{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "JEFYtxdXQP1s"
},
"source": [
"# Введение в анализ данных\n",
"\n",
"\n",
"## PyTorch и полносвязные нейронные сети\n",
"\n",
"![pytorch-logo.png](data:image/png;base64,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)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CKuaI37x8hjJ"
},
"source": [
"### 1. Введение\n",
"\n",
"В данном ноутбуке мы будем пользоваться фреймворком **PyTorch**, который предназначен для работы с нейронными сетями. Как установить `pytorch` можно прочитать [на официальном сайте PyTorch](http://pytorch.org/). Для этого выберите свою OS и вам будет показана нужная команда для ввода в терминале. Больше подробностей о том, как `pytorch` работает будет рассказно на 3 курсе.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Wp5-n1308hjM",
"outputId": "a3d635a0-dab3-4d02-aca0-a996d3d10f59"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.10.0+cu111\n"
]
}
],
"source": [
"import numpy as np\n",
"from sklearn.datasets import load_boston\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from IPython.display import clear_output\n",
"sns.set(palette='Set2', font_scale=1.5)\n",
"\n",
"import torch\n",
"from torch import nn\n",
"import torch.nn.functional as F\n",
"\n",
"print(torch.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y4yG4djE8hjR"
},
"source": [
"#### 1.1 Сравнение NumPy и PyTorch-синтаксиса \n",
"Интерфейс `pytorch` написан подобно интерфесу `numpy` для удобства использования. Главное различие между ними, что `numpy` оперрирует `numpy.ndarray` массивами, а `pytorch` — тензорами `pytorch.Tensor`. Напишем одни и те же операции на `numpy` и `pytorch`."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BW8djI0PNvVE"
},
"source": [
"**numpy**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qh-uD8hE8hjS",
"outputId": "c5692e32-b378-4f24-9ba7-a8d2f49ec3de"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Матрица X:\n",
"[[ 0 1 2 3]\n",
" [ 4 5 6 7]\n",
" [ 8 9 10 11]\n",
" [12 13 14 15]]\n",
"\n",
"Размер: (4, 4)\n",
"\n",
"Добавление константы:\n",
"[[ 5 6 7 8]\n",
" [ 9 10 11 12]\n",
" [13 14 15 16]\n",
" [17 18 19 20]]\n",
"\n",
"X*X^T:\n",
"[[ 14 38 62 86]\n",
" [ 38 126 214 302]\n",
" [ 62 214 366 518]\n",
" [ 86 302 518 734]]\n",
"\n",
"Среднее по колонкам:\n",
"[ 1.5 5.5 9.5 13.5]\n",
"\n",
"Кумулятивная сумма по колонкам:\n",
"[[ 0 1 2 3]\n",
" [ 4 6 8 10]\n",
" [12 15 18 21]\n",
" [24 28 32 36]]\n",
"\n"
]
}
],
"source": [
"x = np.arange(16).reshape(4, 4)\n",
"\n",
"print(\"Матрица X:\\n{}\\n\".format(x))\n",
"print(\"Размер: {}\\n\".format(x.shape))\n",
"print(\"Добавление константы:\\n{}\\n\".format(x + 5))\n",
"print(\"X*X^T:\\n{}\\n\".format(np.dot(x, x.T)))\n",
"print(\"Среднее по колонкам:\\n{}\\n\".format(x.mean(axis=-1)))\n",
"print(\"Кумулятивная сумма по колонкам:\\n{}\\n\".format(np.cumsum(x, axis=0)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Fit0yaPPOCwv"
},
"source": [
"**pytorch**"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uZM_OyVQ8hjV",
"outputId": "8910db0a-65ae-4b09-93d6-af6757ea891d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Матрица X:\n",
"tensor([[ 0., 1., 2., 3.],\n",
" [ 4., 5., 6., 7.],\n",
" [ 8., 9., 10., 11.],\n",
" [12., 13., 14., 15.]])\n",
"Размер: torch.Size([4, 4])\n",
"\n",
"Добавление константы:\n",
"tensor([[ 5., 6., 7., 8.],\n",
" [ 9., 10., 11., 12.],\n",
" [13., 14., 15., 16.],\n",
" [17., 18., 19., 20.]])\n",
"X*X^T:\n",
"tensor([[ 14., 38., 62., 86.],\n",
" [ 38., 126., 214., 302.],\n",
" [ 62., 214., 366., 518.],\n",
" [ 86., 302., 518., 734.]])\n",
"Среднее по колонкам:\n",
"tensor([ 1.5000, 5.5000, 9.5000, 13.5000])\n",
"Кумулятивная сумма по колонкам:\n",
"tensor([[ 0., 1., 2., 3.],\n",
" [ 4., 6., 8., 10.],\n",
" [12., 15., 18., 21.],\n",
" [24., 28., 32., 36.]])\n"
]
}
],
"source": [
"x = np.arange(16).reshape(4, 4)\n",
"x = torch.tensor(x, dtype=torch.float32) # или torch.arange(0,16).view(4,4)\n",
"\n",
"print(\"Матрица X:\\n{}\".format(x))\n",
"print(\"Размер: {}\\n\".format(x.shape))\n",
"print(\"Добавление константы:\\n{}\".format(x + 5))\n",
"print(\"X*X^T:\\n{}\".format(torch.matmul(x, x.transpose(1, 0)))) # кратко: x.mm(x.t())\n",
"print(\"Среднее по колонкам:\\n{}\".format(torch.mean(x, dim=-1)))\n",
"print(\"Кумулятивная сумма по колонкам:\\n{}\".format(torch.cumsum(x, dim=0)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T4_b7OJL8hjX"
},
"source": [
"Всё же некоторые названия методов отличаются от numpy-евских. Полной совместимости с numpy пока нет, но от верчии к версии она увеличвается, и придется сново запоминать новые названия для некоторых методов.\n",
"\n",
"Например, Pytorch имеет другое написание стандартных типов\n",
" * `x.astype('int64') -> x.type(torch.LongTensor)`\n",
"\n",
"\n",
"Для более подробного ознакомления можно посмотреть на [табличку](https://github.com/torch/torch7/wiki/Torch-for-Numpy-users) перевода методов из numpy в pytorch, а также заглянуть в [документацию](http://pytorch.org/docs/master/). Также при возникновении проблем часто помогает зайти на [pytorch forumns](https://discuss.pytorch.org/)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "23HMyfZUZWFH"
},
"source": [
"#### 1.2 NumPy <-> PyTorch\n",
"Можно переводить numpy-массив в torch-тензор и наоборот.\n",
"Например, чтобы сделать из numpy-массива torch-тензор, можно сделать так: "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5F1f3nEWZ6tA",
"outputId": "4aa979ce-404c-4d20-a297-2ee76694ddf7"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" tensor([2, 5, 7, 1])\n",
" tensor([2, 5, 7, 1])\n"
]
}
],
"source": [
"# зададим numpy массив\n",
"x_np = np.array([2, 5, 7, 1])\n",
"\n",
"# 1-й способ\n",
"x_torch = torch.tensor(x_np) \n",
"print(type(x_torch), x_torch)\n",
"\n",
"# 2-й способ\n",
"x_torch = torch.from_numpy(x_np)\n",
"print(type(x_torch), x_torch)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lcFEpTNLa4cP"
},
"source": [
"Аналогично и с переводом обратно: функция `x.numpy()` переведет torch-тензор x в numpy-массив, причем типы переведутся соответственно табличке."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4VsZ0O3Ca8L4",
"outputId": "c50e7637-6cc4-49ac-ec34-146bc1721c85"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [2 5 7 1]\n"
]
}
],
"source": [
"x_np = x_torch.numpy()\n",
"print(type(x_np), x_np)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8kzxCLDZ8hjY"
},
"source": [
"#### 1.3 Еще один пример\n",
"Давайте нарисуем по сетке данную кривую на графике, используя pytorch:\n",
"\n",
"$$x(t) = 2 \\cos t + \\sin 2t \\cos 60t,$$\n",
"\n",
"$$y(t) = \\sin 2t + \\sin 60t.$$"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 298
},
"id": "gQ33m_TF8hjY",
"outputId": "3a1db043-41fc-41b4-ec92-7d565c431fb7"
},
"outputs": [
{
"data": {
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\n",
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