{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zct25DwXc_JI",
        "outputId": "b0d90ebf-7385-4c8d-85a5-186fd0f5b762"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Looks like you're using an outdated `kagglehub` version (installed: 0.3.7), please consider upgrading to the latest version (0.3.8).\n",
            "Downloading from https://www.kaggle.com/api/v1/datasets/download/sayanf/flickr8k?dataset_version_number=5...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1.04G/1.04G [00:19<00:00, 56.7MB/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting files...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Path to dataset files: /root/.cache/kagglehub/datasets/sayanf/flickr8k/versions/5\n"
          ]
        }
      ],
      "source": [
        "import kagglehub\n",
        "# Download latest version\n",
        "path = kagglehub.dataset_download(\"sayanf/flickr8k\")\n",
        "\n",
        "print(\"Path to dataset files:\", path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5LyMFUiWfjfD"
      },
      "outputs": [],
      "source": [
        "!mv  /root/.cache/kagglehub/datasets/sayanf/flickr8k/versions/5/ /content/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7vzGtSKRfj8i",
        "outputId": "d919f49c-c23b-42a2-ce02-39720cd55997"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5  sample_data\n"
          ]
        }
      ],
      "source": [
        "!dir"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fDSGN2lzfruP"
      },
      "outputs": [],
      "source": [
        "def readTextFile(file):\n",
        "  with open(file) as f:\n",
        "    captions = f.read()\n",
        "  return captions\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7UXrpXOiLWb",
        "outputId": "ea1524dc-0718-4d3e-ce81-71af3982aec5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CrowdFlowerAnnotations.txt  Flickr8k.lemma.token.txt  Flickr_8k.trainImages.txt\n",
            "ExpertAnnotations.txt\t    Flickr_8k.testImages.txt  readme.txt\n",
            "Flickr_8k.devImages.txt     Flickr8k.token.txt\n"
          ]
        }
      ],
      "source": [
        "!ls 5/Flickr8k_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "143IpoWkAvWB"
      },
      "outputs": [],
      "source": [
        "#large file so we split\n",
        "captions = readTextFile('5/Flickr8k_text/Flickr8k.token.txt')\n",
        "captions = captions.split(\"\\n\")[:-1] #last line is empty"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ih7IdtFhA9Ga",
        "outputId": "56fbfb29-9a17-423f-e919-d457353ace97"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'997722733_0cb5439472.jpg#3\\tA rock climber in a red shirt .'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "captions[-2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oVmlRWv2BaRr",
        "outputId": "528dc8e0-c03b-47e4-c17a-250925aa8f4b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['1000268201_693b08cb0e.jpg#0',\n",
              " 'A child in a pink dress is climbing up a set of stairs in an entry way .']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "captions[0].split('\\t')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCDOdJe8cyV1",
        "outputId": "3f39ab6d-96c6-4492-f72a-be6a0546458f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['1000268201_693b08cb0e.jpg#4',\n",
              " 'A little girl in a pink dress going into a wooden cabin .']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "captions[4].split('\\t')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qwSyawo3BC6c"
      },
      "outputs": [],
      "source": [
        "#Dictionary to map image with list of captions\n",
        "descriptions = {}\n",
        "for x in captions:\n",
        "  image, caption = x.split('\\t')\n",
        "  image = image.split(\".\")[0]\n",
        "  if image not in descriptions:\n",
        "    descriptions[image] = []\n",
        "  descriptions[image].append(caption)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zo2QwfSsaoa4"
      },
      "source": [
        "# Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q942UcAOa-R4"
      },
      "outputs": [],
      "source": [
        "import regex as re"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B6iIhMEyaqAV"
      },
      "outputs": [],
      "source": [
        "def clean_text(sentence):\n",
        "  sentence = sentence.lower()\n",
        "  sentence = re.sub(\"[^a-z]+\",\" \",sentence)\n",
        "  sentence = sentence.split()\n",
        "  sentence = [s for s in sentence if len(s) > 1]\n",
        "  sentence = \" \".join(sentence)\n",
        "  return sentence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "OotYRWKkbCfn",
        "outputId": "c211a6a4-32a8-4a28-8cc3-c2ef48014ea5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cat is fucking over house'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "clean_text(\"A cat is fucking over house # 5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_6_pREdabFnb"
      },
      "outputs": [],
      "source": [
        "#Clean captions\n",
        "for key,caption_list in descriptions.items():\n",
        "  for i in range(len(caption_list)):\n",
        "    caption_list[i] = clean_text(caption_list[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JaITgJ9tjLTA"
      },
      "outputs": [],
      "source": [
        "# Write the data to text file\n",
        "with open(\"descriptions_1.txt\",\"w\") as f:\n",
        "    f.write(str(descriptions))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aky8JVMUjTgY"
      },
      "source": [
        "# Vocab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U_QCqrAQ1CEi"
      },
      "outputs": [],
      "source": [
        "import json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MJowvLJwjQeZ"
      },
      "outputs": [],
      "source": [
        "descriptions = None\n",
        "with open(\"descriptions_1.txt\",'r') as f:\n",
        "  descriptions = f.read()\n",
        "json_acceptable_string = descriptions.replace(\"'\",\"\\\"\")\n",
        "descriptions = json.loads(json_acceptable_string)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cFbJJFeR06xj",
        "outputId": "09b9b056-5659-4d68-ff79-f4a4a2c5677d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'dict'>\n"
          ]
        }
      ],
      "source": [
        "print(type(descriptions))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NJSCno8J1Ehx",
        "outputId": "6b79e5f7-b1c2-453c-c5ec-90aa5c21e40b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vocab Size : 8424\n"
          ]
        }
      ],
      "source": [
        "# Vocab\n",
        "\n",
        "vocab = set()\n",
        "for key in descriptions.keys():\n",
        "    [vocab.update(sentence.split()) for sentence in descriptions[key]]#keep updating vocab , which is a set\n",
        "\n",
        "print(\"Vocab Size : %d\"% len(vocab))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0SihJdMu1LH9",
        "outputId": "2e7360c9-82f6-43b1-b6a3-6a69839285a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Words 373837\n"
          ]
        }
      ],
      "source": [
        "#total no of words across sentences\n",
        "total_words = []\n",
        "\n",
        "for key in descriptions.keys():\n",
        "  [total_words.append(i) for des in descriptions[key] for i in des.split()]\n",
        "\n",
        "print(\"Total Words %d\"% len(total_words))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Co1cmxOD1Q3A",
        "outputId": "114ab967-a902-4e8c-e3f3-3de3e018df25"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8424\n"
          ]
        }
      ],
      "source": [
        "import collections\n",
        "counter = collections.Counter(total_words)\n",
        "freq_cnt = dict(counter)\n",
        "print(len(freq_cnt.keys()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "tWJuotrs6rLC",
        "outputId": "6035bcb5-5010-4dc9-cc20-56e048c59c81"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'child': 1545,\n",
              " 'in': 18987,\n",
              " 'pink': 739,\n",
              " 'dress': 348,\n",
              " 'is': 9345,\n",
              " 'climbing': 507,\n",
              " 'up': 1302,\n",
              " 'set': 109,\n",
              " 'of': 6723,\n",
              " 'stairs': 109,\n",
              " 'an': 2432,\n",
              " 'entry': 1,\n",
              " 'way': 53,\n",
              " 'girl': 3328,\n",
              " 'going': 149,\n",
              " 'into': 1074,\n",
              " 'wooden': 284,\n",
              " 'building': 511,\n",
              " 'little': 1768,\n",
              " 'playhouse': 6,\n",
              " 'the': 18420,\n",
              " 'to': 3176,\n",
              " 'her': 1178,\n",
              " 'cabin': 4,\n",
              " 'black': 3848,\n",
              " 'dog': 8138,\n",
              " 'and': 8863,\n",
              " 'spotted': 38,\n",
              " 'are': 3505,\n",
              " 'fighting': 133,\n",
              " 'tri': 14,\n",
              " 'colored': 221,\n",
              " 'playing': 2008,\n",
              " 'with': 7765,\n",
              " 'each': 430,\n",
              " 'other': 773,\n",
              " 'on': 10746,\n",
              " 'road': 398,\n",
              " 'white': 3959,\n",
              " 'brown': 2578,\n",
              " 'spots': 29,\n",
              " 'staring': 57,\n",
              " 'at': 2916,\n",
              " 'street': 944,\n",
              " 'two': 5643,\n",
              " 'dogs': 2125,\n",
              " 'different': 46,\n",
              " 'breeds': 5,\n",
              " 'looking': 744,\n",
              " 'pavement': 48,\n",
              " 'moving': 41,\n",
              " 'toward': 146,\n",
              " 'covered': 372,\n",
              " 'paint': 62,\n",
              " 'sits': 577,\n",
              " 'front': 1386,\n",
              " 'painted': 64,\n",
              " 'rainbow': 22,\n",
              " 'hands': 246,\n",
              " 'bowl': 30,\n",
              " 'sitting': 1368,\n",
              " 'large': 1237,\n",
              " 'small': 1278,\n",
              " 'grass': 1622,\n",
              " 'plays': 526,\n",
              " 'fingerpaints': 3,\n",
              " 'canvas': 6,\n",
              " 'it': 401,\n",
              " 'there': 304,\n",
              " 'pigtails': 14,\n",
              " 'painting': 43,\n",
              " 'young': 2630,\n",
              " 'outside': 791,\n",
              " 'man': 7275,\n",
              " 'lays': 56,\n",
              " 'bench': 375,\n",
              " 'while': 1968,\n",
              " 'his': 2357,\n",
              " 'by': 1249,\n",
              " 'him': 403,\n",
              " 'which': 51,\n",
              " 'also': 20,\n",
              " 'tied': 15,\n",
              " 'sleeping': 60,\n",
              " 'next': 749,\n",
              " 'shirtless': 104,\n",
              " 'lies': 43,\n",
              " 'park': 508,\n",
              " 'laying': 189,\n",
              " 'holding': 1324,\n",
              " 'leash': 131,\n",
              " 'ground': 357,\n",
              " 'orange': 745,\n",
              " 'hat': 682,\n",
              " 'starring': 8,\n",
              " 'something': 346,\n",
              " 'wears': 115,\n",
              " 'glasses': 206,\n",
              " 'gauges': 2,\n",
              " 'wearing': 3062,\n",
              " 'blitz': 1,\n",
              " 'beer': 45,\n",
              " 'can': 39,\n",
              " 'crocheted': 1,\n",
              " 'pierced': 6,\n",
              " 'ears': 69,\n",
              " 'rope': 251,\n",
              " 'net': 58,\n",
              " 'red': 2691,\n",
              " 'roping': 2,\n",
              " 'climbs': 201,\n",
              " 'bridge': 141,\n",
              " 'grips': 2,\n",
              " 'onto': 211,\n",
              " 'ropes': 38,\n",
              " 'playground': 201,\n",
              " 'running': 2073,\n",
              " 'grassy': 474,\n",
              " 'garden': 54,\n",
              " 'surrounded': 178,\n",
              " 'fence': 340,\n",
              " 'through': 2032,\n",
              " 'boston': 9,\n",
              " 'terrier': 31,\n",
              " 'lush': 8,\n",
              " 'green': 1234,\n",
              " 'runs': 925,\n",
              " 'near': 1026,\n",
              " 'shakes': 37,\n",
              " 'its': 925,\n",
              " 'head': 377,\n",
              " 'shore': 170,\n",
              " 'ball': 1783,\n",
              " 'edge': 170,\n",
              " 'beach': 1046,\n",
              " 'feet': 87,\n",
              " 'stands': 869,\n",
              " 'shaking': 71,\n",
              " 'off': 766,\n",
              " 'water': 2790,\n",
              " 'standing': 1789,\n",
              " 'turned': 20,\n",
              " 'one': 1223,\n",
              " 'side': 306,\n",
              " 'boy': 3581,\n",
              " 'smiles': 192,\n",
              " 'stony': 3,\n",
              " 'wall': 557,\n",
              " 'city': 319,\n",
              " 'overalls': 24,\n",
              " 'working': 24,\n",
              " 'stone': 128,\n",
              " 'aross': 1,\n",
              " 'walking': 1165,\n",
              " 'paved': 43,\n",
              " 'metal': 115,\n",
              " 'pole': 157,\n",
              " 'behind': 633,\n",
              " 'smiling': 464,\n",
              " 'shirt': 1962,\n",
              " 'blue': 2279,\n",
              " 'jeans': 225,\n",
              " 'rock': 759,\n",
              " 'leaps': 204,\n",
              " 'over': 1415,\n",
              " 'log': 55,\n",
              " 'grey': 249,\n",
              " 'leaping': 138,\n",
              " 'fallen': 70,\n",
              " 'tree': 427,\n",
              " 'mottled': 2,\n",
              " 'collar': 198,\n",
              " 'jumping': 1473,\n",
              " 'jumped': 33,\n",
              " 'stump': 28,\n",
              " 'snow': 1547,\n",
              " 'field': 1283,\n",
              " 'surface': 65,\n",
              " 'displaying': 5,\n",
              " 'pictures': 67,\n",
              " 'skier': 179,\n",
              " 'skis': 82,\n",
              " 'past': 148,\n",
              " 'another': 956,\n",
              " 'paintings': 5,\n",
              " 'person': 1542,\n",
              " 'framed': 7,\n",
              " 'looks': 512,\n",
              " 'trees': 261,\n",
              " 'artwork': 4,\n",
              " 'for': 950,\n",
              " 'sale': 7,\n",
              " 'collage': 2,\n",
              " 'cliff': 195,\n",
              " 'group': 1218,\n",
              " 'people': 2887,\n",
              " 'belays': 1,\n",
              " 'seven': 31,\n",
              " 'climbers': 27,\n",
              " 'ascending': 8,\n",
              " 'face': 486,\n",
              " 'whilst': 152,\n",
              " 'several': 314,\n",
              " 'row': 34,\n",
              " 'watches': 276,\n",
              " 'holds': 471,\n",
              " 'line': 152,\n",
              " 'chases': 99,\n",
              " 'from': 920,\n",
              " 'sprinkler': 53,\n",
              " 'lawn': 117,\n",
              " 'hose': 45,\n",
              " 'away': 170,\n",
              " 'prepares': 50,\n",
              " 'catch': 368,\n",
              " 'thrown': 45,\n",
              " 'object': 155,\n",
              " 'nearby': 153,\n",
              " 'cars': 65,\n",
              " 'about': 152,\n",
              " 'yellow': 1217,\n",
              " 'mouth': 989,\n",
              " 'toy': 582,\n",
              " 'ready': 125,\n",
              " 'flying': 174,\n",
              " 'air': 1062,\n",
              " 'after': 184,\n",
              " 'get': 107,\n",
              " 'jumps': 979,\n",
              " 'towards': 247,\n",
              " 'trying': 163,\n",
              " 'midair': 210,\n",
              " 'woman': 3403,\n",
              " 'waters': 29,\n",
              " 'big': 280,\n",
              " 'lake': 332,\n",
              " 'lone': 82,\n",
              " 'duck': 37,\n",
              " 'swimming': 446,\n",
              " 'around': 648,\n",
              " 'watching': 251,\n",
              " 'waves': 142,\n",
              " 'hand': 349,\n",
              " 'facing': 55,\n",
              " 'skyline': 15,\n",
              " 'couple': 248,\n",
              " 'infant': 15,\n",
              " 'being': 338,\n",
              " 'held': 66,\n",
              " 'male': 114,\n",
              " 'pond': 96,\n",
              " 'stroller': 28,\n",
              " 'sit': 360,\n",
              " 'baby': 419,\n",
              " 'their': 693,\n",
              " 'newborn': 8,\n",
              " 'under': 246,\n",
              " 'care': 3,\n",
              " 'along': 527,\n",
              " 'body': 192,\n",
              " 'outdoors': 175,\n",
              " 'surf': 78,\n",
              " 'lab': 24,\n",
              " 'tags': 7,\n",
              " 'frolicks': 2,\n",
              " 'splashes': 71,\n",
              " 'this': 142,\n",
              " 'splashing': 129,\n",
              " 'drilling': 5,\n",
              " 'hole': 37,\n",
              " 'ice': 222,\n",
              " 'frozen': 26,\n",
              " 'men': 1121,\n",
              " 'fishing': 107,\n",
              " 'play': 747,\n",
              " 'making': 154,\n",
              " 'turn': 70,\n",
              " 'soft': 13,\n",
              " 'sand': 467,\n",
              " 'together': 413,\n",
              " 'tan': 394,\n",
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              " 'jogging': 26,\n",
              " 'headset': 5,\n",
              " 'walkman': 1,\n",
              " 'jogs': 13,\n",
              " 'headphones': 42,\n",
              " 'plant': 16,\n",
              " 'corner': 59,\n",
              " 'bicyclists': 24,\n",
              " 'intersection': 14,\n",
              " 'bikers': 31,\n",
              " 'stop': 36,\n",
              " 'town': 24,\n",
              " 'without': 28,\n",
              " 'guiding': 5,\n",
              " 'wagon': 28,\n",
              " 'escorts': 1,\n",
              " 'leading': 21,\n",
              " 'drawn': 4,\n",
              " 'shetland': 1,\n",
              " 'hits': 49,\n",
              " 'tee': 7,\n",
              " 'practices': 10,\n",
              " 'hitting': 38,\n",
              " 'baseball': 298,\n",
              " 'adult': 123,\n",
              " 'bats': 3,\n",
              " 'put': 26,\n",
              " 'batting': 7,\n",
              " ...}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "freq_cnt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G6UcXipl6sKy"
      },
      "outputs": [],
      "source": [
        "# Sort this dictionary according to the freq count reduce frequency for keeping the most used words\n",
        "sorted_freq_cnt = sorted(freq_cnt.items(),reverse=True,key=lambda x:x[1])\n",
        "\n",
        "# Filter\n",
        "threshold = 10\n",
        "sorted_freq_cnt  = [x for x in sorted_freq_cnt if x[1]>threshold]\n",
        "total_words = [x[0] for x in sorted_freq_cnt]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PDrVgcau68Ge",
        "outputId": "03d511c6-5a09-496f-b2ef-c24a864e4cd7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CrowdFlowerAnnotations.txt  Flickr8k.lemma.token.txt  Flickr_8k.trainImages.txt\n",
            "ExpertAnnotations.txt\t    Flickr_8k.testImages.txt  readme.txt\n",
            "Flickr_8k.devImages.txt     Flickr8k.token.txt\n"
          ]
        }
      ],
      "source": [
        "!dir 5/Flickr8k_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tG_hs3vR62V3"
      },
      "outputs": [],
      "source": [
        "train_file_data = readTextFile(\"5/Flickr8k_text/Flickr_8k.trainImages.txt\")\n",
        "test_file_data = readTextFile(\"5/Flickr8k_text/Flickr_8k.testImages.txt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7zdAdDeC65XN"
      },
      "outputs": [],
      "source": [
        "train = [row.split(\".\")[0] for row in train_file_data.split(\"\\n\")[:-1]]\n",
        "test = [row.split(\".\")[0] for row in test_file_data.split(\"\\n\")[:-1]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-TpHP5XqZ3Lh",
        "outputId": "b5986586-cd59-4008-dc47-3b6f9fa41f60"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['2513260012_03d33305cf',\n",
              " '2903617548_d3e38d7f88',\n",
              " '3338291921_fe7ae0c8f8',\n",
              " '488416045_1c6d903fe0',\n",
              " '2644326817_8f45080b87']"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "train[:5]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QaWy3Ef7Z4HZ"
      },
      "outputs": [],
      "source": [
        "#adding start and end token\n",
        "train_descriptions = {}\n",
        "for img_id in train:\n",
        "  train_descriptions[img_id] = []\n",
        "  for cap in descriptions[img_id]:\n",
        "    cap_to_append = \"startseq \" + cap + \" endseq\"\n",
        "    train_descriptions[img_id].append(cap_to_append)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2-tIXZvaOpB",
        "outputId": "958184cd-cf06-46a7-999e-15cb1ce98c60"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['startseq child in pink dress is climbing up set of stairs in an entry way endseq',\n",
              " 'startseq girl going into wooden building endseq',\n",
              " 'startseq little girl climbing into wooden playhouse endseq',\n",
              " 'startseq little girl climbing the stairs to her playhouse endseq',\n",
              " 'startseq little girl in pink dress going into wooden cabin endseq']"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "train_descriptions[\"1000268201_693b08cb0e\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qp9TvbFae2fD"
      },
      "source": [
        "# Image Feature extraction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aDuDqZxZfAQF"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input\n",
        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
        "from tensorflow.keras.models import Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hkocDRrkaPBN",
        "outputId": "9aace2ed-0902-4ac2-a55f-7f7570a1e584"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5\n",
            "\u001b[1m102967424/102967424\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
          ]
        }
      ],
      "source": [
        "model = ResNet50(weights = \"imagenet\",input_shape = (224,224,3))\n",
        "#model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U0VgKfHTfD72"
      },
      "outputs": [],
      "source": [
        "model_new = Model(model.input,model.layers[-2].output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mU5HI5xxfs0-"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n7qzMeb8faaU"
      },
      "outputs": [],
      "source": [
        "def preprocess_img(img):\n",
        "  img = load_img(img,target_size=(224,224,3))\n",
        "  img = img_to_array(img)\n",
        "  img = np.expand_dims(img,axis = 0)\n",
        "  img = preprocess_input(img)\n",
        "  return img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p8XaI0dsf8jE"
      },
      "outputs": [],
      "source": [
        "IMG_PATH ='/content/5/Flickr8k_Dataset/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h02O2mNA4xpA"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Ykhr4RI5ftqV",
        "outputId": "b25ec7b6-f50c-420b-df94-3fe1157d2624"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-123.68..151.061].\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "img = preprocess_img(IMG_PATH+\"1000268201_693b08cb0e.jpg\")\n",
        "plt.imshow(img[0])\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "onBaPE6UfzRV"
      },
      "outputs": [],
      "source": [
        "def encode_image(img):\n",
        "    img = preprocess_img(img)\n",
        "    feature_vector = model_new.predict(img,verbose=0)\n",
        "\n",
        "    feature_vector = feature_vector.reshape((-1,))\n",
        "    #print(feature_vector.shape)\n",
        "    return feature_vector"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i9XEikY0gLEa",
        "outputId": "463707f6-247b-4260-8c2d-4e0cc88e7bf0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.06536549, 0.1678271 , 0.32518435, ..., 0.05111533, 0.32817906,\n",
              "       1.0043344 ], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "encode_image(IMG_PATH+\"1000268201_693b08cb0e.jpg\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "L-IQNeZx5Z3K",
        "outputId": "c71e637e-3993-4ecc-ab45-2ce3b9226122"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Encoding in Progress Time step 0 \n",
            "Encoding in Progress Time step 100 \n",
            "Encoding in Progress Time step 200 \n",
            "Encoding in Progress Time step 300 \n",
            "Encoding in Progress Time step 400 \n",
            "Encoding in Progress Time step 500 \n",
            "Encoding in Progress Time step 600 \n",
            "Encoding in Progress Time step 700 \n",
            "Encoding in Progress Time step 800 \n",
            "Encoding in Progress Time step 900 \n",
            "Encoding in Progress Time step 1000 \n",
            "Encoding in Progress Time step 1100 \n",
            "Encoding in Progress Time step 1200 \n",
            "Encoding in Progress Time step 1300 \n",
            "Encoding in Progress Time step 1400 \n",
            "Encoding in Progress Time step 1500 \n",
            "Encoding in Progress Time step 1600 \n",
            "Encoding in Progress Time step 1700 \n",
            "Encoding in Progress Time step 1800 \n",
            "Encoding in Progress Time step 1900 \n",
            "Encoding in Progress Time step 2000 \n",
            "Encoding in Progress Time step 2100 \n",
            "Encoding in Progress Time step 2200 \n",
            "Encoding in Progress Time step 2300 \n",
            "Encoding in Progress Time step 2400 \n",
            "Encoding in Progress Time step 2500 \n",
            "Encoding in Progress Time step 2600 \n",
            "Encoding in Progress Time step 2700 \n",
            "Encoding in Progress Time step 2800 \n",
            "Encoding in Progress Time step 2900 \n",
            "Encoding in Progress Time step 3000 \n",
            "Encoding in Progress Time step 3100 \n",
            "Encoding in Progress Time step 3200 \n",
            "Encoding in Progress Time step 3300 \n",
            "Encoding in Progress Time step 3400 \n",
            "Encoding in Progress Time step 3500 \n",
            "Encoding in Progress Time step 3600 \n",
            "Encoding in Progress Time step 3700 \n",
            "Encoding in Progress Time step 3800 \n",
            "Encoding in Progress Time step 3900 \n",
            "Encoding in Progress Time step 4000 \n",
            "Encoding in Progress Time step 4100 \n",
            "Encoding in Progress Time step 4200 \n",
            "Encoding in Progress Time step 4300 \n",
            "Encoding in Progress Time step 4400 \n",
            "Encoding in Progress Time step 4500 \n",
            "Encoding in Progress Time step 4600 \n",
            "Encoding in Progress Time step 4700 \n",
            "Encoding in Progress Time step 4800 \n",
            "Encoding in Progress Time step 4900 \n",
            "Encoding in Progress Time step 5000 \n",
            "Encoding in Progress Time step 5100 \n",
            "Encoding in Progress Time step 5200 \n",
            "Encoding in Progress Time step 5300 \n",
            "Encoding in Progress Time step 5400 \n",
            "Encoding in Progress Time step 5500 \n",
            "Encoding in Progress Time step 5600 \n",
            "Encoding in Progress Time step 5700 \n",
            "Encoding in Progress Time step 5800 \n",
            "Encoding in Progress Time step 5900 \n",
            "Total Time Taken : 2134.884364128113\n"
          ]
        }
      ],
      "source": [
        "from time import time\n",
        "start = time()\n",
        "encoding_train = {}\n",
        "#image_id -->feature_vector extracted from Resnet Image\n",
        "\n",
        "for ix,img_id in enumerate(train):\n",
        "    img_path = IMG_PATH+\"/\"+img_id+\".jpg\"\n",
        "    encoding_train[img_id] = encode_image(img_path)\n",
        "\n",
        "    if ix%100==0:\n",
        "        print(\"Encoding in Progress Time step %d \"%ix)\n",
        "\n",
        "end_t = time()\n",
        "print(\"Total Time Taken :\",end_t-start)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "96LaeADVnF9M"
      },
      "outputs": [],
      "source": [
        "!mkdir saved"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_86blFkOjBqF"
      },
      "outputs": [],
      "source": [
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E9eKSQRcgTK9"
      },
      "outputs": [],
      "source": [
        "# Store everything to the disk\n",
        "with open(\"saved/encoded_train_features.pkl\",\"wb\") as f:\n",
        "    pickle.dump(encoding_train,f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "UdphWcjVnHjb",
        "outputId": "03ebd554-326a-4b81-9ea3-306c7b4c3311"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Encoding in Progress Time step 0 \n",
            "Test Encoding in Progress Time step 100 \n",
            "Test Encoding in Progress Time step 200 \n",
            "Test Encoding in Progress Time step 300 \n",
            "Test Encoding in Progress Time step 400 \n",
            "Test Encoding in Progress Time step 500 \n",
            "Test Encoding in Progress Time step 600 \n",
            "Test Encoding in Progress Time step 700 \n",
            "Test Encoding in Progress Time step 800 \n",
            "Test Encoding in Progress Time step 900 \n",
            "Total Time Taken(test) : 355.45560336112976\n"
          ]
        }
      ],
      "source": [
        "start = time()\n",
        "encoding_test = {}\n",
        "#image_id -->feature_vector extracted from Resnet Image\n",
        "\n",
        "for ix,img_id in enumerate(test):\n",
        "    img_path = IMG_PATH+\"/\"+img_id+\".jpg\"\n",
        "    encoding_test[img_id] = encode_image(img_path)\n",
        "\n",
        "\n",
        "    if ix%100==0:\n",
        "        print(\"Test Encoding in Progress Time step %d \"%ix)\n",
        "\n",
        "end_t = time()\n",
        "print(\"Total Time Taken(test) :\",end_t-start)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K75N5Sf4gQjq"
      },
      "source": [
        "# Data Preprocessing for Captions\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "79YKX6ZEnRww"
      },
      "outputs": [],
      "source": [
        "#create both way mapping\n",
        "word_to_idx = {}\n",
        "idx_to_word = {}\n",
        "\n",
        "for i,word in enumerate(total_words):\n",
        "    word_to_idx[word] = i+1\n",
        "    idx_to_word[i+1] = word"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4hyA5oOfnZfa",
        "outputId": "d72609d2-286c-435e-ccca-aad133ab40f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vocab Size 1848\n"
          ]
        }
      ],
      "source": [
        "#eos and sos\n",
        "idx_to_word[1846] = 'startseq'\n",
        "word_to_idx['startseq'] = 1846\n",
        "\n",
        "idx_to_word[1847] = 'endseq'\n",
        "word_to_idx['endseq'] = 1847\n",
        "\n",
        "vocab_size = len(word_to_idx) + 1\n",
        "print(\"Vocab Size\",vocab_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VEpZdC39nhja",
        "outputId": "87be30f2-45fb-43cf-f787-b66b9075ee9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35\n"
          ]
        }
      ],
      "source": [
        "max_len = 0 #getting max length\n",
        "for key in train_descriptions.keys():\n",
        "    for cap in train_descriptions[key]:\n",
        "        max_len = max(max_len,len(cap.split()))\n",
        "\n",
        "print(max_len)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9y_NLLoinpFr"
      },
      "source": [
        "## Data Loader / Genearator\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nJSLBruwjLF6"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.utils import to_categorical"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RMrqLcfrnoZk"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "def data_generator(train_descriptions, encoding_train, word_to_idx, max_len, batch_size, vocab_size):\n",
        "    X1, X2, y = [], [], []\n",
        "\n",
        "    while True:\n",
        "        for key, desc_list in train_descriptions.items():\n",
        "            photo = encoding_train[key]\n",
        "\n",
        "            for desc in desc_list:\n",
        "                seq = [word_to_idx[word] for word in desc.split() if word in word_to_idx]\n",
        "\n",
        "                for i in range(1, len(seq)):\n",
        "                    xi = seq[:i]\n",
        "                    yi = seq[i]\n",
        "\n",
        "                    # Pad input sequence and convert output to one-hot encoding\n",
        "                    xi = pad_sequences([xi], maxlen=max_len, value=0, padding='post')[0]\n",
        "                    yi = to_categorical([yi], num_classes=vocab_size)[0]\n",
        "\n",
        "                    X1.append(photo)\n",
        "                    X2.append(xi)\n",
        "                    y.append(yi)\n",
        "\n",
        "                    # Yield a batch when the batch size is reached\n",
        "                    if len(X1) == batch_size:\n",
        "                        yield ([np.array(X1), np.array(X2)], np.array(y))\n",
        "                        X1, X2, y = [], [], []\n",
        "\n",
        "        # Handle the last batch if it's smaller than batch_size\n",
        "        if len(X1) > 0:\n",
        "            yield ([np.array(X1), np.array(X2)], np.array(y))\n",
        "            X1, X2, y = [], [], []"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import add\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, Dense, LSTM, Embedding, Dropout"
      ],
      "metadata": {
        "id": "elZC3Otj5hgV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iGZMwBoont0D"
      },
      "source": [
        "# Word Embedding\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p03snDFghV-R",
        "outputId": "515f725d-b239-40e7-f3cf-c1c30974c3bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2025-02-19 20:32:55--  http://nlp.stanford.edu/data/glove.6B.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://nlp.stanford.edu/data/glove.6B.zip [following]\n",
            "--2025-02-19 20:32:55--  https://nlp.stanford.edu/data/glove.6B.zip\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://downloads.cs.stanford.edu/nlp/data/glove.6B.zip [following]\n",
            "--2025-02-19 20:32:55--  https://downloads.cs.stanford.edu/nlp/data/glove.6B.zip\n",
            "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
            "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 862182613 (822M) [application/zip]\n",
            "Saving to: ‘glove.6B.zip’\n",
            "\n",
            "glove.6B.zip        100%[===================>] 822.24M  4.99MB/s    in 2m 39s  \n",
            "\n",
            "2025-02-19 20:35:34 (5.17 MB/s) - ‘glove.6B.zip’ saved [862182613/862182613]\n",
            "\n",
            "Archive:  glove.6B.zip\n",
            "  inflating: glove.6B.50d.txt        \n",
            "  inflating: glove.6B.100d.txt       \n",
            "  inflating: glove.6B.200d.txt       \n",
            "  inflating: glove.6B.300d.txt       \n"
          ]
        }
      ],
      "source": [
        "# prompt: download glove 6B 50d\n",
        "\n",
        "!wget http://nlp.stanford.edu/data/glove.6B.zip\n",
        "!unzip glove.6B.zip\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JWYDT_oBnvXB"
      },
      "outputs": [],
      "source": [
        "f = open(\"./glove.6B.50d.txt\",encoding='utf8')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ftpPXvSopHgD"
      },
      "outputs": [],
      "source": [
        "embedding_index = {}\n",
        "#making embedding mapping\n",
        "for line in f:\n",
        "    values = line.split()\n",
        "\n",
        "    word = values[0]\n",
        "    word_embedding = np.array(values[1:],dtype='float')\n",
        "    embedding_index[word] = word_embedding\n",
        "\n",
        "f.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wxBLMyoOpT-V",
        "outputId": "ca552c53-8b6d-42ed-aacd-6b6bae856f1e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.52042 , -0.8314  ,  0.49961 ,  1.2893  ,  0.1151  ,  0.057521,\n",
              "       -1.3753  , -0.97313 ,  0.18346 ,  0.47672 , -0.15112 ,  0.35532 ,\n",
              "        0.25912 , -0.77857 ,  0.52181 ,  0.47695 , -1.4251  ,  0.858   ,\n",
              "        0.59821 , -1.0903  ,  0.33574 , -0.60891 ,  0.41742 ,  0.21569 ,\n",
              "       -0.07417 , -0.5822  , -0.4502  ,  0.17253 ,  0.16448 , -0.38413 ,\n",
              "        2.3283  , -0.66682 , -0.58181 ,  0.74389 ,  0.095015, -0.47865 ,\n",
              "       -0.84591 ,  0.38704 ,  0.23693 , -1.5523  ,  0.64802 , -0.16521 ,\n",
              "       -1.4719  , -0.16224 ,  0.79857 ,  0.97391 ,  0.40027 , -0.21912 ,\n",
              "       -0.30938 ,  0.26581 ])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "embedding_index['apple']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bsOR7RmhpZqL",
        "outputId": "31861343-5177-4c79-8c3d-8e2c05376faf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1848, 50)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "#making embedding matrix\n",
        "def get_embedding_matrix():\n",
        "    emb_dim = 50\n",
        "    matrix = np.zeros((vocab_size,emb_dim))\n",
        "    for word,idx in word_to_idx.items():\n",
        "        embedding_vector = embedding_index.get(word)\n",
        "\n",
        "        if embedding_vector is not None:\n",
        "            matrix[idx] = embedding_vector\n",
        "\n",
        "    return matrix\n",
        "\n",
        "embedding_matrix = get_embedding_matrix()\n",
        "embedding_matrix.shape\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xbsxP4NYeiLx"
      },
      "source": [
        "# Model Architecture\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VvWkOHf85g2P"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import Model,load_model\n",
        "from tensorflow.keras.layers import Input,Dense,Dropout,Embedding,LSTM\n",
        "from tensorflow.keras.layers import add"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5UPTx-x9ekeK"
      },
      "outputs": [],
      "source": [
        "input_img_features = Input(shape=(2048,))\n",
        "inp_img1 = Dropout(0.3)(input_img_features)\n",
        "inp_img2 = Dense(256,activation='relu')(inp_img1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "98X219Wxeoey"
      },
      "outputs": [],
      "source": [
        "# Captions as Input\n",
        "input_captions = Input(shape=(max_len,))\n",
        "inp_cap1 = Embedding(input_dim=vocab_size,output_dim=50,mask_zero=True)(input_captions)\n",
        "inp_cap2 = Dropout(0.3)(inp_cap1)\n",
        "inp_cap3 = LSTM(256)(inp_cap2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3o1PGmqleqGh"
      },
      "outputs": [],
      "source": [
        "decoder1 = add([inp_img2,inp_cap3])\n",
        "decoder2 = Dense(256,activation='relu')(decoder1)\n",
        "outputs = Dense(vocab_size,activation='softmax')(decoder2)\n",
        "\n",
        "# Combined Model\n",
        "model = Model(inputs=[input_img_features,input_captions],outputs=outputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "7Mov6DvGe5dD",
        "outputId": "71d35a50-b47c-4af4-8739-c90a00d32074"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional_2\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_2\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m       Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to          \u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_4             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m)             │              \u001b[38;5;34m0\u001b[0m │ -                      │\n",
              "│ (\u001b[38;5;33mInputLayer\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ input_layer_3             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)           │              \u001b[38;5;34m0\u001b[0m │ -                      │\n",
              "│ (\u001b[38;5;33mInputLayer\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m50\u001b[0m)         │         \u001b[38;5;34m92,400\u001b[0m │ input_layer_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)           │              \u001b[38;5;34m0\u001b[0m │ input_layer_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m, \u001b[38;5;34m50\u001b[0m)         │              \u001b[38;5;34m0\u001b[0m │ embedding_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ not_equal_1 (\u001b[38;5;33mNotEqual\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m)             │              \u001b[38;5;34m0\u001b[0m │ input_layer_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │        \u001b[38;5;34m524,544\u001b[0m │ dropout_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │        \u001b[38;5;34m314,368\u001b[0m │ dropout_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],       │\n",
              "│                           │                        │                │ not_equal_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ add_1 (\u001b[38;5;33mAdd\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │              \u001b[38;5;34m0\u001b[0m │ dense_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],         │\n",
              "│                           │                        │                │ lstm_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │         \u001b[38;5;34m65,792\u001b[0m │ add_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1848\u001b[0m)           │        \u001b[38;5;34m474,936\u001b[0m │ dense_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)              </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">        Param # </span>┃<span style=\"font-weight: bold\"> Connected to           </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_4             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">35</span>)             │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                      │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ input_layer_3             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)           │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                      │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ embedding_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">35</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">92,400</span> │ input_layer_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)           │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ input_layer_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">35</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)         │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ embedding_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ not_equal_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">NotEqual</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">35</span>)             │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ input_layer_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">524,544</span> │ dropout_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">314,368</span> │ dropout_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],       │\n",
              "│                           │                        │                │ not_equal_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ add_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],         │\n",
              "│                           │                        │                │ lstm_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]           │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">65,792</span> │ add_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1848</span>)           │        <span style=\"color: #00af00; text-decoration-color: #00af00\">474,936</span> │ dense_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,472,040\u001b[0m (5.62 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,472,040</span> (5.62 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,472,040\u001b[0m (5.62 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,472,040</span> (5.62 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xXibVcIWpoao"
      },
      "outputs": [],
      "source": [
        "# Important Thing - Embedding Layer\n",
        "model.layers[2].set_weights([embedding_matrix])\n",
        "model.layers[2].trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r9SEItJvqTvy"
      },
      "outputs": [],
      "source": [
        "model.compile(loss='categorical_crossentropy',optimizer=\"adam\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "--EOaZ2NqYrm"
      },
      "source": [
        "## Training model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lNVXnK4TAgxr"
      },
      "outputs": [],
      "source": [
        "vocab_size = len(word_to_idx) + 1  # Includes padding token at 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8iizdgwWqTuF"
      },
      "outputs": [],
      "source": [
        "epochs = 20\n",
        "batch_size = 3\n",
        "steps = len(train_descriptions)//20"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2FIWejbGAW-P"
      },
      "outputs": [],
      "source": [
        "total_samples = sum(len(desc.split()) - 1 for key in train_descriptions for desc in train_descriptions[key])\n",
        "steps_per_epoch = total_samples // batch_size"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def data_generator(train_descriptions, encoding_train, word_to_idx, max_len, batch_size, vocab_size):\n",
        "    X1, X2, y = [], [], []\n",
        "\n",
        "    while True:\n",
        "        for key, desc_list in train_descriptions.items():\n",
        "            photo = encoding_train[key]\n",
        "\n",
        "            for desc in desc_list:\n",
        "                seq = [word_to_idx[word] for word in desc.split() if word in word_to_idx]\n",
        "\n",
        "                for i in range(1, len(seq)):\n",
        "                    xi = seq[:i]\n",
        "                    yi = seq[i]\n",
        "\n",
        "                    # Pad input sequence and convert output to one-hot encoding\n",
        "                    xi = pad_sequences([xi], maxlen=max_len, value=0, padding='post')[0]\n",
        "                    yi = to_categorical([yi], num_classes=vocab_size)[0]\n",
        "\n",
        "                    X1.append(photo)\n",
        "                    X2.append(xi)\n",
        "                    y.append(yi)\n",
        "\n",
        "                    # Yield a batch when the batch size is reached\n",
        "                    if len(X1) == batch_size:\n",
        "                        yield (np.array(X1), np.array(X2), np.array(y))  # Yield as a tuple of arrays\n",
        "                        X1, X2, y = [], [], []\n",
        "\n",
        "        # Handle the last batch if it's smaller than batch_size\n",
        "        if len(X1) > 0:\n",
        "            yield (np.array(X1), np.array(X2), np.array(y))  # Yield as a tuple of arrays\n",
        "            X1, X2, y = [], [], []"
      ],
      "metadata": {
        "id": "DRAz6NE1A_Nt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "MGos5pGcBN5F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the output signature\n",
        "output_signature = (\n",
        "    tf.TensorSpec(shape=(None, encoding_train[key].shape[0]), dtype=tf.float32),  # X1\n",
        "    tf.TensorSpec(shape=(None, max_len), dtype=tf.int32),                         # X2\n",
        "    tf.TensorSpec(shape=(None, vocab_size), dtype=tf.float32)                     # y\n",
        ")\n",
        "\n",
        "# Create the dataset\n",
        "dataset = tf.data.Dataset.from_generator(\n",
        "    lambda: data_generator(train_descriptions, encoding_train, word_to_idx, max_len, batch_size, vocab_size),\n",
        "    output_signature=output_signature\n",
        ")\n",
        "\n",
        "# Batch the dataset (if not already batched in the generator)\n",
        "dataset = dataset.batch(batch_size)\n",
        "\n",
        "# Prefetch for better performance\n",
        "dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)"
      ],
      "metadata": {
        "id": "kDnnMRna-naC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "UFNBMVsFrooQ",
        "outputId": "db5ba34a-95c7-4c51-bd38-7f00a5e90c66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Layer \"functional_2\" expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor 'data:0' shape=(None, None, 2048) dtype=float32>]",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-101-5fd6a1b7085b>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_descriptions\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/layers/input_spec.py\u001b[0m in \u001b[0;36massert_input_compatibility\u001b[0;34m(input_spec, inputs, layer_name)\u001b[0m\n\u001b[1;32m    158\u001b[0m     \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_spec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m    161\u001b[0m             \u001b[0;34mf'Layer \"{layer_name}\" expects {len(input_spec)} input(s),'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m             \u001b[0;34mf\" but it received {len(inputs)} input tensors. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Layer \"functional_2\" expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor 'data:0' shape=(None, None, 2048) dtype=float32>]"
          ]
        }
      ],
      "source": [
        "# Train the model\n",
        "model.fit(dataset, epochs=10, steps_per_epoch=len(train_descriptions) // batch_size)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoding_train['2513260012_03d33305cf']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4wIt-W9_9JWz",
        "outputId": "1b2f19d5-1d14-4c9e-8216-135b74d51db2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.34708   , 0.5115914 , 0.08728845, ..., 1.1897297 , 0.0404386 ,\n",
              "       0.15271069], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "id": "7aL8dqllBUDs",
        "outputId": "547d836e-1407-42db-a4fa-d320aeb67da5"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'dataset' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-69-2da0ffaf5447>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-68-a7c4e65f723f>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'./model_weights/model_{i}.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined"
          ]
        }
      ],
      "source": [
        "train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HGBGDKNA58mg"
      },
      "outputs": [],
      "source": [
        "model = load_model('./model_weights/model_9.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1zcVwE9NBSq_"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}