FinalModel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\admin\\anaconda3\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"]=\"3\"\n",
    "from numpy import array\n",
    "from pickle import load\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical\n",
    "from keras.utils import plot_model\n",
    "from keras.models import Model\n",
    "from keras.layers import Input\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Embedding\n",
    "from keras.layers import Dropout\n",
    "from keras.layers.merge import add\n",
    "from keras.callbacks import ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load doc into memory\n",
    "def load_doc(filename):\n",
    "    # open the file as read only\n",
    "    file = open(filename, 'r')\n",
    "    # read all text\n",
    "    text = file.read()\n",
    "    # close the file\n",
    "    file.close()\n",
    "    return text\n",
    "\n",
    "# load a pre-defined list of photo identifiers\n",
    "def load_set(filename):\n",
    "    doc = load_doc(filename)\n",
    "    dataset = list()\n",
    "    # process line by line\n",
    "    for line in doc.split('\\n'):\n",
    "        # skip empty lines\n",
    "        if len(line) < 1:\n",
    "            continue\n",
    "        # get the image identifier\n",
    "        identifier = line.split('.')[0]\n",
    "        dataset.append(identifier)\n",
    "    return set(dataset)\n",
    "\n",
    "# load clean descriptions into memory\n",
    "def load_clean_descriptions(filename, dataset):\n",
    "    # load document\n",
    "    doc = load_doc(filename)\n",
    "    descriptions = dict()\n",
    "    for line in doc.split('\\n'):\n",
    "        # split line by white space\n",
    "        tokens = line.split()\n",
    "        # split id from description\n",
    "        image_id, image_desc = tokens[0], tokens[1:]\n",
    "        # skip images not in the set\n",
    "        if image_id in dataset:\n",
    "            # create list\n",
    "            if image_id not in descriptions:\n",
    "                descriptions[image_id] = list()\n",
    "            # wrap description in tokens\n",
    "            desc = 'startseq ' + ' '.join(image_desc) + ' endseq'\n",
    "            # store\n",
    "            descriptions[image_id].append(desc)\n",
    "    return descriptions\n",
    "\n",
    "# load photo features\n",
    "def load_photo_features(filename, dataset):\n",
    "    # load all features\n",
    "    all_features = load(open(filename, 'rb'))\n",
    "    # filter features\n",
    "    features = {k: all_features[k] for k in dataset}\n",
    "    return features\n",
    "\n",
    "# covert a dictionary of clean descriptions to a list of descriptions\n",
    "def to_lines(descriptions):\n",
    "    all_desc = list()\n",
    "    for key in descriptions.keys():\n",
    "        [all_desc.append(d) for d in descriptions[key]]\n",
    "    return all_desc\n",
    "\n",
    "# fit a tokenizer given caption descriptions\n",
    "def create_tokenizer(descriptions):\n",
    "    lines = to_lines(descriptions)\n",
    "    tokenizer = Tokenizer()\n",
    "    tokenizer.fit_on_texts(lines)\n",
    "    return tokenizer\n",
    "\n",
    "# calculate the length of the description with the most words\n",
    "def max_length(descriptions):\n",
    "    lines = to_lines(descriptions)\n",
    "    return max(len(d.split()) for d in lines)\n",
    "\n",
    "# create sequences of images, input sequences and output words for an image\n",
    "def create_sequences(tokenizer, max_length, desc_list, photo):\n",
    "    X1, X2, y = list(), list(), list()\n",
    "    # walk through each description for the image\n",
    "    for desc in desc_list:\n",
    "        # encode the sequence\n",
    "        seq = tokenizer.texts_to_sequences([desc])[0]\n",
    "        # split one sequence into multiple X,y pairs\n",
    "        for i in range(1, len(seq)):\n",
    "            # split into input and output pair\n",
    "            in_seq, out_seq = seq[:i], seq[i]\n",
    "            # pad input sequence\n",
    "            in_seq = pad_sequences([in_seq], maxlen=max_length)[0]\n",
    "                # encode output sequence\n",
    "            out_seq = to_categorical([out_seq], num_classes=vocab_size)[0]\n",
    "            # store\n",
    "            X1.append(photo)\n",
    "            X2.append(in_seq)\n",
    "            y.append(out_seq)\n",
    "    return array(X1), array(X2), array(y)\n",
    " \n",
    "# define the captioning model\n",
    "def define_model(vocab_size, max_length):\n",
    "    # feature extractor model\n",
    "    inputs1 = Input(shape=(4096,))\n",
    "    fe1 = Dropout(0.5)(inputs1)\n",
    "    fe2 = Dense(256, activation='relu')(fe1)\n",
    "    # sequence model\n",
    "    inputs2 = Input(shape=(max_length,))\n",
    "    se1 = Embedding(vocab_size, 256, mask_zero=True)(inputs2)\n",
    "    se2 = Dropout(0.5)(se1)\n",
    "    se3 = LSTM(256)(se2)\n",
    "    # decoder model\n",
    "    decoder1 = add([fe2, se3])\n",
    "    decoder2 = Dense(256, activation='relu')(decoder1)\n",
    "    outputs = Dense(vocab_size, activation='softmax')(decoder2)\n",
    "    # tie it together [image, seq] [word]\n",
    "    model = Model(inputs=[inputs1, inputs2], outputs=outputs)\n",
    "    # compile model\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam')\n",
    "    # summarize model\n",
    "    model.summary()\n",
    "    return model\n",
    "# data generator, intended to be used in a call to model.fit_generator()\n",
    "def data_generator(descriptions, photos, tokenizer, max_length):\n",
    "    # loop for ever over images\n",
    "    while 1:\n",
    "        for key, desc_list in descriptions.items():\n",
    "            # retrieve the photo feature\n",
    "            photo = photos[key][0]\n",
    "            in_img, in_seq, out_word = create_sequences(tokenizer, max_length, desc_list, photo)\n",
    "            yield [[in_img, in_seq], out_word]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getPlot(model):\n",
    "    plot_model(model, to_file='model.png', show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: 6000\n",
      "Descriptions: train=6000\n",
      "Photos: train=6000\n",
      "Vocabulary Size: 7579\n",
      "Description Length: 34\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_6 (InputLayer)            (None, 34)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_5 (InputLayer)            (None, 4096)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_2 (Embedding)         (None, 34, 256)      1940224     input_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 4096)         0           input_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_5 (Dropout)             (None, 34, 256)      0           embedding_2[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 256)          1048832     dropout_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lstm_2 (LSTM)                   (None, 256)          525312      dropout_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 256)          0           dense_5[0][0]                    \n",
      "                                                                 lstm_2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense_6 (Dense)                 (None, 256)          65792       add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_7 (Dense)                 (None, 7579)         1947803     dense_6[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 5,527,963\n",
      "Trainable params: 5,527,963\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "ename": "ImportError",
     "evalue": "Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mc:\\users\\admin\\anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self, prog, format, encoding)\u001b[0m\n\u001b[0;32m   1914\u001b[0m                 \u001b[0marguments\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marguments\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1915\u001b[1;33m                 \u001b[0mworking_dir\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtmp_dir\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1916\u001b[0m             )\n",
      "\u001b[1;32mc:\\users\\admin\\anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mcall_graphviz\u001b[1;34m(program, arguments, working_dir, **kwargs)\u001b[0m\n\u001b[0;32m    135\u001b[0m         \u001b[0mstdout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msubprocess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPIPE\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 136\u001b[1;33m         \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    137\u001b[0m     )\n",
      "\u001b[1;32mc:\\users\\admin\\anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, encoding, errors)\u001b[0m\n\u001b[0;32m    706\u001b[0m                                 \u001b[0merrread\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrwrite\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 707\u001b[1;33m                                 restore_signals, start_new_session)\n\u001b[0m\u001b[0;32m    708\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\admin\\anaconda3\\lib\\subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[1;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_start_new_session)\u001b[0m\n\u001b[0;32m    991\u001b[0m                                          \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfspath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcwd\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcwd\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 992\u001b[1;33m                                          startupinfo)\n\u001b[0m\u001b[0;32m    993\u001b[0m             \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] The system cannot find the file specified",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mc:\\users\\admin\\anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36m_check_pydot\u001b[1;34m()\u001b[0m\n\u001b[0;32m     26\u001b[0m         \u001b[1;31m# to check the pydot/graphviz installation.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m         \u001b[0mpydot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpydot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     28\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\admin\\anaconda3\\lib\\site-packages\\pydot.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self, prog, format, encoding)\u001b[0m\n\u001b[0;32m   1921\u001b[0m                     prog=prog)\n\u001b[1;32m-> 1922\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1923\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] \"dot\" not found in path.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-03f283591847>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;31m# define the model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdefine_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvocab_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_len\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[0mgetPlot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-10-fea5fbfc11fc>\u001b[0m in \u001b[0;36mgetPlot\u001b[1;34m(model)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mgetPlot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mplot_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mto_file\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'model.png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow_shapes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\admin\\anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36mplot_model\u001b[1;34m(model, to_file, show_shapes, show_layer_names, rankdir)\u001b[0m\n\u001b[0;32m    133\u001b[0m             \u001b[1;34m'LR'\u001b[0m \u001b[0mcreates\u001b[0m \u001b[0ma\u001b[0m \u001b[0mhorizontal\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \"\"\"\n\u001b[1;32m--> 135\u001b[1;33m     \u001b[0mdot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_to_dot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow_shapes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow_layer_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrankdir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    136\u001b[0m     \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextension\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplitext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mto_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mextension\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\admin\\anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36mmodel_to_dot\u001b[1;34m(model, show_shapes, show_layer_names, rankdir)\u001b[0m\n\u001b[0;32m     54\u001b[0m     \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m     \u001b[0m_check_pydot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m     \u001b[0mdot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpydot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[0mdot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'rankdir'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrankdir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\admin\\anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36m_check_pydot\u001b[1;34m()\u001b[0m\n\u001b[0;32m     29\u001b[0m         \u001b[1;31m# pydot raises a generic Exception here,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     30\u001b[0m         \u001b[1;31m# so no specific class can be caught.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 31\u001b[1;33m         raise ImportError('Failed to import pydot. You must install pydot'\n\u001b[0m\u001b[0;32m     32\u001b[0m                           ' and graphviz for `pydotprint` to work.')\n\u001b[0;32m     33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work."
     ]
    }
   ],
   "source": [
    "filename = 'Flickr8k_text/Flickr_8k.trainImages.txt'\n",
    "train = load_set(filename)\n",
    "print('Dataset: %d' % len(train))\n",
    "# descriptions\n",
    "train_descriptions = load_clean_descriptions('descriptions.txt', train)\n",
    "print('Descriptions: train=%d' % len(train_descriptions))\n",
    "# photo features\n",
    "train_features = load_photo_features('features.pkl', train)\n",
    "print('Photos: train=%d' % len(train_features))\n",
    "# prepare tokenizer\n",
    "tokenizer = create_tokenizer(train_descriptions)\n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "print('Vocabulary Size: %d' % vocab_size)\n",
    "# determine the maximum sequence length\n",
    "max_len = max_length(train_descriptions)\n",
    "print('Description Length: %d' % max_len)\n",
    " \n",
    "# define the model\n",
    "model = define_model(vocab_size, max_len)\n",
    "getPlot(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train the model, run epochs manually and save after each epoch\n",
    "epochs = 12\n",
    "steps = len(train_descriptions)\n",
    "for i in range(epochs):\n",
    "    # create the data generator\n",
    "    generator = data_generator(train_descriptions, train_features, tokenizer, max_length)\n",
    "    # fit for one epoch\n",
    "    model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)\n",
    "    # save model\n",
    "    model.save('TrainedModels/model_' + str(i) + '.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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