{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MSPWRXaT2jgt"
      },
      "source": [
        "<style>\n",
        "  .justified {\n",
        "    text-align: justify;\n",
        "    text-justify: inter-word;\n",
        "  }\n",
        "</style>\n",
        "\n",
        "<div class=\"justified\">\n",
        "\n",
        "# Elementos de procesamiento de lenguajes naturales, o El Hacedor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-peFXE2g2rLa"
      },
      "source": [
        "Este texto está inspirado en la serie sobre redes neuronales de Andrej Karpathy {cite}`KarpathyZeroToHero` y, más particularmente, en [makemore](https://github.com/karpathy/makemore). A continuación, crearemos un modelo de lenguaje basado en bigramas. Utilizando 21,209 nombres argentinos como base, nuestro modelo aprenderá patrones estadísticos para idear nuevos nombres en español[^1].\n",
        "\n",
        "```{margin}\n",
        "El conjunto de datos original se puede encontrar [aquí](https://www.kaggle.com/datasets/akielbowicz/nombres-de-personas-fsicas-de-argentina).\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UIcRYjz7vVjx"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "\n",
        "#nombres = pd.read_csv('historico-nombres.csv').iloc[0:200000]\n",
        "#regex = \"[^a-z]\"\n",
        "#nombres = nombres['nombre'].str.lower()\n",
        "#filtro = nombres.str.contains(\"[^a-z]\")\n",
        "#nombres = nombres[~filtro].astype('str')\n",
        "\n",
        "#nombres.to_csv(r'nombres.txt', header=None, index=None, mode='a')\n",
        "#nombres.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z10MFTSUfSOv"
      },
      "outputs": [],
      "source": [
        "!wget https://github.com/DanteNoguez/CalculusRatiocinator/raw/main/data/nombres.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cjPrWuX-O5-e"
      },
      "outputs": [],
      "source": [
        "palabras = open('nombres.txt', 'r').read().splitlines()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ndq9xKHVSXrz",
        "outputId": "5d0a02cf-6c8d-4817-8064-70a31f51c96d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['maria',\n",
              " 'rosa',\n",
              " 'jose',\n",
              " 'carmen',\n",
              " 'ana',\n",
              " 'juana',\n",
              " 'antonio',\n",
              " 'elena',\n",
              " 'teresa',\n",
              " 'angela']"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "palabras[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wmSYx3STTUNL",
        "outputId": "956ce5e8-bf21-4f6e-d98b-791e399ad1e6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "21029"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(palabras)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YyY1K36iKgL1"
      },
      "source": [
        "### Bigrama\n",
        "\n",
        "Primero, formaremos bigramas (pares) de caracteres por cada nombre que hay en nuestro conjunto de datos. Al final e inicio de cada nombre, agregaremos un *token* `.` para indicar el inicio y fin de dicho nombre:\n",
        "\n",
        "```{margin}\n",
        "Cada *token* es una unidad indivisible de texto, aunque el diseño o especificación de *tokens* en un modelo es una decisión personal (técnica, para ser más precisos). Por ejemplo, podemos crear un conjunto de datos enfocado en palabras, de manera que «mesa» sea un *token*; pero también podemos considerar a cada letra del alfabeto —junto con el `.`— como un *token*, como estamos haciendo en nuestro caso. Para profundizar, recomiendo el blog de Elena Voita {cite}`VoitaNLP`.\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DBZVt_qPT0hc",
        "outputId": "8c59bc42-2401-46a3-9384-2f5e54556e72"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ". m\n",
            "m a\n",
            "a r\n",
            "r i\n",
            "i a\n",
            "a .\n",
            ". r\n",
            "r o\n",
            "o s\n",
            "s a\n",
            "a .\n",
            ". j\n",
            "j o\n",
            "o s\n",
            "s e\n",
            "e .\n"
          ]
        }
      ],
      "source": [
        "b = {}\n",
        "\n",
        "for p in palabras[:3]: # vemos los primeros tres nombres\n",
        "  cs = ['.'] + list(p) + ['.'] \n",
        "  for c1, c2 in zip(cs, cs[1:]): # iteramos sobre cada caracter para crear bigramas\n",
        "    bigrama = (c1, c2)\n",
        "    b[bigrama] = b.get(bigrama, 0) + 1 # hacemos un conteo de bigramas\n",
        "    print(c1, c2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k2TEq8Ine5Ij"
      },
      "source": [
        "El conteo de bigramas luce así:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4CBHxaLeUfyX",
        "outputId": "9a9dbde8-4ef7-422f-8ef4-de3b1036a205"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{('.', 'm'): 1,\n",
              " ('m', 'a'): 1,\n",
              " ('a', 'r'): 1,\n",
              " ('r', 'i'): 1,\n",
              " ('i', 'a'): 1,\n",
              " ('a', '.'): 2,\n",
              " ('.', 'r'): 1,\n",
              " ('r', 'o'): 1,\n",
              " ('o', 's'): 2,\n",
              " ('s', 'a'): 1,\n",
              " ('.', 'j'): 1,\n",
              " ('j', 'o'): 1,\n",
              " ('s', 'e'): 1,\n",
              " ('e', '.'): 1}"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "b"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dgcHDqlkfxeM"
      },
      "source": [
        "Ahora, crearemos una lista de caracteres únicos (nuestro vocabulario) para luego asignarles un índice en un diccionario de Python. A este proceso de mapear o relacionar cada letra de nuestro vocabulario con un número se le denomina «incrustación» (*embedding*), mientras que el diccionario de Python resultante es una «tabla de consulta» (*lookup table*), debido a que en ella podemos buscar la letra que corresponde a un número y viceversa."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7_IKLGkJXyIA",
        "outputId": "f9a2793b-ebd2-4ac5-b993-386354d02ac7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{1: 'a',\n",
              " 2: 'b',\n",
              " 3: 'c',\n",
              " 4: 'd',\n",
              " 5: 'e',\n",
              " 6: 'f',\n",
              " 7: 'g',\n",
              " 8: 'h',\n",
              " 9: 'i',\n",
              " 10: 'j',\n",
              " 11: 'k',\n",
              " 12: 'l',\n",
              " 13: 'm',\n",
              " 14: 'n',\n",
              " 15: 'o',\n",
              " 16: 'p',\n",
              " 17: 'q',\n",
              " 18: 'r',\n",
              " 19: 's',\n",
              " 20: 't',\n",
              " 21: 'u',\n",
              " 22: 'v',\n",
              " 23: 'w',\n",
              " 24: 'x',\n",
              " 25: 'y',\n",
              " 26: 'z',\n",
              " 0: '.'}"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "caracs = sorted(list(set(''.join(palabras)))) # lista de caracteres únicos (tokens)\n",
        "\n",
        "paf = {p:f+1 for f,p in enumerate(caracs)} # mapeamos letras a números de principio a fin\n",
        "paf['.'] = 0 # agregamos nuestro token «.»\n",
        "fap = {f:p for p,f in paf.items()} # invertimos el orden para que sea apropiado\n",
        "fap"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-74Zf111-qG"
      },
      "source": [
        "Ahora, construiremos una matriz —vía PyTorch— con el conteo de todos los bigramas de nuestro conjunto de datos. Con esta matriz, podremos familiarizarnos más visualmente con lo que hemos estado preparando. Las dimensiones de la matriz serán 27x27 porque tenemos 27 elementos en nuestro vocabulario y queremos emparejarlos (hacer bigramas) con cada uno de los otros elementos del mismo:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1EjZ7r_S1CAZ"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "N = torch.zeros((27,27))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kL1qWrgSXNG2"
      },
      "outputs": [],
      "source": [
        "for p in palabras:\n",
        "  cs = ['.'] + list(p) + ['.']\n",
        "  for c1, c2 in zip(cs, cs[1:]):\n",
        "    ix1 = paf[c1]\n",
        "    ix2 = paf[c2]\n",
        "    N[ix1, ix2] += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        },
        "id": "QdiAmBjNnQet",
        "outputId": "f302181b-29ce-4f02-fe91-95206daece39"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1152x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(16, 16))\n",
        "plt.imshow(N, cmap='Blues')\n",
        "for i in range(27):\n",
        "  for j in range(27):\n",
        "    cts = fap[i] + fap[j]\n",
        "    plt.text(j, i, cts, ha='center', va='bottom', color='gray')\n",
        "    plt.text(j, i, N[i, j].item(), ha='center', va='top', color='gray')\n",
        "plt.axis('off');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1XCnltcd4DdD"
      },
      "source": [
        "Hemos contado la ocurrencia de cada bigrama en el documento de nombres. Ahora, podemos utilizar este conteo como una distribución de probabilidades acerca de cuál letra debe ser consecutiva con otra. Ejemplifiquemos con una fila:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sqWwRm5pnSj5",
        "outputId": "180317d7-87d5-4d77-85c6-73bb3e3ef1ac"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([   0., 2611.,  909., 1465., 1038., 2205.,  941.,  924.,  668.,  726.,\n",
              "         522.,   88., 1230., 1228.,  913.,  584.,  774.,   41., 1014., 1248.,\n",
              "         578.,  154.,  548.,  169.,    0.,  218.,  233.])"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "N[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j7jFf9OX4szH"
      },
      "source": [
        "Obtendremos las probabilidades de cada valor al dividir cada uno por la sumatoria de los demás. Con este truco, todos los valores sumados entre sí nos darán 1:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BvCMSG3mqDXb",
        "outputId": "76604ed8-1362-4658-8adb-0d80b990f56e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([0.0000, 0.1242, 0.0432, 0.0697, 0.0494, 0.1049, 0.0447, 0.0439, 0.0318,\n",
              "        0.0345, 0.0248, 0.0042, 0.0585, 0.0584, 0.0434, 0.0278, 0.0368, 0.0019,\n",
              "        0.0482, 0.0593, 0.0275, 0.0073, 0.0261, 0.0080, 0.0000, 0.0104, 0.0111])"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "p = N[0].float()\n",
        "p = p / p.sum()\n",
        "p"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VBFUGeBOqRP8",
        "outputId": "b2a26f77-b74f-46c9-a688-5099a09d4464"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor(1.0000)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "p.sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-j5qXKy43fz"
      },
      "source": [
        "Ahora utilizaremos `torch.multinomial` para generar números enteros con base en las probabilidades de la distribución que creamos. Primero veamos un ejemplo:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "czj2HBpgqbga",
        "outputId": "023ca1ac-74f0-4ae7-8106-00cc31f4c2bc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([0.7762, 0.0321, 0.0691])\n",
            "tensor([0.8846, 0.0366, 0.0787])\n"
          ]
        }
      ],
      "source": [
        "p = torch.rand(3) #creamos tres valores aleatorios\n",
        "print(p)\n",
        "p = p / p.sum() # ahora, creamos una distribución de probabilidades con base en ellos\n",
        "print(p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KelPqtfxrAfr",
        "outputId": "a25d4a3c-799b-4cba-8843-39041d2e495a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([0, 0, 0, 0, 2, 0, 0, 0, 0, 0])"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.multinomial(p, num_samples=10, replacement=True) # ahora tomamos muestras de números enteros con base en la distribución\n",
        "# Notemos que los números generados reflejan la distribución de probabilidades anteriores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uLB5Od-u8ZyT"
      },
      "source": [
        "Podemos ejemplificar lo mismo con la primera fila de nuestra matriz:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PtHz_W1T7gtv",
        "outputId": "ce35c13c-ce3a-4fb3-e7af-c1e8b3455b5a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([19,  5, 14,  4, 13])"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "p = N[0].float()\n",
        "p = p / p.sum()\n",
        "\n",
        "torch.multinomial(p, num_samples=5, replacement=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t4IFA5zK8_kQ"
      },
      "source": [
        "Pero el resultado obtenido es el índice. Utilicemos nuestra tabla de consulta para obtener la letra correspondiente:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "kZHf54M09GUr",
        "outputId": "63197f8f-4128-4d6a-a443-78de8d00901d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'g'"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "index = torch.multinomial(p, num_samples=1, replacement=True).item()\n",
        "ejemplo = fap[index]\n",
        "ejemplo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9OOyvyyl7Y8N"
      },
      "source": [
        "Ahora haremos lo mismo con todos los bigramas:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ygD1Dvp6LEYX"
      },
      "outputs": [],
      "source": [
        "P = (N+1).float() # agregamos 1 al conteo para que el logaritmo no tenga problemas eventualmente (smoothing)\n",
        "P /= P.sum(1, keepdim=True) # el 1 indica que la sumatoria se hace en la dimensión 1 (i. e., las columnas colapsan para sumarse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9JPyyhANDvs",
        "outputId": "9b6ec448-c5ef-4e14-c75f-f3b206dc2446"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(tensor(1.0000), torch.Size([27, 27]))"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "P[0].sum(), P.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RGR25Noo5Wui"
      },
      "source": [
        "Las probabilidades de nuestra primera fila lucen así:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qqCwRN6Z7VOO",
        "outputId": "e933dea4-2f34-4914-d66a-c0baeeac07c6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([4.7492e-05, 1.2405e-01, 4.3218e-02, 6.9624e-02, 4.9345e-02, 1.0477e-01,\n",
              "        4.4738e-02, 4.3930e-02, 3.1772e-02, 3.4527e-02, 2.4839e-02, 4.2268e-03,\n",
              "        5.8463e-02, 5.8368e-02, 4.3408e-02, 2.7783e-02, 3.6807e-02, 1.9947e-03,\n",
              "        4.8205e-02, 5.9318e-02, 2.7498e-02, 7.3613e-03, 2.6073e-02, 8.0737e-03,\n",
              "        4.7492e-05, 1.0401e-02, 1.1113e-02])"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "P[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ElxBMJ3r5qvT"
      },
      "source": [
        "Ahora que ya tenemos una probabilidad asignada a cada bigrama, podemos comenzar a predecir el carácter que debe acompañar a su precedente con base en nuestra matriz de probabilidades. Experimentemos con cinco palabras:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6MFCryDhrgyk",
        "outputId": "c13eb5b6-9ee7-46dd-829c-11305fb415e3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "macito.\n",
            "esaranartens.\n",
            "milioredia.\n",
            "hinitobal.\n",
            "rinoreria.\n"
          ]
        }
      ],
      "source": [
        "for i in range(5):\n",
        "  out = []\n",
        "  ix = 0\n",
        "  while True:\n",
        "    p = P[ix]\n",
        "    ix = torch.multinomial(p, num_samples=1, replacement=True).item()\n",
        "    out.append(fap[ix])\n",
        "    if ix == 0:\n",
        "      break\n",
        "    \n",
        "  print(''.join(out))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6wvdrpg6ncXq"
      },
      "source": [
        "Aunque quizá no elijamos ninguno de estos nombres para uso personal, podemos ver que el modelo funciona y ha generado palabras que de alguna forma reflejan la estructura del español.\n",
        "\n",
        "También podemos observar las probabilidades asignadas a cada bigrama:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QmKkO8cxmENU",
        "outputId": "3eb94b65-73d8-4cfa-afbe-8a8955a24272"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ".m: 0.0584\n",
            "ma: 0.3492\n",
            "ar: 0.0863\n",
            "ri: 0.2349\n",
            "ia: 0.1907\n",
            "a.: 0.4568\n",
            ".r: 0.0482\n",
            "ro: 0.1212\n",
            "os: 0.0515\n",
            "sa: 0.1840\n",
            "a.: 0.4568\n",
            ".j: 0.0248\n",
            "jo: 0.2045\n",
            "os: 0.0515\n",
            "se: 0.1236\n",
            "e.: 0.0674\n"
          ]
        }
      ],
      "source": [
        "for p in palabras[:3]:\n",
        "  cs = ['.'] + list(p) + ['.']\n",
        "  for c1, c2 in zip(cs, cs[1:]):\n",
        "    ix1 = paf[c1]\n",
        "    ix2 = paf[c2] \n",
        "    prob = P[ix1, ix2]\n",
        "    print(f'{c1}{c2}: {prob:.4f}') "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HEwc0Xyvng9c"
      },
      "source": [
        "Dado que altas probabilidades en nuestros bigramas indican buen «aprendizaje», en el sentido de que nuestro modelo no es completamente aleatorio, sino que concede importancia a bigramas apropiadamente, entonces podemos medir la «precisión» o capacidad de nuestro modelo mediante la función de verosimilitud (*likelihood*), que es el resultado de multiplicar todas las probabilidades entre sí. Si el número es alto, eso indicaría que nuestro modelo funciona bien; si es bajo, eso indicaría que no tiene suficiente información para predecir caracteres.\n",
        "\n",
        "Por conveniencia, esta estimación utiliza el logaritmo natural de las probabilidades: sumar los logaritmos de las probabilidades es equivalente a multiplicar las probabilidades (es decir, podemos emplear cualquiera de las dos formas para estimar la verosimilitud). Esto es particularmente útil porque nuestras probabilidades están dadas en números decimales, de manera que multiplicarlas entre sí nos daría un número pequeño y poco intuitivo."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DOCmJusPqOMN"
      },
      "source": [
        "El logaritmo natural de una serie de números presenta como valor máximo al 0, pero como valor mínimo al infinito:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "_x9MfbXgpZJ2",
        "outputId": "cc7fe8bf-37c0-4c55-f8f3-6cb07dfb5d7c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in log\n",
            "  \"\"\"Entry point for launching an IPython kernel.\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": [
        "plt.plot(np.arange(1, 101, 1), np.log(np.arange(0, 1, 0.01)));"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-DJ2dYfAqZax"
      },
      "source": [
        "Pero, dado que quisiéramos números positivos para hacerlo más intuitivo, podemos volver positivo este número al multiplicarlo por -1:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "NmMa5IkKor2t",
        "outputId": "0ce4c006-0c0e-496f-a9ce-9af4e1ad2f45"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Logaritmo natural de la probabilidad: -2.6970839500427246 | Logaritmo natural negativo: 2.6970839500427246'"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "logprob = torch.log(prob)\n",
        "nlog = -logprob\n",
        "f'Logaritmo natural de la probabilidad: {logprob} | Logaritmo natural negativo: {nlog}'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ed26rvhKqsDL"
      },
      "source": [
        "Y el logaritmo negativo de la verosimilitud (*negative log likelihood*) es la suma de todos los logaritmos negativos. Nuestra función de pérdida entonces podría ser el logaritmo negativo de la verosimilitud (`nll`), normalizada para obtener el promedio[^2]. Mientras esta función de pérdida sea menor, nuestro modelo será mejor:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zjNuu-lZI06c",
        "outputId": "f175920b-aa17-4be1-8f5e-ae381c6dc84a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Logaritmo negativo de verosimilitud: 375152.34375\n",
            "Logaritmo negativo de verosimilitud promedio: 2.2672061920166016\n"
          ]
        }
      ],
      "source": [
        "log_likelihood = 0.0\n",
        "n = 0.0\n",
        "\n",
        "for p in palabras:\n",
        "  cs = ['.'] + list(p) + ['.']\n",
        "  for c1, c2 in zip(cs, cs[1:]):\n",
        "    ix1 = paf[c1]\n",
        "    ix2 = paf[c2]\n",
        "    prob = P[ix1,ix2]\n",
        "    logprob = torch.log(prob)\n",
        "    log_likelihood += logprob\n",
        "    n += 1\n",
        "\n",
        "nll = -log_likelihood\n",
        "print(f'Logaritmo negativo de verosimilitud: {nll}')\n",
        "print(f'Logaritmo negativo de verosimilitud promedio: {nll/n}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bqldf2fVrz-e",
        "outputId": "f26a9c57-4bdf-42c0-ccaf-29fb7acda574"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ".d | prob: 0.0493446 | logaritmo de la verosimilitud: -3.0089\n",
            "da | prob: 0.3054336 | logaritmo de la verosimilitud: -4.1949\n",
            "an | prob: 0.1229762 | logaritmo de la verosimilitud: -6.2907\n",
            "nt | prob: 0.0515025 | logaritmo de la verosimilitud: -9.2568\n",
            "te | prob: 0.1527994 | logaritmo de la verosimilitud: -11.1355\n",
            "e. | prob: 0.0674018 | logaritmo de la verosimilitud: -13.8326\n",
            "logaritmo negativo de la verosimilitud: 13.832551956176758\n",
            "promedio del logaritmo negativo: 2.3054254055023193\n"
          ]
        }
      ],
      "source": [
        "log_likelihood = 0.0\n",
        "n = 0.0\n",
        "\n",
        "for bi in ['dante']:\n",
        "  cs = ['.'] + list(bi) + ['.']\n",
        "  for c1, c2 in zip(cs, cs[1:]):\n",
        "    ix1 = paf[c1]\n",
        "    ix2 = paf[c2]\n",
        "    prob = P[ix1,ix2]\n",
        "    logprob = torch.log(prob)\n",
        "    log_likelihood += logprob\n",
        "    n += 1\n",
        "    print(f'{c1}{c2} | prob: {prob:.7f} | logaritmo de la verosimilitud: {log_likelihood:.4f}')\n",
        "\n",
        "nll = -log_likelihood\n",
        "print(f'logaritmo negativo de la verosimilitud: {nll}')\n",
        "print(f'promedio del logaritmo negativo: {nll/n}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2hHJunjxWHB"
      },
      "source": [
        "### Red neuronal\n",
        "\n",
        "Ahora que tenemos una función de pérdida, podemos adaptar nuestro modelo a una red neuronal y optimizarlo. Crearemos bigramas de la misma manera, pero ahora crearemos un vector $x$ con el primer elemento del bigrama y otro $y$ con el segundo. Las $x$ serán entonces nuestras entradas y las $y$ nuestros objetivos:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8brd5t6xvj5",
        "outputId": "34ef7ba1-549c-4a4a-872d-39c84aa9067d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ". m\n",
            "m a\n",
            "a r\n",
            "r i\n",
            "i a\n",
            "a .\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(tensor([ 0, 13,  1, 18,  9,  1]), tensor([13,  1, 18,  9,  1,  0]))"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Juntaremos los bigramas para el set de entrenamiento (inputs x, objetivos y)\n",
        "# Primero un ejemplo:\n",
        "\n",
        "xs, ys = [], []\n",
        "\n",
        "for p in palabras[:1]:\n",
        "  cs = ['.'] + list(p) + ['.']\n",
        "  for c1, c2 in zip(cs, cs[1:]):\n",
        "    ix1 = paf[c1]\n",
        "    ix2 = paf[c2]\n",
        "    print(c1, c2)\n",
        "    xs.append(ix1)\n",
        "    ys.append(ix2)\n",
        "\n",
        "xs = torch.tensor(xs)\n",
        "ys = torch.tensor(ys)\n",
        "xs, ys"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mow3o5dDb0bi",
        "outputId": "8867daca-f2f0-4e15-e994-09334cbc6ebb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([ 0, 13,  1,  ..., 12, 12,  1])"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Ahora todas las palabras\n",
        "\n",
        "xs, ys = [], []\n",
        "\n",
        "for p in palabras:\n",
        "  cs = ['.'] + list(p) + ['.']\n",
        "  for c1, c2 in zip(cs, cs[1:]):\n",
        "    ix1 = paf[c1]\n",
        "    ix2 = paf[c2]\n",
        "    xs.append(ix1)\n",
        "    ys.append(ix2)\n",
        "\n",
        "xs = torch.tensor(xs) # Pasamos cada bigrama a tensores x (inputs) e y (predicción deseada)\n",
        "ys = torch.tensor(ys)\n",
        "xs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H27cTedAzqFq"
      },
      "source": [
        "Para pasar esta información a una red neuronal, primero la codificaremos (haremos un *encoding*) en vectores vía *one-hot encoding*, ya que este formato es más conveniente para una red neuronal. Esto significa que nuestros vectores tendrán 27 elementos, y todos serán de valor 0 salvo aquel que ocupe el lugar del carácter correspondiente, el cual será 1. \n",
        "\n",
        "Visualicemos, por ejemplo, el vector correspondiente a la letra «a», que se encuentra en la posición 1 de nuestro vocabulario (el punto `.` ocupa la posición 0):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ZbsLepT44s8",
        "outputId": "a57d8491-306b-4c15-f018-284020a5dbbf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 0., 0., 0., 0., 0.])"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import torch.nn.functional as F\n",
        "\n",
        "# Primero veamos un ejemplo:\n",
        "xenc = F.one_hot(xs[0:6], num_classes=27).float()\n",
        "xenc[2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wb-Ik9LdGuUy"
      },
      "source": [
        "Podemos crear una visualización más gráfica de 6 vectores codificados. Como digo, la posición del 1 en cada vector indica el índice de la letra a la que corresponde:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "DvF-_xFF0bf5",
        "outputId": "1b318f7f-dfe0-4e4c-99eb-9fba539ed1d5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7b02839610>"
            ]
          },
          "execution_count": 31,
          "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": [
        "plt.imshow(xenc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M-FskRsJHbm-"
      },
      "source": [
        "Ahora haremos lo mismo con todos los datos:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LQh_XkYx6NHK",
        "outputId": "90854426-65f8-487d-cdbd-39302e98f46f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(tensor([[1., 0., 0.,  ..., 0., 0., 0.],\n",
              "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
              "         [0., 1., 0.,  ..., 0., 0., 0.],\n",
              "         ...,\n",
              "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
              "         [0., 0., 0.,  ..., 0., 0., 0.],\n",
              "         [0., 1., 0.,  ..., 0., 0., 0.]]), torch.Size([165469, 27]))"
            ]
          },
          "execution_count": 166,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "xenc = F.one_hot(xs, num_classes=27).float()\n",
        "xenc, xenc.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PZFTRWmxHqa3"
      },
      "source": [
        "Ahora crearemos una capa de neuronas (*i. e.,* una matriz de pesos, o sea, una *linear layer*)[^3], asignando pesos aleatorios a nuestro modelo para que se multipliquen con las entradas y se optimicen mediante la propagación hacia atrás. Más adelante explicaremos cómo eligiremos las dimensiones de la matriz de pesos.\n",
        "\n",
        "Primero, procuremos entender cómo funcionará la multiplicación de nuestros vectores con la matriz de pesos. Ejemplifiquemos con los primeros tres vectores (o sea, los primeros tres caracteres) de nuestra incrustación:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vfBZ3xmsT2lZ",
        "outputId": "6284ddd8-e217-4221-e6b9-28819e36ce0b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(torch.Size([3, 27]), torch.Size([27, 4]))"
            ]
          },
          "execution_count": 167,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "w = torch.randn(27, 4)\n",
        "xenc[:3].shape, w.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xyjvr_LOUTKe"
      },
      "source": [
        "Nuestra matriz de pesos luce así:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4KDgmeHdUV-J",
        "outputId": "ca0298b5-10e1-4170-f80f-bab4cedca50c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[ 1.6804, -0.3333,  0.1523,  1.0912],\n",
              "        [ 0.0711, -1.5924,  0.6650, -1.9040],\n",
              "        [ 0.2606,  0.2247, -0.6540, -0.6477],\n",
              "        [-0.4081, -2.2263, -0.6014, -1.3560],\n",
              "        [ 1.3712,  0.4519,  1.1165,  0.4909],\n",
              "        [ 0.5392, -0.9536, -0.5489,  0.5621],\n",
              "        [-0.1191,  1.0517, -0.5388,  0.0509],\n",
              "        [-0.2328,  0.5691, -0.1776, -0.8785],\n",
              "        [-0.9944,  0.1690,  0.4808, -0.9270],\n",
              "        [-1.9163, -0.4442, -0.8332,  0.2094],\n",
              "        [-1.5595,  0.3131,  0.3176, -1.0424],\n",
              "        [-0.8103,  0.0612, -0.3940, -0.6969],\n",
              "        [ 1.6637,  0.1493,  0.2939,  0.4968],\n",
              "        [ 0.8579, -0.4684, -0.5580, -0.5566],\n",
              "        [-2.3027, -0.3928,  0.9376,  0.2877],\n",
              "        [ 0.1478, -0.5560,  0.2106,  1.4563],\n",
              "        [ 0.8955,  0.3372, -2.2538,  1.4221],\n",
              "        [ 1.2976, -0.4296, -0.0524, -1.1490],\n",
              "        [ 1.8105,  1.5439,  1.2894,  1.5108],\n",
              "        [-0.9403, -1.0278, -1.1975, -1.4744],\n",
              "        [-1.0490, -0.5257,  0.0466,  0.1303],\n",
              "        [ 0.9318,  0.4428,  0.6996, -1.0788],\n",
              "        [-1.5708, -0.7023, -1.2703, -0.9609],\n",
              "        [ 0.3845,  1.9697, -0.0413,  0.8551],\n",
              "        [-0.2153,  1.3822,  0.0026, -0.8545],\n",
              "        [ 0.6023, -2.3813, -1.3161, -1.5124],\n",
              "        [ 0.3529,  0.2896, -0.4342,  1.6535]])"
            ]
          },
          "execution_count": 168,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "w"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wS7JQVU-dSIi"
      },
      "source": [
        "Nuestra matriz de vectores:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jd9_vn2OdRam",
        "outputId": "5ddf2ecc-2bbb-483d-d915-ec3b4f84f33e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "         0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "         0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "        [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "         0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
            ]
          },
          "execution_count": 169,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "xenc[:3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BJosxvnVUXvK"
      },
      "source": [
        "Si multiplicamos ambas, obtenemos:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGrTSjL5UB_k",
        "outputId": "a8ebbcee-1964-4cd1-ba6f-482c07913916"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[ 1.6804, -0.3333,  0.1523,  1.0912],\n",
              "        [ 0.8579, -0.4684, -0.5580, -0.5566],\n",
              "        [ 0.0711, -1.5924,  0.6650, -1.9040]])"
            ]
          },
          "execution_count": 170,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ejemplo = xenc[:3] @ w\n",
        "ejemplo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mqFqSVulUaC4"
      },
      "source": [
        "El resultado de la multiplicación es una matriz con dimensiones 3x4. Para entender cómo se generó esta matriz, podemos tomar el primer vector de `xenc` y multiplicar cada uno de sus elementos por la primera columna de `w`. El primer vector luce así:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjHdAAujVbMI",
        "outputId": "98819424-c09e-4aa4-ef60-4eddb53c54d5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 0., 0., 0., 0., 0.])"
            ]
          },
          "execution_count": 171,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "xenc[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Va4oIDmxVq4t"
      },
      "source": [
        "De manera que al multiplicarlo por la primera columna, elemento por elemento (es decir, realizando una multiplicación Hadamard, denotada comúnmente por el signo $\\odot$), obtenemos:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6L9FHdjUsKk",
        "outputId": "ef1bb4a5-9943-4a5d-c3c7-c9eb5beed0fb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([1.6804, 0.0000, 0.0000, -0.0000, 0.0000, 0.0000, -0.0000, -0.0000, -0.0000,\n",
              "        -0.0000, -0.0000, -0.0000, 0.0000, 0.0000, -0.0000, 0.0000, 0.0000, 0.0000,\n",
              "        0.0000, -0.0000, -0.0000, 0.0000, -0.0000, 0.0000, -0.0000, 0.0000, 0.0000])"
            ]
          },
          "execution_count": 172,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "xenc[0] * w[:,0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PUMHm9bHWOAw"
      },
      "source": [
        "Y la sumatoria de este vector claramente resulta:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xA3l-t9HWSRi",
        "outputId": "57472b58-da6f-4c46-c147-06e963ff7675"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor(1.6804)"
            ]
          },
          "execution_count": 173,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(xenc[0] * w[:,0]).sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A3SuQaEJWVM9"
      },
      "source": [
        "Que podemos observar en el primer valor de nuestra multiplicación de `xenc` con `w`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3mHX2t-XEJvG",
        "outputId": "08d8869a-dca8-4bab-9022-f8d379a9f5a3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[ 1.6804, -0.3333,  0.1523,  1.0912],\n",
              "        [ 0.8579, -0.4684, -0.5580, -0.5566],\n",
              "        [ 0.0711, -1.5924,  0.6650, -1.9040]])"
            ]
          },
          "execution_count": 174,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ejemplo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pqbG3CyJM8sN"
      },
      "source": [
        "Exactamente lo mismo, aunque de manera más eficiente, sucede cuando multiplicamos ambas matrices. En síntesis: al multiplicar nuestra matriz `w` por la matriz `xenc`, cada columna de pesos evalúa cada vector de `xenc`. Es decir, obtenemos una matriz de dimensiones 3x4 donde cada fila corresponde a cada vector (*i. e.*, cada carácter), pero esta fila tiene 4 valores correspondientes a la evaluación del vector por cada una de las columnas de la matriz `w`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wt-1qLVTYjpW"
      },
      "source": [
        "### Hacia la formulación de un modelo\n",
        "\n",
        "Antes de programar nuestra red neuronal, detengámonos a entender lo que estamos haciendo: en primer lugar, podemos conceptualizar a una red neuronal como una función: nosotros esperamos que nos proporcione un resultado con base en las entradas que le suministremos:\n",
        "\n",
        "<img src='https://miro.medium.com/max/640/1*sPg-0hha7o3iNPjY4n-vow.jpeg' width=400 class='center'>\n",
        "\n",
        "Lo característico de esta función es que podemos entrenarla para que se configure a sí misma, es decir, la función encontrará (o «aprenderá») los parámetros necesarios para transformar las entradas que le proporcionemos en las salidas que queremos. En este caso particular, queremos que «transforme» una letra de entrada en otra de salida, y que realice este proceso hasta conseguir un nombre. La transformación que procuraremos a continuación será lineal y se realizará mediante la matriz de pesos `w`.\n",
        "\n",
        "Antes, recapitulemos: hemos separado cada bigrama de nuestro conjunto de datos en tensores $\\mathbf{x}$ e $\\mathbf{y}$. El tensor $\\mathbf{x}$ contiene las entradas, es decir, el primer carácter de cada bigrama que creamos por cada nombre que tenemos. El tensor $\\mathbf{y}$ contiene el segundo carácter de cada uno de los bigramas. Utilizaremos $\\mathbf{y}$ para entrenar al modelo e indicarle cuál carácter debe suceder a cualquier carácter dado de $\\mathbf{x}$. Por ejemplo, si mi carácter de entrada a la red neuronal es «a», mi modelo podrá aprender que existen altas probabilidades de que esté acompañada por la letra «n»; después, partirá de «n» para generar el siguiente carácter y así sucesivamente hasta generar un nombre.\n",
        "\n",
        "Pero para poder introducir nuestras letras en una red neuronal, debemos transformarlas en números con los que pueda operar. Para ello, codificamos nuestro tensor $\\mathbf{x}$ vía vectores *one-hot* que, concatenados, constituyen la matriz `xenc`. A esta codificación —que equivale a $\\mathbf{x}$ pero bajo una representación numérica basada en el índice de nuestra tabla de consulta— la multiplicamos por `w`, una capa lineal que evaluará nuestras entradas `xenc` por cada una de las neuronas que tenga.\n",
        "\n",
        "Entonces, el resultado de esta multiplicación de matrices nos da un conteo de todos los caracteres de $\\mathbf{x}$ —codificados en vectores—, evaluados por cada neurona de la capa lineal `w`. En ese sentido, este resultado es equivalente a la matriz `N` que graficamos anteriormente, aunque con un grado de complejidad mayor debido a las transformaciones numéricas que hemos realizado.\n",
        "\n",
        "En realidad, lo que queremos hacer a continuación es entrenar nuestra red neuronal para que, con base en cada vector de entrada (de la matriz `xenc`), ese mismo vector sea transformado (mediante la multiplicación por los pesos) en probabilidades correspondientes a cada *token* que debería acompañarlo.\n",
        "\n",
        "Manos a la obra: primero, crearemos una matriz `W` de dimensiones 27x27: necesitamos 27 filas para nuestros 27 *tokens* del vocabulario, y necesitamos 27 evaluaciones (columnas) para cada *token*. Estas evaluaciones tendrán que especificarnos las probabilidades asignadas a cada *token* de acompañar al *token* inicial evaluado."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PlADRGTTE_vj",
        "outputId": "15dd6e9a-6734-4d60-8f70-0a828e644a9b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([165469, 27])"
            ]
          },
          "execution_count": 175,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "W = torch.randn((27, 27), requires_grad=True) # Creamos weights aleatorios\n",
        "logits = xenc @ W #multiplicamos valores de xenc por W para obtener log-counts\n",
        "logits.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hyvUSeEqFFWq"
      },
      "source": [
        "Por lo pronto, nuestros valores son aleatorios y no han sido entrenados. Ahora, dado que nuestros `logits`[^4] tienen valores pequeños, negativos y positivos, queremos transformarlos para que puedan reflejar mejor la naturaleza de un «conteo» y nos faciliten su conversión en probabilidades. Para ello, únicamente necesitamos exponenciarlos, puesto que los números negativos terminarán en un rango del 0 al 1, y los positivos se convertirán en números mayores a 1. Visualicemos la función exponencial y después apliquémosla a nuestra matriz:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Fnrxk59yFExv",
        "outputId": "26029410-c599-46e7-bf87-a7c24316c9d8"
      },
      "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": [
        "plt.plot(np.arange(-3, 3, 0.1), np.exp(np.arange(-3, 3, 0.1)));"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dK3-4qCf6eYY"
      },
      "outputs": [],
      "source": [
        "counts = logits.exp() # exponenciamos para obtener valores mayores a 0, equivalentes a matriz N"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m1drjyCHLvHR"
      },
      "source": [
        "Ahora, convertiremos nuestros conteos en probabilidades, dividiéndolos entre la sumatoria de todos los elementos de su fila correspondiente:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jI3psWjSLzfc",
        "outputId": "bac17c5a-52e5-48cf-c8d8-066df80a8568"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "torch.Size([165469, 27])"
            ]
          },
          "execution_count": 178,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "probs = counts / counts.sum(1, keepdims=True) # normalizar los counts para obtener probabilidades\n",
        "probs # estos últimos dos pasos son equivalentes a la función softmax\n",
        "probs.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VmM4puUmMCZW"
      },
      "source": [
        "Ahora, nuestra primera fila contiene probabilidades que lucen así:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ChqRnqShZ4uG",
        "outputId": "aee822e4-6df9-405c-8d53-40d902c10dad"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([0.0096, 0.0704, 0.0243, 0.0560, 0.0687, 0.0090, 0.0270, 0.0457, 0.0506,\n",
              "        0.0017, 0.0289, 0.0205, 0.0080, 0.0471, 0.0213, 0.0887, 0.0838, 0.0318,\n",
              "        0.0142, 0.0524, 0.0047, 0.0464, 0.0985, 0.0197, 0.0227, 0.0201, 0.0283],\n",
              "       grad_fn=<SelectBackward0>)"
            ]
          },
          "execution_count": 179,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "probs[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YN4JhhUkMHH7"
      },
      "source": [
        "Antes de continuar, podemos visualizar el arreglo de información que tenemos. Tomemos como base nuestro primer nombre («María», que ha quedado ajustado a «.maria.»):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DpdM-K0AaFSi",
        "outputId": "0c86938a-2c5f-41fc-bbf5-ed22a141096d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-----------\n",
            "Bigrama ejemplo 1: .m, índices 0,13\n",
            "Input: 0\n",
            "Probabilidades de cada output calculadas por la red neuronal: tensor([0.0096, 0.0704, 0.0243, 0.0560, 0.0687, 0.0090, 0.0270, 0.0457, 0.0506,\n",
            "        0.0017, 0.0289, 0.0205, 0.0080, 0.0471, 0.0213, 0.0887, 0.0838, 0.0318,\n",
            "        0.0142, 0.0524, 0.0047, 0.0464, 0.0985, 0.0197, 0.0227, 0.0201, 0.0283],\n",
            "       grad_fn=<SelectBackward0>)\n",
            "Output correcto: 13\n",
            "Probabilidad asignada por la red neuronal al carácter correcto: 0.04705430194735527\n",
            "Logaritmo de la verosimilitud -3.056452989578247\n",
            "Logaritmo negativo de la verosimilitud: 3.056452989578247\n",
            "-----------\n",
            "Bigrama ejemplo 2: ma, índices 13,1\n",
            "Input: 13\n",
            "Probabilidades de cada output calculadas por la red neuronal: tensor([0.0171, 0.0113, 0.0380, 0.0324, 0.0241, 0.0180, 0.0223, 0.0269, 0.0354,\n",
            "        0.0124, 0.0092, 0.0323, 0.0372, 0.0657, 0.1751, 0.0092, 0.0080, 0.1024,\n",
            "        0.0069, 0.0092, 0.0266, 0.0315, 0.0494, 0.0146, 0.1493, 0.0156, 0.0198],\n",
            "       grad_fn=<SelectBackward0>)\n",
            "Output correcto: 1\n",
            "Probabilidad asignada por la red neuronal al carácter correcto: 0.011345318518579006\n",
            "Logaritmo de la verosimilitud -4.478950023651123\n",
            "Logaritmo negativo de la verosimilitud: 4.478950023651123\n",
            "-----------\n",
            "Bigrama ejemplo 3: ar, índices 1,18\n",
            "Input: 1\n",
            "Probabilidades de cada output calculadas por la red neuronal: tensor([0.0121, 0.0177, 0.0048, 0.1065, 0.0027, 0.0480, 0.0031, 0.0411, 0.0335,\n",
            "        0.0278, 0.1691, 0.0037, 0.0235, 0.0201, 0.0221, 0.0541, 0.0329, 0.0447,\n",
            "        0.0453, 0.0087, 0.0131, 0.0272, 0.0486, 0.0452, 0.0956, 0.0360, 0.0130],\n",
            "       grad_fn=<SelectBackward0>)\n",
            "Output correcto: 18\n",
            "Probabilidad asignada por la red neuronal al carácter correcto: 0.04529079794883728\n",
            "Logaritmo de la verosimilitud -3.094651460647583\n",
            "Logaritmo negativo de la verosimilitud: 3.094651460647583\n",
            "-----------\n",
            "Bigrama ejemplo 4: ri, índices 18,9\n",
            "Input: 18\n",
            "Probabilidades de cada output calculadas por la red neuronal: tensor([0.1148, 0.0210, 0.0277, 0.0906, 0.0275, 0.0093, 0.0087, 0.0190, 0.0039,\n",
            "        0.0451, 0.0309, 0.0589, 0.1442, 0.0375, 0.0079, 0.0652, 0.0272, 0.0082,\n",
            "        0.0084, 0.0355, 0.0327, 0.0257, 0.0086, 0.0225, 0.0264, 0.0722, 0.0203],\n",
            "       grad_fn=<SelectBackward0>)\n",
            "Output correcto: 9\n",
            "Probabilidad asignada por la red neuronal al carácter correcto: 0.04506437107920647\n",
            "Logaritmo de la verosimilitud -3.099663257598877\n",
            "Logaritmo negativo de la verosimilitud: 3.099663257598877\n",
            "-----------\n",
            "Bigrama ejemplo 5: ia, índices 9,1\n",
            "Input: 9\n",
            "Probabilidades de cada output calculadas por la red neuronal: tensor([0.0452, 0.0301, 0.1283, 0.0392, 0.0107, 0.0309, 0.1220, 0.0093, 0.1139,\n",
            "        0.0111, 0.0087, 0.0042, 0.0337, 0.0945, 0.0093, 0.0085, 0.0163, 0.0475,\n",
            "        0.0031, 0.0154, 0.0541, 0.0090, 0.0990, 0.0133, 0.0252, 0.0098, 0.0078],\n",
            "       grad_fn=<SelectBackward0>)\n",
            "Output correcto: 1\n",
            "Probabilidad asignada por la red neuronal al carácter correcto: 0.030115216970443726\n",
            "Logaritmo de la verosimilitud -3.5027246475219727\n",
            "Logaritmo negativo de la verosimilitud: 3.5027246475219727\n",
            "-----------\n",
            "Bigrama ejemplo 6: a., índices 1,0\n",
            "Input: 1\n",
            "Probabilidades de cada output calculadas por la red neuronal: tensor([0.0121, 0.0177, 0.0048, 0.1065, 0.0027, 0.0480, 0.0031, 0.0411, 0.0335,\n",
            "        0.0278, 0.1691, 0.0037, 0.0235, 0.0201, 0.0221, 0.0541, 0.0329, 0.0447,\n",
            "        0.0453, 0.0087, 0.0131, 0.0272, 0.0486, 0.0452, 0.0956, 0.0360, 0.0130],\n",
            "       grad_fn=<SelectBackward0>)\n",
            "Output correcto: 0\n",
            "Probabilidad asignada por la red neuronal al carácter correcto: 0.0121311629191041\n",
            "Logaritmo de la verosimilitud -4.411977767944336\n",
            "Logaritmo negativo de la verosimilitud: 4.411977767944336\n",
            "----------\n",
            "Promedio de la nll, i. e. pérdida total = 3.607403516769409\n"
          ]
        }
      ],
      "source": [
        "nlls = torch.zeros(6)\n",
        "for i in range(6):\n",
        "  x = xs[i].item()\n",
        "  y = ys[i].item()\n",
        "  print('-----------')\n",
        "  print(f'Bigrama ejemplo {i+1}: {fap[x]}{fap[y]}, índices {x},{y}')\n",
        "  print(f'Input: {x}')\n",
        "  print(f'Probabilidades de cada output calculadas por la red neuronal: {probs[i]}')\n",
        "  print(f'Output correcto: {y}')\n",
        "  p = probs[i, y]\n",
        "  print(f'Probabilidad asignada por la red neuronal al carácter correcto: {p.item()}')\n",
        "  logp = torch.log(p)\n",
        "  print('Logaritmo de la verosimilitud', logp.item())\n",
        "  nll = -logp\n",
        "  print('Logaritmo negativo de la verosimilitud:', nll.item())\n",
        "  nlls[i] = nll\n",
        "\n",
        "print('----------')\n",
        "print(f'Promedio de la nll, i. e. pérdida total = {nlls.mean().item()}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BiORP0mmM6Bg"
      },
      "source": [
        "Bien, tenemos los 6 bigramas del nombre, el índice de cada input y output de cada bigrama, etcétera. Ahora, queremos ajustar nuestro modelo para que, con base en la pérdida de cada bigrama —medida por el logaritmo negativo de la verosimilitud, igual que anteriormente—, optimicemos los pesos de la matriz `W` de tal forma que, al multiplicarla por cada vector input (`xenc`), nos devuelva otro vector con probabilidades asignadas a cada carácter que puede suceder el carácter en cuestión, pero con probabilidad alta asignada al carácter que debe acompañarlo.\n",
        "\n",
        "Tomemos en cuenta que todas las operaciones que hemos realizado hasta ahora son diferenciables (se pueden derivar). Ahora, para poder programar una función de pérdida que entrene todos nuestros términos, ejemplifiquemos también con nuestro primer nombre:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lpvfnq1qiyvz",
        "outputId": "5de74c08-c479-4871-dab8-aa455035d75e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([ 0, 13,  1, 18,  9,  1])"
            ]
          },
          "execution_count": 181,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "xs[:6]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8s8cMAdLk7dr",
        "outputId": "09875f08-f11b-4eac-bb52-bd7d416c682f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([13,  1, 18,  9,  1,  0])"
            ]
          },
          "execution_count": 182,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ys[:6]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q0c4mz7GTDPg"
      },
      "source": [
        "Obtenemos el índice de cada bigrama en nuestros tensores `x` e `y` y, con base en ellos, rastreamos la probabilidad asignada a `y` dado `x`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sxjighDTk0H-",
        "outputId": "4d41e380-d3e0-46e7-8cb6-0f969a53619a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(tensor(0.0471, grad_fn=<SelectBackward0>),\n",
              " tensor(0.0113, grad_fn=<SelectBackward0>),\n",
              " tensor(0.0453, grad_fn=<SelectBackward0>),\n",
              " tensor(0.0451, grad_fn=<SelectBackward0>),\n",
              " tensor(0.0301, grad_fn=<SelectBackward0>),\n",
              " tensor(0.0121, grad_fn=<SelectBackward0>))"
            ]
          },
          "execution_count": 183,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "probs[0,13], probs[1,1], probs[2,18], probs[3,9], probs[4,1], probs[5,0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sLM16b8ObLmC"
      },
      "source": [
        "Que sería equivalente a hacer algo como:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkDbTPQhbO2F",
        "outputId": "9bb086fc-7521-4ae3-fd07-f277870da7d7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([0.0471, 0.0113, 0.0453, 0.0451, 0.0301, 0.0121],\n",
              "       grad_fn=<IndexBackward0>)"
            ]
          },
          "execution_count": 184,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "probs[torch.arange(6), ys[:6]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xdLwxUvvbjeQ"
      },
      "source": [
        "Y para obtener el promedio general del logaritmo negativo de la verosimilitud, únicamente agregamos:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EFIIyx-jbwt7",
        "outputId": "79cde7a1-bb61-49b9-a14b-06aebbed9eab"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor(3.6074, grad_fn=<NegBackward0>)"
            ]
          },
          "execution_count": 185,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "-probs[torch.arange(6), ys[:6]].log().mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6MrKwqeTbhFL"
      },
      "source": [
        "Finalmente, agregaremos un componente de regularización —el cual explicaremos en otra ocasión, aunque de momento se puede visualizar [este video](https://youtu.be/EehRcPo1M-Q)— a nuestra pérdida. Ahora ya podemos entrenar nuestro modelo:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OntNtXegBWOy",
        "outputId": "7adb07f6-5304-49b5-ccad-c0fd6c6d429c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "step: [00]   loss=3.806062\n",
            "step: [05]   loss=2.672217\n",
            "step: [10]   loss=2.497344\n",
            "step: [15]   loss=2.428483\n",
            "step: [20]   loss=2.393344\n",
            "step: [25]   loss=2.371606\n",
            "step: [30]   loss=2.356778\n",
            "step: [35]   loss=2.346093\n",
            "step: [40]   loss=2.338058\n",
            "step: [45]   loss=2.331840\n"
          ]
        }
      ],
      "source": [
        "num = xs.nelement()\n",
        "losses = []\n",
        "\n",
        "# FORWARD PASS\n",
        "for i in range(50):\n",
        "  xenc = F.one_hot(xs, num_classes=27).float() # one-hot encoding\n",
        "  logits = xenc @ W #multiplicamos valores de x por w para obtener logits\n",
        "  counts = logits.exp() # exponenciamos para obtener valores mayores a 0, equivalentes a matriz N\n",
        "  probs = counts / counts.sum(1, keepdims=True) # normalizar los conteos para obtener probabilidades\n",
        "  loss = -probs[torch.arange(num), ys].log().mean() + 0.01*(W**2).mean() # creamos función de pérdida (este último término es la regularización) \n",
        "  \n",
        "  # BACKWARD PASS\n",
        "  W.grad = None # equivalente a reiniciar los gradientes a 0\n",
        "  loss.backward() # propagación hacia atrás\n",
        "  losses.append(loss.item())\n",
        "  if i % 5 == 0:\n",
        "    print(f\"step: [{i:>02d}]   loss={loss:.6f}\")\n",
        "\n",
        "  # UPDATE\n",
        "  W.data += -50 * W.grad # actualizamos los valores de W con base en sus gradientes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8SZOt4JbH_Y3"
      },
      "source": [
        "Podemos visualizar nuestra pérdida a lo largo del entrenamiento:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "YxkgJhRFFhZk",
        "outputId": "a0bbd0d1-1403-429a-eac3-b8d8dad872fa"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(8, 5))\n",
        "ax.plot(losses, color='red') \n",
        "ax.set_facecolor('black')\n",
        "ax.set_xlabel('Pérdida')\n",
        "ax.set_ylabel('Step')\n",
        "plt.tight_layout();"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LnGqKPE5LPsg"
      },
      "source": [
        "Nuestra distribución de probabilidades para cada carácter ahora luce así (por detalles técnicos, debemos leer «.» en lugar de «`»):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "rzfgpkO5JGHb",
        "outputId": "a2462257-e4dc-4d81-8036-ba4daf4dd548"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(8, 5))\n",
        "ax.bar(list(map(chr, range(96, 123))), probs[0].data) \n",
        "ax.set_facecolor('black')\n",
        "plt.tight_layout();"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qQF9Olygo__T"
      },
      "source": [
        "Una vez entrenado el modelo, podemos obtener muestras con base en él:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JwPHLba5CrFC",
        "outputId": "3d3debe2-7ee8-4199-fc37-f3da200a2861"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "a.\n",
            "o.\n",
            "r.\n",
            "osama.\n",
            "dan.\n",
            "iolidenarteceliugialdeminalbqs.\n",
            "cin.\n",
            "limo.\n",
            "enima.\n",
            "cincejumiliva.\n"
          ]
        }
      ],
      "source": [
        "for i in range(10):\n",
        "  out = []\n",
        "  ix = 0\n",
        "  while True:\n",
        "    xenc = F.one_hot(torch.tensor([ix]), num_classes=27).float()\n",
        "    logits = xenc @ W\n",
        "    counts = logits.exp()\n",
        "    p = counts / counts.sum(1, keepdims=True).item()\n",
        "\n",
        "    ix = torch.multinomial(p, num_samples=1, replacement=True).item()\n",
        "    out.append(fap[ix])\n",
        "\n",
        "    if ix == 0:\n",
        "      break\n",
        "\n",
        "  print(''.join(out))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4IDrtTdMdn4"
      },
      "source": [
        "Finalmente, comparemos las matrices entrenadas de ambos métodos:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "IRFECAJ6MoMb",
        "outputId": "3e8154ca-90c0-4a99-9b0c-dfcf4dd933a4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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WRWwt+eFi3/zFCbGNdeUTXveL8u5QXSQoLhoS98I0xa35SWhLMUMFRg3X5mBg4BznfJCkYO4eWpRV0szU1bbmnNf+MLStd/3Ls3KYUTxAccmCXrfm8Mb8wtY2lPzAuev+5g2h8WqRAMJAUNxPKltC4+W51rzhrDVuzVfvzS/sNLLWRMIhN9u+0HhzGv5aGhE9jkOBgMc4K5RHcAAAQOHQ4AAAgMKhwQEAAIVDgwMAAAqHBgcAABQODQ4AACgcGhwAAFA4NDgAAKBwaHAAAEDhWErHOEqwxTRbnk6tfKhtzcZAqqYkVdVwa7oD6cPTU2dovE0lPy1ySooFQ5cDSZD7rOrWRFJKJalu/r6KpH2+u7YiNN6XStvcmmfXZ4a2taa8163ZHLhtommmFx72f8dHyv54WwJJ1JJ0yGpuTSTxtNf6Q+NFRJNKPX39/6RaY+OY5DDPtBVpVekv2tbcV94R2lYkkTVy7i9K00LjPVTe6dZMS7FE3Ury/4btLR12a7qDa1tkXY6sR98+K3b+vPPOJW7NqxqxteaH5q81vyz7EfqR9GFJumLuXLfm+5umujWPVGL3mb3m3861wO23u3QoNF6eys5xvKf6D4OuNTyCAwAACueoPovKzNZJ6pNUl1RLKa3MY1IA0Iq1BsBw5fFhmy9PKcUe6wWAkWOtARDGU1QAAKBwjrbBSZK+Z2b3mNklgxWY2SVmttrMVtcU+1h3ABhgWGtNv2IvvARQXEf7FNVLUkqbzGy+pFvM7JGU0h2tBSmlqyRdJTXfRXWU4wGYnIa11sy0Faw1wCR3VI/gpJQ2Zf9vk/QNSWfmMSkAaMVaA2C4RtzgmNk0M+s58rWkV0p6IK+JAYDEWgNgZI7mKaoFkr5hZke286WU0nfa/cBhq2ttqbftRn+///jQ4F/oWuvWHJYfuBQNSPs/h5/h1nyu6/HQtvLy1npsX32q4u+riFss9rqGzaX9udRI0tn9S92aDZ35vd5ibyCk66eV7bmNNz0Q2BYJfcxTNBTRk+NzRMNeaw5azQ3ye/dUP2hNkv7fQf/2jtxGD5X8AD8pFgD33u27Q9uqWz63wpUndIXq/nStH1wZWZf/4g5/vZWkzRV/n37JDoa29crqQrdmYyBUtBEImJWkBzf1uDX/Hrifi5rR8G/DA4Hg0WjIZCkQGFpKsRzQka6BI25wUkqPSzptpD8PABGsNQBGgreJAwCAwqHBAQAAhUODAwAACocGBwAAFA4NDgAAKBwaHAAAUDg0OAAAoHBocAAAQOEc7YdtDovJTzfcFkhSzNOUFNsFsyrj77P7KrEQyNxsstinwVcCCZZzGlNiY5YPhOry8mQplnqal5cE0lO/07lhFGZSLElSzRptazbu7QxtqxQ40TrN/1uxGkjwlaQTTnjSrZm6zU/BlZrp8Z7uwBo4b8Gu0Hi96/203Mh4D1Vi4/Wr/W0sSdMVu53v7PBTkSP7sxxY/yTpsZwSyntS7Pf7vepit+bzgeTk6Y1YknEjkGXeWzoc2lY5jeyxGB7BAQAAhUODAwAACocGBwAAFA4NDgAAKBwaHAAAUDg0OAAAoHBocAAAQOHQ4AAAgMIZ1aC/zlTW8sZxbWvmBIP38lINBDdJ0vIFgZC7HUc5mWGqj3L24M7SoVBdJOhvZzBQ75TqTL+oHNpUyEPlWMCYpyvFJvVgZU8u4+UpcvtJUi0Q5DVWOlNJyxrtw/BqwekfCISPLmxMdWtKpdh+Xbx8vT+nu2NBmbNTt1vTHwgg3LO7/bp9RE/yd2o1EM5XCQa7lZ0wR0mqBcaTpOnJD7Arm38bRsIHJemujq1uzTPrs9ya7RYLQ/16x2a3JhLCeDAYxhtZA6OBr15oYN8QaxaP4AAAgMKhwQEAAIVDgwMAAAqHBgcAABQODQ4AACgcGhwAAFA4NDgAAKBwaHAAAEDhjGqqXtUa2lxqH5h3lgLBbpKmBAKJQuFbwVCzD2/vdWsqgRAoKRamtM+qbs0nK2tD40VEwt1eUJsf2tZjJX9fva66OLStBwPhgpG5R0Pp5if/mOlu+AFWe60/NN6GUp9bEznWo+FbEdF95QV5xaIcj42qNbTVCUD7jZMPh7b11XWBwLJAoN7Cun9sSdK5X32WW9Ow2NwPyT8uekv+tt6wJnZMRNdAzwX9S0N1d3bsdGs++bz9oW3deu9Ct+aazr1uTT0QPihJJ9f8+7rjAuGD9VJsvLVlf12eFhivN7i2HQqsSXmtNfUhtsMjOAAAoHBocAAAQOHQ4AAAgMKhwQEAAIVDgwMAAAqHBgcAABQODQ4AACgcGhwAAFA4NDgAAKBwRjXJuKGk/U5C76lz/ORaSTq4z09JXFv2UycjKbiS9PL+JW7NLZ0bQ9uKJDxGnFKfFar7RXl3LuPdW9mWy3Yk6ZrO9aG6i/uXuzV3BNMwIyLJwnkmJ19aXeHWXNHhJ1ZHj+PovCI6nL+PLDinY8Fkbkr57Dl7QtsqrZ3u1qwt+WtN1EurC9yahyux8baU/BTfWQ0/hTma+N4IHF/dgbudnwV/v1LgcP7w/ceFtjW/5Gdvn9iY4dZsctL6j9hT8hOBtwXywHdZ7D7zjYf9tebfOp50ayL7QJJ2BVLoq4qlMJ/lnBO3DZHA7D6CY2bXmNk2M3ug5bLZZnaLma3J/o/d0wLAEFhrAOQp8hTVtZLOGXDZByTdmlI6SdKt2fcAcDSuFWsNgJy4DU5K6Q5JuwZcfIGk67Kvr5P0mpznBWCSYa0BkKeRvsh4QUppc/b1Fkn+k8YAMHysNQBG5KhfZJxSSmY25Eu9zOwSSZdIksn/eHgAGMxw1pqKeKkOMNmN9BGcrWa2SJKy/4d8e01K6aqU0sqU0sqSTRvhcAAmqRGtNWXz3/kEoNhG2uB8S9JF2dcXSfpmPtMBgKdhrQEwIpG3id8o6SeSTjGzjWb2JkmXSzrbzNZI+u3sewAYMdYaAHlyX4OTUrpwiKteMdzBulNZz6rPblvzi51+6JQkqcsvyTOQbVP5QKguIq+wtefXYoFLeQX99aTOUF2f+QFWdYsFPJ2+rNcvyi9/UDOCv6OnN7APJOkb5Z25jJdngF80NHCfE9oZCX1rledak5Tc8b/4k5NC2zrQucGtKQf22TOCAWkby3642w7zayRpfmOqW7Oi3uPWHLJ6aLzHAuGqh+QHnZYsdgweUPtjUJLOCK6T7//th9yaN/5woVuzrOHvT0naGQjo65e/3+emKaHxru163K1ZEDheIgF+Uuz8n5Vi9/erO7a3vX5/dfBjio9qAAAAhUODAwAACocGBwAAFA4NDgAAKBwaHAAAUDg0OAAAoHBocAAAQOHQ4AAAgMI56g/bHI66JTcArVexALhIIFt/YFvPq8c+lG9q8nfVHIuFFvWWDrs1keC2ly3eHxrvq4Esuch4pRQL35oTCJ7aWooFJ/7j5kBYXDm0qZBIQN+UwLHQlWKTigSVzWv4+3N7KRb8Nll0pLIWN9p/9t2Z82L77NY9HW7NQfPD655Ri30W36zAAb01eHtvLO1za9aU97g1j731Z6HxTrny9FCdp9KI/e09M/mJrz/o3BjaVv3WZ7s1uyqb3ZrtwbUtoiOwjhwIBCdK0vLGcW5NKbDc7sxxrYkGonrr6VChgjyCAwAACocGBwAAFA4NDgAAKBwaHAAAUDg0OAAAoHBocAAAQOHQ4AAAgMKhwQEAAIVDgwMAAApndJOMlbTX2qf4nvvMXaFtfe8JvyaSLvrTyvbQeBcffoZb858dfsplnvYd8NOc87SzdChUV1Es8Ti2Lb8HjyQLR46FqDy3FUmHjqY+5yWSaj3eHba6Hiv3tq3532+/PbStj/3VKremGkhN//euwKIl6cqFM92aG7b4CcVRkdTtPZtnh7YVOXYi4+2y2Fqzx7k/kWJpwJLUr7pbUzf/do6OF1lHaoGa6HpbS/7cd+SYUhzaD+bvcym23wfDIzgAAKBwaHAAAEDh0OAAAIDCocEBAACFQ4MDAAAKhwYHAAAUDg0OAAAoHBocAABQOKMa9NeZSlrW6Glb09vXEdpWr+11a1bUj3NrFqapofH+q7LHrYkGLnUHgun2WdWt+UFvMFBvdPMAcwv7kqQF9W635v5yfsF7kdtwWvKP0dkNf96StGWUQ/zy5AUsxqLajo3pqUMvri5sW7Nvqx+oJ0n7A+fi6bV5bs2p9Wmh8W7c0O/WlCv++ic194Ont+SH5X3xppeFxqtU1oXqPNFgt0hMXGS9laQzzT9nfxQJwQyG10XWwMjtt6Lh389J0pqyfx8W2e/RINBISGGUtx/2DbFu8wgOAAAoHBocAABQODQ4AACgcGhwAABA4dDgAACAwqHBAQAAhUODAwAACocGBwAAFA4NDgAAKBxLKZZKmIdTZyxMN7z4orY1q26LpYt6KaqSVA6k0nYEe7w+C6SLpti28kqLjCYnR5MnPafV5obqniztd2ueXZ8V2tYdHU+6NZH9EN0HZ/cvdWseLfspsttLB0PjRRKrI8f6wRxTQ/NyoP9K1Rsbg3Hb+XrBad3pP28+vm3N4hP/OLStnuRHgR8KZOrOTrF0662B86cjmATeCBz3kfUoz7Ut4tz+ZaG6BwLpvOcE162ru9aG6jzRffWORvvjU5JuS/6x8Eh5d2i8Q4E1IpLSHkn2lvK9D/P09f+TaoOsNe4tYWbXmNk2M3ug5bLLzGyTmd2X/Tsvl1kCmLRYawDkKdJqXivpnEEu//uU0unZv5vznRaASehasdYAyInb4KSU7pC0axTmAmASY60BkKejeZHx28zs/uxh5SFfUGFml5jZajNbvbs/9roEAGgx7LVmx87YJzoDKK6RNjifkXSipNMlbZb0yaEKU0pXpZRWppRWzuqcMsLhAExSI1pr5s6JvQgXQHGNqMFJKW1NKdVTSg1JV0s6M99pAQBrDYCRG1GDY2aLWr59raQHhqoFgJFirQEwUm7AhpndKGmVpLlmtlHSn0taZWanS0qS1kl6yzGcI4BJgLUGQJ5GNeivUlqaZna8o23NBytLQtu6rP6EW9MVCMM6bLEXI154eIVbc2NOQVFSbO6/2+8HRUn5zWtW6grV7bbDuYwnSecHfsc7O7a6NdE5vaLqB/3d3rHJrYkGC0b2aZ77czSNZdBfZK359st6Q9v6/TvmuDVV+QF39eAx8Sd1P+Tu82X/GJSk2Q0/XHBvIMT0/Ori0Hj/0bHFren2/65WJcUOm0io6PGNntC2TqvNcGu+2+kHj0ad2+/f192a0/6UpEPyg/4iAaXzGrHX0tYCoY9TA8GCkrTLDrW9fnf106qOJOgPAABgoqHBAQAAhUODAwAACocGBwAAFA4NDgAAKBwaHAAAUDg0OAAAoHBocAAAQOGMatBfubQ0Te1866iNV3QVxcKwoqFzGH0beq92a5bNePMozCR/Yxn011lalhZ2vKttTTRkrCd1ujWRvxS3mx+iJsXC1iJBoJJUD4StzQqEAUaVAmvSnOSPFw237Jcf1LogTQ1ta3MgNLAUCCDsVOy2OWBVtyYS1Lir1D4E74i193/WrTnjeZe6NT3B82arHXBroiGF3c7x/kjtch1I6wn6AwAAxUeDAwAACocGBwAAFA4NDgAAKBwaHAAAUDg0OAAAoHBocAAAQOHQ4AAAgMKhwQEAAIUTixHMSadKWt44rm3NWdU5oW19o/MJt2Zm6nJravKTPiXp5MZMt+be8vbQtroDSZe91u/WvLN2Qmi8T1bWhuo804MJlvMaU9yaPaVYUukZtbluzQ86NoW2FRFJh+4IpMge3+gJjfeCnne4NV2BY+Gw+YmuUUVIyO5KZZ1Qb7/W/NH82O/5oZ273ZoFDT8td6pi58/Fhxe5NV8JrH+SNDNwLm4t+Ymz3z0rlsL86h9Pd2v2BFKKo2vNCuf+RJI2BRKKJekPa0vcms9W1rs19WAKczn5jy9EUopPC6yRkvRrz/VTircE9tWmUuw+MyKylkpStdR+feuvD349j+AAAIDCocEBAACFQ4MDAAAKhwYHAAAUDg0OAAAoHBocAABQODQ4AACgcGhwAABA4Yxq0F93KuvkWvtgps7gtiLBZo3kB5HtDAQpSdLUur+r6hYLQOqVP/cpyR9vSzTbLadbuSsYyiPAyhYAAA6USURBVBQJ8dsdDMN6rNzn1kRCwfZZNTReJLyuHKh5bm1GaLw7O/z9ED2u8hIN8PMCAWMxesdGj0p6udqHzk3p3hPaVuTYmRW4jXZYLCxvRuDPzmow2HGP/ONrTqPbrXn8sVgAa912uTWRcLda8JiPhPhFggUl6Ye2163piDwmEMy/LAXOkEjg6+/4GbSSpE/3+fs08vuVUuzMbgR2RPQ4nuas8fuH2Jc8ggMAAAqHBgcAABQODQ4AACgcGhwAAFA4NDgAAKBwaHAAAEDh0OAAAIDCocEBAACFM6pBf/utptUd29vWfGDa3NjGDvgl20uxYK2InkYgnCqa8BTQrVio3miKhiLm6VVV/3i4quvx3MY7se4H9K0v+4FgP+zYEhqvEvgbI8/jKsIL8DvCm9fozvrpdlhV/1J+sm3NbS/5RWhbXV850a3ZZv6CFAknlaS50wKhlH7+W3jMSOBcvTG6fwvvDwZzHgqEpka99xQ/NPDix2puTfR8XVmb79b8vLzTrfnbPj9cUZKmB2J0q/LDAKPBo+XkHzPdgUBbyb+dhwoV5BEcAABQOG6DY2bLzOw2M3vIzB40s3dml882s1vMbE32/6xjP10ARcVaAyBPkUdwapLenVI6VdKLJL3VzE6V9AFJt6aUTpJ0a/Y9AIwUaw2A3LgNTkppc0rp3uzrPkkPS1oi6QJJ12Vl10l6zbGaJIDiY60BkKdhvcjYzE6QdIakuyQtSCltzq7aImnBED9ziaRLJKksHlkG4GOtAXC0wi8yNrPpkr4m6dKU0tPeRpJSShriTRMppatSSitTSitLNu2oJgug+FhrAOQh1OCYWYeaC84XU0pfzy7eamaLsusXSdp2bKYIYLJgrQGQl8i7qEzS5yU9nFL6VMtV35J0Ufb1RZK+mf/0AEwWrDUA8hR5Dc5vSPoDST83s/uyyz4k6XJJN5nZmyStl/S6YzNFAJMEaw2A3LgNTkrpTmnIqMtXDGewhpIOqH0S5D/v6wtta5H859g7A2nA8xrdofEervjptdNTR2hbEX3mR5U+VN4X2lYkmbYj+fvqhbVBX9v5K9aUe92a4xvTQ9u6rnO9W9MVmHs0RXZZYF7z0xS35pelPaHxtpb8BNzIcbUvmPwaEU1ineKkkA439zrPtUYaOt30iIu+tDK0ndmBRPSFjaluzfK6XyNJN9cOuzXd5dj7Q3qSn17bH0gD/u6T/jEvST0d/nidgfP1d/sXhsb7XsVP+j2rNju0rTc9+oRbMydw7u8MJuifGVhrTq3793O3dewIjbfT/HkdFzhe9im/tSaydkvSNLVfA/cN8WQUScYAAKBwaHAAAEDh0OAAAIDCocEBAACFQ4MDAAAKhwYHAAAUDg0OAAAoHBocAABQONb87LrRUSktTT2db2tb8z4tD23rr7UuhxnFfUgnuDWjPaebz/TDACXpvP/2w5sioqFM0VC9iHkNP1hrezBYKyISihgNwpvsDvRfqXpjo79Dj4HO0rI0v+PStjXXnxGLIrzgPv/vwMi50RH8e/KG0/wgtT/4WSxUtKpGqM6z9cYvh+pWXPhGt8YLe81b3WL7YFogULMeOPejoZteUKYkHTR/X0XWLEkqJ//4ixyjkX0g+UGbUvy26Xb21Y7qp1VtbPiVHcEjOAAAoHBocAAAQOHQ4AAAgMKhwQEAAIVDgwMAAAqHBgcAABQODQ4AACgcGhwAAFA4NDgAAKBw/CjFHHWnik6tz2lbc/KKnaFtVZ7IJ3E2kiYpSU/2B3rBrtCmcvP1Hz0rVFfpWuvWRFIu5yY/VViSNtm+UF1Ep2LpyXkhpTh+TkRSVsdKWaaZqbttzQfvmR7a1onyk7n3mp8qfnqt/dp3xFfu9hN139UdS4D9Rs0/F3fbYbfmFa97a2i8SqnPrXllbYlb8+PKttB40+WntHc3YmvInsB+iKTzLmpMC423ot7j1txT2R7aVsSBwPkaSb4+pTErNF6f/HOiEnyMxbtthtoKj+AAAIDCocEBAACFQ4MDAAAKhwYHAAAUDg0OAAAoHBocAABQODQ4AACgcGhwAABA4Yxq0F9J0lQnROwv11ZjGwtkN81ptA/6kqTekh/uJEmbAkFeFfnhg3l6+XM2h+qufdSvqZsf8LTV9ofGy3M/bC35Y05PfjDaoVEOpXvj4RWhuq90PuHWVM0PmstTdDzvdh7ds+Hpqmpoi3O8dljs77sFjaluTSnw224uHwyN94j1ujWPVmOhm+vKe92a3+lf6ta89vmbQuNd/Ii/jtzW4a9bHcG/vVfU/LC8B8u7QtuaFwgyXVvy92dkO5K0puzfzvOSf+ytLfnbkWL79KTGTLdmpx0KjXdSfYZbsz4QDClJZ1Xnt73+Oxr8PoBHcAAAQOHQ4AAAgMKhwQEAAIVDgwMAAAqHBgcAABQODQ4AACgcGhwAAFA4NDgAAKBwRjXob7pKerHahyD9JMW2tV5+4FIkxO/k+qzQeBvKB0J1ET2p063Zbf7c37ImFl5Xs+BOdUSCEyWpM5DCOCt1Bcf0637SscWtqSm2D04MhFPNDwR53di5PjTezMB+2Gl+QFw5xf5WOTyKoYH5HHUjMz916s3149vW7KvHoghvr+x2ayK340HFzlcvDFWSDgVvx1nJP2dvCQTvffeR2L4qBwIPI3OK7AOpGejoOS6w3krS0vo0t2ZTaZ9bsz1wvkrSisZxbk3kfmB2YH9KsXV5q/n3c9Egww2BfTUl2ILc3bGj7fX7a4OfW+6qaGbLzOw2M3vIzB40s3dml19mZpvM7L7s33mhmQLAIFhrAOQp0j7VJL07pXSvmfVIusfMbsmu+/uU0ieO3fQATCKsNQBy4zY4KaXNkjZnX/eZ2cOSlhzriQGYXFhrAORpWC8yNrMTJJ0h6a7soreZ2f1mdo2ZxV7MAgAO1hoARyvc4JjZdElfk3RpSmmvpM9IOlHS6Wr+1fXJIX7uEjNbbWarDyj2yaEAJi/WGgB5CDU4Ztah5oLzxZTS1yUppbQ1pVRPKTUkXS3pzMF+NqV0VUppZUpp5VT5H20PYPJirQGQl8i7qEzS5yU9nFL6VMvli1rKXivpgfynB2CyYK0BkKfIu6h+Q9IfSPq5md2XXfYhSRea2elqxl2sk/SWYzJDAJMFaw2A3ETeRXWnNGh60835TwfAZMVaAyBPo5pkvF9Jq1P7ZMbXz4/ln97th4uGrC35iciS9OLaQrfmkfKu0LYi6ZSVQCLob1YXuTWS9O3OJ0J1np2lQ6G6KYEU0s2l/aFtvbK+zK2JphRHrC/7x8Nj6s1tvGX16W7N1pKfLlrLMaF4euoI1dWd/R47Wo6NPjV0u9ofY3939mOhbX3l9jluTSlwvnYH03l/vTrXrVlXiZ0/0wK35Y6Sn7z70uBa87PKTremX/6xuiCYlvto2T8XD1ksQTqSRu0d85LUCK5H60v+C+H3W9WtiaRHS9LzqrPdmgcDqd3RpOZIGnV0W3Oc46GcBt8HfBYVAAAoHBocAABQODQ4AACgcGhwAABA4dDgAACAwqHBAQAAhUODAwAACocGBwAAFI6llF9ImjuY2XZJ6wdcPFfSjlGbRH4m6rwl5j4WJuq8pZHPfXlKaV7ek4kYZK2ZjPt/PJioc5+o85Ym59wHXWtGtcEZjJmtTimtHNNJjMBEnbfE3MfCRJ23NLHnfsRE/h2Y++ibqPOWmHsrnqICAACFQ4MDAAAKZzw0OFeN9QRGaKLOW2LuY2Gizlua2HM/YiL/Dsx99E3UeUvM/Slj/hocAACAvI2HR3AAAAByNWYNjpmdY2a/MLNHzewDYzWPkTCzdWb2czO7z8xWj/V82jGza8xsm5k90HLZbDO7xczWZP/PGss5DmaIeV9mZpuy/X6fmZ03lnMcipktM7PbzOwhM3vQzN6ZXT6u93ubeU+I/T4U1ppjb6KuM9LEXWsm6jojjd5aMyZPUZlZWdIvJZ0taaOkuyVdmFJ6aNQnMwJmtk7SypTSuM8aMLOXSdon6fqU0nOzy/5O0q6U0uXZgj8rpfT+sZznQEPM+zJJ+1JKnxjLuXnMbJGkRSmle82sR9I9kl4j6WKN4/3eZt6v0wTY74NhrRkdE3WdkSbuWjNR1xlp9NaasXoE50xJj6aUHk8p9Uv6sqQLxmguhZZSukPSrgEXXyDpuuzr69Q8sMaVIeY9IaSUNqeU7s2+7pP0sKQlGuf7vc28JzLWmlEwUdcZaeKuNRN1nZFGb60ZqwZniaQNLd9v1MRaSJOk75nZPWZ2yVhPZgQWpJQ2Z19vkbRgLCczTG8zs/uzh5XH3UOvA5nZCZLOkHSXJtB+HzBvaYLt9xasNWNnwhzvQ5gwx/xEXWekY7vW8CLjkXlJSukFks6V9NbsIc4JKTWfo5wob6X7jKQTJZ0uabOkT47tdNozs+mSvibp0pTS3tbrxvN+H2TeE2q/F0wh1prxfLwPYcIc8xN1nZGO/VozVg3OJknLWr5fml02IaSUNmX/b5P0DTUfBp9ItmbPgR55LnTbGM8nJKW0NaVUTyk1JF2tcbzfzaxDzRP3iymlr2cXj/v9Pti8J9J+HwRrzdgZ98f7UCbKMT9R1xlpdNaasWpw7pZ0kpmtMLNOSa+X9K0xmsuwmNm07EVRMrNpkl4p6YH2PzXufEvSRdnXF0n65hjOJezISZt5rcbpfjczk/R5SQ+nlD7VctW43u9DzXui7PchsNaMnXF9vLczEY75ibrOSKO31oxZ0F/29q8rJJUlXZNS+viYTGSYzOwZav4lJUkVSV8az3M3sxslrVLzU1q3SvpzSf8q6SZJx6v5icuvSymNqxfZDTHvVWo+dJkkrZP0lpbnmscNM3uJpB9J+rmkRnbxh9R8jnnc7vc2875QE2C/D4W15tibqOuMNHHXmom6zkijt9aQZAwAAAqHFxkDAIDCocEBAACFQ4MDAAAKhwYHAAAUDg0OAAAoHBocAABQODQ4AACgcGhwAABA4fx/WhMk6aTg9hAAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 576x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "W_exp = W.exp()\n",
        "P_nn = W_exp / W_exp.sum(dim=1, keepdim=True)\n",
        "P_nn.shape\n",
        "\n",
        "fig, ax = plt.subplots(1, 2, figsize=(8, 5))\n",
        "ax[0].imshow(P.data, cmap='plasma')\n",
        "ax[0].set_title(\"Método de conteo\")\n",
        "ax[1].imshow(P_nn.data, cmap='plasma')\n",
        "ax[1].set_title(\"Red neuronal\")\n",
        "plt.tight_layout();"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6b3GflupLuh"
      },
      "source": [
        "Aunque nuestro modelo de red neuronal no haya superado al de simple conteo y probabilidad, puesto que en realidad los implementamos de tal manera que son prácticamente iguales, lo cierto es que esta estructura de red neuronal contiene ya los rudimentos esenciales para superar con creces al modelo anterior. En realidad, una implementación más compleja y óptima de nuestra red neuronal solo consistirá en cambiar la manera en que lidiamos con los datos (nuestro vocabulario, *tokens*, tabla de consulta, `xenc`, por ejemplo) y con las capas de neuronas (nuestra `W`, por ejemplo). Todo lo demás permanecerá igual. En la próxima lección profundizaremos en esto."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "coUiwWDvWaiY"
      },
      "source": [
        "[^1]: La modelación probabilística del lenguaje tiene origen —no podía ser de otra forma— en el trabajo de Claude Shannon {cite}`Shannon1950`.\n",
        "\n",
        "[^2]: La función de pérdida siempre dependerá de la naturaleza del problema. Dado que este es un problema de clasificación relacionado con probabilidades, hemos elegido una función apta. Durante la implementación de `pequegrad`, habíamos empleado la regresión lineal. <br><br>La formulación matemática de esta nueva función de pérdida puede hacerse como sigue: <br><br>$-\\log\\left(p(X\\mid\\boldsymbol{\\theta})\\right) = -\\log(p(x_1\\mid\\boldsymbol{\\theta})) - \\log(p(x_2\\mid\\boldsymbol{\\theta})) \\cdots - \\log(p(x_n\\mid\\boldsymbol{\\theta})) = -\\sum_{i} \\log(p(x_i \\mid \\theta))$<br><br> Para saber más, véase el apéndice [*Mathematics for deep learning*](https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/maximum-likelihood.html#maximum-likelihood) de {cite}`DIVE`.\n",
        "\n",
        "[^3]: Comúnmente se denomina capa lineal o *lineal layer* a la serie de pesos y/o sesgos que creamos para multiplicarlos y sumarlos por nuestras entradas. Como hemos visto, tanto la función multiplicación como la función suma siempre resultan una línea recta en el plano cartesiano, y de ahí el adjetivo «lineal». Estamos transformando linealmente nuestras entradas, puesto que la graficación de la multiplicación y la suma con los pesos y sesgos es una línea.\n",
        "\n",
        "[^4]: Hemos denominado «logit» al resultado de nuestra multiplicación de matrices, sin embargo, en el ámbito del *deep learning*, el término «logit» puede llegar a ser [bastante ambiguo](https://stackoverflow.com/a/50511692/19440446). En nuestro caso, denominamos así a nuestra matriz porque es la última (y única) capa de la red neuronal, y está representando un conteo en bruto de la ocurrencia de cada $x$ que luego utilizaremos para convertir en probabilidades. Aunque nuestra explicación también sea insatisfactoria, debemos conformarnos con ella por el momento. La ambigüedad del término es tal que su misma composición es extraña: no está prefijado con base en «logaritmo», sino en «logístico», pues es una abreviación de «unidad logística» (*logistic unit*); pero nunca ha estado clara la razón detrás del término «logístico» en matemáticas y, para más inri, el uso de «logit» en *deep learning* no siempre tiene un fundamento matemático riguroso. En fin, no nos perdamos entre las ramas y volvamos a lo nuestro."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
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  "nbformat_minor": 0
}