4-Write-your-own-dataset-class_en.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to write a custom dataset class\n",
    "\n",
    "A typical strategy to improve generalization in deep neural networks is to increase the number of training examples which allows more parameters to be used in the model. Even if the number of parameters is kept unchanged, increasing the number of training examples often improves generalization performance.\n",
    "\n",
    "Since it can be tedious and expensive to manually obtain and label additional training examples, a useful strategy is to consider methods for automatically increasing the size of a training set. Fortunately, for image datasets, there are several augmentation methods that have been found to work well in practice. They include:\n",
    "\n",
    "* Randomly cropping several slightly smaller images from the original training image.\n",
    "* Horizontally flipping the image.\n",
    "* Randomly rotating the image.\n",
    "* Applying various distortions and or noise to the images, etc.\n",
    "\n",
    "In this example, we will write a custom dataset class that performs the first two of these augmentation methods on the CIFAR10 dataset. We will then train our previous deep CNN and check that the generalization performance on the test set has in fact improved.\n",
    "\n",
    "We will create this dataset augmentation class as a subclass of `DatasetMixin`, which has the following API:\n",
    "\n",
    "- `__len__` method to return the size of data in dataset. \n",
    "- `get_example` method to return data or a tuple of data and label, which are passed by `i` argument variable. \n",
    "\n",
    "Other necessary features for a dataset can be prepared by inheriting `chainer.dataset.DasetMixin` class. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Write the dataset augmentation class for CIFAR10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from chainer import dataset\n",
    "from chainer.datasets import cifar\n",
    "\n",
    "gpu_id = 0  # Set to -1 if you don't have a GPU\n",
    "\n",
    "class CIFAR10Augmented(dataset.DatasetMixin):\n",
    "\n",
    "    def __init__(self, train=True):\n",
    "        train_data, test_data = cifar.get_cifar10()\n",
    "        if train:\n",
    "            self.data = train_data\n",
    "        else:\n",
    "            self.data = test_data\n",
    "        self.train = train\n",
    "        self.random_crop = 4\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def get_example(self, i):\n",
    "        x, t = self.data[i]\n",
    "        if self.train:\n",
    "            x = x.transpose(1, 2, 0)\n",
    "            h, w, _ = x.shape\n",
    "            x_offset = np.random.randint(self.random_crop)\n",
    "            y_offset = np.random.randint(self.random_crop)\n",
    "            x = x[y_offset:y_offset + h - self.random_crop,\n",
    "                  x_offset:x_offset + w - self.random_crop]\n",
    "            if np.random.rand() > 0.5:\n",
    "                x = np.fliplr(x)\n",
    "            x = x.transpose(2, 0, 1)\n",
    "        return x, t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This class performs the following types of data augmentation on the CIFAR10 example images:\n",
    "\n",
    "- Randomly crop a 28X28 area form the 32X32 whole image data.\n",
    "- Randomly perform a horizontal flip with 0.5 probability. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Train on the CIFAR10 dataset using our dataset augmentation class\n",
    "\n",
    "Let's now train the same deep CNN from the previous example. The only difference is that we will now use our dataset augmentation class. Since we reuse the same model with the same number of parameters, we can observe how much the augmentation improves the test set generalization performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "epoch       main/loss   main/accuracy  validation/main/loss  validation/main/accuracy  elapsed_time\n",
      "\u001b[J1           1.97079     0.27106        1.53943               0.402667                  18.719        \n",
      "\u001b[J2           1.46539     0.46725        1.32482               0.506071                  36.7491       \n",
      "\u001b[J3           1.20546     0.578145       1.09062               0.599124                  54.8801       \n",
      "\u001b[J4           1.03482     0.649488       0.940756              0.678145                  74.0606       \n",
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      "\u001b[J6           0.818297    0.733995       0.725867              0.76244                   113.789       \n",
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     ]
    }
   ],
   "source": [
    "import chainer\n",
    "import chainer.functions as F\n",
    "import chainer.links as L\n",
    "from chainer.datasets import cifar\n",
    "from chainer import iterators\n",
    "from chainer import optimizers\n",
    "from chainer import training\n",
    "from chainer.training import extensions\n",
    "\n",
    "class ConvBlock(chainer.Chain):\n",
    "    \n",
    "    def __init__(self, n_ch, pool_drop=False):\n",
    "        w = chainer.initializers.HeNormal()\n",
    "        super(ConvBlock, self).__init__()\n",
    "        with self.init_scope():\n",
    "            self.conv = L.Convolution2D(None, n_ch, 3, 1, 1,\n",
    "                                 nobias=True, initialW=w)\n",
    "            self.bn = L.BatchNormalization(n_ch)\n",
    "        \n",
    "        \n",
    "        self.pool_drop = pool_drop\n",
    "        \n",
    "    def __call__(self, x):\n",
    "        h = F.relu(self.bn(self.conv(x)))\n",
    "        if self.pool_drop:\n",
    "            h = F.max_pooling_2d(h, 2, 2)\n",
    "            h = F.dropout(h, ratio=0.25)\n",
    "        return h\n",
    "\n",
    "class LinearBlock(chainer.Chain):\n",
    "    \n",
    "    def __init__(self):\n",
    "        w = chainer.initializers.HeNormal()\n",
    "        super(LinearBlock, self).__init__()\n",
    "        with self.init_scope():\n",
    "            self.fc = L.Linear(None, 1024, initialW=w)\n",
    "        \n",
    "    def __call__(self, x):\n",
    "        return F.dropout(F.relu(self.fc(x)), ratio=0.5)\n",
    "    \n",
    "class DeepCNN(chainer.ChainList):\n",
    "\n",
    "    def __init__(self, n_output):\n",
    "        super(DeepCNN, self).__init__(\n",
    "            ConvBlock(64),\n",
    "            ConvBlock(64, True),\n",
    "            ConvBlock(128),\n",
    "            ConvBlock(128, True),\n",
    "            ConvBlock(256),\n",
    "            ConvBlock(256),\n",
    "            ConvBlock(256),\n",
    "            ConvBlock(256, True),\n",
    "            LinearBlock(),\n",
    "            LinearBlock(),\n",
    "            L.Linear(None, n_output)\n",
    "        )\n",
    "        \n",
    "    def __call__(self, x):\n",
    "        for f in self.children():\n",
    "            x = f(x)\n",
    "        return x\n",
    "\n",
    "def train(model_object, batchsize=64, gpu_id=gpu_id, max_epoch=20):\n",
    "\n",
    "    # 1. Dataset\n",
    "    train, test = CIFAR10Augmented(), CIFAR10Augmented(False)\n",
    "\n",
    "    # 2. Iterator\n",
    "    train_iter = iterators.SerialIterator(train, batchsize)\n",
    "    test_iter = iterators.SerialIterator(test, batchsize, False, False)\n",
    "\n",
    "    # 3. Model\n",
    "    model = L.Classifier(model_object)\n",
    "    if gpu_id >= 0:\n",
    "            model.to_gpu(gpu_id)\n",
    "\n",
    "    # 4. Optimizer\n",
    "    optimizer = optimizers.Adam()\n",
    "    optimizer.setup(model)\n",
    "\n",
    "    # 5. Updater\n",
    "    updater = training.StandardUpdater(train_iter, optimizer, device=gpu_id)\n",
    "\n",
    "    # 6. Trainer\n",
    "    trainer = training.Trainer(updater, (max_epoch, 'epoch'), out='{}_cifar10augmented_result'.format(model_object.__class__.__name__))\n",
    "\n",
    "    # 7. Evaluator\n",
    "\n",
    "    class TestModeEvaluator(extensions.Evaluator):\n",
    "\n",
    "        def evaluate(self):\n",
    "            model = self.get_target('main')\n",
    "            ret = super(TestModeEvaluator, self).evaluate()\n",
    "            return ret\n",
    "\n",
    "    trainer.extend(extensions.LogReport())\n",
    "    trainer.extend(TestModeEvaluator(test_iter, model, device=gpu_id))\n",
    "    trainer.extend(extensions.PrintReport(['epoch', 'main/loss', 'main/accuracy', 'validation/main/loss', 'validation/main/accuracy', 'elapsed_time']))\n",
    "    trainer.extend(extensions.PlotReport(['main/loss', 'validation/main/loss'], x_key='epoch', file_name='loss.png'))\n",
    "    trainer.extend(extensions.PlotReport(['main/accuracy', 'validation/main/accuracy'], x_key='epoch', file_name='accuracy.png'))\n",
    "    trainer.run()\n",
    "    del trainer\n",
    "    \n",
    "    return model\n",
    "    \n",
    "model = train(DeepCNN(10), max_epoch=100, gpu_id=gpu_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the case without the previous data augmentation, it was found that the precision which was capped at about 87% can be improved to 89% or more by applying augmentation to the learning data. It is an improvement of over 2%.\n",
    "\n",
    "Finally, let's take a look at the loss and precision graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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dtMs5+tx1xzamTp2qdAmqw57Ljz2XH3tO5PhycnIwd+7cek19aWquXbuGGTNm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\nAAGIoA8AAAAEIII+AAAAEID+AHDTb9e0nAYkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='DeepCNN_cifar10augmented_result/loss.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FhQkuacnJRFOmCK5ZmzYJLlRRUUTbt+e5BqWnCzInJQn/xsYKz0L9WqEgOnBAGIdI+J0o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\nH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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='DeepCNN_cifar10augmented_result/accuracy.png')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}