a_wider_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "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.layers import RNN\n",
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "text = (open(\"/Users/pranjal/Desktop/text_generator/sonnets.txt\").read())\n",
    "text=text.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating character/word mappings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "characters = sorted(list(set(text)))\n",
    "\n",
    "n_to_char = {n:char for n, char in enumerate(characters)}\n",
    "char_to_n = {char:n for n, char in enumerate(characters)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = []\n",
    "Y = []\n",
    "length = len(text)\n",
    "seq_length = 100\n",
    "\n",
    "for i in range(0, length-seq_length, 1):\n",
    "    sequence = text[i:i + seq_length]\n",
    "    label =text[i + seq_length]\n",
    "    X.append([char_to_n[char] for char in sequence])\n",
    "    Y.append(char_to_n[label])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A wider model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(700, input_shape=(X_modified.shape[1], X_modified.shape[2]), return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(700))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(Y_modified.shape[1], activation='softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.fit(X_modified, Y_modified, epochs=100, batch_size=50)\n",
    "\n",
    "model.save_weights('text_generator_700_0.2_700_0.2_100.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.load_weights('/Users/pranjal/Desktop/text_generator/models/text_generator_700_0.2_700_0.2_100.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "string_mapped = X[99]\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)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"s the riper should by time decease,\\n his tender heir might bear his memory:\\n but thou, contracted to thine own bright eyes,\\n feed'st thy sigh aare so beuider poise,\\n oor maty dis surre that gairs mot me love thee;\\n when i braye the would and lays in the eesire.\\n than thmu disgrmed stand with my bootr still nsentente;\\n tell ia to thou art thou wilt woon'thy sook,\\n and touain then tor, give thy soue semping.\\n whose whod the better of your befaiss nu sante,\\n though i, suoll oas, and i lose bouh twa\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combining text\n",
    "txt=\"\"\n",
    "for char in full_string:\n",
    "    txt = txt+char\n",
    "txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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