model_build.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the file tokens_clean.txt and store the cleaned captions in a dictionary\n",
    "import json\n",
    "\n",
    "content = None\n",
    "\n",
    "with open (\"data/textFiles/tokens_clean.txt\", 'r') as file:\n",
    "    content = file.read()\n",
    "\n",
    "json_acceptable_string = content.replace(\"'\", \"\\\"\")\n",
    "content = json.loads(json_acceptable_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "print(type(content))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Words = 1955246\n"
     ]
    }
   ],
   "source": [
    "#Iterate over the captions word by word, and append each word to total_words\n",
    "total_words = []\n",
    "\n",
    "for key in content.keys():\n",
    "    for caption in content[key]:\n",
    "        for i in caption.split():\n",
    "            total_words.append(i)\n",
    "\n",
    "print(\"Total Words = %d\" %len(total_words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique words = 18079\n"
     ]
    }
   ],
   "source": [
    "# Compute the frequency of occurrence of each word, and store it in a dictionary of word-freq\n",
    "import collections\n",
    "\n",
    "counter = collections.Counter(total_words)\n",
    "freq_cnt = dict(counter)\n",
    "\n",
    "print(\"Number of unique words = \" + str(len(freq_cnt.keys())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the word-freq pairs (from the dictionary freq_cnt) in a list, sorted in decreasing order of frequency\n",
    "sorted_freq_cnt = sorted(freq_cnt.items(), reverse=True, key=lambda x:x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of common unique words = 5136\n"
     ]
    }
   ],
   "source": [
    "threshold = 10\n",
    "\n",
    "#Filter off those words whose frequency of occurrence in less than threshold\n",
    "sorted_freq_cnt = [x for x in sorted_freq_cnt if x[1]>threshold]\n",
    "# Store these common words in total_words\n",
    "total_words = [x[0] for x in sorted_freq_cnt]\n",
    "\n",
    "print(\"Number of common unique words = \" + str(len(total_words)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PREPARE TRAIN AND TEST DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read training and testing image names\n",
    "\n",
    "train_file_data = \"\"\n",
    "test_file_data = \"\"\n",
    "\n",
    "with open (\"data/textFiles/flickr30k_train.txt\", 'r') as file:\n",
    "    train_file_data = file.read()\n",
    "\n",
    "with open (\"data/textFiles/flickr30k_test.txt\", 'r') as file:\n",
    "    test_file_data = file.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain a list of train and test images\n",
    "train_data = [img_file_name for img_file_name in train_file_data.split(\"\\n\")[:-1]]\n",
    "test_data = [img_file_name for img_file_name in test_file_data.split(\"\\n\")[:-1]]\n",
    "\n",
    "# Obtain image ID from image file name\n",
    "train_data = [image.split(\".\")[0] for image in train_data]\n",
    "test_data = [image.split(\".\")[0] for image in test_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1000092795', '10002456', '1000268201', '1000344755', '1000366164']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For each imageID in train_data, store its captions in a dictionary \n",
    "\n",
    "train_content = {}\n",
    "\n",
    "for imageID in train_data:\n",
    "    train_content[imageID] = []\n",
    "    for caption in content[imageID]:\n",
    "        # Add a start sequence token in the beginning and an end sequence token at the end\n",
    "        cap_to_append = \"startseq \" + caption + \" endseq\"\n",
    "        train_content[imageID].append(cap_to_append)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['startseq a man with reflective safety clothes and ear protection drives a john deere tractor on a road  endseq',\n",
       " 'startseq john deere tractors cruises down a street while the driver wears easy to see clothing  endseq',\n",
       " 'startseq a man in a neon green and orange uniform is driving on a green tractor  endseq',\n",
       " 'startseq a man in a tractor wearing headphones driving down a paved street  endseq',\n",
       " 'startseq a man driving a john deere tractor on a main road in the country  endseq']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_content['1001896054']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EXTRACT FEATURES FROM IMAGES USING RESNET50 ARCHIECTURE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Nirajan\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "WARNING:tensorflow:From C:\\Users\\Nirajan\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions\n",
    "\n",
    "model = ResNet50(weights = 'imagenet', input_shape = (224, 224, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"resnet50\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 112, 112, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 56, 56, 64)   4160        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 56, 56, 256)  16640       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 56, 56, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 56, 56, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 56, 56, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 56, 56, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 56, 56, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 56, 56, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 56, 56, 256)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 28, 28, 128)  32896       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 28, 28, 512)  131584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 28, 28, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 28, 28, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 28, 28, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 28, 28, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 28, 28, 512)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 14, 14, 256)  131328      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 14, 14, 1024) 525312      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 14, 14, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 14, 14, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 14, 14, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 14, 14, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 14, 14, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 14, 14, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 14, 14, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 14, 14, 1024) 0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 7, 7, 2048)   0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (GlobalAveragePooling2 (None, 2048)         0           activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "fc1000 (Dense)                  (None, 1000)         2049000     avg_pool[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 25,636,712\n",
      "Trainable params: 25,583,592\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model\n",
    "model_new = Model (model.input, model.layers[-2].output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import image\n",
    "import numpy as np\n",
    "\n",
    "def preprocess_image (img):\n",
    "    img = image.load_img(img, target_size=(224, 224))\n",
    "    img = image.img_to_array(img)\n",
    "\n",
    "    # Convert 3D tensor to a 4D tendor\n",
    "    img = np.expand_dims(img, axis=0)\n",
    "\n",
    "    #Normalize image accoring to ResNet50 requirement\n",
    "    img = preprocess_input(img)\n",
    "\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 224, 224, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "img = preprocess_image(\"data/Images/4376178.jpg\")\n",
    "print(img.shape)\n",
    "plt.imshow(img[0])\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A wrapper function, which inputs an image and returns its encoding (feature vector)\n",
    "def encode_image (img):\n",
    "    img = preprocess_image(img)\n",
    "    feature_vector = model_new.predict(img)\n",
    "\n",
    "    feature_vector = feature_vector.reshape((-1,))\n",
    "    return feature_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Nirajan\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Encoding in progress... STEP 0\n",
      "Encoding in progress... STEP 100\n",
      "Encoding in progress... STEP 200\n",
      "Encoding in progress... STEP 300\n",
      "Encoding in progress... STEP 400\n",
      "Encoding in progress... STEP 500\n",
      "Encoding in progress... STEP 600\n",
      "Encoding in progress... STEP 700\n",
      "Encoding in progress... STEP 800\n",
      "Encoding in progress... STEP 900\n",
      "Encoding in progress... STEP 1000\n",
      "Encoding in progress... STEP 1100\n",
      "Encoding in progress... STEP 1200\n",
      "Encoding in progress... STEP 1300\n",
      "Encoding in progress... STEP 1400\n",
      "Encoding in progress... STEP 1500\n",
      "Encoding in progress... STEP 1600\n",
      "Encoding in progress... STEP 1700\n",
      "Encoding in progress... STEP 1800\n",
      "Encoding in progress... STEP 1900\n",
      "Encoding in progress... STEP 2000\n",
      "Encoding in progress... STEP 2100\n",
      "Encoding in progress... STEP 2200\n",
      "Encoding in progress... STEP 2300\n",
      "Encoding in progress... STEP 2400\n",
      "Encoding in progress... STEP 2500\n",
      "Encoding in progress... STEP 2600\n",
      "Encoding in progress... STEP 2700\n",
      "Encoding in progress... STEP 2800\n",
      "Encoding in progress... STEP 2900\n",
      "Encoding in progress... STEP 3000\n",
      "Encoding in progress... STEP 3100\n",
      "Encoding in progress... STEP 3200\n",
      "Encoding in progress... STEP 3300\n",
      "Encoding in progress... STEP 3400\n",
      "Encoding in progress... STEP 3500\n",
      "Encoding in progress... STEP 3600\n",
      "Encoding in progress... STEP 3700\n",
      "Encoding in progress... STEP 3800\n",
      "Encoding in progress... STEP 3900\n",
      "Encoding in progress... STEP 4000\n",
      "Encoding in progress... STEP 4100\n",
      "Encoding in progress... STEP 4200\n",
      "Encoding in progress... STEP 4300\n",
      "Encoding in progress... STEP 4400\n",
      "Encoding in progress... STEP 4500\n",
      "Encoding in progress... STEP 4600\n",
      "Encoding in progress... STEP 4700\n",
      "Encoding in progress... STEP 4800\n",
      "Encoding in progress... STEP 4900\n",
      "Encoding in progress... STEP 5000\n",
      "Encoding in progress... STEP 5100\n",
      "Encoding in progress... STEP 5200\n",
      "Encoding in progress... STEP 5300\n",
      "Encoding in progress... STEP 5400\n",
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    {
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      "Total time taken: 3827.1696212291718 sec\n"
     ]
    }
   ],
   "source": [
    "from time import time\n",
    "\n",
    "train_encoding = {}\n",
    "# Create a dictionary of iamgeID and its feature vector\n",
    "\n",
    "start_time = time()\n",
    "for index, imageID in enumerate (train_data):\n",
    "    image_path = \"data/Images/\" + imageID + \".jpg\"\n",
    "    \n",
    "    train_encoding[imageID] = encode_image(image_path)\n",
    "\n",
    "    # Print progress\n",
    "    if index%100 == 0:\n",
    "        print(\"Encoding in progress... STEP\", index)\n",
    "\n",
    "end_time = time()\n",
    "print(\"Total time taken:\", end_time-start_time, \"sec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the above computed features on the disk\n",
    "# Use pickle to dump the entire data\n",
    "import pickle\n",
    "\n",
    "with open(\"encoded_train_features.pkl\", \"wb\") as file:\n",
    "    # Pickle allows to store any object as a file on the disk\n",
    "    pickle.dump(train_encoding, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding in progress... STEP 0\n",
      "Encoding in progress... STEP 100\n",
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      "Encoding in progress... STEP 900\n",
      "Total time taken: 149.0283224582672 s\n"
     ]
    }
   ],
   "source": [
    "test_encoding = {}\n",
    "# Create a dictionary of iamgeID and its feature vector\n",
    "\n",
    "start_time = time()\n",
    "for index, imageID in enumerate (test_data):\n",
    "    image_path = \"data/Images/\" + imageID + \".jpg\"\n",
    "    \n",
    "    test_encoding[imageID] = encode_image(image_path)\n",
    "\n",
    "    # Print progress\n",
    "    if index%100 == 0:\n",
    "        print(\"Encoding in progress... STEP\", index)\n",
    "\n",
    "end_time = time()\n",
    "print(\"Total time taken:\", end_time-start_time, \"s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"encoded_test_features.pkl\", \"wb\") as file:\n",
    "    pickle.dump(test_encoding, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PREPROCESS THE CAPTIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the word-to-index and index-to-word mappings\n",
    "word_to_index = {}\n",
    "index_to_word = {}\n",
    "\n",
    "for i, word in enumerate(total_words):\n",
    "    word_to_index[word] = i+1\n",
    "    index_to_word[i+1] = word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5136\n"
     ]
    }
   ],
   "source": [
    "print(len(index_to_word))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "print(index_to_word[5])\n",
    "print(word_to_index['is'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add startseq and endseq also to the mappings\n",
    "index_to_word[5137] = 'startseq'\n",
    "word_to_index['startseq'] = 5137\n",
    "\n",
    "index_to_word[5138] = 'endseq'\n",
    "word_to_index['endseq'] = 5138\n",
    "\n",
    "VOCAB_SIZE = len(word_to_index) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5139\n"
     ]
    }
   ],
   "source": [
    "print(VOCAB_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"data/textFiles/word_to_idx.pkl\", \"wb\") as file:\n",
    "    pickle.dump(word_to_index, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"data/textFiles/idx_to_word.pkl\", \"wb\") as file:\n",
    "    pickle.dump(index_to_word, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the maximum length of a caption\n",
    "max_len = 0\n",
    "\n",
    "for cap_list in train_content.keys():\n",
    "    for caption in train_content[cap_list]:\n",
    "        max_len = max(max_len, len(caption.split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80\n"
     ]
    }
   ],
   "source": [
    "print(max_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the Glove word Embeddings\n",
    "# This contains 50-dimensional embeddings for 6 Billion English words\n",
    "file = open(\"glove.6B.50d.txt\",encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a mapping from word to embedding\n",
    "embeddings_index = {} # empty dictionary\n",
    "\n",
    "for line in file:\n",
    "    values = line.split()\n",
    "\n",
    "    word = values[0]\n",
    "    coefs = np.array (values[1:], dtype='float')\n",
    "    embeddings_index[word] = coefs\n",
    "\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.52042 , -0.8314  ,  0.49961 ,  1.2893  ,  0.1151  ,  0.057521,\n",
       "       -1.3753  , -0.97313 ,  0.18346 ,  0.47672 , -0.15112 ,  0.35532 ,\n",
       "        0.25912 , -0.77857 ,  0.52181 ,  0.47695 , -1.4251  ,  0.858   ,\n",
       "        0.59821 , -1.0903  ,  0.33574 , -0.60891 ,  0.41742 ,  0.21569 ,\n",
       "       -0.07417 , -0.5822  , -0.4502  ,  0.17253 ,  0.16448 , -0.38413 ,\n",
       "        2.3283  , -0.66682 , -0.58181 ,  0.74389 ,  0.095015, -0.47865 ,\n",
       "       -0.84591 ,  0.38704 ,  0.23693 , -1.5523  ,  0.64802 , -0.16521 ,\n",
       "       -1.4719  , -0.16224 ,  0.79857 ,  0.97391 ,  0.40027 , -0.21912 ,\n",
       "       -0.30938 ,  0.26581 ])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_index[\"apple\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_dim = 50\n",
    "\n",
    "embedding_matrix = np.zeros((VOCAB_SIZE, embedding_dim))\n",
    "\n",
    "for word, i in word_to_index.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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5139, 50)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding_matrix.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DEFINE MODEL ARCHITECTURE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Input, Dense, Dropout, Embedding, LSTM\n",
    "\n",
    "#Convert feature vector of image to smaller vector\n",
    "\n",
    "#Output of ResNet goes into following input layer \n",
    "inp_img_features = Input(shape=(2048,))\n",
    "\n",
    "inp_img1 = Dropout(0.3)(inp_img_features)\n",
    "inp_img2 = Dense(256, activation='relu')(inp_img1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Nirajan\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\keras\\backend.py:3994: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    }
   ],
   "source": [
    "#Now take Captions as input\n",
    "\n",
    "#Actual input size will be (batch_size x max_length_of_caption)\n",
    "#But here we specify only for one example\n",
    "inp_cap = Input(shape=(max_len,))\n",
    "inp_cap1 = Embedding(input_dim=VOCAB_SIZE, output_dim=50, mask_zero=True)(inp_cap)\n",
    "inp_cap2 = Dropout(0.3)(inp_cap1)\n",
    "inp_cap3 = LSTM(256)(inp_cap2)\n",
    "# inp_cap3 captures the entire sentence that has been generated till now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers.merge import add\n",
    "\n",
    "# Decode the inputs\n",
    "\n",
    "# So, an image (224x224x3) goes through ResNet50\n",
    "# Then as 2048 dimensional it goes through the above earlier architecture\n",
    "# The final output is inp_img2 (256 dimensional) which now goes through the Decoder \n",
    "\n",
    "# Similarly for the captions which initially have shape (batch_size x max_len)\n",
    "# Then after passing through Embedding layer comes out as (batch_size x max_len x 50(embedding_size)))\n",
    "# Then it passes through the above LSTM layer and comes out as inp_cap3 (a 256 dimensional vector)\n",
    "\n",
    "# Add the two above tensors\n",
    "decoder1 = add([inp_img2, inp_cap3])\n",
    "decoder2 = Dense(256, activation='relu')(decoder1)\n",
    "outputs = Dense(VOCAB_SIZE, activation='softmax')(decoder2)\n",
    "\n",
    "# Combined model\n",
    "model = Model (inputs=[inp_img_features, inp_cap], outputs=outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            (None, 80)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            (None, 2048)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 80, 50)       256950      input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 2048)         0           input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 80, 50)       0           embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 256)          524544      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lstm_1 (LSTM)                   (None, 256)          314368      dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_17 (Add)                    (None, 256)          0           dense_1[0][0]                    \n",
      "                                                                 lstm_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 256)          65792       add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 5139)         1320723     dense_2[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 2,482,377\n",
      "Trainable params: 2,482,377\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils.vis_utils import plot_model\n",
    "\n",
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.layers[2].set_weights([embedding_matrix])\n",
    "model.layers[2].trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "CREATE A DATA LOADER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "def data_generator (train_content, train_encoding, word_to_index, max_len, batch_size):\n",
    "    X1, X2, y = [], [], []\n",
    "    n = 0\n",
    "\n",
    "    while True:\n",
    "        for imageID, cap_list in train_content.items():\n",
    "            n += 1\n",
    "\n",
    "            image = train_encoding [imageID]\n",
    "\n",
    "            for caption in cap_list:\n",
    "                idx_seq = [word_to_index[word] for word in caption.split() if word in word_to_index]\n",
    "\n",
    "                for i in range (1, len(idx_seq)):\n",
    "                    xi = idx_seq[0 : i] # The input sequence of words\n",
    "                    yi = idx_seq[i] # The next word after the above sequence (this is expected to be predicted)\n",
    "\n",
    "                    # Add a padding of zeros ao lengths of input sequences become equal\n",
    "                    xi = pad_sequences([xi], maxlen=max_len, value=0, padding='post')[0] # Take the first row only, since this method inputs & returns a 2D array\n",
    "                    # Convert the expected word to One Hot vector notation\n",
    "                    yi = to_categorical([yi], num_classes=VOCAB_SIZE)[0]\n",
    "\n",
    "                    X1.append(image)\n",
    "                    X2.append(xi)\n",
    "                    y.append(yi)\n",
    "\n",
    "                if n==batch_size:\n",
    "                    yield [[np.array(X1), np.array(X2)], np.array(y)]\n",
    "                    \n",
    "                    X1, X2, y = [], [], []\n",
    "                    n=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TRAIN THE MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 15\n",
    "batch_size = 5\n",
    "steps = len(train_content)//batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "5800/5800 [==============================] - 4879s 841ms/step - loss: 3.8658\n",
      "Epoch 1/1\n",
      "5800/5800 [==============================] - 4925s 849ms/step - loss: 3.3300\n",
      "Epoch 1/1\n",
      "5800/5800 [==============================] - 4944s 852ms/step - loss: 3.1881\n",
      "Epoch 1/1\n",
      "5800/5800 [==============================] - 4951s 854ms/step - loss: 3.1141\n",
      "Epoch 1/1\n",
      "5800/5800 [==============================] - 4956s 854ms/step - loss: 3.0679\n",
      "Epoch 1/1\n",
      "5800/5800 [==============================] - 4960s 855ms/step - loss: 3.0363\n",
      "Epoch 1/1\n",
      "5800/5800 [==============================] - 4952s 854ms/step - loss: 3.0136\n",
      "Epoch 1/1\n",
      "5800/5800 [==============================] - 4958s 855ms/step - loss: 2.9966\n",
      "Epoch 1/1\n",
      "5800/5800 [==============================] - 4951s 854ms/step - loss: 2.9813\n",
      "Epoch 1/1\n",
      "5800/5800 [==============================] - 4954s 854ms/step - loss: 2.9706\n",
      "Epoch 1/1\n",
      "5800/5800 [==============================] - 4939s 852ms/step - loss: 2.9625\n",
      "Epoch 1/1\n",
      "5800/5800 [==============================] - 4923s 849ms/step - loss: 2.9535\n",
      "Epoch 1/1\n",
      "5800/5800 [==============================] - 4926s 849ms/step - loss: 2.9475\n",
      "Epoch 1/1\n",
      "5800/5800 [==============================] - 4919s 848ms/step - loss: 2.9432\n",
      "Epoch 1/1\n",
      "5800/5800 [==============================] - 4897s 844ms/step - loss: 2.9382\n"
     ]
    }
   ],
   "source": [
    "for i in range(epochs):\n",
    "    # Create an instance of the generator\n",
    "    generator = data_generator(train_content, train_encoding, word_to_index, max_len, batch_size)\n",
    "    model.fit_generator(generator, steps_per_epoch=steps)\n",
    "    model.save('model_' + str(i) + '.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('./model_checkpoints/model_14.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = './data/Images/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pickle import dump, load\n",
    "with open(\"./encoded_test_features.pkl\", \"rb\") as encoded_pickle:\n",
    "    encoding_test = load(encoded_pickle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def greedySearch(photo):\n",
    "    in_text = 'startseq'\n",
    "    for i in range(max_len):\n",
    "        sequence = [word_to_index[w] for w in in_text.split() if w in word_to_index]\n",
    "        sequence = pad_sequences([sequence], maxlen=max_len)\n",
    "        yhat = model.predict([photo,sequence], verbose=0)\n",
    "        yhat = np.argmax(yhat)\n",
    "        word = index_to_word[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": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Greedy: a man is walking on a beach with a fishing pole\n",
      "1313869424\n"
     ]
    }
   ],
   "source": [
    "#z = 0\n",
    "z+=1\n",
    "pic = list(encoding_test.keys())[z]\n",
    "image = encoding_test[pic].reshape((1,2048))\n",
    "x=plt.imread(images+pic+'.jpg')\n",
    "plt.imshow(x)\n",
    "plt.show()\n",
    "print(\"Greedy:\",greedySearch(image))\n",
    "print(pic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_content = {}\n",
    "\n",
    "for imageID in test_data:\n",
    "    test_content[imageID] = []\n",
    "    for caption in content[imageID]:\n",
    "        # Add a start sequence token in the beginning and an end sequence token at the end\n",
    "        cap_to_append = caption\n",
    "        test_content[imageID].append(cap_to_append)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['the man with pierced ears is wearing glasses and an orange hat ',\n",
       " 'a man with glasses is wearing a beer can crocheted hat ',\n",
       " 'a man with gauges and glasses is wearing a blitz hat ',\n",
       " 'a man in an orange hat starring at something ',\n",
       " 'a man wears an orange hat and glasses ']"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 0\n",
    "pic = list(encoding_test.keys())[x]\n",
    "test_content[pic]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102617084\n",
      "Greedy Search Caption: a group of people are walking down a snowy path\n",
      "\n",
      "Reference 1: ['five snowmobile riders all wearing helmets and goggles line up in a snowy clearing in a forest in front of their snowmobiles they are all wearing black snow pants and from left to right they are wearing a black coat white coat red coat blue coat and black coat ', 'five people wearing winter jackets and helmets stand in the snow with snowmobiles in the background ', 'five people wearing winter clothing helmets and ski goggles stand outside in the snow ', 'a group of snowmobile riders gather in the snow ', 'group gathered to go snowmobiling ']\n",
      "\n",
      "BLEU-1: 0.738\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction\n",
    "\n",
    "x = 3\n",
    "pic = list(encoding_test.keys())[x]\n",
    "test_content[pic]\n",
    "\n",
    "\n",
    "reference = test_content[pic]\n",
    "#pic = list(encoding_test.keys())[1]\n",
    "print(pic)\n",
    "img = 'twodogs.jpg'\n",
    "e = encoding_test[pic].reshape(1,2048)\n",
    "#image = encoding_test[pic].reshape((1,2048))\n",
    "x=plt.imread(images+pic+'.jpg')\n",
    "#x=plt.imread(img)\n",
    "plt.imshow(x)\n",
    "# plt.show()\n",
    "caption = greedySearch(e)\n",
    "print(\"Greedy Search Caption:\",caption)\n",
    "print()\n",
    "print('Reference 1:',reference)\n",
    "\n",
    "print()\n",
    "print('BLEU-1:', round(sentence_bleu(reference, caption),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}