diff --git a/tutorial-contents-notebooks/201_torch_numpy.ipynb b/tutorial-contents-notebooks/201_torch_numpy.ipynb new file mode 100644 index 0000000..d2cec63 --- /dev/null +++ b/tutorial-contents-notebooks/201_torch_numpy.ipynb @@ -0,0 +1,397 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 201 Torch and Numpy\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\n", + "* numpy\n", + "\n", + "Details about math operation in torch can be found in: http://pytorch.org/docs/torch.html#math-operations\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import torch\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "numpy array: [[0 1 2]\n", + " [3 4 5]] \n", + "torch tensor: \n", + " 0 1 2\n", + " 3 4 5\n", + "[torch.LongTensor of size 2x3]\n", + " \n", + "tensor to array: [[0 1 2]\n", + " [3 4 5]]\n" + ] + } + ], + "source": [ + "# convert numpy to tensor or vise versa\n", + "np_data = np.arange(6).reshape((2, 3))\n", + "torch_data = torch.from_numpy(np_data)\n", + "tensor2array = torch_data.numpy()\n", + "print(\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", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "abs \n", + "numpy: [1 2 1 2] \n", + "torch: \n", + " 1\n", + " 2\n", + " 1\n", + " 2\n", + "[torch.FloatTensor of size 4]\n", + "\n" + ] + } + ], + "source": [ + "# abs\n", + "data = [-1, -2, 1, 2]\n", + "tensor = torch.FloatTensor(data) # 32-bit floating point\n", + "print(\n", + " '\\nabs',\n", + " '\\nnumpy: ', np.abs(data), # [1 2 1 2]\n", + " '\\ntorch: ', torch.abs(tensor) # [1 2 1 2]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + " 1\n", + " 2\n", + " 1\n", + " 2\n", + "[torch.FloatTensor of size 4]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tensor.abs()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "sin \n", + "numpy: [-0.84147098 -0.90929743 0.84147098 0.90929743] \n", + "torch: \n", + "-0.8415\n", + "-0.9093\n", + " 0.8415\n", + " 0.9093\n", + "[torch.FloatTensor of size 4]\n", + "\n" + ] + } + ], + "source": [ + "# sin\n", + "print(\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", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + " 0.2689\n", + " 0.1192\n", + " 0.7311\n", + " 0.8808\n", + "[torch.FloatTensor of size 4]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tensor.sigmoid()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + " 0.3679\n", + " 0.1353\n", + " 2.7183\n", + " 7.3891\n", + "[torch.FloatTensor of size 4]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tensor.exp()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "mean \n", + "numpy: 0.0 \n", + "torch: 0.0\n" + ] + } + ], + "source": [ + "# mean\n", + "print(\n", + " '\\nmean',\n", + " '\\nnumpy: ', np.mean(data), # 0.0\n", + " '\\ntorch: ', torch.mean(tensor) # 0.0\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "matrix multiplication (matmul) \n", + "numpy: [[ 7 10]\n", + " [15 22]] \n", + "torch: \n", + " 7 10\n", + " 15 22\n", + "[torch.FloatTensor of size 2x2]\n", + "\n" + ] + } + ], + "source": [ + "# matrix multiplication\n", + "data = [[1,2], [3,4]]\n", + "tensor = torch.FloatTensor(data) # 32-bit floating point\n", + "# correct method\n", + "print(\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", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "matrix multiplication (dot) \n", + "numpy: [[ 7 10]\n", + " [15 22]] \n", + "torch: 30.0\n" + ] + } + ], + "source": [ + "# incorrect method\n", + "data = np.array(data)\n", + "print(\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", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that:\n", + "\n", + "torch.dot(tensor1, tensor2) → float\n", + "\n", + "Computes the dot product (inner product) of two tensors. Both tensors are treated as 1-D vectors." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + " 7 10\n", + " 15 22\n", + "[torch.FloatTensor of size 2x2]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tensor.mm(tensor)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + " 1 4\n", + " 9 16\n", + "[torch.FloatTensor of size 2x2]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tensor * tensor" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30.0" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tensor.dot(tensor)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/202_variable.ipynb b/tutorial-contents-notebooks/202_variable.ipynb new file mode 100644 index 0000000..8184fc7 --- /dev/null +++ b/tutorial-contents-notebooks/202_variable.ipynb @@ -0,0 +1,293 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 202 Variable\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\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" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch.autograd import Variable" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " 1 2\n", + " 3 4\n", + "[torch.FloatTensor of size 2x2]\n", + "\n", + "Variable containing:\n", + " 1 2\n", + " 3 4\n", + "[torch.FloatTensor of size 2x2]\n", + "\n" + ] + } + ], + "source": [ + "tensor = torch.FloatTensor([[1,2],[3,4]]) # build a tensor\n", + "variable = Variable(tensor, requires_grad=True) # build a variable, usually for compute gradients\n", + "\n", + "print(tensor) # [torch.FloatTensor of size 2x2]\n", + "print(variable) # [torch.FloatTensor of size 2x2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Till now the tensor and variable seem the same.\n", + "\n", + "However, the variable is a part of the graph, it's a part of the auto-gradient.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7.5\n", + "Variable containing:\n", + " 7.5000\n", + "[torch.FloatTensor of size 1]\n", + "\n" + ] + } + ], + "source": [ + "t_out = torch.mean(tensor*tensor) # x^2\n", + "v_out = torch.mean(variable*variable) # x^2\n", + "print(t_out)\n", + "print(v_out)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "v_out.backward() # backpropagation from v_out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$ v_{out} = {{1} \\over {4}} sum(variable^2) $$\n", + "\n", + "the gradients w.r.t the variable, \n", + "\n", + "$$ {d(v_{out}) \\over d(variable)} = {{1} \\over {4}} 2 variable = {variable \\over 2}$$\n", + "\n", + "let's check the result pytorch calculated for us below:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Variable containing:\n", + " 0.5000 1.0000\n", + " 1.5000 2.0000\n", + "[torch.FloatTensor of size 2x2]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variable.grad" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Variable containing:\n", + " 1 2\n", + " 3 4\n", + "[torch.FloatTensor of size 2x2]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variable # this is data in variable format" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + " 1 2\n", + " 3 4\n", + "[torch.FloatTensor of size 2x2]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variable.data # this is data in tensor format" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 2.],\n", + " [ 3., 4.]], dtype=float32)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variable.data.numpy() # numpy format" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we did `.backward()` on `v_out` but `variable` has been assigned new values on it's `grad`.\n", + "\n", + "As this line \n", + "```\n", + "v_out = torch.mean(variable*variable)\n", + "``` \n", + "will make a new variable `v_out` and connect it with `variable` in computation graph." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.autograd.variable.Variable" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(v_out)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.FloatTensor" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(v_out.data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/203_activation.ipynb b/tutorial-contents-notebooks/203_activation.ipynb new file mode 100644 index 0000000..9d8c430 --- /dev/null +++ b/tutorial-contents-notebooks/203_activation.ipynb @@ -0,0 +1,164 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 203 Activation\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\n", + "* matplotlib\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn.functional as F\n", + "from torch.autograd import Variable\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Firstly generate some fake data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "x = torch.linspace(-5, 5, 200) # x data (tensor), shape=(100, 1)\n", + "x = Variable(x)\n", + "x_np = x.data.numpy() # numpy array for plotting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Following are popular activation functions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "y_relu = F.relu(x).data.numpy()\n", + "y_sigmoid = F.sigmoid(x).data.numpy()\n", + "y_tanh = F.tanh(x).data.numpy()\n", + "y_softplus = F.softplus(x).data.numpy()\n", + "\n", + "# y_softmax = F.softmax(x)\n", + "# softmax is a special kind of activation function, it is about probability\n", + "# and will make the sum as 1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot to visualize these activation function" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ASt5SPFlZfo1qdrYf505NDTsikeA552uQP/+8L7Bi5pP23XdrCElKlZK3FM/d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\nWgTPPw+DBsEff8Ahh8A//+l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mZtYybt5mZmYt4+ZtZmbWMm7eZmZmLePmbWZm\n1jJu3mZmZi3zH58alggIp0WVAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(1, figsize=(8, 6))\n", + "plt.subplot(221)\n", + "plt.plot(x_np, y_relu, c='red', label='relu')\n", + "plt.ylim((-1, 5))\n", + "plt.legend(loc='best')\n", + "\n", + "plt.subplot(222)\n", + "plt.plot(x_np, y_sigmoid, c='red', label='sigmoid')\n", + "plt.ylim((-0.2, 1.2))\n", + "plt.legend(loc='best')\n", + "\n", + "plt.subplot(223)\n", + "plt.plot(x_np, y_tanh, c='red', label='tanh')\n", + "plt.ylim((-1.2, 1.2))\n", + "plt.legend(loc='best')\n", + "\n", + "plt.subplot(224)\n", + "plt.plot(x_np, y_softplus, c='red', label='softplus')\n", + "plt.ylim((-0.2, 6))\n", + "plt.legend(loc='best')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/301_regression.ipynb b/tutorial-contents-notebooks/301_regression.ipynb new file mode 100644 index 0000000..8880d2c --- /dev/null +++ b/tutorial-contents-notebooks/301_regression.ipynb @@ -0,0 +1,302 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 301 Regression\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\n", + "* matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch.autograd import Variable\n", + "import torch.nn.functional as F\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.manual_seed(1) # reproducible" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1) # x data (tensor), shape=(100, 1)\n", + "y = 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", + "x, y = Variable(x), Variable(y)\n", + "\n", + "plt.scatter(x.data.numpy(), y.data.numpy())\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class 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" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Net (\n", + " (hidden): Linear (1 -> 10)\n", + " (predict): Linear (10 -> 1)\n", + ")\n" + ] + } + ], + "source": [ + "net = Net(n_feature=1, n_hidden=10, n_output=1) # define the network\n", + "print(net) # net architecture" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "optimizer = torch.optim.SGD(net.parameters(), lr=0.5)\n", + "loss_func = torch.nn.MSELoss() # this is for regression mean squared loss" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "plt.ion() # something about plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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IbALOAeaIiKNGtjFmgjGmtTGmde3atYvvdRkm0FDFkQDlBHzb101L5VByCv2v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\nAG2SsNVBGwMVgO+B5h7bNKC35++Xgdv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5ig8llWFg13tZVreZ6pspQQnF2WQA9X1e1/OUeakMnAwsFJGNwL+BWSLi2OFq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\n4Z7y1SLSoag2FNGuISLys+fafC4ix/r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UUplKqcz9Ns06wj9vY24+/HaX7fbxCzeYb268kfT2Q23POXTuG/ZZRUIA/PWX\nmUl8+3ZL0Y/n1uOBjo9TqnQpmd9M+BRIsMkCXBv1qzu2OZUHLgGWKaW2A1cCc5RSljZFrfUkrXVT\nrXXTqlWrFr3WcczbmJs8L31vrvuvvPoWxl99u/2J77gDliwpdv1EjMnNNTOI//CDpWh76rkM6Pos\nlc+qJPObCb8CCTargXpKqTpKqVJAL2COs1BrfVhrXUVrXVtrXRtYBXSUbLTQ8DbmJtHLGu6u+w9v\nV58p1/Tm/SYdrDvm5poMtZ9/Dko9RQzQGu65BxYutBQdTKlA/+4jKZN2LsvTW0ugEX75DTZa61PA\nEGAh8CvwkdZ6nVJqtFKqY6grKNx5G3PTu1kN2+2uTRudm6TxQtdLmdTtIRbWu9J68iNHTLv8zp0h\nqbuIMqNGwTvvWDbnJJVmQLdn2V45TRY9EwGTiTijkN0M0J2bpHndbisnB9q2NUtIe2rQAL75RqYa\niWeTJ8PAgZbNeSqBe7o8yZf1mgGynHNJiNbUZwk28ezgQTMr72+/WctatDCz9abICopxZ9486NgR\n8vIsRU/dcB8fOJphU5ITpa+mBERrsJHpauLZmWeaFT3PPddatnw59Olj+4YjYlhmJvToYft7f7tl\nT+a26GRZ9MzbuC8hXEmwiXe1aplpa8qXt5Z9+ikMHSpLS8eLrVtNivPff1uKPrm4Fc8178vx3Hxe\n6dm4ICnA27gvCTjCkwSbOOD3zrNRI8jIgORk68ETJ8JY26nwRCw5cMDMCL5vn6Xom1qNSL9xKCjl\nPnYL7+O+XPcRAiTYxLyA7zxbt4b33rM/yRNPwLvvhryuooTk5Jg+mk2bLEW/Vq3NvV2eIDfx9I2I\nawaat2w0yVITngKa9VlEL193npaO3V69zFo3w2wmgxgwgBV/JTL8aLXAst1EdMjLg9tug5UrLUW/\nVzyL/t1H8ldp93n1XMduVUtNIcsmsHgbDybilzzZxLhC33k+8oj58JSXR6NHBlL515+lbT5WaA0P\nPmiaUD2lprJu8occqXy222bPsVvexn0Nb1dfEgeEGwk2Mc7bHabPO8/x481TjodyuceZMnMUNQ/t\nBaRtPuqNHw+vv27dXqoUZGTQpltr28X5XJ9mOzdJs90HkMQB4UbG2cQo5wDPrOwcFOD6Ww5ofMSJ\nE2Y2gaVLLUXbKp1L174v8WfZiihg29ibgl19EWrTppnUdjvTp0PPnsU6fYuxS2yb12QQaPHJOBsR\nMVyTAsCy4FrmAAAdqUlEQVQEGufMaXZ3p7ZKlzapz5deaimqc2gvU2aOJOXkcWmbj0ZLlkD//vZl\nL71U7EADkjggrCTYxCC7pADN6bvKgDv1K1Zkwfgp/J56lqWo8d5NvPnZOB5rUzcINRZhs3atmXA1\n17oAGg8+aN9fVwjOfhpv7SVycxK/JNjEoGDdVWasyeLhr/fTp+sossucYSm/dvNqOr05WgZ9Rotd\nu0zT6JEj1rKuXeGf/wQvs4cHwvOJ2pNncoGIL5L6HIOKm47q2t8DsKVKDe7u+jQfzHiaMqdOuu88\nZQpUr25mCBZhF/Dkq9nZJtBk2XTQt2wJH3wAiYnWskKwe6J2SpNU+bgnTzYxyFc6qj/e7k4zq1/M\ng7c8Sj42d76jR8NbbxWrzqLwAh6we+KEaTpbt85yji1n1mDuc29AmTLFro+3J2cFsuaNkGATi7yl\nowbyz+7r7nThBc155vrB9gfedx/MmWNfJkIioKli8vPhrrtg2TLL8fvKVaJf95E8v8o6RU1RFCnN\nXsQNaUaLUZ2bpBXpTtJfv84nzTpye40k6k/5t3tBfr4Zm7N4MVx1VaFfVxReQH1zI0aYNGcPR0ul\ncGf3keyueDYqSBliw9vVZ8SstW4BUPpphJM82Qg3vu5CnU9I9SdPgH79rDvk5MDNN8MGGegZDn6f\nJF57DV580VKem5DIfZ3SWXd2XZ/nKaziPFGL2CeDOuOYXecyYHt3annTyM2FW26xXZ+eWrXMXFt2\n6+T4eG15UyocZ5+N7e9q27fQrZttpuCjHR5iZsO27vvLtY8aMqhTRBVvnctAYHenyckwcyZcfrn1\n5Dt2QIcO9im2Pl5bpjIpHK9PEjk7zOwANoHm13sfZeXVt8iThwg7ebKJU0GbTuSPP6B5c7Pwlqc2\nbcwSw6VKhea1hdWGDeb38eef1rKBA03WYDHG0oiSJ082IqoEbTqRs882S0tXqWItW7zYZELl54fm\ntYW73383C6DZBZqbbjIL4XkJNDJDswg1CTZxKqhpqvXqwdy5ULastey//4X09NC9tjCOHjUBZft2\na1nTpjBjBiTZJ59Ks6YIBwk2cao4Az9tXXEFfPyx/Sj08eNhwoTQvXa8y82F7t3hhx+sZeedZ24E\nypWzljnI0s4iHCTYxKmQpKl26ACTJtmXPfwwfPRRUF5bmnxcaA2DB5umTA/ZZSvQqs3jtJjyi+01\ncl5Hb3OZZWXnyPUVQSMJAqJIvKUuZ6zJYv9jTzHwy6nWg0qVMqnS111XrNcNKDU7hrle+6cyP2LA\n4vcs++Qklea2XmNYk3YhYL1GdtfRm3i7vpEuWhMEAgo2Sqn2wAQgEZistR7rUf4IcDdwCtgP3KW1\n3uHrnBJsopfdG5VzgTYFaK0Zs+h1+vxovdumYkX4+mto2NDruX2Nv4mVTLaijjNyvfY9flrEiwte\nteyTpxIY3OUJvqh3pdt212vk64nGTrRd31gWrcHGbzOaUioReB24EbgI6K2UushjtzVAU631pcBM\nwDpsWcQMb+vlFHxWimeuv5cvzm9mPfjwYTP78M6dliK7juqHZ/xIbZfmsljIZCtOh7zz2l+3JZPn\nF75mu8+z1w+2BBpwv0aFvV7RdH1FZAqkz+YKYLPWeqvW+iQwHejkuoPWeqnW+pjj21VA9eBWU4Ra\nYfpBAnnjyUtI5IGOw/m+2oXWwqws05TmkTnlK4g535BTyybbvl40ZbIVp0N+T3YODfdu4vXZY0nS\n+dYd0tNZ2qqr7bGu18jb9Ur0khodTddXRKZAgk0asMvl+92Obd4MAOYXp1IivAp7px3oG8/x5DIM\n6PYMO6rWsBZu2wbXXgtbthRs8hfEcnLz0Jqoz2QrztPZ5fnZTJk5inK5x62FffrA888HlO3nbZ/e\nzWpE/fUVkSmo2WhKqb5AU2C8l/JBSqlMpVTm/v37g/nSohgKe6dt90blzYkKldjwzkdwzjnWwp07\nTcBZvx4ILIgdzsl1y2RLTUmmTHICD8/4MWiZU6HOdivyOKMDB3jno2eoeizbUrTvipbMHvocLcYt\n5eEZP1I6KYFKZZO9Zvt5ywj8R+eGMpmmCAm/CQJKqauAkVrrdo7vRwBorV/w2K8t8G/gWq213wUy\nJEEgctRJn2u7ZrwCto29yfYY19U8nckBrsdpTCBQCrKP5XJ1zh7+8146pbNtRreXLw8ffkhGtcYB\nZUg5V32EACcN9cK1k76io66HjuVafh67cxZnIlFfCRZpHpl9zteoXS6BmTOf5syfv7ec73C9Bnw9\n+ROGf7EjrrP04kW0JggEEmySgI1AGyALWA3cprVe57JPE0xiQHut9aZAXliCTeQoboZXoLNHN8ze\nzcczn6bMQZunWqXghRfIuL4P4xdttA1irlKSEymTnMChY7lFqndhUn9dzxuMIOd8fW/BOiU5ka6X\np/HJ91nk5OaRkJ/HxNljab9xpfVE1avDqlW0eH9DTGTpCf9iNtgAKKU6AP/CpD5P0VqPUUqNBjK1\n1nOUUl8CDYG9jkN2aq07+jqnBJvIEYqxK94C2FW5B/hw+hOwZ4/9gbfdBpMnQ0qK2xtyYfh6IvNX\nP3+KG+QCrUeiUuRpDVoz8su36P/D59aDK1aE5cvh4ot9Pp2+0rOxLOcQQ6I12AS0UqfWeh4wz2Pb\nMy5ftw1yvUQYOd94gvmG5K2ze1VyFfjqKzPbwMaN1h2mTTPbMzIKVhv19kbqTSB9P0VN5c3JzfP6\nNFSUc3o7Js9xEzjou1n2gaZUKcjIgIsvBszPbBe0KqYku91IuC4lIQFHhJNMVyMA88azPL0128be\nxPL01sV+I/LZCV63Lnz7LbRrZ39wZqaZPHLVKp/nSk1JLnLmVChSeYtyTl8pyB3XL+OJZe/Ylq8e\n9YrbTAzessuUQuY9ExFBgo0ICb/pt6mpZoLIYcPsT/D77+bN9L33vJ5rZMeLA8qcsssu85dR52vF\nl+IEOU/efrYnyu7lpbn/sj1mzHV3ccff57llyXnLLsu2ae4DGaQpwk/mRhMhE3DG1nvvmYW9Tp60\nP9GwYWT0Gsr4LzcXa3oXJ2d/FGDJRss+lhvQEtmuxxa32dHzOj13Xj6tB3a1Xen0nctvYVSbQaBU\nQH1EsTK9jzgtWvtsJNiIyLBqFXTpYp5o7LRrB9OnmyeiQghFpl1I+zp274YrrzSzLHiYf0Fz7u/0\nOPkJ5kkokEQImbg09kRrsAkoQUCIkLvyStNX07mz+exp4UJo1gzmzIH6gTdXFXcuNWeSQlg4542z\nCTSr0y7ioZuHFQQaCKyPKBTJH0IUhQQbETnS0kym2t13m6w0Txs3moAzfbpZ/jgA3rK0Im6urxMn\nzJPdL79Yiv6qXZehtz7LieTSBdsK00cU1oAphBeSICAiS0oKfPABjBtnBnp6OnzYLH/88stm4TA/\nomJV0Px8uPNOWLrUWnbOOZRf+iWP920hU8iIqCZ9NiJyzZ0LvXvDX3/Zl/frB2++CWXK+DyN3bQ0\nrokAJf6mnZ5ugquncuXMk95ll4W/TiJiRWufjQQbERZF7mj/9Vfo2BE2b7Yvb9YMPv0Uzj03oDpE\nXGf566/DkCHW7YmJ8PnnATc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KBpJTUhk2dzMvJG62LU/845hZ1qhZ09JXw4PbGDdvIq8tdr9vEAQbvv3WxN2z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\nRYJL3TBH+TalVJvcypBLuZ5WSm1xjM2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3oywfpdRFQE2t9YIgyibY4PS5ycjMQgMZmVmkz9rAU3M22JbPueQGeOwxSz+l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\nya53jmyDCTGHnNkI0cdff0F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RNsEG5xRGWnoGGnIE45nUDZbyJxansXDsu7ZpB+7++Wvu/PkrMjKzGTt/S9jf\nhxClZGQwZc6LnHfskKVqYaNWMGxYARglRAP5dhBQShUBxgFPBNC2n1JqtVJq9YEDB/J76kKJtz03\nn67807Z8+DYN06ZBEeu/esR3k2mxa61E3BUCIvXnPSy46mYa7LE+nGw870JOvPmO7edMECAwsUkD\nqru8ruYoc1IGaAgsUUrtBpoBc5RSljlFrfVkrXWy1jq5YsWKebe6EONNGLK9rL3tTc+AG24w0aA9\nKKrP8PrsMTTL/CeoNgqxR+raNHY98Qzt1luzbR4sXZ49733CLVdfWACWCdFCIGKzCqijlKqtlCoG\n9ATmOCu11ke01hW01rW01rWAFUBH8UYLDd723MR52cuQ0/6RR9jd+TZLfblTx3l75rMmlpogeGHl\nuHd5bIk12+apovGc++1c2t9wZQFYJUQTfsVGa50FPATMB34DZmitNyqlnlNKdQy1gYI73vbc9Gpa\n3bY8J3S7UtSaNoUDyVdZ+iz9xy649VYTLVoQPFm/nmemj7atGtL+YbN5UxD8ENA+G631XGCuR9lw\nL22vzb9Zgjd87blJrnmO1704ABQrRsVvvoSmTa3pBxYtgocfhkmTZMe3cJb9+6FjR0plWp1M32za\nlZUtbioAo4RoRKI+F0Y2bzZPo0eOWOsmTjSiIwinTsH118MPP1iqvr2wCQO7D+eFbpfJPq0wE8v7\nbIRYo359mDHD3nNo4EBYsCD8NgmRhdYm46uN0GyuUJOX7xiWIzTe9n0JgisiNoWVdu1MNGhPzpwx\n6zc2KX2FQsSrr8KUKZbiwwll+WPKp3w74uYcobHb9yWCI3giYlMI8Prk+eCD5unVk6NHTQy1gwfD\na6gQGcybZ5sDKbNIHP1ThvDsr2fd773t+5KNwoInIjYxjs8nT6XM6KZNG+uBO3aYKNF2ybCE2OW3\n36BnTzPC9WBY2wdYWaOR214vb/u+ZKOw4ImITYzj98kzPt4ETqxTx3rw99/DQw+Z+Xt8jJCE2ODQ\nIejY0YxsPZhyxS1Mu6wD4L7Xy9u+L2/lQuFFxCbGCejJs3x5+Oor+7QDb78Nr74qc/OxTmYmdO8O\n27dbqpbWaszzrfsCHnu38L7va1D7evJwIrghYhPjBPzkWbeuGeHExVkbP/kkP0z4QObmY5nHHoOF\nCy3F/9Y8nxfvGsGZInEkJSYwuksjN1fnlMZJlgR+o7s0ApCHE8EN2WcTo6SuTWPs/C2kpWegANf/\nckJ8nOW2wgIwAAAdi0lEQVSmkcOkSTBggKX432IJdLnjFbZVrOlWroBdY2RjX1Tz5pv2jiLlysHK\nlVCvnrXOD83HLCLNZlSdlJjAssGt82Kl4ED22QgRg+uUFxihccYEsHs6deOBB8w6jQdlTmfw7ufP\ncc4J942gMjcf5SxebL+Jt0gRsxcrD0ID4jggWBGxiUHsnAI0Z58q/e74Hj/eZPn0oMaRv5n0xYvE\nZ5sYap7z90KUsWOHSXSWlWWtGz/e7MXKJc51Gm/zJfJwUngRsYlB8v1UWbQoTJ/Ov7UusFQ13bOR\nF+a/TlK5Er5HSEJkc/So8Tyzi/bdt2+eQhZ5jqg9kYeTwo2ITQySX3fU1LVpNH9rLbe0/Q/pJUpb\n6rtv+I5lxdaJ0EQAufX4Sl2bRssXv2VR4zawaZO1wTXXwOuv5ykYq92I2onf6Vsh5hGxiUF8uaP6\nw/XpdPc5STyQ8jSZRWw81AYNMu7SQoGRW3d0Z/s7Zr9J6502zjm1asHMmVCsWJ7s8TZyVhDY9K0Q\n04jYxCDe3FED+bJ7Pp0ur3kJI9r2tzbUGnr1gl9/DaLlQm7IbaiYsfO3cNPP87n/p1nWytKlYc4c\nyEcGXdngKfgioHw2QvSR0jgpT0+Sdk+nn1x2A3X++YPea750rzh2zMRQW7kSKlXKq6lCHsnt2lyV\nX9fwwvz/WcrPoCjy8cfQqFG+7BnUvh5DZm1wE0BZpxGcyMhGcMPbU+jzrfuyoo5N6t/du6FLF5P7\nRAgruRpJ/P47k2e/SPFsq+fZmx36GGeBfJKfEbUQ+8imzkKMc+Ona2ZPwPbpdHSXRqScXxquuso+\n/cBdd8HUqQEvLNudW25KucO5BmP7v3K9lseOQYsWsH69pY85DVtz5v33Sbm8WjhMFoKAbOoUogpv\ni8uA96fTcuXgyy/h3HOtHX7wAYwdm69zSyiT3BHQSOLMGfMgYCM0G6tfBG9PFqERwoKMbAop+Qon\nsnSpSRecmelerhR88QV06hS6cwu5Y9gweP55a3lSEqxaBVWqhN8mIV/IyEaIKvK18fOaa0w8LU+0\nhttvNw4DoTq3EDjTp9sLTUICzJ7tJjQSoVkINSI2hZR8u6neey888YS1/PhxaN0a5s8P3bkF/6xe\nDffcY1/3/vtwxRU5L2VaUwgHIjaFlPxs/MzhpZfgJpuIzydOGJfoTz8N3bkF7+zda6YyT5601g0f\nDrfe6lYkqZ2FcCBiU0gJiptqXBx88gk0bGity8yE226DceNyMn0G69wy5eODjAxISTGC40nXrjBi\nRM5L53X0FsssLT1Drq8QNMRBQMgTrq7LDeMy+GjmCMpttYm1BSY/zoQJJsBnEM4bkLtvDOPVbdy5\nZmYzotxU+Xx2zPqGW66uk9OH53X0RmG7vpFOTDsIKKU6KKW2KKW2K6UG29Q/rpTapJT6RSm1UClV\n064fITbwnOPfkJ1AyxtHsKK6zQgH4I03zNP2sWMB9e1r1BIrUz55HZ35XF8ZPdpWaA6UTKRvl2cY\ns/TPnDJfQTM9icbrK0QefsVGKRUHvA7cADQAeimlGng0Wwska60vAWYCLwfbUCFysLtRHS1eiru7\nP8c3da+yP+jrr40Xm930jgO7G+lj09dRy+WGHAuebPlZkPcmtivGvQtDh1ran4oryv1dhrK3bCW3\na5Tb6xVN11eITAIZ2TQBtmutd2qtTwPTALeNFFrrxVrrE46XKwDZJRZl5OZJ29uN51TRYgzoNJgp\nV9xif+DatdC0KWzYYFvtLekbnL0hJ5aMtz02mjzZ8jM6s7v29ffvYtj00bbtB3d4hJ+TLgLcr5G3\n6xXnJQJENF1fITIJRGySgD9dXu9xlHmjDzAvP0YJ4SW3T9q+bjxnisTx7PX382yb+ziDzY1rzx5o\n3hwWLLBU+Xt6zsjMRmui3pMtP6Mzz2t/7vF03vl8FKUyrZ5nk5p244uGZpOs5zXy5hHYq2n1qL++\nQmQSVG80pdQdQDJgG7dEKdVPKbVaKbX6wIEDwTy1kA9y+6Rtd6PyZEpyJ/p3fpqMosWtlf/+Czfe\nCO++61YcyNPzkYxMN0+2xIR4SsQX4bHp64LmORVqb7f87DNyvfbFsjKZlPoi1Y7ut7T7vm5T3ulw\nr1dvP28egc+nNJJgmkJI8OuNppS6ChiptW7veD0EQGs92qPd9cBrQCuttfXT74F4o0UOtQd/bZsz\nXgG7xtjso+GsR1RaegYK3I53vk5MiOfivVuY8NFwKpxItz/500/DqFFQpEjAHlJJgQQNDeDm6OrV\nVS4hHqXg8IlMy/ux6zM/gUTt3qfznEkufXk7R+raNMZ+s5lHPn2JHhu+tfS/uUJNut4xljOly4hQ\nxCDR6o0WiNgUBbYCbYA0YBVwm9Z6o0ubxhjHgA5a622BnFjEJnLIb6wyf9Gjq6XvY8rMZ6lz8E/7\nDnr2hClToEQJnyLmSkJ8HCXii3D4RKalLhC7c+P669pvMETOeX5v7zMhPo6uVyTx+Zo07+d49VV4\n7DFLv4cSytLxrnHsSTwvx2aJNxdbxKzYACilbgReBeKA97TWLyilngNWa63nKKW+AxoBfzkO+UNr\n7TNBhohN5BCKvSueAlb25DHe+uIFrvrD3jmAFi0gNdUtorTrDTk3+BqRebMvUPIrcoHaEacU2Tbf\nzaTEBJZddtpEbjhzxq0us0gcd/R4npU1ziZBU8D4HpdJOocYIlrFJqA1G631XK11Xa31BVrrFxxl\nw7XWcxx/X6+1rqy1vszxk/9MTELYCEXSK8/F7qMlSnNX9+eYdfF19gf88IPJlbN9u5tdywa3tnMz\n8Ekgax95deXNyMy2FZq89untGDuhASixYyv06GERGoBhbR9wExqAcgnxEvdMiAgkLbQA5D2NtDeq\nJiZYntgz4+L57+1D6XK6FYwcaT1o2zYjOLNnw9VX++wLzJrQqawzeUpD7K3P/JAX92BvdtiNbMpl\n/Ms7nz8HR49a2u/odS+zL7gJPK6FUnh1/pDRjRBOJDaaEBK8BtvsUN/E53r/fYi32TPzzz8mavRn\nn/nta2THiwMakdl5l/nzqPM1mkpMiA+ae3CgLshFs7N4ffYYah/+y7MLaNuWCz54y/ZapAdxFCYI\n+UFiowkhw6/H1qJF0KULHDli38FLL8GgQaBUnr2/fK1HARZvtPQTmQGlyHY9Nr/rID69zhxrVs9+\nO4m7f/7aenDdurBiBZQvb9u3JKqLPaJ1zUbERihYNm0ye25+/92+vn9/eO21PAfxDIWnXVinnzIz\nmXJVV3qv+dJSdbR4KcquXwP1vI+oJHBp7BGtYiNrNkLB0qCBeTLv2NGkKfbkzTeNEE2fDmXK5Lr7\n/MZSC/ZaVq7Ytw+6d6f3mv+zVGWrIoy4bTjjfQgNkGO7eKMJBY2IjVDwnHceLFliwuOnplrr582D\nli1NMM+k3N0kvS3AR3Ssr82b4Z13YOpUOHjQtsmYtv1o9fDtAXVXoIIpCA7EQUCIDEqWhJkzYeBA\n+/r1600Qz/Xrc9Vt1GQFPXHCOE20bAkXXQT//a9Xofm8WScuHj1UBESIKmTNRog8Jk40omP32Sxd\n2niqdegQcHd2YWlcHQEK9Ka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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for t in range(100):\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 % 10 == 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[0], fontdict={'size': 20, 'color': 'red'})\n", + " plt.show()\n", + " plt.pause(0.2)\n", + "\n", + "plt.ioff()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/302_classification.ipynb b/tutorial-contents-notebooks/302_classification.ipynb new file mode 100644 index 0000000..dcffc10 --- /dev/null +++ b/tutorial-contents-notebooks/302_classification.ipynb @@ -0,0 +1,315 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 302 Classification\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\n", + "* matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch\n", + "from torch.autograd import Variable\n", + "import torch.nn.functional as F\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "torch.manual_seed(1) # reproducible" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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kQsCU5TB0dYZVh5Yyz3cZPxBh/xxQeA9T93owa1pf5TnGnb0pHdTLKPiQgter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\nOqpwrTy7MA9bfU/jz37TsdfvErbfP8uqhglbPRq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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# make fake data\n", + "n_data = torch.ones(100, 2)\n", + "x0 = torch.normal(2*n_data, 1) # class0 x data (tensor), shape=(100, 2)\n", + "y0 = torch.zeros(100) # class0 y data (tensor), shape=(100, 1)\n", + "x1 = torch.normal(-2*n_data, 1) # class1 x data (tensor), shape=(100, 2)\n", + "y1 = torch.ones(100) # class1 y data (tensor), shape=(100, 1)\n", + "x = torch.cat((x0, x1), 0).type(torch.FloatTensor) # shape (200, 2) FloatTensor = 32-bit floating\n", + "y = torch.cat((y0, y1), ).type(torch.LongTensor) # shape (200,) LongTensor = 64-bit integer\n", + "\n", + "# torch can only train on Variable, so convert them to Variable\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()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class 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" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Net (\n", + " (hidden): Linear (2 -> 10)\n", + " (out): Linear (10 -> 2)\n", + ")\n" + ] + } + ], + "source": [ + "net = Net(n_feature=2, n_hidden=10, n_output=2) # define the network\n", + "print(net) # net architecture\n", + "\n", + "# Loss and Optimizer\n", + "# Softmax is internally computed.\n", + "# Set parameters to be updated.\n", + "optimizer = torch.optim.SGD(net.parameters(), lr=0.02)\n", + "loss_func = torch.nn.CrossEntropyLoss() # the target label is NOT an one-hotted" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "plt.ion() # something about plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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K8rNo1Qh+Jzch7thlqQBV2dv1/8BGxjsvWYQqVO2ojTQWpCiKMgJwAsD/aJqW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\nIIRYLKV82MF6ORy9TyoHIUQc0FRKqRNgGiCE6Ap8CLSXUiY5Wh9nQQj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Ulp9F68bwPbUZccevSAWoyt5u+Ac2Mt55ySJUoWpHbaSxIEVRlBGAkwC+pWla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\nRG/aWOmXVbllJHfdbznvzKd2KAZvLwxenlR94F7ar5yuBaq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ksvxM2jZD17PbEH/qulSAqixs8z+wkPHOSxZhFap21EdqC1IURekBOAPgW5qm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\nhgWsPbGHqMcx+Jcoz6AmL1K/bDWaVqiFb4EiXL1nvoFi3/p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H05bLZPmZtW+O7me2Iv7kNZkAVVn4xn9gJeedlzyiKlTtqI80FqQoijIAcBrA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\nmsQI0RxYAjwF/gfcBZoC44EqQFL+WRYAbYDLwFLgIVAK6Ax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f2YD7k5YoZPNZtGiIjic24+3RSwrBqbSwDf/ARsk7LmUk5ajGUdNoLUBRFGUE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\n6inE5kzwLYzZ/T1yi/57FXfo8VUsOb0NJ6f/gvZ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y/CxaNET705vw7sRVmQBVVvj6/bCR885LHpEaVTuqI60FKYqijACcAvA/mqZl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\nWehweRAouDujKErlaugUSko3cTlcjG3Vg9W6sE+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F/XELZLL8LJrVR7sTG/D26CWZAFVWxNo9sJHzzUsekRpVO6ojrQUpiqKMABwH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\njLzi/3JHDj26igWnN+Pk5BVo81ka9OHHytvDVzV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xaMIiqWw+s5aN0OH0Jrw9cVUqOJUXsX4/rGR845JFqEI1jppGYwGKoigDAKcA\n/I+maalcTYqipgCYAgBOTuxK2BPElyg7PAb3h82W2X6CFokQNGUpDN1dYNWhpczrXccPRMTf+xTe\nw9SzHsya1ld5jQmnr0sHp3IK36fi1crtaLl52X/XnLnJOG/SlbsQFZfIfHXFto2IqvQcbeH75BSi\ndxxD7P6zKExKg66NBWp9OwBuU4ZC26z0+1FmyGvE7DmJgrdJ0DY3gcuoPmW9uhwHdIVt93aIP3YZ\nmc/DwdXWgn2fTrBqX1qf9VOChiKFSSy6C1MUjOu7qf6XrSE0EqAoiuKjNDgdpmla5jZxmqZ3ANgB\nlFYz18R9CaI6ivznoMLeSLRYjNfr9soNUCaNPFDr2wF4s/+MzPMUl4smK39Ua40xu04yjnlz8Bya\nrVtYFmzY1N+jxWKIioplBqiKbCOiY20Bs+YNwOHzUf+nKaj/0xSpMWKRCEGTliB2n+SPqJjdJ2Hl\n0wodzm6BlrEheHq6cB0/SOb1H1g0kixKzQDF4ynMrrTu5KV0ckpNpHaeI1W6a243gHCaptepvySC\n+LIlnr/NOObdRT+FlSBa71oBj/99K7VxVt/ZHu3PbIKdbweZ14lFIry/4o/oHceQcPq6zD5GmS9e\nI+PZK8Y1CnPzUZyeCVFJCZJvBrAKMBwdbbl7nZwGdwdXV4dxDlW4zxjJWN09ZNE6qeD0SapfEB6M\nUBz0KYqSu0m4PA6Pi6Z/zpN/XouP2uMrpofV10YTT1DfABgDIJSiqOcfjy2iafqygmsI4qslKmRu\nE06LRBALhHKzuDg8Hpr/vQgNf/4O787fhiA3HwauTrDzbS93/0zcvxfxfP4aFCQmlx3TMjNB/QWT\nUX/eJBSmpCNg1FzWTQYpLhexe08h8p9DCis0lCcuKkbm83CZrx+1TI1Rd854vFqxldVcbDmP6I0G\ni6ZJr0UgQMLpG0g4dQ0lmTlI8VO8qTnpyl1khryGaZO6Ms9THA6sfFopfDUKlLa1rzt7HHRsLfHs\nf7+jKEXy3524RICHo+eh8H0q6s+bxPC3q9k0kcV3HwDzrxUE8ZUrSstAzO6TrP6/wdDdhVWKsbaZ\nCauOsXH/XkTAqLlS+3hKMrLwfP4aCLJykXj+tlJNEPWc7fDiZ+W3NL49dlnu97HGv80CxDTC1+5h\n1SJeFo4WH/q1HWFczxVuU4fBtls7qSebvLhE+PlOUrqB4dsTV+QGKADwmDVWcYCiqLK29lomhlLB\nqbzn89clAhXEAAAgAElEQVTAvFVjmRuHiVJkKzNBaEDckQs46+iNkIVrUfIhi3G8+/QRGru3WCTC\n8/lrFG4yDftzp9IdevNlZMOxUZSWgdd/78PtbhNws+MYPJu7GrnR8QBKX5M1+X02+if4o/mGxTD3\n8lS6ooK4RIDc17HIi45HfmwCaJFI8rxQCL8ek1XqriurWnl5Dn07o8ESOSWhKArN1i2AZdvSvlIR\nGw4w3i9y40Gl11iTkJbvBKGm1PtPcMtnrNQPSnmsOrREx+t7NLZJ891lf/j3kk4KqCocHW2Iiz57\nzUlRaLZ2AerOHic1vjAlHfFHLqIwKRXxx6+iIP6dUvez7dEB3ue2lH2DenvqGu4P/kGltbfYtFSq\nFYcsKX6BiNx0GGkPnoGiSpMe6swcI9HS/ahWQ4gFAoXz8I0MMCSbXSdjoOa1fCe1+AhCTeFrdrMK\nTtoWpnCdNAQNl85QKTgVvEtB7N5TyItJAN/YAM7De8HCyxMFCcz14SqTVHACAJrGsx9XwsDNCQ59\nOkmc0rW2KAtcVj6tlQ62SVfuIvyvPWiwcCoA1Vurc/V0WfdssvZpLXe/FwDQNM2qHYqmW6Z8bcgr\nPoJQg6ioGO8v+TOO06/lgP6Jd+G5cg54KmSyvVi2EeecO+LFzxsQu+80IjYcwPU2w3C763jWLTKq\ng/A1uyX+nPk8HCl3HiHvTenrRPue3mi2fpHcmnnyRG39F+KPvyQIWbZg/1zTNfM01rOJoihYsGhb\nb+HlqZH7fa3IExRBqEFYUMjq6YlWkLHHJGLjAbz8bbPMc8k3AyAWicA3MVLcy0iVFu0VIO3eExR/\nyETS9Qd4uXwLcsJjys5Zd/KC5+q5qDvrW9j19EbU1n+R8TgUmc/DGYNOQUISChKSYODiAON6rnjH\nItX/E66+LlxG9UFtGXuf1OE+YxRjlfU63zO/TqzJyBMUQahBy8QIOtYWjOMMPWR3YmUiFggQtmqH\nwjGpdwLhMqKXwjEuo/vCpLGHUvc2qqBKB5Fb/kXAyDkSwQkAUm4/wk3vMUgPDIGRuwuar1uIrveO\nwNDdhdW8nzL53KYMU+oJTJRfiJgdx3GpXk/kRMWxvo6Jy4jeqD1BftBznzEKDv26aOx+XyMSoAhC\nDRSHA9eJgxnHuU0ZqtL8qf6PUZiUxrwOPg8Nl84AR0tysyrF4aD2uIFovWsFOt3cB9vu7Vjdt/7C\nqeh8ez/MWmq2uZ62hSlertgi97yooBBPvv9N4ph1J9kVyMszcHOGnpNd6T/XdkTDpcq3ssiPfwe/\nHpMZExuU0XrX7/DavxpmLRqWHTP38kTbI2vRctNSjd3na0Wy+AhCTSWZ2bj+zQipJ4JPbHt0gPeF\nbeBwuUrP/fbkVdwfMotxXK2x/dFm/2oUpX7Am0PnUfD2PbQtTOEysg8MaktWJc8Oj0HS1XsozshC\nxtNXSPV/XFbGyKieK+rNnQDXCaVBl6ZppNx6iLcnr6IkMwfvr9xVq+CrlqkRSjIVt1UHAN+np2HW\nrLS9em7MW1yq11Nh4Gi2fhHqzvpW4lj0zuMIW70TeR875VJcLigel3H/1TfH/obz0J6Ma1SWrN5R\nyqppWXwkQBGEBhSlZyB4zmrEH7tc9gOQb2IEtylD0Xj5LHC1VPuhlPHsFa42Z96o23DZ92j8y0yV\n7iHIyUNebAK4utow8qgtd1z0jmMImlo5v/Vb+bQGxaGgZWIEp2E9ICwsRuCERYCMrDf7/l3Q4dQ/\nMvdT0TSNzOAwCPMKUJyRhXsDFPerAkorU3xzRHHl86pS0wIUSZIgCA3QsTBDm/2r0XTtT8gKiQDF\n48K8ZSPw9HTVmtesWQOYNmuATAW18yguF64KvnUw4RsZwNSzHuM4No0UNSXV77+OPQmnr0Pbylxm\ncAKArOAwFCalQU9GmxKKosqexJJuPGB1b5GM+oXl5ce/w4egFwCHA6sOLaFTgUVwazoSoAhCg3Qs\nzGDTWbrVuTqarVuAO90mQFwi+xVXvfmToG1phpg9J5F09R7EQhHMWzaC68TB0LEyZ5xfLBIh8exN\nxOw6IbHHynXCoNJW6B+xbaRYEYoV1ALMj3+P5wv+QtuDaxTOYVzPFRSXy5h1KSosQm50fFm1cUFu\nHuIOnUdaQDDSHjxDfvw7QFz65omjxYfziN5o8c8S8A1lF8klVEde8RFEBRELhXh34Q6yXkaCp6sD\n+36dYcQyI+1zKX6BeDZ7JTKfh5cd07EyR72fJsOqQwv4956GopR0iWs42lpotWM5ao/t//l0ZURF\nxfDv9x2Sr9+XOqdrb41ON/bCuJ4rAOBm52+RylAotapwtLUw4N1daJubKhzn3286uxR0ioJ9n45w\n6NMJT2f/wZjmbu7liS53DlT4nrSa9oqPBCiCUEJawDPkRr8F38gAtt2+kfsK790lPwRN+RmF78s1\nr6MoOPTvgjb7VsltScHkw5PQ0qccE0NYd2yNkswcXG7QC8Vy6v9RHA463dont+rBk5nLEbnpkNz7\naZkawbanNzKfhSE3Mo7Vni8da3OFRVIrSteAo7Bs01ThmLzYBFz/ZgSKkpkzI5XVaucKuE2S3ylZ\nE0iAqgQkQBFfmhS/QDyZuUKi4Crf2BAe//sWjZZ9L1FNO/XuY9zuMl5u1plVh5bofOeA0kVSZQld\nvhmhSzcqHGPbowM6Xt4pdbwkOxdn7NqzakTIBsXnocmK/6H2uIE4bdtO7jej8ux6eQN06WvG5GvS\nT3HK6BF8ltW3tJzoeFxp3JfxW5OyDFyd0OvVJY3VWJSlpgUosg+KIBik3n2MO90nSlUDF2Tn4uWv\nm6T27bxYulFhSnTq3cd4f+WuRtaWcPIa45jka/chyJNODU+9+1hjwcm0aX34Pj6F+vMnQ8fKHHoO\n0gkLsjRfvxg+l3ag2doFat1f38We9UbknLBojQcnAMiLeYtrrQaz7p1FMCMBiiAYBM/7U26CAgBE\nbTmCnIhYAKUZXqn+ihvjAcCbA2c1sjam9hBAaUFSYb50IKIF8luSs0XxuPC+tB09np2R6KNUf/5k\nxmtNPeuVJSKYNHCH5TfNVF5H3R/Hs34izY2MU/k+TLJeROD+sP9V2Pw1DQlQBKFA1svI0pRiBjG7\nTwIACll+eylKTmcexIIRixJK2uYm0DY3kTpu2rSe2q8ZaaEIkPGVoPa4AdBztFV4bf0FklXLW2xe\nBr6CYq08Od/t6swcA4+ZY5gX+5Gq3//YSvULQoaCbQEEeyRAEYQC+XHsehN9Gqdrw1yXDwB0bC1V\nXlN5blOGMY6pPWEQODzpHSUGtRxh69te/UXIqHvH09dDx+u7oV/LQXo4h4Oma+bDeZhktQbTJnXR\n9f4R2PfpKBE4TZrURbvj6zEg0R8tNi+FTZe2sGjTFK4TB6P745NosXGJUsu179e5rHdURXl34U6F\nzl9TkH1QBKGAlpkx86By4/Sd7GDdyUtxW3CUPmFogkO/zrDv20lu6rShuwvqzZ8k9/qWW5bhRruR\nKEhMVun+XF1tWLaVnTlnXNcVbQ+vQfCc1ch4+gq0mIauvRWarp4L52Gyi9uaNKwD7/PbUJiUivz4\n9+CbGMK4rmvZ+TrfjUKd79SrAK5rbYHaEwchettRxrEUlwNapHzPJkWvhAn2yBMUQShg4eUp8yng\ncy4j+5T9c+Pls6SKtpZn3ckLtt018OSC0qeR9ic3ov6CKRKbajlafLiM6oOu949Ax0J+pQN9Z3t0\ne3Qc7jNGqfTqq9aY/tAyMZJ5LuKfg7jRdgTSHz6HuEQAWihEQfx7PBj+Ix6OU5wUoWtrBQsvT4ng\npEnNNyyG83DFFeCtO7ZGp5v7Vfo2ZtJEucrxhGwkzZwgGMTsPVVaB04OK59W6HLnoMSxpOv3ETj5\nZxS8fV92jOJw4DjEF613rQDfQF/j6xQWFuFD0AvQQhFMGnsoXYJHVFyC4rQMBE5egqSr9xjHG3rU\ngu+TUzL/LulBL3C9teI9QZ5r5qP+3Ins1lZSgoRT15Fw8hoEOXkwrOMCtynDJBIzVJERHIbYfadR\nkJgCUWERdO2sYFjbEXY9vSVS1rNCI/Bo3EJW35Z0bCzR/+2dCnmNWNPSzEmAIggWwtfuQcjiv6Uq\nYdt0a4d2x/6W+RRBi8V4f+Uusl9GgqunC4e+naDvbF9ZS1aZqKgYT2YuR+y+M6CFMjL9KApOw3qg\nzb7Vcvf83OkxiTHI8U2MMCTzMeN68uIScaf7RJnZd3W+H43mG5dI7EOrCDlRcbjo4cvY9JHi8+B9\nfivsfDtUyDpIgKoEJEARX6Ki9Ay82X8WudHx4BsZwHloD5g1b8h8YTUlKilBwslrSPUvDRKW7ZvD\naUiPsqBTmJKOdxfvIPNZGAoSk8E3MYRJA3fUHj+I8ensmF4TVnuNekdeU1j+SSwS4XKjPnJbmQBA\n0zXzUY/lk5iqIjYewNNZvzOOc5s2HK22/lph66hpAYokSRAESzoWZqg3Z0JVL0Mj0h4G4/6gmRLN\nEKN3HEPw3D/R7uQGWLVrAV1rC7hNHAKo8LNf5pOXDPlx7xQGqHfnbysMTgDwet1eeMwaW6GZeWyT\nHnRtNJOdSZQiSRIEUcPkxSbAr8dkmZ16i1LS4ddzCnKj49W6hzaLKuoAGKutJ5y+zjhHYVIa0h+F\nsLqfqkyb1mc5jrnUEsEeCVBEtVH8IROJ524i4cwNldOeCWYRGw9AkJ0r97wwNx+v1+9X6x5s9mfp\n2lkxJjnIqoAhc5yGSjbJY93Ji3FTtJ6THex6+VToOmoaEqCIKifIy0fg5CU46+CNu/1n4N7A73HW\nuSPuDvwehUmpzBMQSok/epl5zL+X1LpHwyXTocOwaZlN/T3jBm6MYygOh1VFDXVQFAWvfavAM9CT\neZ6rq4M2B1aDw+VW6DpqGhKgiColKi6Bn+8kxOw6AVFR8X8nxGIknrmBq62Hoii96hrlfY1KMrMZ\nxwiyctS6B8XhoNfLizCqJ72PicPno8WmpYz7kADAbfJQxnJMNt3bwcCFea+auiy8PNHt4TE4De1R\n9r2L4vHgOLAbuj74F9berSp8DTUNSZIgqlTc4fNIe/BM7vnChCQEz/0TbfatqsRVfd0MajkgJ+KN\nwjFsNicz0TY3Re+wy0gPDEH8scsQ5uXDpGEd1BrTT2JTscJ1ONmh8fJZCFn8t5x7mKDZOvUqoSvD\npGEdtDu2HoKcPBSlZUDb3ETuRmVCfSRAEVUqesdxxjFxh87Da+/KCt/r8qVK8Q9C1ObDSHvwDBSH\nA6uOreExczTMWzaWOd510hAEz/tT4ZxukzXXeM+idRNYtG6i8vUNFk2Drr01wlbtQM7r0qrxFJcL\n+76d4LlqDozqVOzrPVn4RgYVXnSWIPugiCp2wrg5BDl5jON8ru6CnYbKA31NXizdgJfLt8g8Z+XT\nCrbd28NlVB/ol6ssLsjNw412I5H1IkLmdcYN3NEt4Gi1/AGcFRoBQW4+DGo71siU7pq2D4p8gyKq\nFMXyo/Kn35yJ/yReuC03OAGlbR9CFq7F+Vqd8fj73yD+2K6db2iAzrf3w2lYT1DlqpxTPB4cB3dH\n5zsHqmVwAgCTRh6wbNusRganmkgjr/goivIFsAEAF8AumqbJBwOCFeNG7ki7y/w0zdPTrYTVfFki\nNhxgNY4WiRC1+TA4fB6a/11aU1Db3BTtjv6NgvcpSA8IBmgaFm2bQc+eXSdcgqgMaj9BURTFBbAZ\nQA8A9QGMoCiK3a42osZruGga4xiKx4VdT+9KWM2XgxaLGVt6fC5q8xEUpkg2StSzs4bTYF84DelB\nghNR7WjiFV8rANE0TcfSNF0C4CiAfhqYl6gBbLu3h1kLxfXsnIf1JD88P0OLxYyFSz8nFgjw9hjz\nHiiCqC40EaDsASSU+3Pix2MSKIqaQlHUE4qinqSlSZdYIWoun0s7YCKnooCVTyu02v5bJa+o+uPw\neIyBXZbiD1kq3a8kKweFKemlgZEgKkmlpZnTNL0DwA6gNIuvsu5LVH86VuboHnQCCaeu483BcyhO\ny4Cegw1qjx8I+94dGTdq1lR1ZozCo/ELlbpGr1w2HxsJp68jfO2e0u9UAPQcbOA6ZSjqzZlAvgsS\nFU7tNHOKotoA+IWm6e4f/7wQAGiaXinvGpJmTlRnMWmJSM/Lgp2xJRzNqu+rRZqmETB6LuKPXGQ1\nnmeghwHv7rHO0Hv5+1a8WLJe5jmLNk3R6eZeEqQqWU1LM9fEE9RjAO4URdUC8A7AcAAjNTAvUY2I\nxCKcC7mLLf6nEJYcBz2+NoY274I5XUbC3IBdVYDq7npYIJZd3IlHb14CKK2/1qVuS/zedxpaulS/\nvB+KotD20F+w7uiFyE2HkBXyWuH4Rr/MZB2cMkNeyw1OAJD+MBiv/tiGJitmK7VmglCGRjbqUhTV\nE8B6lKaZ76FpWmFnL/IE9WUpFpSg79Z5uB4eKHVOm6eFSzPWonPdllWwMs05/vQmRuxeCjEt/Y1F\nl6+NazM3oL27ZxWsjD1hQSFi953By982o6hctp62hSkaLp0Bj5ljWM8VNHUponccUzhGx8oc/RP9\nK7QPEyGppj1BkUoSBKNZx9dh4x35JYl4HC7ifz8LO5Mvc/NkYUkR7Bf2RWaB/AKp9WxcELbsaCWu\nSnVigQDvr9xF4ftU6FhbwK6nt9zW7PJcaTYAmcFhjOP6RN+AoauTqksllFTTAhT5+kwolFuUjx33\nzyocIxSLMO3f1ZW0Is07/vSWwuAEAOHJcfCPlF/Utjrh8Plw6NsZ7tNGwHFAV6WDE1C694zVvViO\nIwhVkGKxhEIPYl6gSFDCOO56WCDEYjE4Gsy4S87+gF0PzuFi6AOUiATwdKiD6R0Gavx70KskdmWU\nXiXFwrtOM43eu7qy822PjMehCscY1XOFvrPUjhKC0BgSoAiFBCIhq3HFQgGyC/Ngqq+Z1gN3Ip6i\n37Z5yC0qKDsWnBCJvQ8vYkH3sVjZ/zuN3AcA9LR0NDrua+A2dTjC/9oDUWGR3DEes8ZW4oqImoi8\n4iMUaurowXrsi3fRGrlncvYHqeBU3qprB3DgkeYqIgzw9GEco8Xjo1fDbzR2z+pOz94a7U5sAFdX\ndlB2nz4C7lOHV/KqiJqGBChCIQdTK9S1dmY1dtTeZRCyfOJSZOeDc3KD0yezT6zHkJ2LsOjsVsSm\nvVPrfk0c3NG1nuJuqOO8esHS0FSt+3xp7Hv5oNeri6g3byKM6rnCwNUJjgO7odPNfWi55ZeqXh5R\nA5AsPoJR8NsItFg1DmIW/62cmPwHBjfrpNb9Wq+egKA45gyyTyiKwk/dxqj12i8jPxs9N/2IwLhX\nUud6N/oGJyevhDZf+WQDgtCkmpbFR75BfaFScj5gs/8pHAq8ivT8LDiYWGF8m94Y2qILtLl8WBgY\ng8fVzP95mzp5YHan4Vh761/GsQ9jQ9UOUMVCgVLjaZrGqmsHYGNkjlmdhql0TzN9YzyYtwOXQh/g\nUNA1pOVlwtHUGuPb9EZHj+YqzUkQhHpIgPoChSe9QecNM5GU/d9mzPDkOMw/swnzz2wCAFgbmWFi\n2z74qdtYGOnqq33PBnaurMZpoi17U8c6CEmMUvq6NTcOYYb3IInAHBT3Cg9iXoBDcdDJozka2bvJ\nvZ7L4aJvkw7o26SDSusmCEKzSID6wtA0jUE7FkoEJ1lScjLwx9X9uPQyAH6zt8BEz1Ct+3b0aAYO\nxZFZaaG8LhqoKDG9w0Dse3hJ6eveZaUhIDYUHdybIiI5HqP3/YIn8eESY3zqNMPBcb/AwdRK7XUS\nBFGxSJLEF+bm6yCEJ8exHh+SGIUl57erfV8Xczv0bdxe4RgPa2d0r+8ldfx5QiQWnNmM6UdWY831\nQ0jNyVA4TyuXBpjXdZRK68wpysf7rDR0XD9DKjgBgF/kM3T8+ztkFeSqND9BEJWHBKgvzJ0I5asZ\nHAi8jDyGrDg2do5eiMZyXpHZGlvg9NRVEq/4covy0WfLHDT9YyxWXz+IbffOYP6ZTXBc3A9/XNmn\n8F5/DpyJfWN/lns/edwtHbHu1r8KnzCj0xKx8/45peYlCKLykQD1hSkSFCt9TW5RASJT3zKOKxEK\n8Cj2Je5FPUdmvnTpHwsDEwTM24l/hs2Bp0MdmOkboY6VE37tPRnPFx1AfdtaEuOH7lyMi6EPZN5n\n8flt2Op/SuF6vm3TCyFLDiH+97O4M3szKCj+vtXBvSk8bJxZvR7c90j5V4gEQVQu8g3qC5FXVIAl\n57dj1wPVfvPnceTXTBOJRVhxeS+23D2F1NxMAIAOXxvDW3TBiBZdEZ+RDB2+NrrXaw0rIzN87zME\n3/sMUXi/wDcvcTXskcIxv1/dh8nt+jFmGzqZ2cDJzAaLfL/F71f3yRyjr62LdYNmoUQowIf8bIXz\nAaXfqwiCqN5IgKqGrrwMwNa7p/E8MQpaPD661WuNBzEhKldqcDS1RgO72jLP0TSNEbuX4sSzWxLH\niwTF2PfwksTTiBaPj9GtfLFp2BzoMpT9+ffxDcZ1vctKg39UMOtWHSv6TYONsTlWXz+IxMzUsuMd\n3Jti3aBZaO5c2jbeRNcQWYWKvzFZG5qxuidBEFWHBKhqhKZpTDm8ErsenJc4vjUtUa15Z/oMAVfO\nE9TF0PtSwUmeEqEAewIuICEzBVe/X6+wMGwGQ3XwTzKVTFb43mcIpncYiICYUOQWF8DVwh4eNpKV\nLsa09sU/ficUzjPWq4dS9yUIovKRb1DVyNa7p6SCk7rGtO6BOV3kNzhmaqUhy43wIFx+FaBwjIu5\nLau5rr16xJgy/zkuh4v27p7o2bCtVHACgB+7jIC5vvwuv46m1pjafgDScjNxLuQuzj73V3oNBEFU\nPFLqqJqgaRp1fxnGKplBFhNdQ8zsOARnQ/xRUFKMhna1Ma39APg2aKPwOrelgxGjwhNa/ybeODNN\nfg+oN+nv4bZ0MOO+KaA0+eLazPVo5lRX6XXIE5IYhWG7liAiJV7iuIeVE9q7NUHAm5eITHkLoVgE\nAOBzeRjo6YNNw+fCwsBEY+sgCE2qaaWOSICqJuI/JMFlyQCVr3c0tcbbP5RPoPD8fYxKVRuaO9XF\nk4X7FI6Zc3ID1rEojwQA9iaWiF1+Glo8zbUPp2kaN18HISAmFPklRbga9hCh72IUXlPfthYC5u2E\nsa6B0vcTiIQ4+ew2dj84j7iMZJjqGWJEi66Y0LaP2hulCQKoeQGKvOKrJj79Jq+qAZ7eKl03kEWr\nCVkUvUL75K9BP+D3vtNY/XB+l5WGk89uq7QWeSiKQtd6rbGg+1hcecUcnAAgLOkNNjF8v5Ilv7gQ\nXTfMxMg9S3Er4gli0hLxJD4cc05tROMVoxGl4pMxQdRkJEBVE85mNrAxMlfpWl2+Nr73GQwAeJEY\nhZ/ObMKkg7/jt0u78TYjWeG1U9r1h6me8k0GR7f2ZRxDURQGNvVB21qNWM1543WQ0utg4/izW3j5\nnjk4faLKJt4fjq+Df1SwzHMJmSnou3UexGLm150EQfyHBKhqgsflYUq7/kpfZ6Cth9NTV8HBxAqD\nti9Ak9/H4M/rh7A74AKWXdyJ2j8PwtxTGyHvVa6NsTkufbdWqVdr9W1qYWizzgrHFAtKsPDsFtT/\ndQRjQsUnogr6AX446JpS4+MzkiFS4ok2LTeT8R6vk+MZ94URBCGJBKhqZKHvWHRwb8p6/A8+QxG3\n4gx8G7TBuAPLcfq5n9QYkViEtTePYMWVvXLnCU9+gxIlWlxkFuZi3a1/Zf4Qzy8uxIIzm2E5zxer\nrh0ADfbfOFu7NPjvHvk5iE17p5ESTel5WUqNN9DWk5uWL8udyKcoFpYwjrvCMlATBFGK7IOqRnT4\n2rg2cz3GH1iOo09uKhzrYm6Lv4f8DxwOB6+T43D8qeK9TGtvHsGcLiOh99kG2yJBMWaf3KDUOpOy\n07Ho3FY8T4zE0YkryurvFZQUoevGH/AwNlSp+QCAy+Fg5/2zOPL4GgoFxQhJjIaYFkOHr40hzTph\nac8JcLNyVHpeoDSB5Onb16zHD2uu+OnwcwKWXYSFIvW+MxJETUOeoKoZHb429n+7DLXM7RSOm9tl\nVNlG2aNPmKs2ZBfm4fJL6d/g198+hpyifJXWevzpLZwN8S/784bbx1QKTkDp672Qd9EIiA1FcEJk\nWXp6kaAYBwOvwOvPSXj1Plaluce36cV6rJ6WDn5UsG9MlhZO9diNc2Y3jiCIUiRAVUNaPD6u/bAe\ntS3sZZ6f22UUZnxMigDYV2PI/Ky6A03T2H7vjOoLBfDrxd0am0uRD/nZmHToD5Wu7d2oHTp7MGfm\nmukb4fz0NVJFb5l42DijE8P8JrqGGNGym1LzEkRNR17xVVPuVk4IW/ovTjy7hZPBd5BfXIh6Ni6Y\n2n6AVF09pqcteeMyC3IQ9yFJrXWGJ78BUPqEFs+QMaiuR29e4nlCJDwd65Qdyy3Kx8HAK7gR/hhC\nsRBetRpi8jf9YGX0X609DoeD89/9hRlH1+Bw0DWJV3JmekZo69oY/Zt0wIiW3aRegQYnRGDb3TMI\nS3oDfW1dDPD0xuhWvtDX1pUYt2PUArRfO01mRQotHh8Hxy+TmpsgCMXIRt2PEjNTse/hRbz5kART\nPUOMbNlNo5UNKlJ6XhYcFvZV+KG+toU9on49IVE/L7coH0azlfveIgu99REKS4qg/7+OcrMFNWX3\nmMWY0LYPAOB+9HP02zYfGZ+1BtHmaWH3mEUY1eq/VPgHMSHY4n8KgW/CUCQsRn2bWpjWYQAGNu0o\n916zT6zH+ttHpY7bm1ji2swNUr8oJGSkYOW1/TgUdBW5RQWlLeQbt8OC7mPRqlwCCEGoqqZt1CUB\nCsDic1ux+vohqay0Xg2/wdGJy2Ggo1dFK2Nv5dX9WHRuq8xzHIqD01NXoV+TDlLnOqydhnvRz9W6\nt72JJdytHJGem4WXSap9J2KLy+HCwsAYzR3rwi/yKQrk9Mficrjwm70Z7dw88dOZTfjz+iGpMTp8\nbaX8gVUAACAASURBVByduFzmv5dNficw89hauetwMLVC5C/HZVZ1LxaU4EN+Nox09L+I/3aIL0dN\nC1A1/hvUXzcO44+r+2WmTF96+QAj9vxcBatS3kLfb7Fp2Fypzb4e1s44N/1PmT+EASgsJMvWu6w0\n+EU+q/DgBJSmzafkZODyqwC5wenTuL9uHsHhoKsygxNQmoAxfPfPeJP+XuK4WCzG2ptHFK4jMTNV\nbqalNl8LdiaWJDgRhJpq9BNUsaAEjov6IS0vU+G4Z4v2o6mjRyWtSrELL+7hH78TuBcdAgqAt3tT\n/NBxKHo0bAugNOX5dsQTfMjLhqOpNdq5NZFowy7L71f2Ysn57ZWw+srF5XDR2M4VwYmRCsfN6zoK\nfw6cWfbn4IQINPvjW8b5+zZuj3PT16i9ToJgq6Y9QdXoJIlbEU8YgxMA/Pv4erUIUHNPbZT6zf5q\n2CNcDXuExb7jsKLfNPC5PHSv78V6ztPBd3A74il4XB5osRi6fG3o6+jCzdIBTezdkVOYj0OPr2r6\nr1IpRGIRY3ACSv8dlg9QhSXyn8zKK1TwBEcQhPrUClAURa0B0AdACYAYAONpmlZu234V+jztWv44\n5ZrqVYSzz/0Vvnb6/eo+tHf3VCo4zTz2Fzb5nZQ4lldSiLySQkxtNwC/9pkMABjftjf+8TsB/6hg\n5BUXsN6YWtWsDc2QkpvBOK5EKPn3qWPtBC0en7G6RiM7V7XWRxCEYup+g7oBoCFN040BRAJYqP6S\nKg/b9Gx5+5EqE1OHWAD45w77KtzHn96UCk7l/XZ5N269fgwA6FS3Bc5MW42MtddVrn5eFaa07wcH\nUyvGcS2cJbM1LQxMMMhTfnYfUFoId2p71dujEATBTK0ARdP0dZqmP/36+QiAg/pLqjxtXRszbsrk\ncbgYp0QlgorCJtPubrTsatqysAlmsoJi61pfRrp0I3tXzOkyilUB3u86DJI69ufA7+Foai33mmU9\nJ6KOtZNaayQIQjFNZvFNAHBFg/NVivVD/geegsKgi3zHwdbYohJXJBubSt+f8l2YEl/EYjEexL5g\nnO9ulHRQHOfVS6MbTh1N5AcBVejytTGxbR/4z94KY10DzOs6Cu3dPOWO/6nbGLR1bSx13MHUCgHz\ndmJs657Q4WuXHa9vWwv7xv6MZb0naXTdBEFIY8zioyjqJgAbGacW0zR97uOYxQBaABhIy5mQoqgp\nAKYAgJOTU/P4+HhZwyrd24xkrLy6H6ef+yE197+ECWsjMyzoNhb/6zy8CldX6tiTGxi+mzndvbaF\nHXgcHqLSEqCvpYtBTX3wY+cRaOzgDgDIyM/G3ocX8TD2JU4F32Gcj8/l4eG8XWj+2Suwn05vwp83\nZKduK8NM3wijW/li453jas/Vq+E3mNd1FBrbu8FUX7K/VZGgGH/dOIzt988iMTMVQGldvNmdhmNk\nq+6Mc2fm5+DNh/fQ19KFh42z2mslCFXVtCw+tdPMKYoaB2AqgM40TbPqjVAd0swLS4ow9chqHA66\nVlaYFACMdPTxvc9g/NJ7MvjcyktyLBEKUFBSBCMdfdCgEfouBiUiAepYOaHn5h9VLsKqzdPCySl/\noFhQgrH7f0NBSZFS1+vwtXHxu7/QuW7LsmMDtv0kUSRWHTwOFy2d6+Hhm5cqz6HD00LamquM+44+\n7aHS4vFhYWCi8v0IoqqQAKXMxRTlC2AdAG+aptPYXlcdAlTfLXNxIfS+zHMcioML3/2Fnh/3FlWk\nx3Fh+PPGIZx97g+hWAQDbV1wKE5ZhXFdvrba6cy6fG0IxSKVs+/sjC0R//sZ8D4G7NarJyAoLkyt\nNZWnzdPCP0Pn4OjTGwhOiFApa/L89DXo07i9xtZEENVRTQtQ6n6D2gTAEMANiqKeUxS1TQNrqnAB\nMS/kBicAENNiLD5X8X+V8yF30W7tVJx8dhvCj5Us8ooLJdpfaGKvTaGgWK3U8PfZaTgXcrfsz6q2\nppenWFiC1LwM3PrfJsQuP63SHO9lFGklCOLLpm4WnxtN0440TXt+/N80TS2sIh0IZM7leJ4YiZDE\nqApbQ05hPkbtXaZUJ9uq9Cwhouyfx7buofH5Az6+wjTU0YOxroHS12s6aBIEUfVqZCUJWS0RZEnJ\nYd7kWV5WQS72P7qMgNgX4FAcdPJojlGtfGVmvR14dBl5xYVKzV+VtLh8iMVinA3xx/Z7Z6HF5aFE\nzlMZRVFKVzWnUFqOicvhYmzrHqz2fX1iaWCKHg3aKHU/giCqvxoZoNimjduU6ynE5HzIXYza+wvy\niv/LEzn65AYWnduGM1NXod1nqc7Hnipu6V7ddKvfGoN3LsSZ54qTI+xNLNHAthauhwcpNX+XckkY\n87uNUSpALes1EVo8vlL3Iwii+quRAepbr56M3V+bOXqUpWczCU6IwJBdi2W+rkvPy0KvzXPwYskh\nOJvblh2PSk1QbtFq0ObxUazmq0Tff/6nsDW8Ll8bu0YvwtDmnbHZ/5RSAcpQR09iM7SDqRU6eTTH\n7YinCq/jcbj4c+D3Zd2F32YkY2/ARcRlfOrp1R32JpbYef8cnr59DR6Hix4N2mBkq+6keSBBfAFq\nZIBqU7sR+jXpIPHhvzwuh4vf+7H/nPbXjcMKvyXlFOVjs/9J/NFvOnYHXMDWu6dZ1Yhjq0eDNsjI\nz0Fg3Cupc3paOjg8/hcsOb8Dr+S0w/Cp0wzZBXkKC6sqCk5AaSJGRkEOeFweutRtCT6XxyoxQ09L\nB6emrISJnqHE8UW+4xgD1Jlpq9G7UTsAwIIzm/HXzSMSbVP+vnVU6nXj6ed+WHx+Gy5+txYtXeoz\nro8giKpTY/tBHZ24HOPb9JaqImFrbIHjk1bAl+U3DZqmcSrYj3Hc8ae30HvLXEw7slqjyRcd3Jvi\nzNTV8P9xK7aOmI+mjnWgp6UDK0PT/7d33uFRFk0A/20S0ukEKQlEOkiRIiAECL13pAsovTcbYAEF\nRKUpGIogIoJ8FOm9FwGVIlV6Dy2U0CEk2e+PzaVeTbuD7O958oTbd3bfufchN7czszP0qdqSQ8N/\npdmbgewYMo1OFRrg5uIaPTeDuxeDarRlXb9J1ClWIcm6bDixjxWHd1Ju3HtWGacmJapweMQ8ahdN\neO+aRd7i62Z9TM4d16xPtHH6duM8vtk4z2hPL2OxsFsP71F/6mBu2Rhj1Gg0qUua7gcFEBx6i+X/\n7uTh8ycUfi0PjUsERJ/3sYZnL57jMaCaRbnkOM8Um8yeGXi/UiO+atzDaFdXU9x+FMqhK6dwFs6U\n9y8WfbjVd1hjgkOtPspmlIqvF+fQldNmW8/H5u18Jdjz4U9mZfacO8LU7UvYcUbVGaxWsDT9AltF\nlyd69uI5vsOacOfxfZv1Hd2kJyPqv5dg/GnYM24/uk8mT2/Su3vZvK5Gk1KktXNQL5WBuvngDmdD\nruLt5knJ3AUsNuJLLfIMb8qVezfNyljr8rKGxiUC+F+30TYZJku49K1sdAeS0lwas5w8WYxV0rKO\n1Ud30zjog0TNfdO3EIdG/Br9+nxIMKPXzWHh/k08ffEcZydnGpcIYET9LpTLWzTROmo0yUVaM1Av\nhYvv7K0rtJzxCb7DmhAwvidvjnmXIiPb8POeVfZWDYDuAU0tythinCyZ3VVHdzNn72qr17MGWzIW\nk5PQJPbaSsr8x2Exaf4nrl+gwrddmbN3dfRONyIyguWHdxAwvicbTuxLkp4ajcZ2HN5Anbl1mUrf\n9eCPf7dHV1sAOH3rMl3njWHU6ll21E4xsHobSuQ23bzurbzWBeM9Xd3pXbUFczt/blF27Pq5hCdj\n48DOFRsk21rWks7Zxap+TeZISq+uwtlj2mV0nTeG24+M99p8Hh7Gu3NGvTSHqjWaVwWHN1AfLJ1i\nti37qLWzOR8SnIoaJSSDhxfbBwclSELwdvOkb7VWbBk0xaoP4oHV2xDU7iP+vmS5zl1waAi7zh4G\n4Pi18/T9/TvKju3MW+PeY9jyIC7duW7Te+gf2JqMqRxvafFmIFm8MiZpDWt6epni1qN7hIW/4NCV\nU+yzUKw25NE9lhzcmqj7aDSaxOHQBio49BZrju0xKyOlZObu5amkkWmyeGVkbpfPufr1ShZ1G8N3\nLfqxtMfXTGg5gPTuXvSu0sLs/HTOLvSsoprr3X1sXSv6u4/vM2nL75QY3YGgnUs5eOUU+y/9x7gN\nv1J4ZBv+iGqpceP+HU7euMiDp6ZTxXNkzMr2IdOiKzqYwsvVwyrdLJHFKwNfNu6RLGv1C2yFk7D9\nv/LfF0/w0R9TOXDppFXy+y//Z/M9NBpN4nHoc1Anb1yyKnB/4vqFVNDGMjcf3GHo0h9YfHBrtDvI\nxzszfaq1YFjdTvx18Tgrj+xKMM/FyZm5nT8nb9acRERG8M8l6z4Irz+4w5Al3xu99jw8jLazPqWU\nXyH2R63nns6Nd8rUYGTDbuTzSegae9OvEJ0q1mfuvrVG13R2cqZxyQAW7t9klX6mqFawND+2/TDJ\nHWmfhj2jw5wvTFa3yOjhxX0zRhlg1p6VjG/R36r7uTrrahUaTWri0AbK2m/rXm7J860+Kdx+FErA\n+J6cDbkaZzzk0T1GrZnN9J3LGFSjDXWLVuC3fzZw+OoZ3FxcaVIygIE12lDarzAAP+9ZZVWViTJ+\nhVl1xHRFdoAXkRHRxglUSva8v9ax/vg+dg2dnqD53gdLfzBpnDzTufHbeyMpm7coiw9uNfvFIWfG\nrHi5ekQ/i+zpMxNYsAyNSwZQNk8RiibSJRef934dbbb0Ur5suTl0xfThY4DHz5/i6pwOFyfnODFO\nY+h6fxpN6uLQBuot/6L4Zs4e3QXVFM3ftHwOKaUZs+6XBMYpNjcf3mXYiml4u3mytMfXJg/GBu2w\n3G5CAF8360O9qYMSpWvIo3vU+r4/GwZ8Hx2/+fPcYSZsXmByzpMXzzlw5RTNS1fnw9odGLfhV6Ny\nLk7O/NLpc2oXLc+F29eIkBH4Z82V7M0fT9+8zKKDW8zKHA0+Z9VaXm7utC5bkwX/bDQpU9qvENUK\nlbFJR41GkzQcOgbl7OTM0JrtzcoUzO5H8zcDU0chE4SFv+CXvWuskn30/AnNZ3xsNLEjMjKSw8GW\nq0x4uLpTKV8JmyuGx+Zq6C3e+LIdnyz7kVGrZ1Hnh4EW53y9/lcWH9jM1836MK5ZH7LGS3AomsOf\nNX0nUqdYBYQQ5PPJTcHseVKkM/GiA5stvn9LOyIDpXwLMr39x1SOOvwbn/w+vizr+Y3NOmo0mqTh\n0DsogEE123L53g0mbVmY4FoBH1/W95ucqq3ZjXHzwV1Cn1p/HudJ2DN+3LGECa3iGgUnJydcnJwt\nnpnySOeGt7snBXx8ze7arOGbjfOslo2UkbSZ9Rnu6dz4uG4nBtZow+aT/3DvyUPyZctF5fyl4shH\nREaw/vg+zt8OJpNnepqUrJKoXk/xWX98r9Vn4CwVyg0sVIYiOfwB2DY4iCUHtzL7z5VcuXeLrN4Z\n6Vi+Lp0qNLDYTl6j0SQ/L00liaPBZ5mxazmnb13Gy9WDd8rUoFWZGg7RZuHu4/tk/aCuTXPyZcvN\nua+WJhhvOu1Do4kUselUoQFzu3zOxM0LGLr0B5vumxwUzeHPiS8SfmGIzZKDWxm8ZHIc96ynqzv9\nA99hbNPeODlZv3k/FnyOO4/vkydLDubuW8OoNbOtnvth7Y5M2LyASBmZ4Fo270zsGjo92kBpNI5O\nWqsk4fA7KAMlchdgatvElbRJabJ4ZaR6obJsO22++nZsnoQ9Mzo+uGZbVh3dbdJ95ezkzIDqrQHo\nW60Vq4/+adN9k4P/blxk3/ljVMxX3Oj1FYd30mbWpwmMwpOwZ3yzcR4Pnj0mqN1HFu/zx6FtjFoz\nmyPBZxOlZ94sORjXrA+1irzFmPW/sDOqnp+biyutylRnZMNuFMjul6i1NRpNyvPSGChH5+O677L9\nzEGr40LFc+UzOh5YqCw/tB7CgEUTE6zl4uTMrI7DKZu3CABu6VxZ128SX639mTHrf0mS/rZyNdR4\n4oqUko/+mGp0x2Jg+q5lDK3Vnvw+viZlZu1eQff5XydaPyfhxPeth+Dk5ESdYhWoU6wC10JDCH36\niNyZfJLF1ajRaFIWh06SeJmoW6wi09t9nKB9hyl6Vmlu8lq/wHc4+ul8+lRtScncBSjlW5BBNdpy\n/PPf6RyrsR8oIzW6aS9yZMiaJP1txcc7k9HxPeePcPrWZbNzpZTM2WO6luD9p48YtGRyonUrmsOf\nlb2/o2mpqnHGc2XyoVjO17Vx0mheEvQOKhnpUaUZjUpU5sftSwjauZTQp4+MyrUsXZ0WFjIP38iV\njx/bfWj9vQOa8eVa62MzScE/a06qxGthb8DSkYBoORM7MIDf/lrP4+dPTV43h0Bw7LMFNsW4NBqN\nY6L/ipOZXJl8GNOsN5fHrmBg9TZxvq3nzJiNrxr3YGHXr5L9A3RQjTYUiXfwNqUY1ai7Sf19vDNb\ntYY5uf9uXEyMWgAEFCiljVNqMGYMCKF+Tp2ytzYaUwhRCSHWIsRdhHiKEEcQYhBCWOfqiVnHFSE+\nQojDCPEEIR4gxG6EaG1mTgGEmIMQVxEiDCGuI8Q8hDBdWTseegeVQqR392Jy68GMadqLkzcu4ezk\nRPFc+WxqhmgLmb0ysHPIdAYsmsjSQ9uiU9U9Xd3pVKE+hV7LQ9COpdFp6Z6u7rQqXZ2j184Zrbbg\n6uxCWLx09/Tunoxr1od6xSoydt0vrDq6m2cvwiiZuwC9q7agYr7iVCtUGr/Mr1nsj9WpYn2T17yT\nUBmkf+A7iZ6rsRIpYdYsZZykhJ9+gvHj7a2VJj5CNAWWAs+A/wF3gcbAJKAyYN0fixCuwAYgELgI\nzEFtbhoA/0OI4kj5ebw55YCtQHpgC/A7kBdoCzRBiECkPGTx1i9LmrnGem7cv8OByydxEoK385Ug\nk2d6QMV+Tly/wPPwMAr4+JHBw4unYc+Yu28tP+1ewfnb18jo4UXbcrXpW60VLyLCWXRwC6FPHpLf\nJzdty9Xm8NUzNAr6gPtG3JeDa7ZlYqtBzNmzmvfnjTapX+uyNflftzEmr/998TgVvulq8/vuH/gO\nP7QZavM8jY1s2AD16kGXLrB+PYSHQ3AwuLpanKpJGlanmQuRATgLZAQqI+X+qHF3lOF4G2iHlObP\ni6g5g4GJwF6gNlI+jhr3BrYDZYDy0fdQ1w4DJYEhSDkp1nhA1JxjQGlLWWXaF/IKkiNjVhqWqEz9\n4pWijROAEII3cuWjTJ4iZPBQrTU8XN3pVbUFB4bP5d7ETVwcs5xxzfvil+U18vnk5pO6nRjXvC/d\nA5oRFh5O46APjRongElbFvLT7uW8V6kRP7Qegrdb3MOtTsKJjuXrWex3Vd7/DaoXKmtWJl+2XHi6\nuuORzo1aRd5iWc9vtHFKLX76Sf3u3h06dIDbt2HZMuOyEREwfTpUrgwZM4KHBxQoAN26wZkziZPt\n0kXt3i5eTHi/7dvVtZEj444HBqrxsDD48ksoXBjc3NRaAPfvw3ffQY0a4OurjK2PDzRpAnv3mn4W\nJ0/C+++Dv79aL3t2qFIFpk1T1+/dA09PyJ9f7TaN0bix0i15v7S3AnyAhXEMh5TPgE+jXvW2ci1D\nRteYaOOk1noEjEZVX+sTPS5EPpRxugXErWYt5W5gNVAKqGLpxtrFp7Ga2XtWWqyYMWnLQroHNKN/\n9dZ0rtiQhfs3RVeSaF2mptEq6sZY3H0sjYKGGu3T1P6tOszt/HmKuUs1Zrh5E1auhEKFoFIlyJAB\nJkyAmTOhTZu4smFh0KgRbNoEfn7Qvr2Sv3hRGbSAAChY0HbZpNCyJfzzD9SvD82aKYMC8N9/MGIE\nVK0KDRtC5sxw+bJ6r+vWwapVatcYmzVr4J134Plzda1dOwgNhcOH4dtvoXdvtU7btjBnDmzeDLVr\nx13jyhW1ftmyUC5Zz9/WiPq93si1ncAToBJCuCHlcwtr5Yj6fd7INcNYTSPyF5FGz5vEnrPT3I31\nX7jGalYf/dOizH83LnIu5Cr5fXzJ4OFFj6geV7aS1Tsjf34wk/Un9jH/7/XcefyAvFly0K1yE97y\nt65DsSYFmDMHXryI2XkUL64+XLdtg7Nn1Y7HwMiRyuA0bgyLF6sdhoHnz+HBg8TJJoVLl+DYMciW\nLe540aJw7VrC8atXoXx5GDw4roG6fVsZ0fBw2LoVqlVLOM9Anz7quc2YkdBAzZ6tdo49e8YdnzxZ\nGbt4TIBcCDHSyDv7FyljN8YrHPU7YYBZynCEuAC8AeQDLPX3uQ0UBF43Ims40JkHITyQ8mmUPEBe\nhBBG3HiGOYWxgDZQGqt5Hh5mpVzytEZ3cnKiQfFKNCheiaUHtxK08w9qfd+fdM4u1HujIgOrt9HG\nKjUxJEc4OUGnTjHjXbrAgQPK9fdNVFHdiAgIClJuuunT4xocUK99fGyXTSpffZXQCIFyKRrD1xda\ntYIpU9SOKk9UD7O5c5XRHDAgoXEyzDNQrpz6WbECbtyAHFEbjIgIZaDSp1e7r9hMnqyMaTyGQE7g\nCyOazgViGyjDG7pv/I1Fjxs/0BiXNaiY1QiE2BZlhEAIL2B4LLlMwFOkPI0QZ1BGbQCx3XxCVAIa\nRb2ymPKbZmJQ/12/wC97V/PrvrVcuWs+w0xjHEPPKnNk9PDm9aw5k+2eUkre+/UrWv00nK2n9vPg\n2WPuPL7P/L83UPHbbszavSLZ7qWxwNatcO6c2gXkjuWqbd9exWx++UXtrkDFZu7fh5IlIVcu8+va\nIptUypc3fe3PP6F1a+VidHOLSaOfMkVdD47VgWDfPvW7vuls1Dj06aN2Wz//HDO2dq3aaXXsCN7x\nDo9fvKi+EMT7EXAAKYWRny7WKZIovgcOA5WA4wgxFSF+BI6j4lwGYxfbndcLCAMmI8QmhPgOIRai\nEiSOGpE3yiu/g7p45xpd541l66mYOKGzkzMt3gxkRvuPyeyVwY7avVz0rtqCGbtMBMOj6FyxAR6u\n7sl2z5m7l5tsZRIpI+n1+7e8na8Eb5goHaVJRmbOVL8N7j0DWbIo19zSpWqX0KpVjHsqtxUxR1tk\nk4ph9xKfZcuU3u7uygDnzw9eXmq3uH077NihXI0GbNW5bVsYOlTtMj/5RK1reJ7x3XvJg8FomNga\nRo8n9CPGR8pHUdl3w1HJF92Bh8BaYBhwEghHpbEb5mxFiIqohIyqQDVU7OljIBiV9m7xVP8rbaCu\n379NlQm9ElQ3iIiMYPHBLZwLucquD2bgmYwfqK8ypXwL8mn99xi9bo7R68Vz5Wdkw27Jes8p2xab\nvR4RGcGPO5ZYVXxWkwRCQmB5lAepXbuELikDM2eqD/pMUZ6j4IR9zxJgiyyoD3dQO5L4GInbxEEI\n4+OffaZ2gfv3q3hUbHr2VAYqNrF1LlHCss4eHsqwT5oEGzfCG2+o5IgKFaBUqYTySY9BnQLKAYWA\nuNWkhXBBxZPCMZ74kBCVsTecuC49Q8aeN2pn9yLenENAywRrCfFl1L/+sXTbZDFQQoihwHjAR0p5\n25J8ajFh8wKzpXcOXjnFvL/Wma2Lp4nLV016UiRHXsZvWsC/V1X8NbNnBrq83YDPG3SNk9aeVG49\nuMvx65b/flK7mnuaZO5clWlXtiy8abzMFStXqky1CxegSBH1IX7kiEo+MOe6s0UWVGYcqAy42EkZ\nkPhU7bNnldGIb5wiI2H37oTyFSvCkiXKyMTP7jNF797K8MyYoYySseQIA0mPQW0FOgD1UIdkY1MV\n8AR2WpHBZwlDMNJ0O+7YCJEOaAe8AJZYEk9yDEoI4QfUAcxXCE1lpJTM2Wu6IKmB2X9a1/hOE0OH\n8vU4NOJXLo9ZwZlRi7k2bhUTWw1KVuMEILHuELkdzpqnPQxnn4KCVKKEsZ+ePWMSKZydVdzl6VPo\n1SuuewyUsQsJUf+2RRZi4kgGnQwcPQrfxz12YzX+/uqs1bVrMWNSquzCEycSynfurNLgp02DnUYy\npWNn8RkoWBBq1oTVq1UySKZMyvVnjKTHoJagsunaRlV1UKiDuoZT9NPizBDCEyGKIESeBPqog7/x\nx2qjXHbngBnxrnklKKekdm4/AAWAiUh5w/ibjyE5dlCTgI8Ah4pWP3r+hLuPLaemXrp7PRW0eTXx\ny/Jaiq6fPX0WCmb348ytK2blTLVq1yQT27fD6dPKlWUuyaBrV1Wjb84cGDUKvvgC/vpLnSEqVEid\nc0qfXu18Nm5UB2MN8SxbZJs2VR/2v/+uDEGFCirDbsUKdW3RItvf4+DByjiWLq3OSqVLp5ImTpxQ\n8bVV8b7IZssGCxYod2b16ipZomRJldl35IjS+8KFhPfp00ftMm/ehP79lesvJZDyAUJ0Rxmq7VEJ\nCneBJqj07iWoOFBsygPbgB2oskaxOYkQR1Dxpmeo6hG1gBtA0zgHeBXVgVkIsRm4inID1gPyR937\nM2veRpJ2UELVegqWUh5OyjopgaerO+7p3CzKZfUyFUNMewSH3mLC5vkMWx7Ej9uXcPexqQzV1EEI\nQd9qrayQSejm1iQjhp1KNwvxRX9/qFULrl9XH+iurqoU0pQp8Npryk04ZQr8/Tc0b64O3xqwRdbd\nHbZsURl3x47B1Klw/rwyGL2tLY4Qj549lWHNmVPde/58lc33119QpozxOQ0bKpdihw5w6JCqR7h4\nsYpzDRtmfE6TJjFp7imTHBGDiklVQx2GbQn0R7nWhgBtrW5ep5gP5AbeBwYCeYBvgeJIedyI/Gng\nz6j7D0a5G68AHYHWCeJVJrBYi08oC2gs9WUEKmBWR0p5XwhxEShnKgYlhOgB9ADIkydP2UtG/KvJ\nTZe5XzJ331qzMqOb9GRE/fdSXBdHJiIygkGLJzF95zLCIyOix93TuTGiXmc+bfC+XXVr/dMI/vh3\nu9Hr41v2Z2itDqmrlEaTWM6fV3GzypVh1y6bp6e1lu8Wd1BSylpSyuLxf1DZH68Dh6OMky9w+que\ncwAABRVJREFUUAhhNI9TSjlTSllOSlnOJ7kO3Vngozrv4mWmMnaujD70CEhcpYNXiYGLJjF1+5I4\nxgng2YvnfLZqJt9t/M1OmqkjAYu7j2VWx+GU8SuMEAIXJ2caFq/MpgE/aOOkebkYP17Fk/r1s7cm\nLwXJVs3c0g4qNqlZzXzH6YO0nf0ZNx7ciTNe+LW8LOs5jqI5X08VPRyVq/du4f9pcyLiGafYZPJI\nT/C4VQ6Rjh8ZGYkQAmEqXVijcTQuX1buxzNnlBuxZEk4eDAmXd4G0toO6pU+BwVQrVAZLo9dwR+H\ntrH3/DGcnZyoXbQ8dYtV1B9ywIJ/Npg1TgChTx+y+uhuWpetlUpamUY3I9S8dJw/r2JSnp7qEPC0\naYkyTmmRZDNQUkr/5ForuUnn7EKbcrVpU662ZeE0RshDywfJAW49vJfCmmg0ryiBgfosRCLRZjyN\nkzuTdfFA30zZU1gTjUajiYs2UGmcDuXr4uZivhPqaxmy0KB4pVTSSKPRaBTaQKVxfNJn5qM6Hc3K\njGnSC1eXdKmkkUaj0She+SQJjWW+bNwDN5d0fLvxNx48izkQ7uOdmbFNe9G1chM7aqfRaNIqyZZm\nbgupmWausZ5Hz56w8sguQh6F4pc5O41KBOidk0bjQOg0c02axdvdk/bl69pbDY1GowF0DEqj0Wg0\nDoo2UBqNRqNxSLSB0mg0Go1Dog2URqPRaBwSu2TxCSFCgJTvt2GebKiOkxrz6OdkGf2MrEM/J+sw\n95zySilTpx2EA2AXA+UICCH2p6V0zcSin5Nl9DOyDv2crEM/pxi0i0+j0Wg0Dok2UBqNRqNxSNKy\ngZppbwVeEvRzsox+Rtahn5N16OcURZqNQWk0Go3GsUnLOyiNRqPRODDaQAFCiKFCCCmEyGZvXRwR\nIcR3QoiTQogjQohlQohM9tbJURBC1BNCnBJCnBVCfGJvfRwRIYSfEGKbEOKEEOK4EGKgvXVyVIQQ\nzkKIQ0KI1fbWxRFI8wZKCOEH1AEu21sXB2YTUFxKWRI4DQyzsz4OgRDCGfgRqA8UA9oJIYrZVyuH\nJBwYKqUsBlQE+urnZJKBwH/2VsJRSPMGCpgEfAToYJwJpJQbpZThUS/3Ab721MeBKA+clVKel1KG\nAQuBpnbWyeGQUl6XUh6M+vdD1Adwbvtq5XgIIXyBhsAse+viKKRpAyW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xaMIiqWw+s5aN0OH0Jrw9cVUqOJUXsX4/rGR845JFqEI1jppGYwGKoigDAKcA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\nAQuBpnbWyeGQUl6XUh6M+vdD1Adwbvtq5XgIIXyBhsAse+viKKRpAyW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xaMIiqWw+s5aN0OH0Jrw9cVUqOJUXsX4/rGR845JFqEI1jppGYwGKoigDAKcA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\nAQuBpnbWyeGQUl6XUh6M+vdD1Adwbvtq5XgIIXyBhsAse+viKKRpAyW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E44mLpbL5zFo2QoczWxB/8ppUcCovfOMBWMn4xiWLUIVqHDWNxgIURVEGAE4D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\n91mq8/Fnilu6Vzfd67fGkF2LcPaF4uQIexNLNLC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E44mLpbL5zFo2QoczWxB/8ppUcCovfOMBWMn4xiWLUIVqHDWNxgIURVEGAE4D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\n3BfYBpQEykTfQ107BBQDBiLlxBjjFQ1zjgIlrGWVaV/Ia0i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U4cACoLGDdXI6pJRXpZQH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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for 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 % 10 == 0 or t in [3, 6]:\n", + " # plot and show learning process\n", + " plt.cla()\n", + " _, prediction = torch.max(F.softmax(out), 1)\n", + " pred_y = prediction.data.numpy().squeeze()\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 = sum(pred_y == target_y)/200.\n", + " plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 20, 'color': 'red'})\n", + " plt.show()\n", + " plt.pause(0.1)\n", + "\n", + "plt.ioff()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/303_build_nn_quickly.ipynb b/tutorial-contents-notebooks/303_build_nn_quickly.ipynb new file mode 100644 index 0000000..ec795fa --- /dev/null +++ b/tutorial-contents-notebooks/303_build_nn_quickly.ipynb @@ -0,0 +1,133 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 303 Build NN Quickly\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn.functional as F" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# replace following class code with an easy sequential network\n", + "class 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" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "net1 = Net(1, 10, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# easy and fast way to build your network\n", + "net2 = torch.nn.Sequential(\n", + " torch.nn.Linear(1, 10),\n", + " torch.nn.ReLU(),\n", + " torch.nn.Linear(10, 1)\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Net (\n", + " (hidden): Linear (1 -> 10)\n", + " (predict): Linear (10 -> 1)\n", + ")\n", + "Sequential (\n", + " (0): Linear (1 -> 10)\n", + " (1): ReLU ()\n", + " (2): Linear (10 -> 1)\n", + ")\n" + ] + } + ], + "source": [ + "print(net1) # net1 architecture\n", + "print(net2) # net2 architecture" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/304_save_reload.ipynb b/tutorial-contents-notebooks/304_save_reload.ipynb new file mode 100644 index 0000000..11563ae --- /dev/null +++ b/tutorial-contents-notebooks/304_save_reload.ipynb @@ -0,0 +1,204 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 304 Save and Reload\n", + "\n", + "\"\"\"\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\n", + "* matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch\n", + "from torch.autograd import Variable\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "torch.manual_seed(1) # reproducible" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate some fake data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1) # x data (tensor), shape=(100, 1)\n", + "y = x.pow(2) + 0.2*torch.rand(x.size()) # noisy y data (tensor), shape=(100, 1)\n", + "x, y = Variable(x, requires_grad=False), Variable(y, requires_grad=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def 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" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def 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" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def 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()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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24g7JcOLL0HDOFQpWK+iZXJclvGoxhm9fpJSfDnuAsE3JLuGER3WsomLjuX7L\ntD0/zcfXIkFvJvv3wzcG0+qa/Gf+fIodilOKIxr14sTVRCQQfzmBEUv36C+SXGLuRLKvv0WC3ky2\nbYOlakZ4jQswaxaFTh5Xil+t25P4K7e0E3eAuRMJAQWY0cLgcerq1bBmjZNbpskVM2ZQ4OJ5pfjF\nWt2Jv5zgEk54VMcqMjqO5LSskffayo3ZVq6WWnH0aEhIcGLLNDmSmGj6u1ixu0Jtfq5gmZolITmV\nyGi1A6ZRsXZiWa027Ct1n1rxrbfUmGGa/OX6ddMjcCv+rN6UTWUsU7NoJ3KPtRM/1OvAkeJl1IoR\nEWAV4V6Tz1y4AJMnK8W/1mvHzrssn1DlpxMe1bHK2C2QiRBMbDNArXjiBMyc6ZxGaXLHrFlw9KhS\n/H5YP8Nn5srfWmOI9e9JCh/jmdyDB+Hrr53UKk2umD7dMEL+uBZ9DKtrJ3KH9e8pxdePKa36qRV3\n7IDFi53UKk2umDjRlKrOHD8/xjc1WD9K/jnhl3MV96FscBDxVr/Iv8rVZHXVZjz0zxaL8itvv8uj\nF+7jhAykbHAQwztU15GL84tr1wxH5huqNmFr+fsNTzGPpKyxjZET6yo1ZPO9dWl+bLdF+dkhEXT5\nrzSnU3y1E/nN+fMQGakUr6rzAPuNZhzRTuQWIydW1mjJwG1LqXPaMtTCsZdep9u+wpxPlNqJ/ObE\nCVOKOisW1u/I0eJlDU/JLyc8asZqeIfqmXFKzJnUuj+pVgmai926zlO/f+8Sz2O9nunTTcmBrRjf\n0mA9EBDk76uTzeYSQyeEYKLBrFWp6xcJ37hUO+EKGIzMk318mdj8KcPq2oncY+SEFD5MbPOMUvfe\niyfpsGWldsIVePdd05IRMxL8Aols2tOwen46kauOlRCioxAiTghxSAgRYaNOTyHEfiHEPiHEfPs2\nM3dYxynJ4FCJe1l8f3ul/oAdyyh9zbQITq9RyCdsjMyjarXhgMHI3FcIJnStk++jRnd3YlfZ6qyq\n1kKp/9KWxRRLuAZoJ/KNEyfg44+V4h/qdeCYwVog7cTtYcuJTRXrs6FCfaX+4E3zKZhkmuHSTuQT\ncXGGSxW+Dn2Mc4XvUsrz24kcO1ZCCF9gJtAJqAX0FkLUsqpTFRgBhEkpawP5lnckI06JtTTTWvbh\nlp9lHKQCKUkM3vR95mu9RiEfmDDB9CjQjGQfX6bamK1Kk9IVvkA8wokPWvcnxWomt2jiDQZtydrK\nrJ3IB2wXONBUAAAgAElEQVSMzD80ikOGduJOsOXEpLbPKHVL3rjMszE/Zb7WTuQDb7+thEm6EliI\nz5p2N6ye307kZsaqCXBISnlYSpkE/AB0sarzf8BMKeUlACmluuLSyVjf/KeLlmBOw0eVek/uXk3l\nC6btzHqNgpM5dsxwE4GtkTm4zN/II5w4fHc5FtZ9WKn3zI7llLlqejTrIr9v7+Hvv+Grr5RiWyNz\ncJm/kUc4sfeeKiyv0UqpN3DrEorfvAK4zO/be4iJgUVq3KrPmnXnagHjFGf5/TfKTccqBDAPpHIi\nvcycakA1IcQmIcQWIURHezXwTjH6xX7arAdXAi2zwPvKNIat/1avUXAyUbHxLO82UBmZ3/S3PTL3\n9xWu8jfyGCemh/UmwS/QoiwwNZnXNs7XTjiZqNh41vYYqGzxz25krp3IG0ZOfNC6H8k+lmuwiiQl\n8PLmhdoJJxMVG8+23i8q5WcK38XXjR4zPMcVnLDX4nU/oCrQFugNfCGEUMLZCiFeEELECCFizhks\nVrYnRgsUrwQV4VODBM2dDv7JZ5WT8n063dOJio0nbOJaKkas5OOZy3lkx69Kna9CuxiOzIsX9Cey\nez13+hu5hRNni9zN16HqB1T3vb/xcf1Ad/p9uyXmTnw1fRHt9q5X6nzarIfhyFw7kXeMnPiveFl+\nqNdBqds/dhXTmxV3p9+3W2LuxMLJc2lySE0v9GGLXtzyL6CUu4oTuQm3EA+UN3tdLr3MnBPAVill\nMnBECHEQk0DbzStJKWcBswBCQ0MdmkMj4xcbGR1nsbX260aP8fSO5ZS5fsGifpuvp8DTj7lEniFP\nJCo2nhFL95CQbHpOPnTDt/hKy5H55QKFmdUkK3dXkL+vSyzKNcCjnPisaXee2vkLwbeuZ5b5yjTa\nf/chdHvAkU3yaqydMEq0fKbwXcxplLWEQTthX2w58WGL3nTb+xsFk7Nm1ANSk+mw6FPo3NSRTfJq\nLJyQkjfWzVXqHA0uwwKzJQyu6ERuZqy2A1WFEJWEEAFAL8A6k3EUplEIQogSmKZ8D9uxnXeEecLN\nDBL9A5ne0mDL8vr18PPPTmubtxEZHZf5BVL31EE6HfxTqfNJsx4UuackAggJDnI5WczwKCeuFijM\nzGYGW5ajomDzZqe1zdswd6LF0Z20Phqr1JkR1pu7SxbXTjgQIyfOFS7O7NBwtfI338CePU5rm7dh\n7kSHfzZT/9RBpc6UVn0pfXcRl3YixxkrKWWKEOIVIBrwBb6SUu4TQowFYqSUy9KPPSyE2A+kAsOl\nlBdsX9W5WAeEW1znQf5v249UuXjCot6BZ17m/177gmGdarrcH8rdMV8kajQKOV34Lla368GmiHbO\nbNYd4YlOfNPoUQbsWEbZa5Y5uLY++QJDXprO8I41tBN2JtMJKXljverEkeJl2Njqce2Ek7B2YlbT\nbvTd+TN3JZjFE5OS3554jtHPT9TBQh1AhhO+aakMW/+tcnxfqfuIbfawyzuRqzVWUspVUspqUsrK\nUsr308tGp8uCNDFESllLSllHSvmDIxt9u1g/R0/18WVGu2eUejXPHSV08y86CJwDyFgkGnZ0Jy3/\n26Uc/zDsKY7cSMv3rOS5xdOcSPQLYGYbNcRF0+N7qfrXBu2EA8hwosPBzdQ/9Y9yfGrLvhy7lqyd\ncBLWTlwPLMislupGmvb/bqfM3hjthAPIcKLr3rVUvaAmH49s3Z8TVxNd3gmPirxuC/OAcAIIDvJn\n4/0t+ausunNg6IbvSE24pYPA2ZnhHaoT5OfDm+vmKMf+vSuEBXUfAnR0Y2dh5ER0w4f55+7ySt03\n180lMTFJO2FnhneoTiFfGLbBeGS+oqZp2792wjkYOfFj08eIL1pSqRvxxxwSklK0E3ZmeIfqFBOp\nDN6kxo7dWv5+/rivEeD6TnhFxwqynqNPe7I+iSlpXEpIYWJbNUFz+Stn6LNzlQ4CZ2fCG4Qwt9gx\n6lrl4gLTyDzVbHuzjm7sHKydOJ+YRmTr/kq9mueO0mX/Ou2EnQlvEMK3AQdtjsylWfBW7YRzsHbi\nTLIwDFYcGn+ABw9t007YmfAGIXyXHEu5q+pu0Emtn7bYXObKTnhNxyoD88Vx28rfz2+VGyt1Xvlz\nAVUKpCnlmjyQkkKT2VOV4t33VGFVjTClXH9gOQ9zJ36t2owdZWsodYZu+I4KhdU8nJo8kJhIw6+m\nK8VbzEbm5mgnnIe5Ez/WbsvfJSoodYavn0u5ogFKuSYPXLtGnblqOqfVVZryV7maSrmrOuF1HSvr\nP8TkNk+ThmWIhbsTrvLRyd+c2SzPZ84cOKju8Jjc+mmLkXkG+R0515uwcEIIw7Qe5a6e5cMrW53X\nKG/gs89M2QesmNzmacOwL9oJ52HuRJqPr+lvYkX188eYkbzXmc3yfKZONeWPNSMNQWTrfobVXdUJ\nr+tYWf8h4kpW5MfabZV6NeZ9AadPO6lVHk5CArzzjlK8qUJdNlZUk54KyPfIud6EtRO2ZnLrfv0R\nXL2qlGvugGvXYNw4pXh1lSb8FaKOzLUTzsXaibWVG7OtXC2lXsPZU+HWLWc1y7M5dw6mTFGKo2q3\n5WDJikq5KzvhdR0ro0i701r1JdHXKvLEzZvw3ntObJkHM3MmxKuLDCe3Nh6ZS9DbmJ2IkRNGM7lc\nuAAffODElnkwNkfm6ho30E44G8UJIZjYRl2Ty4kThvlONXfAhAmmAYcZST5+TG3Zx7C6KzvhdR0r\n850fGZwoVprvGnRWK8+aBYfUxdaa2+DyZRg/Xin+o3YrdhnsygSUjPMax2LkhK2ZXKZO1TO5eeXc\nOcMOanSDBw1H5qCdcDZGTvxVriarqxhEXR8/3vQ5p7lzjh0z7KAua/ooJ4LvMTzFlZ3wuo4VZO38\nMP/DfNy8J9cCrP5QKSkwapSTW+dZxA0bA5cuWZSlCh9S3h3L9CfrKzMlOslp/mDkhOFM7o0beiY3\njxx6fSRcv25Rluzrh+/Yd7UTLoSRE5Nb9yfVek3oxYswebKTW+dZ/PfqG5CUZFGW4F+AQu+Odksn\nvLJjlYH5dO+lgsX4vGk3tdKCBbBDTQKpyZmfV8dS/pvPlfLF97fnfztN6xLM48a4anoCb8LcCT2T\na3+iV23j3h++Vsrn1evI4K1XAO2Eq2HuxD8lK7DkfoOo39Onw8mTTm6ZZ7BmyR+UW7ZIKZ8d2oUh\n688A7udEbpIweyzmCThPXk7g5wd78cr+aApcsIqhEREBq1fnQwvdm5uj37VIYgqQ6OvHjJa9M2OQ\nbIpo59KCeBvWTizp+DT9D/yG/w2zGZaUFHj7bfj++3xqpfuSPHoMAakpFmU3/AvwcYsntRMuirUT\n3z/yHN3iNuCbZPbZlpAAY8eadnpqbgu/MaPxlZbhjS4VKMKspl3d1gmv7liBSRqLP1jJd2HQIMtK\na9aYOlYPPeTcxrkzhw/z+LYVSvF3DTpzsmgpwHVjkHg7ihMBb8Do0ZaVfvgBhg+Hhg2d2zh3Zv9+\nOv31q1L8VWgXzhcqDmgnXBXFiZQYdQfb7NkwZAhUq+bcxrkz27bRdt8GpfjTZt25FlgIcE8nvPpR\noCHPPw9VqijFl18dCmk6aGiuGT0a/7RUi6JrAUHMbN4z87WrxiDRWPH661C6tFJ85uUh+dAYN2bU\nKGVkfrlAYWY17Zr5WjvhJowYQXLhopZlqal6Te7t8tZbStGpwnczt+Gjma/d0QndsTIjKjaesCkb\neLmWutYq+O89bJ+sp3lzxa5dMF/N9fRFk65cLFgMcP3FhxoTUbHxhH28jVF1uyrHSm9Zx8bPFuRD\nq9yQbdvgxx+V4pnNemaOzLUTrk9UbDxhE9dSMXIL0xqGqxUWLYLt253fMHdkzRr4TQ3EPSOsN4n+\ngYD7OqE7VulExcYzYuke4i8nsKpGGLvvUWetykS+r+xc0BgwciRIaVF0vmAx5jQ2fRC5w+JDjaUT\nP9TrwNHgMkqdEuNG65ncnJASRoxQik8Vvpt5jUwjc+2E62PuA8BXoY9zpvBdasU331Q+/zRW2HDi\n37tC+LGuacmNOzvh9WusMjDPDSWFDxPbPMP8BZbTuuUunjQ9R7deg6XJYsMGWLlSKS4xYSy7X+2e\nDw3S3CnmTqT4+vFB6358vMxyW3mN+IOweDH07Gl0CQ2YRuZr1yrFZaZNZP/zT+RDgzR3grkPALf8\nCzA97CkmRFvltvv9d/j1V+jQwcktdCOWLIGYGKW48mfTiOvxeD40yL7oGat0rBfI/VmxPusrNlAr\nvvuuEoNGYyLqrxPs6mfQ6axQAQYOdH6DNHnC2omVNVqyp3RlteLIkZCc7KRWuQ9RsfGETfiN3f0N\nnKhWDZ55xult0tw5RouoF9Z9iH/vMphRiYjQM7kGRMXG0/r91fw78HX1YMOG0M0g5JEbkquOlRCi\noxAiTghxSAgRkU29bkIIKYQItV8TnYPRArlJBok3OXsWpk1zQovci6jYeKInf0m9/wySko4dC4GB\nzm+UA/FGJ6TwYVKbZ9SKhw7Bl186p1FuQsZjo7pb11D3tEHMr3HjwM+zHhh4uhNG3xGpPr580Mog\nQfDOnaYYiJpMMpxotnEFlS+eUCtMmAA+njHXk+NPIYTwBWYCnYBaQG8hhJKNUghRBBgMbLV3I52B\nUb60/fdUYVnN1krd6+Mm0HnUEqJi1fx33soHPx/g1bVzlPLDpStCH+NcT+6KNzuxqVIDNlaop9Q9\nO+wt2o9dqZ1IJzI6jqTEJIZt+E492KiRx4zMM/AGJ4x8EMDP1cPYX05dYP3foCG0GRetnUgnMjqO\ntJs3eW2TurGJBx7wqHBGuekeNgEOSSkPSymTgB+ALgb13gMmAW6Z6jsjN1RwkH9mWZC/D188PIBk\nH0uZCicl0P2XuYxYukdLk07o5l+oee6oUj4xrC/4+qonuDde7cSnHZ5X6pa6cYmOaxZoJ9I5eTmB\nbnt+Mx6Zjx/vMSNzMzzeCev8gb5CIIHgggFMe/BZpX6Fy6dps+5H7UQ6Jy8n0C92JWWvnVcPjh8P\nQqjlbkpu7A4Bjpu9PpFelokQoiFQXkqprlp2MxJTsp6L30xOY0+Bksyv31Gp1yf2Z+4+F09kdJwz\nm+eS/LTtCEMMRuY7ytZgX2hb5zfI8Xi1E5uKV2JFjVZKvYFblxB49ZJ2Aijtn2Y4Mt9xX32PGpmb\n4RVOhDcIyZy5Sk3f+Xc5IZnVpWuzrpIaLPd/fy5A3LiunQDK+SQxaMtipXxdrTBo1iwfWuQ48jxs\nEkL4AFOBobmo+4IQIkYIEXPu3Lmcqjsd610fGXzUohc3/AtYlAWkpTBk4zy3jAprT6Ji49k95gPu\nvXJGOTa9/bMM71gjH1qVv3iDEx+06qvM5BZNusnLmxdqJ2Lj6fLnT4Yj8+tjxnrUyDy3eIMTRusP\nS968zPPbo7QTsfE8uX4hdyVctShPFT6kvDs2n1rlOHLTsYoHypu9LpdelkER4H7gDyHEUaAZsMxo\nYaKUcpaUMlRKGVqyZMk7b7WDsHXzny9UnNmN1W3R4fv+oFWCdyfe/HjZTl7coI7M193XiG6vP+WW\nMUhygdc7cfSuEBbUfVgp7//XChpw1eAM7+GTn3bw4p8LlfK1NVrQpv9j+dAip+D1TuwvfR8/1Wyj\nlL+wbSm1/BINzvAeZi/ezIBtUUr5yrrtad/dIKm1m5ObjtV2oKoQopIQIgDoBSzLOCilvCKlLCGl\nrCilrAhsAR6XUqpBKlyc7ELnf9HkCc6nRw3PwAfJ5L9+cHSzXJqOvy2g5I3LSvmk1k97aqcKtBOA\nKULyTX/L3Z6BqSlM269+gHoTj/06n+K3rlmUpQofJoT1zacWOQXtBDDFYCa3cFICHx5e5ehmuTTd\no7+hULLlsrpEXz8mN++dTy1yLDl2rKSUKcArQDRwAFgopdwnhBgrhHD/SF5mGO36yOB6YEE+bdFL\nKb9n41pYt87RTXNNLlzgxW1LleKfarZhf+n7CJu41iMXbWonTJwrfBdzGqvrkyusXAz79jm6aa7J\nmTM8t+MnpXjJ/e34p8S92gkPIDsnjhUvww/1OynllRd/A0ePOrhlLsqRI/TZ+bNS/F2DzpwoVsoj\nncjVGisp5SopZTUpZWUp5fvpZaOllMsM6rZ1x1EIWO76EEBwkD/FC/ojMIXXrzfuDahUST3RW1MY\nTJxI4cQbFkXJPr5MbWUKrxB/OcFjd8RoJ0xO3DvhHbjLKq1HWpphclVv4N/XRlAwSR2Zzwh7CtBO\nmNX1WCdKTHoPChWyPCk5GUaPzo/m5jvHXn0D/9QUi7LrAUF80qwH4JlOeFaEOjsQ3iDE8BFWVGw8\nk6LjWFu7K9OPTLE8uHWrKcFqVzVRrcdy4gSpH36E9bjth3od+K942czXCcmpREbHefJjQY8nOycm\nRMexq144I3//yvLgsmWwaROEhTmplfnPryu30Hbht0r5dw06E1+sVOZr7YT7k50T46Lj+KfeY7z6\np9Uyke++g2HDoG5dJ7Uy//lt0W88sGKJUj67cTgXCgVnvvY0JzwumIojME+++VOtNuwvZTBr9dZb\nkJKilnsq776Lb5LlgswEv0A+NHhc6u07YjwRcye+afgo8UUMFhl72Uxu6tujCUhTR+Yzm6t5FLUT\nnoe5E7OadOVCUFHLCjYSD3syAWNG44PlZ8CFoKKGm8E8yQndscoF1gmaDdN6xMUR++5UwiaupVLE\nSo98bpxJXBx89ZVS/HXoY5wzyPYuwbN/H16IuROJfgFMa2UQXX/TJrbMmOsdTuzdS4fYNUrx7Mbh\nXLTa9ALaCU/E3InrgQX5uMWTaqVVq9gwe4l3OLFlC60O/KkUf9K8J9cDCyrlnuSE7ljlAuue9LpK\nDfnzXnU6N2T6JC6cu4TEM58bZzJqlJJg9HKBwnzWtLvNUzz69+GFWDuxtPYDxJW4V6lX4v0xnLp4\n3SucsB6ZX0wfmduKWuXRvw8vxNqJefUf4Xix0kq9Iu+MIv7STc92QkpTImor4ouU5LsGj3i8E7pj\nlQuCC/pbFghhmKC51PWLDNixPPN1xnNjj2L7dlisRs/9rGl3rhYojL+PoLj17ysdj/x9eCFRsfH4\nWAW5TPPxZbKBE1XOH6Prvt8zX3vkPbB5M/yk7gSc2awHqYWL0KfZvZlpUKzxyN+HF2LkRJKfP1Na\nqSE26sf/zcP/bMl87ZH3wK+/Gu6Wn96yNz5BQR7vhO5Y5UBUbDzXb6lrp/aXr0F8+0eU8pe2LKZY\nQlYMG096bgwY7vY6V/Ru5jZ6lJDgICJ71CN29MM2RyQe9/vwMjLWkaQarJ36s0ZzLtRvrJS/vmEe\ngSlJma896h6wsW4mvkhJ1j7QnQld6zAuvA6bItppJzyU7Jz4tV47rlStqZS/sW4uvmlZ0ds96h5I\nSzN04tBd5dga9qhXOKE7VjkQGR1HcpoqTKEAP0JmTlUSDBdNvMGgLYsyX2cXTM7tWLPG9M+KkpPf\n58CUbmyKaEd4gxDD0VsGHvX78EJspfPwFYIJ3epy98fTlGMh187R96+s9HAedQ/YGJmHzJjE7293\nzNzlpJ3wXLJzYny3ehSb/oFyrMrFE3Tb81vma4+6BxYvhthYpbjKFzNYP/Ihr3BCd6xywFbP+UpC\nMlSvzvb26u6GZ3Ysp8zVcwT5+zK8Q3VHN9GuRMXGGy6sjPrrBAeeeUU9oUoVePZZi/Ntjd7c8feh\nscSWD2lSmj4ww8I40FhN6/HK5oUUSbzhlveATSd2HCfuWQMnatSAfv0sztdOeC45OtGpE0dqK5l7\neH3jPAKTE93yHrDlxE/bjnJs0BD1hMaN4YknLM73ZCd0HKscKBscRLyBOGWDg0xxfKo8xh9rlxOU\nkhV6IDA1mSEb5+M/92u3isuRcbNnjL4yFhLG/HeRq98tIDze4Ln3uHHgn7WmKtsZja513Or3oVHJ\nzgcw3UOf1unBqpgN+MqsDQ7Fb13jpa1LKDtzqlvdA9k5cfOb+YSfPKSe9P774Jf10aqd8GxydGLn\nSeY26MWP+yzjoZa5foFn/1pB9anvudU9kJ0TPl98QZcLBgvPJ0ywSD7u6U7oGascMEpfkNGjjoyO\n40yRu/kqVM3Y0HXvb4QHqDn0XBmjmz0hOZWFm48y+Pe5Sv24slWgRw+LshxHbxq3JjsfwHQPxZWs\nyNLaamLVAdt/Ivwe9/rIseXEoj+P8L8/VCf2l6tuMTIH7YSnkxsnYkNq8Eu15sq5L25eSHhFNfSA\nK2PLiR83/sOg9fOU+turNIT27S3KPN0J9/qUywes0xeEBAfRrVEIkdFxmaOUz5t243KBwhbn+Urj\ntB62plBdAVs3e/juNVS+eEIpHx/WD3wsbyFbz8bd/Zm5xoSRDxO61gFMMWgynJjW6ikSfS13hwal\nJMLYsco13dGJbrt/pdKlU0r5+2H9LEbmoJ3wdHLrRGSr/qQKy8/LYok3YNIk5Zru6ETfHcu55/pF\npXxcC3VnpKc7oTtWuSC8QQibItpxZGJ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1iAoWzPYaIcFBbBrYADZuzBJkxw5IzTnmCQA+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\ngg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# save net1\n", + "save()\n", + "# restore entire net (may slow)\n", + "restore_net()\n", + "# restore only the net parameters\n", + "restore_params()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/305_batch_train.ipynb b/tutorial-contents-notebooks/305_batch_train.ipynb new file mode 100644 index 0000000..0200d69 --- /dev/null +++ b/tutorial-contents-notebooks/305_batch_train.ipynb @@ -0,0 +1,177 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 305 Batch Train\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch\n", + "import torch.utils.data as Data\n", + "\n", + "torch.manual_seed(1) # reproducible" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "BATCH_SIZE = 5\n", + "# BATCH_SIZE = 8" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "x = torch.linspace(1, 10, 10) # this is x data (torch tensor)\n", + "y = torch.linspace(10, 1, 10) # this is y data (torch tensor)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)\n", + "loader = 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", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | Step: 0 | batch x: [ 6. 7. 2. 3. 1.] | batch y: [ 5. 4. 9. 8. 10.]\n", + "Epoch: 0 | Step: 1 | batch x: [ 9. 10. 4. 8. 5.] | batch y: [ 2. 1. 7. 3. 6.]\n", + "Epoch: 1 | Step: 0 | batch x: [ 3. 4. 2. 9. 10.] | batch y: [ 8. 7. 9. 2. 1.]\n", + "Epoch: 1 | Step: 1 | batch x: [ 1. 7. 8. 5. 6.] | batch y: [ 10. 4. 3. 6. 5.]\n", + "Epoch: 2 | Step: 0 | batch x: [ 3. 9. 2. 6. 7.] | batch y: [ 8. 2. 9. 5. 4.]\n", + "Epoch: 2 | Step: 1 | batch x: [ 10. 4. 8. 1. 5.] | batch y: [ 1. 7. 3. 10. 6.]\n" + ] + } + ], + "source": [ + "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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Suppose a different batch size that cannot be fully divided by the number of data entreis:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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", + "Epoch: 0 | Step: 1 | batch x: [ 5. 6.] | batch y: [ 6. 5.]\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", + "Epoch: 1 | Step: 1 | batch x: [ 7. 9.] | batch y: [ 4. 2.]\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", + "Epoch: 2 | Step: 1 | batch x: [ 7. 1.] | batch y: [ 4. 10.]\n" + ] + } + ], + "source": [ + "BATCH_SIZE = 8\n", + "loader = 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", + "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())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/306_optimizer.ipynb b/tutorial-contents-notebooks/306_optimizer.ipynb new file mode 100644 index 0000000..dc831dc --- /dev/null +++ b/tutorial-contents-notebooks/306_optimizer.ipynb @@ -0,0 +1,295 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 206 Optimizers\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\n", + "* matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.utils.data as Data\n", + "import torch.nn.functional as F\n", + "from torch.autograd import Variable\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.manual_seed(1) # reproducible" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "LR = 0.01\n", + "BATCH_SIZE = 32\n", + "EPOCH = 12" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate some fake data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Ko2Mtk7k2DHNAKGHCv4l4B4RaUt82m4eeftWEfxtQi2quFSYoSQhasZoDQgkz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\nrNhK+2L5/I2mMzhcYO2mPb4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# fake dataset\n", + "x = torch.unsqueeze(torch.linspace(-1, 1, 1000), dim=1)\n", + "y = x.pow(2) + 0.1*torch.normal(torch.zeros(*x.size()))\n", + "\n", + "# plot dataset\n", + "plt.scatter(x.numpy(), y.numpy())\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Put dataset into torch dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)\n", + "loader = Data.DataLoader(\n", + " dataset=torch_dataset, \n", + " batch_size=BATCH_SIZE, \n", + " shuffle=True, num_workers=2,)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Default network" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class 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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Different nets" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "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]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Different optimizers" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "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]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "loss_func = torch.nn.MSELoss()\n", + "losses_his = [[], [], [], []] # record loss" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0\n", + "Epoch: 1\n", + "Epoch: 2\n", + "Epoch: 3\n", + "Epoch: 4\n", + "Epoch: 5\n", + "Epoch: 6\n", + "Epoch: 7\n", + "Epoch: 8\n", + "Epoch: 9\n", + "Epoch: 10\n", + "Epoch: 11\n" + ] + }, + { + "data": { + "image/png": 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wcKBTVISXg3nhhRdw5ZVXYsKECRg2bBgeffRRAKYKOvnkk/G1r30No0aNwvbt2zFjxgyM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\nzOg+o3bWv2ul5iAqI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FxhBoPBYHArpsdiMBgMBrdieiwGg8FgcCumYTEYDAaDWzENi8Fg\nMBjcimlYDAaDweBWTMNiMBgMBrfy/yB4amTjla2eAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# training\n", + "for epoch in range(EPOCH):\n", + " print('Epoch: ', epoch)\n", + " for step, (batch_x, batch_y) in enumerate(loader): # for each training step\n", + " b_x = Variable(batch_x)\n", + " b_y = Variable(batch_y)\n", + "\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[0]) # 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()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/401_CNN.ipynb b/tutorial-contents-notebooks/401_CNN.ipynb new file mode 100644 index 0000000..2f85435 --- /dev/null +++ b/tutorial-contents-notebooks/401_CNN.ipynb @@ -0,0 +1,529 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 401 CNN\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\n", + "* torchvision\n", + "* matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch.autograd import Variable\n", + "import torch.utils.data as Data\n", + "import torchvision\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.manual_seed(1) # reproducible" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Hyper Parameters\n", + "EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch\n", + "BATCH_SIZE = 50\n", + "LR = 0.001 # learning rate\n", + "DOWNLOAD_MNIST = True # set to False if you have downloaded" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Mnist digits dataset\n", + "train_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", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([60000, 28, 28])\n", + "torch.Size([60000])\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot one example\n", + "print(train_data.train_data.size()) # (60000, 28, 28)\n", + "print(train_data.train_labels.size()) # (60000)\n", + "plt.imshow(train_data.train_data[0].numpy(), cmap='gray')\n", + "plt.title('%i' % train_data.train_labels[0])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)\n", + "train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# convert test data into Variable, pick 2000 samples to speed up testing\n", + "test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)\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", + "test_y = test_data.test_labels[:2000]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class 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 con2d, 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 (1, 28, 28)\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" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CNN (\n", + " (conv1): Sequential (\n", + " (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", + " (1): ReLU ()\n", + " (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", + " )\n", + " (conv2): Sequential (\n", + " (0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", + " (1): ReLU ()\n", + " (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", + " )\n", + " (out): Linear (1568 -> 10)\n", + ")\n" + ] + } + ], + "source": [ + "cnn = CNN()\n", + "print(cnn) # net architecture" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "optimizer = torch.optim.Adam(cnn.parameters(), lr=LR) # optimize all cnn parameters\n", + "loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | train loss: 0.0623 | test accuracy: 0.98\n" + ] + }, + { + "data": { + "image/png": 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J7l8BVPMM1c9b8BRR/HlMQVVCP9uiLqhC//4cEuPWUyHVHE3Qq5etMstGyM87\nf5ConkRqa6NFr6y8ar1Ii/EX3byDpqdvp8vZE31b86tP13P7cTfzP6d27oAVJ+4DeOiHU/hmzde+\nyZZV1NdHC+LFXwZcUXmxqank550/yI4/ETs7J5d2W0GhI63seHAcXUb+ksK+qRuquI0Zx6D7yUFb\n2LgL9i5zNE3gWI3Br+rbw9R1Uj0zfxDFn8fo9nZ2PjyB4mO+S8kxIz1bp4xSW3HjbicHrec9zvnb\nqYzgPgBuoXOOwkPAbKIZr0uA94HCJPu+lWbsRjgdv4smvup3Lu0bOt/B+rZ2LoF9x3KjSP7OOPl5\nf/7eqg6hnakitcSPEA5E8ecxrYtfoHXpPNq3baTlrdkU9juUrhdOdn2d84u+z6MWa/J7Uc1xJ5so\npTrtNZkyXsFaM3YjnI4HDJW+E5z+vM2GdprxI8T/Vsoolbo9HiGKPwN26ppnCyVDRlEyZFTQYhji\nODno8lNJLkZcRg+aSJ89OwyIADXAH1Nc47QZu2fN3B3gVzKWlZ4DUrfHO0Txp8FuXXOAMRMibLFY\nC626O8yaKr8ScKF2TUPnGkP9OI5XuZk2WmnkK8NhZjJenTZj96SZu0O8KIBnhPgRwoFE9aQhVV1z\nM1hV+nbHZAtmE5C8laGaY7mCv3Iyz2CtPHUiic3Yl3/3QG5cWm9c38ej8dlMoh9h07pvApYmf5Ht\nZRrM1DUf870v2PKjiGtrjojNlWu7fzt2fi84mnEczbjYJ3sNWJw2Y/eqmfsHTKMvJc4n8gjpwhUe\nckezeICZuuZbyryJ48/l3X+247QZuxfN3HexhXe5n5Fc7WgeL5GibuFBFH8GzER5ZAt7lyrLpZn3\nLrW3Kw4KSyal/zwYyootr+G0vo8X9YHW8x77cqKrc7qNX34EITOi+DNgJsojW3C9m1eApA31u/xU\nk5NYV/phxUyoqh/E4/RdK/0wJimOrnsN/MlsiIWQClH8GfCjrnmucdLatWxqazM4M5Qr+7/T4Uiy\n3XfG5Y9y48LOZRwuLRpjXgCDiJ5cx0yoqh94Xu8nxL/bKyPvsY1WS2O6UcyUoiEeSZQaUfwus+j1\ns9m29QMGHPALvnVofoaqGSt9Y7y0+5aXfmOqNHMYqX7c2MlTUNxO7ZChjE46Hg9VBW+UiNmMW697\nLIQZq0rf7hg38FzxK6WGA/cRNY8/qrW+y+s1bcfQu7D24cdO5ZsNr9C8a70Ls+U+Xtp9rz73tE7H\nNjdH6NFb1epyAAAgAElEQVTF+M/eaSkFt9hQkrq2TnursRM4HqrqFWZ38l73WBDcwVPFr5QqJGoa\nP5NoNOQipdTzWutPvVw3yBj60q7BJ+MIqalOkyXrRikFy/KM+H+uzRUNU/UmZNZsxm2+Kf17amFH\nrE5jf443NaatVwvr6xZ5KFVmvE7gGgKs1Fqv1lq3AE8C4awRIOQF8+tTN3YPYymFsHDnyvu45tXf\ncNJPTmf6hEcMr0luvn7H8b/1U8RA2GGjOG/hhuBzLbw29fQlWu0gTh1wXOIFSqkJwASA/v37eyxO\nejZ36UWP5uwusxxG2hobWXfJJaiSEnjTv5r/RqQLZg1DKQUz7TDXjBkDvGM43gg3KmKaqdwpcfrZ\nQ+DOXa31VGAqwODBg13IX7TP+aPqglw+ZykoL2fAk0+iiorY3vIeFSXWyhO4We7hjF6ps0UTSyk0\ntLZx4oLVDO9dQZc0XcKs8PPF6zP6D8y0wxwwa1Y0o9AkTiNtzGbcSpx+9uC14l9PtMxNnH6xY77j\nV7TNJ4t+ytZN79De3kzD5iUcfcIznq2VLaiCAiiIKs8HXq1g84wZ9Lmrs48/OdQT3C/NW1iQOiHN\nq1IKccz4D8y2w9xV1UTZVnM3xEz2+T/XHc3OdpPmh6qhlD0xmj/XtfDTfu+bG+OA5rZ21268fvEB\n01jCVBSKETxAH44OWqROeK34FwEHKKUGElX4PyZaBcF3/Iq2OezYP3s6f7bSWl9P3cSJtHz+OX3u\nvtvwmmue+C5bxnb3WbI9eFFKIU671pb8B/F2mF0vfaDTuc9Hj+aegs/p88jdVJ7WMXLpSoO5MlXE\nNK30HY6xg5s3Xj+Il864lHdoZD1zGct43gxarE54qvi11hGl1M+Bl4mGc/5Fa73MyzVTIdE2wVJc\nW8vAOXNoqatj7ZgxnRRWGPCilEIcBab9B5naYQ6cM8fS2m53MvMTt268fhEvnVFECdUMpIVGIjRT\nRJegReuA5zZ+rfWLwIterxNnzITUlTL7DbzILzHymvr3qlPGm0MN5Xcu48uFHY8WFOd2WeJE/8Ep\n10/jxm7V3Aj0IUUm6skPxd44y1SVipj+klw6o5QqdrGZSnoHKFVnAnfuuo3bVS0lE9c6qZV+5jGp\nMlbj7F2qsrLmUKL/YHM3/2rqhC3SZsfm7ZT36HjzmXz67TnTfze5dEYTDZRhrtm9n2SN4reTjesG\nx57s28NKB6qDM3WHGqvVRcNCov+A2/1b106kTWL4bfuuXex99dVUHG8uOSkTk77z207N3z2t7eMz\nyV3eSqjwxMyzfO09tLXt2P35k1W3HrNs9W2Z/nNsGLTfLbWQRYo/X+rTvzQ7a34lHPTUtqxVxH4T\n9x+cdN1fgxYlI4nhty3r1lE3caJtxe/U1PTojOt3vy/bup3zJz5oSw6/SOzyplAM5z5P1klU+hbY\nHTKWPVpGCB2i9K2zqTL40smZSAy/bd++ndKDOzuYzeKmqWlXVXb4Jzp2eQsnovg9YNWnd6T0B9R9\nPp3mXevFX2CAmaxVU3SvCaZ876yP9rxPriOfZZgJvzWD60ld3WvcmyuPyWrF77fj1Y311nx2b9Ym\nd3lt2jGTtQomHMCnPZfeAXz5qZZvDE1t7ZRmWSKRE0IbfptjTVi6EUwzoKxW/H6XQHZjvROGL3VR\nIn/x2rRjNms1ExnlNFIeSTv0eV818qeVm3n6+P67yze8P3z/1FmkPuxEk52u+z37bKdrdrQV879f\nHe4owaq9uZmCLlGHZGFFBQXl5YbXTVk3FICuBe5k8bpRUyhbeKzIHWd5Mss+/pI/3LOASGs7hx3R\nh6uuH2Z4XVYrfidJWXZ275IEZo/G351lyXyTLmvVL0yVb4iZdnZ3HFu9evep1yuq6Lk9c0csKxE0\nyU5XI6auP8bU90tH84oVbJg0CQoK0G1t9Lo5fdSNW1m8nnfvynFaWyJMmbyA+x76EeUV6SOJslrx\nO0EapviHGfNNnExZq35hpXyDUcexk6+fZmodKxE0yU5XI9oaGymsdFYTv+zwwxnw5JOO5rCD2Zr/\nYaK8l/XSzOW9Ml9jhw8/qKNr1xKuvfIZdu1s4We/OpVjjt3X8Nq8Vfyye/cPVRZTRBnMN7q9nZ0P\nT6D4mO9ScszIlNdZfYKwg5flGxKxGkGT6HQ9aFHnZh6pzDJ+MuYXD9C1IX24YWKYZpxMNYXCyNX1\nQUuwh683NLJieT1Pv3AZO3a0cOnY6Tw/7+co1bkwYd4qfi9Zv2ZGVjpvrWIlCseM+aZ18Qu0Lp1H\n+7aNtLw1m8J+h9L1wsmdrqv8zctufYVQYCWCJtHpaoQqMH4q8TIpK5lMSj8V2VxTKAx0ryrjyKP3\noaKylIrKUqqqu7J50w5qenYOgw1c8Y/4UaSeWGLBXt96hxE/Sl1rJxm/SyCbXe+ks5fbmj/bsnXN\nRuGYNd+UDBlFyZD8a9BmNoIm2elqBTeTsrxAago55/Cj+vLAlFeJRNpoboqwedMOqqq7Gl4buOIn\nIZvMKn6XQHZjvWzKzM2E2SgcM+abXCO+wx749NNprzMbQQPsvg6gsMpaL2A3k7K8IGw1hbKRbt3K\nGHPhcVwyZhqRSDtXXnsmhSmi0HJHC1lEGqa4RyYzjhnzTeBYTfrKEL4Z32FnwmoEjVMiW7ey9qKL\nHCVleUG+dO9KbM5ulvJe5n0J55x7JOece2TG6/JW8QfWMMVG8hDda0KbuGLGjFP1iHeRU20b1xj6\nFdIleRlW+HT555u4w06H3xE0RVVVDHz2WVtJWXFT1AFvvGFpnNaaXyz5skPbyXzFTnP2HRvgtgT/\nbHkvGPGWMzlCp/hzuQxydXfslRIIovyACcxG4XhJpvBQIzY26YzZv3GytQx0OjKZlNwet6yhuVPb\nyRsszyLEsXPzSCZ0ij/X4us72fQDaTzpDWajcLzEbnavWcJSiG7NmDGuReOs++lPbZmUCquq+NZL\nL1ke16esyFLbScF7Qqf4Jb4+ewhDFE6XsycGur5fDJg1K2M0jtmQzYGzZ3stbgeqSwo7tZ382lcJ\nco/CwnI7pZl3PyuETvHnI0Y20PMGWIvayDXM5gik8iu4meRV/XhDKEw+maJxwhqymdh2Ml73yKgp\nfK5hx5FrloP3vbrDZ6XUEq31YLPjc0LxZ7tfwMgGmu+K32yOQCrMjLWSgBakyUe3tbHmvPMyJngF\nGbIZL9gGcGnSOaO6R8kkx/HPuPxRblz4394K7TFeKX03yAnFH7Rf4P03/8tROKjYQDtjOkdgxjWG\nfgUzZSKc3lzskhi737Z1K2vOOy+t7VwVFpoukexWHX03Map7lIzE8e/hA6axhKkoFCN4gD4c7foa\noVP88fj6IacuMD0maL+A0xwAIxtoGNm7VFne+eq2iCMHbKYcgXTO5Exj3SoDbRUrsftWErwgnHX0\njeoePZp0Tb7E8WdiF1t4l/u5lHdoZD1dMDYv3pZUfudW9DG3KeL/OTfcoqlNt07oFH9g8fUBYmQD\nHd67InXt94CI27gzhULqSCs77ruAklPGOgrzdFKp0+rY4kEnUzzoZLuiWsJK7L6XNwk3+XJh7nXG\n8jrZyoj1vMe+nEgRJVQz0O4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | train loss: 0.0904 | test accuracy: 0.98\n" + ] + }, + { + "data": { + "image/png": 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6WyqiinJAi2vqCh/n+C1iMlEpCDG2wye8HCthN92YsZVRgD4GqY8fml0MLROT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\nvks+W71lzGZQFTmZ6CSAv//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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | train loss: 0.1173 | test accuracy: 0.98\n" + ] + }, + { + "data": { + "image/png": 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16ray0Rm393ww+IJ2diSfdqWifq8UDmQSBzLJ9evmm9Hbzba1K9/UOe+64X21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\nulJJLxO7Fr69Iq8E08rnClPdITfQVOl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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | train loss: 0.0559 | test accuracy: 0.98\n" + ] + }, + { + "data": { + "image/png": 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GvwgJw8gAbCrPbJS9pPT5MRqmYwM6L0/d1VXQmUQ72cKr3MaFLKCRdcxhIpN5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\nyyyXt3mbwAQlvex2dVNSoufF/tKrv+6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | train loss: 0.0859 | test accuracy: 0.98\n" + ] + }, + { + "data": { + "image/png": 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VxmZjPcHJQAToAfwxg3bc0xu2elwkXO5gOUg7/rBipzftzGTZtEa8/frT9zfd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\nSC0meaZesearaTRtX+sqDdP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KnzBo+F4AHHH+URx9ybHt9i9deXvScwsLy9lvwLU+Wqdx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\nQzt+jUajyTP+PylDpLvHb7ZhAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | train loss: 0.0169 | test accuracy: 0.98\n" + ] + }, + { + "data": { + "image/png": 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+dxXWaCqmMKmz2GuHrsN2UqV72XtXO7ajEC2NphFM0BjHbygpog7fVDRanrQ9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\nnXWZ6+O9koQ0TL+I3PGP+2a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SHQXOZTAURKXa2XDDZHoc8yMqB+8StTndyJcGmZsl4zbz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | train loss: 0.0625 | test accuracy: 0.98\n" + ] + }, + { + "data": { + "image/png": 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613LiDafQY2DHZMq7M/hZ8j3pppJqvzdBMYV/lsa+psRYtNK9p6qf1beXrNWC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\nPUHLD7kyO81fdBVc/cOs18U2bGXP6//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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | train loss: 0.0136 | test accuracy: 0.98\n" + ] + }, + { + "data": { + "image/png": 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eufO88IfpH1ue92tmn2t4EVoZtCgduxjDn8/M/ByaNQp1nDzE12E4mRVWlTXr\nXVhT0z4s8sz9bPdlyC10jfaipbckbGPYIf0Zdkh/2toUa9c0csG4Kbww6zI/h+0pxvDnK2MfgxdT\naWXmADZj4dNJ2ur0aiRdOU3MysaErqjR9oKCAqG6piqjG7VOMIY/iJR2sSXNbLDBthYoK7Z1y7rt\nISqLCyxj+90Y73TV6e3auzvXvP5r19c4vW9a6Omc3eAF2LK5ifX16VXXdIsx/EHk1AdTXwMwPaUz\n3Bu2t0CpTWNZ3D4+2UnmqC+zyWc/73gszv3z6qpGZtQ28NChfWhpU3xv1lcsHG1ditKN8U5Xnd5N\ndRu56+hbKe9RkTDpSucap20HdYPXKy44ZypXXGu936S7iVxYWO7nEDtgDL9hBwlnr68t3nHNEIex\n5E7++J3OQt1iJ7Z/cJdOvHnMHgBUFhfylY0CLDphoE2tbY4loKP8Ycm9VPas5NOZHydMutK5xmnb\n2YyO4X7s6Z9a3qu7iTxkj5s8G68uxvDnId1aH4RHG9gQdzxdrgddnM5CU3ETMAsowboqmJ3Y/gIX\nKfc6chFelG+s7BmuDJYs6UrnmlT37Tsq8Z5RUOP7Yw37w9PPbXfObfRPkCN/jOHPIewoZlqRLteD\nLvdvfMjzNj8C5gLvAd+gVxXML2LlIrp3KmLRSXt3uMZypTF2f21Jh+2bt1NSVkJBYQErPl5ORY+O\nEgE616Rq2y5BcP+kMuxuDbeXm8heYwx/DmFXMTMeHddDqiQiKzoXaIZepoHFwEGRz9GqYJki6lJy\nREO91mWrPlvJo5dMprSyFBFh3AMTHF2TrO3fzv+9raEHhXjD/tC0c9udD5qwmpcYw5+j6ChmxqPj\nenjnzvMSJhDFY1XFya/wv16len+M+xJ27zQD/yNcFcxL7ET5eCEXkYoBw/dMaZh1rvHyvqAQb9jj\nCZqwmpcYw5+DOFXM9EKpMhVebthuGGdfKmIwMBY4DtgT8Do1LWj7JJkiSPH9hYXllrLK8YY9nqAJ\nq3mJMfxBIT7DVIdpZ1gedqqY6YVSZSp0N2x1DIfVikJn0/CSyNenhKuCOYntT0TQ9kkyRaYisqzY\np9/Vllm48YY93s+vK6z28vOfcOIpHTPCg1RqMZ68MvxjLwyxwWZeVLcuMH1iGn5MHmaYOlXM9ML1\nkCpeXzf8z6nh2MZ2JoWmJ30BjAJCQA8iVcEsYvtvof1S56Yz9bKcg6LUmWn8isjyknjDbhWWqSOs\n1qVrWYesNM6ZAAAgAElEQVRjQSu1GE9eGX67Rt/pPZnEE8VMF6SK1tANG3RrOJKNY6atluzhRUWv\nXCBb4vu9UMzcb2jvDseCHMoJeWL4ncz0sxW/tfrdYCdsMFsMRzxO9km80uwpo9RWmKSffnineQHZ\nSFVVxxl/0COC8sLwuzX6o38SSp/LJ4ZUiUbZhp2wwWw1HE72SbzaEI66tqz2Pqzwyw+v+4KPHWdQ\nErq8IugRQXlh+L1gQ0P4BRDF7xdBkBKNvEI3/E/XcPznobcCpwbpZJ8kUxvCfvnhneQFRPdm7BDk\nl4VuRFC6NXqiGMPvEL9dR1aJRk2Al9tEq0u6U9283t5NXewVRneCruHQMvrV1bY3zhtJXRXMSzK1\nIeyXOy1d8f1ByP5NhG5E0D79rs7A6IzhDyxWiUYbgJrYizZuA4uIgmSsVlU7Pu9zzHOAs3h4P9E1\nHOuWrU09S62ra/ftLXEu1o+ZTj2LOYqbAXiYI/gxj1NpZ8AuydSGcLa605yQKJbfT3Q2jieEZmu1\nVUVx6prSNjCGP6BYJRp1MHGXPtculr9bq6acc47gxSy1jO5sZ6dswnYaKKO726HZwk3inI7c9fbN\n2ymtKO1wzIk+T7YSnVlbxfPP+2AZjz48h/setM6LCQKb8LaesjH8Eea9fSKbNn5I/4GXsefgYESQ\nxCca5WcqUGLql69z3UYfDuENfk0rLTSyihIqKPLUoZYaN4lzOu6OVZ+tZMDwPTscc6LP4yUXnHOH\n5fGtXcqZ/pfL0pb9G43AccLzzyzkycfm8+gTej+/Fw65iqZ1O1+yu2vc01rdzMoV8xyNLxG+G34R\nOQG4l7DdmqSUsv7fzjD7DZvIutWv07RtZaaHsoMOiUaGdngxSy2jG8O4hIc5AkE4gXs9GJk9/Nbs\niTf60WNB1dnp3BB2yaQr+zcageMEu3kAsUZfl8LVJbbvSYWvhl9ECgnbrOMIu6nnicjzSqnP/OzX\nCaWdg5dhqZVoNPaxHR97PbmJNdvtievoips55eu5X7WbWZ5xzzn0PcCbRBavZqkHcj4Hcn7Sa3QL\nosxc1cjflqznqRG77/DX//eEvfIugcsL0pX9G43AySf8nvEPB5YopZYCiMjjwBggLYbfbuJWnwHj\n/RtMGvjitKrUF6UZP2eWqV4gn9f+0WJDz1m1I12fezqE7vKFdCXxVVWV8fgzF/rSdlDxexrSm3AY\nepQVkWM7EJELRWS+iMxfu3atp53nS7auwRovozh0fe7HVlfQhmLkrK/4/qyvfBG6ywSXVJ7LXcfc\nxl3H3MZ/HnrLkzaHvbqEx5YlrkcQG3XkxX5OUPmQR5jE95nMCL7lv2npM+Obu0qpicBEgIMPPtjM\njQxZTTo09lOxaXUDVdXehuj64W9/4+gBCbOUgxZ1NGSPmywjgnSIVek8KW7FuY0NfMB9XMAcGlnJ\n04xjAu96MeSk+G34VxLOP4rSJ3LMYGjHPcsP7XDsit3neN5Pp56bbW+wlZdmZrbZ0qZYuz3EmP/U\nMu/4vbTvEx/2E3T87XcdcxugH4GTLEs5CFFH8TjJBYhX6Xwqbp99JXPpx2EUUUI3BtBMIyGafI8s\n89vwzwMGisgAwgb/DMLh6RkncOGbDjJMqU5vhmku8IMP/pTymiG//lcaRpIap5m8UReJl+j42+2u\nCIb++8uEz5bOqCNd3fz4LFudFUC8SueJcTP+rdRTys5SpqV0ZRvrqWRXB0+ij6+GXykVEpGfA68S\nDud8SCm1yM8+dfEifDOq3eOJbk+dszjioGFXIRJgS6t1EZQtrcWUFzpLXCmjY0UlCHZxjHicZPKm\nKoDuNDZeJ8vXbgTOn1Y8wNquFUzSGoE/uNHN11kBxKt0/mdE+/OZSiD03cevlHoZeNnvfuIZe2Eo\n6XkvwzfNJvJOdESzhixdqtXWxJUHtfvejuvnrKJTWcQn7Y4FvThGPE4ig2JdJNe++dsO55346nX9\n7de88RtbEThNXTOfLexGN99KZyd+FZCqvGOmEggzvrnrF8YYG+IJenEMK4oLhI9GD9SODErlInES\nG2/H355tuj9+6+bHq3TGk6kEwqw2/PlUYCWXaW1sZPl55yElJbRt20avq6+mYsSI1DfaJOjFMRJR\nHjH60+77OdtiZ8k2ZYzBWWy8HX97uiNwkkk568g2+62bH6/S+X90/DnqJBB6TVYb/nQZ/cBtBOcY\nBeXl9H/8caSoiObly1lx+eW+GH63f+RKqYy+JLZ54BrxU5Hz/x31O08icLzS6NHZa9LVzXdDrKxD\nfFRPpshqw++GT+ddxMb6ObS1NdGwfgEHjkwcyRFEHZ9cQgoKoCA8q23bvJnSffZx1V6ijV23f+Sz\n6jZntRyD37HxVnsKTkiXRg/o6+Znmt2LR3ALUF4NV3sQB5K3hn/fYX/Xvra0cx/6DBhPKLTZxxHl\nNy11day4/HKav/6a3e680/Iaq1j/RPyepTwRd8ztH7m2HMP0j9t/PzZ50Uyvau6mIoix8VakS6Mn\nihcF19PFFpsR34nIOcMfdcscM2aV520XFWU+CiFXKa6pYcATT9C8YgW1Y8dSefTRvvTj5o/cjXxy\nMryquZuKICtyxqK7D5Eu2eZcJOcMf9QtY8gMg7QUQtuXbywoaqXmkHAsc2FFBQXlmalDmgq/5Bh0\nau4Oue1ZrsA6nDXXDKDuPkQ6XUJe4SRz3A9yzvAHUV45n7ArCw3QFipk2RlnQEEBqrWV6l9n+I95\n+scp3TNekqrm7uG/ejjp/dlmAKMvKqsx29mHSLdLSIdUSV1WmePb15bz4qH6tXfjy4cC3Iw66BbB\n6o9v9U2qfcVWyEHD7zctzRsoLumW+kKDLfo//rjte9IVBuo3qWru1lcm/30LogFMRrIXlZ19iHTJ\nNtshUfF0K2OdJix1XYzht4kx+sEhVRjoRkrpalM+orAw/W4mtxr+qQxg/KZ454JmLuqTXP737ysO\nZGubvcpPOu3CzhfVNW/8psM5O/sQTkJTnQitpft34kMeYQETEYTR/IXdONDzPgJl+Hv0e2mH/o0f\nREM4R56w0Lc+DB0JLVvItkevhYICpKCIsgl/obBXf9ftpgoDvZDTAFi0xx6u+9KmS/v9i8Nra6m/\n7dkd3y/69Q873OJ209iuAdzaVtLhZRD9GUUTouwafTv3RF9UbnAamppoRh4U0iXTHCjDX1DUI/VF\nGiSqpGUnhNPgHQVda6i4+imkrJKWhTPZ/vTvKb94oidt64SBHl5bS31rq612e1w/hXf+YLMiW5ce\n8Lc3k/Y3JOYl0K6/xg28e+d59vrDu9j8w2treadfP0cie3bxQj00W0JT7ZIumeZAGf5MYrJz/aOg\na4ybsagTUmj9a1c3txttLfbCIwuKu6UMA7Vr9AHqy7t0jMfXvddJfyn8+InwygBGxxyVOLgHPSE9\nu6RSD9UlW0JT7WJXptmpW8gY/ggmO9d/VNMWtj91G50v+IvlebtGP/aeIIeB+omXBjA667fCq430\nVOqh+Y4dmWZdt1Ak2qdddI8x/BHchIF287bKXU6iQi1suf98Op38Cwp7d5Rk+Ha2czffsrFjgxEG\n6pJEbqB0kWyloqunFL93EC+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M/SOdetjzoW+v78xH23dBldpLWCtubR+E8dGHK+jcuYRr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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | train loss: 0.0033 | test accuracy: 0.98\n" + ] + }, + { + "data": { + "image/png": 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C07j6t/7vVaaM3ofDh4Qon9mmgOUqJ7v8oNGO9lfkv8maFzJl7Pg10jf4gxH+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\nknkG3nPaZ+/67tbMHvASiz/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | train loss: 0.1449 | test accuracy: 0.98\n" + ] + }, + { + "data": { + "image/png": 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VPHTRvdQ21CIinP2z8z3f61XQ41hVKfGTOLV75W8nsmH9vxi+xzf5xL7+f8nU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\nm/Oc1xVBUrttFYpEylW6OZt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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | train loss: 0.0121 | test accuracy: 0.98\n" + ] + }, + { + "data": { + "image/png": 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po5VGVmastnU63g5B32z9xqnTjy2eX8w7gduSCuP4PXDAQeNZu+p1mptW+HYN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\nT0M2G6DoupbfTy4z6zezoinC4lP2oaG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | train loss: 0.0233 | test accuracy: 0.99\n" + ] + }, + { + "data": { + "image/png": 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nFUJf3VScaLt4VX6cQh/6kameYlSXd3bTPBP4pfljRSEodSZCcFfP+VHYpmTF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\n75eCZ6Qdvzrd6GI3N90ubqt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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# following function (plot_with_labels) is for visualization, can be ignored if not interested\n", + "from matplotlib import cm\n", + "try: from sklearn.manifold import TSNE; HAS_SK = True\n", + "except: HAS_SK = False; print('Please install sklearn for layer visualization')\n", + "def 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", + "\n", + "plt.ion()\n", + "# training and testing\n", + "for epoch in range(EPOCH):\n", + " for step, (x, y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader\n", + " b_x = Variable(x) # batch x\n", + " b_y = Variable(y) # batch y\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 % 100 == 0:\n", + " test_output, last_layer = cnn(test_x)\n", + " pred_y = torch.max(test_output, 1)[1].data.squeeze()\n", + " accuracy = sum(pred_y == test_y) / float(test_y.size(0))\n", + " print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| 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)\n", + "plt.ioff()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[7 2 1 0 4 1 4 9 5 9] prediction number\n", + "[7 2 1 0 4 1 4 9 5 9] real number\n" + ] + } + ], + "source": [ + "# print 10 predictions from test data\n", + "test_output, _ = cnn(test_x[:10])\n", + "pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()\n", + "print(pred_y, 'prediction number')\n", + "print(test_y[:10].numpy(), 'real number')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/402_RNN.ipynb b/tutorial-contents-notebooks/402_RNN.ipynb new file mode 100644 index 0000000..f2a3660 --- /dev/null +++ b/tutorial-contents-notebooks/402_RNN.ipynb @@ -0,0 +1,318 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 402 RNN\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\n", + "* matplotlib\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch import nn\n", + "from torch.autograd import Variable\n", + "import torchvision.datasets as dsets\n", + "import torchvision.transforms as transforms\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.manual_seed(1) # reproducible" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Hyper Parameters\n", + "EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch\n", + "BATCH_SIZE = 64\n", + "TIME_STEP = 28 # rnn time step / image height\n", + "INPUT_SIZE = 28 # rnn input size / image width\n", + "LR = 0.01 # learning rate\n", + "DOWNLOAD_MNIST = True # set to True if haven't download the data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Mnist digital dataset\n", + "train_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", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([60000, 28, 28])\n", + "torch.Size([60000])\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot one example\n", + "print(train_data.train_data.size()) # (60000, 28, 28)\n", + "print(train_data.train_labels.size()) # (60000)\n", + "plt.imshow(train_data.train_data[0].numpy(), cmap='gray')\n", + "plt.title('%i' % train_data.train_labels[0])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Data Loader for easy mini-batch return in training\n", + "train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# convert test data into Variable, pick 2000 samples to speed up testing\n", + "test_data = dsets.MNIST(root='./mnist/', train=False, transform=transforms.ToTensor())\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", + "test_y = test_data.test_labels.numpy().squeeze()[:2000] # covert to numpy array" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class 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" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RNN (\n", + " (rnn): LSTM(28, 64, batch_first=True)\n", + " (out): Linear (64 -> 10)\n", + ")\n" + ] + } + ], + "source": [ + "rnn = RNN()\n", + "print(rnn)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters\n", + "loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | train loss: 2.3127 | test accuracy: 0.14\n", + "Epoch: 0 | train loss: 1.1182 | test accuracy: 0.56\n", + "Epoch: 0 | train loss: 0.9374 | test accuracy: 0.68\n", + "Epoch: 0 | train loss: 0.6279 | test accuracy: 0.75\n", + "Epoch: 0 | train loss: 0.8004 | test accuracy: 0.75\n", + "Epoch: 0 | train loss: 0.3407 | test accuracy: 0.88\n", + "Epoch: 0 | train loss: 0.3342 | test accuracy: 0.89\n", + "Epoch: 0 | train loss: 0.3200 | test accuracy: 0.91\n", + "Epoch: 0 | train loss: 0.3430 | test accuracy: 0.92\n", + "Epoch: 0 | train loss: 0.1234 | test accuracy: 0.93\n", + "Epoch: 0 | train loss: 0.2714 | test accuracy: 0.92\n", + "Epoch: 0 | train loss: 0.1043 | test accuracy: 0.93\n", + "Epoch: 0 | train loss: 0.2893 | test accuracy: 0.93\n", + "Epoch: 0 | train loss: 0.0635 | test accuracy: 0.94\n", + "Epoch: 0 | train loss: 0.2412 | test accuracy: 0.94\n", + "Epoch: 0 | train loss: 0.1203 | test accuracy: 0.92\n", + "Epoch: 0 | train loss: 0.0807 | test accuracy: 0.94\n", + "Epoch: 0 | train loss: 0.1307 | test accuracy: 0.94\n", + "Epoch: 0 | train loss: 0.1166 | test accuracy: 0.95\n" + ] + } + ], + "source": [ + "# training and testing\n", + "for epoch in range(EPOCH):\n", + " for step, (x, y) in enumerate(train_loader): # gives batch data\n", + " b_x = Variable(x.view(-1, 28, 28)) # reshape x to (batch, time_step, input_size)\n", + " b_y = Variable(y) # batch y\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().squeeze()\n", + " accuracy = sum(pred_y == test_y) / float(test_y.size)\n", + " print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[7 2 1 0 4 1 4 9 5 9] prediction number\n", + "[7 2 1 0 4 1 4 9 5 9] real number\n" + ] + } + ], + "source": [ + "# print 10 predictions from test data\n", + "test_output = rnn(test_x[:10].view(-1, 28, 28))\n", + "pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()\n", + "print(pred_y, 'prediction number')\n", + "print(test_y[:10], 'real number')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/403_RNN_regressor.ipynb b/tutorial-contents-notebooks/403_RNN_regressor.ipynb new file mode 100644 index 0000000..0c103b3 --- /dev/null +++ b/tutorial-contents-notebooks/403_RNN_regressor.ipynb @@ -0,0 +1,264 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 403 RNN Regressor\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\n", + "* matplotlib\n", + "* numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch import nn\n", + "from torch.autograd import Variable\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.manual_seed(1) # reproducible" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Hyper Parameters\n", + "TIME_STEP = 10 # rnn time step\n", + "INPUT_SIZE = 1 # rnn input size\n", + "LR = 0.02 # learning rate" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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gqKTCu+8CuXKp1+AiLVtK2rO2nEoDa9YAK1dKwkNIiGk1lueff4C6dYH9+71/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\nEBoqvvmOHV7Ox/QPgoPlNB0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# show data\n", + "steps = np.linspace(0, np.pi*2, 100, dtype=np.float32)\n", + "x_np = np.sin(steps) # float32 for converting torch FloatTensor\n", + "y_np = np.cos(steps)\n", + "plt.plot(steps, y_np, 'r-', label='target (cos)')\n", + "plt.plot(steps, x_np, 'b-', label='input (sin)')\n", + "plt.legend(loc='best')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class 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" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RNN (\n", + " (rnn): RNN(1, 32, batch_first=True)\n", + " (out): Linear (32 -> 1)\n", + ")\n" + ] + } + ], + "source": [ + "rnn = RNN()\n", + "print(rnn)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters\n", + "loss_func = nn.MSELoss()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "h_state = None # for initial hidden state" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(1, figsize=(12, 5))\n", + "plt.ion() # continuously plot" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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EcmL3HQ0DEMEgLg2oGED9mNSr856JfWibxcOsqG92doBXTNiBOwdRDwPIXYzs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\ncykIZc7aR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for step in range(60):\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)\n", + " x_np = np.sin(steps) # float32 for converting torch FloatTensor\n", + " y_np = np.cos(steps)\n", + "\n", + " x = Variable(torch.from_numpy(x_np[np.newaxis, :, np.newaxis])) # shape (batch, time_step, input_size)\n", + " y = Variable(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 = Variable(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(); plt.pause(0.05)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/404_autoencoder.ipynb b/tutorial-contents-notebooks/404_autoencoder.ipynb new file mode 100644 index 0000000..67bec42 --- /dev/null +++ b/tutorial-contents-notebooks/404_autoencoder.ipynb @@ -0,0 +1,407 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 404 Autoencoder\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "Dependencies:\n", + "\n", + "* torch: 0.1.11\n", + "* matplotlib\n", + "* numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch.autograd import Variable\n", + "import torch.utils.data as Data\n", + "import torchvision\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib import cm\n", + "import numpy as np\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.manual_seed(1) # reproducible" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Hyper Parameters\n", + "EPOCH = 10\n", + "BATCH_SIZE = 64\n", + "LR = 0.005 # learning rate\n", + "DOWNLOAD_MNIST = False\n", + "N_TEST_IMG = 5" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Mnist digits dataset\n", + "train_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", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([60000, 28, 28])\n", + "torch.Size([60000])\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot one example\n", + "print(train_data.train_data.size()) # (60000, 28, 28)\n", + "print(train_data.train_labels.size()) # (60000)\n", + "plt.imshow(train_data.train_data[2].numpy(), cmap='gray')\n", + "plt.title('%i' % train_data.train_labels[2])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)\n", + "train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class 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" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AutoEncoder (\n", + " (encoder): Sequential (\n", + " (0): Linear (784 -> 128)\n", + " (1): Tanh ()\n", + " (2): Linear (128 -> 64)\n", + " (3): Tanh ()\n", + " (4): Linear (64 -> 12)\n", + " (5): Tanh ()\n", + " (6): Linear (12 -> 3)\n", + " )\n", + " (decoder): Sequential (\n", + " (0): Linear (3 -> 12)\n", + " (1): Tanh ()\n", + " (2): Linear (12 -> 64)\n", + " (3): Tanh ()\n", + " (4): Linear (64 -> 128)\n", + " (5): Tanh ()\n", + " (6): Linear (128 -> 784)\n", + " (7): Sigmoid ()\n", + " )\n", + ")\n", + "Epoch: 0 | train loss: 0.2323\n" + ] + }, + { + "data": { + "image/png": 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Pj87JybFqo6urK0aNGoXKykpEREQAgBTg5/dHjRqFEydOAIA8h3379gEwnmJcVlYGQDuY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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | train loss: 0.0559\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5 | train loss: 0.0374\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 5 | train loss: 0.0349\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9 | train loss: 0.0367\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 9 | train loss: 0.0351\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "autoencoder = AutoEncoder()\n", + "print(autoencoder)\n", + "\n", + "optimizer = torch.optim.Adam(autoencoder.parameters(), lr=LR)\n", + "loss_func = nn.MSELoss()\n", + "\n", + "# original data (first row) for viewing\n", + "view_data = Variable(train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.)\n", + "\n", + "for epoch in range(EPOCH):\n", + " for step, (x, y) in enumerate(train_loader):\n", + " b_x = Variable(x.view(-1, 28*28)) # batch x, shape (batch, 28*28)\n", + " b_y = Variable(x.view(-1, 28*28)) # batch y, shape (batch, 28*28)\n", + " b_label = Variable(y) # batch label\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 % 500 == 0 and epoch in [0, 5, EPOCH-1]:\n", + " print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0])\n", + "\n", + " # plotting decoded image (second row)\n", + " _, decoded_data = autoencoder(view_data)\n", + " \n", + " # initialize figure\n", + " f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))\n", + " \n", + " for 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", + " \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.show(); plt.pause(0.05)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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IBB0dHaivr1f0t1XTZ9i4uQl3v3RE9jE5KIqCw+GAw+HIaIDmCj7C4TACgQAmJiaQSCRg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\nrUZ1dbUkXWlYlsV///tf/O53v8Pq1auxdOlS2Wx0Ozo6sGzZMlitVth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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualize in 3D plot\n", + "view_data = Variable(train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.)\n", + "encoded_data, _ = autoencoder(view_data)\n", + "fig = plt.figure(2); ax = Axes3D(fig)\n", + "X, Y, Z = encoded_data.data[:, 0].numpy(), encoded_data.data[:, 1].numpy(), encoded_data.data[:, 2].numpy()\n", + "values = train_data.train_labels[:200].numpy()\n", + "for 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)\n", + "ax.set_xlim(X.min(), X.max()); ax.set_ylim(Y.min(), Y.max()); ax.set_zlim(Z.min(), Z.max())\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/405_DQN_Reinforcement_learning.ipynb b/tutorial-contents-notebooks/405_DQN_Reinforcement_learning.ipynb new file mode 100644 index 0000000..a233789 --- /dev/null +++ b/tutorial-contents-notebooks/405_DQN_Reinforcement_learning.ipynb @@ -0,0 +1,433 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 405 DQN Reinforcement Learning\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "More about Reinforcement learning: https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\n", + "* gym: 0.8.1\n", + "* numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch.autograd import Variable\n", + "import torch.nn.functional as F\n", + "import numpy as np\n", + "import gym" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2017-06-20 22:23:40,418] Making new env: CartPole-v0\n" + ] + } + ], + "source": [ + "# Hyper Parameters\n", + "BATCH_SIZE = 32\n", + "LR = 0.01 # learning rate\n", + "EPSILON = 0.9 # greedy policy\n", + "GAMMA = 0.9 # reward discount\n", + "TARGET_REPLACE_ITER = 100 # target update frequency\n", + "MEMORY_CAPACITY = 2000\n", + "env = gym.make('CartPole-v0')\n", + "env = env.unwrapped\n", + "N_ACTIONS = env.action_space.n\n", + "N_STATES = env.observation_space.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class Net(nn.Module):\n", + " def __init__(self, ):\n", + " super(Net, self).__init__()\n", + " self.fc1 = nn.Linear(N_STATES, 10)\n", + " self.fc1.weight.data.normal_(0, 0.1) # initialization\n", + " self.out = nn.Linear(10, 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" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class 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 = Variable(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()[0, 0] # return the argmax\n", + " else: # random\n", + " action = np.random.randint(0, N_ACTIONS)\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 = Variable(torch.FloatTensor(b_memory[:, :N_STATES]))\n", + " b_a = Variable(torch.LongTensor(b_memory[:, N_STATES:N_STATES+1].astype(int)))\n", + " b_r = Variable(torch.FloatTensor(b_memory[:, N_STATES+1:N_STATES+2]))\n", + " b_s_ = Variable(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] # 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()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "dqn = DQN()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Collecting experience...\n", + "Ep: 201 | Ep_r: 1.59\n", + "Ep: 202 | Ep_r: 4.18\n", + "Ep: 203 | Ep_r: 2.73\n", + "Ep: 204 | Ep_r: 1.97\n", + "Ep: 205 | Ep_r: 1.18\n", + "Ep: 206 | Ep_r: 0.86\n", + "Ep: 207 | Ep_r: 2.88\n", + "Ep: 208 | Ep_r: 1.63\n", + "Ep: 209 | Ep_r: 3.91\n", + "Ep: 210 | Ep_r: 3.6\n", + "Ep: 211 | Ep_r: 0.98\n", + "Ep: 212 | Ep_r: 3.85\n", + "Ep: 213 | Ep_r: 1.81\n", + "Ep: 214 | Ep_r: 2.32\n", + "Ep: 215 | Ep_r: 3.75\n", + "Ep: 216 | Ep_r: 3.53\n", + "Ep: 217 | Ep_r: 4.75\n", + "Ep: 218 | Ep_r: 2.4\n", + "Ep: 219 | Ep_r: 0.64\n", + "Ep: 220 | Ep_r: 1.15\n", + "Ep: 221 | Ep_r: 2.3\n", + "Ep: 222 | Ep_r: 7.37\n", + "Ep: 223 | Ep_r: 1.25\n", + "Ep: 224 | Ep_r: 5.02\n", + "Ep: 225 | Ep_r: 10.29\n", + "Ep: 226 | Ep_r: 17.54\n", + "Ep: 227 | Ep_r: 36.2\n", + "Ep: 228 | Ep_r: 6.61\n", + "Ep: 229 | Ep_r: 10.04\n", + "Ep: 230 | Ep_r: 55.19\n", + "Ep: 231 | Ep_r: 10.03\n", + "Ep: 232 | Ep_r: 13.25\n", + "Ep: 233 | Ep_r: 8.75\n", + "Ep: 234 | Ep_r: 3.83\n", + "Ep: 235 | Ep_r: -0.92\n", + "Ep: 236 | Ep_r: 5.12\n", + "Ep: 237 | Ep_r: 3.56\n", + "Ep: 238 | Ep_r: 5.69\n", + "Ep: 239 | Ep_r: 8.43\n", + "Ep: 240 | Ep_r: 29.27\n", + "Ep: 241 | Ep_r: 17.95\n", + "Ep: 242 | Ep_r: 44.77\n", + "Ep: 243 | Ep_r: 98.0\n", + "Ep: 244 | Ep_r: 38.78\n", + "Ep: 245 | Ep_r: 45.02\n", + "Ep: 246 | Ep_r: 27.73\n", + "Ep: 247 | Ep_r: 36.96\n", + "Ep: 248 | Ep_r: 48.98\n", + "Ep: 249 | Ep_r: 111.36\n", + "Ep: 250 | Ep_r: 95.61\n", + "Ep: 251 | Ep_r: 149.77\n", + "Ep: 252 | Ep_r: 29.96\n", + "Ep: 253 | Ep_r: 2.79\n", + "Ep: 254 | Ep_r: 20.1\n", + "Ep: 255 | Ep_r: 24.25\n", + "Ep: 256 | Ep_r: 3074.75\n", + "Ep: 257 | Ep_r: 1258.49\n", + "Ep: 258 | Ep_r: 127.39\n", + "Ep: 259 | Ep_r: 283.46\n", + "Ep: 260 | Ep_r: 166.96\n", + "Ep: 261 | Ep_r: 101.71\n", + "Ep: 262 | Ep_r: 63.45\n", + "Ep: 263 | Ep_r: 288.94\n", + "Ep: 264 | Ep_r: 130.49\n", + "Ep: 265 | Ep_r: 207.05\n", + "Ep: 266 | Ep_r: 183.71\n", + "Ep: 267 | Ep_r: 142.75\n", + "Ep: 268 | Ep_r: 126.53\n", + "Ep: 269 | Ep_r: 310.79\n", + "Ep: 270 | Ep_r: 863.2\n", + "Ep: 271 | Ep_r: 365.12\n", + "Ep: 272 | Ep_r: 659.52\n", + "Ep: 273 | Ep_r: 103.98\n", + "Ep: 274 | Ep_r: 554.83\n", + "Ep: 275 | Ep_r: 246.01\n", + "Ep: 276 | Ep_r: 332.23\n", + "Ep: 277 | Ep_r: 323.35\n", + "Ep: 278 | Ep_r: 278.71\n", + "Ep: 279 | Ep_r: 613.6\n", + "Ep: 280 | Ep_r: 152.21\n", + "Ep: 281 | Ep_r: 402.02\n", + "Ep: 282 | Ep_r: 351.4\n", + "Ep: 283 | Ep_r: 115.87\n", + "Ep: 284 | Ep_r: 163.26\n", + "Ep: 285 | Ep_r: 631.0\n", + "Ep: 286 | Ep_r: 263.47\n", + "Ep: 287 | Ep_r: 511.21\n", + "Ep: 288 | Ep_r: 337.18\n", + "Ep: 289 | Ep_r: 819.76\n", + "Ep: 290 | Ep_r: 190.83\n", + "Ep: 291 | Ep_r: 442.98\n", + "Ep: 292 | Ep_r: 537.24\n", + "Ep: 293 | Ep_r: 1101.12\n", + "Ep: 294 | Ep_r: 178.42\n", + "Ep: 295 | Ep_r: 225.61\n", + "Ep: 296 | Ep_r: 252.62\n", + "Ep: 297 | Ep_r: 617.5\n", + "Ep: 298 | Ep_r: 617.8\n", + "Ep: 299 | Ep_r: 244.01\n", + "Ep: 300 | Ep_r: 687.91\n", + "Ep: 301 | Ep_r: 618.51\n", + "Ep: 302 | Ep_r: 1405.07\n", + "Ep: 303 | Ep_r: 456.95\n", + "Ep: 304 | Ep_r: 340.33\n", + "Ep: 305 | Ep_r: 502.91\n", + "Ep: 306 | Ep_r: 441.21\n", + "Ep: 307 | Ep_r: 255.81\n", + "Ep: 308 | Ep_r: 403.03\n", + "Ep: 309 | Ep_r: 229.1\n", + "Ep: 310 | Ep_r: 308.49\n", + "Ep: 311 | Ep_r: 165.37\n", + "Ep: 312 | Ep_r: 153.76\n", + "Ep: 313 | Ep_r: 442.05\n", + "Ep: 314 | Ep_r: 229.23\n", + "Ep: 315 | Ep_r: 128.52\n", + "Ep: 316 | Ep_r: 358.18\n", + "Ep: 317 | Ep_r: 319.03\n", + "Ep: 318 | Ep_r: 381.76\n", + "Ep: 319 | Ep_r: 199.19\n", + "Ep: 320 | Ep_r: 418.63\n", + "Ep: 321 | Ep_r: 223.95\n", + "Ep: 322 | Ep_r: 222.37\n", + "Ep: 323 | Ep_r: 405.4\n", + "Ep: 324 | Ep_r: 311.32\n", + "Ep: 325 | Ep_r: 184.85\n", + "Ep: 326 | Ep_r: 1026.71\n", + "Ep: 327 | Ep_r: 252.41\n", + "Ep: 328 | Ep_r: 224.93\n", + "Ep: 329 | Ep_r: 620.02\n", + "Ep: 330 | Ep_r: 174.54\n", + "Ep: 331 | Ep_r: 782.45\n", + "Ep: 332 | Ep_r: 263.79\n", + "Ep: 333 | Ep_r: 178.63\n", + "Ep: 334 | Ep_r: 242.84\n", + "Ep: 335 | Ep_r: 635.43\n", + "Ep: 336 | Ep_r: 668.89\n", + "Ep: 337 | Ep_r: 265.42\n", + "Ep: 338 | Ep_r: 207.81\n", + "Ep: 339 | Ep_r: 293.09\n", + "Ep: 340 | Ep_r: 530.23\n", + "Ep: 341 | Ep_r: 479.26\n", + "Ep: 342 | Ep_r: 559.77\n", + "Ep: 343 | Ep_r: 241.39\n", + "Ep: 344 | Ep_r: 158.83\n", + "Ep: 345 | Ep_r: 1510.69\n", + "Ep: 346 | Ep_r: 425.17\n", + "Ep: 347 | Ep_r: 266.94\n", + "Ep: 348 | Ep_r: 166.08\n", + "Ep: 349 | Ep_r: 630.52\n", + "Ep: 350 | Ep_r: 250.95\n", + "Ep: 351 | Ep_r: 625.88\n", + "Ep: 352 | Ep_r: 417.7\n", + "Ep: 353 | Ep_r: 867.81\n", + "Ep: 354 | Ep_r: 150.62\n", + "Ep: 355 | Ep_r: 230.89\n", + "Ep: 356 | Ep_r: 1017.52\n", + "Ep: 357 | Ep_r: 190.28\n", + "Ep: 358 | Ep_r: 396.91\n", + "Ep: 359 | Ep_r: 305.53\n", + "Ep: 360 | Ep_r: 131.61\n", + "Ep: 361 | Ep_r: 387.54\n", + "Ep: 362 | Ep_r: 298.82\n", + "Ep: 363 | Ep_r: 207.56\n", + "Ep: 364 | Ep_r: 248.56\n", + "Ep: 365 | Ep_r: 589.12\n", + "Ep: 366 | Ep_r: 179.52\n", + "Ep: 367 | Ep_r: 130.19\n", + "Ep: 368 | Ep_r: 1220.84\n", + "Ep: 369 | Ep_r: 126.35\n", + "Ep: 370 | Ep_r: 133.31\n", + "Ep: 371 | Ep_r: 485.81\n", + "Ep: 372 | Ep_r: 823.4\n", + "Ep: 373 | Ep_r: 253.26\n", + "Ep: 374 | Ep_r: 466.06\n", + "Ep: 375 | Ep_r: 203.27\n", + "Ep: 376 | Ep_r: 386.5\n", + "Ep: 377 | Ep_r: 491.02\n", + "Ep: 378 | Ep_r: 239.45\n", + "Ep: 379 | Ep_r: 276.93\n", + "Ep: 380 | Ep_r: 331.98\n", + "Ep: 381 | Ep_r: 764.79\n", + "Ep: 382 | Ep_r: 198.29\n", + "Ep: 383 | Ep_r: 717.18\n", + "Ep: 384 | Ep_r: 562.15\n", + "Ep: 385 | Ep_r: 29.44\n", + "Ep: 386 | Ep_r: 344.95\n", + "Ep: 387 | Ep_r: 671.87\n", + "Ep: 388 | Ep_r: 299.81\n", + "Ep: 389 | Ep_r: 899.76\n", + "Ep: 390 | Ep_r: 319.04\n", + "Ep: 391 | Ep_r: 252.11\n", + "Ep: 392 | Ep_r: 865.62\n", + "Ep: 393 | Ep_r: 255.64\n", + "Ep: 394 | Ep_r: 81.74\n", + "Ep: 395 | Ep_r: 213.13\n", + "Ep: 396 | Ep_r: 422.33\n", + "Ep: 397 | Ep_r: 167.47\n", + "Ep: 398 | Ep_r: 507.34\n", + "Ep: 399 | Ep_r: 614.0\n" + ] + } + ], + "source": [ + "\n", + "print('\\nCollecting experience...')\n", + "for 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_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/406_GAN.ipynb b/tutorial-contents-notebooks/406_GAN.ipynb new file mode 100644 index 0000000..617f4a9 --- /dev/null +++ b/tutorial-contents-notebooks/406_GAN.ipynb @@ -0,0 +1,286 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 406 GAN\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\n", + "* numpy\n", + "* matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch.autograd import Variable\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "torch.manual_seed(1) # reproducible\n", + "np.random.seed(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Hyper Parameters\n", + "BATCH_SIZE = 64\n", + "LR_G = 0.0001 # learning rate for generator\n", + "LR_D = 0.0001 # learning rate for discriminator\n", + "N_IDEAS = 5 # think of this as number of ideas for generating an art work (Generator)\n", + "ART_COMPONENTS = 15 # it could be total point G can draw in the canvas\n", + "PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AkrsxxsQgS+7GGBODLLkbY0wMsuRujDExyJK7McbEIEvu\nxhgTgyy5G2NMDPp/MA9tfAK2AEYAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 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()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def 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 Variable(paintings)\n", + "\n", + "G = 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", + "\n", + "D = 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", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "opt_D = torch.optim.Adam(D.parameters(), lr=LR_D)\n", + "opt_G = torch.optim.Adam(G.parameters(), lr=LR_G)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ampp6lZmfnx8eHh7odDoMBoO632QykZubS3R0NDExMQAEBQVRU1NDdna2S2Vm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\nLWb+0WIGymX/i8dUVVgXGf3g5D+prPxhxqbGdXxZ9T7/q5nv8wQPyjF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wWVlZTJgwwaP+mjVrEELw66+/AnDw4EGEECxcuJCbb76ZiIgIxo5V8s29++67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MfHx8cHNzQ6fTYTAY1P0mk4m8vDwiIyOJiooCICAggNraWnJyclwqM5PJRG5u\nLuHh4Q7r5EJCQtQlC7Nnz2bo0KF89NFHfPzxx6ellAus38sLQohnrYrKxutSyv+AMr8G5AGjgHel\nlKlCiL0oymu9tY4XMAZFOSKE8EdReM9KKedZ+1wnhPAF/imEeEdKaZuPCAGGSSn32E4uhHgZqARG\nSymrrPtOoyhSWx2Bomw/llL+zW6/EXhLCPGClLIQQEpZIoQ4AfSjEWWmmRkboKO/o3fj6ovE3NjU\nSENKyRtvvEH37t3x8fHBw8ODu+++G6PRqK7p+emnnwgODnZQZPZs3bqVqqqqRj3NWsLIkSPr7UtN\nTeXWW28lPDwcd3d3PDw8OHjwIOnp6YDyx719+3YmTZrkcs3U9ddfj06nU81HZrOZ4uJihzklX19f\nLr300ia3xhwlmktAQABRUVEEBAQQEBBAfHw8QUFB5OTkNHuE2Bi1tbVNjgpbQnh4OGFhYfj5+REc\nHExCQgIeHh7Nnhu0p7KyEr1e76BYPD09MRgM9UbPLR3Z28yL9t6L3md4G6qqqrBYLA2akaurq116\ngVZUVGCxWFwqO7PZzO7dux2WVlj5AuU//Gqn/T/YPlgVwikgxqndbXYmypsAP8AWIuZqQA8sF0Lo\nbBvwExDu1FeWvSKzchWwzqbIrHzrVCcB6AQsa+Ac3kAPp/oFKCM8l2jKzAVJ8XVZqW3mRssFs9ig\n5VRXV1NYWKi+5TfEG2+8wYwZM7j11lv55ptv2LFjhzoqspmkCgsLHd6UnbHNHzRWpyU4y1tWVsYN\nN9xAZmYmr732Gps3b2bnzp1cccUVqozFxcVIKRuVQQiBXq+nsLAQKSVFRUVIKQkOrjPbu7m5qSO1\nxjbb6MU20nB2srGVW6pMgoKCMJlM6pylu7s7ZrO5nnIzm824ubk16nwhpax3/Gz6c8bd3Z2AgACH\nBdHNpaampsF7o9Pp6o1mW3oP7c2LbgKCvFHvZ0tfQmzKyrmdrezKucp2Da7OV1BQQG1tbUPPps2M\n4jyXVOJUrkFREDa+AEKB66zl8cBWKaVtlbntjW0/UGu32cyS9naqhkw5EYBDiCcpZTVg/+ZhO8dq\np3McbeAcAEana6iHZmZ0gc3c+K+LxNy4fv16TCYTV1/t/JJXx/Llyxk3bhzPPfecuu/AAcd40yEh\nIY2+fdvePnNychxGOfZ4e3vXcypxtabHeWS1detWTp48ybp16xzWVdlPpAcFBeHm5tbkKMFgMGA0\nGikrK6OXREeHAAAgAElEQVSwsJDAwECHP8uWmhm9vLwQQlBdXe0wd2RTsg2ZtFqCbW7LaDQ6zHNV\nV1c36RWo0+nq/dmeTX8N0ZzIIQ3h6enZoKeqyWSqp7xacg6zVJJu2rB5L54+fRoPD48Wfx82ZeQ8\nyrUpOWfzsg1b3dra2gYVWmhoKB4eHg15kNq0W9MTmHZIKTOEECnAeCHEz8BolDkrG7b+RtGwsrL/\n0Tf0ip8LdLDfIYTwBgx2u2znuB/4tYE+jjqVA2niOrWRWSPE+MO1F4G5saSkhCeffJKuXbs2uki1\nqqqq3gNun1oEFPNcUVERK1eubLCPq6++Gh8fn0ajM8TExJCWluaw74cffnBRu76M4KgYtmzZwrFj\nx9SyXq+nf//+fPzxx42a6HQ6Hf7+/mRnZ1NeXl5P+bbUzGjzFnR2nigqKsJgMLR4VFFcXIxOp1MX\nHBsMBtzd3R36N5vNlJSUNGl+8/Lywmg0Ouw7m/6csVgslJSUqPONLUGv11NRUeEgX01NDeXl5Q5z\nVi2l2m5QZ1scffr0aYqLix0caxpCCFHP9d82l+b84lVcXIy3t7fLkZder8fNzc2l16O7uzt9+vRx\nCPZs5XbAguIg0VKSgVutmw9g3/lWoAqIklKmNLCVNdH3TiBJCGHv8OE873AQyALiXJxDvRlWz8tO\nQHpjJ9VGZk0wLB7250Ou1btxWSo8eAF7N5pMJrZt2wYoJrldu3bxzjvvUFlZyZo1a1y+PQIkJSWx\ncOFC+vfvT5cuXVi6dKlD7ilbneHDh3PXXXcxe/ZsrrzySnJycti0aROLFy8mMDCQp59+mlmzZlFT\nU8OIESMwGo2sWrWKOXPmEB0dza233soHH3zAI488wsiRI1m/fr260LspBgwYgMFg4L777uOJJ57g\n5MmTzJ07V51It/Hiiy8ybNgwbrrpJu6//370ej1bt26lb9++jBo1Sq0XGhrKkSNH8PT0xN/f36EP\nd3d3l84MroiMjOTgwYOcOHGCoKAgSktLKS0tpVu3bmodo9HIvn37iIuLUxXo4cOH0ev1+Pr6IqWk\nR48ezJw5k3HjxqmjETc3NyIiIsjJyVEXG9scemyLg11hMBjquds3t7+CggK2bNnCmDFj1FHI4cOH\nCQkJwcvLS3WMqK2tPSPzckhICLm5uRw6dIioqCjVm1Gn0zWodIYOHcqECRP461//6rJPiwRTbS21\nVeUIAcK9luOnSiksLMTf35+IiEanZ/Dx8VG/O51Oh5eXFzqdjvDwcHJychBC4OvrS0lJCaWlpQ4L\n0Z3R6XRERkaSlZWFlJKAgAA1kktWVhbR0dHMmzeP4cOH2+aa/YUQM1AcNv7t5PzRXJahOGC8DGyy\npvoCVIeLucCbQohYYBPKwCcBuFZKeWsTfb8BTAW+E0K8jmJ2/AeKU4jFeg6LEOIx4BOrw8n3KObQ\nzsAtwDgppW3okIgyqvulsZNqyqwJnM2NR0vgl0z4U6em256PlJaWcvXVVyOEwN/fn65duzJhwgT+\n7//+r8kHePbs2eTn56vJEseOHcvChQvV9V2gvLF+9dVXPP3007zxxhvk5+cTFRXFXXfdpdaZOXMm\nwcHBvPnmmyxevJigoCAGDx6smt5GjhzJ888/z9tvv83777/PmDFjePPNNxkzZkyT1xceHs7y5cuZ\nMWMGY8aMoVu3brz77rssWLDAod7gwYNZt24dTz/9NBMmTMDT05PevXurYaFsBAYGIoQgJCTkjM1k\n9vj5+dGlSxeys7PJz8/Hy8uLzp07NznS8fb2prCwUHX4sM35Oc+j2L7DnJwcTCYTer1edb5ojKCg\nIHJzcx28N8+0P5unX05ODrW1tbi5uaHX60lMTGyx8rf1l5CQQGZmpjrCtt3HM3FaqTYptrHqsiKq\ny4oQQnBap8PHx4e4uDiCg4Ob/K4jIyMxGo0cOXIEs9msvnjYvBjz8/NVb8j4+HiHuVZX/el0OvLy\n8sjPz0en02GxWNRn4oYbbiA5OdkWtaUrMB14FcXrsMVIKTOFEFtQwhXOa+D4AiFENvAI8BjKGrJ0\n7DwSG+k7SwgxEngT+C+QCtwLrAPsg9J/YfVyfMp63AwcAVaiKDYbN1r3N2SOVNHCWTWTNRnw4zHl\ns4cbPNIfOrTcYqJxAZGamkpUVBSHDh2iR48e5ySs0pkSFxfH+++/36LYhU2xf/9+QkJCmnypaWiu\n6tixY8THx7e6V+SZ0NjIzCKVNaQ2pw8fHYT4nJ/Zo9tTOCshxCBgM3CdlLLh9OSu224FVkkpn22s\nnjZn1kyGxUOE1Txfa4HlB9q3d+PFTnZ2NtXV1Zw8eZKAgIDzSpE5YzQamT59OlFRUURFRTF9+nR1\nfmnIkCF8+eWXAPzyyy8IIVi1ahUAP/74I7169VL7+emnn7jmmmsICgpi+PDhHD9+XD0mhOCtt96i\nW7duDiZRZ/7zn/8QFRVFZGSkw5rExmT88MMPGTRokEM/QgjVhD158mSmTp3KyJEj8fPzo3///mRk\nZKh1bc4+AQEBTJs2rdF50FIn78VA7/NTkV3oCCFeEkLcYY0E8gDKHN1eoHlpEOr66Q9cAixqqq5m\nZmwmOjcYf6mdubH0wjY3ajTOe++9x4ABA4iNjaVTp06w6K6mG7UW0z5rUfXnnnuObdu2sWfPHoQQ\njBkzhmeffZZnnnmGIUOGsGHDBm677TY2btxI586d2bRpEyNHjmTjxo0MGTIEgG+++YY333yTDz/8\nkD59+vD6669z5513OmSA/vrrr9m+fXu9CCb2rF+/nkOHDnHkyBGuu+46evXqxbBhwxqVsTkkJyfz\n/fffc+WVVzJp0iRmzZpFcnIyBQUFjB07liVLljBmzBgWLVrEu+++yz333FOvj2qnxdFa7MVzihfK\nfFw4UIay9u1RKWXjATPrEwxMklI6Lzeoh/ZVtoAYf7gurq78fQbkt0PvRg0lw0BsbCyXXnrpWbvM\nn2uWLl3K7NmzCQsLo0OHDsyZM4dPPvkEUEZmtpxgmzZtYubMmWrZXpm9++67zJw5k8GDB6PX63nq\nqafYs2ePw+jMNtfZmDKbM2cOer2enj17MmXKFD7//PMmZWwOt956K/369UOn03H33XezZ4+yTnf1\n6tVcdtlljBs3Dg8PD6ZPn96gmdQiodgulKOPi9QuGq2DlHK6lLKjlNJTShkipbzT3smkBf18L6V0\nXnDdIJoyayHXx0GknblxmWZu1GhjsrOziY2tW0MSGxtLdnY2oCyFSE9PJy8vjz179jBx4kQyMzMp\nKChgx44dDB48GFDSvzz88MMEBgYSGBhIcHAwUkqysrLUfu1DLbnCvo69HI3J2BzsFZSvr68a+cOW\nnsaGEKJBOTXzYvtHMzO2EJt348KdihI7Vgo/Z8JgzdzYvmmh6e+PJCoqiuPHj3PZZZcBcOLECdWr\nztfXlz59+vDmm2/So0cPPD09ueaaa3jttdfo0qWL6vrfsWNHZs2axd133+3yPM3x5szMzFQXq9vL\n0ZiMer3eITJISxLFRkZGOmQxkFLWy2qgmRcvDrSv9AyI9lNGaDY0c6NGW3LnnXfy7LPPkp+fT0FB\nAfPnz2fChAnq8SFDhrBo0SLVpDh06FCHMsCDDz7ICy+8oKZNKS0tbWiRbpM888wzVFZWsn//fpYs\nWcL48eOblPGKK65g//797Nmzh+rqaubOndvs840cOZL9+/fz3//+F5PJxMKFCx2UoWZevHjQlNkZ\ncl1cnbnRZIEvNHOjRhvxz3/+k759+3L55ZfTs2dPrrzySnUtICjKrKysTDUpOpdBmZN68sknueOO\nO/D396dHjx58//33LZZlyJAhdO3aleuvv54ZM2Zwww03NCljQkICs2fPZtiwYXTr1q2eZ2NjhIaG\nsnz5cv7xj38QEhLCoUOHGDhwoHq8tLp+7EXNvNg+0daZnQVZZXXmRoBR3WCIZm5sN7ha56NxYVBt\ncrSYBHuDvlXzIJ9b2tM6sz8CbWR2FjibG9dkwKmKNhNHQ0PDimZevPjQHEDOkuvjlNiN2eWKOWNZ\nKvytz/kZu3Hu3LnMm6dErhFCEBAQQNeuXbnhhhuaFc7qYqe6upq8vDzKy8upqqrCz8+PxMTEJttV\nVVWRmZlJVVUVJpMJDw8P/P39iYqKUoME2zCZTGRlZVFSUoLJZMLLy4uIiAiXGQZsSCk5cOAA4eHh\njda1WCxkZWVRUVFBRUUFUkr69m36Jd9isXD06FEqKyupqanB3d0dX19foqOj64WoKioqIjc3l+rq\natzd3fH39yc6OrretTpTUlLCyZMnMRqNeHh4cPnllzcplysaMy/a4h4WFBSoOcg8PDzw8/OjQ4cO\njQYvrqiooLS0VHVe0Tg3WLNVHwQul1IeaU4bbWR2lrhbvRttyut4KWw+0XibtiQgIICtW7eyZcsW\nkpOTGTt2LJ988gk9e/Zk165dbS3eeU11dTWlpaV4e3u3KCKI2WzGy8uLmJgYEhISiIqK4vTp0xw+\nfNghWoXZbCYtLY3Kyko6duxIt27dCAsLa1byzeLiYsxmc5MxAC0WCwUFBbi5uZ1RxPmIiAi6detG\nbGwsFouF9PR0h2j2JSUlHDlyBIPBQNeuXYmJiaGsrKzetTojpeTo0aP4+PiQkJBA165dWyybjWoT\nlNvlwQz0Vp5T23mOHDnC8ePH8fX1JT4+noSEBDXWYlpaWqNyVlRUtGhJgcaZIaXMQokDObupuja0\nkVkrEOUHw+LgB2sGnjVH4NJQCGt5TNVzjk6nY8CAAWp5+PDhPPTQQwwePJg77riDtLS0RiPntwVV\nVVWNLtT9owgICFBHCxkZGfUSQ7rCYDA4KA4/Pz88PT1JT09XsygDahDhxMRENfGlc6R+V5w6dYqQ\nkJAmE2bqdDp69eqFEIJTp05RVtZUNg8FNzc3unTp4rDP39+fPXv2UFxcrI7qCwsL8fX1VaKmWHF3\nd+fw4cNUV1e7/B5ra2sxm82EhIQ45HprKRYJRVUWkAKEUMyLdv9yp06dori4mISEBId7axuV5efn\nN9CrRlMIIXycMku3BkuAH4UQj9mnhHGFNjJrJa6LU+bQoM7ceKF4NwYGBrJgwQIOHz7MunXrXNYr\nKSnhr3/9K1FRUXh7e9OpUyfuu+8+hzp79+5l9OjRBAYGYjAY6Nevn0OfR48e5ZZbbsHf3x8/Pz9G\njx5dL42MEILXXnuN6dOn06FDB3r27Kke++abb+jbty/e3t5ERETwxBNPuExH3xrYv6W3RtR8G7YX\nBvv+CwoKCA0NbVEGZ1BGjOXl5QQFBTWrfmtdhy3btP01SCnrvQw19XJUUFDA3r17ASV1TEpKijr6\nMZvNnDhxgt9++41du3Zx4MCBeqlqDh48SEZGBvn5+ezbt4/sg7uxmGob9F7My8sjKCjI5UtChw4d\nXN6fgoICTpxQzC4pKSmkpKQ4JGc9ffo0qamp7Nq1S42e4iq7tD2VlZUcOnSIX3/9ld27d5Oamupw\njc7PDNBVCOEwdBVCSCHEw0KI54UQ+UKIU0KIt4QQXtbj8dY6I53auQshcoUQz9rt6yGEWCWEKLNu\ny4UQEXbHh1r7Gi6E+FYIUY41dqIQIkgIkSyEqBBCZAshnhRCvCKEOOZ03k7WekVCiEohxFohhLPN\n/heUhJx3NHkT0UZmrYa7G9x+qeLdaJaKuXHTCRga23Tb84GhQ4ei0+nYtm0bN954Y4N1Hn30UbZs\n2cLrr79OREQEmZmZbNq0ST2elpbGwIEDSUxM5N133yUkJISUlBR1EavRaOT666/Hw8ODf//73+h0\nOubMmcOQIUPYt2+fg4ns5ZdfZvDgwXzyySdqEsRly5Zx55138sADD/D888+TkZHBzJkzsVgsalBb\nKWWz/kCaE9ndlnaltdK/2FK31NTUkJWVhV6vV0dlRqMRk8mEu7s7hw4d4vTp07i7uxMSEkJ0dHSj\nCq6srAw3N7c/ZPRqU1wmk0ldz2X/vYWGhpKRkUFBQQFBQUHU1taSlZWFn5+fS/kCAgLo0qULGRkZ\nxMTEYDAY1Pm148ePU1JSQnR0NN7e3uTn53P48GESEhIcRnDl5eVUVRvxDYlGuLkj3N0JsjMvgpLQ\ns6am5oxyqtnkDA8PJy8vT10YblPUVVVVHDp0CH9/f7p06aJ+x0ajkYSEBJd9VlVVkZaWhre3N7Gx\nseh0OsrLyyksLMTb27vBZ2bcuHFewEYhRE8ppX2m18eAn4AJwOXAC8BxYIGU8qgQYgdKQs9Vdm2G\noMRPTAawKslfgBRrPzqUvGnfCSH6SUcb7Acoo6c3UFLEAHwIDAIeRsk4/QhKHjT1oRRCBAM/A4XA\ngyh5zv4B/E8IkWAb4UkppRBiGzAMeMvlTbSiKbNWJMoPro+HH6zTlWuPQPfz1NzojLe3N6GhoWry\nxYbYsWMHU6dOVRfCAg6Lc+fNm0dAQACbN29W/7iSkpLU40uWLOHEiROkp6eryQr79+9P586dWbx4\nMTNnzlTrRkZG8sUXdamTpJQ8/vjjTJw4kbffflvd7+XlxdSpU5k5cyYhISFs3LiRa6+9tsnrPXr0\nKHFxcY3WiYmJ4eTJkw2anvLz8zGbzfWyDTdGXl4e1dXKM+/p6UlYWJiaUdtoNFJQUEBhYaGq5IxG\nIwcOHCAzM7PRUVdhYSE1NTX1snM3RVlZGUVFRaSmpja7TWlpKSUlSsxXNzc3wsLCOHLEcX5eSsmu\nXbtUxefl5UVYWFij5zGZTOpcnu23U1tbS3Z2NiEhIWq2ayklJSUl7Nq1S83llpubS01NDX6h0XBK\n+f16uEGFk7+J7R67ublRUFDgIK89jb242O6Zc5SR/Px8ampq8PHxISdHCUFoNps5cuQIlZWVLuN7\n5ufnYzQaiY6Odnj2vL29iYmJ4YMPPqj3zKDkFbsUeABFYdk4JqWcbP28VggxEBgL2JL5JQNzhBBe\nUkrbROd4YL+U8ndreQ6KErpJSlljvR97gTRgBI6KcLmU8mm7+9YDJaP07VLK5dZ9PwKZQLldu0cA\nPdDLpoyFEL8Ax1Dymtkrrt8AR/OPK2xvi3/01qdPH9keMZmlfH27lDP+p2wLd0hptrS1VApz5syR\nISEhLo+Hh4fLBx980OXxu+++W3bs2FG+9dZb8uDBg/WOh4WFyUcffdRl+ylTpsirrrqq3v6hQ4fK\nESNGqGVAzpo1y6FOWlqaBOTq1atlbW2tuh09elQCcsOGDVJKKU+fPi137tzZ5GY0Gl3KaTKZHM5h\nsdT/Am+77TY5ZMgQl300RHp6uty2bZv85JNPZGJiorzyyitlVVWVlFLKX375RQKyf//+Dm3mzZsn\nvby8ZEVFhct+R48eLW+88UaHfWaz2eEazGZzvXb/+te/pPIX0HxycnLkzp075bfffitvvPFGGRIS\nIvfv368e/+mnn6TBYJBPPPGEXL9+vUxOTpaXXHKJHDp0qDSZTC77tX2P3333nbrvo48+kkC9a587\nd6709fVVy0OGDJGXXDlQfeZmb5CytLr+ObZt2yYBuXbtWof9U6dOlSj5OuvJ4IyrexYfHy8ff/xx\nh30mk0nqdDq5YMECl/2dyTODMmpaj5Ljy6aMJfBPafcfCzwPnLQrR6Nkeh5jLeuAfOBpuzo5wIvW\nY/ZbBjDHWmeo9XzDnM432brf22l/MoqitZW3Wvc5n+MnYIlT22lALdY10Y1t2pxZK2PzbnS3vtyd\nOK2YG893qqurKSwsrJe52J5FixZxyy23MH/+fBITE+nWrRvJycnq8cLCwkZNODk5OQ32Hx4err55\n2++zx/YmPWLECDw8PNQtPj4eQH1TNhgM9OrVq8mtMTdxm1nHttmizJ8t3bp1o3///kyYMIG1a9fy\n66+/8tlnSsxH28jLeVR53XXXYTQaHfJ3OVNdXV3vzX/+/PkO1zB//vxWuYaIiAj69u3L6NGj+e67\n7wgJCeHFF19Ujz/22GPcfPPNvPTSSwwdOpTx48fz9ddfs2HDBr755psWnSsnJweDwYCvr2MW3PDw\ncCorK1UvyioTmH3rfi+3JIJ/AwMhmzv9yZMnHfY/8cQT7Ny5k2+/bVZwdpeyOv9mbWZi59+2PWf6\nzAB5KOlR7HFOk1IDqG63UvEQ/BllNAZwPRCK1cRoJRR4EkWB2G+dAecIzs5mnAigTEpZ7bTf2bQR\napXB+RzXNnAOI3XKrlGarCCE6Ah8jGJXlcB7Uso3neoIlBTZI1Dsn5OllLub6ru9EmlQknmutTM3\nJgQrZsjzlfXr12Mymbj66qtd1gkMDGThwoUsXLiQvXv3smDBAu6++24uv/xyunfvTkhIiGpiaYjI\nyEg19p89eXl59VzKnU09tuPvvfcevXv3rteHTam1hplx8eLFDl5+zVlL1lJiY2MJDg5WTXRdunTB\n09OznsnLVm5sziw4OLhecN7777+fUaNGqeVzsS5Kp9PRs2dPBzNjWload955p0O9xMREfHx8GlXI\nDREZGUl5eTmVlZUOCi0vLw9fX1+8vLyoqFUCFXj4Kb+XHh2gl4v3sY4dOxIXF8cPP/zAvffeq+7v\n1KkTnTp14tixYy2Sz1nWU6dOOewzm80UFhY2ulziTJ8ZlP9j11rSNV8ALwohfFAUyq9SykN2x4uA\nr4D3G2hb4FR2dnHLBfyEEN5OCq2DU70i4FuUuThnnN1rA4FyKWWTXl7NGZmZgMeklN2BAcBUIUR3\npzo3Ad2s2/3AO83ot11zbayjd+OHe6GipvE2bUVJSQlPPvkkXbt2ZdiwYc1qc/nll/Pyyy9jsVjU\nuZrrr7+eZcuWqfNCzvTv359du3Zx9OhRdV9WVhZbtmxpMh5fYmIi0dHRHDt2jL59+9bbQkJCAOjT\npw87d+5scmvszz0xMdGh77NxFXfFwYMHKSwsVJWwp6cnSUlJrF/vmFH+xx9/xNfXt9F1V4mJiQ73\nFBTlZX8N50KZVVdXs3v3bvUaQFHSu3c7vsempqZSVVXV5BylM1dddRVCCFasWKHuk1KyYsUKBg0a\nhNkCn+6rWxzt6wFjExuPvTh9+nRWrFjBhg0bWiSLDduI3vk33r9/f7766isH5yNb8OPGfttn8swA\nHsA1KKOslrIc8AFutW7JTsd/BC4DdkkpU5y2Y030bYtPeLNth1VpJjnVs51jfwPnOOhUNw5ljrBp\nmrJDOm/AN0CS077FwJ125YNAZGP9tNc5M3tyy6Wctb5u/uydFGVOra2YM2eODAgIkFu3bpVbt26V\nP/zwg3zhhRdkp06dZGhoqExJSWm0/cCBA+Urr7wi16xZI9euXSvHjRsn9Xq9zMzMlFIq81p+fn7y\nqquuksnJyXLdunVywYIF8oMPPpBSSlldXS3j4+NlYmKi/OKLL+SKFStkz549ZVRUlCwsLFTPA8h/\n/etf9c6fnJwsPTw85LRp0+SqVavkunXr5OLFi+VNN93U6JxSa1FRUSGXL18uly9fLgcMGCC7d++u\nlu3P36VLF3nvvfeq5ccee0w++eST8r///a/86aef5FtvvSVjY2Nlly5dZHl5uVpv+/bt0sPDQ06e\nPFmuXbtWvvzyy9LLy0s+++yzjcq1du1aCchTp0416zpWr14tly9fLv/yl79IQL2GY8eOqXXuvfde\n2aVLF7X82WefyXvuuUcuXbpUrl+/Xn722Wdy0KBB0tvbW+7evVut98Ybb0ghhHz00UflunXr5Kef\nfioTEhJkXFycw7U609CcmZRS3nXXXdLPz08uWrRIfv/993Ls2LFSp9PJzZs3y6/SlOcq5vIhstuf\nbpP7mnH5ZrNZjh07Vnp7e8uHH35Yrly5Um7cuFEuX75cjh8/XgJy/fr1Lttv3LhRAvLFF1+UO3bs\nkGlpaVJKKX///Xfp4eEhR40aJVetWiUXL14sAwMD5fDhwxuV50yeGRTrVxYQLB3nzKZJx//luUCB\nrP8f/j8g29omzulYAoq5cjUwDmV+7G4UL8Wh0nHOrEcDfX+L4qX4F2CkVXFlAkfs6oQCJ1Dmzu5C\n8ai8HcXx406n/rYDC53P09DWUkUWZxXC32n/SmCQXflHoG8D7e9H0d4pnTp1avRLbi/sPyXl4/+r\nU2hfpradLHPmzFEnuYUQMiAgQPbp00c+9dRTMicnp8n2M2bMkD169JAGg0EGBATIoUOHyk2bNjnU\n+e233+RNN90kDQaDNBgMsl+/fvJ///ufejwjI0OOGTNGGgwGqdfr5ciRI2V6erpDH66UmZTKH/Gg\nQYOkr6+v9PPzk1dccYWcNWuWrK2tPYM70jJsf7gNbUePHlXrxcbGykmTJqnlzz//XF5zzTUyKChI\n+vj4yMTERPnoo4/K/Pz8eudYs2aN7N27t/T09JQxMTFy/vz5DTpv2GM0GmVwcLD8+OOPm3UdsbGx\nDV7DkiVL1DqTJk2SsbGxann37t1yxIgRMjw8XHp6esrY2Fh5++23y99//92hb4vFIt9++23Zs2dP\n6evrK6OiouTtt98uMzIyGpXJlTKrqKiQ06ZNk2FhYdLT01P26dNHrlmzRm7PqnumYi4fIgfdeFuz\nrl1KRaF98MEHcuDAgdLPz096eHjI2NhYOWHCBLlly5ZG21osFvn444/LyMhIKYRwcAL63//+J/v1\n6ye9vLxkhw4d5EMPPSTLysqalKelz4xV2XSTjv+tLVFmf7XW3+p8zHr8EmAFijmwCjhsHbDEyKaV\nWTCKKbMCZU5tNvBvYI9TvSgUt/48lHmxY8CnwGV2dTqgWAaHNCSn89bsqPlCCAOwEXhOSvlfp2Mr\ngRellD9byz8CT0opXYbFbw9R85vLj8eUIMQ2brsEBkS3mTga7ZCHH36Yw4cPs2rVqqYrX+AcLYHF\nu5X1nAA9O8CEnudnPNRzwYUUNV8IoQN+B7ZLKSe1sO0DwAwgQTZDUTVrnZkQwgP4EljqrMisZOHo\nhRJj3acBXBcLOWXwm3V++KuDEOYLnZsXsEFDo0kef/xxEhISSE9Pb3SR7oVOSTV8vLdOkUUaHGOj\napic1toAACAASURBVLQtQog/o4y69gH+KGvEugETW9iPQFl4/VxzFBk0wwHE2ukHQKqU8jUX1b4F\nJgqFAUCplNK1i85FhhBwe/c6hxCLhI/3QXFrRzLTuGiJiYnhP//5T6OecRc6NWbFkcoWRFjvAZMv\nBy8t9MP5RAUwBUUnfI5iKhwtpdzRwn4igKXAJ81t0KSZUQgxCNiMomlt4Q6eAjoBSCnftSq8RcCN\nKJOTUxozMcLFZWa0UVwNC3fUPYxRBpjaFzzPr7i+GhrnHVLCZ/thj3Vlk5uA+3tDl4vQunEhmRn/\nSJp8p7HOgzU6iLcOA6e2llDtlSBvmHh5nb0/uxy+OAATemip3DU0GmP98TpFBjAm4eJUZBqu0SKA\n/MHEB8Ktdmtw956Cn461mTgaGuc9BwocHagGRMM1MW0nj8b5iabM2oD+Tg/jmiNKtmoNDQ1H8irg\ns9/rQk3EByqjMg0NZzRl1kbc3M3RTPL5fsgtd11fQ+Nio7IWPvwNjNagGkHeMLEn6LR/LY0G0H4W\nbYS7G9zTQ3lAQXlgP9yrPMAaGhc7Zgss/R0KrB6/Hm6K56LBdXxojYscTZm1IXpPmHJFnTdjYRV8\n+rvyIGtoXMyszoB0uzC6d3Q/vwN1a7Q9mjJrYyINyoNq41ARrDzcdvJoaLQ1KTmOaZOGxcHlrjMT\naWgAmjI7L+gZBkl1gcf5ORN2ZredPBoabcWJUvjSLmH2ZR0gqbPr+hoaNjRldp4wLF6JMWfjyzQ4\nVtp28mho/NGUGuGjvXUpXcL1itVCC1Wl0RwuOGUmpWRn+c9YZPuaWHITSoy5SINSNkvlwS5pOM2R\nhka7otas/N5PW3P++eqU+WRvLVSVRjO5oJSZ0VLNmznzmJv5d5YV/qetxWl1vHSKx5avh1Iur1Ee\n8Fpz4+00NC5kpIQVaZB5Wim7CbinJ4T4tK1c54qC2lNNV9JoMReUMltdvIJ1pd8C8Gn+O+wu39rG\nErU+wT7KWhqbaeVkGSxPVR54DY32yKYTsDu3rjy6G3QNbjt5ziUnjEd48MhY/p33KmZpamtx2hUX\nlDK7OfgOevr2AUAieTl7Fqdq25+nRJcgxygHv+bBhuNtJ4+GxrkirRBW2Xnv9ouCge00VFWFuYxn\nTj5KlaWSr4uWsjDnmbYWqV1xQSkzd6HjiegXCNaFAnDaXMILJ5+k1lLTxpK1PldHQ/+ouvL3GZBa\n0HbyaGi0NqcqlIXRNqNDbIASt7Q9Bt22SAuvZP+T7BplzYGX8OaW4LvbWKr2xQWlzACCdaHMjF6A\nuzXgf3r1ft7Le6WNpWp9hIBbEpVYdKA88J/9rsSq09C40KkyKRFvqq2WtgAvmNSOQ1V9XvAeO8o3\nq+WHI+cQ760FmWxNLsifTnffXvwlfLpaXl2ygh9LVrahROcGnZsyfxZoDXlVbVZi1WkhrzQuZCxS\neTHLr1TKOmuoKj+vtpXrXLGtbOP/t3fe4VFV6R//nJn0XkgjoSNKryIoKqKIoqIoK+raddEtunZF\n/anrrmvDte669oKuBVYUFSviYgGRIlKU3pJAeu+ZOb8/ztTkBgLM5E45n+eZJ3PPvTPz5mZyv/e8\n5y38p/R51/a5aZdwYvIUEy0KTYJSzACmpV7ICUmnuraf2fcA2xs3m2iRf0iIUv/okY6/VGmDcs3Y\ndUCIJkj5ZJtaK3Ny/kDISzLPHn+yp2kHcwrvdm0PjxvL5ZnXmWhR6BK0YiaE4Pqce+gRpUpnNMsm\n/p5/C7W2GpMt8z25iSoHzcnmcu9Fc40mWFi9zzuY6aReMDLbPHv8Sb2tlr/l30yDXa0NZEbmcHvu\ng1iFTp7zB0ErZgCxljjuyptDrCUOgL0t+TxW+H8hl1ANMDwLTu7t3l66W9Ww02iChT3VKs3EycB0\nOK2fefb4E7u084/Ce8lv3glAlIjm7rzHSI7Q7bH9RVCLGUCP6D7ckHOfa3tF7VLmlb1inkF+5NS+\nMKibe3v+L/BzUcfHazSBQn41vPSTu1RVZhxcOCR0S1W9W/Yyy2qXuLavy7mbfjFHmWhR6BP0YgYw\nIekUpqdd7Np+o+RZ1tT9YKJF/sEi4MLBqmYdqJJXb6zXMzRNYLOzCp5bA3WOwKXYCLh8uPoZiqyo\n+YY3Sp51bZ+ddhGTks8w0aLwICTEDODyzOsYHDsSADt2HimYTUnLvgO8KviIiYDfjVB3tqBC9t/Z\nCN/nm2qWRmPI1nJ4YY07BD82Aq4eARlx5trlLwqadzOn8C6kI3tuaNwYrsz8s8lWhQchI2YRIpI7\nch8i1eqZUH1bSCZUJ8fA70e7ixIDLNikq4RoAotfSuGltdDsqC0aHwnXjoKeyeba5S/qbXX8bc9N\n1NlrAciIyOaO3IeIEJEmWxYehIyYAaRFZnBH3kNYUK2bNzWu54Xix0y2yj8kRDkuDB4hzR9vhc+2\n6zqOGvNZW6SSop1rZMnR8IfRodstWkrJE3vvY3fzdgAiRRR35c0hJSJEi0wGICElZgBD4kZ5Tes/\nrpjHV1Ufm2iR/4iLhN+NhL4p7rEvd6hO1VrQNGaxcq93LmRajBKyzHhz7fIn88pe5buaxa7tP2bf\nyRGxg/bzCo2vCTkxAzgn7bdMSJzs2n5m7wPsCMGEalBraFeNgCPT3WNLd8N7m3Ritabr+T5freE6\nv3qZcUrI0kK0nQvAytrveL3kGdf2makzmZwyzUSLwpOQFDMhBH/OuYe8qN4ANMlG/p5/a0gmVANE\nWVWVkCEenaqXF6iLii30Uu40AcqSXWrt1kn3BLW2mxxjnk3+Zm/zHh4puNMV8DE4diS/y7rJZKvC\nk5AUM4A4azx35c0hRqhbwsKWPTxeeC8yRP1vERa4eAiM8qimsHqfCt1v1YKm8SNSwmfbYJFHVZqe\nSXDNKLW2G6o02hv4W/7N1NnVTXJ6RCaz8x7RAR8mcUAxE0K8LIQoFkKs72D/RCFElRDiJ8fjHt+b\neWj0jO7LDd3vdW0vr/2a+WWvmWiRf7FaVNmrcbnusfUlaiG+WXer1vgBKeHDLfDlTvdYvxS1lhsX\nwtd0KSVP7v0LO5uUgkeISO7Me5TUiPQDvFLjLzozM3sVOO0Ax3wjpRzheNx/+Gb5juOTTuXstItc\n26+XPMPauhUmWuRfLALOPRJO6Oke21Smqi806sa2Gh9il/DfX+GbPe6xo9LVGm5MiCZEO3mvfC5L\nqz93bf8hezZHxQ410SLNAcVMSrkUKO8CW/zGlZl/ZnDsCEAlVD9cMJvSltCtAyUEnNkfJvdxj22v\nhOfX6PYxGt9gs8PbG+EHj0bvQzPgsmEQaTXPrq5gTd0PvFr8lGt7asoMpqScY6JFGvDdmtl4IcRa\nIcQnQojBHR0khJglhFgphFhZUlLio48+MCqh+mFSrMoFUGWr4MGC22iRoXtlF0LVcjyjv3tsTzX8\nezXUNJlnlyb4abXD3PWwxqPAzqhs+O2Q0G2u6WRfcwEPF9yBHbUQPTB2OLOybzXZKg34RsxWA72k\nlMOBp4H3OzpQSvm8lHKMlHJMRkZGR4f5hbTIDGZ7JFT/2rCOF4v+0aU2mMHEXqoVvZO9tfDsaqhs\nNM8mTfDSbINX1sIGj3vRcblqrdYa4kLWaG/ggfxbqLFVAarr/Z25jxCpAz4CgsP++kkpq6WUtY7n\ni4BIIUS3A7zMFIbEjeaKzOtd2x9VvMOSqkUmWtQ1HJunLjbOAuUl9fCvVVDWYKpZmiCjsVWtvW72\nWHQ4sadaow3V6vdOpJQ8vfdvbG9SuQcRRDA791HSIrv2plzTMYctZkKIbCGEcDwf63jPsv2/yjym\np13McYknu7af3vs3djaGfqfLMTlw8VCwOi46FY1K0IrqzLVLExzUt6g11+2V7rHJfZQbW4S4kAEs\nrHiLr6s/cW1fm30bg+KGm2iRpi2dCc1/C1gGHCmEyBdCXCWEuFYIca3jkBnAeiHEWuAp4AIZwMlc\nQghuyLnXO6G64FbqbbXmGtYFDMtUC/TOdY3qJnh2FRSEZi65xkfUNCnX9J5q99iZ/dWabDgI2c91\nK3mx6HHX9pSU6ZyWcp6JFmmMEGbpzpgxY+TKlStN+WyAXU3buHHHJTRJtXh0bOIk7sx9FBEG/51b\ny+EVj9yzWEdJrF4hWs1cc+hUNqoZWUm92haoNdjxeaaa1WUUt+zlhh0XU2WrAODImCE83OtFIi3m\nZYMLIVZJKceYZkCAEuJLth3TK7off85xJ1R/X/MV75W/bqJFXUf/NJg10p0L1NCqLljbKsy1SxNY\nlDrWVj2FbOag8BGyJnsjD+Tf4hKyFGs6d+bNMVXINB0TtmIGcGLyFKalXujafrX4adbW/WiiRV1H\nr2TVQibeEYjVbIMXf1I9qDSaojrlWqxwRL1ahVpzHZ1jrl1dhZSSf+57kK2NvwBgJYLZeQ/TLTLT\nZMs0HRHWYgZwZdYNDIxVC7l27DxYcBt7m8OjbXNuoioEmxSttlvt8NrP8GOhbiETzmyvUGup1Y58\nxAiLKmQ9LEyu4zZp499Fj7C46kPX2KysmxkSN8pEqzQHIuzFLNKRUO3sUF1jq+Kv+TeGRUAIQFa8\natGR6qhsbpPw7i/w+jqoDb0m3Zr90GJTdRb/vRrqHPUEoq1w9Qg4KiCTbXxPo72Bv+ffykcV77jG\nTkk+izNSzzfRKk1nCHsxA+gWmcndPeYQKZQvfFfTNh4tvBubDI/qvOmxjuaJce6x9SUwZzmsKzbP\nLk3XsacanliheuE5J+WxEapgcL9UU03rMipby5m96xqW137tGjs+cTJ/yr4rLALDgh0tZg6Oih3G\n9Tl3u7ZX1C5lbsm/TLSoa0mJgeuP9q64X9eiZmhvbdA1HUOVVjt8th2eWQnF9e7xAWlw0zHhE+Fa\n0LSLm3dezuZGd3OQ89Iu5bbcB3XAR5AQ4rWtD45JyWeys3Er/3VENc4re4Ve0f04KXmqyZZ1DdER\ncN5RqsnnvF+gyrFmsnofbK2A3wxUVdE1ocG+WlUs2DPPMMqqcsjG5YZHDhnAxvqfuD//RleZKgsW\nrsm6lTPTZppsmeZg0DOzNlyWeR1HJ0xwbT+59342NRi2cgtZjkyHm4/xbvRZ3aRKGf33V91KJtix\nS9UV+okV3kLWJ0XNxsbnhY+QfVv9JXfuvtYlZNEihrvyHtNCFoSEbdL0/qi31XLzzsvZ3bwdUAVF\nH+/9RliG5a4rVgJW5+FmTItR+UZ9w2QtJZQoqYd3NsKuKvdYhAVO6wfH9wj9GotOpJS8X/4mLxU/\njnSsEiZbU7m3x5McGTvEZOv2j06aNkaLWQfsbd7DjTsvdd2xHREziId7vUi0JcZky7qe2mYlaOs9\nKqULYEIPOL1f6PevCgXsEpblw8dbocXuHs9LhAsGq6jWcMEmbbxQ9BgfVrztGsuN6sVfejxNTlTg\nZ4RrMTNGuxk7ICeqB7NzH3a1jNnSuJEn9v6FAC476TcSouDSoXDRYBXhBiri7Zs98PgK2F2135dr\nTKaiAV5YA+9vdguZxdHv7k9jwkvInKH3nkI2KHYEc3q9EhRCpukYLWb7YXj8WK7JcjfeW1r9Ge+W\nvWyiReYhBIzMVmtpR3oEgZTUwz9XwafbVGScJnCQUiXAP/aDCuBxkh2vIlcn9wn9HmSeGIXeT0ic\nzAM9nyUpIsU8wzQ+QUczHoAz085nV9NWFlXOB+D1kn/SM7of4xMnmmuYSSTHwFXDYUWhSrBtsikX\n1uKdsLEULhgE3RPNtlJT3QTzf/UuTyZQzVpP7Rv6HaHbUtC0i3v2XMe+Fnd1n3PTLuGKzD9jEWF2\nMkIUvWbWCVplC3fv/iPr6pW9MSKWOb1foU/MAJMtM5fyBhVM4NnjyupwX53YM7zu+gOJn4pgwa9Q\n7xF12i0WZg6G3mGSN+aJUej9rKxbOCvtApMtOzT0mpkxWsw6SXVrJTfuvNR1Z5cZmcMTvd8gOSK8\nQ/rsEr7bA4vauBl7JqmIx8wwWo8xm7pmWLAJ1rap2nJcHkztr3LIwo1vq79kTuHdtEhVmy1axHBr\n7t+D2rOixcwYfe/cSZIiUrinx+PEWtTVubhlLw/k30KLDO/SGBYBx/eEG8dCjyT3+G5HeaRv9yjB\n0/iXjSUw5wdvIUuJgWtGwjlHhp+QSSlZUPYGDxXc7hKyZGsqf+/1XFALmaZjtJgdBL2i+3Fb9wcQ\nqGScDQ1reHbfQ2EZ4diWzHj442iVr2R15Cq12OGDzfD8auWS1PiehlZ4d6NqtupZGHpsdxWs0z/N\nPNvMwiZtPFf0KC8W/8OVQ9Y9qieP9X6Vo2KHmmydxl9oN+MhMK/0VV4tecq1fU3WbUwLUv+7Pyis\nUWWS9no0HoiywtE5qkxSdoJ5toUKVU2wogCWF0C1h4glRsGMgTAoTKrct6XR3sCcgrtZVrvENTYw\ndjj35D0eMhGL2s1ojI5mPARmpF/GrqatLKleBMALRY/RI7oPI+OPMdmywKB7ogr9/nIHfLVT5aQ1\n2+C7fPXom6JEbWhm+EXVHQ52CVvLYVmBihxt674dkaVcis6Gq+FGZWs59++5gU0exYKPSzyFm7vf\nH5bFDsINPTM7RJrtTdy+63euKtvxlkQe7zOX3KieJlsWWOyuUkWL99W13xcfqdxh43IhLbbrbQsW\n6lpgZaGahZUauGsTouCcATA8q+ttCxSMQu+np13ClSEYeq9nZsZoMTsMyltKuGHnxZS1qjpPeVG9\neaz3ayRYdaKVJ3YJ2ypUOaUNBjMKAQxIh/G5qiq/DulXCc+7qtQs7Odi44T0vinqnA0J8xnuxvq1\n/DX/RqptKkdEIJiVdWvIuv61mBmjxeww2dKwkdt2XUWzVP1SRscfy709nsQqwix8rJNUNamE6x8K\n3C1mPEmOhmNy1YwtObrr7TObxlbVcmd5gfeao5OYCBiTrWazWXrtka+rPuGJvX8JqdD7A6HFzBgt\nZj5gafVnPFww27U9Pe1irs66yUSLAh+bHX4tUxftTWXu7sZOLAIGd4NxedA/NfSruRfWqFnYmn2q\nqkpb8hJVa5YRWeEXZm+EXdp5o+RZ3il7yTWWbE3lnh5PhHzEohYzY3QAiA84IWkKOxu3uv6xFpS/\nQa/o/kxOmWayZYGL1QKDM9SjrEHN1FYUulvN2CWsK1GPbrFqJjKme2gFN7TYVF7YsnyVl9eWSIuq\nhzku1zuHL9xptDfwWOH/8X3NV66xvKje3NfjSXKiephomcZM9MzMR9ilnb/n3+oKCY4QkTzY83kG\nxQ032bLgodUO64vVDMWzRJaTCAsMy1QzlF5JwdtAsqRezUhXFnqXnHKSFa/WwkZlQ2wIibcvKG0p\n4v78G9nW+KtrbFT8eO7IfYj4MFmr1jMzY7SY+ZAGez237LycnU1bAUixpvF4n7lkRuaYbFnwUVSr\nRG3VPuPO1jkJ6oI/LCs4ZmvNNuVWXZbvXcHeiVWoVIXxuarjc7AKtT/Z3LCBv+bfSHmru3ry2akX\nclXWjVhF+DiZtJgZo8XMxxQ1F3LDzotdkVV9ogcwp/crxFh07Pmh0GxThXOX5UN+jfExsRGQHuvx\niFM/02JVEElXrLdJqSpwlDVCWb1ynTof5Q1Q02z8urQY5UY8ursKsdcY80315/yj8F5XoJWVCH6f\nfRunp84w2bKuR4uZMQcUMyHEy8CZQLGUsl0/cSGEAJ4EpgL1wOVSytUH+uBQFTOA9fWruHPX77Gh\nphTHJk7i9twHiRBBMIUIYPZUK/fcmn3e3ZL3h1UoUUs3eKTFHlyXbJsdKhq9RcrzuVHghhECGNhN\nuUsHpIV+cMvhIKXkrdLnebP0OddYgiWJO/MeYXj8WBMtMw8tZsZ0RsxOAGqB1zsQs6nAdSgxOwZ4\nUkp5wFIYhyxmtRXw82dwzAywBq5r4bOKBTy176+u7T7RA7gh5176xw400arQoKFFuR9X7YWius4L\nmxHJ0e3FLiXGMctq8H5UNh560WSrUO89LFOlHqToghQHpMneyBN772Np9eeusdyoXtzb48ngLk6w\n9lPI6g/Z/Q/p5VrMjDmgGkgplwoheu/nkLNRQieB5UKIFCFEjpRyr49sdNNcDx89AqW7oGwXTPkz\nRAXmVWFK6nR2NW3lg4q3ANjRtJkbd17KeemXclG3WURZwjCJykfERsKEHuohpXLhuUSn3tvVV3eA\npgZVTeqxwyDg5GCJsbpdnM6Zn6dA6hlY5ylrKeFv+TexuXGDa2xE/DHckfswidYgDe2Udvj2TVj7\nCcQkwoz7IEWvp/sKX0xtcoE9Htv5jrF2YiaEmAXMAujZ8xDurDZ+rYQMYNdaWPBXOOs2iAvMjoNX\nZ91Mt8hs5pb8i2bZhB0b88peYVnNEv6ccw+D4kaYbWLQIwQkRatHH4M6so2t7V2CTtGrbDr4mVZS\ntLHLMj0W4iJ14IYv2NrwC/fn30hZq7ufzRmpv2FW1i3B66pvbYYvn4WtP6jtxhpY8R6c+kdz7Qoh\nOhUA4piZfdSBm/Ej4CEp5beO7cXA7VLK/foQD8nNKCX8MA9Wvu8eS8qAs26H1O4H915dSGHzbp7a\n+1fW1a9yjQkEZ6VewGWZf9LBISbRdg3M+ahqVMEYh7vGpjl4vqtezGOF/0eTbATAgpVrsm7hzLSZ\nJlt2GDTWwqJ/QKE7nYC+Ryshizj4qB/tZjTGF2L2HPC1lPItx/YmYOKB3IyHFQCyfjH872UlbgAx\nCXDGLZAz4NDerwuwSzufVr7Hy8VP0GCvd41nReZyfc7djNAV9zVhjJSSd8peYm7Jv1xj8ZYEZuc+\nwsiEcSZadphUl8CHj0BFgXts2BSYcAlYDq2gphYzY3xRnnQhcKlQjAOq/LJe5smQk2HqzRDhWHdq\nrIX3H4BtP/r1Yw8Hi7AwNXUG/+o7j9Hxx7rGi1oKuGv373lq71+ps3UQe67RhDDN9ibmFN7tJWTd\nI3vwWO/XglvISnbC/Hu9hezYi+D4Sw9ZyDQd05loxreAiUA3oAi4F4gEkFL+2xGa/wxwGio0/4oD\nuRjBR6H5RVvhoznQ4KwFJOCEy2DYqYf3vn5GSslXVR/zfNEcau3uOkbpERn8MftOjkk80UTrNJqu\no7y1lAfyb+bXhnWusWFxY7gz71ESrYG5Ft4pdq+DT56AFkfPHksEnHItDDh2/6/rBHpmZkzwJ01X\nFcHCh9RPJ6POgvEzIcD7GJW3lvLvfQ/zXc1ir/GJSaczK+sWkiNSTbJMo/E/2xs3c/+eGyhp3eca\nm5IynT9k3xG8gR4Av34DXz0PdkfiYVQcTL0J8gb55O21mBkT/GIGamb20Rw1U3My4Fg4+RqwBv4/\nxbfVX/LsvoeotJW7xpKtqVybfRvHJ56K0CFymhBjWc3XzCm4i0apZi4WLFyddRPTUi8M3u+7lLDq\nA1j+rnssIU0FqKX7rgCyFjNjQkPMAFqa4LOnYadH8ZHcQTD1RoiO993n+Inq1kpeKH6Mr6o+9hof\nlzCRP2TPJj0ywyTLNBrfIaVkftlrvFbyNNLR+CfOksDtuQ8yJuE4k607DOw2WPqqCk5zkt5DpQ4l\npPv0o7SYGRM6YgbGX6i0HjDN918of/Fj7bc8s/cBSlvdbtN4SwJXZ93E5OSzg/euVRP2tNibeXrf\nAyyu+tA1lh2Zx709nqBndF8TLTtMWhrhs2e8b6TzBsPpN0J0nM8/TouZMaElZqCm+qs/hGVvu8f8\nMNX3J/W2Wl4pfopFlfO9xkfGj+O67LvJigrcnDqNxojilkIeyr+DTY3rXWODY0dyV96c4F4bbqiG\njx6Fom3usQHHOZY4/FNuT4uZMaEnZk4MF2FvVHdMQcLPdSt5au/97G3Jd43FiFguz7yOM1LPxxLg\nAS4aDcCKmqU8VniPV+Tu5OSz+WPOnUQGc6BH5T748OE2wWfTYPz5fg0+02JmTOiKGcCedbDIMzzW\nCqf83ifhsV1Fo72BN0qe5f3yN11rDACDY0dwfc495EX3Ns84jWY/tMoWXi/+J/8tf901ZiWCKzKv\n55y03wa3y9zEtCAtZsaEtpiBquX44SNQ59ER8dgLYeSZQVVI79eGn3mi8C/sad7hGosggjPTZnJB\nt98Fb/FVTUhS2lLEwwWz2djwk2usW0QWd+Q+xMBg776+Y5UKNmt1NKmzRsKUP6kSVV2AFjNjQl/M\nAGpKlTug3CMTf+ipQZeJ32Jv5u3SF5lX9qqrVxpAkjWF33a7htNTzwurjruawGRV7ffMKbzb1aAW\nYEz8cdzU/f7gXh+DgCilp8XMmPAQM/B5sU8z2da4ief2PcwGj7tegB5Rfbg666bgDnHWBC02aeM/\nJc/xTtlLLpe4BQuXZPyRGemXBfcab4dFzu+A1K5t46LFzJjwETMAWwt88SxsXe4eyx4AZ9wMsYld\na8thIqXku5rFvFz8BEUthV77Rscfy9VZNwV3uLMmqChvKeGRwrtYV+/+n06L6MbtuQ8yJG60iZb5\nAFsrfPUCbPrGPZbZF8681ZT2U1rMjAkvMQPVIO+7/8BPi9xjKTkw7XZIyux6ew6TZnsTH5S/xTtl\nL9Fgr3ONW7AyNfU8Lup2TfC7djQBzdq6FTxScBeVtjLX2Ij4Y7i1+wOkRKSZaJkPaK6HT55UwWRO\neo2AKdeb1hhYi5kx4SdmTn76BL59A5wRgnHJ6k4rMzhnMxWtZcwt+RdfVH6AHbtrPN6SwIXdZnFm\n2szgDoPWBBw2aeOd0pf4T+lzLreiQHBRt2uY2e0qrCLIm7/VVrg72zsZdBJMvFJFRpuEFjNjwlfM\nQHV9/eJfyv0IEBmtQvf7jTXXrsNgR+NmXij6B2vrV3iNd4/swZVZNzIu4cTgDonWBASVreU8WngX\nP9X94BpLsaZxa+4DodGbr3i7qnpfU+oeGzsDjp5uehS0FjNjwlvMQAWEfPwYNLlddAybAsddFBRF\nio2QUvJD7VJeKn6cwubdXvuGxY3hd1m30DcmcBuZagKb9fWreLhgNuWt7gv90Lgx3Nb9AdKCSYSS\nLAAAGepJREFUvYaolLDuc/j2TbA7IoaFBU66GgZNNNU0J1rMjNFiBipk/6NHVFdYJxl94LTrITnL\nPLsOkxbZwscV7/Kfkueps7sbfwoEp6acwyUZfyA1IjhqVmrMxy7tzC97lbkl/3K5sgWCmelXcVHG\nrOBPC2mqg8XPw3aPJr+Rseo60CtwcuO0mBmjxcyJ0Rc5KhYmzYL+we02qW6t5M3S51hUMR87Ntd4\nrCWe89Ov5Jy0i4iyRJtooSbQqWqt4B+F97Cy7jvXWJI1hVu6/43RCcFTUadDirbBZ0+1uaHtrQI9\nUrJNM8sILWbGaDHzREr4+TP47k13TUeAoZPhuN8GXT5aW3Y3beeloidYWfet13hWZHeuyPwzExJP\n0etpmnZsrF/LwwV3eHVyGBw7gttyH6RbZPB6LgDH//ynKsLZ63/+VJjw24BcatBiZowWMyOC6C7t\nUFhV+z0vFv2D3c3bvcYHx47gd1m3cESsbzriaoIbKSXvlc/lteJnvCrOnJd+GZdm/CG4u0GDKqTw\n1fOw3eM6FATeGC1mxmgx64iO/OeTfgdHjDPPLh9hk618WrmAN0qe9So7BDA+8STOTbuUQcFeQ09z\nyNTYqnm88F5+qP2fayzBksTN3e9nbOIJJlrmI4q2wqdPQ03wrZNrMTNGi9n+MIpsAhhyCky4OOjd\njgC1threLn2RD8vfotXj7hvgqNihnJt2KeMSJwZ/zpCmU5S2FPFtzZd8UP4filv2usaPjBnCHXkP\nkRkZ5L30pIS1n8L3bdyKQRTBrMXMGC1mnaF4O3z6FFQXu8e69VJ3cSldW5fNXxQ27+bl4idZVrOk\n3b7syDymp/2WU1KmEWOJNcE6jT8paynhu5rFfFP9uVeVeydnp13EFZl/Dv6k+8ZaWPycqnrvJCoO\nTp4VVLmlWsyM0WLWWZrqYckLKtHaSWQsTLoajhhvnl0+ZnfTdhaUvcFX1R/TKlu89iVak5maMoMz\n02aSFtHNJAs1vqCitYzvqhfzTc3nbKhf49Urz0m8JYEbcu7j2KRJJljoY/ZtVevgnknQmX3VDWmQ\nlbHTYmaMFrODQUpY/yV8M7eN2/FkmHBJSLgdnZS3lvJR+TssqpxPja3Ka1+EiOSkpKmcm36JLmYc\nRFS1VvB9zWKWVn/O+vrVXmXPnFiwMjz+aI5PnMyxSScHf588KVUd1mV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+uLm5odPpMBgM6n6TyURubi6RkZFERUUBEBAQQE1NDdnZ2S6VmclkIicnh/Dw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gq6knHf1ri7jVMC5z46ubFSV2oFAJddW34aU15zR+fn6Eh4eTk5NTZ50uXbrw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\nkglOc5M80CwdSrS39puImX96j5iB/w/UgIFwqv0fKGFvdjVs563Sf/B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XS+inQBSQ7HXOGGCzlHIfgBDiUeBtlGVWQ52fnxFCTPQ6Lx6YDbwAXA8cFEK0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\nhQ52bSe3dgMrqhazvHIRG2vWeGTsaE5aVD8uTL2GGT1OlHyKgcJhh29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GcnJyiIyMJCoqCgB/f3+qq6vJyspyqsyMRiPZ2dmEh4fbrZMLDg5WTbnz5s0j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\nq3qVWme1z77+1lTOSrmIk/ufZd6NRhPWVDrKeK3iGRbXLXTnDwWIEJH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NDZ1Oh8FgUPebTCZycnKIiooiOjoagICAAKqrq8nKynKpzEwmE9nZ2URERDis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Z+fj44Obmhk6nw2AwqPtNJhP5+flERkYSFRUFQEBAAHV1deTm5rpUZiaTiby8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\n9AZEzNwjYhYM2Jpg82r45t/QVO+wRyfA/J/BEcdDB6GavXU7WVP6bxm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z8/PDw8MDnU6HwWBQ99fU1JCbm0t0dDQxMTEABAUFYTKZyM7OdqvMampqyMnJ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\na9jnMX5CzBSuTLpZF5RuC1LChv/BD/8Eu+GBEBY46So4ZrKppjnQYuY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for step in range(10000):\n", + " artist_paintings = artist_works() # real painting from artist\n", + " G_ideas = Variable(torch.randn(BATCH_SIZE, N_IDEAS)) # random ideas\n", + " G_paintings = G(G_ideas) # fake painting from G (random ideas)\n", + "\n", + " prob_artist0 = D(artist_paintings) # D try to increase this prob\n", + " prob_artist1 = D(G_paintings) # D try to reduce this prob\n", + "\n", + " D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1))\n", + " G_loss = torch.mean(torch.log(1. - prob_artist1))\n", + "\n", + " opt_D.zero_grad()\n", + " D_loss.backward(retain_variables=True) # retain_variables for 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 % 1000 == 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': 15})\n", + " plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 15})\n", + " plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=12);plt.draw();plt.pause(0.01)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/501_why_torch_dynamic_graph.ipynb b/tutorial-contents-notebooks/501_why_torch_dynamic_graph.ipynb new file mode 100644 index 0000000..9c87636 --- /dev/null +++ b/tutorial-contents-notebooks/501_why_torch_dynamic_graph.ipynb @@ -0,0 +1,262 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 501 Why Torch Dynamic Graph\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\n", + "* matplotlib\n", + "* numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch import nn\n", + "from torch.autograd import Variable\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "torch.manual_seed(1) # reproducible\n", + "\n", + "# Hyper Parameters\n", + "INPUT_SIZE = 1 # rnn input size / image width\n", + "LR = 0.02 # learning rate" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class 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" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RNN (\n", + " (rnn): RNN(1, 32, batch_first=True)\n", + " (out): Linear (32 -> 1)\n", + ")\n" + ] + } + ], + "source": [ + "\n", + "rnn = RNN()\n", + "print(rnn)\n", + "\n", + "optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters\n", + "loss_func = nn.MSELoss() # the target label is not one-hotted" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n", + 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SBEvM8DcWf4DdJW1tWOceLEdNnFfHrG00vroNx1WAxvWZtEpEK2dJhRo2jrCD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\njg/izq99PtlWul0e3hxp1aDfvvOt+Dhej196if5+3nYb8Cd/om3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EQoNYKOgT2NhYWPkG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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "h_state = None # for initial hidden state\n", + "\n", + "plt.figure(1, figsize=(12, 5))\n", + "plt.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 #########\n", + "step = 0\n", + "for 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 = Variable(torch.from_numpy(x_np[np.newaxis, :, np.newaxis])) # shape (batch, time_step, input_size)\n", + " y = Variable(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 = Variable(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", + "\n", + "plt.ioff()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/502_GPU.ipynb b/tutorial-contents-notebooks/502_GPU.ipynb new file mode 100644 index 0000000..37bdf0f --- /dev/null +++ b/tutorial-contents-notebooks/502_GPU.ipynb @@ -0,0 +1,330 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 502 GPU\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\n", + "* torchvision" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch.autograd import Variable\n", + "import torch.utils.data as Data\n", + "import torchvision\n", + "\n", + "torch.manual_seed(1)\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "EPOCH = 1\n", + "BATCH_SIZE = 50\n", + "LR = 0.001\n", + "DOWNLOAD_MNIST = False" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "train_data = torchvision.datasets.MNIST(\n", + " root='./mnist/', \n", + " train=True, \n", + " transform=torchvision.transforms.ToTensor(), \n", + " download=DOWNLOAD_MNIST,)\n", + "\n", + "train_loader = Data.DataLoader(\n", + " dataset=train_data, \n", + " batch_size=BATCH_SIZE, \n", + " shuffle=True)\n", + "\n", + "test_data = torchvision.datasets.MNIST(\n", + " root='./mnist/', train=False)\n", + "\n", + "# !!!!!!!! Change in here !!!!!!!!! #\n", + "test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1)).type(torch.FloatTensor)[:2000].cuda()/255. # Tensor on GPU\n", + "test_y = test_data.test_labels[:2000].cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class CNN(nn.Module):\n", + " def __init__(self):\n", + " super(CNN, self).__init__()\n", + " self.conv1 = nn.Sequential(\n", + " nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2,), \n", + " nn.ReLU(), \n", + " nn.MaxPool2d(kernel_size=2),)\n", + " self.conv2 = nn.Sequential(\n", + " nn.Conv2d(16, 32, 5, 1, 2), \n", + " nn.ReLU(), \n", + " 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" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CNN (\n", + " (conv1): Sequential (\n", + " (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", + " (1): ReLU ()\n", + " (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", + " )\n", + " (conv2): Sequential (\n", + " (0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n", + " (1): ReLU ()\n", + " (2): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n", + " )\n", + " (out): Linear (1568 -> 10)\n", + ")" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cnn = CNN()\n", + "\n", + "# !!!!!!!! Change in here !!!!!!!!! #\n", + "cnn.cuda() # Moves all model parameters and buffers to the GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)\n", + "loss_func = nn.CrossEntropyLoss()\n", + "\n", + "losses_his = []" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 | train loss: 2.3145 | test accuracy: 0.10\n", + "Epoch: 0 | train loss: 0.5546 | test accuracy: 0.83\n", + "Epoch: 0 | train loss: 0.5856 | test accuracy: 0.89\n", + "Epoch: 0 | train loss: 0.1879 | test accuracy: 0.92\n", + "Epoch: 0 | train loss: 0.0601 | test accuracy: 0.94\n", + "Epoch: 0 | train loss: 0.1772 | test accuracy: 0.95\n", + "Epoch: 0 | train loss: 0.0993 | test accuracy: 0.94\n", + "Epoch: 0 | train loss: 0.2210 | test accuracy: 0.95\n", + "Epoch: 0 | train loss: 0.0379 | test accuracy: 0.96\n", + "Epoch: 0 | train loss: 0.0535 | test accuracy: 0.96\n", + "Epoch: 0 | train loss: 0.0268 | test accuracy: 0.96\n", + "Epoch: 0 | train loss: 0.0910 | test accuracy: 0.96\n", + "Epoch: 0 | train loss: 0.0982 | test accuracy: 0.97\n", + "Epoch: 0 | train loss: 0.3154 | test accuracy: 0.97\n", + "Epoch: 0 | train loss: 0.0418 | test accuracy: 0.97\n", + "Epoch: 0 | train loss: 0.1072 | test accuracy: 0.96\n", + "Epoch: 0 | train loss: 0.0652 | test accuracy: 0.97\n", + "Epoch: 0 | train loss: 0.1042 | test accuracy: 0.97\n", + "Epoch: 0 | train loss: 0.1346 | test accuracy: 0.97\n", + "Epoch: 0 | train loss: 0.0431 | test accuracy: 0.98\n", + "Epoch: 0 | train loss: 0.0276 | test accuracy: 0.97\n", + "Epoch: 0 | train loss: 0.0153 | test accuracy: 0.98\n", + "Epoch: 0 | train loss: 0.0438 | test accuracy: 0.98\n", + "Epoch: 0 | train loss: 0.0082 | test accuracy: 0.97\n" + ] + } + ], + "source": [ + "for epoch in range(EPOCH):\n", + " for step, (x, y) in enumerate(train_loader):\n", + "\n", + " # !!!!!!!! Change in here !!!!!!!!! #\n", + " b_x = Variable(x).cuda() # Tensor on GPU\n", + " b_y = Variable(y).cuda() # Tensor on GPU\n", + "\n", + " output = cnn(b_x)\n", + " loss = loss_func(output, b_y)\n", + " losses_his.append(loss.data[0])\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.squeeze() # move the computation in GPU\n", + "\n", + " accuracy = sum(pred_y == test_y) / test_y.size(0)\n", + " print('Epoch: ', epoch, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.2f' % accuracy)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Y1XuO4rJXluD1H+3NgFc4UlKB7EnfYUNBccSybq0+qO2MFJ+qxHPfbMZd7+eY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\njHQAMQdFWIGVQgIx/uKzcMZ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hNhLR05C84LMA/AfAh0SUB+AIJMXhJjGboBIIvpbEpLZc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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(losses_his, label='loss')\n", + "plt.legend(loc='best')\n", + "plt.xlabel('Steps')\n", + "plt.ylabel('Loss')\n", + "plt.ylim((0, 1))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " 7\n", + " 2\n", + " 1\n", + " 0\n", + " 4\n", + " 1\n", + " 4\n", + " 9\n", + " 5\n", + " 9\n", + "[torch.cuda.LongTensor of size 10 (GPU 0)]\n", + " prediction number\n", + "\n", + " 7\n", + " 2\n", + " 1\n", + " 0\n", + " 4\n", + " 1\n", + " 4\n", + " 9\n", + " 5\n", + " 9\n", + "[torch.cuda.LongTensor of size 10 (GPU 0)]\n", + " real number\n" + ] + } + ], + "source": [ + "# !!!!!!!! Change in here !!!!!!!!! #\n", + "test_output = cnn(test_x[:10])\n", + "pred_y = torch.max(test_output, 1)[1].cuda().data.squeeze() # move the computation in GPU\n", + "\n", + "print(pred_y, 'prediction number')\n", + "print(test_y[:10], 'real number')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/503_dropout.ipynb b/tutorial-contents-notebooks/503_dropout.ipynb new file mode 100644 index 0000000..89a340c --- /dev/null +++ b/tutorial-contents-notebooks/503_dropout.ipynb @@ -0,0 +1,295 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 503 Dropout\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\n", + "* matplotlib\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch\n", + "from torch.autograd import Variable\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "torch.manual_seed(1) # reproducible" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "N_SAMPLES = 20\n", + "N_HIDDEN = 300" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# training data\n", + "x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)\n", + "y = x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))\n", + "x, y = Variable(x), Variable(y)\n", + "\n", + "# test data\n", + "test_x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)\n", + "test_y = test_x + 0.3*torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))\n", + "test_x, test_y = Variable(test_x, volatile=True), Variable(test_y, volatile=True)\n", + "\n", + "# show data\n", + "plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=50, alpha=0.5, label='train')\n", + "plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.5, label='test')\n", + "plt.legend(loc='upper left')\n", + "plt.ylim((-2.5, 2.5))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "net_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", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "net_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", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sequential (\n", + " (0): Linear (1 -> 300)\n", + " (1): ReLU ()\n", + " (2): Linear (300 -> 300)\n", + " (3): ReLU ()\n", + " (4): Linear (300 -> 1)\n", + ")\n", + "Sequential (\n", + " (0): Linear (1 -> 300)\n", + " (1): Dropout (p = 0.5)\n", + " (2): ReLU ()\n", + " (3): Linear (300 -> 300)\n", + " (4): Dropout (p = 0.5)\n", + " (5): ReLU ()\n", + " (6): Linear (300 -> 1)\n", + ")\n" + ] + } + ], + "source": [ + "print(net_overfitting) # net architecture\n", + "print(net_dropped)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "optimizer_ofit = torch.optim.Adam(net_overfitting.parameters(), lr=0.01)\n", + "optimizer_drop = torch.optim.Adam(net_dropped.parameters(), lr=0.01)\n", + "loss_func = torch.nn.MSELoss()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ntR9+cGsQkmVCgkWLJJeMyQ8/iMNzVsybJ5omSByuw4ft87blhMuXxTP87FnZ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\nfGtTtsJyzrWZ9HGTpY+3FExXcFHBZWU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rpfYrpU4mKfXrWqUef1wpf3+VDOoywWov9dQqOqpdhCnVooVS8+Ypdfu2uqyU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\nCnRzliJH3jzly8vDRlSUCF2zJ4GbmQx0BsYbBm2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lalVH/ty5rvHfPv7Y2GgWUYxwM+Qvq1c79rt1c1/moYfUU0VCgnquWLMm47Il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\n14azZ9WZywcfOOyoL13S99p9+/Svd911+sJy6JB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s9nG33w779mVvGTApSZYiL16U62XL4L6uolKagEjrithPBxISxKzF7NMyKkoe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\nXCjCzewv+ORJa15mfPfa0qePmFb27m0dx5gxYmK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kbEFBpgbbTkCHjrIfEwpxPeHChczZaV0Owvje8HAz196EpkDQgZM13uPUKahS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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for 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 % 100 == 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[0], fontdict={'size': 20, 'color': 'red'})\n", + " plt.text(0, -1.5, 'dropout loss=%.4f' % loss_func(test_pred_drop, test_y).data[0], 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", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorial-contents-notebooks/504_batch_normalization.ipynb b/tutorial-contents-notebooks/504_batch_normalization.ipynb new file mode 100644 index 0000000..71736d6 --- /dev/null +++ b/tutorial-contents-notebooks/504_batch_normalization.ipynb @@ -0,0 +1,371 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 504 Batch Normalization\n", + "\n", + "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n", + "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n", + "\n", + "Dependencies:\n", + "* torch: 0.1.11\n", + "* matplotlib\n", + "* numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch.autograd import Variable\n", + "from torch import nn\n", + "from torch.nn import init\n", + "import torch.utils.data as Data\n", + "import torch.nn.functional as F\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "%matplotlib inline\n", + "\n", + "torch.manual_seed(1) # reproducible\n", + "np.random.seed(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Hyper parameters\n", + "N_SAMPLES = 2000\n", + "BATCH_SIZE = 64\n", + "EPOCH = 12\n", + "LR = 0.03\n", + "N_HIDDEN = 8\n", + "ACTIVATION = F.tanh\n", + "B_INIT = -0.2 # use a bad bias constant initializer" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ypRmYn4LKUv3vEU1A71GJfd8I3P9eOHqPifwO09pCH4vBXY8ATq6Xrv6tLQOD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\nUsONmxd/AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# training data\n", + "x = np.linspace(-7, 10, N_SAMPLES)[:, np.newaxis]\n", + "noise = np.random.normal(0, 2, x.shape)\n", + "y = np.square(x) - 5 + noise\n", + "\n", + "# test data\n", + "test_x = np.linspace(-7, 10, 200)[:, np.newaxis]\n", + "noise = np.random.normal(0, 2, test_x.shape)\n", + "test_y = np.square(test_x) - 5 + noise\n", + "\n", + "train_x, train_y = torch.from_numpy(x).float(), torch.from_numpy(y).float()\n", + "test_x = Variable(torch.from_numpy(test_x).float(), volatile=True) # not for computing gradients\n", + "test_y = Variable(torch.from_numpy(test_y).float(), volatile=True)\n", + "\n", + "train_dataset = Data.TensorDataset(data_tensor=train_x, target_tensor=train_y)\n", + "train_loader = Data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2,)\n", + "\n", + "# show data\n", + "plt.scatter(train_x.numpy(), train_y.numpy(), c='#FF9359', s=50, alpha=0.2, label='train')\n", + "plt.legend(loc='upper left')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class 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" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Net (\n", + " (bn_input): BatchNorm1d(1, eps=1e-05, momentum=0.5, affine=True)\n", + " (fc0): Linear (1 -> 10)\n", + " (fc1): Linear (10 -> 10)\n", + " (fc2): Linear (10 -> 10)\n", + " (fc3): Linear (10 -> 10)\n", + " (fc4): Linear (10 -> 10)\n", + " (fc5): Linear (10 -> 10)\n", + " (fc6): Linear (10 -> 10)\n", + " (fc7): Linear (10 -> 10)\n", + " (predict): Linear (10 -> 1)\n", + ") Net (\n", + " (bn_input): BatchNorm1d(1, eps=1e-05, momentum=0.5, affine=True)\n", + " (fc0): Linear (1 -> 10)\n", + " (bn0): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", + " (fc1): Linear (10 -> 10)\n", + " (bn1): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", + " (fc2): Linear (10 -> 10)\n", + " (bn2): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", + " (fc3): Linear (10 -> 10)\n", + " (bn3): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", + " (fc4): Linear (10 -> 10)\n", + " (bn4): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", + " (fc5): Linear (10 -> 10)\n", + " (bn5): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", + " (fc6): Linear (10 -> 10)\n", + " (bn6): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", + " (fc7): Linear (10 -> 10)\n", + " (bn7): BatchNorm1d(10, eps=1e-05, momentum=0.5, affine=True)\n", + " (predict): Linear (10 -> 1)\n", + ")\n" + ] + } + ], + "source": [ + "nets = [Net(batch_normalization=False), Net(batch_normalization=True)]\n", + "\n", + "print(*nets) # print net architecture" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "opts = [torch.optim.Adam(net.parameters(), lr=LR) for net in nets]\n", + "\n", + "loss_func = torch.nn.MSELoss()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0\n", + "Epoch: 1\n", + "Epoch: 2\n", + "Epoch: 3\n", + "Epoch: 4\n", + "Epoch: 5\n", + "Epoch: 6\n", + "Epoch: 7\n", + "Epoch: 8\n", + "Epoch: 9\n", + "Epoch: 10\n", + "Epoch: 11\n" + ] + }, + { + "data": { + "image/png": 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YYDDdtCRJWiQzK/9iNEv99XXc1zpqPBu4CXgQuBH4beBJjD41dz1wKfDEVe7z\n34/3dT9wC3BR0znX+rNY4346l9/sZje72c1v9uW+XBFckiSpgCuCS5IkFbBpkiRJKmDTJEmSVMCm\nSZIkqYBNkyRJUgGbJkmSpAI2TZIkSQVsmiRJkgr8fyHKwy7zLMiUAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f, axs = plt.subplots(4, N_HIDDEN+1, figsize=(10, 5))\n", + "plt.ion() # something about plotting\n", + "def 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: p_range = (-7, 10);the_range = (-7, 10)\n", + " else: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", + "# training\n", + "losses = [[], []] # recode loss for two networks\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[0])\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", + " b_x, b_y = Variable(b_x), Variable(b_y)\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()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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P92PXxM6t6977ItU1rX36/ypd/y+2kFpkU9V2m+3fvATiSjT2rofB8H9KOoKW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\nR0TkQNYikoeZZQN/Ak4GBgL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Hj4pxBvKb7q6y2DhpGn/2RubxL8yGOepdHZCxYaGDnMVLS0XMExPlTJ6cHLLI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\nfOJKC2swvr1wKb8r+Swd7sgrqTkNe7l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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 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()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}