text_prediction_using_LSTM_.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://machinelearningmastery.com/develop-word-based-neural-language-models-python-keras/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary Size: 22\n",
      "Total Sequences: 24\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 1, 10)             220       \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 50)                12200     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 22)                1122      \n",
      "=================================================================\n",
      "Total params: 13,542\n",
      "Trainable params: 13,542\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 155/500\n",
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 0s - loss: 0.2385 - acc: 0.8750\n",
      "Epoch 491/500\n",
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      "Epoch 499/500\n",
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      "Epoch 500/500\n",
      " - 0s - loss: 0.2353 - acc: 0.8750\n",
      "Jack and jill came tumbling after up\n"
     ]
    }
   ],
   "source": [
    "from numpy import array\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.utils import to_categorical\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Embedding\n",
    "\n",
    "# generate a sequence from the model\n",
    "def generate_seq(model, tokenizer, seed_text, n_words):\n",
    "\tin_text, result = seed_text, seed_text\n",
    "\t# generate a fixed number of words\n",
    "\tfor _ in range(n_words):\n",
    "\t\t# encode the text as integer\n",
    "\t\tencoded = tokenizer.texts_to_sequences([in_text])[0]\n",
    "\t\tencoded = array(encoded)\n",
    "\t\t# predict a word in the vocabulary\n",
    "\t\tyhat = model.predict_classes(encoded, verbose=0)\n",
    "\t\t# map predicted word index to word\n",
    "\t\tout_word = ''\n",
    "\t\tfor word, index in tokenizer.word_index.items():\n",
    "\t\t\tif index == yhat:\n",
    "\t\t\t\tout_word = word\n",
    "\t\t\t\tbreak\n",
    "\t\t# append to input\n",
    "\t\tin_text, result = out_word, result + ' ' + out_word\n",
    "\treturn result\n",
    "\n",
    "# source text\n",
    "data = \"\"\" Jack and Jill went up the hill\\n\n",
    "\t\tTo fetch a pail of water\\n\n",
    "\t\tJack fell down and broke his crown\\n\n",
    "\t\tAnd Jill came tumbling after\\n \"\"\"\n",
    "# integer encode text\n",
    "tokenizer = Tokenizer()\n",
    "tokenizer.fit_on_texts([data])\n",
    "encoded = tokenizer.texts_to_sequences([data])[0]\n",
    "# determine the vocabulary size\n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "print('Vocabulary Size: %d' % vocab_size)\n",
    "# create word -> word sequences\n",
    "sequences = list()\n",
    "for i in range(1, len(encoded)):\n",
    "\tsequence = encoded[i-1:i+1]\n",
    "\tsequences.append(sequence)\n",
    "print('Total Sequences: %d' % len(sequences))\n",
    "# split into X and y elements\n",
    "sequences = array(sequences)\n",
    "X, y = sequences[:,0],sequences[:,1]\n",
    "# one hot encode outputs\n",
    "y = to_categorical(y, num_classes=vocab_size)\n",
    "# define model\n",
    "model = Sequential()\n",
    "model.add(Embedding(vocab_size, 10, input_length=1))\n",
    "model.add(LSTM(50))\n",
    "model.add(Dense(vocab_size, activation='softmax'))\n",
    "print(model.summary())\n",
    "# compile network\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "# fit network\n",
    "model.fit(X, y, epochs=500, verbose=2)\n",
    "# evaluate\n",
    "print(generate_seq(model, tokenizer, 'Jack', 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jack and jill came tumbling after up the hill to fetch\n"
     ]
    }
   ],
   "source": [
    "print(generate_seq(model, tokenizer, 'Jack', 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jack and jill came tumbling after up the hill to fetch a pail of water jack and jill came tumbling after\n"
     ]
    }
   ],
   "source": [
    "print(generate_seq(model, tokenizer, 'Jack', 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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