redes-neuronales/Cuadernos/Pythorch_02_tensores.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48c72371",
   "metadata": {},
   "source": [
    "<figure>\n",
    "<img src=\"../Imagenes/logo-final-ap.png\"  width=\"80\" height=\"80\" align=\"left\"/> \n",
    "</figure>\n",
    "\n",
    "# <span style=\"color:#4361EE\"><left>Aprendizaje Profundo</left></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04c28920",
   "metadata": {},
   "source": [
    "# <span style=\"color:red\"><center>Diplomado en Inteligencia Artificial y Aprendizaje Profundo</center></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recorded-timber",
   "metadata": {
    "tags": []
   },
   "source": [
    "# <span style=\"color:green\"><center>Tensores</center></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c22e8c31",
   "metadata": {},
   "source": [
    "<figure>\n",
    "<center>\n",
    "<img src=\"https://raw.githubusercontent.com/AprendizajeProfundo/Alejandria/main/Archivos_Generales/Imagenes/Estampillas.jpg\" width=\"600\" height=\"400\" align=\"center\" /> \n",
    "</center>   \n",
    "</figure>\n",
    "<center>\n",
    "    \n",
    "Fuente: [Pexels](https://www.pexels.com/es-es/foto/coleccion-de-sellos-248993/)   \n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a3d8cb2",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Profesores</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29268792",
   "metadata": {},
   "source": [
    "1. Alvaro  Montenegro, PhD, ammontenegrod@unal.edu.co\n",
    "1. Camilo José Torres Jiménez, Msc, cjtorresj@unal.edu.co\n",
    "1. Daniel  Montenegro, Msc, dextronomo@gmail.com "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e5ecb45",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Asesora Medios y Marketing digital</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42eb9757",
   "metadata": {},
   "source": [
    "4. Maria del Pilar Montenegro, pmontenegro88@gmail.com\n",
    "5. Jessica López Mejía, jelopezme@unal.edu.co\n",
    "6. Venus Puertas, vpuertasg@unal.edu.co"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85931ef1",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Jefe Jurídica</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2849ea37",
   "metadata": {},
   "source": [
    "7. Paula Andrea Guzmán, guzmancruz.paula@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60bb1120",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Coordinador Jurídico</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d16ac691",
   "metadata": {},
   "source": [
    "8. David Fuentes, fuentesd065@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "615f913d",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Desarrolladores Principales</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19122023",
   "metadata": {},
   "source": [
    "9. Dairo Moreno, damoralesj@unal.edu.co\n",
    "10. Joan Castro, jocastroc@unal.edu.co\n",
    "11. Bryan Riveros, briveros@unal.edu.co\n",
    "12. Rosmer Vargas, rovargasc@unal.edu.co"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72645d63",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Expertos en Bases de Datos</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "506116c1",
   "metadata": {},
   "source": [
    "13. Giovvani Barrera, udgiovanni@gmail.com\n",
    "14. Camilo Chitivo, cchitivo@unal.edu.co"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "coral-jackson",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Referencias</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "based-integration",
   "metadata": {},
   "source": [
    "1. Basado en los [tutoriales de Pytorch](https://pytorch.org/tutorials/)\n",
    "1. [Deep learning for coders with FastAI and Pytorch](http://library.lol/main/F13E85845AE48D9FD7488FE7630A9FD3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recorded-vector",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Contenido</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "functional-ancient",
   "metadata": {},
   "source": [
    "* [Introducción](#Introducción)\n",
    "* [Inicializa un Tensor](#Inicializa-un-Tensor)\n",
    "* [Operaciones sobre Tensores](#Operaciones-sobre-Tensores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "limiting-glasgow",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Introducción</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "detailed-agent",
   "metadata": {},
   "source": [
    "Los tensores son similares a los ndarrays de `NumPy`, excepto que los tensores pueden ejecutarse en GPU u otros aceleradores de hardware. De hecho, los tensores y las matrices NumPy a menudo pueden compartir la misma memoria subyacente, lo que elimina la necesidad de copiar datos (consulte [Puente con NumPy](https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#bridge-to-np-label)). Los tensores también están optimizados para la diferenciación automática."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prostate-garden",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Inicializa un Tensor</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cubic-matthew",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "brutal-lending",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Directamente de los datos</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "parental-concentration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1, 2],\n",
       "        [3, 4]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [[1, 2], [3, 4]]\n",
    "x_data = torch.tensor(data)\n",
    "x_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "south-province",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">A partir de arreglos Numpy</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "choice-holly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1, 2],\n",
       "        [3, 4]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array = np.array(data)\n",
    "\n",
    "x_np = torch.from_numpy(np_array)\n",
    "x_np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "documented-acrylic",
   "metadata": {},
   "source": [
    "Desde otro tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "amino-conviction",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ones Tensor: \n",
      " tensor([[1, 1],\n",
      "        [1, 1]]) \n",
      "\n",
      "Random Tensor: \n",
      " tensor([[0.6667, 0.5778],\n",
      "        [0.5316, 0.7024]]) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "x_ones = torch.ones_like(x_data) # retiene las propiedades de x_data\n",
    "print(f\"Ones Tensor: \\n {x_ones} \\n\")\n",
    "\n",
    "x_rand = torch.rand_like(x_data, dtype=torch.float)# sobre escribe le tipo de dato\n",
    "print(f\"Random Tensor: \\n {x_rand} \\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "governing-tradition",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Con valores aleatorios y constantes</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "economic-ticket",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Tensor: \n",
      " tensor([[0.5011, 0.7800, 0.2040],\n",
      "        [0.9878, 0.6407, 0.6404]]) \n",
      "\n",
      "Ones Tensor: \n",
      " tensor([[1., 1., 1.],\n",
      "        [1., 1., 1.]]) \n",
      "\n",
      "Zeros Tensor: \n",
      " tensor([[0., 0., 0.],\n",
      "        [0., 0., 0.]])\n"
     ]
    }
   ],
   "source": [
    "shape = (2,3)\n",
    "rand_tensor = torch.rand(shape)\n",
    "ones_tensor = torch.ones(shape)\n",
    "zeros_tensor = torch.zeros(shape)\n",
    "\n",
    "print(f\"Random Tensor: \\n {rand_tensor} \\n\")\n",
    "print(f\"Ones Tensor: \\n {ones_tensor} \\n\")\n",
    "print(f\"Zeros Tensor: \\n {zeros_tensor}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "expected-albania",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of tensor: torch.Size([3, 4])\n",
      "Datatype of tensor: torch.float32\n",
      "Device tensor is stored on: cpu\n"
     ]
    }
   ],
   "source": [
    "tensor = torch.rand(3,4)\n",
    "\n",
    "print(f\"Shape of tensor: {tensor.shape}\")\n",
    "print(f\"Datatype of tensor: {tensor.dtype}\")\n",
    "print(f\"Device tensor is stored on: {tensor.device}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "manufactured-ground",
   "metadata": {},
   "source": [
    "## <span style=\"color:#4361EE\">Operaciones sobre Tensores</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "caring-analyst",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Movemos nuestro tensor a la GPU si está disponible\n",
    "if torch.cuda.is_available():\n",
    "    tensor.to('cuda')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "frozen-niger",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Indexación y rebanado(slicing) al estilo  numpy</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "personalized-laundry",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First row:  tensor([1., 1., 1., 1.])\n",
      "First column:  tensor([1., 1., 1., 1.])\n",
      "Last column:  tensor([1., 1., 1., 1.])\n",
      "tensor([[1., 0., 1., 1.],\n",
      "        [1., 0., 1., 1.],\n",
      "        [1., 0., 1., 1.],\n",
      "        [1., 0., 1., 1.]])\n"
     ]
    }
   ],
   "source": [
    "tensor = torch.ones(4, 4)\n",
    "print('First row: ', tensor[0])\n",
    "print('First column: ', tensor[:,0])\n",
    "print('Last column: ', tensor[...,-1])\n",
    "tensor[:,1] = 0\n",
    "print(tensor)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aggressive-yesterday",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Concatenación de tensores</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "talented-performer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\n",
      "        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\n",
      "        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.],\n",
      "        [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.]])\n"
     ]
    }
   ],
   "source": [
    "t1 = torch.cat([tensor, tensor, tensor], dim=1)\n",
    "print(t1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "subject-wellington",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Operaciones aritméticas</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e7fa3b7a-e355-4a6a-8faa-f7857cf8cc20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[3., 3., 3., 3.],\n",
       "        [3., 3., 3., 3.],\n",
       "        [3., 3., 3., 3.],\n",
       "        [3., 3., 3., 3.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiplicación matricial\n",
    "y1 = tensor @ tensor.T\n",
    "y2 = tensor.matmul(tensor.T)\n",
    "\n",
    "y3 = torch.rand_like(tensor)\n",
    "torch.matmul(tensor, tensor.T, out=y3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "18fe5809-b59b-42c5-a56a-830f8b1b8b29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 0., 1., 1.],\n",
       "        [1., 0., 1., 1.],\n",
       "        [1., 0., 1., 1.],\n",
       "        [1., 0., 1., 1.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multiplicación elemento a elemento\n",
    "z1 = tensor * tensor\n",
    "z2 = tensor.mul(tensor)\n",
    "\n",
    "z3 = torch.rand_like(tensor)\n",
    "torch.mul(tensor, tensor, out = z3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intelligent-check",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Agregación</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "august-theta",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.0 <class 'float'>\n"
     ]
    }
   ],
   "source": [
    "agg = tensor.sum()\n",
    "agg_item = agg.item()\n",
    "print(agg_item, type(agg_item))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "marked-paradise",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Operaciones in place</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "little-storm",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 0., 1., 1.],\n",
      "        [1., 0., 1., 1.],\n",
      "        [1., 0., 1., 1.],\n",
      "        [1., 0., 1., 1.]]) \n",
      "\n",
      "tensor([[6., 5., 6., 6.],\n",
      "        [6., 5., 6., 6.],\n",
      "        [6., 5., 6., 6.],\n",
      "        [6., 5., 6., 6.]])\n"
     ]
    }
   ],
   "source": [
    "print(tensor, '\\n')\n",
    "tensor.add_(5)\n",
    "print(tensor)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "creative-southwest",
   "metadata": {},
   "source": [
    "### <span style=\"color:#4CC9F0\">Puente con Numpy</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "outstanding-lodge",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t: tensor([1., 1., 1., 1., 1.])\n",
      "n: [1. 1. 1. 1. 1.]\n",
      "t: tensor([2., 2., 2., 2., 2.])\n",
      "n: [2. 2. 2. 2. 2.]\n"
     ]
    }
   ],
   "source": [
    "t = torch.ones(5)\n",
    "print(f't: {t}')\n",
    "n = t.numpy()\n",
    "print(f'n: {n}')\n",
    "\n",
    "# el cambio se refleja en el arreglo numpy\n",
    "t.add_(1)\n",
    "print(f't: {t}')\n",
    "print(f'n: {n}')"
   ]
  }
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