cifar10_SOLVED.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cifar10 Lab 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Pytorch Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Download cifar 10 image data and transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(),\n",
    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "\n",
    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
    "                                        download=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n",
    "                                          shuffle=True, num_workers=2)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
    "                                       download=True, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n",
    "                                         shuffle=False, num_workers=2)\n",
    "\n",
    "classes = ('plane', 'car', 'bird', 'cat',\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Show some images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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": [
      "plane   car plane  bird\n"
     ]
    }
   ],
   "source": [
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build our neural network architecture\n",
    "<img src=\"./screenshots/cnn_architecture.jpeg\" alt=\"drawing\" width=\"800\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
    "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = x.view(-1, 16 * 5 * 5)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "net = Net()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define our optimizer and criterion loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,  2000] loss: 2.236\n",
      "[1,  4000] loss: 1.892\n",
      "[1,  6000] loss: 1.693\n",
      "[1,  8000] loss: 1.605\n",
      "[1, 10000] loss: 1.536\n",
      "[1, 12000] loss: 1.500\n",
      "[2,  2000] loss: 1.433\n",
      "[2,  4000] loss: 1.390\n",
      "[2,  6000] loss: 1.375\n",
      "[2,  8000] loss: 1.339\n",
      "[2, 10000] loss: 1.308\n",
      "[2, 12000] loss: 1.308\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(2):  # loop over the dataset multiple times\n",
    "\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        # get the inputs; data is a list of [inputs, labels]\n",
    "        inputs, labels = data\n",
    "\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # print statistics\n",
    "        running_loss += loss.item()\n",
    "        if i % 2000 == 1999:    # print every 2000 mini-batches\n",
    "            print('[%d, %5d] loss: %.3f' %\n",
    "                  (epoch + 1, i + 1, running_loss / 2000))\n",
    "            running_loss = 0.0\n",
    "\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Show some images and our models predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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": [
      "GroundTruth:    cat  ship  ship plane\n"
     ]
    }
   ],
   "source": [
    "dataiter = iter(testloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# print images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assess the accuracy of our model on all images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 53 %\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        outputs = net(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
    "    100 * correct / total))"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}