POET experiments and fixations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "import glob, os, sys\n",
    "import pickle\n",
    "from subprocess import call\n",
    "\n",
    "import numpy as np\n",
    "from keras import backend as K\n",
    "from keras.preprocessing import image\n",
    "import keras\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D\n",
    "from keras import optimizers\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from keras.applications.resnet50 import ResNet50 as resnet\n",
    "from keras.applications.resnet50 import preprocess_input as resnet_pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def save_object(obj, filename):\n",
    "    with open(filename, 'wb') as output:  # Overwrites any existing file.\n",
    "        pickle.dump(obj, output, pickle.HIGHEST_PROTOCOL)\n",
    "        \n",
    "def load_object(filename):\n",
    "    with open(filename, 'rb') as fp:\n",
    "        return pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Build a model by adding preprocessing before the pretrained CNN\n",
    "def get_feature_extraction_model():\n",
    "    cnn_object, pp_function, img_size = _get_pretrained_model()\n",
    "    model = keras.models.Sequential()\n",
    "    cnn_model = cnn_object(weights='imagenet', include_top=False)\n",
    "    model.add(keras.layers.Lambda(pp_function, name='preprocessing', input_shape=(img_size, img_size, 3)))\n",
    "    model.add(cnn_model)\n",
    "    return model\n",
    "\n",
    "# Unpacking information from the models dictionary\n",
    "def _get_pretrained_model():\n",
    "    cnn_object = resnet\n",
    "    pp_function = resnet_pp\n",
    "    img_size = 244\n",
    "    return cnn_object, pp_function, img_size\n",
    "\n",
    "def get_features(files, model):\n",
    "    # Load images based on the size of the Lambda layer \n",
    "    # provided as the first layer before the pretrained CNN\n",
    "    x = np.array([image.img_to_array(image.load_img(file, target_size=(model.layers[0].input_shape[1], model.layers[0].input_shape[1]))) for file in files])\n",
    "    return model.predict(x, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\keras\\applications\\resnet50.py:281: UserWarning: You are using the TensorFlow backend, yet you are using the Theano image data format convention (`image_data_format=\"channels_first\"`). For best performance, set `image_data_format=\"channels_last\"` in your Keras config at ~/.keras/keras.json.\n",
      "  warnings.warn('You are using the TensorFlow backend, yet you '\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Dimensions must be equal, but are 244 and 3 for 'preprocessing/add' (op: 'Add') with input shapes: [?,244,244,3], [1,3,1,1].",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\common_shapes.py\u001b[0m in \u001b[0;36m_call_cpp_shape_fn_impl\u001b[1;34m(op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn)\u001b[0m\n\u001b[0;32m    685\u001b[0m           \u001b[0mgraph_def_version\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnode_def_str\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_shapes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_tensors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 686\u001b[1;33m           input_tensors_as_shapes, status)\n\u001b[0m\u001b[0;32m    687\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, type_arg, value_arg, traceback_arg)\u001b[0m\n\u001b[0;32m    515\u001b[0m             \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc_api\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\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[1;32m--> 516\u001b[1;33m             c_api.TF_GetCode(self.status.status))\n\u001b[0m\u001b[0;32m    517\u001b[0m     \u001b[1;31m# Delete the underlying status object from memory otherwise it stays alive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInvalidArgumentError\u001b[0m: Dimensions must be equal, but are 244 and 3 for 'preprocessing/add' (op: 'Add') with input shapes: [?,244,244,3], [1,3,1,1].",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-ee4b6bff8db7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_feature_extraction_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-5-8765ca8a3ac0>\u001b[0m in \u001b[0;36mget_feature_extraction_model\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSequential\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mcnn_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcnn_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'imagenet'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minclude_top\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLambda\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpp_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'preprocessing'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimg_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\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      7\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcnn_model\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\keras\\models.py\u001b[0m in \u001b[0;36madd\u001b[1;34m(self, layer)\u001b[0m\n\u001b[0;32m    465\u001b[0m                 \u001b[1;31m# and create the node connecting the current layer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    466\u001b[0m                 \u001b[1;31m# to the input layer we just created.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 467\u001b[1;33m                 \u001b[0mlayer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    468\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    469\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_inbound_nodes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput_tensors\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\keras\\engine\\topology.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs, **kwargs)\u001b[0m\n\u001b[0;32m    617\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    618\u001b[0m             \u001b[1;31m# Actually call the layer, collecting output(s), mask(s), and shape(s).\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 619\u001b[1;33m             \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    620\u001b[0m             \u001b[0moutput_mask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_mask\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprevious_mask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    621\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\keras\\layers\\core.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, inputs, mask)\u001b[0m\n\u001b[0;32m    661\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhas_arg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'mask'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    662\u001b[0m             \u001b[0marguments\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'mask'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 663\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\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[0m\u001b[0;32m    664\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    665\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcompute_mask\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\keras\\applications\\imagenet_utils.py\u001b[0m in \u001b[0;36mpreprocess_input\u001b[1;34m(x, data_format, mode)\u001b[0m\n\u001b[0;32m    176\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    177\u001b[0m         return _preprocess_symbolic_input(x, data_format=data_format,\n\u001b[1;32m--> 178\u001b[1;33m                                           mode=mode)\n\u001b[0m\u001b[0;32m    179\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    180\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\keras\\applications\\imagenet_utils.py\u001b[0m in \u001b[0;36m_preprocess_symbolic_input\u001b[1;34m(x, data_format, mode)\u001b[0m\n\u001b[0;32m    141\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias_add\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_IMAGENET_MEAN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_format\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    142\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 143\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias_add\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_IMAGENET_MEAN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_format\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    144\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mstd\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    145\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m/=\u001b[0m \u001b[0mstd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36mbias_add\u001b[1;34m(x, bias, data_format)\u001b[0m\n\u001b[0;32m   3761\u001b[0m                                        data_format='NCHW')\n\u001b[0;32m   3762\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3763\u001b[1;33m                     \u001b[0mx\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias_shape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\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   3764\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3765\u001b[0m                 \u001b[0mx\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias_shape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mbias_shape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\u001b[0m in \u001b[0;36mbinary_op_wrapper\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m    969\u001b[0m           \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    970\u001b[0m             \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 971\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    973\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mbinary_op_wrapper_sparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msp_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\u001b[0m in \u001b[0;36madd\u001b[1;34m(x, y, name)\u001b[0m\n\u001b[0;32m    303\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0m_ctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecuting_eagerly\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[0;32m    304\u001b[0m     _, _, _op = _op_def_lib._apply_op_helper(\n\u001b[1;32m--> 305\u001b[1;33m         \"Add\", x=x, y=y, name=name)\n\u001b[0m\u001b[0;32m    306\u001b[0m     \u001b[0m_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\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[0;32m    307\u001b[0m     \u001b[0m_inputs_flat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[1;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[0;32m    785\u001b[0m         op = g.create_op(op_type_name, inputs, output_types, name=scope,\n\u001b[0;32m    786\u001b[0m                          \u001b[0minput_types\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mattr_protos\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 787\u001b[1;33m                          op_def=op_def)\n\u001b[0m\u001b[0;32m    788\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0moutput_structure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_def\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_stateful\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    789\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mcreate_op\u001b[1;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device)\u001b[0m\n\u001b[0;32m   3290\u001b[0m           op_def=op_def)\n\u001b[0;32m   3291\u001b[0m       self._create_op_helper(ret, compute_shapes=compute_shapes,\n\u001b[1;32m-> 3292\u001b[1;33m                              compute_device=compute_device)\n\u001b[0m\u001b[0;32m   3293\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3294\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m_create_op_helper\u001b[1;34m(self, op, compute_shapes, compute_device)\u001b[0m\n\u001b[0;32m   3330\u001b[0m     \u001b[1;31m# compute_shapes argument.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3331\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_c_op\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mcompute_shapes\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3332\u001b[1;33m       \u001b[0mset_shapes_for_outputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3333\u001b[0m     \u001b[1;31m# TODO(b/XXXX): move to Operation.__init__ once _USE_C_API flag is removed.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3334\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mset_shapes_for_outputs\u001b[1;34m(op)\u001b[0m\n\u001b[0;32m   2494\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_set_shapes_for_outputs_c_api\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2495\u001b[0m   \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2496\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_set_shapes_for_outputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2497\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2498\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m_set_shapes_for_outputs\u001b[1;34m(op)\u001b[0m\n\u001b[0;32m   2467\u001b[0m       \u001b[0mshape_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_call_cpp_shape_fn_and_require_op\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2468\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2469\u001b[1;33m   \u001b[0mshapes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mshape_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2470\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mshapes\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2471\u001b[0m     raise RuntimeError(\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mcall_with_requiring\u001b[1;34m(op)\u001b[0m\n\u001b[0;32m   2397\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2398\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mcall_with_requiring\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2399\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mcall_cpp_shape_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequire_shape_fn\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[0;32m   2400\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2401\u001b[0m   \u001b[0m_call_cpp_shape_fn_and_require_op\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall_with_requiring\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\common_shapes.py\u001b[0m in \u001b[0;36mcall_cpp_shape_fn\u001b[1;34m(op, require_shape_fn)\u001b[0m\n\u001b[0;32m    625\u001b[0m     res = _call_cpp_shape_fn_impl(op, input_tensors_needed,\n\u001b[0;32m    626\u001b[0m                                   \u001b[0minput_tensors_as_shapes_needed\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 627\u001b[1;33m                                   require_shape_fn)\n\u001b[0m\u001b[0;32m    628\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    629\u001b[0m       \u001b[1;31m# Handles the case where _call_cpp_shape_fn_impl calls unknown_shape(op).\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\common_shapes.py\u001b[0m in \u001b[0;36m_call_cpp_shape_fn_impl\u001b[1;34m(op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn)\u001b[0m\n\u001b[0;32m    689\u001b[0m       \u001b[0mmissing_shape_fn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    690\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 691\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    692\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    693\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mmissing_shape_fn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Dimensions must be equal, but are 244 and 3 for 'preprocessing/add' (op: 'Add') with input shapes: [?,244,244,3], [1,3,1,1]."
     ]
    }
   ],
   "source": [
    "model = get_feature_extraction_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "files_plane = glob.glob(r'C:\\Users\\Michal\\Desktop\\cogsci3\\POETdataset\\PascalImages\\aeroplane*')\n",
    "files_boat = glob.glob(r'C:\\Users\\Michal\\Desktop\\cogsci3\\POETdataset\\PascalImages\\boat*')\n",
    "files_dog = glob.glob(r'C:\\Users\\Michal\\Desktop\\cogsci3\\POETdataset\\PascalImages\\dog*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PLANE\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-523d850d784e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"PLANE\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplane_features\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_features\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiles_plane\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[0;32m      3\u001b[0m \u001b[0msave_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplane_features\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'plane_features.pkl'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"BOAT\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "print(\"PLANE\")\n",
    "plane_features = get_features(files_plane, model)\n",
    "save_object(plane_features, 'plane_features.pkl')\n",
    "\n",
    "print(\"BOAT\")\n",
    "boat_features = get_features(files_boat, model)\n",
    "save_object(boat_features, 'boat_features.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plane = load_object('plane_features.pkl').reshape(-1,2048)\n",
    "boat = load_object('boat_features.pkl').reshape(-1,2048)\n",
    "X = np.concatenate([plane, boat])\n",
    "y = np.concatenate([[1]*plane.shape[0],[0]*boat.shape[0]])\n",
    "plane.shape, boat.shape, X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)\n",
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adam = optimizers.Adam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 936 samples, validate on 234 samples\n",
      "Epoch 1/10\n",
      "936/936 [==============================] - 2s 2ms/step - loss: 0.1154 - acc: 0.9541 - val_loss: 0.2468 - val_acc: 0.9316\n",
      "Epoch 2/10\n",
      "936/936 [==============================] - 0s 315us/step - loss: 0.0522 - acc: 0.9818 - val_loss: 0.1159 - val_acc: 0.9573\n",
      "Epoch 3/10\n",
      "936/936 [==============================] - 0s 313us/step - loss: 0.0083 - acc: 0.9989 - val_loss: 0.1031 - val_acc: 0.9573\n",
      "Epoch 4/10\n",
      "936/936 [==============================] - 0s 311us/step - loss: 0.0038 - acc: 1.0000 - val_loss: 0.1093 - val_acc: 0.9573\n",
      "Epoch 5/10\n",
      "936/936 [==============================] - 0s 324us/step - loss: 0.0022 - acc: 1.0000 - val_loss: 0.1151 - val_acc: 0.9573\n",
      "Epoch 6/10\n",
      "936/936 [==============================] - 0s 316us/step - loss: 0.0014 - acc: 1.0000 - val_loss: 0.1301 - val_acc: 0.9573\n",
      "Epoch 7/10\n",
      "936/936 [==============================] - 0s 331us/step - loss: 0.0011 - acc: 1.0000 - val_loss: 0.1263 - val_acc: 0.9573\n",
      "Epoch 8/10\n",
      "936/936 [==============================] - 0s 338us/step - loss: 9.0343e-04 - acc: 1.0000 - val_loss: 0.1225 - val_acc: 0.9573\n",
      "Epoch 9/10\n",
      "936/936 [==============================] - 0s 329us/step - loss: 8.1286e-04 - acc: 1.0000 - val_loss: 0.1313 - val_acc: 0.9573\n",
      "Epoch 10/10\n",
      "936/936 [==============================] - 0s 352us/step - loss: 6.5229e-04 - acc: 1.0000 - val_loss: 0.1317 - val_acc: 0.9530\n"
     ]
    }
   ],
   "source": [
    "dense_model = Sequential()\n",
    "# model.add(Flatten(input_shape=(1,1,2048)))\n",
    "dense_model.add(Dense(units=128, activation='relu', input_shape=(2048,)))\n",
    "dense_model.add(Dense(units=1, activation='sigmoid'))\n",
    "\n",
    "dense_model.compile(loss='binary_crossentropy',\n",
    "             optimizer=adam,\n",
    "             metrics=['accuracy'])\n",
    "history = dense_model.fit(X_train,y_train,\n",
    "                    batch_size = 32,\n",
    "                    epochs = 10,\n",
    "                    validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "files_plane = glob.glob(r'C:\\Users\\Michal\\Desktop\\cogsci3\\POETdataset\\PascalImages\\aeroplane*')\n",
    "files_boat = glob.glob(r'C:\\Users\\Michal\\Desktop\\cogsci3\\POETdataset\\PascalImages\\boat*')\n",
    "files_dog = glob.glob(r'C:\\Users\\Michal\\Desktop\\cogsci3\\POETdataset\\PascalImages\\dog*')\n",
    "\n",
    "files = files_plane\n",
    "x1 = np.array([image.img_to_array(image.load_img(file, target_size=(128, 128))) for file in files])\n",
    "files = files_dog\n",
    "x2 = np.array([image.img_to_array(image.load_img(file, target_size=(128, 128))) for file in files])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1923, 128, 128, 3), (1923,))"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_images = np.concatenate([x1,x2])\n",
    "y = np.concatenate([[1]*x1.shape[0],[0]*x2.shape[0]])\n",
    "X_images.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1730, 128, 128, 3), (1730,), (193, 128, 128, 3), (193,))"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_images, y, test_size=0.1, stratify=y)\n",
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##model building\n",
    "model = Sequential()\n",
    "#convolutional layer with rectified linear unit activation\n",
    "model.add(Conv2D(32, kernel_size=(3, 3),\n",
    "                 activation='relu',\n",
    "                 input_shape=X_train.shape[1:]))\n",
    "#32 convolution filters used each of size 3x3\n",
    "#again\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "#64 convolution filters used each of size 3x3\n",
    "#choose the best features via pooling\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "#randomly turn neurons on and off to improve convergence\n",
    "model.add(Dropout(0.25))\n",
    "#flatten since too many dimensions, we only want a classification output\n",
    "model.add(Flatten())\n",
    "#fully connected to get all relevant data\n",
    "model.add(Dense(128, activation='relu'))\n",
    "#one more dropout for convergence' sake :) \n",
    "model.add(Dropout(0.5))\n",
    "#output a softmax to squash the matrix into output probabilities\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss=keras.losses.binary_crossentropy,\n",
    "              optimizer=keras.optimizers.Adadelta(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1730 samples, validate on 193 samples\n",
      "Epoch 1/10\n",
      "1730/1730 [==============================] - 165s 95ms/step - loss: 5.6497 - acc: 0.6468 - val_loss: 5.5954 - val_acc: 0.6528\n",
      "Epoch 2/10\n",
      " 160/1730 [=>............................] - ETA: 2:15 - loss: 6.2458 - acc: 0.6125"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-121-486029cc0dae>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m           \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m           \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m           validation_data=(X_test, y_test))\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\keras\\models.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m    961\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    962\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 963\u001b[1;33m                               validation_steps=validation_steps)\n\u001b[0m\u001b[0;32m    964\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    965\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m   1703\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1704\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1705\u001b[1;33m                               validation_steps=validation_steps)\n\u001b[0m\u001b[0;32m   1706\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1707\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[1;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[0;32m   1233\u001b[0m                         \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1234\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1235\u001b[1;33m                     \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1236\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1237\u001b[0m                         \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2476\u001b[0m         \u001b[0msession\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2477\u001b[0m         updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[1;32m-> 2478\u001b[1;33m                               **self.session_kwargs)\n\u001b[0m\u001b[0;32m   2479\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2480\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    903\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    904\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 905\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    906\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    907\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1138\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1139\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1140\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1141\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1142\u001b[0m       \u001b[0mresults\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[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1319\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1320\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1321\u001b[1;33m                            run_metadata)\n\u001b[0m\u001b[0;32m   1322\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1323\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1325\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1326\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1327\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\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   1328\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1329\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1310\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1311\u001b[0m       return self._call_tf_sessionrun(\n\u001b[1;32m-> 1312\u001b[1;33m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m   1313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1314\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Michal\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m   1418\u001b[0m         return tf_session.TF_Run(\n\u001b[0;32m   1419\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1420\u001b[1;33m             status, run_metadata)\n\u001b[0m\u001b[0;32m   1421\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1422\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "num_epoch = 10\n",
    "#model training\n",
    "model_log = model.fit(X_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          epochs=num_epoch,\n",
    "          verbose=1,\n",
    "          validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.io\n",
    "mat = scipy.io.loadmat('./POETdataset/etData/etData_aeroplane.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'etData' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-cf78ce9cf8f8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0metData\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m607\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'fixations'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'imgCoord'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'fixR'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'pos'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'etData' is not defined"
     ]
    }
   ],
   "source": [
    "etData[607,0]['fixations'][0]['imgCoord'][0]['fixR'][0][0]['pos'][0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'cv2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-9c00d6965b36>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m \u001b[0mfix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetFixationPositions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmat\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'etData'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'2008_006109'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'./POETdataset/PascalImages/aeroplane_2008_006109.jpg'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     34\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'cv2' is not defined"
     ]
    }
   ],
   "source": [
    "def getFixationPositions(etData, imgFilename, setUsers=list(range(6)), first=False):\n",
    "\n",
    "    fix = [];\n",
    "\n",
    "    imgIndex = 0;\n",
    "    if (imgFilename):\n",
    "        for i in range(etData.shape[0]):\n",
    "            if (etData[i,0]['filename'] == imgFilename):\n",
    "                imgIndex = i;\n",
    "                break\n",
    "\n",
    "    if (imgIndex <= 0 or imgIndex > etData.shape[0]):\n",
    "        raise('Invalid image index or image filename!')\n",
    "\n",
    "    etData[imgIndex,0]\n",
    "    print(imgIndex)\n",
    "\n",
    "    for s in range(len(setUsers)):\n",
    "        fixR = etData[imgIndex,0]['fixations'][0]['imgCoord'][0]['fixR'][0][0]['pos'][0][0]\n",
    "        fixL = etData[imgIndex,0]['fixations'][0]['imgCoord'][0]['fixL'][0][0]['pos'][0][0]\n",
    "        \n",
    "    if not first:\n",
    "        fixR = fixR[1:]\n",
    "        fixL = fixL[1:]\n",
    "        \n",
    "    return fixL, fixR\n",
    "\n",
    "# fix = ceil(fix);\n",
    "\n",
    "# end\n",
    "\n",
    "fix = getFixationPositions(mat['etData'], '2008_006109')\n",
    "img = cv2.imread('./POETdataset/PascalImages/aeroplane_2008_006109.jpg', 1)    \n",
    "    \n",
    "plt.figure()\n",
    "plt.imshow(img)\n",
    "plt.scatter(fix[0][:,0], fix[0][:,1], c='b')\n",
    "plt.scatter(fix[1][:,0], fix[1][:,1], c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "recurrent_model = Sequential()\n",
    "recurrent_model.add(LSTM(32, return_sequences=False))\n",
    "recurrent_model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "adam = Adam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy',\n",
    "             optimizer=adam,\n",
    "             metrics='accuracy')\n",
    "history = model.fit(X_train,y_train,\n",
    "                    batch_size = 32,\n",
    "                    epochs = 10,\n",
    "                    validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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