test_animals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep Learning in the Eye Tracking World \n",
    "#### the tutorial presented during ETRA 2021 (https://etra.acm.org/2021/acceptedtutorials.html)\n",
    "#### the code downloaded from: https://github.com/kasprowski/etra2021\n",
    "@author: pawel@kasprowski.pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import os\n",
    "import numpy as np\n",
    "from tensorflow.python.keras.models import load_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import zipfile\n",
    "r = requests.get(\"http://www.kasprowski.pl/etra2021/animals.zip\", allow_redirects=True)\n",
    "open('animals.zip', 'wb').write(r.content)\n",
    "print(\"Downloaded animals.zip\")\n",
    "with zipfile.ZipFile(\"animals.zip\",\"r\") as zip_ref:\n",
    "    zip_ref.extractall(\"animals\")\n",
    "print(\"Uzipped to /animals directory\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "r = requests.get(\"http://www.kasprowski.pl/etra2021/model_rnn.h5\", allow_redirects=True)\n",
    "open('model_rnn.h5', 'wb').write(r.content)\n",
    "print(\"Downloaded model_rnn.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing A10\n",
      "Processing A11\n",
      "Processing A12\n",
      "Processing A13\n",
      "Processing A14\n",
      "Processing A15\n",
      "Processing A16\n",
      "Processing A17\n",
      "Processing A18\n",
      "Processing A19\n",
      "Processing A20\n",
      "Processing A21\n",
      "Processing A22\n",
      "Processing A23\n",
      "Processing A24\n",
      "Processing A25\n"
     ]
    },
    {
     "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-2-3def4270ec68>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     51\u001b[0m     \u001b[1;31m# predict subsequent samples based on 20 samples from genuine sample\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     52\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m20\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---> 53\u001b[1;33m         \u001b[0mpredictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msampleSequence\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     54\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mroll\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msampleSequence\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\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[0;32m     55\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m19\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msample\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m20\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[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    128\u001b[0m       raise ValueError('{} is not supported in multi-worker mode.'.format(\n\u001b[0;32m    129\u001b[0m           method.__name__))\n\u001b[1;32m--> 130\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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[0mkwargs\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    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    132\u001b[0m   return tf_decorator.make_decorator(\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1567\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistribute_strategy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscope\u001b[0m\u001b[1;33m(\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   1568\u001b[0m       \u001b[1;31m# Creates a `tf.data.Dataset` and handles batch and epoch iteration.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1569\u001b[1;33m       data_handler = data_adapter.DataHandler(\n\u001b[0m\u001b[0;32m   1570\u001b[0m           \u001b[0mx\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[0m\n\u001b[0;32m   1571\u001b[0m           \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\data_adapter.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution)\u001b[0m\n\u001b[0;32m   1103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1104\u001b[0m     \u001b[0madapter_cls\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mselect_data_adapter\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[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1105\u001b[1;33m     self._adapter = adapter_cls(\n\u001b[0m\u001b[0;32m   1106\u001b[0m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1107\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:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\data_adapter.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)\u001b[0m\n\u001b[0;32m    360\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mflat_dataset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    361\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 362\u001b[1;33m     \u001b[0mindices_dataset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindices_dataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflat_map\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mslice_batch_indices\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    363\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    364\u001b[0m     \u001b[0mdataset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mslice_inputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindices_dataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py\u001b[0m in \u001b[0;36mflat_map\u001b[1;34m(self, map_func)\u001b[0m\n\u001b[0;32m   1725\u001b[0m       \u001b[0mDataset\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mA\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1726\u001b[0m     \"\"\"\n\u001b[1;32m-> 1727\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mFlatMapDataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmap_func\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   1728\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1729\u001b[0m   def interleave(self,\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, input_dataset, map_func)\u001b[0m\n\u001b[0;32m   4120\u001b[0m     \u001b[1;34m\"\"\"See `Dataset.flat_map()` for details.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4121\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_input_dataset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minput_dataset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4122\u001b[1;33m     self._map_func = StructuredFunctionWrapper(\n\u001b[0m\u001b[0;32m   4123\u001b[0m         map_func, self._transformation_name(), dataset=input_dataset)\n\u001b[0;32m   4124\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_map_func\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput_structure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDatasetSpec\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:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, func, transformation_name, dataset, input_classes, input_shapes, input_types, input_structure, add_to_graph, use_legacy_function, defun_kwargs)\u001b[0m\n\u001b[0;32m   3369\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mtracking\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresource_tracker_scope\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresource_tracker\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   3370\u001b[0m         \u001b[1;31m# TODO(b/141462134): Switch to using garbage collection.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3371\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwrapper_fn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_concrete_function\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   3372\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0madd_to_graph\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3373\u001b[0m           \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_to_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[1;33m(\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:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mget_concrete_function\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2936\u001b[0m       \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0minputs\u001b[0m \u001b[0mto\u001b[0m \u001b[0mspecialize\u001b[0m \u001b[0mon\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2937\u001b[0m     \"\"\"\n\u001b[1;32m-> 2938\u001b[1;33m     graph_function = self._get_concrete_function_garbage_collected(\n\u001b[0m\u001b[0;32m   2939\u001b[0m         *args, **kwargs)\n\u001b[0;32m   2940\u001b[0m     \u001b[0mgraph_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_garbage_collector\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_get_concrete_function_garbage_collected\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2904\u001b[0m       \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2905\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2906\u001b[1;33m       \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\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   2907\u001b[0m       \u001b[0mseen_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\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   2908\u001b[0m       captured = object_identity.ObjectIdentitySet(\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m   3211\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3212\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3213\u001b[1;33m       \u001b[0mgraph_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\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   3214\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3215\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\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[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[1;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[0;32m   3063\u001b[0m     \u001b[0marg_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbase_arg_names\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mmissing_arg_names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3064\u001b[0m     graph_function = ConcreteFunction(\n\u001b[1;32m-> 3065\u001b[1;33m         func_graph_module.func_graph_from_py_func(\n\u001b[0m\u001b[0;32m   3066\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3067\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_python_function\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[1;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[0;32m    988\u001b[0m       \u001b[1;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    989\u001b[0m       \u001b[1;31m# TensorArrays and `None`s.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 990\u001b[1;33m       func_outputs = nest.map_structure(convert, func_outputs,\n\u001b[0m\u001b[0;32m    991\u001b[0m                                         expand_composites=True)\n\u001b[0;32m    992\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\util\\nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[1;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[0;32m    633\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    634\u001b[0m   return pack_sequence_as(\n\u001b[1;32m--> 635\u001b[1;33m       \u001b[0mstructure\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentries\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    636\u001b[0m       expand_composites=expand_composites)\n\u001b[0;32m    637\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\util\\nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    633\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    634\u001b[0m   return pack_sequence_as(\n\u001b[1;32m--> 635\u001b[1;33m       \u001b[0mstructure\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mentries\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    636\u001b[0m       expand_composites=expand_composites)\n\u001b[0;32m    637\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36mconvert\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m    948\u001b[0m               (str(python_func), type(x)))\n\u001b[0;32m    949\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0madd_control_dependencies\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 950\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdeps_ctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmark_as_return\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[0m\n\u001b[0m\u001b[0;32m    951\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    952\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\framework\\auto_control_deps.py\u001b[0m in \u001b[0;36mmark_as_return\u001b[1;34m(self, tensor)\u001b[0m\n\u001b[0;32m    219\u001b[0m     \u001b[1;31m# of a new identity operation that the stateful operations definitely don't\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;31m# depend on.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 221\u001b[1;33m     \u001b[0mtensor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor\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    222\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_returned_tensors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    223\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    199\u001b[0m     \u001b[1;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    200\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 201\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mtarget\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[0mkwargs\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    202\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\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    203\u001b[0m       \u001b[1;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\ops\\array_ops.py\u001b[0m in \u001b[0;36midentity\u001b[1;34m(input, name)\u001b[0m\n\u001b[0;32m    285\u001b[0m     \u001b[1;31m# variables. Variables have correct handle data when graph building.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    286\u001b[0m     \u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 287\u001b[1;33m   \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\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[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    288\u001b[0m   \u001b[1;31m# Propagate handle data for happier shape inference for resource variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    289\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"_handle_data\"\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:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\ops\\gen_array_ops.py\u001b[0m in \u001b[0;36midentity\u001b[1;34m(input, name)\u001b[0m\n\u001b[0;32m   3998\u001b[0m       \u001b[1;32mpass\u001b[0m  \u001b[1;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3999\u001b[0m   \u001b[1;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4000\u001b[1;33m   _, _, _op, _outputs = _op_def_library._apply_op_helper(\n\u001b[0m\u001b[0;32m   4001\u001b[0m         \"Identity\", input=input, name=name)\n\u001b[0;32m   4002\u001b[0m   \u001b[0m_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_outputs\u001b[0m\u001b[1;33m[\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:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[1;34m(op_type_name, name, **keywords)\u001b[0m\n\u001b[0;32m    740\u001b[0m       \u001b[1;31m# Add Op to graph\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    741\u001b[0m       \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 742\u001b[1;33m       op = g._create_op_internal(op_type_name, inputs, dtypes=None,\n\u001b[0m\u001b[0;32m    743\u001b[0m                                  \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscope\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_types\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    744\u001b[0m                                  attrs=attr_protos, op_def=op_def)\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36m_create_op_internal\u001b[1;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)\u001b[0m\n\u001b[0;32m    589\u001b[0m       \u001b[0minp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcapture\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    590\u001b[0m       \u001b[0minputs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 591\u001b[1;33m     return super(FuncGraph, self)._create_op_internal(  # pylint: disable=protected-access\n\u001b[0m\u001b[0;32m    592\u001b[0m         \u001b[0mop_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_types\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_def\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    593\u001b[0m         compute_device)\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m_create_op_internal\u001b[1;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)\u001b[0m\n\u001b[0;32m   3475\u001b[0m     \u001b[1;31m# Session.run call cannot occur between creating and mutating the op.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3476\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mutation_lock\u001b[0m\u001b[1;33m(\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-> 3477\u001b[1;33m       ret = Operation(\n\u001b[0m\u001b[0;32m   3478\u001b[0m           \u001b[0mnode_def\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3479\u001b[0m           \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)\u001b[0m\n\u001b[0;32m   1972\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mop_def\u001b[0m \u001b[1;32mis\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[0;32m   1973\u001b[0m         \u001b[0mop_def\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_op_def\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode_def\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-> 1974\u001b[1;33m       self._c_op = _create_c_op(self._graph, node_def, inputs,\n\u001b[0m\u001b[0;32m   1975\u001b[0m                                 control_input_ops, op_def)\n\u001b[0;32m   1976\u001b[0m       \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_str\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\programs\\miniconda3\\envs\\deep\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m_create_c_op\u001b[1;34m(graph, node_def, inputs, control_inputs, op_def)\u001b[0m\n\u001b[0;32m   1810\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1811\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1812\u001b[1;33m     \u001b[0mc_op\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpywrap_tf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_FinishOperation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop_desc\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   1813\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[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1814\u001b[0m     \u001b[1;31m# Convert to ValueError for backwards compatibility.\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": [
    "if not os.path.exists(\"scanpaths\"):\n",
    "    os.makedirs(\"scanpaths\")\n",
    "    \n",
    "def load_files(indir):\n",
    "    samples = []\n",
    "    names = []\n",
    "    for file in os.listdir(indir):\n",
    "        if file.endswith(\"csv\"):\n",
    "            sample = np.genfromtxt(os.path.join(indir, file), delimiter=',')\n",
    "            samples.append(sample)\n",
    "            names.append(file[:-4])\n",
    "    return samples,names\n",
    "\n",
    "# creates an image with scanpaths taken from a sample\n",
    "# and saves it in /scanpaths folder\n",
    "def save_scanpath(sample,prefix):\n",
    "    imname = \"animal-11\"    \n",
    "    image = cv2.imread(\"animals/{}.jpg\".format(imname))\n",
    "    ox=0\n",
    "    oy=0\n",
    "    for j in range(sample.shape[0]):\n",
    "        sx = int(sample[j,1])\n",
    "        sy = int(sample[j,2])\n",
    "        if sx<=0 or sy<=0: continue\n",
    "        if j>0: cv2.line(image,(ox,oy),(sx,sy),(255,0,0),3)\n",
    "        cv2.circle(image,(sx,sy),10,(0,0,255),3)\n",
    "        ox = sx\n",
    "        oy = sy\n",
    "    cv2.imwrite(\"scanpaths/{}_{}_sp.jpg\".format(prefix,imname),image)\n",
    "    \n",
    "\n",
    "\n",
    "model = load_model(\"model_rnn.h5\")\n",
    "samples,names = load_files(\"animals\")\n",
    "\n",
    "for i in range(len(samples)):\n",
    "    sample = samples[i]\n",
    "    name = names[i]\n",
    "    print(\"Processing\",name)\n",
    "    #genuine scanpath\n",
    "    save_scanpath(sample, \"{}_real\".format(name))\n",
    "    sampleSequence = sample[0:20,1:]\n",
    "    sampleSequence = np.expand_dims(sampleSequence, axis=0)\n",
    "    ts = 0\n",
    "    sequence = []\n",
    "    # predict subsequent samples based on 20 samples from genuine sample\n",
    "    for i in range(sample.shape[0]-20):\n",
    "        predictions = model.predict(sampleSequence)\n",
    "        x = np.roll(sampleSequence,-1,axis=1)\n",
    "        x[0,19] = sample[i+20,1:]\n",
    "        sampleSequence = x\n",
    "\n",
    "        item = np.zeros((3))\n",
    "        item[0] = ts\n",
    "        item[1] = predictions[0,0]\n",
    "        item[2] = predictions[0,1] \n",
    "        sequence.append(item)\n",
    "        ts += 1\n",
    "    sequence = np.array(sequence)\n",
    "    save_scanpath(sequence, \"{}_gen\".format(name))"
   ]
  }
 ],
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