{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, import all needed packages, particularly: **torch** and **torchvision**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original Source: https://github.com/pytorch/examples/blob/master/mnist/main.py\n",
    "# Code from: https://github.com/jiapei100/PyTorchZeroToAll/blob/master/10_1_cnn_mnist.py\n",
    "\n",
    "from __future__ import print_function\n",
    "import argparse\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading MNIST dataset. You'll see 4 datasets are downloaded during the running process.\n",
    "* Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
    "* Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
    "* Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
    "* Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Training settings\n",
    "batch_size = 64\n",
    "\n",
    "# MNIST Dataset\n",
    "train_dataset = datasets.MNIST(root='./data/',\n",
    "                               train=True,\n",
    "                               transform=transforms.ToTensor(),\n",
    "                               download=True)\n",
    "\n",
    "test_dataset = datasets.MNIST(root='./data/',\n",
    "                              train=False,\n",
    "                              transform=transforms.ToTensor())\n",
    "\n",
    "# Data Loader (Input Pipeline)\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
    "                                           batch_size=batch_size,\n",
    "                                           shuffle=True)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n",
    "                                          batch_size=batch_size,\n",
    "                                          shuffle=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Afterwards, define CNN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class Net(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
    "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
    "        self.mp = nn.MaxPool2d(2)\n",
    "        self.fc = nn.Linear(320, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        in_size = x.size(0)\n",
    "        x = F.relu(self.mp(self.conv1(x)))\n",
    "        x = F.relu(self.mp(self.conv2(x)))\n",
    "        x = x.view(in_size, -1)  # flatten the tensor\n",
    "        x = self.fc(x)\n",
    "        return F.log_softmax(x, dim=1)\n",
    "\n",
    "\n",
    "model = Net()\n",
    "\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define training function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def train(epoch):\n",
    "    model.train()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        data, target = Variable(data), Variable(target)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = F.nll_loss(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if batch_idx % 10 == 0:\n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "                100. * batch_idx / len(train_loader), loss.item()))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define testing function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def test():\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    for data, target in test_loader:\n",
    "        data, target = Variable(data, requires_grad=True), Variable(target)\n",
    "        with torch.no_grad():\n",
    "            output = model(data)\n",
    "            # sum up batch loss\n",
    "            test_loss += F.nll_loss(output, target, size_average=False).item()\n",
    "            # get the index of the max log-probability\n",
    "            pred = output.data.max(1, keepdim=True)[1]\n",
    "            correct += pred.eq(target.data.view_as(pred)).cpu().sum()\n",
    "\n",
    "    test_loss /= len(test_loader.dataset)\n",
    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
    "        test_loss, correct, len(test_loader.dataset),\n",
    "        100. * correct / len(test_loader.dataset)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.302750\n",
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      "\n",
      "Test set: Average loss: 0.1844, Accuracy: 9473/10000 (95%)\n",
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 2 [45440/60000 (76%)]\tLoss: 0.053797\n",
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      "\n",
      "Test set: Average loss: 0.1250, Accuracy: 9608/10000 (96%)\n",
      "\n",
      "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.040007\n",
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      "\n",
      "Test set: Average loss: 0.0861, Accuracy: 9740/10000 (97%)\n",
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 4 [29440/60000 (49%)]\tLoss: 0.034178\n",
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      "\n",
      "Test set: Average loss: 0.0799, Accuracy: 9755/10000 (98%)\n",
      "\n",
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      "\n",
      "Test set: Average loss: 0.0678, Accuracy: 9802/10000 (98%)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 6 [14080/60000 (23%)]\tLoss: 0.202451\n",
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      "\n",
      "Test set: Average loss: 0.0627, Accuracy: 9794/10000 (98%)\n",
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 7 [58880/60000 (98%)]\tLoss: 0.028213\n",
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      "\n",
      "Test set: Average loss: 0.0580, Accuracy: 9822/10000 (98%)\n",
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      "Train Epoch: 8 [48640/60000 (81%)]\tLoss: 0.103640\n",
      "Train Epoch: 8 [49280/60000 (82%)]\tLoss: 0.031259\n",
      "Train Epoch: 8 [49920/60000 (83%)]\tLoss: 0.048746\n",
      "Train Epoch: 8 [50560/60000 (84%)]\tLoss: 0.063274\n",
      "Train Epoch: 8 [51200/60000 (85%)]\tLoss: 0.071457\n",
      "Train Epoch: 8 [51840/60000 (86%)]\tLoss: 0.044408\n",
      "Train Epoch: 8 [52480/60000 (87%)]\tLoss: 0.194396\n",
      "Train Epoch: 8 [53120/60000 (88%)]\tLoss: 0.176485\n",
      "Train Epoch: 8 [53760/60000 (90%)]\tLoss: 0.047335\n",
      "Train Epoch: 8 [54400/60000 (91%)]\tLoss: 0.118688\n",
      "Train Epoch: 8 [55040/60000 (92%)]\tLoss: 0.045645\n",
      "Train Epoch: 8 [55680/60000 (93%)]\tLoss: 0.067245\n",
      "Train Epoch: 8 [56320/60000 (94%)]\tLoss: 0.030975\n",
      "Train Epoch: 8 [56960/60000 (95%)]\tLoss: 0.049449\n",
      "Train Epoch: 8 [57600/60000 (96%)]\tLoss: 0.022767\n",
      "Train Epoch: 8 [58240/60000 (97%)]\tLoss: 0.014989\n",
      "Train Epoch: 8 [58880/60000 (98%)]\tLoss: 0.019494\n",
      "Train Epoch: 8 [59520/60000 (99%)]\tLoss: 0.046725\n",
      "\n",
      "Test set: Average loss: 0.0554, Accuracy: 9816/10000 (98%)\n",
      "\n",
      "Train Epoch: 9 [0/60000 (0%)]\tLoss: 0.157581\n",
      "Train Epoch: 9 [640/60000 (1%)]\tLoss: 0.014102\n",
      "Train Epoch: 9 [1280/60000 (2%)]\tLoss: 0.050544\n",
      "Train Epoch: 9 [1920/60000 (3%)]\tLoss: 0.144397\n",
      "Train Epoch: 9 [2560/60000 (4%)]\tLoss: 0.050650\n",
      "Train Epoch: 9 [3200/60000 (5%)]\tLoss: 0.015721\n",
      "Train Epoch: 9 [3840/60000 (6%)]\tLoss: 0.063486\n",
      "Train Epoch: 9 [4480/60000 (7%)]\tLoss: 0.048654\n",
      "Train Epoch: 9 [5120/60000 (9%)]\tLoss: 0.022528\n",
      "Train Epoch: 9 [5760/60000 (10%)]\tLoss: 0.031864\n",
      "Train Epoch: 9 [6400/60000 (11%)]\tLoss: 0.064015\n",
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      "Train Epoch: 9 [27520/60000 (46%)]\tLoss: 0.127449\n",
      "Train Epoch: 9 [28160/60000 (47%)]\tLoss: 0.043093\n",
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      "Train Epoch: 9 [38400/60000 (64%)]\tLoss: 0.031180\n",
      "Train Epoch: 9 [39040/60000 (65%)]\tLoss: 0.018087\n",
      "Train Epoch: 9 [39680/60000 (66%)]\tLoss: 0.121827\n",
      "Train Epoch: 9 [40320/60000 (67%)]\tLoss: 0.026022\n",
      "Train Epoch: 9 [40960/60000 (68%)]\tLoss: 0.094626\n",
      "Train Epoch: 9 [41600/60000 (69%)]\tLoss: 0.060430\n",
      "Train Epoch: 9 [42240/60000 (70%)]\tLoss: 0.027267\n",
      "Train Epoch: 9 [42880/60000 (71%)]\tLoss: 0.105337\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 9 [43520/60000 (72%)]\tLoss: 0.029487\n",
      "Train Epoch: 9 [44160/60000 (74%)]\tLoss: 0.029931\n",
      "Train Epoch: 9 [44800/60000 (75%)]\tLoss: 0.064974\n",
      "Train Epoch: 9 [45440/60000 (76%)]\tLoss: 0.090586\n",
      "Train Epoch: 9 [46080/60000 (77%)]\tLoss: 0.092096\n",
      "Train Epoch: 9 [46720/60000 (78%)]\tLoss: 0.010928\n",
      "Train Epoch: 9 [47360/60000 (79%)]\tLoss: 0.056327\n",
      "Train Epoch: 9 [48000/60000 (80%)]\tLoss: 0.031470\n",
      "Train Epoch: 9 [48640/60000 (81%)]\tLoss: 0.062591\n",
      "Train Epoch: 9 [49280/60000 (82%)]\tLoss: 0.079878\n",
      "Train Epoch: 9 [49920/60000 (83%)]\tLoss: 0.025575\n",
      "Train Epoch: 9 [50560/60000 (84%)]\tLoss: 0.016678\n",
      "Train Epoch: 9 [51200/60000 (85%)]\tLoss: 0.036606\n",
      "Train Epoch: 9 [51840/60000 (86%)]\tLoss: 0.009055\n",
      "Train Epoch: 9 [52480/60000 (87%)]\tLoss: 0.077593\n",
      "Train Epoch: 9 [53120/60000 (88%)]\tLoss: 0.049766\n",
      "Train Epoch: 9 [53760/60000 (90%)]\tLoss: 0.029348\n",
      "Train Epoch: 9 [54400/60000 (91%)]\tLoss: 0.026008\n",
      "Train Epoch: 9 [55040/60000 (92%)]\tLoss: 0.007919\n",
      "Train Epoch: 9 [55680/60000 (93%)]\tLoss: 0.065004\n",
      "Train Epoch: 9 [56320/60000 (94%)]\tLoss: 0.020004\n",
      "Train Epoch: 9 [56960/60000 (95%)]\tLoss: 0.023373\n",
      "Train Epoch: 9 [57600/60000 (96%)]\tLoss: 0.013297\n",
      "Train Epoch: 9 [58240/60000 (97%)]\tLoss: 0.091797\n",
      "Train Epoch: 9 [58880/60000 (98%)]\tLoss: 0.027091\n",
      "Train Epoch: 9 [59520/60000 (99%)]\tLoss: 0.082391\n",
      "\n",
      "Test set: Average loss: 0.0504, Accuracy: 9839/10000 (98%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for epoch in range(1, 10):\n",
    "    train(epoch)\n",
    "    test()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we copy and paste the last 5 lines of the testing result:\n",
    "\n",
    "* Train Epoch: 9 [56960/60000 (95%)]\tLoss: 0.005480\n",
    "* Train Epoch: 9 [57600/60000 (96%)]\tLoss: 0.103070\n",
    "* Train Epoch: 9 [58240/60000 (97%)]\tLoss: 0.003925\n",
    "* Train Epoch: 9 [58880/60000 (98%)]\tLoss: 0.018122\n",
    "* Train Epoch: 9 [59520/60000 (99%)]\tLoss: 0.025634\n",
    "\n",
    "*Test set: Average loss: 0.0484, Accuracy: 9849/10000 (98%)\n",
    "\n",
    "**NOTE**: The above result changes every time, but fianl Accuracy is always roughly the same."
   ]
  }
 ],
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