Basic/08_NN_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.init\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x236e9f2f1d0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torchvision.utils as utils\n",
    "import torchvision.datasets as dsets\n",
    "import torchvision.transforms as transforms\n",
    "torch.manual_seed(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MNIST dataset\n",
    "mnist_train = dsets.MNIST(root='data/',\n",
    "                          train=True,\n",
    "                          transform=transforms.ToTensor(),\n",
    "                          download=True)\n",
    "\n",
    "mnist_test = dsets.MNIST(root='data/',\n",
    "                         train=False,\n",
    "                         transform=transforms.ToTensor(),\n",
    "                         download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([60000, 28, 28])\n",
      "torch.Size([60000])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hwnau\\.conda\\envs\\cuda\\lib\\site-packages\\torchvision\\datasets\\mnist.py:53: UserWarning: train_data has been renamed data\n",
      "  warnings.warn(\"train_data has been renamed data\")\n",
      "C:\\Users\\hwnau\\.conda\\envs\\cuda\\lib\\site-packages\\torchvision\\datasets\\mnist.py:43: UserWarning: train_labels has been renamed targets\n",
      "  warnings.warn(\"train_labels has been renamed targets\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '5')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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08RW2JzZ+OJHtiZK+rf6binqNpAWNxwskPV5jL3+kX6bxbjbNuGr+7mqf/jwien6TdLmGf5H/naR/qKOHJn3NkPRq4/ZG3b1JWq3h3br/0/Ae0Y2S/kzSs5LeatxP7qPe/l3DU3u/puFgTa2pt7kaPjR8TdLGxu3yur+7Ql89+d44XRZIgjPogCQIO5AEYQeSIOxAEoQdSIKwA0kQdiCJ/wdTTaw/QgR51gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(mnist_train.train_data.size())\n",
    "print(mnist_train.train_labels.size())\n",
    "\n",
    "idx=0\n",
    "plt.imshow(mnist_train.train_data[idx,:,:].numpy(), cmap='gray')\n",
    "plt.title('%i' % mnist_train.train_labels[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "\n",
    "data_loader  = torch.utils.data.DataLoader(dataset=mnist_train,\n",
    "                                          batch_size=batch_size,\n",
    "                                          shuffle=True,\n",
    "                                          num_workers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([100, 1, 28, 28])\n",
      "torch.Size([100])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([3, 2, 3, 2, 1, 6, 0, 4, 7, 0, 0, 4, 9, 2, 4, 9, 3, 3, 0, 3, 6, 1,\n",
       "       4, 5, 2, 5, 7, 6, 7, 8, 8, 2, 4, 1, 8, 4, 3, 6, 9, 2, 2, 4, 1, 0,\n",
       "       3, 9, 2, 1, 5, 6, 6, 1, 1, 1, 0, 0, 5, 0, 7, 9, 2, 3, 9, 6, 4, 6,\n",
       "       9, 6, 9, 0, 8, 0, 1, 7, 3, 3, 8, 7, 9, 4, 7, 4, 2, 2, 8, 5, 6, 9,\n",
       "       6, 7, 9, 1, 2, 0, 3, 6, 9, 2, 9, 6], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def imshow(img):\n",
    "    img = img / 2 + 0.5\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1,2,0)))\n",
    "    plt.show()\n",
    "    \n",
    "batch_images, batch_labels = next(iter(data_loader))\n",
    "\n",
    "print(batch_images.size())\n",
    "print(batch_labels.size())\n",
    "\n",
    "#숫자 100개 표시\n",
    "imshow(utils.make_grid(batch_images))\n",
    "batch_labels.numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Linear(in_features=784, out_features=512, bias=True)\n",
      "  (1): ReLU()\n",
      "  (2): Linear(in_features=512, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "linear1 = torch.nn.Linear(784, 512, bias = True)\n",
    "linear2 = torch.nn.Linear(512, 10, bias = True)\n",
    "relu = torch.nn.ReLU()\n",
    "\n",
    "model = torch.nn.Sequential(linear1, relu, linear2)\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cost_func = torch.nn.CrossEntropyLoss()\n",
    "# Softmax 내장"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mdel Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch:    1] cost = 0.114310443\n",
      "[Epoch:    2] cost = 0.0709680021\n",
      "[Epoch:    3] cost = 0.0501046963\n",
      "[Epoch:    4] cost = 0.0371314399\n",
      "[Epoch:    5] cost = 0.0269469228\n",
      "Learning Ginished!\n"
     ]
    }
   ],
   "source": [
    "learning_rate = 0.001\n",
    "training_epochs = 5\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "for epoch in range(training_epochs):\n",
    "    avg_cost = 0\n",
    "    total_batch = len(mnist_train) // batch_size\n",
    "    \n",
    "    for i, (batch_images, batch_labels) in enumerate(data_loader):\n",
    "        \n",
    "        X = Variable(batch_images.view(-1, 28 * 28))\n",
    "        Y = Variable(batch_labels)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        Y_prediction = model(X)\n",
    "        cost = cost_func(Y_prediction, Y)\n",
    "        cost.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        avg_cost += cost / total_batch\n",
    "        \n",
    "    print(\"[Epoch: {:>4}] cost = {:>.9}\".format(epoch + 1, avg_cost.data))\n",
    "    \n",
    "print(\"Learning Ginished!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.load_state_dict(torch.load('NN.pkl'))\n",
    "#model.state_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measure performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 98 %\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "for images, labels in mnist_test:\n",
    "    images  = Variable(images.view(-1, 28 * 28))\n",
    "    outputs = model(images)\n",
    "    _, predicted = torch.max(outputs.data, 1)\n",
    "    \n",
    "    total += 1\n",
    "    correct += (predicted == labels).sum()\n",
    "    \n",
    "print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hwnau\\.conda\\envs\\cuda\\lib\\site-packages\\torchvision\\datasets\\mnist.py:58: UserWarning: test_data has been renamed data\n",
      "  warnings.warn(\"test_data has been renamed data\")\n",
      "C:\\Users\\hwnau\\.conda\\envs\\cuda\\lib\\site-packages\\torchvision\\datasets\\mnist.py:48: UserWarning: test_labels has been renamed targets\n",
      "  warnings.warn(\"test_labels has been renamed targets\")\n"
     ]
    }
   ],
   "source": [
    "r = random.randint(0, len(mnist_test)-1)\n",
    "X_single_data = Variable(mnist_test.test_data[r:r + 1].view(-1,28*28).float())\n",
    "Y_single_data = Variable(mnist_test.test_labels[r:r + 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label :  [5]\n",
      "Prediction :  [5]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "single_prediction = model(X_single_data)\n",
    "plt.imshow(X_single_data.data.view(28,28).numpy(), cmap='gray')\n",
    "\n",
    "print('Label : ', Y_single_data.data.view(1).numpy())\n",
    "print('Prediction : ', torch.max(single_prediction.data, 1)[1].numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZVI/q4adOnZqpsf7nALBkyRKqjxs3juo33HBDphb1N4j2H0T7NqLzwmYNRL0Zjhw5kqmx0eThld3MGs3sb2a228x2mtlzudvrzextM9uX+5pdUS+EKDv5vIzvBfAzd18K4B8A/MTMlgF4HsC77r4QwLu5/wshKpTQ7O7e5u6bc993AdgNYA6AhwGszf3YWgCPjNQihRDF860+oDOzeQC+B+AzADPcvQ0Y+IMAYMjBWma22sxazKwleu8phBg58ja7mY0H8GcAP3V33q1vEO6+xt2b3b157NixhaxRCDEM5GV2M6vGgNH/4O6v524+aWazcvosAO0js0QhxHAQpt5sIAfxCoDd7v7LQdKbAJ4G8FLu67roWDU1NZg7d26mHrUt3rZtW6YWjbldunQpX1wAa9fMxu8CwMqVK6l+4sQJqh89epTqrIw1Sr1F5bVRiWyUwmKpVpYyBOIy00hnraqLbVMdpVOj5zIbZc00gJfAsudiPnn2OwA8BWC7mX1VmP0CBkz+JzN7BsARAD/M41hCiDIRmt3d/w4ga4fBPcO7HCHESKHtskIkgswuRCLI7EIkgswuRCLI7EIkQklLXHt7e+kI3+7ubhq/aNGiTC1q3cvKAgFg9OjRVGelg9F43h07dlA9GukclXqyEtloLHLUQjva4tzZ2Ul1VkLLngtAvL+Alc8CPNcdlVOzPR0AMGvWLKofPnyY6qwNdrR3gflg1KhRmZqu7EIkgswuRCLI7EIkgswuRCLI7EIkgswuRCLI7EIkQknz7NXV1Zg+fcjuVQCAvr4+Gl9Vlb3cjo4OGstGRQNx62BWk/7pp5/S2GhtLDcK8McN8Jxxsa3Aoj0EUa6ctXuOcvx33nkn1aMc/4oVKzK1qN68ubmZ6lH77/Z23suF1cvX1tbSWPY7KaqVtBDiu4HMLkQiyOxCJILMLkQiyOxCJILMLkQiyOxCJEJJ8+x9fX203zYbiwwAEyZMyNSievY9e/ZQvaGhgeozZ87M1BYsWEBjozp9lhsF+OOOqKuro3rUVz7Ks0ePjdXTR2O2o/tmMwgAnoePctlRX/jodxaNo37ssccytei5zGYYsP0iurILkQgyuxCJILMLkQgyuxCJILMLkQgyuxCJILMLkQj5zGdvBPB7ADMB9ANY4+6/NrMXAfwLgFO5H33B3dfncbxMjdU+AzxnG+Umo97rUR9wltNdt46Ppp89ezbVo5wt6wEA8Dnl0bGjtUVE+eTJkydnau+//z6NjWrCo30ZU6ZMydSiHP+BAweozubOA8BHH31EdTYffuLEiTSW7btgvRHy2VTTC+Bn7r7ZzCYA2GRmb+e0X7n7f+RxDCFEmclnPnsbgLbc911mthvAnJFemBBiePlW79nNbB6A7wH4LHfTs2a2zcxeNbMhX6+Z2WozazGzlmj7oxBi5Mjb7GY2HsCfAfzU3c8D+A2A6wDciIEr/y+GinP3Ne7e7O7N0XtyIcTIkZfZzawaA0b/g7u/DgDuftLd+9y9H8BvAdwycssUQhRLaHYb+Pj8FQC73f2Xg24f/PH1DwDwUaVCiLKSz6fxdwB4CsB2M9uSu+0FAE+Y2Y0AHMAhAD+ODmRmNDUQtfdlZaqPP/44jY1SLWyELsBbJq9atYrGRm2qe3t7qR69/Tl9+nSmNnbsWBrL0nYA0NjYSPXosbHxw1H6KmpTHZXXsrVHo6yjkuimpiaqP/nkk1RnbbSj8eLsd8aeS/l8Gv93AEP9RsOcuhCictAOOiESQWYXIhFkdiESQWYXIhFkdiESQWYXIhFK2ko6oqenh+qstG/r1q00NsplR22JWSlo1Hb4ypUrVK+vr6f6iRMnqM7aXEf5ZDY6GACOHj1K9ej4LP7YsWM09t5776U6218AALt27crUotLcaH9CVNZ8//33U52V544ZM4bGsr0NaiUthJDZhUgFmV2IRJDZhUgEmV2IRJDZhUgEmV2IRLBoZO+w3pnZKQCHB900FUBHyRbw7ajUtVXqugCtrVCGc21N7j5tKKGkZv/GnZu1uHtz2RZAqNS1Veq6AK2tUEq1Nr2MFyIRZHYhEqHcZl9T5vtnVOraKnVdgNZWKCVZW1nfswshSke5r+xCiBIhswuRCGUxu5k9YGb/a2b7zez5cqwhCzM7ZGbbzWyLmbWUeS2vmlm7me0YdFu9mb1tZvtyX7NnIpd+bS+a2bHcudtiZg+VaW2NZvY3M9ttZjvN7Lnc7WU9d2RdJTlvJX/PbmajAOwFcC+AVgAbATzh7tmdBkqImR0C0OzuZd+AYWZ3AbgA4PfuviJ3278DOOPuL+X+UE5293+tkLW9COBCucd456YVzRo8ZhzAIwD+GWU8d2Rd/4QSnLdyXNlvAbDf3Q+4ezeAPwJ4uAzrqHjc/UMAV4+yeRjA2tz3azHwZCk5GWurCNy9zd03577vAvDVmPGynjuyrpJQDrPPATC4V1ErKmveuwP4q5ltMrPV5V7MEMxw9zZg4MkDYHqZ13M14RjvUnLVmPGKOXeFjD8vlnKYfagmWZWU/7vD3W8C8CCAn+Reror8yGuMd6kYYsx4RVDo+PNiKYfZWwEMnrjXACB7yl2Jcffjua/tAN5A5Y2iPvnVBN3c1/Yyr+f/qaQx3kONGUcFnLtyjj8vh9k3AlhoZteaWQ2AHwF4swzr+AZmVpv74ARmVgvgPlTeKOo3ATyd+/5pALzNaQmplDHeWWPGUeZzV/bx5+5e8n8AHsLAJ/JfAPi3cqwhY13zAWzN/dtZ7rUBeA0DL+t6MPCK6BkAUwC8C2Bf7mt9Ba3tvwBsB7ANA8aaVaa1/SMG3hpuA7Al9++hcp87sq6SnDdtlxUiEbSDTohEkNmFSASZXYhEkNmFSASZXYhEkNmFSASZXYhE+D9baDVq+uQDDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    },
    {
     "data": {
      "image/png": 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sPT09NP8Y1YWz+uQ//elPNDbKdUd509deey2pffLJJzR21qxZVG9qaqJ6VBvNzls0/jfqlx/l2Ts7O6k+ffr0pLZs2TIaG/W0j/oAsOfL5cuXaeyCBQuovmnTJqqzHD8ALFmyJKlFtfKsF39JfePNrNnMXjezfWbWZmbfLtzeaGZbzKyj8H18dF9CiMoxmJfxvQC+6+6LAawG8E0zWwLgewC2uvsCAFsL/xZCVCmh2d29y93fLfx8AcA+ANMBPALgxcKvvQiA7+kUQlSUv+sDOjObDeAOADsATHH3LqDvPwQAAw7WMrP1ZtZqZq3R/nMhxPAxaLOb2VgAvwLwHXfn3R374e4b3L3F3VvGjh1bzBqFEEPAoMxuZrXoM/ov3P3XhZtPmNnUgj4VQHEtL4UQZSFMvVnfZ/nPAdjn7v1r7zYBeBzAM4Xvr0b3VVNTA3Z1j0oW58+fn9QOHz5MY2/cuEH1aPwvS/NE7ZijVEqUBopKQVkraZamGcyxo5TklClTqF5TU5PUzp07R2Oj9t5RSTSLj2JbW1upHo2TjtKK7P6jFDRL67l7UhtMnn0NgK8B2GNm7xVu+z76TL7RzL4O4AiArwzivoQQFSI0u7tvB5D67/2+oV2OEGK40HZZITJBZhciE2R2ITJBZhciE2R2ITKhrCWu165do+2go1G1dXV1Sa2hoSE8NmP37t1Uv3TpUlKL2gZHOf4oTx+NLmbx0cjlqBV0VL47depUqrM8/M0330xjWY4eiMtz//jHPya16JyOH8+LOGfOnEn1o0ePUp2dt2inKYtl+0F0ZRciE2R2ITJBZhciE2R2ITJBZhciE2R2ITJBZhciE8qaZx81ahTNjUY53eXLlye1KGcb1R9HsHr3qCactVMG4j0C06ZNozprmRzlwbu7ec+RcePGUT1qNc1y5VGbsqgF965du6jOart7enpo7NWrV6ke7dtYsWJF0fGnT5+msWzPB9vToSu7EJkgswuRCTK7EJkgswuRCTK7EJkgswuRCTK7EJlQ1jy7mdG8a9SDnI25jfKit9xyC9VZ7hLgufSWlhYa+9Zbb1F98uQBJ2f9haium9XTt7W10dhoD0CUTz5w4ADVV65cmdSiUdRsTDYANDY2Un3RokVJ7dSpUzQ2qin/0pe+RPWOjg6qs7rzVatW0Vi2H4XtLdCVXYhMkNmFyASZXYhMkNmFyASZXYhMkNmFyASZXYhMGMx89mYAPwfQBOAGgA3u/mMzexLAvwE4WfjV77v75uC+aH7x+vXrdC2vvpoeAR/VAEf90Zubm6m+d+/epMZymwBwzz33UJ310geA+vp6qrN53XPnzqWx69ato3p7ezvVoz4CV65cSWrRjHT2XAGApqamonW2ZwOI90ZEfeVXr15NdTYDYefOnTSW1awzbTCbanoBfNfd3zWzBgDvmNmWgvYjd//PQdyHEKLCDGY+exeArsLPF8xsHwC+7UoIUXX8Xe/ZzWw2gDsA7Cjc9C0z221mz5vZgK9rzGy9mbWaWWvUdkoIMXwM2uxmNhbArwB8x93PA/gJgHkAlqPvyv/DgeLcfYO7t7h7S9TPTAgxfAzK7GZWiz6j/8Ldfw0A7n7C3a+7+w0APwXAd+8LISpKaHYzMwDPAdjn7s/2u71/29IvA0h/XC2EqDiD+TR+DYCvAdhjZu8Vbvs+gMfMbDkAB3AYwDeiO+rt7aUpsqjMlKUr5s2bR2OjksUtW7ZQ/eTJk0ktanm8ceNGqs+fP5/qUQksG3Udtak+d+4c1aPzFj1mLPUWlddGj+krr7xCdfZci8qpWXtuIB6FHcWz40ftv9ljxkZRD+bT+O0AbACJ5tSFENWFdtAJkQkyuxCZILMLkQkyuxCZILMLkQkyuxCZUNZW0jU1NbQ0cMyYMTT+pptuSmpRiWvUUjnKu7Iy1h07diQ1IC6fff3116kelXqyNtmHDh2isRMmTKB6NC76c5/7HNW3bduW1Pbv309jWStoIC7fZSXRa9eupbFRuXVU4hqV/rL23+x5DvC9D6ztuK7sQmSCzC5EJsjsQmSCzC5EJsjsQmSCzC5EJsjsQmSCRW2Qh/RgZicBfNTvpokA+OzcylGta6vWdQFaW7EM5dpmufuADQ7Kava/ObhZq7vz4eYVolrXVq3rArS2YinX2vQyXohMkNmFyIRKm31DhY/PqNa1Veu6AK2tWMqytoq+ZxdClI9KX9mFEGVCZhciEypidjNbZ2b7zewDM/teJdaQwswOm9keM3vPzForvJbnzazbzPb2u63RzLaYWUfhOy+sLu/anjSzY4Vz956ZPVyhtTWb2etmts/M2szs24XbK3ruyLrKct7K/p7dzGoAHABwP4BOAG8DeMzd3y/rQhKY2WEALe5e8Q0YZnYvgIsAfu7uSwu3/QeAM+7+TOE/yvHu/kSVrO1JABcrPca7MK1oav8x4wAeBfCvqOC5I+v6F5ThvFXiyr4KwAfufsjdrwH4JYBHKrCOqsfdtwE485mbHwHwYuHnF9H3ZCk7ibVVBe7e5e7vFn6+AODPY8Yreu7IuspCJcw+HcDRfv/uRHXNe3cAr5nZO2a2vtKLGYAp7t4F9D15APDZUOUnHONdTj4zZrxqzl0x489LpRJmH2iUVDXl/9a4+woADwH4ZuHlqhgcgxrjXS4GGDNeFRQ7/rxUKmH2TgD9OzDOAJDuvldm3P144Xs3gN+g+kZRn/jzBN3C9+4Kr+cvVNMY74HGjKMKzl0lx59XwuxvA1hgZnPMrA7AVwFsqsA6/gYzqy98cAIzqwfwAKpvFPUmAI8Xfn4cQLqFapmpljHeqTHjqPC5q/j4c3cv+xeAh9H3ifxBAP9eiTUk1jUXwK7CV1ul1wbgJfS9rOtB3yuirwOYAGArgI7C98YqWtv/ANgDYDf6jDW1Qmv7Z/S9NdwN4L3C18OVPndkXWU5b9ouK0QmaAedEJkgswuRCTK7EJkgswuRCTK7EJkgswuRCTK7EJnw/2EILBzYM6TEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n"
     ]
    },
    {
     "data": {
      "image/png": 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v7GbWYmZ/NrODZvaxmf2ocH+9mb1hZkcKt6VVcBBCTCrjeRt/A8BP3P1vAPwtgB+a2VoATwN4091XAXiz8LcQokIJze7uve6+t/D7JQAHATQDeBTAjsLDdgB4bLImKYQona/1BZ2ZLQOwHsB7ABa6ey8w8g8BQGPGmG1m1m5m7YODg6XNVghRNOM2u5nVAfgDgB+7O69SOAp33+7ube7eFjURFEJMHuMyu5lNxYjRf+vufyzc3WdmTQW9CQAvxymEyJUw9GYj3+W/AOCgu/98lPQagK0AnivcvlrqZB544AGqs3TLqM1tpEchpPfeey9Ti9IZI6LxURho0aJFmVoUOovSSNesWUP1aPssFBSFQ0tpewzw8FoULr158ybVI6Ly4iwcG7X4ZmE/9nqMJ86+CcD3AXSY2b7Cfc9gxOS/N7MnAHwK4Lvj2JYQIidCs7v7XwBk/XveMrHTEUJMFlouK0QiyOxCJILMLkQiyOxCJILMLkQilDXFFeBx1yj2yVbgReWYWctloLTWw1FMNio7fPLkSarPnz+f6levXi16bFRKOmplHcWTFy9enKlFr3dUznn58uVUZ2WsI86dO0f16Hmz9uIAf82iVtVRafEsdGUXIhFkdiESQWYXIhFkdiESQWYXIhFkdiESQWYXIhHKGmcfHh6mZY9Zi12AtwcuNf84iumyeHNHRwcdG8Wyo7jp8ePHqc5aWUdx8qGhIapH8eYo1s3qCEQltKMy2I2NY1ZC+z/Y+oaohHZ7ezvVo/ULUQvwAwcOZGqsZDoQH7fMcUWNEkL8v0NmFyIRZHYhEkFmFyIRZHYhEkFmFyIRZHYhEqHs+ewsRhjFyvfu3ZupRfHirq4uqq9YsYLqLJ99w4YNJe07imVH+e4slh5tO8qdbmlpoXq0PoHp0bZLackM8PUJUc35qL5BtH7hvvvuo/rtt9+eqUXnMsuFZ2s2dGUXIhFkdiESQWYXIhFkdiESQWYXIhFkdiESQWYXIhHG05+9BcBvACwCMAxgu7v/0syeBfBPAL4oyP6Mu7/OtjU8PEx7kUc5wqwOeBSTjfKLd+3aRXWWa7969Wo6NspnX79+PdVffvllqrM1AFGP887OTqr39fVRfc+ePVRnrwvrnw7E9fajfHcWr54yhZ/6U6dOpXqUSx+NnzFjRqYWrTdhawRYX4bxLKq5AeAn7r7XzGYB2GNmbxS0X7j7v41jG0KInBlPf/ZeAL2F3y+Z2UEAzZM9MSHExPK1PrOb2TIA6wG8V7jrSTP7yMxeNLMx++GY2TYzazez9mhppRBi8hi32c2sDsAfAPzY3T8D8DyAFQDuxMiV/2djjXP37e7e5u5tpfTeEkKUxrjMbmZTMWL037r7HwHA3fvc/aa7DwP4FYCNkzdNIUSphGa3ka/3XgBw0N1/Pur+plEP+w6A/RM/PSHERDGeb+M3Afg+gA4z21e47xkAj5vZnQAcQBeAH0Qbqq6upq1uo5BDKeGKqFxz9BGD7fvixYt0bFQiu7e3l+pLliyhOgu9Rdt+8MEHqR6Ft5qb+Xe1LD23qakpUwOAmpoaqkfhs0OHDmVqrP33ePbd399P9cOHD1Od7T86V1k5djZ2PN/G/wXAWME7GlMXQlQWWkEnRCLI7EIkgswuRCLI7EIkgswuRCLI7EIkQllLSd+4cQNnzpzJ1BsaGuh4Fl+M4qK1tbVUj1ISGVHZYdZqGgC6u7up3traSnWWyhml3w4ODlI9Oi7RGoI1a9ZkalHacZTyzEoqA7xUNVubAMRrAKK1FdEaAHbOlNIOWqWkhRAyuxCpILMLkQgyuxCJILMLkQgyuxCJILMLkQgWxRsndGdmZwCcGHXXAgBnyzaBr0elzq1S5wVobsUykXNb6u5jLlgpq9m/snOzdndvy20ChEqdW6XOC9DciqVcc9PbeCESQWYXIhHyNvv2nPfPqNS5Veq8AM2tWMoyt1w/swshykfeV3YhRJmQ2YVIhFzMbmYPmdlhMztqZk/nMYcszKzLzDrMbJ+Ztec8lxfNrN/M9o+6r97M3jCzI4Xb7EL85Z/bs2bWUzh2+8zs4Zzm1mJmfzazg2b2sZn9qHB/rseOzKssx63sn9nNrBrAJwD+HkA3gN0AHnf3A2WdSAZm1gWgzd1zX4BhZn8HYBDAb9z9tsJ9/wrgvLs/V/hHOc/d/7lC5vYsgMG823gXuhU1jW4zDuAxAP+IHI8dmdc/oAzHLY8r+0YAR929092vA/gdgEdzmEfF4+47AZz/0t2PAthR+H0HRk6WspMxt4rA3XvdfW/h90sAvmgznuuxI/MqC3mYvRnA6J5A3aisfu8O4E9mtsfMtuU9mTFY6O69wMjJA6Ax5/l8mbCNdzn5Upvxijl2xbQ/L5U8zD5WK6lKiv9tcve7AHwbwA8Lb1fF+BhXG+9yMUab8Yqg2PbnpZKH2bsBjK4EeAuAUznMY0zc/VThth/AK6i8VtR9X3TQLdzyDoNlpJLaeI/VZhwVcOzybH+eh9l3A1hlZq1mVgPgewBey2EeX8HMZha+OIGZzQTwLVReK+rXAGwt/L4VwKs5zuWvqJQ23lltxpHzscu9/bm7l/0HwMMY+Ub+GIB/yWMOGfNaDuDDws/Hec8NwEsYeVs3hJF3RE8AmA/gTQBHCrf1FTS3/wTQAeAjjBirKae5bcbIR8OPAOwr/Dyc97Ej8yrLcdNyWSESQSvohEgEmV2IRJDZhUgEmV2IRJDZhUgEmV2IRJDZhUiE/wWp5FKA5Sb0DwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZPnsuhK/sZjbbzP5mZl1mtsvMfpq/fJqZvWtme/OfeRcDIURFGcmf8QMAfu7uSwGsBvATM1sG4HkA77n7IgDv5b8XQlQpodndvcfdt+S/Pg+gC8AsAI8CWJ//sfUAHivVJoUQxfOt3qAzs7kA7gCwEUCzu/cAg78QADRlrFlrZh1m1sH+TxFClJYRm93MJgF4HcDP3J132xuCu69z93Z3b2cDCIUQpWVEZjezsRg0+h/c/c/5i0+YWUtebwHA31oVQlSUMHqzwffyXwHQ5e6/GiK9BeBpAC/lP79Z7GY+/vhjqrMoJopxojLUqNU0i7+idszRSOaoHXMU7bES2d27d9O10VjkYu8bK++N4q2oxDVq58zGeB8+fJiuPX78ONXb2tqoHrX/ZlHwsmXL6FoWrzFGkrPfA+BHAHaY2db8ZS9g0OR/MrNnAHQD+EFBOxBClIXQ7O7+dwBZv0q+O7rbEUKUCp0uK0QiyOxCJILMLkQiyOxCJILMLkQilLXEdWBggGbCUTkla98bjf9tahr2bN5/MGPGDKqzzDbKe1mrZyAeDxyV5+ZyuYI0ID7mkyZNovrly5epzsqSo/vNRnQDcbtnltNH5w9EGX9UttzQ0EB1dt5GNOKbHdOiSlyFEP8cyOxCJILMLkQiyOxCJILMLkQiyOxCJILMLkQilDVnd3c6UjaqjX7ggQcytW3bthW8LwCYPn061VkeHWW227dvp3pUnxxluo2NjQVfd3QOQFTv3tLSQnWWV0fnJ0THddeuXVRnOX7U3yC67Wgkc8SaNWsytah9G/MJa0OtV3YhEkFmFyIRZHYhEkFmFyIRZHYhEkFmFyIRZHYhEqHsOTsbdRvVlLNs9M4776Rro6w76hu/cOHCTI3l3EA8kvnChQtUjybpsPVR//Mob476BESZcF9fX6Z266230rUbN26k+rFjxwq+7aVLl9K1UY4e9Z1fvnw51Xt6ejK16PnCPKScXQghswuRCjK7EIkgswuRCDK7EIkgswuRCDK7EIkwkvnsswH8HsAMADkA69z9ZTN7EcC/A/iqifUL7r6BXdfYsWNp/fPp06fpXj766KNMjfXSBuKZ10uWLKE66zsf5b0rV66kend3N9U3b95MdVZr//jjj9O10XGLzgGIasrZsYl6+ff391O9ubmZ6qxWP+qHv3jxYqoXc+4DEJ/XwZgyZUqmxmbej+SkmgEAP3f3LWY2GcBmM3s3r/3a3f/r22xUCFEZRjKfvQdAT/7r82bWBWBWqTcmhBhdvtX/7GY2F8AdAL46j/FZM9tuZq+aWX3GmrVm1mFmHdGplUKI0jFis5vZJACvA/iZu38J4DcAFgBYgcFX/l8Ot87d17l7u7u3R//nCCFKx4jMbmZjMWj0P7j7nwHA3U+4+3V3zwH4LYBVpdumEKJYQrPbYHvSVwB0ufuvhlw+9G317wPYOfrbE0KMFiN5N/4eAD8CsMPMtuYvewHAE2a2AoADOATgx9EVRSWuLFIAeAvdqDw2Gv8bxTys7JCVKwLxyOUoBorKJdno4mIjoqjUc/Xq1VR/8MEHM7XOzk669t5776V69B4QizSnTp1K1y5YsIDqrJQUiI8ri6DZWHOA+4D5ayTvxv8dwHDNx2mmLoSoLnQGnRCJILMLkQgyuxCJILMLkQgyuxCJILMLkQhlbSWdy+Vonh1lumz8cJR1R+15o9HGbN9RO+YoD45Kezs6OqjOMt1o3POpU6eoXldXR3U2RhsAurq6MrWJEyfStblcjuoXL16ken39sOUaAOLzMvbs2UP1aMR3dO4EG1cdHXPWIpsdM72yC5EIMrsQiSCzC5EIMrsQiSCzC5EIMrsQiSCzC5EIFtXljuqNmZ0EMLSwvBEAD3orR7XurVr3BWhvhTKae5vj7sOeBFBWs3/jxs063L29YhsgVOveqnVfgPZWKOXam/6MFyIRZHYhEqHSZl9X4dtnVOveqnVfgPZWKGXZW0X/ZxdClI9Kv7ILIcqEzC5EIlTE7Ga2xsx2m9k+M3u+EnvIwswOmdkOM9tqZryQvPR7edXMes1s55DLppnZu2a2N/85u2i7/Ht70cyO5o/dVjN7pEJ7m21mfzOzLjPbZWY/zV9e0WNH9lWW41b2/9nNrBbAHgAPATgC4FMAT7g7nxhQJszsEIB2d6/4CRhm9i8ALgD4vbvflr/sPwGccfeX8r8o6939uSrZ24sALlR6jHd+WlHL0DHjAB4D8G+o4LEj+/pXlOG4VeKVfRWAfe5+wN2vAvgjgEcrsI+qx90/BHDjeJBHAazPf70eg0+WspOxt6rA3XvcfUv+6/MAvhozXtFjR/ZVFiph9lkADg/5/giqa967A/irmW02s7WV3swwNLt7DzD45AHQVOH93Eg4xruc3DBmvGqOXSHjz4ulEmYfrtlbNeV/97j7SgDfA/CT/J+rYmSMaIx3uRhmzHhVUOj482KphNmPAJg95PtWAMcqsI9hcfdj+c+9AN5A9Y2iPvHVBN38594K7+cfVNMY7+HGjKMKjl0lx59XwuyfAlhkZvPMbByAHwJ4qwL7+AZmVpd/4wRmVgfgYVTfKOq3ADyd//ppAG9WcC9fo1rGeGeNGUeFj13Fx5+7e9k/ADyCwXfk9wP4j0rsIWNf8wFsy3/sqvTeALyGwT/rrmHwL6JnADQAeA/A3vznaVW0t/8FsAPAdgwaq6VCe7sXg/8abgewNf/xSKWPHdlXWY6bTpcVIhF0Bp0QiSCzC5EIMrsQiSCzC5EIMrsQiSCzC5EIMrsQifD/OsNY2qL4TskAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(20):\n",
    "    weight = model[0].weight[i, :].data.view(28,28)\n",
    "    weight = (weight - torch.min(weight))/(torch.max(weight)-torch.min(weight))\n",
    "    print(i)\n",
    "    plt.imshow(weight.numpy(), cmap='gray')\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cuda",
   "language": "python",
   "name": "cuda"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}