generating_poetry_part_1.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "generating poetry part 1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "zX4Kg8DUTKWO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BOwsuGQQY9OL",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import numpy as np "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PRnDnCW-Z7qv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "d3fcf0dc-21d8-41df-96bc-10bbda8cd35e"
      },
      "source": [
        "tokenizer = Tokenizer()\n",
        "\n",
        "data=\"In the town of Athy one Jeremy Lanigan \\n Battered away til he hadnt a pound. \\nHis father died and made him a man again \\n Left him a farm and ten acres of ground. \\nHe gave a grand party for friends and relations \\nWho didnt forget him when come to the wall, \\nAnd if youll but listen Ill make your eyes glisten \\nOf the rows and the ructions of Lanigans Ball. \\nMyself to be sure got free invitation, \\nFor all the nice girls and boys I might ask, \\nAnd just in a minute both friends and relations \\nWere dancing round merry as bees round a cask. \\nJudy ODaly, that nice little milliner, \\nShe tipped me a wink for to give her a call, \\nAnd I soon arrived with Peggy McGilligan \\nJust in time for Lanigans Ball. \\nThere were lashings of punch and wine for the ladies, \\nPotatoes and cakes; there was bacon and tea, \\nThere were the Nolans, Dolans, OGradys \\nCourting the girls and dancing away. \\nSongs they went round as plenty as water, \\nThe harp that once sounded in Taras old hall,\\nSweet Nelly Gray and The Rat Catchers Daughter,\\nAll singing together at Lanigans Ball. \\nThey were doing all kinds of nonsensical polkas \\nAll round the room in a whirligig. \\nJulia and I, we banished their nonsense \\nAnd tipped them the twist of a reel and a jig. \\nAch mavrone, how the girls got all mad at me \\nDanced til youd think the ceiling would fall. \\nFor I spent three weeks at Brooks Academy \\nLearning new steps for Lanigans Ball. \\nThree long weeks I spent up in Dublin, \\nThree long weeks to learn nothing at all,\\n Three long weeks I spent up in Dublin, \\nLearning new steps for Lanigans Ball. \\nShe stepped out and I stepped in again, \\nI stepped out and she stepped in again, \\nShe stepped out and I stepped in again, \\nLearning new steps for Lanigans Ball. \\nBoys were all merry and the girls they were hearty \\nAnd danced all around in couples and groups, \\nTil an accident happened, young Terrance McCarthy \\nPut his right leg through miss Finnertys hoops. \\nPoor creature fainted and cried Meelia murther, \\nCalled for her brothers and gathered them all. \\nCarmody swore that hed go no further \\nTil he had satisfaction at Lanigans Ball. \\nIn the midst of the row miss Kerrigan fainted, \\nHer cheeks at the same time as red as a rose. \\nSome of the lads declared she was painted, \\nShe took a small drop too much, I suppose. \\nHer sweetheart, Ned Morgan, so powerful and able, \\nWhen he saw his fair colleen stretched out by the wall, \\nTore the left leg from under the table \\nAnd smashed all the Chaneys at Lanigans Ball. \\nBoys, oh boys, twas then there were runctions. \\nMyself got a lick from big Phelim McHugh. \\nI soon replied to his introduction \\nAnd kicked up a terrible hullabaloo. \\nOld Casey, the piper, was near being strangled. \\nThey squeezed up his pipes, bellows, chanters and all. \\nThe girls, in their ribbons, they got all entangled \\nAnd that put an end to Lanigans Ball.\"\n",
        "\n",
        "corpus = data.lower().split(\"\\n\")\n",
        "\n",
        "print(corpus)\n",
        "tokenizer.fit_on_texts(corpus)\n",
        "total_words = len(tokenizer.word_index) + 1\n",
        "\n",
        "print(tokenizer.word_index)\n",
        "print(total_words)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['in the town of athy one jeremy lanigan ', ' battered away til he hadnt a pound. ', 'his father died and made him a man again ', ' left him a farm and ten acres of ground. ', 'he gave a grand party for friends and relations ', 'who didnt forget him when come to the wall, ', 'and if youll but listen ill make your eyes glisten ', 'of the rows and the ructions of lanigans ball. ', 'myself to be sure got free invitation, ', 'for all the nice girls and boys i might ask, ', 'and just in a minute both friends and relations ', 'were dancing round merry as bees round a cask. ', 'judy odaly, that nice little milliner, ', 'she tipped me a wink for to give her a call, ', 'and i soon arrived with peggy mcgilligan ', 'just in time for lanigans ball. ', 'there were lashings of punch and wine for the ladies, ', 'potatoes and cakes; there was bacon and tea, ', 'there were the nolans, dolans, ogradys ', 'courting the girls and dancing away. ', 'songs they went round as plenty as water, ', 'the harp that once sounded in taras old hall,', 'sweet nelly gray and the rat catchers daughter,', 'all singing together at lanigans ball. ', 'they were doing all kinds of nonsensical polkas ', 'all round the room in a whirligig. ', 'julia and i, we banished their nonsense ', 'and tipped them the twist of a reel and a jig. ', 'ach mavrone, how the girls got all mad at me ', 'danced til youd think the ceiling would fall. ', 'for i spent three weeks at brooks academy ', 'learning new steps for lanigans ball. ', 'three long weeks i spent up in dublin, ', 'three long weeks to learn nothing at all,', ' three long weeks i spent up in dublin, ', 'learning new steps for lanigans ball. ', 'she stepped out and i stepped in again, ', 'i stepped out and she stepped in again, ', 'she stepped out and i stepped in again, ', 'learning new steps for lanigans ball. ', 'boys were all merry and the girls they were hearty ', 'and danced all around in couples and groups, ', 'til an accident happened, young terrance mccarthy ', 'put his right leg through miss finnertys hoops. ', 'poor creature fainted and cried meelia murther, ', 'called for her brothers and gathered them all. ', 'carmody swore that hed go no further ', 'til he had satisfaction at lanigans ball. ', 'in the midst of the row miss kerrigan fainted, ', 'her cheeks at the same time as red as a rose. ', 'some of the lads declared she was painted, ', 'she took a small drop too much, i suppose. ', 'her sweetheart, ned morgan, so powerful and able, ', 'when he saw his fair colleen stretched out by the wall, ', 'tore the left leg from under the table ', 'and smashed all the chaneys at lanigans ball. ', 'boys, oh boys, twas then there were runctions. ', 'myself got a lick from big phelim mchugh. ', 'i soon replied to his introduction ', 'and kicked up a terrible hullabaloo. ', 'old casey, the piper, was near being strangled. ', 'they squeezed up his pipes, bellows, chanters and all. ', 'the girls, in their ribbons, they got all entangled ', 'and that put an end to lanigans ball.']\n",
            "{'and': 1, 'the': 2, 'a': 3, 'in': 4, 'all': 5, 'i': 6, 'for': 7, 'of': 8, 'lanigans': 9, 'ball': 10, 'were': 11, 'at': 12, 'to': 13, 'she': 14, 'stepped': 15, 'his': 16, 'girls': 17, 'as': 18, 'they': 19, 'til': 20, 'he': 21, 'again': 22, 'got': 23, 'boys': 24, 'round': 25, 'that': 26, 'her': 27, 'there': 28, 'three': 29, 'weeks': 30, 'up': 31, 'out': 32, 'him': 33, 'was': 34, 'spent': 35, 'learning': 36, 'new': 37, 'steps': 38, 'long': 39, 'away': 40, 'left': 41, 'friends': 42, 'relations': 43, 'when': 44, 'wall': 45, 'myself': 46, 'nice': 47, 'just': 48, 'dancing': 49, 'merry': 50, 'tipped': 51, 'me': 52, 'soon': 53, 'time': 54, 'old': 55, 'their': 56, 'them': 57, 'danced': 58, 'dublin': 59, 'an': 60, 'put': 61, 'leg': 62, 'miss': 63, 'fainted': 64, 'from': 65, 'town': 66, 'athy': 67, 'one': 68, 'jeremy': 69, 'lanigan': 70, 'battered': 71, 'hadnt': 72, 'pound': 73, 'father': 74, 'died': 75, 'made': 76, 'man': 77, 'farm': 78, 'ten': 79, 'acres': 80, 'ground': 81, 'gave': 82, 'grand': 83, 'party': 84, 'who': 85, 'didnt': 86, 'forget': 87, 'come': 88, 'if': 89, 'youll': 90, 'but': 91, 'listen': 92, 'ill': 93, 'make': 94, 'your': 95, 'eyes': 96, 'glisten': 97, 'rows': 98, 'ructions': 99, 'be': 100, 'sure': 101, 'free': 102, 'invitation': 103, 'might': 104, 'ask': 105, 'minute': 106, 'both': 107, 'bees': 108, 'cask': 109, 'judy': 110, 'odaly': 111, 'little': 112, 'milliner': 113, 'wink': 114, 'give': 115, 'call': 116, 'arrived': 117, 'with': 118, 'peggy': 119, 'mcgilligan': 120, 'lashings': 121, 'punch': 122, 'wine': 123, 'ladies': 124, 'potatoes': 125, 'cakes': 126, 'bacon': 127, 'tea': 128, 'nolans': 129, 'dolans': 130, 'ogradys': 131, 'courting': 132, 'songs': 133, 'went': 134, 'plenty': 135, 'water': 136, 'harp': 137, 'once': 138, 'sounded': 139, 'taras': 140, 'hall': 141, 'sweet': 142, 'nelly': 143, 'gray': 144, 'rat': 145, 'catchers': 146, 'daughter': 147, 'singing': 148, 'together': 149, 'doing': 150, 'kinds': 151, 'nonsensical': 152, 'polkas': 153, 'room': 154, 'whirligig': 155, 'julia': 156, 'we': 157, 'banished': 158, 'nonsense': 159, 'twist': 160, 'reel': 161, 'jig': 162, 'ach': 163, 'mavrone': 164, 'how': 165, 'mad': 166, 'youd': 167, 'think': 168, 'ceiling': 169, 'would': 170, 'fall': 171, 'brooks': 172, 'academy': 173, 'learn': 174, 'nothing': 175, 'hearty': 176, 'around': 177, 'couples': 178, 'groups': 179, 'accident': 180, 'happened': 181, 'young': 182, 'terrance': 183, 'mccarthy': 184, 'right': 185, 'through': 186, 'finnertys': 187, 'hoops': 188, 'poor': 189, 'creature': 190, 'cried': 191, 'meelia': 192, 'murther': 193, 'called': 194, 'brothers': 195, 'gathered': 196, 'carmody': 197, 'swore': 198, 'hed': 199, 'go': 200, 'no': 201, 'further': 202, 'had': 203, 'satisfaction': 204, 'midst': 205, 'row': 206, 'kerrigan': 207, 'cheeks': 208, 'same': 209, 'red': 210, 'rose': 211, 'some': 212, 'lads': 213, 'declared': 214, 'painted': 215, 'took': 216, 'small': 217, 'drop': 218, 'too': 219, 'much': 220, 'suppose': 221, 'sweetheart': 222, 'ned': 223, 'morgan': 224, 'so': 225, 'powerful': 226, 'able': 227, 'saw': 228, 'fair': 229, 'colleen': 230, 'stretched': 231, 'by': 232, 'tore': 233, 'under': 234, 'table': 235, 'smashed': 236, 'chaneys': 237, 'oh': 238, 'twas': 239, 'then': 240, 'runctions': 241, 'lick': 242, 'big': 243, 'phelim': 244, 'mchugh': 245, 'replied': 246, 'introduction': 247, 'kicked': 248, 'terrible': 249, 'hullabaloo': 250, 'casey': 251, 'piper': 252, 'near': 253, 'being': 254, 'strangled': 255, 'squeezed': 256, 'pipes': 257, 'bellows': 258, 'chanters': 259, 'ribbons': 260, 'entangled': 261, 'end': 262}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rB0ngqNpx7Oa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ffb52828-7be1-456f-d781-39a2605604ef"
      },
      "source": [
        "input_sequences = []\n",
        "for line in corpus:\n",
        "  token_list = tokenizer.texts_to_sequences([line])[0]  # [] is me is lye dala \"line\" ko blc texts_to_sequence sirf ak line ko nhi blky multiple line ko encode krta hai\n",
        "                                                        #  so it takes in list of multiple lines. but we have oneline at a time. so we covered that in a list.\n",
        "  print(token_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[4, 2, 66, 8, 67, 68, 69, 70]\n",
            "[71, 40, 20, 21, 72, 3, 73]\n",
            "[16, 74, 75, 1, 76, 33, 3, 77, 22]\n",
            "[41, 33, 3, 78, 1, 79, 80, 8, 81]\n",
            "[21, 82, 3, 83, 84, 7, 42, 1, 43]\n",
            "[85, 86, 87, 33, 44, 88, 13, 2, 45]\n",
            "[1, 89, 90, 91, 92, 93, 94, 95, 96, 97]\n",
            "[8, 2, 98, 1, 2, 99, 8, 9, 10]\n",
            "[46, 13, 100, 101, 23, 102, 103]\n",
            "[7, 5, 2, 47, 17, 1, 24, 6, 104, 105]\n",
            "[1, 48, 4, 3, 106, 107, 42, 1, 43]\n",
            "[11, 49, 25, 50, 18, 108, 25, 3, 109]\n",
            "[110, 111, 26, 47, 112, 113]\n",
            "[14, 51, 52, 3, 114, 7, 13, 115, 27, 3, 116]\n",
            "[1, 6, 53, 117, 118, 119, 120]\n",
            "[48, 4, 54, 7, 9, 10]\n",
            "[28, 11, 121, 8, 122, 1, 123, 7, 2, 124]\n",
            "[125, 1, 126, 28, 34, 127, 1, 128]\n",
            "[28, 11, 2, 129, 130, 131]\n",
            "[132, 2, 17, 1, 49, 40]\n",
            "[133, 19, 134, 25, 18, 135, 18, 136]\n",
            "[2, 137, 26, 138, 139, 4, 140, 55, 141]\n",
            "[142, 143, 144, 1, 2, 145, 146, 147]\n",
            "[5, 148, 149, 12, 9, 10]\n",
            "[19, 11, 150, 5, 151, 8, 152, 153]\n",
            "[5, 25, 2, 154, 4, 3, 155]\n",
            "[156, 1, 6, 157, 158, 56, 159]\n",
            "[1, 51, 57, 2, 160, 8, 3, 161, 1, 3, 162]\n",
            "[163, 164, 165, 2, 17, 23, 5, 166, 12, 52]\n",
            "[58, 20, 167, 168, 2, 169, 170, 171]\n",
            "[7, 6, 35, 29, 30, 12, 172, 173]\n",
            "[36, 37, 38, 7, 9, 10]\n",
            "[29, 39, 30, 6, 35, 31, 4, 59]\n",
            "[29, 39, 30, 13, 174, 175, 12, 5]\n",
            "[29, 39, 30, 6, 35, 31, 4, 59]\n",
            "[36, 37, 38, 7, 9, 10]\n",
            "[14, 15, 32, 1, 6, 15, 4, 22]\n",
            "[6, 15, 32, 1, 14, 15, 4, 22]\n",
            "[14, 15, 32, 1, 6, 15, 4, 22]\n",
            "[36, 37, 38, 7, 9, 10]\n",
            "[24, 11, 5, 50, 1, 2, 17, 19, 11, 176]\n",
            "[1, 58, 5, 177, 4, 178, 1, 179]\n",
            "[20, 60, 180, 181, 182, 183, 184]\n",
            "[61, 16, 185, 62, 186, 63, 187, 188]\n",
            "[189, 190, 64, 1, 191, 192, 193]\n",
            "[194, 7, 27, 195, 1, 196, 57, 5]\n",
            "[197, 198, 26, 199, 200, 201, 202]\n",
            "[20, 21, 203, 204, 12, 9, 10]\n",
            "[4, 2, 205, 8, 2, 206, 63, 207, 64]\n",
            "[27, 208, 12, 2, 209, 54, 18, 210, 18, 3, 211]\n",
            "[212, 8, 2, 213, 214, 14, 34, 215]\n",
            "[14, 216, 3, 217, 218, 219, 220, 6, 221]\n",
            "[27, 222, 223, 224, 225, 226, 1, 227]\n",
            "[44, 21, 228, 16, 229, 230, 231, 32, 232, 2, 45]\n",
            "[233, 2, 41, 62, 65, 234, 2, 235]\n",
            "[1, 236, 5, 2, 237, 12, 9, 10]\n",
            "[24, 238, 24, 239, 240, 28, 11, 241]\n",
            "[46, 23, 3, 242, 65, 243, 244, 245]\n",
            "[6, 53, 246, 13, 16, 247]\n",
            "[1, 248, 31, 3, 249, 250]\n",
            "[55, 251, 2, 252, 34, 253, 254, 255]\n",
            "[19, 256, 31, 16, 257, 258, 259, 1, 5]\n",
            "[2, 17, 4, 56, 260, 19, 23, 5, 261]\n",
            "[1, 26, 61, 60, 262, 13, 9, 10]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUKncGAE0HA0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "98c10c94-74ef-4e73-f281-d852493b5624"
      },
      "source": [
        "input_sequences = []\n",
        "for line in corpus:\n",
        "  token_list = tokenizer.texts_to_sequences([line])[0] \n",
        "  # len(token_list) sy muraad, har ak encoded line me total no of words. \n",
        "  for i in range(1, len(token_list)):\n",
        "    n_gram_sequence = token_list[:i+1]\n",
        "    print(n_gram_sequence)\n",
        "    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[4, 2]\n",
            "[4, 2, 66]\n",
            "[4, 2, 66, 8]\n",
            "[4, 2, 66, 8, 67]\n",
            "[4, 2, 66, 8, 67, 68]\n",
            "[4, 2, 66, 8, 67, 68, 69]\n",
            "[4, 2, 66, 8, 67, 68, 69, 70]\n",
            "[71, 40]\n",
            "[71, 40, 20]\n",
            "[71, 40, 20, 21]\n",
            "[71, 40, 20, 21, 72]\n",
            "[71, 40, 20, 21, 72, 3]\n",
            "[71, 40, 20, 21, 72, 3, 73]\n",
            "[16, 74]\n",
            "[16, 74, 75]\n",
            "[16, 74, 75, 1]\n",
            "[16, 74, 75, 1, 76]\n",
            "[16, 74, 75, 1, 76, 33]\n",
            "[16, 74, 75, 1, 76, 33, 3]\n",
            "[16, 74, 75, 1, 76, 33, 3, 77]\n",
            "[16, 74, 75, 1, 76, 33, 3, 77, 22]\n",
            "[41, 33]\n",
            "[41, 33, 3]\n",
            "[41, 33, 3, 78]\n",
            "[41, 33, 3, 78, 1]\n",
            "[41, 33, 3, 78, 1, 79]\n",
            "[41, 33, 3, 78, 1, 79, 80]\n",
            "[41, 33, 3, 78, 1, 79, 80, 8]\n",
            "[41, 33, 3, 78, 1, 79, 80, 8, 81]\n",
            "[21, 82]\n",
            "[21, 82, 3]\n",
            "[21, 82, 3, 83]\n",
            "[21, 82, 3, 83, 84]\n",
            "[21, 82, 3, 83, 84, 7]\n",
            "[21, 82, 3, 83, 84, 7, 42]\n",
            "[21, 82, 3, 83, 84, 7, 42, 1]\n",
            "[21, 82, 3, 83, 84, 7, 42, 1, 43]\n",
            "[85, 86]\n",
            "[85, 86, 87]\n",
            "[85, 86, 87, 33]\n",
            "[85, 86, 87, 33, 44]\n",
            "[85, 86, 87, 33, 44, 88]\n",
            "[85, 86, 87, 33, 44, 88, 13]\n",
            "[85, 86, 87, 33, 44, 88, 13, 2]\n",
            "[85, 86, 87, 33, 44, 88, 13, 2, 45]\n",
            "[1, 89]\n",
            "[1, 89, 90]\n",
            "[1, 89, 90, 91]\n",
            "[1, 89, 90, 91, 92]\n",
            "[1, 89, 90, 91, 92, 93]\n",
            "[1, 89, 90, 91, 92, 93, 94]\n",
            "[1, 89, 90, 91, 92, 93, 94, 95]\n",
            "[1, 89, 90, 91, 92, 93, 94, 95, 96]\n",
            "[1, 89, 90, 91, 92, 93, 94, 95, 96, 97]\n",
            "[8, 2]\n",
            "[8, 2, 98]\n",
            "[8, 2, 98, 1]\n",
            "[8, 2, 98, 1, 2]\n",
            "[8, 2, 98, 1, 2, 99]\n",
            "[8, 2, 98, 1, 2, 99, 8]\n",
            "[8, 2, 98, 1, 2, 99, 8, 9]\n",
            "[8, 2, 98, 1, 2, 99, 8, 9, 10]\n",
            "[46, 13]\n",
            "[46, 13, 100]\n",
            "[46, 13, 100, 101]\n",
            "[46, 13, 100, 101, 23]\n",
            "[46, 13, 100, 101, 23, 102]\n",
            "[46, 13, 100, 101, 23, 102, 103]\n",
            "[7, 5]\n",
            "[7, 5, 2]\n",
            "[7, 5, 2, 47]\n",
            "[7, 5, 2, 47, 17]\n",
            "[7, 5, 2, 47, 17, 1]\n",
            "[7, 5, 2, 47, 17, 1, 24]\n",
            "[7, 5, 2, 47, 17, 1, 24, 6]\n",
            "[7, 5, 2, 47, 17, 1, 24, 6, 104]\n",
            "[7, 5, 2, 47, 17, 1, 24, 6, 104, 105]\n",
            "[1, 48]\n",
            "[1, 48, 4]\n",
            "[1, 48, 4, 3]\n",
            "[1, 48, 4, 3, 106]\n",
            "[1, 48, 4, 3, 106, 107]\n",
            "[1, 48, 4, 3, 106, 107, 42]\n",
            "[1, 48, 4, 3, 106, 107, 42, 1]\n",
            "[1, 48, 4, 3, 106, 107, 42, 1, 43]\n",
            "[11, 49]\n",
            "[11, 49, 25]\n",
            "[11, 49, 25, 50]\n",
            "[11, 49, 25, 50, 18]\n",
            "[11, 49, 25, 50, 18, 108]\n",
            "[11, 49, 25, 50, 18, 108, 25]\n",
            "[11, 49, 25, 50, 18, 108, 25, 3]\n",
            "[11, 49, 25, 50, 18, 108, 25, 3, 109]\n",
            "[110, 111]\n",
            "[110, 111, 26]\n",
            "[110, 111, 26, 47]\n",
            "[110, 111, 26, 47, 112]\n",
            "[110, 111, 26, 47, 112, 113]\n",
            "[14, 51]\n",
            "[14, 51, 52]\n",
            "[14, 51, 52, 3]\n",
            "[14, 51, 52, 3, 114]\n",
            "[14, 51, 52, 3, 114, 7]\n",
            "[14, 51, 52, 3, 114, 7, 13]\n",
            "[14, 51, 52, 3, 114, 7, 13, 115]\n",
            "[14, 51, 52, 3, 114, 7, 13, 115, 27]\n",
            "[14, 51, 52, 3, 114, 7, 13, 115, 27, 3]\n",
            "[14, 51, 52, 3, 114, 7, 13, 115, 27, 3, 116]\n",
            "[1, 6]\n",
            "[1, 6, 53]\n",
            "[1, 6, 53, 117]\n",
            "[1, 6, 53, 117, 118]\n",
            "[1, 6, 53, 117, 118, 119]\n",
            "[1, 6, 53, 117, 118, 119, 120]\n",
            "[48, 4]\n",
            "[48, 4, 54]\n",
            "[48, 4, 54, 7]\n",
            "[48, 4, 54, 7, 9]\n",
            "[48, 4, 54, 7, 9, 10]\n",
            "[28, 11]\n",
            "[28, 11, 121]\n",
            "[28, 11, 121, 8]\n",
            "[28, 11, 121, 8, 122]\n",
            "[28, 11, 121, 8, 122, 1]\n",
            "[28, 11, 121, 8, 122, 1, 123]\n",
            "[28, 11, 121, 8, 122, 1, 123, 7]\n",
            "[28, 11, 121, 8, 122, 1, 123, 7, 2]\n",
            "[28, 11, 121, 8, 122, 1, 123, 7, 2, 124]\n",
            "[125, 1]\n",
            "[125, 1, 126]\n",
            "[125, 1, 126, 28]\n",
            "[125, 1, 126, 28, 34]\n",
            "[125, 1, 126, 28, 34, 127]\n",
            "[125, 1, 126, 28, 34, 127, 1]\n",
            "[125, 1, 126, 28, 34, 127, 1, 128]\n",
            "[28, 11]\n",
            "[28, 11, 2]\n",
            "[28, 11, 2, 129]\n",
            "[28, 11, 2, 129, 130]\n",
            "[28, 11, 2, 129, 130, 131]\n",
            "[132, 2]\n",
            "[132, 2, 17]\n",
            "[132, 2, 17, 1]\n",
            "[132, 2, 17, 1, 49]\n",
            "[132, 2, 17, 1, 49, 40]\n",
            "[133, 19]\n",
            "[133, 19, 134]\n",
            "[133, 19, 134, 25]\n",
            "[133, 19, 134, 25, 18]\n",
            "[133, 19, 134, 25, 18, 135]\n",
            "[133, 19, 134, 25, 18, 135, 18]\n",
            "[133, 19, 134, 25, 18, 135, 18, 136]\n",
            "[2, 137]\n",
            "[2, 137, 26]\n",
            "[2, 137, 26, 138]\n",
            "[2, 137, 26, 138, 139]\n",
            "[2, 137, 26, 138, 139, 4]\n",
            "[2, 137, 26, 138, 139, 4, 140]\n",
            "[2, 137, 26, 138, 139, 4, 140, 55]\n",
            "[2, 137, 26, 138, 139, 4, 140, 55, 141]\n",
            "[142, 143]\n",
            "[142, 143, 144]\n",
            "[142, 143, 144, 1]\n",
            "[142, 143, 144, 1, 2]\n",
            "[142, 143, 144, 1, 2, 145]\n",
            "[142, 143, 144, 1, 2, 145, 146]\n",
            "[142, 143, 144, 1, 2, 145, 146, 147]\n",
            "[5, 148]\n",
            "[5, 148, 149]\n",
            "[5, 148, 149, 12]\n",
            "[5, 148, 149, 12, 9]\n",
            "[5, 148, 149, 12, 9, 10]\n",
            "[19, 11]\n",
            "[19, 11, 150]\n",
            "[19, 11, 150, 5]\n",
            "[19, 11, 150, 5, 151]\n",
            "[19, 11, 150, 5, 151, 8]\n",
            "[19, 11, 150, 5, 151, 8, 152]\n",
            "[19, 11, 150, 5, 151, 8, 152, 153]\n",
            "[5, 25]\n",
            "[5, 25, 2]\n",
            "[5, 25, 2, 154]\n",
            "[5, 25, 2, 154, 4]\n",
            "[5, 25, 2, 154, 4, 3]\n",
            "[5, 25, 2, 154, 4, 3, 155]\n",
            "[156, 1]\n",
            "[156, 1, 6]\n",
            "[156, 1, 6, 157]\n",
            "[156, 1, 6, 157, 158]\n",
            "[156, 1, 6, 157, 158, 56]\n",
            "[156, 1, 6, 157, 158, 56, 159]\n",
            "[1, 51]\n",
            "[1, 51, 57]\n",
            "[1, 51, 57, 2]\n",
            "[1, 51, 57, 2, 160]\n",
            "[1, 51, 57, 2, 160, 8]\n",
            "[1, 51, 57, 2, 160, 8, 3]\n",
            "[1, 51, 57, 2, 160, 8, 3, 161]\n",
            "[1, 51, 57, 2, 160, 8, 3, 161, 1]\n",
            "[1, 51, 57, 2, 160, 8, 3, 161, 1, 3]\n",
            "[1, 51, 57, 2, 160, 8, 3, 161, 1, 3, 162]\n",
            "[163, 164]\n",
            "[163, 164, 165]\n",
            "[163, 164, 165, 2]\n",
            "[163, 164, 165, 2, 17]\n",
            "[163, 164, 165, 2, 17, 23]\n",
            "[163, 164, 165, 2, 17, 23, 5]\n",
            "[163, 164, 165, 2, 17, 23, 5, 166]\n",
            "[163, 164, 165, 2, 17, 23, 5, 166, 12]\n",
            "[163, 164, 165, 2, 17, 23, 5, 166, 12, 52]\n",
            "[58, 20]\n",
            "[58, 20, 167]\n",
            "[58, 20, 167, 168]\n",
            "[58, 20, 167, 168, 2]\n",
            "[58, 20, 167, 168, 2, 169]\n",
            "[58, 20, 167, 168, 2, 169, 170]\n",
            "[58, 20, 167, 168, 2, 169, 170, 171]\n",
            "[7, 6]\n",
            "[7, 6, 35]\n",
            "[7, 6, 35, 29]\n",
            "[7, 6, 35, 29, 30]\n",
            "[7, 6, 35, 29, 30, 12]\n",
            "[7, 6, 35, 29, 30, 12, 172]\n",
            "[7, 6, 35, 29, 30, 12, 172, 173]\n",
            "[36, 37]\n",
            "[36, 37, 38]\n",
            "[36, 37, 38, 7]\n",
            "[36, 37, 38, 7, 9]\n",
            "[36, 37, 38, 7, 9, 10]\n",
            "[29, 39]\n",
            "[29, 39, 30]\n",
            "[29, 39, 30, 6]\n",
            "[29, 39, 30, 6, 35]\n",
            "[29, 39, 30, 6, 35, 31]\n",
            "[29, 39, 30, 6, 35, 31, 4]\n",
            "[29, 39, 30, 6, 35, 31, 4, 59]\n",
            "[29, 39]\n",
            "[29, 39, 30]\n",
            "[29, 39, 30, 13]\n",
            "[29, 39, 30, 13, 174]\n",
            "[29, 39, 30, 13, 174, 175]\n",
            "[29, 39, 30, 13, 174, 175, 12]\n",
            "[29, 39, 30, 13, 174, 175, 12, 5]\n",
            "[29, 39]\n",
            "[29, 39, 30]\n",
            "[29, 39, 30, 6]\n",
            "[29, 39, 30, 6, 35]\n",
            "[29, 39, 30, 6, 35, 31]\n",
            "[29, 39, 30, 6, 35, 31, 4]\n",
            "[29, 39, 30, 6, 35, 31, 4, 59]\n",
            "[36, 37]\n",
            "[36, 37, 38]\n",
            "[36, 37, 38, 7]\n",
            "[36, 37, 38, 7, 9]\n",
            "[36, 37, 38, 7, 9, 10]\n",
            "[14, 15]\n",
            "[14, 15, 32]\n",
            "[14, 15, 32, 1]\n",
            "[14, 15, 32, 1, 6]\n",
            "[14, 15, 32, 1, 6, 15]\n",
            "[14, 15, 32, 1, 6, 15, 4]\n",
            "[14, 15, 32, 1, 6, 15, 4, 22]\n",
            "[6, 15]\n",
            "[6, 15, 32]\n",
            "[6, 15, 32, 1]\n",
            "[6, 15, 32, 1, 14]\n",
            "[6, 15, 32, 1, 14, 15]\n",
            "[6, 15, 32, 1, 14, 15, 4]\n",
            "[6, 15, 32, 1, 14, 15, 4, 22]\n",
            "[14, 15]\n",
            "[14, 15, 32]\n",
            "[14, 15, 32, 1]\n",
            "[14, 15, 32, 1, 6]\n",
            "[14, 15, 32, 1, 6, 15]\n",
            "[14, 15, 32, 1, 6, 15, 4]\n",
            "[14, 15, 32, 1, 6, 15, 4, 22]\n",
            "[36, 37]\n",
            "[36, 37, 38]\n",
            "[36, 37, 38, 7]\n",
            "[36, 37, 38, 7, 9]\n",
            "[36, 37, 38, 7, 9, 10]\n",
            "[24, 11]\n",
            "[24, 11, 5]\n",
            "[24, 11, 5, 50]\n",
            "[24, 11, 5, 50, 1]\n",
            "[24, 11, 5, 50, 1, 2]\n",
            "[24, 11, 5, 50, 1, 2, 17]\n",
            "[24, 11, 5, 50, 1, 2, 17, 19]\n",
            "[24, 11, 5, 50, 1, 2, 17, 19, 11]\n",
            "[24, 11, 5, 50, 1, 2, 17, 19, 11, 176]\n",
            "[1, 58]\n",
            "[1, 58, 5]\n",
            "[1, 58, 5, 177]\n",
            "[1, 58, 5, 177, 4]\n",
            "[1, 58, 5, 177, 4, 178]\n",
            "[1, 58, 5, 177, 4, 178, 1]\n",
            "[1, 58, 5, 177, 4, 178, 1, 179]\n",
            "[20, 60]\n",
            "[20, 60, 180]\n",
            "[20, 60, 180, 181]\n",
            "[20, 60, 180, 181, 182]\n",
            "[20, 60, 180, 181, 182, 183]\n",
            "[20, 60, 180, 181, 182, 183, 184]\n",
            "[61, 16]\n",
            "[61, 16, 185]\n",
            "[61, 16, 185, 62]\n",
            "[61, 16, 185, 62, 186]\n",
            "[61, 16, 185, 62, 186, 63]\n",
            "[61, 16, 185, 62, 186, 63, 187]\n",
            "[61, 16, 185, 62, 186, 63, 187, 188]\n",
            "[189, 190]\n",
            "[189, 190, 64]\n",
            "[189, 190, 64, 1]\n",
            "[189, 190, 64, 1, 191]\n",
            "[189, 190, 64, 1, 191, 192]\n",
            "[189, 190, 64, 1, 191, 192, 193]\n",
            "[194, 7]\n",
            "[194, 7, 27]\n",
            "[194, 7, 27, 195]\n",
            "[194, 7, 27, 195, 1]\n",
            "[194, 7, 27, 195, 1, 196]\n",
            "[194, 7, 27, 195, 1, 196, 57]\n",
            "[194, 7, 27, 195, 1, 196, 57, 5]\n",
            "[197, 198]\n",
            "[197, 198, 26]\n",
            "[197, 198, 26, 199]\n",
            "[197, 198, 26, 199, 200]\n",
            "[197, 198, 26, 199, 200, 201]\n",
            "[197, 198, 26, 199, 200, 201, 202]\n",
            "[20, 21]\n",
            "[20, 21, 203]\n",
            "[20, 21, 203, 204]\n",
            "[20, 21, 203, 204, 12]\n",
            "[20, 21, 203, 204, 12, 9]\n",
            "[20, 21, 203, 204, 12, 9, 10]\n",
            "[4, 2]\n",
            "[4, 2, 205]\n",
            "[4, 2, 205, 8]\n",
            "[4, 2, 205, 8, 2]\n",
            "[4, 2, 205, 8, 2, 206]\n",
            "[4, 2, 205, 8, 2, 206, 63]\n",
            "[4, 2, 205, 8, 2, 206, 63, 207]\n",
            "[4, 2, 205, 8, 2, 206, 63, 207, 64]\n",
            "[27, 208]\n",
            "[27, 208, 12]\n",
            "[27, 208, 12, 2]\n",
            "[27, 208, 12, 2, 209]\n",
            "[27, 208, 12, 2, 209, 54]\n",
            "[27, 208, 12, 2, 209, 54, 18]\n",
            "[27, 208, 12, 2, 209, 54, 18, 210]\n",
            "[27, 208, 12, 2, 209, 54, 18, 210, 18]\n",
            "[27, 208, 12, 2, 209, 54, 18, 210, 18, 3]\n",
            "[27, 208, 12, 2, 209, 54, 18, 210, 18, 3, 211]\n",
            "[212, 8]\n",
            "[212, 8, 2]\n",
            "[212, 8, 2, 213]\n",
            "[212, 8, 2, 213, 214]\n",
            "[212, 8, 2, 213, 214, 14]\n",
            "[212, 8, 2, 213, 214, 14, 34]\n",
            "[212, 8, 2, 213, 214, 14, 34, 215]\n",
            "[14, 216]\n",
            "[14, 216, 3]\n",
            "[14, 216, 3, 217]\n",
            "[14, 216, 3, 217, 218]\n",
            "[14, 216, 3, 217, 218, 219]\n",
            "[14, 216, 3, 217, 218, 219, 220]\n",
            "[14, 216, 3, 217, 218, 219, 220, 6]\n",
            "[14, 216, 3, 217, 218, 219, 220, 6, 221]\n",
            "[27, 222]\n",
            "[27, 222, 223]\n",
            "[27, 222, 223, 224]\n",
            "[27, 222, 223, 224, 225]\n",
            "[27, 222, 223, 224, 225, 226]\n",
            "[27, 222, 223, 224, 225, 226, 1]\n",
            "[27, 222, 223, 224, 225, 226, 1, 227]\n",
            "[44, 21]\n",
            "[44, 21, 228]\n",
            "[44, 21, 228, 16]\n",
            "[44, 21, 228, 16, 229]\n",
            "[44, 21, 228, 16, 229, 230]\n",
            "[44, 21, 228, 16, 229, 230, 231]\n",
            "[44, 21, 228, 16, 229, 230, 231, 32]\n",
            "[44, 21, 228, 16, 229, 230, 231, 32, 232]\n",
            "[44, 21, 228, 16, 229, 230, 231, 32, 232, 2]\n",
            "[44, 21, 228, 16, 229, 230, 231, 32, 232, 2, 45]\n",
            "[233, 2]\n",
            "[233, 2, 41]\n",
            "[233, 2, 41, 62]\n",
            "[233, 2, 41, 62, 65]\n",
            "[233, 2, 41, 62, 65, 234]\n",
            "[233, 2, 41, 62, 65, 234, 2]\n",
            "[233, 2, 41, 62, 65, 234, 2, 235]\n",
            "[1, 236]\n",
            "[1, 236, 5]\n",
            "[1, 236, 5, 2]\n",
            "[1, 236, 5, 2, 237]\n",
            "[1, 236, 5, 2, 237, 12]\n",
            "[1, 236, 5, 2, 237, 12, 9]\n",
            "[1, 236, 5, 2, 237, 12, 9, 10]\n",
            "[24, 238]\n",
            "[24, 238, 24]\n",
            "[24, 238, 24, 239]\n",
            "[24, 238, 24, 239, 240]\n",
            "[24, 238, 24, 239, 240, 28]\n",
            "[24, 238, 24, 239, 240, 28, 11]\n",
            "[24, 238, 24, 239, 240, 28, 11, 241]\n",
            "[46, 23]\n",
            "[46, 23, 3]\n",
            "[46, 23, 3, 242]\n",
            "[46, 23, 3, 242, 65]\n",
            "[46, 23, 3, 242, 65, 243]\n",
            "[46, 23, 3, 242, 65, 243, 244]\n",
            "[46, 23, 3, 242, 65, 243, 244, 245]\n",
            "[6, 53]\n",
            "[6, 53, 246]\n",
            "[6, 53, 246, 13]\n",
            "[6, 53, 246, 13, 16]\n",
            "[6, 53, 246, 13, 16, 247]\n",
            "[1, 248]\n",
            "[1, 248, 31]\n",
            "[1, 248, 31, 3]\n",
            "[1, 248, 31, 3, 249]\n",
            "[1, 248, 31, 3, 249, 250]\n",
            "[55, 251]\n",
            "[55, 251, 2]\n",
            "[55, 251, 2, 252]\n",
            "[55, 251, 2, 252, 34]\n",
            "[55, 251, 2, 252, 34, 253]\n",
            "[55, 251, 2, 252, 34, 253, 254]\n",
            "[55, 251, 2, 252, 34, 253, 254, 255]\n",
            "[19, 256]\n",
            "[19, 256, 31]\n",
            "[19, 256, 31, 16]\n",
            "[19, 256, 31, 16, 257]\n",
            "[19, 256, 31, 16, 257, 258]\n",
            "[19, 256, 31, 16, 257, 258, 259]\n",
            "[19, 256, 31, 16, 257, 258, 259, 1]\n",
            "[19, 256, 31, 16, 257, 258, 259, 1, 5]\n",
            "[2, 17]\n",
            "[2, 17, 4]\n",
            "[2, 17, 4, 56]\n",
            "[2, 17, 4, 56, 260]\n",
            "[2, 17, 4, 56, 260, 19]\n",
            "[2, 17, 4, 56, 260, 19, 23]\n",
            "[2, 17, 4, 56, 260, 19, 23, 5]\n",
            "[2, 17, 4, 56, 260, 19, 23, 5, 261]\n",
            "[1, 26]\n",
            "[1, 26, 61]\n",
            "[1, 26, 61, 60]\n",
            "[1, 26, 61, 60, 262]\n",
            "[1, 26, 61, 60, 262, 13]\n",
            "[1, 26, 61, 60, 262, 13, 9]\n",
            "[1, 26, 61, 60, 262, 13, 9, 10]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0AKIbwTG1bsS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "59f901c2-ba8e-4e31-b2c9-ea63474cd0f4"
      },
      "source": [
        "input_sequences = []\n",
        "for line in corpus:\n",
        "  token_list = tokenizer.texts_to_sequences([line])[0] \n",
        "  # len(token_list) sy muraad, har ak encoded line me total no of words. \n",
        "  for i in range(1, len(token_list)):\n",
        "    n_gram_sequence = token_list[:i+1]\n",
        "    input_sequences.append(n_gram_sequence)\n",
        "\n",
        "# Pad Sequences:\n",
        "max_sequence_len = max([len(x) for x in input_sequences])\n",
        "# for finding which line contains the maximum words or what is length of longest line so we can pad other lines according to that line length.\n",
        "print(max_sequence_len)\n",
        "paddings = pad_sequences(input_sequences, maxlen = max_sequence_len, padding = 'pre')\n",
        "print(paddings)\n",
        "print()\n",
        "input_sequences = np.array(paddings)\n",
        "print(input_sequences)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11\n",
            "[[  0   0   0 ...   0   4   2]\n",
            " [  0   0   0 ...   4   2  66]\n",
            " [  0   0   0 ...   2  66   8]\n",
            " ...\n",
            " [  0   0   0 ...  60 262  13]\n",
            " [  0   0   0 ... 262  13   9]\n",
            " [  0   0   0 ...  13   9  10]]\n",
            "\n",
            "[[  0   0   0 ...   0   4   2]\n",
            " [  0   0   0 ...   4   2  66]\n",
            " [  0   0   0 ...   2  66   8]\n",
            " ...\n",
            " [  0   0   0 ...  60 262  13]\n",
            " [  0   0   0 ... 262  13   9]\n",
            " [  0   0   0 ...  13   9  10]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y0_G5y9Z3xXa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "4b764cf3-b7f3-4dfa-f369-48a836540c31"
      },
      "source": [
        "input_sequences = []\n",
        "for line in corpus:\n",
        "  token_list = tokenizer.texts_to_sequences([line])[0] \n",
        "  # len(token_list) sy muraad, har ak encoded line me total no of words. \n",
        "  for i in range(1, len(token_list)):\n",
        "    n_gram_sequence = token_list[:i+1]\n",
        "    input_sequences.append(n_gram_sequence)\n",
        "\n",
        "# Pad Sequences:\n",
        "max_sequence_len = max([len(x) for x in input_sequences])\n",
        "# for finding which line contains the maximum words or what is length of longest line so we can pad other lines according to that line length.\n",
        "print(max_sequence_len)\n",
        "paddings = pad_sequences(input_sequences, maxlen = max_sequence_len, padding = 'pre')\n",
        "input_sequences = np.array(paddings)\n",
        "\n",
        "# create training set and labels\n",
        "xs, labels = input_sequences[:, :-1], input_sequences[:, -1]\n",
        "\n",
        "# now we will do onehot encoding on labels.\n",
        "ys = tf.keras.utils.to_categorical(labels, num_classes=total_words)\n",
        "print(ys)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11\n",
            "[[0. 0. 1. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "soPGVheskaQP",
        "colab": {}
      },
      "source": [
        "input_sequences = []\n",
        "for line in corpus:\n",
        "\ttoken_list = tokenizer.texts_to_sequences([line])[0]\n",
        "\tfor i in range(1, len(token_list)):\n",
        "\t\tn_gram_sequence = token_list[:i+1]\n",
        "\t\tinput_sequences.append(n_gram_sequence)\n",
        "\n",
        "# pad sequences \n",
        "max_sequence_len = max([len(x) for x in input_sequences])\n",
        "input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))\n",
        "\n",
        "# create predictors and label\n",
        "xs, labels = input_sequences[:,:-1],input_sequences[:,-1]\n",
        "\n",
        "ys = tf.keras.utils.to_categorical(labels, num_classes=total_words)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pJtwVB2NbOAP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "8ecad32b-b126-47bf-a03a-e3ef23ae5a52"
      },
      "source": [
        "print(tokenizer.word_index['in'])\n",
        "print(tokenizer.word_index['the'])\n",
        "print(tokenizer.word_index['town'])\n",
        "print(tokenizer.word_index['of'])\n",
        "print(tokenizer.word_index['athy'])\n",
        "print(tokenizer.word_index['one'])\n",
        "print(tokenizer.word_index['jeremy'])\n",
        "print(tokenizer.word_index['lanigan'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4\n",
            "2\n",
            "66\n",
            "8\n",
            "67\n",
            "68\n",
            "69\n",
            "70\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "49Cv68JOakwv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f10bec28-53e5-48ff-e529-7a543907359c"
      },
      "source": [
        "print(xs[6])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 0  0  0  4  2 66  8 67 68 69]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iY-jwvfgbEF8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "eadbff82-030c-4dbc-aebb-c20e47f6a831"
      },
      "source": [
        "print(ys[6])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wtzlUMYadhKt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "99bf7e30-6133-4a21-f921-45c3d820c584"
      },
      "source": [
        "print(xs[5])\n",
        "print(ys[5])  # ys ka mtlb hai k is  input sequence k baad konsa word aye ga. so basically yeh simple ak word predict kry ga. \n",
        "# this is classification problem, where u r predicting word after each sequence of sentence. "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 0  0  0  0  4  2 66  8 67 68]\n",
            "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "H4myRpB1c4Gg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "a222aa09-9741-492b-a50e-273617a6f0ab"
      },
      "source": [
        "print(tokenizer.word_index)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'and': 1, 'the': 2, 'a': 3, 'in': 4, 'all': 5, 'i': 6, 'for': 7, 'of': 8, 'lanigans': 9, 'ball': 10, 'were': 11, 'at': 12, 'to': 13, 'she': 14, 'stepped': 15, 'his': 16, 'girls': 17, 'as': 18, 'they': 19, 'til': 20, 'he': 21, 'again': 22, 'got': 23, 'boys': 24, 'round': 25, 'that': 26, 'her': 27, 'there': 28, 'three': 29, 'weeks': 30, 'up': 31, 'out': 32, 'him': 33, 'was': 34, 'spent': 35, 'learning': 36, 'new': 37, 'steps': 38, 'long': 39, 'away': 40, 'left': 41, 'friends': 42, 'relations': 43, 'when': 44, 'wall': 45, 'myself': 46, 'nice': 47, 'just': 48, 'dancing': 49, 'merry': 50, 'tipped': 51, 'me': 52, 'soon': 53, 'time': 54, 'old': 55, 'their': 56, 'them': 57, 'danced': 58, 'dublin': 59, 'an': 60, 'put': 61, 'leg': 62, 'miss': 63, 'fainted': 64, 'from': 65, 'town': 66, 'athy': 67, 'one': 68, 'jeremy': 69, 'lanigan': 70, 'battered': 71, 'hadnt': 72, 'pound': 73, 'father': 74, 'died': 75, 'made': 76, 'man': 77, 'farm': 78, 'ten': 79, 'acres': 80, 'ground': 81, 'gave': 82, 'grand': 83, 'party': 84, 'who': 85, 'didnt': 86, 'forget': 87, 'come': 88, 'if': 89, 'youll': 90, 'but': 91, 'listen': 92, 'ill': 93, 'make': 94, 'your': 95, 'eyes': 96, 'glisten': 97, 'rows': 98, 'ructions': 99, 'be': 100, 'sure': 101, 'free': 102, 'invitation': 103, 'might': 104, 'ask': 105, 'minute': 106, 'both': 107, 'bees': 108, 'cask': 109, 'judy': 110, 'odaly': 111, 'little': 112, 'milliner': 113, 'wink': 114, 'give': 115, 'call': 116, 'arrived': 117, 'with': 118, 'peggy': 119, 'mcgilligan': 120, 'lashings': 121, 'punch': 122, 'wine': 123, 'ladies': 124, 'potatoes': 125, 'cakes': 126, 'bacon': 127, 'tea': 128, 'nolans': 129, 'dolans': 130, 'ogradys': 131, 'courting': 132, 'songs': 133, 'went': 134, 'plenty': 135, 'water': 136, 'harp': 137, 'once': 138, 'sounded': 139, 'taras': 140, 'hall': 141, 'sweet': 142, 'nelly': 143, 'gray': 144, 'rat': 145, 'catchers': 146, 'daughter': 147, 'singing': 148, 'together': 149, 'doing': 150, 'kinds': 151, 'nonsensical': 152, 'polkas': 153, 'room': 154, 'whirligig': 155, 'julia': 156, 'we': 157, 'banished': 158, 'nonsense': 159, 'twist': 160, 'reel': 161, 'jig': 162, 'ach': 163, 'mavrone': 164, 'how': 165, 'mad': 166, 'youd': 167, 'think': 168, 'ceiling': 169, 'would': 170, 'fall': 171, 'brooks': 172, 'academy': 173, 'learn': 174, 'nothing': 175, 'hearty': 176, 'around': 177, 'couples': 178, 'groups': 179, 'accident': 180, 'happened': 181, 'young': 182, 'terrance': 183, 'mccarthy': 184, 'right': 185, 'through': 186, 'finnertys': 187, 'hoops': 188, 'poor': 189, 'creature': 190, 'cried': 191, 'meelia': 192, 'murther': 193, 'called': 194, 'brothers': 195, 'gathered': 196, 'carmody': 197, 'swore': 198, 'hed': 199, 'go': 200, 'no': 201, 'further': 202, 'had': 203, 'satisfaction': 204, 'midst': 205, 'row': 206, 'kerrigan': 207, 'cheeks': 208, 'same': 209, 'red': 210, 'rose': 211, 'some': 212, 'lads': 213, 'declared': 214, 'painted': 215, 'took': 216, 'small': 217, 'drop': 218, 'too': 219, 'much': 220, 'suppose': 221, 'sweetheart': 222, 'ned': 223, 'morgan': 224, 'so': 225, 'powerful': 226, 'able': 227, 'saw': 228, 'fair': 229, 'colleen': 230, 'stretched': 231, 'by': 232, 'tore': 233, 'under': 234, 'table': 235, 'smashed': 236, 'chaneys': 237, 'oh': 238, 'twas': 239, 'then': 240, 'runctions': 241, 'lick': 242, 'big': 243, 'phelim': 244, 'mchugh': 245, 'replied': 246, 'introduction': 247, 'kicked': 248, 'terrible': 249, 'hullabaloo': 250, 'casey': 251, 'piper': 252, 'near': 253, 'being': 254, 'strangled': 255, 'squeezed': 256, 'pipes': 257, 'bellows': 258, 'chanters': 259, 'ribbons': 260, 'entangled': 261, 'end': 262}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "w9vH8Y59ajYL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "46d7956f-e8da-44e5-dd3a-164fb6140892"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(total_words, 64, input_length=max_sequence_len-1)) # we subtracted -1 blc we cropped off the last word in the sequence which became our label.\n",
        "model.add(Bidirectional(LSTM(20)))\n",
        "model.add(Dense(total_words, activation='softmax'))\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "history = model.fit(xs, ys, epochs=500, verbose=1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 5.5679 - accuracy: 0.0066\n",
            "Epoch 2/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 5.5407 - accuracy: 0.0508\n",
            "Epoch 3/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 5.4743 - accuracy: 0.0508\n",
            "Epoch 4/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 5.3045 - accuracy: 0.0508\n",
            "Epoch 5/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 5.1120 - accuracy: 0.0508\n",
            "Epoch 6/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 5.0532 - accuracy: 0.0508\n",
            "Epoch 7/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 5.0143 - accuracy: 0.0508\n",
            "Epoch 8/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.9816 - accuracy: 0.0508\n",
            "Epoch 9/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.9498 - accuracy: 0.0508\n",
            "Epoch 10/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.9169 - accuracy: 0.0574\n",
            "Epoch 11/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 4.8817 - accuracy: 0.0530\n",
            "Epoch 12/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.8491 - accuracy: 0.0596\n",
            "Epoch 13/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.8131 - accuracy: 0.0618\n",
            "Epoch 14/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.7676 - accuracy: 0.0706\n",
            "Epoch 15/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 4.7223 - accuracy: 0.0618\n",
            "Epoch 16/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.6693 - accuracy: 0.0795\n",
            "Epoch 17/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.6190 - accuracy: 0.0839\n",
            "Epoch 18/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 4.5661 - accuracy: 0.0839\n",
            "Epoch 19/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.5301 - accuracy: 0.1170\n",
            "Epoch 20/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.4907 - accuracy: 0.1192\n",
            "Epoch 21/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.4455 - accuracy: 0.1192\n",
            "Epoch 22/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 4.3986 - accuracy: 0.1258\n",
            "Epoch 23/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.3620 - accuracy: 0.1148\n",
            "Epoch 24/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.3345 - accuracy: 0.1280\n",
            "Epoch 25/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.2815 - accuracy: 0.1236\n",
            "Epoch 26/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.2352 - accuracy: 0.1413\n",
            "Epoch 27/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.1958 - accuracy: 0.1391\n",
            "Epoch 28/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.1548 - accuracy: 0.1413\n",
            "Epoch 29/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.1212 - accuracy: 0.1435\n",
            "Epoch 30/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.0761 - accuracy: 0.1523\n",
            "Epoch 31/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 4.0321 - accuracy: 0.1545\n",
            "Epoch 32/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.9885 - accuracy: 0.1589\n",
            "Epoch 33/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.9484 - accuracy: 0.1766\n",
            "Epoch 34/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.9057 - accuracy: 0.1854\n",
            "Epoch 35/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 3.8613 - accuracy: 0.1943\n",
            "Epoch 36/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.8289 - accuracy: 0.1921\n",
            "Epoch 37/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.7744 - accuracy: 0.2075\n",
            "Epoch 38/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.7293 - accuracy: 0.2119\n",
            "Epoch 39/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.6965 - accuracy: 0.2208\n",
            "Epoch 40/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.6502 - accuracy: 0.2230\n",
            "Epoch 41/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.6074 - accuracy: 0.2494\n",
            "Epoch 42/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 3.5612 - accuracy: 0.2428\n",
            "Epoch 43/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.5199 - accuracy: 0.2384\n",
            "Epoch 44/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 3.4845 - accuracy: 0.2759\n",
            "Epoch 45/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.4549 - accuracy: 0.2693\n",
            "Epoch 46/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.4108 - accuracy: 0.2804\n",
            "Epoch 47/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 3.3647 - accuracy: 0.2693\n",
            "Epoch 48/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.3316 - accuracy: 0.2848\n",
            "Epoch 49/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.3172 - accuracy: 0.2892\n",
            "Epoch 50/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.2670 - accuracy: 0.2980\n",
            "Epoch 51/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.2288 - accuracy: 0.3223\n",
            "Epoch 52/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.1949 - accuracy: 0.3311\n",
            "Epoch 53/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.1500 - accuracy: 0.3355\n",
            "Epoch 54/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.1149 - accuracy: 0.3267\n",
            "Epoch 55/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.0789 - accuracy: 0.3664\n",
            "Epoch 56/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.0361 - accuracy: 0.3885\n",
            "Epoch 57/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 3.0056 - accuracy: 0.3885\n",
            "Epoch 58/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.9727 - accuracy: 0.4018\n",
            "Epoch 59/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.9385 - accuracy: 0.4194\n",
            "Epoch 60/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.9067 - accuracy: 0.4084\n",
            "Epoch 61/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 2.8746 - accuracy: 0.4216\n",
            "Epoch 62/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.8403 - accuracy: 0.4305\n",
            "Epoch 63/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.8080 - accuracy: 0.4305\n",
            "Epoch 64/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 2.7922 - accuracy: 0.4437\n",
            "Epoch 65/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.7799 - accuracy: 0.4437\n",
            "Epoch 66/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.7515 - accuracy: 0.4437\n",
            "Epoch 67/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.7102 - accuracy: 0.4459\n",
            "Epoch 68/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.6773 - accuracy: 0.4547\n",
            "Epoch 69/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.6500 - accuracy: 0.4481\n",
            "Epoch 70/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.6138 - accuracy: 0.4614\n",
            "Epoch 71/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 2.5826 - accuracy: 0.4834\n",
            "Epoch 72/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.5671 - accuracy: 0.4746\n",
            "Epoch 73/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.5876 - accuracy: 0.4658\n",
            "Epoch 74/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.5565 - accuracy: 0.4923\n",
            "Epoch 75/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.5245 - accuracy: 0.4834\n",
            "Epoch 76/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.4975 - accuracy: 0.4923\n",
            "Epoch 77/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.4575 - accuracy: 0.5166\n",
            "Epoch 78/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.4238 - accuracy: 0.5033\n",
            "Epoch 79/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.3909 - accuracy: 0.5430\n",
            "Epoch 80/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.3649 - accuracy: 0.5430\n",
            "Epoch 81/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 2.3398 - accuracy: 0.5519\n",
            "Epoch 82/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 2.3106 - accuracy: 0.5497\n",
            "Epoch 83/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 2.2890 - accuracy: 0.5673\n",
            "Epoch 84/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.2563 - accuracy: 0.5784\n",
            "Epoch 85/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.2286 - accuracy: 0.5850\n",
            "Epoch 86/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.2026 - accuracy: 0.5938\n",
            "Epoch 87/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.1783 - accuracy: 0.5916\n",
            "Epoch 88/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.1572 - accuracy: 0.6115\n",
            "Epoch 89/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.1333 - accuracy: 0.6115\n",
            "Epoch 90/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.1176 - accuracy: 0.6159\n",
            "Epoch 91/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.0947 - accuracy: 0.6093\n",
            "Epoch 92/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.0658 - accuracy: 0.6269\n",
            "Epoch 93/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 2.0501 - accuracy: 0.6358\n",
            "Epoch 94/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.0366 - accuracy: 0.6313\n",
            "Epoch 95/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 2.0224 - accuracy: 0.6313\n",
            "Epoch 96/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 1.9977 - accuracy: 0.6556\n",
            "Epoch 97/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 1.9667 - accuracy: 0.6689\n",
            "Epoch 98/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 1.9476 - accuracy: 0.6821\n",
            "Epoch 99/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 1.9199 - accuracy: 0.6843\n",
            "Epoch 100/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 1.9032 - accuracy: 0.6909\n",
            "Epoch 101/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 1.8863 - accuracy: 0.6821\n",
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            "15/15 [==============================] - 0s 7ms/step - loss: 0.1438 - accuracy: 0.9492\n",
            "Epoch 456/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1439 - accuracy: 0.9492\n",
            "Epoch 457/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1436 - accuracy: 0.9492\n",
            "Epoch 458/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1436 - accuracy: 0.9448\n",
            "Epoch 459/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1455 - accuracy: 0.9426\n",
            "Epoch 460/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1442 - accuracy: 0.9536\n",
            "Epoch 461/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1428 - accuracy: 0.9492\n",
            "Epoch 462/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1416 - accuracy: 0.9492\n",
            "Epoch 463/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1407 - accuracy: 0.9492\n",
            "Epoch 464/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1400 - accuracy: 0.9492\n",
            "Epoch 465/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1400 - accuracy: 0.9448\n",
            "Epoch 466/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1403 - accuracy: 0.9514\n",
            "Epoch 467/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1401 - accuracy: 0.9448\n",
            "Epoch 468/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1386 - accuracy: 0.9536\n",
            "Epoch 469/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1376 - accuracy: 0.9470\n",
            "Epoch 470/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1383 - accuracy: 0.9492\n",
            "Epoch 471/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1378 - accuracy: 0.9470\n",
            "Epoch 472/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1368 - accuracy: 0.9514\n",
            "Epoch 473/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1369 - accuracy: 0.9492\n",
            "Epoch 474/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1371 - accuracy: 0.9448\n",
            "Epoch 475/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1358 - accuracy: 0.9514\n",
            "Epoch 476/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1353 - accuracy: 0.9426\n",
            "Epoch 477/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1357 - accuracy: 0.9404\n",
            "Epoch 478/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1350 - accuracy: 0.9536\n",
            "Epoch 479/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1342 - accuracy: 0.9514\n",
            "Epoch 480/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1340 - accuracy: 0.9470\n",
            "Epoch 481/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1339 - accuracy: 0.9492\n",
            "Epoch 482/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1328 - accuracy: 0.9492\n",
            "Epoch 483/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1318 - accuracy: 0.9514\n",
            "Epoch 484/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1335 - accuracy: 0.9470\n",
            "Epoch 485/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1322 - accuracy: 0.9492\n",
            "Epoch 486/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1327 - accuracy: 0.9470\n",
            "Epoch 487/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1309 - accuracy: 0.9426\n",
            "Epoch 488/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1314 - accuracy: 0.9470\n",
            "Epoch 489/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1315 - accuracy: 0.9492\n",
            "Epoch 490/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1307 - accuracy: 0.9470\n",
            "Epoch 491/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1298 - accuracy: 0.9492\n",
            "Epoch 492/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1294 - accuracy: 0.9448\n",
            "Epoch 493/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1296 - accuracy: 0.9448\n",
            "Epoch 494/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1287 - accuracy: 0.9492\n",
            "Epoch 495/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1290 - accuracy: 0.9448\n",
            "Epoch 496/500\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.1286 - accuracy: 0.9426\n",
            "Epoch 497/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1280 - accuracy: 0.9448\n",
            "Epoch 498/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1282 - accuracy: 0.9426\n",
            "Epoch 499/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1276 - accuracy: 0.9470\n",
            "Epoch 500/500\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1267 - accuracy: 0.9514\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3YXGelKThoTT",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "poeprYK8h-c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "982402dd-6e5e-4ff0-cb87-af1ba91c5262"
      },
      "source": [
        "plot_graphs(history, 'accuracy')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6Vc6PHgxa6Hm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "957d74e2-877b-49bf-b4b7-7ae812f1b01e"
      },
      "source": [
        "seed_text = \"Laurence went to dublin\"\n",
        "next_words = 100\n",
        "# mtlb seed_text k baad or mazeed 100 words add kro. ya predict kro. \n",
        "  \n",
        "for _ in range(next_words):\n",
        "\ttoken_list = tokenizer.texts_to_sequences([seed_text])[0]\n",
        "\ttoken_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')\n",
        "\tpredicted = model.predict_classes(token_list, verbose=0)\n",
        "\toutput_word = \"\"\n",
        "\tfor word, index in tokenizer.word_index.items():\n",
        "\t\tif index == predicted:\n",
        "\t\t\toutput_word = word\n",
        "\t\t\tbreak\n",
        "\tseed_text += \" \" + output_word\n",
        "print(seed_text)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-35-39d0f938e410>:8: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "Laurence went to dublin as red as a cask cask water rose glisten able able hearty together boys mavrone mavrone mavrone mavrone mavrone mavrone mavrone mavrone me ground me three again mavrone mavrone mavrone mavrone mavrone ball me me three mavrone mavrone mavrone mavrone mavrone mavrone ball me me three again mavrone mavrone mavrone mavrone mavrone ball me me three mavrone mavrone mavrone mavrone mavrone mavrone ball me me three again mavrone mavrone mavrone mavrone mavrone ball me me three mavrone mavrone mavrone mavrone mavrone mavrone ball me me three again mavrone mavrone mavrone mavrone mavrone ball me me three mavrone mavrone mavrone mavrone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bodAosgd6ctZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "709f4fc5-9389-4b0b-9f9f-ae34ca1cba02"
      },
      "source": [
        "# breaking above code into cells for test.\n",
        "\n",
        "seed_text = \"Laurence went to dublin\"\n",
        "next_words = 100\n",
        "\n",
        "for _ in range(next_words):\n",
        "  token_list = tokenizer.texts_to_sequences([seed_text])[0]\n",
        "  token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')\n",
        "  print(token_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  0   0   0   0   0   0   0 134  13  59]]\n",
            "[[  0   0   0   0   0   0   0 134  13  59]]\n",
            "[[  0   0   0   0   0   0   0 134  13  59]]\n",
            "[[  0   0   0   0   0   0   0 134  13  59]]\n",
            "[[  0   0   0   0   0   0   0 134  13  59]]\n",
            "[[  0   0   0   0   0   0   0 134  13  59]]\n",
            "[[  0   0   0   0   0   0   0 134  13  59]]\n",
            "[[  0   0   0   0   0   0   0 134  13  59]]\n",
            "[[  0   0   0   0   0   0   0 134  13  59]]\n",
            "[[  0   0   0   0   0   0   0 134  13  59]]\n",
            "[[  0   0   0   0   0   0   0 134  13  59]]\n",
            "[[  0   0   0   0   0   0   0 134  13  59]]\n",
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            "[[  0   0   0   0   0   0   0 134  13  59]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d4GWzu_47BO0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "077a369c-82c3-446f-e293-9d61834de396"
      },
      "source": [
        "seed_text = \"Laurence went to dublin\"\n",
        "next_words = 100\n",
        "\n",
        "for _ in range(next_words):\n",
        "  token_list = tokenizer.texts_to_sequences([seed_text])[0]\n",
        "  token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')\n",
        "  predicted = model.predict_classes(token_list, verbose=0)\n",
        "  # this will give us the prediction of most likely next word in the form of token. \n",
        "  output_word = \"\"\n",
        "  # print(tokenizer.word_index.items()) # .items list and tuple me convert kr dy ga output ko\n",
        "  # now we will turn token back into word. This is known as reverse lookup. \n",
        "  for word, index in tokenizer.word_index.items():\n",
        "    if index == predicted:\n",
        "      output_word = word\n",
        "      break\n",
        "  seed_text += \" \" + output_word\n",
        "print(seed_text)\n",
        "    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Laurence went to dublin as red as a cask cask water rose glisten able able hearty together boys mavrone mavrone mavrone mavrone mavrone mavrone mavrone mavrone me ground me three again mavrone mavrone mavrone mavrone mavrone ball me me three mavrone mavrone mavrone mavrone mavrone mavrone ball me me three again mavrone mavrone mavrone mavrone mavrone ball me me three mavrone mavrone mavrone mavrone mavrone mavrone ball me me three again mavrone mavrone mavrone mavrone mavrone ball me me three mavrone mavrone mavrone mavrone mavrone mavrone ball me me three again mavrone mavrone mavrone mavrone mavrone ball me me three mavrone mavrone mavrone mavrone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wA920ChS7BM1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "seed_text = \"Laurence went to dublin\"\n",
        "next_words = 100\n",
        "# mtlb seed_text k baad or mazeed 100 words add kro. ya predict kro. \n",
        "  \n",
        "for _ in range(next_words):\n",
        "\ttoken_list = tokenizer.texts_to_sequences([seed_text])[0]\n",
        "\ttoken_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')\n",
        "\tpredicted = model.predict_classes(token_list, verbose=0)\n",
        "\toutput_word = \"\"\n",
        "\tfor word, index in tokenizer.word_index.items():\n",
        "\t\tif index == predicted:\n",
        "\t\t\toutput_word = word\n",
        "\t\t\tbreak\n",
        "\tseed_text += \" \" + output_word\n",
        "print(seed_text)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xxu5MtPv7BJ2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNuVedvg6TRs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}