src/custom_scripts/TextGeneration.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TextGeneration.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "NadIRfUq3Ex0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import LSTM\n",
        "from keras.utils import np_utils\n",
        "import requests\n",
        "import re"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4isrTwSG3GE9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sonnet_url = \"http://www.gutenberg.org/cache/epub/1041/pg1041.txt\"\n",
        "sonnets = requests.get(sonnet_url).text\n",
        "text = sonnets.lower()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6s1Gqf4G515y",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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wdXRFbGVtZW50LmRpc2FibGVkID0gdHJ1ZTsKCiAgZm9yIChjb25zdCBmaWxlIG9mIGZpbGVzKSB7CiAgICBjb25zdCBsaSA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2xpJyk7CiAgICBsaS5hcHBlbmQoc3BhbihmaWxlLm5hbWUsIHtmb250V2VpZ2h0OiAnYm9sZCd9KSk7CiAgICBsaS5hcHBlbmQoc3BhbigKICAgICAgICBgKCR7ZmlsZS50eXBlIHx8ICduL2EnfSkgLSAke2ZpbGUuc2l6ZX0gYnl0ZXMsIGAgKwogICAgICAgIGBsYXN0IG1vZGlmaWVkOiAkewogICAgICAgICAgICBmaWxlLmxhc3RNb2RpZmllZERhdGUgPyBmaWxlLmxhc3RNb2RpZmllZERhdGUudG9Mb2NhbGVEYXRlU3RyaW5nKCkgOgogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAnbi9hJ30gLSBgKSk7CiAgICBjb25zdCBwZXJjZW50ID0gc3BhbignMCUgZG9uZScpOwogICAgbGkuYXBwZW5kQ2hpbGQocGVyY2VudCk7CgogICAgb3V0cHV0RWxlbWVudC5hcHBlbmRDaGlsZChsaSk7CgogICAgY29uc3QgZmlsZURhdGFQcm9taXNlID0gbmV3IFByb21pc2UoKHJlc29sdmUpID0+IHsKICAgICAgY29uc3QgcmVhZGVyID0gbmV3IEZpbGVSZWFkZXIoKTsKICAgICAgcmVhZGVyLm9ubG9hZCA9IChlKSA9PiB7CiAgICAgICAgcmVzb2x2ZShlLnRhcmdldC5yZXN1bHQpOwogICAgICB9OwogICAgICByZWFkZXIucmVhZEFzQXJyYXlCdWZmZXIoZmlsZSk7CiAgICB9KTsKICAgIC8vIFdhaXQgZm9yIHRoZSBkYXRhIHRvIGJlIHJlYWR5LgogICAgbGV0IGZpbGVEYXRhID0geWllbGQgewogICAgICBwcm9taXNlOiBmaWxlRGF0YVByb21pc2UsCiAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgYWN0aW9uOiAnY29udGludWUnLAogICAgICB9CiAgICB9OwoKICAgIC8vIFVzZSBhIGNodW5rZWQgc2VuZGluZyB0byBhdm9pZCBtZXNzYWdlIHNpemUgbGltaXRzLiBTZWUgYi82MjExNTY2MC4KICAgIGxldCBwb3NpdGlvbiA9IDA7CiAgICB3aGlsZSAocG9zaXRpb24gPCBmaWxlRGF0YS5ieXRlTGVuZ3RoKSB7CiAgICAgIGNvbnN0IGxlbmd0aCA9IE1hdGgubWluKGZpbGVEYXRhLmJ5dGVMZW5ndGggLSBwb3NpdGlvbiwgTUFYX1BBWUxPQURfU0laRSk7CiAgICAgIGNvbnN0IGNodW5rID0gbmV3IFVpbnQ4QXJyYXkoZmlsZURhdGEsIHBvc2l0aW9uLCBsZW5ndGgpOwogICAgICBwb3NpdGlvbiArPSBsZW5ndGg7CgogICAgICBjb25zdCBiYXNlNjQgPSBidG9hKFN0cmluZy5mcm9tQ2hhckNvZGUuYXBwbHkobnVsbCwgY2h1bmspKTsKICAgICAgeWllbGQgewogICAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgICBhY3Rpb246ICdhcHBlbmQnLAogICAgICAgICAgZmlsZTogZmlsZS5uYW1lLAogICAgICAgICAgZGF0YTogYmFzZTY0LAogICAgICAgIH0sCiAgICAgIH07CiAgICAgIHBlcmNlbnQudGV4dENvbnRlbnQgPQogICAgICAgICAgYCR7TWF0aC5yb3VuZCgocG9zaXRpb24gLyBmaWxlRGF0YS5ieXRlTGVuZ3RoKSAqIDEwMCl9JSBkb25lYDsKICAgIH0KICB9CgogIC8vIEFsbCBkb25lLgogIHlpZWxkIHsKICAgIHJlc3BvbnNlOiB7CiAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgIH0KICB9Owp9CgpzY29wZS5nb29nbGUgPSBzY29wZS5nb29nbGUgfHwge307CnNjb3BlLmdvb2dsZS5jb2xhYiA9IHNjb3BlLmdvb2dsZS5jb2xhYiB8fCB7fTsKc2NvcGUuZ29vZ2xlLmNvbGFiLl9maWxlcyA9IHsKICBfdXBsb2FkRmlsZXMsCiAgX3VwbG9hZEZpbGVzQ29udGludWUsCn07Cn0pKHNlbGYpOwo=",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "6f7e5034-243b-4347-c472-987b80e649de"
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-183447c2-d91b-4ffe-b197-867a272b9db9\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-183447c2-d91b-4ffe-b197-867a272b9db9\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving all.csv to all.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Qa1-Dfbe5_4Q",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "poems = pd.read_csv(\"all.csv\", encoding=\"utf8\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5kCTG0SM6UGB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "4f1e076c-ff9e-4c65-ab59-312d80aef856"
      },
      "cell_type": "code",
      "source": [
        "poem_content[:5000]"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Let the bird of loudest lay\\r\\nOn the sole Arabian tree\\r\\nHerald sad and trumpet be,\\r\\nTo whose sound chaste wings obey.\\r\\n\\r\\nBut thou shrieking harbinger,\\r\\nFoul precurrer of the fiend,\\r\\nAugur of the fever\\'s end,\\r\\nTo this troop come thou not near.\\r\\n\\r\\nFrom this session interdict\\r\\nEvery fowl of tyrant wing,\\r\\nSave the eagle, feather\\'d king;\\r\\nKeep the obsequy so strict.\\r\\n\\r\\nLet the priest in surplice white,\\r\\nThat defunctive music can,\\r\\nBe the death-divining swan,\\r\\nLest the requiem lack his right.\\r\\n\\r\\nAnd thou treble-dated crow,\\r\\nThat thy sable gender mak\\'st\\r\\nWith the breath thou giv\\'st and tak\\'st,\\r\\n\\'Mongst our mourners shalt thou go.\\r\\n\\r\\nHere the anthem doth commence:\\r\\nLove and constancy is dead;\\r\\nPhoenix and the Turtle fled\\r\\nIn a mutual flame from hence.\\r\\n\\r\\nSo they lov\\'d, as love in twain\\r\\nHad the essence but in one;\\r\\nTwo distincts, division none:\\r\\nNumber there in love was slain.\\r\\n\\r\\nHearts remote, yet not asunder;\\r\\nDistance and no space was seen\\r\\n\\'Twixt this Turtle and his queen:\\r\\nBut in them it were a wonder.\\r\\n\\r\\nSo between them love did shine\\r\\nThat the Turtle saw his right\\r\\nFlaming in the Phoenix\\' sight:\\r\\nEither was the other\\'s mine.\\r\\n\\r\\nProperty was thus appalled\\r\\nThat the self was not the same;\\r\\nSingle nature\\'s double name\\r\\nNeither two nor one was called.\\r\\n\\r\\nReason, in itself confounded,\\r\\nSaw division grow together,\\r\\nTo themselves yet either neither,\\r\\nSimple were so well compounded;\\r\\n\\r\\nThat it cried, \"How true a twain\\r\\nSeemeth this concordant one!\\r\\nLove has reason, reason none,\\r\\nIf what parts can so remain.\"\\r\\n\\r\\nWhereupon it made this threne\\r\\nTo the Phoenix and the Dove,\\r\\nCo-supremes and stars of love,\\r\\nAs chorus to their tragic scene:\\r\\n\\r\\n                 threnos\\r\\n\\r\\nBeauty, truth, and rarity,\\r\\nGrace in all simplicity,\\r\\nHere enclos\\'d, in cinders lie.\\r\\n\\r\\nDeath is now the Phoenix\\' nest,\\r\\nAnd the Turtle\\'s loyal breast\\r\\nTo eternity doth rest,\\r\\n\\r\\nLeaving no posterity:\\r\\n\\'Twas not their infirmity,\\r\\nIt was married chastity.\\r\\n\\r\\nTruth may seem but cannot be;\\r\\nBeauty brag but \\'tis not she;\\r\\nTruth and beauty buried be.\\r\\n\\r\\nTo this urn let those repair\\r\\nThat are either true or fair;\\r\\nFor these dead birds sigh a prayer.\\n\\n\\nSir Charles into my chamber coming in,\\r\\nWhen I was writing of my Fairy Queen;\\r\\nI praysaid hewhen Queen Mab you do see\\r\\nPresent my service to her Majesty:\\r\\nAnd tell her I have heard Fame\\'s loud report\\r\\nBoth of her beauty and her stately court.\\r\\nWhen I Queen Mab within my fancy viewed,\\r\\nMy thoughts bowed low, fearing I should be rude;\\r\\nKissing her garment thin which fancy made,\\r\\nI knelt upon a thought, like one that prayed;\\r\\nAnd then, in whispers soft, I did present\\r\\nHis humble service which in mirth was sent;\\r\\nThus by imagination I have been\\r\\nIn Fairy court and seen the Fairy Queen.\\n\\n\\nOur vice runs beyond all that old men saw,\\r\\nAnd far authentically above our laws,\\r\\nAnd scorning virtues safe and golden mean,\\r\\nSits uncontrolled upon the high extreme.\\r\\nCirces, thy monsters painted out the hue,\\r\\nOf feigned filthiness, but ours is true.\\r\\nOur vice puts down all proverbs and all themes,\\r\\nOur vice excels all fables and all dreams.\\n\\n\\nLo I the man, whose Muse whilome did maske,\\r\\nAs time her taught in lowly Shepheards weeds,\\r\\nAm now enforst a far unfitter taske,\\r\\nFor trumpets sterne to chaunge mine Oaten reeds,\\r\\nAnd sing of Knights and Ladies gentle deeds;\\r\\nWhose prayses having slept in silence long,\\r\\nMe, all too meane, the sacred Muse areeds\\r\\nTo blazon broad emongst her learned throng:\\r\\nFierce warres and faithful loves shall moralize my song.\\r\\nHelpe then, O holy Virgin chiefe of nine,\\r\\nThy weaker Novice to performe thy will,\\r\\nLay forth out of thine everlasting scryne\\r\\nThe antique rolles, which there lye hidden still,\\r\\nOf Faerie knights and fairest Tanaquill,\\r\\nWhom that most noble Briton Prince so long\\r\\nSought through the world, and suffered so much ill,\\r\\nThat I must rue his undeserved wrong:\\r\\nO helpe thou my weake wit, and sharpen my dull tong.\\r\\nAnd thou most dreaded impe of highest Jove,\\r\\nFaire Venus sonne, that with thy cruell dart\\r\\nAt that good knight so cunningly didst rove,\\r\\nThat glorious fire it kindled in his hart,\\r\\nLay now thy deadly Heben bow apart,\\r\\nAnd with thy mother milde come to mine ayde:\\r\\nCome both, and with you bring triumphant Mart,\\r\\nIn loves and gentle jollities arrayd,\\r\\nAfter his murdrous spoiles and bloudy rage allayd.\\r\\nAnd with them eke, O Goddesse heavenly bright,\\r\\nMirrour of grace and Majestie divine,\\r\\nGreat Lady of the greatest Isle, whose light\\r\\nLike Phoebus lampe throughout the world doth shine,\\r\\nShed thy faire beames into my feeble eyne,\\r\\nAnd raise my thoughts too humble and too vile,\\r\\nTo thinke of that true glorious type of thine,\\r\\nThe argument of mine afflicted stile:\\r\\nThe which to heare, vouchsafe, O dearest dred a-while.\\r\\n\\r\\ni\\r\\nA Gentle Knight was pricking on the plaine,\\r\\nY cladd in mightie armes and silver shielde,\\r\\nWherein old dints of deepe wounds did remaine,\\r\\nThe cruell markes of many a bloudy fielde;\\r\\nYet armes till that time did he never wield:\\r\\nHis angry steede did chide his foming bitt,'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "qNtClGsy6Hef",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "poem_clean = re.sub(r'[^\\x00-\\x7F]+',' ', poem_content)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dYW2RjWU8Ifw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0532a8c5-dd3d-4cfa-a28c-62f2b818d289"
      },
      "cell_type": "code",
      "source": [
        "len(poem_clean)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "568917"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "id": "jJgTpy2m3oEA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "characters = sorted(list(set(poem_clean)))\n",
        "n_to_char = {n:char for n, char in enumerate(characters)}\n",
        "char_to_n = {char:n for n, char in enumerate(characters)}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "snhWsVKf35KK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X = []\n",
        "Y = []\n",
        "length = len(poem_clean)\n",
        "seq_length = 100\n",
        "for i in range(0, length-seq_length, 1):\n",
        "   sequence = poem_clean[i:i + seq_length]\n",
        "   label = poem_clean[i + seq_length]\n",
        "   X.append([char_to_n[char] for char in sequence])\n",
        "   Y.append(char_to_n[label])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rR00DKUX3-R9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_modified = np.reshape(X, (len(X), seq_length, 1))\n",
        "X_modified = X_modified / float(len(characters))\n",
        "Y_modified = np_utils.to_categorical(Y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4MetiiBt48_q",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(400, input_shape=(X_modified.shape[1], X_modified.shape[2]), return_sequences=True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(400))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(Y_modified.shape[1], activation='softmax'))\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cFoDk6r35TN0",
        "colab_type": "code",
        "outputId": "db5f92c6-d29b-4938-c2c5-f440a4587875",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 803
        }
      },
      "cell_type": "code",
      "source": [
        "model.fit(X_modified, Y_modified, epochs=1, batch_size=100)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1\n",
            "  1400/568817 [..............................] - ETA: 5:33:02 - loss: 3.6130"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-c9653ec66936>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_modified\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_modified\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[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\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/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\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    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\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    200\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\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/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\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   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\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[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\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/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\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   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\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   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "9KkcW5lU5BJO",
        "colab_type": "code",
        "outputId": "d929fb0b-1b0b-4ef2-dc15-dd15567f960f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "string_mapped = X[97]\n",
        "print(len(string_mapped))\n",
        "full_string = [n_to_char[value] for value in string_mapped]\n",
        "# generating characters\n",
        "for i in range(400):\n",
        "    x = np.reshape(string_mapped,(1,len(string_mapped), 1))\n",
        "    x = x / float(len(characters))\n",
        "\n",
        "    pred_index = np.argmax(model.predict(x, verbose=0))\n",
        "    seq = [n_to_char[value] for value in string_mapped]\n",
        "    full_string.append(n_to_char[pred_index])\n",
        "\n",
        "    string_mapped.append(pred_index)\n",
        "    string_mapped = string_mapped[1:len(string_mapped)]"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LoH0W8XLQc_S",
        "colab_type": "code",
        "outputId": "d2351204-b6ef-4acd-8659-75bf33f9db2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "#combining text\n",
        "txt=\"\"\n",
        "for char in full_string:\n",
        "    txt = txt+char\n",
        "txt"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'r the use of anyone anywhere at no cost and with\\r\\nalmost no restrictions whatsoever.  you may copy io the the the the thee \\r  the the the the the the the she thee \\r\\n    xxi \\n\\r\\n  th the the the the the the the the shee \\r  the the the the the the the she thee \\r  the the the the the the the she thee \\r  the the the the the the the she thee \\r  the the the the the the the she thee \\r  the the the the the the the she thee \\r  the the the the the the the she thee \\r  the the the the the the the she thee \\r '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "FCsb83fAQhfH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "full_string"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}