Notebooks/Cats_Dogs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Кошки против собак\n",
    "\n",
    "Распознавание фотографий котиков и их отличие от собак является давней проблемой, которая волнует человечество. В 2007 году считалось, что точность распознавания не может привысить 60%. Давайте посмотрим, какой точности нам удастся достичь!\n",
    "\n",
    "В качестве исходных данных мы берем набор [Dogs vs. Cats](https://www.kaggle.com/c/dogs-vs-cats/data) с сайта [Kaggle](http://kaggle.com). Предполагаем следующую конфигурацию входных данных:\n",
    "\n",
    "   * Все картинки распакованы в директорию `C:\\Cats_Dogs`\n",
    "   * Файлы с отображением картинок на категории находятся в той же директории с именами `Test.txt` и `Train.txt`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "source_dir = r'c:\\Cats_Dogs'\n",
    "train_file = os.path.join(source_dir,'CTrain.txt')\n",
    "test_file = os.path.join(source_dir,'CTest.txt')\n",
    "\n",
    "test_epoch_size = 5000\n",
    "train_epoch_size= 20000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для начала импортируем все необходимые модули для работы Cognitive Toolkit и задаём вычисления с использованием графического процессора"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cntk\n",
    "from cntk.io import *\n",
    "from cntk.io.transforms import *\n",
    "from cntk.layers import *\n",
    "from cntk.ops import *\n",
    "import numpy as np\n",
    "\n",
    "cntk.set_default_device(cntk.device.gpu(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Задаём разрешение модели. Будем масштабировать картинки до $64\\times64$ пикселей, при этом распознавать два класса - кошки (0) и собаки (1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Задаём разрешение модели\n",
    "image_height = 64\n",
    "image_width = 64\n",
    "num_channels = 3\n",
    "num_classes = 2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для чтения входных данных с диска используется `reader` (который также называется `MinibatchSource`). При этом мы можем задавать преобразования, которые на лету будут происходить с входными данными - это полезно для реализации **Data Augmentation**. В нашем случае мы используем случайный crop.\n",
    "После этого создаём объекты для чтения данных из входных файлов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_reader(map_file,train):\n",
    "    trans = []\n",
    "    if train: trans += [crop(crop_type='randomside',side_ratio=0.8)]\n",
    "    trans += [scale(width=image_width,height=image_height,channels=num_channels)]\n",
    "    return MinibatchSource(ImageDeserializer(map_file, StreamDefs(\n",
    "        features = StreamDef(field='image',transforms=trans), # first column in map file is referred to as 'image'\n",
    "        labels   = StreamDef(field='label', shape=num_classes)      # and second as 'label'\n",
    "    )))\n",
    "\n",
    "reader_train = create_reader(train_file,True)\n",
    "reader_test = create_reader(test_file,False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Архитектура сети\n",
    "Описываем архитектуру нашей нейронной сети. В нашем случае мы будем использовать свёрточную сеть с тремя свёрточными слоями и двумя промежуточными полносвязными слоями. Функция активации по умолчанию - `relu`. `model` - это функция, которая может быть применена ко входным переменным для получения результата."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    LayerStack(3, lambda i: [\n",
    "        Convolution((5,5), [32,32,64][i], init=glorot_uniform(), pad=True,activation=relu),\n",
    "        BatchNormalization(map_rank=1),\n",
    "        MaxPooling((3,3), strides=(2,2))\n",
    "    ]),\n",
    "    #Dropout(0.1),\n",
    "    Dense(64, init=glorot_uniform(),activation=relu),\n",
    "    Dense(64, init=glorot_uniform(),activation=relu),\n",
    "    #Dropout(0.25),\n",
    "    Dense(num_classes, init=glorot_uniform(), activation=None)\n",
    "])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\def\\model{\\rm model}\n",
    "\\def\\input{\\rm input}\n",
    "$\n",
    "Далее определяем саму нейросеть: входные переменные (это переменные, соответствующие входным фичам - пикселям изображения, и выходным категориям). После этого граф вычислений сети определяется по формуле $$z = \\model(\\input)$$.\n",
    "На самом деле мы здесь же производим нормирование входных величин, приводя их к диапазону $[-1;1]$, т.е. формула имеет вид $$z = \\model({1\\over256}\\times(\\input-128)$$.\n",
    "Переменные (тензоры) `ce` и `pe` определяют ошибку cross-entropy loss и ошибку классификации, соответственно."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "input_var = input_variable((num_channels, image_height, image_width))\n",
    "label_var = input_variable((num_classes))\n",
    "\n",
    "input_var_norm = element_times(1.0/256.0, minus(input_var,128.0))\n",
    "    \n",
    "z = model(input_var_norm)\n",
    "\n",
    "ce = cntk.cross_entropy_with_softmax(z, label_var)\n",
    "pe = cntk.classification_error(z, label_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Определяем алгоритм обучения\n",
    "Далее задаём алгоритм обучения `learner` и обучающий объект `trainer`. При этом мы описываем, как будет изменяться скорость обучения *learning rate* по мере обучения в зависимости от количества эпох."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training 284642 parameters in 18 parameter tensors.\n"
     ]
    }
   ],
   "source": [
    "minibatch_size = 64\n",
    "\n",
    "lr_per_minibatch = cntk.learning_rate_schedule([0.01] * 10 + [0.003] * 10 + [0.001], cntk.UnitType.minibatch, train_epoch_size)\n",
    "    \n",
    "learner = cntk.adagrad(z.parameters, lr = lr_per_minibatch)\n",
    "trainer = cntk.Trainer(z, (ce, pe), [learner])\n",
    "\n",
    "input_map = {\n",
    "    input_var: reader_train.streams.features,\n",
    "    label_var: reader_train.streams.labels\n",
    "}\n",
    "\n",
    "cntk.logging.log_number_of_parameters(z)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В процессе обучения нам будет полезно контролировать качество обучения на тестовой выборке. Для этого мы определим функцию, которая проходится по всей тестовой выборке и вычисляет совокупную ошибку (в процентах)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_eval():\n",
    "    test_epoch_size = 5000\n",
    "    minibatch_size = 16\n",
    "\n",
    "    metric_numer = 0\n",
    "    metric_denom = 0\n",
    "    sample_count = 0\n",
    "    minibatch_index = 0\n",
    "\n",
    "    while sample_count < test_epoch_size:\n",
    "        current_minibatch = min(minibatch_size, test_epoch_size - sample_count)\n",
    "        data = reader_test.next_minibatch(current_minibatch, input_map=input_map)\n",
    "        metric_numer += trainer.test_minibatch(data) * current_minibatch\n",
    "        metric_denom += current_minibatch\n",
    "        sample_count += data[label_var].num_samples\n",
    "        minibatch_index += 1\n",
    "\n",
    "    return (metric_numer * 100.0) / metric_denom\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Приступаем к обучению!\n",
    "Теперь всё готово для обучения! Определяем количество эпох и запускаем процесс обучения!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "max_epochs = 30\n",
    "progress_printer = cntk.logging.ProgressPrinter(tag='Training',num_epochs=max_epochs)\n",
    "for epoch in range(max_epochs):\n",
    "    sample_count = 0\n",
    "    n=0\n",
    "    while sample_count < train_epoch_size:\n",
    "        data = reader_train.next_minibatch(min(minibatch_size, train_epoch_size - sample_count), input_map=input_map) # fetch minibatch.\n",
    "        t = trainer.train_minibatch(data)\n",
    "        sample_count += data[label_var].num_samples                        \n",
    "        progress_printer.update_with_trainer(trainer, with_metric=True) # log progress\n",
    "        n+=1\n",
    "\n",
    "    progress_printer.epoch_summary(with_metric=True)\n",
    "    print(\"Evaluation result: {:0.1f}\".format(test_eval()))\n",
    "    trained_model = cntk.softmax(z)\n",
    "    #trained_model.save_model('c:\\\\Learn\\\\Models\\\\CatDog_' + str(epoch))\n",
    "\n",
    "trained_model.save_model(os.path.join(source_dir,'Model'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "После обучения полученная нейросеть хранится в тензоре `trained_model`. Для получения на выходе распределения вероятностей мы применяем к выходу сети $z$ функцию `softmax`. Вычислить значение нейросети на некоторых входных данных мы можем с помощью функции `eval`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "\n",
    "def evaluate(image_path):\n",
    "    image_data   = np.array(Image.open(image_path).resize((image_width,image_height)), dtype=np.float32)\n",
    "    image_data   = np.ascontiguousarray(np.transpose(image_data, (2, 0, 1)))\n",
    "    return np.squeeze(trained_model.eval({trained_model.arguments[0]:[image_data]}))\n",
    "\n",
    "import IPython\n",
    "\n",
    "def show(image_path):\n",
    "    IPython.display.display(IPython.display.Image(open(image_path,'rb').read(),format='jpg'))\n",
    "    print(evaluate(image_path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на нескольких примерах из нашей выборки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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eqKwoNOAI7U3ryOtdBzsSlyOtJkelHFAhyvgn0qJlDMSKcAfSomJDkU0AHj3p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\njOxWM8gqeFOO47elQaZJCsz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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.63091594  0.36908403]\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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P1qXYpHvSLSKzAKMnmnxjcM9qS4OFAHrUsMZKjtQOwICWwoqZYj3qaKMIuKQg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\nfL2A60UUwE3IOiD8aKKKQH//2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.19271372  0.80728626]\n"
     ]
    }
   ],
   "source": [
    "show(os.path.join(source_dir,'cat.1.jpg'))\n",
    "show(os.path.join(source_dir,'dog.0.jpg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Проверяем качество модели на тестовой выборке\n",
    "Вручную переберем все изображение из тестовой выборки и посмотрим, на скольких из них нейросеть правильно угадывать категорию. Для этого опишем функцию `eval_best_cat`, которая возращает номер категории (0 или 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct: 3326\n"
     ]
    }
   ],
   "source": [
    "def eval_best_cat(image_path):\n",
    "    result = evaluate(image_path)\n",
    "    return (-np.array(result)).argsort()[0]\n",
    "\n",
    "correct = 0; total=0\n",
    "with open(os.path.join(source_dir,'Test.txt')) as f:\n",
    "    for x in f:\n",
    "        cat = int(x.split()[1])\n",
    "        res = eval_best_cat(x.split()[0])\n",
    "        if (cat==res): correct+=1\n",
    "        total+=1\n",
    "\n",
    "print (\"Correct: {} of {}\".format(correct,total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В заключении проверим модель на паре произвольных изображений, скачанных из интернет"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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dyuXJR6M4+i55/wAZ8j9SpW7jC5cmbPZwfMfZTt/3+q8XJAW0xur7ZggmBueX\nguXIERnLvMpjSH5fRcuQxEKrBJwPZQpMAggCZ+y5ckNFV/uPI/ZEWjQGCANgfkMrlyQBAaIOEov9\n/wDpcuXIGC2/5Z8T9UyiGYxnl5LlyYDfg9JpbJaCZGSAUZxId0+f7Lly0AbbgaB4ALiMH0XLk2IT\n3R78csfdVhxnf9LT697K5ckhsJIl5nOCfWd0RTYPhbD9XJerloRU+i0h0tByOQQV+0A4AH+KocYy\nNj6LlyQxPw0TRE5/Jv5rQAZH+4f/AGC5ctIQtvx/kH+5N20xBwOXLxXLkgEFz+Yrly5ZEf/Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.14863679  0.85136318]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.3418062   0.65819377]\n"
     ]
    }
   ],
   "source": [
    "show('l:\\\\CIFAR\\\\Real_Life\\\\Kitten.jpg')\n",
    "show('l:\\\\CIFAR\\\\Real_Life\\\\Doggie.jpg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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