01-tensor_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Traduction en français du notebook *01* du cours ***Deep Learning*** d'Alfredo Canziani, professeur assistant à la *New York University*  : \n",
    "https://github.com/Atcold/pytorch-Deep-Learning/blob/master/01-tensor_tutorial.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Qu'est-ce que PyTorch ?\n",
    "\n",
    "Il s'agit d'un package de calcul scientifique basé sur Python et destiné à deux types de public :\n",
    "\n",
    "- La librairie tensorielle qui utilise la puissance des GPU\n",
    "- Une plateforme pour la recherche en apprentissage profond qui offre un maximum de flexibilité et de rapidité\n",
    "\n",
    "\n",
    "\n",
    "## Importer la librairie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch  # <Ctrl> ou <Shift> + <Entrée>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtenir de l'aide dans Jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.sq  # <Tab>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Qu'en est-il de tous les \"tenseurs\" ?\n",
    "# Appuyez sur <ech> pour sortir de l'aide\n",
    "torch.*Tensor?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.nn.Module()  # <Shift>+<Tab>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Annotez vos fonctions / cours !\n",
    "torch.nn.Module?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.nn.Module??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bash: magic!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Pour lister tous les fichiers dans le répertoire courant\n",
    "!ls -lh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "# Lister tous les fichiers mais avec des sorties plus propres pour la lisibilité\n",
    "for f in $(ls *.*); do\n",
    "    echo $(wc -l $f)\n",
    "done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pour obtenir une aide générale\n",
    "%magic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Types de données en Python\n",
    "\n",
    "Python possède de nombreux types de données. Voici les plus importants :\n",
    "\n",
    " - Les **booléens** (*booleans*) sont soit `True` soit `False`.\n",
    " - Les **nombres** (*numbers*) peuvent être des entiers (1 et 2), des flottants (1.1 et 1.2), des fractions (1/2 et 2/3), ou même des nombres complexes.\n",
    " - **Les chaînes de caractères** (*strings*) sont des séquences de caractères Unicode, par exemple un document html.\n",
    " - Les **listes** (*list*) sont des séquences ordonnées de valeurs.\n",
    " - Les **tuples** (*tuples*) sont des séquences de valeurs ordonnées et immuables.\n",
    " - Les **ensembles** (*set*) sont des sacs de valeurs non ordonnées.\n",
    " - **Les dictionnaires** (*dictionaries*) sont des sacs non ordonnés de paires clés/valeurs.\n",
    " \n",
    "Voir [ici](http://www.diveintopython3.net/native-datatypes.html) pour une vue d'ensemble complète.\n",
    "\n",
    "### Plus de ressources (en anglais)\n",
    "\n",
    " 1. Brève présentation de Python [ici](https://learnxinyminutes.com/docs/python3/).\n",
    " 2. Tutoriel Python complet [ici](https://docs.python.org/3/tutorial/).\n",
    " 3. Un tour d'horizon de Python [ici](https://github.com/jakevdp/WhirlwindTourOfPython).\n",
    " 4. Python Data Science Handbook [ici](https://github.com/jakevdp/PythonDataScienceHandbook)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Torch!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Tensor"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour générer un tenseur de taille 2x3x4\n",
    "t = torch.Tensor(2, 3, 4)\n",
    "type(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 3, 4])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour obtenir la taille du tenseur\n",
    "t.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t size: 2 × 3 × 4\n"
     ]
    }
   ],
   "source": [
    "# t.size() est un tuple classique =>\n",
    "print('t size:', ' \\u00D7 '.join(map(str, t.size())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "points dans un espace de dimension 24\n",
      "organisés en 3 sous-dimensions\n"
     ]
    }
   ],
   "source": [
    "# Pour afficher l'espace dimensionnel et les sous-dimensions\n",
    "print(f'points dans un espace de dimension {t.numel()}')\n",
    "print(f'organisés en {t.dim()} sous-dimensions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[1.0102e-38, 1.0561e-38, 9.0919e-39, 9.2755e-39],\n",
       "         [2.9389e-39, 1.2960e-19, 2.9389e-39, 8.9082e-39],\n",
       "         [2.9389e-39, 1.0745e-38, 2.1490e-38, 1.0469e-38]],\n",
       "\n",
       "        [[2.9389e-39, 9.0919e-39, 9.2755e-39, 2.1398e-38],\n",
       "         [2.9389e-39, 1.0010e-38, 2.1398e-38, 9.6429e-39],\n",
       "         [1.0653e-38, 1.0010e-38, 1.0102e-38, 1.6255e-43]]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[6., 3., 7., 4.],\n",
       "         [4., 8., 2., 9.],\n",
       "         [0., 2., 9., 2.]],\n",
       "\n",
       "        [[1., 3., 4., 4.],\n",
       "         [2., 1., 8., 8.],\n",
       "         [1., 7., 4., 7.]]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Attention au soulignement !\n",
    "# Toute opération qui fait muter un tenseur en place est post-fixée avec un _.\n",
    "# Par exemple : x.copy_(y), x.t_(), x.random_(n) changera x.\n",
    "t.random_(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[6., 3., 7., 4.],\n",
       "         [4., 8., 2., 9.],\n",
       "         [0., 2., 9., 2.]],\n",
       "\n",
       "        [[1., 3., 4., 4.],\n",
       "         [2., 1., 8., 8.],\n",
       "         [1., 7., 4., 7.]]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[6., 3., 7., 4., 4., 8., 2., 9.],\n",
       "        [0., 2., 9., 2., 1., 3., 4., 4.],\n",
       "        [2., 1., 8., 8., 1., 7., 4., 7.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ce qui suit permet de redimensionner le tenseur de façon permanente \n",
    "r = torch.Tensor(t)\n",
    "r.resize_(3, 8)\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comme vous pouvez le voir, zero_ remplace r par des 0, qui était à l'origine rempli d'entiers\n",
    "r.zero_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0.],\n",
       "         [0., 0., 0., 0.]]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# C'est important\n",
    "s = r.clone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remplissage par des 1\n",
    "s.fill_(1)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parce que nous avons cloné r, même si nous avons fait une nouvelle opération, cela n'affecte pas r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vecteurs (Tenseurs 1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 2., 3., 4.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour créer un tenseur 1D des entiers 1 à 4\n",
    "v = torch.Tensor([1, 2, 3, 4])\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dim: 1, taille: 4\n"
     ]
    }
   ],
   "source": [
    "# Pour afficher le nombre de dimensions (1D) et la taille du tenseur\n",
    "print(f'dim: {v.dim()}, taille: {v.size()[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 0., 2., 0.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = torch.Tensor([1, 0, 2, 0])\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 0., 6., 0.])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiplication par éléments\n",
    "v * w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(7.)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Produit scalaire : 1*1 + 2*0 + 3*2 + 4*0\n",
    "v @ w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([7., 4., 9., 1., 1.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remplacement de nombre aléatoire de 0 à 10\n",
    "x = torch.Tensor(5).random_(10)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "premier: 7.0, dernier: 1.0\n"
     ]
    }
   ],
   "source": [
    "print(f'premier: {x[0]}, dernier: {x[-1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([4., 9.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extrait du sous-tenseur [de:à]\n",
    "x[1:2 + 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 2., 3., 4.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 2, 3, 4])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour créer un tenseur avec des nombres entiers allant de 1 à 5 (5 exclus)\n",
    "v = torch.arange(1, 4 + 1)\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 1,  4,  9, 16]) tensor([1, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "# Pour mettre au carré tous les éléments d'un tenseur\n",
    "print(v.pow(2), v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrices (Tenseurs 2D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2., 5., 3., 7.],\n",
       "        [4., 2., 1., 9.]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour créer un tenseur 2x4 \n",
    "m = torch.Tensor([[2, 5, 3, 7],\n",
    "                  [4, 2, 1, 9]])\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.dim()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 -- 4 -- torch.Size([2, 4])\n"
     ]
    }
   ],
   "source": [
    "print(m.size(0), m.size(1), m.size(), sep=' -- ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renvoie le nombre total d'éléments, donc numel (nombre d'éléments)\n",
    "m.numel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(3.)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour indexer la ligne 0, la colonne 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(3.)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour indexer la ligne 0, la colonne 2\n",
    "m[0, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([5., 2.])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour indexer la colonne, toutes les lignes (retourne une taille de 2)\n",
    "m[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[5.],\n",
       "        [2.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour indexer la colonne 1, toutes les lignes (retourne une taille 2x2)\n",
    "m[:, [1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2., 5., 3., 7.]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour indexer la ligne 0, toutes les colonnes (retourne une taille 1x4)\n",
    "m[[0], :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([2., 5., 3., 7.])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour indexer la ligne 0, toutes les colonnes (retourne une taille de 4) \n",
    "m[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 2., 3., 4.])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour créer un tenseur de nombres de 1 à 5 (5 exclus)\n",
    "v = torch.arange(1., 4 + 1)\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2., 5., 3., 7.],\n",
       "        [4., 2., 1., 9.]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([49., 47.])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Produit scalaire\n",
    "m @ v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([49.])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculé par 1*2 + 2*5 + 3*3 + 4*7\n",
    "m[[0], :] @ v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([47.])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculé par\n",
    "m[[1], :] @ v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2.2849, 5.2500, 3.8638, 7.9089],\n",
       "        [4.2126, 2.7432, 1.9940, 9.2226]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour ajouter un tenseur aléatoire de taille 2x4 à m\n",
    "m + torch.rand(2, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1.0262, 4.1877, 2.2834, 6.5255],\n",
       "        [3.0378, 1.3799, 0.0300, 8.3211]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour soustraire un tenseur aléatoire de taille 2x4 à m\n",
    "m - torch.rand(2, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.7473, 2.5695, 1.0966, 1.9768],\n",
       "        [0.1088, 1.7619, 0.4187, 0.3696]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour multiplier un tenseur aléatoire de taille 2x4 à m\n",
    "m * torch.rand(2, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[4.1579, 5.3197, 4.7460, 8.6796],\n",
       "        [8.5406, 4.0876, 1.7447, 9.1747]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour diviser m par un tenseur aléatoire de taille 2x4\n",
    "m / torch.rand(2, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 4])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2., 4.],\n",
       "        [5., 2.],\n",
       "        [3., 1.],\n",
       "        [7., 9.]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose m (passe de 2x4 à 4x2)\n",
    "m.t()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2., 4.],\n",
       "        [5., 2.],\n",
       "        [3., 1.],\n",
       "        [7., 9.]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Même chose\n",
    "m.transpose(0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constructeurs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([3., 4., 5., 6., 7., 8.])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour créer des tenseurs de 3 à 8, ayant chacun un pas de 1\n",
    "torch.arange(3., 8 + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 5.7000,  2.7000, -0.3000])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour créer un tenseur de 5,7 à -2,1 ayant chacun un pas de -3\n",
    "torch.arange(5.7, -2.1, -3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[3.0000, 3.2632, 3.5263, 3.7895, 4.0526, 4.3158, 4.5789, 4.8421, 5.1053,\n",
       "         5.3684, 5.6316, 5.8947, 6.1579, 6.4211, 6.6842, 6.9474, 7.2105, 7.4737,\n",
       "         7.7368, 8.0000]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# renvoie un tenseur 1D de points équidistants entre start=3, end=8 et steps=20\n",
    "torch.linspace(3, 8, 20).view(1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour créer un tenseur rempli de 0\n",
    "torch.zeros(3, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[1., 1., 1., 1., 1.],\n",
       "         [1., 1., 1., 1., 1.]],\n",
       "\n",
       "        [[1., 1., 1., 1., 1.],\n",
       "         [1., 1., 1., 1., 1.]],\n",
       "\n",
       "        [[1., 1., 1., 1., 1.],\n",
       "         [1., 1., 1., 1., 1.]]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour créer un tenseur rempli de 1\n",
    "torch.ones(3, 2, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 0., 0.],\n",
       "        [0., 1., 0.],\n",
       "        [0., 0., 1.]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour créer un tenseur dont la diagonale est remplie de 1\n",
    "torch.eye(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pour définir des tracés par défaut\n",
    "from res.plot_lib import set_default\n",
    "from matplotlib import pyplot as plt\n",
    "set_default()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# En NumPy\n",
    "plt.hist(torch.randn(1000).numpy(), 100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(torch.randn(10**6).numpy(), 100);   # combien pèse ce graphique ?\n",
    "# utiliser rasterized=True pour SVG/EPS/PDF!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(torch.rand(10**6).numpy(), 100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Casting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aide pour obtenir le type de tenseur\n",
    "torch.*Tensor?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2., 5., 3., 7.],\n",
       "        [4., 2., 1., 9.]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2., 5., 3., 7.],\n",
       "        [4., 2., 1., 9.]], dtype=torch.float64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Il s'agit essentiellement d'un tenseur de flotteur de 64 bits\n",
    "m_double = m.double()\n",
    "m_double"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2, 5, 3, 7],\n",
       "        [4, 2, 1, 9]], dtype=torch.uint8)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cela crée un tenseur de type int8\n",
    "m_byte = m.byte()\n",
    "m_byte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2., 5., 3., 7.],\n",
       "        [4., 2., 1., 9.]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Déplace votre tenseur sur le GPU 0 s'il y en a un (premier GPU dans le système)\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "m.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 5., 3., 7.],\n",
       "       [4., 2., 1., 9.]], dtype=float32)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convertit le tenseur en tableau numpy\n",
    "m_np = m.numpy()\n",
    "m_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.,  5.,  3.,  7.],\n",
       "       [ 4.,  2.,  1.,  9.]], dtype=float32)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remplissage de la colonne 0 et de la ligne 0 avec la valeur -1\n",
    "m_np[0, 0] = -1\n",
    "m_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1.,  5.,  3.,  7.],\n",
       "        [ 4.,  2.,  1.,  9.]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4] tensor([0, 1, 2, 3, 4], dtype=torch.int32)\n"
     ]
    }
   ],
   "source": [
    "# Pour céer un tenseur d'entiers allant de 0 à 4\n",
    "import numpy as np\n",
    "n_np = np.arange(5)\n",
    "n = torch.from_numpy(n_np)\n",
    "print(n_np, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiplication de tous les éléments par 2 pour le tenseur n\n",
    "# Parce que n est essentiel n_np, pas un clone, cela affecte n_np\n",
    "n.mul_(2)\n",
    "n_np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plus de fun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 4]) tensor([[5., 6., 7., 8.]])\n"
     ]
    }
   ],
   "source": [
    "# Pour créer deux tenseurs de taille 1x4\n",
    "a = torch.Tensor([[1, 2, 3, 4]])\n",
    "b = torch.Tensor([[5, 6, 7, 8]])\n",
    "print(a.size(), b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 2., 3., 4.],\n",
       "        [5., 6., 7., 8.]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour concaténer sur l'axe 0, donc vous obtenez 2x4\n",
    "torch.cat((a, b), 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 2., 3., 4., 5., 6., 7., 8.]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pour concaténer sur l'axe 1, donc vous obtenez 1x8\n",
    "torch.cat((a, b), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plus encore\n",
    "\n",
    "Il y a certainement beaucoup plus à voir, mais c'est l'essentiel pour les `Tensor`.\n",
    "\n",
    "L'API complète de *Torch* doit être lue au moins une fois.\n",
    "Pour cela, allez [ici](https://pytorch.org/docs/stable/index.html).\n",
    "Vous y trouverez plus de 100 opérations `Tensor`, y compris la transposition, l'indexation, le découpage, les opérations mathématiques, l'algèbre linéaire, les nombres aléatoires, etc."
   ]
  }
 ],
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