deepface/my_deepface.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from deepface import DeepFace\n",
    "import os\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "# read images\n",
    "imgs = []\n",
    "img_dir = 'test_imgs/'\n",
    "for img in os.listdir(img_dir):\n",
    "    if img.endswith('jpg') or img.endswith('jpeg'):\n",
    "        img_path = os.path.join(img_dir, img)\n",
    "        imgs.append(img_path)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x7fe392cbb1f0>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img1 = plt.imread(imgs[0])\n",
    "plt.imshow(img1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x7fe395337100>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img2 = plt.imread(imgs[1])\n",
    "plt.imshow(img2)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Action: emotion: 100%|██████████| 3/3 [00:14<00:00,  4.96s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 17 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ddd7caf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:6 out of the last 19 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dd798ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:7 out of the last 20 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dd9e09d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 21 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de88c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de88c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de88c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de00a5e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dd9e0f70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dd9e0f70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dd9e0f70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dd798160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ddc954c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dd9e01f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dd9e01f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dd9e01f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de88c1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de88c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de88c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de88c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de00a1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de3ec790> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe31d495e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe31d495e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe31d495e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dfd92c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2df888940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dfd92430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dfd92430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dfd92430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dfcc9d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dfcc93a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dfcc93a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dfcc93a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de5544c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e239f790> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de554ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de554ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de554ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e2147310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e2147ee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2df888940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2df888940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2df888940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de3ec160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de00a8b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2df888310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2df888310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2df888310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de7e4670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ddc95dc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e239fa60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e239fa60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e239fa60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de840b80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2df2435e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dd510700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dd510700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dd510700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de5540d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dfcc9670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de554c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de554c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de554c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e274cc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d12940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e43f94c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e444c040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d5c280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d5c280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d5c280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e4faad30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e444cc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e444cc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e444cc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d121f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dfcc91f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d5c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d5c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d5c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dd5109d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2df243820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d5c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d5c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d5c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de7e4670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ddc95040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ddc95040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ddc95040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de00a040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ddc95280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ddc95280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ddc95280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de554af0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2dfd92550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e239f820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e239f820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e239f820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e444cee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e274c820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e274c820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e274c820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e4a9d5e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e2828310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "[262  48  88  88]\n",
      "[649  54  99  99]\n",
      "[57 68 88 88]\n",
      "[448  57  92  92]\n",
      "[450 251  87  87]\n",
      "[831 250  96  96]\n",
      "[264 255  85  85]\n",
      "[653 272  84  84]\n",
      "[448 438  96  96]\n",
      "[256 456  93  93]\n",
      "[649 446 101 101]\n",
      "[643 644 104 104]\n",
      "[ 65 639  79  79]\n",
      "[847 641  86  86]\n",
      "[248 655 114 114]\n",
      "[835 819  92  92]\n",
      "[450 830  93  93]\n",
      "[256 832  88  88]\n",
      "[643 838  91  91]\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e659b160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e6572e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e659be50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    }
   ],
   "source": [
    "demography, imgs = DeepFace.analyze(imgs[0], actions=['age', 'gender', 'emotion'],\n",
    "                                    detector_backend='mtcnn')\n",
    "# print(\"Age: \", demography[\"age\"])\n",
    "# print(\"Gender: \", demography[\"gender\"])\n",
    "# print(\"Emotion: \", demography[\"dominant_emotion\"])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age:  34.399849031832304\n",
      "Gender:  Woman\n",
      "Emotion:  happy\n"
     ]
    },
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x7fe2e8006c10>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "demo = demography[0]\n",
    "print(\"Age: \", demo[\"age\"])\n",
    "print(\"Gender: \", demo[\"gender\"])\n",
    "print(\"Emotion: \", demo[\"dominant_emotion\"])\n",
    "plt.imshow(imgs[0][:,:,::-1])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age:  24.706228912787836\n",
      "Gender:  Man\n",
      "Emotion:  happy\n"
     ]
    },
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x7fe2f42eab50>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "demo = demography[1]\n",
    "print(\"Age: \", demo[\"age\"])\n",
    "print(\"Gender: \", demo[\"gender\"])\n",
    "print(\"Emotion: \", demo[\"dominant_emotion\"])\n",
    "plt.imshow(imgs[1][:,:,::-1])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age:  36.036305006492086\n",
      "Gender:  Man\n",
      "Emotion:  happy\n"
     ]
    },
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x7fe2e80e3b80>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "demo = demography[2]\n",
    "print(\"Age: \", demo[\"age\"])\n",
    "print(\"Gender: \", demo[\"gender\"])\n",
    "print(\"Emotion: \", demo[\"dominant_emotion\"])\n",
    "plt.imshow(imgs[2][:,:,::-1])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age:  35.126786088918394\n",
      "Gender:  Man\n",
      "Emotion:  happy\n"
     ]
    },
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x7fe2e82a95e0>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "demo = demography[3]\n",
    "print(\"Age: \", demo[\"age\"])\n",
    "print(\"Gender: \", demo[\"gender\"])\n",
    "print(\"Emotion: \", demo[\"dominant_emotion\"])\n",
    "plt.imshow(imgs[3][:,:,::-1])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age:  37.149563607061395\n",
      "Gender:  Man\n",
      "Emotion:  happy\n"
     ]
    },
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x7fe39372f040>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "demo = demography[4]\n",
    "print(\"Age: \", demo[\"age\"])\n",
    "print(\"Gender: \", demo[\"gender\"])\n",
    "print(\"Emotion: \", demo[\"dominant_emotion\"])\n",
    "plt.imshow(imgs[4][:,:,::-1])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "19"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(demography)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e444ce50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:6 out of the last 14 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e274cd30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:7 out of the last 15 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e43f9ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 16 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e43f9ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 17 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e43f9ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e4a9d9d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e65b71f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e76cf8b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e649d5e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e659b8b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e659b8b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e659b8b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e896b160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e87dcca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e80c9d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e80c9d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e80c9d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e913f9d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e9125940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e896baf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e896baf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e896baf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e921eb80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ea9b7550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ea373ee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e896bdc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e9d5f280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e9d5f280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e9d5f280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e921ea60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ea9b7c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e913f1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e913f1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e913f1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e913f550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e896b430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e913f3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e913f3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e913f3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e649daf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e76cf940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e896b700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e896b700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e896b700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e659b670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e4a9d5e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e649d310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e649d310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e649d310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:7 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d12940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:7 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e43f9550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e274cf70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e274cf70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e274cf70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:7 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2df243c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2de554700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e444c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e444c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e444c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e9d5f940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ea373670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d5caf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d5caf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d5caf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ec5a4430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ec5a40d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ea373a60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ea373a60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ea373a60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2edd11d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ebd9fc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2eac0c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2eac0c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2eac0c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ee2afe50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2eda6ac10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e444c940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e444c940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e444c940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ee208d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2eda6ac10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ebd9f550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ebd9f550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ebd9f550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e3d5c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ec5a4430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2edd11430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2edd11430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2edd11430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:7 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2ec5a4e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e6572af0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2df243c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2df243c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2df243c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2df243550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e4a9d790> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e444cee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e444cee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e444cee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:7 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e2828310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:7 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e649da60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e659b550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e659b550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e659b550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:7 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e896b040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e91253a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e87dc3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e87dc3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:10 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e87dc3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e913fdc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:8 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e921e280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:9 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:7 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e9d5f1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:7 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe2e9d5fd30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    }
   ],
   "source": [
    "faces = DeepFace.detectFace(img1, detector_backend='mtcnn')\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x7fe2f19cde80>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(faces[0])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "(83, 64, 3)"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faces[0].shape\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}