Training_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c13bdcc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import string\n",
    "from PIL import Image\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import os\n",
    "from pickle import dump, load\n",
    "\n",
    "# Tensorflow Imports\n",
    "from tensorflow import keras\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.applications.xception import Xception, preprocess_input\n",
    "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras_preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical\n",
    "from keras.layers import Input, Dense, LSTM, Embedding, Dropout\n",
    "from keras.layers.merging import add\n",
    "from keras.models import Model, load_model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91dd14a9",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bd54710f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of Descriptions:  8092\n",
      "Length of Vocabulary:  8767\n"
     ]
    }
   ],
   "source": [
    "# text file loading\n",
    "def load_doc(file_name):\n",
    "    file= open(file_name, 'r')# reading mode\n",
    "    text= file.read()\n",
    "    file.close()\n",
    "    return text\n",
    "\n",
    "# Data cleaning (lower casing, puntuataions removal, word containing number)\n",
    "def clean_text(captions):\n",
    "    table= str.maketrans('','',string.punctuation)\n",
    "    for img,cap in captions.items():\n",
    "        for i,img_cap in enumerate(cap):\n",
    "            \n",
    "            img_cap.replace(\"-\",\" \")\n",
    "            descr= img_cap.split()\n",
    "            \n",
    "            # to lowercase\n",
    "            descr= [wd.lower() for wd in descr]\n",
    "            # removing 's and 'a'\n",
    "            descr= [wd.translate(table) for wd in descr if(len(wd)>1)]\n",
    "            # punctuation removal\n",
    "            descr= [wd.translate(table) for wd in descr]\n",
    "            # token removal\n",
    "            descr= [wd for wd in descr if(wd.isalpha())]\n",
    "            # convert to string\n",
    "            img_cap= ' '.join(descr)\n",
    "            captions[img][i]= img_cap\n",
    "    return captions\n",
    "\n",
    "# get imgs with respective captions\n",
    "def img_captions(file_name):\n",
    "    file= load_doc(file_name)\n",
    "    captions= file.split('\\n')\n",
    "    descr= {}\n",
    "    for caption in captions[:-1]:\n",
    "        img, caption= caption.split('\\t')\n",
    "        if img[:-2] not in descr:\n",
    "            descr[img[:-2]]= [caption]\n",
    "        else:\n",
    "            descr[img[:-2]].append(caption)\n",
    "    return descr\n",
    "\n",
    "# vocabulary building\n",
    "def text_vocab(descr):\n",
    "    vocab= set()\n",
    "    \n",
    "    for key in descr.keys():\n",
    "        [vocab.update(d.split()) for d in descr[key]]\n",
    "    \n",
    "    return vocab\n",
    "\n",
    "# saving descriptions in one file\n",
    "def save_descr(descr, file_name):\n",
    "    lines= list()\n",
    "    for key,descr_list in descr.items():\n",
    "        for descr in descr_list:\n",
    "            lines.append(key+'\\t'+descr)\n",
    "    data= \"\\n\".join(lines)\n",
    "    file= open(file_name, \"w\")\n",
    "    file.write(data)\n",
    "    file.close()\n",
    "    \n",
    "# define paths\n",
    "dataset_txt= './Flickr8k_text/'\n",
    "dataset_img= './Flickr8k_Dataset/Flicker8k_Dataset/'\n",
    "\n",
    "file_name= dataset_txt + \"/\" + \"Flickr8k.token.txt\"\n",
    "\n",
    "description= img_captions(file_name)\n",
    "print(\"Length of Descriptions: \",len(description))\n",
    "\n",
    "cleaned_descr= clean_text(description)\n",
    "\n",
    "vocab= text_vocab(cleaned_descr)\n",
    "print(\"Length of Vocabulary: \",len(vocab))\n",
    "\n",
    "save_descr(cleaned_descr, \"descriptions.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63afb3c6",
   "metadata": {},
   "source": [
    "### Feature Exctraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "da23f856",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DEV\\AppData\\Local\\Temp/ipykernel_13776/2125275388.py:4: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for img_iter in tqdm(os.listdir(data_path)):\n"
     ]
    },
    {
     "data": {
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       "model_id": "18df24e8c83747d4802d53e3531d3d07",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8091 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "def feature_extractor(data_path):\n",
    "    model= Xception(include_top=False, pooling='avg')\n",
    "    features= {}\n",
    "    for img_iter in tqdm(os.listdir(data_path)):\n",
    "        file_name= data_path + \"/\" + img_iter\n",
    "        img= Image.open(file_name)\n",
    "        img= img.resize((299,299)) # deafault dim for xception-net input\n",
    "        img= np.expand_dims(img, axis=0)\n",
    "        img= img /127.5\n",
    "        img= img - 1.0\n",
    "        with tf.device('/GPU:0'):\n",
    "            feature= model.predict(img)\n",
    "        features[img_iter]= feature\n",
    "    \n",
    "    return features\n",
    "\n",
    "# length of feature vactor will be 2048\n",
    "features= feature_extractor(dataset_img)\n",
    "dump(features, open('feature.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "45e15cdd",
   "metadata": {},
   "outputs": [
    {
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       "         5.4940983e-04, 2.2314616e-02]], dtype=float32),\n",
       " '2085557551_7a88d01d4e.jpg': array([[0.01295645, 0.09690738, 0.07360303, ..., 0.23918225, 0.        ,\n",
       "         0.25982574]], dtype=float32),\n",
       " '2085726719_a57a75dbe5.jpg': array([[0.1469548 , 0.25012502, 0.6390337 , ..., 0.        , 0.09385143,\n",
       "         0.00651392]], dtype=float32),\n",
       " '2086513494_dbbcb583e7.jpg': array([[0.2022644 , 0.52103406, 0.25482488, ..., 0.08147587, 0.19518024,\n",
       "         0.1008712 ]], dtype=float32),\n",
       " '2086532897_b8714f2237.jpg': array([[0.3585378 , 0.13491902, 0.        , ..., 0.        , 0.        ,\n",
       "         0.08998943]], dtype=float32),\n",
       " '2086534745_1e4ab80078.jpg': array([[0.34620926, 0.225614  , 0.        , ..., 0.00096278, 0.        ,\n",
       "         0.        ]], dtype=float32),\n",
       " '2086678529_b3301c2d71.jpg': array([[0.03183535, 0.07166884, 0.00132104, ..., 0.        , 0.0193813 ,\n",
       "         0.19092411]], dtype=float32),\n",
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       "         0.26461694]], dtype=float32),\n",
       " '2087640654_1a84577a44.jpg': array([[0.        , 0.        , 0.        , ..., 0.09196783, 0.        ,\n",
       "         0.01740481]], dtype=float32),\n",
       " '2088120475_d6318364f5.jpg': array([[0.0842405 , 0.00451043, 0.02103063, ..., 0.00309263, 0.08552115,\n",
       "         0.5761038 ]], dtype=float32),\n",
       " '2088460083_42ee8a595a.jpg': array([[0.        , 0.00238221, 0.01395728, ..., 0.17590156, 0.14221925,\n",
       "         0.0711683 ]], dtype=float32),\n",
       " '2088532947_c628e44c4a.jpg': array([[0.07564949, 0.26135102, 0.        , ..., 0.00044112, 0.00044449,\n",
       "         0.28413162]], dtype=float32),\n",
       " '2088910854_c6f8d4f5f9.jpg': array([[0.01043891, 0.12267007, 0.32248864, ..., 0.24689335, 0.00242845,\n",
       "         0.37608662]], dtype=float32),\n",
       " '2089122314_40d5739aef.jpg': array([[0.00472166, 0.        , 0.        , ..., 0.0007786 , 0.2431325 ,\n",
       "         0.07210749]], dtype=float32),\n",
       " '2089350172_dc2cf9fcf6.jpg': array([[0.01496254, 0.00884967, 0.        , ..., 0.00856715, 0.01658827,\n",
       "         0.        ]], dtype=float32),\n",
       " '2089426086_7acc98a3a8.jpg': array([[0.19880539, 0.99435514, 0.4438995 , ..., 0.        , 0.0017262 ,\n",
       "         0.17032757]], dtype=float32),\n",
       " '2089442007_6fc798548c.jpg': array([[0.09638581, 0.        , 0.03298196, ..., 0.        , 0.        ,\n",
       "         0.        ]], dtype=float32),\n",
       " '2089539651_9e518ec7de.jpg': array([[0.00018042, 0.05034869, 0.10285719, ..., 0.        , 0.01383946,\n",
       "         0.05858595]], dtype=float32),\n",
       " '2089542487_b4c1ee7025.jpg': array([[0.        , 0.08535706, 0.07075832, ..., 0.02574035, 0.01132963,\n",
       "         0.11395586]], dtype=float32),\n",
       " '2089555297_95cf001fa7.jpg': array([[0.03973217, 0.05567721, 0.08934455, ..., 0.1612063 , 0.03053952,\n",
       "         0.01135804]], dtype=float32),\n",
       " '2090327868_9f99e2740d.jpg': array([[0.0137747 , 0.48259562, 0.1600346 , ..., 0.00863115, 0.10806284,\n",
       "         0.05342406]], dtype=float32),\n",
       " '2090339522_d30d2436f9.jpg': array([[0.08856998, 0.        , 0.        , ..., 0.02536854, 0.        ,\n",
       "         0.07943949]], dtype=float32),\n",
       " '2090386465_b6ebb7df2c.jpg': array([[2.1908203e-01, 1.9597279e-05, 0.0000000e+00, ..., 2.2890042e-01,\n",
       "         0.0000000e+00, 1.8314125e-01]], dtype=float32),\n",
       " '2090387793_274ab4cf7d.jpg': array([[0.00525392, 0.08557562, 0.2001088 , ..., 0.00405648, 0.29294804,\n",
       "         0.17882986]], dtype=float32),\n",
       " '2090545563_a4e66ec76b.jpg': array([[0.29514125, 0.38058975, 0.3375303 , ..., 0.01377559, 0.14705141,\n",
       "         0.        ]], dtype=float32),\n",
       " '2090723611_318031cfa5.jpg': array([[0.01564293, 0.01861249, 0.3764048 , ..., 0.29914972, 0.14460409,\n",
       "         0.00947149]], dtype=float32),\n",
       " '2090997177_76d482b158.jpg': array([[0.3273176 , 0.06677656, 0.        , ..., 0.01038916, 0.19783978,\n",
       "         0.        ]], dtype=float32),\n",
       " '2091171488_c8512fec76.jpg': array([[0.1210022 , 0.11309416, 0.3249876 , ..., 0.10195586, 0.05513741,\n",
       "         0.3227354 ]], dtype=float32),\n",
       " '2092177624_13ab757e8b.jpg': array([[0.        , 0.02075454, 0.25294006, ..., 0.03298885, 0.        ,\n",
       "         0.02885886]], dtype=float32),\n",
       " '2092419948_eea8001d0f.jpg': array([[0.0026054 , 0.02265801, 0.17466109, ..., 0.02268933, 0.06207491,\n",
       "         0.18546936]], dtype=float32),\n",
       " '2092870249_90e3f1855b.jpg': array([[0.01079444, 0.07992654, 0.3692476 , ..., 0.06306618, 0.        ,\n",
       "         0.18063459]], dtype=float32),\n",
       " '2094323311_27d58b1513.jpg': array([[0.05584421, 0.05111977, 0.08582029, ..., 0.        , 0.28682604,\n",
       "         0.        ]], dtype=float32),\n",
       " '2094543127_46d2f1fedf.jpg': array([[0.12320828, 0.01626882, 0.00414124, ..., 0.1620282 , 0.22282028,\n",
       "         0.        ]], dtype=float32),\n",
       " '2094810449_f8df9dcdf7.jpg': array([[0.        , 0.12729235, 0.        , ..., 0.04852335, 0.01361983,\n",
       "         0.        ]], dtype=float32),\n",
       " '2095007523_591f255708.jpg': array([[0.35969993, 0.        , 0.09254701, ..., 0.        , 0.        ,\n",
       "         0.01608977]], dtype=float32),\n",
       " '2095078658_c14ba89bc2.jpg': array([[0.01489676, 0.19258526, 0.05912961, ..., 0.27777436, 0.1315775 ,\n",
       "         0.        ]], dtype=float32),\n",
       " '2095435987_1b7591d214.jpg': array([[7.8476180e-05, 0.0000000e+00, 7.8832678e-02, ..., 3.8792025e-02,\n",
       "         0.0000000e+00, 8.7656498e-02]], dtype=float32),\n",
       " '2095444126_201ff9f222.jpg': array([[0.        , 0.        , 0.27939516, ..., 0.24108751, 0.04609753,\n",
       "         0.16221634]], dtype=float32),\n",
       " '2095478050_736c4d2d28.jpg': array([[0.        , 0.11078712, 0.36210114, ..., 0.16942401, 0.01011732,\n",
       "         0.3982932 ]], dtype=float32),\n",
       " '209605542_ca9cc52e7b.jpg': array([[0.01206791, 0.20603868, 0.00443362, ..., 0.00037145, 0.        ,\n",
       "         0.03064098]], dtype=float32),\n",
       " '2096771662_984441d20d.jpg': array([[0.16744806, 0.        , 0.16094261, ..., 0.00679035, 0.        ,\n",
       "         0.09256122]], dtype=float32),\n",
       " '2097398349_ff178b3f1b.jpg': array([[0., 0., 0., ..., 0., 0., 0.]], dtype=float32),\n",
       " '2097403787_77a154f5b9.jpg': array([[0.04171897, 0.        , 0.03998678, ..., 0.02031385, 0.        ,\n",
       "         0.00627977]], dtype=float32),\n",
       " '2097407245_c798e0dcaf.jpg': array([[0.16434798, 0.        , 0.03250214, ..., 0.21270378, 0.04411808,\n",
       "         0.08630718]], dtype=float32),\n",
       " '2097420505_439f63c863.jpg': array([[0.1890308 , 0.00634667, 0.00365071, ..., 0.0406222 , 0.04351391,\n",
       "         0.02923799]], dtype=float32),\n",
       " '2097489021_ca1b9f5c3b.jpg': array([[0.0676318 , 0.        , 0.7377324 , ..., 0.05721454, 0.08540612,\n",
       "         0.15602535]], dtype=float32),\n",
       " '2098174172_e57d86ea03.jpg': array([[0.        , 0.        , 0.        , ..., 0.00060547, 0.        ,\n",
       "         0.        ]], dtype=float32),\n",
       " '2098418613_85a0c9afea.jpg': array([[0.32865664, 0.05962682, 0.00240411, ..., 0.        , 0.        ,\n",
       "         0.00058935]], dtype=float32),\n",
       " ...}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features= load(open(\"feature.pkl\", \"rb\"))\n",
    "features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "940c1bd9",
   "metadata": {},
   "source": [
    "### Helper Functions for Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ee754b2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DEV\\AppData\\Local\\Temp/ipykernel_13776/3804803303.py:10: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for line in tqdm(file.split(\"\\n\")):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f0d28789eac24cc2b57be6535d6efe59",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/40460 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load data (list of images)\n",
    "def load_imgs(file_name):\n",
    "    file= load_doc(file_name)\n",
    "    imgs= file.split(\"\\n\")[:-1]\n",
    "    return imgs\n",
    "\n",
    "def load_cleaned_descrs(file_name, imgs):\n",
    "    file= load_doc(file_name)\n",
    "    descrs= {}\n",
    "    for line in tqdm(file.split(\"\\n\")):\n",
    "        word= line.split()\n",
    "        if len(word)<1:\n",
    "            continue\n",
    "         \n",
    "        img, img_cap= word[0], word[1:]\n",
    "        if img in imgs:\n",
    "            if img not in descrs:\n",
    "                descrs[img]= []\n",
    "            des= '<start> '+ \" \".join(img_cap)+' <end>'\n",
    "            descrs[img].append(des)\n",
    "    \n",
    "    return descrs\n",
    "\n",
    "def load_feature(imgs):\n",
    "    feature= load(open(\"feature.pkl\", \"rb\"))\n",
    "    features= {k:feature[k] for k in imgs}\n",
    "    \n",
    "    return features\n",
    "\n",
    "file_name= dataset_txt + \"/\" + \"Flickr_8k.trainImages.txt\"\n",
    "train_img= load_imgs(file_name)\n",
    "train_descrs= load_cleaned_descrs(\"descriptions.txt\", train_img)\n",
    "train_feature= load_feature(train_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e7eb4c3",
   "metadata": {},
   "source": [
    "### Vocabulary Tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e30e60b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total words: 7581\n"
     ]
    }
   ],
   "source": [
    "# converting dict to list of discriptions\n",
    "def dict_to_list(descr):\n",
    "    all_descr= []\n",
    "    for key in descr.keys():\n",
    "        [all_descr.append(d) for d in descr[key]]\n",
    "    return all_descr\n",
    "\n",
    "# creating tokenizer function (it'll vectorize text corpus)\n",
    "def create_tokens(descr):\n",
    "    descr_list= dict_to_list(descr)\n",
    "    tokenizer= Tokenizer()\n",
    "    tokenizer.fit_on_texts(descr_list)\n",
    "    return tokenizer\n",
    "\n",
    "tokens= create_tokens(train_descrs)\n",
    "dump(tokens, open(\"tokens.pkl\", \"wb\"))\n",
    "vocab_size= len(tokens.word_index) + 1\n",
    "print('total words:', vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2d8f3a4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating max length for description of image \n",
    "def max_descr_len(descr):\n",
    "    descr_list= dict_to_list(descr)\n",
    "    return max(len(d.split()) for d in descr_list)\n",
    "\n",
    "max_len= max_descr_len(description)\n",
    "max_len"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05de283b",
   "metadata": {},
   "source": [
    "#### Defining Input and Output of model\n",
    "- Input: it will be in form of [X1,X2] and output will be Y  <br>        (where X1 is 2048 length of feature vector and X2 is input text sequence)\n",
    "- Output: it will be text sequence that model will predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d42da257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((37, 2048), (37, 32), (37, 7581))"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating Input-Output pairs from image description\n",
    "\n",
    "def data_generator(descrs, feature, tokens, max_len):\n",
    "    while 1:\n",
    "        for key, descr_list in description.items():\n",
    "            # retrve img feature\n",
    "            feature= feature[key][0]\n",
    "            input_img, input_seq, output_word= create_sequences(tokens, max_len, descr_list, feature)\n",
    "            yield [[input_img, input_seq], output_word]\n",
    "\n",
    "def create_sequences(tokens, max_len, descr_list, feature):\n",
    "    X1, X2, Y= list(), list(), list()\n",
    "    for desc in descr_list:\n",
    "        # sequence encoding\n",
    "        seq= tokens.texts_to_sequences([desc])[0]\n",
    "        #splitting one sequence into multiple X,Y pairs\n",
    "        for i in range(1, len(seq)):\n",
    "            in_seq, out_seq = seq[:i], seq[i]\n",
    "            # pad sequence\n",
    "            in_seq= pad_sequences([in_seq], maxlen=max_len)[0]\n",
    "            # encode output sequence\n",
    "            out_seq= to_categorical([out_seq], num_classes=vocab_size)[0]\n",
    "            # store\n",
    "            X1.append(feature)\n",
    "            X2.append(in_seq)\n",
    "            Y.append(out_seq)\n",
    "            \n",
    "    return np.array(X1), np.array(X2), np.array(Y)\n",
    "\n",
    "# checking shape of I/P and O/P data\n",
    "[a,b], c= next(data_generator(train_descrs, features, tokens, max_len))\n",
    "a.shape, b.shape, c.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdbf3b2e",
   "metadata": {},
   "source": [
    "#### Creating CNN-RNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4a74f788",
   "metadata": {},
   "outputs": [],
   "source": [
    "def final_model(vocab_size, max_length):\n",
    "    # features from the CNN model squeezed from 2048 to 256 nodes\n",
    "    input1 = Input(shape=(2048,))\n",
    "    fl1= Dropout(0.5)(input1)\n",
    "    fl2= Dense(256, activation='relu')(fl1)\n",
    "    \n",
    "    # LSTM Cell\n",
    "    input2 = Input(shape=(max_length,))\n",
    "    sl1= Embedding(vocab_size, 256, mask_zero=True)(input2)\n",
    "    sl2= Dropout(0.5)(sl1)\n",
    "    sl3= LSTM(256)(sl2)\n",
    "    \n",
    "    # Merge both models\n",
    "    decoder1= add([fl2,sl3])\n",
    "    decoder2 = Dense(256, activation='relu')(decoder1)\n",
    "    outputs = Dense(vocab_size, activation='softmax')(decoder2)\n",
    "    \n",
    "    # tie it together [img, seq] ---> [word]\n",
    "    model = Model(inputs=[input1, input2], outputs=outputs)\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam')\n",
    "    \n",
    "    print(model.summary())\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "247a5602",
   "metadata": {},
   "source": [
    "#### Model Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d21b5e13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset:  6000\n",
      "Training Description:  6000\n",
      "Description Length:  32\n",
      "Train Images:  6000\n",
      "Vocabulary:  7581\n",
      "Model: \"model_7\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_29 (InputLayer)          [(None, 32)]         0           []                               \n",
      "                                                                                                  \n",
      " input_28 (InputLayer)          [(None, 2048)]       0           []                               \n",
      "                                                                                                  \n",
      " embedding_11 (Embedding)       (None, 32, 256)      1940736     ['input_29[0][0]']               \n",
      "                                                                                                  \n",
      " dropout_22 (Dropout)           (None, 2048)         0           ['input_28[0][0]']               \n",
      "                                                                                                  \n",
      " dropout_23 (Dropout)           (None, 32, 256)      0           ['embedding_11[0][0]']           \n",
      "                                                                                                  \n",
      " dense_29 (Dense)               (None, 256)          524544      ['dropout_22[0][0]']             \n",
      "                                                                                                  \n",
      " lstm_11 (LSTM)                 (None, 256)          525312      ['dropout_23[0][0]']             \n",
      "                                                                                                  \n",
      " add_71 (Add)                   (None, 256)          0           ['dense_29[0][0]',               \n",
      "                                                                  'lstm_11[0][0]']                \n",
      "                                                                                                  \n",
      " dense_30 (Dense)               (None, 256)          65792       ['add_71[0][0]']                 \n",
      "                                                                                                  \n",
      " dense_31 (Dense)               (None, 7581)         1948317     ['dense_30[0][0]']               \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 5,004,701\n",
      "Trainable params: 5,004,701\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DEV\\AppData\\Local\\Temp/ipykernel_13776/420966462.py:11: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for i in tqdm(range(epochs)):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "83369ad36c3b4901b0768e7c49a31d53",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DEV\\AppData\\Local\\Temp/ipykernel_13776/420966462.py:13: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/6000 [..............................] - ETA: 7:03:25 - loss: 8.9269"
     ]
    },
    {
     "ename": "UnknownError",
     "evalue": "Graph execution error:\n\n2 root error(s) found.\n  (0) UNKNOWN:  IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices\nTraceback (most recent call last):\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\script_ops.py\", line 270, in __call__\n    ret = func(*args)\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\tensorflow\\python\\autograph\\impl\\api.py\", line 642, in wrapper\n    return func(*args, **kwargs)\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py\", line 1030, in generator_py_func\n    values = next(generator_state.get_iterator(iterator_id))\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\keras\\engine\\data_adapter.py\", line 831, in wrapped_generator\n    for data in generator_fn():\n\n  File \"C:\\Users\\DEV\\AppData\\Local\\Temp/ipykernel_13776/2864613916.py\", line 7, in data_generator\n    feature= feature[key][0]\n\nIndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices\n\n\n\t [[{{node PyFunc}}]]\n\t [[IteratorGetNext]]\n\t [[IteratorGetNext/_2]]\n  (1) UNKNOWN:  IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices\nTraceback (most recent call last):\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\script_ops.py\", line 270, in __call__\n    ret = func(*args)\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\tensorflow\\python\\autograph\\impl\\api.py\", line 642, in wrapper\n    return func(*args, **kwargs)\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py\", line 1030, in generator_py_func\n    values = next(generator_state.get_iterator(iterator_id))\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\keras\\engine\\data_adapter.py\", line 831, in wrapped_generator\n    for data in generator_fn():\n\n  File \"C:\\Users\\DEV\\AppData\\Local\\Temp/ipykernel_13776/2864613916.py\", line 7, in data_generator\n    feature= feature[key][0]\n\nIndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices\n\n\n\t [[{{node PyFunc}}]]\n\t [[IteratorGetNext]]\n0 successful operations.\n0 derived errors ignored. [Op:__inference_train_function_591415]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnknownError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_13776/420966462.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m         \u001b[0mgenerator\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mdata_generator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_descrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_feature\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtokens\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_len\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m         \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_generator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[1;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[0;32m   2258\u001b[0m         \u001b[1;34m'Please use `Model.fit`, which supports generators.'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2259\u001b[0m         stacklevel=2)\n\u001b[1;32m-> 2260\u001b[1;33m     return self.fit(\n\u001b[0m\u001b[0;32m   2261\u001b[0m         \u001b[0mgenerator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2262\u001b[0m         \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     68\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m       \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     52\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 54\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     55\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     56\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mUnknownError\u001b[0m: Graph execution error:\n\n2 root error(s) found.\n  (0) UNKNOWN:  IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices\nTraceback (most recent call last):\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\script_ops.py\", line 270, in __call__\n    ret = func(*args)\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\tensorflow\\python\\autograph\\impl\\api.py\", line 642, in wrapper\n    return func(*args, **kwargs)\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py\", line 1030, in generator_py_func\n    values = next(generator_state.get_iterator(iterator_id))\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\keras\\engine\\data_adapter.py\", line 831, in wrapped_generator\n    for data in generator_fn():\n\n  File \"C:\\Users\\DEV\\AppData\\Local\\Temp/ipykernel_13776/2864613916.py\", line 7, in data_generator\n    feature= feature[key][0]\n\nIndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices\n\n\n\t [[{{node PyFunc}}]]\n\t [[IteratorGetNext]]\n\t [[IteratorGetNext/_2]]\n  (1) UNKNOWN:  IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices\nTraceback (most recent call last):\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\script_ops.py\", line 270, in __call__\n    ret = func(*args)\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\tensorflow\\python\\autograph\\impl\\api.py\", line 642, in wrapper\n    return func(*args, **kwargs)\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py\", line 1030, in generator_py_func\n    values = next(generator_state.get_iterator(iterator_id))\n\n  File \"C:\\Users\\DEV\\anaconda3\\lib\\site-packages\\keras\\engine\\data_adapter.py\", line 831, in wrapped_generator\n    for data in generator_fn():\n\n  File \"C:\\Users\\DEV\\AppData\\Local\\Temp/ipykernel_13776/2864613916.py\", line 7, in data_generator\n    feature= feature[key][0]\n\nIndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices\n\n\n\t [[{{node PyFunc}}]]\n\t [[IteratorGetNext]]\n0 successful operations.\n0 derived errors ignored. [Op:__inference_train_function_591415]"
     ]
    }
   ],
   "source": [
    "print('Dataset: ',len(train_img))\n",
    "print('Training Description: ',len(train_descrs))\n",
    "print('Description Length: ',max_len)\n",
    "print('Train Images: ',len(train_feature))\n",
    "print('Vocabulary: ',(vocab_size))\n",
    "\n",
    "model= final_model(vocab_size, max_len)\n",
    "epochs= 10\n",
    "steps= len(train_descrs)\n",
    "with tf.device('/GPU:0'):\n",
    "    for i in tqdm(range(epochs)):\n",
    "        generator= data_generator(train_descrs, train_feature, tokens, max_len)\n",
    "        model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ead0fa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode.save('model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae8b77de",
   "metadata": {},
   "source": [
    "#### Testing Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "667c312e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "1/1 [==============================] - 9s 9s/step\n",
      " two dogs are playing in the grass \n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import argparse\n",
    "import numpy as np\n",
    "import string\n",
    "from PIL import Image\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import os\n",
    "from pickle import dump, load\n",
    "\n",
    "# Tensorflow Imports\n",
    "from tensorflow import keras\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.applications.xception import Xception, preprocess_input\n",
    "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras_preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical\n",
    "from keras.layers import Input, Dense, LSTM, Embedding, Dropout\n",
    "from keras.layers.merging import add\n",
    "from keras.models import Model, load_model\n",
    "\n",
    "\n",
    "\n",
    "ap = argparse.ArgumentParser()\n",
    "ap.add_argument('-i', '--image', required=True, help=\"Image Path\")\n",
    "args = vars(ap.parse_args())\n",
    "img_path = args['image']\n",
    "\n",
    "def extract_features(model, filename):\n",
    "        try:\n",
    "            image = Image.open(filename)\n",
    "\n",
    "        except:\n",
    "            print(\"ERROR: Couldn't open image! Make sure the image path and extension is correct\")\n",
    "        image = Image.open(filename)\n",
    "        image= image\n",
    "        image = image.resize((299,299))\n",
    "        image = np.array(image)\n",
    "        # for images that has 4 channels, we convert them into 3 channels\n",
    "        if image.shape[2] == 4: \n",
    "            image = image[..., :3]\n",
    "        image = np.expand_dims(image, axis=0)\n",
    "        image = image/127.5\n",
    "        image = image - 1.0\n",
    "        feature = model.predict(image)\n",
    "        return feature\n",
    "\n",
    "def word_for_id(integer, tokenizer):\n",
    "    for word, index in tokenizer.word_index.items():\n",
    "         if index == integer:\n",
    "                return word\n",
    "    return None\n",
    "\n",
    "\n",
    "def generate_desc(model, tokenizer, photo, max_length):\n",
    "    in_text = 'start'\n",
    "    for i in range(max_length):\n",
    "        sequence = tokenizer.texts_to_sequences([in_text])[0]\n",
    "        sequence = pad_sequences([sequence], maxlen=max_length)\n",
    "        pred = model.predict([photo,sequence], verbose=0)\n",
    "        pred = np.argmax(pred)\n",
    "        word = word_for_id(pred, tokenizer)\n",
    "        if word is None:\n",
    "            break\n",
    "        in_text += ' ' + word\n",
    "        if word == 'end':\n",
    "            break\n",
    "    return in_text\n",
    "\n",
    "\n",
    "# img_path = 'Flickr8k_Dataset/Flicker8k_Dataset/1001773457_577c3a7d70.jpg'\n",
    "max_length = 32\n",
    "tokenizer = load(open(\"tokens.pkl\",\"rb\"))\n",
    "model = load_model('model_9.h5')\n",
    "xception_model = Xception(include_top=False, pooling=\"avg\")\n",
    "\n",
    "photo = extract_features(xception_model, img_path)\n",
    "img = Image.open(img_path)\n",
    "\n",
    "description = generate_desc(model, tokenizer, photo, max_length)[5:-3]\n",
    "print(\"\\n\\n\")\n",
    "print(description)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "7eb9af6d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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