MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "A simple convolutional neural network to classify mnist dataset.\n",
    "Implemented with PyTorch, Python 3.6, Jupyter Notebook.\n",
    "\"\"\"\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as func\n",
    "import torch.autograd as atgd\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.io as sio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of training images:  60000\n",
      "The number of testing images:  10000\n",
      "Data successfully loaded!\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "loading training and testing data. \n",
    "\"\"\"\n",
    "\n",
    "batchsize = 16\n",
    "\n",
    "transform = transforms.Compose(\n",
    "    [\n",
    "    transforms.Resize((32,32)),    \n",
    "    transforms.ToTensor(),\n",
    "    ])\n",
    "\n",
    "\"\"\"\n",
    "download mnist dataset from internet \n",
    "\"\"\"\n",
    "train_dataset = torchvision.datasets.MNIST(root = './data/mnist', \n",
    "                           train = True, \n",
    "                           transform = transform,\n",
    "                           download = True) \n",
    "test_dataset = torchvision.datasets.MNIST(root = './data/mnist',\n",
    "                           train = False,\n",
    "                           transform = transform,\n",
    "                           download = True)\n",
    "\n",
    "\"\"\"\n",
    "use dataloader to go through images and labels\n",
    "\"\"\"\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batchsize,\n",
    "                                          shuffle=True, num_workers=1)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batchsize,\n",
    "                                          shuffle=True, num_workers=1)\n",
    "single_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1,\n",
    "                                          shuffle=True, num_workers=1)\n",
    "\n",
    "Trainsize = len(train_loader) * batchsize\n",
    "Testsize = len(test_loader) * batchsize\n",
    "\n",
    "print(\"The number of training images: \", Trainsize)\n",
    "print(\"The number of testing images: \", Testsize)\n",
    "print(\"Data successfully loaded!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=400, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "The definition for network structure. This is a simple network using 2 convolutional\n",
    "layers, 2 max pooling layers and one fully connection layers. We use cross entropy loss \n",
    "and SGD method for the BP. \n",
    "\"\"\"\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 6, 5)\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.fc1 = nn.Linear(16*5*5, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = func.relu(self.conv1(x))\n",
    "        self.after_conv1 = x\n",
    "        \n",
    "        x = self.pool(x)\n",
    "        self.after_pool1 = x\n",
    "        \n",
    "        x = func.relu(self.conv2(x))\n",
    "        self.after_conv2 = x\n",
    "        x = self.pool(x)\n",
    "        \n",
    "        self.after_pool2 = x\n",
    "        \n",
    "        x = x.view(-1, 16 * 5 * 5)\n",
    "        x = self.fc1(x)\n",
    "        return x\n",
    "\n",
    "net = Net()\n",
    "net = net.cuda()\n",
    "print(net)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "This function is used to compute the accuracy and loss for model(net) \n",
    "and input data (data_loader). \n",
    "\"\"\"\n",
    "\n",
    "def compute_acc_loss(net, data_loader):\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    running_loss = 0\n",
    "    for i, data in enumerate(data_loader, 0):\n",
    "        images, labels = data\n",
    "        images = images.cuda()\n",
    "        labels = labels.cuda()\n",
    "        outputs = net(images)\n",
    "        \n",
    "        train_loss = criterion(outputs, labels)\n",
    "        running_loss += train_loss.item()\n",
    "        _, predicted = torch.max(outputs, 1)\n",
    "        total += batchsize\n",
    "        correct += (predicted == labels).sum().item()\n",
    "        \n",
    "    return 100 * correct / total, running_loss / (len(data_loader) * batchsize) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch number 1: training acc 95.910%, training loss 0.009,  testing acc 96.350%, testing loss 0.008\n",
      "epoch number 2: training acc 97.227%, training loss 0.006,  testing acc 97.660%, testing loss 0.005\n",
      "epoch number 3: training acc 97.912%, training loss 0.004,  testing acc 98.060%, testing loss 0.004\n",
      "epoch number 4: training acc 98.282%, training loss 0.004,  testing acc 98.270%, testing loss 0.003\n",
      "epoch number 5: training acc 98.312%, training loss 0.003,  testing acc 98.410%, testing loss 0.003\n",
      "epoch number 6: training acc 98.450%, training loss 0.003,  testing acc 98.660%, testing loss 0.003\n",
      "epoch number 7: training acc 98.373%, training loss 0.003,  testing acc 98.470%, testing loss 0.003\n",
      "epoch number 8: training acc 98.700%, training loss 0.003,  testing acc 98.720%, testing loss 0.003\n",
      "epoch number 9: training acc 98.630%, training loss 0.003,  testing acc 98.490%, testing loss 0.003\n",
      "epoch number 10: training acc 98.677%, training loss 0.003,  testing acc 98.680%, testing loss 0.003\n",
      "epoch number 11: training acc 98.908%, training loss 0.002,  testing acc 98.800%, testing loss 0.002\n",
      "epoch number 12: training acc 98.983%, training loss 0.002,  testing acc 98.750%, testing loss 0.002\n",
      "epoch number 13: training acc 99.042%, training loss 0.002,  testing acc 98.800%, testing loss 0.002\n",
      "epoch number 14: training acc 98.980%, training loss 0.002,  testing acc 98.830%, testing loss 0.002\n",
      "epoch number 15: training acc 99.037%, training loss 0.002,  testing acc 98.700%, testing loss 0.002\n",
      "epoch number 16: training acc 99.023%, training loss 0.002,  testing acc 98.820%, testing loss 0.002\n",
      "epoch number 17: training acc 99.132%, training loss 0.002,  testing acc 98.870%, testing loss 0.002\n",
      "epoch number 18: training acc 99.133%, training loss 0.002,  testing acc 98.820%, testing loss 0.002\n",
      "epoch number 19: training acc 99.117%, training loss 0.002,  testing acc 98.780%, testing loss 0.002\n",
      "epoch number 20: training acc 99.213%, training loss 0.002,  testing acc 98.830%, testing loss 0.002\n",
      "Training process end!\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "This is the main process of training. We can define the epoch_num at the beginning\n",
    "of training process.\n",
    "\"\"\"\n",
    "\n",
    "epoch_num = 20\n",
    "accuracy1 = []\n",
    "accuracy2 = []\n",
    "loss1 = []\n",
    "loss2 = []\n",
    "\n",
    "for epoch in range(epoch_num): \n",
    "    for i, data in enumerate(train_loader, 0):     \n",
    "        inputs, labels = data         \n",
    "        inputs = inputs.cuda()\n",
    "        labels = labels.cuda()\n",
    "        optimizer.zero_grad()     \n",
    "        outputs = net(inputs)\n",
    "        train_loss = criterion(outputs, labels)\n",
    "        train_loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    \"\"\"\n",
    "    training accuracy and loss.\n",
    "    save the accuracy and loss in lists to visualize the result\n",
    "    \"\"\"\n",
    "    \n",
    "    acc, loss = compute_acc_loss(net, train_loader)\n",
    "    accuracy1.append(acc)\n",
    "    loss1.append(loss)\n",
    "    print(\"epoch number %d: training acc %.3f%%, training loss %.3f, \" \n",
    "          % (epoch + 1, acc, loss), end = \" \")\n",
    "    \n",
    "    acc, loss = compute_acc_loss(net, test_loader)\n",
    "    accuracy2.append(acc)\n",
    "    loss2.append(loss)\n",
    "    print(\"testing acc %.3f%%, testing loss %.3f\" % (acc, loss))\n",
    "\n",
    "print(\"Training process end!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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gOxH5BngLeMAYs1pEbgIeBB6vcq478F/gYiAJiBaRr4wx210Vr1KNTanNsCs1h9iDmcQlZhB7MJPdabmUtUJ3Cffnoh7h9GsbhAjE7M9g44F0VmxPBcDLw40+kYEM7BBEVPtgBrYPItjP67RiMMZw4Fgem5MyiT2YyeakTLalZJcvYxHi50W/ti0Y1DGY/KJScgtLOF5YQlZeESmZpRwvLCkvO9XIo06hfjx2WQ8mD2hD0GnGqZzjyhpGD2C9MSYPQERWAxOBbsAa+zErgO+pkjCwksxuY8xe+7mfAlcAmjCUqsGRnIITNYeDmWxJyuR4kTX6LKiZJ/3bBTGhbwT92wXRp21gtSaaawa3AyAtp5CNB6zkEXMgg7fX7uON1XsB6BTmZ9VA2gczsEMQnUL9Kn2CP5pbWF5riEvKYnNiJln5xQD4errTOzKQG4a2p2/bFvRt04I2Qb5O1QCMMRSW2MqTR25hCXkVEkx4gA+DOmhtwtVcmTDigadFJATIB8YDMfbyCcCXwNVAWwfnRgKJFZ4nAQ6H3IjILGAWQLt27c5W7ErVe8dyC/k54ShrdqWxYX86SRnW3BwPN+HciOZcNbAN/dsF0a9ti9PqnwgL8GZcr1aM69UKgILiUrYkZRFzIJ2N+zNYvj2VRTFJAAT7eTGgXRDenm5sTswsj8FNoGvLAC7t1ao8OXRt6X/GS1iICD6e7vh4uhPq733qE5RLuCxhGGN2iMi/sGoRucBmoAS4Cfi3iDwBfIXVXFWVo3/ZDiukxpj5wHywRkmdhdCVqpdKSm3EJmayemcaaxLS2JqchTHWm/aQTsHcOKwD/du1oGdE4Fkdy+/j6c7gjsEM7mjNurfZDHuP5hKzP4OYAxlsPJBBUYmNfm1bcP3Q9vRt04LebQJp5qVjahobl/5FjTELgAUAIvIMkGSM+R0Yay/rClzm4NQkKtc82gAprozVVTIzM/n4448drlbrjJdffplZs2aVL5Uxfvx4Pv74Y1q00KGBTUFyZj5rdqWxZlcaa3cfJaegBHc3oX/bFvzloq6M6hZGr4jAWp0R7OYmdA4PoHN4ANMGa62+KXH1KKlwY8wREWkHTAKGVihzAx7DGjFVVTTQRUQ6AsnANOBaV8bqKidb3twZL7/8MtOnTy9PGM4seV7f6HLmzisoLmXDvnTW7Epj9a40Eo7kAtA60IfLerdmVNcwhnUOJdBXh4iq2ufq/8WL7X0YxcCdxpgMEblXRO60v74EeAdARCKwhs+ON8aUiMhdWB3i7sDbxphtLo7VJSoub37xxRfz/PPP8/zzz7No0SIKCwuZOHEiTz75JMePH2fKlCkkJSVRWlrK448/TmpqKikpKYwZM4bQ0FBWrVpFhw4diImJITc3l0svvZQRI0bwyy+/EBkZyZdffomvry/R0dHcfPPN+Pn5MWLECL799lvi4+MrxZWbm8sVV1xBRkYGxcXF/OMf/+CKK64A4P3332fu3LmICH369OGDDz4gNTWV2267jb17rc7P1157jYiICC6//PLya8+dO5fc3FzmzJnD6NGjGTZsGOvWrWPChAl07dqVf/zjHxQVFRESEsJHH31Ey5Ytyc3N5e677yYmJgYRYfbs2WRmZhIfH89LL70EwJtvvsmOHTt48cUXa/EvV3sOZxXwbfwhVu9KY/3eYxQU2/DycOO8jsFMHdSW87uG0SXcXzt0VZ1zdZPUSAdl87DmV1QtT8HqGC97vgw4ux+nv30YDm89q5ekVW+4tOYpIlWXN1++fDkJCQls2LABYwwTJkxgzZo1pKWlERERwTfffANYay0FBgby4osvsmrVKkJDQ6tdu6Ylz2fMmMH8+fMZNmwYDz/8sMO4fHx8WLp0Kc2bN+fo0aMMGTKECRMmsH37dp5++mnWrVtHaGho+bLl99xzD6NGjWLp0qWUlpaSm5vrcE2qijIzM1m9ejUAGRkZrF+/HhHhrbfe4rnnnuOFF17g73//O4GBgWzdurX8OC8vL/r06cNzzz2Hp6cn77zzDm+88cYp/hAN0297jzHrg41k5RfTKdSPaYPaMapbGEM6huDrVX/XFFJNk7YT1LLly5ezfPly+vfvD1if9BMSEhg5ciQPPPAADz30EJdffjkjR1bLtdU4WvI8MzOTnJwchg0bBsC1117L119/Xe1cYwyPPvooa9aswc3NjeTkZFJTU/nxxx+56qqryhNU2fLiP/74I++//z5grYQbGBh4yoQxderU8u+TkpKYOnUqhw4doqioqHx595UrV/Lpp5+WH1e29PoFF1zA119/TY8ePSguLi7fbKox+SI2mf/7fAttg3357Lah9WYJa6Vq0rQSxklqArXFGMMjjzzCrbfeWu21jRs3smzZMh555BHGjh3LE088cdJrOVry3Nm1wT766CPS0tLYuHEjnp6edOjQ4aTLmTtScSlzOPly5nfffTd/+ctfmDBhAj/99BNz5swBal7O/JZbbuGZZ56he/fuzJgxw6l4GgpjDK/8uJsXVuxiSKdg3pgeRWAz7ZOoFcZAVpLV0nB4KxzeYj2WFEKrXlaLQave0KoPBHcCN63lVdS0EkYdqLq8+SWXXMLjjz/Oddddh7+/P8nJyXh6elJSUkJwcDDTp0/H39+fd999t9L5jpqkHAkKCiIgIID169czZMiQSp/eK8rKyiI8PBxPT09WrVrFgQMHALjwwguZOHEi999/PyEhIeXLi1944YW89tpr3HfffZSWlnL8+HFatmzJkSNHOHbsGP7+/nz99deMGzeuxvtFRkYC8N5775WXjx07lldeeYWXX34ZsJqkgoKCOO+880hMTGTTpk1s2bLFqZ+9ISgutfHokq18tjGJSf0jeXZyH7w8dKdklygpgqM7KyQH+1dBpv0AgZBzIHIAePjA4XjYuxps1kRDPJtB+LmVk0jLc8HLr8ZbNnaaMFys6vLmzz//PDt27GDo0KEA+Pv78+GHH7J7924efPBB3Nzc8PT05LXXXgNg1qxZXHrppbRu3ZpVq1Y5dc8FCxYwc+ZM/Pz8GD16NIGBgdWOue666/jTn/5EVFQU/fr1o3v37gD07NmTv/3tb4waNQp3d3f69+/Pu+++y7x585g1axYLFizA3d2d1157jaFDh/LEE09w3nnn0bFjx/JrODJnzhyuvvpqIiMjGTJkCPv27QPgscce484776RXr164u7sze/bs8l0Dp0yZQlxcXI07BDY0WfnF3PHRRtbtPsa9F3bhvou6NLyObGOsN9ysZMhOgewk6/vmraHvteDVrG7iys+w3vArJoa030+8+Xv4Qsue0HPiiQQQfi54+1e+jqMks20JbHzHfoBASOcKtZE+1qN/S6jtv2Vu2okaUmEOXFh1wYyzT5c3b4Ryc3Px97f+Izz77LMcOnSIefOqjTOo9y6//HLuv/9+LrzwQqeOr89/66SMPG56N5p9R4/zz0l9uGpgm7oOybGCrOrJoOr3xccrnyNuYGzgFwZD74JBN4N3LfTHlJbAjq/g1/9CcoX/9/4tK9QKekPL3lZN4kybl2pqxso8cOIY32ArkYR2se4V0tn6Cu4Enn9wTwxbKaTvPXHfssSYe/jEMSFd4K7oM0paurx5E/fNN9/wz3/+k5KSEtq3b1/evNVQZGZmMnjwYPr27et0sqjPtiRlcvN7MRQUl/LejMEM61yhefHwVvjtdcjLqNyG3qK96z6xGmO92VX8FH1sj5UMiqruDikQ0AqaR1rNMV0utr4PjLQem0dab9CJv8Ga52HlbFj3Mgy5AwbPAl8XTDAtzIFN78P61yHrIASfAxc8DhH97Z/2w8/u/USgRVvrq/v4E+UFWZC6DQ5tsWozx3bDnh8h7qOKJ0Ng2xNJpGJCCWxbPYkVHYfU7SeSQ2q8dY/iPOt1N08I6w7nXGD/t9ILWvaCZsFn92eugdYwVKNRH//WK7ancs8nsQT7efHujEF0aRkANhvs+QF++Q/sWw2eftYb8LHd1id1AO/m9k/HFZJIeA/wOM11lIoLrDeziskhNR4K7TsKiJv16TS0CwS2qZ4MAlqB+2l0yCfFwJq5sOtb62cYPMtKHn5nYQ/2rCQruW58z4q/3TAYdhd0vRTc6lE/UGEupO+x/p5Hd1uPZV+FFXZycPeyaiAhna3f8eF465iyVZB8Ak80eZV9hXYDj7O7Eq/WMKo4nZE/qmGqjx983l23jye/3k6fyEDevCGKcB+sN7tf/2u1kwe0hovmwMAbwTcIivLgyA5IrfDmHvvhiSYgNw8I7Vq9uaXszfj4scrnHt4KR3eBrcR63dPP+kTaZ8qJ88N6nN1+hzZRcO2n1qfun+fCzy/A+lch6iYYdreVgE5XSiz88gpss++J0vNKGHonRA48e3GfTd7+0Lqv9VWRMXA8zZ5IEuxJZI/1fWmh9eGg99Un/jaBbWq/X+QUGn0NY9++fQQEBBASEqJJo5EyxnDs2DFycnLK53fUpVKb4elvdvD2un2MPbcl8/7UFt/N70L0m9YbRqveMPRuqwP2VJ8WbTbI2Fd9pE9OhaXVAiKsN5bs5MplFRNLq94Q1LH2P4kf+R3WvghbP7OaUwbeAMPvtd4MT8Zmg13fWcn1wFrwCrDOPe9WaKHrV51Np1PDaPQJo7i4mKSkpGpzBFTj4uPjQ5s2bfD0rNv5DHlFJdz7aRwrtqfy4EDhdu/vcdvyKZQUQJexVqdwx/P/+CfH40dPNC+VrV5QsQnLz7lh2LXm2B5Y+xJs/gQQ6HcNjPgLBFdJ8EV51jHrX7U+gQe2hfNugwHXg0/zOgm9sdOEoZqO3CPw6XXWG+TEN+r0TeVITgG3vBuN/6Ff+VfEz7Q9ugbcvaHvNKsJJaxbncVWb2QehHX/tjqtbSVWE8zIv1rt9dFvQvQCyE+3OrCH3gXnXgnuTaLlvM5owlBNQ2Yi5v0rKMlIxM2UkO1/DjmTP6Zth9qf35CQks7H78zjqqIv6Sn7oFkIDJoJg24B/7BajaVByD4Ev74CMW9Dcb7V6VtaDN3GWx3Z7YbWu/b7xkoThmr80nbBB1dSeDyL6/L+Qpiv4V+lc8nHm4e9/kZI1/MY3jmEYeeE0rK5j0tCKLUZDhzNIfPn+URu/S8tSacgsDM+598Nfab+8fH3TcHxo/DbG9Zw0qibILRzXUfU5GjCULUrKQZ8WtTef/aUOPhwEoWlhonZD9Ct33BenNKXpJ2bCPpiOp6F6fwf9/BlvrUwY+dwf4afE8KwzqEM6Rhy2us2GWNIzS5kZ2oOOw9n8/vhHHal5pCZmsgz8irnu29li3tPIi57mNB+l9evIZ5KnYImDFV7Yj+Er+62xpRf/JQ17t6VTQkHfoGPp1Ls6c+E7AfxatmNhbOGnNiSNCcVPpmGSYnl8NAn+Mp7Auv2phO9L5384lLcBHpFBjLsnFCGdw5hUIfgStuZZuUXsys1x0oKh3PYeTiHnak5ZOUXlx8THuDNNYFbuTXzJbxshaQOfZyw0bfjdRa3RVWqtmjCULXj1//C949as07dPCBhOXS+GK74LwS0PPv327UcFv2Z0uZtuPr4QyTZgvnf3SOqNzkV5cGSmfD711YCu+SfFBk34hIzWbf7KL/sOUrswUxKbAYvdzcGtG+Bj6c7Ow/ncCjrxGi6AG8PurYKoFurALq1tD8GuRG09klrbaFWfWDyAgjrevZ/VqVqiSYM5VrGwKpnYM1z1iiWSW9anZbRb8Hyx8DL30oa3RyvXHtG4hfDklmY8HOZZXuU1Smw6Nah9Gtbw9ITNhusfMKaTd1lLFz1dqX1jY4XlrBhfzq/7D7KL3uOUWozdG8VQLdWzenWyp9urZoTEehTufM8JRYW32INER1+D4x57KzPulWqtmnCUK5js8F3D8OGN6D/n+FP8yqvh3Nkh/WmmhoPUTfD2H/88ZnEMe/A1/dDu6H8PfAJFkSn89LUvkzs78QCftELYNmD1sqk1y60lr04XbZS+OXf8OM/wC8cJr1hzaVQqhE4nYShvXPKeaXF8MXtVrIYdjdM+E/1xdPCe8DMH60x9DELYP4oOLT5zO+59iX4+j7ocjGfdHuJBdHp3Hp+J+eSBVgrp163CDL2w1sXnn4sWUnw3gRYOQe6Xw63r9NkoZosTRjsAV4LAAAgAElEQVTKOcUFsOh62PKptTLoxX+vuXPbwxsueRr+/AUUZMObF8K6eVbtxFnGWG/SK+dAr8n8NvjfPP7NXkZ3C+P/xtW874ZDnS+Cm78HcYe3L4Wd3zl3XvwSeG0YHIqDK16Fq9+ttVVBlaqPNGGoUyvMgY+ugp3LYPxcOP8B50ZCnTMG7vgVul4CK56AD66w9lQ4FVup1QS19iUYOIPE0fO47ZOttAtpxr+v6Y+72xmMwmrZE2b+YK3K+uk18Nv8mo8tyIalt8PnM6yVXG/7GfpfpxPJVJPn0oQhIveKSLyIbBOR++xl/URkvYjEiUiMiAyu4dxS+zFxIvKVK+NUJ5GXbjXJHPjF6twePPP0zm8WDFM/tJqvkmKsT+zbvqj5+JIiqw9k4zsw4n6OX/w8Mz+MpdRmeOv6KJr7/IG1ogJawYxl1nLY3z4I3z5sJaeKEjfAGyOtmtSoh+Cm76wlqJVSrksYItILmAkMBvoCl4tIF+A54EljTD/gCftzR/KNMf3sXxNcFac6iewUeOdSawOXaR9Zy2KfCRFr8bjb1lpvvp/dAF/cadVcKirKg0+vtbbEvOhJbBfM5i+fbWZXag6vXDuATmH+jq9/Orz8YOoH1h4Nv71mrUNVmGvt3vbTs/D2OGtPihnfwphHT28vCKUaOVeu6tUDWG+MyQMQkdXARKzdQcpWiAsEUhyfrupU+l54/0rIOwbTF0PHkX/8miHnwM3LrTfmn1+AA+tg8lvWHgoFWfDxNDj4K1z+MkTNYN6KXXy/LZXHLuvB+V3P4npMbu4w7p/Wct/fPQTvjrcWCUzaAH2mwfjnrMXwlFKVuGxYrYj0AL4EhgL5wA9ADPAq8D0gWDWcYcaYAw7OLwHigBLgWWOMw3YMEZkFzAJo167dwAMHql1Kna7U7fDBldaoqOmLIXLA2b/HgV9gySyrFjPyr9beB0e2w6T50Gsyy7Ye4o6PNnHVwDY8f1Uf1y0muOt7+GyGNfHw8heh91WuuY9S9VS9mYchIjcDdwK5wHasxOEOrDbGLBaRKcAsY8xFDs6NMMakiEgn4EfgQmPMnpPdT+dhnAWJ0VYHt2cz+PNSCD/NEUmnIz8TvvkrxH8OHr5WU1GXi9mWksVVr/1Kj9YBfDJrCN4eLl5yIzPRGtl1tveCVqoBqDcJo9KNRJ4BkoB/Ai2MMUasj41ZxpiTbmIgIu8CXxtjPj/ZcZow/qA9q6w2ff9wuP5LCGp/ylNiD2bw2k9WHh/VLYzR3cKJbHGaq7Tu/A6at4bWfTmaW8gVr6zDZgxf3jWc8ADXrDSrlLLUmz29RSTcGHNERNoBk7Cap+4GRgE/ARcACQ7OCwLyjDGFIhIKDKfmznF1Nuz4H3x+kzWM9M9LT7kW1PaUbF5csZOVO44Q1MyTZl4eLN+eCkDXlv6M7hbO6G5hRLUPxsvjFGMr7EuIFJXYuOPDTRzNLeTz24ZpslCqnnH1VlaLRSQEKAbuNMZkiMhMYJ6IeAAF2PsfRCQKuM0YcwtWh/kbImLD6ud41hiz3cWxNk02mzWEddkDEBllzYr2Darx8N1Hcnlp5S6+2XKIAB8P/npxV2aM6Iiflzu7j+SyaucRftqZxjvr9jF/zV78vNwZ3jmUMd2tBNI60HHtwxjD7K/i2bA/nXnT+tG7jXY6K1Xf6FpSTZUx1uqyP/wdUrdCpzHWfAlvx0NXDx7L4+UfdvFFbDI+nu7cNLwjM0d2qnFvidzCEn7ZfZRVO9NYvfMIKfZVYLu1DGB09zBGdw0nqkMQnu5W7eP9X/fzxJfbuGP0Oac/k1spdcbqZR9GbdCE4aT9a+GHpyDxN2to6ZhHodfk6utCAYey8vnPj7tZFJ2Im5tw/ZD23D76HEL8vZ2+nTGGhCO5/LTzCKt+TyPmQDrFpQZ/bw9GdA7l3IjmzPshgTHdwpj/5yjczmQmt1LqjGjCUI4lb7RqFHtXQUAEjPo/6D/d4eS0tJxCXvtpDx/+dgBjDNMGtePOMZ1pFfjH+xVyC0tYt/soP9mbrw5lFdAl3J8ldwwj4I/M5FZKnbZ60+mt6okjO6yluX//GnyDYezT1iquDvaczswr4o01e3l33X4KS0qZPKAN91zYhbbBf3CJ8gr8vT24pGcrLunZCmMMe9JyCfX31mShVD2nCaMxS99nzarestDaPGj0ozDkdvCpPoo5p6CYt9fu562f95JTWMKf+kZw30VdOOdsLMdxEiJC5/CAUx+olKpzmjAao+wUWPM8bHof3Dyt3eGG3wfNgjHGkJVXxKGsAg5l5ZOSWcDB9Dw+i0kkI6+Ysee25P6Lu9Kj9UmnxiilmiBNGI2IOX6UotUv4rnxLbCVsrvtZFaFX09CZgCHPt5lJYnMAvKLK6/Q6u4mjOgcyl8u7krfmrY8VUo1eZowGjibzfCvr6KJ2PE2kwu+wJcCltpG8HLJJBJ3tcQtIYvwgEJaBfrQvVUAY7qF0zrQh9aBvrRu4UPrQB/C/L3xcNetUZRSJ6cJo4H75pulzIj9C60kg+1Bo4nvdhe+ET15uYUPrQJ9CQ/wLp/roJRSf4QmjIbKGFKWz2NczFNkeLbE3Pg557aJ4ty6jksp1WhpwmiIivIo+fIeIrZ9xlq3KHrd/ikSchb3i1BKKQe0raKhSd8LCy7GbdvnvFByNW7XfUILTRZKqVqgNYyGZNf3sGQmRaUws+j/6DVqEsM66x4OSqnaoQmjIbDZYPW/YPWzFIX2YkLabfi26cR9F3Wt68iUUk2IJoz6Li8dlt4KCcux9bmG6w9NIZlilk3rr6OflFK1ShNGfXZoCyycbs3cvuwFXkofwfrEPcyb1u+sru2klFLO0I+o9dXmT2HBxVBaDDO+ZX3IRF75aQ+TB7Thin6RdR2dUqoJ0hpGfVNSBN8/CtFvQvsRcPU7ZLq14P55P9MhxI8nr+hZ1xEqpZooTRj1SXYKLLoBkjbA0Lvgoicxbu489OFGjuYWsuT24fh7659MKVU39N2nvti/Fj6bAUXH4ap3oNckAD7+7QDfb0vl0fHddZ9rpVSd0oRRH2x4E759CII7wg1fQXgPAHal5vDU/7Yzsksot4zoVMdBKqWaOk0YdW3/Wlj2AHQdB5Pmg49ViygoLuWeT2Lx9/bghSl9dZ9rpVSdc+koKRG5V0TiRWSbiNxnL+snIutFJE5EYkRkcA3n3iAiCfavG1wZZ50pOg5f3glBHeGqt8uTBcA/l+3g98M5zJ3Sl/CAP76PtlJK/VEuq2GISC9gJjAYKAK+E5FvgOeAJ40x34rIePvz0VXODQZmA1GAATaKyFfGmAxXxVsnVs6BjP1w4zLw8jtRvD2V9349wE3DOzKmmy79oZSqH1xZw+gBrDfG5BljSoDVwESsBFC2/2cgkOLg3EuAFcaYdHuSWAGMc2GstW/fz7BhPpx3G3QYXl58OKuABz/fzLmtm/PQpd3qMECllKrMlX0Y8cDTIhIC5APjgRjgPuB7EZmLlbCGOTg3Ekis8DzJXlaNiMwCZgG0a9furAXvUoW58OUdENwJLnyivLjUZvjLojgKim38+5r+eHu412GQSilVmctqGMaYHcC/sGoH3wGbgRLgduB+Y0xb4H5ggYPTHfXwmhruM98YE2WMiQoLayDLfK+cDZmJcMWrlZqi3lizh1/2HGPOhHPpHO5fhwEqpVR1Lu30NsYsMMYMMMacD6QDCcANwBL7IZ9h9XFUlQS0rfC8DY6brhqevash+i0Ycju0H1peHJeYyYvLd3FZ79ZMiWp7kgsopVTdcPUoqXD7YztgEvAJ1hv/KPshF2Alkaq+B8aKSJCIBAFj7WUNW2EOfHkXBJ8DFzxeXpxTUMw9n8TSsrkPz0zqjYgOoVVK1T+unoex2N6HUQzcaYzJEJGZwDwR8QAKsPc/iEgUcJsx5hZjTLqI/B2Itl/nKWNMuotjdb0VT0BWItz0HXidWG129lfbSMrIY9GtQwn09azDAJVSqmYuTRjGmJEOytYCAx2UxwC3VHj+NvC2K+OrVXtWQczb1hpR7YaUFyem57FkUzK3jupEVIfgOgxQKaVOzqkmKRFZLCKXiYguh34mCrLhq7shpDNc8Fillz6LSUQEbhjaoW5iU0opJzmbAF4DrgUSRORZEenuwpganxWPQ3YyXPkaePqWF5faDItikhjVNYyIFr4nuYBSStU9pxKGMWalMeY6YACwH1ghIr+IyAwR0Ub3k9n9A2x8F4beCW0rDwhbsyuNw9kFTBuko6KUUvWf001M9s7rG7H6GWKBeVgJZIVLImsMCrLgq3sgpAuM+Vu1lz+NPkiovxcXdG9ZB8EppdTpcarTW0SWAN2BD4A/GWMO2V9aKCIxrgquwVv+GOSkwE3LKzVFARzJKeCHHUe4eURHvDy0a0gpVf85O0rqFWPMj45eMMZEncV4Go+ElbDpfRh+L7QdVO3lJZuSKbEZpmhzlFKqgXD2o20PEWlR9sQ+oe4OF8XU8BVkwf/ugdBuMPrRai8bY1gYncjgDsGcE6ZLgCilGgZnE8ZMY0xm2RP7CrIzXRNSI/D9o5BzyD4qqvpeFr/tS2ff0eNM1dqFUqoBcTZhuEmF9SpExB3wck1IDdyu5RD7odUU1aba/EQAFkYnEuDtwfjerWs5OKWUOnPO9mF8DywSkdexVo29DWsFWlVRfqbVFBXWHUY/4vCQrLxilm09xNVRbfD10uXLlVINh7MJ4yHgVqylyQVYDrzlqqAarO8fhdwjMO0j8PB2eMiXm5MpLLExbVAD2btDKaXsnEoYxhgb1mzv11wbTgO263uI+whG/hUiHTdFGWP4ZEMiPSOa0ysy0OExSilVXzm7llQXEflcRLaLyN6yL1cH12DkZ1gT9MLPhVEP1XhYfHI2Ow5l68xupVSD5Gyn9ztYtYsSYAzwPtYkPgXw/d/geBpc+WqNTVFgzez29nBjQj+Hu80qpVS95mzC8DXG/ACIMeaAMWYO1uZHqjgfNn8Kg26GiP41HpZXVMJXcSlc1ru17nmhlGqQnO30LrAvbZ4gIncByUC468JqQA5vBVMKHUed9LBlWw+TU1jCtMHa2a2UapicrWHcBzQD7sHa/Gg61t7cKiXWejxJ7QJgYfRBOoX6MahDUC0EpZRSZ98pE4Z9kt4UY0yuMSbJGDPDGDPZGLO+FuKr/1Jiwb8lNI+o8ZDdR3KJ3p/B1EFtdb9upVSDdcqEYYwpBQaKvtM5lrzJql2c5NezKCYRDzdh0oA2tRiYUkqdXc72YcQCX4rIZ8DxskJjzBKXRNVQFObA0V3Qa1KNhxSV2Fi8MYmLerQkLKDmEVRKKVXfOZswgoFjVB4ZZYCmnTAObQEMRAyo8ZAfdqRy7HgRUwfr3AulVMPm7EzvGWdycRG5F2tVWwHeNMa8LCILgW72Q1oAmcaYfg7O3Q/kAKVASb3cdyNlk/UYUS38cp9GJ9I60Ifzu4TVUlBKKeUazu649w5WjaISY8xNJzmnF1ayGAwUAd+JyDfGmKkVjnkByDrJrccYY446E2OdSImF5m3A3/EI4+TMfNYkpHH3BV1wd9MuIKVUw+Zsk9TXFb73ASYCKac4pwew3hiTByAiq+3nPWd/LsAUGvIEwJRYiKx5OO1nMYkAXD1QO7uVUg2fs01Siys+F5FPgJWnOC0eeFpEQoB8YDxQcf/vkUCqMSahptsCy0XEAG8YY+Y7OkhEZgGzANq1q8VJcfkZkL4X+k93+HKpzfBZTBIjOofSNrhZ7cWllFIu4uzEvaq6ACd9dzbG7AD+BazA2jtjM9ZaVGWuAT45ySWGG2MGAJcCd4rI+TXcZ74xJsoYExUWVov9BClx1mMNE/bW7j5Kcma+LmOulGo0nF2tNkdEssu+gP9h7ZFxUsaYBcaYAcaY84F0IMF+PQ9gErDwJOem2B+PAEux+kLqj1PM8F4YfZBgPy8uOldXUFFKNQ7ONkkFnMnFRSTcGHNERNphJYih9pcuAn43xiTVcJ4f4GaMybF/PxZ46kxicJmUWAjqCL7Vl/o4mlvIiu2p3DC0A94euqueUqpxcLaGMVFEAis8byEiVzpx6mIR2Y5VI7nTGJNhL59GleYoEYkQkWX2py2BtSKyGdgAfGOMqV9bwqbE1li7WLopmeJSw1Td90Ip1Yg4O0pqtjFmadkTY0ymiMwGvjjZScaYkTWU3+igLAWrYxxjzF6gr5Ox1b7cNMhKhPNurfaSMYZPog8ysH0QXVqeUcVMKaXqJWc7vR0d52yyaXwO1dzhHXMgg71px7V2oZRqdJxNGDEi8qKInCMinUTkJWCjKwOr15I3AQKt+lR76dMNifh7e3BZ79a1H5dSSrmQswnjbqzZ2guBRVjzKu50VVD1XkoshHYBn+aVirMLivlmawp/6huBn3fTrYAppRonZ0dJHQcednEsDUdKLHQaXa34q7gUCoptTNPmKKVUI+TsKKkVItKiwvMgEfnedWHVY9kpkHvYYf/FwuhEurcKoE+bQAcnKqVUw+Zsk1SoMSaz7Il9eGzTnJFWw4S9+OQstiZnMU131VNKNVLOJgybffIdACLSAQer1zYJKbEg7tCqd6XiRTGJeHm4cWX/yDoKTCmlXMvZntm/YU2kW21/fj72Bf+anJRYCO8BXicWFCwoLmVpbDKX9mpFi2ZedRicUkq5jlM1DPss6yhgJ9ZIqb9ijZRqWoyx7+FdecOkb+MPkVNQonMvlFKNmrMbKN0C3Au0AeKAIcCvNOS9LM5E5kHIT6+2JeunGxJpH9KMIR1D6igwpZRyPWf7MO4FBgEHjDFjgP5Amsuiqq8cdHgfLywhen86f+oTgZvuqqeUasScTRgFxpgCABHxNsb8zol9uZuOlE3g5gkte5YXbT+Ujc1A/3YtTnKiUko1fM52eifZ52F8AawQkQxOvUVr45MSC616gYd3edGWJGtL8t6ROvdCKdW4OTvTe6L92zkisgoIxNpFr+mw2SBlM/SeXKl4a1ImrZr7EN7cp44CU0qp2nHaCx4ZY1af+qhGKH0vFGZVm7C3JTmL3jqzWynVBJzpnt5NT3mH94kRUjkFxexNO04fbY5SSjUBmjCclRILHj4Q1r28KD45G0BrGEqpJkEThrNSNln7X7ifaMXbmmwtr6Ud3kqppkAThjNspXBoM0RWnrC3JSmLyBa+hPh713CiUko1HpownHF0FxTnVevw3pqcpUuZK6WaDE0YzkjeZD1WSBhZecUcOJan/RdKqSbDpQlDRO4VkXgR2SYi99nLFopInP1rv4jE1XDuOBHZKSK7RaRud/tLiQUvfwjpXF60NdmasNcnUmd4K6WaBpdtPC0ivYCZwGCs/cC/E5FvjDFTKxzzApDl4Fx34L/AxUASEC0iXxljtrsq3pNKiYXW/cDNvbxoi3Z4K6WaGFfWMHoA640xecaYEmA1UDZjHLG2pZsCfOLg3MHAbmPMXmNMEfApcIULY61ZaTEc3lptSfOtSVm0D2lGYDPPOglLKaVqmysTRjxwvoiEiEgzYDxQccOIkUCqMSbBwbmRQGKF50n2smpEZJaIxIhITFqaCxbQPbIdSgurz/BOyqJPG22OUko1HS5LGMaYHcC/gBVY605tBkoqHHINjmsXAI7WCXe4JawxZr4xJsoYExUWFvYHIq5B2QzvCkNqj+UWkpyZrzO8lVJNiks7vY0xC4wxA4wx5wPpQAKAiHgAk7B273Mkicq1kTbU1eq4KbHgEwhBHcuLttg7vHWElFKqKXH1KKlw+2M7rARRVqO4CPjdGJNUw6nRQBcR6SgiXsA04CtXxlqj5E1Wc5ScqPRsTcpCBHpGNK+TkJRSqi64eh7GYhHZDvwPuNMYk2Evn0aV5igRiRCRZQD2TvK7gO+BHcAiY8w2F8daXXGB1YcRUX2Gd6dQPwJ8tMNbKdV0uGxYLYAxZmQN5Tc6KEvB6hgve74MWOay4JyRug1sJQ5meGcy7JzQOgpKKaXqhs70PpmU6jO8U7MLSM0u1PkXSqkmRxPGyaTEgl8YBLYpL9pq35JV15BSSjU1mjBOJiW2Wof3luQs3ATO1Q5vpVQTowmjJkXHIe336v0XSZl0CQ+gmZdLu3+UUqre0YRRk0NbwNgqjZAyxrBV9/BWSjVRmjBqUr6H94k1pA5lFXA0t0j7L5RSTZImjJqkxEJABAS0Ki/aYu/w1hFSSqmmSBNGTVI2VduSdWtyJh5uQo/W2uGtlGp6NGE4UpAFx3ZXW9J8S1IWXVsG4OPpXsOJSinVeGnCcOTQZuuxwgipsg5v7b9QSjVVmjAcKdvDu/WJhJGUkU9mXrGOkFJKNVmaMBxJiYUW7cEvpLyorMNb9/BWSjVVmjAcKZvhXcGW5Ey83N3o2sq/joJSSqm6pQmjquPHIPOAgxneWXRvHYC3h3Z4K6WaJk0YVR2qviWrzaYd3koppQmjqrIZ3q37lhcdSM8jp6BE+y+UUk2aJoyqkmMhpLO1j7fdlqRMQPfwVko1bZowqkqJdbglq7eHG13CtcNbKdV0acKoKOcw5KQ47PDuGdEcD3f9dSmlmi59B6woJc56rJAwSm2G+JQs+rTR/gulVNPm0oQhIveKSLyIbBOR+yqU3y0iO+3lz9Vw7n4R2SoicSIS48o4y6VsAnGD1n3Ki/am5ZJXVKor1CqlmjyXbRsnIr2AmcBgoAj4TkS+AdoAVwB9jDGFIhJ+ksuMMcYcdVWM1aTEQlh38PIrL9qie3grpRTgwoQB9ADWG2PyAERkNTARiAKeNcYUAhhjjrgwBucZYyWMLmMrFW9NzqKZlzudwrTDWynVtLmySSoeOF9EQkSkGTAeaAt0BUaKyG8islpEBtVwvgGWi8hGEZnlwjgtWUlwPK36kiBJmfSKCMTdTVweglJK1Wcuq2EYY3aIyL+AFUAusBkosd8zCBgCDAIWiUgnY4ypconhxpgUe5PVChH53Rizpup97MlkFkC7du3OPODyLVlPDKktKbWxLSWb6UPan/l1lVKqkXBpp7cxZoExZoAx5nwgHUgAkoAlxrIBsAGhDs5NsT8eAZZi9YU4usd8Y0yUMSYqLCzszINNiQU3D2jZs7wo4UguhSU27b9QSilcP0oq3P7YDpgEfAJ8AVxgL+8KeAFHq5znJyIBZd8DY7GauFwnZZOVLDx9you26h7eSilVzpWd3gCLRSQEKAbuNMZkiMjbwNsiEo81euoGY4wRkQjgLWPMeKAlsFREymL82BjzncuiLOvw7jmxUvGW5EwCvD3oEOJXw4lKKdV0uDRhGGNGOigrAqY7KE/B6hjHGLMX6Fv1GJfJ2Gft4+1ghnevyEDctMNbKaV0pjdwYkvWCh3eRSU2dhzK0f4LpZSy04QBVnOUuzeE9ygv2pWaQ1GpTVeoVUopO00YYK0h1ao3uHuWF+ke3kopVZkmDFspHIqr3n+RnEmgrydtg33rKDCllKpfXD1Kqv4zBq56GwJaVyrekmRtyWofqaWUUk2e1jDcPaDrJZVWqC0oLmXnYe3wVkqpijRhOPD74RxKbIbe2n+hlFLlNGE4sNW+h7fWMJRS6gRNGA5sScoi1N+L1oE+pz5YKaWaCE0YDmxJyqJ3pHZ4K6VURZowqsgrKiHhSA69dQ9vpZSqRBNGFdtTsrEZ6KMr1CqlVCWaMKoom+GtS4IopVRlmjCq2JqcRcvm3rRsrh3eSilVkSaMKrYkZer8C6WUckATRgU5BcXsPXpc518opZQDmjAq2JaSjTHaf6GUUo5owqhA9/BWSqmaacKoYEtyFpEtfAn1967rUJRSqt7RhFHB1qRMrV0opVQNNGHYZeUVs/9YnvZfKKVUDVyaMETkXhGJF5FtInJfhfK7RWSnvfy5Gs4dZz9mt4g87Mo4AeJT7FuyasJQSimHXLbjnoj0AmYCg4Ei4DsR+QZoA1wB9DHGFIpIuINz3YH/AhcDSUC0iHxljNnuqni3aIe3UkqdlCu3aO0BrDfG5AGIyGpgIhAFPGuMKQQwxhxxcO5gYLcxZq/93E+xkozLEsbW5EzaBTejRTMvV91CKaUaNFc2ScUD54tIiIg0A8YDbYGuwEgR+U1EVovIIAfnRgKJFZ4n2ctcpmwPb6WUUo65LGEYY3YA/wJWAN8Bm4ESrFpNEDAEeBBYJNU3nnC0EYVxdB8RmSUiMSISk5aWdkaxph8vIikjXxOGUkqdhEs7vY0xC4wxA4wx5wPpQAJWbWGJsWwAbEBolVOTsGojZdoAKTXcY74xJsoYExUWFnZGcW5NLuu/0DWklFKqJq4eJRVuf2wHTAI+Ab4ALrCXdwW8gKNVTo0GuohIRxHxAqYBX7kqzrI9vHtFNnfVLZRSqsFzZac3wGIRCQGKgTuNMRki8jbwtojEY42eusEYY0QkAnjLGDPeGFMiIncB3wPuwNvGmG2uCnJLUhadwvwI8PF01S2UUqrBc2nCMMaMdFBWBEx3UJ6C1TFe9nwZsMyV8ZXZmpzFeR2Da+NWSinVYLm6hlHvFZXYGN45lBGdq3ajKKWUqqjJJwwvDzfmXt23rsNQSql6T9eSUkop5RRNGEoppZyiCUMppZRTNGEopdT/t3fuwVZVdRz/fOH64KGADwqFpHwwmKMXMLJUVFBHzCEt6aWORk2To6U2ljo4PmtGVKo/atRSyxTNF5oyk/IoMJ0E4crjIqipJCiCY+VzRB6//li/MxzonHP32vcScu/vM7PnrLPP/q7f2uesvX5rrb3PbwWFCIcRBEEQFCIcRhAEQVCIcBhBEARBIcJhBEEQBIWQWc2o4Tskkt4E/llSvhf/GwQx9KEPfeg7u34/MysW6tvMYktOc37oQx/60HdFfdEtpqSCIAiCQoTDCIIgCAoRDmMzvwl96EMf+i6qL0SnuukdBEEQbDtihBEEQRAUIhxGEARBUIgu7zAk3S5pra8xnqsdJOmvkpZJWirpgkz9rpLmSVrk+qtzy+D5dIp+PTcAAAjCSURBVJf0rKRpJbQrJC2RtFDS/BL6vpIekLTcv4cvZOqHuO3K9o6kCzP0F/l31yrpHkm7Ztq/wLVLi9qtVWck7SFphqQX/bVfpn68l2GTpMNL2L/Bf4PFkh6S1DdTf61rF0qaLmmfHH3VZxdLMkl1l7CsY/8qSa9V1YOTc/S+/weSnvfv8fpM+/dW2V4haWGmvlnS05XrSNLITP1hkv7u1+KjknZvoK/Z7uTUwdL8P57d/ThvwChgONBaQjsAGO7p3YAXgIMz9AJ6e3onYC5wRIly/Ai4G5hWQrsC2Ksd398dwHc9vTPQtx15dQfeIP2RqMjx+wKvAD38/X3AORn2DgFagZ6k1SdnAgeWqTPA9cClnr4UmJSpHwoMAWYDh5ewfyLQ5OlJJezvXpX+IXBzjt73DwIeJ/15tm6dqmP/KuDigr9bLf1x/vvt4u/755a/6vPJwBWZ9qcDYz19MjA7U/8McIynJwDXNtDXbHdy6mDZrcuPMMzsCeBfJbWrzazF0+8Cy0iNWFG9mdl7/nYn37KeQpA0EPgScGuOriPwXtAo4DYAM/vIzP7TjizHAC+ZWc6/9ZuAHpKaSA3/6xnaocDTZvaBmW0A5gCntSWqU2e+THKe+OupOXozW2ZmzxcpdB39dD8HgKeBgZn6d6re9qJBPWxwzfwC+EkjbRv6QtTRnwtcZ2br/Ji1ZexLEvA14J5MvQGVUUEfGtTDOvohwBOengF8tYG+XrtTuA6Wpcs7jI5C0mBgGGmUkKPr7sPftcAMM8vSA78kXaSbMnUVDJguaYGk72VqPwO8CfzOp8RuldSrZDkAvkGDC3VrzOw14EbgVWA18LaZTc+w1wqMkrSnpJ6knuGgDH01nzCz1V6u1UD/kvl0BBOAP+eKJP1M0krgDOCKTO044DUzW5Rrt4rzfVrs9hLTKQcBR0uaK2mOpM+VLMPRwBozezFTdyFwg39/NwKXZepbgXGeHk/BerhVu7PN62A4jA5AUm/gQeDCrXpqbWJmG82smdQjHCnpkAy7pwBrzWxBVoG35EgzGw6MBc6TNCpD20QaWt9kZsOA90lD4Wwk7Uy6YO7P0PQj9ao+DewD9JJ0ZlG9mS0jTd/MAB4DFgEbGoo+5kiaSDqHKblaM5toZoNce36GzZ7ARDKdzFbcBOwPNJOc/+RMfRPQDzgC+DFwn48WcvkmGZ2WKs4FLvLv7yJ81J3BBNL1t4A0zfRRW4L2tDtlCYfRTiTtRPrRppjZ1LL5+FTObOCkDNmRwDhJK4A/AqMl3ZVp93V/XQs8BNS9WVeDVcCqqlHRAyQHUoaxQIuZrcnQHA+8YmZvmtl6YCrwxRyjZnabmQ03s1GkaYLcnmWFNZIGAPhr3SmRbYWks4FTgDPMJ7JLcjcNpkRqsD/JaS/yujgQaJH0yaIZmNka7zxtAn5LXj2EVBen+jTvPNKIu+6N91r4tOZXgHszbQOcTap/kDo9WeU3s+VmdqKZjSA5rJfaKGutdmeb18FwGO3AezC3AcvM7Ocl9HtXnmaR1IPUAC4vqjezy8xsoJkNJk3n/MXMCvewJfWStFslTbpxWvhpMTN7A1gpaYjvGgM8V1S/FWV6dq8CR0jq6b/FGNJ8bmEk9ffXT5EaizK9S4BHSI0G/vqnkvmUQtJJwCXAODP7oIT+wKq348irh0vMrL+ZDfa6uIp0U/aNDPsDqt6eRkY9dB4GRnteB5EewMiN3no8sNzMVmXqIN2zOMbTo8nseFTVw27A5cDNDY6t1+5s+zrY0XfRd7SN1ECsBtaTKvp3MrRHke4BLAYW+nZyhv5Q4FnXt9LgyYwCeR1L5lNSpHsQi3xbCkwsYbcZmO/n8DDQr0QePYG3gD4ltFeTGrdW4E78KZkM/d9ITm4RMKZsnQH2BGaRGopZwB6Z+tM8vQ5YAzyeqf8HsLKqHjZ6yqmW/kH/DhcDjwL7lr1maOPJuzr27wSWuP1HgAGZ+p2Bu/wcWoDRueUHfg98v+TvfxSwwOvRXGBEpv4C0tNOLwDX4VE46uhrtjs5dbDsFqFBgiAIgkLElFQQBEFQiHAYQRAEQSHCYQRBEASFCIcRBEEQFCIcRhAEQVCIcBhBUAJJx6pEdOAOtH+OpF9tL/tB1yQcRhB0QSR1395lCHY8wmEEnRZJZyqtN7JQ0i2VRlLSe5ImS2qRNEvS3r6/sqZBZU2Jfr7/AEkzldYtaZG0v5vorc1rgUypFbtI0mxJk7wcL0g62vdvMUKQNE3SsVXlm+QBIWdKGun5vOxB/ioMkvSY0hoQVxY872skzQWy1i0JAgiHEXRSJA0Fvk4KrtgMbCRFYYUUvrvFUtDFOUClsf0DcImZHUr613Fl/xTg12Z2GClW1WrfP4wUpfRg0r/mj6xTnCYzG+nHXlnnmGp6kdZTGAG8C/wUOIH0b/Brqo4b6efUDIyXdHiB8241s8+b2ZMFyhEEW9C0vQsQBNuIMcAI4Bnv+PdgczC2TWwOMHcXMFVSH9LiT3N8/x3A/R5ra18zewjAzD4E8DznmccdUgpRPxio1RBXgsMt8GPa4iNS9FxIjmudma2XtGQr/Qwze8vtTyWFjNjQ4Lw3kkKABEEpwmEEnRUBd5hZkXUJGsXHaRQie11VeiP1r6d1NY7ZwJYj/OqlZdfb5pg9myp6M9vkEVXrldtofN4fmtnGOmUMgjaJKamgszILOL0qCugekvbzz7oBp3v6W8CTZvY28O/KPQbgLGCOpXUGVkk61fPZxdd/aC8rgGZJ3SQNIj+cN8AJfl49SKurPUXj8w6CdhEjjKBTYmbPSbqctJpgN1Jk0PNI602/D3zWF6t5mzTnDykk9M3uEF4Gvu37zwJukXSN5zO+A4r4FGk98iVsjrCay5OkKK8HAHeb2XyABucdBO0iotUGXQ5J75lZ7+1djiDY0YgpqSAIgqAQMcIIgiAIChEjjCAIgqAQ4TCCIAiCQoTDCIIgCAoRDiMIgiAoRDiMIAiCoBD/BdHsPQ8Z8jEOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Use plotmatlib to visualize the accuracy and loss by line chart.\n",
    "\"\"\"\n",
    "\n",
    "x = range(1,epoch_num+1)\n",
    "plt.plot(x, accuracy1, label=\"training accuracy\")\n",
    "plt.plot(x, accuracy2, label=\"testing accuracy\")\n",
    "plt.xlabel(\"epoch number\")\n",
    "plt.xticks(np.linspace(1,epoch_num,epoch_num))\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.title(\"accuracy graph\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(x, loss1, label=\"training loss\")\n",
    "plt.plot(x, loss2, label=\"testing loss\")\n",
    "plt.xlabel(\"epoch number\")\n",
    "plt.xticks(np.linspace(1,epoch_num,epoch_num))\n",
    "plt.ylabel(\"loss\")\n",
    "plt.title(\"loss graph\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number 0 test_acc: 99.388\n",
      "number 1 test_acc: 99.559\n",
      "number 2 test_acc: 98.353\n",
      "number 3 test_acc: 98.911\n",
      "number 4 test_acc: 98.880\n",
      "number 5 test_acc: 98.430\n",
      "number 6 test_acc: 99.165\n",
      "number 7 test_acc: 97.957\n",
      "number 8 test_acc: 98.665\n",
      "number 9 test_acc: 98.910\n"
     ]
    },
    {
     "data": {
      "image/png": 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Hnm+V5Fvj15YkqW+jHsW0zfBlL6pqJd6TWpImtVED4t7miCMAkuxIy9VdJUmTx6iT1B8Czm8uiwEwh+ZsZknS5DTqJPU5ScYYhMIlwJcZXCZDkjRJjXqxvrcBRwLbMwiIvYAfcP9bkEqSJpFR5yCOBJ4DXFVVLwSeBawYt64kSb0bNSDurKo7AZI8pqp+Cjxt/NqSJPVt1EnqZc15EF9icCmMlXjLUUma1EadpD6oefiRJN8FtmBwqW5J0iS1zldkrarz1r6WJOmRbn3vSS1JmuQMCElSKwNCktTKgJAktTIgJEmtDAhJUisDQpLUyoCQJLXqJSCSHJlkSZLLkhzVLHt8c2vTy5vPW/XRmyRpoPOASLIrcBiwB/BM4IAks4APAN+pqlnAd5rnkqSe9LEHsQuwqKpur6pVwHnAQcCBwMnNOicDr+qhN0lSo4+AWALMSbJ1kqnA/sAOwBOq6nqA5vOMto2TzE2yOMniFSu8JYUkjZfOA6KqlgLHAwsZXBH2UmDVOmw/r6rGqmps+vTp49SlJKmXSeqqml9Vs6tqDnAjcDnw6yTbAjSfl/fRmyRpoK+jmGY0n2cCBwOnAV8BDm1WORT4ch+9SZIG1vl+EBvIgiRbA3cDh1fVyiTHAWckeStwNfCnPfUmSaKngKiqfVqW/RbYr4d2JEktPJNaktTKgJAktTIgJEmtDAhJUisDQpLUyoCQJLUyICRJrQwISVIrA0KS1MqAkCS1MiAkSa0MCElSKwNCktTKgJAktTIgJEmtDAhJUisDQpLUyoCQJLUyICRJrQwISVKrXgIiydFJLkuyJMlpSTZNsl+Si5JckuT8JE/pozdJ0kDnAZFkO+A9wFhV7QpsArwW+DTwhqraHfg88OGue5Mk3aevIaYpwGZJpgBTgeuAAjZvXt+iWSZJ6smUrgtW1bVJTgCuBu4Azq2qc5O8DfhGkjuAW4C92rZPMheYCzBz5syOupakjU8fQ0xbAQcCOwFPAqYlOQQ4Gti/qrYHPgt8om37qppXVWNVNTZ9+vSu2pakjU4fQ0wvAq6oqhVVdTdwFvA84JlVdUGzzunA3j30Jklq9BEQVwN7JZmaJMB+wE+ALZI8tVnnxcDSHnqTJDX6mIO4IMmZwEXAKuBiYB6wDFiQ5F5gJfCWrnuTJN2n84AAqKpjgGPWWHx28yFJmgA8k1qS1MqAkCS1MiAkSa0MCElSKwNCktTKgJAktTIgJEmtDAhJUisDQpLUyoCQJLUyICRJrQwISVIrA0KS1MqAkCS1MiAkSa0MCElSKwNCktTKgJAktTIgJEmtDAhJUqteAiLJ0UkuS7IkyWlJNs3AsUl+nmRpkvf00ZskaWBK1wWTbAe8B3h6Vd2R5AzgtUCAHYCdq+reJDO67k2SdJ/OA2Ko7mZJ7gamAtcBfw+8vqruBaiq5T31JkmihyGmqroWOAG4GrgeuLmqzgWeDPxZksVJvplkVtv2SeY26yxesWJFd41L0kam84BIshVwILAT8CRgWpJDgMcAd1bVGHAicFLb9lU1r6rGqmps+vTpXbUtSRudPiapXwRcUVUrqupu4Cxgb2AZsKBZ52xgtx56kyQ1+giIq4G9kkxNEmA/YCnwJWDfZp3nAz/voTdJUqPzSeqquiDJmcBFwCrgYmAesBlwapKjgduAt3XdmyTpPr0cxVRVxwDHrLH498DLe2hHktTCM6klSa0MCElSKwNCktTKgJAktTIgJEmtDAhJUisDQpLUyoCQJLUyICRJrVJVffew3pKsAK7qsOQ2wG86rGdta1vb2uPhD6tqrZfDfkQHRNeSLG4uR25ta1vb2pOi9kNxiEmS1MqAkCS1MiDWzTxrW9va1p5ktR+UcxCSpFbuQUiSWhkQkqRWBsQIkrw0yc+S/CLJBzqufVKS5UmWdFx3hyTfTbI0yWVJjuyw9qZJ/jPJpU3tj3ZVe6iHTZJcnORrPdS+MsmPk1ySZHHHtbdMcmaSnzY/++d2VPdpzde7+uOWJEd1Ubupf3Tzb21JktOSbNph7SObupd1+TWPwjmItUiyCfBz4MXAMuCHwOuq6icd1Z/D4B7dp1TVrl3UbOpuC2xbVRcleRxwIfCqLr7uJAGmVdVtSf4AOB84sqoWjXftoR7eC4wBm1fVAV3VbWpfCYxVVecnbSU5Gfi/VfWZJI8GplbVTR33sAlwLbBnVY37ibBJtmPwb+zpVXVHkjOAb1TVv3VQe1fgC8AewF3AOcA7q+ry8a49Cvcg1m4P4BdV9auquovBD/PAropX1X8AN3ZVb6ju9VV1UfP4VmApsF1Htauqbmue/kHz0dlfMkm2Z3B/9M90VXMiSLI5MAeYD1BVd3UdDo39gF92EQ5DpgCbJZkCTAWu66juLsCiqrq9qlYB5wEHdVR7rQyItdsOuGbo+TI6+kU5USTZEXgWcEGHNTdJcgmwHFhYVZ3VBj4J/AVwb4c1hxVwbpILk8ztsO4fASuAzzbDa59JMq3D+qu9Fjitq2JVdS1wAnA1cD1wc1Wd21H5JcCcJFsnmQrsD+zQUe21MiDWLi3LNppxuSSPBRYAR1XVLV3Vrap7qmp3YHtgj2ZXfNwlOQBYXlUXdlHvQTyvqmYDLwMOb4YZuzAFmA18uqqeBfwO6HrO7dHAK4EvdlhzKwajAjsBTwKmJTmki9pVtRQ4HljIYHjpUmBVF7VHYUCs3TLun+jb093uZ6+a8f8FwKlVdVYfPTRDHN8DXtpRyecBr2zmAb4A7Jvkcx3VBqCqrms+LwfOZjDM2YVlwLKhvbUzGQRGl14GXFRVv+6w5ouAK6pqRVXdDZwF7N1V8aqaX1Wzq2oOg+HkCTH/AAbEKH4IzEqyU/PXzWuBr/Tc07hrJornA0ur6hMd156eZMvm8WYM/gP/tIvaVfVXVbV9Ve3I4Gf971XVyV+TAEmmNQcF0AzvvITBMMS4q6obgGuSPK1ZtB/QycEYQ15Hh8NLjauBvZJMbf7d78dgzq0TSWY0n2cCB9P91/+gpvTdwERXVauSvBv4FrAJcFJVXdZV/SSnAS8AtkmyDDimquZ3UPp5wJ8DP27mAgA+WFXf6KD2tsDJzdEsjwLOqKrODzftyROAswe/p5gCfL6qzumw/hHAqc0fQ78C3txV4WYM/sXA27uqCVBVFyQ5E7iIwfDOxXR76YsFSbYG7gYOr6qVHdZ+SB7mKklq5RCTJKmVASFJamVASJJaGRCSpFYGhCSplQEhdSDJ95JMuJvSSw/FgJAmuOYCclLnDAhpSJIdm/sgnNhcn//cJJsN7wEk2aa5FAdJ3pTkS0m+muSKJO9O8t7mYneLkjx+6O0PSfL95tr/ezTbT8vgnh8/bLY5cOh9v5jkq0BXF46T7seAkB5oFvBPVfUM4CbgT9ay/q7A6xlcM+lY4PbmYnc/AN44tN60qtobeBdwUrPsQwwu5/Ec4IXAx4euoPpc4NCq2ncDfE3SOnPXVXqgK6pq9eVFLgR2XMv6323umXFrkpuBrzbLfwzsNrTeaTC4x0eSzZvrTb2EwcUB39essykws3m8sKo6vxeItJoBIT3Q74ce3wNsxuAaPav3uNe8HeXw+vcOPb+X+/8fW/O6NsXgcvJ/UlU/G34hyZ4MLrct9cYhJmk0VwLPbh6/ej3f488Akvwxg5vS3MzgIpBHNFcRJcmzHmaf0gZjQEijOQF4Z5LvA9us53usbLb/F+CtzbK/Y3BL1R8lWdI8lyYEr+YqSWrlHoQkqZUBIUlqZUBIkloZEJKkVgaEJKmVASFJamVASJJa/X9fPo/JJfO1mwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Here we calculate the accuracy for different numbers. \n",
    "\"\"\"\n",
    "\n",
    "correct = [0.0]*10\n",
    "total = [0.0]*10\n",
    "for data in single_loader:\n",
    "    images, labels = data \n",
    "    images = images.cuda()\n",
    "    labels = labels.cuda()\n",
    "    outputs = net(images)\n",
    "    _, predicted = torch.max(outputs, 1)\n",
    "    total[labels] += 1\n",
    "    correct[labels] += (predicted == labels).sum().item()  \n",
    "    \n",
    "num_acc = []\n",
    "for i in range(10):\n",
    "    num_acc.append(100*correct[i]  / total[i])\n",
    "    print(\"number %d test_acc: %.3f\" % (i, 100*correct[i]  / total[i]))\n",
    "    \n",
    "\"\"\"\n",
    "draw a histogram to show the classification accuracy for different numbers. \n",
    "\"\"\"\n",
    "\n",
    "num = range(0,10)\n",
    "plt.bar(num, num_acc)\n",
    "\n",
    "plt.xlabel(\"number\")\n",
    "plt.ylim((85,100))\n",
    "plt.xticks(np.linspace(0,9,10))\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.title(\"number-acc graph\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ground Truth:  7\n",
      "Model Result:  8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Here we can randomly show the incorrect result.\n",
    "\"\"\"\n",
    "\n",
    "for data in single_loader:\n",
    "    images, labels = data\n",
    "    images = images.cuda()\n",
    "    labels = labels.cuda()\n",
    "    outputs = net(images)\n",
    "    _, predicted = torch.max(outputs, 1)\n",
    "    \n",
    "    if predicted != labels:\n",
    "        plt.imshow(images.squeeze(), cmap = \"gray\")\n",
    "        print(\"Ground Truth: \", labels.item())\n",
    "        print(\"Model Result: \", predicted.item())\n",
    "        break    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Here we output the confusion matrix. This matrix will show how many numbers are \n",
    "classified correctly and how incorrect results are distributed. The row above is\n",
    "ground truth and the columm on the left is the predicted result. \n",
    "\"\"\"\n",
    "\n",
    "cells = [([0]*10)for i in range(10)]\n",
    "for data in single_loader:\n",
    "    images, labels = data\n",
    "    images = images.cuda()\n",
    "    labels = labels.cuda()\n",
    "    outputs = net(images)\n",
    "    _, predicted = torch.max(outputs, 1)\n",
    "    cells[predicted.item()][labels.item()] += 1\n",
    "\n",
    "\"\"\"\n",
    "draw the table\n",
    "\"\"\"\n",
    "labels_num = [i for i in range(10)]\n",
    "predicted_num = [i for i in range(10)]\n",
    "plt.subplot2grid((4,3), (0,0), colspan=5, rowspan=3)\n",
    "plt.axis(\"off\")\n",
    "plt.table(rowLabels = predicted_num, colLabels = labels_num ,cellText = cells,loc=\"upper center\",\n",
    "         colWidths= [0.1]*10)\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Output the hidden layers.\n",
    "Here we output only the feature maps after the first convolutional layers. \n",
    "\"\"\"\n",
    "\n",
    "for data in single_loader:\n",
    "    images, labels = data\n",
    "    images = images.cuda()\n",
    "    labels = labels.cuda()\n",
    "    \n",
    "    plt.figure(0)\n",
    "    plt.imshow((images.squeeze()), cmap = \"gray\")\n",
    "    outputs = net(images)\n",
    "    \n",
    "    for i in range(0,6):\n",
    "        plt.figure(i+1)\n",
    "        plt.title(\"after conv1 %d feature map\" % (i+1))\n",
    "        plt.imshow(net.after_conv1.squeeze().detach()[i], cmap = \"gray\")\n",
    "        plt.colorbar()\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "save the module in .mat file\n",
    "\"\"\"\n",
    "\n",
    "weight = dict()\n",
    "weight['conv1_w'] = net._modules['conv1']._parameters['weight'].data.cpu().numpy()\n",
    "weight['conv2_w'] = net._modules['conv2']._parameters['weight'].data.cpu().numpy()\n",
    "weight['fc_w'] = net._modules['fc1']._parameters['weight'].data.cpu().numpy()\n",
    "\n",
    "weight['conv1_b'] = net._modules['conv1']._parameters['bias'].data.cpu().numpy()\n",
    "weight['conv2_b'] = net._modules['conv2']._parameters['bias'].data.cpu().numpy()\n",
    "weight['fc_b'] = net._modules['fc1']._parameters['bias'].data.cpu().numpy()\n",
    "\n",
    "sio.savemat('mnist_module.mat', mdict=weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}