Text_creation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "data = '''In the town of Athy one Jeremy Lanigan\r\n",
    "Battered away til he hadnt a pound.\r\n",
    "His father died and made him a man again\r\n",
    "Left him a farm and ten acres of ground.\r\n",
    "\r\n",
    "He gave a grand party for friends and relations\r\n",
    "Who didnt forget him when come to the wall,\r\n",
    "And if youll but listen Ill make your eyes glisten\r\n",
    "Of the rows and the ructions of Lanigan’s Ball.\r\n",
    "\r\n",
    "Myself to be sure got free invitation,\r\n",
    "For all the nice girls and boys I might ask,\r\n",
    "And just in a minute both friends and relations\r\n",
    "Were dancing round merry as bees round a cask.\r\n",
    "\r\n",
    "Judy ODaly, that nice little milliner,\r\n",
    "She tipped me a wink for to give her a call,\r\n",
    "And I soon arrived with Peggy McGilligan\r\n",
    "Just in time for Lanigans Ball.\r\n",
    "'''\r\n",
    "\r\n",
    "\r\n",
    "#the above string would be used for this session \r\n",
    "#our aim is to use Tensorflow to create Text"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "from tensorflow.keras.preprocessing.text import Tokenizer\r\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\r\n",
    "from tensorflow.keras import utils\r\n",
    "from tensorflow.keras.models import Sequential\r\n",
    "from tensorflow.keras.layers import Embedding, Bidirectional, LSTM, Dense\r\n",
    "\r\n",
    "import numpy as np\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "\r\n",
    "tokenizer = Tokenizer()\r\n",
    "corpus = data.lower().split('\\n')\r\n",
    "tokenizer.fit_on_texts(corpus)\r\n",
    "total_words = len(tokenizer.word_index) + 1\r\n",
    "\r\n",
    "input_sequences = []\r\n",
    "for line in corpus:\r\n",
    "    token_list = tokenizer.texts_to_sequences([line])[0]\r\n",
    "    for i in range(1, len(token_list)):\r\n",
    "        n_gram_sentences = token_list[:i+1]\r\n",
    "        input_sequences.append(n_gram_sentences)\r\n",
    "\r\n",
    "max_sequence_len = max([len(x) for x in input_sequences]) #get the length of the longest seq\r\n",
    "input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre')\r\n",
    "\r\n",
    "xs, labels = input_sequences[:, :-1], input_sequences[:, -1]\r\n",
    "ys = utils.to_categorical(labels, num_classes=total_words)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "model = Sequential()\r\n",
    "model.add(Embedding(total_words, 8))\r\n",
    "model.add(Bidirectional(LSTM(max_sequence_len-1))) #subtract one as we would be predicting the last WORD\r\n",
    "model.add(Dense(total_words, activation='softmax'))\r\n",
    "\r\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\r\n",
    "history = model.fit(xs, ys, epochs=1500, verbose=0)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "plt.plot(history.history['loss'])\r\n",
    "plt.title('MODEL LOSS')\r\n",
    "plt.ylabel('loss')\r\n",
    "plt.xlabel('epoch')\r\n",
    "plt.legend('train', loc='best')\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "plt.plot(history.history['accuracy'])\r\n",
    "plt.title('MODEL accuracy')\r\n",
    "plt.xlabel('epoch')\r\n",
    "plt.ylabel('accuracy')\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"277.314375pt\" version=\"1.1\" viewBox=\"0 0 385.78125 277.314375\" width=\"385.78125pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2021-08-29T17:41:25.610586</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.4.2, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  </rdf:RDF>\r\n </metadata>\r\n <defs>\r\n  <style 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262.043886 53.009416 \r\nL 262.449975 49.541473 \r\nL 262.653019 51.275444 \r\nL 262.856064 54.743387 \r\nL 263.059108 53.009416 \r\nL 263.262152 49.541473 \r\nL 263.465197 51.275444 \r\nL 263.668241 49.541473 \r\nL 263.871286 51.275444 \r\nL 264.07433 49.541473 \r\nL 264.277375 51.275444 \r\nL 264.480419 47.807502 \r\nL 264.683464 49.541473 \r\nL 265.089553 49.541473 \r\nL 265.292597 51.275444 \r\nL 265.495641 46.073531 \r\nL 265.698686 49.541473 \r\nL 265.90173 46.073531 \r\nL 266.916953 46.073531 \r\nL 267.119997 47.807502 \r\nL 267.323041 47.807502 \r\nL 267.526086 46.073531 \r\nL 268.541308 46.073531 \r\nL 268.744353 47.807502 \r\nL 268.947397 47.807502 \r\nL 269.150442 46.073531 \r\nL 269.353486 46.073531 \r\nL 269.55653 44.33956 \r\nL 269.962619 47.807502 \r\nL 270.165664 46.073531 \r\nL 270.571753 46.073531 \r\nL 270.774797 47.807502 \r\nL 270.977842 46.073531 \r\nL 271.180886 46.073531 \r\nL 271.383931 47.807502 \r\nL 271.586975 47.807502 \r\nL 271.993064 44.33956 \r\nL 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "source": [
    "#let's try to predict text\r\n",
    "seed_text = 'in the town of athy'\r\n",
    "toks_list = tokenizer.texts_to_sequences([seed_text])[0]\r\n",
    "toks_list = pad_sequences([toks_list], maxlen=max_sequence_len-1, padding='pre')\r\n",
    "\r\n",
    "#check the first line of our poem for the answer, which is 'ONE\"\r\n",
    "predicted = np.argmax(model.predict(toks_list), axis=-1)\r\n",
    "predicted"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([19], dtype=int64)"
      ]
     },
     "metadata": {},
     "execution_count": 51
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "#check if we got the answer correctly\r\n",
    "for word, index in tokenizer.word_index.items():\r\n",
    "    if index == predicted:\r\n",
    "        print(word)\r\n",
    "        break"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "one\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "source": [
    "#now let's try to create text by continously appending previous words.\r\n",
    "\r\n",
    "seed_text = 'sweet jeremy saw dublin'\r\n",
    "next_words = 10\r\n",
    "\r\n",
    "for _ in range(next_words):\r\n",
    "    toks_list = tokenizer.texts_to_sequences([seed_text])[0]\r\n",
    "    toks_list = pad_sequences([toks_list], maxlen=max_sequence_len-1, padding='pre')\r\n",
    "    predicted = np.argmax(model.predict(toks_list, verbose=0), axis=-1)\r\n",
    "    output_word = ''\r\n",
    "\r\n",
    "    for word, index in tokenizer.word_index.items():\r\n",
    "        if index == int(predicted):\r\n",
    "            output_word = word\r\n",
    "            break\r\n",
    "    seed_text += ' ' + output_word\r\n",
    "\r\n",
    "print(seed_text)\r\n",
    "#the text being output is non-sensical because the training data is little.\r\n",
    "#we can get better TEXT CREATED if the training data is huge"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "sweet jeremy saw dublin youll but but listen make your eyes glisten ground ground\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "#another model architecture to try\r\n",
    "model = Sequential()\r\n",
    "model.add(Embedding(total_words, 8))\r\n",
    "model.add(Bidirectional(LSTM(max_sequence_len-1, return_sequences=True)))\r\n",
    "model.add(Bidirectional(LSTM(max_sequence_len-1)))\r\n",
    "model.add(Dense(total_words, activation='softmax'))\r\n",
    "\r\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\r\n",
    "history = model.fit(xs, ys, epochs=1000, verbose=0)\r\n",
    "\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "window_size = 10\r\n",
    "sentences = []\r\n",
    "alltext = []\r\n",
    "data = open('enter file path')\r\n",
    "corpus = data.lower()\r\n",
    "words = corpus.split(' ')\r\n",
    "range_size = len(words) - max_sequence_len\r\n",
    "for i in range(0, range_size):\r\n",
    "    thissentence = ''\r\n",
    "    for word in range(0, window_size-1):\r\n",
    "        word = words[i+word]\r\n",
    "        thissentence = thissentence + word\r\n",
    "        thissentence = thissentence + ' '\r\n",
    "    sentences.append(thissentence)\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "\r\n"
   ],
   "outputs": [],
   "metadata": {}
  }
 ],
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