmU2f/589Ho9PXv2ZNq0aXz22WcNytgYxo0bR9++fdHpdEycOJG9\\ne5V1uuvXr6dHjx5MmDABNzc3Zs6cWaeZ1Cyh0CaUo5eT1C4azYOUcqaUsr2U0l1KGSylvM3WyaQJ\\n7WyQUjouuK4TTZk1ketiIdLG3LhSMzdqtDCZmZnExNSsIYmJiVEdPwYMGEBqaio5OTns3buXyZMn\\nc+rUKfLy8ti5cyeDBw8GlPQvDzzwAAEBAQQEBBAUFISUkoyMDLVd21BLzrCtYytHfTI2BlsF5e3t\\nrUb+sKansSKEqFNOzbzY9tHMjE3E6t24ZJeixI4Xw8+nYLBmbmzbNNH091cSFRXFiRMn6NGjBwAn\\nT55Uveq8vb3p3bs3r776KvHx8bi7u3P11Vfz0ksv0blzZ9X1v3379sydO5eJEyc67acx3pynTp1S\\nF6vbylGfjHq93i4ySFMSxUZGRtplMZBS1spqoJkXLw60f+k50M5XGaFZ0cyNGi3JbbfdxtNPP01u\\nbi55eXksXLiQSZMmqccTEhJ4/fXXVZNiYmKiXRng3nvv5bnnnlMj7RcXF9e1SLdBnnrqKcrLyzlw\\n4ABLly7llltuaVDGyy+/nAMHDrB3714qKytZsGBBo/sbOXIkBw4c4L///S9Go5ElS5bYKUPNvHjx\\noCmzc+Ta2Bpzo9EMn2vmRo0W4vHHH6dPnz5cdtll9OzZkyuvvFJdCwiKMispKVFNio5lUOakZs+e\\nza233oqfnx/x8fFs2LChybIkJCTQpUsXrrvuOmbNmsX111/foIzdunVj3rx5DBkyhK5du9bybKyP\\nkJAQVq1axWOPPUZwcDBHjhxh4MCB6vHiytqxFzXzYttEW2f2J8goqTE3AozqCgmaubHN4Gydj0br\\noNJobzEJ8gT9n4/89ZfRltaZ/RVoI7M/gaO5cWM6nClrMXE0NDQsaObFiw/NAeRPcl2sErsxs1Qx\\nZ6xMgX/0vjBjNy5YsIAnn1Qi1wgh8Pf3p0uXLlx//fWNCmd1sVNZWUlOTg6lpaVUVFTg6+tLXFxc\\ng+dVVFRw6tQpKioqMBqNuLm54efnR1RUlF2QYLPZTHZ2Nvn5+VRVVeHu7k5QUBCRkZENpluRUnLw\\n4EHCw8OdZiOw9pGRkUFZWRllZWVIKenTp+GXfLPZzLFjxygvL6eqqgpXV1e8vb1p165drRBVBQUF\\nZGdnU1lZiaurK35+frRr167BgMhFRUWcPn0ag8GAm5sbl112WYNyOaM+86I17mFeXp6ag8zNzQ1f\\nX19CQ0PrDV5cVlZGcXGx6ryicX6wZKs+DFwmpTzamHO0kdmfxNXi3WhVXieK4aeT9Z/Tkvj7+7N9\\n+3a2bdvGihUrGD9+PB9//DE9e/Zk9+7dLS3eBU1lZSXFxcV4eno2KSKIyWTCw8OD6OhounXrRlRU\\nFGfPniUtLc0uWkVGRgbZ2dmEhobStWtXQkNDyc7O5vTphuPIFhYWYjKZGowBaDabycvLw8XF5Zwi\\nzkdERNC1a1diYmIwm82kpqbaRbMvKiri6NGj+Pj40KVLF6KjoykpKal1rY5IKTl27BheXl5069aN\\nLl26NFk2K5VGKLXJgxngqTyn1n6OHj3KiRMn8Pb2pmPHjnTr1k2NtXjo0KF65SwrK2vSkgKNc0NK\\nmYESB3JeQ3WtaCOzZiDKF4bEwneWDDwbj8IlIRDW9Jiq5x2dTkf//v3V8tChQ7nvvvsYPHgwt956\\nK4cOHao3cn5LUFFRUe9C3b8Kf39/dbSQnp5eKzGkM3x8fOwUh6+vL+7u7qSmpqpZlEEZ0YSGhqoj\\nZD8/P6qrq8nPz1eikNTDmTNnCA4ObnAEp9PpuOKKKxBCcObMGUpKGsrmoeDi4kLnzp3t9vn5+bF3\\n714KCwtVmfPz8/H29raT19XVlbS0NCorK53+H6urqzGZTAQHB9vlemsqZgkFFWaQAoRQzIs2v3Jn\\nzpyhsLCQbt262WVBsI7KcnNz62hVoyGEEF4OmaWbg6XA90KIh21TwjhDG5k1E9fGKnNoUGNubC3e\\njQEBASxevJi0tDQ2bdrktF5RURF///vfiYqKwtPTkw4dOnDXXXfZ1dm3bx+jR48mICAAHx8f+vbt\\na9fmsWPHGDt2LH5+fvj6+jJ69OhaaWSEELz00kvMnDmT0NBQevbsqR77+uuv6dOnD56enkRERPDo\\no486TUffHNi+pTdH1Hwr1hcG2/allLVeJBrzYlFZWUlpaSmBgYGN6ru5rsOabfrPXkNeXh779u0D\\nlNQxycnJ6ujHZDJx8uRJfv/9d3bv3s3Bgwdrpao5fPgw6enp5Obmsn//fjIP78FsrK7TezEnJ4fA\\nwMBa6XyshIaGOr0/eXl5nDypmF2Sk5NJTk62S8569uxZUlJS2L17txo9xVl2aVvKy8s5cuQIv/32\\nG3v27CElJcXuGh2fGaCLEMJu6CqEkEKIB4QQzwohcoUQZ4QQbwghPCzHO1rqjHQ4z1UIkS2EeNpm\\nX7wQYp0QosSyrRJCRNgcT7S0NVQI8Y0QohRL7EQhRKAQYoUQokwIkSmEmC2EeEEIcdyh3w6WegVC\\niHIhxLdCCEeb/S8oCTlvbfAmoo3Mmg1XF7j5EsW70SQVc+PWk5AY0/C5FwKJiYnodDp27NjBsGHD\\n6qzz0EMPsW3bNl5++WUiIiI4deoUW7duVY8fOnSIgQMHEhcXx9tvv01wcDDJycnqIlaDwcB1112H\\nm5sb7733Hjqdjvnz55OQkMD+/fvtTGTPP/88gwcP5uOPP1aTIK5cuZLbbruNe+65h2effZb09HTm\\nzJmD2WxWg9pKKRv1A9KYyO7WtCvNlf7FmrqlqqqKjIwM9Hq93XxTSEgIubm5+Pn54eXlRXl5Obm5\\nuXa5w+qipKQEFxeXv2T0alVcRqNRXc9l+38LCQkhPT2dvLw8AgMDqa6uJiMjA19fX6fy+fv707lz\\nZ9LT04mOjsbHx0edXztx4gRFRUW0a9cOT09PcnNzSUtLo1u3bnYjuNLSUioqDXgHt0O4uCJcXQm0\\nMS+CktCzqqrqnHKqWeUMDw8nJydHXRhuVdQVFRUcOXIEPz8/OnfurP6PDQYD3bp1c9pmRUUFhw4d\\nwtPTk5iYGHQ6HaWlpeTn5+Pp6VnnMzNhwgQP4EchRE8ppW2m14eBH4BJwGXAc8AJYLGU8pgQYidK\\nQs91NuckoMRPXAFgUZK/AMmWdnQoedPWCCH6Snsb7Acoo6dXUFLEACwDBgEPoGScfhAlD5r6UAoh\\ngoCfgXzgXpQ8Z48B/xNCdLOO8KSUUgixAxgCvOH0JlrQlFkzEuUL13WE7yzTld8ehUsvUHOjI56e\\nnoSEhKjJF+ti586dTJ8+XV0IC9gtzn3yySfx9/fnp59+Un+4kpKS1ONLly7l5MmTpKamqskK+/Xr\\nR6dOnXjnnXeYM2eOWjcyMpLPP69JnSSl5JFHHmHy5Mm8+eab6n4PDw+mT5/OnDlzCA4O5scff+Sa\\na65p8HqPHTtGbGxsvXWio6M5ffp0naan3NxcTCZTrWzD9ZGTk0NlpfLMu7u7ExYWViujdmlpKT//\\n/LNatpokHUcjtlgdRhzbaoiSkhIKCgpISUlp9DnFxcUUFSkxX11cXAgLC+PoUfv5eSklu3fvVhWf\\nh4cHYWFh9fZjNBrVuTzrd6e6uprMzEyCg4PVbNdSSoqKiti9e7eayy07O5uqqip8Q9rBGeX76+YC\\nZQ7+JgaDQe0jLy/PTl5b6ntxsd4zxygjubm5VFVV4eXlRVaWEoLQZDJx9OhRysvLncb3zM3NxWAw\\n0K5dO7tnz9PTk+joaD744INazwxKXrFLgHtQFJaV41LKqZbP3wohBgLjAWsyvxXAfCGEh5TSOtF5\\nC3BASvmHpTwfRQkNl1JWWe7HPuAQMAJ7RbhKSvmEzX2LR8kofbOUcpVl3/fAKaDU5rwHAT1whVUZ\\nCyF+AY6j5DWzVVy/A/bmH2dY3xb/6q13796yLWI0Sfnyr1LO+p+yLdkppcnc0lIpzJ8/XwYHBzs9\\nHh4eLu+9916nxydOnCjbt28v33jjDXn48OFax8PCwuRDDz3k9Pxp06bJq666qtb+xMREOWLECLUM\\nyLlz59rVOXTokATk+vXrZXV1tbodO3ZMAnLLli1SSinPnj0rd+3a1eBmMBicymk0Gu36MJtr/wNv\\nvPFGmZCQ4LSNukhNTZU7duyQH3/8sYyLi5NXXnmlrKioUI8vWrRIBgYGytdee03++OOPcsmSJdLf\\n318+8cQT9bY7evRoOWzYMLt9JpPJ7hpMJlOt81577TWp/AQ0nqysLLlr1y75zTffyGHDhsng4GB5\\n4MAB9fgPP/wgfXx85KOPPio3b94sV6xYIbt37y4TExOl0Wh02q71/7hmzRp134cffigBWVZWZld3\\nwYIF0tvbWy0nJCTI7lcOVJ+5eVukLK6s3ceOHTskIL/99lu7/dOnT5co+TpryeCIs3vWsWNH+cgj\\nj9jtMxqNUqfTycWLFztt71yeGZRR02aUHF9WZSyBx6XNbyzwLHDaptwOJdPzGEtZB+QCT9jUyQL+\\nbTlmu6UD8y11Ei39DXHob6plv6fD/hUoitZa3m7Z59jHD8BSh3NnANVY1kTXt2lzZs2M1bvR1fJy\\nd/KsYm680KmsrCQ/P79W5mJbXn/9dcaOHcvChQuJi4uja9eurFixQj2en59frwknKyurzvbDw8PV\\nN2/bfbZY36RHjBiBm5ubulmzJ1vflH18fLjiiisa3OpzE7eadaybNcr8n6Vr167069ePSZMm8e23\\n3/Lbb7/x6aefqtf3+OOPs2jRImbMmMHgwYO5//77WbRoEc899xxnzpxx2m5lZWWtN/+FCxfaXcPC\\nhQub5RoiIiLo06cPo0ePZs2aNQQHB/Pvf/9bPf7www9zww03sGjRIhITE7nlllv46quv2LJlC19/\\n/XWT+srKysLHxwdvb/ssuOHh4ZSXl6telBVGMHnXfF/GxoFfHQMhqzu9o3foo48+yq5du/jmm0YF\\nZ3cqq+N31tXV1W5UWRfn+swAOSjpUWxxTJNSBahut1LxEPwZZTQGcB0QgsXEaCEEmI2iQGy3ToBj\\nBGdHM04EUCKlrHTY72jaCLHI4NjHNXX0YaBG2dVLgxWEEO2Bj1DsqhJ4V0r5qkMdgZIiewSK/XOq\\nlHJPQ223VSJ9lGSe39qYG7sFKWbIC5XNmzdjNBoZMGCA0zoBAQEsWbKEJUuWsG/fPhYvXszEiRO5\\n7LLLuPTSSwkODlZNLHURGRmpxv6zJScnp5ZLuaOpx3r83XffpVevXrXasCq15jAzvvPOO3Zefo1Z\\nS9ZUYmJiCAoKUk10R48epbq62i5ZJkCvXr0wGo2cOHHC6dxZUFBQreC8d999N6NGjVLL52NdlE6n\\no2fPnnZmxkOHDnHbbbfZ1YuLi8PLy8suoWZjiIyMpLS0lPLycjuFlpOTg7e3Nx4eHpRVK4EK3HyV\\n70t8KFzh5H2sffv2xMbG8t1333HnnXeq+zt06ECHDh04fvx4k+RzlNXxhcNkMpGfn1/vcolzfWZQ\\nfo+da0nnfA78WwjhhaJQfpNSHrE5XgB8Cbxfx7l5DmVHF7dswFcI4emg0EId6hUA36DMxTni6F4b\\nAJRKKRv08mrMyMwIPCylvBToD0wXQlzqUGc40NWy3Q281Yh22zTXxNh7Ny7bB2VV9Z/TUhQVFTF7\\n9my6dOnCkCFDGnXOZZddxvPPP4/ZbFbnaq677jpWrlypzgs50q9fP3bv3s2xY8fUfRkZGWzbtq3B\\neHxxcXG0a9eO48eP06dPn1pbcHAwAL1792bXrl0NbvX9uMfFxdm1/WdcxZ1x+PBh8vPzVSVsTY+y\\nZ4/9O6B17V9983txcXF29xQU5WV7DedDmVVWVrJnzx71GkC5DsdrSElJoaKiosE5SkeuuuoqhBCs\\nXr1a3SelZPXq1QwaNAiTGT7ZX7M42tsNxsfVH3tx5syZrF69mi1btjRJFivWEb3jd7xfv358+eWX\\nds5H1uDH9X23z+WZAdyAq1FGWU1lFeAFjLNsKxyOfw/0AHZLKZMdtuMNtG2NT3iDdYdFaSY51LP2\\ncaCOPg471I1FmSNskAZHZlJJqJZl+VwihEhBsb0etKk2BvjIYs/dIYQIEEJEynNIxtZWcHWB23rA\\na7vAYFJC63y8H+7qZe9h9VdjNBrZsWMHoExm7969m7feeovy8nI2btxYrxv1oEGDGDduHPHx8Qgh\\neO+999Dr9fTt2xdQEjNeddVVDB48mIcffpjg4GB+++03goODufPOO5k6dSqLFi1i+PDhLFy4EFdX\\nV5588klCQkK455576pXbxcWFF198kTvuuIOzZ88yfPhw3N3dOXr0KF999RWrV6/G29sbX1/fRkW0\\nOBfKy8tZv349oCjhs2fPqj+0I0aMUEcPXbp0ISEhgQ8++ACAWbNmodPp6NevHwEBAaSkpLB48WI6\\nd+7MrbcqXsfh4eGMHTuW2bNnU1lZyWWXXcbevXtZsGABN910E6Ghji+3NQwcOJCFCxeSm5tbbz0r\\nGzZsoKysTE1wab2Gq666SlWq//d//8ePP/6oLpv47LPP2LBhA8OGDSMqKoqsrCzefPNNsrKyeOih\\nh9S27733Xh588EGioqIYPnw4OTk5LFy4kNjYWEaMGNH4mw1ccskl3HbbbcyYMYOSkhI6d+7Me++9\\nx6FDh3jrrbdYcwTSCmvq33QJ+DaQR/X+++9n69atDB8+nHvuuYekpCR8fX05c+aMeh/qW0xu9WJ8\\n9dVXufbaa/Hz8yMuLo7HH3+cXr16MXbsWO677z5Onz7N7NmzGTp0aL3WjnN5ZlAGDXnAO427kzVI\\nKc8IIbYAL6CMelY6VFkA7ATWCSH+Y+mnHYpCWial3FJP238IIdYAbwkhfFFGag+hWOtsPaVeQvGU\\n/EEI8RqQgTLSTAB+llJ+ZlO3D4p3ZaMurtEbipY8Cfg57F8LDLIpfw/0qeP8u1G0d3KHDh2cTnq2\\nJQ6ckfKR/9U4hHyR0nKyzJ8/X53kFkJIf39/2bt3b/mvf/1LZmVlNXj+rFmzZHx8vPTx8ZH+/v4y\\nMTFRbt261a7O77//LocPHy59fHykj4+P7Nu3r/zf//6nHk9PT5djxoyRPj4+Uq/Xy5EjR8rU1FS7\\nNgD52muv1SnD+vXr5aBBg6S3t7f09fWVl19+uZw7d66srq4+hzvSNKxOCnVtx44dU+vFxMTIKVOm\\nqOXPPvtMXn311TIwMFB6eXnJuLg4+dBDD8nc3Fy79ouLi+XDDz8sO3XqJD09PWXnzp3lI488Is+e\\nPVuvXAaDQQYFBcmPPvqoUdcRExNT5zUsXbpUrTNlyhQZExOjlvfs2SNHjBghw8PDpbu7u4yJiZE3\\n33yz/OOPP+zaNpvN8s0335Q9e/aU3t7eMioqSt58880yPT29XpnqcgCRUsqysjI5Y8YMGRYWJt3d\\n3WXv3r3lxo0b5a8ZNc9U9GUJctCwGxt17VIqzjEffPCBHDhwoPT19ZVubm4yJiZGTpo0SW7btq3e\\nc81ms3zkkUdkZGSkFELYOQH973//k3379pUeHh4yNDRU3nfffbKkpKRBeZr6zKDMjXWV9r+tEpjh\\nsG8BkCdr/w7/3VJ/u+Mxy/HuwGoUc2AFkIaiOKOlvQNIfB3nBqGYMstQ5tTmAe8Bex3qRaG49eeg\\nzIsdBz4BetjUCUWxDCbUJafj1uio+UIIH+BH4Bkp5X8djq0F/i2l/NlS/h6YLaV0Gha/LUTNbyzf\\nH1eCEFu5sTv0b9di4mi0QR544AHS0tJYt25dw5VbOceK4J09ynpOgJ6hMKnnhRkP9XzQmqLmCyF0\\nwB/Ar1LKKU089x5gFtBNNkJRNWqdmRDCDfgCWO6oyCxkYO+FEm3ZpwFcGwNZJfC7ZX74y8MQ5g2d\\nGkQwRxkAACAASURBVBewQUOjQR555BG6detGampqvYt0WztFlfDRvhpFFuljHxtVo2URQtyEMura\\nD/ihrBHrCkxuYjsCZeH1M41RZNAIBxBLox8AKVLKl5xU+waYLBT6A8XyIp4vc0QIuPnSGocQs4SP\\n9kNhc0cy07hoiY6O5j//+U+9nnGtnSqT4khlDSKsd4Opl4GHFvrhQqIMmIaiEz5DMRWOllLubGI7\\nEcBy4OPGntCgmVEIMQj4CUXTWifx/gV0AJBSvm1ReK8Dw1Am+6bVZ2KEi8vMaKWwEpbsrHkYo3xg\\neh9wv7Di+mpoXHBICZ8egL2WlU0uAu7uBZ0vQutGazIz/pU0xpvxZ6DeQbxlGDi9uYRqqwR6wuTL\\nauz9maXw+UGYFK+lctfQqI/NJ2oUGcCYbhenItNwjhYB5C+mYwCMs1mDu+8M/HC8xcTR0LjgOZhn\\n70DVvx1cHd1y8mhcmGjKrAXo5/AwbjyqZKvW0NCwJ6cMPv2jJtRExwBlVKah4YimzFqIG7ram0k+\\nOwDZpc7ra2hcbJRXw7LflaADYDHT9wSd9qulUQfa16KFcHWBO+KVBxSUB3bZPuUB1tC42DGZYfkf\\nkGfx+HVzUTwXfZzHh9a4yNGUWQuid4dpl9d4M+ZXwCd/KA+yhsbFzPp0SLUJo3vrpRd2oG6NlkdT\\nZi1MpI/yoFo5UgBr01pOHg2NliY5yz5t0pBYuMx5ZiINDUBTZhcEPcMgqSbwOD+fgl2ZLSePhkZL\\ncbIYvrBJmN0jFJI6Oa+voWFFU2YXCEM6KjHmrHxxCI4Xt5w8Ghp/NcUG+HBfTUqXcL1itdBCVWk0\\nBk2ZXSC4CCXGXKQl+4RJKg92Ud1pjjQ02hTVJuX7ftaS889bp8wne2qhqjQaiabMLiA8dIrHlreb\\nUi6tUh7walP952lotGakhNWH4NRZpewi4I6eEOzVsnJptC40ZXaBEeSlrKWxmlZOl8CqFOWB19Bo\\ni2w9CXuya8qju0KXoJaTR6N1oimzC5DOgfZRDn7LgS0nWk4eDY3zxaF8WGfjvds3CgZqoao0zgFN\\nmV2gDGgH/aJqyhvSISWv5eTR0GhuzpQpC6OtRocYfyVuqRZ0W+Nc0JTZBYoQMDZOiUUHygP/6R9K\\nrDoNjdZOhVGJeFNpVMr+HjBFC1Wl8SfQvjoXMDoXZf4swBLyqtKkxKrTQl5ptGbMUnkxyy1XyjpL\\nqCpfj5aVS6N1oymzCxwfd+VBd7P8p/IqFNOMWXMI0WilbEhX5sqs3HwJRPu1nDwabQNNmbUC2vkq\\na9CspBbYT5praLQW9mTbOzNdEwO9IlpOHo22g6bMWgmXh8N1sTXlrSeVGHYaGq2FU2eVZSZWLgmG\\nYZ1bTh6NtoWmzFoR13eCS0NqyqtTYF+O8/oaGhcKp8/CB3trQlWFecNt8VqoKo3mQ1NmrQgXAbf1\\nUGLWgRLy6pM/tBGaxoXN8WJ45zcoszgueelg6uXKXw2N5kJTZq0Mz/9v77zDo6rSP/45M+kJSSCN\\nkAYhgIjSpdgXrIht7buusspiWcuu7s+6q1ixroq69rrrWhYbuior2JUOoogCAQIJhPQ2SSaZzJzf\\nH+dmSphACHMzmcn5PM88mXvuvXPe3Ezu955z3hIBfxirnmxBuey/uQG+KwmqWRqNXwqr4bm1Hhf8\\n2AiYPRbS4oJrlyb80GIWgiTFwBUTPEmJAd7dqLOEaHoXP1fCC+ug1cgtGh8Jl4+H3KTg2qUJT7SY\\nhSgJUcaNwcul+b+FsGirzuOoCT7rylRQdPsaWVI0XDlBV4vWmIcWsxAmLhL+MA7ykz1ti7epStVa\\n0DTBYlWpbyzkgBglZOnxwbVLE95oMQtxYiLg0rEwIsXT9tUOeGejDqzW9Dzflag13PavXnqcErIB\\nupyLxmS0mIUBUVaVJeQQr0rVy3aqm4rTFTy7NH2Lz7ertdt2BiWotd2kmODZpOk7aDELEyIscOEh\\nMN4rm8Ka3cp1v00LmsZEpIRFW+Ajr6w0uYlw2Xi1tqvR9AT7FDMhxItCiHIhxPpO9h8rhKgTQnxv\\nvG4LvJmarmC1qLRXU7I8besr1EJ8q65WrTEBKeGDzbC4yNM2NFmt5bZXTNdoeoKujMxeBk7axzFf\\nSynHGq87D9wsTXexCPj1CDg619O2sUplX2iP9dFoAoFLwtu/wNfFnraDUtQabowOiNb0MPsUMynl\\nV0B1D9iiCRBCwMwCOH6Ip21rLTy7VpeP0QQGpwve2ADLd3naDk2Di0dDpDV4dmn6LoFaM5sqhFgn\\nhPhYCDGqs4OEEHOEEKuEEKsqKioC1LXGH0KoXI6nFHjaiuvh6TXQ0BI8uzShT5sL/rke1u72tI0f\\nCL89RBfX1ASPQHz11gB5UsoxwOPAe50dKKV8Vko5UUo5MS0trbPDNAHk2DxVir6dUhs8tQZq7cGz\\nSRO6tDrhpXXwk9ez6JQstVZr1UKmCSIH/PWTUtZLKW3G+4+ASCFE6j5O0/Qgh2erm017gvKKJvjH\\naqhqDqpZmhDD3qbWXjd5LTock6vWaHX2+65T7ihF6qwGAeeAxUwIMVAIIYz3k4zPrNr7WZqeZmIm\\nXHgoWI2bTo1dCVpZY3Dt0oQGTQ615rq11tN2/BA1jS20kHWZHS1buWrr+fxj9zycUntkBZKuuOa/\\nDiwFRgghSoQQlwohLhdCXG4ccjawXgixDpgPnC/1Y0evZHS6WqBvX9eob4GnVsPOhuDapendNLSo\\nqeniek/bzAK1JquFrOtUOyq4bcdVNLoa+Kh2AU+U3htsk8IKESzdmThxoly1atV+n1fTVsVrFU8z\\nO+M6Yiw6R053KKyGl7xiz2KNlFh5Opu5pgO1djUiq2hS2wK1Bjs1O6hmhRzNriZuLJrNlpZfAIgR\\nsdyf9zwFsSP3+7OEEKullBMDbWOoE1JLtlWOCm7a/gc+rn2be0qup9Wl3fK6Q8EAmDPOEwvU3KZu\\nWFtqgmuXpndRaaytegvZeQdrIdtfnLKN+0pudAuZBSs3Zz/QLSHTdE5IidkK21eUtBYBsKZxGfN2\\n3oBD6sCp7pCXpErIxBtZGlqd8Pz3qgaVRlPWqKYWawyvV6tQa64TMoNrV6ghpeTJ3fNY1fitu+2q\\ngbcwMeGIIFoVnoSUmJ3c/ywuTL3Cvb3C9jUP7rxFL6R2k6x+KhFsYrTabnPBKz/Ayl26hExfZmuN\\nWkutNyY+IiwqkfXo9ODaFYq8VfUii2rfdW+fnzKbE/ufGUSLwpeQEjOA81Nnc27K793b3zYs4e+7\\nbscpdfLB7pARr0p09DcymzslvPUzvPoj2FqDa5umZ3E4VZ7Fp9dAozHhEW2F2WPhIB1ss998Vvdf\\nXq140r09LekULky7Yi9naA6EkBMzIQQXpV3FGQN+6277ov5jHi+9G5fU6eG7Q0qsUTwxztO2vgIe\\nWgY/lgfPLk3PUVwPj65QtfDaB+WxESph8ND+QTUtJFnXuILHdt3h3h4TdxjXZN6G0O6fphFyYgZK\\n0GanX8eM5HPcbZ/Wvc9Tu+/XwYjdJDkGrjnMN+N+o0ON0F7/Sed0DFfaXLBoKzyxCsqbPO3DB8B1\\nk7WHa3coshdyd8lfaEMtf+RFF3Br9kNECl1GwExCNre1EIIrBt6IQ7bwad1CAD6q/Q9Rlihmp1+n\\nn4C6QXQEnHWQKvL5n5+hzlgzWbMbCmvgnJEqK7omPNhtU8mCveMMo6wqhmxKlo4h6w6VjnJuL76a\\nJpcNgJSINO7ImU+8tV+QLQt/QnJk1o5FWLg6828ck+ipUPNe9Ws+89Sa/WdEClw/2bfQZ32LSmX0\\n9i+6lEyo45KqKvSjK3yFbEiyGo1NzdZC1h2anI3MLb6GyrYyAGIt8czNeZy0yIH7OFMTCEJ2ZNaO\\nVVi5ftCdOGQr3zV8BigPomhLDOenzg6ydaFLbCRcMEqN0t7+xeMQsGwnbKpS8Ub5ei0l5Khogjc3\\nwPY6T1uEBU4aCkfl6ByL3aVNOpi38//Y1rIJACsR3JL1APkxw4NsWd8hpEdm7VhFBDdkzeOwhCPd\\nbf+s+AdvV70aRKvCg0PT4S9TlKi1U21XHm8LNykPOE3vxyXh22J4ZLmvkGX3gz9NUgmDtZB1Dykl\\nT5Tey5rGZe62qzNvZXzC1CBa1fcICzEDiBSR3JL1IOPip7jbXix/lA+q3wiiVeFBQhRcdCj8ZpTy\\ncAPl8fZ1MTyyAnbU7fV0TZCpaYbn1sJ7m8BhOPxajHp3V01U4Rma7vN65XN8Wve+e/s3qXM4Pvn0\\nIFrUNwkbMQOIskTz1+yHOSRuvLvt6bIH+KTmnSBaFR4IAeMGqrW0EV5OIBVN8ORq+GSL8ozT9B6k\\nVAHwDy9XDjztDIxXnqvHD9E1yA6UxbUf8Frl0+7t45JO5TeplwXRor5L2H2VYyyx3J79GAfFjna3\\nPbH7Hj6r+zCIVoUPSTFw6Rg4+yAVUAtqCmtJEcxfCbt0Bv5eQX2LSib91s/QYkwFC+BXeXDtJJX9\\nRXNgrLUtY37pXe7tcfFTuDrzr9qTOkiEnZgBxFnjuSPncQpiVCJPieSRXXP5uv5/QbYsPBACJmcp\\nz7f8ZE97qU0J2mdF4NSjtKDxfRk8vMw3z2ZqLFw5EWYUeEoAabrPNvsm7tn5fziNWLIh0cO4JesB\\nInQsWdAI2691grUfd+U8yeDoAgBcuHhw519Z2vBFcA0LIwbEwmXj4bRhnhukU8LHW1S29XJd+LNH\\naWyFf/0Ir62HJq/wiSOy4c+TYbAOgA4IlY4ybi++hmaX+oKnRmQwN+dx4qwJQbasbxO2YgaQGJHM\\nPblPkx01GAAnbdy380ZW2b7d+4maLmMRcFQu/HkS5CR62ncY6ZG+KVbTkBpz2VABDy2HdV7px5Jj\\n4LJxcMYIFQytOXAanQ3cXnw1VW3qQsdZErgjZz6pkToLc7AJazEDSI4YwL25z5AZqYowtUkH95T8\\nhXWNK4JsWXiRHg9/nKDilazGkoHDBe9vgmfXQHVzcO0LV5rb4K0Nan3MOzH0pEHKWadgQPBsCzcc\\n0sG9O2+gqKUQULFkt2Y/yOCYYUG2TAMhWGm6u5Q7Srlx+2zKHaUARIsY7sp9klFx43rMhr7CrgaV\\nJqnU5mmLssJhmSpN0kA9G3PA1LXAip0qiL3eS8T6RcHZI+FgneU+oEgpeaT0dpZ4OZJdl3kn05Nn\\n9rgtutK0f/qMmAGUtpZw4/bZ7imCWEs89+Q+xYjYQ3rUjr5AmwsWb1POIB2/YfnJStQOTdfOCPuD\\nS0JhNSzdCRsq95y+HZuhphTjtQ9CwPlXxVO8Xvmce/t3aVcGLcOQFjP/9CkxAyhpKeLG7X+g1lkF\\nQLylH/PynmFozEE9bktfYEedSlq8248zSHykmg6bkqWcSTT+aXTAql1qFFbpZ7o2IQrOGA5jMnre\\ntr7Aotr3mF96p3v7xOQzuXpg8FzwtZj5p8+JGagSDTfvmEO9sxaARGsy83KfZXBMQVDsCXdcErbU\\nwNIS+MnPiEIAw1NgapbKyq8DeVXA8/Y6NQr7odx/QHp+srpmh+gRrmmstn3H3OJrcaGC9SbEH85t\\nOY8E1QVfi5l/+qSYAWyx/8LN2y+j0aWifJOtKTyQ9zxZ0XlBs6kvUNcCK3bB8p2eEjPeJEWrGLZJ\\ng9T7voa9TZXcWbbTd82xnZgImDhQjWYz9NqjqWyxb+TG7ZfS7FKF3vKjR3B/3vPEWYOb/0uLmX/6\\nrJgBbGxez607rnDHi6REpHNf3rMMisoNql19AacLfqlSN+2NVXuuq1kEjEqFKdlQ0D/8k+DualCj\\nsLW7PRk7vMnup0qzjM3QbvY9QUlLETfvmEN1m4o8T4sYyMODXyElMm0fZ5qPFjP/9GkxA/ipaS1/\\n2/FHWqQdgCRrf+bmzGd47KggW9Z3qGpWI7UVuzylZrxJjVUjkYmDwsu5weFUcWFLS1RcXkciLSof\\n5pQs3xg+jbmsbVzOvJL/o9EosBlvSeDBwS+RFz00yJYptJj5p8+LGcC6xhXMLb6WVqnmvaJFDDdn\\nP+BTUkZjPm0uWF+uRihba/fcH2GB0elqhJKXGLoFJCua1Ih01S7fTB3tZMSrtbDxA1VdOU3P8VHN\\nAp7afb97jSxaxDA3Zz6j43uPdmgx848WM4MNTeu4s+RPNDhVPRMLVq7OvJUTks8IsmV9kzKbErXV\\nu/1Xts5MUDf80RmhMVprdapp1aUlvhns27EKFaowNUtVfA5VoQ5VnNLJC2V/5/2a191tKRHp3J7z\\naK/zdNZi5h8tZl4Ut2zjtuKr3IHVAL9NvZwLUv+gM2EHiVanSpy7tARKOsnIHxsBKbFerzj1c0Cs\\nciLpifU2KVUGjio7VDWpqdP2V3UzNLT6P29AjJpGPGyQcrHX9DxNThv377yFVY3fuNsKYkZyW/aj\\nvWKNrCNazPyzTzETQrwIzATKpZR7RBcLdZd/DJgBNAGzpJRr9tVxbxQzgGpHBbcXX8PWlo3uthOT\\nz+SPA2/GKiKCaJmmuF5Nz63d7SkyuS+sQolaip/XgFiI3A9nCqcLauy+IuX93p/jhj8EMDJVTZcO\\nHxD+zi29mbLWXdxR8ie2GymqAI7odxzXDbqDGEvvDH7UYuafrojZ0YANeLUTMZsBXI0Ss8nAY1LK\\nyfvquNtiZquBHxbB5LPBao64NDlt3LvzBtZ6lUGflHAUN2bd12u/4H2JZoeaflxdCmWNXRc2fyRF\\n7yl2yTHGKKvZ91Vr737SZKtQnz06XYUeJMd032ZNYNjQtI67S66jzumZ9z0v5VIuTLsCizAxcG/d\\nJ5BRAAO7F9eqxcw/XZpmFEIMBj7sRMyeAb6QUr5ubG8EjpVSlnY81ptuiVlrE7xzF1Ruh7wxcOK1\\nEGXOXcEhHcwvvZPP6v7rbhsecwhzcx4jKaK/KX1q9h8p1RSeW3SafKf6/HlHmkWM1TPF2T7y8xZI\\nPQLrPXxe9xGPlt5Bm1RfkAgRybWZf2Nakom5FqULvnkN1n0MMf3g7LmQnLnfH6PFzD+BGNpkAcVe\\n2yVG2x5iJoSYA8wByM3tRizXhi+UkAFsXwfv3gWn3gBxgS/UFCkiuS7zTlIj0nmr6iUANtnX85ei\\nWdyZ+wSZUTkB71Oz/wgBidHqNSR5z/32tj2nBNtFr7Zl/0daidH+pyxTYiEuUjtu9HZc0sVrlc/w\\nhleexURrMn/L/jsHx401r+O2Vlj8FBQuV9v2BljxDpzwR/P67GP06CKQlPJZ4FlQI7P9/oAxJ4Pd\\nBqveU9sV22DBbXDqjdB/UCBNBUAIwcXpV5MSkcHTZfcjkexyFHN90SwdixYixERAVj/16kjHNbD2\\nV51dOWMc6BqbpndhdzXzyK65fNPwqbstNyqf23MeY2BUlokd2+Cjv8OuXzxt+YfBtD+Y12cfJBBi\\nthPwHqZkG22BRwiYci4kpMCXL6o5pvoKeHsunPIXyBxuSrczB5zLgIhUHtx1K62yhTpnDTdt/4OO\\nRQtxrBZIjVMvTXhT7ajgrpLr2GT/yd02If5wbsyaR7zVz5NOoKivgA8egBqvW+LoE+HI34FFJ9QM\\nJIG4mguBi4RiClC3r/WyA+aQ6TDjeogwkvfZbfDePbBlpWldHp44jXtyn6afVU1ptkg7dxb/mf/V\\nvmdanxqN5sDZYt/In4su8hGy0/pfwO05j5orZBVFsOB2XyE7/Ddw1EVayExgn1dUCPE6sBQYIYQo\\nEUJcKoS4XAhxuXHIR8BWoBB4DrjSNGu9GTIezrwVYo08P04HfPwo/PA/07o8OG4MD+a9SHqkWrR1\\n4eSx0jv5d8WzBCteT6PRdM7Shi+4oegSKtvKAJUM4YqMm7hs4P+ZG2qz40flrNZkpLKxRMAJV8H4\\nmXph1SRCP2i6rgwW3qd+tjP+VJh6HpjkXqtj0TSa3o2UkneqX+Wl8vlII411vCWBm7MeYFzCFHM7\\n/+Vr+OxZcBmBh1FxMOM6yD44IB+vvRn9E/pj3aQMOPsOFbfRzpoP4NN/qNGaCQyITOP+vOcYF+/5\\np1hU+y53l1yP3eWneqJGo+kxHNLBY6V38mL5Y24hGxiZzUODXzZXyKRUzmmLn/IIWcIAOOv2gAmZ\\npnNCX8xATTWecSsMHu9p2/QdLLwfWvyUOA4AcdYEbs95jGlJp7jbVti+5ubtl1HX5if5nkajMZ36\\ntlr+tuNKPq173902KnYcfx/8CrnR+eZ17HIqp7Rlb3naUnLUg3aKDuPpCcJDzAAio2HGn5VzSDs7\\nN8Dbd4KtypwujVi0c1N+725rj0UrbS3ey5kajSbQFLds47qii/ixabW7bXrSqdyT+5S5iQ4cdvjo\\nEVi/xNOWPQp+fbvyvNb0COEjZgAWKxxzCUw939NWXaw8iqrMEZf2WLQrMm5CoBZ222PRNjX/tI+z\\nNRpNIFjbuJzriy6m1FHibpuVdg1/zpxLpMXEDM7N9cqTusgrHe3wI1Tsa7SO+ehJwkvMQHkKTTgN\\njrtCiRuArRrevgNKzBOXmQPO5ZasB4kSKlygPRZtle1b0/rUaDTwcc0CbttxlbuYZrSI4dashzgn\\ndZa51S5qd6sH5bItnrbxp8HxV5iWN1bTOeEnZu0cdJRKdRVpJAZubVJej5u+M61Lf7FodxT/Scei\\naTQm4JIuXiqfzxO773UX00yJSOOBvBc4PHGauZ2XFapkDW4vagFHz4LDzzfNi1qzd8L7quccCmfd\\nBvHGfLnLCf97Qnk7mhSSoGPRNBrzcbhaeWjXX1lQ9bK7rSBmJH8f/E8KYkea2/m21fDu3WqKEcAa\\nCTP+BKNPMLdfzV4JbzEDSM1THkUDvHKvffc6fPUKuA6gdsheyIkewsN5L5MfPcLd9lrl0zy++26c\\n0k/ZZI1G02UanPX8rfiPfFn/ibttUsLR3J/3PKmR6eZ2vn6JyrPYZlRbjUlQntT5h5nbr2afhL+Y\\nAfRLVZ5Fg7zKn//4P/jkMc+XMsB0Fot2V8l1OhZNo+km5Y5d/F/R7308Fk/pfw5/zX7Y3FqDUiq3\\n+y9e8MzqJKbBWXeYlhNWs3/0DTED9QR1+s1Q4BU0uXUlvHcvNDeY0mV7LNp0rxpJK23fcPP2OdS2\\nVZvSp0YTrhQ2/8x122ZR3LrN3XZJ+rVckXETVmFiOQNnGyx+2lOtAyA9H86+E/rvfz0yjTn0HTED\\nNbd94lUwdoanbfcmtZBbX25Kl5Eikj9n3sG5KZe42zbZf+IvRbPY1brDlD41mnBjle1bbtw+mxpn\\nJaCKad4waB5npVxsrsdiaxN8+CBs/NrTljcWzvirKXUUNd2nb4kZKE+jIy9UJRiMuDBqS5WLbflW\\nc7oUgovTr+LKgTdjMS55qaOEvxT9no3N603pU6MJFz6peYc7iv+EXarp+XhLP+7J/QfHJJ1obse2\\nGpUsuPhHT9vBv4JTrjetwr2m+/Q9MWtn7Mlw0jVqtAbQVKcqV29ZYVqXp/Q/h1uyH/KJRbt5+xxW\\nNHxlWp8aTagipeTV8id5fPfdbtf79MhMHhr8EofETTC38/Kt8Pbtnsr2AJPOhl/N9sSvanoVfVfM\\nAAomq3W06Hi17WhRZWS+esW0JMVT+x3LvblPk2hNBlQs2l0l1/FJzTum9KfRhCIO6eDhXX/jzaoX\\n3G1DYw7iYbNzLEoJPyyCBXOhQU1pIiwwbQ5M+rUu39KL6dtiBsrD8ay5yjOpnfYvs3dZmQAyMm4M\\nD+W9REakChdw4eLx3Xfzr4qndCyaps9jczZw244/8nn9R+62ifFHcn/e8wyISDWv45ZGz8Osywih\\niYyFmf8HBx9rXr+agKDFDFQM2nn3+saKVGyDN2+BwuWmdJkVncdDg1+iIMYT4Pl65XM8VnoHbdKc\\nUaFG09upcOzmhu2X8EOTp9bhScm/5racvxNrMTHXYdkW9f++1atafdpgOO8eyBtjXr+agBH6xTkD\\nSfsUw7eveeoRARx6PBzxW4gIfMLSZlcT80puYHWjJ83WxPgjuCn7fnP/eTWaXsZW+ybmFl9NVVuF\\nu+3itKs4J+X35nksSgk/fALf/rvD//wJcORvPWvqvQhdnNM/Wsz8UbYFFs2Hes8/FWmD4cRrIHlg\\nwLtrkw6eKL2HT+sWutsKYkYyN2c+/SN0CQlN+LPGtpR7d95As0vVH4wggj8Nmsuvkmbs48wDwG5T\\nFaG3et2HomLV+ljBZPP6PUC0mPlHi1lntDTCkmd9px0iY2HaH2BY4KvVSin5V+XTvFH5nLstIzKL\\nu3KeICs6L+D9aTS9hU9r3+fx0ntwotap4i0J3Jr9MGPiTUwRVVYInzwODd4PrEOUh3NShnn9BgAt\\nZv7RYrY3pFRpr755zbMgDHDIcSpWzYRpx49r3uYfu+fhQuWNTLQmc3vOoxwUOzrgfWk0wURKyb8r\\nn+Hflc+629IiBjI3Zz6DYwrM6hTWfQLfdZhWHH0iHPGbXjmt2BEtZv7RYtYVyrfCJ/N9s4Sk5qmn\\nuOTAp7NZ0fAV9+28iRZpB1R9phuy5jGl3zEB70ujCQZt0sHjpXezuO4Dd1t+9Ajm5swnJTJtL2ce\\nAHYbLHlGZb1vJyoOps+BoZPM6dMEtJj5R4tZV2lpgs+f8/VujIyFabNh2NSAd7exeT1zi6+h3lkL\\ngAULVwy8iRn9zw54XxpNT9LktHHvzhtY27jM3TY+fio3Zz1AnDXenE53F6p18PbYMVD5FU+6BhJN\\nzrQfYLSY+UeL2f4gJaxfDF//s8O043SVHivA0467Wndw246rfErBn5dyKb9Lu9LcfHQajUlUOsqZ\\nW3w121o2u9uOTzqdqzJvIUKYMMUnJXz/ESx9w3dacczJcPgFIVkRWouZf7SYdYfybeopzzuoOjVP\\neTsGOIt2bVs1dxRfyyb7T+626Umnck3mX83559doTGJz8wbuLrmeyjbP/82FqVdwfupscx7O/E0r\\nRsfB9MtCuv6YFjP/aDHrLq1N8NnzUOiZKiEyRuVuG354QLuyu5qZV3Ijqxq/cbeNj5/CzVkPPNXa\\nqwAAGEtJREFUmjcto9EEiLLWXbxa8SRf1H/sbrMSwTWZf+O45FPN6XT3Zlj0uO+0YsZQ9cCZaNKa\\nXA+hxcw/WswOBCnhpyVq2tE7l+OoaXDURQGddnTKNp7cPY9Fte+62zIjs7kgdQ7HJp2EVYTedIkm\\nvKlrq+HNqhf4b81/fLLaxFriuTX7IcbFmxDLJV2w9iNY9mbYTCt2RIuZf7SYBYKKIlW12nvaMSVX\\nLS73HxSwbqSUvF75LK9VPuPTnhmZzXmpl/KrpBl66lETdOyuZt6v/jcLql6hyWXz2Tcl4VguSb/W\\nnNjJ5gZY8jQUrfW0RcfB9MshP3zu/VrM/NMlMRNCnAQ8BliB56WU93XYPwt4ENhpND0hpXx+b58Z\\nVmIGatrx8+dhs/e0YzQceymMODKgXS2u/YBnyx6kscONIiMyi/NSLmFa8kwitahpehinbOPT2oW8\\nVvk01W2VPvtGxo7hkvRrOThurDmdl25S04q2Kk9bRgGceHXITyt2RIuZf/YpZkIIK7AJOB4oAVYC\\nF0gpN3gdMwuYKKW8qqsdh52YgTHt+Bl8/arvtOPBx8IRF6qnxABhczbwQfUbvFf9GjZXvc++tIiB\\nnJt6CccnnUakJfCB3RqNN1JKltm+4OXyxylpLfLZlx01mFnp1zAl4RhznDycbbD2v7D8P2qKsZ2x\\np8DU88JiWrEjWsz80xUxmwrMlVKeaGzfDCClnOd1zCy0mHmo3K6CrGtLPW1xySpryLCpAa2J1OS0\\n8UHNm7xb/S8anHU++1IjMjgnZRYnJJ9BlCU6YH1qNO1saPqeF8sf4+fmdT7tKRFp/Db1co5LPtW8\\n9dzSjfD5i1Bd7GmLjofjLochJhfvDCJazPzTFTE7GzhJSjnb2P4dMNlbuAwxmwdUoEZxf5ZSFvv5\\nODdhLWYArc3w+Quw+Tvf9uxRcMzvA7qWBtDkbOS/Nf/hnepX3YHW7aREpHF2yixOTD6TaIsu9645\\ncHa0bOXl8sdZbvvSpz3OksA5KbM4bcAFxFhizem8uR6+ex1+9u2bjAK1Tt3PxJpnvQAtZv4JlJil\\nADYpZYsQ4jLgPCnlND+fNQeYA5Cbmzth+/btHQ8JL6RUGUO+fhWavATGEgHjZ8LEMwIeaG13NStR\\nq3qVWme1z77+1lTOSrmIk/ufZd6NRhPWVDrKeK3iGRbXLXTnDwWIEJHM7H8u56ZcQlJEf3M6ly7Y\\n8KUSshav9eKIaFUFeszJYTmt2BEtZv4JyDRjh+OtQLWUMmlvnxv2IzNvWptg+QJVK837eiemwzGz\\nIC/wi+J2VzOf1L7DgspXqHH6LsYnWwfw65SLOKX/OVrUNF3C5mxgQdXLvF/9b1pli7tdIDg28WR+\\nl3YlGVGBnW3woXI7fPGiih/zJn+iCoMJ89GYN1rM/NMVMYtATR1OR3krrgR+I6X8yeuYTCllqfH+\\nTOBGKeVe66T0KTFrp6JI/UOWFfq2D50ER/0OEgJfu6zFZWdR7XssqHrJp+ghqIz8vx6gRE0HX2v8\\n4XC18mHNW7xZ9cIea7Lj46cyK/0ahsaMMM+A1mavB0EvB49+aXD0xTBkvHl991K0mPmnq675M4BH\\nUa75L0op7xFC3AmsklIuFELMA04D2oBq4Aop5S97+8w+KWag/iF/+lzlimtp9LRHRsOks1UpChOm\\nSlpdLfyv9n3+U/WSTzohgH7WJM4ccCGn9j+POGtCwPvWhB5O6eTL+o/5Z8VTlDtKffYNjTmIS9Kv\\nZawZQc/tSAlbVqiEBI1e0+UWK4wzpugj+6ZTkxYz/+ig6WDRVKfm/n/5yrc9JQeOvQQyzXnadbha\\nWVy3kDcrX6SibbfPvgRLImcM+C3Tk2eSHhn40jaa3o/N2cDX9Yv4b81/fJIBAwyMzOaitD9yVOLx\\nWITFPCPqyuDLl2GHr4ckWQcr56kBWeb1HQJoMfOPFrNgs/Nn+PJFqN7p2z7yWDj8fIhNNKVbh3Tw\\nWe2HvFn1AmWOXXvsHxxdwMSEIzks4QhGxo7R6bLCGKdsY03jMpbUfsAy25c4ZKvP/kRrMhekzuHk\\n/meZG4zvdMDqD2D1+75xmrGJKqxl+BEBDWsJVbSY+UeLWW/A2QbrPoYV70CbZ3Gd6AQ44gIYeQyY\\n9CTcJh18XvcRb1a+4FNqxpt4Sz/Gx0/hsISjmJBwOMkRA0yxRdOzFNk3s7juQ76o+3gPJyFQRWHP\\nTPkdZw34nfnTzzt+hC9fgjrv2QIBhx4HU85V8WMaQItZZ2gx603UVyg3fu+SFQADh6upx9Rc07p2\\nyja+rP+Ez+s+4oem1T6JYb0RCIbFjOKwhCM4LOEohsYcZO6Ukyag1LXV8EX9xyyp/ZAtLf6XtYdG\\nH8T05Jkcm3iyeW727dhq4Nt/+qaBA0gbor7zGUPN7T8E0WLmHy1mvZFtq+GrV3zLVwgLjDkJJp0F\\nUea60ze7mljXuJJVtm9YaftmD4cRb5KtKUxMOJyJCUcyPn4K8dZ+ptqm2X8c0sHKhq9ZUvchK23f\\n4KRtj2OSrSn8KmkGxyXNZHDMMPONcjnhx//BsgXgaPa0R8XClPPgkOPAoh+S/KHFzD9azHorjhZY\\n9a7KO+ddyiJ+gHLjHzqpR9YPpJRsbylkhe0bVtm+4efmH3Dh9HuslQgOjhvDRGPUlhuVrytiBwkp\\nJZvtG1hS9wFf1i/aw60eIFJEMSXhGKYnn8r4+Ck9ty66u1CtE1cU+bYPP1zlMI1P7hk7QhQtZv7R\\nYtbbqd6p1hJ2bvBtzx0DR18EyT3rddjgrGdt41JW2r5hle3bPVJneZMemcnEeOVEMjr+MB2g3QNU\\nOsr5vO4jPqv7kB2tW/0ec1DsaI5LmsmRiSfQz2qOg5Ffmhtg2VsqGTde953kTDWlmD2q52wJYbSY\\n+UeLWSggJWz6Fr75l8pL144QKnHx+NNMXU/rDKd0stm+wT0dWWj/udNjrUSQEz2Y/JgR5EePID9m\\nOEOih5MYoZ/CDxS7q5llDV+wpO4Dvm9c4ZNmqp20iIFMSzqF6Ukzzakltjds1fD9R6qQrcPLwcka\\nCYedCeNOUe81XUKLmX+0mIUSdpt6sl2/BJ8nW1BZwiecDgMLgmIaQHVbJatt37LS9i1rG5ftUZjR\\nH6kRGeTHDDcETolcRmSWdirZB3ZXM2sbl7G84Uu+bfjM77WOEbEckTid6UkzOTRuYs9f07oyWPMB\\n/PwVuDqs0+WNVRk8kjJ61qYwQIuZf7SYhSJlW5SoFf+4577sUUrUskcFNSanTTrY0LTOPWrrbMrL\\nH7GWePKjhzHES+Tyoof2+TI2lY4yVti+ZnnDV6xrWrFHPFg7o+MmMj3pVI5InE6sJXA19LpM5Q5Y\\nsxA2L/XNRQoqKcCks1VORb2e2i20mPlHi1koU7YFVi+ErSv33JcxFCacpkZsvWCU0+S0sa1lM1vt\\nG9lq38S2lk0UtRR2ekPuiAUrOdGDGRI93BjBjSA/erj5ruNBREpJof1nVti+YnnDV5260gMMispl\\netJMpiXNID3SxIS/e2P3Zlj1PhSt2XNfRoFKQTV4nBaxA0SLmX+0mIUDVSXqSXjTd77JWEGl/plw\\nulpbs1iDY18nOGUbJa3bDYHbyLaWzWyx/7JXp5KOJFsHkBmVw6CoXAZF5TAoKofMyByyonJDMs9k\\ni8vOusaVrLB9xQrbV3skh/YmL7qAyQlHMbnfsYyIOSQ4nqNSQsl6JWIdnZQAcg5V37+skVrEAoQW\\nM/9oMQsn6sthzYeqaKGzQ9BzYhqMPxUOOjrgNdQCiZSS6rZKtrYYAmffxNaWTexq3YHsuE64D5Ks\\n/b1ELpfMyBy34PWmeLjqtkpW2r5hecOXfN+4nBZp93tcBBEcEj+ByQnHMCnhKAZGBTFHoXSpeMhV\\n70O5nynk/MPUzIAOeg44Wsz8o8UsHGmsge8/hvWLwdHhxhiXDGNnwCHTTQ++DiTNriaK7JvZ2rJJ\\nCZx9I0UthZ3e+PdFojXZI3SRuWRGqdFcZlQOCSYLnZSSopbNLDemDzfZ13d6bD9rEhPjj2Byv2OY\\nED81+KNNl1Otha1+f898osKiYsUmnAYDsoNjXx9Ai5l/tJiFM3Yb/PA/WPeJb2VeULnuRp+oXrG9\\nZ5SyP7iki8q2Mna17qC0tZidrcWUtharbUdJl9fjOpJoTSbekkCUJZooYbzc76OIssQQLaKJtESp\\nnyKaaH/HWozjRTRRlhhq26rc618dKxZ4kx01mMkJRzOp39GMjB3dO5I8t7WqCg9rPlBp17yxRsLB\\nxyoX+8T0oJjXl9Bi5h8tZn2BVjts+ExlE2ms8d0XGQ2jjlOjtYTwcaYwS+jMwIKVUXFj1fRhv6PJ\\niur5mMFOaW1WI/zvP4amDmuZkTFw6PEw5mSdtaMH0WLmHy1mfQmnA375Wj1d13XIt2iJgJFHq3W1\\nMI/96Q1CF29JYELC4UxOOIYJCYfTz5pkep/7RXODqu78wyLfIrIAMQkqT+ihJ6j3mh5Fi5l/tJj1\\nRVxOKFyuFu+ri333CQHZhyjvx/yJfe5m5ZIuatqqsMtmWl12WmUrra4WWqV6tbhacLh/ttIi7cb+\\nVlqlnVZXqzrW65z29wILh8aNZ3K/YxgVN44IM2uDdYe2VlUQc/My2LbGtxwRQHx/NZU4apoalWmC\\nghYz/2gx68tIFxStVaJWVrjnfosVckcrYRsyHqKCEICrMRdnmwq+37xUeSe2Nu95TFKGSpl20JE6\\n7VQvQIuZf3rByrImaAiLCqoePF5VvF79vm9WEZdTiV3RWnUTyxsLw6aowFf9ZB66uJxQ8pMagW1d\\nuec0YjupeWrauWByr4tR1Gg6osVMY0wtHqxeDZXqJle4zDd+yOlQN76tKyEiGoaMg4KpkDemV8et\\naQxcLtj1CxQuhcIVYG/wf1xSBhRMUaPxlBwd6KwJGfQ0o6Zz6so8wla53f8xkbGQP0HdAHNHg1U/\\nH/UapEulmNq8TK2RdvRGbKdfqiFgU1SFZy1gvRo9zegfLWaarlGzU90UNy9T7/0RHacyPwybqhId\\n66mpnkdKNaJufwixVfk/Lr6/mj4cNlXlTdQCFjJoMfOPFjPN/iElVBWrG+XmpXu6+LcT009Vwx42\\nBQaNBEvwkx2HLVKqkXO7gNWX+z8uNlEJWMEUGDSiVySg1uw/Wsz8o8VM032khIoij7A1VPo/Li5Z\\nTUUOHAbp+ZA8SIvbgSClutblW1XlhG2robbU/7HRCTDUGC1njdSj5TBAi5l/tJhpAoOU6sa6eala\\nn2ms7vzYyBhIG6yErf2VlKGnujrDVgMVhnCVb1Mi1pkDB6gQivyJnulevY4ZVmgx84/+lmsCgxCq\\nyvXAAjjyt1C6ySNszfW+xzrsyrNul1d9rug45XyQng/pQyF9iHJM6GsC11yvxKp8K5QZPztz3PAm\\nMkbFAg6bajji6HgwTd9Cj8w05uJywa6foXSjGlWUbenazRnUult6PmQYo7e0/LDKH4ndBhXbPNel\\nYlvnU7UdiYpTgp8+VD1A5I7WIRJ9BD0y848emWnMxWJRU13Zozxt7dNm3qMPf9Nm9gaVXmnHOk9b\\nXLKqkZVujOKSBqoKANHxvXMdTrpUoucWGzRUeUZd5Vs7d57pSGS016hVT8tqNP7okpgJIU4CHgOs\\nwPNSyvs67I8GXgUmAFXAeVLKosCaqgkbEvpDwgSVfQR8HRrcr23Q2rTnuU21yuFh2+o990XFKVGL\\nMcQtJsEQugRPm89749jI2L0Lg5Qqb2GLDeyNKmOGz3vjZbd1+NkIrY3q/K5ijfRaTzRGXsmZvVOo\\nNZpexD7FTAhhBZ4EjgdKgJVCiIVSSu8a6ZcCNVLKAiHE+cD9wHlmGKwJQ4RQlbAT05TrOKgRTV2Z\\nx+GhfKuahnO0dP45rU3q1VDR+TF++7f4il9UrEqya/cSKVdb93+/zrBYISXXM42ang/9s7TDhkbT\\nDbryXzMJKJRSbgUQQrwBnA54i9npwFzj/QLgCSGEkMFakNOEPsKiRiTJmap6Maj1t9pdnpFb+TZo\\nqjFGRn5GcV1FutSU5t48BA+EyBgllDH9VL7DDGP9LzVHO2poNAGiK2KWBXjXCSkBJnd2jJSyTQhR\\nB6QAPqvZQog5wBxj0yaE2Ngdo4HUjp/dS+itdkHvtU3btX9ou/aPcLQrL5CGhAs9Op8hpXwWePZA\\nP0cIsao3evP0Vrug99qm7do/tF37h7ar79CVVeWdQI7XdrbR5vcYIUQEkIRyBNFoNBqNxnS6ImYr\\ngWFCiCFCiCjgfGBhh2MWAhcb788GPtPrZRqNRqPpKfY5zWisgV0FLEK55r8opfxJCHEnsEpKuRB4\\nAfinEKIQqEYJnpkc8FSlSfRWu6D32qbt2j+0XfuHtquPELQMIBqNRqPRBAodianRaDSakEeLmUaj\\n0WhCnl4rZkKIc4QQPwkhXEKITl1YhRAnCSE2CiEKhRA3ebUPEUIsN9rfNJxXAmHXACHEp0KIzcbP\\nPTLfCiF+JYT43utlF0KcYex7WQixzWvf2J6yyzjO6dX3Qq/2YF6vsUKIpcbf+wchxHle+wJ6vTr7\\nvnjtjzZ+/0Ljegz22nez0b5RCHHigdjRDbuuE0JsMK7PEiFEntc+v3/THrJrlhCiwqv/2V77Ljb+\\n7puFEBd3PNdkux7xsmmTEKLWa5+Z1+tFIUS5EGJ9J/uFEGK+YfcPQojxXvtMu159Aillr3wBI4ER\\nwBfAxE6OsQJbgHwgClgHHGzsews433j/NHBFgOx6ALjJeH8TcP8+jh+AcoqJM7ZfBs424Xp1yS7A\\n1kl70K4XMBwYZrwfBJQCyYG+Xnv7vngdcyXwtPH+fOBN4/3BxvHRwBDjc6w9aNevvL5DV7Tbtbe/\\naQ/ZNQt4ws+5A4Ctxs/+xvv+PWVXh+OvRjmumXq9jM8+GhgPrO9k/wzgY0AAU4DlZl+vvvLqtSMz\\nKeXPUsp9ZQhxp9qSUrYCbwCnCyEEMA2VWgvgFeCMAJl2uvF5Xf3cs4GPpZQHkG+pS+yvXW6Cfb2k\\nlJuklJuN97uAciAtQP174/f7shd7FwDTjetzOvCGlLJFSrkNKDQ+r0fsklJ+7vUdWoaK9zSbrlyv\\nzjgR+FRKWS2lrAE+BU4Kkl0XAK8HqO+9IqX8CvXw2hmnA69KxTIgWQiRibnXq0/Qa8Wsi/hLtZWF\\nSqVVK6Vs69AeCDKklO016ncDGfs4/nz2/Ee6x5hieESoigM9aVeMEGKVEGJZ+9Qnveh6CSEmoZ62\\nt3g1B+p6dfZ98XuMcT3aU7N15Vwz7fLmUtTTfTv+/qY9addZxt9ngRCiPcFCr7hexnTsEOAzr2az\\nrldX6Mx2M69XnyCo6bmFEIuBgX523SqlfL+n7Wlnb3Z5b0gppRCi09gG44nrUFSMXjs3o27qUahY\\nkxuBO3vQrjwp5U4hRD7wmRDiR9QNu9sE+Hr9E7hYSukymrt9vcIRIcSFwETgGK/mPf6mUsot/j8h\\n4HwAvC6lbBFCXIYa1U7rob67wvnAAiml06stmNdLYxJBFTMp5XEH+BGdpdqqQg3fI4yna38puLpl\\nlxCiTAiRKaUsNW6+5Xv5qHOBd6WUDq/Pbh+ltAghXgL+0pN2SSl3Gj+3CiG+AMYBbxPk6yWESAT+\\ni3qQWeb12d2+Xn7Yn9RsJcI3NVtXzjXTLoQQx6EeEI6RUrpr4XTyNw3EzXmfdkkpvdPWPY9aI20/\\n99gO534RAJu6ZJcX5wN/9G4w8Xp1hc5sN/N69QlCfZrRb6otKaUEPketV4FKtRWokZ536q59fe4e\\nc/XGDb19neoMwK/Xkxl2CSH6t0/TCSFSgSOADcG+Xsbf7l3UWsKCDvsCeb0OJDXbQuB8obwdhwDD\\ngBUHYMt+2SWEGAc8A5wmpSz3avf7N+1BuzK9Nk8DfjbeLwJOMOzrD5yA7wyFqXYZth2EcqZY6tVm\\n5vXqCguBiwyvxilAnfHAZub16hsE2wOlsxdwJmreuAUoAxYZ7YOAj7yOmwFsQj1Z3erVno+62RQC\\n/wGiA2RXCrAE2AwsBgYY7RNRVbjbjxuMetqydDj/M+BH1E35X0BCT9kFHG70vc74eWlvuF7AhYAD\\n+N7rNdaM6+Xv+4KatjzNeB9j/P6FxvXI9zr3VuO8jcDJAf6+78uuxcb/Qfv1Wbivv2kP2TUP+Mno\\n/3PgIK9zLzGuYyHw+560y9ieC9zX4Tyzr9frKG9cB+r+dSlwOXC5sV+gih1vMfqf6HWuaderL7x0\\nOiuNRqPRhDyhPs2o0Wg0Go0WM41Go9GEPlrMNBqNRhPyaDHTaDQaTcijxUyj0Wg0IY8WM41Go9GE\\nPFrMNBqNRhPy/D9TK2HEl34cegAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f38380b3dd8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n9qiiu5MaAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f383801a978>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n5BExEwRBEEKe/wdydEUjSCJJkQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f383db88f28>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbMAAAD8CAYAAAD9lEqKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3825bf7da0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for step in range(10000):\\n\",\n    \"    artist_paintings = artist_works()           # real painting from artist\\n\",\n    \"    G_ideas = Variable(torch.randn(BATCH_SIZE, N_IDEAS))    # random ideas\\n\",\n    \"    G_paintings = G(G_ideas)                    # fake painting from G (random ideas)\\n\",\n    \"\\n\",\n    \"    prob_artist0 = D(artist_paintings)          # D try to increase this prob\\n\",\n    \"    prob_artist1 = D(G_paintings)               # D try to reduce this prob\\n\",\n    \"\\n\",\n    \"    D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1))\\n\",\n    \"    G_loss = torch.mean(torch.log(1. - prob_artist1))\\n\",\n    \"\\n\",\n    \"    opt_D.zero_grad()\\n\",\n    \"    D_loss.backward(retain_variables=True)      # retain_variables for reusing computational graph\\n\",\n    \"    opt_D.step()\\n\",\n    \"\\n\",\n    \"    opt_G.zero_grad()\\n\",\n    \"    G_loss.backward()\\n\",\n    \"    opt_G.step()\\n\",\n    \"\\n\",\n    \"    if step % 1000 == 0:  # plotting\\n\",\n    \"        plt.cla()\\n\",\n    \"        plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)\\n\",\n    \"        plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')\\n\",\n    \"        plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')\\n\",\n    \"        plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 15})\\n\",\n    \"        plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 15})\\n\",\n    \"        plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=12);plt.draw();plt.pause(0.01)\\n\",\n    \"        plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# below is for pytorch version => 1.5\\n\",\n    \"# this fix is done by albanD - https://github.com/pytorch/pytorch/issues/39141\\n\",\n    \"\\n\",\n    \"for step in range(10000):\\n\",\n    \"    \\n\",\n    \"    artist_paintings = artist_works()           # real painting from artist\\n\",\n    \"    G_ideas = torch.randn(BATCH_SIZE, N_IDEAS, requires_grad=True)  # random ideas\\n\",\n    \"    G_paintings = G(G_ideas)                    # fake painting from G (random ideas)\\n\",\n    \"\\n\",\n    \"    prob_artist1 = D(G_paintings)               # D try to reduce this prob\\n\",\n    \"    \\n\",\n    \"    G_loss = torch.mean(torch.log(1. - prob_artist1))  \\n\",\n    \"    opt_G.zero_grad()\\n\",\n    \"    G_loss.backward()\\n\",\n    \"    opt_G.step()\\n\",\n    \"     \\n\",\n    \"    prob_artist0 = D(artist_paintings)          # D try to increase this prob\\n\",\n    \"    prob_artist1 = D(G_paintings.detach())  # D try to reduce this prob\\n\",\n    \"    D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1))      \\n\",\n    \"\\n\",\n    \"    opt_D.zero_grad()\\n\",\n    \"    D_loss.backward(retain_graph=True)      # reusing computational graph\\n\",\n    \"    opt_D.step()\\n\",\n    \"\\n\",\n    \"    if step % 1000 == 0:  # plotting\\n\",\n    \"        plt.cla()\\n\",\n    \"        plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], c='#4AD631', lw=3, label='Generated painting',)\\n\",\n    \"        plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + 1, c='#74BCFF', lw=3, label='upper bound')\\n\",\n    \"        plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + 0, c='#FF9359', lw=3, label='lower bound')\\n\",\n    \"        plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size': 15})\\n\",\n    \"        plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 15})\\n\",\n    \"        plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=12);plt.draw();plt.pause(0.01)\\n\",\n    \"        plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/501_why_torch_dynamic_graph.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 501 Why Torch Dynamic Graph\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* matplotlib\\n\",\n    \"* numpy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"from torch import nn\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1)    # reproducible\\n\",\n    \"\\n\",\n    \"# Hyper Parameters\\n\",\n    \"INPUT_SIZE = 1          # rnn input size / image width\\n\",\n    \"LR = 0.02               # learning rate\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class RNN(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(RNN, self).__init__()\\n\",\n    \"\\n\",\n    \"        self.rnn = nn.RNN(\\n\",\n    \"            input_size=1,\\n\",\n    \"            hidden_size=32,     # rnn hidden unit\\n\",\n    \"            num_layers=1,       # number of rnn layer\\n\",\n    \"            batch_first=True,   # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)\\n\",\n    \"        )\\n\",\n    \"        self.out = nn.Linear(32, 1)\\n\",\n    \"\\n\",\n    \"    def forward(self, x, h_state):\\n\",\n    \"        # x (batch, time_step, input_size)\\n\",\n    \"        # h_state (n_layers, batch, hidden_size)\\n\",\n    \"        # r_out (batch, time_step, output_size)\\n\",\n    \"        r_out, h_state = self.rnn(x, h_state)\\n\",\n    \"\\n\",\n    \"        outs = []                                   # this is where you can find torch is dynamic\\n\",\n    \"        for time_step in range(r_out.size(1)):      # calculate output for each time step\\n\",\n    \"            outs.append(self.out(r_out[:, time_step, :]))\\n\",\n    \"        return torch.stack(outs, dim=1), h_state\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"RNN (\\n\",\n      \"  (rnn): RNN(1, 32, batch_first=True)\\n\",\n      \"  (out): Linear (32 -> 1)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"\\n\",\n    \"rnn = RNN()\\n\",\n    \"print(rnn)\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all cnn parameters\\n\",\n    \"loss_func = nn.MSELoss()                                # the target label is not one-hotted\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"20\\n\",\n      \"30\\n\",\n      \"20\\n\",\n      \"20\\n\",\n      \"30\\n\",\n      \"30\\n\",\n      \"10\\n\",\n      \"10\\n\",\n      \"30\\n\",\n      \"20\\n\",\n      \"20\\n\",\n      \"20\\n\",\n      \"30\\n\",\n      \"10\\n\",\n      \"10\\n\",\n      \"30\\n\",\n      \"10\\n\",\n      \"10\\n\",\n      \"30\\n\",\n      \"20\\n\",\n      \"20\\n\",\n      \"10\\n\",\n      \"30\\n\",\n      \"10\\n\",\n      \"30\\n\",\n      \"10\\n\",\n      \"20\\n\",\n      \"20\\n\",\n      \"30\\n\",\n      \"30\\n\",\n      \"20\\n\",\n      \"20\\n\",\n      \"30\\n\",\n      \"30\\n\",\n      \"10\\n\",\n      \"20\\n\",\n      \"20\\n\",\n      \"10\\n\",\n      \"30\\n\",\n      \"10\\n\",\n      \"20\\n\",\n      \"10\\n\",\n      \"20\\n\",\n      \"10\\n\",\n      \"20\\n\",\n      \"30\\n\",\n      \"30\\n\",\n      \"10\\n\",\n      \"10\\n\",\n      \"20\\n\",\n      \"20\\n\",\n      \"20\\n\",\n      \"20\\n\",\n      \"10\\n\",\n      \"10\\n\",\n      \"20\\n\",\n      \"10\\n\",\n      \"10\\n\",\n      \"20\\n\",\n      \"30\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAs4AAAEyCAYAAADqVFbTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fed213b0d30>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"h_state = None   # for initial hidden state\\n\",\n    \"\\n\",\n    \"plt.figure(1, figsize=(12, 5))\\n\",\n    \"plt.ion()   # continuously plot\\n\",\n    \"\\n\",\n    \"########################  Below is different #########################\\n\",\n    \"\\n\",\n    \"################ static time steps ##########\\n\",\n    \"# for step in range(60):\\n\",\n    \"#     start, end = step * np.pi, (step+1)*np.pi   # time steps\\n\",\n    \"#     # use sin predicts cos\\n\",\n    \"#     steps = np.linspace(start, end, 10, dtype=np.float32)\\n\",\n    \"\\n\",\n    \"################ dynamic time steps #########\\n\",\n    \"step = 0\\n\",\n    \"for i in range(60):\\n\",\n    \"    dynamic_steps = np.random.randint(1, 4)  # has random time steps\\n\",\n    \"    start, end = step * np.pi, (step + dynamic_steps) * np.pi  # different time steps length\\n\",\n    \"    step += dynamic_steps\\n\",\n    \"\\n\",\n    \"    # use sin predicts cos\\n\",\n    \"    steps = np.linspace(start, end, 10 * dynamic_steps, dtype=np.float32)\\n\",\n    \"\\n\",\n    \"#######################  Above is different ###########################\\n\",\n    \"\\n\",\n    \"    print(len(steps))       # print how many time step feed to RNN\\n\",\n    \"\\n\",\n    \"    x_np = np.sin(steps)    # float32 for converting torch FloatTensor\\n\",\n    \"    y_np = np.cos(steps)\\n\",\n    \"\\n\",\n    \"    x = Variable(torch.from_numpy(x_np[np.newaxis, :, np.newaxis]))    # shape (batch, time_step, input_size)\\n\",\n    \"    y = Variable(torch.from_numpy(y_np[np.newaxis, :, np.newaxis]))\\n\",\n    \"\\n\",\n    \"    prediction, h_state = rnn(x, h_state)   # rnn output\\n\",\n    \"    # !! next step is important !!\\n\",\n    \"    h_state = Variable(h_state.data)        # repack the hidden state, break the connection from last iteration\\n\",\n    \"\\n\",\n    \"    loss = loss_func(prediction, y)         # cross entropy loss\\n\",\n    \"    optimizer.zero_grad()                   # clear gradients for this training step\\n\",\n    \"    loss.backward()                         # backpropagation, compute gradients\\n\",\n    \"    optimizer.step()                        # apply gradients\\n\",\n    \"\\n\",\n    \"    # plotting\\n\",\n    \"    plt.plot(steps, y_np.flatten(), 'r-')\\n\",\n    \"    plt.plot(steps, prediction.data.numpy().flatten(), 'b-')\\n\",\n    \"    plt.draw()\\n\",\n    \"    plt.pause(0.05)\\n\",\n    \"\\n\",\n    \"plt.ioff()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/502_GPU.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 502 GPU\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* torchvision\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import torch.utils.data as Data\\n\",\n    \"import torchvision\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1)\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"EPOCH = 1\\n\",\n    \"BATCH_SIZE = 50\\n\",\n    \"LR = 0.001\\n\",\n    \"DOWNLOAD_MNIST = False\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_data = torchvision.datasets.MNIST(\\n\",\n    \"    root='./mnist/', \\n\",\n    \"    train=True, \\n\",\n    \"    transform=torchvision.transforms.ToTensor(), \\n\",\n    \"    download=DOWNLOAD_MNIST,)\\n\",\n    \"\\n\",\n    \"train_loader = Data.DataLoader(\\n\",\n    \"    dataset=train_data, \\n\",\n    \"    batch_size=BATCH_SIZE, \\n\",\n    \"    shuffle=True)\\n\",\n    \"\\n\",\n    \"test_data = torchvision.datasets.MNIST(\\n\",\n    \"    root='./mnist/', train=False)\\n\",\n    \"\\n\",\n    \"# !!!!!!!! Change in here !!!!!!!!! #\\n\",\n    \"test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1)).type(torch.FloatTensor)[:2000].cuda()/255.   # Tensor on GPU\\n\",\n    \"test_y = test_data.test_labels[:2000].cuda()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class CNN(nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(CNN, self).__init__()\\n\",\n    \"        self.conv1 = nn.Sequential(\\n\",\n    \"            nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2,),                      \\n\",\n    \"            nn.ReLU(), \\n\",\n    \"            nn.MaxPool2d(kernel_size=2),)\\n\",\n    \"        self.conv2 = nn.Sequential(\\n\",\n    \"            nn.Conv2d(16, 32, 5, 1, 2), \\n\",\n    \"            nn.ReLU(), \\n\",\n    \"            nn.MaxPool2d(2),)\\n\",\n    \"        self.out = nn.Linear(32 * 7 * 7, 10)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.conv1(x)\\n\",\n    \"        x = self.conv2(x)\\n\",\n    \"        x = x.view(x.size(0), -1)\\n\",\n    \"        output = self.out(x)\\n\",\n    \"        return output\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"CNN (\\n\",\n       \"  (conv1): Sequential (\\n\",\n       \"    (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\\n\",\n       \"    (1): ReLU ()\\n\",\n       \"    (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\\n\",\n       \"  )\\n\",\n       \"  (conv2): Sequential (\\n\",\n       \"    (0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\\n\",\n       \"    (1): ReLU ()\\n\",\n       \"    (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\\n\",\n       \"  )\\n\",\n       \"  (out): Linear (1568 -> 10)\\n\",\n       \")\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"cnn = CNN()\\n\",\n    \"\\n\",\n    \"# !!!!!!!! Change in here !!!!!!!!! #\\n\",\n    \"cnn.cuda()      # Moves all model parameters and buffers to the GPU.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)\\n\",\n    \"loss_func = nn.CrossEntropyLoss()\\n\",\n    \"\\n\",\n    \"losses_his = []\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0 | train loss: 2.3145 | test accuracy: 0.10\\n\",\n      \"Epoch:  0 | train loss: 0.5546 | test accuracy: 0.83\\n\",\n      \"Epoch:  0 | train loss: 0.5856 | test accuracy: 0.89\\n\",\n      \"Epoch:  0 | train loss: 0.1879 | test accuracy: 0.92\\n\",\n      \"Epoch:  0 | train loss: 0.0601 | test accuracy: 0.94\\n\",\n      \"Epoch:  0 | train loss: 0.1772 | test accuracy: 0.95\\n\",\n      \"Epoch:  0 | train loss: 0.0993 | test accuracy: 0.94\\n\",\n      \"Epoch:  0 | train loss: 0.2210 | test accuracy: 0.95\\n\",\n      \"Epoch:  0 | train loss: 0.0379 | test accuracy: 0.96\\n\",\n      \"Epoch:  0 | train loss: 0.0535 | test accuracy: 0.96\\n\",\n      \"Epoch:  0 | train loss: 0.0268 | test accuracy: 0.96\\n\",\n      \"Epoch:  0 | train loss: 0.0910 | test accuracy: 0.96\\n\",\n      \"Epoch:  0 | train loss: 0.0982 | test accuracy: 0.97\\n\",\n      \"Epoch:  0 | train loss: 0.3154 | test accuracy: 0.97\\n\",\n      \"Epoch:  0 | train loss: 0.0418 | test accuracy: 0.97\\n\",\n      \"Epoch:  0 | train loss: 0.1072 | test accuracy: 0.96\\n\",\n      \"Epoch:  0 | train loss: 0.0652 | test accuracy: 0.97\\n\",\n      \"Epoch:  0 | train loss: 0.1042 | test accuracy: 0.97\\n\",\n      \"Epoch:  0 | train loss: 0.1346 | test accuracy: 0.97\\n\",\n      \"Epoch:  0 | train loss: 0.0431 | test accuracy: 0.98\\n\",\n      \"Epoch:  0 | train loss: 0.0276 | test accuracy: 0.97\\n\",\n      \"Epoch:  0 | train loss: 0.0153 | test accuracy: 0.98\\n\",\n      \"Epoch:  0 | train loss: 0.0438 | test accuracy: 0.98\\n\",\n      \"Epoch:  0 | train loss: 0.0082 | test accuracy: 0.97\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for epoch in range(EPOCH):\\n\",\n    \"    for step, (x, y) in enumerate(train_loader):\\n\",\n    \"\\n\",\n    \"        # !!!!!!!! Change in here !!!!!!!!! #\\n\",\n    \"        b_x = Variable(x).cuda()    # Tensor on GPU\\n\",\n    \"        b_y = Variable(y).cuda()    # Tensor on GPU\\n\",\n    \"\\n\",\n    \"        output = cnn(b_x)\\n\",\n    \"        loss = loss_func(output, b_y)\\n\",\n    \"        losses_his.append(loss.data[0])\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        loss.backward()\\n\",\n    \"        optimizer.step()\\n\",\n    \"\\n\",\n    \"        if step % 50 == 0:\\n\",\n    \"            test_output = cnn(test_x)\\n\",\n    \"\\n\",\n    \"            # !!!!!!!! Change in here !!!!!!!!! #\\n\",\n    \"            pred_y = torch.max(test_output, 1)[1].cuda().data.squeeze()  # move the computation in GPU\\n\",\n    \"\\n\",\n    \"            accuracy = torch.sum(pred_y == test_y).type(torch.FloatTensor) / test_y.size(0)\\n\",\n    \"            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f5b979dcb70>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(losses_his, label='loss')\\n\",\n    \"plt.legend(loc='best')\\n\",\n    \"plt.xlabel('Steps')\\n\",\n    \"plt.ylabel('Loss')\\n\",\n    \"plt.ylim((0, 1))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Test\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \" 7\\n\",\n      \" 2\\n\",\n      \" 1\\n\",\n      \" 0\\n\",\n      \" 4\\n\",\n      \" 1\\n\",\n      \" 4\\n\",\n      \" 9\\n\",\n      \" 5\\n\",\n      \" 9\\n\",\n      \"[torch.cuda.LongTensor of size 10 (GPU 0)]\\n\",\n      \" prediction number\\n\",\n      \"\\n\",\n      \" 7\\n\",\n      \" 2\\n\",\n      \" 1\\n\",\n      \" 0\\n\",\n      \" 4\\n\",\n      \" 1\\n\",\n      \" 4\\n\",\n      \" 9\\n\",\n      \" 5\\n\",\n      \" 9\\n\",\n      \"[torch.cuda.LongTensor of size 10 (GPU 0)]\\n\",\n      \" real number\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# !!!!!!!! Change in here !!!!!!!!! #\\n\",\n    \"test_output = cnn(test_x[:10])\\n\",\n    \"pred_y = torch.max(test_output, 1)[1].cuda().data.squeeze() # move the computation in GPU\\n\",\n    \"\\n\",\n    \"print(pred_y, 'prediction number')\\n\",\n    \"print(test_y[:10], 'real number')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/503_dropout.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 503 Dropout\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* matplotlib\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<torch._C.Generator at 0x7f41f8033918>\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1)    # reproducible\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"N_SAMPLES = 20\\n\",\n    \"N_HIDDEN = 300\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n8/93Ljvg2d5oywAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f41a7abbf98>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# training data\\n\",\n    \"x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)\\n\",\n    \"y = x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))\\n\",\n    \"x, y = Variable(x), Variable(y)\\n\",\n    \"\\n\",\n    \"# test data\\n\",\n    \"test_x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)\\n\",\n    \"test_y = test_x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))\\n\",\n    \"test_x, test_y = Variable(test_x, volatile=True), Variable(test_y, volatile=True)\\n\",\n    \"\\n\",\n    \"# show data\\n\",\n    \"plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=50, alpha=0.5, label='train')\\n\",\n    \"plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.5, label='test')\\n\",\n    \"plt.legend(loc='upper left')\\n\",\n    \"plt.ylim((-2.5, 2.5))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"net_overfitting = torch.nn.Sequential(\\n\",\n    \"    torch.nn.Linear(1, N_HIDDEN),\\n\",\n    \"    torch.nn.ReLU(),\\n\",\n    \"    torch.nn.Linear(N_HIDDEN, N_HIDDEN),\\n\",\n    \"    torch.nn.ReLU(),\\n\",\n    \"    torch.nn.Linear(N_HIDDEN, 1),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"net_dropped = torch.nn.Sequential(\\n\",\n    \"    torch.nn.Linear(1, N_HIDDEN),\\n\",\n    \"    torch.nn.Dropout(0.5),  # drop 50% of the neuron\\n\",\n    \"    torch.nn.ReLU(),\\n\",\n    \"    torch.nn.Linear(N_HIDDEN, N_HIDDEN),\\n\",\n    \"    torch.nn.Dropout(0.5),  # drop 50% of the neuron\\n\",\n    \"    torch.nn.ReLU(),\\n\",\n    \"    torch.nn.Linear(N_HIDDEN, 1),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sequential (\\n\",\n      \"  (0): Linear (1 -> 300)\\n\",\n      \"  (1): ReLU ()\\n\",\n      \"  (2): Linear (300 -> 300)\\n\",\n      \"  (3): ReLU ()\\n\",\n      \"  (4): Linear (300 -> 1)\\n\",\n      \")\\n\",\n      \"Sequential (\\n\",\n      \"  (0): Linear (1 -> 300)\\n\",\n      \"  (1): Dropout (p = 0.5)\\n\",\n      \"  (2): ReLU ()\\n\",\n      \"  (3): Linear (300 -> 300)\\n\",\n      \"  (4): Dropout (p = 0.5)\\n\",\n      \"  (5): ReLU ()\\n\",\n      \"  (6): Linear (300 -> 1)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(net_overfitting)  # net architecture\\n\",\n    \"print(net_dropped)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer_ofit = torch.optim.Adam(net_overfitting.parameters(), lr=0.01)\\n\",\n    \"optimizer_drop = torch.optim.Adam(net_dropped.parameters(), lr=0.01)\\n\",\n    \"loss_func = torch.nn.MSELoss()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbcAAAD8CAYAAAD0f+rwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4FMX7wD+bAgESktAJhARUegeR3mwIiIAoNgQUUaz4\\nUxT9qoBYUGyoKIoUC4KAUqQooFQFkRKld0IJ0gktCSSZ3x/vba4nl+SSyyXzeZ597nZ2dmb2yr77\\nzrzFUEqh0Wg0Gk1hIsDXA9BoNBqNxtto4abRaDSaQocWbhqNRqMpdGjhptFoNJpChxZuGo1Goyl0\\naOGm0Wg0mkKHFm4ajUajKXRo4abRaDSaQocWbhqNRqMpdAT5otNy5cqp2NhYX3St0Wg0fsvGjRtP\\nKaXK+3oc/oBPhFtsbCwbNmzwRdcajUbjtxiGEe/rMfgLelpSo9FoNIUOLdw0Go1GU+jQwk2j0Wg0\\nhQ6frLm54urVqxw5coTk5GRfD0XjgpCQEKpWrUpwcLCvh6LRaDRZUmCE25EjRwgLCyM2NhbDMHw9\\nHI0NSilOnz7NkSNHqF69uq+Ho9FoNFlSYKYlk5OTKVu2rBZsBRDDMChbtqzWqjUajd9QYIQboAVb\\nAUZ/NxqNxp8oUMJNo9FoNBpvoIVbHjNr1izq1KlDp06d2LBhA08//TQAK1as4M8//8yoN3fuXLZv\\n356x/9prr7Fs2bJ8H69Go9EUBgqMQUlhQymFUopJkyYxceJE2rZtC0Dz5s0BEW6hoaG0bt0aEOHW\\nvXt36tatC8Drr7/um4FrNBpNIUBrbjZ88MEH1K9fn/r16/PRRx8xfPhwxo8fn3F85MiRvPfeewCM\\nHTuW66+/noYNGzJixAgADh48SK1atXjwwQepX78+o0ePZs2aNTz88MMMGzaMFStW0L17dw4ePMiE\\nCRP48MMPady4MStXrmT+/PkMGzaMxo0bs2/fPgYMGMDs2bMBCVc2YsQImjZtSoMGDdi5cycAJ0+e\\n5Oabb6ZevXoMGjSImJgYTp06lc+fmkaj0RQ8CqZwM4y829ywceNGpkyZwl9//cW6deuYOHEiffv2\\nZebMmRl1Zs6cSd++fVmyZAl79uxh/fr1xMXFsXHjRlatWgXAnj17ePzxx9m2bRsjRoygefPmTJs2\\njbFjx2a0Exsby2OPPcazzz5LXFwcHTp0oEePHowdO5a4uDiuueYap/GVK1eOTZs2MWTIkAwBO2rU\\nKDp37sy2bdvo06cPhw4d8tY3oNFoNH6Nnpa0sGbNGnr16kWpUqUA6N27N6tXr+bEiRMkJCRw8uRJ\\nIiMjiY6OZty4cSxZsoQmTZoAcPHiRfbs2UO1atWIiYmhZcuWXh9f7969AWjWrBk//fRTxpjnzJkD\\nQJcuXYiMjPR6vxqNRuOPaOGWBXfddRezZ8/mv//+o2/fvoCsp7300ks8+uijdnUPHjyYIRy9TfHi\\nxQEIDAwkNTU1T/rQaDSawkLBnJZUKu82N7Rr1465c+dy+fJlLl26xJw5c2jXrh19+/ZlxowZzJ49\\nm7vuuguAW2+9lcmTJ3Px4kUAjh49yokTJ7J1iWFhYVy4cMHtvie0adMmY9p0yZIlnD17NlvnazQa\\nTWGlYAo3H9C0aVMGDBhAixYtuOGGGxg0aBBNmjShXr16XLhwgSpVqlC5cmUAbrnlFu677z5atWpF\\ngwYN6NOnT7YF0+23386cOXNo3Lgxq1ev5p577mHs2LE0adKEffv2edTGiBEjWLJkCfXr12fWrFlU\\nqlSJsLCwbF+7RqPRFDYMlYk241EDhhENfANUBBTwpVJqXGbnNG/eXDkmK92xYwd16tTJ1ViKGikp\\nKQQGBhIUFMTatWsZMmQIcXFxedaf/o40Gt9iGMZGpVRzX4/DH/DGmlsq8JxSapNhGGHARsMwliql\\ntmd1oiZ3HDp0iLvvvpv09HSKFSvGxIkTfT0kjUajKRDkWrgppY4BxyzvLxiGsQOoAmjhlsdcd911\\nbN682dfD0Gg0mgKHV9fcDMOIBZoAf3mzXY1Go9FosoPXhJthGKHAj8BQpdR5F8cHG4axwTCMDSdP\\nnvRWtxqNRqPROOEV4WYYRjAi2KYppX5yVUcp9aVSqrlSqnn58uW90a1Go9FoNC7JtXAzJNHXJGCH\\nUuqD3A9Jo9FoNJrc4Q3NrQ3QD+hsGEacZevqhXZ9im2Q5PzmrbfesttPSkqiQ4cOpKWlARKlpHHj\\nxjRu3JgePXpk1Dtw4AA33HAD1157LX379uXKlSsAfPLJJ9SvX5+uXbtmlK1Zs4Znn30249yTJ0/S\\npUuXvL40jUajyRdyLdyUUmuUUoZSqqFSqrFlW+SNwRU08ivslaNwmzx5Mr179yYwMBCAEiVKEBcX\\nR1xcHPPnz8+o9+KLL/Lss8+yd+9eIiMjmTRpEgDTpk3j33//pXXr1vz6668opRg9ejSvvvpqxrnl\\ny5encuXK/PHHH/lwhRqNRpO36AglNrz55pvUrFmTtm3bsmvXLgA6duzI0KFDad68OePGjePgwYN0\\n7tyZhg0bcuONN2ZE4h8wYACPPfYYzZs3p2bNmixYsACA5ORkBg4cSIMGDWjSpAnLly8HYOrUqTz5\\n5JMZfXfv3p0VK1YwfPhwkpKSaNy4Mffffz8gwumOO+7IdOxKKX7//Xf69OkDQP/+/Zk7d27GsatX\\nr3L58mWCg4P57rvvuO222yhTpoxdGz179mTatGm5/Rg1Go3G5xRY4TZypOdZbAYPdj5/8GD7OiNH\\nZt7fxo0bmTFjBnFxcSxatIi///4749iVK1fYsGEDzz33HE899RT9+/fn33//5f7778/IrA0SOHn9\\n+vUsXLiQxx57jOTkZMaPH49hGGzZsoXp06fTv39/kpOT3Y5jzJgxGZrZtGnTuHLlCvv37yc2Njaj\\nTnJyMs2bN6dly5YZAuz06dNEREQQFCSui1WrVuXo0aMAPPnkk7Rs2ZJDhw7Rpk0bpkyZwhNPPOHU\\nd/PmzVm9enXmH5RGo9H4ATorgIXVq1fTq1cvSpYsCWC3lmVmAwBYu3ZtRsqZfv368cILL2Qcu/vu\\nuwkICOC6666jRo0a7Ny5kzVr1vDUU08BULt2bWJiYti9e7fH4zp16hQRERF2ZfHx8VSpUoX9+/fT\\nuXNnGjRoQHh4uNs2+vXrR79+/QDJ8P3000+zePFivvnmG6Kjo3n//fcJCAigQoUKJCQkeDw2jUaj\\nKagUWM2tIOFpGhvDIRmq474tQUFBpKenZ+y70+ZKlCjhdKxKlSoA1KhRg44dO7J582bKli3LuXPn\\nMtYFjxw5klHPJCEhgfXr19OzZ0/ef/99fvjhByIiIvjtt98yxlCiRAmPrlWj0WgKMgVWuI0c6XkW\\nmy+/dD7/yy/t62Q1Ldm+fXvmzp1LUlISFy5c4Oeff3ZZr3Xr1syYMQOQtbB27dplHJs1axbp6ens\\n27eP/fv3U6tWLdq1a5exjrV7924OHTpErVq1iI2NJS4ujvT0dA4fPsz69esz2gkODubq1asAREZG\\nkpaWliHgzp49S0pKCiBa3R9//EHdunUxDINOnToxe/ZsAL7++mundbpXX32V119/HRALTMMwCAgI\\n4PLlyxnjq1+/fuYflEaj0fgBelrSQtOmTenbty+NGjWiQoUKXH/99S7rffLJJwwcOJCxY8dSvnx5\\npkyZknGsWrVqtGjRgvPnzzNhwgRCQkJ4/PHHGTJkCA0aNCAoKIipU6dSvHhx2rRpQ/Xq1albty51\\n6tShadOmGe0MHjyYhg0b0rRpU6ZNm8Ytt9zCmjVruOmmm9ixYwePPvooAQEBpKenM3z4cOrWrQvA\\nO++8wz333MMrr7xCkyZNePjhhzPaNGNQmv3cd999NGjQgOjo6Iyp1eXLl9OtWzfvfrAajUbjA3Kd\\n8iYnFMaUNwMGDKB79+4Z1oreZNOmTXz44Yd8++23Xm/blvbt2zNv3jwiIyNdHvf370ij8Xd0yhvP\\nKbDTkhorTZs2pVOnThlO3HnByZMn+b//+z+3gk2j0Wj8CT0t6SWmTp2ap+0/9NBDedp++fLl6dmz\\nZ572odFoNPmF1tw0Go1GU+jQwk2j0Wg0hQ4t3DQajUZT6NDCTaPRaDSFDi3cLJw7d47PPvss2+d1\\n7dqVc+fO5cGINBqNP5MKHAK2W17zJ6eIxsR/hZuXfznuhFtWaW4WLVrkFPtRo9EUbc4A04EFwGrL\\n63RLuSZ/8E9XgDPAQuACEAikAWFAN6BMJudlwvDhw9m3bx+NGzcmODiYkJAQIiMj2blzJ7t376Zn\\nz54cPnyY5ORknnnmGQZbUhHExsayYcMGLl68yG233Ubbtm35888/qVKlCvPmzdOxGjWaIkYqcnsy\\ngGib8kRL+b34643Xv/A/zc3xlxNleTUs5TnU4MaMGcM111xDXFwcY8eOZdOmTYwbNy4jgv/kyZPZ\\nuHEjGzZs4OOPP+b06dNObezZs4cnnniCbdu2ERERwY8//pizwWg0Gr8lAXnudszTEW4p13k38gf/\\nE2759Mtp0aIF1atXz9j/+OOPadSoES1btuTw4cPs2bPH6Zzq1avTuHFjAJo1a8bBgwe9MxiNRuM3\\nXEQmlFwRCFzKx7EUZfxPO86nX45tmpsVK1awbNky1q5dS8mSJenYsaPLFDXFixe3DiUwkKSkJO8M\\nRqPR+A2hyEqJK9IAzxJoaXKL/2luefTLCQsL48KFCy6PJSYmEhkZScmSJdm5cyfr1q3LWScajabQ\\nE4WYACQ6lCdayqPyfURFE//T3Gx/ObZTk7n85ZQtW5Y2bdpQv359SpQoQcWKFTOOdenShQkTJlCn\\nTh1q1apFy5Ytczx8jUaTf6QiKxUXkefiKPL+pheE2LYtBA7jbPPmfzdd/8Q/U97kgbWkJmt0yhuN\\nP+Hr24QpWC8hE0reEKw65Y3n+OdDRBnEntbbvxyNRlMoKAjm+EFAtTzuQ+Me/xUH+pej0WjcYBpV\\nRzuUhyNThQno20dhx/8MSjQajSYLtDm+xn81N41Go3FDhlF1GrL4lgSUAMpAWqA2xy8KaOGm0WgK\\nHVFA2EVIjIPwRDIsShLDIawxRIX6eICaPEdPS2o0mkJHUCp0WwgKOBwNCZXlVSHlQTpEf6FHCzcL\\nOU15A/DRRx9x+fJlL49Io9HkmAQocxruPQfdT0D7M/J67zkp1wEeCz9+K9y8nStJCzeNpuCR4/+5\\nxaIkSEG1JKhzUV6DFNqipIjgl2tueeGcaZvy5uabb6ZChQrMnDmTlJQUevXqxahRo7h06RJ33303\\nR44cIS0tjVdffZXjx4+TkJBAp06dKFeuHMuXL/fORWo0RZxc/c91gMcij98Jt7xyzhwzZgxbt24l\\nLi6OJUuWMHv2bNavX49Sih49erBq1SpOnjxJVFQUCxculD4TEwkPD+eDDz5g+fLllCtXLpdXp9Fo\\nwAv/8zwK06fxH/xuWjI/Mt4sWbKEJUuW0KRJE5o2bcrOnTvZs2cPDRo0YOnSpbz44ousXr2a8HDH\\nUWg0Gm+Q6/+5GeBRYfXaPmzZ9zDAo7eXPjT5i99pbvnhnKmU4qWXXuLRRx91OrZp0yYWLVrEK6+8\\nwo033shrr73mhR41Go0tXvmf5yJMn6/jUmpyj99pbnk1lW6b8ubWW29l8uTJXLx4EYCjR49y4sQJ\\nEhISKFmyJA888ADDhg1j06ZNTudqNJrc47X/uRmmr47l1UONzXZKNMryaljKtQbnH3hFczMMYzLQ\\nHTihlKrvjTbdkVdT6bYpb2677Tbuu+8+WrVqBUBoaCjfffcde/fuZdiwYQQEBBAcHMznn38OwODB\\ng+nSpQtRUVHaoESj8QK+XDLTcSkLB15JeWMYRntkJuEbT4RbblPe6CkD36BT3mjyE1/9z7cDq3Et\\nQBOA9ogi6At0yhvP8YrmppRaZRhGrDfa8gSd8Uaj8RNykS3UV/9z7UVQOPBbeaAz3mg0BRwvqF65\\n/p8rBVu3QuXK4KGrjvYiKBzkm0GJYRiDDcPYYBjGhpMnT7qs44us4BrP0N+NJlsUFKuMN9+Ehg2h\\nWTM4ccKjU7zgRaApAOSbcFNKfamUaq6Ual6+fHmn4yEhIZw+fVrfRAsgSilOnz5NSEiIr4ei8Rfy\\nwyE1KxIT4e235f2hQ5CN8HrmlGh3ZI2tu2Vfr+n7DwXmIaRq1aocOXIEd1qdxreEhIRQtWpVXw9D\\n4y8UhGyh334LtjFfJ0yAl16C4sU9Oj3XU6K5WG/U5B5vuQJMBzoC5QzDOAKMUEpNyk4bwcHBVK9e\\n3RvD0Wg0vsbXVhlKOWtqx4/DzJnQr18ed4426S4AeGVaUil1r1KqslIqWClVNbuCTVNE0PGMig62\\nVhm25JdVxqpVsGOHc/m4cSL48pKCst5YxPG7CCUaP+UMMB1YgDgRLbDsn/HloDR5hq+tMiwBFgC4\\n6y4w14s3boS1a/O274Kw3qjRM8CafCCvUjloCja+clT77z/48Ufr/quvQunSMMkyoTRuHLRunXf9\\nF4T1Ro3W3DT5gH6SLbrkILZjrpk0CVItc39t20KDBvD009bjP/4Ihw/nXf++Xm/UAFq4afID/SSr\\nyS/S0uCLL6z7Q4bIa8OG0LGjtY7ttKW38fV6owbQwk2TH+gnWU1+sXChVSsrXx7uvNN67JlnrO+/\\n/BKSkrzevVJw/DT8VRF+WAsTvkd7gfsI/TFr8h4dz0iTX9ia/z/8sL1P2+23Q2wsHDwIp0/DtGkw\\naFC2mlcKTp2CoCCIjLQ/1rMnLFliLzODg2HwKxBg/s71HTff0JqbJu/xteVcUaeouGDs2we//irv\\nDQMckw0HBsKTT1r3P/7YyS3AFF4bNsCsWTB2LDzxBHTrBvXqQWgoVKgAEyc6d5+W5qwMXr0KxyLI\\nv/VGTQb649bkDzqVg28oSs7EEyZY33ftKlqaA+qhhzn36gdcToIqW7bAihXQqRMAI0fC+++DJUdx\\nphw86FxmdhceDtWry35sLARoFcIn6FuLJv8IAtIPwtG90Lmz/tfnNUXJBSM5GaZMAeACoRzo8gIH\\n5sGBAyKIDh4030dwPukodzCXufQStwCLcCtWzDPBFhbmuvy112D0aIiIsCk8flzW/vQkWb5TWH7a\\nGn/g6FGoXx8uXZIYf2+95esRFW4KSkrprVvhl1/gnnvAC/FJU1IkwH+07XXNmsX80615iMmcphw8\\nlXkbB7CE+ps/X6Re9eqY0f9CQ+01r9hY+/2ICJn1dMQpHvzly2KhGRMDX38NFStm/2I1OUYLN03+\\nMXeuCDaADz+EoUNlAUOTNxQEF4xJk+Dxx+HKFTH2+Ocf96qPhZQUCeIfH2/dDhywbgkJIkjsMth8\\n9hmlKS6CLQtKloSSwcVEg1UKPv0U3n+fHj1kva1MGdfCK9s8+yzs3Clb69byGhzshYY1nqCFmyb/\\nWLXK+j45WRb033jDd+Mp7FhcMFINSAiBi4EQmgZRyRCU1y4YV6/Kw4ut9eKBAzBsGBffm0B8PNSs\\naX+vP3IEWrSAY8eybv7kSZlCDA0FNm+GdeuoblFDixVTxMQYVK+O3WZqYOXKgbH4gKw7ggjgUaMo\\nFRpKKW99Jj/+KO4GJi+/rAVbPmP4In9a8+bN1YYNG/K9X40PUQqioiQ0kklEhDyWly7tu3EVZlLh\\nzI+wsApcKA2BCtIMCDsP3Y5CmTvx+uOtUnBuz0ni7x3OwU2niSeGeGI4SGzGe1O72r0brrvOem5y\\nMpQokXUfAQEyu7lihQgrBg+GiRNJx+BYz8ep/OOnWS/npqdDnToyCIDx40XD9AaHDkGjRnDunOzf\\nfTfMmOEVddAwjI1Kqea5bqgIoIWbJn/YvRtq1XIuf/ddGDYs/8dTBEgFpl8EIw7CE8mwlkwMB9UY\\n7g3NvmxTSrSm+HhZSnKcVb6magr7j3qWL23pUrjpJvuyypVlurFKFdG0YmKsm6mBRUeL8QcgCUmj\\noqx529asgTZtPLuYTz+FpyyLc7VqwfbtuTdySk0VY6nVq2U/Jgbi4hysTHKOFm6eo6clNfmD7ZRk\\nqVLWtbcPPpAbjM7y7XUSgAuhEN0KcQlIBkIgvAwcDnRtT5KWJmtatutdjpvpyzV1KvTvb3Py998T\\nmVALaJbpuIK5QrWws1y54mxgsWmTTBt6PIP3zTdWwdagQfYCIvfvD//7H5w/D7t2ibS99VbPz3fF\\nm29aBVtAgDiKe0mwabKHFm6a/MFWuA0fLrH9EhJkmvLbb+GRR3w3tkJKhj1JIGCx5LuSDCf3wa54\\nqFQeqjW2P6dPH7H78YT4eMubtDT5Tt97j1hmsYPaxAQcpkqTMqRfX4GwGCgXA/X2/0bfV/pRif8I\\nuKBALcC68CVUrpyNC1TKPkbk449nb+ovLAweegg++kj2x43LnXBbswZef926P3Kk51qkxuto4abJ\\nH1autL6/+WZZXHn+edl/9125yQS6M+3TeMqFC7B/vwiezfGwOh6SD8HJeDgRD+eOW+ueGgy9v7A/\\nP9rRbcAFpUtbTeI5cwbuvVfiTgHf0o+QWrGkzZvL9FoVMLCNuHYjV/5tR8DMmbL7yCPiJlAmh97k\\nK1daE5KGhsL992e/jSeftCYwXbxYNDhX0+dZcfas9J+eLvvt24sRicZnaOGmyXvi42WRHWRKsmlT\\nqFtXLCXPnYO9e+GnnySppMYt6emi6JrTg0FBomnZ8tlnokR5wql457LYWFlHs13rctwyZtm2boUW\\nPSXslYUSt98M335LQni4Sxe7XePHU2HFCkqeOCFmkc88I5p7TrDV2vr1y9LFwCXXXAPdu8PPP8v+\\np5/CJ59krw2lxKjF/I1HRsJ33+mHNV+jlMr3rVmzZkpThPj6a6XkFqDUzTdby195xVrepIlS6em+\\nG2MB4dw5pZYsUeqrr5R69VWl+vdXqlNHpa6JUapYsPXjAqXq13c+f/p0+zqOWyBXVQwHVNuQ9Wr4\\nDb8rtXatUmlpGed7/BX89JNSpUrZN/7KKxltbVNKTVBKzXexLZ471/68OXOy/0ElJCgVFGRt499/\\ns9+GybJl1nZCQ+VLyA4TJ9pfz08/5XwsWQBsUD64Z/vjpjU3Td5ju97Wvr31/dNPSzC/pCTxVVq6\\nFG65Jf/Hlw+kp4uD8KFDVgfl48dhzBj7elu2eP4RmIpCBvv3U+O336gd3ImYq3uJIZ5qHLIY4MsW\\nRQJBpIlxyV9AK8TasGdP6NULo0OHzK050tNh1Cj7taVSpSQCh016mcyyHO2/4w4uPfAApb77Tgoe\\nfVSSipbL2gE7A1cJSXNK584SFXnbNnGemzxZHLA9YccO+0Sojz4KvXrlfCwa7+ELiao1tyLGdddZ\\nn2pXrbI/9tRT1mOdOvlmfF4kKUmpjz9Watgwpe69V6l27ZSqXl2pYsVca1Lnz9ufHx/vup65lQlT\\nqkmMUj2bKfXMU0pdPXVO1Lx27dyfdN11Sr3xhlLTpinVt69oJ+7qRkYq1a+faFOXLtkPLjFRqR49\\n7OvXqOFSa7qqlPpGKfWtstfavrWUXz1zRqmoKGs7d9/t+Yd89apS0dHWc7//3vNz3fHFF/bXlJoq\\nFxGvRA2Nt1yULUlJSjVqZD2vbl3nz8zLoDU3jzft56bJW44dE80AJLfWuXP2Zv/x8bLukWZ5zl+3\\nDm64If/H6YYrV+QSEg7B0a1w9BAcOQOHz8GRo/KQX7Omtf7Vq3J5pl1BVmzdKkqDSWqq2NtER0O1\\ncKh2GqrVhGrlZQstgXxWy5bB4a9hyRzxfnYkIkJiOT74ILRsaW9FmJwMv/0m65zz54tK6YoSJaBL\\nF9FE6tSRtkwDDhAntR9+cGsQkmVCgkWLJJeMyQ8/iMNzVsybJ5omSByuw4ft87blhMuXxTP87FnZ\\n/24epPfIPJvC0KFijALS/99/506D9ADt55YNfCFRteZWhJgxw/pk26GD6zr9+lnr9OyZL8NKT1fq\\n1ClnzUkppR56SJYAK1Rwr+CY2y+/OJ9vq5DYbhGllGoYq1T365V6/Eal3umrVMKhTAbpuHD16Tal\\ner+gVBk3HQQGKtWtm1IzZ4pW4QlXryq1YoVSTz9trw1ltT33nJybVfNKlJ7tyrXyox56yNpm2bJK\\n/fdf1mO+9VbrOS+95Nl1esILL1jbrds5E7VTKbVggf3n8emn3htHJqA1N483rblp8pYnnrDGF3z1\\nVfu1GpNt2yRbgMn27aIp5JBz5yRO4bFjrrejR8XFLjlZwv85uti1bAl//eVZX199AQ8Pti8bM0aU\\nq6pVoWowVN0FVepZtC5bDgPdcR2ZPyUFFm+G79bBiXWwa53Y8ruiUSNxSL73XqhUybOBu0Ip2LgR\\n5syRzVZLMwkJkUydDzyQ835sSUwUbefwYdnv2VM0Snf+anv3WmN2GYb4PbjI25YjDh2CGjWsswif\\nbIGY+vZ1DgPXH4OuDa0ab48e4hzolWjLmaM1N8/RBiWavMXWv83WmMSWevXkBjF/vuy/+25Gbi5b\\nzp+Xe9nRo1ZB1aiRnGrLY4/JDJcnHD3qXFalivW9YUCl0lClPFQpC1FlILocVC0HVdOhQQvn8+1M\\n8bcjU1uuYiaakfmVkunZdeus2+bNMifqjtIVYOD9MLC/fAjewDCgeXPZ3nxTotjPmQM//gQbN0DV\\n6jBhFtyaeQSSbBEeLsYhphXN3Lnw/ffufda+sHHM69bNe4INoFo1mYKdPVv2f/4YnvzSvo6RDs88\\naBVsUVEy/nwQbJrsoTU3Td5x6pQ1yVVQkKhU7sKur12bETppR2B9Vr+xkv3nynDggAi0/fvFX9iR\\nAQOc5eCzz1qDTmRGWJgYujkmJvjnH9HqqlSBSmcgaC2SNdyRBKA9kJmSeQhYgL3DV9JF2LsB1q+D\\npHXwzzoxncyK4BJQvxt07A8v3goV8yHKvLlwduK8JapxcN5k8h4yxJpJOyJCtPkohw89KUnUYfOH\\nsHChZNz2JqtXWx/CipWAyYehdFnr8cnvwtwX5b1hyNqlJdlpfqA1N8/Rmpsm7zBj7IFoA6VKkZIi\\nSokptEJDxY+XVq3kprJqFQvSuvDCS57dOQ+5SI9So4YYeVSubL9FRVlfq1Rx7/Nrpwil496m3ZO0\\nMZUVXNoDmDOLAAAgAElEQVQLC/6EQ5bpxfh/PbM4ufZa+Vyubwk1WkJ0A4gIFkGbH/9cSybv1ABI\\naFTamjLnOAR5O5P32LGS0PTgQXkIGjxYHKttNaJZs6yCrXr13MeBdEXbttC4CcRthitJsPQruNMi\\nzDb/DT//z1p3+PB8FWya7KGFmybvcPBvW7NGDPhspwLr1LEIN5CbxapV1GC/y+aCi0PF6lAuGiIr\\nQ6nKULWh3INtf8hPPWUN9p5rohBNJRHbOFKyH4azRnfpkljN/fmnaKPr1rm3RrQlLEysRFu2lO2G\\nG7Ln95UXJMCZK7CwCVwIskmZUwa6bYYy3szkHRoqKrgpLBYulMjMAwda69jmhnv00byJAGIYMPQZ\\nmRIAmD8eWjwHyUkw/l5Is/jW3XCD+PtpCix6WlKTdzRrBps2oYDPh2zhmYn1M/xuTUJCxArbMJC1\\npyZN2PVPEm/zEtU7xFDj4U6UrA67akC9Ss4ZSTKzyfAa7mzauypIPCBCzBRm//5rNUhwh2FA3XrQ\\nqqVVmNWuXeDCNaVuh+n/gREB4TbfW2IQqHNwb2UIyrndj2ueeUaS2IIEsdy6VfwiNm+WsG0g+W6O\\nHLFOeXub5GRZfzt5UvY/nAWr58NPljBhYWGSxqZGjbzpPxP0tKTnaM1NkzckJkJcHMkU5wk+Y/Ln\\nVquzyEjo2FHuDTVqiG9XcDBy0x8+nFr33stUBsKWMtArnu2hoZwCXGXaMm0yPCIVWSe7iITQ8HR6\\nrwwyBbc/Gf76G3ashW1/wvNrJflYVkRGyvRiq1YiyFq08IsErQnhcOEkRDs8kISnWlLmlM6Dh4q3\\n3xb/t717xYLooYfhq1/hLZs4knfdlXeCDeSJ67HHYPRoy5iesP+ev/jCJ4JNkz20cNPkDX/8wZH0\\nytzJj6zH6pTdtKlYesfEuDmvTx/JsWVakEycSOizz+Zq2QvwwKM4EzZtgq++ktxc589nXtcwxPrT\\nFGatWskCYG6TYPqAixUh8DDy9GD7IV+CwBJwyTkdW+4pWVKmI9u1E01+2VIYOhYWTbPWuX9IHnTs\\nwJAhImhTU+0Fm+lyoSnw+N8/TuMXbJ61l2ZstBNs/fpJyiu3gg3EqtI2M/f77xN15UrGspct7pa9\\nnLAYRmAgVotRllfDUp7q4pyzZ2H8eGjSRKZXP//ctWALDxfDhpEj4ddf5bwtW8SBbuBAmW70Q8EG\\nEBoEaabCfRJ5QLDM1KXVh1J59Wjcpg08+5x1f+6LcMWSkDS6IZxs7fo78yaVKztHS7n22uxnDND4\\nDP/812kKPDFbFxLKRQACA9IZN05i65Zw5e/lyIABUNGiFhw9StC0aXQDFLLGlmB5VYjileU9NgHR\\n2MIdysMt5QmWfaVgxQpxUI6KklxfcXH251SvLrnnJk6U9aAzZ8TKb8QI8dUKd+zEf4kCwkIhsT3Q\\nAqgnr4ntpTzLh4rc8OhoqFjbubz7ELhoWL+zvCTD0gmZN58xI2dpdTQ+QRuUaLzPpUsQEcG/qXW4\\ng3lMmVuGjndk86b/zjtWb+hatWD7dlIDAkjAOkvmsUX8dmA17n3VaifAn1+LM65NbrIMQkJknWfQ\\nIJkuK0IOu7mZzc0V24Fv1sPYVla3iRJhMOUonAvL2r/QW7zxhrggvPqqc/I8H6ANSjzHK8LNMIwu\\nwDjk9/+VUmpMZvW1cCt8XLhg81C7bJlE/wWu1m1E8LY49ye6IzFRLNbMqcCffsp5KhGLI3VqNUgI\\nQfy1rqRSZc0iAud9BdsXubZwbNoUHn4Y7rvPJkNn0cO0w8n2Q0VuMJ3fV/wPZr0lZd2fgsEf55OJ\\nbMFECzfPyfVv1DCMQGA8cDNwBPjbMIz5SqntuW1b4x+sXi2KzaefWh5ubfzbgju0zlmj4eHw+OPW\\nhGdvvw3de8IxI/vWjlFwpiwsjICAs3uoO3cyMfOnEnjqP9f93n+/CDXT9NzH5NTI01sE4QM5YvoX\\ndhsFJUvDhTNw78hsLLRqijre+I+0APYqpfYDGIYxA7gDmVjQFGKUEr/aoUPFqGzAALGfqG/rvN2h\\nQ847eOYZ+PBDCSL899/w2nKI7pzt+bHUlEvsSPyRW8ZMpmLcSteVOnSQacc77/RwYTB/8Nm0oK8J\\nQi5yYRC0eFEu/gTWi9d23pos8MZPpAoyUWByBCg4Cbk0eUJysihWtnEdS5aExBMpEpXDpF27nHdS\\nqZIYb3xu8XFaMAbe6mw9nojc+V2FgVJKQvtPnkzAjBm0uXDB+RoqVWL7gAFUeughosxI8wUIRyNP\\nk8wuu1Bh+hfm+5yopjCQbz8TwzAGA4MBqlUrgpPlhYjDh6F3b7BdNm3WTJbFqsWvF00LxHTaMfht\\ndnn+eXGaTU+HrUth70a41hKVPhyr+aT5kzp+HL79VrKIWlK22JoEq4AAjnftyqFBgzjRtStHg4Np\\nT8Gc5TKNPKMdyl1ddqHFJ3OimsKAN1wBjmL//6tqKbNDKfWlUqq5Uqp5+byMLqDJU5YtkxjItoKt\\nf39Zd6tWDft4krmZkjSpUQO69LXuz3awVQoEElMlXU7PnhI1ftgwp1xkZ2vWZPuYMSw9fJi/f/6Z\\n43fcgQoO9twJ3AdcRC7PFdmKzKLRFEG8obn9DVxnGEZ1RKjdA9znhXY1BYjjx+G55yRIh0lgoCyJ\\nPfmkjXW8J/nbsstTL8Ki6fJ+7Y9wdDdUqQlHdsJPk+HVb+GkC+OQUqWgb19SH3qIBa1bYxiGR7GP\\nCwqh5C4hgUZTlMm1cFNKpRqG8STwK/JAOVkptS3XI9PkCTm1vOvZ034prXx5cf+xU86uXpUAwibe\\n0NwAbmoEjbrCP4tkLW38YEi9AjvXuq7ftq2s1d11F4SGWm0TkOk8R8OMgrqEk92EBBqNxop24i5C\\n5Mby7vff4cYb5f0998AHH0iEIjv++ksCA4PMUcbHe23sLFwN3TPRBCtXlvnRgQMllqMLfOKvlUuK\\nrLWkxiXaz81zCvp/W+MlsmN5d+mSWD7aBuLo3BlefhnadoB6t8BZ4CoOAsIhf5tX6doWWrWGtTaa\\nYVAQ3H67+KTdeqvsZ4I/2iZog0GNJmfo2JJFBE/CKyolFo+1asEPPzi38dybcOoWCRyxGnmdjmgX\\ngPeNSWwxDPhqoqTJbtYM3n9fsp7+9BN065alYPNnTKFcx/JaeK9Uo/Ee+n9SRMjK8m7XAXj8KUmA\\nDPDss3DbbdY4wFlqfmlpBK1ebT3gbc0NoG5d50DGGo1G4wKtuRUR3FneXb0Ci8dAj3pWwQbiVrZz\\np3U/K83v5JYtEg8SJKJ/AXSKzg2pSLjD7ZbXvM64otFocofW3IoIrizvtq2GTx+DozaB0gwDHn0U\\n3npLEkibZKX5GY5TkoUocr426tBo/A8t3IoItubwO07BvBfgzyn2dRo1ggkTrAaPtmTlc1U6L/zb\\nCgBFPgSWRuOn6P9lEaIMUO9feKojJJ61lpcqBaNHw1NPubfLyNTnSilK5KUxiQ/RIbA0Gv9Er7kV\\nMerXhpBi1v3evSVS1bPPZm5waGp+rrJh375jB8apU1KxTBkx/Cgk6BBYGo1/UjQ1N18nyMoHLlyQ\\n+MENGtgH5i9WDAYPljBa48ZB9+6et+nW58pWa2vXDgIKzzOTDoGl0fgnheyW7gH+bh2QhWDetQvG\\nj4epU0XAde3qnHVm+HAYMUJiQ2YXl47QHk5J+uMzhQ6BpdH4JwX93uJd/N06wI1gTusCC9dKJuyl\\nS+1PWbwY9u2Da66xlpUs6cUxKeVRsGR/fabwVlxKfxTsGo0/U7T+X/5sHeBCMJ8+D5N+hs/+D+JP\\nOZ9Sq5ZE7K9QIQ/HtX8/JCTI+7AwMbl0wN+fKXIbAstfBbtG488U5HuK9/Fn6wCLYE6tBkeLwwuf\\nwLxfIeWKfbWAAOjRQ4Ra584u3M28rULYTkm2bevSKsWfnylMchqX0t8Fu0bjrxSt/5U/WwdchDMl\\nYWEUXAiC/VftBVvZCHjkMXjsMYiJcdNGXqgQHkxJ+vMzRW4pDIJdo/FHipZw8zPrgLQ0+O03yRwz\\n8FZYWNWiASRDn16w4ReoXgdu6g4fDIJQ15lehLxSITwwJvHnZ4rcUpQFu0bjS4qWcPOTrJUHD8KU\\nKWLxeOgQhIZC28NwIQyiE4FSUKcRfPgN1KgCR0rBmRoiRNySFyrE4cNw4IC8L1FCovW7wM+eKbxK\\nURbsGo0vKSC383ykgCbISk6GOXNg8mTR1mxzyF68CD/NgXJ3AX8DJ8EIhGvKAAYE1oNLWY0/L1QI\\nW62tVStxonOBnzxT5AlFWbBrNL6kMN9X3FOAslbGxcGkSeJUffas8/GyZaFfP2jdGnaEAu2RtbNk\\nIAQoA2mBHmgAeaFCZCPkVgF9pshzirJg12h8if5v+ZBHH4Uvv3QuNwxJLP3ww2L5WKyYLJkdARID\\nIby8ta7HGkBeqBDZDJZcgJ4p8pWiKtg1Gl+i/195QHo6HDki+dB27ZLXCxfgm2/s67VpYy/cqleH\\nhx6C/v0h2mFtLNcagLdViOPH5eJApO8NN2SzgaJFURXsGo2v0MItF6Smwr//yj3eFGLm+6Qk+7oB\\nATBxIhQvbi3r0wdeeAFuvFG0tI4dMw/LmGsNwJsqhG3W7RYtxKBEo9FoCghauOWCCxfcGgg6kZ4O\\ne/dCvXrWspIlxRrSjR2GS3KtAXhLhSik+ds0Gk3hoOgKtz/+gD//hEGD7FNOuyElBYKD7TWryEgJ\\nbXXihHP9cuUk/FXt2tZXx6lGyJ5gK1AU0vxtGo2mcFA0hVtCglhsXLokC2Hr17udVktOFmvGMWPg\\nww9lKtGWW2+FxER7IVarllg5FkpSge1nYMsW2Q8MFDcAjUajKUAUSeGWtmwZgZcsjl1bt5L+/PME\\njB9vVycpSYw93nkHjh2TstGjJbmnrfbmaCRSqDHDd/2xxuqIF9MUrob5clQajUbjROHJKukhZ4B9\\nf/xhVxbw2WdcmDMHEGXugw/EcnHoUKtgA/jvP2tAjiKHbfiu4zZTkrU7SHmqb4al0Wg0rihSmpt5\\nf77dQbgBXHnoacb83ZEPvork5En7Y5Urw4svwiOPeDkXmjdJS4PTp+HkSet26pT9vm152bKSxbR9\\ne9misnB0sw3ftdXGmKRpeynXEYA1Gk0BokgJtwTgypkzRGzbBkB6YCApUVF8frgPb517mdNv2xuW\\nVKkiWasHDYKQEB8M2B3TpkmsrhMnrALrzBn7mF1ZcewYbN0Kn38u+9dcI0KuQwd5jY21z5djhu+6\\nfAH2b5Iyw4C6beE8OgKwRqMpUBQp4XYRiFq7NmM/sWlTtn/wAXvabeE05TLKq1WDl16CgQPt/dIK\\nBMuXwwMPeL/dfftkmzJF9qtWtWp17dtDqdqQZsDOP8WvASC2IYRGwll0BGCNRlOgKFLCLRSouGZN\\nxv7ZNm0407Ytd/3fRiZ9cIUqHOV/xts8OOkBit1UAH230tPh+efdH4+MhPLl7bdy5ZzLypYVQbZy\\npZj0r1snZqG2HDkC338vG8h5se0h6by1Tr32OgKwRqMpkBQp4RYFFLdZbzvTti0Ax959ktnLh3Lb\\n5gkEq1QYsAj++afg2fNPnw6bLFOCISHw44/iPGcKrOBgz9uKiZFU3SBOfBs2iKBbtQrWrJFUBLac\\nPAknf7Qvq9QBFDoCsEajKXAYKjvrNF6iefPmasOGDfneLykprCrdjcNXKnIf3/PtsWNcrFSJMKD7\\n0aNENmokRhkgEYvnzrVfdzJJRRbwLiLqYH5EwU1OFie6+HjZf/llePPNvOkrNVWEu6nZrV4ta3q2\\nBAXBX0ehYQUt2DSafMIwjI1Kqea+Hoc/UKSEW9KKv2jUKZI91OSWEqsYvrs911S1kU0//yxCzeST\\nT+DJJ+0bOQOpiyAhHS4Wh9AUiAqAoK5I7Ma84r33YNgweV+unMTyCg/P/BxvkZ4O27dbNbv4eFmQ\\nHDw4f/rXaDSAFm7ZoUj5uY0erdhDTQD+Sm1GrQCxXs9QPG6/HZ56ynrC88+LBmOSCmeWwvRYWNAY\\nVteW1+mxUp5nvl5nzthraSNG5J9gA/Far18fHn8cZsyAtWu1YCuIXL0qv43rrhNLKMOQ2YeDB+X9\\ngAHe7S82VraCxMiRcq0rVvh6JBofU2SE27//wtjl1geed+/e6Nq16913oVEjeZ+SAvfcI57dQGoC\\nLCwLRkmIToaoFHk1Skp5akIeDf6NN+DcOXl/3XWSCE6jceT99+H118Vn8fnnRdDVru2+/oABIggO\\nHnR9vGNH19PyGt9iGHUxjJkYxgkMIxnD2IVhjMIwspeawzDewTB+wzAOYxhJGMYZDGMzhjECw/DM\\n4MAwvsIwlGW71sXxnhjGDxjGTgzjrKWfPRjGdAzDvQZqGG0xjHkYxkHLNR7CMBZhGF08vbxcrZYY\\nhnEXMBKoA7RQSvlgIS1r0tJg0CBFqpLLbccqBg0v57pySAj88AM0bQqXL0sem2eega++IiEZLhSD\\naAcNLTwVDheDhBTP/JiztWR34AB8+ql1f8yY7BmOaIoOCxZAaCgsXWofkfvqVdixw/va/m+/ebc9\\nTZZ0Eqebv4FgYDaSnbEz8BpwI4ZxI0qleNjcs8AmYClwAmm7JXJPH4xhtESpw27PNozbgYex3spc\\ncQdwvWXMCcAV4FqgF9AXwxiMUl85tDsE+Azxnp2D5GmuCvQGbsMwXkGpLA0OcmsKsNXS4Re5bCdP\\n+fRT+PtveQItRgpflh5GQN217k+oVUtOeugh2Z80CW6+mYvt+hKY5vqUwDS45IGvlxme8QLO+UJd\\nLtm9/LLcnABat4ZevbLuRFM0SUgQq1nHVBPBwZlrcDnlmmu836bGPWlpTIRYIAS4A6XmA2AYAcBM\\n4E5EYI3xsMXSKJXsVGoYbwIvAy8Bj7s80zDKAxOBH4BKgLvUIEPc9NEAEXjvYRjfoNQVS3kw8DaQ\\nDDRDqV0257wFbAb+h2G8l5UQz9W0pFJqh7LtvAASHw//+591/xXeoHa78plnBQWZsrn3Xuv+4MGE\\nXzpAWkmco3FcgrSSUKpC5k3ahmeMRjS2aMu+y/CMf/8ta1wmY8fqaaL8ZOZMcWAPD5esEQ0awNtv\\ny3S1SXIyRERI7qNUN4uuQ4bI97ZggX35zp3yO4uOFoFUsSLcd581w7kt5hTi/v1i6NSwoYypY0fr\\nsQMH5AdvGLKZ62Gu1twMA77+Wt5Xr25/jlnfzNlnHjMM6c/E1Zrb1KlSb+pUCTjQsSOEhUHp0tCt\\nm2iQrti9G+68U3w1S5WSB7mFC+3byy2//QZdukCZMrImWbOmhCBKTHSuu3+/rCtfe618zmXKyPf/\\n2GNWi2qAK1fg449lpicyUuLzxcbCHXfAsmW5H7MtK1dyjQi2VRmCDUCpdOAFy95jGB7eJFwJHWGm\\n5fW6TM7+0vL6RI76UGoLsAMIB8rbHCljKduNo2xRagewGyiBe00xg0JtxK2U3FfMBAD12MqLvANt\\nX8/6ZMOQ0FTr1slN4/x5ovrdS+nFq0ncFkz4STJUr8RwCKsHUVl8mrbhGW0JR+YW7MIzKmXvsH3n\\nnfKH1+QPL78sgqxcORE4oaGweLGU//orLFkiAikkBPr2lRQSixeLUZItKSkyzV2xotxYTX75RVJM\\nXL0q51x7rTjO//ST3NSXL5cbpiPPPCOuGd26QdeuknLo+uvlhvrRR1Jn6FB5jYhwf30jRoixyT//\\nSJtm3YgI2UaMEIESHy/vTTw1IFmwAObNg9tuE4GwfTssWiQPbNu3y+dqsnOn/LbPnpXrathQhEuv\\nXnKN3uCLL+RmUKoU3HWXPIysWCFpP37+WfI7mp/BsWPymZ4/L/3feac8xBw4AN9+KxbUpg/sgAHi\\nf1q/Pjz4oAjChATxFf3lF7jpJu+MH+D33813vzgdU2o/hrEbqAnUAPbloifzR/yvy6OGMQDoCfRE\\nqdM5euA2jJpALeAUYBOenhPASaAmhnEdSu1xOOc6IA6lbJ4w3KCUynQDliHTj47bHTZ1VgDNs2hn\\nMLAB2FCtWjWVH3z/vVIiJZQySFN/0lJ2Vq3yvJG//lIqKCijocvDh6tvUpUaf0qpCQny+k2qUqc9\\naGqbUmqCUmq+i22CUmq7beX5862DDwpSavduz8esyR1//imfe3S0UseOWcuvXlWqe3c59uabzvXv\\nvNO5rZkz5dj//Z+17MwZpSIilCpbVqlt2+zrb9miVKlSSjVpYl/ev7+0ExWl1P79rscdEyObIwcO\\nyLn9+7tu88AB1+116CDH3eGqvylT5JzAQKWWLbM/Nny4HHvnHfvyzp2l/LPP7MsXLbL+B6ZMcT8O\\nW0aMkPrLl1vLDh5UqlgxpcLClNqxw77+kCFS/5FHrGUffyxlH33k3P7Fi0pdvizvz51TyjCUatZM\\nqdRU57qnTtnvT5ki4/N0c7zmPn3Mz+NO5eo+Cwssx29zedzdBs8rGKngQwWrLW38o6C8i7oxChIV\\nfGtTtsJyzrWZ9HGTpY+3FExXcFHBZWUjR2zq3qUgRcF5BV8reFvBNwouKNiQaT+2MidbH4J7wZWl\\ncLPdmjVr5vxD8DJpaUrVr2/9bzzBJ/KmWDGlkpKy19g779hISUOlLl2q4pUIo3il1FUPm4lXSo1X\\nroXbeMtxpZTcRGvXtvb55JPZG68mdwwaJJ/7F184H9u1S6mAAKWqV7cvr1lTflunHR5zunWTtv75\\nx1r20UdS9umnrvsfOlSO2wo+UxC5uuGaFCThdv/9zvX373d+CDh0SMquvVb+tI7cdFPuhdsbb0jZ\\nSy851z9zRoReSIhSyclSZgo3V9+/LYmJUq91a6XS07Mem/l5erp16GB//s03m8duUq4FyDTL8Xtd\\nHncveP5z6Huxgoou6gVYBNlRBZE25Z4ItzEOfRxTcGsm9dsoOORwzn8KnlAQ4Ml1FVpXgIAAmXUY\\nMACiy13mLV6WA82aZT/E//PPwy23yHulCOzXj2onTlAHBz+5LIhCjEccZ/idwjNOmiRTNSBrFa+9\\nlr3xanKHGeLMDE9mS82aElT6wAH7tZr+/WX9xXaN9PhxmcJs0kSm2kzM4N3//CN+WY7b7t1y3NX6\\nVIsWOb6sfKW5CyvvaMuE/Nmz1rK4OHlt1cr1OrglRF6uyOz7jIyU7yc52fqf69FDpqGfeEKmJL/8\\nErZtc866Ubq0TCn/+Sc0bixuGMuXi5W1K1asyI5oyz9fPaUqoZSBGIb0RqY1N2MYjvPizyKGI4+g\\n1Fmyg1LDLX2EAk2B34HFGMb/nOoaxgPIjOFqxBK/pOX1N+BTYIbTOS7IlXAzDKOXYRhHgFbAQsMw\\nfs1Ne96mbFkJcv/vPW9TmgtSmJM/S0CALL5XsFiM/Pef3MzM6PgeEoRYRSqsa2yHcQjPeOGC/RrH\\n8OESO1KTf5hCq3Jl18fNctP3EGS9xfydmEybJkYm/fvbn28aJEycCKNGOW+LFslxx/ieAJUqZf96\\nfIGr9b4gy2Ngmo3JsflZV6zouh135dkhu99nTAysXy9rosuWiV9p/fpS/vHH9uf+8IP8X5OS5LVz\\nZ7nx9OsnDzfexOrK4c6nwyw/5+Z45ih1HKXmALcAZYFvMo7JetebwBSUWpSj9qWPSyi1GaXuB34F\\nRmMY1zv0MxnYBvRDqZ0olYRSO4F+wEbgLgyjY1Zd5cqgRMkHMSc3beQHERtt/HHatMlZI5UqwTff\\nWI0CfvkFPvwQnnsuW82UAe5FBNslxLHEzs/t/fetf4qqVa3GAZr8w7yJ/Pefa3N3Mz27rd9Y1apy\\nY1u2TDSA2rVF0AUHi0GKq/b/+cdeo/OEwmYtW7q0vLoTBN4QELbfZ716zsddfZ916ojgMuOsLlsm\\nVqrPPCNGKQ8/LPVKlLBq3IcPS3i6qVPhu+/E6nT1amubU6e6d5h3RWysvYVrrVrmu5puzjCtG3d7\\n3okLlIrHMLYDjTGMcih1CqgLFAcGYhgD3Zy5x/L77IVScz3o6RegC6IN/m0puwXx4VuJWIHajisd\\nw1gFNLNsKzJr3G+tJd05Qh86JPnYMkhKkoj3JrmxOLz1VpmifO892X/pJbmp9e2brWaCcOPsfeyY\\nmPubjB4tfx5N/tKkiUxlrVjhLNz27hWrxurVnbWTAQPkJvj11/Kb+PdfmeJy1LxbtpSMDqtXZ1+4\\neZPAQHlNc+e8aXPcfO9tGjeW17VrZSbEcWrSJkVVjmnSRKxQV6yAG2+0P3bunEyNhoSIQHMkKEiW\\nMpo1k3tH+/ZiZWoKN1uio+H++8WFqFYtGfvp01bLyqlTre4VntChg71w69zZDMPXBfEFs2IYNRCh\\nFw/s97wTt5irJOaP4yAwyU3dbsiU5iwkdfFBD/uoYnm19aExM2i6m64yy69k1bhfrrmdAaYDC5BJ\\n2QWW/ZVxci8aPNhmxmjDBqsTdK1auZ/ie/NNMRMGafeeeyQeZYqnQQEyYcQI63x9w4YytaHJf0zn\\n/TfekFQ/Jmlp8nCTnu765ta7t2gi331n9ctyFc9x4EARjKNGyfSXI+np+bPeYt50Dx3K2XFvUK2a\\n+MLt3Svm+rb88ot3fMUeeEA06E8+kX5sefVVMfl/4AFrZuKNG137vplaZMmS8nryJGzZ4lzv0iWZ\\nUg4Ksneoz+2aW4cO7BPn5vYYhjXCuzhxv2PZm4CyWRw0jGAMozaGYf+UZhg1MQzn6U3DCLA4cVcA\\n/sxYW1MqDqUGudzA9Ed72VIWZ2mrOIbRyPkDAstU5GOI8LR1bTBV3T4YRkOHcxoDfZCVnN/JAr/T\\n3BwdoU3OpMKAQTKLMHGiCLeZM7F/8svplKQtxYpJwzfdJAk/QaKZrF8v5TExOWt32zYxJDEZOzbv\\nnpY1mdO6NbzwgsQZrV8f+vSRqajFi2HrVmKLJ8DEShx0XAovUUJ8qCZNgs8+E+HQrZtz+2XLwuzZ\\n4sfVsqVoE/XqyZTj4cOixZw+7ZxA1tvceKP8zh55RAwnwsJE6JqZMG68EWbNEqHdtatcX0xMjh+6\\nOqC+IUUAABV/SURBVHaElSgUHe0PjB8v/83HH5f1RtPP7ccfxRl63rysgy5khukD+MQT4jt4993y\\nkLtypXzWtWuLv5vJt9+KoG3bVp6WIyPlv/7zzyIAzaWCo0dFK2zQQMYcHS2CcsECmQJ9+mn5TL1F\\nYCCPwMHfZeJnNoYxGzgE3Ag0B/4APnQ4qwriLB2PRDcx6Qq8jWGsAQ4Ap4GKyBRhDeA/4JFcjrgE\\nEIdh/Iu4jx3BahxiWvcMs6ynCUqtxzCmAAOBvzGMOTZj7wkUAz5CqW1Z9p4tk1EvbblxBXBnTv/Q\\n+9ZHnuLFxWJbKWU1xQalJk/Ocb9OnDunVK9e9s9akZFKLViQs/ZM/ylQ6pZbvDdOTc6ZPl2pNm2U\\nCg2VH1Xdukq98YaKqZbu0uJeKaXU6tXW7zErF44DB5R64gkxgy9eXEzSa9VS6oEHlJozx75uVmb7\\nSmXfFUAppd5/X9xOihWTOrbnp6aK+Xz16lZfzw4drFbqmbkCuDDdz/AscDRxV0r8z3r1Uio8XKmS\\nJZVq2VL+S2PHykmOn4c7XLkCmPz6q5jTR0TI9V5zjVLDhil19qx9vXXrlHrsMaUaNpT/dEiI1B0w\\nQPwQTc6eVWrUKKU6dRL/w2LF1M8RD6gO4ZtV6RIpqlSpdNWihVJTp3o2dJMrV8TjY8AApRo1Uio4\\nWC5p4kSlED+vugpmKTilxB9st4JRN/NrNVDvgtoK6gKo08VJ2vIuz6tzlI5X9qb29f/H6N9uYkli\\ndfalhZGoSnJRVWdfUntWbLyVxS1UJvdwUDeCmgPqv2BS0itzVIWRuApUV5s+ghW8omCpgiMKkhUk\\nKdirxG/tBpftg6FggMXF4KyCVAVnFPym4J7MxmU3Rk8renPLjXBz5Qj95X6lipe03lPeeMNSOS1N\\nfpzmgQyJ5yXS05X64AM7J29l+tNc9dT7Tckf0caPTsXFeXecGq/iToYUFVy5YHlCVm5zLrnvPjlp\\n587sd5jPfGJxpS1bVqnHHxd3xapVpey55zxv5+xZ6+2gYkWJJWAn3FwLm1hQxy3nLQc1FtQnoHZZ\\nyv4BVcLhnN9B7QA1DdT7lnMWgUoFlQLKpTO4RYAqUIdBfQnqLVATQW0C9a6rc3yx+Z1wc9Tc5qUr\\n1eRW64+hVn2lUlIslbdutR4oX94zR8uc8Mcf1l+xuXXsaB/dwh1paRLhwDzP1dO1pkChhZuXhVta\\nmuv/yrJlEumkbt3sd5bPHDggyneZMvbK9ZkzovSBBLLxhJQUCc6SkCD7pjKahXAbb7mFjHAoDwT1\\nm+XYgw7HQty0dbOl/nYXxx6xHJsKqpiL48Gu2vTF5ncGJY6O0Cu/h80W7zrDgElf2azh/vGH9cQ2\\nbfLOjLp1a7GuMx29QRaDmzTJ2jpqxgxZwAax2HrjjbwZoyZbKCVLqfXqyddSpYosRbmyMwD7+L6/\\n/CLrS+Hhzj+57MTuNdOppaTAK6+IgWbx4rIMNGqU+Iy7Ijt9ZJZv1DHvp3mNID9r23jKI0e6bsMT\\n0pOvMKHKaK4P30VosRRKBadwffguPr/pR9IDg2VNzobVq8V3umpVub5KlWTpctQo+3aPHxf7n1q1\\nZMk0IkLeDxggS3reZPJk+Z6efNL+84yMlHCkABMmeNZWsWISktOdW54balhe59sWKkUaYqYADhaI\\nSuFyUVcpliK+cnb52QyD4oiv2yFgsFLOFotKcTVbo85D/M6gxHSEXgjsOAVf2riBDXoS2txgU9nb\\nxiSZUb68LIa/+ab805WSReXOnUVgvfiidVHc9GM4kwIvvmxt49ln5R+r8TlDh4q/buXKYn0bHCx2\\nDX/9JULFMauMyezZItzMeMHx8dZj2Ynda8vdd0u84T59rOMYOVIMgefPtxegOe3DExo3FoPeUaPE\\nrsTWENQ2WUB26TeoGN+njyf68jEGBUzCSL3KnIu9eZzPWHPjG0zraE0G9csvYqNTurR4WVSpIonq\\nd+wQGx4z/sHly/KX37cPbr5ZhKFS8n3MmyefZY0abgaUA8yYxraxsU1uu82+Th6xDXER6IakhQHA\\nMAgAbgPS8cDC0HJOWyACyfVmy82IgPwISDcMugH1EQvO9UqRSR4xH+ALddEbsSWvKqXufNA6mxcd\\nrdT58w6VatSwVli7Ntd9esySJTINajtN2a2bxB08rZT6Rsnc6p3vWY+XLSdGKhqf88cf8pVcc419\\nqMikJLFzcLS7UMpqR2EYSi1e7NxmdmP3KmWdxrvuOpnecjWOb77JXR+ZTbG6s83w5rSkGdy8SROl\\nLlywll+8aJ2tnzbNWt67t5S5WpY+edL63ow7PnSoc72UFOd7RXbiGY8Y4fyZlCsn/TnGSjYpVUqO\\nX7rk+nhmeDgtWQHUTsvt5DfL+tk4y5raWVADXZ1nObcPqJGg3rEYiaSAOg2qlUO9UZb23wa1xfb2\\nZtlWgnIOtuyjzW+F26+/2n+wTkaKCQnWgyEhNgtx+cSRI2JpZzvImBilRq5X6lul1LTTSpWKsB57\\n4GPPIzBr8hQzbrIr41rT9sedcOvZ03Wb2Y3dq5RVGNgKMMdxdOyYuz58LdzMuMi//upcf9kyOdap\\nk7XMFG5Z2YaZws3VZ+EKFzfqTLcRI+zPNy0a3dmRRUXJcXMdLTt4ItyUCJ8IUD85jDUd1BegojM5\\nb4bDObtBOQXCB/W55XgqqH9BtQUVCqoBqF8tx1a46ye/N79bczOpUcMaB7VvXxfuRLbrbS1auJ9H\\nyiuqVJEgqrY52eLjYXQbWP0pzHwTLlk8zStfC9c/KlOVGp9jxtnt0MH5WNu2mbsfuotrnN3YvbZk\\nNo7Nm61luenDV2zaJLP1rqY1O3Rwvsb775fXG26Qad8ffpCAMa7OrVIFxoyRqcKPP5albXfBWLIr\\n3nKzxpgXGAaxwCqgAeLDFg5UBoYA9wN/GwbVXZ2rFPcohWE5pw3i9/aHYTDAoaopL1KBHkqxRiku\\nKsUWoBfix9bBMGjlzWvLKX4r3K69VoIXTJ0K48a5qOBoTOILgoPFSXbOHGvcurSr8OVTMO8Da73+\\nY6B4MecM3xqfkFks36Ag+zybjriLa5yTWMwmmY3j/Hnv9OErEhPF8MXVs6d5jbaGML17i490kyZi\\nxHHPPeI73bw5LF1qrVe6tOQZHjhQhNozz0idSpVkXe6ql80ezL+3O4MjszzcXcjj3DMVEWx3KsVi\\npTivFP8pxRfA/xAH7RGZNWA5508kWeku4HPDwNYIwPzlbFbKPsSWUlxGAiEDFIjUFX5nUGKLYTgH\\nXM8gP41JsqJnT3lE7dEHtm22P1a7FbTqLc88pXwyOo0D5g3o+HFno4PUVDh1yr3djzuD3JzE7jU5\\nftwhXqrNOMy4wzntIyDAvdVlfgjB8HAxCLl6VZ4FbXF1jSCzNN26SZSrv/4SYff559C9u2h5detK\\nvapVJViMUpL8+/ffxfDy9dclwtno0dY2s6uJdexor23WqiVj3b1bsvfYcuyYjLVqVWvkLm9iGIQh\\nkUXOKOUye/Zyy2szT9pTiiuGwW+IsGwJzLYcMsNsuftlmGlwCkRAXL8Wbm65dMl+LiM3wZK9RY0a\\nsO5P6DEUltvE0Bv4Hpw3HBK6aXxJ06byLLJypbNwW7PG/dRWZuQmdu/Klc4Rr8xxNGmSuz4iIyW+\\nsyvhYhtv3JaAgJx9Bq5o0kRcF1atch7zqlXST1PHrGIWSpWSKdjOneU6XntNIqSZws3EMETY16sn\\nz5nVqknsY1vh5uhG4Am2wq1zZ5ks+uUXZ+G2eLG1Th5h6r2lDYNiytlE3+Ngwza4Cmr8G6CAuoZB\\ngFI45vyqb3k9kI1+8gy/nZbMlPXrrf++evXkl18QCA2B2RPg8elQszP0/RhCW2Of0E3ja0wT9zff\\nFK3CJDlZEkHkhOzG7rVl9Gj7/J624xhok3wkJ320aCEa0pQp9vWnTrWf2belbFkJgekNzBjVL71k\\nn+Pz8mXxzQP7GNWrVsl4HXGMabxtm+tsOY71THK75jZwoHyun35qn9Xm7Fn+v717j5GqPOM4/v0J\\nRUFQwetaAWswEW1BZNOAFWvBeKEE2tIi/GGxxYh/1Br9o0FprdFGbZvUxGrUxhpvjaWSNGJs03pJ\\n1aSgRaOWS61oVFQKKoqKIi48/eM9y55ddnYHdpkzc+b3SSZ7bnPm2XfPzLPnnPd9huuuS9MXX9z5\\nOVu2pPuf7WfUeyuC90j1IwcCP8uvkzgA+Gk2+1hu+aES3Q6GkJhBuof2MbBroG4ErwMPkWpbXtrl\\nOWcBZ5PO6vKFkItTRC+W/ugt2aNrruk4Dhcu3LevtTc+j1RqZU32070k684ll6TDp6UlTV9+eRoa\\n0Nqalu1BScVdbrklbTNsWMSCBRGLFkVMnpyWnXBC52EHER29C2fO3D0OSKNLuhbd2dPXWL06VdbY\\nb7+IOXNSmahp01J5x/Zyp117S86dm5bPmBFx1VUR114b8cQTvbdppQolc+ak5ccem7ruX3ZZKmcJ\\nEeed13nb8eNTt/tZsyIuvTSVhpw6NXb1YG0fMnHjjakq3pQpqR2uuCLi/PMjDjoo/a4PPNB7vHvq\\npptSHNWW32o/ZrorSnT99Wn5/Pnpd4aIU0+NgKXvkqqDXBi5z1SIM0ld+ANiBcRvst6Nr2XLXoY4\\nNLf9yRA7IJ6GuIfUvf9WiOXZ9tshzsu/Rva8YyDeyLZ5lDTkYGnWg/JziNldn1PUo5zJ7excPa7u\\n+lGb9WLnzlQrsL2mcEtL+sD64IM9rhfcSbW1eyM6ksG2bRGLF6cP/0GD0gf/1Vd37tK/t68RkWo9\\nT5kSMXhwSorTp0e88ELloQAbN0bMmxdxxBEpUXTXNb47lZLbjh0pKU+cmGIYPDjilFMibr45rctb\\nsiQl1zFj0tixYcMiTjop4sorIzZt6thuzZqUJCdOTMlw0KD0N5s9O41j3FeWLYs4/fRUa3vIkPTP\\nUKXCyT0lt/a26uFxV+yeeMZB3Jsln+0Qn0KsJtV+PKTLtsMhfgHxFMSGbPutpHFxt0GM7br/3HMP\\nJ9WtfD173ruk8XE9Fluu9UMp2NpqbW2NlZUu6PfVjh3pMuRHH6X5V17p31IEZjVyxhnpflsBb1Gr\\nU5KejYjWouNoBOW757ZqVUdia2lJBfnMzKyplC+51apYspmZ1a3yJbf8+LbTTisuDjMzK0z5Op/X\\nQ2USs37Q/lUzZrbnynXmtn49vPFGmh4yBMaPLzYeMzMrRLmSW/6sbdKk3UsumJlZUyhXcqunepJm\\nZlaYciW3/JmbO5OYmTWt8iS3Dz9MFWAhVXadNKnYeMzMrDDlSW4rVqTvsQAYN27378kwM7OmUZ7k\\n5iEAZmaWadxxbm3A26QvZRgKPOXOJGZmljRmctsMPAx8BAwAtrfBP5/uWO/OJGZmTa3xLku2kRKb\\ngJGkb69uewE+25rWjxyZHmZm1rQaL7m9TTpjOzi3bE3ukuQEn7WZmTW7xktuH5MuReatzXUmGef7\\nbWZmza5PyU3SryX9R9KLkv4s6ZD+CqyiocCO3HwErM2duZ3q5GZm1uz6eub2CPDliBgH/Be4ou8h\\n9eJoYBiwJZvf+Bps3pCmDxgGU7+yz0MwM7P61qfkFhF/j4i2bHYFcEzfQ+rFQOCbQADrgeW5S5KT\\nJ8P+Xa9ZmplZs+nPe24/BP7aj/urbAQwD5gBbM1dkvyGO5OYmVkV49wkPQoc1c2qxRHxYLbNYlIn\\n/T/0sJ+LgIsARo0atVfBdjIQGAWscWUSMzPrTBHRtx1IFwALgWkR8Uk1z2ltbY2VK1f26XUBeP99\\nGDEiTQ8YAFu2wIEH9n2/ZmZ1SNKzEdFadByNoE8VSiSdA/wE+Hq1ia1fLV/eMT1hghObmZkBfb/n\\ndjOp7+Ijkp6XdFs/xFQ9F0s2M7Nu9OnMLSLG9FcgeyX/zduuJ2lmZpnGq1DSbvt2eOaZjnmfuZmZ\\nWaZxk9tzz8G2bWn6uOOgpaXYeMzMrG40bnLz/TYzM6vAyc3MzEqnMZNbhDuTmJlZRY2Z3LZuhbPO\\ngtGjYfhwGDu26IjMzKyO9GkoQGGGDoX77kvTmzfDfo2Zo83MbN9o/KzQXn7LzMws0/jJzczMrAsn\\nNzMzKx0nNzMzKx0nNzMzKx0nNzMzKx0nNzMzKx0nNzMzKx0nNzMzKx0nNzMzKx1FRO1fVHoHeL2f\\ndncY8G4/7avM3E7VcTtVz21Vnf5sp9ERcXg/7avUCklu/UnSyohoLTqOeud2qo7bqXpuq+q4nYrh\\ny5JmZlY6Tm5mZlY6ZUhuvys6gAbhdqqO26l6bqvquJ0K0PD33MzMzLoqw5mbmZlZJw2X3CR9T9Jq\\nSTslVeyBJOkcSS9JWidpUS1jrAeSRkh6RNLL2c/hFbbbIen57LGs1nEWpbfjQ9L+kpZk65+WdGzt\\noyxeFe10gaR3csfQhUXEWTRJd0raJGlVhfWSdFPWji9KOqXWMTabhktuwCrgO8CTlTaQNAC4BTgX\\nOBGYJ+nE2oRXNxYBj0XE8cBj2Xx3Po2Ik7PHzNqFV5wqj48FwPsRMQa4EfhlbaMs3h68j5bkjqE7\\nahpk/bgLOKeH9ecCx2ePi4BbaxBTU2u45BYRayPipV42+yqwLiJejYjtwB+BWfs+uroyC7g7m74b\\n+FaBsdSbao6PfPstBaZJUg1jrAd+H1UpIp4ENvewySzgnkhWAIdIaqlNdM2p4ZJblb4IrM/Nv5kt\\nayZHRsSGbPp/wJEVtjtA0kpJKyQ1SwKs5vjYtU1EtAFbgENrEl39qPZ9NDu71LZU0sjahNZw/JlU\\nYwOLDqA7kh4Fjupm1eKIeLDW8dSrntopPxMRIalSt9jREfGWpOOAxyX9OyJe6e9YrbQeAu6PiM8k\\nLSSd7U4tOCaz+kxuEXFmH3fxFpD/D/KYbFmp9NROkjZKaomIDdnlj00V9vFW9vNVSf8AJgBlT27V\\nHB/t27wpaSBwMPBebcKrG722U0Tk2+QO4Fc1iKsRNcVnUj0p62XJfwHHS/qSpEHAXKBpegJmlgHz\\ns+n5wG5nvJKGS9o/mz4M+BqwpmYRFqea4yPfft8FHo/mGxTaazt1uW80E1hbw/gayTLg+1mvyUnA\\nltxtA9sH6vLMrSeSvg38FjgceFjS8xFxtqSjgTsiYnpEtEn6EfA3YABwZ0SsLjDsItwA/EnSAtI3\\nMMwByIZPXBwRFwJjgdsl7ST9o3NDRJQ+uVU6PiRdA6yMiGXA74F7Ja0jdRSYW1zExaiynX4saSbQ\\nRmqnCwoLuECS7gfOAA6T9Cbwc+ALABFxG/AXYDqwDvgE+EExkTYPVygxM7PSKetlSTMza2JObmZm\\nVjpObmZmVjpObmZmVjpObmZmVjpObmZmVjpObmZmVjpObmZmVjr/B9CwJ2pOgtodAAAAAElFTkSu\\nQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f41a025ffd0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbcAAAD8CAYAAAD0f+rwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nrkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f41a7a476d8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbcAAAD8CAYAAAD0f+rwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f41a0259240>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbcAAAD8CAYAAAD0f+rwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnWd4VcXWgN+dQk1IAiR0CEiRppQoSG8qTQVEmg0bggVB\\n5YoFQdF78UMpIsqVKyCKSFNEOihIEURKEOm9BSFAgARISJnvxzq9JCfJSTnJvM+zn7P39NP22jOz\\niqGUQqPRaDSagoRfXg9Ao9FoNBpvo4WbRqPRaAocWrhpNBqNpsChhZtGo9FoChxauGk0Go2mwKGF\\nm0aj0WgKHFq4aTQajabAoYWbRqPRaAocWrhpNBqNpsARkBedli1bVkVGRuZF1xqNRuOz7Nix46JS\\nKjyvx+EL5Ilwi4yMZPv27XnRtUaj0fgshmGczOsx+Ap6WVKj0Wg0BQ4t3DQajUZT4NDCTaPRaDQF\\njjzZc3NFcnIyZ86cITExMa+HonFBsWLFqFy5MoGBgXk9FI1Go8mQfCPczpw5Q3BwMJGRkRiGkdfD\\n0diglOLSpUucOXOG6tWr5/VwNBqNJkPyzbJkYmIiZcqU0YItH2IYBmXKlNGzao1G4zPkG+EGaMGW\\nj9HfjUaj8SXylXDTaDQajcYbaOGWwyxYsIC6devSvn17tm/fztChQwFYv349v//+u6Xc4sWL2bdv\\nn+X63XffZe3atbk+Xo1GoykI5BuFkoKGUgqlFF999RXTp0+nVatWAERFRQEi3IKCgmjRogUgwq17\\n9+7Uq1cPgPfffz9vBq7RaDQFAD1zs2HChAk0aNCABg0aMGnSJEaOHMnUqVMt+WPGjOHjjz8GYPz4\\n8dx1113ccccdjB49GoATJ05Qp04dnnjiCRo0aMDYsWPZtGkTzzzzDCNGjGD9+vV0796dEydOMG3a\\nNCZOnEijRo347bffWLJkCSNGjKBRo0YcPXqUgQMHsnDhQkDclY0ePZomTZrQsGFDDhw4AEBsbCz3\\n3nsv9evX59lnn6VatWpcvHgxlz81jUajyX/kT+FmGDl3uGHHjh3MnDmTP/74g61btzJ9+nT69u3L\\n/PnzLWXmz59P3759Wb16NYcPH2bbtm1ER0ezY8cONmzYAMDhw4d54YUX2Lt3L6NHjyYqKoo5c+Yw\\nfvx4SzuRkZEMHjyY4cOHEx0dTdu2bXnwwQcZP3480dHR3HbbbU7jK1u2LDt37mTIkCEWAfvee+/R\\noUMH9u7dS+/evTl16pS3vgGNRqPxafSypIlNmzbRs2dPSpYsCUCvXr3YuHEjFy5cICYmhtjYWMLC\\nwqhSpQqTJ09m9erVNG7cGICEhAQOHz5M1apVqVatGs2bN/f6+Hr16gVA06ZN+eGHHyxj/vHHHwHo\\n3LkzYWFhXu9Xo9FofBEt3DLgkUceYeHChfzzzz/07dsXkP20N998k+eff96u7IkTJyzC0dsULVoU\\nAH9/f1JSUnKkD41Goyko5M9lSaVy7nBD69atWbx4MTdu3OD69ev8+OOPtG7dmr59+/L999+zcOFC\\nHnnkEQDuv/9+ZsyYQUJCAgBnz57lwoULmXqLwcHBxMfHu732hJYtW1qWTVevXk1cXFym6ms0Gk1B\\nJX8KtzygSZMmDBw4kLvvvptmzZrx7LPP0rhxY+rXr098fDyVKlWiQoUKANx3330MGDCAe+65h4YN\\nG9K7d+9MC6YHHniAH3/8kUaNGrFx40b69evH+PHjady4MUePHvWojdGjR7N69WoaNGjAggULKF++\\nPMHBwZl+7xqNRlPQMFQ6sxmPGjCMKsBsoByggC+VUpPTqxMVFaUcg5Xu37+funXrZmsshY2kpCT8\\n/f0JCAhgy5YtDBkyhOjo6BzrT39HGk3eYhjGDqVUVF6Pwxfwxp5bCvCaUmqnYRjBwA7DMNYopfZl\\nVFGTPU6dOkWfPn1IS0ujSJEiTJ8+Pa+HpNFoNPmCbAs3pdQ54JzpPN4wjP1AJUALtxymVq1a7Nq1\\nK6+HodFoNPkOr+65GYYRCTQG/vBmuxqNRqPRZAavCTfDMIKARcAwpdQ1F/mDDMPYbhjG9tjYWG91\\nq9FoNBqNE14RboZhBCKCbY5S6gdXZZRSXyqlopRSUeHh4d7oVqPRaDQal2RbuBkS6OsrYL9SakL2\\nh6TRaDQaTfbwxsytJfA40MEwjGjT0dUL7eYptk6Sc5t///vfdtc3b96kbdu2pKamAuKlpFGjRjRq\\n1IgHH3zQUu748eM0a9aMmjVr0rdvX27dugXAlClTaNCgAV27drWkbdq0ieHDh1vqxsbG0rlz55x+\\naxqNRpMrZFu4KaU2KaUMpdQdSqlGpmO5NwaX38gtt1eOwm3GjBn06tULf39/AIoXL050dDTR0dEs\\nWbLEUu6NN95g+PDhHDlyhLCwML766isA5syZw19//UWLFi1YtWoVSinGjh3LqFGjLHXDw8OpUKEC\\nmzdvzoV3qNFoNDmL9lBiw4cffkjt2rVp1aoVBw8eBKBdu3YMGzaMqKgoJk+ezIkTJ+jQoQN33HEH\\nHTt2tHjiHzhwIIMHDyYqKoratWuzdOlSABITE3nqqado2LAhjRs3Zt26dQDMmjWLl156ydJ39+7d\\nWb9+PSNHjuTmzZs0atSIRx99FBDh9NBDD6U7dqUUv/76K7179wbgySefZPHixZa85ORkbty4QWBg\\nIN9++y1dunShdOnSdm306NGDOXPmZPdj1Gg0mjwn3wq3MWM8j2IzaJBz/UGD7MuMGZN+fzt27OD7\\n778nOjqa5cuX8+eff1rybt26xfbt23nttdd4+eWXefLJJ/nrr7949NFHLZG1QRwnb9u2jWXLljF4\\n8GASExOZOnUqhmGwZ88e5s6dy5NPPkliYqLbcYwbN84yM5szZw63bt3i2LFjREZGWsokJiYSFRVF\\n8+bNLQLs0qVLhIaGEhAgpouVK1fm7NmzALz00ks0b96cU6dO0bJlS2bOnMmLL77o1HdUVBQbN25M\\n/4PSaDQaH0BHBTCxceNGevbsSYkSJQDs9rLM0QAAtmzZYgk58/jjj/Ovf/3LktenTx/8/PyoVasW\\nNWrU4MCBA2zatImXX34ZgNtvv51q1apx6NAhj8d18eJFQkND7dJOnjxJpUqVOHbsGB06dKBhw4aE\\nhIS4bePxxx/n8ccfByTC99ChQ1mxYgWzZ8+mSpUqfPLJJ/j5+REREUFMTIzHY9NoNJr8Sr6dueUn\\nPA1jYzgEQ3W8tiUgIIC0tDTLtbvZXPHixZ3yKlWqBECNGjVo164du3btokyZMly5csWyL3jmzBlL\\nOTMxMTFs27aNHj168MknnzBv3jxCQ0P55ZdfLGMoXry4R+9Vo9Fo8jP5VriNGeN5FJsvv3Su/+WX\\n9mUyWpZs06YNixcv5ubNm8THx/Pzzz+7LNeiRQu+//57QPbCWrdubclbsGABaWlpHD16lGPHjlGn\\nTh1at25t2cc6dOgQp06dok6dOkRGRhIdHU1aWhqnT59m27ZtlnYCAwNJTk4GICwsjNTUVIuAi4uL\\nIykpCZBZ3ebNm6lXrx6GYdC+fXsWLlwIwNdff+20Tzdq1Cjef/99QDQwDcPAz8+PGzduWMbXoEGD\\n9D8ojUaj8QH0sqSJJk2a0LdvX+68804iIiK46667XJabMmUKTz31FOPHjyc8PJyZM2da8qpWrcrd\\nd9/NtWvXmDZtGsWKFeOFF15gyJAhNGzYkICAAGbNmkXRokVp2bIl1atXp169etStW5cmTZpY2hk0\\naBB33HEHTZo0Yc6cOdx3331s2rSJTp06sX//fp5//nn8/PxIS0tj5MiR1KtXD4CPPvqIfv368c47\\n79C4cWOeeeYZS5tmH5TmfgYMGEDDhg2pUqWKZWl13bp1dOvWzbsfrEaj0eQB2Q55kxUKYsibgQMH\\n0r17d4u2ojfZuXMnEydO5JtvvvF627a0adOGn376ibCwMJf5vv4daTS+jg554zn5dllSY6VJkya0\\nb9/eYsSdE8TGxvLqq6+6FWwajUbjS+hlSS8xa9asHG3/6aefztH2w8PD6dGjR472odFoNLmFnrlp\\nNBqNpsChhZtGo9FoChxauGk0Go2mwKGFm0aj0WgKHFq4mbhy5Qqff/55put17dqVK1eu5MCINBqN\\nL5MCnAL2mV5zJ6aIxozvCjcv/3LcCbeMwtwsX77cyfejRqMp3FwG5gJLgY2m17mmdE3u4JumAJeB\\nZUA84A+kAsFAN6B0OvXSYeTIkRw9epRGjRoRGBhIsWLFCAsL48CBAxw6dIgePXpw+vRpEhMTeeWV\\nVxhkCkUQGRnJ9u3bSUhIoEuXLrRq1Yrff/+dSpUq8dNPP2lfjRpNISMFuT0ZQBWb9Kum9P746o3X\\nt/C9mZvjL6ei6dUwpWdxBjdu3Dhuu+02oqOjGT9+PDt37mTy5MkWD/4zZsxgx44dbN++nU8//ZRL\\nly45tXH48GFefPFF9u7dS2hoKIsWLcraYDQajc8Sgzx3O8bpCDGl67gbuYPvCbdc+uXcfffdVK9e\\n3XL96aefcuedd9K8eXNOnz7N4cOHnepUr16dRo0aAdC0aVNOnDjhncFoNBqfIQFZUHKFP3A9F8dS\\nmPG92XEu/XJsw9ysX7+etWvXsmXLFkqUKEG7du1chqgpWrSodSj+/ty8edM7g9FoND5DELJT4opU\\nwLMAWprs4nsztxz65QQHBxMfH+8y7+rVq4SFhVGiRAkOHDjA1q1bs9aJRqMp8FREVACuOqRfNaVX\\nzPURFU58b+Zm+8uxXZrM5i+nTJkytGzZkgYNGlC8eHHKlStnyevcuTPTpk2jbt261KlTh+bNm2d5\\n+BqNJvdIQXYqEpDn4ork/E0vANFtWwacxlnnzfduur6Jb4a8yQFtSU3G6JA3Gl8ir28TZsF6HVlQ\\n8oZg1SFvPMc3HyJKI/q03v7laDSaAkF+UMcPAKrmcB8a9/iuONC/HI1G4wazUnUVh/QQZKkwBn37\\nKOj4nkKJRqPRZIBWx9f47sxNo9Fo3GBRqk5FNt9uAsWB0pDqr9XxCwNauGk0mgJHRSA4Aa5GQ8hV\\nLBolV0MguBFUDMrjAWpyHL0sqdFoChwBKdBtGSjgdBWIqSCvCkkP0C76CzxauJnIasgbgEmTJnHj\\nxg0vj0ij0WSZGCh9Cfpfge4XoM1lee1/RdK1g8eCj88KN2/HStLCTaPJf2T5f27SKAlQUPUm1E2Q\\n1wCF1igpJPjknltOGGfahry59957iYiIYP78+SQlJdGzZ0/ee+89rl+/Tp8+fThz5gypqamMGjWK\\n8+fPExMTQ/v27Slbtizr1q3zzpvUaAo52fqfawePhR6fE245ZZw5btw4/v77b6Kjo1m9ejULFy5k\\n27ZtKKV48MEH2bBhA7GxsVSsWJFly5ZJn1evEhISwoQJE1i3bh1ly5bN5rvTaDTghf95Drnp0/gO\\nPrcsmRsRb1avXs3q1atp3LgxTZo04cCBAxw+fJiGDRuyZs0a3njjDTZu3EhIiOMoNBqNN8j2/9zs\\n4FFhtdo+bbr20MGjt7c+NLmLz83ccsM4UynFm2++yfPPP++Ut3PnTpYvX84777xDx44deffdd73Q\\no0ajscUr//NsuOnLa7+UmuzjczO3nFpKtw15c//99zNjxgwSEhIAOHv2LBcuXCAmJoYSJUrw2GOP\\nMWLECHbu3OlUV6PRZB+v/c/Nbvrqml49nLHZLolWNL0apnQ9g/MNvDJzMwxjBtAduKCUauCNNt2R\\nU0vptiFvunTpwoABA7jnnnsACAoK4ttvv+XIkSOMGDECPz8/AgMD+eKLLwAYNGgQnTt3pmLFilqh\\nRKPxAl77n1+9CrNnQ5Mm0LKlR1W0X8qCgVdC3hiG0QZZSZjtiXDLbsgbvWSQN+iQN5rcxCv/88cf\\nh2+/hSJF4K+/oE6dDKvsAzbiWoDGAG2QiWBeoEPeeI5XZm5KqQ2GYUR6oy1P0BFvNBofIRvRQrP9\\nP79yBebPl/Nbt2DaNJg4McNq2oqgYOCz8kBHvNFo8jlemHpl63++eLEINTOzZ8N//gPFiqVbTVsR\\nFAxyTaHEMIxBhmFsNwxje2xsrMsyeREVXOMZ+rvRZIr8oJXx/ff215cvww8/ZFjNC1YEmnxArgk3\\npdSXSqkopVRUeHi4U36xYsW4dOmSvonmQ5RSXLp0iWIZPPFqNBZywyA1PWJjYe1a5/Qvv/SounlJ\\ntDuyx9bddK339H2HfPMQUrlyZc6cOYO7WZ0mbylWrBiVK1fO62FofIW8jha6aBGkmnbO6tWDgwfl\\n+rff5NwDxZJsb31kY79Rk328ZQowF2gHlDUM4wwwWin1VWbaCAwMpHr16t4YjkajyWvyWitj7lzr\\n+QsvwJo18NNPcj19Onz8cc72r1W68xyvmAJkFlemAJpCgH6SLTykAHORPTZHrQxF1p3AesKZM1C1\\nKigFfn4QEwM7dkC3bpJftqyUKVo0Z/rPwfeuTQE8x+c8lGh8lMvIH34pYkS01HR9OS8Hpckx8lIr\\nY8ECEWwAHTpAuXJw//1QxWSWffGiaFLmFHm936gBtHDT5Ab5QXNOk/uYtTLCfoVfB0ON3bmjlWGr\\nJdmvn7z6+8Ozz1rTPVQsyRJ5vd+oAbRw0+QG+km28HLsEDzZGeb/F57qDCmJOdzfMdi2Tc4DA6FX\\nL2ve00/LMiXAr7/CkSM5M4a83m/UAFq4aXID/SRbeHntNUhOlvN//rFX9MgJbGdtnTtDWJj1unJl\\n6NrVev2//+XMGGytwG3RVuC5ihZumpxHP8kWTlauhKVL7dMmTbLuh+UErpYkbRk0yHo+c6a9BxNv\\noa3A8wVauGlyHv0kW/hITobhw53T//oLcipyxt69sGePnBcvDg8+6FymSxeoVEnOL1yAJUtyZiza\\nCjzP0cJNk/PoJ9m8JS9CSn/+ORw4IOfBwfDII9a8SZNyps9586zn3btDUJBzmYAA2XszM316zowF\\nshRLTuM9tJ2bJve4mQzde8LeaJjwNfTpqP/wOU1eGBPHxkKtWhJLDeD//k9mUbffLteGIV5CatXy\\nXp9KideRw4fl+ocfoGdP12VPnoTq1a3Lo8eOybUPoO3cPEfP3DS5x6pl8OsyOH8WxgwBv7S8HlHB\\nJq9MMN591yrYataEoUNF8JiNqJWCyZO92+fOnVbBFhwsy4/uqFZNlE3M5JBiSVISnNt8LGf29TQZ\\nooWbJvfYtMl6fvgwLFuWd2MpDOSFCcbu3fY2ZBMmWD2B2O7BzZwJcXHe69dWkaRnzwzD2tgplsyY\\nYdXoTIcUFw8DBw7AW2/Bc8/BQw9BixYiz0NCZAjVW1VE3dMCDh3y8I1ovIVeFNLkHps3219PmAAP\\nPJA3YykM5LYJhlIwbBikmWbk990ne19mOnSAhg1F6ePGDZkxjRiR/X7T0uz321xpSTrSrRuUL0/i\\nP3Hs/acisWP/JLZWC2JjcXsUKSI6KLacPi0h4tyRRDESdh4kuFMnsasrUiRr71GTabRw0+QOiYni\\n38+W9etlOalJkzwZUoHHZIKRYkBMMUjwh6BUqJgIATlhgvHDD/KdgngEmThR9tfMGIYIv2eekesp\\nU2Q2F5DN29CWLXD6NDcpRmxobWJLd+LCChFIFy7Yv86bByVLIgbeTz/N6X/PJ4odMDbjbgxDAgv4\\n2zwwuIjeZcGfFMpykauEEDx5shZsuYxWKNHkDps2QevWzumPPQbffJP74ykMpMDlRbCsEsSXAn8F\\nqQYEX4NuZ6H0w3jv8TYxEerWhRMn5HroUNf7aomJ4tTYHNpq3jzo08dlk7duWQWT+ejc2V6gpKZC\\nndKxnL9WjASCMxzm8eMQGWm9uFKjMWFc8egt+vnB+fPid9lMXBx89pmMKSJCXsPjjxHeuy1hN8/i\\nh4Lnn4dp0zzqIyO0Qonn6JmbJnf4/XfreZMmMmMD2SsZN85qe6TxGikBsKwbGNFQ5TQWbcmrIZLe\\nP8CLN4AJE6yCrUwZGDPGqUhaGqQFFCNgyBB4/31JnDiRiWf7cOiQvRC7cAGuuJA5v/4K7dtbr/1V\\nCpfiAz0SbCDC0iLcqlcn5N5mNF6zkzDiCK8fQUSHhiKgXBylS1u9d5kJC4NRo2wSkpKg+cNw84xc\\n16snn40m19HCTZM72O63vfACzJ4NGzbILv1nn6W/caHJEjFAfBA0SlpP3dEjKXLxIn+PmwKdu3Da\\nX/KzFYzTxD+7znFm7Aou0IULRHC+9fNc+CCMCxfg/AU4ex5iL0BcLHzxJTw7ZIg80Ny6BVu38s3l\\n6+w65NkaqeOeF+vXE6GqcIVQArlFeMVAwsMNyyzKMpsyHbfdZl/dGPQcO9c0lYsrlWDCiewtk44c\\nCdHRcl60qLgbK1Ei6+1psowWbpqcRyn7mVuLFrK2s2GDXE+bBm+/7droVpNlbsbG0vH116kze7Yl\\n7a7HHyQ6dRYxjz7qUp8kLQ0uX3aeRV24IEty7ds762u82OMMPyRutCakE01mzQXoVb48pfv3h6+/\\nBiDi2mGgkVNZf39nAVWhgkOh779nLasIJp6QoQMxJmfSQPzBB6WDCxfg7FlYsSLrSk4rVtgbqI8f\\nD3fckbW2NNlHKZXrR9OmTZWmEHHwoFIi4pQqXVqp1FSlUlKUqlnTmv7ZZ3k9yoJDaqpS06erlLAw\\nlQbqGkHqCDXU7zRXO2isFKgNkyerk6biX3yhVMOGSpUrp5Sfn/UrcXU8/7xDX1u2qMF8nm4d26P7\\nG0rNVkol79plSVzo11tNef+ymjdPqXXrlNq3T6mLF+VtpEtSklKhodbGt2zJ2uf1xhs2A+yetTbO\\nnVMqPNy+nbS0rLWVDsB2lQf3bF889MxNk/PYLknec49142LYMHjpJTmfNAkGD7ZXRdN4xP798PPP\\nJo3Aw3Fc2HCQC1caE0s0F4ggCavN1/2sZCVdaP3KK6TFxsL773PtmmFxyZgRdsuCaWnwyivUoiWN\\n2EVEhEHEfY0oVw6KRMDpCKgRAaHlICQcQiKgSDGTB7ZGjajarh2sX8/DaQshoQb0+Shzb3zVKuvG\\nXGQkNGuWufpmnn0WPjL1vXy5ROmuXNnz+mlp8OSTViWZChXEds5WU1ST62jhpsl5bJckW7a0ng8c\\nKLvxcXFiA7R0qVjCFjKSkkQXIzZWgkQ7vV6Ai+cg9iKEhcKu3dj9c/fsgTfeMF+FAc3d9nW2ZHWL\\nfZvfBx9AbCwRd3+OrT+H0FBZqbM9wsMloHW9ejaNffstbNvGq2zj1aKfw5Z9UEOy9iEB1135xLaY\\n2A0bZjUd+PJL8WxSMhP2CY4RALIqTGrWhI4d4ZdfRFDNmCFj8ZSJE2H1ajk3DNlPTs9GQJMraOGm\\nyXlsZ24tWljPS5YUNelx4+R6wgSfFm6JibJfZXtcuuScNn++/X14zx646y7P+rgaB8zFzjdkxNEt\\nwD1u6xQvDmUiIDQcbq9zG2mXu+C3YoVk/ve/dDuXzM6tXxBRuQjh4R6aY8XHi/KEmddegxo1LJce\\nRTnq3l00PI4elRnY11+LspEn3LgBP/1kvfbEcDs9nntOhBuIcfnbb3u2irBjB7z5pvV6xAjo1Cl7\\nY9F4BW3npslZLl8W1XAQLbSrV+21x86elSUls2+jP/+EqLwz40lLkyHGxclx+bL9a1yc3L9sH8yv\\nXYPy5eHmTc/6iPsLQutiebQ8edJGPd0DkmZCEX+g9Rl47RXO/PAHExlOBBeI4ALh9SKIGPU84c1q\\nEBHhYjKUnAxPPQVz5ljTOnWCH3/0XKnnrbesGq4VKoh7KZu6KYgMNrD3/nUVCQbRH9PbnzJFbOJA\\nHCkfOOCsb++KBQus9nG33w779mVvGTApSZYiL16U62XL4L6uolKagEjrithPBxISxKzF7NMyKkoe\\n5HLQWFvbuXmOnrlpcpYtW6znTZo4q0VXqiRP3d9+K9cTJ9rfdLOAUiJwzMLI8ejZU1aibImKEufw\\nV65kHEuzX1sIvx/LvycoSO6NnhK7CEJvwzL7KltWxlO2rBzh4abXACh7GspGQngpKFsKIkIhsEgK\\nzPkMhoyC6wlUBj7hdXmIGD9elnvTu9EHBsrSWZky8OmnkrZ2rbjHWr7c3krZFceOwSefWK8/+shJ\\nKJqjHC1D9tgcAxJYbjwDB8I778gXdviwaByaHSynh+2SZP/+2d/fKlpUxvLxx3I9dTpc6pp+NIWh\\nQ62CLShI1P61F5J8gxZumpzF0QTAFcOHW4Rb8rwfSBh5hoTQyiQk4PJo1sxZw3rgQHlovnxZBFRa\\nOgEHqld3Fm5mwecJl1choWRMNzo/PzHmvXZNDH3tjlAoEwulgyCsrElA3Y7VY39/mVmZ75F2uNq4\\nOrQNPh8Mx3bZl336aREyGQkmM35+osQTHm61Qv7zT/Eis3o1VKnivu7rr1s93TdrBo8+6rKYOV5n\\nDLLHVhLnyQ/BwbIkaBaWEydmLNyuXrV3ut23b/rlPeW556zCbeXP0DkGIm0+/KtYvjMWzRPnz2am\\nTnX+UWnyFC3cNDmL7X5by5ZMny6rk089ZVOmSROer7CEWefu41ZqUcjANOijj5yF29mzopPiCZcv\\nO6eVLi0TEpD7bViYpIWFQlgChJU0XQdB9QZYw8aY1tdOnZK9LacJxClgKRJqxhFz4FZ3ltTmjavU\\nFIg5DMs+gxVf2E8t69WDL76ANm08e/O2GIbMmsqWlb0upWRZsGVLEXDm+Gu2/PKLLF+amTw53WVE\\nc7zOdHnpJRFqaWnS/p494mDZHT/9ZJ0qN24s4XS8Qe3a0LYt/PYbpKXCtpkQ+bY1PwT5zv44YR9V\\nYMAAePxx74xB4zW0cNPkHMnJsG2b5XLVrfYMGiQPyHbCDTDubMitc0U9atbVDCsszP66ZElJc3U0\\naOBcf/FiWZkKDXVwUOGhcHLrhCIznvnT0kRtcu9e+Ptv+Otv2Pw3nDsAKQ4xwQKLweh3YcRr2V8K\\nGzxYJPdjj8l3dvo0tGolS5R33y2zzBjgSgq8OMxa7/HHs65+b0tkJPTqBQsXyvWkSfDVV+7LO2pJ\\nepNBg0S4Aaz+H/R+00F4p8BLA2SaDrIM8MUXWu0/H6KFmybniI62allERvLBF6JYcuWKOLC1DX4c\\nVK8arAQ7LEPhAAAgAElEQVQ/UgkmnqCQAILKBxEUhOUoGQSUhIimInNsl7g++gjGjhXhFRqa+fu9\\nW9eW2Q0b40ptUCm4HAN/74Uzf8PZv0WY7d0rWoAZcUcXmPkZNKmRcVlP6dNHPryePeH6dVHz7NAB\\nZi+G651k72njl3DwbylfsqR3XaYNG2YVbnPmSNsREc7lLl6ENWus195akjTTqxeEloYrl+HCCdi9\\nFhrfZ81f+j5Em/aR/f3hu++gVCnvjkHjFbRw0+QcNvttG2s+xaa1cr5ggb0+AsD7Yw0+qPYlRV95\\nHgOgTA3Ye8iijn0ZWQU07+8vxX5/31ZQehWPdNqxV7O0Va28GAfrL8P1OLh1CWIOwcm/4bpnnugt\\nVKgMNRvCo8/C0z0hMAdmCvfeK56Ju3YV4Xb9OvTpSuoLczjXriPll4+y3jC6vAXlvOjsukULsYf4\\n809Zcpw2zbWt2aJFVs3aFi0kqrY3KVZMDLInT5TrVV9ahdsfG2Dlh9ay778Pzd3bFGryFm0KoMk5\\n+vQRSQZ0rXeCFfvkRvTMM2JK5MT16xIOxbwp9sMP0LOn52rlOYG589RkiP4ODm6BhDiIMwksP5Mw\\nu3o1YzVLTyhbVvabGjSwHvXqyXQ0t9i/XwKNnhHP9sowuFD/Lsr9LUvMCRUjuTVqP6W7FvOO52Uz\\n331nVU4pV05sJIo6LFW3b281/P70U3j5ZS8OwMT+/VZrdb8AGHcGCIRxd8Jlk7f/du1EwzSXPepo\\nUwDP0cJNkzMoJXZDMTHsohFNEO0+Pz/RWahVy029t9+Gf/9bzlu1go0bM9z26o5377F2pKXBzAXw\\n9jtw3kONFU8oVUoEV/369oLM1VJcXnD6NKrdfRjHDjhl/TZ+Eaea9KJ/BQio68U+k5NlCn72rFzP\\nmiWzKDMxMfKbUkp+SGfPioFhTtC6tcQgBBj+H9j/J6z8Qa5Ll4a//sqTME1auHmOXpbU5AynTsnN\\nCPhPwCiZAQG9e6cj2ABefFFstZKT5eaybRsJd9+drW0vC2bFCHdGubYoJRqDb74Ju3a5KeRAcLBJ\\npTLMRt3SQZulalWZmVWunL+VEKpU4ezijQT26Uq5A39aki82bc+11j2JT4SYUl5+qAgMFM1Js8eP\\niRNhwBNwzpDvbP4C6+y4XbucE2wgiiVm4TbtfXsL/RkzdPxBH0ALN03OYDIBOEhtFqb0sCTbeipy\\nScWKYpRrDtMycSJBc+d6tO2VLo6bdq6Mcs1s3SoDNS9/mQkNhVdeEdVzRwHmpGbp+1yrW5atn/3C\\nA2/0InzHWlKLFufvVyfDDQP/4nC9XA50OmiQ7GXdvAm7d8Po36ByO/nOZueglqQjvXuLkfaVK/aC\\nbcgQn3YRV5jwwM+NRpMFTMokH/EGyvQz69oVGjmH7XJm+HDr+YIFVDx1imBkj82Wq4h8cuWc1w6z\\nwbSBrG1WNL2abdVMs0r27RNtwXvusRdsxYuLsDt2TCJM9+8PnTuLGnytWrJPVsAEG0BQACQ2C+aP\\ncSv4Y/QyNkzZSXyI2J+lNoCSOfGWS5e2X4pcNVG+K7/jcHyrpPkHwEMP50DnNhQv7my7Vr++syaU\\nJt+ihZsmZ9i8mVNU4RusN4i33vKwbqNGooYOkJpKwJQpdEOUR8ymZadN13aunNwRg8zYQhzSQ0zp\\nf54SDx8NG4rBm5mAAHlSP3pU9gEdjekKOBWB4CC40j6ACy92JaHz7XA3XG0j6Rk+VGSVV16xnu/6\\nGWKOwKZ51rS690Oi43Q7B7A11DZH1S5ePOf71XgFLdw03ic+Hv76iym8TAqBgDjQsI12kyGvvmo9\\n//JLSsfH0x9RHmljeu2P84qiS9zZql27CD+8Cm1riSslW59d/fqJ1tznn7sI/1w4MPuHVP5wOhxi\\nqsir8vfwoSKr3H47tO4i50rB0k9ho82S5F39MrHRmg0aNBCD8pYtRXM3Pa8pmvyHNyKeAp2Bg8AR\\nYGRG5XUk7gLOmjVKgUqghJpY4SNVqZJSK1Zkso3UVKXq1LFGNp40KevjOamUmqpU8s9KnVyj1L5l\\n8SruifdUWvFg+zDRoFTnzkrt3Jn1vgogyUo+wn2m1+Tc6PSbVdbvJLCo9bxIMaU+vqosYcQLGehI\\n3B4f2Z65GYbhD0wFugD1gP6GYdRLv5amQGPabyvJDYb1OMHRo3D//Zlsw8/Pfu9t0iRIShXXJPuQ\\n1xQ3dR2pCJfLwLwSSZxcO4XIx2oQOns0xs14a5lmzWDdOvFK37hxJgebs6SQtbftLcz+IeuaXnNl\\nd7HvvVDJdBtJtgm5cGc3iCiVg2uimoKCN36ndwNHlFLHAAzD+B54CPkvagojDsFJHe1wPebxx8Xu\\n7dIl8bk4YjHc/nDG2o4OpJw6RszW//LwzBkUu3rRLu9KnboEj/s3/g89lC9V8zOj5FmgCDTg9WEw\\nfJB9evP+ObwmqikoeGPPrRKyv2/mjClNUxhJTRVVejOZ2mhzoEQJUegws3JC+tqOtqSkiPf4zp0J\\nuO02Gkz6PzvBdrNSFXZ9NZO5e/dwtkePfCnYPFXyLLA8/5h9CJ+SQfBh1wIu1TXeItcUSgzDGGQY\\nxnbDMLbHxsbmVreaXObG9n0MvDaZ7TQVRYzMhJh2xYsvWr0gH/4dDtgITrO2Y4xN+ZgYsZOqXh16\\n9IBVq+yau1mlCnsnTODXI4c48/RA/Pz9c0U3IStkpOQZ41SjgFG8uEQsMNOzBwRrbUWNZ3hDuJ3F\\n3jNSZVOaHUqpL5VSUUqpqPDwcC90q8mPfDXhKl8zkLvYzjNFv8n+jKh8eeg6wHq9ZKJ9vj8QnyZ+\\n/nr3Fg8go0db/CKC+EY82bUr25YsYe3x4xwbPpy0YsWATBiB5wHZDUhQIPjXv+DBB8VJ8tixeT0a\\njQ/hjZXrP4FahmFUR4RaP2BA+lU0BZFbt2D8UmuAyyZNvLTUN3g4LJ4l578vhPMnoFwkxF+G1bPg\\no2lw3EUo6/BweOYZUgcNYkP16i4dL3tkBJ5HeBqQoEATHCzLyxpNJsm2cFNKpRiG8RKwCnmgnKGU\\n2pvtkWlyhMy4V8wsc+bA6RuyRxLBeZ4eGuSdhjveAfU7wd61Yov29RsSrHPTPHtNOjNt28pyVs+e\\nULSoxV5rGbI57KiYkV91EyqCxTOLLwlljSY/oKMCFCJyUvMuNRXq10nh4FERFf8JGMXI66OyHyXa\\nzPwV0Ler+/xSpcRt0+DB1nAlDpgF+3Vk1uNNwZ5TFFptSY1LdFQAz8nv/22Nl3DUvDNz1ZTuaUw0\\ndzO/H3/EIthCuMKQu7Z7T7AB9L4f6tYVryG2NG0qGpX9+kl06HQw22v5EqWR78bXhLJGk9fo/0gh\\nwax55xgTLQSrv8aMbvzuZhFdlTUEG8BLfEZIW088JGcCPz+YPBkeeEDO+/cXoRZV8B9ifVEoazR5\\njRZuhYTsat6lN/Mbt8oa8qw4N3iFydBiRrbG65J774W4OIl+7M1ZoUajKXBo4VZIyK7mXXozvx9t\\nZm3PMZ1wLkrYmJwgj7yy56Qijkaj8T76/1lIyK7mnbuZ375NcHSjnAeQzOt8LME8bT1L+DhaqUOj\\n8T10yJtCgiV8CVmLieZu5lc8GOp1lvMnmE0VzmTP5VY+o9C7wNJofBQ9cytEZEfzzt3Mr/SdMHIF\\n1G0zjLIbTYE+W7Tw4qjzFm8o4mg0mtxHC7dCRlY179I1hFaK0vu+BS5J4QI0c9MusDQa36RwCjet\\nHZAl3M78Dh2SsDQApUtD7dp5NUSvo11gaTS+SeG7pfu6dkAeC2bzzG/9elGIDCiKJTgpIEuSfq63\\ncn3xmUK7wNJofJP8fm/xLt5y05FX5BPBfOqUmJyFh8Nrr8Hwfb9bNZPcLEnmk6FnGm/5pfRFwa7R\\n+DKF6//ly9oB+Ugwf/KJxAI9d04ctr8Wu8ma6UKZJB8NPUtk1wWWrwp2jcaXKVymAL6sHWASzCmh\\ncKo47AuS15RQMhe5MgU4BewzvWZSl/3CBZg+3Xr91kvX4MABuQgIgLvucjd0nw66aV6OrWt6zcyM\\nTZsSaDS5T35+YPY+vqwdkACXS8CyihAfAP4KUg0IToFuF6G0J4I5m1OI69dh5Ei4eVOuGzeG+4tv\\nsBZo0sSlBxFffqbILr68WKDR+DKFa+Zmqx1giw9oB6QEwbLKphlAIlRMklcDSU/JSDBnYwpx5Qp8\\n8AFUqwYzZ1rT33oLjC02yiRu9tt8+ZkiuxRmwa7R5CWFS7hl101HHhJTEeKDIcRBMIdclfSYjARz\\nFtYGL1yAN9+EqlVh1Cirtj9Ahw4SC5TNm62Jboy3ffiZItsUZsGu0eQl+fh2nkP4aICshADwrw/8\\nCcRiXVYsIenXMxp/FqYQv/0G48bZp0VGwhtvwFNPgX9aMmzbZs10I9x8NRK2N9CmBBpN3lCQ7yvu\\n8cEAWUFAahDQBtk7SwSKAaUh1d+DGUAWphC9eokP5IMHJU7om29KTNDAQFOBbbsgMVHOIyOhovtb\\ntY8+U2SbwizYNZq8RP+3fATLDMAfQsKt6R7PANKZQuy5Av95A/oPkFigZvz9YcIEUSDp2dOFbfbv\\nGe+32eKDzxReobAKdo0mL9H/Lx8h2zMAFw1sOwofroUlpkCjR45C9+5gGNZqXbum06YH+20aobAK\\ndo0mr9DCzYcwzwAu/vknxqZN+FWpQtjttxNQsyYUK+ZRA1e6wNbl8Mk0WLvFPvvPP2HHDoiK8mAw\\nSmV65qbRaDS5ReEVblOmwJo1MHo0NG2a16PxmIDDhynfpo11rwtkqlW9umyQ3X67/Wu5cvxnnMHG\\njbBnD5w547rdnj1Ftd8jwQZw8iTEmFQsg4OhQYNsvS+NRqPxJoVTuO3dC0OHyvmWLXLXL18+b8fk\\nKV9+aSfY0jA4rqrz97EG7DnWkHtWbKEjE63lQ0JYq9bw6zVnzyF+ftC/vyiK1K+fyXHYztqaN5cN\\nOo1Go8knFE7htm6d9fziRdFrX77cfrMpH5Jy4xZ7/reDLQxhJ03YE3QPe69Hcl1ZVR2HMpmO/Gqt\\ndPUqDdnMr4hwK0ISddlP6+I7GDYmlNtG9Mrc+zZ7AP5Z77dpNJr8S6EUbmmbN9tbr69cCZ99Bi+/\\nnFdDSpclS2DSJNi2xY/riTaCK8G57J7qD0HL7eLv8eBBiI/ncb6hBb/TkD3U5AiBpMBN4A3g51bw\\n+efQsGHGA7F137XJRrg11PttGo0mf1HohNtloOimTU5mXWrECIwOHbKwPucdUlNltfT0aejWzT4v\\nLs482XT9dYWHi2xq2BCaN4+Eft9IhlJw7hxNDxyg6cGDcKACHKwmWiMXL0qZTZvESeTw4bL/GBTk\\neoC27rvKXIOYPZJu+MGlZpJf6H5NGo0mv1KobkcpwLpTp3jYpFWRUrIk12vWJGT3boykJNSAARjb\\ntkHRojk+lrg42LpVtvy2bIE//oD4eAgNFTdXtjZl99xjPa/EGVrwO83e6sid7cvQsCGUK+emE8MQ\\nw+qKFcVflpnr12HsWGvsmtRU+PhjmDsXJk8W623HpUpbD8C7/oC0NEmPbAgppbQHYI1Gk68oVL4l\\nY4BgG9usuObN2TlnDqkmNXrjr7/g7be93m9KCqxeLa6s+vSBmjWhdGmxIRs7FtauFcEG4qT44EH7\\n+rVqwYI+CzhFFc5QhfmdpvPah2Xo1CkdwZYeJUvKYHbvhrZtrelnz0Lv3jKwI0fs69i67zpgo0xS\\nt6X2AKzRaPIdhUq4JQCVNlkDa15u2ZKE+vXZN368tdAnn4i0yQJKiXwwCyozhgE9eohW4oIFcPSo\\n6/oVKsikKdXBTZaRlkrvra9TBZMe/7PPZml8TtSrJ+ud33wDERHW9JUrRbX/vfesmpm27rv22+y3\\n3d5CewDWaDT5jkIl3IKAcjYzt8utWgFw4sUXOdmli7Xgk0/au8B3gVJw/DgsWiSTvS5dxJqgcmVY\\nscK+rL8/NGpknxYQIDZlL78M330HJ06IYFy0yIXJ2C+/wKlTcl66tEhKb2EY8NhjMl188UXrcmRS\\nEowZI4NZudLqvisuFQ5utdav3FJ7ANZoNPmOQrXnVvHqVfz2iCKE8vMjrnlzAK4aBttmzKBqw4YY\\nFy+KcfLzz8s0y3SzVwoOHxY58+uvsH69VSfDkZ07ZfnRlt694c47JZ5n06ait+Lx1t7//mc9f+KJ\\nnNkTDA0VjdGBA+GFF8RdCcg0s0sXeQPvToQll+CmaWoaUgHKVNMegDUaTb6jUN2SArZutShCXLzz\\nTk4HB1v8M3YsXx5jxgx48EEpvGgRzJolNnDIpOaLLzLuIygIbt1yTn/11SwOOjYWFi+2Xj/zTBYb\\n8pCoKNFwmT5d1lGvXJH0hQtlStrCRu2/ZUsYYBSyX5FGo/EFCtWypK2j32ItW9IG6I74aywNXGj2\\nAPM6fsl0THtaQ4daNshceegqVRJa1YfXu8Hc6bKyd/WqeNL3Gt98A8nJct68ee64ufL3h8GD5Q09\\n+aQ1/fp1WLPaet2phRZsGo0mX1K4hJuNMklwy5ZUvArRS+D1YVhU6vv98hxv+49DASQkwKOPQnIy\\nHTpAqVLQuSk89gq8vRA+2gT9v4M7RsJ9wVC7houwMNlBKfjqK+u1txRJPCUiQmavv/3m2v5PO0vO\\nXyQni61irVqydG0YMus/cULOBw70bn+RkXLkJ8aMkfe6fn1ej0STxxQe4ZacLMZkwBVC6DvnQUqX\\nhoceEtOuv/+2Fo1NLcNe/zvl4o8/4IMPqF4dLkTDgHFw/xPQrApUugVVEsEoAcvKQEqMl8e8dSvs\\n2yfnJUs6b+TlFm3awK5dMH68jAPkpuaoJaPJWz75BN5/X+waX39dBN3tt7svP3CgCIITJ1znt2uX\\n713SFUoMox6GMR/DuIBhJGIYBzGM9zCM4pls5yMM4xcM4zSGcRPDuIxh7MIwRmMYZdKp549hPIth\\nbMAw4kx1j2EY8zCM2m7qPIlhbMMwEjCMqxjGegyjewZ9DMcw/rIZ23IMw2Nff9laVDIM4xFgDFAX\\nuFsptT077eUou3fDjRvspR49ApZyZGkJpyIBAbLy17EjhCQ+Bx+9JBkffAD338/5si2ILwJVUuzr\\nhaTA6SIQk+SZHbPZPWMCosHpNnClrSJJv37ifT+vCAyUG2b//hJNoUMHKFIk78ajcWbpUtn0XbPG\\n/rtJTob9+yEkxH3drPDLL95tT5Mh7cXo5k8gEFiIRGfsALwLdMQwOqJUkofNDQd2AmuAC0jbzZF7\\n+iAMozlKnbarYRhBwE+mPqOBr4FEoBLQGqgNHHKo8zHwGnAGmA4UAfoBP2MYL6PUZw7lDeB7oDdw\\nEPgM2TnqC2zAMB5GqZ8yenPZ3TH5G+gF/Deb7eQ8mzbxIz14gtkkpFiFRNOmIsw6dIBWrawTE1IH\\nw5b5sGGDKKE89hg3VkTjn1rKZfP+qXDdA1svW/eMjgFHS9sWvHYNvv/eep3bS5LuqFTJ+8tbGu8Q\\nEwNlyjg/dAQGpj+Dyyq33eb9NjXuSU1lOkQCxYCHUGoJAIbhB8wHHkYE1jgPWyyFUolOqYbxIfAW\\n8CbwgkPufxHBNhilnO/7hhHocN0CEWxHgbtQKs6UPh7YAXyMYSxFqRM2tfohgu13oKNljIYxDdgE\\nTMcwfkUpB4tie7K1LKmU2q+UOphxyXzA5s1E04gERLCVKAHz58P27fDRR3D//TaCDUSp4ptvrE+7\\nx49T+cOhpJbA2RvHdUgtASUjSBdb94xVkBlbFdP1MlO+hXnz4MYNOa9fH5o1y/x71mSP+fNlSTYk\\nBIoXl43Z//xHbADNJCaKGUVEhLiiccWQIbK8t3SpffqBA/KgUKWKCKRy5WDAAGcXNWBdQjx2TGIR\\n3nGHjKldO2ve8eMSZ88w5DDvh7naczMM+PprOa9e3b6Oufxvv1nLmo927axtuNpzmzVLys2aJQ4C\\n2rWTFYdSpcRp6v79rj+jQ4fg4YchLEz+iC1awLJl9u1ll19+gc6dxVa0aFGoXRtGjhQtMEeOHYNB\\ng8SdUPHiUqdhQ1G0srWBvXULPv1UbHzCwuTGEhkp+x1ZdAbhlt9+4zYRbBssgg1AqTTgX6arwaaZ\\nT8a4EmzCfNNrLbtUw2gCDADmuRRs0mayQ8pg0+uHFsEm5U4AU4GiwFMOdYaYXt+xG6NSfwLzgHBE\\n+KVL4dB1Uwo2b2Y0i9hFY/6udD+LlxfljjsyqFe1quj/DxgAQNA3X1P/3m6crv4IIbFYpl5XQyC4\\nPlTM4NO0dc9oSwiytmDnntF2SfLZZ/XeR27z1lsiyMqWle8/KEhMId56C1atEn9qRYpIBPS+fSXO\\n3ooV8MAD9u0kJcmDSrlycmM1s3KluKNJTpY6NWtKJNkffpCb+rp1csN05JVXYONGERRdu8pD2F13\\nyQ110iQpM2yYvIaGun9/o0eLssnu3dKmuWxoqByjR4tAOXlSzs14qkCydCn89JPYSA4eLHvHy5eL\\n/eS+ffK5mjlwQIRZXJy8rzvuEOHSs6e8R2/w3//KQ0bJkvDII/Iwsn69PNn+/LNoUps/g3Pn5DO9\\ndk36f/hheYg5flweeF96SWbIIA8Mc+eKFvMTT4ggjIkR5bWVK6FTJ++MH8TAVljplKfUMQzjELIs\\nWAOZKWUV84/4L4f0AabXuRhGiKlcFeAS8CtKOfjsA2SW53rMsAIYZSojPzLDKAa0AG4AG93UedxU\\nZ2a670Iple4BrEWWHx2Ph2zKrAeiMmhnELAd2F61alWVqxw9qpSIOHU1uJK6eD4lc/Ufe8xSPzU0\\nVC08fkpNvajUtBilpl5UanaKUpc8aGavUmqaUmqJi2OaUmqfueDu3Zb+VJEiSsXGZm68muzx++/y\\n2VepotS5c9b05GSluneXvA8/dC7/8MPObc2fL3mvvmpNu3xZqdBQpcqUUWrvXvvye/YoVbKkUo0b\\n26c/+aS0U7GiUseOuR53tWpyOHL8uNR98knXbR4/7rq9tm0l3x2u+ps5U+r4+yu1dq193siRkvfR\\nR/bpHTpI+uef26cvX279H8yc6X4ctoweLeXXrbOmnTgh/6PgYKX277cvP2SIlH/uOWvap59K2qRJ\\nzu0nJCh144acX7milGEo1bSpUiku7ikXL9pfz5wp4/P0cHzPvXubP4+Hlav7LCw15Xdxme/ugNcV\\njFEwUcFGUxu7FYQ7lPvNlDdUwUXLdyNHmoKpCvxtypc05cW76besKf+8TVp9U9oeN3WiTPl/ZPS+\\nPP8A0hdcGQo326Np06bOPwQvs3SpUr16yf1IzZ5t/RLuvz/zjV25In9is4Br316dTE1V+5RSJ5VS\\nyR42c1IpNVW5Fm5TTflKKaWGDrWOt2/fzI9Xkz2efVY++//+1znv4EGl/PyUql7dPr12bbmBXnJ4\\nzOnWTdravduaNmmSpH32mev+hw2TfFvBZxZErm64ZvKTcHv0Uefyx445PwScOiVpNWsqlZrqXKdT\\np+wLtw8+kLQ333Quf/myCL1ixZRKTJQ0s3Bz9f3bcvWqlGvRQqm0tIzHZv48PT3atrWvf++95rxO\\nyvWNf44pv7/LfPfC7R+HvlcoKOei3H5TfoqChQpuVxCkoKOCw6a8MTblK5rSzrjpN9CUn2ST1sKU\\ntslNnVqm/IMZva8CZwqgFHz4oaz0/PAD/Otf2Nm3Zck2KyQEvv3WYsTmt24dVT/5hLrIMqKna7tm\\n94yOK/xXsXHPmJgofZnJL4okhYmdO+XVNkyQmdq1xYHo8eP2ezVPPin7L7ZKQOfPyxJm48bYrYFv\\n2SKvu3eLXZbjccikbOZqf+ruu7P8tnKVqCjntCqmBfk469YL0dHyes89ro1ETf5fs0V632dYmHw/\\niYmyPAripSgoSNwSPfywLDnv3Ss3F1tKlZIbze+/i1nM++/LcrJ5r9yR9eszI9pyz1ZPqfIoZQDl\\nEQXBGsAu0x6bLeYv6ADQF6UOoFQCSv2C7IGlAa9iGPlCjTpbws0wjJ6GYZwB7gGWGYaxyjvDyhoJ\\nCbKc/s471t/hokVwZYPN0nFW/yytWok7KjNvvy22X5kgANGKVFj32E6bri3uGRcvhsuXpUJkpOs/\\npCZnMQutChVc55vTza7JQPZb/PysShoAc+aIkomtlxewKiRMny6RFxyP5cslP8FFqPXy5TP/fvIC\\nV/t9AabHQNuwF+bP2l3spizFdHIgs99ntWqwbZvsia5dK35mGzSQ9E8/ta87b57sSd68Ka8dOsh+\\n3OOPy8ONN7Gacriz6TCnX3GTnz5KnUepH4H7gDLAbIcS5nZ/RqlUh7q7gePIc3pdU6r56S8z481K\\nHZdkS6FEyQfxY3ba8BZHj4qzfFtj7HbtYP6XVwitbfJi7++fvSff0aNFkeDPP0UR4NFHRd2yhLPN\\nnDtKI+6+YhCly5I42LnZKpI884yXXZ5oPMJ8E/nnH9fq7ufO2ZcDmc116CA3wwMHRPX+669FDX/A\\nAPv65nq7d5OxVpMDBU2xqJTJtMadIPCGgLD9Pl152nH1fdatK4IrJUW+p7VrRUv1lVdEKcXs47V4\\nceuM+/RpMR2aNUtWX06cEOUfM7NmuTeYd0VkpL2Ga5065jPXhtJW7cZDbvI9Q6mTGMY+oBGGURal\\nzC7iDwJ3416wmKfkxU3tXMcwzgKVMIwKKHXOg/EeRSykamAYASjlqILs+XvM1Nqslw5v7LklK9mj\\n2quU+maVUmFh9nP6oUOVunVLyeabOfGuu7Ldrzp4UKkSJaxtPvigaWPPC9govig/P6VOn/ZOu5rM\\n8cwz8h3873/OeYcPu95zU0qpb7+VeiNHKrVrl/X34cj48SrdPTdXZLQ/plTm99yeflrSjxxx3Z5Z\\n0cOVsoS7/sx7bu72yBz3kk6eVDm+5zZ2rKS9845z+bg4pUqVst9zc8eGDdJO9+7pl0tNlfcD9kol\\n2d1z++UXc95vynkvqoYp74QCwyk/swecN7UXZpP2hCntGxfliyq4Ysovb5M+25T2lIs675vy3nNI\\n34GfDxcAABcnSURBVGBKb++ijvv2HA6fnBZcBuYCPysY9TE80cW6jF+0KMycKS61AgOxc5bsFV+I\\ntWvbL00sWSIqxuZ10OwwY4b1vHNnmQ1ocp+nn5bXDz6QqAxmUlPFS0tamuvoDL16yUzk22+tdlmu\\nDN6fekqW7d57T5a/HElLy539FrM6uzlWYGbzvUHVqrLEcuSIqOvbsnKld2zFHntMbgZTpjhHmB81\\nSlT+H3vMGkpqxw7Xtm/mWaR5pSY2FkwhtOy4fl2WlAMC7A3qs7vn1rYtR8UbSBsM40FLuhhxf2S6\\nmoayuRkZRiCGcTuGYb8EYRi1Ter8OKT7mYy4I4DfsbVNg0XIolNfDMNxCWwUsmS4DqX+sUmfZnp9\\nG8MIs+knEngRSMJZpd8cf+UDk2mAuc5diJeSWNNY0ifbEj4LR3ZmbslKqdlKqf9dV6pNf/tfQ1gl\\npTb/4VChTRtrgQULstyvE//6l33no0Zlr73kZFHzNrf3ww/eGacma5i/34gIpV54QakRI5Rq0EAp\\nUNWKxqhq1dxox5lnfYGBouqflOS63Nq1oqVnGDI7eeUV0ZJ8+GH5HRQtal8+J2ZuK1dK+m23yfsd\\nO1apKVOs+dOmSX6jRkq99Zbkz56dfn/pzNwsypeOM5K9e8U0wjwreustpfr1k8/woYck/euv3b9v\\nW1zN3JRSaupUSQ8Olu9o5Eil7rlH0m6/3V7L9ZVXZCbXqZNSzz8vZR95RNKKFhXTD6Wss/OGDUU7\\ndORI+a1Uraosy0depr1oLF5XcEvBdwrGKfhTmTUMoaiyn+lEWmZ09unDFNxUsEbBlwr+o2CGgqOm\\n8ucU1LOrI/XuVZBkOuYq+FhZzQfOK6jlos4npvzTSswNpiqrKcFLLsobChaY8vcr+D8FXylIUKKp\\n+ZBTHReHzwk3szr9vHilqjW0yoK6LZX69zkbdXql5MZSrJi1UExMlvt1Ii1NqSeesBdwU6dmvb2f\\nf7a2ExFhWlPV5Clz5yrVsqVSQUFyU6tXT6kPPlDVqqa5lCFKKaU2brR+jy+9lH77x48r9eKLsoRV\\ntKjceOvUEbvKH3+0L5sTwk0ppT75RG7uRYpIGdv6KSmiPl+9ulIBAZLftq35xXvCTSmxP+vZU6mQ\\nEFn2b95cthTMS7iOn4c73Ak3pZRatUrU6UND5f3edps8tMTF2ZfbulWpwYOVuuMO2e8oVkzKDhwo\\ndohm4uKUeu89pdq3lweSIkXUz6GPqbYhu1Sp4kmqZMk0dffdSs2a5dnQzRw6pNS4cdJs5coi4yMi\\nZIUbuh9UUM90879oEjKHdtB4QnGujwa1ANQRUGmg1FAmtXUj3Boo+ExBtKmdlP3UiS/GjVRQqjjX\\n5ysX925Q/m1Z92YD/oqN4J+04lxXNTiS3JE1Bx5hXjtXdUz9DTQJ4esK4pXYzHVPp3yAguEK9piE\\ncJyC5QpauK3j68LN1hD6v0eUCgpTqvNgpRYlORhCK6XUli3WG02NGlnu0y23binVpYu1D8PI+uzQ\\n/IQK8hStybe4kyGFBVfbQZ6QkdmcSwYMkEoHDmS+w1xmyhQZapkyMoEbNkyEEyj12muet9O3r9Sp\\nV0+pQYNkQtizp9jFm24RQ5Wz0OlhyksDdRRUnOm6pmNZVweoAFB/gIo31fvWTbl5pvzToD4H9RGo\\n5aZ+k0B18KS/3Dh8Trg5GkLPPOPGEFop61MfyCwrJ0hIUKpZM2s/RYoo9euvmWvj3Dm7X646eDBn\\nxqrxClq4eVm4pabae4Ixs3at/C/q1ct8Z7nM8eMy+S5d2n5yffmyTPrAupqZETNnKrVzp3P6+vVK\\nwS2zEKmg7IVOZVCtQZUyXa/PpHB719TuUHfCDdRdpry/QZVwyHvKlPerJ/3lxuFzCiWOhtBlKsmr\\nnSG0GW8rk7iiZEnxo2dW0711S5ymmo1TPeHrr622P23aiNKKJk9RCj77TDTHixWTYAgvveRazwDs\\n/fuuXCk6EiEhzpr7mfHdaw6nlpQktpvVq0ud224TXZRbt1yPJTN9pBdv1DHup/k9gvhUtvWnPGaM\\n6zY8IS3xFtMqjeWukIMEFUmiZGASd4Uc5ItOi0jzD4SpU+3Kb9wottOVK8v7K19eQlW99559u+fP\\ni/5PnTryNw0NlfOBA8V1pTeZMUO+p5desv88w8LEHSnAtGkuqzoxcKDYlTvSti3AtngkZIxdXDOl\\nOKMUG5XiWmbHbhhEIQohY3H2J2lLDdPrL0rhaKluDkETntn+cwqfc5xsNoRehhhAO4aNsbwhpXJH\\nuIE4gV21Spy/xsRAfLw4jN28GWrUcC5vG9CtpHJ2kqzJc4YNE6XYChXEOXxgoPgB/uMPESruQtkt\\nXCjCzewv+ORJa15mfPfa0qePmFb27m0dx5gxYmK5ZIm9AM1qH57QqJGYer73ntgz2yqC2gYLyCyP\\nP1uE79KmUuXGOZ71+wojJZkfE3rxAp+zqeMHzGlnDQa1cqX4Vi5VShyJVKokPg/274fPP7f6eL5x\\nQ/7yR4/CvfeKMFRKvo+ffpLP0tVfM6uYfRrb+sY206WLfZnskaxMJ25CUGQOw6A48A0Sm20ckJ6X\\ni72m1w6GQXGluGmTZw486uVQCNkgL6aL3rRzc+vf8eBB6zJfWJhrGxpv89dfsiFu7rdmTaXOn7cv\\nc0mJuudUJZuEw9Zby4eEKHX9es6PU5MumzfL13HbbfZKdDdvip6Do96FUlY9CsNQasUK5zYz67tX\\nKesyXq1asrzlahy2yotZ6SO9JVZ3uhneXJb87jtJa9xYqfh4a3pCgvgjBqXmzLGm9+oladHRzu3b\\n+hdfskTKDRvmXC4pSalr1+zTMuPPePRo58+kbFnpz9FXspmSJSU/O3/vEyeUgsQ0UNdBWe3PXC8z\\nrvdkWRLUZFCJoOqZrttlsOc2wZR/EtRUUONA/QwqGdRcUCXT6y83D58VbhkyY4ZVaHTrlvP9mdmw\\nQRbfzX1HRVn/SWY7hm+UdaOwnTXigHp+SO6NU+MWs9/kGTOc89atS1+49ejhus3M+u5VyioMbAWY\\n4zjatcteH3kt3Mw22qtWOZdfu1by2re3ppmFW0bb0mbh5uqzcIX5L+jpMXq0ff3AQEl358/BbOWT\\nVYXtxERR3DX1P0JldGP3QLiB6mhSBPmXTVq6ws1U5nlQNxw+k+2gOmc0rtw8fG7PzWNya0nSkdat\\nxXmu2W3W9u3ifPXWLWtAN7PpZEIc/L7QWvcBvSSZHzD72ZU9DntatRIvbu5w590ts757bUlvHLbu\\nTbPTR16xc6f8VVwta7Zt6/weH31UXps1k2XfefMkDJ6rupUqwbhxslT46adim52a6lwWMivasrfH\\nmFlSU8VVpdzSlsYBH2e3TcMgFJgF/AF84mEdwzD4FAky+j4Syy0YaA0oYIVh8GJ2x+YtCq5ws40E\\n4A3P4pmhRw8JcmpmzRrxSnEtTTYJzfz2HdwyBZqt0hhquAhOqcl10vPlGxBgH2fTEXd+jbPii9lM\\neuO4ZqM+kJ0+8oqrV0XxxdUepvk92irC9Ool+luNG4sSR79+EmwgKkr+ZmZKlYKtW+Vvt2OHuISM\\nipLvZ/RocQ3rTcxuKd0pHJnTQ9y5A3ZDaqo4T1mwQPZeoecxpVAZ1fOACYhz5IFK4UbkO/Ek8DLw\\nqVKMU6LEkqAUm5DApTeBcYZBkBfGl218TqHEI2Jj4eBBOQ8MdB1+I6cZNEgctZp3uL/7DoqXg8af\\nAIY8/q2ebi3f4lnxoqzJc8w3oPPnnZUOUlLg4kX3ntHc+TXOiu9eM+fPi5cqV+Mw+x3Oah9+fu61\\nLnNDCIaEiEJIcrLJXZ4Nrt4jiEJJt27i5eqPP0TYffEFdO8us7x69aRc5crw1VfyV9u3TxQ6pk6V\\nyDRpaTB2rLXNzM7E2rWzn23WqSNjPXRIovfYcu6cjLVy5Uz5WLf4Zl+wQHxvz54N8+d7RY8E+P/2\\n7j1GqvKM4/j34aKgWMRCFJCbAl5QA+uKVaolYFqlKr0rmhYqhGIUE6MQG6IlNTFS09j0Yq0RrW2M\\nhZgaqIqtlwJBwboYoKhoVbQuCN6KwVt13ad/PDPd2WVmd3Z3ds7Mmd8n2ezMnDPnPPvu2X3mfc97\\noY6Y4HhHgWv2UjMuBba6MzHzWrbTyN/b7uzOHjN2AJOA44DNpQq0q9KZ3J56quVxfX3M3J2E66+P\\n/zTZWtzyW+GioXD+Inj7Wdi5NV7v2w+mXtJmHIMkpa4umsvWrTswuW3YULhpqz2TJsX6gmvXwvTp\\nrbft2xcjR/r1i8no21q3Lpql8sWR22W8K+cYNAi2bcufXBoa8v8svXp1rQzymTQphi6sX39gzOvX\\nx3nqCjRoHHpoNMFOmxY/xw03wJo1LcktyyyS/YQJ0agycmSsLJWb3NoOIyhGbnKbNi2aDR955MDk\\ntmZNyz7F+vTTqKmtWhWrKd19d8kXCPkzkO83PBSYQczOvxbInVg0M/lmwe7+2dcLfFwqsyRu9PV4\\nh5JFi1qax6+9tmfP1ZGmppgvMLfJfv497mdd3vJ8yvejF6VUhA0bvMu9JQtNXr9zZ3Q6GDgwFhbI\\ndeWV8d5581q/XkxvydwpF7tyjgUL4vW2i05nf558HUqGDOnaIPZ8HUruvTdeO+201j0JP/wwXoNY\\nbCFr3br8nTauuCL2ve22eL59u/uePQfu98wzsd/kyZ2Pvz2vvtr5Qdz79kWv1radTD75xH3GjHjP\\n3LmtO3oDDV7E/9hie0vmed/UQh1KwBdntm0HH9hm24LMtjfBe3fmnD31lc6aW1KdSfLp3TtmiX/n\\nnfgIDrD8Mjjo4JZ9bpwXC71JRZgyBRYujEnkTzqp9fiyQYMK39Nqz+jR8ItfxOLOdXXxqXzIkLgk\\nNm6M5d+WLcv/3hNOiFpHbhyvvBJNc7k1uq6cY+HCqBVcfnnUoEaMiBrexo3RzPfggwfGM3169Jm6\\n4II4T9++MffA2Wd3vlwuuSR+npUrW2pWZlGz2rkTLrqopRMJwFVXwa5d8TsaPTru1W3eHE2Oo0bF\\nPTiI+2+LFkUtavz4GO/X2Bjn6tUrtpXSmDFwyy0RX319xH3QQTHusbERrrnmwBrdAw/EPcHZs1sW\\nkYDoKPPww3G/cfjwaEZt8fNhZiwF1rqzNneLGTlH4fjM92Vm7M88vjNzf6yrbgMuBU4BXjJjNbG2\\nWx0wjRhyfIUXfw+vZyWRUXu05vbxxy2TwIL7W2/13Lk6Y9++mIi1bcerceNiEmapKM3NMVdgdk7h\\noUNjvsB9+7q2jFlWsXP3urfUdD75xH3JEvfRo+M9Y8a4L11aePmxzpzDPeZ6Puss9/79Y7jAjBnu\\nW7cWHgqwd6/7rFkxmW+vXp63a3w+habf+vzzmHP81FMjhv793evqYrm7tsNTV6yIBQPGjo2xY4cd\\n5j5hQiwkkPun/vzz7ldfHcccPDjKYdSoaER58smOY+2q1atjIZIBA2L+5/r6whMnZ6+ZtvNZF7ns\\n21I/sGbV0XvmtH1PnmNMbW8oAPgAYqquLcR4u8/Ad4OvBJ/c0fHL+WURcHnV19d7Q6EG/e7asCG6\\n40N8ZMt2LKkEu3fHR87c1XiXLYPFixMLSSrX1KlR60rgT1QqlJltdvcEeshVn/QNBaikJsm2hg2L\\nabqyfckPOSTuFouISEml755bkuPbijF+fAzAWb48Jp0rNDBKRES6LF3Jrbm59TCASqu5ZR17LNx0\\nU9JRiIikVrqS244dMSIUoulPS8dIFcsuNSMinZeue25t77cVmi5CRERSLd3JTUREalK6kluldyYR\\nEZGySE9y27s3pm2AWHu+0IR0IiKSeulJbrlNkpMnR4ITEZGalJ7kltskqfttIiI1LT3JTZ1JREQk\\no3rHuTUBu4EPgN4fxQJcWWeemVBQIiJSCaozub0HPATsB3oDL/wjlu2FWKnwCK0fIyJSy6qvWbKJ\\nSGwGjCBWr343p0nyDDVJiojUuupLbruJGtvAnNeez+lMMkHj20REal31JbcPiKbIrOZmeHFjy/MT\\nVXMTEal13UpuZnaLme0ws21m9oCZHV6qwAoaAK0WMf/3c/Dh+/H4C0fB8cf0eAgiIlLZultzexQ4\\nyd1PAV4Cftz9kDowDDgMyOSzVk2Sx02B4ZosWUSk1nUrubn739w9002RTcDR3Q+pA32ArwMOvAE8\\nm9OZZOaUau3/KSIiJVTKe26XAWtKeLzCjgBmAecDjTk1t6+qM4mIiBRRzzGzx4Cj8mxa4u6rMvss\\nITrp39vOceYD8wFGjhzZpWBb6QP03gW7X4/nhxwCEyd2/7giIlL1Okxu7n5Oe9vNbA5Rh5ru7t7O\\nce4A7gCor68vuF+n5E65dfrp0LdvSQ4rIiLVrVt3qMzsXGAx8BV3/6g0IXWCJksWEZE8unvP7ddE\\n38VHzWyLmd1egpiKp8mSRUQkj27V3Nx9bKkC6bT9+2HLlnhsBmeckVgoIiJSWapvhpKsp5+O2UkA\\nTj4ZBg5sf38REakZ1Zvc1CQpIiIFVG9yy+1M8mWNbxMRkRbVmdyammDTppbnqrmJiEiO6pys6rPP\\n4MYbo2nytdegFIPCRUQkNaydcdc9pr6+3hsaGsp+XhGRamZmm929Puk4qkF1NkuKiIi0Q8lNRERS\\nR8lNRERSR8lNRERSR8lNRERSR8lNRERSR8lNRERSR8lNRERSR8lNRERSR8lNRERSR8lNRERSJ5G5\\nJc3sbeD1Eh1uMPBOiY6VZiqn4qiciqeyKk4py2mUuw8p0bFSLZHkVkpm1qCJRDumciqOyql4Kqvi\\nqJySoWZJERFJHSU3ERFJnTQktzuSDqBKqJyKo3IqnsqqOCqnBFT9PTcREZG20lBzExERaaXqkpuZ\\nfdfMnjOzZjMr2APJzM41sxfN7GUzu66cMVYCMzvCzB41s39lvg8qsN/nZrYl87W63HEmpaPrw8wO\\nNrMVme1Pm9no8keZvCLKaY6ZvZ1zDc1LIs6kmdldZvaWmW0vsN3M7JeZctxmZnXljrHWVF1yA7YD\\n3wLWF9rBzHoDvwHOA04EZpnZieUJr2JcBzzu7uOAxzPP8/nY3Sdmvi4sX3jJKfL6mAv8x93HArcC\\ny8obZfI68Xe0IucaurOsQVaO3wPntrP9PGBc5ms+8NsyxFTTqi65ufsL7v5iB7tNBl5291fd/VPg\\nT8DMno+uoswE7sk8vgf4RoKxVJpiro/c8rsfmG5mVsYYK4H+jork7uuB99rZZSbwBw+bgMPNbGh5\\noqtNVZfcijQceCPneWPmtVpypLu/mXm8BziywH79zKzBzDaZWa0kwGKuj//v4+5NwPvAF8sSXeUo\\n9u/o25mmtvvNbER5Qqs6+p9UZn2SDiAfM3sMOCrPpiXuvqrc8VSq9sop94m7u5kV6hY7yt13mdkx\\nwBNm9k93f6XUsUpq/QW4z93/a2Y/Imq70xKOSaQyk5u7n9PNQ+wCcj9BHp15LVXaKycz22tmQ939\\nzUzzx1sFjrEr8/1VM1sLTALSntyKuT6y+zSaWR9gIPBuecKrGB2Wk7vnlsmdwM/KEFc1qon/SZUk\\nrc2SzwDjzGyMmR0EXAzUTE/AjNXA7Mzj2cABNV4zG2RmB2ceDwamAM+XLcLkFHN95Jbfd4AnvPYG\\nhXZYTm3uG10IvFDG+KrJauAHmV6TXwLez7ltID2gImtu7TGzbwK/AoYAD5nZFnf/mpkNA+509xnu\\n3mRmVwJ/BXoDd7n7cwmGnYSbgZVmNpdYgeF7AJnhEwvcfR5wAvA7M2smPujc7O6pT26Frg8z+ynQ\\n4O6rgeXAH83sZaKjwMXJRZyMIsvpKjO7EGgiymlOYgEnyMzuA6YCg82sEfgJ0BfA3W8HHgZmAC8D\\nHwE/TCbS2qEZSkREJHXS2iwpIiI1TMlNRERSR8lNRERSR8lNRERSR8lNRERSR8lNRERSR8lNRERS\\nR8lNRERS538wMxeFt66dhAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f41a36df198>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbcAAAD8CAYAAAD0f+rwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f41a36fc710>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for t in range(500):\\n\",\n    \"    pred_ofit = net_overfitting(x)\\n\",\n    \"    pred_drop = net_dropped(x)\\n\",\n    \"    loss_ofit = loss_func(pred_ofit, y)\\n\",\n    \"    loss_drop = loss_func(pred_drop, y)\\n\",\n    \"\\n\",\n    \"    optimizer_ofit.zero_grad()\\n\",\n    \"    optimizer_drop.zero_grad()\\n\",\n    \"    loss_ofit.backward()\\n\",\n    \"    loss_drop.backward()\\n\",\n    \"    optimizer_ofit.step()\\n\",\n    \"    optimizer_drop.step()\\n\",\n    \"\\n\",\n    \"    if t % 100 == 0:\\n\",\n    \"        # change to eval mode in order to fix drop out effect\\n\",\n    \"        net_overfitting.eval()\\n\",\n    \"        net_dropped.eval()  # parameters for dropout differ from train mode\\n\",\n    \"\\n\",\n    \"        # plotting\\n\",\n    \"        plt.cla()\\n\",\n    \"        test_pred_ofit = net_overfitting(test_x)\\n\",\n    \"        test_pred_drop = net_dropped(test_x)\\n\",\n    \"        plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=50, alpha=0.3, label='train')\\n\",\n    \"        plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.3, label='test')\\n\",\n    \"        plt.plot(test_x.data.numpy(), test_pred_ofit.data.numpy(), 'r-', lw=3, label='overfitting')\\n\",\n    \"        plt.plot(test_x.data.numpy(), test_pred_drop.data.numpy(), 'b--', lw=3, label='dropout(50%)')\\n\",\n    \"        plt.text(0, -1.2, 'overfitting loss=%.4f' % loss_func(test_pred_ofit, test_y).data[0], fontdict={'size': 20, 'color':  'red'})\\n\",\n    \"        plt.text(0, -1.5, 'dropout loss=%.4f' % loss_func(test_pred_drop, test_y).data[0], fontdict={'size': 20, 'color': 'blue'})\\n\",\n    \"        plt.legend(loc='upper left'); plt.ylim((-2.5, 2.5));plt.pause(0.1)\\n\",\n    \"\\n\",\n    \"        # change back to train mode\\n\",\n    \"        net_overfitting.train()\\n\",\n    \"        net_dropped.train()\\n\",\n    \"        plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "tutorial-contents-notebooks/504_batch_normalization.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 504 Batch Normalization\\n\",\n    \"\\n\",\n    \"View more, visit my tutorial page: https://mofanpy.com/tutorials/\\n\",\n    \"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\\n\",\n    \"\\n\",\n    \"Dependencies:\\n\",\n    \"* torch: 0.1.11\\n\",\n    \"* matplotlib\\n\",\n    \"* numpy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"from torch.autograd import Variable\\n\",\n    \"from torch import nn\\n\",\n    \"from torch.nn import init\\n\",\n    \"import torch.utils.data as Data\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1)    # reproducible\\n\",\n    \"np.random.seed(1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Hyper parameters\\n\",\n    \"N_SAMPLES = 2000\\n\",\n    \"BATCH_SIZE = 64\\n\",\n    \"EPOCH = 12\\n\",\n    \"LR = 0.03\\n\",\n    \"N_HIDDEN = 8\\n\",\n    \"ACTIVATION = F.tanh\\n\",\n    \"B_INIT = -0.2   # use a bad bias constant initializer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x7f1a8e78f240>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nUsONmxd/AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f1a8e78f3c8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# training data\\n\",\n    \"x = np.linspace(-7, 10, N_SAMPLES)[:, np.newaxis]\\n\",\n    \"noise = np.random.normal(0, 2, x.shape)\\n\",\n    \"y = np.square(x) - 5 + noise\\n\",\n    \"\\n\",\n    \"# test data\\n\",\n    \"test_x = np.linspace(-7, 10, 200)[:, np.newaxis]\\n\",\n    \"noise = np.random.normal(0, 2, test_x.shape)\\n\",\n    \"test_y = np.square(test_x) - 5 + noise\\n\",\n    \"\\n\",\n    \"train_x, train_y = torch.from_numpy(x).float(), torch.from_numpy(y).float()\\n\",\n    \"test_x = Variable(torch.from_numpy(test_x).float(), volatile=True)  # not for computing gradients\\n\",\n    \"test_y = Variable(torch.from_numpy(test_y).float(), volatile=True)\\n\",\n    \"\\n\",\n    \"train_dataset = Data.TensorDataset(train_x, train_y)\\n\",\n    \"train_loader = Data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,)\\n\",\n    \"\\n\",\n    \"# show data\\n\",\n    \"plt.scatter(train_x.numpy(), train_y.numpy(), c='#FF9359', s=50, alpha=0.2, label='train')\\n\",\n    \"plt.legend(loc='upper left')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class Net(nn.Module):\\n\",\n    \"    def __init__(self, batch_normalization=False):\\n\",\n    \"        super(Net, self).__init__()\\n\",\n    \"        self.do_bn = batch_normalization\\n\",\n    \"        self.fcs = []\\n\",\n    \"        self.bns = []\\n\",\n    \"        self.bn_input = nn.BatchNorm1d(1, momentum=0.5)   # for input data\\n\",\n    \"\\n\",\n    \"        for i in range(N_HIDDEN):               # build hidden layers and BN layers\\n\",\n    \"            input_size = 1 if i == 0 else 10\\n\",\n    \"            fc = nn.Linear(input_size, 10)\\n\",\n    \"            setattr(self, 'fc%i' % i, fc)       # IMPORTANT set layer to the Module\\n\",\n    \"            self._set_init(fc)                  # parameters initialization\\n\",\n    \"            self.fcs.append(fc)\\n\",\n    \"            if self.do_bn:\\n\",\n    \"                bn = nn.BatchNorm1d(10, momentum=0.5)\\n\",\n    \"                setattr(self, 'bn%i' % i, bn)   # IMPORTANT set layer to the Module\\n\",\n    \"                self.bns.append(bn)\\n\",\n    \"\\n\",\n    \"        self.predict = nn.Linear(10, 1)         # output layer\\n\",\n    \"        self._set_init(self.predict)            # parameters initialization\\n\",\n    \"\\n\",\n    \"    def _set_init(self, layer):\\n\",\n    \"        init.normal(layer.weight, mean=0., std=.1)\\n\",\n    \"        init.constant(layer.bias, B_INIT)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        pre_activation = [x]\\n\",\n    \"        if self.do_bn: x = self.bn_input(x)     # input batch normalization\\n\",\n    \"        layer_input = [x]\\n\",\n    \"        for i in range(N_HIDDEN):\\n\",\n    \"            x = self.fcs[i](x)\\n\",\n    \"            pre_activation.append(x)\\n\",\n    \"            if self.do_bn: x = self.bns[i](x)   # batch normalization\\n\",\n    \"            x = ACTIVATION(x)\\n\",\n    \"            layer_input.append(x)\\n\",\n    \"        out = self.predict(x)\\n\",\n    \"        return out, layer_input, pre_activation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Net (\\n\",\n      \"  (bn_input): BatchNorm1d(1, eps=1e-05, momentum=0.5, affine=True)\\n\",\n      \"  (fc0): Linear (1 -> 10)\\n\",\n      \"  (fc1): Linear (10 -> 10)\\n\",\n      \"  (fc2): Linear (10 -> 10)\\n\",\n      \"  (fc3): Linear (10 -> 10)\\n\",\n      \"  (fc4): Linear (10 -> 10)\\n\",\n      \"  (fc5): Linear (10 -> 10)\\n\",\n      \"  (fc6): Linear (10 -> 10)\\n\",\n      \"  (fc7): Linear (10 -> 10)\\n\",\n      \"  (predict): Linear (10 -> 1)\\n\",\n      \") Net (\\n\",\n      \"  (bn_input): BatchNorm1d(1, eps=1e-05, momentum=0.5, affine=True)\\n\",\n      \"  (fc0): Linear (1 -> 10)\\n\",\n      \"  (bn0): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\\n\",\n      \"  (fc1): Linear (10 -> 10)\\n\",\n      \"  (bn1): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\\n\",\n      \"  (fc2): Linear (10 -> 10)\\n\",\n      \"  (bn2): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\\n\",\n      \"  (fc3): Linear (10 -> 10)\\n\",\n      \"  (bn3): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\\n\",\n      \"  (fc4): Linear (10 -> 10)\\n\",\n      \"  (bn4): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\\n\",\n      \"  (fc5): Linear (10 -> 10)\\n\",\n      \"  (bn5): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\\n\",\n      \"  (fc6): Linear (10 -> 10)\\n\",\n      \"  (bn6): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\\n\",\n      \"  (fc7): Linear (10 -> 10)\\n\",\n      \"  (bn7): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\\n\",\n      \"  (predict): Linear (10 -> 1)\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"nets = [Net(batch_normalization=False), Net(batch_normalization=True)]\\n\",\n    \"\\n\",\n    \"print(*nets)    # print net architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"opts = [torch.optim.Adam(net.parameters(), lr=LR) for net in nets]\\n\",\n    \"\\n\",\n    \"loss_func = torch.nn.MSELoss()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch:  0\\n\",\n      \"Epoch:  1\\n\",\n      \"Epoch:  2\\n\",\n      \"Epoch:  3\\n\",\n      \"Epoch:  4\\n\",\n      \"Epoch:  5\\n\",\n      \"Epoch:  6\\n\",\n      \"Epoch:  7\\n\",\n      \"Epoch:  8\\n\",\n      \"Epoch:  9\\n\",\n      \"Epoch:  10\\n\",\n      \"Epoch:  11\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAk0AAAE/CAYAAABb+PcPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f1a8af019b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"f, axs = plt.subplots(4, N_HIDDEN+1, figsize=(10, 5))\\n\",\n    \"plt.ion()   # something about plotting\\n\",\n    \"def plot_histogram(l_in, l_in_bn, pre_ac, pre_ac_bn):\\n\",\n    \"    for i, (ax_pa, ax_pa_bn, ax,  ax_bn) in enumerate(zip(axs[0, :], axs[1, :], axs[2, :], axs[3, :])):\\n\",\n    \"        [a.clear() for a in [ax_pa, ax_pa_bn, ax, ax_bn]]\\n\",\n    \"        if i == 0: p_range = (-7, 10);the_range = (-7, 10)\\n\",\n    \"        else:p_range = (-4, 4);the_range = (-1, 1)\\n\",\n    \"        ax_pa.set_title('L' + str(i))\\n\",\n    \"        ax_pa.hist(pre_ac[i].data.numpy().ravel(), bins=10, range=p_range, color='#FF9359', alpha=0.5);ax_pa_bn.hist(pre_ac_bn[i].data.numpy().ravel(), bins=10, range=p_range, color='#74BCFF', alpha=0.5)\\n\",\n    \"        ax.hist(l_in[i].data.numpy().ravel(), bins=10, range=the_range, color='#FF9359');ax_bn.hist(l_in_bn[i].data.numpy().ravel(), bins=10, range=the_range, color='#74BCFF')\\n\",\n    \"        for a in [ax_pa, ax, ax_pa_bn, ax_bn]: a.set_yticks(());a.set_xticks(())\\n\",\n    \"        ax_pa_bn.set_xticks(p_range);ax_bn.set_xticks(the_range)\\n\",\n    \"        axs[0, 0].set_ylabel('PreAct');axs[1, 0].set_ylabel('BN PreAct');axs[2, 0].set_ylabel('Act');axs[3, 0].set_ylabel('BN Act')\\n\",\n    \"    plt.pause(0.01)\\n\",\n    \"    \\n\",\n    \"# training\\n\",\n    \"losses = [[], []]  # recode loss for two networks\\n\",\n    \"for epoch in range(EPOCH):\\n\",\n    \"    print('Epoch: ', epoch)\\n\",\n    \"    layer_inputs, pre_acts = [], []\\n\",\n    \"    for net, l in zip(nets, losses):\\n\",\n    \"        net.eval()              # set eval mode to fix moving_mean and moving_var\\n\",\n    \"        pred, layer_input, pre_act = net(test_x)\\n\",\n    \"        l.append(loss_func(pred, test_y).data)\\n\",\n    \"        layer_inputs.append(layer_input)\\n\",\n    \"        pre_acts.append(pre_act)\\n\",\n    \"        net.train()             # free moving_mean and moving_var\\n\",\n    \"    plot_histogram(*layer_inputs, *pre_acts)     # plot histogram\\n\",\n    \"\\n\",\n    \"    for step, (b_x, b_y) in enumerate(train_loader):\\n\",\n    \"        b_x, b_y = Variable(b_x), Variable(b_y)\\n\",\n    \"        for net, opt in zip(nets, opts):     # train for each network\\n\",\n    \"            pred, _, _ = net(b_x)\\n\",\n    \"            loss = loss_func(pred, b_y)\\n\",\n    \"            opt.zero_grad()\\n\",\n    \"            loss.backward()\\n\",\n    \"            opt.step()    # it will also learns the parameters in Batch Normalization\\n\",\n    \"            \\n\",\n    \"plt.ioff()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAY8AAAEKCAYAAADq59mMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n/weQ2pRl6ztibwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f1a8c6594a8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nSUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f1a8bdbecf8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot training loss\\n\",\n    \"plt.figure(2)\\n\",\n    \"plt.plot(losses[0], c='#FF9359', lw=3, label='Original')\\n\",\n    \"plt.plot(losses[1], c='#74BCFF', lw=3, label='Batch Normalization')\\n\",\n    \"plt.xlabel('step');plt.ylabel('test loss');plt.ylim((0, 2000));plt.legend(loc='best')\\n\",\n    \"\\n\",\n    \"# evaluation\\n\",\n    \"# set net to eval mode to freeze the parameters in batch normalization layers\\n\",\n    \"[net.eval() for net in nets]    # set eval mode to fix moving_mean and moving_var\\n\",\n    \"preds = [net(test_x)[0] for net in nets]\\n\",\n    \"plt.figure(3)\\n\",\n    \"plt.plot(test_x.data.numpy(), preds[0].data.numpy(), c='#FF9359', lw=4, label='Original')\\n\",\n    \"plt.plot(test_x.data.numpy(), preds[1].data.numpy(), c='#74BCFF', lw=4, label='Batch Normalization')\\n\",\n    \"plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='r', s=50, alpha=0.2, label='train')\\n\",\n    \"plt.legend(loc='best')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  }
]