cifar10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "\n",
    "trainset = torchvision.datasets.CIFAR10(root=\"./data\", train=True, download=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root=\"./data\", train=False, download=True, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)\n",
    "\n",
    "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
      "  cat horse horse   cat\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "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.axis(\"off\")\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "print(\" \".join(\"%5s\" % classes[labels[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "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",
    "net = Net()        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,  2000] loss: 2.186\n",
      "[1,  4000] loss: 1.858\n",
      "[1,  6000] loss: 1.653\n",
      "[1,  8000] loss: 1.559\n",
      "[1, 10000] loss: 1.513\n",
      "[1, 12000] loss: 1.443\n",
      "[2,  2000] loss: 1.374\n",
      "[2,  4000] loss: 1.359\n",
      "[2,  6000] loss: 1.351\n",
      "[2,  8000] loss: 1.309\n",
      "[2, 10000] loss: 1.278\n",
      "[2, 12000] loss: 1.269\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(2):\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        inputs, labels = data\n",
    "        optimizer.zero_grad()\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.item()\n",
    "        if i % 2000 == 1999:\n",
    "            print(\"[%d, %5d] loss: %.3f\" % (epoch + 1, i + 1, running_loss / 2000))\n",
    "            running_loss = 0.0\n",
    "print(\"Finished Training\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = \"./cifar_net.pth\"\n",
    "torch.save(net.state_dict(), PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
    "imshow(torchvision.utils.make_grid(images))\n",
    "print(\"GroundTruth: \", \" \".join(\"%5s\" % classes[labels[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.5451, -1.7960,  0.5097,  2.0415, -0.8244,  0.4333,  0.6961, -1.0456,\n",
       "          0.0212, -1.3671],\n",
       "        [ 6.4444,  4.6309,  0.2469, -2.8568, -2.8006, -5.5423, -4.9816, -4.3379,\n",
       "          7.2409,  1.5677],\n",
       "        [ 2.9393,  2.0849,  0.5286, -0.8872, -1.4641, -2.7166, -1.9935, -2.5186,\n",
       "          3.8002,  0.1525],\n",
       "        [ 3.2590,  0.2729,  0.8116, -0.7059, -1.3174, -2.6206, -1.5734, -1.8614,\n",
       "          2.6480,  0.3214]], grad_fn=<AddmmBackward>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs = net(images)\n",
    "outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([3, 8, 8, 0])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_, predicted = torch.max(outputs, 1)\n",
    "predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted:    cat  ship  ship plane\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted: \", \" \".join(\"%5s\" % classes[predicted[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(predicted == labels)[0].item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([False, False, False, False])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(predicted == labels).squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 11.95 %\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, label = data\n",
    "        outputs = net(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "    print(f\"Accuracy of the network on the 10000 test images: {100*correct/total} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of plane : 64 %\n",
      "Accuracy of   car : 68 %\n",
      "Accuracy of  bird : 47 %\n",
      "Accuracy of   cat : 52 %\n",
      "Accuracy of  deer : 21 %\n",
      "Accuracy of   dog : 29 %\n",
      "Accuracy of  frog : 69 %\n",
      "Accuracy of horse : 68 %\n",
      "Accuracy of  ship : 70 %\n",
      "Accuracy of truck : 62 %\n"
     ]
    }
   ],
   "source": [
    "class_correct = list(0. for  i in range(10))\n",
    "class_total = list(0. for i in range(10))\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        outputs = net(images)\n",
    "        _, predicted = torch.max(outputs, 1)\n",
    "        c = (predicted == labels).squeeze()\n",
    "        for i in range(4):\n",
    "            label = labels[i]\n",
    "            class_correct[label] += c[i].item()\n",
    "            class_total[label] += 1\n",
    "            \n",
    "for i in range(10):\n",
    "    #print(f\"Accuracy of {classes[i]}   : {100 * class_correct[i] / class_total[i]}\")\n",
    "    print('Accuracy of %5s : %2d %%' % (classes[i], 100 * class_correct[i] / class_total[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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