Initial_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import csv\n",
    "from os import listdir\n",
    "import keras as K\n",
    "from keras.models import Model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Input, LSTM, Embedding, TimeDistributed,BatchNormalization, Dense,Dropout, RepeatVector, Activation, Flatten\n",
    "from keras.preprocessing import image, sequence\n",
    "from keras.layers.wrappers import Bidirectional\n",
    "from keras.applications import VGG16\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from keras.utils import plot_model\n",
    "from keras.optimizers import Adam\n",
    "import scipy\n",
    "%pylab inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from matplotlib.pyplot import imshow\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.applications.imagenet_utils import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "embedding_dim = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"total_string = \"<sss> \" + row[4] + \" . \" + row[5] + \" \" + row[6]\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_caption_list = []\n",
    "\n",
    "with open('preprocess.csv') as csv_file:\n",
    "    csv_reader = csv.reader(csv_file, delimiter=',')\n",
    "    for row in csv_reader:\n",
    "        row_insert = []\n",
    "        row_insert.append(row[0])\n",
    "        total_string = \"<sss> \" + row[5] + \" \" + row[6] + \" <eee>\"\n",
    "        row_insert.append(total_string)\n",
    "        all_caption_list.append(row_insert)\n",
    "\n",
    "with open(\"train_caption_final.csv\", \"w\", newline='') as output:\n",
    "    writer = csv.writer(output)\n",
    "    writer.writerows(all_caption_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoded_images = pickle.load( open( \"visual_features.pickle\", \"rb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#find vocab_size\n",
    "#find total_sample\n",
    "def initialize():\n",
    "        df = pd.read_csv('train_caption_final.csv', delimiter=',')\n",
    "        nb_samples = df.shape[0]\n",
    "        iteration = df.iterrows()\n",
    "        caps = []\n",
    "        for i in range(nb_samples):\n",
    "            x = iteration.__next__()\n",
    "            caps.append(x[1][1])\n",
    "\n",
    "        total_samples=0\n",
    "        for text in caps:\n",
    "            total_samples+=len(text.split())-1\n",
    "        print(\"Total samples : \"+str(total_samples))\n",
    "        \n",
    "        words = [txt.split() for txt in caps]\n",
    "        unique = []\n",
    "        for word in words:\n",
    "            unique.extend(word)\n",
    "        unique = list(set(unique))\n",
    "        vocab_size = len(unique)\n",
    "        word_index = {}\n",
    "        index_word = {}\n",
    "        for i, word in enumerate(unique):\n",
    "            word_index[word]=i\n",
    "            index_word[i]=word\n",
    "\n",
    "        max_len = 0\n",
    "        for caption in caps:\n",
    "            if(len(caption.split()) > max_len):\n",
    "                max_len = len(caption.split())\n",
    "        max_cap_len = max_len\n",
    "        print(\"Vocabulary size: \"+str(vocab_size))\n",
    "        print(\"Maximum caption length: \"+str(max_cap_len))\n",
    "        \n",
    "        return max_cap_len, vocab_size, total_samples, word_index, index_word\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total samples : 119082\n",
      "Vocabulary size: 3395\n",
      "Maximum caption length: 177\n"
     ]
    }
   ],
   "source": [
    "max_capt_len, vocab_size, total_samples, word_to_index, index_to_word = initialize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Bakwaas\n",
    "def linear_model_combined(optimizer='Adadelta'):    \n",
    "\n",
    "    # declare input\n",
    "    inlayer =Input(shape=(2048,1))\n",
    "    flatten = Flatten()(inlayer)\n",
    "\n",
    "    modela = Dense(1024)(flatten)\n",
    "    modela = Activation('relu')(modela)\n",
    "    modela = Dense(512)(modela)\n",
    "\n",
    "    modelb = Dense(1024)(flatten)\n",
    "    modelb = Activation('relu')(modelb)\n",
    "    modelb = Dense(512)(modelb)\n",
    "\n",
    "    model_concat = concatenate([modela, modelb])\n",
    "\n",
    "\n",
    "    model_concat = Activation('relu')(model_concat)\n",
    "    model_concat = Dense(256)(model_concat)\n",
    "    model_concat = Activation('relu')(model_concat)\n",
    "\n",
    "    model_concat = Dense(4)(model_concat)\n",
    "    model_concat = Activation('softmax')(model_concat)\n",
    "\n",
    "    model_combined = Model(inputs=inlayer,outputs=model_concat)\n",
    "\n",
    "    model_combined.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "\n",
    "    return model_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"EMBEDDING_DIM = 300\n",
    "\n",
    "image_model = Sequential()\n",
    "image_model.add(Dense(EMBEDDING_DIM, input_shape=(2048,), activation='relu'))\n",
    "image_model.add(RepeatVector(max_capt_len))\n",
    "\n",
    "   \n",
    "lang_model = Sequential()\n",
    "lang_model.add(Embedding(vocab_size,EMBEDDING_DIM , input_length=max_capt_len))\n",
    "lang_model.add(Bidirectional(LSTM(256,return_sequences=True)))\n",
    "lang_model.add(Dropout(0.5))\n",
    "lang_model.add(BatchNormalization())\n",
    "lang_model.add(TimeDistributed(Dense(EMBEDDING_DIM)))\n",
    "\n",
    "   \n",
    "fin_model = Sequential()\n",
    "#fin_model.add(Merge([image_model, lang_model], mode='concat'))\n",
    "fin_model.add(K.layers.concatenate([image_model, lang_model],axis=1))\n",
    "fin_model.add(Dropout(0.5))\n",
    "fin_model.add(BatchNormalization())\n",
    "fin_model.add(Bidirectional(LSTM(1000,return_sequences=False)))\n",
    "\n",
    "fin_model.add(Dense(vocab_size))\n",
    "fin_model.add(Activation('softmax'))\n",
    "print (\"Model created!\")\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"fin_model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])\n",
    "image_model.summary()\n",
    "lang_model.summary()\n",
    "fin_model.summary()\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_model(return_model = False):\n",
    "        \n",
    "        image_decod_input = Input(shape=(2048,))\n",
    "        image_decod = Dense(embedding_dim, input_dim=2048, activation='relu')(image_decod_input)\n",
    "        image_decod = RepeatVector(max_capt_len)(image_decod)\n",
    "        \n",
    "        \n",
    "        capt_decod_input = Input(shape=(max_capt_len,))\n",
    "        capt_decod = Embedding(vocab_size, 256, input_length=max_capt_len)(capt_decod_input)\n",
    "        capt_decod = LSTM(256,return_sequences=True)(capt_decod)\n",
    "        capt_decod = TimeDistributed(Dense(embedding_dim))(capt_decod)\n",
    "        \n",
    "        model = K.layers.concatenate([image_decod, capt_decod])\n",
    "        model = LSTM(1000,return_sequences=False)(model)\n",
    "        model = Dense(vocab_size)(model)\n",
    "        prediction = Activation('softmax')(model)\n",
    "        \n",
    "        model = Model(inputs=[image_decod_input, capt_decod_input], outputs=prediction)\n",
    "\n",
    "        if(return_model==True):\n",
    "            return model\n",
    "\n",
    "        model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])\n",
    "        return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate_a_batch(batch_size = 32):\n",
    "        partial_caps = []\n",
    "        next_words = []\n",
    "        images = []\n",
    "        gen_count = 0\n",
    "        df = pd.read_csv('train_caption_final.csv', delimiter=',')\n",
    "        nb_samples = df.shape[0]\n",
    "        iter = df.iterrows()\n",
    "        caps = []\n",
    "        imgs = []\n",
    "        for i in range(nb_samples):\n",
    "            x = iter.__next__()\n",
    "            caps.append(x[1][1])\n",
    "            imgs.append(x[1][0])\n",
    "\n",
    "\n",
    "        total_count = 0\n",
    "        while True:\n",
    "            image_counter = -1\n",
    "            for text in caps:\n",
    "                image_counter+=1\n",
    "                current_image = encoded_images[imgs[image_counter]][0]\n",
    "                #print(current_image.shape)\n",
    "                for i in range(len(text.split())-1):\n",
    "                    total_count+=1\n",
    "                    partial = [word_to_index[txt] for txt in text.split()[:i+1]]\n",
    "                    partial_caps.append(partial)\n",
    "                    next = np.zeros(vocab_size)\n",
    "                    next[word_to_index[text.split()[i+1]]] = 1\n",
    "                    next_words.append(next)\n",
    "                    images.append(current_image)\n",
    "                    #print(images[0].shape)\n",
    "\n",
    "                    if total_count>=batch_size:\n",
    "                        next_words = np.asarray(next_words)\n",
    "                        images = np.asarray(images)\n",
    "                        partial_caps = sequence.pad_sequences(partial_caps, maxlen=max_capt_len, padding='post')\n",
    "                        total_count = 0\n",
    "                        gen_count+=1\n",
    "                        if gen_count%10 == 0:\n",
    "                            print(\"batch count: \"+str(gen_count))\n",
    "                        yield [[images, partial_caps], next_words]\n",
    "                        partial_caps = []\n",
    "                        next_words = []\n",
    "                        images = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 16\n",
    "epochs = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "image_caption_model = create_model()\n",
    "file_name = 'saved_weights_epoch_{epoch:02d}.hdf5'\n",
    "checkpoint = ModelCheckpoint(file_name, monitor='loss', verbose=1, save_best_only=True, mode='min')\n",
    "checkpoints_list = [checkpoint]\n",
    "image_caption_model.fit_generator(generate_a_batch(batch_size=batch_size), steps_per_epoch=total_samples/batch_size, epochs=epochs, verbose=2, callbacks=checkpoints_list)"
   ]
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