notebooks/Lab1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/iu5git/Deep-learning/blob/main/notebooks/Lab1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HsvLg8M8ebUX"
   },
   "source": [
    "# Лабораторная работа №1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VrOocc6D_O7M"
   },
   "source": [
    "# Задание\n",
    "\n",
    "Необходимо познакомиться с фреймворком машинного обучения PyTorch и выполнить три задания:\n",
    "1. Регрессия по теореме универсальной аппроксимации, ручное дифференцирование\n",
    "2. Бинарная классификация с помощью автодиффиренцирования PyTorch\n",
    "3. Обучить полносвязную нейронную сеть классификации 3 классов изображений из набора данных CIFAR100 по варианту из примера и затем повысить точность на тестовой выборке.\n",
    "\n",
    "Для задания 3 нужно сформировать свою подвыборку CIFAR100 по варианту.\n",
    "\n",
    "Лабораторные выполняются на платформе Google Colab - просто перейдите по ссылки в начале ноутбука. Также можно работать с ноубуками лабораторных локально.\n",
    "\n",
    "Отчет должен содержать: титульный лист, задание с вариантом, скриншоты и краткие пояснения по каждому этапу лабораторной работы, итоговую таблицу со результатами для всех вариантов обучения."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fpq20OpJhje4"
   },
   "source": [
    "# Варианты для Задания 3\n",
    "Вы должны использовать следующие классы из CIFAR100:\n",
    "1. Номер группы + 11\n",
    "2. Номер варианта + 37\n",
    "3. ИУ5 (Номер варианта + 42); ГУИМЦ (70); Иностранцы (71)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UNO1xJtZdf37"
   },
   "source": [
    "# Задания для самостоятельной работы\n",
    "1. Проанализируйте результаты обучения вашей модели. Что говорит о ней точность на обучающей и тестовой выборке? С какими классами модель справляется лучше и почему?\n",
    "2. Проанализируйте результаты обучения. Возникает ли переобучение вашей модели? Что необходимо сделать, чтобы нивелировать это (не используя регуляризацию)?\n",
    "3. Измените размер батча, но сохраните общее количество итераций. Проанализируйте результаты обучения с новыми гиперпараметрами. Что изменилось и почему?\n",
    "4. Уменьшите скорость обучения и увеличьте общее количество итераций, чтобы повысить точность модели.\n",
    "5. Измените вашу модель - поменяйте количество нейронов и слоев. Проанализируйте результаты обучения новой модели. Найдите лучшие гиперпараметры для этой модели.\n",
    "6. Укажите, какие действия помогли повысить точность вашей модели и объясните почему.\n",
    "\n",
    "# Комментарии к самостоятельной работе\n",
    "- Изменение скорости обучения и размера батча делаем только после нахождения момента переобучения - эпохи где ошибка на тестовой выборке самая маленькая. Иначе улучшением шагов вы можете усилить переобучение (улучшаться при обучении, ухудшаться для теста).\n",
    "\n",
    "- увеличение размера батча при том же числе итераций должно хоть немного (в среднем) улучшить обучение. Потому что мы делаем такое же количество, но более хороших шагов. Это может не сходится с опытом по разным причинам, но из теории мы ожидаем какое-то улучшение\n",
    "\n",
    "- Во сколько раз меняете скорость обучения или размер батча - во столько же меняете количество эпох для сохранения итераций. На практике это условие не нужно, потому что при новом обучении меняют сразу много гиперпараметров. Но мы учимся, поэтому исследуем поведение каждого гиперпараметра отдельно: мы не можем сравнить 5 больших шагов и 7 маленьких, можем сравнить 5 больших и 5 маленьких.\n",
    "\n",
    "- При изменении модели нужно искать гиперпараметры заново - это новая модель. Не нужно расписывать снова все шаги, просто сделайте это сами - найдите переобучение и поищите скорость и размер батча, которые будут лучше.\n",
    "\n",
    "# Контрольные вопросы для защиты\n",
    "1. Полносвязная нейронная сеть, объясните структуру, вычисления и назначение слоев и составляющих нейронов.\n",
    "2. Укажите количество нейронов, связей и весов в полносвязной нейронной сети\n",
    "3. Опишите задачи регрессии и классификации. Какие функции потерь применяются в этих задачах?\n",
    "4. Опишите структуру набора данных, назначение его частей\n",
    "5. Опишите алгоритм стохастического градиентного спуска. Укажите назначение гиперпараметров. В чем отличие пакетного и стохастического спуска?\n",
    "6. Что такое эпоха, итерация, батч обучения. Как они взаимосвязаны?\n",
    "7. Что такое обучение с учителем, без учителя, с подкреплением? Приведите примеры методов и задач для каждого вида обучения."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Описание итоговой таблицы\n",
    "\n",
    "|  Конфигурация нейросети   |  Гиперпараметры  |  Точность  |  Комментарий\n",
    "|----------|----------|----------|----------|\n",
    "|  FC(10), FC(3)   | lr = 0.003, batch_size = 128, epochs = 100  |  test = 70%, train = 98%  |  Базовый вариант\n",
    "|  FC(10), FC(3)   | lr = 0.001, batch_size = 128, epochs = 300  |  test = 72%, train = 99%  |  Уменьшили скорость обучения в 3 раза, для компенсации увеличили количество эпох в столько же раз"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FDgKl3yGd-W_"
   },
   "source": [
    "### Методы и функции PyTorch\n",
    "\n",
    "(Документация: https://pytorch.org/docs/stable/index.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IKXfCiiWf2MK"
   },
   "source": [
    "Библиотеки:\n",
    "\n",
    "* __np__ - библиотека NumPy для работы с многомерными массивами данных\n",
    "* __pickle__ - библиотека Pickle для сериализации и десериализации структур данных ЯП Python\n",
    "* __sklearn__ - библиотека, реализующая в основном методы классического машинного обучения и инструменты для работы с ними\n",
    "* __PIL__ - легковесная библиотека Pillow для работы с изображениями и вывода графических элементов напрямую в Jupyter Notebook\n",
    "* __matplotlib__ - библиотека для построения графиков, по большей части повторяет API Matlab'a\n",
    "* __torch__ - библиотека Pytorch для глубокого обучения нейронных сетей"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MMmc6jvid-XB"
   },
   "source": [
    "__Принятые сокращения__: \n",
    "* torch.nn - nn\n",
    "* torch.nn.functional - F\n",
    "* torch.optim - optim\n",
    "\n",
    "__Методы__:\n",
    "* __torch.Tensor__ - cоздает тензор из многомерного массива Numpy и наследует его тип данных. По умолчанию память под тензоры выделяется на CPU. При выставлении флага __requires_grad__ автоматически отслеживает градиенты с помощью движка autograd, который строит динамический вычислительный граф. Включить отслеживания тензора __t__ можно так же при помощи метода __t.requires_grad_(True)__. В таком случае после вызова метода __backward__, в поле __grad__ будут записаны производные. Производные тензора __t__ можно очистить вызовом метода __t.grad.zero_()__. Для того чтобы отсечь ненужные вычисления производных используется метод __detach__, который создаёт копию тензора, при этом флаг __requires_grad__ снимается и отслеживание движком autograd прекращается.\n",
    "\n",
    "* __torch.numpy__ - создает многомерный NumPy массив данных из тензора\n",
    "\n",
    "* __torch.item__ - возвращает число, но только если ранг тензора 0. В противном случае выдаёт ошибку и следует использовать torch.numpy\n",
    "\n",
    "* __torch.uint8__, __torch.int16__, __torch.int64__, __torch.float32__ - приведение массива к новому типу, аналогично NumPy. Для приведения используется метод .to (например `t.to(torch.int64)`). По умолчанию все вычисления на графе производятся в float64, есть также возможность использования mixed precision (что-то во float16, что-то во float64), но это считается продвинутой техникой.\n",
    "\n",
    "* __torch.ones__, __torch.zeros__, __torch.transpose__, __torch.reshape__ - API похожий, как у NumPy\n",
    "\n",
    "* __torch.rand__ - создание случайного тензора с числами в диапазоне от 0 до 1. Размерность перечисляется через запятую\n",
    "\n",
    "* __torch.t__ - транспонирование тензора, похоже на рассмотренный ранее __numpy.transpose__. Если дан тензор X, то можно его транспонировать при помощи `X.t()` \n",
    "\n",
    "* __torch.sum__ - суммирование элементов тензора вдоль указанной оси __axis__. Если суммирование производится вдоль последней оси, то разрешается указать вместо номера -1. Для сохранения исходной размерности тензора, необходимо выставить флаг __keepdims__.\n",
    "\n",
    "* __torch.maximum__ - производит поэлементное сравнение тензоров и возвращает максимальный из элементов. На практике используется для реализации некоторых функций активации нейронной сети\n",
    "\n",
    "* __torch.mm__ - произведение тензоров. Для 2 двухмерных матриц с размерностями (M, N) и (N, K) результатом данного метода будет двухмерная матрица размерностью (M, K)\n",
    "\n",
    "* __torch.exp__ - повторяет функционал __numpy.exp__ - поэлементное возведение тензора в степень экспоненты\n",
    "\n",
    "* __torch.log__ - поэлементная операция логарифмирования тензора - взятие натурального логарифма, обратная операция потенциирования\n",
    "\n",
    "* __torch.flatten__ - аналогично NumPy .reshape(-1), если указан параметр start_dim, то начинает \"выпрямление\" массива начиная с указанного номера. Т.е. для того, чтобы перевести тензор t с формой (100, 32, 32, 3) в форму (100, 3072) достаточно написать `torch.flatten(t, start_dim=1)`\n",
    "\n",
    "* __F.one_hot__ - один из многих способов получить горячую кодировку класса в виде PyTorch тензора. Например, для 5 классов, горячая кодировка класса \"4\" будет [0, 0, 0, 1, 0]\n",
    "\n",
    "* __torch.utils.data.TensorDataset__ - создание связанных тензоров, например обучающих примеров и соответствующих меток. В качестве аргумента передаются тензоры. Приемлемый способ создания набора данных, когда обучающая выборка некрупная и полностью помещается в оперативной памяти.\n",
    "\n",
    "* __torch.utils.data.DataLoader__ - В основе утилиты загрузки данных PyTorch лежит класс DataLoader. Он представляет собой Python объект, повторяющийся по набору данных, с поддержкой набора данных в стиле map и итератора; настройки порядка загрузки данных; автоматического разбиения на минибатчи;загрузки данных в один и несколько процессов/потоков. Самые полезные аргументы в конструкторе - размер мини-батча __batch_size__ и число параллельных процессов __num_workers__. Чтобы перемешать данные (для лучшей сходимости), следует выставить флаг __shuffle__ в True\n",
    "\n",
    "* __torch.save__ - сохранение параметров модели на постоянный носитель информации. Для этого первым аргументом передаётся model.state_dict(), где model - обученная нейросетевая модель, а вторым аргументов передаётся путь с именем файла.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mM59NsM-d-XC"
   },
   "source": [
    "__Создание моделей__:\n",
    "\n",
    "Создание моделей осуществляется при помощи модуля nn, при этом в модуле уже реализованы самые популярные блоки нейронных сетей или слои, такие как: \n",
    "* полносвязный слов Linear\n",
    "* свёрточный слой Conv2d\n",
    "* пуллинг MaxPool2d\n",
    "* нормализация BatchNorm2d\n",
    "* множество активационных функций ReLU, Softmax, Tanh\n",
    "* слои-регуляризаторы, например Dropout\n",
    "\n",
    "В данной лабораторной работе мы рассмотрим лишь 2 блока-кирпичика нейронной сети из выше приведённого списка, а именно Linear и ReLU.\n",
    "\n",
    "Задать модель можно 2 способам: \n",
    "\n",
    "1. при помощи nn.Sequential\n",
    "2. при помощи наследования от класса nn.Module\n",
    "\n",
    "Первый способ подходит для создания простых моделей без ответвлений. По сути их можно представить как конвейер, где входной тензор передается ряду последовательно приведённых трансформаций для получения выходного тензора.\n",
    "\n",
    "Если необходимо применять более сложные архитектуры, где конвейерные дорожки могут разветвляться на несколько частей, то используется nn.Module. Данный подход позволяет реализовать самые разные архитектуры.\n",
    "\n",
    "Для создания простого многослойного перцептрона с одним скрытым слоем и функцией нелинейности, согласно первому способу достаточно написать следующий код:\n",
    "\n",
    "    model = nn.Sequential(\n",
    "      nn.Linear(input_dims, hidden_dims),\n",
    "      nn.ReLU(),\n",
    "      nn.Linear(hidden_dims, num_classes) \n",
    "    )\n",
    "\n",
    "Для создания простого многослойного перцептрона с одним скрытым слоем и функцией нелинейности, согласно второму способу необходимо создать класс и модель как экземпляр этого класса:\n",
    "\n",
    "    class MLP(nn.Module):\n",
    "        def __init__(self, input_dims, hidden_dims, num_classes,\n",
    "                     *args, **kwargs):\n",
    "            super(MLP, self).__init__()\n",
    "            self.fc1 = Linear(input_dims, hidden_dims)\n",
    "            self.fc2 = Linear(hidden_dims, num_classes)\n",
    "        \n",
    "        def forward(self, input):\n",
    "             x = self.fc1(input)\n",
    "             x = F.relu(x)\n",
    "             x = self.fc2(x)\n",
    "             return x\n",
    "    \n",
    "    model = MLP(input_dims, hidden_dims, num_classes) \n",
    "\n",
    "При этом допускается вкладывать nn.Module и nn.Sequential внутри других модулей, что позволяет создавать очень сложные архитектуры моделей.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FmxEqwWLd-XD"
   },
   "source": [
    "__Обучение моделей__:\n",
    "\n",
    "Перед обучением моделей необходимо выбрать функцию потерь и оптимизатор. Различные функции потерь представлены также в модуле nn:\n",
    "* __nn.MSELoss__ - среднеквадратическая ошибка (y_true-y_pred)**2\n",
    "* __nn.BCEWithLogitsLoss__ - бинарная перекрёстная энтропия для задач бинарной классификации\n",
    "* __nn.CrossEntropyLoss__ - категориальная перекрёстная энтропия для задач многоклассовой классификации\n",
    "\n",
    "В качестве альтернативы можно собственноручно реализовать функцию потерь, например для MSELoss:\n",
    "\n",
    "    inputs, y = batch\n",
    "    ...\n",
    "    output = model(inputs)\n",
    "    loss = ((output - y)**2).sum()\n",
    "    ...\n",
    "\n",
    "Оптимизаторы содержатся в модуле __torch.optim__. Существует множество оптимизаторов целевой функции, классическим является стохастический градиентный спуск Stochastic Gradient Descent или SGD. В конструктор класса необходимо передать веса модели, а также указать шаг обучения или learning rate. \n",
    "\n",
    "Для перевода модели в состояние обучения необходимо вызвать метод __train__. После чего модель готова для обучения.\n",
    "\n",
    "Для обучения нейросетевых моделей используется градиентный спуск и его разновидности, в основе которых лежит метод последовательных приближений. \n",
    "\n",
    "За одну эпоху условно выбирают прохождение итератора через весь набор данных, за одну итерацию - оптимизация параметров модели с помощью текущего мини-батча. PyTorch автоматически считает производные при вызове метода __backward__, применённому к функции потерь. \n",
    "\n",
    "При этом при повторном вызове, значения новых градиентов добавятся к предыдущим расчитанным. Поэтому, для избежания нежелательных эффектов принято очищать прошлые значения градиентов на каждой итерации при помощи метода __zero_grad__, применённого к экземпляру класса оптимизатора.\n",
    "\n",
    "Для обновления параметров нейронной сети используется метод __step__, применённый к экземпляру класса оптимизатора."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oJ2fTXUPd-XD"
   },
   "source": [
    "__Проверка качества моделей__:\n",
    "\n",
    "Для перевода модели в состояние проверки необходимо вызвать метод __eval__. После чего модель готова для проверки.\n",
    "\n",
    "Выходной тензор предсказаний модели необходимо отсечь от вычислительного графа. Для этого используется метод __detach__, применённый к выходному тензору модели. В противном случае возможны утечки памяти. Метод __numpy__ конвертирует тензор в многомерный массив NumPy.\n",
    "\n",
    "По умолчанию модель выводит так называемые логиты классов, а не их вероятности. Для получения вероятностей необходимо применить функцию активации __Softmax__. Однако на практике это необязательно, поскольку величина логитов согласуется с вероятностью классов, и для получения номера наиболее вероятного класса этот этап можно опустить. Номер класса получается при помощи либо метода __argmax__, либо метода __argsort__, причём последний позволяет считать такие метрики, как Accuracy@5 и метрики ранжирования.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fzjC1ECbdj-Z"
   },
   "source": [
    "## Импортирование необходимых библиотек"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "3O2PElov-nod"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "import pickle\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.datasets import make_circles, make_moons\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CTpTR1x0YYdn"
   },
   "source": [
    "# Часть 1. Задача регрессии по теореме универсальной аппроксимации, ручное дифференцирование"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sqpCNuNKufbZ"
   },
   "source": [
    "## Генерация выборки и инициализация параметров нейронной сети"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "ity2_RGkYX8S",
    "outputId": "001940fc-8cbc-494c-83f5-e98a325eb31a"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = (np.arange(100)/100 - 0.5).repeat(5)\n",
    "\n",
    "y = 1/(1+np.exp(-10*X))\n",
    "yn = np.random.normal(scale=0.05, size=y.size)+y\n",
    "\n",
    "plt.plot(X, yn)\n",
    "plt.plot(X, y, linestyle='--', c='k')\n",
    "################################################\n",
    "tensor_X = torch.Tensor(X.reshape(-1, 1))\n",
    "tensor_y = torch.Tensor(yn.reshape(-1, 1))\n",
    "\n",
    "HIDDEN_SIZE = 64\n",
    "# Инициализация весов MLP с одним скрытым слоём\n",
    "weights_1 = (torch.rand(1, HIDDEN_SIZE)-.5)/10\n",
    "bias_1 = torch.zeros(HIDDEN_SIZE)\n",
    "\n",
    "weights_2 = (torch.rand(HIDDEN_SIZE, 1)-.5)/10\n",
    "bias_2 = torch.zeros(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1UZe3zl6uj7J"
   },
   "source": [
    "## Обучение нейронной сети задачи регрессии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "LFZI-IamaceN",
    "outputId": "433622df-5dba-4173-9d78-51f62598ff72"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.002543415\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Определяем функцию нелинейности\n",
    "relu = lambda x: torch.maximum(x, torch.Tensor([0]))\n",
    "# Прямой проход\n",
    "forward = lambda x: (weights_2.t()*relu((weights_1*x) + bias_1)\n",
    "                      ).sum(axis=-1,keepdims=True) + bias_2\n",
    "loss = lambda y, y_: ((y-y_)**2).sum(axis=-1)\n",
    "\n",
    "# # обратный проход\n",
    "def backward(X, y, y_pred):\n",
    "    # производная функции потерь по y_pred\n",
    "    dL = 2*(y_pred-y)\n",
    "    # значения нейронов скрытого слоя до применения активации\n",
    "    Ax = (weights_1*X) + bias_1\n",
    "    # значения нейронов скрытого слоя после применения активации\n",
    "    A = relu(Ax)\n",
    "    # производная функции потерь по weight_2\n",
    "    dW2 = torch.mm(A.t(), dL)\n",
    "    # производная функции потерь по bias_2\n",
    "    db2 = dL.sum(axis=0)\n",
    "    # производная функции потерь по значениям скрытого слоя после активации\n",
    "    dA = torch.mm(dL, weights_2.t())\n",
    "    # производная функции потерь по значениям скрытого слоя до активации\n",
    "    dA[Ax<=0] = 0\n",
    "    # производная функции потерь по weight_1\n",
    "    dW = torch.mm(X.t(), dA)\n",
    "    # производная функции потерь по bias_1\n",
    "    db = dA.sum(axis=0)\n",
    "    #print(dW.shape, db.shape, dW2.shape, db2.shape)\n",
    "    return dW, db, dW2, db2\n",
    "\n",
    "def optimize(params, grads, lr=0.001):\n",
    "    # градиентный спуск по всей обучающей выборке\n",
    "    W1, b1, W2, b2 = params\n",
    "    W1 -= lr*grads[0]\n",
    "    W2 -= lr*grads[2]\n",
    "    b1 -= lr*grads[1]\n",
    "    b2 -= lr*grads[3]\n",
    "    return W1, b1, W2, b2\n",
    "\n",
    "for i in range(50000): # 50 тысяч итераций градиентного спуска == 50 тысяч эпох\n",
    "  output = forward(tensor_X)\n",
    "  cur_loss = loss(output, tensor_y)\n",
    "  grads = backward(tensor_X, tensor_y, output)\n",
    "  params = [weights_1, bias_1, weights_2, bias_2]\n",
    "  weights_1, bias_1, weights_2, bias_2 = optimize(params, grads, 1e-4)\n",
    "  if (i+1)%10000 == 0:\n",
    "      plt.plot(X, output.numpy(), label=str(i+1), alpha=0.5)\n",
    "plt.plot(X, y, linestyle='--', c='k', label='real')\n",
    "plt.legend()\n",
    "plt.ylim(y.min(), y.max())\n",
    "print(cur_loss.numpy().mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qq1r9u6hSucR"
   },
   "source": [
    "# Часть 2. Бинарная классификация с помощью автодиффиренцирования PyTorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z_96UjOJuOPs"
   },
   "source": [
    "## Генерация выборки и инициализация параметров нейронной сети"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "fNVT1K3IS33Y",
    "outputId": "352bffca-70fa-4e5c-fd03-635671ec6b62"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.random.randint(2, size=(1000, 2))\n",
    "\n",
    "y = (X[:, 0] + X[:, 1]) % 2 # XOR\n",
    "X = X + np.random.normal(0, scale=0.1, size=X.shape)\n",
    "#X, y = make_circles(n_samples=1000, noise=0.025)\n",
    "#X, y = make_moons(n_samples=1000, noise=0.025)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y)\n",
    "####################################################\n",
    "tensor_X = torch.Tensor(X.reshape(-1, 2))\n",
    "tensor_y = torch.Tensor(y.reshape(-1, 1))\n",
    "\n",
    "HIDDEN_SIZE = 16\n",
    "# Инициализация весов MLP с одним скрытым слоём\n",
    "weights_1 = ((torch.rand(2, HIDDEN_SIZE)-.5)/10).detach().requires_grad_(True)\n",
    "bias_1 = torch.zeros(HIDDEN_SIZE, requires_grad=True)\n",
    "\n",
    "weights_2 = ((torch.rand(HIDDEN_SIZE, 1)-.5)/10).detach().requires_grad_(True)\n",
    "bias_2 = torch.zeros(1, requires_grad=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YSKqixxWuTb8"
   },
   "source": [
    "## Обучение нейронной сети задачи классификации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "XioBMI-KW1qx",
    "outputId": "eebd6148-a9ae-41d7-9175-92d6d8f904dd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff170558b10>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Определяем функцию нелинейности\n",
    "def sigmoid(x):\n",
    "    return 1/(1+torch.exp(-x))\n",
    "\n",
    "# Прямой проход\n",
    "def forward(x):\n",
    "    hidden = torch.mm(x, weights_1) + bias_1\n",
    "    hidden_nonlin = sigmoid(hidden)\n",
    "    output = (weights_2.t()*hidden_nonlin).sum(axis=-1,keepdims=True) + bias_2\n",
    "    return sigmoid(output)\n",
    "                       \n",
    "# Logloss\n",
    "def loss(y_true, y_pred):\n",
    "    return -1*(y_true*torch.log(y_pred)+(1-y_true)*torch.log(1-y_pred)).sum()\n",
    "\n",
    "# задаём шаг обучения\n",
    "lr = 1e-3\n",
    "# задаём число итераций\n",
    "iters = 10000\n",
    "params = [weights_1, bias_1, weights_2, bias_2]\n",
    "losses = []\n",
    "for i in range(iters):\n",
    "    output = forward(tensor_X)\n",
    "    lossval = loss(tensor_y, output)\n",
    "    lossval.backward() # тут включается в работу autograd\n",
    "    for w in params:\n",
    "        with torch.no_grad():\n",
    "            w -= w.grad*lr # обновляем веса\n",
    "        w.grad.zero_() # зануляем градиенты, чтобы не накапливались за итерации\n",
    "    losses.append(lossval.item())\n",
    "# выводим историю функции потерь по итерациям\n",
    "plt.plot(losses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pghpgU0WuXCS"
   },
   "source": [
    "## Проверка результатов обучения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "qMNBdwBOikFj",
    "outputId": "cd1a7a95-608d-411d-a929-0fc95425b121"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.4265555623302785, 1.5671068095642362)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD4CAYAAADhNOGaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9d5Qc13no+bsVuicnDHKOBAESIAIBghFgBHOUSAVKsqWlbIv2Wu9obe9bvydb+/ye12ffWpJlP1uWZUmWxCiJYk5iBAiQAEgi55yBybm7wrd/VE/P9HT1TM9MT76/c4acvnWr6jam+371ZSUiaDQajWbsYgz1AjQajUYztGhBoNFoNGMcLQg0Go1mjKMFgUaj0YxxtCDQaDSaMY411AvoCxXjCmX6jIqhXkaviHmtAPiqtNt5zW4MSxWmjTc5cQqMaMpYS8yhyIykjrXGASi07dDrtza3URgNP6bRaEY3R08dqhKR8V3HR6QgmD6jgtfe/uZQL6PXHG7aDUCTtT7jnC1VR5O/V9grk79vPnUy+fvywvkAbD3UMXbNuBnJ3z/ZfgKANVOnpl1/x6aDyd9XzJ2Y9do1Gs3I59Fv3Xk8bFybhgaRuUWLAShyX6XIfTV0zpWVs7mycjYANc7W5PhV06Zz1bTpAHzcHGzmK+dNZ+W8YGxj9Ynk3GVLA6Gw6fRpNp0+nXL9JWvms2RNIEi2HT7f7/ek0WhGPloQDDJzixanCIRMdBYGXQUCBMKgs0CAQBi0C4RlS2ekCISudBYGWiBoNGMbLQiGCK0daDSa4UJOBIFS6sdKqQtKqV0Zjq9VStUrpT5N/PzXTsfWK6X2K6UOKaX+IhfrGSlo7UCj0QwHcqUR/ATI7AENeF9Erkj8fAdAKWUC/wjcDiwCPqeUWpSjNY0YtHag0WiGkpwIAhF5D6jpw6mrgEMickRE4sCTwL25WNNIQ2sHGo1mqBhMH8EapdR2pdQrSqnFibGpwMlOc04lxtJQSj2mlNqqlNpaXdU80GsdMrR2oNFoBpvBEgQfAzNFZCnwD8Bzvb2AiPxQRFaKyMpxlekJV6MJrR1oNJrBZFAEgYg0iEhT4veXAVspVQmcBqZ3mjotMaZBawcajWZwGBRBoJSapJRSid9XJe5bDWwB5iulZiulIsAjwPODsaaRgtYONBrNQJOr8NEngE3AJUqpU0qpryql/kAp9QeJKQ8Bu5RS24HvA49IgAs8DrwG7AWeFpHduVjTaENrBxqNZqBQI7FV5dJl02Uk1hrKFbpmkUaj6QuPfuvObSKysuu4ziwegaRqB68AftocrR1oNJps0RrBSEU8itzNRN3dGPg0qKns4x4amJY2VWsHGo0GtEYw6ihx3iDf24OpfJSCUk6zUv6FfKrT5mrtQKPRdIcWBCMQQxqJ+idReCnjCp95/hMZz9ORRZqhQEQ4c+A0G5/ewKZnN3LhqP5sDDe0aWgEYnunKHVexyCedqzBL2a3fwWQnTO5s6kIOsxF7aYi6DAXhZmKoHtzkTYVjW1EhI1Pb+D4jmO4cRcUmJbFousWsfz2FUO9vDGHNg2NIjyjLE0bABAMLHuWzjvQDBsuHr/YIQQABDzHZc97u2moahjaxWmSaEEwAvFVETFjFj5mckwAwaTFWgLovAPN8ODknhMdQqALp/edGuTVaDKhBcEIpcG+kRZzCT5RBANHTaE2ch++Kk7O0VnJmqHGjtgYZvo2owyFFRmRLdNHJVoQjFSUSYu9mqq83+Ni3mPURe/BM8aFTtXagWaomL1sDspQaeMiwozLZg7BijRhaEEwRtDagWYoKB5XzJoHrsa0TKyohR21sWyLtY+uI1oQHerlaRLoqKExSHuJCtCRRZrBIdYS4+zBMyhDMWXBVOyoPdRLGpNkihrSgmAMo2sWaTRjCx0+qklD+w40Gg1oQTDm0b4DzXBDRPC99EKKmoFDm4Y0SbTvQDOUiAj7Nu5l+5ufEmuOUVBawIo7r2TOsjlDvbRRgzYNaXpEaweaoWTP+7vZ9vI2Ys0xAFrqW/jgmQ0c33l8iFc2+tGCQJOG9h1oBhsRYcfvtuM5qVnInuPxyavbhmhVYwctCIYj4hLxThLxToCEp+cPNFo70AwmruPitDmhx5pqmwZ5NWOPnPgIlFI/Bu4CLojIZSHHvwD8OaCARuAPRWR74tixxJgHuGH2q66MZh9BxDtBifNGyliDfQtxc0aGMwYe7TvQDDQiwtN//SRtzW1px0onljF53mSO7zyOaRksWH0Ji2+4LLR0haZ7BjSPQCl1PdAE/CyDILga2CsitUqp24G/EpHViWPHgJUiUpXt/YazIFASJ+IfR4lH3JyOrwpTJ4hPgfsJ+d4uDOI4ahKN9tV4xjiUtFIZ+wWKVC1AsKiKfgFR+YP4TlIx/XrOtuymhQLqrbszztN5B5q+cmDzfj56/qMU85BhGUTzo8RaYslIItM2mTxvMjf9/i1DtdQRy4A6i0XkPaCmm+MfiEht4uVmCOmnOAqIeCeojP2MYuc9itwNjIv9knxne8qcYuddCrxPMGlF4WHLacrjz2H4DeR5hwkTy4KQ5x3ueQEimH41ln8eJL1MdV9QfhvlsWeoiD/FImsvK82PWOj9c6JXcjrad6DpKwuuuoQ1D11NUUUxhmlQNrGMS9YsxIk5KeGknuNx9tBZas5k3HI0vSRn4aNKqVnAi2EaQZd53wIWisjXEq+PArUElZT/RUR+mOG8x4DHAKZOK1+xdedf5mTduUJJnMrYz0Ke5k1qI/fjGpVEnb2UeO/StQSXoGg1L8VXRRS6W1BdxIGgiKtpmAS20lbjElqty0F1lKE2/VpKnVcwpIXAAqdosG8kbs7qx3tqZVxCQ+m8Zh+LQ95cqmWCzkrWdIv4ght3saIWSqUXn+uJDU+9z+Gth9LGrYjFlfesZsHqBblY5pghk0YwqHVglVLrgK8C13YavlZETiulJgBvKKX2JTSMFBIC4ocQmIYGZcG9IOKfQFBpmzx4RL39GNJEibch5DgoBNu/SKN9KYV8DKQ7iG05jUHwVFTkbSXqH6cucg8oBeJRFn8eg9aU65c6b1KjPoNnlPbpPZXE30wTAgAGLrOtGqqdCUlHcphAaNcMtlQdpcbZmhQG7ZrB5lMn+bj5IMsL5yc1g62HTrKx+kRSGCxbOoNPtp9IagadBUK7ZrBj00G2HT6vhUGOibfG+fiVbRz99Agiwqwls1h+x0ryCvN6PFdE2PnWDna9sxM37hItiLL8jpXMv3J+j+d2pqSyBNMy8dwuGq6CovLC8JM0vWbQvC1KqSXAj4B7RSTZYV1ETif+fwH4DbBqsNaUS4KOYeHySeFR5H4Y2lWMxFlKWihyNuBS0qXhjCLoPdahGitcLLmI7QebY8Q/Hbphg0eeu6dvb0gcInImVHABGNKqI4tGMb7v88o/vczBjw4Qb43jtDkc3naYl3/wUlrW77nD53j1f73C0995ktf/9TUObTnIh89tZseb23HaHMQX2pra+PA3mzi241iv1jF/1YI0p7BSirzCfCbPm9Lft6lJMCiCQCk1A/g18KiIHOg0XqhU0ElFKVUI3ArsGow15Zq4MT3NpAOBozdmzMWUxm7PN2kmIuexqEHhJzqOQWbh4lLi/I58dyeGNIfOUwh5/r4+haAqMqf4CxDrFMWk8w5GH2cOnKGppjFl0/c9n9bGFk7s7vibnNxzkjf/7XXOHzlHa2MrZw+cYePTG9j/wb60p3jP8fj0tY8BiLfGuHD0fI+hofnF+dz69fWUjC/FsAwM02DC7Ams/6PbQ/scaPpGrqKGngDWApXAeeDbgA0gIv+slPoR8CDQniLoishKpdQcAi0AAjPVL0Xkb3q633CNGsp3d1LkbgZ8AhevRcyYQ6O9jor4M1iS7twSCPEZpI9lwsfCVeOw5UIGQQQt5lKa7TW9ei8AFbGnsJI+/o7rgUl19PPpEVHoiqajhZ1v7+CTVz9G/PTP1JKblrJs/XIAfvU/nqGpJvs4f9M2GT9zAucOn8U0TUSESXMns/bRddh53Zembm1owbBM3cegH+gy1IOE6deQ5+1H4RIz5uAYU0ApIt5xSp3Xun3S7is+Fi7l2FwMFSA+BlV5j/X6upZ/nrL4iwmzkyS0FIvayH14RmXG83Tewcjn+I5jbHj6fdxYqjZpRSyuun8Nc1fOw3M9fv6ff5ZJaQ0nsHSmjdlRG9/zKSovYtn65cy8fFbWl2xtbOXD5zZzcvcJUDBj8UxW3bea/KKhC7ceruhaQ4OEZ1TQbK+hyb4Ox5waOHOBuDmTmBqYpLAgUimzDqHwUdLa6+u6xkRqog/TbC6nzZhDs7mKOvtuPFXW7XnadzDymbZoOpH8aPLzC4Ft3orYzFwyCwDDNHrfYCY8PhqnzcFzPOov1PPOf7zN0995kg+f20xLQ0u3l/Ncj5f+4UVO7DqO7/n4rs/xncd4JcSXocmMFgSDSMyai0/PX5ze62gWnlHc7Yyos4dCZxPj2n5BRewp8t1dID1/UXxVRIt1BSAUetsoc16kMvZT8t2dPZ6rfQcjF9MyuePxO5k0d1J7NDIT507ijj++M9l0XinFousXY9pmj9cqn1KRvU1fgqf8fRv38ux/e5p9HwQBD06bQ1NtU8oGf3L3CWItsRQTlvhCa1MrJ/ecTLu0JpxBDR8dbRjSSL67C0tqcNREWq3F3Wb/xow5FKmPEHEz2vMBXMqwqAOy9RV4tKqF5BGedKaAYn8LoIL7ChS6m7H8czRGbu7x6iXO20T944FmkYh8KnI346niIE9BPBQuQiTlCRI6hMHhpt0Uua9mNBVdWTk7GWYKpISatoeZAslQ0/YwUwjMRe3CoD3UtKupaMma+ckwU9Dmop4QX9j9zi4uHruAFbHwPZ/ySWUUlqX6hZbedAVuzGXfB3vxXZ/OpmYrYjF14TTWProOJ+7w5H/5ZcLA2It1iPDhcx9yaMshas/VopSBaRmsvPtK5l+5gLrzdbix9BpFbtyl/kIdMLNP73+soX0EfcTyL1AWfwGFl4jyMREsaiIP4hslXeZWYUk1rirFo4QidzN5fhA81XnbFKDevpU87yBR/2jWTmQfBVgonKydzMH1DGoin0WUheVX46liPKMiZY6StkSiXLr24BHBwCMoE6XwKaTRvj5jXSTtOxg57Pjd9kQ10I7IH9M2WXLjEpbcfEXafNdxaalr4fSB0xz95DDKMFiwagFzls9NagJv/OtrnD14llzsOaZtse7LN+K0xdn49AbceBdfRtTm2oev7ZWvYSygncU5pjz2DHZHOgQQxPzHjFk0RG5LDLiUxV/BlvPJfABPldJoraXYeRsrUZWjw39mUG/dTKn7VlqGcne0/wV7G0wngEcRJq0IJgofh1JaraW4xgQ8owzTr6Mi/lRGDSZdWFnURu7BNSZkvK+OLBpa3LjL+aPnMS2TCbMmhBZve/LbvyTWEksbjxRE+Nxff6FP921paOHVf3qZloaWFAHTVybOnsgtj93Gb/7uV7TUtyTNQ8pQFJYVcv+fPagL03VBO4tziTihoaAKIeKfSr4udLdgyzkULgYOBi6W1FLu/CaRL9CxkQa/+5S4byN9+LP0JATCxL0CTJpQeBjEUbjYVFPivk1F/BlKYy/hkZ/h7Ez3dClwP+l2Ldp3MHD4ns+5I+c4c+B02lMywNFPj/DUXz3Buz9/m7f+/U2e/s6TXDxxMW1erDVdCADEW+KhT/TiC2cOnGHfB3s5d/hc6JyCkgJu/fptTFmQLpj7QlNtE6ZlcufjdzF98QwMI8gzmLF4Jnc8fpcWAr1A+wj6hEF4HFzwRBz84pPv7U7LJm5PFsu8cft4FKGIZ/2En828THO6jgevA10hImco8j4meL/ZRWAowJK6Hudp30HuqTp5kd/925tBIpcKNuerP3sts5cGArWhqoGNT28InsY7mdXf+NfX+ex/fRjL7tgOKiZXhBZ1K59SkVYzqK25jVf+8SUaqxuTT+X5pQVc89lrKa4opqSyBBGh/kI9L//Di+nlIvqAUooJs4O/VX5JAeu+dGNS+PSlptFYR5uG+khx/E3y/KMpG71g0mbMRZRNvreXdut9b+irmWeg8LGJG9OJ+kfS/BlhaxQUbcYlNEbWZn0P7TvoP67j8vR3nsJpi6eMm7bJPf/pPkoqS/j4lW3sencn4qV+562oxTWfvY5ZibBQgPNHz/PGv74WbNqJP7Zpmdz8tVuZNGdSyvlv/eRNTu7OHKGjDBUIiPBnp96jwIrY3PW/303p+KCOVlNNI1Unq8gvKWDCrAlaGGRgWBSdG0002ddjxpuwpQrBQOHhYxH1DyWia8LpaaMfbh9fhYPlXwA6Nv9M3+X2hLNma3mv7qG1g/5zeu+pUHOM7/sc2nKQ5bevINYaSxMCwSTSBMjE2RO5/Rt3suPNT6k+XU35pHKuuHUZ46alJhKKL90KgfY5wS+9e0+dMUyDvKI8xBcmzJrIsvXLKR1fivjCpl99wJGPD6NMBRJoCLd9fX1ahJMmM1oQ9BFREeqi92F6VUT9Y0S8Y9hUhzpVQ8+nY9PvTUmJbK5LH68Xto4OP0LqWOd7BRjEjak0WWvSoqayZW7R4qQwAF3RtDfEWuMQUg5CPEk2g59+6XSObDuc5jsQESbNm5x2rmEaNNY00drQSkt9C2cPnsX3fApKC1hx15XMvGwmB7ccTDtvIDBMg7v/9F7yilIrnx7cepAjnxwJNJfE22qsbuCFv/8t+cX5lE8u57J1S6iYUhFyVU072pvSH8SnyPuQAu9TbKqyEgKq009HYbkcLafTtftCb7WU4D2YNJpXczH6VVrMpUEBvH70WdZZyX1j8vzJoRqBFbGYvjh4/1MvmcaEWROSCWHtxxdecynFFakJifHWOK/+08vUnqnB93zEFzzXQ0RormvmvZ+/w6/+9lm2PP/hwL4xAtPSdV+4IVljSHxJahm739mZ1vAegVhLjLrzdRzdfpRXfvAS54+cG/B1dqX+Yj0Xjp3HiYf3Yh5OaB9BP8jz9lPkvI/Ri1DPruTKJzCUvoW4mpBwEAeiTeERV1NosZYnay31Be076B1bXviIA5v3J5/4lVKICKZtMnfFPFbedSWmZXJsx1GOfHwEK2Ixf9UCpl4SvOf6i/XsePNTLh6/iGEaNNU05cSx21+CPASFaZsUlhXScLEeBCqmVlB9qrrH8yFoeTll/hQW33AZk+amaz+5pKWhhbf+/U3qztdhGAa+L6y8cyULr7l0QO+bDTqPYAAoi/2WiJzt1zX6axbq/NcbKCHQ3RoTcRpp2lAgEkzazEtpsq8NOTN7dN5BULZ5/6b9nDlwmsLyIhZdtzjN3CEinNl/mn0f7OX0/tMpZRcM02DctEpu/8YdKKVoa27j45e3cnzncZShmHrJNE7sOo7neDlJ+BqumLbJqntXs2D1JQN2jxe/9zw1Z2pS/v1N2+Lmr9484EKoJ3QeQQ4x/DpK4y9j9yAEhJ7NP/3dvDubmnJJ53V3jRZKxQjNe1CAgUe+txfT719v2bGed9DW1MZv/9/n2P7mp5w7fI4j2w7z8g9e5PjOYynzlFJMXTiNSXMnp8XQ+55P7dkaqk9VBYXavv8ih7ceJt4aJ9Yc48gnge9gNAsBCHoibH1hy4BpOuePnaf2bG1a+W7Pcdnz3u4MZw09WhD0AiWtlMV+y7j4k0T8Ez1uvgpoUws6cguyZDC+ij3dI0y4BMLBQDDxsfGxaTPm9OAbEaL+iW6OZ8dY9h3sfHsHbc1tyWxcEcFzPDY9+wG+n57fUX26OmPmbs2ZGjY9u5Gm2sbUc0f3/p9G/fmec116y653dvL6P7+aseppS2PvKwAPFloQ9ILS+KuJTOHsnsAFE08V9apcBFleu7/05R4K8NQ4auwHqIvcRVX0K7Sai+ku2UxQSBYVV7NlLGoHJ/ecDN1cPNej4WJD2njFlIrQiqAiwievfszhbYfH3MbfGd/ziRb13He5N1w4dp5PX/80oxAwLINpl07L6T1ziRYEWWL69djSfWRQ1yMCROV42NTQ+bmkp2v35d4CWHKRUvd1fFWIwqHEebvbcxQ+bg/9C3rLWNMOovmR0HHfFyJ56cfCCq0ZpoFpmbQ1teV6eUNHH55mDNNg/MzxFJbmNsfgwOYDeG74A59hGuQX5XPpNYtyes9cogVBlihae6wB1BESauBRRJtxKZbUd/t5HQhhIF3+H0ZfNQIFmFJPafwVyuIvYdKQIcO447cy5xWibu7jzceKdrDo+sUpIZ8QRNJUTq+koLQgZfz4zmP89n8G3V/bs2uVUsxZPje09tBIRqGYMHtCj7uYYRrYeTamZTJ+5nhueHRdxrmxlhiN1Y2hJrfucNriGQt6zbhsJnd/895h3WIzJwllSqkfA3cBF0TkspDjCvgecAfQAnxFRD5OHPsy8JeJqf9NRH6aizXlBHHI849gSCMeZWl1g8JQQJwK6qIPUh7/dQ9mITPx2fFyag4aaNNSUE+oGjB7zD0I/u9S7L5HzJwNKrc5jGMhK3nW0tnUnK5h74Y9GJaBeELJ+BLWdtnQ2prbeP+J99L8A8pQXLb2co58HN6vYqQiIlSfqsY0TTw/83fz9m/cifg++cX5FCXyJUSE0/tOcXjbIUAx8/KZHNp6kLOHzmIoAzNictUDV6eU3eiOmUtncebgmTRha5omax5cE3R7G8bkqnn99UAT8LMMguAO4I8JBMFq4HsislopVQFsBVYSyNNtwAqRLh3TuzAY4aOmX095/DcEm7SDYCNYiSqdCacd4ZtuzJhJfeT2HsNLc5VRPBghpJnume39fGzqInfiGpN6ntxHRnveQVtTG9Wngno6YZmyBz86wIfPbQ51FC+46hJiLTFO7Doe2pA+E8k6QSOU4nHFPPAXD6WNb3jqfY7vONaxcYfUTjFtk/V/eDuV08f3eB/f83n9h69RfaoKN+6ilMKwDFbetYqFVy/MwTvJDQMaPioi7wHdxQjeSyAkREQ2A2VKqcnAbcAbIlKT2PzfADJ/gweRYuctFG0YiWYvQRnpGDGmJCJnwuvu+Fi0moEsbLUWdVuXJ1eb9lDVJ+reJNR1rgQdzAaQ0e47yCvKY+rCaRnLJXiul3HTPvrJEVbdu5rCskJMq/vWku0oQ2XfXnIYogzF2i/dmDZedbKKY9uPpj69h/yzea7H7nezC/k0TINbH7uNaz57LbOWzmL+6gXc/kd3DCsh0B2D5SOYCnSuTHUqMZZpPA2l1GNKqa1Kqa3VVc0DtlAAJXFsuRhSd8cnwpnE76T8vz2ntsVcTtwMNo+YMQ+Pwoy9AHK65gG4Zk/360rwb2Djd/lYCQpPFeGp8kFZ21jxHXRl2qXTM0at+L5Pa0ML9//Zg1z3+eu54rZlLFjTfVKV+ILvjtAG8Aouv3FpqNA8c+A0XjbvS6CxpjHrWxqmwayls7nhi+tY8+DVaQX6hjMjxlksIj8UkZUisrKofOiWHVQWDf8QCRat1uUoiVHobKYi/iSBG0Z1mZeZkaqEC+CoSdREH6HFvCIl18BXRdTZd/S51ERfGO3aQRhF5UUZK24ahoHneBimwczLZ7H05iu46v41FFUUDeiahpILR8+x8ekNaX0V7KiNmUXTGsM0mDR34EyZw4nB2lFPA9M7vZ6WGMs03iOHm3an2IRziagIjpqQaC/ZaRwDn4LQc9obukS9A1TEn6XA24kl9VjUI0hWdvxcmotySTbCSbBotlfhq0Ja7FVURb9Ig30j9fZt1Fl3Iiq3cdvZMta0g8vWXY5hhXytFYybnvqEqpTi5q/emlbRc1QgcO7wOQ5vPcTLP3iRYzuOJQ/NWjq7xy+aMhR21GbxdYsHdp3DhMESBM8DX1IBVwH1InIWeA24VSlVrpQqB25NjHWLr0qTzsCBEgYN9o345OFjIwTOTldVBGWWM/yzKVyi3jEMaU2JMGrvZ9Yd2YR8DgWZG9B0/t3AU2U4anKnsTxMv45S51XKnV9RGfspRc67IINfxGwsaQfzVy1g3JRxyXBTZQbF2q59+LpQ30DphFJW379meD6B5ICOLOyNSbNZfnE+N3xxHVbEwo7a2FEbK2KxeO1lVEypoKCskHkr53P3N+8lvyT8wW+0kauooSeAtUAlcB74NgTppCLyz4nw0R8QOIJbgN8Tka2Jc38f+M+JS/2NiPx7T/ebunia/Ph3fw+kfrHbv+w5Q1yi/lFMacRVlcSN6aAURfG3yPcPhJZg8CjGIju7Yuq/fHrhtvY5w/E7GqzUSBSWm0eztQZRHc7gPHc/RW5qZVYfkzZzEU32NYO+3nZGe2QRBBEsJ3Yd59S+UxQU5zNv1QJKKjP3iNjywkfDug5OLrAiFnc8fiflkzt8Bq7jcu5QENU3ad7klFado5VRVX106uJp8odP/QnQ8RTXLhByLgxCsLyzlDu/Dd2gPfIxaAutxhlWvC3obtbuak6nJ0EwWIKi832CyKjFNNtrMs6viD2BJfUh17G4GP09UNlFrgwUuqJpB+//8l2OfHJkyO4/GJiWyf1//gCFZaPXJ5INo6r6aJtrc+B88MVp/7J2NhUNlLkoibIRwjcyT5VCl2Pt0TRdK3oGPz4qgxBon9eVbKqa9ofw6yp8ognz2LU0W1d1ew1DMhXY8lEMfaOOseY76I6CUd7SUSlF+ZSKMS8EumNECgKcYNmdhcGWqqM0WesH3HcA4KpxCAWhOQQt1lLqrZsS/gULwcRhPD75QHgryN7SOXS1N+f3VXgEPpI8qqJfpib6WdqshR0RQOJje6eJeocwpCl5jqvGh97LJw9heGRZjiXfQXfMu3J+1rkF2VI8rhgrag0Lu2bpxDLWfTk9n0DTwcgUBIB/Noh0OHB+4uBqByJYcoE2cy4+EXyshEPZpM1YQJ67j1L3DRRxfPKpt2+iKXItBi1DXkYiu4qpXXMjAnNOnb0eVOrHxfTrGBf7BaXOqxQ77zIu9gSFziYQocleA1hdnMoWTdY1gxpGmg1jXTs4d/gcvuQ2X6CxuhE35g559INpmVx+4+XkF+cP7UKGOSPSRzB+2ly57W//e/K1MTmoqLhgYscXaEB8B+JR6ryG7Z9B4SfMQ4pmazkxcz2qDaIAACAASURBVC6l8TewpColz8DHosm6miJ3E0aWJpGBbjvZXSRQ+LiJT5Ta6AP4KqFei1ARfxKzS1E9waLevom4ORvTr6bQ3YrtX8RTJTRbK3DM0HzBYcNY8x2cO3yWN//tzfS+vyG0h1QqQxFrjg3outLubSrE62GvCkv1J3AUX7JmISvvunJA1jaSGFU+AoCCUx1frM7aQfsXM0w76C/57i5s/zQGLgo/UX4iTp53AENimFKTlmym8LH8ixmT0MJI1IwcsIep3jep9zBopcj5IDlmSi2mNIeYulzy3V0AeMY4GiK3UZ33Reqi9wx7IQBjTzvY/e6urIQAgCDJjmaDRbQgyg1fWssVty3vdp5hGlx67aK0zmwAbtxl78a9tA7jxjBDzYgUBIV20Ojkk+0nKDgVjPln8zhT08DmUydDfQfQf1NRvr8Po0sF0qASZz2mf5GwrVThY9JMi3kZfqdirx1mFyNDCYrhpampLp3GgkJ84aLDID5YyxoQxpLvoKW+JXTcMA3sqI1hGh2b6xBUm4i1xNj45IZkmGcmSipLWHXPaibMDtegTMug6mTVQCxxVDAiBUFrcxuFJ1qSanW7djApNiGjdpAbR3LmZChPlYY+9fuYOGoSzdZVNNrriKtJuKqcZnMF1ZHP02CvJdOzeG+Kug0Gggr6D4vgqvA6KoGvZO4gr2xgGAvawZRLpoY+RStD8dBffpaHv/3IoDt8lZl6Qzfucu5gZkFgWAbXPnIdAMUVxck+DJ0RXygo0X6CTIxIQVAYDTSCHZsOJoVBV+0AyLl20GbMDw0b9SjENSYTM2Z3eepXgE2rtQiUImbOpS56HzXRh2mxr8Q3SoiZC3DJvoPXYJeZ7vxa4VIe/zXjYv+BLedptNYiWEnNwMfCoxjPKMX0c98TdigY7drB4usvI5IfTREGlm2xbP1yInkRPMfrV+E5wzLSNvaeCPMFGJYRWjpDGYqbf+/mZIG3S6+9NG2eUoqi8iIqpo7r1TrGEiPSWTxn+nz5zp9+L/mFaP+StH+BWjq1Bm13JF81bXryC9pnR7I4lMefw5QGDBx8TMCgLnI3rjEBxCff3UGBtwuFQ8yYQbO9usPBGoLp11ARf3pAN/jgL6wQLALx5GZd8qKzB67zOT4WNdHPoSRGvrcHw29CESMi5xEsFB6OmkB9ZD2ihke4aH8ZqqzkmuMXaDhbS9nUcZRNr8x5VnJrUyu739nF6X2nyC/OZ9H1i3FiDgc27yfe5lBzpjprVdSO2pSML6F0Qhn5xflMmjuJyfOn8OJ3n6fuQl2fVVorYlEyvoT6Cw1Jn4YVsZi+aDrXf2FtytzjO4/xwbMb8V0f8YXyKeWs+9JNad3cxiKjKrO4XRBAqrq8ZM38lKepdoHQLgwgPbKo1yUqxCfiH8f2z+BTTJs1H1F9VDlFGBf7JQaNvRYEvW8MYxA3ZtFqBn1Ti533MraZTL+PkWb2EkyazRW02IETL9/dSaH7YUpJCcEgZsyiIXJrlqscGQxWZNG2jw5z7icbcM/WJxvEVM6bzA3fvIc92zqun+vIoo1Pb0iv158FhmWw7LblXLb28rRjnutxaMtBDm87jGkZnDt8rlfXLqoo4t5v3c/hrYc4vO0QhmEwf/UlzFk2J7Rngu/51F+sx47aFJXrRLJ2Rl3UUDsr5k5MfhHaTUWdfQcQmIpy5jtQBnFzNs32NbTaS/ouBAjaPSpaB8XcY+AT8U/hmNNwzGnU5H2emDG7xwe0dqNP+riHQUdfiHx3Z4oQCOb4RP1jIEOfSZxLBst3wAeHiJ2qxYu7uG0OXtzl4sEzbH9m44D5DurO13H00yN96m8ciUaYv2pByljtuVr2btzD/k37qD1bi2EqKqaMY8qCqb2yc17zcOADEBGsiE1BaQHF44oyNs4xTIPySeVaCGTJiNcIOtNX7aCrqQgGqWaRf56y+ItZ5xe0I0Cbmo8YFvne3qy/T4LJxbz/rdOAR7HzNnn+oV7XMxKg0VobZBkDlW0/waCt66kIBlXRR/slMIczA6UdiAhVX3sOP6TtpJ0f4TP//IfJ17nMO9i/aR9bXvgotN1lUUURzbXNhO0Z0y+bzup7r8K0LXa9vZMTu48Tb4njxILPdljDHNMy8X0/q1aYJeNLMEyDpuom3IRpyLRNVty5kkuvWdTbtzlmGbUaQWf6oh3A4JeoaCeIvOn+TyAhP46aSGPkeprMzFU80529ipgxPXVQmTRGbqbBuiVRDqP7dXTF8jsEb8yYHhpOGnRoG4X17hMMmHYg4LvhUWpOl6f1XGoH0cI8DCP9M2mYBvNWzmfaoukdJa6NoMT1moeuZt2jN1F7tpbn/u7X7Nmwm8aqRmItMXzPz9g1rbvWml1pqmmioaohKQQAPMdj24tbibeN7HDl4cCoEgTtdBYGnSOLCk6RFlk06CUqOqNMGuybEpE3wZ+iaz2g9npCgkmrsZga+0HqoveDshMlHzL/Cdv7JgSbfDS0/LOSFkQZ1FvriTEtVIC0ryPlPCDP76hY2WyvQogko6ok4ZxutG8YdiUlcs1ARBZduWAG9vyQKBelyJ87YcAii6ZfOg2VIZx03pXzWPelG7nh0XVMXzyD6Yumc8vXbmPKgqn8+v95lrd++haxlljPGcB9IFPbTMMyuHj8Ys7vN9YYVaahMHoTWTTgJSoyYEgjee4+DFqIeCexaEqb42NTF7kH1xifMl4Sf5Oof6RLWQuTNuMSfFWMJdU4ajxt1sK06J0C5yMKve2dhFCENjWTAtmXLI8dV5OJyOnQBDePfKrzvpx8raSFfHcXEf8srlFGq7kEzxicPsXDhVxGFjUereaDrz8Z1OxxfIyIhRmxWPf3n+PgxY6eF7nod9Da1Mo7P32LC8cvJG2BpmlimAbKUFz/+RuYunAaTbVNvPHD12hpaEEphe/5RPIjA5+1qxSE7FVWxOLWr69n/IzxISdpujJqo4ayYST5DspizxGR9IgKH4u6yH24Rpd2gxKjLP48Zqfa/64aT13kjkBryEDEO06J80aXKJ8gMa7WfgCTOnxViK+KKIu9gC1nUoSBj0mreVm3PQnGMrnyHSyOTeHEczs5/ukJrNnlFKydzXWzOsJPc1GzyHVcnvnOU6EmlqseWMP8VQuSeQbP/3/PUXeuLtRPkCvaHcDtZiNlKEzLDHVgWxGLR/768zmvnjpaGRM+gkyMJN9Bm3VpSlJaO0IUV6WbCkRFqY08RF3kLpqsa6m3byFmziPPO4jK2BMA8r1dIVE+YEgzBk24xsRk/kNDZB0+BYkqq0aQOKYqaLbSPk+aBLnyHeyOnmH+71/Fzd//LGu/eQtGcTTnWcmHtx3KaGc/sHl/UgjUX6ynoaphQIWAaZusvu8q5q9aQH5xPirhr8gUxSS+sOvtnQO2nrHCmBAE7XTnOwgrYDcUvoM2Yz5xY0bSb+Bj4xOhPrI+s61dKVxjEoo4Zc7rFLofUOR+QGXs50Tdg+GnSCYHm0qrFeSrIqqjn6fBvpEmazX19u3URh7oVuPQjJys5HOHMsf0N1Q1JH932pxQR3JWKKiYNo7K6ZUYppHsFzxp3iRM28S0Tew8mxV3rOSSNQtZ8+DVXH7TUgzT6Nah7Lke+z7Y27c1aZLkpEmnUmo98D2C1lw/EpG/7XL874F1iZcFwAQRKUsc84B2kX5CRO7JxZoy0S4Mth0+HwiDdt/B9hMUEJiK2oWBMbmNA+cnsmDi+aQwuLIyEAZF7qscbtqde1ORMmiI3IrlXwyS1lQ+MWN2j5uu6ddS5H6Iwktx7Ja471BlTkVUalZlzJiD7VWhQuonOSrE3qpM4ubsvryjMc/cosUcbtqdFAZh5qJ2YbCl6ig1ztakqahdGGw+dZKPmw+yvHB+UhhsPXSSjdUnkg8xy5bO4JPtJ5LCoLO5qF0Y7Nh0kG2Hz6eYisoml8H28LUXdorDL58c7u8xTAMRSfoMQhGoO1vLZ/7Lw4gIseYYxeOKA5OP4xJrbiO/uCCl1MWZ/aezqozqtI2uPJWhoN8agVLKBP4RuB1YBHxOKZUS2Csi3xSRK0TkCuAfgF93OtzafmyghUBnhrt24BrjabWWEjMXZPXkHfUOEZb4JSii3rG08VZrEZ4qTZqhklE+1nWgRn8T78FmOGsHl6xeGFp4DmD1vauTv5uWyVUPXo1pm8nCbqZtUlBWyLov38glVy9k1pJZRArCS4oYhkG8NU5+UT5lE8uSdn3LtigsK0pbQ2F5YWgBua5UztSO4v6SC9PQKuCQiBwRkTjwJHBvN/M/BzyRg/v2mzDfAaQXsBsOvoOeUBkbUUp4LwRlUxN5gCbrGtqMWbSaC6mN3EfMWpA+V5MzhmNF07yiPG5//E7yijvyPUzb5NqHr2Py/Ckp585ZNoc7vnEn866cx5QFUxk/cwIt9c2894t3ObB5P8WVJcxaMis049eKWhRVZJ/pu/Dq9AJyYVSduMiZA+lCT5M9/Y4aUko9BKwXka8lXj8KrBaRx0PmzgQ2A9NExEuMucCngAv8rYg8l+E+jwGPAYwrH7/iu//XT/q17q5kiixatnRG8guW0wJ2OcbyL1Ie/y2qiwNYMKmOPoKviodkXZrMDMduaG3NbShDEc3vuVDgjre2s/PNHSlJXpZtseiGxRzYvB+nzcFzPZRSGJbB9V9Yy4zFM7q5Yjondp9g49Mb8BPJZ/kl+TTVNqU981gRi89++xHsiPZbdceAhY/2UhD8OYEQ+ONOY1NF5LRSag7wFnCTiBzu7p69DR/tDZ3zDga8gF2OKXQ2ke/tTggDBRg0matotZcO+lo02TFUFU270peKpk98+xfEW9KDDvKK8rj3W/ezb+Nezh48Q9G4YhZffxkVUyqyvnZnfM+n/kIddtRm1zu72L95X5ogsKM21zx8LTMvn9Wne4wVBjJ89DTQuXbBtMRYGI/QxSwkIqcT/z8CvAMsy8Ga+kxnU1FY85ucFrADDL+BQucjiuNvEfUOgmRuftMTzfYa6iL30mIuo9lcTk3kIS0EhjnD2XfQHSISKgQAYs0x8grzuOLWZdz+jTu57pHr+ywEIFFAbnIFRRXFeK4XagEVJLOjWtMjuRAEW4D5SqnZSqkIwWb/fNdJSqmFQDmwqdNYuVJBuqtSqhK4BtiTgzX1i2x9B9C/5jcR7yTj4k9T4H1Kvn+AYuddyuO/7le1TtcYT7O9mhb7yjGX1TuSGY6+g+5QSlE6oTT0WFmG6KJcMGvJrGSto86IJ2n+DE329FsQiIgLPA68BuwFnhaR3Uqp7yilOkcBPQI8Kam2qEuBrUqp7cDbBD6CIRcE7QxoeWvxKXF+l2gSEzzJGLhYUpds/q4ZW4w07WDVvVdh2qkZvaZtsuqeVd28y/4x5ZKpTF88I63w3ZX3riavcPQWNxxoxkSJif4yEM1vLL+KsvhvQ0tQO2octdHP5Gz9mpHHSPEdXDx+gU9f/5S687WUT67giluvoHL6wIZzigjnj5zjxK4TWBGLuSvmUjoh+3avY5kxXWsoV+SyNabp11EefzatzANAXE0MKoxqxjzDMbJIM3LRgiBH5K6AnXClvQtT6tJ6ATfa1weJZBoNI0c70Ax/tCDIMbkob51HC0usvSgc2nN724z5Y6KGv6b3aO1A01+0IBgAcqMdCGWqlqnR8TjGJDxD2zo1mdHagaY/aEEwgIyE5jea0YXWDjR9QQuCAWYkNb/RjA60dqDpLVoQDBJaO9AMNlo70GSLFgSDSG8K2IHWDjT9R2sHmmzQgmAI6KmAHWjtQJNbtHag6Y4x3bN4qOipgB2QsUQFMGx6HWhGDiOtZpFmeKA1gkGiJ+3gXPQCUypKgOFX3lozMtHagaYrWiMYYnrSDibFJuS0vLVGo7UDTbZojWAIGMnNbzQjE60daEA7i4cduSxgp9Fkg44s0mhBMAwZiPLWGk1PaO1g7KIFwTBGaweawUZrB2MTLQiGObpEhWYoyEY7gHCBoLWDkYcWBCMEXaJCM9ho7WDsoAXBCEJrB5qhQGsHo58BzSNQSq1XSu1XSh1SSv1FyPGvKKUuKqU+Tfx8rdOxLyulDiZ+vpyL9Yx0VsydmJJ3sGbq1JS8AwgykjvnHUDwBdV5B5q+kk3eAZCSd9Dur9J5ByObfmsESikTOADcApwCtgCfE5E9neZ8BVgpIo93ObcC2AqsBATYBqwQkdru7jnaNYLOaO1AMxRo7WB0MpAawSrgkIgcEZE48CRwb5bn3ga8ISI1ic3/DaD7T94YQ2sHmqFAawdji1wIgqnAyU6vTyXGuvKgUmqHUupZpdT0Xp6LUuoxpdRWpdTWhub6HCx7ZNFZGLQLBAiEQWeBAIEw6CwQILWAnRYImmyYW7Q4RSBkorsyFRAIg64CYWP1iaRAWLZ0RopA6EpnYaAFwsAwWLWGXgBmicgSgqf+n/b2AiLyQxFZKSIrSwpLc77AkYDWDjRDQa61A0BrB8OMXAiC08D0Tq+nJcaSiEi1iMQSL38ErMj2XE063WkHYeWttXag6S9aOxjd5MJZbBE4i28i2MS3AJ8Xkd2d5kwWkbOJ3+8H/lxErko4i7cByxNTPyZwFtd0d8+x5CzuCd38RjPY9DbvAMKdyWF5B9DxMKPzDnLPgDmLRcQFHgdeA/YCT4vIbqXUd5RS9ySm/YlSardSajvwJ8BXEufWAP83gfDYAnynJyGgSUU3v9EMNgOpHQBaOxgCdELZKEI3v9EMNlo7GFnoxjRjgO4cybr5jWYg0NrB6EBrBKMU3fxGM9ho7WD4o2sNjUF0eWvNUJDrrGTovoidzkrOHi0Ixii6+Y1mKNDawfBEC4JRTO3ZGvZt3EtzXTNTLpnG/FXzsaN2yhytHWiGAq0dDC+0IBilHNtxlA1Pvo/v+ogIpm2SX1zA3X96N5H8aMpcXcBOM1Tkoj2m1g76j44aGoX4ns+mZz/AczzaBbrneLQ0NLPn/T1p83WJCs1QkU2ZCh1ZNHRoQTCCqTtXi++na3S+63N857GM5+kCdpqhoDehptD7mkWdBQLomkW9QQuCEYydF0F8P/RYV7NQV7R2oBkqtHYw/NCCYARTPK6Y0gllKKVSxq2IxaXXLsrqGlo70AwFWjsYXmhn8Qinua6J13/4Gi31LSil8DyPRdcuZvkdK9IERE/0JrJIF7DT5IrehprqyKK+o6OGRjEiQtXJKlobWxk/vZL8koI+XytTZNGypTOST1k6skgzEOjIooFHCwJNr9DlrTVDgdYOBhYdPqrpFb0tb619B5pcoH0HQ4PWCDQ9orUDzVCgtYPcozUCTZ/RzW80nYl4xyiP/ZpxbT+jJP46pl87IPfR2sHgoTWCYYATcxBfiORHhnopPaLLW49t8tzdFLmbMHABCHYPg7iajGNModVahKj8nN9Xawe5QTuLhyGtDS1seOp9zh0+hwiUTyrjmoevo2JKxVAvrVt0AbsxiHggPpXx/8AgnnoIUICPCZjURh7AM8oGZBk6sqh/DKggUEqtB74HmMCPRORvuxz/T8DXABe4CPy+iBxPHPOAnYmpJ0TkHnpgNAgC8YXf/N2vaKptQjqVibDzbB74i4fIK8wbwtVlh9YORj+mX02x8x62nCewJAuKzHuGAI6aSl307gFbk9YO+s6A+QiUUibwj8DtwCLgc0qprmmtnwArRWQJ8Czwd52OtYrIFYmfHoXAaOHsobO0NrWlCAEIzETbXtwyRKvqHb0tUaFbY44sDGmhPP4ctpxHAQofuhECEGgGtpwZ0HVp30HuyYWzeBVwSESOiEgceBK4t/MEEXlbRFoSLzcD0xjjNNU2htcJEjj88WF2v7tr8BfVB3pTomLzqZOhNYtAh5kOR/Lc3Sh8OuenK3oSBSAYIOE1sHKJrlmUO3IhCKYCJzu9PpUYy8RXgVc6vc5TSm1VSm1WSt2X6SSl1GOJeVsbmuv7t+JhQMWUcRmPiS988trHOHFnEFfUd/pSwE5rB8MfS2pQeCFHjISBKN1IFPgLfCpjPyHf+RhEUBIjz91LgfsJln8ecuiX1NpBbui3j0Ap9RCwXkS+lnj9KLBaRB4PmftF4HHgBhGJJcamishppdQc4C3gJhE53N09R4OPAOD1f3mVs4fOhh6zoza3PHYb42eM79U13bjL4W2HOL3vFAWlhVyy5hLKJ/fd+ey0Oex5fzfHdhzFjtgsvOZSZi+bk7GOkW5+M3oocD6hwNuK0UUYCIp661Zco5SS+HvYVAFe4kjneSBEEscUCg/BJG5Mp8G+BVQOnkPFJeKfROFwsLUZhyDyTvsOwhkwZ7FSag3wVyJyW+L1/wkgIv+jy7ybgX8gEAIXMlzrJ8CLIvJsd/ccLYLAc1x++z+fo7G6Me2YaZnc92cPUFRelPX1nJjDS99/gea6Zty4C0phWgbXPHwds5fOzvo6vufT2tiKaRu8+k+v0FTThOcGm4EVsZizfC5rHry622voAnYjDBFsOUvEO4lPlJg1D8FiXOwJFLGQDd5ORAeVE/GOUuq8kfAh9IyPRau5JLiu+LSZc3GMqdDp4aJdi7DlIq6qpNVamBaWavnnKIu/TLuxSuHTZK5gVywowa4ji9IZSEFgAQeAm4DTwBbg8yKyu9OcZQRO4vUicrDTeDnQIiIxpVQlsAm4V0TS22t1YrQIAoBzh8/y5r+9ged0PHUZpsH4WRNY/we3p833XI+zB8/gxBwmzZtMflHHl2PnWzvY/sanyU27HdM2ueGLa6k5U0NhaSEzl8xK62nczsEtB9n64kd4jofvBV/srg5t0zK57/+4n6KK4m7fm9YORggilDhvEPFPoHAJLMaKevsWXFVBRfzpZN5A8hQgbsygPnIHtn+G0viraWGl3d4y5XeLmDGPRvsGUArbO02Z8zLgJXwSBoKVGpYqHpWxn4aEslrURu5if0t1cqy32gF0LxBGsnYw0OGjdwDfJQgf/bGI/I1S6jvAVhF5Xin1JnA50G4HOSEi9yilrgb+BfAJPn3fFZF/6+l+I1kQ+J5PvC1OJC+CYRq0NbWxd+Me9ry3G6UUvudTMqEU3/VorG6ksKyQK25bzpxlc7h44iJv/uh1xBcEQTxh2frlLL7hMgBe/O7zVJ+u7vb+VsTCMA3W/9EdlE8qTzl2at8p3vnZWylCKdM11jx0DXOWzUk75rkeSikMs0Pt19rBECM+llwEwFXj00wyUe8wxc7baZu9j01V5POMj/8sNGTUx6Iq72soiVEZ+1kGf0J2+FjURe5CiFARf4YwM5OjplAXDQILI94JSpw3MEj1ownQZiykMbIWyE3eAXQIhJGuHeiEsiHGdVze+elbnN7f8VRcWFZIa1MrpmXiuz4zl8xi8oIpbP7VBymbsWmbrLp3Ndte3ka8JZZ6YQULr7mUpTdfwbs/f4dzGXwOXSmbWMa937o/ZezlH7zExeOhVrsUrKjFjV+5mcnzJifH6s7X8cEzG6g6UQUKpi+awZqHrk7mQ2jtYGgIntZfh+QmbVIfuQ3H6PjblcRfIc8/nnauj02DfQulzquhZh+PAqrzvgRAgfMRhd72PgsDAeJqGpZUY9BKmAdKUFyMPgZKEfWOJIRXekBFmzGHhsitydf9yTuA0aUd6FpDQ8zr//JaihAAaK5rxnd9nDYHz/U48vFhPvrN5rQncs/x2PbSVnw35EsmsH/jPn71358h1lVIdENDdSPN9c0pY0216b6KNBRE86NMmjMpORRrifHKP77ExeMXERHEF07tPclr/+sV2h80dGvMwcXwGyh0NlMWfwGDNgycxE8bpfGXUNL5s5J5GxAMfCKh0UGOmpB83WJdSYO1Fh+7i9nHRFD4mD2GndpyCjODEAjoOBI3poYKJx+LmDkvZaw/kUWQOdQURk9kkRYEg0BjTSMXT/T8pA2BwzeMeFtm+6uI4MZdas/UZL2msC9bfnHmGjHKUBiWQfmkcm77w9tRRscVDm45kOaX8D2fpromzh85lzLeXd6BLm+dG6LuIcbFn6LA206mqP+o1xGY12YuxMcKmaXwVDEG8bTPiwJM6RTGrRQxaz5V0a/QaN2AoyqSQsGllDa1AMHMuGZF95tRYPKZm3Qoi4rSaF2DYCWNSD4WjjGZmDEr9Br9zTvoGmo6mvIOtCAYBBqrGvp9jUh+JOm8zQXFlSU4bQ7v/fJdfvN3v+Ltn/6OlrqWjPOVoSgqL+L2b9xJcRcncf35+nC/gkBDVbqWEaYdQKBaa+2gfyhxKHHfQeElIv1D5uBh0GGG84kmQjsDJPHTaF2ddNaG3ivMDKRMfFWAJQ0JDcTDpo582Ztyj94ggE8eTfZ1IB4R7zhR7yBxcxY1kftpMS+j1VhAg30z9fbt3Yalau0gHC0IBoHSCaX9Ot+yLa64ZRlX3r0K0878VJUthmlw2brLeen7L3Ds06M0XGzgxO4TtDW3ZTzHd32aa5vZuyE9oGvctHFYkbAnSiifXB46Drq89UBg+2cybP8dBLH8U0EE06+mxHkrRWi0/z/PO4SnShCiIddQxIz0YAGAIndzIvqoA9XpJ1vaBVKciVTbn8eUOipjP6PEeZNi5z3GxX5OofMRjjGVRvsG4uasrHMTtHaQihYEg8D5o737IytTES2MBvb4gijL71jBJVcvZNLcScy4fCaG1b8/m+/7bHtxC27cTdrws3lU81yPo58eTRufu2IedtROSTIzLZOKqeOonF7Z7TWz1Q7O1DToEhVZ0f1nw8cibkzHp5Dy+DNUxH+NSbrGqoCInAWlaLBvTLHxS+K/ETkNkm7KNKWuv28iuQaACOepdH5JWfx5DGJJf4fCJyrHKXHeYFzsF5h+7yoODKZ2sGzpjGGtHeiooQFm3wf7+PA3m9IPKJi1dBbHth9L2YQN06BsUhm3f+NOTuw8TktDC0opdr+3i9aG1kFbdyYqp1dy55+kV5Zsrm9m24tbOLn3JKZpMnflPJbdtjyjphBGpjDTMXoGmwAAIABJREFUZUtnJL9Uurx1D2SMr1e4VNBiLyVmzKM8/mssqe62kqhPHlV5XwGgLPY8tpxJeaL3MWk1F9NsrSLf20vUOwjYmFKFSc+BC+3lqzO97ml+2vtTFdRGP9PjfQ2/gWLnfSJyCjBoU/M47irGqSD0+qRxO9UsCL1briKLID26aDAii3T4aI5pbWhhz4Y9nD9yjpLxpSy+fnFaKQfP9fjlX/481LavDMX9f/YgzfXNfPD0hiCCR2Dy/CksvfUK3vrx73AdB8/x0hK6hgorYnHVA2uYu2Jez5P7gS5vnQXiUeB+Sr63B4VD3JhOk30VvirG9k5T6rT/OwRF41qMxbRYVyDKxpRmKuLPdBvq6WPSYl5Bi30liMv42I8zROlE8FQpptR2alZjJO+bXC7pm75LBQZxDNpwVRmWVPXKdJT2T4JJdfRz+CpzNr6SGONiv0QRTwrB9m9X+709MTirlrOX+0Ov0d+8A8gu1HQghIEWBDmkqbaJF7/7PE7Mwff8IIHKMlj35ZuI5Ec4sHk/8bY45ZPK2f7Gp6HXUEpxy2O3MnneFESEtsZWzIhFJC/Ci99LJIYNsz/NxLmTuO3r6zPWGcoluvlN95TEXyfin+i0+SqEKNXRRxCVh5I4Ef8YhsQRTArdrRgEGmVcTcGWc2kJZO3XAYOYMZsG+8bA5i5OQhCkfyADO74ZUo8oIDwfINA2aqMP4qsg8EBJK5Wxn/ZTEFjURD6DZ2T2yeU72yn0toS+9854YrDbW8p566GMc0aidqAFQQ55/4l3OfLJ0bQqinaeje/5QSilBHbyrmGVnfnsf30kGbLpuR7bX/+U/Zv3EW/NPlV/MJkwawK3f+POrOfXna/j45e3cuH4BfKL87n8xqWh2ciZ6GsSGoxu7cD066mIP532RB88xa+gxV6eHLP8Ksrjv0mZm2mTFgzajLk026tTnqoNv4Gy+AuYNIac0zsHcPs5LqXU5n0uZbws9ltsOdujeShs7e3rbzLX0GpdCircJFkcf4t8/0AWa1Sc9GZwWmYMaFYyDK52oBPKcsiZA2dCS+k6bYEpp/3T2p0QmLJgSkrc/rv/8TZ7NuwetkIA0nMcmuub+eS1j3nnZ2+x+91dKWuvv1DPS99/gZN7TxJrjlF3ro5Nz2xk59s7sr6fLm8djiXVoSGdBh62BHkbpl9LafwVyuO/gi4Co30TDWr4BPiY+KqQJutqTGnE9s+A71LkvM+4+FMYpCYf/v/tnWmUHNWVoL8bEZlZi1SbSruEhHYQAiGJfbfABsRiMCAWu8HGbbu9TI+7p6fxcbeP2zOMPfacg497Nnts2rRXsMFsttkEGBuQQCCB2LSDJJDQUiWp1lwi7vyIyFJWZWRlVlVmZVbV+87JU5EvXkTejIqM++67992bea6BDiUFcDhCVfINxieepan7lzTEH6LLXkySiT3RQtmL2GzanAvosk7EI4oXXINjysGj1l1HY+Ihv7RmCCmZkGPNRN/PsmiM+aONUkYWQf5QUyh9ZJGxCAbB7757P0cPFL42IL34Kj3XP3HWRC79m8t78vEcOXCER+56KG+On3JiOzYLz16EqvLepneJt8XxPH9azPM87IhNtCrKqr+9ktr6Wp775Z94d+NO+t5fTtRh9TdvwokU7kQGk6IiE3+U/2BWiKZi0Wkvocs5iab4fQjJfpyr0G3NQ1As7SBhzSJlNVGXfBqCSmQSklq6WGTeFenz+zWPBSEVankAtERv9BPPqUuV+zbjU89nTVl5OLRFLiBuz6cvuXwE2Z/ncDB2Myo1wPDkLILSWwdmaqiIbH7xHV5+5GXc5LEfYt+HfU+7CHNXzGP6wunsf3c/3e3d1NTXUF1XzbaXt9FxuJ2a+lraW9twE5WpCMQWnIjjh5v247gWSzh+6RzOu+l87v/2b2hvac/q48QcVn3lShomD664uUlg59MQf4CIHuzlwPUf7gsBpcrb2m9EEEBcptLlLCVhzURIMiH+s7xz5+XET2sxhcMxv35VzN3C+OSfc+QbmsfR6MWh57G9I4xL/tkPf8UmIVMCS+qYrXQ0cgkJe1av44Y7ZxEU33dgFEERUVVeemgdW9ZtwXYsPNdjwsxmDu9tzZrasSM2K2+/hLUPvMDR/UNfYVwOIrEM30ceotVRbvrWLTz+w8dCE+BZjsUN/3wjsZrsRUqFYqwDf2Q7PvknYt5OjuXjT4/f84/ij/3qBcWhyz6RavfNilYE4Mt9KHqzH22kbdQlnw7JQCp02kvoiPRfM6P3QUminl9rOWFNAwlP0w4j2zowiqAEdLV3cXhvK7UNtdRNrOfQnoM8+f+e6AkX9VyPZZcv553n3w4tPjMaqamv4fp/Wh1aZ8F2bGadPJvzbjq/KJ9lrIPw+P58hIVypil9PNjQyH5a2aTrFhzr49ASve5Y7QIIaih7WMSJuu8BFnF7Vlaxm0IZqdaBUQTDhOd67Nu+l2Q8xZS5U/yMog+vq7hQ0FJh2RYTZ01i+aoVtB9q46WH15Hs9kdsc5bN5YxrzsR2hp4mI81Ytw4mdv+ooMpg6api/vx7+M04lAVd5SL9vTJpcy4k7swNOqQYl3qRavcd0k5z7fFFKEedi4g7g18XM9KsA6MIhplUMsWanzzJhzv3o17xksWVg8apjbTuax2QMrMjNpd98XKapk2gu72LSHV0wA7igTDmrAP1iHp7qEs+UdB0jp/L/zM0x/89dE493cd/QGbfr/2FbZYbBVoj1yDikaKOGvc1qtydqDioCg6HcypLxeZg7JODtgxgZFkHJnx0mNnw2Kvsf+/AiFcCAK17fSWQmXo6H27SZcPjGxBLqK6rKakSgLGV3tryjjIh/gvqkk8WlNHTL/oyHSRC3Do+Z3/FptNanHN/rsVhlTCU9KSalDTTlHyQGvcNbNpwtBWHln4tJkWIudn5swbCcOcsguwkdkPNWWQUQYnY9vLW8EIyI5zquppeZSj7oyVP2cxik2vdwWhLb12ffAKLziDxWto9nC4CYwWrjIMoNgQlQnvgOG2PnIVHTY6HtxUoimylnWsIMNCMokOhP4XjSTVV7mZEu3o9+PPLpgVNrRXCcGQ0hdKsOyiKIhCRS0Vks4hsE5E7QvbHROTeYP86EZmdse9rQftmEflYMeSpBAqJsBlpqKckOuMF10XIV9y+VAzUOhhJ6a0tbcfR1qx5fgE8qmmJrqYlej3d1nyS0kyXfUKQdsHPg6VSzaHYLcEDX/ArkEXwiNDhnEZ96sms9QnlIMzSyGWRJJgGOMTcHQOOehI84pI97z5YRqp1MGQfgYjYwBbgEmAP8DJwk6q+ldHni8DJqvoFEbkRuEZVV4vIicCvgNOBacBTwALVHMsCA0aCj+CZe9aw641d+TtWGGJJv2sFIrFI7/TVObAjNhfdupLpC4v3IxsM+RLYwcjyHfjpJX4T+rBOUUdL1c0Fn8vSdqLuHlQcEjKJCYn7BhU+Wm4nctrqkSDj6kAjqDxidNsLUKkhbh2Ha00oilyV6DsopY/gdGCbqu5Q1QTwa+DqPn2uBu4Jtn8LrBQ/c9nVwK9VNa6qO4FtwflGPKdddcaA5tQrgfpJ9Sw698R++yjKxNkT++1jR23Ouf7csisBGH3Fb1ypw6Mqq12xidtzB3QuT8bR7Swibs8LitcPfFBYCf4BfyIsMahpqiDFHjXuJmpT62hKPMC45F9CU8gMlJFkHRRDEUwHdme83xO0hfZR1RRwBJhQ4LEAiMjnRGS9iKw/2jGwAhTlYFzjuILn0stG8KuxIzbzz1zARbeuZMf6bf0ekoqniFbHEDvHT07g8i9dwfEDSC5XagotfgNUfvEbEY5GV+Lh9OQb8nBwZTydzqmDPq2lnf2mpc4pzqA/sbjkmjYq9NhjL5cq9x0imr0YcrBUiu8grRDCqPAn1TFU9UequkJVV9TVDq3043BR21BbbhH6J50cL+myde0WHvzeA8Q78xcUef+dPf3+ysY15s4HX05GSwK7pDWVltiNdNjL6LIW0h45n5bo9ahEe/qIdjM+8SzN3XfT3H0345J/QjT3/zZpTQt1EhdCpSiDMAYzrhdSVKW25u84ACrBOuiPYiiC94GZGe9nBG2hfUTEAeqBQwUeW3Ec/vAwb//lLba/sq1nsVQYJ55XGfPKxUY9zelHsG2bRHeCZDyZ14/wwZYPeOKHj/G7797Pi/e/QMfh7NxExaY/R/JIsg48GUdnZAVt0YvotheAZCzSU4/GxINUeVuCwi8Jqt3NNCZ+F6ywzSZhzSAlk3pl5qyEaZ9CGEi4a3/9B9NroFSCdRBGMZzFDr6zeCX+Q/xl4GZVfTOjz5eAJRnO4mtV9QYRWQz8kmPO4jXA/Ep1Fqsqax94ke2vbENVsSwLRLjk9kuYdHz2Io7f/+ARDu4+OOxylhM7amNZFqlEilhNjOWrVjBvRbZJumXdZl56aF1PCgqxhEgswpVfvXrYLIrRWvwm6u4MzcHjEeFoZCUJezaOdwDH248n40lYM4ICNC7V7ttUuZsBwdIjBZWcHAzD6WDOXAxXSCSSh8ORyGUk7dL6uMqxKvkzf3VeaZzFwZz/l4HHgbeB+1T1TRH5lohcFXT7CTBBRLYBfwfcERz7JnAf8BbwGPClfEqgnOx+azc7Xt2Om3TxUh6pRIpUPMnTP12TFVJ5ZP9hfzXuKMWJOqF+AjfpkuxOop7S3d7N2gde5L1N7/buk3JZ/8jLvfIQqack40lef+q1Uovew2ixDvri1yLOtlSFJI53gPr4ozQmHmJc6oWg8PsvsbQdxKbLOYnW2CdojV1LSvoPChgqw2VxpOf/NWO7rwzHaiBYdNsLSVrTSi5XOayDXBTFR6Cqf1DVBao6V1XvDNq+oaoPB9vdqnq9qs5T1dNVdUfGsXcGxy1U1T8WQ55Sse2lLaQS2eF1ruux/739vdq62roq31k8SET80XsofX7d6RXGmbQdakPDyh56yt5tHxRLzIIYjcVvXKnPyr8Dfk4eR1uJ6l6EFBYuFkksOqhLrMnq3+UsKaiIy2Ao9kK0QpSK9NlWLBIyjSSNuNJIt7WI1ui1tEfOg2Eoxwrl8R2EMTqfVCUi10IqAbTPvqZpTXipkZ9eogfxFYBYwvQTZnDSRSdj24Ulj+to7T33X1VblfNa1tTVDFnUwTAU6wAqK0WFv1gs0isZtR9rHyXi7c+KDhKUiH6Y5UxO2LPotJcNeeReKWko+mLhISitVatpia2mLXohKau5LLIMl3WQC6MIBsCc5fNwotkjJMWv55tJtDrGkpUnh/YfadgRm6apTdz0X27hU9+5lZWfvhjLKnz1dP2k3kVoqsZVMW3BdCyn9+1nRxxOuujkosk9UAZjHUAFpqgQh9bYNSRkek+6iYRMpzV2DfSbTiH7/9kZWUaHLBnSg7yUFc7SSqbw+gu92zypnMi+4bIOwjCKYADMPmU2U+ZO7Xm4W46FHbE5/+bzsUOSqp1y8VLOvfF8mmeWZ5RRLOYum8vlX7mCSCyCiJDsTrLxiY39rkBOY0dslq/K8k1x/k0XMG3+NCzHJhKL4EQdll22jJknzgw5y/AyEOugUhPYeTKeI7ErOBD7LAdin+VI7Ao8GUfcnktYvWNX6nvKMvbFlu7Q9oFQbGWQa86/P7JrIDt0OuUbeOSiVNZBWimEYdJQDxBVZd/2vbz/zh5iNVXMWTa3oPUCm555nQ2PvVrQw7OSEEv4yG0rmXHCsZto84vvsP7Rl0P9JZk0TW9ixRWnM3Xe1Jx9uo520tXeTd3EupJnKB0Moy29tWicxsT9WNqJRSqoE2xxOHoVKSvEOaweE+M/qui1AoWSruOWXi/R5pxH3FlQTpHyUuzIoq+edY6pR1BOkokkD/2P39HR2lFuUQZEtCrK6m/e1Mvx/cL9z7N17ZZ+j4vEIlzy1x9l4qxJ/fYbCYy64jeaosrbgePtxaWebmdhznz8ltfGhMQvyq4IwqqqDUYmj4iv9KSp99qLCqaYOYtyKQIzNTRMvPqHV0acErBsi8u+vKpHCRzYdYCH73oorxIA33IaLVFTo8Z3kEYcuu0FtEcuoCuytN+iLP6agqEz1OHmsVTbkJlme+Dn8XzLZ4QoASiu7yAXo+OXOgLYtr64S9aLzeyls5lz6hxqG2qpGlfF/NMXsPqbN9Ew2Xf0trW08dj//gOtH7QUdL5odYym6cXJ4lgpjKXiN+BnJ611N+Tv2A/HIoakl4N3sHRbCzkQ+SSCNcjzuFSl3qIpfh8Tun/G+MSz/hqKEUCxfAdhmKmhYeJnX7unIsNJxRKu+/oNecM2H/3BIxzKtUpa/AKHlm0htmBZFh/7/KWjThFkMtrSW4dRm3yRGnfTkAq3eFh02qcSt+cRdffgePuI6CEs2rD6VFcrJPLHIxakyu5bsL6wqSP/86ye7+RbFzEOxVYPqVzlcDNY38GXFq00U0PDQeu+VjY89iqv/H49B3cf6GmftqD8KZlDUT+yp98unuZWAsDKT1/MVX//cVZceTrn3HAeN/zz6lGtBGD0pbcOI+IdGJASCBtSCh417gaaEr8hqntoj5xFu3M2Hc7pdFkL6LJPpM0+h4RMyzvCT6eMlj5KIL0vc9pIqcohD30qmPkprGtSrxf8PSuBoVgHYVRemMYI5o1nN7HxiQ14KQ9Fefv5t1h41iJOu/J0zr7+HB749m/zRtoMN6rK28+/zdKLl+bs097a1u85GiY3EKmKcnhfK+++vpN1lsXcZXM55aNLiURzrEAeBaSVwSvbP/SnigLrYMNru6jBtw68vVXsi+1nLbuBySyY/GGPMjitOa0MKsc6EE3g5xlqR7QrdGSda7Sdqy394I16u2hO/Bwlil/ZoorD0VW4VgPdLCHqbqc++WS/lkE+q+FA7HYQB9tr8ZPskcx7jOAR8UJWtKsS87ZT5W4BhC57EQlr9rCtOs5H+n7Z3v4m41KP5bQOTms+npcP7uxXGRiLoEi0t7az8fENuEnXz7qpfnqFzS++w6E9B6keV80N37iRBWctpKa+hvETxtM4rancYgPwxtOv96ugotWxfo+vrqvh9//6KFte2kK8I053WxfvPP82T/zw8bwZSEcD/TmSz5lwHFPikyreOrC9Vhrj99Mc/ynN8btpStyLQ2tWoja/tGVskOmdNRjVJ4LUFm00JB7B8vyBRsKeS0omDsGHYIH4Y1vXaqI1+glc8od2K37Bn96NSl3yScYnnyXm7SLmvUddcg3jk88MWrpSMVDrIAyjCIrEnrd2h7a7KZf3Nr0H+CGVZ117Ntf/02quveM65iydk3daJpPxE+rydwpBLCFaFUVyjGTEEtpbczvMYjWxfkdBOzfuoOtoZ680G27KpeX9Q2x9qbKd5MUiHVnU15E8Eorf+GsLHsRRfyoo/cBO/8czp1267MUcin2KuDUPZWiRN75S6GBC4lc0xe/F9lpojV6V97jcKOixZHuu1UDCnltAhJFFl3NKr5aI7iPq7epVutMiHXZbeRmFC40syoVRBEXCsq1Qu1VEcoZRzj9jgb+IKuO4XOUtnajDkpUnc8udn6KuuS5vGUyxLZyqCM0zJ3LShUtwYpGco3PP9aip699RFonlmEUUOLTnYKhF4bkeax94ged+8eyIW0g3WEZiAruYu42+zte++PvUz0ek4FIFgbM38zVQ0lNHtrbSmHgIQVHCLdD8nyHQRzl12Yuz2vqes9uan5VjKOruCa0LDR5Rb0+/UpSTQqyDMIwiKBIzTwrP7GfZFscvDS/bGKuJsepvr2TGCTOxHZtIVZR5K+Zn5eABQGDWklk4UYdLv3g50xZMw7ItLMuiYUojC89exMRZE5kydwqLLziJS26/hJv/5RZW/YcrmDhrIonu8LzyYgtzls7NO/1z3EmzQtNOT5jeTMPkxtAUG+A7mne/tZvtr/RfAnM0Ufb01gOcjrP1SEFF6wWIedupTz5OtfdOrxKP/uM895RRvoe4fw6XmLudTntxVtZT7dUv/PwesayEeq5Vz+HIx/CI5sg3FCFuZ/8+PYmFWjzpqbFKZjDWgXEWF4nqcdWcff25vHDfXxBLevwEyy5bTv2k3KU1xzeNZ+WnL+7VNuvk2Tz382d7RvCWbXHRbSuJVvmlCKvHV3Px7R8lmUiirpf3Id52qC1n6GrD5AbO/MRZeb/f8lUr2LdtL/HOOKlECjtiY9s256w+l9qGWl597BXcHMXaUokUW9ZuZt5puWumjjbCHMngp6io2XPMkQx+mOmWDyf3FL95+eDOHkfyuNRjbG9/M78jWVOMS62lyn0HIUVSptAWOQ/Xyh+9lbIm4blOQcrAwiWqu0OidhSLOG3WGYzz1mc9kJMyCUu7sOjCIhXqcPZTY3fQ6SzH1naqvO0oNoJLQqYQ0f2hdRbgWERRbWqtn0Y687PtmRyUT9Kc+AWQ6FnRoAieVJOwsuPru+15jEu9FPpZYYqjEpk7bnGPIxn6DzU16wiKTHd7N7vf2oXnesw4Yeag6xa7KZcDuw5gidB83MQhrdLdu20vT//bU1nTN07U4azrzmbOqXMLOk8qkWLnxh0c2HWA+on1zDttvu8/wC/E8+zPnuHwvsOhxzZNa+LKr1496O8wkhmOFBX1iT8Q9d7veQD7I/AILbHVeJKn4pu6NCXuw9a2jPh6n1yx+P0Vi/dH0n4QZ4e9jE5neVABLUm1u5lY6m0itGQsMfPxK6hdQsL2rWtLO7H1MK7UY2mH71jOoQgyz3Gw6vbQfZZ3lPHJ54iqf/3j1izaIufnTLYXdXdRl3wq45tZHIl8tOSVy0pB2rI8d+bdJtfQWEVV+f2/Pkrr3pYey8CyLGoaavj4P1yL7YSYwKp0HO7Admyqxxe20MbzPO771q+Jd/SehrIjNssuXc6J55c/PLKclKo0pu0dpinxm6xRuGLRaS+hI5Lf4hPtpjb1MlXu9qBK1wKi3i4imr2S3CUWxPPnR7E5FLsZjxpqUq9S476OEEdxAse019MvJRNojV4THpigyoT4z7Ho6PdzFZsDVX+dR6jAOpYCBlfqEvH2AZC0poyo1BRhTG38e6MIxjLJeJINj7/Kjle3o54ya8ksll2+gqraqqy++9/9kD//6jm62rpQT5kwYwLn33JhQbWE923fy5qfPIWnHl7Kw4k6NExp5NIvXJrTjzCWKIV1EHX90EaLRNbnxWU6R2JXDkpWx9tPY+JhfEeyBtE3Nkecj1CfeqIgReBh0+Gcia1HqXbf7uWAVX+5GYpNt72ATucUkNzrTmzvEI2JRwJ5UsEZjqFA3JrN0WjuKZCxTkkUgYg0AfcCs4F3gRtUtbVPn6XA/wHq8Ctf3Kmq9wb7fgpcABwJut+mqhvzfa5RBKWj40gHD373gV7TSCJCbUMt195xXd5oJYDOo51sX7+NzqOdTJ03lRknzBw1CeiKRTHTW9veEZoS9+WwCE6mI3LmgGSLuu9RnXoDizgJmYJFF462kpJmOp2luFYDjfH7ieiBvOdSLDrs5dS6r4bIN4gHt7pEvd04Xgs17kb8JBYpFAePCK2xa/Fk/IC+71gilyIY6q/zDmCNqs4H1gTv+9IJ/JWqLgYuBb4vIpklq/5BVZcGr7xKwFBatr60Bc/r7VhWVbo7u9m7bW9B56ipq2HJR07mjI+fyXEnzTJKIIRiFr/Z0rmHhDWjV5SL7yPwi9EPhJrkeuqSTxLT3UR0PzXeJqq87QgJXKntqejVFjkPD6dnTJ57OCkkrebQYjgCOF5hSQyPHWSTsGfTGVnGodgtdNin02Utot05k5bYTUYJDJKh/kKvBu4Jtu8BPt63g6puUdWtwfYHwH4gpAKGoRJoP9QeHmGkvrVgKB7FTG+9ITGDLvskvKBWcVKm0Rq9Jr+jOAPRLmrdDb2ihySYwHH0KDXuRj9tg7qkrEm0Rj9BtzW3X+dxh72UlDU1NGeRAilr8KvrVWJ0RU6mLXohXc5JqEQHfa6xzlAVwWRVTQ8T9wGT++ssIqcDUWB7RvOdIvK6iNwlIpUdoDsGmDx3SnhdZlUmHmf0dykoRnprxeK1eB3rUmdyoOrzHI5dhTvAh2zE+zB05J7GwsXSo8S8HQC4ViMdkTPIFYWuVNEZOQ2VKF32iVlrA8DxI4oMZSevIhCRp0TkjZBXr1hA9Z0NOS1EEZkK/Az4tGrabc/XgEXAaUAT8I/9HP85EVkvIuuPdhzJ1c0wRI5fejzV42t6TefYEZsZi2b21CYwFJ8w6wCyU1SUsviNV0AaZotUrwRtHrU9pR8zUYS4dczh0e6cTae9DI+qwGKZwOHo5eHlMQ3DzlCdxZuBC1V1b/Cgf1ZVF4b0qwOeBf6bqv42x7kuBP6Tql6R73ONs7i0JLrivL7mdd59/V2ciM2CMxex6OxFZq5/mMgVWXTqKcfx/KFdQIlKY6rSlPhVsJ4g/Lmg2HTYy+mMLOtpi6W2Upf6E5AK8hIJSsRP+mblXkxpGH5KFTX0PeCQqn5HRO4AmlT1P/fpEwX+CDyiqt/vs29qoEQEuAvoVtUwh3MvjCIwjAXKUfzG8o7SkPwjth6FIMonc/7fwwmcsr0XSka8D6hJbcDWNhLWVDrtU/GswSVJNJSOUkUNfQe4RES2AhcH7xGRFSLy46DPDcD5wG0isjF4pZPf/0JENgGbgGbgvw5RHoNh1FCO4jeeVUdL9AZaop/gSOQSUtKEYuPh4FLLkeiqLCUAkLSmcSS6ipbYjbRHLjBKYIRhFpQZDCOAfNZB2FQR+NbBoKeKAixtQ9TFlfqKKcpiGBylsggMBsMwkK/4TSnTW3syHtdqMEpgFGMUgcEwQsgVZjoSit8YKhujCAyGEcRgFqFVQvEbQ2VjFIHBMAIpe/Ebw6jCKAKDYYRirANDsTCKwGAY4QzFOgCMdWAwisBgGA0UM4GdUQZjD6MIDIZRRDHTWxuFMHYwisBgGGUY68AwUIwiMBhGKcYkOr/xAAAGBElEQVQ6MBSKUQQGwyjGWAeGQjCKwGAYAxSj+A0Y62C0YhSBwTBGqITiN4bKxCgCg2GMkS+BHRQ/vbWhshmRaahF5ADw3jB/bDNwcJg/czAYOYuLkbO4GDmLy0DlnKWqWfVBR6QiKAcisj4sj3elYeQsLkbO4mLkLC7FktNMDRkMBsMYxygCg8FgGOMYRVA4Pyq3AAVi5CwuRs7iYuQsLkWR0/gIDAaDYYxjLAKDwWAY4xhFYDAYDGMcowgyEJEmEXlSRLYGfxtD+iwVkRdF5E0ReV1EVmfs+6mI7BSRjcFraZHlu1RENovINhG5I2R/TETuDfavE5HZGfu+FrRvFpGPFVOuQcj5dyLyVnD91ojIrIx9bsb1e7jMct4mIgcy5Plsxr5bg/tkq4jcWmY578qQcYuIHM7YNyzXU0TuFpH9IvJGjv0iIj8IvsPrIrIsY99wXst8ct4SyLdJRF4QkVMy9r0btG8UkfVllvNCETmS8b/9Rsa+fu+XUFTVvIIX8F3gjmD7DuC/h/RZAMwPtqcBe4GG4P1PgetKJJsNbAfmAFHgNeDEPn2+CPzfYPtG4N5g+8Sgfww4PjiPXUY5LwJqgu2/ScsZvG8fpv91IXLeBvzPkGObgB3B38Zgu7Fccvbp/xXg7jJcz/OBZcAbOfZfDvwREOBMYN1wX8sC5Tw7/fnAZWk5g/fvAs0Vcj0vBB4d6v2SfhmLoDdXA/cE2/cAH+/bQVW3qOrWYPsDYD+QtVKvBJwObFPVHaqaAH4dyJtJpvy/BVaKiATtv1bVuKruBLYF5yuLnKr6jKp2Bm/XAjNKJEt/FHI9c/Ex4ElVbVHVVuBJ4NIKkfMm4FclkiUnqvoc0NJPl6uBf1eftUCDiExleK9lXjlV9YVADijfvVnI9czFoO5rowh6M1lV9wbb+4DJ/XUWkdPxte72jOY7A9PyLhGJFVG26cDujPd7grbQPqqaAo4AEwo8djjlzOR2/JFimioRWS8ia0UkSxEXkULl/ETw//ytiMwc4LHFoODPCqbYjgeezmgeruuZj1zfYziv5UDpe28q8ISIvCIinyuTTJmcJSKvicgfRWRx0Dao6+mUQrpKRkSeAqaE7Pp65htVVRHJGVsbjGZ+Btyqql7Q/DV8BRLFj+/9R+BbxZB7NCIinwRWABdkNM9S1fdFZA7wtIhsUtXt4WcoOY8Av1LVuIh8Ht/a+kiZZCmEG4Hfqqqb0VZJ13PEICIX4SuCczOazw2u5STgSRF5Jxi5l4NX8f+37SJyOfAgMH+wJxtzFoGqXqyqJ4W8HgI+DB7w6Qf9/rBziEgd8Hvg64GZmz733sD0jQP/RnGnX94HZma8nxG0hfYREQeoBw4VeOxwyomIXIyvfK8KrhcAqvp+8HcH8CxwarnkVNVDGbL9GFhe6LHDKWcGN9JnWmgYr2c+cn2P4byWBSEiJ+P/v69W1UPp9oxruR/4HaWbXs2Lqh5V1fZg+w9ARESaGez1HA7Hx0h5Ad+jt7P4uyF9osAa4D+G7Jsa/BXg+8B3iiibg+9IO55jTqDFffp8id7O4vuC7cX0dhbvoHTO4kLkPBV/Om1+n/ZGIBZsNwNbKcDRVUI5p2ZsXwOsDbabgJ2BvI3BdlO55Az6LcJ3Zko5rmfwGbPJ7dxcRW9n8UvDfS0LlPM4fB/a2X3aa4HxGdsvAJeWUc4p6f81vkLaFVzbgu6XrPOV8ouMtBf+fPqa4AfzVPqGxJ+++HGw/UkgCWzMeC0N9j0NbALeAH4OjCuyfJcDW4KH6NeDtm/hj6oBqoDfBDfyS8CcjGO/Hhy3GbisxNcxn5xPAR9mXL+Hg/azg+v3WvD39jLL+W3gzUCeZ4BFGcd+JrjO24BPl1PO4P036TPwGM7riW+J7A1+G3vwp1W+AHwh2C/A/wq+wyZgRZmuZT45fwy0Ztyb64P2OcF1fC24J75eZjm/nHFvriVDcYXdL/leJsWEwWAwjHHGnI/AYDAYDL0xisBgMBjGOEYRGAwGwxjHKAKDwWAY4xhFYDAYDGMcowgMBoNhjGMUgcFgMIxx/j/hqpC/2QvKhAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_diff = X.max() - X.min()\n",
    "X_left = X.min() - 0.1*X_diff\n",
    "X_right = X.max() + 0.1*X_diff\n",
    "grid = np.arange(X_left, X_right, 0.01)\n",
    "grid_width = grid.size\n",
    "surface = []\n",
    "# создаем точки по сетке\n",
    "for x1 in grid:\n",
    "    for x2 in grid:\n",
    "        surface.append((x1, x2))\n",
    "surface = np.array(surface)\n",
    "# получаем предсказания для всех точек\n",
    "with torch.no_grad():\n",
    "    Z = forward(torch.Tensor(surface)).detach().numpy()\n",
    "# меняем форму в виде двухмерного массива\n",
    "Z = Z.reshape(grid_width, grid_width)\n",
    "xx = surface[:, 0].reshape(grid_width, grid_width)\n",
    "yy = surface[:, 1].reshape(grid_width, grid_width)\n",
    "# рисуем разделяющие поверхности классов\n",
    "plt.contourf(xx, yy, Z, alpha=0.5)\n",
    "# рисуем обучающую выборку\n",
    "plt.scatter(X[:, 0], X[:, 1], c=output.detach().numpy()>0.5)\n",
    "# задаём границы отображения графика\n",
    "plt.xlim(X_left, X_right)\n",
    "plt.ylim(X_left, X_right)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OF4X4J8_YlLo"
   },
   "source": [
    "# Часть 3. Классификация изображений CIFAR100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4zoT9OgeY7cZ"
   },
   "source": [
    "## Загрузка и распаковка набора данных CIFAR100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QDPzQmviB8IT",
    "outputId": "2fd6d7fa-094f-4d72-ce4b-c349d1ac9276"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-02-10 21:29:10--  https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\n",
      "Resolving www.cs.toronto.edu (www.cs.toronto.edu)... 128.100.3.30\n",
      "Connecting to www.cs.toronto.edu (www.cs.toronto.edu)|128.100.3.30|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 169001437 (161M) [application/x-gzip]\n",
      "Saving to: ‘cifar-100-python.tar.gz’\n",
      "\n",
      "cifar-100-python.ta 100%[===================>] 161.17M  46.1MB/s    in 3.9s    \n",
      "\n",
      "2022-02-10 21:29:14 (41.2 MB/s) - ‘cifar-100-python.tar.gz’ saved [169001437/169001437]\n",
      "\n",
      "cifar-100-python/\n",
      "cifar-100-python/file.txt~\n",
      "cifar-100-python/train\n",
      "cifar-100-python/test\n",
      "cifar-100-python/meta\n"
     ]
    }
   ],
   "source": [
    "!wget https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\n",
    "!tar -xvzf cifar-100-python.tar.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mtz5rqMlZD2x"
   },
   "source": [
    "## Чтение тренировочной и тестовой выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 273
    },
    "id": "49KAR3NoDbEp",
    "outputId": "0daba920-a164-471d-e719-c834d5c1a14d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=256x256 at 0x7FF170B00F50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('cifar-100-python/train', 'rb') as f:\n",
    "    data_train = pickle.load(f, encoding='latin1')\n",
    "with open('cifar-100-python/test', 'rb') as f:\n",
    "    data_test = pickle.load(f, encoding='latin1')\n",
    "\n",
    "# Здесь указать ваши классы по варианту!!!\n",
    "CLASSES = [0, 55, 58]\n",
    "\n",
    "train_X = data_train['data'].reshape(-1, 3, 32, 32)\n",
    "train_X = np.transpose(train_X, [0, 2, 3, 1]) # NCHW -> NHWC\n",
    "train_y = np.array(data_train['fine_labels'])\n",
    "mask = np.isin(train_y, CLASSES)\n",
    "train_X = train_X[mask].copy()\n",
    "train_y = train_y[mask].copy()\n",
    "train_y = np.unique(train_y, return_inverse=1)[1]\n",
    "del data_train\n",
    "\n",
    "test_X = data_test['data'].reshape(-1, 3, 32, 32)\n",
    "test_X = np.transpose(test_X, [0, 2, 3, 1])\n",
    "test_y = np.array(data_test['fine_labels'])\n",
    "mask = np.isin(test_y, CLASSES)\n",
    "test_X = test_X[mask].copy()\n",
    "test_y = test_y[mask].copy()\n",
    "test_y = np.unique(test_y, return_inverse=1)[1]\n",
    "del data_test\n",
    "Image.fromarray(train_X[50]).resize((256,256))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VJHI8GhtZO8F"
   },
   "source": [
    "## Создание Pytorch DataLoader'a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a77Fex1TIhGE",
    "outputId": "247fd07b-67fd-4451-a2a2-b6e6878cfb42"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'test': <torch.utils.data.dataloader.DataLoader at 0x7ff2823c59d0>,\n",
       " 'train': <torch.utils.data.dataloader.DataLoader at 0x7ff1706411d0>}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 128\n",
    "dataloader = {}\n",
    "for (X, y), part in zip([(train_X, train_y), (test_X, test_y)],\n",
    "                        ['train', 'test']):\n",
    "    tensor_x = torch.Tensor(X)\n",
    "    tensor_y = F.one_hot(torch.Tensor(y).to(torch.int64),\n",
    "                                     num_classes=len(CLASSES))/1.\n",
    "    dataset = TensorDataset(tensor_x, tensor_y) # создание объекта датасета\n",
    "    dataloader[part] = DataLoader(dataset, batch_size=batch_size, shuffle=True) # создание экземпляра класса DataLoader\n",
    "dataloader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FxcEeFaHZV-G"
   },
   "source": [
    "## Создание Pytorch модели многослойного перцептрона с одним скрытым слоем"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jxfiec1w_bLr",
    "outputId": "1028043b-aef1-4f87-f184-84a087bd00e7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cifar100_MLP(\n",
       "  (norm): Normalize()\n",
       "  (seq): Sequential(\n",
       "    (0): Linear(in_features=3072, out_features=10, bias=True)\n",
       "    (1): ReLU()\n",
       "    (2): Linear(in_features=10, out_features=3, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Normalize(nn.Module):\n",
    "    def __init__(self, mean, std):\n",
    "        super(Normalize, self).__init__()\n",
    "        self.mean = torch.tensor(mean)\n",
    "        self.std = torch.tensor(std)\n",
    "\n",
    "    def forward(self, input):\n",
    "        x = input / 255.0\n",
    "        x = x - self.mean\n",
    "        x = x / self.std\n",
    "        return torch.flatten(x, start_dim=1) # nhwc -> nm\n",
    "\n",
    "class Cifar100_MLP(nn.Module):\n",
    "    def __init__(self, hidden_size=32, classes=100):\n",
    "        super(Cifar100_MLP, self).__init__()\n",
    "        # https://blog.jovian.ai/image-classification-of-cifar100-dataset-using-pytorch-8b7145242df1\n",
    "        self.norm = Normalize([0.5074,0.4867,0.4411],[0.2011,0.1987,0.2025])\n",
    "        self.seq = nn.Sequential(\n",
    "            nn.Linear(32*32*3, hidden_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_size, classes),\n",
    "        )\n",
    "\n",
    "    def forward(self, input):\n",
    "        x = self.norm(input)\n",
    "        return self.seq(x)\n",
    "\n",
    "HIDDEN_SIZE = 10\n",
    "model = Cifar100_MLP(hidden_size=HIDDEN_SIZE, classes=len(CLASSES))\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "raKMPtc4ZgsZ"
   },
   "source": [
    "## Выбор функции потерь и оптимизатора градиентного спуска"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "-sRf5LGwHIZB"
   },
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.005)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hFtkRYFQZ0xb"
   },
   "source": [
    "## Обучение модели по эпохам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "j3N4gdE9KKd1",
    "outputId": "fffa5ef5-7980-4598-8a21-489542b2af86"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,    12] loss: 1.015\n",
      "[1,     3] val loss: 0.921\n",
      "[2,    12] loss: 0.860\n",
      "[2,     3] val loss: 0.837\n",
      "[3,    12] loss: 0.777\n",
      "[3,     3] val loss: 0.793\n",
      "[4,    12] loss: 0.721\n",
      "[4,     3] val loss: 0.743\n",
      "[5,    12] loss: 0.674\n",
      "[5,     3] val loss: 0.727\n",
      "[6,    12] loss: 0.638\n",
      "[6,     3] val loss: 0.681\n",
      "[7,    12] loss: 0.612\n",
      "[7,     3] val loss: 0.658\n",
      "[8,    12] loss: 0.589\n",
      "[8,     3] val loss: 0.667\n",
      "[9,    12] loss: 0.569\n",
      "[9,     3] val loss: 0.622\n",
      "[10,    12] loss: 0.552\n",
      "[10,     3] val loss: 0.632\n",
      "[11,    12] loss: 0.537\n",
      "[11,     3] val loss: 0.604\n",
      "[12,    12] loss: 0.527\n",
      "[12,     3] val loss: 0.618\n",
      "[13,    12] loss: 0.513\n",
      "[13,     3] val loss: 0.578\n",
      "[14,    12] loss: 0.502\n",
      "[14,     3] val loss: 0.626\n",
      "[15,    12] loss: 0.490\n",
      "[15,     3] val loss: 0.631\n",
      "[16,    12] loss: 0.482\n",
      "[16,     3] val loss: 0.577\n",
      "[17,    12] loss: 0.475\n",
      "[17,     3] val loss: 0.558\n",
      "[18,    12] loss: 0.465\n",
      "[18,     3] val loss: 0.553\n",
      "[19,    12] loss: 0.459\n",
      "[19,     3] val loss: 0.556\n",
      "[20,    12] loss: 0.450\n",
      "[20,     3] val loss: 0.542\n",
      "[21,    12] loss: 0.443\n",
      "[21,     3] val loss: 0.542\n",
      "[22,    12] loss: 0.438\n",
      "[22,     3] val loss: 0.585\n",
      "[23,    12] loss: 0.428\n",
      "[23,     3] val loss: 0.546\n",
      "[24,    12] loss: 0.423\n",
      "[24,     3] val loss: 0.536\n",
      "[25,    12] loss: 0.416\n",
      "[25,     3] val loss: 0.528\n",
      "[26,    12] loss: 0.409\n",
      "[26,     3] val loss: 0.537\n",
      "[27,    12] loss: 0.403\n",
      "[27,     3] val loss: 0.550\n",
      "[28,    12] loss: 0.399\n",
      "[28,     3] val loss: 0.512\n",
      "[29,    12] loss: 0.391\n",
      "[29,     3] val loss: 0.512\n",
      "[30,    12] loss: 0.387\n",
      "[30,     3] val loss: 0.528\n",
      "[31,    12] loss: 0.381\n",
      "[31,     3] val loss: 0.515\n",
      "[32,    12] loss: 0.378\n",
      "[32,     3] val loss: 0.551\n",
      "[33,    12] loss: 0.374\n",
      "[33,     3] val loss: 0.506\n",
      "[34,    12] loss: 0.368\n",
      "[34,     3] val loss: 0.519\n",
      "[35,    12] loss: 0.364\n",
      "[35,     3] val loss: 0.535\n",
      "[36,    12] loss: 0.358\n",
      "[36,     3] val loss: 0.491\n",
      "[37,    12] loss: 0.355\n",
      "[37,     3] val loss: 0.529\n",
      "[38,    12] loss: 0.349\n",
      "[38,     3] val loss: 0.535\n",
      "[39,    12] loss: 0.344\n",
      "[39,     3] val loss: 0.490\n",
      "[40,    12] loss: 0.341\n",
      "[40,     3] val loss: 0.544\n",
      "[41,    12] loss: 0.337\n",
      "[41,     3] val loss: 0.512\n",
      "[42,    12] loss: 0.334\n",
      "[42,     3] val loss: 0.535\n",
      "[43,    12] loss: 0.329\n",
      "[43,     3] val loss: 0.490\n",
      "[44,    12] loss: 0.323\n",
      "[44,     3] val loss: 0.504\n",
      "[45,    12] loss: 0.323\n",
      "[45,     3] val loss: 0.531\n",
      "[46,    12] loss: 0.317\n",
      "[46,     3] val loss: 0.536\n",
      "[47,    12] loss: 0.315\n",
      "[47,     3] val loss: 0.509\n",
      "[48,    12] loss: 0.312\n",
      "[48,     3] val loss: 0.532\n",
      "[49,    12] loss: 0.306\n",
      "[49,     3] val loss: 0.525\n",
      "[50,    12] loss: 0.303\n",
      "[50,     3] val loss: 0.538\n",
      "[51,    12] loss: 0.301\n",
      "[51,     3] val loss: 0.533\n",
      "[52,    12] loss: 0.297\n",
      "[52,     3] val loss: 0.536\n",
      "[53,    12] loss: 0.292\n",
      "[53,     3] val loss: 0.507\n",
      "[54,    12] loss: 0.288\n",
      "[54,     3] val loss: 0.485\n",
      "[55,    12] loss: 0.288\n",
      "[55,     3] val loss: 0.487\n",
      "[56,    12] loss: 0.284\n",
      "[56,     3] val loss: 0.514\n",
      "[57,    12] loss: 0.281\n",
      "[57,     3] val loss: 0.521\n",
      "[58,    12] loss: 0.276\n",
      "[58,     3] val loss: 0.525\n",
      "[59,    12] loss: 0.274\n",
      "[59,     3] val loss: 0.519\n",
      "[60,    12] loss: 0.270\n",
      "[60,     3] val loss: 0.533\n",
      "[61,    12] loss: 0.270\n",
      "[61,     3] val loss: 0.535\n",
      "[62,    12] loss: 0.265\n",
      "[62,     3] val loss: 0.557\n",
      "[63,    12] loss: 0.264\n",
      "[63,     3] val loss: 0.489\n",
      "[64,    12] loss: 0.260\n",
      "[64,     3] val loss: 0.502\n",
      "[65,    12] loss: 0.257\n",
      "[65,     3] val loss: 0.550\n",
      "[66,    12] loss: 0.251\n",
      "[66,     3] val loss: 0.495\n",
      "[67,    12] loss: 0.252\n",
      "[67,     3] val loss: 0.485\n",
      "[68,    12] loss: 0.247\n",
      "[68,     3] val loss: 0.507\n",
      "[69,    12] loss: 0.244\n",
      "[69,     3] val loss: 0.525\n",
      "[70,    12] loss: 0.241\n",
      "[70,     3] val loss: 0.526\n",
      "[71,    12] loss: 0.240\n",
      "[71,     3] val loss: 0.517\n",
      "[72,    12] loss: 0.239\n",
      "[72,     3] val loss: 0.508\n",
      "[73,    12] loss: 0.236\n",
      "[73,     3] val loss: 0.495\n",
      "[74,    12] loss: 0.232\n",
      "[74,     3] val loss: 0.519\n",
      "[75,    12] loss: 0.231\n",
      "[75,     3] val loss: 0.540\n",
      "[76,    12] loss: 0.227\n",
      "[76,     3] val loss: 0.517\n",
      "[77,    12] loss: 0.226\n",
      "[77,     3] val loss: 0.479\n",
      "[78,    12] loss: 0.223\n",
      "[78,     3] val loss: 0.518\n",
      "[79,    12] loss: 0.223\n",
      "[79,     3] val loss: 0.529\n",
      "[80,    12] loss: 0.218\n",
      "[80,     3] val loss: 0.544\n",
      "[81,    12] loss: 0.217\n",
      "[81,     3] val loss: 0.503\n",
      "[82,    12] loss: 0.214\n",
      "[82,     3] val loss: 0.519\n",
      "[83,    12] loss: 0.210\n",
      "[83,     3] val loss: 0.486\n",
      "[84,    12] loss: 0.209\n",
      "[84,     3] val loss: 0.566\n",
      "[85,    12] loss: 0.207\n",
      "[85,     3] val loss: 0.482\n",
      "[86,    12] loss: 0.205\n",
      "[86,     3] val loss: 0.557\n",
      "[87,    12] loss: 0.205\n",
      "[87,     3] val loss: 0.507\n",
      "[88,    12] loss: 0.201\n",
      "[88,     3] val loss: 0.528\n",
      "[89,    12] loss: 0.199\n",
      "[89,     3] val loss: 0.484\n",
      "[90,    12] loss: 0.198\n",
      "[90,     3] val loss: 0.523\n",
      "[91,    12] loss: 0.196\n",
      "[91,     3] val loss: 0.534\n",
      "[92,    12] loss: 0.193\n",
      "[92,     3] val loss: 0.514\n",
      "[93,    12] loss: 0.191\n",
      "[93,     3] val loss: 0.533\n",
      "[94,    12] loss: 0.189\n",
      "[94,     3] val loss: 0.546\n",
      "[95,    12] loss: 0.187\n",
      "[95,     3] val loss: 0.561\n",
      "[96,    12] loss: 0.186\n",
      "[96,     3] val loss: 0.546\n",
      "[97,    12] loss: 0.183\n",
      "[97,     3] val loss: 0.501\n",
      "[98,    12] loss: 0.182\n",
      "[98,     3] val loss: 0.538\n",
      "[99,    12] loss: 0.179\n",
      "[99,     3] val loss: 0.566\n",
      "[100,    12] loss: 0.177\n",
      "[100,     3] val loss: 0.530\n",
      "[101,    12] loss: 0.176\n",
      "[101,     3] val loss: 0.547\n",
      "[102,    12] loss: 0.174\n",
      "[102,     3] val loss: 0.546\n",
      "[103,    12] loss: 0.173\n",
      "[103,     3] val loss: 0.543\n",
      "[104,    12] loss: 0.170\n",
      "[104,     3] val loss: 0.507\n",
      "[105,    12] loss: 0.168\n",
      "[105,     3] val loss: 0.538\n",
      "[106,    12] loss: 0.167\n",
      "[106,     3] val loss: 0.539\n",
      "[107,    12] loss: 0.166\n",
      "[107,     3] val loss: 0.546\n",
      "[108,    12] loss: 0.164\n",
      "[108,     3] val loss: 0.521\n",
      "[109,    12] loss: 0.163\n",
      "[109,     3] val loss: 0.587\n",
      "[110,    12] loss: 0.159\n",
      "[110,     3] val loss: 0.541\n",
      "[111,    12] loss: 0.158\n",
      "[111,     3] val loss: 0.595\n",
      "[112,    12] loss: 0.156\n",
      "[112,     3] val loss: 0.560\n",
      "[113,    12] loss: 0.155\n",
      "[113,     3] val loss: 0.522\n",
      "[114,    12] loss: 0.154\n",
      "[114,     3] val loss: 0.583\n",
      "[115,    12] loss: 0.152\n",
      "[115,     3] val loss: 0.564\n",
      "[116,    12] loss: 0.152\n",
      "[116,     3] val loss: 0.485\n",
      "[117,    12] loss: 0.149\n",
      "[117,     3] val loss: 0.538\n",
      "[118,    12] loss: 0.148\n",
      "[118,     3] val loss: 0.606\n",
      "[119,    12] loss: 0.147\n",
      "[119,     3] val loss: 0.526\n",
      "[120,    12] loss: 0.145\n",
      "[120,     3] val loss: 0.566\n",
      "[121,    12] loss: 0.144\n",
      "[121,     3] val loss: 0.592\n",
      "[122,    12] loss: 0.142\n",
      "[122,     3] val loss: 0.530\n",
      "[123,    12] loss: 0.141\n",
      "[123,     3] val loss: 0.563\n",
      "[124,    12] loss: 0.138\n",
      "[124,     3] val loss: 0.563\n",
      "[125,    12] loss: 0.137\n",
      "[125,     3] val loss: 0.519\n",
      "[126,    12] loss: 0.136\n",
      "[126,     3] val loss: 0.632\n",
      "[127,    12] loss: 0.136\n",
      "[127,     3] val loss: 0.541\n",
      "[128,    12] loss: 0.136\n",
      "[128,     3] val loss: 0.572\n",
      "[129,    12] loss: 0.133\n",
      "[129,     3] val loss: 0.591\n",
      "[130,    12] loss: 0.133\n",
      "[130,     3] val loss: 0.572\n",
      "[131,    12] loss: 0.130\n",
      "[131,     3] val loss: 0.543\n",
      "[132,    12] loss: 0.128\n",
      "[132,     3] val loss: 0.599\n",
      "[133,    12] loss: 0.128\n",
      "[133,     3] val loss: 0.531\n",
      "[134,    12] loss: 0.126\n",
      "[134,     3] val loss: 0.601\n",
      "[135,    12] loss: 0.127\n",
      "[135,     3] val loss: 0.607\n",
      "[136,    12] loss: 0.123\n",
      "[136,     3] val loss: 0.627\n",
      "[137,    12] loss: 0.123\n",
      "[137,     3] val loss: 0.574\n",
      "[138,    12] loss: 0.122\n",
      "[138,     3] val loss: 0.614\n",
      "[139,    12] loss: 0.121\n",
      "[139,     3] val loss: 0.556\n",
      "[140,    12] loss: 0.119\n",
      "[140,     3] val loss: 0.589\n",
      "[141,    12] loss: 0.118\n",
      "[141,     3] val loss: 0.587\n",
      "[142,    12] loss: 0.118\n",
      "[142,     3] val loss: 0.593\n",
      "[143,    12] loss: 0.118\n",
      "[143,     3] val loss: 0.606\n",
      "[144,    12] loss: 0.115\n",
      "[144,     3] val loss: 0.631\n",
      "[145,    12] loss: 0.114\n",
      "[145,     3] val loss: 0.557\n",
      "[146,    12] loss: 0.113\n",
      "[146,     3] val loss: 0.640\n",
      "[147,    12] loss: 0.113\n",
      "[147,     3] val loss: 0.590\n",
      "[148,    12] loss: 0.111\n",
      "[148,     3] val loss: 0.576\n",
      "[149,    12] loss: 0.111\n",
      "[149,     3] val loss: 0.596\n",
      "[150,    12] loss: 0.110\n",
      "[150,     3] val loss: 0.571\n",
      "[151,    12] loss: 0.109\n",
      "[151,     3] val loss: 0.584\n",
      "[152,    12] loss: 0.107\n",
      "[152,     3] val loss: 0.596\n",
      "[153,    12] loss: 0.107\n",
      "[153,     3] val loss: 0.580\n",
      "[154,    12] loss: 0.106\n",
      "[154,     3] val loss: 0.598\n",
      "[155,    12] loss: 0.105\n",
      "[155,     3] val loss: 0.624\n",
      "[156,    12] loss: 0.103\n",
      "[156,     3] val loss: 0.581\n",
      "[157,    12] loss: 0.103\n",
      "[157,     3] val loss: 0.606\n",
      "[158,    12] loss: 0.102\n",
      "[158,     3] val loss: 0.569\n",
      "[159,    12] loss: 0.101\n",
      "[159,     3] val loss: 0.556\n",
      "[160,    12] loss: 0.100\n",
      "[160,     3] val loss: 0.582\n",
      "[161,    12] loss: 0.100\n",
      "[161,     3] val loss: 0.614\n",
      "[162,    12] loss: 0.099\n",
      "[162,     3] val loss: 0.615\n",
      "[163,    12] loss: 0.098\n",
      "[163,     3] val loss: 0.559\n",
      "[164,    12] loss: 0.097\n",
      "[164,     3] val loss: 0.590\n",
      "[165,    12] loss: 0.095\n",
      "[165,     3] val loss: 0.596\n",
      "[166,    12] loss: 0.095\n",
      "[166,     3] val loss: 0.663\n",
      "[167,    12] loss: 0.094\n",
      "[167,     3] val loss: 0.666\n",
      "[168,    12] loss: 0.093\n",
      "[168,     3] val loss: 0.591\n",
      "[169,    12] loss: 0.093\n",
      "[169,     3] val loss: 0.627\n",
      "[170,    12] loss: 0.091\n",
      "[170,     3] val loss: 0.598\n",
      "[171,    12] loss: 0.090\n",
      "[171,     3] val loss: 0.683\n",
      "[172,    12] loss: 0.090\n",
      "[172,     3] val loss: 0.652\n",
      "[173,    12] loss: 0.090\n",
      "[173,     3] val loss: 0.601\n",
      "[174,    12] loss: 0.089\n",
      "[174,     3] val loss: 0.624\n",
      "[175,    12] loss: 0.088\n",
      "[175,     3] val loss: 0.649\n",
      "[176,    12] loss: 0.088\n",
      "[176,     3] val loss: 0.624\n",
      "[177,    12] loss: 0.088\n",
      "[177,     3] val loss: 0.656\n",
      "[178,    12] loss: 0.086\n",
      "[178,     3] val loss: 0.645\n",
      "[179,    12] loss: 0.085\n",
      "[179,     3] val loss: 0.614\n",
      "[180,    12] loss: 0.084\n",
      "[180,     3] val loss: 0.650\n",
      "[181,    12] loss: 0.085\n",
      "[181,     3] val loss: 0.581\n",
      "[182,    12] loss: 0.083\n",
      "[182,     3] val loss: 0.594\n",
      "[183,    12] loss: 0.082\n",
      "[183,     3] val loss: 0.603\n",
      "[184,    12] loss: 0.082\n",
      "[184,     3] val loss: 0.650\n",
      "[185,    12] loss: 0.082\n",
      "[185,     3] val loss: 0.647\n",
      "[186,    12] loss: 0.082\n",
      "[186,     3] val loss: 0.646\n",
      "[187,    12] loss: 0.081\n",
      "[187,     3] val loss: 0.580\n",
      "[188,    12] loss: 0.079\n",
      "[188,     3] val loss: 0.676\n",
      "[189,    12] loss: 0.079\n",
      "[189,     3] val loss: 0.597\n",
      "[190,    12] loss: 0.078\n",
      "[190,     3] val loss: 0.620\n",
      "[191,    12] loss: 0.077\n",
      "[191,     3] val loss: 0.655\n",
      "[192,    12] loss: 0.077\n",
      "[192,     3] val loss: 0.590\n",
      "[193,    12] loss: 0.076\n",
      "[193,     3] val loss: 0.668\n",
      "[194,    12] loss: 0.075\n",
      "[194,     3] val loss: 0.686\n",
      "[195,    12] loss: 0.075\n",
      "[195,     3] val loss: 0.628\n",
      "[196,    12] loss: 0.074\n",
      "[196,     3] val loss: 0.627\n",
      "[197,    12] loss: 0.074\n",
      "[197,     3] val loss: 0.625\n",
      "[198,    12] loss: 0.074\n",
      "[198,     3] val loss: 0.620\n",
      "[199,    12] loss: 0.073\n",
      "[199,     3] val loss: 0.623\n",
      "[200,    12] loss: 0.072\n",
      "[200,     3] val loss: 0.600\n",
      "[201,    12] loss: 0.072\n",
      "[201,     3] val loss: 0.624\n",
      "[202,    12] loss: 0.070\n",
      "[202,     3] val loss: 0.700\n",
      "[203,    12] loss: 0.070\n",
      "[203,     3] val loss: 0.709\n",
      "[204,    12] loss: 0.070\n",
      "[204,     3] val loss: 0.681\n",
      "[205,    12] loss: 0.069\n",
      "[205,     3] val loss: 0.683\n",
      "[206,    12] loss: 0.069\n",
      "[206,     3] val loss: 0.689\n",
      "[207,    12] loss: 0.068\n",
      "[207,     3] val loss: 0.627\n",
      "[208,    12] loss: 0.068\n",
      "[208,     3] val loss: 0.615\n",
      "[209,    12] loss: 0.068\n",
      "[209,     3] val loss: 0.684\n",
      "[210,    12] loss: 0.067\n",
      "[210,     3] val loss: 0.610\n",
      "[211,    12] loss: 0.066\n",
      "[211,     3] val loss: 0.670\n",
      "[212,    12] loss: 0.067\n",
      "[212,     3] val loss: 0.685\n",
      "[213,    12] loss: 0.065\n",
      "[213,     3] val loss: 0.678\n",
      "[214,    12] loss: 0.066\n",
      "[214,     3] val loss: 0.683\n",
      "[215,    12] loss: 0.065\n",
      "[215,     3] val loss: 0.622\n",
      "[216,    12] loss: 0.063\n",
      "[216,     3] val loss: 0.658\n",
      "[217,    12] loss: 0.063\n",
      "[217,     3] val loss: 0.691\n",
      "[218,    12] loss: 0.063\n",
      "[218,     3] val loss: 0.661\n",
      "[219,    12] loss: 0.063\n",
      "[219,     3] val loss: 0.696\n",
      "[220,    12] loss: 0.062\n",
      "[220,     3] val loss: 0.602\n",
      "[221,    12] loss: 0.061\n",
      "[221,     3] val loss: 0.665\n",
      "[222,    12] loss: 0.062\n",
      "[222,     3] val loss: 0.679\n",
      "[223,    12] loss: 0.061\n",
      "[223,     3] val loss: 0.650\n",
      "[224,    12] loss: 0.061\n",
      "[224,     3] val loss: 0.694\n",
      "[225,    12] loss: 0.060\n",
      "[225,     3] val loss: 0.654\n",
      "[226,    12] loss: 0.060\n",
      "[226,     3] val loss: 0.670\n",
      "[227,    12] loss: 0.059\n",
      "[227,     3] val loss: 0.687\n",
      "[228,    12] loss: 0.059\n",
      "[228,     3] val loss: 0.666\n",
      "[229,    12] loss: 0.059\n",
      "[229,     3] val loss: 0.734\n",
      "[230,    12] loss: 0.058\n",
      "[230,     3] val loss: 0.729\n",
      "[231,    12] loss: 0.057\n",
      "[231,     3] val loss: 0.701\n",
      "[232,    12] loss: 0.057\n",
      "[232,     3] val loss: 0.627\n",
      "[233,    12] loss: 0.056\n",
      "[233,     3] val loss: 0.642\n",
      "[234,    12] loss: 0.056\n",
      "[234,     3] val loss: 0.644\n",
      "[235,    12] loss: 0.056\n",
      "[235,     3] val loss: 0.684\n",
      "[236,    12] loss: 0.056\n",
      "[236,     3] val loss: 0.684\n",
      "[237,    12] loss: 0.055\n",
      "[237,     3] val loss: 0.673\n",
      "[238,    12] loss: 0.055\n",
      "[238,     3] val loss: 0.684\n",
      "[239,    12] loss: 0.054\n",
      "[239,     3] val loss: 0.723\n",
      "[240,    12] loss: 0.054\n",
      "[240,     3] val loss: 0.747\n",
      "[241,    12] loss: 0.054\n",
      "[241,     3] val loss: 0.702\n",
      "[242,    12] loss: 0.053\n",
      "[242,     3] val loss: 0.742\n",
      "[243,    12] loss: 0.053\n",
      "[243,     3] val loss: 0.641\n",
      "[244,    12] loss: 0.053\n",
      "[244,     3] val loss: 0.685\n",
      "[245,    12] loss: 0.052\n",
      "[245,     3] val loss: 0.615\n",
      "[246,    12] loss: 0.052\n",
      "[246,     3] val loss: 0.753\n",
      "[247,    12] loss: 0.051\n",
      "[247,     3] val loss: 0.744\n",
      "[248,    12] loss: 0.052\n",
      "[248,     3] val loss: 0.696\n",
      "[249,    12] loss: 0.051\n",
      "[249,     3] val loss: 0.681\n",
      "[250,    12] loss: 0.051\n",
      "[250,     3] val loss: 0.706\n",
      "Обучение закончено\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 250\n",
    "steps_per_epoch = len(dataloader['train'])\n",
    "steps_per_epoch_val = len(dataloader['test'])\n",
    "for epoch in range(EPOCHS):  # проход по набору данных несколько раз\n",
    "    running_loss = 0.0\n",
    "    model.train()\n",
    "    for i, batch in enumerate(dataloader['train'], 0):\n",
    "        # получение одного минибатча; batch это двуэлементный список из [inputs, labels]\n",
    "        inputs, labels = batch\n",
    "\n",
    "        # очищение прошлых градиентов с прошлой итерации\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # прямой + обратный проходы + оптимизация\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        #loss = F.cross_entropy(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # для подсчёта статистик\n",
    "        running_loss += loss.item()\n",
    "    print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / steps_per_epoch:.3f}')\n",
    "    running_loss = 0.0\n",
    "    model.eval()\n",
    "    with torch.no_grad(): # отключение автоматического дифференцирования\n",
    "        for i, data in enumerate(dataloader['test'], 0):\n",
    "            inputs, labels = data\n",
    "\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            running_loss += loss.item()\n",
    "    print(f'[{epoch + 1}, {i + 1:5d}] val loss: {running_loss / steps_per_epoch_val:.3f}')\n",
    "print('Обучение закончено')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pM3jjyu2Z6cf"
   },
   "source": [
    "## Проверка качества модели по классам на обучающей и тестовой выборках"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5RlNWKIRM8Hj",
    "outputId": "6a7cb3c0-e9a0-442f-e132-8bcbb81dc721"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     1.0000    0.9900    0.9950       500\n",
      "          55     0.9881    1.0000    0.9940       500\n",
      "          58     0.9980    0.9960    0.9970       500\n",
      "\n",
      "    accuracy                         0.9953      1500\n",
      "   macro avg     0.9954    0.9953    0.9953      1500\n",
      "weighted avg     0.9954    0.9953    0.9953      1500\n",
      "\n",
      "--------------------------------------------------\n",
      "test\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.8174    0.9400    0.8744       100\n",
      "          55     0.7158    0.6800    0.6974       100\n",
      "          58     0.6889    0.6200    0.6526       100\n",
      "\n",
      "    accuracy                         0.7467       300\n",
      "   macro avg     0.7407    0.7467    0.7415       300\n",
      "weighted avg     0.7407    0.7467    0.7415       300\n",
      "\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for part in ['train', 'test']:\n",
    "    y_pred = []\n",
    "    y_true = []\n",
    "    with torch.no_grad(): # отключение автоматического дифференцирования\n",
    "        for i, data in enumerate(dataloader[part], 0):\n",
    "            inputs, labels = data\n",
    "\n",
    "            outputs = model(inputs).detach().numpy()\n",
    "            y_pred.append(outputs)\n",
    "            y_true.append(labels.numpy())\n",
    "        y_true = np.concatenate(y_true)\n",
    "        y_pred = np.concatenate(y_pred)\n",
    "        print(part)\n",
    "        print(classification_report(y_true.argmax(axis=-1), y_pred.argmax(axis=-1),\n",
    "                                    digits=4, target_names=list(map(str, CLASSES))))\n",
    "        print('-'*50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rwf4xLEjnJ95"
   },
   "source": [
    "## Визуализация весов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118
    },
    "id": "yRxbB8D7nYaM",
    "outputId": "d3a98553-d144-4db9-91d6-68099b7282bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 3072)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2160x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = list(model.parameters())[0].detach().numpy()\n",
    "print(weights.shape)\n",
    "fig, ax = plt.subplots(1, weights.shape[0], figsize=(3*weights.shape[0], 3))\n",
    "for i, ω in enumerate(weights):\n",
    "    ω = ω.reshape(32, 32, 3)\n",
    "    ω -= np.percentile(ω, 1, axis=[0, 1])\n",
    "    ω /= np.percentile(ω, 99, axis=[0, 1])\n",
    "    ω = np.clip(ω, 0, 1) \n",
    "    ax[i].imshow(ω)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HFzEnQacoQwb"
   },
   "source": [
    "# Описание методов библиотек"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mv5qVoyxfoPZ"
   },
   "source": [
    "### Методы и функции NumPy:\n",
    "\n",
    "(Подробнее в документации https://numpy.org/doc/1.22/reference/index.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9aWaJ2_EfoPZ"
   },
   "source": [
    "* __np.array__ - создание массива из списка или другого массива\n",
    "* __np.shape__ - выводит размерность многомерного массива (т.е. для массива 2х2 будет выведен кортеж (2, 2))\n",
    "* __np.size__ - выводит число элементов в массиве (т.е. для массива 2х2 будет выведено число 4)\n",
    "* __np.uint8__, __np.int16__, __np.int64__, __np.float32__ - приведение массива к новому типу, при этом в памяти выделяется место под новый массив выбранного типа. Число после типа обозначет, сколько бит данных используется для хранения одного элемента массива. Для хранения картинок зачастую используется экономный uint8 - беззнаковый 8-битный целочисленный тип данных (диапазон чисел 0-255)\n",
    "* __np.ones__, __np.zeros__  - создание уже заполненных массивов либо единицами, либо нулями. В качестве аргумента передается список или кортеж с требуемой размерностью. Например `np.ones((10,))` создаст вектор из 10 единичек. А `np.zeros((32, 32, 3))` создаст двузмерный массив разрешением 32 на 32 пикселя с 3 каналами. На практике используется для проверки архитектуры модели в прямом направлении\n",
    "* __np.arange__ - создание уже заполненного массива в виде возрастающей арифметической прогресии от первого аргумента до второго аргумента не включительно с шагом, который задаётеся третьим аргументом. Первый и третий аргументы можно опускать, в таком случае получается компактная запись `np.arange(3)` => [0, 1, 2]\n",
    "* __np.repeat__ - дублирование элементов массива на количество, указанное первым аргументом. Таким образом, для массива `arr = [0, 1]` `arr.repeat(2)` вернёт [0, 0, 1, 1]\n",
    "* __np.exp__ - применение поэлементной операции потенциирования к массиву\n",
    "* __np.random.normal__ - генерация массива, заполненного случайными нормальными величинами со стандартным отклонением, задающимся через аргумент scale и со средним значением, равным аргументу mean. Число элементов в массиве задаётся числом или списком, переданным аргументу size.\n",
    "* __np.random.randint__ - генерация массива, заполненного случайными целыми числами в диапазоне, задающимся аналогично __np.arange__. Число элементов в массиве задаётся числом или списком, переданным аргументу size.\n",
    "* __np.reshape__  - буквально изменение размерности многомерного массива с учётом числа элементов. В качестве аргумента передается многомерный массив, а также список или кортеж с новой размерностью. Например `np.reshape([0, 1, 2, 3], (2,2))` создаст двухмерный массив размером 2х2. При этом в памяти новый массив не выделяется, а меняется лишь способ обхода по нему. Разрешается также и следующий способ вызова метода: `arr.reshape(2, 2)`. Обратите внимание на отсутствие дополнительных скобок. Если вместо конкретного числа подставить -1, то размерность будет подсчитана автоматически. На практике используется для выпрямление картинок в виде одномерного массива: `X.reshape(-1, 3072)`# [100, 32, 32, 3] -> [100, 3072]\n",
    "* __np.transpose__  - переименование осей многомерного массива. Для работы с изображениями принято два формата NHWC и NCHW (N - число картинок в массиве, C - число каналов, H - высота, W - ширина). В качестве аргумента передается многомерный массив, а также список или кортеж с новой расстановкой осей. Например `np.transpose([[0, 1, 2, 3]], (1,0))` создаст двухмерный вектор-столбец [[[0], [1], [2], [3]]. Заметьте, что отсчет осей начинается с 0. На практике используется для перевода NHWC в NCHW и обратно. В первом случае 0 ось N остаётся на своём первом месте, первая и вторая оси H и W сдвигаются на одну позицию вправо, а 3 ось - C ставится на второе место. Т.е. получим следующую перестановку: [0, 3, 1, 2] \n",
    "* __np.isin__ - аналог SQL оператора IN, поэлементная проверка вхождения массива в коллекцию. `np.isin([0, 2, 1], [2, 3])` вернёт [False, True, False]\n",
    "* __индексирование__ - выбор подмассива или среза массива осуществляется с помощью квадратных скобок []. Если `arr = np.array([2, 1, 0])`, то `arr[0]` вернёт первый элемент. `arr[[0, 1]]` - обращение по индексу, `arr[[True, False, True]]` - обращение по булевой маске. Заметьте, что обращение по индексу необязательно должно совпадать с размерностью массива, в отличие от обращения по маске. На практике удобно записывать значения маски в отдельную переменную. Для выбора конкретного столбца в многомерном массиве используется синтаксис срезов [:, k], где k - номер столбца. Если k равняется -1, то используется последний столбец или элемент. Так, например, для массива `arr = np.array([[0, 1], [2, 3], [4, 5])` выражение `arr[:, 0]` вернет массив [0, 2, 4]. Поскольку используется индекс срезов (стандартный синтаксис Python), то можно также выполнять срезы многомерных массивов. Для предыдущего примера `arr[1:2, 0:1]` вернёт [[2]]\n",
    "* __np.unique__ - аналог SELECT DISTINCT в SQL. При стандартных параметрах возвращает одномерный подмассив, содержащий уникальные элементы. Если указать выставить флаг __return_inverse__, то вернется массив с номерами отсчётов массива с уникальными элементами. По сути выполняется Label Encoding\n",
    "* __np.concatenate__ - конкатенация многомерного массива вдоль указанной оси. Номер оси указывается через аргумент __axis__. Например может быть использован для объединения нескольких признаков или нескольких наборов данных. В контексте изображений может использоваться для объединения или склейки нескольких изображений в одно как вертикально, так и горизонтально. В контексте звука - склеивание двух аудиодорожек.\n",
    "* __np.max__, __np.min__ - возвращает максимальный и минимальный элементы массива вдоль указанной оси, соответственно. Если номер оси не указан, то возвращается число. Номер оси указывается через аргумент __axis__. Если указывается -1, то полагается, что используется последний номер оси. Разрешается также и вызов функции в качестве метода многомерного массива: `arr.max()`\n",
    "* __np.argmax__ - возвращает индекс максимального элемента массива вдоль указанной оси. Если номер оси не указан, то возвращается первый индекс, соответвующих максимальному значению в массиве, т.е. одно число. Номер оси указывается через аргумент __axis__. Если указывается -1, то полагается, что используется последний номер оси. На практике используется для расчёта метрики доли правильных ответов модели (Accuracy). Разрешается также и вызов функции в качестве метода многомерного массива: `arr.argmax(axis=-1)`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pL2CwPP4foPa"
   },
   "source": [
    "### Методы и функции Pickle\n",
    "(Документация: https://docs.python.org/3/library/pickle.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OCx3YmHafoPa"
   },
   "source": [
    "* __pickle.dump__ - сериализация структуры данных Python. Первым аргументом идёт сама структура, а вторым FileObject. При этом FileObject должен быть открыт в режиме записи байт (wb). Можно указать кодировку байт (big endian/ little endian). Тем самым можно хранить на постоянном носителе стандартные структуры данных, в том числе NumPy массивы.\n",
    "* __pickle.load__ - десериализация структуры данных Python. Первым аргументом идёт FileObject. При этом FileObject должен быть открыт в режиме чтения байт (rb). Можно указать кодировку байт (big endian/ little endian). Тем самым можно загружать ранее сохранённые структуры данных, что может быть полезно, если для их создания требуется длительное время (например, параметры модели глубокого обучения)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QC12OZoxfoPa"
   },
   "source": [
    "### Методы и функции Sklearn\n",
    "(Документация: https://scikit-learn.org/stable/modules/classes.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xIvGpvkYfoPb"
   },
   "source": [
    "* __datasets.make_circles__, __datasets.make_moons__ - генерация синтетической обучающей выборки для задачи классификации, возвращает X - двухмерный массив с числом примеров и числом признаков (признаков 2), а также одномерный массив с метками классов (0 или 1)\n",
    "\n",
    "* __metrics.classification_report__ - cоздает текстовый отчет, показывающий основные метрики классификации (доля правильных ответов, полнота, точность, f1-мера). В качестве первого аргумента передаются истинные метки класса, в качестве второго - метки класса, предсказанные моделью. Дополнительные полезные аргументы: digits - число выводимых знаков после запятой (по умолчанию 2), output_dict - возвращает словарь с расчитанными метриками вместо строки, sample_weight - расчитывает взвешенные метрики на основе веса каждого примера\n",
    "\n",
    "* __metrics.confusion_matrix__ - вычисляет матрицу ошибок модели для оценки точности классификации. Матрица ошибок идеальной модели имеет значения только на главной диагонали. Может быть использована для подсчёта всех классических метрик классификации (доля правильных ответов, полнота, точность, специфичность, f1-мера)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IHlZJ6u8foPb"
   },
   "source": [
    "### Методы и функции PIL\n",
    "\n",
    "(Документация: https://pillow.readthedocs.io/en/stable/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LiZ6VpgQfoPb"
   },
   "source": [
    "* __Image.fromarray__ - cоздает объект Image на основе двухмерного массива или двухмерного массива с каналами. Часто ругается, если тип данных не uint8. Часто ругается, если производится попытка создать черно-белое изображения из картинки размерностью (W, H, 1). Для того, чтобы получить обратно массив из объекта Image, достаточно привести его к NumPy массиву, например np.array(img)\n",
    "\n",
    "* __Image.resize__ - меняет разрешение изображения с помощью интерполяции. Первым аргументом указывается список с новой шириной и высотой изображения. При желании можно указать тип интерполяции через аргумент resample. Поддерживаемые значения: PIL.Image.NEAREST, PIL.Image.BOX, PIL.Image.BILINEAR, PIL.Image.HAMMING, PIL.Image.BICUBIC, PIL.Image.LANCZOS. По умолчанию используется бикубическая интерполяция.\n",
    "\n",
    "* __Image.convert__ - переводит изображение из одной цветовой схемы в другую. Новая цветовая схема передается строкой, L - черно белая, LA - черно-белая с прозрачностью, RGB - стандартная цветовая схема с 3 каналами, RGBA - стандартная цветовая схема с 3 каналами цвета и одним каналом прозрачности, HSV - альтернативное цветовое представление и т.д.\n",
    "\n",
    "* __Image.open__ - считывает изображение по указанному пути в виде строки или FileObject. При создании набора данных может неправильно определить формат (например L вместо RGB), поэтому рекомендуется сразу после open приводить к нужному формату при помощи метода convert\n",
    "\n",
    "* __Image.save__ - сохраняет изображение по указанному пути в виде строки или FileObject. Если указывается FileObject, то нужно также указать формат изображения в аргументе format, например 'PNG' или 'JPEG'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lbgjhQ_QfoPb"
   },
   "source": [
    "### Методы и функции Matplotlib\n",
    "\n",
    "(Документация: https://matplotlib.org/stable/api/index.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WkICIncTfoPc"
   },
   "source": [
    "Принятые сокращения:\n",
    "* matplotlib.pyplot - plt\n",
    "\n",
    "Методы:\n",
    "* __plt.plot__ - рисует график по точкам и соединяет их линией. Первым аргументом передаются x-координаты, вторым - у-координаты. Если не передавать второй аргумент, х координаты будут приняты за у, а в качестве х будут использованы отсчёты массива. Дополнительные полезные аргументы: linestyle - тип отображаемой линиии ('--', '-', '-.' и т.д.), color - цвет линии ('k' - черный, 'r' - красный, 'white' - белый и т.д.), alpha - прозрачность линии, число от 0 (линия не видна) до 1 (нет прозрачности), label - текстовая метка данного графика.\n",
    "* __plt.scatter__ - рисует график по точкам юез соединения линиями. Первым аргументом передаются x-координаты, вторым - у-координаты. Если не передавать второй аргумент, х координаты будут приняты за у, а в качестве х будут использованы отсчёты массива. Дополнительные полезные аргументы: s - размер точек, color - цвет точек ('k' - черный, 'r' - красный, 'white' - белый и т.д.), alpha - прозрачность точек, число от 0 (линия не видна) до 1 (нет прозрачности), label - текстовая метка данного графика.\n",
    "* __plt.contourf__ - рисует заполненные контурные линии, разграничивающие границы.\n",
    "* __plt.show__ - принудительная отрисовка графика, может использоваться для вывода нескольких графиков в одном блоке кода.\n",
    "* __plt.legend__ - отображает ранее указанные метки графиков\n",
    "* __plt.xlim__ - ограничивает диапазон x-координат от первого до второго аргумента. По умолчанию диапазон горизонтальной оси подбирается автоматически на основе используемых данных. Для задания диапозана значений горизонтальной оси вручную и используется данный метод\n",
    "* __plt.ylim__ - аналогично __plt.xlim__, но для вертикальнйо оси.\n",
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "VrOocc6D_O7M",
    "CTpTR1x0YYdn",
    "qq1r9u6hSucR",
    "OF4X4J8_YlLo",
    "4zoT9OgeY7cZ",
    "HFzEnQacoQwb"
   ],
   "include_colab_link": true,
   "name": "Lab1.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}