Image CAPTION - Working Sample Ipython Notebook-Attention.ipynb

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    {
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     "output_type": "stream",
     "text": [
      "Using Theano backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\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 text_processor_utils as tp_utils\n",
    "import cv2\n",
    "import numpy as np\n",
    "import theano\n",
    "from keras.preprocessing import sequence\n",
    "import pickle\n",
    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
    "import image_classes as img_class\n",
    "import json\n",
    "from collections import Counter\n",
    "import random\n",
    "import lasagne\n",
    "import theano.tensor as T\n",
    "import matplotlib.pyplot as plt\n",
    "import skimage.transform\n",
    "\n",
    "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 = 20\n",
    "CNN_FEATURE_SIZE = 1000\n",
    "EMBEDDING_SIZE = 256\n",
    "\n",
    "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, '#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,val_set,test_set\n",
    "    \n",
    "\n",
    "def pop(model):\n",
    "    '''Removes a layer instance on top of the layer stack.\n",
    "    This code is thanks to @joelthchao https://github.com/fchollet/keras/issues/2371#issuecomment-211734276\n",
    "    '''\n",
    "    if not model.outputs:\n",
    "        raise Exception('Sequential model cannot be popped: model is empty.')\n",
    "    else:\n",
    "        model.layers.pop()\n",
    "        if not model.layers:\n",
    "            model.outputs = []\n",
    "            model.inbound_nodes = []\n",
    "            model.outbound_nodes = []\n",
    "        else:\n",
    "            model.layers[-1].outbound_nodes = []\n",
    "            model.outputs = [model.layers[-1].output]\n",
    "        model.built = False\n",
    "\n",
    "    return model\n",
    "\n",
    "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",
    "\n",
    "    return model\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])\n",
    "\n",
    "def chunks(l, n):\n",
    "    for i in xrange(0, len(l), n):\n",
    "        yield l[i:i + n]\n",
    "\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",
    "        rawim_input = floatX(np.zeros((len(chunk), 224, 224,3)))\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",
    "                rawim_input[i], cnn_input[i] = prep_image(im)\n",
    "            except IOError:\n",
    "                continue\n",
    "        features = model.predict(cnn_input)\n",
    "        print \"Processing Features For Chunk\"\n",
    "        for i, image in enumerate(chunk):\n",
    "            image['cnn features'] = features[i]\n",
    "            image['raw image'] = rawim_input[i]\n",
    "\n",
    "def get_data_batch(dataset, size, split='train'):\n",
    "    items = []\n",
    "    \n",
    "    while len(items) < size:\n",
    "        item = random.choice(dataset)\n",
    "        if item['split'] != split:\n",
    "            continue\n",
    "        sentence = random.choice(item['sentences'])['tokens']\n",
    "        if len(sentence) > MAX_SENTENCE_LENGTH:\n",
    "            continue\n",
    "        items.append((item['cnn features'], sentence, item['filename']))\n",
    "    \n",
    "    return items\n",
    "\n",
    "def calc_cross_ent(net_output, mask, targets):\n",
    "    # Helper function to calculate the cross entropy error\n",
    "    preds = T.reshape(net_output, (-1, len(vocab)))\n",
    "    targets = T.flatten(targets)\n",
    "    cost = T.nnet.categorical_crossentropy(preds, targets)[T.flatten(mask).nonzero()]\n",
    "    return cost\n",
    "\n",
    "\n",
    "# Convert a list of tuples into arrays that can be fed into the network\n",
    "def prep_batch_for_network(batch,word_to_index):\n",
    "    x_cnn = floatX(np.zeros((len(batch), 1000)))\n",
    "    x_sentence = np.zeros((len(batch), SEQUENCE_LENGTH - 1), dtype='int32')\n",
    "    y_sentence = np.zeros((len(batch), SEQUENCE_LENGTH), dtype='int32')\n",
    "    mask = np.zeros((len(batch), SEQUENCE_LENGTH), dtype='bool')\n",
    "\n",
    "    for j, (cnn_features, sentence, _) in enumerate(batch):\n",
    "        x_cnn[j] = cnn_features\n",
    "        i = 0\n",
    "        for word in ['#START#'] + sentence + ['#END#']:\n",
    "            if word in word_to_index:\n",
    "                mask[j, i] = True\n",
    "                y_sentence[j, i] = word_to_index[word]\n",
    "                x_sentence[j, i] = word_to_index[word]\n",
    "                i += 1\n",
    "        #mask[j, 0] = False\n",
    "                \n",
    "    return x_cnn, x_sentence, y_sentence, mask\n",
    "\n",
    "def predict(x_cnn):\n",
    "    x_sentence = np.zeros((BATCH_SIZE, SEQUENCE_LENGTH - 1), dtype='int32')\n",
    "    words = []\n",
    "    i = 0\n",
    "    while True:\n",
    "        i += 1\n",
    "        p0 = f(x_cnn, x_sentence)\n",
    "        pa = p0.argmax(-1)\n",
    "        tok = pa[0][i]\n",
    "        word = index_to_word[tok]\n",
    "        if word == '#END#' or i >= SEQUENCE_LENGTH - 1:\n",
    "            return ' '.join(words)\n",
    "        else:\n",
    "            x_sentence[0][i] = tok\n",
    "            if word != '#START#':\n",
    "                words.append(word)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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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"
     ]
    }
   ],
   "source": [
    "model = VGG_16('weights/vgg16_weights.h5')\n",
    "sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "model.compile(optimizer=sgd, loss='categorical_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "dataset = json.load(open('captions/dataset_flickr8k.json'))['images']\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",
    "dataset = train_set+val_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "process_cnn_features(dataset, model, False, \"Flicker8k_Dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(dataset, open('flickr8k_Entire_dataset_with_cnn_features.pkl','w'), protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2763\n"
     ]
    }
   ],
   "source": [
    "vocab,wrd_to_ind, ind_to_wrd = word_processing(dataset)\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 = 20\n",
    "CNN_FEATURE_SIZE = 1000\n",
    "EMBEDDING_SIZE = 256\n",
    "print len(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# sentence embedding maps integer sequence with dim (BATCH_SIZE, SEQUENCE_LENGTH - 1) to \n",
    "# (BATCH_SIZE, SEQUENCE_LENGTH-1, EMBEDDING_SIZE)\n",
    "l_input_sentence = lasagne.layers.InputLayer((BATCH_SIZE, SEQUENCE_LENGTH - 1))\n",
    "l_sentence_embedding = lasagne.layers.EmbeddingLayer(l_input_sentence,\n",
    "                                                     input_size=len(vocab),\n",
    "                                                     output_size=EMBEDDING_SIZE,\n",
    "                                                    )\n",
    "\n",
    "# cnn embedding changes the dimensionality of the representation from 1000 to EMBEDDING_SIZE, \n",
    "# and reshapes to add the time dimension - final dim (BATCH_SIZE, 1, EMBEDDING_SIZE)\n",
    "l_input_cnn = lasagne.layers.InputLayer((BATCH_SIZE, CNN_FEATURE_SIZE))\n",
    "l_cnn_embedding = lasagne.layers.DenseLayer(l_input_cnn, num_units=EMBEDDING_SIZE,\n",
    "                                            nonlinearity=lasagne.nonlinearities.identity)\n",
    "\n",
    "l_cnn_embedding = lasagne.layers.ReshapeLayer(l_cnn_embedding, ([0], 1, [1]))\n",
    "\n",
    "# the two are concatenated to form the RNN input with dim (BATCH_SIZE, SEQUENCE_LENGTH, EMBEDDING_SIZE)\n",
    "l_rnn_input = lasagne.layers.ConcatLayer([l_cnn_embedding, l_sentence_embedding])\n",
    "\n",
    "l_dropout_input = lasagne.layers.DropoutLayer(l_rnn_input, p=0.5)\n",
    "l_lstm = lasagne.layers.LSTMLayer(l_dropout_input,\n",
    "                                  num_units=EMBEDDING_SIZE,\n",
    "                                  unroll_scan=True,\n",
    "                                  grad_clipping=5.)\n",
    "l_dropout_output = lasagne.layers.DropoutLayer(l_lstm, p=0.5)\n",
    "\n",
    "# the RNN output is reshaped to combine the batch and time dimensions\n",
    "# dim (BATCH_SIZE * SEQUENCE_LENGTH, EMBEDDING_SIZE)\n",
    "l_shp = lasagne.layers.ReshapeLayer(l_dropout_output, (-1, EMBEDDING_SIZE))\n",
    "\n",
    "# decoder is a fully connected layer with one output unit for each word in the vocabulary\n",
    "l_decoder = lasagne.layers.DenseLayer(l_shp, num_units=len(vocab), nonlinearity=lasagne.nonlinearities.softmax)\n",
    "\n",
    "# finally, the separation between batch and time dimension is restored\n",
    "l_out = lasagne.layers.ReshapeLayer(l_decoder, (BATCH_SIZE, SEQUENCE_LENGTH, len(vocab)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# cnn feature vector\n",
    "x_cnn_sym = T.matrix()\n",
    "\n",
    "# sentence encoded as sequence of integer word tokens\n",
    "x_sentence_sym = T.imatrix()\n",
    "\n",
    "# mask defines which elements of the sequence should be predicted\n",
    "mask_sym = T.imatrix()\n",
    "\n",
    "# ground truth for the RNN output\n",
    "y_sentence_sym = T.imatrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output = lasagne.layers.get_output(l_out, {\n",
    "                l_input_sentence: x_sentence_sym,\n",
    "                l_input_cnn: x_cnn_sym\n",
    "                })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss = T.mean(calc_cross_ent(output, mask_sym, y_sentence_sym))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "MAX_GRAD_NORM = 15\n",
    "\n",
    "all_params = lasagne.layers.get_all_params(l_out, trainable=True)\n",
    "\n",
    "all_grads = T.grad(loss, all_params)\n",
    "all_grads = [T.clip(g, -5, 5) for g in all_grads]\n",
    "all_grads, norm = lasagne.updates.total_norm_constraint(\n",
    "    all_grads, MAX_GRAD_NORM, return_norm=True)\n",
    "\n",
    "updates = lasagne.updates.adam(all_grads, all_params, learning_rate=0.001)\n",
    "\n",
    "f_train = theano.function([x_cnn_sym, x_sentence_sym, mask_sym, y_sentence_sym],\n",
    "                          [loss, norm],\n",
    "                          updates=updates\n",
    "                         )\n",
    "\n",
    "f_val = theano.function([x_cnn_sym, x_sentence_sym, mask_sym, y_sentence_sym], loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "for iteration in range(2000):\n",
    "    print iteration\n",
    "    x_cnn, x_sentence, y_sentence, mask = prep_batch_for_network(get_data_batch(dataset, BATCH_SIZE),wrd_to_ind)\n",
    "    loss_train, norm = f_train(x_cnn, x_sentence, mask, y_sentence)\n",
    "    if not iteration % 250:\n",
    "        print('Iteration {}, loss_train: {}, norm: {}'.format(iteration, loss_train, norm))\n",
    "        try:\n",
    "            batch = get_data_batch(dataset, BATCH_SIZE, split='val')\n",
    "            x_cnn, x_sentence, y_sentence, mask = prep_batch_for_network(batch,wrd_to_ind)\n",
    "            loss_val = f_val(x_cnn, x_sentence, mask, y_sentence)\n",
    "            print('Val loss: {}'.format(loss_val))\n",
    "        except IndexError:\n",
    "            continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "param_values = lasagne.layers.get_all_param_values(l_out)\n",
    "d = {'param values': param_values,\n",
    "     'vocab': vocab,\n",
    "     'word_to_index': wrd_to_ind,\n",
    "     'index_to_word': ind_to_wrd,\n",
    "    }\n",
    "pickle.dump(d, open('flickr8k_lstm_final.pkl','w'), protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = theano.function([x_cnn_sym, x_sentence_sym], output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2016-12-09 10:52:56--  http://akhopecenter.org/wp-content/uploads/2013/05/Dog-and-Cat-Wallpaper-teddybear64-16834786-1280-800-1024x640.jpg\n",
      "Resolving akhopecenter.org... 209.236.122.74\n",
      "Connecting to akhopecenter.org|209.236.122.74|:80... connected.\n",
      "HTTP request sent, awaiting response... 304 Not Modified\n",
      "File ‘Dog-and-Cat-Wallpaper-teddybear64-16834786-1280-800-1024x640.jpg’ not modified on server. Omitting download.\n",
      "\n",
      "(224, 224, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2413f6b50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4+OmniCVEBkYdUXFfgNfTV+Y8c8kLl/VELmeauTV1UKWKYs1rH3xRJ8xAdcsS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\n99xzz5fcJ4R77rnnS/5vDZfkt3kyc5YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2413eb8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "!wget -N http://akhopecenter.org/wp-content/uploads/2013/05/Dog-and-Cat-Wallpaper-teddybear64-16834786-1280-800-1024x640.jpg\n",
    "im = plt.imread('Dog-and-Cat-Wallpaper-teddybear64-16834786-1280-800-1024x640.jpg')\n",
    "rawim, cnn_im = prep_image(im)\n",
    "print rawim.shape\n",
    "plt.imshow(rawim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1000)\n"
     ]
    }
   ],
   "source": [
    "x_cnn = model.predict(cnn_im)\n",
    "print x_cnn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "SEQUENCE_LENGTH = 32\n",
    "MAX_SENTENCE_LENGTH = SEQUENCE_LENGTH - 3 # 1 for image, 1 for start token, 1 for end token\n",
    "BATCH_SIZE = 1\n",
    "CNN_FEATURE_SIZE = 1000\n",
    "EMBEDDING_SIZE = 256\n",
    "\n",
    "d = pickle.load(open('flickr8k_lstm_check.pkl'))\n",
    "vocab = d['vocab']\n",
    "word_to_index = d['word_to_index']\n",
    "index_to_word = d['index_to_word']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l_input_sentence = lasagne.layers.InputLayer((BATCH_SIZE, SEQUENCE_LENGTH - 1))\n",
    "l_sentence_embedding = lasagne.layers.EmbeddingLayer(l_input_sentence,\n",
    "                                                     input_size=len(vocab),\n",
    "                                                     output_size=EMBEDDING_SIZE,\n",
    "                                                    )\n",
    "\n",
    "l_input_cnn = lasagne.layers.InputLayer((BATCH_SIZE, CNN_FEATURE_SIZE))\n",
    "l_cnn_embedding = lasagne.layers.DenseLayer(l_input_cnn, num_units=EMBEDDING_SIZE,\n",
    "                                            nonlinearity=lasagne.nonlinearities.identity)\n",
    "\n",
    "l_cnn_embedding = lasagne.layers.ReshapeLayer(l_cnn_embedding, ([0], 1, [1]))\n",
    "\n",
    "l_rnn_input = lasagne.layers.ConcatLayer([l_cnn_embedding, l_sentence_embedding])\n",
    "l_dropout_input = lasagne.layers.DropoutLayer(l_rnn_input, p=0.5)\n",
    "l_lstm = lasagne.layers.LSTMLayer(l_dropout_input,\n",
    "                                  num_units=EMBEDDING_SIZE,\n",
    "                                  unroll_scan=True,\n",
    "                                  grad_clipping=5.)\n",
    "l_dropout_output = lasagne.layers.DropoutLayer(l_lstm, p=0.5)\n",
    "l_shp = lasagne.layers.ReshapeLayer(l_dropout_output, (-1, EMBEDDING_SIZE))\n",
    "l_decoder = lasagne.layers.DenseLayer(l_shp, num_units=len(vocab), nonlinearity=lasagne.nonlinearities.softmax)\n",
    "\n",
    "l_out = lasagne.layers.ReshapeLayer(l_decoder, (BATCH_SIZE, SEQUENCE_LENGTH, len(vocab)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lasagne.layers.set_all_param_values(l_out, d['param values'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_cnn_sym = T.matrix()\n",
    "x_sentence_sym = T.imatrix()\n",
    "\n",
    "output = lasagne.layers.get_output(l_out, {\n",
    "                l_input_sentence: x_sentence_sym,\n",
    "                l_input_cnn: x_cnn_sym\n",
    "                })\n",
    "\n",
    "f = theano.function([x_cnn_sym, x_sentence_sym], output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a man in a blue shirt is sitting on a bench with a large blue and white and white shirt\n",
      "a man in a pink shirt and a black shirt is walking on a large tree\n",
      "a man in a red shirt and a black and white shirt is sitting on a rock\n",
      "a man in a red shirt and a pink shirt is jumping down a rock\n",
      "a man is sitting on a swing with a red ball\n",
      "a man is standing on a bench in a pool\n"
     ]
    }
   ],
   "source": [
    "x_cnn = np.reshape(dataset[1]['cnn features'], (1,1000))\n",
    "for _ in range(6):\n",
    "    print(predict((x_cnn)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cnn features': array([  8.41589998e-09,   3.18888098e-08,   5.94773581e-08,\n",
       "          1.34680349e-07,   5.59009152e-07,   5.02435995e-08,\n",
       "          2.87913409e-08,   3.16422643e-07,   4.71011759e-07,\n",
       "          1.63704699e-05,   5.04545845e-08,   3.01402210e-08,\n",
       "          1.73545772e-07,   2.87365314e-08,   3.70973687e-08,\n",
       "          3.02698652e-07,   3.40937760e-07,   7.39699644e-08,\n",
       "          8.50642834e-07,   1.02383908e-07,   8.51808082e-08,\n",
       "          4.70065800e-07,   3.04309833e-06,   1.10937754e-06,\n",
       "          1.28562112e-06,   6.56475976e-08,   3.00554461e-07,\n",
       "          5.61098602e-07,   2.35904185e-08,   1.75602523e-07,\n",
       "          9.52802814e-09,   3.71192392e-08,   8.47269490e-08,\n",
       "          1.25647404e-07,   1.49475881e-07,   5.19190202e-09,\n",
       "          4.43526780e-08,   6.13872331e-09,   3.86867532e-06,\n",
       "          5.82997984e-07,   2.10374651e-07,   3.08866106e-07,\n",
       "          3.79565961e-07,   2.09374690e-07,   4.47989748e-07,\n",
       "          2.64900677e-07,   1.89438595e-07,   5.67727483e-08,\n",
       "          1.38458446e-07,   6.04028219e-06,   6.86134172e-07,\n",
       "          4.24259596e-08,   7.19297333e-08,   2.04784385e-08,\n",
       "          2.83503397e-07,   7.41681339e-09,   9.44634166e-08,\n",
       "          6.53844001e-09,   1.66423845e-07,   8.98615937e-08,\n",
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       "          2.11517431e-06], dtype=float32),\n",
       " u'filename': u'2513260012_03d33305cf.jpg',\n",
       " u'imgid': 0,\n",
       " 'raw image': array([[[  90.,   76.,   59.],\n",
       "         [  74.,   60.,   51.],\n",
       "         [  70.,   57.,   51.],\n",
       "         ..., \n",
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       "         [  28.,   25.,   19.],\n",
       "         [  43.,   36.,   31.]],\n",
       " \n",
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       "         [  53.,   42.,   35.],\n",
       "         ..., \n",
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       " \n",
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       "         [  73.,   56.,   46.],\n",
       "         ..., \n",
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       "         [  56.,   46.,   34.]],\n",
       " \n",
       "        ..., \n",
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       "         ..., \n",
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       " \n",
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       "         ..., \n",
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       " \n",
       "        [[ 215.,  215.,  213.],\n",
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       "         [ 228.,  228.,  227.],\n",
       "         ..., \n",
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       "         [ 218.,  219.,  226.]]], dtype=float32),\n",
       " u'sentences': [{u'imgid': 0,\n",
       "   u'raw': u'A black dog is running after a white dog in the snow .',\n",
       "   u'sentid': 0,\n",
       "   u'tokens': [u'a',\n",
       "    u'black',\n",
       "    u'dog',\n",
       "    u'is',\n",
       "    u'running',\n",
       "    u'after',\n",
       "    u'a',\n",
       "    u'white',\n",
       "    u'dog',\n",
       "    u'in',\n",
       "    u'the',\n",
       "    u'snow']},\n",
       "  {u'imgid': 0,\n",
       "   u'raw': u'Black dog chasing brown dog through snow',\n",
       "   u'sentid': 1,\n",
       "   u'tokens': [u'black',\n",
       "    u'dog',\n",
       "    u'chasing',\n",
       "    u'brown',\n",
       "    u'dog',\n",
       "    u'through',\n",
       "    u'snow']},\n",
       "  {u'imgid': 0,\n",
       "   u'raw': u'Two dogs chase each other across the snowy ground .',\n",
       "   u'sentid': 2,\n",
       "   u'tokens': [u'two',\n",
       "    u'dogs',\n",
       "    u'chase',\n",
       "    u'each',\n",
       "    u'other',\n",
       "    u'across',\n",
       "    u'the',\n",
       "    u'snowy',\n",
       "    u'ground']},\n",
       "  {u'imgid': 0,\n",
       "   u'raw': u'Two dogs play together in the snow .',\n",
       "   u'sentid': 3,\n",
       "   u'tokens': [u'two', u'dogs', u'play', u'together', u'in', u'the', u'snow']},\n",
       "  {u'imgid': 0,\n",
       "   u'raw': u'Two dogs running through a low lying body of water .',\n",
       "   u'sentid': 4,\n",
       "   u'tokens': [u'two',\n",
       "    u'dogs',\n",
       "    u'running',\n",
       "    u'through',\n",
       "    u'a',\n",
       "    u'low',\n",
       "    u'lying',\n",
       "    u'body',\n",
       "    u'of',\n",
       "    u'water']}],\n",
       " u'sentids': [0, 1, 2, 3, 4],\n",
       " u'split': u'train'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0]"
   ]
  }
 ],
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