Keras Implementation Pre-Trained CNN TEST DANIEL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "import cv2, numpy as np\n",
    "import time\n",
    "import theano\n",
    "import os\n",
    "from collections import OrderedDict, defaultdict\n",
    "import six.moves.cPickle as pkl\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten, LSTM\n",
    "from keras.layers import Embedding\n",
    "from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D \n",
    "from keras.optimizers import SGD\n",
    "from keras.layers import GRU, TimeDistributed, RepeatVector, Merge, TimeDistributedDense\n",
    "import h5py\n",
    "import json\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "import skimage.transform\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MEAN_VALUES = np.array([104, 117, 123]).reshape((3,1,1))\n",
    "SEQUENCE_LENGTH = 32\n",
    "MAX_SENTENCE_LENGTH = SEQUENCE_LENGTH - 3 # 1 for image, 1 for start token, 1 for end token\n",
    "BATCH_SIZE = 200\n",
    "CNN_FEATURE_SIZE = 1000\n",
    "EMBEDDING_SIZE = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def word_processing(dataset):\n",
    "    allwords = Counter()\n",
    "    for item in dataset:\n",
    "        for sentence in item['sentences']:\n",
    "            allwords.update(sentence['tokens'])\n",
    "            \n",
    "    vocab = [k for k, v in allwords.items() if v >= 5]\n",
    "    vocab.insert(0, '<NULL>')\n",
    "    vocab.append('<UNK>')\n",
    "    vocab.append('<START>')\n",
    "    vocab.append('<END>')\n",
    "\n",
    "    word_to_index = {w: i for i, w in enumerate(vocab)}\n",
    "    index_to_word = {i: w for i, w in enumerate(vocab)}\n",
    "    return vocab, word_to_index, index_to_word\n",
    "\n",
    "def import_flickr8kdataset():\n",
    "    dataset = json.load(open('captions/dataset_flickr8k.json'))['images']\n",
    "    #reduced length to a 300 for testing\n",
    "    val_set = list(filter(lambda x: x['split'] == 'val', dataset))\n",
    "    train_set = list(filter(lambda x: x['split'] == 'train', dataset))\n",
    "    test_set = list(filter(lambda x: x['split'] == 'test', dataset))\n",
    "    return train_set[:800]+val_set[:200]\n",
    "\n",
    "\n",
    "# In[101]:\n",
    "\n",
    "def floatX(arr):\n",
    "    return np.asarray(arr, dtype=theano.config.floatX)\n",
    "\n",
    "#Prep Image uses an skimage transform\n",
    "def prep_image(im):\n",
    "    if len(im.shape) == 2:\n",
    "        im = im[:, :, np.newaxis]\n",
    "        im = np.repeat(im, 3, axis=2)\n",
    "    # Resize so smallest dim = 224, preserving aspect ratio\n",
    "    h, w, _ = im.shape\n",
    "    if h < w:\n",
    "        im = skimage.transform.resize(im, (224, w*224//h), preserve_range=True)\n",
    "    else:\n",
    "        im = skimage.transform.resize(im, (h*224//w, 224), preserve_range=True)\n",
    "\n",
    "    # Central crop to 224x224\n",
    "    h, w, _ = im.shape\n",
    "    im = im[h//2-112:h//2+112, w//2-112:w//2+112]\n",
    "    \n",
    "    rawim = np.copy(im).astype('uint8')\n",
    "    \n",
    "    # Shuffle axes to c01\n",
    "    im = np.swapaxes(np.swapaxes(im, 1, 2), 0, 1)\n",
    "    \n",
    "    # Convert to BGR\n",
    "    im = im[::-1, :, :]\n",
    "\n",
    "    im = im - MEAN_VALUES\n",
    "    return rawim, floatX(im[np.newaxis])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def VGG_16(weights_path=None):\n",
    "    model = Sequential()\n",
    "    model.add(ZeroPadding2D((1,1),input_shape=(3,224,224)))\n",
    "    model.add(Convolution2D(64, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(64, 3, 3, activation='relu'))\n",
    "    model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    "\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(128, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(128, 3, 3, activation='relu'))\n",
    "    model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    "\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(256, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(256, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(256, 3, 3, activation='relu'))\n",
    "    model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    "\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    "\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(ZeroPadding2D((1,1)))\n",
    "    model.add(Convolution2D(512, 3, 3, activation='relu'))\n",
    "    model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    "\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(4096, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(4096, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(1000, activation='softmax'))\n",
    "\n",
    "    if weights_path:\n",
    "        model.load_weights(weights_path)\n",
    "\n",
    "    return model\n",
    "\n",
    "\n",
    "# In[125]:\n",
    "\n",
    "def language_model():\n",
    "    model = Sequential()\n",
    "    print('Adding Embedding')\n",
    "    model.add(Embedding(VOCAB_COUNT, EMBEDDING_SIZE, input_length=SEQUENCE_LENGTH-1))\n",
    "    print('Adding LSTM')\n",
    "    model.add(LSTM(EMBEDDING_SIZE, return_sequences=True))\n",
    "    print('Adding TimeDistributed Dense')\n",
    "    model.add(TimeDistributed(Dense(EMBEDDING_SIZE)))\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = import_flickr8kdataset()\n",
    "# Currently testing it out\n",
    "#dataset = [i for i in dataset[:100]]\n",
    "vocab,word_to_index, index_to_word = word_processing(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def chunks(l, n):\n",
    "    for i in range(0, len(l), n):\n",
    "        yield l[i:i + n]\n",
    "\n",
    "def process_images(dataset, coco=False, d_set=\"Flicker8k_Dataset\"):\n",
    "    ind_process = 1\n",
    "    total = len(dataset)\n",
    "    cnn_input = floatX(np.zeros((len(dataset), 3, 224, 224)))\n",
    "    rawim_input = []\n",
    "    sentences_tokens = []\n",
    "    for i, image in enumerate(dataset):\n",
    "        #print(\"ind_process %s total %s\" %(str(ind_process),str(total)))\n",
    "        ind_process+=1\n",
    "        if coco:\n",
    "            fn = './coco/{}/{}'.format(image['filepath'], image['filename'])\n",
    "        else:\n",
    "            fn = d_set+'/{}'.format(image['filename'])\n",
    "        try:\n",
    "            im = plt.imread(fn)\n",
    "            rawim, cnn_input[i] = prep_image(im)\n",
    "            sentences_tokens.append(image['sentences'][0]['tokens'])\n",
    "            rawim_input.append(rawim)\n",
    "        except IOError:\n",
    "            continue\n",
    "    return rawim_input, cnn_input, sentences_tokens\n",
    "\n",
    "def process_cnn_features(dataset, model, coco=False, d_set=\"Flicker8k_Dataset\"):\n",
    "    ind_process = 1\n",
    "    total = len(dataset)\n",
    "    for chunk in chunks(dataset, 25):\n",
    "        cnn_input = floatX(np.zeros((len(chunk), 3, 224, 224)))\n",
    "        for i, image in enumerate(chunk):\n",
    "            #print(\"ind_process %s total %s\" %(str(ind_process),str(total)))\n",
    "            ind_process+=1\n",
    "            if coco:\n",
    "                fn = './coco/{}/{}'.format(image['filepath'], image['filename'])\n",
    "            else:\n",
    "                fn = d_set+'/{}'.format(image['filename'])\n",
    "            try:\n",
    "                im = plt.imread(fn)\n",
    "                _, cnn_input[i] = prep_image(im)\n",
    "            except IOError:\n",
    "                continue\n",
    "        features = model.predict(cnn_input)\n",
    "        print(features.shape)\n",
    "        print(features[0].shape)\n",
    "        print(\"Processing Features For Chunk\")\n",
    "        for i, image in enumerate(chunk):\n",
    "            image['cnn features'] = features[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/danielsampetethiyagu/anaconda/lib/python2.7/site-packages/keras/backend/theano_backend.py:1500: UserWarning: DEPRECATION: the 'ds' parameter is not going to exist anymore as it is going to be replaced by the parameter 'ws'.\n",
      "  mode='max')\n",
      "/Users/danielsampetethiyagu/anaconda/lib/python2.7/site-packages/keras/backend/theano_backend.py:1500: UserWarning: DEPRECATION: the 'st' parameter is not going to exist anymore as it is going to be replaced by the parameter 'stride'.\n",
      "  mode='max')\n",
      "/Users/danielsampetethiyagu/anaconda/lib/python2.7/site-packages/keras/backend/theano_backend.py:1500: UserWarning: DEPRECATION: the 'padding' parameter is not going to exist anymore as it is going to be replaced by the parameter 'pad'.\n",
      "  mode='max')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "image_model = VGG_16('weights/vgg16_weights.h5')\n",
    "rawim_array, cnnim_array, sentences_tokens = process_images(dataset, coco=False, d_set=\"Flicker8k_Dataset\")\n",
    "process_cnn_features(dataset, image_model, False, \"Flicker8k_Dataset\")\n",
    "pkl.dump(dataset, open('flickr8k_100_with_cnn_features.pkl','wb'), protocol=pkl.HIGHEST_PROTOCOL)\n",
    "#get_ipython().magic(u'matplotlib inline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gen_image_partial_captions(images, captions, word_to_index, vocab_count):\n",
    "    a_features = []\n",
    "    a_captions = []\n",
    "    next_words = []\n",
    "    #vocab_size = len(vocab)\n",
    "    for ind, image in enumerate(dataset):\n",
    "        sentence = captions[ind]\n",
    "        partial_caption_ar = np.zeros(SEQUENCE_LENGTH-1, dtype=np.int)\n",
    "        \n",
    "        words = ['<START>'] + sentence + ['<END>']\n",
    "        #assert len(words)<SEQUENCE_LENGTH\n",
    "        for i in range(len(words) - 1):\n",
    "            if i >= SEQUENCE_LENGTH-1:\n",
    "                continue\n",
    "            pc_copy = partial_caption_ar.copy()\n",
    "            if words[i] in word_to_index:\n",
    "                pc_copy[i] = word_to_index[words[i]]\n",
    "            else:\n",
    "                pc_copy[i] = word_to_index[\"<UNK>\"]\n",
    "            a_features.append(image['cnn features'])\n",
    "            a_captions.append(pc_copy)\n",
    "            #Generate next word output vector\n",
    "            next_word = words[i + 1]\n",
    "            if next_word in word_to_index:\n",
    "                next_word_index = word_to_index[next_word]\n",
    "            else:\n",
    "                next_word_index = word_to_index[\"<UNK>\"]\n",
    "            next_word_ar = np.zeros(vocab_count, dtype=np.int)\n",
    "            next_word_ar[next_word_index] = 1\n",
    "            next_words.append(next_word_ar)\n",
    "    v_i = np.array(a_features)\n",
    "    print(v_i.shape)\n",
    "    v_c = np.array(a_captions)\n",
    "    v_nw = np.array(next_words)\n",
    "    return v_i, v_c, v_nw "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 3, 224, 224)\n",
      "(12200, 1000)\n"
     ]
    }
   ],
   "source": [
    "vocab_count = len(word_to_index)\n",
    "print(cnnim_array.shape)\n",
    "v_i, v_c, v_nw = gen_image_partial_captions(cnnim_array, sentences_tokens, word_to_index, vocab_count)\n",
    "\n",
    "\n",
    "# In[156]:\n",
    "\n",
    "VOCAB_COUNT = len(word_to_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_model(weights_path):\n",
    "    #image_model = VGG_16(weights_path)\n",
    "    #image_model.add(Dense(EMBEDDING_SIZE, activation='tanh'))\n",
    "    #image_model.add(RepeatVector(SEQUENCE_LENGTH-1))\n",
    "    print('Built Image Model')\n",
    "    print('Building Language Model')\n",
    "    image_model = Sequential()\n",
    "    image_model.add(Dense(CNN_FEATURE_SIZE, input_dim=CNN_FEATURE_SIZE))\n",
    "    image_model.add(RepeatVector(SEQUENCE_LENGTH-1))\n",
    "    lang_model = language_model()\n",
    "    #model = lang_model\n",
    "    model = Sequential()\n",
    "    model.add(Merge([image_model, lang_model], mode='concat',  concat_axis=-1))\n",
    "    #model.add(Merge([image_model, lang_model], mode='concat',  concat_axis=-1))\n",
    "    model.add(LSTM(EMBEDDING_SIZE, return_sequences=False))\n",
    "    #print(vocab_size)\n",
    "    model.add(Dense(VOCAB_COUNT, activation='softmax'))\n",
    "\n",
    "    print(model.summary())\n",
    "    return model\n",
    "\n",
    "def predict(model, images, index_to_word, word_to_index):\n",
    "    for ind, image in enumerate(dataset):\n",
    "        caption = np.zeros(SEQUENCE_LENGTH - 1).reshape(1, SEQUENCE_LENGTH - 1)\n",
    "        #print(caption.shape)\n",
    "        caption[0,0] = 0\n",
    "        count=0\n",
    "        sentence = []\n",
    "        #a = image.reshape(1,3,224,224)\n",
    "        #a = np.array([image])\n",
    "        f = image['cnn features'].reshape(1, CNN_FEATURE_SIZE)\n",
    "        while True:\n",
    "            out = model.predict([f, caption])\n",
    "            index = out.argmax(-1)\n",
    "            #print(index)\n",
    "            index = index[0]\n",
    "            word = index_to_word[index]\n",
    "            sentence.append(word)\n",
    "            count+= 1\n",
    "            if count >= SEQUENCE_LENGTH - 1 or index == word_to_index[\"<END>\"]: #max caption length reach of '<eos>' encountered\n",
    "                break\n",
    "            caption[0,count] = index\n",
    "        sent_str = \" \".join(sentence)\n",
    "        print(\"The Oracle says : %s\" %sent_str)\n",
    "\n",
    "# In[158]:\n",
    "\n",
    "def train():\n",
    "    model=build_model('weights/vgg16_weights.h5')\n",
    "    print('Built model.')\n",
    "    print('Compiling Now')\n",
    "    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    print('Fitting Now')\n",
    "    model.fit([v_i, v_c], v_nw, batch_size=BATCH_SIZE, nb_epoch=10)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Built Image Model\n",
      "Building Language Model\n",
      "Adding Embedding\n",
      "Adding LSTM\n",
      "Adding TimeDistributed Dense\n",
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "dense_4 (Dense)                  (None, 1000)          1001000     dense_input_1[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "repeatvector_1 (RepeatVector)    (None, 31, 1000)      0           dense_4[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)          (None, 31, 256)       235776      embedding_input_1[0][0]          \n",
      "____________________________________________________________________________________________________\n",
      "lstm_1 (LSTM)                    (None, 31, 256)       525312      embedding_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "timedistributed_1 (TimeDistribute(None, 31, 256)       65792       lstm_1[0][0]                     \n",
      "____________________________________________________________________________________________________\n",
      "lstm_2 (LSTM)                    (None, 256)           1549312     merge_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_6 (Dense)                  (None, 921)           236697      lstm_2[0][0]                     \n",
      "====================================================================================================\n",
      "Total params: 3613889\n",
      "____________________________________________________________________________________________________\n",
      "None\n",
      "Built model.\n",
      "Compiling Now\n",
      "Fitting Now\n",
      "Epoch 1/10\n",
      "12200/12200 [==============================] - 209s - loss: 5.0264 - acc: 0.1506   \n",
      "Epoch 2/10\n",
      "12200/12200 [==============================] - 211s - loss: 4.7705 - acc: 0.1546   \n",
      "Epoch 3/10\n",
      "12200/12200 [==============================] - 1216s - loss: 4.7705 - acc: 0.1546   \n",
      "Epoch 4/10\n",
      "12200/12200 [==============================] - 2211s - loss: 4.7664 - acc: 0.1546   \n",
      "Epoch 5/10\n",
      "12200/12200 [==============================] - 240s - loss: 4.7660 - acc: 0.1546   \n",
      "Epoch 6/10\n",
      "12200/12200 [==============================] - 238s - loss: 4.7653 - acc: 0.1546   \n",
      "Epoch 7/10\n",
      "12200/12200 [==============================] - 204s - loss: 4.7653 - acc: 0.1546   \n",
      "Epoch 8/10\n",
      "12200/12200 [==============================] - 210s - loss: 4.7651 - acc: 0.1546   \n",
      "Epoch 9/10\n",
      "12200/12200 [==============================] - 228s - loss: 4.7649 - acc: 0.1546   \n",
      "Epoch 10/10\n",
      "12200/12200 [==============================] - 208s - loss: 4.7641 - acc: 0.1546   \n",
      "Trained on 1000 images, saved weights to weights_20161207-222331.hf5\n",
      "(1000, 3, 224, 224)\n"
     ]
    }
   ],
   "source": [
    "model = train()\n",
    "timestr = time.strftime(\"%Y%m%d-%H%M%S\")\n",
    "file_name = 'weights_'+timestr+'.hf5'\n",
    "#model.save_weights(file_name)\n",
    "print('Trained on %s images, saved weights to %s'%(len(cnnim_array), file_name))\n",
    "print(cnnim_array.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
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      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n",
      "The Oracle says : a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n"
     ]
    }
   ],
   "source": [
    "predict(model, cnnim_array, index_to_word, word_to_index)"
   ]
  }
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