DesafioGeneracionTexto_Martin_Fierro.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/c-e-leiva/procesamientoDelHabla/blob/main/DesafioGeneracionTexto_Martin_Fierro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Generación de Texto en el Estilo de Martín Fierro 📝**\n",
        "\n",
        "---\n",
        "## **Alumno**  \n",
        "- **Nombre y Apellido**: Carlos Ezequiel Leiva  \n",
        "- **DNI**: 39595128  \n",
        "---\n",
        "## **Consignas - Tema 9**\n",
        "- Entrenar al menos un modelo **seq2seq**.  \n",
        "- Utilizar al menos un modelo pre-entrenado de **Transformers** (ejemplo: GPT-2, LLaMA3, etc.).  \n",
        "\n",
        "---\n",
        "\n",
        "## **Instrucciones**    \n",
        "- Instalar las librerías necesarias: *pip install transformers torch numpy pdfplumber*\n",
        "\n",
        "- Descargar el texto El Gaucho Martín Fierro desde el siguiente enlace: [El Gaucho Martín Fierro (PDF)](https://www.argentina.gob.ar/sites/default/files/hernandez_jose_-_el_gaucho_martin_fierro.pdf)\n",
        "- Convertir el PDF a texto plano (se explicará en el preprocesamiento).\n",
        "---\n"
      ],
      "metadata": {
        "id": "pvLJyzqtc8sU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Introducción**\n",
        "El objetivo de este proyecto es generar texto en el estilo del poema El Martín Fierro utilizando técnicas de procesamiento de lenguaje natural (NLP).\n",
        "\n",
        "🔹 El poema, escrito por José Hernández, es un ícono de la literatura gauchesca argentina.\n",
        "\n",
        "🔹 Modelos a Utilizar: **seq2seq** y **GPT-2**\n",
        "\n",
        "Este proyecto busca no solo reproducir el estilo del poema, sino también explorar cómo los modelos actuales pueden adaptarse a textos literarios únicos.\n",
        "_____\n",
        "_______\n",
        "_______\n"
      ],
      "metadata": {
        "id": "lp9GXJiO67Zl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Modelo Seq2Seq: Martín Fierro**\n",
        "\n",
        "El modelo Seq2Seq está diseñado para generar texto inspirado en El Martín Fierro a partir de una frase inicial. Utilizando una arquitectura de codificador-decodificador, el objetivo es recrear el estilo del poema con ajustes de temperatura para controlar la creatividad y fluidez en las predicciones, logrando estrofas coherentes y fieles al espíritu de la obra."
      ],
      "metadata": {
        "id": "7usN4Mnp-3ra"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Instalación de librerías"
      ],
      "metadata": {
        "id": "1ihV_LQ3uEZo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers torch numpy pdfplumber tensorflow keras -q"
      ],
      "metadata": {
        "id": "x_baVvaluMmu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importación de librerias"
      ],
      "metadata": {
        "id": "X2EVN0VeMcHt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kVmtnPXut5I3"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "import os\n",
        "import pdfplumber\n",
        "import re\n",
        "import numpy as np\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, LSTM, Embedding, Dense\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.utils import to_categorical"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Verificación y descarga de archivo PDF desde URL\n",
        "\n",
        "Comprobamos la respuesta del servidor (código 200) antes de descargar el archivo PDF a una ubicación local. Si la descarga es exitosa, mostramos el tamaño del archivo; de lo contrario, mostramos un mensaje de error."
      ],
      "metadata": {
        "id": "Cea7ydmBuYpW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Descargar el PDF\n",
        "def descargar_pdf(url, file_name):\n",
        "    response = requests.get(url)\n",
        "    if response.status_code == 200:\n",
        "        with open(file_name, \"wb\") as file:\n",
        "            file.write(response.content)\n",
        "        file_size = os.path.getsize(file_name)\n",
        "        print(f\"PDF descargado exitosamente, tamaño: {file_size} bytes\")\n",
        "    else:\n",
        "        print(f\"Error al descargar el PDF. Código de estado: {response.status_code}\")\n",
        "\n",
        "# Variables para la URL y los nombres de los archivos\n",
        "url = \"https://www.argentina.gob.ar/sites/default/files/hernandez_jose_-_el_gaucho_martin_fierro.pdf\"\n",
        "file_name_pdf = \"el_gaucho_martin_fierro.pdf\"\n",
        "file_name_txt = \"texto_martin_fierro.txt\"\n",
        "\n",
        "# Descargar el archivo PDF\n",
        "descargar_pdf(url, file_name_pdf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJ-R4gPruach",
        "outputId": "f8cc669f-e78d-4ebb-d2c4-d18487256b5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PDF descargado exitosamente, tamaño: 513796 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Extracción y limpieza de texto desde un archivo PDF\n",
        "\n",
        "El código tiene como objetivo extraer el texto relevante de un PDF, omitiendo secciones no deseadas, limpiando los datos de números de página, líneas vacías y eliminando números romanos.\n",
        "\n",
        "**Detección de páginas relevantes:** La función ***pagina_a_procesar*** verifica si el texto de una página contiene palabras clave que deben ser omitidas, como 'Índice' o 'Prólogo'.\n",
        "\n",
        "**Extracción de texto:** Utilizando pdfplumber, la función ***extraer_texto*** extrae el texto de las páginas del PDF que cumplen con los criterios de relevancia.\n",
        "\n",
        "**Limpieza de texto:** La función ***limpiar_texto*** elimina números de página y líneas vacías, mientras que eliminar_numeros_romanos elimina números romanos del texto extraído.\n",
        "\n",
        "**Guardar el texto limpio:** Finalmente, el texto limpio y procesado se guarda en un archivo de texto para su posterior uso."
      ],
      "metadata": {
        "id": "cbq4OnHjN0aW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Determinar si procesar una página\n",
        "def pagina_a_procesar(texto_pagina):\n",
        "    palabras_omitir = ['Índice', 'Prólogo', 'Anexo', 'Imágenes', 'Agradecimientos', 'Dedicatoria', 'http']\n",
        "    return all(palabra.lower() not in texto_pagina.lower() for palabra in palabras_omitir)\n",
        "\n",
        "# Extraer texto relevante del PDF\n",
        "def extraer_texto(filename, pagina_inicio=0, pagina_fin=None):\n",
        "    texto_relevante = ''\n",
        "    with pdfplumber.open(filename) as pdf:\n",
        "        if pagina_fin is None:\n",
        "            pagina_fin = len(pdf.pages)\n",
        "        for pagina_numero in range(pagina_inicio, pagina_fin):\n",
        "            pagina = pdf.pages[pagina_numero]\n",
        "            texto_pagina = pagina.extract_text()\n",
        "            if texto_pagina and len(texto_pagina.split()) > 10 and pagina_a_procesar(texto_pagina):\n",
        "                texto_relevante += texto_pagina + '\\n'\n",
        "    return texto_relevante\n",
        "\n",
        "# Limpiar texto de números de página y líneas vacías\n",
        "def limpiar_texto(texto):\n",
        "    # Expresión regular para detectar y eliminar \"Página X\"\n",
        "    texto = re.sub(r'Página\\s+\\d+', '', texto)\n",
        "    # Eliminar líneas vacías adicionales\n",
        "    texto = re.sub(r'\\n\\s*\\n', '\\n', texto)\n",
        "    return texto.strip()\n",
        "\n",
        "# Eliminar números romanos de I a XIII\n",
        "def eliminar_numeros_romanos(texto):\n",
        "    # Expresión regular para detectar números romanos de I a XIII\n",
        "    patron_romanos = r'\\b(I{1,3}|IV|V{0,2}I{0,3}|VI{0,2}I{0,3}|VII{0,2}I{0,3}|VIII{0,2}I{0,3}|IX|X{1,3}|XI|XII|XIII)\\b'\n",
        "    texto = re.sub(patron_romanos, '', texto)\n",
        "    return texto\n",
        "\n",
        "# Guardar texto extraído en archivo\n",
        "def guardar_texto(texto, file_name):\n",
        "    with open(file_name, \"w\", encoding=\"utf-8\") as file:\n",
        "        file.write(texto)\n",
        "    print(f\"Texto guardado en {file_name}\")\n",
        "\n",
        "# Procesamiento del PDF\n",
        "try:\n",
        "    # Extraer texto del PDF\n",
        "    texto = extraer_texto(file_name_pdf)\n",
        "    # Limpiar el texto extraído\n",
        "    texto_limpio = limpiar_texto(texto)\n",
        "    # Eliminar números romanos\n",
        "    texto_sin_romanos = eliminar_numeros_romanos(texto_limpio)\n",
        "    # Guardar texto limpio en archivo\n",
        "    guardar_texto(texto_sin_romanos, file_name_txt)\n",
        "except Exception as e:\n",
        "    print(f\"Error durante el procesamiento: {e}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07-hGts9w_zn",
        "outputId": "ddfccfe9-437a-4545-d7bf-cd2a8ab67b31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Texto guardado en texto_martin_fierro.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mostrar texto completo o resumido desde un archivo\n",
        "El código tiene como objetivo mostrar el texto completo o una parte resumida (limitada a un número de caracteres) de un archivo de texto extraído de un PDF.\n",
        "\n",
        "**Mostrar texto completo:** La función mostrar_texto_completo abre el archivo de texto y muestra su contenido completo.\n",
        "\n",
        "**Mostrar texto resumido:** La función mostrar_texto_resumido limita el contenido del archivo a una cantidad específica de caracteres (por defecto 2980), mostrando solo una parte del texto.\n",
        "\n",
        "**Control de ejecución:** El código está diseñado para mostrar por defecto el texto resumido. Para mostrar el texto completo, se debe descomentar la línea correspondiente."
      ],
      "metadata": {
        "id": "nj0mPix3OPma"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Función para mostrar el texto completo\n",
        "def mostrar_texto_completo(file_name=\"texto_martin_fierro.txt\"):\n",
        "    \"\"\"\n",
        "    Esta función muestra el texto completo extraído del PDF.\n",
        "    Está comentado por defecto para que no se ejecute inmediatamente.\n",
        "    Si se desea mostrar todo el texto, solo se debe descomentar la línea correspondiente\n",
        "    en el bloque de código más abajo.\n",
        "    \"\"\"\n",
        "    # Abro el archivo de texto extraído\n",
        "    with open(file_name, \"r\", encoding=\"utf-8\") as file:\n",
        "        texto = file.read()  # Leo todo el contenido del archivo\n",
        "\n",
        "    # Muestra todo el texto\n",
        "    print(\"Texto completo extraído del PDF:\\n\")\n",
        "    print(f'{texto}')  # Muestra el texto completo\n",
        "\n",
        "\n",
        "# Función para mostrar el texto resumido (por defecto, se usa esta)\n",
        "def mostrar_texto_resumido(file_name=\"texto_martin_fierro.txt\", limite=2980):\n",
        "    \"\"\"\n",
        "    Esta función muestra una parte del texto extraído, limitado a un número de caracteres.\n",
        "    \"\"\"\n",
        "    # Abro el archivo de texto extraído\n",
        "    with open(file_name, \"r\", encoding=\"utf-8\") as file:\n",
        "        texto = file.read()  # Leo todo el contenido del archivo\n",
        "\n",
        "    # Texto resumido (solo los primeros 'limite' caracteres)\n",
        "    texto_resumido = texto[:limite]\n",
        "\n",
        "    # Muestra solo los primeros 'limite' caracteres del texto\n",
        "    print(f\"Texto resumido (primeros {limite} caracteres):\\n\")\n",
        "    print(texto[:limite])  # Muestra solo el texto resumido\n",
        "    #return texto_resumido  # Retorna el texto resumido\n",
        "\n",
        "# Ejemplo de uso:\n",
        "# Por defecto, se ejecutará esta línea para mostrar el texto resumido.\n",
        "mostrar_texto_resumido()  # Muestra el texto resumido\n",
        "\n",
        "# Si deseas mostrar el texto completo, descomenta la siguiente línea:\n",
        "# mostrar_texto_completo()  # Descomentar para ver el texto completo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BO_2JIwhxFio",
        "outputId": "67b577d7-b63e-4b05-b68b-f25890cbb61e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Texto resumido (primeros 2980 caracteres):\n",
            "\n",
            "El Gaucho Martín Fierro\n",
            "\n",
            "Aquí me pongo a cantar\n",
            "al compás de la vigüela,\n",
            "que el hombre que lo desvela\n",
            "una pena estrordinaria,\n",
            "como la ave solitaria\n",
            "con el cantar se consuela.\n",
            "Pido a los santos del cielo\n",
            "que ayuden mi pensamiento:\n",
            "les pido en este momento\n",
            "que voy a cantar mi historia\n",
            "me refresquen la memoria\n",
            "y aclaren mi entendimiento.\n",
            "Vengan santos milagrosos,\n",
            "vengan todos en mi ayuda,\n",
            "que la lengua se me añuda\n",
            "y se me turba la vista;\n",
            "pido a mi Dios que me asista\n",
            "en una ocasión tan ruda.\n",
            "Yo he visto muchos cantores,\n",
            "con famas bien otenidas\n",
            "y que después de alquiridas\n",
            "no las quieren sustentar:\n",
            "parece que sin largar\n",
            "se cansaron en partidas.\n",
            "Mas ande otro criollo pasa\n",
            "Martín Fierro ha de pasar;\n",
            "nada lo hace recular,\n",
            "ni las fantasmas lo espantan,\n",
            "y dende que todos cantan\n",
            "yo también quiero cantar.\n",
            "Cantando me he de morir,\n",
            "cantando me han de enterrar,\n",
            "y cantando he de llegar\n",
            "al pie del Eterno Padre;\n",
            "dende el vientre de mi madre\n",
            "vine a este mundo a cantar.\n",
            "Que no se trabe mi lengua\n",
            "ni me falte la palabra;\n",
            "el cantar mi gloria labra\n",
            "y, poniéndome a cantar,\n",
            "cantando me han de encontrar\n",
            "aunque la tierra se abra.\n",
            "Me siento en el plan de un bajo\n",
            "a cantar un argumento;\n",
            "como si soplara el viento\n",
            "hago tiritar los pastos.\n",
            "Con oros, copas y bastos\n",
            "juega allí mi pensamiento.\n",
            "Yo no soy cantor letrao\n",
            "mas si me pongo a cantar\n",
            "no tengo cuándo acabar\n",
            "y me envejezco cantando:\n",
            "las coplas me van brotando\n",
            "como agua de manantial.\n",
            "Con la guitarra en la mano\n",
            "ni las moscas se me arriman;\n",
            "naides me pone el pie encima,\n",
            "y, cuando el pecho se entona,\n",
            "hago gemir a la prima\n",
            "y llorar a la bordona.\n",
            "Yo soy toro en mi rodeo\n",
            "y torazo en rodeo ajeno;\n",
            "siempre me tuve por güeno\n",
            "y si me quieren probar\n",
            "salgan otros a cantar\n",
            "y veremos quien es menos.\n",
            "No me hago al lao de la güeya\n",
            "aunque vengan degollando;\n",
            "con los blandos yo soy blando\n",
            "y soy duro con los duros,\n",
            "y ninguno en un apuro\n",
            "me ha visto andar tutubiando.\n",
            "En el peligro ¡qué Cristos!\n",
            "el corazón se me enancha,\n",
            "pues toda la tierra es cancha,\n",
            "y de esto naides se asombre;\n",
            "el que se tiene por hombre\n",
            "donde quiera hace pata ancha.\n",
            "Soy gaucho, y entiéndalo\n",
            "como mi lengua lo esplica:\n",
            "para mí la tierra es chica\n",
            "y pudiera ser mayor;\n",
            "ni la víbora me pica\n",
            "ni quema mi frente el sol.\n",
            "Nací como nace el peje\n",
            "en el fondo de la mar;\n",
            "naides me puede quitar\n",
            "aquéllo que Dios me dio:\n",
            "lo que al mundo truje yo\n",
            "del mundo lo he de llevar.\n",
            "Mi gloria es vivir tan libre\n",
            "como el pájaro del cielo;\n",
            "no hago nido en este suelo\n",
            "ande hay tanto que sufrir,\n",
            "y naides me ha de seguir\n",
            "cuando yo remuento el vuelo.\n",
            "Yo no tengo en el amor\n",
            "quien me venga con querellas;\n",
            "como esas aves tan bellas\n",
            "que saltan de rama en rama,\n",
            "yo hago en el trébol mi cama\n",
            "y me cubren las estrellas.\n",
            "Y sepan cuantos escuchan\n",
            "de mis penas el relato\n",
            "que nunca peleo ni mato\n",
            "sino por necesidá\n",
            "y que a tanta alversidá\n",
            "sólo me arrojó el mal trato.\n",
            "Y atiendan la relación\n",
            "que hace un gaucho perseguido,\n",
            "que padre y marido ha sido\n",
            "empeñoso y diligente,\n",
            "y sin embargo la gente\n",
            "lo tiene por un bandido.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " ## División y tokenización de texto resumido\n",
        "\n",
        "El código divide un texto resumido en líneas o frases y realiza el proceso de tokenización para contar las palabras únicas en el texto.\n",
        "\n",
        "\n",
        "**División del texto:** La función ***dividir_y_tokenizar*** lee el archivo de texto, toma los primeros limite caracteres y los divide en líneas utilizando el salto de línea como delimitador.\n",
        "\n",
        "**Tokenización:** Se utiliza el objeto Tokenizer de Keras para procesar las líneas divididas y calcular el número total de palabras únicas en el texto.\n",
        "\n",
        "**Resultados:** La función devuelve una lista de líneas (texts), el objeto tokenizer y el total de palabras únicas en el texto tokenizado. Además, imprime un resumen con el número de líneas/frases y el número de palabras únicas."
      ],
      "metadata": {
        "id": "9Le-BBhYOf_2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# División del texto y tokenización\n",
        "def dividir_y_tokenizar(file_name=\"texto_martin_fierro.txt\", limite=2980):\n",
        "    \"\"\"\n",
        "    Esta función divide el texto resumido en líneas y realiza el proceso de tokenización.\n",
        "\n",
        "    Parámetros:\n",
        "    - file_name (str): Nombre del archivo que contiene el texto.\n",
        "    - limite (int): Número máximo de caracteres del texto a considerar (por defecto 2980).\n",
        "\n",
        "    Retorna:\n",
        "    - texts (list): Lista de líneas o frases del texto resumido.\n",
        "    - tokenizer (Tokenizer): Objeto tokenizer ajustado al texto.\n",
        "    - total_words (int): Número total de palabras únicas en el texto tokenizado.\n",
        "    \"\"\"\n",
        "    # Abrir el archivo y extraer el texto resumido\n",
        "    with open(file_name, \"r\", encoding=\"utf-8\") as file:\n",
        "        texto_resumido = file.read()[:limite]  # Extraer los primeros 'limite' caracteres\n",
        "\n",
        "    # Dividir el texto en líneas (o frases si se cambia el delimitador)\n",
        "    texts = texto_resumido.split('\\n')  # Cambia a split('.') para separar por frases\n",
        "\n",
        "    # Tokenización del texto\n",
        "    #from keras.preprocessing.text import Tokenizer  # Importar Tokenizer\n",
        "    tokenizer = Tokenizer()\n",
        "    tokenizer.fit_on_texts(texts)\n",
        "    total_words = len(tokenizer.word_index) + 1  # +1 para incluir el índice 0\n",
        "\n",
        "    print(f\"Texto dividido en {len(texts)} líneas/frases.\")\n",
        "    print(f\"Tokenización completa. Palabras únicas: {total_words}.\")\n",
        "\n",
        "    return texts, tokenizer, total_words\n",
        "\n",
        "# Llamada al método\n",
        "texts, tokenizer, total_words = dividir_y_tokenizar()\n",
        "\n",
        "# Imprimir ejemplo de resultados\n",
        "print(f\"Ejemplo de líneas del texto dividido: {texts[:5]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IN4YQy4t5ppQ",
        "outputId": "98a3b7dd-09f5-4853-81ce-3834122cc954"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Texto dividido en 116 líneas/frases.\n",
            "Tokenización completa. Palabras únicas: 275.\n",
            "Ejemplo de líneas del texto dividido: ['El Gaucho Martín Fierro', '', 'Aquí me pongo a cantar', 'al compás de la vigüela,', 'que el hombre que lo desvela']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creación y padding de secuencias para modelado de texto\n",
        "\n",
        "El código crea secuencias de entrada y salida a partir de un texto tokenizado y aplica padding para que todas las secuencias tengan la misma longitud.\n",
        "\n",
        "**Creación de secuencias:** Para cada línea del texto tokenizado, el código convierte las palabras en secuencias de números mediante texts_to_sequences. Luego, genera secuencias de entrada (input_sequences) y las correspondientes palabras de salida (output_words), donde cada secuencia de entrada contiene una parte de la secuencia original y la salida es el siguiente token en la secuencia.\n",
        "\n",
        "**Padding de secuencias:** Se calcula la longitud máxima de las secuencias y se aplica padding a las secuencias de entrada con pad_sequences de Keras para que todas tengan la misma longitud, añadiendo ceros al principio de cada secuencia según sea necesario."
      ],
      "metadata": {
        "id": "Mq8ae9VSOwnj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear secuencias de entrada y salida\n",
        "input_sequences = []\n",
        "output_words = []\n",
        "\n",
        "for line in texts:\n",
        "    token_list = tokenizer.texts_to_sequences([line])[0]\n",
        "    for i in range(1, len(token_list)):\n",
        "        input_sequences.append(token_list[:i])\n",
        "        output_words.append(token_list[i])\n",
        "\n",
        "# Padding de secuencias\n",
        "max_sequence_len = max([len(seq) for seq in input_sequences])\n",
        "input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_len, padding=\"pre\")"
      ],
      "metadata": {
        "id": "lge8Qvry932E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conversión de salidas a formato one-hot y reshaping\n",
        "\n",
        "Convierte las palabras de salida a formato one-hot y ajustar su forma para el entrenamiento de un modelo secuencial.\n",
        "\n",
        "**Conversión a formato one-hot:** Se utiliza la función to_categorical de Keras para convertir las palabras de salida (output_words) en vectores one-hot. Cada palabra se representa como un vector con un 1 en la posición correspondiente a la palabra en el vocabulario, y 0 en las demás posiciones.\n",
        "\n",
        "**Reshape de las salidas:** Se usa np.expand_dims para agregar una dimensión adicional a las salidas, cambiando la forma de las mismas a (batch_size, 1, total_words). Esto es necesario para que las salidas puedan ser procesadas adecuadamente por un modelo de redes neuronales, especialmente en arquitecturas secuenciales."
      ],
      "metadata": {
        "id": "aUfizatoO93A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convertir las salidas a formato one-hot (esto será la salida esperada del decodificador)\n",
        "output_words = np.array(output_words)\n",
        "output_words_one_hot = to_categorical(output_words, num_classes=total_words)\n",
        "\n",
        "# Reshape de las salidas para que tengan la forma correcta (batch_size, 1, total_words)\n",
        "output_words_one_hot = np.expand_dims(output_words_one_hot, axis=1)  # Esto agrega una dimensión para que sea (batch_size, 1, total_words)"
      ],
      "metadata": {
        "id": "nZB405sm98x2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Definición del modelo Seq2Seq y espacio latente\n",
        "\n",
        "Establece la dimensión del espacio latente para el modelo Seq2Seq.\n",
        "\n",
        "La variable **latent_dim** con un valor de 100, que corresponde a la dimensión del espacio latente del modelo Seq2Seq. Este valor controla la capacidad del modelo para aprender representaciones de alta calidad durante el entrenamiento. Al aumentar la dimensión, se incrementa la capacidad de aprendizaje del modelo, permitiéndole capturar patrones más complejos en los datos, pero también aumentando la complejidad computacional."
      ],
      "metadata": {
        "id": "ybrUJRFpPNGJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Definir el modelo Seq2Seq\n",
        "latent_dim = 100  # Dimensión del espacio latente, aumentamos para mejorar la capacidad de aprendizaje"
      ],
      "metadata": {
        "id": "eULSja-p9_oi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Definición del Codificador del Modelo Seq2Seq\n",
        "\n",
        "Implementa la parte del codificador en el modelo Seq2Seq utilizando una capa LSTM.\n",
        "\n",
        "El codificador toma como entrada las secuencias de entrada (tokens) y genera una representación del contexto utilizando una capa LSTM (Long Short-Term Memory). Se definen los siguientes pasos:\n",
        "\n",
        "**encoder_inputs:** Se crea una capa de entrada con la forma (max_sequence_len,), que corresponde a la longitud máxima de las secuencias de entrada.\n",
        "\n",
        "**encoder_embedding:** La capa de Embedding convierte los índices de palabras en vectores densos, usando el tamaño total de palabras (total_words) y la dimensión latente (latent_dim).\n",
        "\n",
        "**encoder_lstm:** Se agrega una capa LSTM con la dimensión latente especificada. Esta capa devuelve el estado de la memoria (state_h y state_c), que se utilizan como el \"estado\" del codificador para pasar al decodificador."
      ],
      "metadata": {
        "id": "SmB_18DwPYYp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Codificador\n",
        "encoder_inputs = Input(shape=(max_sequence_len,))\n",
        "encoder_embedding = Embedding(input_dim=total_words, output_dim=latent_dim)(encoder_inputs)\n",
        "encoder_lstm = LSTM(latent_dim, return_state=True)\n",
        "encoder_outputs, state_h, state_c = encoder_lstm(encoder_embedding)\n",
        "encoder_states = [state_h, state_c]"
      ],
      "metadata": {
        "id": "wgDkJWYk-CpZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Definición del Decodificador del Modelo Seq2Seq\n",
        "\n",
        "Implementa la parte del decodificador en el modelo Seq2Seq utilizando una capa LSTM y una capa densa para generar la predicción de la secuencia de salida.\n",
        "\n",
        "El decodificador genera la secuencia de salida palabra por palabra a partir del contexto proporcionado por el codificador:\n",
        "\n",
        "**decoder_inputs:** Define la entrada para el decodificador, que en este caso es una sola palabra a la vez, con forma (1,).\n",
        "decoder_embedding: La capa de Embedding convierte las palabras de entrada en vectores densos, con la misma dimensión latente (latent_dim) que el codificador.\n",
        "\n",
        "**decoder_lstm:** Una capa LSTM que toma como entrada los vectores de palabras y utiliza los estados del codificador (encoder_states) como el estado inicial para generar la secuencia de salida. Esta capa devuelve la secuencia completa (return_sequences=True) y los estados internos (return_state=True).\n",
        "\n",
        "**decoder_dense:** Una capa densa con activación softmax para predecir la siguiente palabra en la secuencia de salida, generando una distribución de probabilidad sobre el vocabulario de salida (total_words)."
      ],
      "metadata": {
        "id": "fudLMzrvPpjX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Decodificador\n",
        "decoder_inputs = Input(shape=(1,))  # Usaremos una sola palabra a la vez\n",
        "decoder_embedding = Embedding(input_dim=total_words, output_dim=latent_dim)(decoder_inputs)\n",
        "decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)\n",
        "decoder_outputs, _, _ = decoder_lstm(decoder_embedding, initial_state=encoder_states)\n",
        "decoder_dense = Dense(total_words, activation=\"softmax\")\n",
        "decoder_outputs = decoder_dense(decoder_outputs)"
      ],
      "metadata": {
        "id": "GHFZ3Vdx-GKB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Definición y Compilación del Modelo Seq2Seq\n",
        "\n",
        "El objetivo Crear y compilar el modelo Seq2Seq que utiliza las entradas del codificador y el decodificador para predecir la secuencia de salida.\n",
        "\n",
        "\n",
        "**model** = Model([encoder_inputs, decoder_inputs], decoder_outputs): Se define el modelo como una instancia de la clase Model, que toma dos entradas: las del codificador (encoder_inputs) y las del decodificador (decoder_inputs), y tiene como salida las predicciones generadas por el decodificador (decoder_outputs).\n",
        "\n",
        "**model.compile**(loss=\"categorical_crossentropy\", optimizer=\"adam\"): El modelo se compila utilizando la función de pérdida categorical_crossentropy, adecuada para tareas de clasificación multiclase como la predicción de palabras en secuencias, y el optimizador adam para ajustar los pesos del modelo durante el entrenamiento."
      ],
      "metadata": {
        "id": "oeLgpt_wP7fM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Modelo\n",
        "model = Model([encoder_inputs, decoder_inputs], decoder_outputs)\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\")"
      ],
      "metadata": {
        "id": "bRAZFO5H-Jcf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Entrenamiento del Modelo Seq2Seq\n",
        "\n",
        "Se Entrena el modelo Seq2Seq utilizando las secuencias de entrada y salida con un enfoque de optimización para mejorar la precisión del modelo.\n",
        "\n",
        "model.fit([input_sequences, output_words], output_words_one_hot, epochs=200, batch_size=32): El modelo se entrena utilizando el método fit, donde:\n",
        "\n",
        "**[input_sequences, output_words] **son las secuencias de entrada y las palabras de salida correspondientes.\n",
        "\n",
        "**output_words_one_hot** es la representación one-hot de las palabras de salida.\n",
        "\n",
        "**epochs**=200 especifica que el modelo pasará por 200 iteraciones completas del conjunto de datos durante el entrenamiento.\n",
        "\n",
        "**batch_size**=32 define el tamaño del lote, es decir, cuántas muestras se procesan antes de actualizar los pesos del modelo."
      ],
      "metadata": {
        "id": "4IdBHm9dQMai"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Entrenamiento del modelo\n",
        "model.fit([input_sequences, output_words], output_words_one_hot, epochs=200, batch_size=32)  # Aumentar épocas para mejorar el entrenamiento"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Ju1Lu9O-NdG",
        "outputId": "f0661201-9f31-414f-e34b-c3d40d041f62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - loss: 5.6093\n",
            "Epoch 2/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 5.4811\n",
            "Epoch 3/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 5.1156\n",
            "Epoch 4/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - loss: 5.0640\n",
            "Epoch 5/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 4.8778\n",
            "Epoch 6/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 4.7530\n",
            "Epoch 7/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 4.5431\n",
            "Epoch 8/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 4.2032\n",
            "Epoch 9/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 3.8988\n",
            "Epoch 10/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 3.6871\n",
            "Epoch 11/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 3.4348\n",
            "Epoch 12/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 3.1226\n",
            "Epoch 13/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.9968\n",
            "Epoch 14/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.7654\n",
            "Epoch 15/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.5899\n",
            "Epoch 16/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.5088\n",
            "Epoch 17/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.2390\n",
            "Epoch 18/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.2075\n",
            "Epoch 19/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.8960\n",
            "Epoch 20/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.7467\n",
            "Epoch 21/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6119\n",
            "Epoch 22/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.4653\n",
            "Epoch 23/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.3461\n",
            "Epoch 24/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.1826\n",
            "Epoch 25/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.0063\n",
            "Epoch 26/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.0281\n",
            "Epoch 27/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.8655\n",
            "Epoch 28/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.6807\n",
            "Epoch 29/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.6452\n",
            "Epoch 30/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.5931\n",
            "Epoch 31/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.4990\n",
            "Epoch 32/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.4423\n",
            "Epoch 33/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.3776\n",
            "Epoch 34/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.3552\n",
            "Epoch 35/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.2783\n",
            "Epoch 36/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.2638\n",
            "Epoch 37/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.2263\n",
            "Epoch 38/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.2038\n",
            "Epoch 39/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.1914\n",
            "Epoch 40/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 0.1604\n",
            "Epoch 41/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.1554\n",
            "Epoch 42/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 0.1324\n",
            "Epoch 43/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.1304\n",
            "Epoch 44/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.1188\n",
            "Epoch 45/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.1017\n",
            "Epoch 46/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.1024\n",
            "Epoch 47/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0866\n",
            "Epoch 48/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0846\n",
            "Epoch 49/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0816\n",
            "Epoch 50/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0758\n",
            "Epoch 51/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0705\n",
            "Epoch 52/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0654\n",
            "Epoch 53/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0627\n",
            "Epoch 54/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0602\n",
            "Epoch 55/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0557\n",
            "Epoch 56/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0547\n",
            "Epoch 57/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0521\n",
            "Epoch 58/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0477\n",
            "Epoch 59/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0465\n",
            "Epoch 60/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0446\n",
            "Epoch 61/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0448\n",
            "Epoch 62/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0408\n",
            "Epoch 63/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0392\n",
            "Epoch 64/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0377\n",
            "Epoch 65/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0371\n",
            "Epoch 66/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0339\n",
            "Epoch 67/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0345\n",
            "Epoch 68/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0323\n",
            "Epoch 69/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0319\n",
            "Epoch 70/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0320\n",
            "Epoch 71/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0284\n",
            "Epoch 72/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0285\n",
            "Epoch 73/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0268\n",
            "Epoch 74/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0281\n",
            "Epoch 75/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0252\n",
            "Epoch 76/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0249\n",
            "Epoch 77/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0238\n",
            "Epoch 78/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0234\n",
            "Epoch 79/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0228\n",
            "Epoch 80/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0210\n",
            "Epoch 81/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0214\n",
            "Epoch 82/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0209\n",
            "Epoch 83/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0195\n",
            "Epoch 84/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0196\n",
            "Epoch 85/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0194\n",
            "Epoch 86/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0181\n",
            "Epoch 87/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0183\n",
            "Epoch 88/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0179\n",
            "Epoch 89/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0168\n",
            "Epoch 90/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0170\n",
            "Epoch 91/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0169\n",
            "Epoch 92/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0162\n",
            "Epoch 93/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0155\n",
            "Epoch 94/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0154\n",
            "Epoch 95/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0150\n",
            "Epoch 96/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0143\n",
            "Epoch 97/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0141\n",
            "Epoch 98/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0137\n",
            "Epoch 99/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0136\n",
            "Epoch 100/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0136\n",
            "Epoch 101/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0132\n",
            "Epoch 102/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0125\n",
            "Epoch 103/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0130\n",
            "Epoch 104/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0119\n",
            "Epoch 105/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0114\n",
            "Epoch 106/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0110\n",
            "Epoch 107/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0115\n",
            "Epoch 108/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0113\n",
            "Epoch 109/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0110\n",
            "Epoch 110/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0108\n",
            "Epoch 111/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0103\n",
            "Epoch 112/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.0101\n",
            "Epoch 113/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0101\n",
            "Epoch 114/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0103\n",
            "Epoch 115/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0100\n",
            "Epoch 116/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0095\n",
            "Epoch 117/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0092\n",
            "Epoch 118/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - loss: 0.0091\n",
            "Epoch 119/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0085\n",
            "Epoch 120/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0091\n",
            "Epoch 121/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0083\n",
            "Epoch 122/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0086\n",
            "Epoch 123/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0083\n",
            "Epoch 124/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0080\n",
            "Epoch 125/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0083\n",
            "Epoch 126/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0076\n",
            "Epoch 127/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 0.0076\n",
            "Epoch 128/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 0.0077\n",
            "Epoch 129/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 0.0073\n",
            "Epoch 130/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 0.0073\n",
            "Epoch 131/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - loss: 0.0071\n",
            "Epoch 132/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0071\n",
            "Epoch 133/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0073\n",
            "Epoch 134/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0068\n",
            "Epoch 135/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0066\n",
            "Epoch 136/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0070\n",
            "Epoch 137/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0066\n",
            "Epoch 138/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0064\n",
            "Epoch 139/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0066\n",
            "Epoch 140/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0063\n",
            "Epoch 141/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0062\n",
            "Epoch 142/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0062\n",
            "Epoch 143/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0058\n",
            "Epoch 144/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0059\n",
            "Epoch 145/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0057\n",
            "Epoch 146/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0054\n",
            "Epoch 147/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0055\n",
            "Epoch 148/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0057\n",
            "Epoch 149/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0055\n",
            "Epoch 150/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0052\n",
            "Epoch 151/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0052\n",
            "Epoch 152/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0053\n",
            "Epoch 153/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0051\n",
            "Epoch 154/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0053\n",
            "Epoch 155/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0049\n",
            "Epoch 156/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0049\n",
            "Epoch 157/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0048\n",
            "Epoch 158/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0047\n",
            "Epoch 159/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0049\n",
            "Epoch 160/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0047\n",
            "Epoch 161/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0044\n",
            "Epoch 162/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0043\n",
            "Epoch 163/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0044\n",
            "Epoch 164/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0045\n",
            "Epoch 165/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0044\n",
            "Epoch 166/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0043\n",
            "Epoch 167/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0042\n",
            "Epoch 168/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0044\n",
            "Epoch 169/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0041\n",
            "Epoch 170/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0042\n",
            "Epoch 171/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0038\n",
            "Epoch 172/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0040\n",
            "Epoch 173/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 0.0040\n",
            "Epoch 174/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0039\n",
            "Epoch 175/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 0.0037\n",
            "Epoch 176/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 0.0039\n",
            "Epoch 177/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0038\n",
            "Epoch 178/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0036\n",
            "Epoch 179/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0037\n",
            "Epoch 180/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0035\n",
            "Epoch 181/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0035\n",
            "Epoch 182/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0035\n",
            "Epoch 183/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0035\n",
            "Epoch 184/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0034\n",
            "Epoch 185/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 0.0034\n",
            "Epoch 186/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0031\n",
            "Epoch 187/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0033\n",
            "Epoch 188/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0033\n",
            "Epoch 189/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0032\n",
            "Epoch 190/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0031\n",
            "Epoch 191/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0032\n",
            "Epoch 192/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0030\n",
            "Epoch 193/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 0.0030\n",
            "Epoch 194/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0031\n",
            "Epoch 195/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0030\n",
            "Epoch 196/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0028\n",
            "Epoch 197/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0029\n",
            "Epoch 198/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0028\n",
            "Epoch 199/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 0.0029\n",
            "Epoch 200/200\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0030\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7881eb464d60>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generación de Texto con Temperatura Controlada\n",
        "\n",
        "el objetivo es Generar texto secuencial utilizando un modelo Seq2Seq, controlando la creatividad del modelo con un parámetro de \"temperatura\".\n",
        "\n",
        "La función generar_texto_con_temperature tiene como objetivo predecir una secuencia de palabras (basada en seed_text) utilizando el modelo entrenado, ajustando la \"temperatura\" de la predicción para modificar la aleatoriedad del proceso:\n",
        "\n",
        "**seed_text:** El texto inicial que el modelo usará para generar el resto del texto.\n",
        "\n",
        "**next_words:** Número de palabras a generar a partir de la semilla.\n",
        "model: El modelo entrenado que se utilizará para predecir las palabras.\n",
        "\n",
        "**tokenizer:** Objeto que convierte palabras a secuencias y viceversa.\n",
        "\n",
        "**max_sequence_len:** Longitud máxima de las secuencias de entrada.\n",
        "\n",
        "**temperature:** Controla la \"creatividad\" en la generación. Un valor de temperatura más bajo (cerca de 0) hace que las predicciones sean más deterministas, mientras que valores altos (por ejemplo, 1.0 o superior) generan más aleatoriedad.\n",
        "\n",
        "El proceso se lleva a cabo en un bucle donde:\n",
        "\n",
        "El texto de entrada se tokeniza y ajusta con padding.\n",
        "Se realiza una predicción para la siguiente palabra utilizando el modelo.\n",
        "La probabilidad de la siguiente palabra se ajusta según la temperatura: una mayor temperatura suaviza las probabilidades, aumentando la diversidad de palabras elegidas.\n",
        "Finalmente, se selecciona la palabra más probable (ajustada por la temperatura) y se agrega a la secuencia de salida."
      ],
      "metadata": {
        "id": "x9yPD8PeQl0I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Función modificada para generar texto con temperatura\n",
        "def generar_texto_con_temperature(seed_text, next_words, model, tokenizer, max_sequence_len, temperature=1.0):\n",
        "    for _ in range(next_words):\n",
        "        # Tokenizar el texto de entrada\n",
        "        token_list = tokenizer.texts_to_sequences([seed_text])[0]\n",
        "        # Realizamos padding para asegurar que la entrada tenga el tamaño correcto\n",
        "        token_list = pad_sequences([token_list], maxlen=max_sequence_len, padding='pre')\n",
        "\n",
        "        # Predecir la siguiente palabra\n",
        "        predicted_probs = model.predict([token_list, np.zeros((token_list.shape[0], 1))])[0, -1, :]\n",
        "\n",
        "        # Ajustar las probabilidades con temperatura\n",
        "        predicted_probs = np.asarray(predicted_probs).astype('float64')\n",
        "        predicted_probs = np.exp(predicted_probs / temperature)\n",
        "        predicted_probs = predicted_probs / np.sum(predicted_probs)\n",
        "\n",
        "        # Obtener el índice de la palabra con la probabilidad más alta\n",
        "        predicted_word_index = np.random.choice(range(len(predicted_probs)), p=predicted_probs)\n",
        "\n",
        "        # Convertir el índice a palabra\n",
        "        predicted_word = tokenizer.index_word.get(predicted_word_index, \"\")\n",
        "\n",
        "        # Agregar la palabra predicha a la semilla\n",
        "        seed_text += \" \" + predicted_word\n",
        "\n",
        "    return seed_text"
      ],
      "metadata": {
        "id": "O6GvBu6t-SYr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generación de Texto con Temperatura Ajustada\n",
        "\n",
        "Se genera texto a partir de una semilla, controlando la creatividad mediante el parámetro de temperatura.\n",
        "\n",
        "La función **generar_texto_con_temperature** predice palabras a partir de un texto inicial (seed_text), ajustando la aleatoriedad con la temperatura. Un valor de temperatura más bajo genera texto más predecible, mientras que uno más alto lo hace más aleatorio. El resultado es un texto generado que se basa en la semilla proporcionada y las palabras predichas por el modelo.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "v1nflX60RUe3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Parámetros para la prueba\n",
        "seed_text = \"me refresquen la memoria\"\n",
        "next_words = 3  # Número de palabras a predecir\n",
        "temperature = 0.6  # Ajustar la temperatura\n",
        "\n",
        "# Generar texto con el modelo\n",
        "generated_text = generar_texto_con_temperature(seed_text, next_words, model, tokenizer, max_sequence_len, temperature)\n",
        "\n",
        "print(\"Texto generado:\", generated_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0jXv3W5X-Vm7",
        "outputId": "0cd85e80-d911-4fd1-e438-c04ba7db314b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
            "Texto generado: me refresquen la memoria cuando fierro llorar\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Segunda Generación de Texto con Temperatura Ajustada"
      ],
      "metadata": {
        "id": "ArS235DnRiTx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Parámetros para la prueba\n",
        "seed_text = \"pido a mi Dios\"\n",
        "next_words = 3  # Número de palabras a predecir\n",
        "temperature = 0.6  # Ajustar la temperatura\n",
        "\n",
        "# Generar texto con el modelo\n",
        "generated_text = generar_texto_con_temperature(seed_text, next_words, model, tokenizer, max_sequence_len, temperature)\n",
        "\n",
        "print(\"Texto generado:\", generated_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNIuDqyW-arY",
        "outputId": "f50307ef-8c13-426b-a99d-8cb1a8da1898"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "Texto generado: pido a mi Dios amor poniéndome escuchan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se introdujo la frase \"pido a mi Dios\" como texto inicial del Martín Fierro para generar predicciones con el modelo. Se ajustó la temperatura a 0.6, lo que modula la aleatoriedad en la generación de texto: un valor más bajo produce resultados más deterministas, mientras que uno más alto genera mayor diversidad en las predicciones. El parámetro next_words se fijó en 3, esperando que el modelo predijera tres palabras adicionales. Aunque los resultados reflejan algo del estilo de Martín Fierro, la coherencia del texto generado es limitada. Esto puede mejorar con ajustes en la temperatura o ampliando la cantidad y diversidad de los datos de entrada, lo que permitiría al modelo generar predicciones más coherentes y estilísticamente correctas."
      ],
      "metadata": {
        "id": "bZZyf2PIrn98"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "----\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "towa3wdgTTdM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## MODELO GPT2 MARTIN FIERRO\n",
        "\n",
        "El objetivo de este modelo es utilizar **GPT-2** para generar texto coherente y alineado con el estilo de *Martín Fierro*, completando fragmentos o creando nuevas estrofas inspiradas en la obra de **José Hernández**."
      ],
      "metadata": {
        "id": "e3XpX-DPTWp4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Carga del Modelo y Tokenizador GPT-2\n",
        "\n",
        "Este código carga el modelo de lenguaje GPT-2 y su tokenizador para generar texto:\n",
        "\n",
        "**GPT2LMHeadModel:** Es el modelo de lenguaje preentrenado GPT-2, utilizado para generar texto a partir de un prompt o fragmento inicial.\n",
        "\n",
        "**GPT2Tokenizer:** Convierte el texto en una secuencia de números que el modelo puede entender y luego convierte la salida del modelo en texto legible."
      ],
      "metadata": {
        "id": "vs6-0eswj_vq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
        "\n",
        "# Cargar el modelo y el tokenizador GPT-2\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
        "model = GPT2LMHeadModel.from_pretrained(\"gpt2\")"
      ],
      "metadata": {
        "id": "zBErQrIAjtwU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365,
          "referenced_widgets": [
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        "outputId": "71e967b2-b7ab-4def-d3b7-98f8707f9089"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Función para Generar Texto con GPT-2\n",
        "\n",
        "Esta función utiliza **GPT-2** para generar texto coherente a partir de un *prompt* inicial, permitiendo personalizar la cantidad de texto generado y varios parámetros que afectan la creatividad y coherencia de la salida.\n",
        "\n",
        "\n",
        "La función **generar_texto** toma un *prompt* y varios parámetros opcionales como el número de tokens generados (**max_tokens**), la temperatura (**temperature**), y los métodos de muestreo como **top_p** y **top_k**. Estos parámetros controlan la variabilidad y creatividad del texto generado. Luego, codifica el *prompt* y pasa los datos al modelo GPT-2, que genera el texto. Finalmente, decodifica la salida en formato legible y la devuelve.\n",
        "\n",
        "- **prompt**: Texto inicial a partir del cual se generará el contenido.\n",
        "- **max_tokens**: Número máximo de tokens (palabras o fragmentos) que se generarán.\n",
        "- **temperature**: Controla la aleatoriedad; valores más altos aumentan la variabilidad.\n",
        "- **top_p y top_k**: Controlan el muestreo del texto para hacer que el modelo elija las palabras más probables de manera más flexible o restringida.\n"
      ],
      "metadata": {
        "id": "N8onE7Z4klYC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Función para generar texto\n",
        "def generar_texto(prompt, max_tokens=50, temperature=0.5, top_p=0.7, top_k=40):\n",
        "    \"\"\"\n",
        "    Genera texto a partir de un prompt utilizando GPT-2.\n",
        "    \"\"\"\n",
        "    # Codificar el prompt y generar el texto\n",
        "    input_ids = tokenizer.encode(prompt, return_tensors='pt')\n",
        "\n",
        "    # Generación de texto con el modelo\n",
        "    output = model.generate(input_ids,\n",
        "                            max_new_tokens=max_tokens,  # Número de tokens generados\n",
        "                            do_sample=True,\n",
        "                            top_p=top_p,\n",
        "                            top_k=top_k,\n",
        "                            temperature=temperature)\n",
        "\n",
        "    # Decodificar la salida y devolver el texto generado\n",
        "    return tokenizer.decode(output[0], skip_special_tokens=True)"
      ],
      "metadata": {
        "id": "owVnvm0RkW-E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Función para Mostrar Texto Resumido y Generar Continuación con GPT-2\n",
        "\n",
        "Esta función extrae una porción de un archivo de texto, mostrando los primeros caracteres según un límite especificado, y luego usa **GPT-2** para generar una continuación coherente del texto, ajustada al número de palabras deseado.\n",
        "\n",
        "La función **mostrar_texto_resumido_y_generar** realiza dos tareas principales: primero, toma una porción del texto inicial especificada por el parámetro limite, y luego genera una continuación utilizando el modelo GPT-2.\n",
        "\n",
        "El número de palabras generadas se ajusta a través del parámetro palabras, el cual se convierte en tokens (conversión aproximada: 1 palabra ≈ 1.33 tokens). El texto generado se imprime después de mostrar el texto resumido.\n",
        "\n",
        "- **file_name**: Nombre del archivo de texto del cual se extraerá el contenido.\n",
        "- **limite**: Número de caracteres que se mostrarán como resumen del texto.\n",
        "- **palabras**: Número de palabras deseadas para la continuación generada por el modelo GPT-2.\n"
      ],
      "metadata": {
        "id": "qVrYgPBZlFNk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Función para mostrar el texto resumido y generar el resto con GPT-2\n",
        "def mostrar_texto_resumido_y_generar(file_name=\"texto_martin_fierro.txt\", limite=220, palabras=50):\n",
        "    \"\"\"\n",
        "    Esta función muestra una parte del texto extraído, limitado a un número de caracteres,\n",
        "    y luego usa el modelo GPT-2 para predecir el resto del texto, ajustado al número de palabras deseado.\n",
        "    \"\"\"\n",
        "    # Aproximadamente 1 palabra ≈ 1.33 tokens\n",
        "    max_tokens = int(palabras * 1.33)  # Convertir palabras a tokens aproximados\n",
        "\n",
        "    # Abro el archivo de texto extraído\n",
        "    with open(file_name, \"r\", encoding=\"utf-8\") as file:\n",
        "        texto = file.read()  # Leo todo el contenido del archivo\n",
        "\n",
        "    # Texto resumido (solo los primeros 'limite' caracteres)\n",
        "    texto_resumido = texto[:limite]\n",
        "\n",
        "    # Muestra solo los primeros 'limite' caracteres del texto\n",
        "    print(f\"\\nTexto resumido (primeros {limite} caracteres):\\n\")\n",
        "    print(texto_resumido)  # Muestra solo el texto resumido\n",
        "\n",
        "    # Generar la continuación del texto usando GPT-2\n",
        "    texto_completo = generar_texto(texto_resumido, max_tokens=max_tokens)\n",
        "\n",
        "    print(\"\\nTexto generado (continuación):\\n\")\n",
        "    print(texto_completo)  # Muestra la continuación generada"
      ],
      "metadata": {
        "id": "YyNTHUOqlDpE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejemplo de Uso: Generar Aproximadamente 50 Palabras\n",
        "\n",
        "Este ejemplo demuestra cómo utilizar la función **mostrar_texto_resumido_y_generar** para generar una continuación de texto a partir de un resumen, ajustado a aproximadamente 50 palabras.\n",
        "\n",
        "Se llama a la función **mostrar_texto_resumido_y_generar** con el parámetro **palabras=50**, lo que indica que se generará una continuación del texto con un número aproximado de 50 palabras. La función toma los primeros caracteres del texto y usa el modelo **GPT-2** para predecir el texto restante.\n"
      ],
      "metadata": {
        "id": "SIfbCYCClEfe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Llamada a la función mostrar_texto_resumido_y_generar\n",
        "# Esta llamada utilizará los valores predeterminados de los parámetros:\n",
        "# - file_name=\"texto_martin_fierro.txt\" (nombre del archivo de texto)\n",
        "# - limite=220 (limitación en el número de caracteres para mostrar como resumen)\n",
        "# - palabras=50 (valor predeterminado, generará aproximadamente 50 palabras de continuación)\n",
        "\n",
        "mostrar_texto_resumido_y_generar()  # Llama la función con los valores predeterminados"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "suIT-5XqlT2f",
        "outputId": "447cf7cf-cd85-4ce4-cdd2-7064f35c5c58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Texto resumido (primeros 220 caracteres):\n",
            "\n",
            "El Gaucho Martín Fierro\n",
            "\n",
            "Aquí me pongo a cantar\n",
            "al compás de la vigüela,\n",
            "que el hombre que lo desvela\n",
            "una pena estrordinaria,\n",
            "como la ave solitaria\n",
            "con el cantar se consuela.\n",
            "Pido a los santos del cielo\n",
            "que ayuden mi pen\n",
            "\n",
            "Texto generado (continuación):\n",
            "\n",
            "El Gaucho Martín Fierro\n",
            "\n",
            "Aquí me pongo a cantar\n",
            "al compás de la vigüela,\n",
            "que el hombre que lo desvela\n",
            "una pena estrordinaria,\n",
            "como la ave solitaria\n",
            "con el cantar se consuela.\n",
            "Pido a los santos del cielo\n",
            "que ayuden mi pena estrordinaria.\n",
            "\n",
            "Pido a los santos del cielo,\n",
            "\n",
            "que el hombre que lo desvela,\n",
            "\n",
            "como la ave solitaria con el cantar se consuela.\n",
            "\n",
            "Pido a los santos del cielo\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "El código utiliza un fragmento de 220 caracteres de \"Martín Fierro\" para generar un texto de aproximadamente 50 palabras con GPT-2, ajustando parámetros como temperature, top_p, y top_k para controlar la creatividad y coherencia. Los resultados muestran repetición de frases, lo que sugiere que los parámetros actuales no evitan la redundancia. Para mejorar, se podría aumentar la temperature y top_p para mayor diversidad y ajustar la longitud del fragmento o la cantidad de tokens para evitar repeticiones."
      ],
      "metadata": {
        "id": "tOJRljJyptPo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ANÁLISIS COMPARATIVO\n",
        "\n",
        "**Primer modelo (frase inicial y temperatura ajustada):** Este modelo genera texto a partir de una frase del Martín Fierro y ajusta la temperatura a 0.6. Aunque refleja parcialmente el estilo del autor, la coherencia es limitada. La predicción controlada de tres palabras (next_words = 3) no logra mantener la cohesión dentro del contexto del poema. La principal ventaja de este modelo es su simplicidad y control sobre las predicciones, pero su desventaja radica en la falta de coherencia, lo que dificulta la generación de textos más largos y cohesionados. Aumentar la temperatura y expandir los datos de entrada podría mejorar los resultados.\n",
        "\n",
        "**Segundo modelo (GPT-2 con fragmentos de 220 caracteres):** Este modelo genera textos más largos y permite mayor diversidad mediante parámetros como temperature, top_p y top_k. Sin embargo, presenta repetición de frases, lo que sugiere que los parámetros actuales no evitan la redundancia. Su ventaja es generar textos más completos y variados, pero la desventaja es la falta de coherencia en textos extendidos. Para mejorar, se podría ajustar mejor la temperature y los parámetros.\n"
      ],
      "metadata": {
        "id": "pfH_nNeMtWAu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EXPERIENCIA\n",
        "\n",
        "\n",
        "- En mi experiencia con el primer **modelo seq2seq**, encontré varios desafíos, especialmente en el proceso de preparación del texto. Tuve que descargar el PDF completo del Martín Fierro y realizar varios pasos previos al entrenamiento del modelo. Uno de los principales problemas fue eliminar los números romanos y el nombre \"Página X\" que aparecían en cada hoja del texto. Para esto, utilicé expresiones regulares, como **texto = re.sub(r'Página\\s+\\d+', '', texto)** para eliminar la referencia a las páginas, y una expresión regular para los números romanos: **patron_romanos = r'\\b(I{1,3}|IV|V{0,2}I{0,3}|VI{0,2}I{0,3}|VII{0,2}I{0,3}|VIII{0,2}I{0,3}|IX|X{1,3}|XI|XII|XIII)\\b' texto = re.sub(patron_romanos, '', texto)**, lo que permitió limpiar el texto correctamente. En cuanto a la división del texto, me resultó útil establecer un límite de caracteres en la función mostrar_texto_resumido, lo que me permitió entrenar el modelo con fragmentos más manejables, con un límite de 2980 caracteres por fragmento.\n",
        "\n",
        "- Con respecto al modelo preentrenado de **GPT-2**, me pareció que el proceso era menos complejo en comparación con el modelo seq2seq, ya que requería menos pasos. Utilicé fragmentos de 220 caracteres para pasarle a GPT-2, lo que permitió generar textos más largos y con mayor diversidad. Sin embargo, al principio, el modelo generaba texto en inglés, lo cual era inesperado, probablemente debido a que el modelo no estaba específicamente entrenado para generar en español. Para corregir esto, tuve que ajustar el prompt de entrada y asegurarme de proporcionar contexto mas amplio para que el modelo generara texto en el idioma correcto.\n",
        "\n",
        "\n",
        "A lo largo de este proceso, me encontré con diversos obstáculos que retrasaban el avance del proyecto. En varios momentos, tuve que recurrir a material de clase o buscar ayuda en línea, para superar los errores que surgían. Sin embargo, cada uno de estos desafíos se convirtió en una valiosa oportunidad de aprendizaje.\n"
      ],
      "metadata": {
        "id": "ULjft1nLvvqS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RECURSOS\n",
        "\n",
        "## Recursos Utilizados 🛠️📚\n",
        "\n",
        "- **Clases de Procesamiento del Habla**: Se utilizaron recursos y ejemplos aprendidos en las clases para la generación de contenido.\n",
        "  \n",
        "- **Asistencia de ChatGPT**: Utilicé ChatGPT para obtener aclaraciones sobre conceptos técnicos y recomendaciones.\n",
        "\n",
        "## Prompts Utilizados ✍️\n",
        "\n",
        "- **Prompt**: \"Quiero eliminar las ocurrencias de 'Página' seguidas de un número en un documento. ¿Qué expresión regular puedo usar?\"\n",
        "  \n",
        "- **Prompt**: \"¿Cómo elimino números romanos del texto, especialmente de I a XIII?\"\n",
        "  \n",
        "- **Prompt**: \"¿Cómo funciona la temperatura en la generación de texto con un modelo de lenguaje?\"\n",
        "  \n",
        "- **Prompt**: \"¿Cómo puedo generar texto en un modelo de lenguaje con variación utilizando una función de temperatura?\"\n",
        "  \n",
        "- **Prompt**: \"¿Cómo puedo predecir la siguiente palabra en una secuencia de texto con un modelo LSTM?\"\n",
        "  \n",
        "- **Prompt**: \"¿Qué diferencia hay entre usar una temperatura alta y baja en la generación de texto?\"\n",
        "  \n",
        "- **Prompt**: \"Generame un ejemplo de como crear un modelo GPT-2 para generar texto?\"\n",
        "  \n",
        "- **Prompt**: \"¿Cómo puedo usar GPT-2 para generar el texto restante a partir de un resumen?\"\n",
        "  \n",
        "- **Prompt**: \"¿Cómo se convierte el número de palabras a tokens para alimentar el modelo GPT-2?\""
      ],
      "metadata": {
        "id": "C76EZO1W1cS3"
      }
    }
  ]
}