a_more_trained_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": 55,
   "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)}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": 57,
   "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 more trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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",
    "\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_400_0.2_400_0.2_100.h5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.load_weights('/Users/pranjal/Desktop/text_generator/models/text_generator_400_0.2_400_0.2_100.h5')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'love, he past doth fiamee.\\n to diserace but in the orsths of are orider,\\n waie agliemt would have me'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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 besire,\\n that in the breath ther doomownd wron to ray,\\n dorh part nit backn oy steresc douh dxcel;\\n for that i have beauty lekeng norirness,\\n for all the foowing of a former sight,\\n which in the remame douh a foure to his,\\n that in the very bumees of toue mart detenese;\\n how ap i am nnw love, he past doth fiamee.\\n to diserace but in the orsths of are orider,\\n waie agliemt would have me '"
      ]
     },
     "execution_count": 63,
     "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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