Image Captioning Basic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from numpy import array\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import string\n",
    "import os\n",
    "from PIL import Image\n",
    "import glob\n",
    "from pickle import dump, load\n",
    "from time import time\n",
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Embedding, TimeDistributed, Dense, RepeatVector,\\\n",
    "                         Activation, Flatten, Reshape, concatenate, Dropout, BatchNormalization\n",
    "from keras.optimizers import Adam, RMSprop\n",
    "from keras.layers.wrappers import Bidirectional\n",
    "from keras.layers.merge import add\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.preprocessing import image\n",
    "from keras.models import Model\n",
    "from keras import Input, layers\n",
    "from keras import optimizers\n",
    "from keras.applications.inception_v3 import preprocess_input\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000268201_693b08cb0e.jpg#0\tA child in a pink dress is climbing up a set of stairs in an entry way .\n",
      "1000268201_693b08cb0e.jpg#1\tA girl going into a wooden building .\n",
      "1000268201_693b08cb0e.jpg#2\tA little girl climbing into a wooden playhouse .\n",
      "1000268201_693b08cb0e.jpg#3\tA little girl climbing the s\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 = \"../../storage/image_caption/dataset/TextFiles/Flickr8k.token.txt\"\n",
    "# load descriptions\n",
    "doc = load_doc(filename)\n",
    "print(doc[:300])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded: 8092 \n"
     ]
    }
   ],
   "source": [
    "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# extract 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",
    "# parse descriptions\n",
    "descriptions = load_descriptions(doc)\n",
    "print('Loaded: %d ' % len(descriptions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1000268201_693b08cb0e',\n",
       " '1001773457_577c3a7d70',\n",
       " '1002674143_1b742ab4b8',\n",
       " '1003163366_44323f5815',\n",
       " '1007129816_e794419615']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(descriptions.keys())[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A child in a pink dress is climbing up a set of stairs in an entry way .',\n",
       " 'A girl going into a wooden building .',\n",
       " 'A little girl climbing into a wooden playhouse .',\n",
       " 'A little girl climbing the stairs to her playhouse .',\n",
       " 'A little girl in a pink dress going into a wooden cabin .']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptions['1000268201_693b08cb0e']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A black dog and a spotted dog are fighting',\n",
       " 'A black dog and a tri-colored dog playing with each other on the road .',\n",
       " 'A black dog and a white dog with brown spots are staring at each other in the street .',\n",
       " 'Two dogs of different breeds looking at each other on the road .',\n",
       " 'Two dogs on pavement moving toward each other .']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptions['1001773457_577c3a7d70']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "# clean descriptions\n",
    "clean_descriptions(descriptions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['child in pink dress is climbing up set of stairs in an entry way',\n",
       " 'girl going into wooden building',\n",
       " 'little girl climbing into wooden playhouse',\n",
       " 'little girl climbing the stairs to her playhouse',\n",
       " 'little girl in pink dress going into wooden cabin']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptions['1000268201_693b08cb0e']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['black dog and spotted dog are fighting',\n",
       " 'black dog and tricolored dog playing with each other on the road',\n",
       " 'black dog and white dog with brown spots are staring at each other in the street',\n",
       " 'two dogs of different breeds looking at each other on the road',\n",
       " 'two dogs on pavement moving toward each other']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptions['1001773457_577c3a7d70']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Vocabulary Size: 8763\n"
     ]
    }
   ],
   "source": [
    "# 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",
    "# summarize vocabulary\n",
    "vocabulary = to_vocabulary(descriptions)\n",
    "print('Original Vocabulary Size: %d' % len(vocabulary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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",
    "save_descriptions(descriptions, 'descriptions.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: 6000\n"
     ]
    }
   ],
   "source": [
    "# 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 training dataset (6K)\n",
    "filename = '../../storage/image_caption/dataset/TextFiles/Flickr_8k.trainImages.txt'\n",
    "train = load_set(filename)\n",
    "print('Dataset: %d' % len(train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Below path contains all the images\n",
    "images = '../../storage/image_caption/dataset/Flicker8k_Dataset/'\n",
    "# Create a list of all image names in the directory\n",
    "img = glob.glob(images + '*.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Below file conatains the names of images to be used in train data\n",
    "train_images_file = '../../storage/image_caption/dataset/TextFiles/Flickr_8k.trainImages.txt'\n",
    "# Read the train image names in a set\n",
    "train_images = set(open(train_images_file, 'r').read().strip().split('\\n'))\n",
    "\n",
    "# Create a list of all the training images with their full path names\n",
    "train_img = []\n",
    "\n",
    "for i in img: # img is list of full path names of all images\n",
    "    if i[len(images):] in train_images: # Check if the image belongs to training set\n",
    "        train_img.append(i) # Add it to the list of train images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Below file conatains the names of images to be used in test data\n",
    "test_images_file = '../../storage/image_caption/dataset/TextFiles/Flickr_8k.testImages.txt'\n",
    "# Read the validation image names in a set# Read the test image names in a set\n",
    "test_images = set(open(test_images_file, 'r').read().strip().split('\\n'))\n",
    "\n",
    "# Create a list of all the test images with their full path names\n",
    "test_img = []\n",
    "\n",
    "for i in img: # img is list of full path names of all images\n",
    "    if i[len(images):] in test_images: # Check if the image belongs to test set\n",
    "        test_img.append(i) # Add it to the list of test images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Descriptions: train=6000\n"
     ]
    }
   ],
   "source": [
    "# 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",
    "# descriptions\n",
    "train_descriptions = load_clean_descriptions('descriptions.txt', train)\n",
    "print('Descriptions: train=%d' % len(train_descriptions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(image_path):\n",
    "    # Convert all the images to size 299x299 as expected by the inception v3 model\n",
    "    img = image.load_img(image_path, target_size=(299, 299))\n",
    "    # Convert PIL image to numpy array of 3-dimensions\n",
    "    x = image.img_to_array(img)\n",
    "    # Add one more dimension\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "    # preprocess the images using preprocess_input() from inception module\n",
    "    x = preprocess_input(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the inception v3 model\n",
    "model = InceptionV3(weights='imagenet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new model, by removing the last layer (output layer) from the inception v3\n",
    "model_new = Model(model.input, model.layers[-2].output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to encode a given image into a vector of size (2048, )\n",
    "def encode(image):\n",
    "    image = preprocess(image) # preprocess the image\n",
    "    fea_vec = model_new.predict(image) # Get the encoding vector for the image\n",
    "    fea_vec = np.reshape(fea_vec, fea_vec.shape[1]) # reshape from (1, 2048) to (2048, )\n",
    "    return fea_vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Call the funtion to encode all the train images\n",
    "# This will take a while on CPU - Execute this only once\n",
    "start = time()\n",
    "encoding_train = {}\n",
    "for img in train_img:\n",
    "    encoding_train[img[len(images):]] = encode(img)\n",
    "print(\"Time taken in seconds =\", time()-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the bottleneck train features to disk\n",
    "with open(\"../../storage/image_caption/dataset/Pickle/encoded_train_images.pkl\", \"wb\") as encoded_pickle:\n",
    "    pickle.dump(encoding_train, encoded_pickle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Call the funtion to encode all the test images - Execute this only once\n",
    "start = time()\n",
    "encoding_test = {}\n",
    "for img in test_img:\n",
    "    encoding_test[img[len(images):]] = encode(img)\n",
    "print(\"Time taken in seconds =\", time()-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the bottleneck test features to disk\n",
    "with open(\"../../storage/image_caption/dataset/Pickle/encoded_test_images.pkl\", \"wb\") as encoded_pickle:\n",
    "    pickle.dump(encoding_test, encoded_pickle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Photos: train=6000\n"
     ]
    }
   ],
   "source": [
    "train_features = load(open(\"../../storage/image_caption/dataset/Pickle/encoded_train_images.pkl\", \"rb\"))\n",
    "print('Photos: train=%d' % len(train_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list of all the training captions\n",
    "all_train_captions = []\n",
    "for key, val in train_descriptions.items():\n",
    "    for cap in val:\n",
    "        all_train_captions.append(cap)\n",
    "len(all_train_captions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preprocessed words 7578 -> 1651\n"
     ]
    }
   ],
   "source": [
    "# Consider only words which occur at least 10 times in the corpus\n",
    "word_count_threshold = 10\n",
    "word_counts = {}\n",
    "nsents = 0\n",
    "for sent in all_train_captions:\n",
    "    nsents += 1\n",
    "    for w in sent.split(' '):\n",
    "        word_counts[w] = word_counts.get(w, 0) + 1\n",
    "\n",
    "vocab = [w for w in word_counts if word_counts[w] >= word_count_threshold]\n",
    "print('preprocessed words %d -> %d' % (len(word_counts), len(vocab)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ixtoword = {}\n",
    "wordtoix = {}\n",
    "\n",
    "ix = 1\n",
    "for w in vocab:\n",
    "    wordtoix[w] = ix\n",
    "    ixtoword[ix] = w\n",
    "    ix += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1652"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_size = len(ixtoword) + 1 # one for appended 0's\n",
    "vocab_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Description Length: 34\n"
     ]
    }
   ],
   "source": [
    "# convert 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",
    "# 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",
    "# determine the maximum sequence length\n",
    "max_length = max_length(train_descriptions)\n",
    "print('Description Length: %d' % max_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data generator, intended to be used in a call to model.fit_generator()\n",
    "def data_generator(descriptions, photos, wordtoix, max_length, num_photos_per_batch):\n",
    "    X1, X2, y = list(), list(), list()\n",
    "    n=0\n",
    "    # loop for ever over images\n",
    "    while 1:\n",
    "        for key, desc_list in descriptions.items():\n",
    "            n+=1\n",
    "            # retrieve the photo feature\n",
    "            photo = photos[key+'.jpg']\n",
    "            for desc in desc_list:\n",
    "                # encode the sequence\n",
    "                seq = [wordtoix[word] for word in desc.split(' ') if word in wordtoix]\n",
    "                # split one sequence into multiple X, y pairs\n",
    "                for i in range(1, len(seq)):\n",
    "                    # split into input and output pair\n",
    "                    in_seq, out_seq = seq[:i], seq[i]\n",
    "                    # pad input sequence\n",
    "                    in_seq = pad_sequences([in_seq], maxlen=max_length)[0]\n",
    "                    # encode output sequence\n",
    "                    out_seq = to_categorical([out_seq], num_classes=vocab_size)[0]\n",
    "                    # store\n",
    "                    X1.append(photo)\n",
    "                    X2.append(in_seq)\n",
    "                    y.append(out_seq)\n",
    "            # yield the batch data\n",
    "            if n==num_photos_per_batch:\n",
    "                yield [[array(X1), array(X2)], array(y)]\n",
    "                X1, X2, y = list(), list(), list()\n",
    "                n=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 400000 word vectors.\n"
     ]
    }
   ],
   "source": [
    "# Load Glove vectors\n",
    "glove_dir = '../../storage/glove'\n",
    "embeddings_index = {} # empty dictionary\n",
    "f = open(os.path.join(glove_dir, 'glove.6B.200d.txt'), encoding=\"utf-8\")\n",
    "\n",
    "for line in f:\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    coefs = np.asarray(values[1:], dtype='float32')\n",
    "    embeddings_index[word] = coefs\n",
    "f.close()\n",
    "print('Found %s word vectors.' % len(embeddings_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_dim = 200\n",
    "\n",
    "# Get 200-dim dense vector for each of the 10000 words in out vocabulary\n",
    "embedding_matrix = np.zeros((vocab_size, embedding_dim))\n",
    "\n",
    "for word, i in wordtoix.items():\n",
    "    #if i < max_words:\n",
    "    embedding_vector = embeddings_index.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        # Words not found in the embedding index will be all zeros\n",
    "        embedding_matrix[i] = embedding_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1652, 200)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs1 = Input(shape=(2048,))\n",
    "fe1 = Dropout(0.5)(inputs1)\n",
    "fe2 = Dense(256, activation='relu')(fe1)\n",
    "inputs2 = Input(shape=(max_length,))\n",
    "se1 = Embedding(vocab_size, embedding_dim, mask_zero=True)(inputs2)\n",
    "se2 = Dropout(0.5)(se1)\n",
    "se3 = LSTM(256)(se2)\n",
    "decoder1 = add([fe2, se3])\n",
    "decoder2 = Dense(256, activation='relu')(decoder1)\n",
    "outputs = Dense(vocab_size, activation='softmax')(decoder2)\n",
    "model = Model(inputs=[inputs1, inputs2], outputs=outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_4 (InputLayer)            (None, 34)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_3 (InputLayer)            (None, 2048)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_2 (Embedding)         (None, 34, 200)      330400      input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 2048)         0           input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 34, 200)      0           embedding_2[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 256)          524544      dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lstm_2 (LSTM)                   (None, 256)          467968      dropout_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 256)          0           dense_2[0][0]                    \n",
      "                                                                 lstm_2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 256)          65792       add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 1652)         424564      dense_3[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 1,813,268\n",
      "Trainable params: 1,813,268\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.layers.embeddings.Embedding at 0x7ff80cc3fc18>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.layers[2].set_weights([embedding_matrix])\n",
    "model.layers[2].trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 10\n",
    "number_pics_per_bath = 3\n",
    "steps = len(train_descriptions)//number_pics_per_bath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 134s 67ms/step - loss: 4.1161\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 131s 65ms/step - loss: 3.4084\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 121s 60ms/step - loss: 3.1949\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 122s 61ms/step - loss: 3.0587\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 118s 59ms/step - loss: 2.9650\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 119s 59ms/step - loss: 2.8900\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 118s 59ms/step - loss: 2.8345\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 118s 59ms/step - loss: 2.7878\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 118s 59ms/step - loss: 2.7484\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 118s 59ms/step - loss: 2.7090\n"
     ]
    }
   ],
   "source": [
    "for i in range(epochs):\n",
    "    generator = data_generator(train_descriptions, train_features, wordtoix, max_length, number_pics_per_bath)\n",
    "    model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)\n",
    "    model.save('./model_weights/model_' + str(i) + '.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 118s 59ms/step - loss: 2.6823\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 118s 59ms/step - loss: 2.6567\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 118s 59ms/step - loss: 2.6342\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 118s 59ms/step - loss: 2.6129\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 118s 59ms/step - loss: 2.5916\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 118s 59ms/step - loss: 2.5761\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 118s 59ms/step - loss: 2.5610\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 118s 59ms/step - loss: 2.5488\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 118s 59ms/step - loss: 2.5363\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 118s 59ms/step - loss: 2.5255\n"
     ]
    }
   ],
   "source": [
    "for i in range(epochs):\n",
    "    generator = data_generator(train_descriptions, train_features, wordtoix, max_length, number_pics_per_bath)\n",
    "    model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)\n",
    "    model.save('./model_weights/model_' + str(i) + '.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.optimizer.lr = 0.0001\n",
    "epochs = 10\n",
    "number_pics_per_bath = 6\n",
    "steps = len(train_descriptions)//number_pics_per_bath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "1000/1000 [==============================] - 68s 68ms/step - loss: 2.4589\n",
      "Epoch 1/1\n",
      "1000/1000 [==============================] - 68s 68ms/step - loss: 2.4495\n",
      "Epoch 1/1\n",
      "1000/1000 [==============================] - 68s 68ms/step - loss: 2.4380\n",
      "Epoch 1/1\n",
      "1000/1000 [==============================] - 68s 68ms/step - loss: 2.4300\n",
      "Epoch 1/1\n",
      "1000/1000 [==============================] - 68s 68ms/step - loss: 2.4200\n",
      "Epoch 1/1\n",
      "1000/1000 [==============================] - 68s 68ms/step - loss: 2.4085\n",
      "Epoch 1/1\n",
      "1000/1000 [==============================] - 67s 67ms/step - loss: 2.3994\n",
      "Epoch 1/1\n",
      "1000/1000 [==============================] - 67s 67ms/step - loss: 2.3957\n",
      "Epoch 1/1\n",
      "1000/1000 [==============================] - 67s 67ms/step - loss: 2.3877\n",
      "Epoch 1/1\n",
      "1000/1000 [==============================] - 67s 67ms/step - loss: 2.3766\n"
     ]
    }
   ],
   "source": [
    "for i in range(epochs):\n",
    "    generator = data_generator(train_descriptions, train_features, wordtoix, max_length, number_pics_per_bath)\n",
    "    model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)\n",
    "    #model.save('./model_weights/model_' + str(i) + '.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights('./model_weights/model_30.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('./model_weights/model_30.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = '../../storage/image_caption/dataset/Flicker8k_Dataset/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"../../storage/image_caption/dataset/Pickle/encoded_test_images.pkl\", \"rb\") as encoded_pickle:\n",
    "    encoding_test = load(encoded_pickle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def greedySearch(photo):\n",
    "    in_text = 'startseq'\n",
    "    for i in range(max_length):\n",
    "        sequence = [wordtoix[w] for w in in_text.split() if w in wordtoix]\n",
    "        sequence = pad_sequences([sequence], maxlen=max_length)\n",
    "        yhat = model.predict([photo,sequence], verbose=0)\n",
    "        yhat = np.argmax(yhat)\n",
    "        word = ixtoword[yhat]\n",
    "        in_text += ' ' + word\n",
    "        if word == 'endseq':\n",
    "            break\n",
    "    final = in_text.split()\n",
    "    final = final[1:-1]\n",
    "    final = ' '.join(final)\n",
    "    return final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Greedy: motorcyclist is riding an orange motorcycle\n"
     ]
    }
   ],
   "source": [
    "z+=1\n",
    "pic = list(encoding_test.keys())[z]\n",
    "image = encoding_test[pic].reshape((1,2048))\n",
    "x=plt.imread(images+pic)\n",
    "plt.imshow(x)\n",
    "plt.show()\n",
    "print(\"Greedy:\",greedySearch(image))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}