Img_captioning_flickr8K.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "408a1e7a",
   "metadata": {},
   "source": [
    "# Image captioning with visual attention\n",
    "# Flickr 8K \n",
    "## By: Inbasekaran"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c37c20b",
   "metadata": {},
   "source": [
    "# Loading all the dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c3f90018",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import tensorflow as tf\n",
    "from tqdm import tqdm\n",
    "from glob import glob\n",
    "from PIL import Image\n",
    "import nltk\n",
    "import string\n",
    "import warnings\n",
    "import os\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "import numpy as np\n",
    "from tensorflow.compat.v1 import ConfigProto\n",
    "from tensorflow.compat.v1 import InteractiveSession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e7084b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "config = ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "session = InteractiveSession(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20c25c05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),\n",
       " PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.config.experimental.list_physical_devices()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6d5041d",
   "metadata": {},
   "source": [
    "# Defining the constants "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b552961",
   "metadata": {},
   "outputs": [],
   "source": [
    "# image height according the the model architecture\n",
    "IMG_H = 224\n",
    "# image width according the the model architecture\n",
    "IMG_W = 224"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f9c900d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Batch size\n",
    "BATCH_SIZE = 100\n",
    "# Buffer size \n",
    "BUFFER_SIZE = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "858bfbd8",
   "metadata": {},
   "source": [
    "# Defining img file paths "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "02bb551d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dir which contains all the images. \n",
    "IMG_FILE_PATH = \"C:\\\\Users\\\\inba2\\\\Documents\\\\DataSet\\\\8k\\\\Images\\\\Images\\\\\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91313d5f",
   "metadata": {},
   "source": [
    "# Defining txt file paths "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36f93f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# txt file containg the unprocessed captions\n",
    "CAP_TEXT_PATH = r\"C:\\Users\\inba2\\Documents\\DataSet\\8k\\Flickr_TextData\\Flickr8k.token.txt\"\n",
    "# txt file containing the names of the train imgs \n",
    "TRAIN_TXT_PATH = r\"C:\\Users\\inba2\\Documents\\DataSet\\8k\\Flickr_TextData\\Flickr_8k.trainImages.txt\"\n",
    "# txt file containing the names of the test imgs \n",
    "TEST_TXT_PATH = r\"C:\\Users\\inba2\\Documents\\DataSet\\8k\\Flickr_TextData\\Flickr_8k.testImages.txt\"\n",
    "# txt file containing the names of the validation imgs \n",
    "VAL_TXT_PATH = r\"C:\\Users\\inba2\\Documents\\DataSet\\8k\\Flickr_TextData\\Flickr_8k.devImages.txt\"\n",
    "\n",
    "# Text file path\n",
    "TXT_PATH = r\"C:\\Users\\inba2\\Documents\\DataSet\\8k\\Flickr_TextData\\Flickr_8k.trainImages.txt\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0891cce",
   "metadata": {},
   "source": [
    "# Utility Functions to load and clean images\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82bff665",
   "metadata": {},
   "outputs": [],
   "source": [
    "def all_img_name_vector(images_path: str, ext: str = r\".jpg\") -> list:\n",
    "    \"\"\"\n",
    "    :param ext: extension of the image\n",
    "    :returns the path of all the images in that dir\n",
    "    :param images_path: the path of the dir in which the images are present\n",
    "    \"\"\"\n",
    "    # Creating a list of the all the image paths\n",
    "    images_path_list = glob(images_path + '*.jpg')\n",
    "    # Printing the number of images in the given directory\n",
    "    print(f\"{len(images_path_list)} images found from {images_path}.\")\n",
    "    return images_path_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5dcd45fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cnn_model() -> tf.keras.Model:\n",
    "    \"\"\"\n",
    "    returns the cnn model need for feature extraction\n",
    "    :return:Vgg16 without the last layer.\n",
    "    \"\"\"\n",
    "    # load the model\n",
    "    model = tf.keras.applications.VGG16(include_top=False, weights='imagenet')\n",
    "    # re-structure the model\n",
    "    model = tf.keras.Model(inputs=model.inputs, outputs=model.layers[-1].output)\n",
    "    # summarize the model\n",
    "    print(model.summary())\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f1d6a7ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_img(image_path: str, img_h: str = IMG_H, img_w: str = IMG_W) -> (object, str):\n",
    "    \"\"\"\n",
    "    Returns the numpy image and the image path\n",
    "    :param image_path: the path of the image\n",
    "    EG: 'C:\\\\Users\\\\inba2\\\\Documents\\\\DataSet\\\\8k\\\\Images\\\\Images\\\\1000268201_693b08cb0e.jpg'\n",
    "    :param img_w: the width of image taken into the cnn\n",
    "    :param img_h: the height of image taken into the cnn\n",
    "    :return: image and image_path\n",
    "    \"\"\"\n",
    "    # Read the image\n",
    "    img = tf.io.read_file(image_path)\n",
    "    img = tf.image.decode_jpeg(img, channels=3)\n",
    "    # Resizing the image according the the model architecture\n",
    "    img = tf.image.resize(img, (img_h, img_w))\n",
    "    img = tf.keras.applications.vgg16.preprocess_input(img)\n",
    "    return img, image_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2fe694db",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_doc(filename: str) -> str:\n",
    "    \"\"\"\n",
    "    to open the file as read only\n",
    "    :param filename: name of the file\n",
    "    :return: the entire text as a string\n",
    "    \"\"\"\n",
    "    with open(filename, 'r') as file:\n",
    "        text = file.read()\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4edc83ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_set(text_file_path: str) -> set:\n",
    "    \"\"\"\n",
    "    to load a pre-defined list of photo names\n",
    "    returns the names of the images form the set_text_file\n",
    "    :param text_file_path:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    # user defined to load the document\n",
    "    doc = load_doc(text_file_path)\n",
    "    dataset = list()\n",
    "    # process line by line\n",
    "    for line in doc.split('\\n'):\n",
    "        # skip empty lines\n",
    "        if len(line) < 1:\n",
    "            continue\n",
    "        # get the image name without the extension\n",
    "        identifier = line.split('.')[0]\n",
    "        dataset.append(identifier)\n",
    "    return set(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b7252e87",
   "metadata": {},
   "outputs": [],
   "source": [
    "def img_name_2_path(image_name: str, img_file_path: str = IMG_FILE_PATH, ext: str = r\".jpg\") -> str:\n",
    "    \"\"\"\n",
    "    Converts the name of the image to image path\n",
    "    :param image_name: The name of the image\n",
    "    :param img_file_path: The path where the image is stored\n",
    "    :param ext:The extension of the image default is .jpg\n",
    "    :return: The image path\n",
    "    \"\"\"\n",
    "    image_path = img_file_path + str(image_name) + ext\n",
    "    return image_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "01274c55",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_img_dataset(txt_path: str, batch_size=BATCH_SIZE):\n",
    "    \"\"\"\n",
    "    To load the train or test dataset\n",
    "    :param txt_path: The text file which has the names of the training images\n",
    "    :param batch_size: The batch size for processing\n",
    "    :return: image_dataset\n",
    "    \"\"\"\n",
    "    img_name_vector = load_set(txt_path)\n",
    "    img_path_list = map(img_name_2_path, img_name_vector)\n",
    "    encode_train = sorted(img_path_list)\n",
    "    image_dataset = tf.data.Dataset.from_tensor_slices(encode_train)\n",
    "    image_dataset = image_dataset.map(load_img, num_parallel_calls=tf.data.experimental.AUTOTUNE).batch(batch_size)\n",
    "    return image_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9527e6ec",
   "metadata": {},
   "source": [
    "# Features extractraction "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93225784",
   "metadata": {},
   "source": [
    "## Loading the train images "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e6998874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\inba2\\\\Documents\\\\DataSet\\\\8k\\\\Flickr_TextData\\\\Flickr_8k.trainImages.txt'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TRAIN_TXT_PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b3273fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_train_dataset = load_img_dataset(TRAIN_TXT_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3e047e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BatchDataset shapes: ((None, 224, 224, 3), (None,)), types: (tf.float32, tf.string)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_train_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f9e1aa",
   "metadata": {},
   "source": [
    "## Creating our image features extraction model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c2dbcae6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, None, None, 3)]   0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, None, None, 64)    1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, None, None, 64)    36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, None, None, 64)    0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, None, None, 128)   73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, None, None, 128)   147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, None, None, 128)   0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, None, None, 256)   295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, None, None, 256)   590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, None, None, 256)   590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, None, None, 256)   0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "image_features_extract_model = cnn_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24df3685",
   "metadata": {},
   "source": [
    "## Extracting features and saving it in the same dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d4415242",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do you want to extract the features of the images[y/Y]: n\n"
     ]
    }
   ],
   "source": [
    "if str(input(\"Do you want to extract the features of the images[y/Y]: \")).casefold() == 'y':\n",
    "    for img, path in tqdm(image_train_dataset):\n",
    "        batch_features = image_features_extract_model(img)\n",
    "        batch_features = tf.reshape(batch_features, (batch_features.shape[0], -1, batch_features.shape[3]))\n",
    "        for bf, p in zip(batch_features, path):\n",
    "            path_of_feature = p.numpy().decode(\"utf-8\")\n",
    "            np.save(path_of_feature, bf.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e606133",
   "metadata": {},
   "source": [
    "## Loading the test images "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9eecc0fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\inba2\\\\Documents\\\\DataSet\\\\8k\\\\Flickr_TextData\\\\Flickr_8k.testImages.txt'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TEST_TXT_PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9dc940ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_test_dataset = load_img_dataset(TEST_TXT_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a77023",
   "metadata": {},
   "source": [
    "## Extracting features and saving it in the same dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "391f14ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do you want to extract the features of the images[y/Y]: n\n"
     ]
    }
   ],
   "source": [
    "if str(input(\"Do you want to extract the features of the images[y/Y]: \")).casefold() == 'y':\n",
    "    for img, path in tqdm(image_test_dataset):\n",
    "        batch_features = image_features_extract_model(img)\n",
    "        batch_features = tf.reshape(batch_features, (batch_features.shape[0], -1, batch_features.shape[3]))\n",
    "        for bf, p in zip(batch_features, path):\n",
    "            path_of_feature = p.numpy().decode(\"utf-8\")\n",
    "            np.save(path_of_feature, bf.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11c5861e",
   "metadata": {},
   "source": [
    "# Word/Sentence preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4dc7b33",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1b2a614a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_cap(caption: str) -> str:\n",
    "    \"\"\"\n",
    "    preprocessing the caption\n",
    "    :param caption: unprocessed caption\n",
    "    :return: processed caption\n",
    "    \"\"\"\n",
    "    # Removes Punctuations\n",
    "    cap = ''.join([ch for ch in caption if ch not in string.punctuation])\n",
    "    # tokenize\n",
    "    cap = cap.split()\n",
    "    # convert to lower case\n",
    "    cap = [word.casefold() for word in cap]\n",
    "    # remove hanging 's' and 'a'\n",
    "    cap = [word for word in cap if len(word) > 1]\n",
    "    # remove tokens with numbers in them\n",
    "    cap = [word for word in cap if word.isalpha()]\n",
    "    # Lemmatizing\n",
    "    lemmatizer = nltk.WordNetLemmatizer()\n",
    "    cap = [lemmatizer.lemmatize(word) for word in cap]\n",
    "    # store as string\n",
    "    return ' '.join(cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bd6de9da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_cap(caption_txt_path: str) -> dict:\n",
    "    \"\"\"\n",
    "    To read teh text file containing captions and store it in a dict.\n",
    "    mapping(dict) contains image_name as key and the corresponding captions\n",
    "    as a list of list of words.\n",
    "    :param caption_txt_path:\n",
    "    :return: mapping(dict) contains image_name as key and the\n",
    "    corresponding captions as a list of captions\n",
    "    \"\"\"\n",
    "    # opening the text file containing the captions\n",
    "    with open(caption_txt_path, 'r', encoding='utf-8') as caption_txt:\n",
    "        # each line in the txt file is stored in captions_list(list)\n",
    "        captions_list = caption_txt.readlines()\n",
    "    # initializing the dict\n",
    "    mapping = dict()\n",
    "    # each line in the text file\n",
    "    for line in captions_list:\n",
    "        # split the text by \\t\n",
    "        caption = line.split('\\t')\n",
    "        # the first item of the list\n",
    "        # -2 ignoring the last two characters and removing the extension\n",
    "        image_name = caption[0][:-2].split('.')[0]\n",
    "        # cleaning up the caption(-1 for removing the \\n)\n",
    "        image_caption = clean_cap(caption[-1][:-1])\n",
    "        # adding start and end of seq\n",
    "        image_caption = ('startofseq ' + image_caption + ' endofseq')\n",
    "        # appending the caption with the name as key\n",
    "        if image_name in mapping:\n",
    "            mapping[image_name].append(image_caption)\n",
    "        # if the image_name key is new the creating a key value\n",
    "        else:\n",
    "            mapping[image_name] = [image_caption]\n",
    "    return mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e079f545",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_captions(mapping: dict, filename: str) -> bool:\n",
    "    \"\"\"\n",
    "    To write the mappings in the disk, one per line.\n",
    "    written as \"key<space>caption\"\n",
    "    :param mapping: dict with image_name as key and the\n",
    "    corresponding captions as a list of captions\n",
    "    :param filename: file name to save\n",
    "    \"\"\"\n",
    "    lines = list()\n",
    "    for key, cap_list in mapping.items():\n",
    "        for cap in cap_list:\n",
    "            lines.append(key + ' ' + cap)\n",
    "    data = '\\n'.join(lines)\n",
    "    with open(filename, 'w') as file:\n",
    "        file.write(data)\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "12dc08bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_doc(filename: str) -> str:\n",
    "    \"\"\"\n",
    "    to open the file as read only\n",
    "    :param filename: name of the file\n",
    "    :return: the entire text as a string\n",
    "    \"\"\"\n",
    "    with open(filename, 'r', encoding='utf-8') as file:\n",
    "        text = file.read()\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "82e61657",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_set(text_file_path: str) -> set:\n",
    "    \"\"\"\n",
    "    to load a pre-defined list of photo names\n",
    "    returns the names of the images form the set_text_file\n",
    "    :param text_file_path:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    # user defined to load the document\n",
    "    doc = load_doc(text_file_path)\n",
    "    dataset = list()\n",
    "    # process line by line\n",
    "    for line in doc.split('\\n'):\n",
    "        # skip empty lines\n",
    "        if len(line) < 1:\n",
    "            continue\n",
    "        # get the image name without the extension\n",
    "        identifier = line.split('.')[0]\n",
    "        dataset.append(identifier)\n",
    "    return set(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cc4dc247",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_clean_cap(caption_txt_path: str, dataset: set) -> dict:\n",
    "    \"\"\"\n",
    "    load clean descriptions into memory\n",
    "    :param caption_txt_path: The path where the clean captions were saved\n",
    "    :param dataset: the img names of the tining or test data set\n",
    "    :return: dict of captions mapped with its curr name\n",
    "    \"\"\"\n",
    "    doc = load_doc(caption_txt_path)\n",
    "    clean_captions = dict()\n",
    "    for line in doc.split('\\n'):\n",
    "        # split line by white space\n",
    "        tokens = line.split()\n",
    "        # split name and captions\n",
    "        image_name, image_cap = tokens[0], \" \".join(tokens[1:])\n",
    "        # skip the images which are not in the set.\n",
    "        if image_name in dataset:\n",
    "            # creating a list\n",
    "            if image_name not in clean_captions:\n",
    "                clean_captions[image_name] = list()\n",
    "            clean_captions[image_name].append(image_cap)\n",
    "    return clean_captions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2abbfc23",
   "metadata": {},
   "outputs": [],
   "source": [
    "def max_len(clean_captions: dict) -> int:\n",
    "    \"\"\"\n",
    "    Returns the length of the caption with most words\n",
    "    :param clean_captions: a dictionary of captions \n",
    "    :return: length of the longest caption \n",
    "    \"\"\"\n",
    "    # Converts a dictionary of clean captions and returns a list of captions.\n",
    "    clean_captions_list = [caption.split() for captions in clean_captions.values()\n",
    "                           for caption in captions]\n",
    "    return max(len(caption) for caption in clean_captions_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "02bd0dad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_tokenizer(captions_dict: dict, top_k: int = 2000) -> Tokenizer:\n",
    "    \"\"\"\n",
    "    Fit a tokenizer given caption descriptions\n",
    "    :param captions_dict: (dict) of clean captions\n",
    "    :param top_k: number of words in vocabulary\n",
    "    :return: tokenizer object\n",
    "    \"\"\"\n",
    "    clean_captions_list = [caption for captions in captions_dict.values()\n",
    "                           for caption in captions]\n",
    "    tokenizer = Tokenizer(num_words=top_k, oov_token=\"<unk>\")\n",
    "    # Map '<pad>' to '0'\n",
    "    tokenizer.word_index['<pad>'] = 0\n",
    "    tokenizer.index_word[0] = '<pad>'\n",
    "    tokenizer.fit_on_texts(clean_captions_list)\n",
    "    return tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "db249b69",
   "metadata": {},
   "outputs": [],
   "source": [
    "def path_cap_list(img_names_set: set, tokenizer: Tokenizer, captions_dict) -> (list, list):\n",
    "    \"\"\"\n",
    "    a list of image paths and a list of captions for images with corresponding values\n",
    "    Note: the captions will be tokenized and padded in this function\n",
    "    :param img_names_set: The set on which the processing is done\n",
    "    :param tokenizer: tokenizer\n",
    "    :param captions_dict: clean captions for that set without any tokenization\n",
    "    \"\"\"\n",
    "    tokenized_caps_dict = tokenize_cap(tokenizer, captions_dict)\n",
    "    image_name_list = sorted(img_names_set)\n",
    "    capt_list = [cap for name in image_name_list for cap in tokenized_caps_dict[name]]\n",
    "    img_path_list = [img_name_2_path(name) for name in image_name_list for i in range(len(tokenized_caps_dict[name]))]\n",
    "    return img_path_list, capt_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7b728502",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_npy(image_path: str, cap: str) -> (str, str):\n",
    "    \"\"\"\n",
    "    :returns image tensor vector with the image path \n",
    "    :param image_path: \n",
    "    :param cap: \n",
    "    \"\"\"\n",
    "    img_tensor = np.load(image_path.decode('utf-8') + '.npy')\n",
    "    return img_tensor, cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c9acb671",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(img_path_list: str, cap_list: str) -> object:\n",
    "    \"\"\"\n",
    "    :param img_path_list: The ordered list of img paths with duplication acc to number of captions  \n",
    "    :param cap_list: the padded caption list with the curr order \n",
    "    :return: dataset \n",
    "    \"\"\"\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((img_path_list, cap_list))\n",
    "    # Use map to load the numpy files in parallel\n",
    "    dataset = dataset.map(lambda item1, item2: tf.numpy_function(load_npy, [item1, item2], [tf.float32, tf.int32]),\n",
    "                          num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
    "    # Shuffle and batch\n",
    "    dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE).prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "66942098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\inba2\\\\Documents\\\\DataSet\\\\8k\\\\Flickr_TextData\\\\Flickr8k.token.txt'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CAP_TEXT_PATH"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6812454c",
   "metadata": {},
   "source": [
    "Pre processing is also done while loading the captions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0f2b7889",
   "metadata": {},
   "outputs": [],
   "source": [
    "cap_dict = load_cap(CAP_TEXT_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af989d70",
   "metadata": {},
   "source": [
    "Finding the maximum len of the captption "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0398b64d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34\n"
     ]
    }
   ],
   "source": [
    "MAX_CAP_LEN = max_len(cap_dict)\n",
    "print(MAX_CAP_LEN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c34308bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_cap(tokenizer: Tokenizer, captions_dict: dict, pad_len: int = MAX_CAP_LEN) -> dict:\n",
    "    \"\"\"\n",
    "    Tokenizes the captions and\n",
    "    :param pad_len: The maximum caption length in the whole dataset\n",
    "    (should include both train and test dataset)\n",
    "    :param tokenizer: Tokenizer object\n",
    "    :param captions_dict: The dict of train/test cap which have to be tokenized\n",
    "    :return: A dict of tokenized captions\n",
    "    \"\"\"\n",
    "    pad_caps_dict = {img_name: pad_sequences(tokenizer.texts_to_sequences(captions), maxlen=pad_len, padding='post')\n",
    "                     for img_name, captions in captions_dict.items()}\n",
    "    return pad_caps_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1399c87e",
   "metadata": {},
   "outputs": [],
   "source": [
    "CLEAN_CAP_TEXT_PATH = r'C:\\Users\\inba2\\Documents\\DataSet\\8k\\Flickr_TextData\\Flickr8k_clean_cap.txt'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe6885d",
   "metadata": {},
   "source": [
    "## Saving the clean captions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a3d6c103",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_captions(cap_dict,CLEAN_CAP_TEXT_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8db16a16",
   "metadata": {},
   "source": [
    "# Tokenizing words"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31cc55f2",
   "metadata": {},
   "source": [
    "## Loading the train img names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1b5857c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_img_names = sorted(load_set(TRAIN_TXT_PATH))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34065a09",
   "metadata": {},
   "source": [
    "Loading the train img captions as a dict "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e6dc2cc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_img_cap = load_clean_cap(CLEAN_CAP_TEXT_PATH, train_img_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cd753a57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_img_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "139d68ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['startofseq child in pink dress is climbing up set of stair in an entry way endofseq', 'startofseq girl going into wooden building endofseq', 'startofseq little girl climbing into wooden playhouse endofseq', 'startofseq little girl climbing the stair to her playhouse endofseq', 'startofseq little girl in pink dress going into wooden cabin endofseq']\n"
     ]
    }
   ],
   "source": [
    "for x in train_img_cap.values():\n",
    "    print(x)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a87570bb",
   "metadata": {},
   "source": [
    "## Creating a tokenizer for training set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cd0edcc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = create_tokenizer(train_img_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6f767b88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6537\n"
     ]
    }
   ],
   "source": [
    "VOCAB_SIZE = len(tokenizer.word_index) + 1\n",
    "print(VOCAB_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d48890bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'<unk>': 1,\n",
       " 'startofseq': 2,\n",
       " 'endofseq': 3,\n",
       " 'in': 4,\n",
       " 'the': 5,\n",
       " 'on': 6,\n",
       " 'dog': 7,\n",
       " 'is': 8,\n",
       " 'and': 9,\n",
       " 'with': 10,\n",
       " 'man': 11,\n",
       " 'of': 12,\n",
       " 'two': 13,\n",
       " 'boy': 14,\n",
       " 'girl': 15,\n",
       " 'woman': 16,\n",
       " 'white': 17,\n",
       " 'black': 18,\n",
       " 'are': 19,\n",
       " 'to': 20,\n",
       " 'wearing': 21,\n",
       " 'at': 22,\n",
       " 'people': 23,\n",
       " 'water': 24,\n",
       " 'brown': 25,\n",
       " 'red': 26,\n",
       " 'young': 27,\n",
       " 'child': 28,\n",
       " 'an': 29,\n",
       " 'his': 30,\n",
       " 'blue': 31,\n",
       " 'running': 32,\n",
       " 'through': 33,\n",
       " 'playing': 34,\n",
       " 'shirt': 35,\n",
       " 'while': 36,\n",
       " 'ball': 37,\n",
       " 'down': 38,\n",
       " 'standing': 39,\n",
       " 'little': 40,\n",
       " 'grass': 41,\n",
       " 'snow': 42,\n",
       " 'person': 43,\n",
       " 'jumping': 44,\n",
       " 'over': 45,\n",
       " 'three': 46,\n",
       " 'stand': 47,\n",
       " 'jump': 48,\n",
       " 'sitting': 49,\n",
       " 'field': 50,\n",
       " 'run': 51,\n",
       " 'front': 52,\n",
       " 'it': 53,\n",
       " 'holding': 54,\n",
       " 'play': 55,\n",
       " 'small': 56,\n",
       " 'yellow': 57,\n",
       " 'green': 58,\n",
       " 'group': 59,\n",
       " 'up': 60,\n",
       " 'by': 61,\n",
       " 'large': 62,\n",
       " 'one': 63,\n",
       " 'walking': 64,\n",
       " 'her': 65,\n",
       " 'men': 66,\n",
       " 'mouth': 67,\n",
       " 'air': 68,\n",
       " 'player': 69,\n",
       " 'into': 70,\n",
       " 'rock': 71,\n",
       " 'near': 72,\n",
       " 'beach': 73,\n",
       " 'street': 74,\n",
       " 'another': 75,\n",
       " 'bike': 76,\n",
       " 'for': 77,\n",
       " 'from': 78,\n",
       " 'riding': 79,\n",
       " 'walk': 80,\n",
       " 'a': 81,\n",
       " 'outside': 82,\n",
       " 'hat': 83,\n",
       " 'other': 84,\n",
       " 'looking': 85,\n",
       " 'jacket': 86,\n",
       " 'look': 87,\n",
       " 'orange': 88,\n",
       " 'out': 89,\n",
       " 'pink': 90,\n",
       " 'mountain': 91,\n",
       " 'next': 92,\n",
       " 'ride': 93,\n",
       " 'camera': 94,\n",
       " 'off': 95,\n",
       " 'tree': 96,\n",
       " 'pool': 97,\n",
       " 'their': 98,\n",
       " 'toy': 99,\n",
       " 'behind': 100,\n",
       " 'around': 101,\n",
       " 'soccer': 102,\n",
       " 'some': 103,\n",
       " 'wall': 104,\n",
       " 'sits': 105,\n",
       " 'background': 106,\n",
       " 'hand': 107,\n",
       " 'face': 108,\n",
       " 'ha': 109,\n",
       " 'building': 110,\n",
       " 'stick': 111,\n",
       " 'dressed': 112,\n",
       " 'dirt': 113,\n",
       " 'kid': 114,\n",
       " 'hold': 115,\n",
       " 'top': 116,\n",
       " 'watch': 117,\n",
       " 'along': 118,\n",
       " 'park': 119,\n",
       " 'football': 120,\n",
       " 'climbing': 121,\n",
       " 'car': 122,\n",
       " 'catch': 123,\n",
       " 'skateboard': 124,\n",
       " 'four': 125,\n",
       " 'picture': 126,\n",
       " 'crowd': 127,\n",
       " 'trick': 128,\n",
       " 'hill': 129,\n",
       " 'smiling': 130,\n",
       " 'grassy': 131,\n",
       " 'swing': 132,\n",
       " 'across': 133,\n",
       " 'swimming': 134,\n",
       " 'carrying': 135,\n",
       " 'bicycle': 136,\n",
       " 'sand': 137,\n",
       " 'short': 138,\n",
       " 'race': 139,\n",
       " 'baby': 140,\n",
       " 'wave': 141,\n",
       " 'dress': 142,\n",
       " 'helmet': 143,\n",
       " 'each': 144,\n",
       " 'snowy': 145,\n",
       " 'tennis': 146,\n",
       " 'hair': 147,\n",
       " 'together': 148,\n",
       " 'ocean': 149,\n",
       " 'arm': 150,\n",
       " 'back': 151,\n",
       " 'doing': 152,\n",
       " 'bench': 153,\n",
       " 'tan': 154,\n",
       " 'road': 155,\n",
       " 'slide': 156,\n",
       " 'head': 157,\n",
       " 'him': 158,\n",
       " 'area': 159,\n",
       " 'that': 160,\n",
       " 'game': 161,\n",
       " 'sidewalk': 162,\n",
       " 'sit': 163,\n",
       " 'ground': 164,\n",
       " 'fence': 165,\n",
       " 'basketball': 166,\n",
       " 'pose': 167,\n",
       " 'uniform': 168,\n",
       " 'horse': 169,\n",
       " 'something': 170,\n",
       " 'skateboarder': 171,\n",
       " 'being': 172,\n",
       " 'purple': 173,\n",
       " 'track': 174,\n",
       " 'side': 175,\n",
       " 'frisbee': 176,\n",
       " 'guy': 177,\n",
       " 'several': 178,\n",
       " 'ramp': 179,\n",
       " 'boat': 180,\n",
       " 'lake': 181,\n",
       " 'coat': 182,\n",
       " 'covered': 183,\n",
       " 'there': 184,\n",
       " 'city': 185,\n",
       " 'path': 186,\n",
       " 'suit': 187,\n",
       " 'smile': 188,\n",
       " 'posing': 189,\n",
       " 'baseball': 190,\n",
       " 'rope': 191,\n",
       " 'big': 192,\n",
       " 'long': 193,\n",
       " 'climb': 194,\n",
       " 'high': 195,\n",
       " 'motorcycle': 196,\n",
       " 'wooden': 197,\n",
       " 'sign': 198,\n",
       " 'wood': 199,\n",
       " 'pant': 200,\n",
       " 'lady': 201,\n",
       " 'jean': 202,\n",
       " 'couple': 203,\n",
       " 'take': 204,\n",
       " 'them': 205,\n",
       " 'rocky': 206,\n",
       " 'watching': 207,\n",
       " 'glass': 208,\n",
       " 'table': 209,\n",
       " 'under': 210,\n",
       " 'bird': 211,\n",
       " 'grey': 212,\n",
       " 'sunglass': 213,\n",
       " 'dark': 214,\n",
       " 'racing': 215,\n",
       " 'snowboarder': 216,\n",
       " 'older': 217,\n",
       " 'towards': 218,\n",
       " 'doe': 219,\n",
       " 'beside': 220,\n",
       " 'leap': 221,\n",
       " 'get': 222,\n",
       " 'rider': 223,\n",
       " 'adult': 224,\n",
       " 'bag': 225,\n",
       " 'cliff': 226,\n",
       " 'above': 227,\n",
       " 'river': 228,\n",
       " 'who': 229,\n",
       " 'ice': 230,\n",
       " 'team': 231,\n",
       " 'colorful': 232,\n",
       " 'pole': 233,\n",
       " 'yard': 234,\n",
       " 'open': 235,\n",
       " 'striped': 236,\n",
       " 'backpack': 237,\n",
       " 'skier': 238,\n",
       " 'onto': 239,\n",
       " 'cap': 240,\n",
       " 'against': 241,\n",
       " 'outfit': 242,\n",
       " 'taking': 243,\n",
       " 'he': 244,\n",
       " 'flower': 245,\n",
       " 'midair': 246,\n",
       " 'asian': 247,\n",
       " 'blonde': 248,\n",
       " 'swim': 249,\n",
       " 'chair': 250,\n",
       " 'light': 251,\n",
       " 'collar': 252,\n",
       " 'after': 253,\n",
       " 'body': 254,\n",
       " 'blond': 255,\n",
       " 'chasing': 256,\n",
       " 'hockey': 257,\n",
       " 'surfer': 258,\n",
       " 'fountain': 259,\n",
       " 'laying': 260,\n",
       " 'leg': 261,\n",
       " 'inside': 262,\n",
       " 'costume': 263,\n",
       " 'during': 264,\n",
       " 'wet': 265,\n",
       " 'many': 266,\n",
       " 'surrounded': 267,\n",
       " 'old': 268,\n",
       " 'drink': 269,\n",
       " 'edge': 270,\n",
       " 'toddler': 271,\n",
       " 'colored': 272,\n",
       " 'forest': 273,\n",
       " 'very': 274,\n",
       " 'five': 275,\n",
       " 'playground': 276,\n",
       " 'performing': 277,\n",
       " 'hanging': 278,\n",
       " 'step': 279,\n",
       " 'guitar': 280,\n",
       " 'others': 281,\n",
       " 'object': 282,\n",
       " 'outdoors': 283,\n",
       " 'flag': 284,\n",
       " 'ski': 285,\n",
       " 'tshirt': 286,\n",
       " 'leash': 287,\n",
       " 'wear': 288,\n",
       " 'whilst': 289,\n",
       " 'brick': 290,\n",
       " 'carry': 291,\n",
       " 'night': 292,\n",
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       " 'decorated': 963,\n",
       " 'tail': 964,\n",
       " 'robe': 965,\n",
       " 'bounce': 966,\n",
       " 'tutu': 967,\n",
       " 'grind': 968,\n",
       " 'kayaking': 969,\n",
       " 'beam': 970,\n",
       " 'helping': 971,\n",
       " 'pier': 972,\n",
       " 'closed': 973,\n",
       " 'blurry': 974,\n",
       " 'waterskiing': 975,\n",
       " 'balcony': 976,\n",
       " 'taken': 977,\n",
       " 'lift': 978,\n",
       " 'training': 979,\n",
       " 'sniffing': 980,\n",
       " 'embrace': 981,\n",
       " 'hillside': 982,\n",
       " 'lick': 983,\n",
       " 'surrounding': 984,\n",
       " 'thumb': 985,\n",
       " 'strip': 986,\n",
       " 'fan': 987,\n",
       " 'working': 988,\n",
       " 'happily': 989,\n",
       " 'cave': 990,\n",
       " 'pine': 991,\n",
       " 'sharp': 992,\n",
       " 'wade': 993,\n",
       " 'biking': 994,\n",
       " 'eat': 995,\n",
       " 'suspended': 996,\n",
       " 'bear': 997,\n",
       " 'soda': 998,\n",
       " 'rug': 999,\n",
       " 'pair': 1000,\n",
       " ...}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.word_index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db3281e6",
   "metadata": {},
   "source": [
    "# Train dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4568dc89",
   "metadata": {},
   "source": [
    "## Tokenizing the train captions and storing it in lists "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cf4a1ece",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_name_train, caption_train = path_cap_list(train_img_names, tokenizer, train_img_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3263c645",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:\\\\Users\\\\inba2\\\\Documents\\\\DataSet\\\\8k\\\\Images\\\\Images\\\\1000268201_693b08cb0e.jpg',\n",
       " 'C:\\\\Users\\\\inba2\\\\Documents\\\\DataSet\\\\8k\\\\Images\\\\Images\\\\1000268201_693b08cb0e.jpg',\n",
       " 'C:\\\\Users\\\\inba2\\\\Documents\\\\DataSet\\\\8k\\\\Images\\\\Images\\\\1000268201_693b08cb0e.jpg',\n",
       " 'C:\\\\Users\\\\inba2\\\\Documents\\\\DataSet\\\\8k\\\\Images\\\\Images\\\\1000268201_693b08cb0e.jpg',\n",
       " 'C:\\\\Users\\\\inba2\\\\Documents\\\\DataSet\\\\8k\\\\Images\\\\Images\\\\1000268201_693b08cb0e.jpg',\n",
       " 'C:\\\\Users\\\\inba2\\\\Documents\\\\DataSet\\\\8k\\\\Images\\\\Images\\\\1001773457_577c3a7d70.jpg',\n",
       " 'C:\\\\Users\\\\inba2\\\\Documents\\\\DataSet\\\\8k\\\\Images\\\\Images\\\\1001773457_577c3a7d70.jpg',\n",
       " 'C:\\\\Users\\\\inba2\\\\Documents\\\\DataSet\\\\8k\\\\Images\\\\Images\\\\1001773457_577c3a7d70.jpg',\n",
       " 'C:\\\\Users\\\\inba2\\\\Documents\\\\DataSet\\\\8k\\\\Images\\\\Images\\\\1001773457_577c3a7d70.jpg',\n",
       " 'C:\\\\Users\\\\inba2\\\\Documents\\\\DataSet\\\\8k\\\\Images\\\\Images\\\\1001773457_577c3a7d70.jpg']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_name_train[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ab8d56fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([  2,  28,   4,  90, 142,   8, 121,  60, 367,  12, 358,   4,  29,\n",
       "          1, 618,   3,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0]),\n",
       " array([  2,  15, 316,  70, 197, 110,   3,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0]),\n",
       " array([   2,   40,   15,  121,   70,  197, 1840,    3,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0]),\n",
       " array([   2,   40,   15,  121,    5,  358,   20,   65, 1840,    3,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0]),\n",
       " array([  2,  40,  15,   4,  90, 142, 316,  70, 197,   1,   3,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0]),\n",
       " array([  2,  18,   7,   9, 817,   7,  19, 349,   3,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0]),\n",
       " array([   2,   18,    7,    9, 1418,    7,   34,   10,  144,   84,    6,\n",
       "           5,  155,    3,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0]),\n",
       " array([  2,  18,   7,   9,  17,   7,  10,  25, 746,  19, 608,  22, 144,\n",
       "         84,   4,   5,  74,   3,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0]),\n",
       " array([   2,   13,    7,   12,  798, 1285,   85,   22,  144,   84,    6,\n",
       "           5,  155,    3,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0]),\n",
       " array([  2,  13,   7,   6, 646, 818, 318, 144,  84,   3,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0])]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caption_train[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "227dfe97",
   "metadata": {},
   "source": [
    "## Creating train dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e5788a07",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = create_dataset(img_name_train, caption_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a49c00e0",
   "metadata": {},
   "source": [
    "# Test Dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08711bbe",
   "metadata": {},
   "source": [
    "## Loading the test img names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4327dd79",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_img_names = sorted(load_set(TEST_TXT_PATH))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d9eae5",
   "metadata": {},
   "source": [
    "## Loading the test img captions as a dict "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "65bdcc33",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_img_cap = load_clean_cap(CLEAN_CAP_TEXT_PATH, test_img_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "69e34070",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_img_cap)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96e95350",
   "metadata": {},
   "source": [
    "## Creating the test dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "994e85ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_name_test, caption_test = path_cap_list(test_img_names, tokenizer, test_img_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c5e313d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset = create_dataset(img_name_test, caption_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea53b761",
   "metadata": {},
   "source": [
    "# Defining some constants "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8c007f7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_dim = 256\n",
    "units = 512\n",
    "vocab_size = len(tokenizer.word_index) + 1 # 6537\n",
    "num_steps = len(train_img_names) // BATCH_SIZE  # 187\n",
    "EPOCHS = 20\n",
    "# Shape from last layer of VGG-16 :(7,7,512)\n",
    "# So, say there are 49 pixel locations now and each pixel is 512 dimensional\n",
    "features_shape = 512\n",
    "attention_features_shape = 49 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e576d4",
   "metadata": {},
   "source": [
    "# Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4422635b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BahdanauAttention(tf.keras.Model):\n",
    "  def __init__(self, units):\n",
    "    super(BahdanauAttention, self).__init__()\n",
    "    self.W1 = tf.keras.layers.Dense(units)\n",
    "    self.W2 = tf.keras.layers.Dense(units)\n",
    "    self.V = tf.keras.layers.Dense(1)\n",
    "\n",
    "  def call(self, features, hidden):\n",
    "    # features(CNN_encoder output) shape == (batch_size, 64, embedding_dim)\n",
    "\n",
    "    # hidden shape == (batch_size, hidden_size)\n",
    "    # hidden_with_time_axis shape == (batch_size, 1, hidden_size)\n",
    "    hidden_with_time_axis = tf.expand_dims(hidden, 1)\n",
    "\n",
    "    # attention_hidden_layer shape == (batch_size, 64, units)\n",
    "    attention_hidden_layer = (tf.nn.tanh(self.W1(features) +\n",
    "                                         self.W2(hidden_with_time_axis)))\n",
    "\n",
    "    # score shape == (batch_size, 64, 1)\n",
    "    # This gives you an unnormalized score for each image feature.\n",
    "    score = self.V(attention_hidden_layer)\n",
    "\n",
    "    # attention_weights shape == (batch_size, 64, 1)\n",
    "    attention_weights = tf.nn.softmax(score, axis=1)\n",
    "\n",
    "    # context_vector shape after sum == (batch_size, hidden_size)\n",
    "    context_vector = attention_weights * features\n",
    "    context_vector = tf.reduce_sum(context_vector, axis=1)\n",
    "\n",
    "    return context_vector, attention_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "67d423e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNN_Encoder(tf.keras.Model):\n",
    "    # Since you have already extracted the features and dumped it\n",
    "    # This encoder passes those features through a Fully connected layer\n",
    "    def __init__(self, embedding_dim):\n",
    "        super(CNN_Encoder, self).__init__()\n",
    "        # shape after fc == (batch_size, 49, embedding_dim)\n",
    "        self.fc = tf.keras.layers.Dense(embedding_dim)\n",
    "\n",
    "    def call(self, x):\n",
    "        x = self.fc(x)\n",
    "        x = tf.nn.relu(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0f85f22a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rnn_type(units):\n",
    "  # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)\n",
    "  # the code automatically does that.\n",
    "    if tf.test.is_gpu_available():\n",
    "        return tf.compat.v1.keras.layers.CuDNNLSTM(units, \n",
    "                                        return_sequences=True, \n",
    "                                        return_state=True, \n",
    "                                        recurrent_initializer='glorot_uniform')\n",
    "    else:\n",
    "        return tf.keras.layers.GRU(units, \n",
    "                                   return_sequences=True, \n",
    "                                   return_state=True, \n",
    "                                   recurrent_activation='sigmoid', \n",
    "                                   recurrent_initializer='glorot_uniform')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fae868ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "class RNN_Decoder(tf.keras.Model):\n",
    "  def __init__(self, embedding_dim, units, vocab_size):\n",
    "    super(RNN_Decoder, self).__init__()\n",
    "    self.units = units\n",
    "\n",
    "    self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n",
    "#     self.gru = rnn_type(self.units)\n",
    "    self.gru = tf.keras.layers.GRU(self.units,\n",
    "                                   return_sequences=True,\n",
    "                                   return_state=True,\n",
    "                                   recurrent_initializer='glorot_uniform')\n",
    "    self.fc1 = tf.keras.layers.Dense(self.units)\n",
    "    self.fc2 = tf.keras.layers.Dense(vocab_size)\n",
    "\n",
    "    self.attention = BahdanauAttention(self.units)\n",
    "\n",
    "  def call(self, x, features, hidden):\n",
    "    # defining attention as a separate model\n",
    "    context_vector, attention_weights = self.attention(features, hidden)\n",
    "\n",
    "    # x shape after passing through embedding == (batch_size, 1, embedding_dim)\n",
    "    x = self.embedding(x)\n",
    "\n",
    "    # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)\n",
    "    x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)\n",
    "\n",
    "    # passing the concatenated vector to the GRU\n",
    "    output, state = self.gru(x)\n",
    "\n",
    "    # shape == (batch_size, MAX_CAP_LEN, hidden_size)\n",
    "    x = self.fc1(output)\n",
    "\n",
    "    # x shape == (batch_size * MAX_CAP_LEN, hidden_size)\n",
    "    x = tf.reshape(x, (-1, x.shape[2]))\n",
    "\n",
    "    # output shape == (batch_size * MAX_CAP_LEN, vocab)\n",
    "    x = self.fc2(x)\n",
    "\n",
    "    return x, state, attention_weights\n",
    "\n",
    "  def reset_state(self, batch_size):\n",
    "    return tf.zeros((batch_size, self.units))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9e666edd",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = CNN_Encoder(embedding_dim)\n",
    "decoder = RNN_Decoder(embedding_dim, units, vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fff6313e",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.keras.optimizers.Adam()\n",
    "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\n",
    "    from_logits=True, reduction='none')\n",
    "\n",
    "\n",
    "def loss_function(real, pred):\n",
    "  mask = tf.math.logical_not(tf.math.equal(real, 0))\n",
    "  loss_ = loss_object(real, pred)\n",
    "\n",
    "  mask = tf.cast(mask, dtype=loss_.dtype)\n",
    "  loss_ *= mask\n",
    "\n",
    "  return tf.reduce_mean(loss_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22687584",
   "metadata": {},
   "source": [
    "# Checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a7ce368b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T01:48:39.553957Z",
     "iopub.status.busy": "2021-04-02T01:48:39.552992Z",
     "iopub.status.idle": "2021-04-02T01:48:39.555163Z",
     "shell.execute_reply": "2021-04-02T01:48:39.555549Z"
    },
    "id": "PpJAqPMWo0uE"
   },
   "outputs": [],
   "source": [
    "checkpoint_path = \"./checkpoints/train\"\n",
    "ckpt = tf.train.Checkpoint(encoder=encoder,\n",
    "                           decoder=decoder,\n",
    "                           optimizer=optimizer)\n",
    "ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ca6e9174",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T01:48:39.560183Z",
     "iopub.status.busy": "2021-04-02T01:48:39.559277Z",
     "iopub.status.idle": "2021-04-02T01:48:39.561984Z",
     "shell.execute_reply": "2021-04-02T01:48:39.561485Z"
    },
    "id": "fUkbqhc_uObw"
   },
   "outputs": [],
   "source": [
    "start_epoch = 0\n",
    "if ckpt_manager.latest_checkpoint:\n",
    "  start_epoch = int(ckpt_manager.latest_checkpoint.split('-')[-1])\n",
    "  # restoring the latest checkpoint in checkpoint_path\n",
    "  ckpt.restore(ckpt_manager.latest_checkpoint)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9cae28b",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "* You extract the features stored in the respective `.npy` files and then pass those features through the encoder.\n",
    "* The encoder output, hidden state(initialized to 0) and the decoder input (which is the start token) is passed to the decoder.\n",
    "* The decoder returns the predictions and the decoder hidden state.\n",
    "* The decoder hidden state is then passed back into the model and the predictions are used to calculate the loss.\n",
    "* Use teacher forcing to decide the next input to the decoder.\n",
    "* Teacher forcing is the technique where the target word is passed as the next input to the decoder.\n",
    "* The final step is to calculate the gradients and apply it to the optimizer and backpropagate.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7500e35d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T01:48:39.566197Z",
     "iopub.status.busy": "2021-04-02T01:48:39.565231Z",
     "iopub.status.idle": "2021-04-02T01:48:39.567763Z",
     "shell.execute_reply": "2021-04-02T01:48:39.567232Z"
    },
    "id": "Vt4WZ5mhJE-E"
   },
   "outputs": [],
   "source": [
    "# adding this in a separate cell because if you run the training cell\n",
    "# many times, the loss_plot array will be reset\n",
    "loss_plot = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8b2bf971",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T01:48:39.575388Z",
     "iopub.status.busy": "2021-04-02T01:48:39.574440Z",
     "iopub.status.idle": "2021-04-02T01:48:39.576980Z",
     "shell.execute_reply": "2021-04-02T01:48:39.576396Z"
    },
    "id": "sqgyz2ANKlpU"
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def train_step(img_tensor, target):\n",
    "  loss = 0\n",
    "\n",
    "  # initializing the hidden state for each batch\n",
    "  # because the captions are not related from image to image\n",
    "  hidden = decoder.reset_state(batch_size=target.shape[0])\n",
    "\n",
    "  dec_input = tf.expand_dims([tokenizer.word_index['startofseq']] * target.shape[0], 1)\n",
    "\n",
    "  with tf.GradientTape() as tape:\n",
    "      features = encoder(img_tensor)\n",
    "\n",
    "      for i in range(1, target.shape[1]):\n",
    "          # passing the features through the decoder\n",
    "          predictions, hidden, _ = decoder(dec_input, features, hidden)\n",
    "\n",
    "          loss += loss_function(target[:, i], predictions)\n",
    "\n",
    "          # using teacher forcing\n",
    "          dec_input = tf.expand_dims(target[:, i], 1)\n",
    "\n",
    "  total_loss = (loss / int(target.shape[1]))\n",
    "\n",
    "  trainable_variables = encoder.trainable_variables + decoder.trainable_variables\n",
    "\n",
    "  gradients = tape.gradient(loss, trainable_variables)\n",
    "\n",
    "  optimizer.apply_gradients(zip(gradients, trainable_variables))\n",
    "\n",
    "  return loss, total_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "74ca1aec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do you want to load weights ? [y/Y] y\n"
     ]
    }
   ],
   "source": [
    "# Loading weights \n",
    "if str(input(\"Do you want to load weights ? [y/Y] \")).casefold() == 'y':\n",
    "    encoder.load_weights(r\"C:\\Users\\inba2\\Documents\\IEEE\\Envision\\Image_Captioning\\Encoder_train_1_8k.h5\")\n",
    "    decoder.load_weights(r\"C:\\Users\\inba2\\Documents\\IEEE\\Envision\\Image_Captioning\\Decoder_train_1_8k.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4df1b5f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T01:48:39.583840Z",
     "iopub.status.busy": "2021-04-02T01:48:39.582898Z",
     "iopub.status.idle": "2021-04-02T02:05:08.368215Z",
     "shell.execute_reply": "2021-04-02T02:05:08.368655Z"
    },
    "id": "UlA4VIQpRPGo"
   },
   "outputs": [],
   "source": [
    "EPOCHS = 20\n",
    "\n",
    "for epoch in range(start_epoch, EPOCHS):\n",
    "    start = time.time()\n",
    "    total_loss = 0\n",
    "\n",
    "    for (batch, (img_tensor, target)) in enumerate(train_dataset):\n",
    "        batch_loss, t_loss = train_step(img_tensor, target)\n",
    "        total_loss += t_loss\n",
    "\n",
    "        if batch % 100 == 0:\n",
    "            average_batch_loss = batch_loss.numpy()/int(target.shape[1])\n",
    "            print(f'Epoch {epoch+1} Batch {batch} Loss {average_batch_loss:.4f}')\n",
    "    # storing the epoch end loss value to plot later\n",
    "    loss_plot.append(total_loss / num_steps)\n",
    "\n",
    "    if epoch % 5 == 0:\n",
    "      ckpt_manager.save()\n",
    "\n",
    "    print(f'Epoch {epoch+1} Loss {total_loss/num_steps:.6f}')\n",
    "    print(f'Time taken for 1 epoch {time.time()-start:.2f} sec\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eeab9827",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T02:05:08.388459Z",
     "iopub.status.busy": "2021-04-02T02:05:08.387197Z",
     "iopub.status.idle": "2021-04-02T02:05:08.531959Z",
     "shell.execute_reply": "2021-04-02T02:05:08.531457Z"
    },
    "id": "1Wm83G-ZBPcC",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.plot(loss_plot)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Loss Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85238025",
   "metadata": {},
   "source": [
    "# Saving Weights "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37ac0fbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.path.isfile(\"Encoder_train_1_8k.h5\") is False:\n",
    "    encoder.save_weights(\"Encoder_train_1_8k.h5\")\n",
    "    print(\"Encoder saved\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "caffc283",
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.path.isfile(\"Decoder_train_1_8k.h5\") is False:\n",
    "    decoder.save_weights(\"Decoder_train_1_8k.h5\")\n",
    "    print(\"Decoder saved\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d27c08",
   "metadata": {
    "id": "xGvOcLQKghXN"
   },
   "source": [
    "# Caption!\n",
    "\n",
    "* The evaluate function is similar to the training loop, except you don't use teacher forcing here. The input to the decoder at each time step is its previous predictions along with the hidden state and the encoder output.\n",
    "* Stop predicting when the model predicts the end token.\n",
    "* And store the attention weights for every time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1c1cb74e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T02:05:08.540198Z",
     "iopub.status.busy": "2021-04-02T02:05:08.539560Z",
     "iopub.status.idle": "2021-04-02T02:05:08.541377Z",
     "shell.execute_reply": "2021-04-02T02:05:08.541803Z"
    },
    "id": "RCWpDtyNRPGs"
   },
   "outputs": [],
   "source": [
    "def evaluate(image):\n",
    "    attention_plot = np.zeros((MAX_CAP_LEN, attention_features_shape))\n",
    "\n",
    "    hidden = decoder.reset_state(batch_size=1)\n",
    "\n",
    "    temp_input = tf.expand_dims(load_img(image)[0], 0)\n",
    "    img_tensor_val = image_features_extract_model(temp_input)\n",
    "    img_tensor_val = tf.reshape(img_tensor_val, (img_tensor_val.shape[0],\n",
    "                                                 -1,\n",
    "                                                 img_tensor_val.shape[3]))\n",
    "    features = encoder(img_tensor_val)\n",
    "\n",
    "    dec_input = tf.expand_dims([tokenizer.word_index['startofseq']], 0)\n",
    "    result = []\n",
    "\n",
    "    for i in range(MAX_CAP_LEN):\n",
    "        predictions, hidden, attention_weights = decoder(dec_input,\n",
    "                                                         features,\n",
    "                                                         hidden)\n",
    "\n",
    "        attention_plot[i] = tf.reshape(attention_weights, (-1, )).numpy()\n",
    "\n",
    "        predicted_id = tf.random.categorical(predictions, 1)[0][0].numpy()\n",
    "        result.append(tokenizer.index_word[predicted_id])\n",
    "\n",
    "        if tokenizer.index_word[predicted_id] == 'endofseq':\n",
    "            return result, attention_plot\n",
    "\n",
    "        dec_input = tf.expand_dims([predicted_id], 0)\n",
    "\n",
    "    attention_plot = attention_plot[:len(result), :]\n",
    "    return result, attention_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f3d436ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T02:05:08.547848Z",
     "iopub.status.busy": "2021-04-02T02:05:08.547263Z",
     "iopub.status.idle": "2021-04-02T02:05:08.549193Z",
     "shell.execute_reply": "2021-04-02T02:05:08.549567Z"
    },
    "id": "fD_y7PD6RPGt"
   },
   "outputs": [],
   "source": [
    "def plot_attention(image, result, attention_plot):\n",
    "    temp_image = np.array(Image.open(image))\n",
    "\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "\n",
    "    len_result = len(result)\n",
    "    for i in range(len_result - 1):\n",
    "        temp_att = np.resize(attention_plot[i], (8, 8))\n",
    "        grid_size = max(np.ceil(len_result/2), 2)\n",
    "        ax = fig.add_subplot(grid_size, grid_size, i+1)\n",
    "        ax.set_title(result[i])\n",
    "        img = ax.imshow(temp_image)\n",
    "        ax.imshow(temp_att, cmap='gray', alpha=0.6, extent=img.get_extent())\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21bb4d14",
   "metadata": {},
   "source": [
    "# Validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9512ffe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "VAL_TXT_PATH = r\"C:\\Users\\inba2\\Documents\\DataSet\\8k\\Flickr_TextData\\Flickr_8k.devImages.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2e1d2bf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_img_names = sorted(load_set(TEST_TXT_PATH))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0cfe0db2",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_img_cap = load_clean_cap(CLEAN_CAP_TEXT_PATH, val_img_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7c2da0a4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(val_img_cap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "74ba918f",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_name = val_img_names[np.random.randint(0, len(val_img_names))]\n",
    "img_path = img_name_2_path(image_name)\n",
    "real_captions = val_img_cap[image_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7dde31d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-02T02:05:08.555410Z",
     "iopub.status.busy": "2021-04-02T02:05:08.554801Z",
     "iopub.status.idle": "2021-04-02T02:05:10.500706Z",
     "shell.execute_reply": "2021-04-02T02:05:10.501131Z"
    },
    "id": "7x8RiPHe_4qI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction Caption: brown dog is jumping in the grass wearing large field of grass and grass\n",
      "Real Captions:\n",
      "1)  brown dog walk on the rock near river \n",
      "2)  dog is walking through some gravel beside river \n",
      "3)  dog with snub nose smell for somthing on riverbank \n",
      "4)  large brown dog is walking along side river \n",
      "5)  large tan bulldog walking along the rocky shore of small stream \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x1BA1D996130>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result, attention_plot = evaluate(img_path)\n",
    "print('Prediction Caption:', ' '.join(result[:-1]))\n",
    "print('Real Captions:')\n",
    "for i, cap in enumerate(real_captions):\n",
    "    print(f\"{i+1}) {cap[10:-8]}\")\n",
    "plot_attention(img_path, result, attention_plot)\n",
    "Image.open(img_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9489a825",
   "metadata": {
    "id": "Rprk3HEvZuxb"
   },
   "source": [
    "# Try it on your own images\n",
    "For fun, below we've provided a method you can use to caption your own images with the model we've just trained. Keep in mind, it was trained on a relatively small amount of data, and your images may be different from the training data (so be prepared for weird results!)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a3aeb73",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "img_path = str(input(\"Enter the image path: \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "600bb8bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa81b76f",
   "metadata": {},
   "outputs": [],
   "source": [
    "result, attention_plot = evaluate(img_path)\n",
    "print('Prediction Caption:', ' '.join(result))\n",
    "plot_attention(img_path, result, attention_plot)\n",
    "# opening the image\n",
    "Image.open(img_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "371b1bbb",
   "metadata": {},
   "source": [
    "# ------------------------------------ THANK YOU ------------------------------------"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}