Image_Captioning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Captioning\n",
    "\n",
    "Image Captioning using Deep learning models in Keras. The models were trained on Flickr_8k Dataset using Google Colab."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives:\n",
    "1. Prepare photo and text data for training a deep learning model.\n",
    "2. Design and train a deep learning model.\n",
    "3. Evaluate the model \n",
    "4. Using this model generate caption for new pictures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "zGM31U6dJpMC",
    "outputId": "83340919-8b48-479a-c7c3-d97b0d6c166c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Importing libraries for image classification.\n",
    "from os import listdir\n",
    "from pickle import dump\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.preprocessing.image import load_img\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from keras.applications.vgg16 import preprocess_input\n",
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "vENNdaqVhPgf",
    "outputId": "d2cecb07-81c0-4db2-aae5-92cae6c8b44a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "#mount drive\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset:\n",
    "After requesting the dataset from the author's website. I got these two files.\n",
    "1. Flickr8k_Dataset: Contains 8092 photographs in JPEG format.\n",
    "2. Flickr8k_text: Contains a number of files containing different sources of descriptions for the photographs.\n",
    "\n",
    "\n",
    "The dataset has a pre-defined training dataset (6,000 images), development dataset (1,000 images), and test dataset (1,000 images)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "qsGix6uYjy0z",
    "outputId": "daf3d5e9-c2ad-40d8-bca3-323bbe51a04a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  inflating: __MACOSX/Flicker8k_Dataset/._98377566_e4674d1ebd.jpg  \n",
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      "  inflating: Flicker8k_Dataset/997338199_7343367d7f.jpg  \n",
      "  inflating: __MACOSX/Flicker8k_Dataset/._997338199_7343367d7f.jpg  \n",
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      "  inflating: __MACOSX/Flicker8k_Dataset/._997722733_0cb5439472.jpg  \n",
      "  inflating: __MACOSX/._Flicker8k_Dataset  \n",
      "Archive:  /content/drive/My Drive/Deep Learning/Image_Captioning/Flickr8k_text.zip\n",
      "  inflating: CrowdFlowerAnnotations.txt  \n",
      "  inflating: ExpertAnnotations.txt   \n",
      "  inflating: Flickr8k.lemma.token.txt  \n",
      "  inflating: __MACOSX/._Flickr8k.lemma.token.txt  \n",
      "  inflating: Flickr8k.token.txt      \n",
      "  inflating: Flickr_8k.devImages.txt  \n",
      "  inflating: Flickr_8k.testImages.txt  \n",
      "  inflating: Flickr_8k.trainImages.txt  \n",
      "  inflating: readme.txt              \n"
     ]
    }
   ],
   "source": [
    "!unzip '/content/drive/My Drive/Deep Learning/Image_Captioning/Flickr8k_Dataset.zip'\n",
    "!unzip '/content/drive/My Drive/Deep Learning/Image_Captioning/Flickr8k_text.zip'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "15In5gp9kLdr",
    "outputId": "40795afc-1cce-4967-c5a4-2c00b457fd15",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CrowdFlowerAnnotations.txt  Flickr_8k.devImages.txt   Flickr_8k.trainImages.txt\n",
      "drive\t\t\t    Flickr8k.lemma.token.txt  __MACOSX\n",
      "ExpertAnnotations.txt\t    Flickr_8k.testImages.txt  readme.txt\n",
      "Flicker8k_Dataset\t    Flickr8k.token.txt\t      sample_data\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing Image Data:\n",
    "1. Using pretrained model VGG16 the Image features were extracted. \n",
    "2. Restructuring the model to remove final layer, to perform Transfer learning. Else, it's an Image classification model.\n",
    "3. The extracted features are then stored in a pickle file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "RjgROZXtifo3",
    "outputId": "93187a8c-39b4-4760-de1e-bb34c7316287"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      ">2702506716_17a7fb3ba4.jpg\n",
      "Extracted Features: 8091\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "\n",
    "# extract features from each photo in the directory\n",
    "def extract_features(directory):\n",
    "\t# load the model\n",
    "\tmodel = VGG16()\n",
    "\t# re-structure the model\n",
    "\tmodel.layers.pop()\n",
    "\tmodel = Model(inputs=model.inputs, outputs=model.layers[-1].output)\n",
    "\t# summarize\n",
    "\tprint(model.summary())\n",
    "\t# extract features from each photo\n",
    "\tfeatures = dict()\n",
    "\tfor name in listdir(directory):\n",
    "\t\t# load an image from file\n",
    "\t\tfilename = directory + '/' + name\n",
    "\t\timage = load_img(filename, target_size=(224, 224))\n",
    "\t\t# convert the image pixels to a numpy array\n",
    "\t\timage = img_to_array(image)\n",
    "\t\t# reshape data for the model\n",
    "\t\timage = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))\n",
    "\t\t# prepare the image for the VGG model\n",
    "\t\timage = preprocess_input(image)\n",
    "\t\t# get features\n",
    "\t\tfeature = model.predict(image, verbose=0)\n",
    "\t\t# get image id\n",
    "\t\timage_id = name.split('.')[0]\n",
    "\t\t# store feature\n",
    "\t\tfeatures[image_id] = feature\n",
    "\t\tprint('>%s' % name)\n",
    "\treturn features\n",
    "\n",
    "# extract features from all images\n",
    "# zip = '/content/drive/My Drive/Deep Learning/Image_Captioning/Flickr8k_Dataset.zip'\n",
    "directory = '/content/Flicker8k_Dataset'\n",
    "features = extract_features(directory)\n",
    "print('Extracted Features: %d' % len(features))\n",
    "# save to file\n",
    "dump(features, open('features.pkl', 'wb'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing Text Data:\n",
    "1. Load and clean contents of the file.\n",
    "\n",
    "\n",
    "    An example of content(line) in the file.\n",
    "    \"1000268201_693b08cb0e.jpg#0\tA child in a pink dress is climbing up a set of stairs in an entry way.\" \n",
    "2. Some ideas of data cleaning we can form include:\n",
    "    1. Normalizing the case of all tokens to lowercase.\n",
    "    2. Remove all punctuation from tokens.\n",
    "    3. Removing all tokens that contain one or fewer characters (after punctuation is removed), e.g. ‘a’ and hanging ‘s’ characters.\n",
    "    \n",
    "3. Summarize the vocabulary.\n",
    "4. Store in a text file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "loL5is-skWJf"
   },
   "outputs": [],
   "source": [
    "#Importing Libraries for Text classification.\n",
    "import string\n",
    "from numpy import array\n",
    "from numpy import argmax\n",
    "from pickle import load\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.utils import plot_model\n",
    "from keras.models import Model\n",
    "from keras.layers import Input\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Embedding\n",
    "from keras.layers import Dropout\n",
    "from keras.layers.merge import add\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "x6KkRH5rIpNI",
    "outputId": "73246e0c-9bc9-4255-9de2-407210ffc8ae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded: 8092 \n",
      "Vocabulary Size: 8763\n"
     ]
    }
   ],
   "source": [
    "# load doc into memory\n",
    "def load_doc(filename):\n",
    "\t# open the file as read only\n",
    "\tfile = open(filename, 'r')\n",
    "\t# read all text\n",
    "\ttext = file.read()\n",
    "\t# close the file\n",
    "\tfile.close()\n",
    "\treturn text\n",
    " \n",
    "filename = '/content/Flickr8k.token.txt'\n",
    "# load descriptions\n",
    "doc = load_doc(filename)\n",
    "\n",
    "# extract descriptions for images\n",
    "def load_descriptions(doc):\n",
    "\tmapping = dict()\n",
    "\t# process lines\n",
    "\tfor line in doc.split('\\n'):\n",
    "\t\t# split line by white space\n",
    "\t\ttokens = line.split()\n",
    "\t\tif len(line) < 2:\n",
    "\t\t\tcontinue\n",
    "\t\t# take the first token as the image id, the rest as the description\n",
    "\t\timage_id, image_desc = tokens[0], tokens[1:]\n",
    "\t\t# remove filename from image id\n",
    "\t\timage_id = image_id.split('.')[0]\n",
    "\t\t# convert description tokens back to string\n",
    "\t\timage_desc = ' '.join(image_desc)\n",
    "\t\t# create the list if needed\n",
    "\t\tif image_id not in mapping:\n",
    "\t\t\tmapping[image_id] = list()\n",
    "\t\t# store description\n",
    "\t\tmapping[image_id].append(image_desc)\n",
    "\treturn mapping\n",
    "\n",
    "def clean_descriptions(descriptions):\n",
    "\t# prepare translation table for removing punctuation\n",
    "\ttable = str.maketrans('', '', string.punctuation)\n",
    "\tfor key, desc_list in descriptions.items():\n",
    "\t\tfor i in range(len(desc_list)):\n",
    "\t\t\tdesc = desc_list[i]\n",
    "\t\t\t# tokenize\n",
    "\t\t\tdesc = desc.split()\n",
    "\t\t\t# convert to lower case\n",
    "\t\t\tdesc = [word.lower() for word in desc]\n",
    "\t\t\t# remove punctuation from each token\n",
    "\t\t\tdesc = [w.translate(table) for w in desc]\n",
    "\t\t\t# remove hanging 's' and 'a'\n",
    "\t\t\tdesc = [word for word in desc if len(word)>1]\n",
    "\t\t\t# remove tokens with numbers in them\n",
    "\t\t\tdesc = [word for word in desc if word.isalpha()]\n",
    "\t\t\t# store as string\n",
    "\t\t\tdesc_list[i] =  ' '.join(desc)\n",
    "            \n",
    "# convert the loaded descriptions into a vocabulary of words\n",
    "def to_vocabulary(descriptions):\n",
    "\t# build a list of all description strings\n",
    "\tall_desc = set()\n",
    "\tfor key in descriptions.keys():\n",
    "\t\t[all_desc.update(d.split()) for d in descriptions[key]]\n",
    "\treturn all_desc\n",
    "\n",
    "# save descriptions to file, one per line\n",
    "def save_descriptions(descriptions, filename):\n",
    "\tlines = list()\n",
    "\tfor key, desc_list in descriptions.items():\n",
    "\t\tfor desc in desc_list:\n",
    "\t\t\tlines.append(key + ' ' + desc)\n",
    "\tdata = '\\n'.join(lines)\n",
    "\tfile = open(filename, 'w')\n",
    "\tfile.write(data)\n",
    "\tfile.close()\n",
    " \n",
    "filename = '/content/Flickr8k.token.txt'\n",
    "# load descriptions\n",
    "doc = load_doc(filename)\n",
    "# parse descriptions\n",
    "descriptions = load_descriptions(doc)\n",
    "print('Loaded: %d ' % len(descriptions))\n",
    "# clean descriptions\n",
    "clean_descriptions(descriptions)\n",
    "# summarize vocabulary\n",
    "vocabulary = to_vocabulary(descriptions)\n",
    "print('Vocabulary Size: %d' % len(vocabulary))\n",
    "# save to file\n",
    "save_descriptions(descriptions, 'descriptions.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model:\n",
    "    The model is trained by loading data Progressively.\n",
    "    1. While Training the model conventionally, the session expired because of limited GPU memory(8 GB).\n",
    "    2. So, progressively load the photos and descriptions as-needed by the model.\n",
    "\n",
    "Model Training:\n",
    "1. Cleaning the photo and text data. Then, wrap the tokens/descriptions between two strings: Here, 'startseq', 'endseq'.\n",
    "2. After cleaning, create sequences of image and text data to output words of that image.\n",
    "3. Using LSTM model, train the data using 2 input layers and 7 hidden layers. The input layers are photo and text features.\n",
    "4. Model is fit using generator for progressive loading.\n",
    "5. Save the model in .h5 file. \n",
    "\n",
    "        One of the disadvantage of using Progressive loading is it takes too long to train the model.(Took ~30 mins for each epoch.)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 819
    },
    "colab_type": "code",
    "id": "nrJC1LBLObPQ",
    "outputId": "334d8882-af8c-45e8-cabb-db457d37ac47"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: 6000\n",
      "Descriptions: train=6000\n",
      "Photos: train=6000\n",
      "Vocabulary Size: 7579\n",
      "Description Length: 34\n",
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_4 (InputLayer)            (None, 34)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_3 (InputLayer)            (None, 4096)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_2 (Embedding)         (None, 34, 256)      1940224     input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 4096)         0           input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 34, 256)      0           embedding_2[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 256)          1048832     dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lstm_2 (LSTM)                   (None, 256)          525312      dropout_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 256)          0           dense_4[0][0]                    \n",
      "                                                                 lstm_2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 256)          65792       add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_6 (Dense)                 (None, 7579)         1947803     dense_5[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 5,527,963\n",
      "Trainable params: 5,527,963\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/indexed_slices.py:434: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "6000/6000 [==============================] - 909s 151ms/step - loss: 4.6874\n",
      "Epoch 1/1\n",
      "6000/6000 [==============================] - 906s 151ms/step - loss: 3.8994\n",
      "Epoch 1/1\n",
      "6000/6000 [==============================] - 902s 150ms/step - loss: 3.6506\n",
      "Epoch 1/1\n",
      "6000/6000 [==============================] - 904s 151ms/step - loss: 3.4852\n",
      "Epoch 1/1\n",
      "6000/6000 [==============================] - 918s 153ms/step - loss: 3.3733\n"
     ]
    }
   ],
   "source": [
    "# load doc into memory\n",
    "def load_doc(filename):\n",
    "\t# open the file as read only\n",
    "\tfile = open(filename, 'r')\n",
    "\t# read all text\n",
    "\ttext = file.read()\n",
    "\t# close the file\n",
    "\tfile.close()\n",
    "\treturn text\n",
    "\n",
    "# load a pre-defined list of photo identifiers\n",
    "def load_set(filename):\n",
    "\tdoc = load_doc(filename)\n",
    "\tdataset = list()\n",
    "\t# process line by line\n",
    "\tfor line in doc.split('\\n'):\n",
    "\t\t# skip empty lines\n",
    "\t\tif len(line) < 1:\n",
    "\t\t\tcontinue\n",
    "\t\t# get the image identifier\n",
    "\t\tidentifier = line.split('.')[0]\n",
    "\t\tdataset.append(identifier)\n",
    "\treturn set(dataset)\n",
    "\n",
    "# load clean descriptions into memory\n",
    "def load_clean_descriptions(filename, dataset):\n",
    "\t# load document\n",
    "\tdoc = load_doc(filename)\n",
    "\tdescriptions = dict()\n",
    "\tfor line in doc.split('\\n'):\n",
    "\t\t# split line by white space\n",
    "\t\ttokens = line.split()\n",
    "\t\t# split id from description\n",
    "\t\timage_id, image_desc = tokens[0], tokens[1:]\n",
    "\t\t# skip images not in the set\n",
    "\t\tif image_id in dataset:\n",
    "\t\t\t# create list\n",
    "\t\t\tif image_id not in descriptions:\n",
    "\t\t\t\tdescriptions[image_id] = list()\n",
    "\t\t\t# wrap description in tokens\n",
    "\t\t\tdesc = 'startseq ' + ' '.join(image_desc) + ' endseq'\n",
    "\t\t\t# store\n",
    "\t\t\tdescriptions[image_id].append(desc)\n",
    "\treturn descriptions\n",
    "\n",
    "# load photo features\n",
    "def load_photo_features(filename, dataset):\n",
    "\t# load all features\n",
    "\tall_features = load(open(filename, 'rb'))\n",
    "\t# filter features\n",
    "\tfeatures = {k: all_features[k] for k in dataset}\n",
    "\treturn features\n",
    "\n",
    "# covert a dictionary of clean descriptions to a list of descriptions\n",
    "def to_lines(descriptions):\n",
    "\tall_desc = list()\n",
    "\tfor key in descriptions.keys():\n",
    "\t\t[all_desc.append(d) for d in descriptions[key]]\n",
    "\treturn all_desc\n",
    "\n",
    "# fit a tokenizer given caption descriptions\n",
    "def create_tokenizer(descriptions):\n",
    "\tlines = to_lines(descriptions)\n",
    "\ttokenizer = Tokenizer()\n",
    "\ttokenizer.fit_on_texts(lines)\n",
    "\treturn tokenizer\n",
    "\n",
    "# calculate the length of the description with the most words\n",
    "def max_length(descriptions):\n",
    "\tlines = to_lines(descriptions)\n",
    "\treturn max(len(d.split()) for d in lines)\n",
    "\n",
    "# create sequences of images, input sequences and output words for an image\n",
    "def create_sequences(tokenizer, max_length, desc_list, photo, vocab_size):\n",
    "\tX1, X2, y = list(), list(), list()\n",
    "\t# walk through each description for the image\n",
    "\tfor desc in desc_list:\n",
    "\t\t# encode the sequence\n",
    "\t\tseq = tokenizer.texts_to_sequences([desc])[0]\n",
    "\t\t# split one sequence into multiple X,y pairs\n",
    "\t\tfor i in range(1, len(seq)):\n",
    "\t\t\t# split into input and output pair\n",
    "\t\t\tin_seq, out_seq = seq[:i], seq[i]\n",
    "\t\t\t# pad input sequence\n",
    "\t\t\tin_seq = pad_sequences([in_seq], maxlen=max_length)[0]\n",
    "\t\t\t# encode output sequence\n",
    "\t\t\tout_seq = to_categorical([out_seq], num_classes=vocab_size)[0]\n",
    "\t\t\t# store\n",
    "\t\t\tX1.append(photo)\n",
    "\t\t\tX2.append(in_seq)\n",
    "\t\t\ty.append(out_seq)\n",
    "\treturn array(X1), array(X2), array(y)\n",
    "\n",
    "# define the captioning model\n",
    "def define_model(vocab_size, max_length):\n",
    "\t# feature extractor model\n",
    "\tinputs1 = Input(shape=(4096,))\n",
    "\tfe1 = Dropout(0.5)(inputs1)\n",
    "\tfe2 = Dense(256, activation='relu')(fe1)\n",
    "\t# sequence model\n",
    "\tinputs2 = Input(shape=(max_length,))\n",
    "\tse1 = Embedding(vocab_size, 256, mask_zero=True)(inputs2)\n",
    "\tse2 = Dropout(0.5)(se1)\n",
    "\tse3 = LSTM(256)(se2)\n",
    "\t# decoder model\n",
    "\tdecoder1 = add([fe2, se3])\n",
    "\tdecoder2 = Dense(256, activation='relu')(decoder1)\n",
    "\toutputs = Dense(vocab_size, activation='softmax')(decoder2)\n",
    "\t# tie it together [image, seq] [word]\n",
    "\tmodel = Model(inputs=[inputs1, inputs2], outputs=outputs)\n",
    "\t# compile model\n",
    "\tmodel.compile(loss='categorical_crossentropy', optimizer='adam')\n",
    "\t# summarize model\n",
    "\tmodel.summary()\n",
    "\tplot_model(model, to_file='model.png', show_shapes=True)\n",
    "\treturn model\n",
    "\n",
    "# data generator, intended to be used in a call to model.fit_generator()\n",
    "def data_generator(descriptions, photos, tokenizer, max_length, vocab_size):\n",
    "\t# loop for ever over images\n",
    "\twhile 1:\n",
    "\t\tfor key, desc_list in descriptions.items():\n",
    "\t\t\t# retrieve the photo feature\n",
    "\t\t\tphoto = photos[key][0]\n",
    "\t\t\tin_img, in_seq, out_word = create_sequences(tokenizer, max_length, desc_list, photo, vocab_size)\n",
    "\t\t\tyield [[in_img, in_seq], out_word]\n",
    "\n",
    "# load training dataset (6K)\n",
    "filename = '/content/Flickr_8k.trainImages.txt'\n",
    "train = load_set(filename)\n",
    "print('Dataset: %d' % len(train))\n",
    "# descriptions\n",
    "train_descriptions = load_clean_descriptions('/content/drive/My Drive/Deep Learning/Image_Captioning/descriptions.txt', train)\n",
    "print('Descriptions: train=%d' % len(train_descriptions))\n",
    "# photo features\n",
    "train_features = load_photo_features('/content/drive/My Drive/Deep Learning/Image_Captioning/features.pkl', train)\n",
    "print('Photos: train=%d' % len(train_features))\n",
    "# prepare tokenizer\n",
    "tokenizer = create_tokenizer(train_descriptions)\n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "print('Vocabulary Size: %d' % vocab_size)\n",
    "# determine the maximum sequence length\n",
    "max_length = max_length(train_descriptions)\n",
    "print('Description Length: %d' % max_length)\n",
    "\n",
    "# define the model\n",
    "model = define_model(vocab_size, max_length)\n",
    "# train the model, run epochs manually and save after each epoch\n",
    "epochs = 5\n",
    "steps = len(train_descriptions)\n",
    "for i in range(epochs):\n",
    "\t# create the data generator\n",
    "\tgenerator = data_generator(train_descriptions, train_features, tokenizer, max_length, vocab_size)\n",
    "\t# fit for one epoch\n",
    "\tmodel.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)\n",
    "\t# save model\n",
    "\tmodel.save('model_' + str(i) + '.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation\n",
    "\n",
    "    Model is evaluated by generating descriptions for all photos in the test dataset and evaluating those predictions with a standard cost function.\n",
    "    \n",
    "    The actual and predicted descriptions are collected and evaluated collectively using the corpus BLEU score that summarizes how close the generated text is to the expected text.\n",
    "    \n",
    "    BLEU scores are used in text translation for evaluating translated text against one or more reference translations. A higher score close to 1.0 is better, a score closer to zero is worse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 258
    },
    "colab_type": "code",
    "id": "uuWPZsUBQw0R",
    "outputId": "9f90bc7f-f23b-4105-c526-7ef2f3188a54"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: 6000\n",
      "Descriptions: train=6000\n",
      "Vocabulary Size: 7579\n",
      "Description Length: 34\n",
      "Dataset: 1000\n",
      "Descriptions: test=1000\n",
      "Photos: test=1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/indexed_slices.py:434: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BLEU-1: 0.556602\n",
      "BLEU-2: 0.301163\n",
      "BLEU-3: 0.205805\n",
      "BLEU-4: 0.097146\n"
     ]
    }
   ],
   "source": [
    "# Importing libraries for model evaluation\n",
    "from numpy import argmax\n",
    "from pickle import load\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.models import load_model\n",
    "from nltk.translate.bleu_score import corpus_bleu\n",
    "\n",
    "# load doc into memory\n",
    "def load_doc(filename):\n",
    "\t# open the file as read only\n",
    "\tfile = open(filename, 'r')\n",
    "\t# read all text\n",
    "\ttext = file.read()\n",
    "\t# close the file\n",
    "\tfile.close()\n",
    "\treturn text\n",
    " \n",
    "# load a pre-defined list of photo identifiers\n",
    "def load_set(filename):\n",
    "\tdoc = load_doc(filename)\n",
    "\tdataset = list()\n",
    "\t# process line by line\n",
    "\tfor line in doc.split('\\n'):\n",
    "\t\t# skip empty lines\n",
    "\t\tif len(line) < 1:\n",
    "\t\t\tcontinue\n",
    "\t\t# get the image identifier\n",
    "\t\tidentifier = line.split('.')[0]\n",
    "\t\tdataset.append(identifier)\n",
    "\treturn set(dataset)\n",
    " \n",
    "# load clean descriptions into memory\n",
    "def load_clean_descriptions(filename, dataset):\n",
    "\t# load document\n",
    "\tdoc = load_doc(filename)\n",
    "\tdescriptions = dict()\n",
    "\tfor line in doc.split('\\n'):\n",
    "\t\t# split line by white space\n",
    "\t\ttokens = line.split()\n",
    "\t\t# split id from description\n",
    "\t\timage_id, image_desc = tokens[0], tokens[1:]\n",
    "\t\t# skip images not in the set\n",
    "\t\tif image_id in dataset:\n",
    "\t\t\t# create list\n",
    "\t\t\tif image_id not in descriptions:\n",
    "\t\t\t\tdescriptions[image_id] = list()\n",
    "\t\t\t# wrap description in tokens\n",
    "\t\t\tdesc = 'startseq ' + ' '.join(image_desc) + ' endseq'\n",
    "\t\t\t# store\n",
    "\t\t\tdescriptions[image_id].append(desc)\n",
    "\treturn descriptions\n",
    " \n",
    "# load photo features\n",
    "def load_photo_features(filename, dataset):\n",
    "\t# load all features\n",
    "\tall_features = load(open(filename, 'rb'))\n",
    "\t# filter features\n",
    "\tfeatures = {k: all_features[k] for k in dataset}\n",
    "\treturn features\n",
    " \n",
    "# covert a dictionary of clean descriptions to a list of descriptions\n",
    "def to_lines(descriptions):\n",
    "\tall_desc = list()\n",
    "\tfor key in descriptions.keys():\n",
    "\t\t[all_desc.append(d) for d in descriptions[key]]\n",
    "\treturn all_desc\n",
    " \n",
    "# fit a tokenizer given caption descriptions\n",
    "def create_tokenizer(descriptions):\n",
    "\tlines = to_lines(descriptions)\n",
    "\ttokenizer = Tokenizer()\n",
    "\ttokenizer.fit_on_texts(lines)\n",
    "\treturn tokenizer\n",
    " \n",
    "# calculate the length of the description with the most words\n",
    "def max_length(descriptions):\n",
    "\tlines = to_lines(descriptions)\n",
    "\treturn max(len(d.split()) for d in lines)\n",
    "\n",
    "# map an integer to a word\n",
    "def word_for_id(integer, tokenizer):\n",
    "\tfor word, index in tokenizer.word_index.items():\n",
    "\t\tif index == integer:\n",
    "\t\t\treturn word\n",
    "\treturn None\n",
    " \n",
    "# generate a description for an image\n",
    "def generate_desc(model, tokenizer, photo, max_length):\n",
    "\t# seed the generation process\n",
    "\tin_text = 'startseq'\n",
    "\t# iterate over the whole length of the sequence\n",
    "\tfor i in range(max_length):\n",
    "\t\t# integer encode input sequence\n",
    "\t\tsequence = tokenizer.texts_to_sequences([in_text])[0]\n",
    "\t\t# pad input\n",
    "\t\tsequence = pad_sequences([sequence], maxlen=max_length)\n",
    "\t\t# predict next word\n",
    "\t\tyhat = model.predict([photo,sequence], verbose=0)\n",
    "\t\t# convert probability to integer\n",
    "\t\tyhat = argmax(yhat)\n",
    "\t\t# map integer to word\n",
    "\t\tword = word_for_id(yhat, tokenizer)\n",
    "\t\t# stop if we cannot map the word\n",
    "\t\tif word is None:\n",
    "\t\t\tbreak\n",
    "\t\t# append as input for generating the next word\n",
    "\t\tin_text += ' ' + word\n",
    "\t\t# stop if we predict the end of the sequence\n",
    "\t\tif word == 'endseq':\n",
    "\t\t\tbreak\n",
    "\treturn in_text\n",
    " \n",
    "# evaluate the skill of the model\n",
    "def evaluate_model(model, descriptions, photos, tokenizer, max_length):\n",
    "\tactual, predicted = list(), list()\n",
    "\t# step over the whole set\n",
    "\tfor key, desc_list in descriptions.items():\n",
    "\t\t# generate description\n",
    "\t\tyhat = generate_desc(model, tokenizer, photos[key], max_length)\n",
    "\t\t# store actual and predicted\n",
    "\t\treferences = [d.split() for d in desc_list]\n",
    "\t\tactual.append(references)\n",
    "\t\tpredicted.append(yhat.split())\n",
    "\t# calculate BLEU score\n",
    "\tprint('BLEU-1: %f' % corpus_bleu(actual, predicted, weights=(1.0, 0, 0, 0)))\n",
    "\tprint('BLEU-2: %f' % corpus_bleu(actual, predicted, weights=(0.5, 0.5, 0, 0)))\n",
    "\tprint('BLEU-3: %f' % corpus_bleu(actual, predicted, weights=(0.3, 0.3, 0.3, 0)))\n",
    "\tprint('BLEU-4: %f' % corpus_bleu(actual, predicted, weights=(0.25, 0.25, 0.25, 0.25)))\n",
    " \n",
    "# prepare tokenizer on train set\n",
    " \n",
    "# load training dataset (6K)\n",
    "filename = '/content/Flickr_8k.trainImages.txt'\n",
    "train = load_set(filename)\n",
    "print('Dataset: %d' % len(train))\n",
    "# descriptions\n",
    "train_descriptions = load_clean_descriptions('/content/drive/My Drive/Deep Learning/Image_Captioning/descriptions.txt', train)\n",
    "print('Descriptions: train=%d' % len(train_descriptions))\n",
    "# prepare tokenizer\n",
    "tokenizer = create_tokenizer(train_descriptions)\n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "print('Vocabulary Size: %d' % vocab_size)\n",
    "# determine the maximum sequence length\n",
    "max_length = max_length(train_descriptions)\n",
    "print('Description Length: %d' % max_length)\n",
    " \n",
    "# prepare test set\n",
    " \n",
    "# load test set\n",
    "filename = '/content/Flickr_8k.testImages.txt'\n",
    "test = load_set(filename)\n",
    "print('Dataset: %d' % len(test))\n",
    "# descriptions\n",
    "test_descriptions = load_clean_descriptions('/content/drive/My Drive/Deep Learning/Image_Captioning/descriptions.txt', test)\n",
    "print('Descriptions: test=%d' % len(test_descriptions))\n",
    "# photo features\n",
    "test_features = load_photo_features('/content/drive/My Drive/Deep Learning/Image_Captioning/features.pkl', test)\n",
    "print('Photos: test=%d' % len(test_features))\n",
    " \n",
    "# load the model\n",
    "filename = '/content/model_4.h5'\n",
    "model = load_model(filename)\n",
    "# evaluate model\n",
    "evaluate_model(model, test_descriptions, test_features, tokenizer, max_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating New Captions\n",
    "\n",
    "    1. Using vocabulary file, map the integers during training.\n",
    "    2. Save the new content in a pickle as a tokenizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "85rP5dh68Hao",
    "outputId": "b6e8a9c9-5763-4c6b-f15a-3efc645a863c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: 6000\n",
      "Descriptions: train=6000\n"
     ]
    }
   ],
   "source": [
    "# Importing libraries\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from pickle import dump\n",
    " \n",
    "# load doc into memory\n",
    "def load_doc(filename):\n",
    "\t# open the file as read only\n",
    "\tfile = open(filename, 'r')\n",
    "\t# read all text\n",
    "\ttext = file.read()\n",
    "\t# close the file\n",
    "\tfile.close()\n",
    "\treturn text\n",
    " \n",
    "# load a pre-defined list of photo identifiers\n",
    "def load_set(filename):\n",
    "\tdoc = load_doc(filename)\n",
    "\tdataset = list()\n",
    "\t# process line by line\n",
    "\tfor line in doc.split('\\n'):\n",
    "\t\t# skip empty lines\n",
    "\t\tif len(line) < 1:\n",
    "\t\t\tcontinue\n",
    "\t\t# get the image identifier\n",
    "\t\tidentifier = line.split('.')[0]\n",
    "\t\tdataset.append(identifier)\n",
    "\treturn set(dataset)\n",
    " \n",
    "# load clean descriptions into memory\n",
    "def load_clean_descriptions(filename, dataset):\n",
    "\t# load document\n",
    "\tdoc = load_doc(filename)\n",
    "\tdescriptions = dict()\n",
    "\tfor line in doc.split('\\n'):\n",
    "\t\t# split line by white space\n",
    "\t\ttokens = line.split()\n",
    "\t\t# split id from description\n",
    "\t\timage_id, image_desc = tokens[0], tokens[1:]\n",
    "\t\t# skip images not in the set\n",
    "\t\tif image_id in dataset:\n",
    "\t\t\t# create list\n",
    "\t\t\tif image_id not in descriptions:\n",
    "\t\t\t\tdescriptions[image_id] = list()\n",
    "\t\t\t# wrap description in tokens\n",
    "\t\t\tdesc = 'startseq ' + ' '.join(image_desc) + ' endseq'\n",
    "\t\t\t# store\n",
    "\t\t\tdescriptions[image_id].append(desc)\n",
    "\treturn descriptions\n",
    " \n",
    "# covert a dictionary of clean descriptions to a list of descriptions\n",
    "def to_lines(descriptions):\n",
    "\tall_desc = list()\n",
    "\tfor key in descriptions.keys():\n",
    "\t\t[all_desc.append(d) for d in descriptions[key]]\n",
    "\treturn all_desc\n",
    " \n",
    "# fit a tokenizer given caption descriptions\n",
    "def create_tokenizer(descriptions):\n",
    "\tlines = to_lines(descriptions)\n",
    "\ttokenizer = Tokenizer()\n",
    "\ttokenizer.fit_on_texts(lines)\n",
    "\treturn tokenizer\n",
    " \n",
    "# load training dataset (6K)\n",
    "filename = '/content/Flickr_8k.trainImages.txt'\n",
    "train = load_set(filename)\n",
    "print('Dataset: %d' % len(train))\n",
    "# descriptions\n",
    "train_descriptions = load_clean_descriptions('/content/drive/My Drive/Deep Learning/Image_Captioning/descriptions.txt', train)\n",
    "print('Descriptions: train=%d' % len(train_descriptions))\n",
    "# prepare tokenizer\n",
    "tokenizer = create_tokenizer(train_descriptions)\n",
    "# save the tokenizer\n",
    "dump(tokenizer, open('tokenizer.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result:\n",
    "    After extracting features for a new input image. The word sequence(tokens) is wrapped between two strings, using Tokenizer and trained Model files respectively. Then an output of Image description is printed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "colab_type": "code",
    "id": "nIhPZSda9SW0",
    "outputId": "1626379b-ece1-45aa-d134-9e75f8256d18"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/indexed_slices.py:434: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "startseq dog is running through the water endseq\n"
     ]
    }
   ],
   "source": [
    "# extract features from each photo in the directory\n",
    "def extract_features(filename):\n",
    "\t# load the model\n",
    "\tmodel = VGG16()\n",
    "\t# re-structure the model\n",
    "\tmodel.layers.pop()\n",
    "\tmodel = Model(inputs=model.inputs, outputs=model.layers[-1].output)\n",
    "\t# load the photo\n",
    "\timage = load_img(filename, target_size=(224, 224))\n",
    "\t# convert the image pixels to a numpy array\n",
    "\timage = img_to_array(image)\n",
    "\t# reshape data for the model\n",
    "\timage = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))\n",
    "\t# prepare the image for the VGG model\n",
    "\timage = preprocess_input(image)\n",
    "\t# get features\n",
    "\tfeature = model.predict(image, verbose=0)\n",
    "\treturn feature\n",
    " \n",
    "# map an integer to a word\n",
    "def word_for_id(integer, tokenizer):\n",
    "\tfor word, index in tokenizer.word_index.items():\n",
    "\t\tif index == integer:\n",
    "\t\t\treturn word\n",
    "\treturn None\n",
    " \n",
    "# generate a description for an image\n",
    "def generate_desc(model, tokenizer, photo, max_length):\n",
    "\t# seed the generation process\n",
    "\tin_text = 'startseq'\n",
    "\t# iterate over the whole length of the sequence\n",
    "\tfor i in range(max_length):\n",
    "\t\t# integer encode input sequence\n",
    "\t\tsequence = tokenizer.texts_to_sequences([in_text])[0]\n",
    "\t\t# pad input\n",
    "\t\tsequence = pad_sequences([sequence], maxlen=max_length)\n",
    "\t\t# predict next word\n",
    "\t\tyhat = model.predict([photo,sequence], verbose=0)\n",
    "\t\t# convert probability to integer\n",
    "\t\tyhat = argmax(yhat)\n",
    "\t\t# map integer to word\n",
    "\t\tword = word_for_id(yhat, tokenizer)\n",
    "\t\t# stop if we cannot map the word\n",
    "\t\tif word is None:\n",
    "\t\t\tbreak\n",
    "\t\t# append as input for generating the next word\n",
    "\t\tin_text += ' ' + word\n",
    "\t\t# stop if we predict the end of the sequence\n",
    "\t\tif word == 'endseq':\n",
    "\t\t\tbreak\n",
    "\treturn in_text\n",
    " \n",
    "# load the tokenizer\n",
    "tokenizer = load(open('/content/tokenizer.pkl', 'rb'))\n",
    "# pre-define the max sequence length (from training)\n",
    "max_length = 34\n",
    "# load the model\n",
    "model = load_model('/content/model_3.h5')\n",
    "# load and prepare the photograph\n",
    "photo = extract_features('/content/eagle.jpg')\n",
    "# generate description\n",
    "description = generate_desc(model, tokenizer, photo, max_length)\n",
    "print(description)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comments:\n",
    "1. From the result you can see it's not accurate because model was trained for 5 epochs due to limited GPU time Google colab. \n",
    "2. Using Checkpoints can make a difference but it will be updated.\n",
    "3. Have to try for other techniques like different pretrained models for feature etraction and word to vec for token generation.\n",
    "4. This is Implemented by understanding the tutorial of Jason Brownlee(Machine learning mastery)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "14ocN43O_W4j"
   },
   "source": [
    "# References:\n",
    "1. [Flickr_8k Dataset](http://nlp.cs.illinois.edu/HockenmaierGroup/Framing_Image_Description/KCCA.html)\n",
    "\n",
    "\n",
    "2. [Deep learning caption generation - Jason Brownlee](https://machinelearningmastery.com/develop-a-deep-learning-caption-generation-model-in-python/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V1fnmdHd-652"
   },
   "outputs": [],
   "source": []
  }
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