notebooks/Convolutional_neural_networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convolutional Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this notebook, we will train CNN to classify CIFAR-10 database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test for CUDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA is available, Training on GPU...\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "# check if cuda is available\n",
    "train_on_gpu = torch.cuda.is_available()\n",
    "\n",
    "if not train_on_gpu:\n",
    "    print('CUDA is not available, Training on CPU...')\n",
    "else:\n",
    "    print('CUDA is available, Training on GPU...')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "from torchvision import datasets\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data.sampler import SubsetRandomSampler\n",
    "\n",
    "# number of subprocesses to use for data loading\n",
    "num_workers = 0\n",
    "# how many samples per batch to load\n",
    "batch_size = 20\n",
    "# percentage of training set to use as validation\n",
    "valid_size = 0.2\n",
    "\n",
    "# convert data to a normalized torch.FloatTensor\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "])\n",
    "\n",
    "# choose the training and test datasets\n",
    "train_data = datasets.CIFAR10('data', train=True, download=True, transform=transform)\n",
    "test_data = datasets.CIFAR10('data', train=True, download=True, transform=transform)\n",
    "\n",
    "# obtain training indices that will be use for validation\n",
    "num_train = len(train_data)\n",
    "indices = list(range(num_train))\n",
    "np.random.shuffle(indices)\n",
    "split = int(np.floor(valid_size * num_train))\n",
    "train_idx, valid_idx = indices[split:], indices[:split]\n",
    "\n",
    "# define samplers for obtaining training and validation batches\n",
    "train_sampler = SubsetRandomSampler(train_idx)\n",
    "valid_sampler = SubsetRandomSampler(valid_idx)\n",
    "\n",
    "# prepare data loaders (combine dataset and sampler)\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers)\n",
    "valid_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, sampler=valid_sampler, num_workers=num_workers)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, num_workers=num_workers)\n",
    "\n",
    "\n",
    "\n",
    "# specify the image classes\n",
    "classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize a Batch of Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# helper function to un-normalize and display an image\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5 # unnormalize\n",
    "    plt.imshow(np.transpose(img, (1, 2, 0))) # convert from Tensor image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f32aa8fec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# obtain one batch of training images\n",
    "dataiter = iter(train_loader)\n",
    "images, labels = dataiter.next()\n",
    "images = images.numpy()\n",
    "\n",
    "# plot the images in the batch, along with the corresponding labels\n",
    "fig = plt.figure(figsize=(25, 4))\n",
    "# display 20 images\n",
    "for idx in np.arange(20):\n",
    "    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])\n",
    "    imshow(images[idx])\n",
    "    ax.set_title(classes[labels[idx]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the Network Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)\n",
    "        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)\n",
    "        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.fc1 = nn.Linear(64 * 4 * 4, 500)\n",
    "        self.fc2 = nn.Linear(500, 10)\n",
    "        self.dropout = nn.Dropout(0.2)\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 = self.pool(F.relu(self.conv3(x)))\n",
    "        x = x.view(-1, 64 * 4 * 4)\n",
    "        x = self.dropout(x)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "    \n",
    "model = Net()\n",
    "model\n",
    "\n",
    "if train_on_gpu:\n",
    "    model.cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define loss and optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train the Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 \tTraning Loss: 1.690684 \tValidation Loss: 0.365476\n",
      "Validation loss decreased (inf --> 0.365476). Saving model...\n",
      "Epoch: 2 \tTraning Loss: 1.340417 \tValidation Loss: 0.301782\n",
      "Validation loss decreased (0.365476 --> 0.301782). Saving model...\n",
      "Epoch: 3 \tTraning Loss: 1.176079 \tValidation Loss: 0.272880\n",
      "Validation loss decreased (0.301782 --> 0.272880). Saving model...\n",
      "Epoch: 4 \tTraning Loss: 1.072479 \tValidation Loss: 0.266662\n",
      "Validation loss decreased (0.272880 --> 0.266662). Saving model...\n",
      "Epoch: 5 \tTraning Loss: 0.989130 \tValidation Loss: 0.230523\n",
      "Validation loss decreased (0.266662 --> 0.230523). Saving model...\n",
      "Epoch: 6 \tTraning Loss: 0.922807 \tValidation Loss: 0.218496\n",
      "Validation loss decreased (0.230523 --> 0.218496). Saving model...\n",
      "Epoch: 7 \tTraning Loss: 0.865578 \tValidation Loss: 0.215179\n",
      "Validation loss decreased (0.218496 --> 0.215179). Saving model...\n",
      "Epoch: 8 \tTraning Loss: 0.811760 \tValidation Loss: 0.194288\n",
      "Validation loss decreased (0.215179 --> 0.194288). Saving model...\n",
      "Epoch: 9 \tTraning Loss: 0.768174 \tValidation Loss: 0.189534\n",
      "Validation loss decreased (0.194288 --> 0.189534). Saving model...\n",
      "Epoch: 10 \tTraning Loss: 0.729652 \tValidation Loss: 0.188486\n",
      "Validation loss decreased (0.189534 --> 0.188486). Saving model...\n",
      "Epoch: 11 \tTraning Loss: 0.691221 \tValidation Loss: 0.186144\n",
      "Validation loss decreased (0.188486 --> 0.186144). Saving model...\n",
      "Epoch: 12 \tTraning Loss: 0.658079 \tValidation Loss: 0.174696\n",
      "Validation loss decreased (0.186144 --> 0.174696). Saving model...\n",
      "Epoch: 13 \tTraning Loss: 0.626362 \tValidation Loss: 0.170737\n",
      "Validation loss decreased (0.174696 --> 0.170737). Saving model...\n",
      "Epoch: 14 \tTraning Loss: 0.595495 \tValidation Loss: 0.162641\n",
      "Validation loss decreased (0.170737 --> 0.162641). Saving model...\n",
      "Epoch: 15 \tTraning Loss: 0.566794 \tValidation Loss: 0.161092\n",
      "Validation loss decreased (0.162641 --> 0.161092). Saving model...\n",
      "Epoch: 16 \tTraning Loss: 0.536215 \tValidation Loss: 0.160751\n",
      "Validation loss decreased (0.161092 --> 0.160751). Saving model...\n",
      "Epoch: 17 \tTraning Loss: 0.508971 \tValidation Loss: 0.155263\n",
      "Validation loss decreased (0.160751 --> 0.155263). Saving model...\n",
      "Epoch: 18 \tTraning Loss: 0.479288 \tValidation Loss: 0.157150\n",
      "Epoch: 19 \tTraning Loss: 0.456750 \tValidation Loss: 0.153389\n",
      "Validation loss decreased (0.155263 --> 0.153389). Saving model...\n",
      "Epoch: 20 \tTraning Loss: 0.433392 \tValidation Loss: 0.154594\n",
      "Epoch: 21 \tTraning Loss: 0.409697 \tValidation Loss: 0.152046\n",
      "Validation loss decreased (0.153389 --> 0.152046). Saving model...\n",
      "Epoch: 22 \tTraning Loss: 0.383094 \tValidation Loss: 0.159066\n",
      "Epoch: 23 \tTraning Loss: 0.364693 \tValidation Loss: 0.152422\n",
      "Epoch: 24 \tTraning Loss: 0.342039 \tValidation Loss: 0.157912\n",
      "Epoch: 25 \tTraning Loss: 0.322577 \tValidation Loss: 0.161259\n",
      "Epoch: 26 \tTraning Loss: 0.311067 \tValidation Loss: 0.160412\n",
      "Epoch: 27 \tTraning Loss: 0.290859 \tValidation Loss: 0.164783\n",
      "Epoch: 28 \tTraning Loss: 0.272252 \tValidation Loss: 0.161772\n",
      "Epoch: 29 \tTraning Loss: 0.262171 \tValidation Loss: 0.165940\n",
      "Epoch: 30 \tTraning Loss: 0.242050 \tValidation Loss: 0.168018\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "n_epochs = 30\n",
    "\n",
    "valid_loss_min = np.Inf\n",
    "\n",
    "for epoch in range(1, n_epochs+1):\n",
    "    train_loss = 0\n",
    "    valid_loss = 0\n",
    "    \n",
    "    #train the model\n",
    "    model.train()\n",
    "    for data, target in train_loader:\n",
    "        if train_on_gpu:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = criterion(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item()*data.size(0)\n",
    "\n",
    "    #validate the model\n",
    "    model.eval()\n",
    "    for data, target in valid_loader:\n",
    "        if train_on_gpu:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        \n",
    "        output = model(data)\n",
    "        loss = criterion(output, target)\n",
    "        valid_loss += loss.item()*data.size(0)\n",
    "    \n",
    "    \n",
    "    # calculate average losses\n",
    "    train_loss = train_loss/len(train_loader.dataset)\n",
    "    valid_loss = valid_loss/len(valid_loader.dataset)\n",
    "    \n",
    "    print('Epoch: {} \\tTraning Loss: {:.6f} \\tValidation Loss: {:.6f}'.format(\n",
    "        epoch, train_loss, valid_loss))\n",
    "    \n",
    "    # save model\n",
    "    if valid_loss <= valid_loss_min:\n",
    "        print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model...'.format(\n",
    "            valid_loss_min, valid_loss))\n",
    "        torch.save(model.state_dict(), 'model_cifar.pt')\n",
    "        valid_loss_min = valid_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the model with lowest validation loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_state_dict(torch.load('model_cifar.pt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test the train network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.410045\n",
      "\n",
      "Test Accuracy of airplane: 91% (4557/5000)\n",
      "Test Accuracy of automobile: 94% (4707/5000)\n",
      "Test Accuracy of  bird: 78% (3903/5000)\n",
      "Test Accuracy of   cat: 78% (3912/5000)\n",
      "Test Accuracy of  deer: 83% (4160/5000)\n",
      "Test Accuracy of   dog: 84% (4245/5000)\n",
      "Test Accuracy of  frog: 88% (4409/5000)\n",
      "Test Accuracy of horse: 90% (4541/5000)\n",
      "Test Accuracy of  ship: 94% (4736/5000)\n",
      "Test Accuracy of truck: 94% (4704/5000)\n",
      "\n",
      "Test Accuracy (Overall): 87% (43874/50000)\n"
     ]
    }
   ],
   "source": [
    "# track test loss\n",
    "test_loss = 0.0\n",
    "class_correct = list(0. for i in range(10))\n",
    "class_total = list(0. for i in range(10))\n",
    "\n",
    "model.eval()\n",
    "# iterate over test data\n",
    "for data, target in test_loader:\n",
    "    # move tensors to GPU if CUDA is available\n",
    "    if train_on_gpu:\n",
    "        data, target = data.cuda(), target.cuda()\n",
    "    # forward pass: compute predicted outputs by passing inputs to the model\n",
    "    output = model(data)\n",
    "    # calculate the batch loss\n",
    "    loss = criterion(output, target)\n",
    "    # update test loss \n",
    "    test_loss += loss.item()*data.size(0)\n",
    "    # convert output probabilities to predicted class\n",
    "    _, pred = torch.max(output, 1)    \n",
    "    # compare predictions to true label\n",
    "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
    "    correct = np.squeeze(correct_tensor.numpy()) if not train_on_gpu else np.squeeze(correct_tensor.cpu().numpy())\n",
    "    # calculate test accuracy for each object class\n",
    "    for i in range(batch_size):\n",
    "        label = target.data[i]\n",
    "        class_correct[label] += correct[i].item()\n",
    "        class_total[label] += 1\n",
    "\n",
    "# average test loss\n",
    "test_loss = test_loss/len(test_loader.dataset)\n",
    "print('Test Loss: {:.6f}\\n'.format(test_loss))\n",
    "\n",
    "for i in range(10):\n",
    "    if class_total[i] > 0:\n",
    "        print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (\n",
    "            classes[i], 100 * class_correct[i] / class_total[i],\n",
    "            np.sum(class_correct[i]), np.sum(class_total[i])))\n",
    "    else:\n",
    "        print('Test Accuracy of %5s: N/A (no training examples)' % (classes[i]))\n",
    "\n",
    "print('\\nTest Accuracy (Overall): %2d%% (%2d/%2d)' % (\n",
    "    100. * np.sum(class_correct) / np.sum(class_total),\n",
    "    np.sum(class_correct), np.sum(class_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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}