Chord Detection model training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pip\n",
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      "\u001b[K     |████████████████████████████████| 1.5 MB 4.3 MB/s eta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: pip\n",
      "  Attempting uninstall: pip\n",
      "    Found existing installation: pip 20.0.2\n",
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      "      Successfully uninstalled pip-20.0.2\n",
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      "\u001b[?25h  Created wheel for resampy: filename=resampy-0.2.2-py3-none-any.whl size=320720 sha256=000e32506683efbf5774f644f47bbcb6444835b4e87fc075e89074371782d45a\n",
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      "Installing collected packages: audioread, resampy, soundfile, appdirs, pooch, librosa\n",
      "Successfully installed appdirs-1.4.4 audioread-2.1.8 librosa-0.8.0 pooch-1.1.1 resampy-0.2.2 soundfile-0.10.3.post1\n",
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      "Collecting numba==0.45\n",
      "  Downloading numba-0.45.0-cp36-cp36m-manylinux1_x86_64.whl (3.5 MB)\n",
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      "Requirement already satisfied: numpy in /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages (from numba==0.45) (1.18.1)\n",
      "Installing collected packages: numba\n",
      "  Attempting uninstall: numba\n",
      "    Found existing installation: numba 0.48.0\n",
      "    Uninstalling numba-0.48.0:\n",
      "      Successfully uninstalled numba-0.48.0\n",
      "Successfully installed numba-0.45.0\n"
     ]
    }
   ],
   "source": [
    "# Dependencies\n",
    "!pip3 install --upgrade pip\n",
    "!pip3 install matplotlib\n",
    "!pip3 install numpy\n",
    "!pip3 install librosa\n",
    "!pip3 install pandas\n",
    "\n",
    "!pip3 install boto3\n",
    "!pip3 install numba==0.45 # for librosa, https://github.com/librosa/librosa/issues/1160"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  Downloading oauthlib-3.1.0-py2.py3-none-any.whl (147 kB)\n",
      "\u001b[K     |████████████████████████████████| 147 kB 67.2 MB/s eta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: flatbuffers, pyasn1-modules, cachetools, google-auth, oauthlib, requests-oauthlib, google-auth-oauthlib, tensorboard-plugin-wit, tb-nightly, tf-estimator-nightly, astunparse, gast, tf-nightly\n",
      "  Attempting uninstall: gast\n",
      "    Found existing installation: gast 0.2.2\n",
      "    Uninstalling gast-0.2.2:\n",
      "      Successfully uninstalled gast-0.2.2\n",
      "\u001b[31mERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.\n",
      "\n",
      "We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.\n",
      "\n",
      "tensorflow 1.15.2 requires gast==0.2.2, but you'll have gast 0.3.3 which is incompatible.\u001b[0m\n",
      "Successfully installed astunparse-1.6.3 cachetools-4.1.1 flatbuffers-1.12 gast-0.3.3 google-auth-1.20.1 google-auth-oauthlib-0.4.1 oauthlib-3.1.0 pyasn1-modules-0.2.8 requests-oauthlib-1.3.0 tb-nightly-2.4.0a20200820 tensorboard-plugin-wit-1.7.0 tf-estimator-nightly-2.4.0.dev2020082001 tf-nightly-2.4.0.dev20200820\n"
     ]
    }
   ],
   "source": [
    "!pip install tf-nightly --upgrade # Need nightly version for TFlite conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow version: 1.15.2\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "sndfile library not found",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-f26f1b93f966>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Tensorflow version: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/librosa/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[0;31m# And all the librosa sub-modules\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbeat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdecompose\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/librosa/core/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m  \u001b[0;31m# pylint: disable=wildcard-import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0maudio\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m  \u001b[0;31m# pylint: disable=wildcard-import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mspectrum\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m  \u001b[0;31m# pylint: disable=wildcard-import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mpitch\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m  \u001b[0;31m# pylint: disable=wildcard-import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/librosa/core/audio.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0msoundfile\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maudioread\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/soundfile.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    140\u001b[0m     \u001b[0m_libname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_find_library\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sndfile'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_libname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sndfile library not found'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m     \u001b[0m_snd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ffi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_libname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: sndfile library not found"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(\"Tensorflow version: {}\".format(tf.__version__))\n",
    "\n",
    "import librosa, librosa.display\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset: only download from s3 IF ./audioData is empty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Dataset\n",
    "import boto3\n",
    "import sagemaker\n",
    "sagemaker_session = sagemaker.Session()\n",
    "\n",
    "BUCKET = sagemaker_session.default_bucket()\n",
    "FOLDER = 'live'\n",
    "OUTPUT_FOLDER = 'live-model-v4'\n",
    "LOCAL_TRAIN_DATA = './audioData'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23653\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "if len(os.listdir(LOCAL_TRAIN_DATA) ) == 0:\n",
    "    !aws s3 cp s3://$BUCKET/$FOLDER/ ./audioData --recursive\n",
    "else:\n",
    "    print(len(os.listdir(LOCAL_TRAIN_DATA) ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualize audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'librosa' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-68ff33426305>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mfilepath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'./audioData/A Test2 32446.0-35746.0 iter0.wav'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msample_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaveplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mipd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAudio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'librosa' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import IPython.display as ipd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "filepath = './audioData/A Test2 32446.0-35746.0 iter0.wav'\n",
    "plt.figure(figsize=(12,4))\n",
    "data,sample_rate = librosa.load(filepath, sr=None)\n",
    "_ = librosa.display.waveplot(data,sr=sample_rate)\n",
    "ipd.Audio(filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract audio_data and label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  EG filename = 'F#m Test3 34330-41240 iter254.wav' , ie excluding LOCAL_TRAIN_DATA folder\n",
    "\n",
    "CHORDS = ['Noise', 'A', 'Am', 'Bb', 'Bbm', 'B', 'Bm', 'C', 'Cm', 'C#', 'C#m',\n",
    "          'D', 'Dm', 'D#', 'D#m','E', 'Em', 'F', 'Fm', 'F#', 'F#m', 'G',\n",
    "          'Gm', 'G#', 'G#m']\n",
    "\n",
    "def extract_data(filename): # len 8160\n",
    "    try:\n",
    "        filepath = '{}/{}'.format(LOCAL_TRAIN_DATA, filename)\n",
    "        audio, sr = librosa.load(filepath, sr=None, res_type='kaiser_fast')  \n",
    "    except Exception as e:\n",
    "        print(\"Error encountered while parsing file: {} {}\".format(filename, e))\n",
    "        return None \n",
    "    return audio\n",
    "\n",
    "def data_to_chroma(data):\n",
    "    hop_length = 512\n",
    "    sr = 48000\n",
    "    chroma = librosa.feature.chroma_stft(data, sr=sr, hop_length=hop_length)\n",
    "    chroma = np.mean(chroma.T,axis=0)\n",
    "    return chroma\n",
    "\n",
    "## Extract Label\n",
    "def get_chord_name(filename):\n",
    "    return filename.split()[0]\n",
    "\n",
    "def chord_name_to_onehot(c): # O\n",
    "    # 'Noise' would return all 1.0\n",
    "    # \"not an identifiable chord\" will return all 0.0\n",
    "    return [1.0 if c == chord else 0.0 for chord in CHORDS]\n",
    "\n",
    "def onehot_to_chord(onehot): # O\n",
    "    onehot = list(onehot) # convert to python if it is numpy array\n",
    "    try:\n",
    "        i = onehot.index(1.0)\n",
    "    except:\n",
    "        print(\"no valid chord from onehot\")\n",
    "    return CHORDS[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For one file C# - Chroma features using Librosa Library. shape = (12,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'C# Test3 3827-11140 iter514.wav'\n",
    "filepath = LOCAL_TRAIN_DATA + '/' + filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.04885864 -0.08361816 -0.09838867 ...  0.12991333  0.13528442\n",
      "  0.13726807]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.36102372, 0.38479376, 0.33067036, 0.32812068, 0.43780902,\n",
       "       0.7073642 , 0.45068264, 0.73381793, 1.        , 0.57231593,\n",
       "       0.28627527, 0.26780984], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audio = extract_data(filename)\n",
    "print(audio)\n",
    "chroma = data_to_chroma(audio)\n",
    "chroma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C#'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = get_chord_name(filename)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'C#'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onehot = chord_name_to_onehot(c)\n",
    "print(onehot) # C# index 9\n",
    "onehot_to_chord(onehot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract data, labels for all files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get ~600 samples for each chord to have evenly distributed data. \n",
    "### Original distribution of data is uneven: \n",
    "```\n",
    "# from collections import Counter, OrderedDict\n",
    "# count = Counter()\n",
    "# for chord in [get_chord_name(clip) for clip in train_files]:\n",
    "#     count[chord] += 1\n",
    "# count\n",
    "# OrderedDict(sorted(count.items(), key=lambda t: t[1]))\n",
    "\n",
    "OrderedDict([('.ipynb_checkpoints', 1),\n",
    "             ('F#', 583),\n",
    "             ('Bm', 591),\n",
    "             ('Fm', 593),\n",
    "             ('Bb', 595),\n",
    "             ('Bbm', 597),\n",
    "             ('Gm', 597),\n",
    "             ('C#', 599),\n",
    "             ('G#m', 599),\n",
    "             ('Cm', 599),\n",
    "             ('F#m', 599),\n",
    "             ('Dm', 600),\n",
    "             ('D#m', 600),\n",
    "             ('Noise', 600),\n",
    "             ('D#', 600),\n",
    "             ('G#', 600),\n",
    "             ('A', 900),\n",
    "             ('D', 1200),\n",
    "             ('E', 1200),\n",
    "             ('Em', 1200),\n",
    "             ('Am', 1200),\n",
    "             ('B', 1200),\n",
    "             ('C#m', 1200),\n",
    "             ('F', 1800),\n",
    "             ('G', 2400),\n",
    "             ('C', 2400)])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_files = os.listdir(LOCAL_TRAIN_DATA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('.ipynb_checkpoints', 1),\n",
       "             ('F#', 583),\n",
       "             ('Bm', 591),\n",
       "             ('Fm', 593),\n",
       "             ('Bb', 595),\n",
       "             ('Bbm', 597),\n",
       "             ('Gm', 597),\n",
       "             ('C#', 599),\n",
       "             ('G#m', 599),\n",
       "             ('Cm', 599),\n",
       "             ('F#m', 599),\n",
       "             ('D', 600),\n",
       "             ('Dm', 600),\n",
       "             ('G', 600),\n",
       "             ('C', 600),\n",
       "             ('D#m', 600),\n",
       "             ('E', 600),\n",
       "             ('Em', 600),\n",
       "             ('F', 600),\n",
       "             ('Am', 600),\n",
       "             ('Noise', 600),\n",
       "             ('B', 600),\n",
       "             ('A', 600),\n",
       "             ('C#m', 600),\n",
       "             ('D#', 600),\n",
       "             ('G#', 600)])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter, OrderedDict\n",
    "count = Counter()\n",
    "\n",
    "even_dist_train_files = []\n",
    "for clip in train_files:\n",
    "    chord = get_chord_name(clip)\n",
    "    if count[chord] < 600:\n",
    "        count[chord] += 1\n",
    "        even_dist_train_files.append(clip)\n",
    "        \n",
    "OrderedDict(sorted(count.items(), key=lambda t: t[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract audio data from all files into \"features\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "finished parsing files:  14952\n"
     ]
    }
   ],
   "source": [
    "features = []\n",
    "i = 0\n",
    "\n",
    "for file in even_dist_train_files:\n",
    "    if not file.startswith('.'):\n",
    "        data = extract_data(file)\n",
    "        chord_class = get_chord_name(file)\n",
    "        features.append([data, chord_class])\n",
    "        i += 1\n",
    "        print(i)\n",
    "    \n",
    "print(\"finished parsing files: \", i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Convert into a Panda dataframe \n",
    "import pandas as pd\n",
    "featuresdf = pd.DataFrame(features, columns=['feature','class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CONVERT DATA AND LABELS\n",
    "# Convert features and corresponding classification labels into numpy arrays\n",
    "X = np.array(featuresdf.feature.tolist())\n",
    "class_list = featuresdf['class']\n",
    "y = class_list.tolist() # list of chord labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode the classification labels to one hot vectors\n",
    "yy = [chord_name_to_onehot(label) for label in y]\n",
    "yy = np.array(yy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shuffle and split dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# split the dataset \n",
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, yy, test_size=0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11961, 8160)\n",
      "(2991, 8160)\n",
      "(11961, 25)\n",
      "(2991, 25)\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)\n",
    "print(yy.shape[1]) # num labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.0004272e-01, -1.9592285e-01, -1.9146729e-01, ...,\n",
       "         8.6059570e-03,  1.2420654e-02,  1.4434814e-02],\n",
       "       [-1.7694092e-01, -1.9152832e-01, -2.0111084e-01, ...,\n",
       "        -1.2207031e-03, -5.0354004e-03, -9.3383789e-03],\n",
       "       [-8.4594727e-02, -7.9956055e-02, -7.6263428e-02, ...,\n",
       "        -1.5304565e-01, -1.4086914e-01, -1.3394165e-01],\n",
       "       ...,\n",
       "       [ 3.0517578e-05,  9.1552734e-05,  3.0517578e-05, ...,\n",
       "        -3.9672852e-04, -9.1552734e-05, -1.5258789e-04],\n",
       "       [ 7.7056885e-02,  7.1289062e-02,  6.1157227e-02, ...,\n",
       "        -3.7179565e-01, -3.2748413e-01, -3.0020142e-01],\n",
       "       [-1.1425781e-01, -1.1654663e-01, -1.2109375e-01, ...,\n",
       "         5.2185059e-02,  5.2612305e-02,  5.2917480e-02]], dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('x_train_small.npy', 'wb') as f:\n",
    "    np.save(f, x_train[:10])\n",
    "with open('x_test_small.npy', 'wb') as f:\n",
    "    np.save(f, x_train[10:15])\n",
    "    \n",
    "with open('y_train_small.npy', 'wb') as f:\n",
    "    np.save(f, x_train[:10])\n",
    "with open('y_test_small.npy', 'wb') as f:\n",
    "    np.save(f, x_train[10:15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.0004272e-01 -1.9592285e-01 -1.9146729e-01 ...  8.6059570e-03\n",
      "   1.2420654e-02  1.4434814e-02]\n",
      " [-1.7694092e-01 -1.9152832e-01 -2.0111084e-01 ... -1.2207031e-03\n",
      "  -5.0354004e-03 -9.3383789e-03]\n",
      " [-8.4594727e-02 -7.9956055e-02 -7.6263428e-02 ... -1.5304565e-01\n",
      "  -1.4086914e-01 -1.3394165e-01]\n",
      " ...\n",
      " [ 3.0517578e-05  9.1552734e-05  3.0517578e-05 ... -3.9672852e-04\n",
      "  -9.1552734e-05 -1.5258789e-04]\n",
      " [ 7.7056885e-02  7.1289062e-02  6.1157227e-02 ... -3.7179565e-01\n",
      "  -3.2748413e-01 -3.0020142e-01]\n",
      " [-1.1425781e-01 -1.1654663e-01 -1.2109375e-01 ...  5.2185059e-02\n",
      "   5.2612305e-02  5.2917480e-02]]\n"
     ]
    }
   ],
   "source": [
    "with open('x_train_small.npy', 'rb') as f:\n",
    "    x_train_small = np.load(f)\n",
    "print(x_train_small)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Custom Keras Layer AudioToChromaLayerLARGE\n",
    "transforms raw audio into chromas averaged across time. Using CQT\n",
    "\n",
    "See more indepth exploration of chroma extraction: https://oslyn-le-v1.notebook.ca-central-1.sagemaker.aws/notebooks/oslyn-LE/Live-model-v4/Librosa%20Features%20Analysis.ipynb\n",
    "\n",
    "Input = (batchsize, 8160), Output = (batchsize, 12)\n",
    "\n",
    "### Note 1: Used magic numbers for `K`, `nfft` in `def CQT_fast` to avoid np/tf math calculations\n",
    "assumed sample rate 48,000, sample length 170ms\n",
    "we can calculate it from scratch, the code is commented on those lines\n",
    "\n",
    "### Note 2: Used `scipy.fftpack fft` in creating the sparse kernel because it can (to handle the math operations) but have to use `ft = tf.signal.fft(x)` on the input tensor because cannot convert tensor to numpy/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation, PReLU, LayerNormalization\n",
    "from sklearn import metrics "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Couldnt use librosa due to numpy tensor errors. \n",
    "Also couldnt use https://github.com/zafarrafii/Z#cqtkernel-constant-q-transform-cqt-kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from scipy.signal import hamming\n",
    "from scipy.fftpack import fft\n",
    "\n",
    "class AudioToChromaLayerLARGE(tf.keras.layers.Layer):\n",
    "    \"\"\"input is raw audio of size (*, 8160), output chromagram of size (*, 12).\"\"\"\n",
    "\n",
    "    def __init__(self, **kwargs):\n",
    "        super(AudioToChromaLayerLARGE, self).__init__(**kwargs)\n",
    "        \n",
    "    def call(self, audio): # LOGIC\n",
    "        apply_to_batch = tf.map_fn(self.compute_chroma, audio, fn_output_signature=tf.TensorSpec((12,), dtype=tf.float32))\n",
    "        return apply_to_batch\n",
    "    \n",
    "    def get_config(self):\n",
    "        config = super(AudioToChromaLayerLARGE, self).get_config()\n",
    "        return config\n",
    "    \n",
    "#     def log2(self, x):\n",
    "#         x = tf.cast(x, tf.float16)\n",
    "#         two = tf.constant(2, tf.float16)\n",
    "#         return tf.math.log(x)/tf.math.log(two)\n",
    "    \n",
    "#     def nearestPow2(self, inp):\n",
    "#         power = tf.math.ceil(self.log2(inp))\n",
    "#         return 2**power\n",
    "\n",
    "    \"\"\"Function to compute CQT using sparse matrix multiplication, Brown and Puckette 1992- fast\"\"\"\t\n",
    "    def CQT_fast(self, x,fs,bins,fmin,fmax,M):\n",
    "        threshold = 0.0054 #for Hamming window\n",
    "        K = 48          # int(bins*np.ceil(self.log2(fmax/fmin)))\n",
    "        Q = 1/(2**(1/bins)-1)\n",
    "        nfft = 8192     # tf.cast(self.nearestPow2(tf.math.ceil(Q*fs/fmin)), dtype=tf.int32)\n",
    "        tempKernel = np.zeros(nfft, dtype = np.complex)\n",
    "        specKernel = np.zeros(nfft, dtype = np.complex)\n",
    "        sparKernel = []\n",
    "\n",
    "        #create sparse Kernel \n",
    "        for k in range(K-1,-1,-1):\n",
    "            fk = (2**(k/bins))*fmin\n",
    "            N = np.int32(np.round((Q*fs)/fk))\n",
    "            tempKernel[:N] = hamming(N)/N * np.exp(-2*np.pi*1j*Q*np.arange(N)/N)\n",
    "            specKernel = fft(tempKernel)\n",
    "            specKernel[np.where(np.abs(specKernel) <= threshold)] = 0\n",
    "            if k == K-1:\n",
    "                sparKernel = specKernel\n",
    "            else:\n",
    "                sparKernel = np.vstack((specKernel, sparKernel))\n",
    "        sparKernel = np.transpose(np.conjugate(sparKernel))/nfft\n",
    "        sparKernel = tf.convert_to_tensor(sparKernel, dtype=tf.complex64)\n",
    "        x = tf.cast(x, tf.complex64)\n",
    "        padding = tf.constant([[16, 16]]) # 4112 = (nfft - x.shape[0])/2\n",
    "        x = np.pad(x, padding, mode='constant')\n",
    "        ft = tf.signal.fft(x)\n",
    "        cqt = tf.tensordot(ft, sparKernel,axes=1)\n",
    "        return cqt\n",
    "\n",
    "    @tf.function(input_signature=[tf.TensorSpec(shape=(8160,), dtype=tf.float32)])\n",
    "    def compute_chroma(self, x, fs=48000):\n",
    "        fmin = 110\n",
    "        fmax = 1760\n",
    "        bins = 12\n",
    "        M = 1\n",
    "        nOctave = np.int32(np.ceil(np.log2(fmax/fmin)))\n",
    "        CH = np.zeros(bins)\n",
    "        #Compute constant Q transform\n",
    "        cqt_fast = self.CQT_fast(x,fs,bins,fmin,fmax,M)\n",
    "        ## Compute Pitch Class Profile from constant Q transform\n",
    "        cqt_abs = tf.math.abs(cqt_fast)\n",
    "        reshaped = tf.reshape(cqt_abs, [-1, bins])\n",
    "        return tf.reduce_sum(reshaped, 0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test just AudioToChromaLayer, batched"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "in user code:\n\n    <ipython-input-26-8de17001358b>:67 compute_chroma  *\n        cqt_fast = self.CQT_fast(x,fs,bins,fmin,fmax,M)\n    <ipython-input-28-18a61cca59da>:53 CQT_fast  *\n        x = np.pad(x, padding, mode='constant')\n    /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/numpy/lib/arraypad.py:1168 pad  **\n        if not np.asarray(pad_width).dtype.kind == 'i':\n    /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/numpy/core/numeric.py:538 asarray\n        return array(a, dtype, copy=False, order=order)\n    /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/framework/ops.py:849 __array__\n        \" a NumPy call, which is not supported\".format(self.name))\n\n    NotImplementedError: Cannot convert a symbolic Tensor (Const_1:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-698506dbd5f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAudioToChromaLayerLARGE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# activation = Activation('softmax')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mchroma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;31m# chroma = norm.call(x_batch)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchroma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-28-18a61cca59da>\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, audio)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maudio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# LOGIC\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m         \u001b[0mapply_to_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_chroma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maudio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn_output_signature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensorSpec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mapply_to_batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    572\u001b[0m                   \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'in a future version'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m                   if date is None else ('after %s' % date), instructions)\n\u001b[0;32m--> 574\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m     doc = _add_deprecated_arg_value_notice_to_docstring(\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m                 \u001b[0;34m'in a future version'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'after %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m                 instructions)\n\u001b[0;32m--> 507\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m     doc = _add_deprecated_arg_notice_to_docstring(\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/ops/map_fn.py\u001b[0m in \u001b[0;36mmap_fn_v2\u001b[0;34m(fn, elems, dtype, parallel_iterations, back_prop, swap_memory, infer_shape, name, fn_output_signature)\u001b[0m\n\u001b[1;32m    646\u001b[0m       \u001b[0mswap_memory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mswap_memory\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m       \u001b[0minfer_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minfer_shape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 648\u001b[0;31m       name=name)\n\u001b[0m\u001b[1;32m    649\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    650\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m                 \u001b[0;34m'in a future version'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'after %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m                 instructions)\n\u001b[0;32m--> 507\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m     doc = _add_deprecated_arg_notice_to_docstring(\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/ops/map_fn.py\u001b[0m in \u001b[0;36mmap_fn\u001b[0;34m(fn, elems, dtype, parallel_iterations, back_prop, swap_memory, infer_shape, name, fn_output_signature)\u001b[0m\n\u001b[1;32m    504\u001b[0m         \u001b[0mback_prop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mback_prop\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m         \u001b[0mswap_memory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mswap_memory\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 506\u001b[0;31m         maximum_iterations=n)\n\u001b[0m\u001b[1;32m    507\u001b[0m     \u001b[0mresult_batchable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mr_a\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py\u001b[0m in \u001b[0;36mwhile_loop\u001b[0;34m(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name, maximum_iterations, return_same_structure)\u001b[0m\n\u001b[1;32m   2733\u001b[0m                                               list(loop_vars))\n\u001b[1;32m   2734\u001b[0m       \u001b[0;32mwhile\u001b[0m \u001b[0mcond\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mloop_vars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2735\u001b[0;31m         \u001b[0mloop_vars\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mloop_vars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2736\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtry_to_pack\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloop_vars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_basetuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2737\u001b[0m           \u001b[0mpacked\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(i, lv)\u001b[0m\n\u001b[1;32m   2724\u001b[0m         cond = lambda i, lv: (  # pylint: disable=g-long-lambda\n\u001b[1;32m   2725\u001b[0m             math_ops.logical_and(i < maximum_iterations, orig_cond(*lv)))\n\u001b[0;32m-> 2726\u001b[0;31m         \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlv\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig_body\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2727\u001b[0m       \u001b[0mtry_to_pack\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/ops/map_fn.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(i, tas)\u001b[0m\n\u001b[1;32m    488\u001b[0m       \u001b[0mag_ctx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mautograph_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol_status_ctx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m       \u001b[0mautographed_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mautograph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_convert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mag_ctx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 490\u001b[0;31m       \u001b[0mresult_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mautographed_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melems_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    491\u001b[0m       \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_same_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_output_signature\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0melems\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m       \u001b[0mresult_value_flat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    794\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    795\u001b[0m         \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 796\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    797\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    798\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    837\u001b[0m       \u001b[0;31m# This is the first call of __call__, so we have to initialize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    838\u001b[0m       \u001b[0minitializers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 839\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitializers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    840\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    841\u001b[0m       \u001b[0;31m# At this point we know that the initialization is complete (or less\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[0;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[1;32m    710\u001b[0m     self._concrete_stateful_fn = (\n\u001b[1;32m    711\u001b[0m         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\n\u001b[0;32m--> 712\u001b[0;31m             *args, **kwds))\n\u001b[0m\u001b[1;32m    713\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    714\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minvalid_creator_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0munused_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0munused_kwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2946\u001b[0m       \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2947\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2948\u001b[0;31m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2949\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2950\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   3317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3318\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3319\u001b[0;31m       \u001b[0mgraph_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3320\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3321\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m   3179\u001b[0m             \u001b[0marg_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3180\u001b[0m             \u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3181\u001b[0;31m             capture_by_value=self._capture_by_value),\n\u001b[0m\u001b[1;32m   3182\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_attributes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3183\u001b[0m         \u001b[0mfunction_spec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction_spec\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m    984\u001b[0m         \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    985\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 986\u001b[0;31m       \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    987\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    988\u001b[0m       \u001b[0;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    612\u001b[0m         \u001b[0;31m# __wrapped__ allows AutoGraph to swap in a converted function. We give\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m         \u001b[0;31m# the function a weak reference to itself to avoid a reference cycle.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 614\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    615\u001b[0m     \u001b[0mweak_wrapped_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweakref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapped_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    616\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mbound_method_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3846\u001b[0m     \u001b[0;31m# However, the replacer is still responsible for attaching self properly.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3847\u001b[0m     \u001b[0;31m# TODO(mdan): Is it possible to do it here instead?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3848\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3849\u001b[0m   \u001b[0mweak_bound_method_wrapper\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweakref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbound_method_wrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3850\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    971\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    972\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ag_error_metadata\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m               \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    974\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m               \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: in user code:\n\n    <ipython-input-26-8de17001358b>:67 compute_chroma  *\n        cqt_fast = self.CQT_fast(x,fs,bins,fmin,fmax,M)\n    <ipython-input-28-18a61cca59da>:53 CQT_fast  *\n        x = np.pad(x, padding, mode='constant')\n    /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/numpy/lib/arraypad.py:1168 pad  **\n        if not np.asarray(pad_width).dtype.kind == 'i':\n    /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/numpy/core/numeric.py:538 asarray\n        return array(a, dtype, copy=False, order=order)\n    /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/framework/ops.py:849 __array__\n        \" a NumPy call, which is not supported\".format(self.name))\n\n    NotImplementedError: Cannot convert a symbolic Tensor (Const_1:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported\n"
     ]
    }
   ],
   "source": [
    "notes = ['A','A#','B','C','C#','D','D#','E','F','F#','G','G#']\n",
    "\n",
    "x_Bbm, sample_rate = librosa.load('./24chords/F#m.wav', sr=None)\n",
    "x_C, sample_rate = librosa.load('./24chords/C.wav', sr=None)\n",
    "x_batch = np.array([x_Bbm, x_C])\n",
    "layer = AudioToChromaLayerLARGE()\n",
    "# activation = Activation('softmax')\n",
    "chroma = layer.call(x_batch)\n",
    "# chroma = norm.call(x_batch)\n",
    "print(chroma[0])\n",
    "print(chroma[1])\n",
    "\n",
    "plt.xticks(np.arange(12),notes)\n",
    "plt.title('F#m')\n",
    "plt.plot(chroma[0]) # Plot list. x-values assumed to be [0, 1, 2, 3]\n",
    "plt.show()\n",
    "plt.xticks(np.arange(12),notes)\n",
    "plt.title('C')\n",
    "plt.plot(chroma[1]) # Plot list. x-values assumed to be [0, 1, 2, 3]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "['jhngbfvdcs giubhj  # Realised: The Y-axis range varies widely, F#m had the highest values resulting in the model only predicting F#m.\n",
    "\n",
    "### Solution: Need to normalise each sample within the layer\n",
    "We can use Keras' [LayerNormalization()](https://www.tensorflow.org/api_docs/python/tf/keras/layers/LayerNormalization) after AudioToChromaLayer\n",
    "\n",
    "Normalize the activations of the previous layer for each given example in a batch independently, rather than across a batch like Batch Normalization. i.e. applies a transformation that maintains the mean activation within each example close to 0 and the activation standard deviation close to 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# THE MODEL (with LayerNormalisation after AudioToChromaLayer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # Construct model \n",
    "num_labels = yy.shape[1]\n",
    "\n",
    "model = Sequential()\n",
    "model.add(AudioToChromaLayerLARGE(input_shape=(8160,), name=\"chroma\"))\n",
    "\n",
    "model.add(LayerNormalization()) ### NECESSARY!!! Cos Chromas all diff y scale resulting only 1 prediction - the one with the highest\n",
    "\n",
    "model.add(Dense(64, name=\"dense1\"))\n",
    "model.add(PReLU())\n",
    "\n",
    "model.add(Dense(32, name=\"dense2\"))\n",
    "model.add(PReLU())\n",
    "\n",
    "model.add(Dense(32, name=\"dense3\"))\n",
    "model.add(PReLU())\n",
    "\n",
    "model.add(Dense(num_labels))\n",
    "model.add(Activation('softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display model architecture summary \n",
    "model.build(input_shape=(8160,))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = tf.keras.optimizers.Adam()\n",
    "model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-training accuracy, predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate pre-training accuracy \n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "accuracy = 100*score[1]\n",
    "\n",
    "print(\"Pre-training accuracy: %.4f%%\" % accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ACTUAL Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from datetime import datetime\n",
    "\n",
    "num_epochs = 25\n",
    "num_batch_size = 64\n",
    "\n",
    "model_path = \"./model/epoch{epoch:02d}-acc{val_accuracy:.2f}.hdf5\" # Use this to save each epoch where accuracy improves\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath=model_path, monitor='val_accuracy',\n",
    "                               verbose=1, save_best_only=True, mode='max')\n",
    "start = datetime.now()\n",
    "\n",
    "model.fit(x_train, y_train, batch_size=num_batch_size, epochs=num_epochs, validation_data=(x_test, y_test), callbacks=[checkpointer], verbose=1)\n",
    "\n",
    "duration = datetime.now() - start\n",
    "print(\"Training completed in time: \", duration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict / Validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluating the model on the training and testing set\n",
    "score = model.evaluate(x_train, y_train, verbose=0)\n",
    "print(\"Training Accuracy: \", score[1])\n",
    "\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print(\"Testing Accuracy: \", score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CHORDS = ['Noise', 'A', 'Am', 'Bb', 'Bbm', 'B', \n",
    "          'Bm', 'C', 'Cm', 'C#', 'C#m', 'D',\n",
    "          'Dm', 'D#', 'D#m','E', 'Em', 'F',\n",
    "          'Fm', 'F#', 'F#m', 'G', 'Gm', 'G#',\n",
    "          'G#m']\n",
    "\n",
    "def predict_extract_features(filename): ## NOTE NP.array at the end, diff from extract_features\n",
    "    hop_length = 512\n",
    "    try:\n",
    "        filepath = '{}/{}'.format(LOCAL_TRAIN_DATA, filename)\n",
    "        audio, sr = librosa.load(filepath, sr=None, res_type='kaiser_fast') \n",
    "    except Exception as e:\n",
    "        print(\"Error encountered while parsing file: {} {}\".format(filename, e))\n",
    "        return None \n",
    "    return np.array([audio])\n",
    "\n",
    "def predict(file_name):\n",
    "    feature = predict_extract_features(file_name)\n",
    "    predicted_vector = model.predict(feature) \n",
    "    predicted_proba = predicted_vector[0]\n",
    "    predicted_i = np.argmax(predicted_proba)\n",
    "    return CHORDS[predicted_i]\n",
    "\n",
    "def predict_proba(file_name):\n",
    "    feature = predict_extract_features(file_name)\n",
    "    predicted_vector = model.predict(feature) \n",
    "    predicted_proba = predicted_vector[0]\n",
    "    predicted_i = np.argmax(predicted_proba)\n",
    "\n",
    "    # to see probabilities of each chord:\n",
    "    for i in range(len(predicted_proba)):\n",
    "        category = CHORDS[i]\n",
    "        print(category, \"\\t\\t : \", format(predicted_proba[i], '.32f'))\n",
    "    \n",
    "    return CHORDS[predicted_i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'A Test2 32446.0-35746.0 iter0.wav'\n",
    "print('predicted: ' + predict_proba(filename) + '\\n')\n",
    "\n",
    "filename = 'C# Test3 3827-11140 iter514.wav'\n",
    "print('predicted: ' + predict_proba(filename) + '\\n')\n",
    "\n",
    "filename = 'F# Test3 34330-41240 iter254.wav'\n",
    "print('predicted: ' + predict_proba(filename) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "val_files = even_dist_train_files[:200]\n",
    "\n",
    "val_files.sort()\n",
    "\n",
    "print('chord   prediction')\n",
    "for i in range(len(val_files)):\n",
    "    file = val_files[i]\n",
    "    actual = get_chord_name(file)\n",
    "    pred = predict(file)\n",
    "    print(i, actual, ' '*(6-len(actual)), pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert keras .hdf5 file to .tflite\n",
    "\n",
    "### Problem 1: AudioToChromaLayerLARGE not found\n",
    "Solution: add `custom_objects` in `tf.keras.models.load_model`\n",
    "\n",
    "###  Problem 2: Not all tf operations are supported in tf.lite\n",
    "Solution:\n",
    "```\n",
    "converter.experimental_new_converter = True\n",
    "converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]\n",
    "```\n",
    "\n",
    "### Problem 3: `map_fn`:\n",
    "Solution: https://github.com/tensorflow/tensorflow/issues/40221\n",
    "Use TF 2.3.0 (experimental) and `@tf.function(input_signature=[tf.TensorSpec(shape=(8160,), dtype=tf.float32)])`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_best = tf.keras.models.load_model('./model/epoch24-acc0.99.hdf5',\n",
    "                        custom_objects={'AudioToChromaLayerLARGE': AudioToChromaLayerLARGE})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmpc2hyev0l/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmpc2hyev0l/assets\n"
     ]
    },
    {
     "ename": "ConverterError",
     "evalue": "/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py:1089:0: error: 'tf.FusedBatchNormV3' op is neither a custom op nor a flex op\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py:1183:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/lite/python/lite.py:810:0: note: called from\n<ipython-input-20-3a990714fb94>:5:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2963:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2903:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2785:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2662:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/ipykernel/zmqshell.py:537:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/ipykernel/ipkernel.py:208:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py:1089:0: note: see current operation: %y, %batch_mean, %batch_variance, %reserve_space_1, %reserve_space_2, %reserve_space_3 = \"tf.FusedBatchNormV3\"(%13, %9, %10, %cst_4, %cst_4) {data_format = \"NCHW\", device = \"\", epsilon = 1.000000e-03 : f32, exponential_avg_factor = 1.000000e+00 : f32, is_training = true} : (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<0xf32>, tensor<0xf32>) -> (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<*xf32>)\n<unknown>:0: error: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Pad@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): 'tf.Pad' op is neither a custom op nor a flex op\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n<unknown>:0: note: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Pad@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): see current operation: %4 = \"tf.Pad\"(%3, %cst_6) {device = \"\"} : (tensor<8160xcomplex<f32>>, tensor<1x2xi32>) -> tensor<8192xcomplex<f32>>\n<unknown>:0: error: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/FFT@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): 'tf.FFT' op is neither a custom op nor a flex op\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n<unknown>:0: note: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/FFT@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): see current operation: %5 = \"tf.FFT\"(%4) {device = \"\"} : (tensor<8192xcomplex<f32>>) -> tensor<8192xcomplex<f32>>\n<unknown>:0: error: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Tensordot/MatMul@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): 'tf.MatMul' op is neither a custom op nor a flex op\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n<unknown>:0: note: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Tensordot/MatMul@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): see current operation: %7 = \"tf.MatMul\"(%6, %cst_10) {transpose_a = false, transpose_b = true} : (tensor<1x8192xcomplex<f32>>, tensor<48x8192xcomplex<f32>>) -> tensor<1x48xcomplex<f32>>\n<unknown>:0: error: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Abs@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): 'tf.ComplexAbs' op is neither a custom op nor a flex op\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n<unknown>:0: note: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Abs@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): see current operation: %9 = \"tf.ComplexAbs\"(%8) {device = \"\"} : (tensor<48xcomplex<f32>>) -> tensor<48xf32>\n<unknown>:0: error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):\n\ttf.ComplexAbs {device = \"\"}\n\ttf.FFT {device = \"\"}\n\ttf.FusedBatchNormV3 {data_format = \"NCHW\", device = \"\", epsilon = 1.000000e-03 : f32, exponential_avg_factor = 1.000000e+00 : f32, is_training = true}\n\ttf.MatMul {transpose_a = false, transpose_b = true}\n\ttf.Pad {device = \"\"}\n<unknown>:0: note: see current operation: \"func\"() ( {\n^bb0(%arg0: tensor<?x8160xf32>):  // no predecessors\n  %cst = \"std.constant\"() {value = dense<[0.147737071, -0.112439565, 0.0381680913, -0.117878117, -0.0436722748, 0.00939085335, -0.0897184237, 0.128659829, -0.047683455, 0.0467018858, 0.0466206186, -0.00430281786, -0.0359039567, -0.0798944235, 0.0561503395, -0.0470165387, -0.0136118941, 0.0447785147, -0.148503736, 0.0860366448, 0.0881774276, 0.0692368522, 0.0460185111, 0.00844638515, -0.117945939]> : tensor<25xf32>} : () -> tensor<25xf32>\n  %cst_0 = \"std.constant\"() {value = dense<[0.168076098, 0.189250961, 0.331452399, 0.241827965, -0.0550747886, 0.221319988, 0.300828785, 0.148949519, 0.393287301, 0.196752965, 0.175839543, 0.161021888, 0.168520689, 0.117292024, 0.17293328, 0.16789341, 0.036989145, 0.27923131, -0.149837464, 0.264275849, 0.172219113, 0.10350284, 0.303251654, 0.00179201632, 0.126206487, 0.303781778, 0.357120544, 0.222722366, 0.31719318, 0.279861689, 0.141150013, 0.0332527235, 0.254058361, 0.220147535, 0.245440096, 0.136673525, 0.112707727, 0.185189515, 0.24833253, 2.750830e-01, 0.176150858, 0.268469721, 0.143250883, 0.167769864, 0.238804296, 0.0973271057, 0.224579096, 0.321996093, 0.202449262, 0.0528840162, 0.172480404, -0.106782943, 0.150560513, 0.241444588, 0.0187574979, 0.157111406, 0.200682238, 0.0350796692, 0.208003864, -0.00685206195, 0.140565231, 0.0904081687, 0.189968288, 0.0431946218]> : tensor<64xf32>} : () -> tensor<64xf32>\n  %cst_1 = \"std.constant\"() {value = dense<[0.0549458079, 0.238936529, 0.103667565, 0.0921238735, 0.143788829, 0.0911116451, 0.175867394, 0.21835795, -0.0401271246, 0.0155029483, 0.122010075, 0.223504156, 0.100209974, -0.0316341706, 0.0610777251, 0.0399796627, 0.204122216, 0.131453395, 0.252934813, 0.182648122, 0.104780763, 0.126945794, 0.0563390441, 0.0932675302, -0.00845822785, 0.171583086, 0.182540447, 0.0219219904, 0.075256735, -0.0730319172, 0.0945926234, 0.03456036]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %cst_2 = \"std.constant\"() {value = dense<[-0.0118571837, 0.0800256952, 0.170635894, 0.028398335, 0.259022981, 0.0898712798, 0.175803497, 0.161798835, 0.0338865407, -3.69851245E-4, 0.0425762795, 0.212983623, -0.0411456116, 0.0527183078, 0.119301379, 0.065352641, 0.0161737688, 0.0426695272, -0.00505993469, 0.085072346, 0.0391224585, 0.0117550725, 0.039498996, -0.00897677056, -8.358640e-02, 0.179748133, -0.0227445941, 0.0897767916, 0.0937709659, 0.091977097, -0.112039283, 0.133670092]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %cst_3 = \"std.constant\"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>\n  %cst_4 = \"std.constant\"() {value = dense<> : tensor<0xf32>} : () -> tensor<0xf32>\n  %cst_5 = \"std.constant\"() {value = dense<[0.00725550949, -0.155836254, 0.117213838, -7.415890e-02, -0.22646898, -0.17580685, 0.145644963, -0.127022445, -0.0325459577, -0.229648903, -0.162604809, -0.104097113]> : tensor<12xf32>} : () -> tensor<12xf32>\n  %cst_6 = \"std.constant\"() {value = dense<[1.40223753, 1.42622852, 1.53603494, 1.36610556, 1.69401383, 1.55997336, 1.62918568, 1.57092464, 1.45249867, 1.6583848, 1.62247539, 1.52738059]> : tensor<12xf32>} : () -> tensor<12xf32>\n  %cst_7 = \"std.constant\"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>\n  %cst_8 = \"std.constant\"() {value = dense<12> : tensor<1xi32>} : () -> tensor<1xi32>\n  %cst_9 = \"std.constant\"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>\n  %cst_10 = \"std.constant\"() {value = dense<0.000000e+00> : tensor<f32>} : () -> tensor<f32>\n  %cst_11 = \"std.constant\"() {value = dense<\"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tensor<25x32xf32>} : () -> tensor<25x32xf32>\n  %cst_12 = \"std.constant\"() {value = dense<\"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tensor<64x12xf32>} : () -> tensor<64x12xf32>\n  %cst_13 = \"std.constant\"() {value = dense<\"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\"> : tensor<32x64xf32>} : () -> tensor<32x64xf32>\n  %cst_14 = \"std.constant\"() {value = dense<\"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\"> : tensor<32x32xf32>} : () -> tensor<32x32xf32>\n  %cst_15 = \"std.constant\"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>\n  %cst_16 = \"std.constant\"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>\n  %cst_17 = \"std.constant\"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>\n  %cst_18 = \"std.constant\"() {value = dense<[-0.18137075, -0.190813139, -0.044286862, 0.211237565, -1.746690e-01, -0.151737764, 0.16834183, 0.119328104, 0.105832867, -0.0240005907, -0.0751447379, 0.037534222, -0.342453718, 0.186597332, -0.310402393, 0.018641986, 0.161040291, 0.232865468, -0.081227988, 0.160159558, -0.437950075, -0.137296706, -0.329482079, -0.239970639, -0.0312041733, 0.190941975, -0.402803183, 0.483502597, 0.173778772, 0.298825443, 0.334926784, 0.363431662, 0.524393857, -0.133963794, 0.304172069, -0.42725718, 0.16676645, 0.0580276921, 0.193012163, 0.0510975271, 0.813377559, 0.0750992373, 0.161859617, -0.254672736, 0.190442935, -0.226609036, -0.343025893, -0.335672051, -0.34258005, -0.254799277, 0.181894705, -0.34304902, 0.131500974, 0.0329634733, 0.0467517935, 0.325228602, -0.512234688, 0.171455756, -0.0636215433, -0.16159603, 0.178863093, -0.261178225, -0.211183608, 0.135612398]> : tensor<64xf32>} : () -> tensor<64xf32>\n  %cst_19 = \"std.constant\"() {value = dense<[0.360518038, -0.168143243, 0.11197973, 0.127239093, 0.0691332668, -0.266665071, 0.0290160123, 0.169769675, 0.236813396, 0.418850154, -0.415096372, -0.178710625, 0.493837386, -0.269553035, 0.242321983, 0.225369349, -0.294833422, -0.269634217, 0.524404645, 0.0572571382, -0.173829466, -0.321533293, 0.339223772, -0.0190662649, 0.203185797, -0.0887959301, 0.0538560525, -0.392667502, 0.318463176, -0.404079944, 0.492643982, 0.248903751]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %cst_20 = \"std.constant\"() {value = dense<[-0.274599046, 0.2693699, -0.0384969115, 0.547718585, 0.44997403, -0.281009167, -0.0743118376, 0.0901606306, 0.306658685, -0.00219570659, -0.0720264911, -0.340246856, -0.0986638963, 0.286379397, -0.0639733076, 0.49492836, 0.157945499, -0.232450366, 0.11595349, 0.357020527, -0.306564212, -0.223095223, 0.32178998, 0.656092644, 0.211648986, 0.0336364061, -0.346420258, -0.214254037, 0.199852765, -0.188247502, 0.677568316, 0.299702734]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %0 = \"tfl.shape\"(%arg0) : (tensor<?x8160xf32>) -> tensor<2xi32>\n  %1 = \"tfl.strided_slice\"(%0, %cst_15, %cst_17, %cst_17) {begin_mask = 0 : i32, ellipsis_mask = 0 : i32, end_mask = 0 : i32, new_axis_mask = 0 : i32, shrink_axis_mask = 1 : i32} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %2 = \"tfl.reshape\"(%1, %cst_17) : (tensor<i32>, tensor<1xi32>) -> tensor<1xi32>\n  %3 = \"tfl.concatenation\"(%2, %cst_8) {axis = 0 : i32, fused_activation_function = \"NONE\"} : (tensor<1xi32>, tensor<1xi32>) -> tensor<2xi32>\n  %4 = \"tfl.fill\"(%3, %cst_10) : (tensor<2xi32>, tensor<f32>) -> tensor<?x?xf32>\n  %5:5 = \"tfl.while\"(%cst_9, %cst_9, %4, %1, %arg0) ( {\n  ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<?x?xf32>):  // no predecessors\n    %25 = \"std.call\"(%arg1, %arg2, %arg3, %1, %arg0) {callee = @\"sequential/chroma/map/while_cond\"} : (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> tensor<i1>\n    \"tfl.yield\"(%25) : (tensor<i1>) -> ()\n  },  {\n  ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<?x?xf32>):  // no predecessors\n    %25:5 = \"std.call\"(%arg1, %arg2, %arg3, %1, %arg0) {callee = @\"sequential/chroma/map/while_body\"} : (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n    \"tfl.yield\"(%25#0, %25#1, %25#2, %25#3, %25#4) : (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> ()\n  }) {is_stateless = true} : (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n  %6 = \"tfl.shape\"(%5#2) : (tensor<?x?xf32>) -> tensor<2xi32>\n  %7 = \"tfl.strided_slice\"(%6, %cst_15, %cst_17, %cst_17) {begin_mask = 0 : i32, ellipsis_mask = 0 : i32, end_mask = 0 : i32, new_axis_mask = 0 : i32, shrink_axis_mask = 1 : i32} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %8 = \"tfl.pack\"(%7) {axis = 0 : i32, values_count = 1 : i32} : (tensor<i32>) -> tensor<1xi32>\n  %9 = \"tfl.fill\"(%8, %cst_3) : (tensor<1xi32>, tensor<f32>) -> tensor<?xf32>\n  %10 = \"tfl.fill\"(%8, %cst_10) : (tensor<1xi32>, tensor<f32>) -> tensor<?xf32>\n  %11 = \"tfl.strided_slice\"(%6, %cst_17, %cst_16, %cst_17) {begin_mask = 0 : i32, ellipsis_mask = 0 : i32, end_mask = 0 : i32, new_axis_mask = 0 : i32, shrink_axis_mask = 1 : i32} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %12 = \"tfl.pack\"(%cst_7, %7, %11, %cst_7) {axis = 0 : i32, values_count = 4 : i32} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<4xi32>\n  %13 = \"tfl.reshape\"(%5#2, %12) : (tensor<?x?xf32>, tensor<4xi32>) -> tensor<1x?x?x1xf32>\n  %y, %batch_mean, %batch_variance, %reserve_space_1, %reserve_space_2, %reserve_space_3 = \"tf.FusedBatchNormV3\"(%13, %9, %10, %cst_4, %cst_4) {data_format = \"NCHW\", device = \"\", epsilon = 1.000000e-03 : f32, exponential_avg_factor = 1.000000e+00 : f32, is_training = true} : (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<0xf32>, tensor<0xf32>) -> (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<*xf32>)\n  %14 = \"tfl.reshape\"(%y, %6) : (tensor<1x?x?x1xf32>, tensor<2xi32>) -> tensor<?x?xf32>\n  %15 = \"tfl.mul\"(%14, %cst_6) {fused_activation_function = \"NONE\"} : (tensor<?x?xf32>, tensor<12xf32>) -> tensor<?x12xf32>\n  %16 = \"tfl.add\"(%15, %cst_5) {fused_activation_function = \"NONE\"} : (tensor<?x12xf32>, tensor<12xf32>) -> tensor<?x12xf32>\n  %17 = \"tfl.fully_connected\"(%16, %cst_12, %cst_0) {fused_activation_function = \"NONE\", keep_num_dims = false, weights_format = \"DEFAULT\"} : (tensor<?x12xf32>, tensor<64x12xf32>, tensor<64xf32>) -> tensor<?x64xf32>\n  %18 = \"tfl.prelu\"(%17, %cst_18) : (tensor<?x64xf32>, tensor<64xf32>) -> tensor<?x64xf32>\n  %19 = \"tfl.fully_connected\"(%18, %cst_13, %cst_1) {fused_activation_function = \"NONE\", keep_num_dims = false, weights_format = \"DEFAULT\"} : (tensor<?x64xf32>, tensor<32x64xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %20 = \"tfl.prelu\"(%19, %cst_19) : (tensor<?x32xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %21 = \"tfl.fully_connected\"(%20, %cst_14, %cst_2) {fused_activation_function = \"NONE\", keep_num_dims = false, weights_format = \"DEFAULT\"} : (tensor<?x32xf32>, tensor<32x32xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %22 = \"tfl.prelu\"(%21, %cst_20) : (tensor<?x32xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %23 = \"tfl.fully_connected\"(%22, %cst_11, %cst) {fused_activation_function = \"NONE\", keep_num_dims = false, weights_format = \"DEFAULT\"} : (tensor<?x32xf32>, tensor<25x32xf32>, tensor<25xf32>) -> tensor<?x25xf32>\n  %24 = \"tfl.softmax\"(%23) {beta = 1.000000e+00 : f32} : (tensor<?x25xf32>) -> tensor<?x25xf32>\n  \"std.return\"(%24) : (tensor<?x25xf32>) -> ()\n}) {sym_name = \"main\", tf.entry_function = {control_outputs = \"\", inputs = \"chroma_input\", outputs = \"Identity\"}, type = (tensor<?x8160xf32>) -> tensor<?x25xf32>} : () -> ()\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/lite/python/convert.py\u001b[0m in \u001b[0;36mtoco_convert_protos\u001b[0;34m(model_flags_str, toco_flags_str, input_data_str, debug_info_str, enable_mlir_converter)\u001b[0m\n\u001b[1;32m    198\u001b[0m                                                  \u001b[0mdebug_info_str\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                                                  enable_mlir_converter)\n\u001b[0m\u001b[1;32m    200\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_str\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/lite/python/wrap_toco.py\u001b[0m in \u001b[0;36mwrapped_toco_convert\u001b[0;34m(model_flags_str, toco_flags_str, input_data_str, debug_info_str, enable_mlir_converter)\u001b[0m\n\u001b[1;32m     37\u001b[0m       \u001b[0mdebug_info_str\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m       enable_mlir_converter)\n\u001b[0m\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mException\u001b[0m: /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py:1089:0: error: 'tf.FusedBatchNormV3' op is neither a custom op nor a flex op\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py:1183:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/lite/python/lite.py:810:0: note: called from\n<ipython-input-20-3a990714fb94>:5:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2963:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2903:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2785:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2662:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/ipykernel/zmqshell.py:537:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/ipykernel/ipkernel.py:208:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py:1089:0: note: see current operation: %y, %batch_mean, %batch_variance, %reserve_space_1, %reserve_space_2, %reserve_space_3 = \"tf.FusedBatchNormV3\"(%13, %9, %10, %cst_4, %cst_4) {data_format = \"NCHW\", device = \"\", epsilon = 1.000000e-03 : f32, exponential_avg_factor = 1.000000e+00 : f32, is_training = true} : (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<0xf32>, tensor<0xf32>) -> (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<*xf32>)\n<unknown>:0: error: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Pad@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): 'tf.Pad' op is neither a custom op nor a flex op\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n<unknown>:0: note: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Pad@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): see current operation: %4 = \"tf.Pad\"(%3, %cst_6) {device = \"\"} : (tensor<8160xcomplex<f32>>, tensor<1x2xi32>) -> tensor<8192xcomplex<f32>>\n<unknown>:0: error: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/FFT@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): 'tf.FFT' op is neither a custom op nor a flex op\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n<unknown>:0: note: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/FFT@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): see current operation: %5 = \"tf.FFT\"(%4) {device = \"\"} : (tensor<8192xcomplex<f32>>) -> tensor<8192xcomplex<f32>>\n<unknown>:0: error: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Tensordot/MatMul@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): 'tf.MatMul' op is neither a custom op nor a flex op\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n<unknown>:0: note: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Tensordot/MatMul@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): see current operation: %7 = \"tf.MatMul\"(%6, %cst_10) {transpose_a = false, transpose_b = true} : (tensor<1x8192xcomplex<f32>>, tensor<48x8192xcomplex<f32>>) -> tensor<1x48xcomplex<f32>>\n<unknown>:0: error: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Abs@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): 'tf.ComplexAbs' op is neither a custom op nor a flex op\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n<unknown>:0: note: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Abs@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): see current operation: %9 = \"tf.ComplexAbs\"(%8) {device = \"\"} : (tensor<48xcomplex<f32>>) -> tensor<48xf32>\n<unknown>:0: error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):\n\ttf.ComplexAbs {device = \"\"}\n\ttf.FFT {device = \"\"}\n\ttf.FusedBatchNormV3 {data_format = \"NCHW\", device = \"\", epsilon = 1.000000e-03 : f32, exponential_avg_factor = 1.000000e+00 : f32, is_training = true}\n\ttf.MatMul {transpose_a = false, transpose_b = true}\n\ttf.Pad {device = \"\"}\n<unknown>:0: note: see current operation: \"func\"() ( {\n^bb0(%arg0: tensor<?x8160xf32>):  // no predecessors\n  %cst = \"std.constant\"() {value = dense<[0.147737071, -0.112439565, 0.0381680913, -0.117878117, -0.0436722748, 0.00939085335, -0.0897184237, 0.128659829, -0.047683455, 0.0467018858, 0.0466206186, -0.00430281786, -0.0359039567, -0.0798944235, 0.0561503395, -0.0470165387, -0.0136118941, 0.0447785147, -0.148503736, 0.0860366448, 0.0881774276, 0.0692368522, 0.0460185111, 0.00844638515, -0.117945939]> : tensor<25xf32>} : () -> tensor<25xf32>\n  %cst_0 = \"std.constant\"() {value = dense<[0.168076098, 0.189250961, 0.331452399, 0.241827965, -0.0550747886, 0.221319988, 0.300828785, 0.148949519, 0.393287301, 0.196752965, 0.175839543, 0.161021888, 0.168520689, 0.117292024, 0.17293328, 0.16789341, 0.036989145, 0.27923131, -0.149837464, 0.264275849, 0.172219113, 0.10350284, 0.303251654, 0.00179201632, 0.126206487, 0.303781778, 0.357120544, 0.222722366, 0.31719318, 0.279861689, 0.141150013, 0.0332527235, 0.254058361, 0.220147535, 0.245440096, 0.136673525, 0.112707727, 0.185189515, 0.24833253, 2.750830e-01, 0.176150858, 0.268469721, 0.143250883, 0.167769864, 0.238804296, 0.0973271057, 0.224579096, 0.321996093, 0.202449262, 0.0528840162, 0.172480404, -0.106782943, 0.150560513, 0.241444588, 0.0187574979, 0.157111406, 0.200682238, 0.0350796692, 0.208003864, -0.00685206195, 0.140565231, 0.0904081687, 0.189968288, 0.0431946218]> : tensor<64xf32>} : () -> tensor<64xf32>\n  %cst_1 = \"std.constant\"() {value = dense<[0.0549458079, 0.238936529, 0.103667565, 0.0921238735, 0.143788829, 0.0911116451, 0.175867394, 0.21835795, -0.0401271246, 0.0155029483, 0.122010075, 0.223504156, 0.100209974, -0.0316341706, 0.0610777251, 0.0399796627, 0.204122216, 0.131453395, 0.252934813, 0.182648122, 0.104780763, 0.126945794, 0.0563390441, 0.0932675302, -0.00845822785, 0.171583086, 0.182540447, 0.0219219904, 0.075256735, -0.0730319172, 0.0945926234, 0.03456036]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %cst_2 = \"std.constant\"() {value = dense<[-0.0118571837, 0.0800256952, 0.170635894, 0.028398335, 0.259022981, 0.0898712798, 0.175803497, 0.161798835, 0.0338865407, -3.69851245E-4, 0.0425762795, 0.212983623, -0.0411456116, 0.0527183078, 0.119301379, 0.065352641, 0.0161737688, 0.0426695272, -0.00505993469, 0.085072346, 0.0391224585, 0.0117550725, 0.039498996, -0.00897677056, -8.358640e-02, 0.179748133, -0.0227445941, 0.0897767916, 0.0937709659, 0.091977097, -0.112039283, 0.133670092]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %cst_3 = \"std.constant\"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>\n  %cst_4 = \"std.constant\"() {value = dense<> : tensor<0xf32>} : () -> tensor<0xf32>\n  %cst_5 = \"std.constant\"() {value = dense<[0.00725550949, -0.155836254, 0.117213838, -7.415890e-02, -0.22646898, -0.17580685, 0.145644963, -0.127022445, -0.0325459577, -0.229648903, -0.162604809, -0.104097113]> : tensor<12xf32>} : () -> tensor<12xf32>\n  %cst_6 = \"std.constant\"() {value = dense<[1.40223753, 1.42622852, 1.53603494, 1.36610556, 1.69401383, 1.55997336, 1.62918568, 1.57092464, 1.45249867, 1.6583848, 1.62247539, 1.52738059]> : tensor<12xf32>} : () -> tensor<12xf32>\n  %cst_7 = \"std.constant\"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>\n  %cst_8 = \"std.constant\"() {value = dense<12> : tensor<1xi32>} : () -> tensor<1xi32>\n  %cst_9 = \"std.constant\"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>\n  %cst_10 = \"std.constant\"() {value = dense<0.000000e+00> : tensor<f32>} : () -> tensor<f32>\n  %cst_11 = \"std.constant\"() {value = dense<\"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tensor<25x32xf32>} : () -> tensor<25x32xf32>\n  %cst_12 = \"std.constant\"() {value = dense<\"0x40AAFD3E3D83163EE94C2CBE7D0EFC3AEB638C3E97A1E2BCBF9EA6BE11709DBE73EA8ABEC78EC53D5DC3E13EAFB08DBE8888E23EDDCD0CBDD55303BFC489E7BE18822ABEBAB7A13E5E6F363EDD009ABE954510BF1D8FA93EC983833EF787393E7FDEA43E5079B43E04B84EBF22E398BEB191823C083076BE73D15A3DFEA75F3EAE0E113DEE1B723E9CFE95BE1E2155BFFDC38C3C77C0733E8F2A8B3E181A4EBE62B9FBBE114D873D3DA7EB3E6108043F17D27C3DE8E3A6BDC65BF5BE56B58ABE1799C53EE531F23BB9BA20BE9215E7BEBA0DA0BEAEFAD13EBE1C433EFD361EBED47F493EB9FA533EB7EA4DBE815A96BE58ECB43E43468ABEA4C410BEFA24A23E2387B13D77075FBC417609BF273038BEDAFC9D3EA1B9ACBE27337CBE1E9668BE12BD72BEB0C6493E3A51AB3EE0B15CBE443190BE6A0C2DBE04F86D3E090B863D906CFF3EDDCB233E84B4E4BE164AF3BCA1969D3E4A9309BF173203BD766D88BE11D4033FC032893D93F9AEBE8EB300BD9D875EBEF3BD2E3EAE8AB2BE591B49BD1C140FBE399645BDEC3CBFBE12BF04BF00315A3E5F1AE13E7A5E6BBEE9B6E3BD1EEB093F4E8C71BEC14044BE1F5D193E8BE65CBE7871FF3E668404BDC2DC58BD9770F9BE0D52E2BDEDBF5E3EA98CF4BEB25EC93DD77F7DBE2068193FD5CFAEBE9D98953E77F2993C8071803E69AAA3BDAE73743EC788AEBE922797BE7DD2E7BE8639553EF6EC343FC2B334BEEA5EE4BE6242E63ED60CFBBE944A3BBF24443E3E7A0671BC1B78513E2117983EA55C7CBE71B8453E2C5A3B3E66D627BF9675203E831BB23E8C90AC3EE42E1CBFE135CBBEF62FC0BEE7AD21BE6C6D363FBE1D65BD5ADC1CBE7973DBBEE9B8AC3E18DB97BEB3F285BEC1E739BE9A82473E732E933EFD8EF73D3BEC9E3EF3A7E23E265093BED514DDBEE96F1BBF96CDA73E8CCE9A3C73FD863C1F4B7CBEB110C13ED4D5C8BDB6DB6CBE070751BED43206BEAC178F3E50805C3CA5C92ABED7250ABFABE5D23DACEB12BFA94EFBBD5CA0C73EC381443E49B3F6BCAEA3E23EFDB045BEB8BAC3BD4A2DAD3D0199F9BE22A5503EFB0416BF075809BE30DD82BEA18518BE2671EB3E23F8223F9BDB6EBEA756EE3BC075813E1334953E958F13BF5E0C98BEBC89E53E84BBD5BEB6BF87BD4FC0E63EB1F50A3C2EA733BEA0FD9E3DDF6BCE3EE219093F4A63ACBE1E8D80BD3B0B1ABC416010BF38F6A33ED630A5BEC70C913EFD8AD63E81EE1FBE62CCEBBB30B968BB50D3F03D109AECBECC822DBE9B3601BE5CF3783E19638A3DB08322BF66AB143E0CF5B13E9FB2E43E17EE883E73C6003F35D328BF0EFFD3BEC976E3BD34CE5EBE0578B43D324F8E3EDCCF993E48D9BBBE7A484EBD242711BD23F8363EB63D1C3FF053DB3DA9F7E53D8F3591BEF9020DBFE2B59FBE34EE703E0FCBEFBE50C7BD3E075E4CBEB16DBF3D840B083F015B49BE99D51E3FF5FCA6BEEDF20DBFC0E283BCEEB203BEEED3B4BEDC8AB9BE413144BC23D99CBB46BCCFBEFA9FDFBEA5486D3DFDDE1C3E2FDDFEBDA834D4BE5979E83E6F86D83E23DB2D3E8B3227BE75E88DBED1273BBE159997BD272C113F8CA5D63E9DF259BD278BB23EDB9897BE19492CBD5ED7DF3EF697E53C3D3A72BDEF5D953D4AD3ACBC5C80F73EC518D53E502325BE4F61D1BE3E3F07BFECBC0D3E7D6CC03E429485BE819F11BEA63B83BDD4C7C83E3E1A8FBEC879233F87C6A93E77DD02BF1119E5BE0D2F853EB23F8BBE4621B5BEB5B2C73EE17230BECDF3913DBC6D11BFDC803EBFA1D6A63E542870BBFD8E0B3D413FA4BECBB65CBD3E83693E69198FBE9106693D25819FBE5C1A3E3EDD10CE3E285AA6BC32FF12BF12E5D83D8B04B53EF0D608BFC5BC08BFB2E4813EA5ED913D563A90BEFE8FDFBE6101BDBEF380A239A40FDEBC3DD5BFBED83A98BD61CB29BB52DF32BE4D81613E6119D3BE1B28003F51BAF93EE65C7D3E0D48B2BDEFAE11BDE385B7BE093CAABC771AE23E7D7602BF91FCD8BE48C2F8BEC710BC3E80E6843D92ABCBBE7B50A03EE2BD8CBEADC506BED1B9B53EBCCB713E5FBEE8BE6F527A3D50C2AE3E1426803E6A5A51BE13565FBE5A12923DD9FF7CBE8B125D3E2B3EBF3E7BABAEBE5E37C03E4959AB3E154F163EAAFC03BF946B02BE9EB287BE20F9E13EBE582C3E6E1A793EA2EC1D3EE0875D3E8CA3AE3E5E3231BDC2A915BF1434B43E2103D23E8070713CFD4DE3BE6A1EBA3D1B79EE3DBB188A3ED6FDC93D08EB49BE51E98B3C496D243F38EAE5BE080E05BFB197DC3D87DB4C3D31CECDBE5BFDB3BB063D2CBE0E822B3C17B6ECBC1CA224BEDF93B83D599295BD784DB3BE0425253CCA7916BF56AE003F38E53DBEE8167E3D8C71573E6A4C833D83361F3E62598B3ECA40D63C60DE273D331BF7BD207A1ABF2BCD87BE91C91C3F8D63163F5CE5B7BE8B23503E26CA633EF77C763D431C0B3FD5A0633D1ADA013E915B0FBFBD3A42BE85D27F3DEA98A2BC102FF7BE6F6C1BBFF6BB0C3FF88EA73E79E2A4BD5DB3833E2AF884BE409F10BF76EC22BF1BE21C3F305B6D3DC3651DBF7D5FE03EEC08A5BE07C1ED3DB13FECBE3D03CEBE65A07B3EF90A2D3ED22BCABE0A57653DD46B943DB6CBB5BDBE1AFBBE43323A3E7E6D453EC0CD6C3EBC4D6EBEC7E270BE3D65E7BEDE855DBE2F59EB3EF639703ED595D13D4C131CBF09F0293EB0947ABE0E93EA3C48DB713E76DD2CBE4AC75ABED616903E78DFBEBDB30E28BF175AA2BC2713D23E70E2E6BE19ACDBBEA2595D3BD66E65BE00323F3EF05F33BE4BB6EFBE4C7684BE0D88953E80210BBEB42DFEBED405D53D3AEC76BE3F41053ECE5115BCCD2D113F23B5E3BEA0D17DBEC16082BE0DEAE2BEEE35BABEDCBB9CBE72CB233F42A7CFBE0FF2E7BE4A1DB73EDA18AFBE28D9ECBED2BA343C181FBCBEC388663E666D003E2E60F9BE4B690BBD65EF4E3D8B799FBEFB04DEBE1A531D3F56E300BD696438BEFA998EBEA69B73BE688FAE3EA485423E534B19BE032989BEDB7E80BD99A396BEDC055DBE4CA1063CF026C4BE7BCC2B3E431CDCBEE695D13C653033BF8C09ABBF985516BE9BFC123E08D130BEADFF783DC493033EF9FEAE3E1B3C47BD2DE2F63E1AB96C3EF2663DBEE73310BF592C79BE81A9293F55E3A1BE08BEDBBE87548F3E4BF6B3BE71D3C63D926CD93E45FCADBE262AAFBD1A6004BF93900ABF9666813E0A36333FDF2E84BE07AE5BBE59CB5D3E31267E3EF2C6AEBEC0ED253A5A038C3E994037BFEA4934BEC65BD13E69F03CBE6B5E363EBF04253F279DF43D677F88BE25D593BE957025BF1B77573E2BC50F3D1A2814BF5204AB3ECE40CDBCD41845BECA64FABE5EA8F4BE36E07FBEC159F83E1EA0973EBB9FA5BE60B7433E5A2DA23C10A298BEC502B9BE1388A7BD90CCD03D653B3F3DA6E392BE36D7E3BE7D2320BF32AB263F081C383E416606BF557ACD3D4B3AC73D0A007F3E017F033F839648BE14E125BEB5E998BE3CE67A3D31BA563E983F0FBF8925913E6187A9BED477763E52A82BBEB1C98F3E0FE3123D4F0605BEEB48543BD4D42CBE4581F9BE399B4C3F7FE5E73D3F519B3DED8308BFB88CB5BE55F13F3EFDA22E3EC2B7853E2B5249BEEE34143E7B594C3E4E27A83E8C890DBFEEE6923B63E1183FD3953DBD57E05BBD1AB38EBEDBF520BF387CB43DBDAFD03E894C863EE6736BBE14BEDB3EC757B53D4544313FD1EEB1BD8F71BCBEDA77743EE1D900BE6604743EA5B602BFFFBE3E3D2765BE3E13B70C3F823BC3BEE8A4683E711E2D3E825E0E3F70D8C5BE6CDE17BF0C2BB63D97237C3E555AD3BEAE52783DFA610DBF873EB3BECEDB113EC7DEC93ECAEF253D8B741B3E274083BEA7CD63BE8522873E18829CBDE8BC323E78173E3E98C7C73E5EF0CD3E69D7923E036E233F2B8EDCBEB167EDBEDDBAF8BE676E773EA3FA5A3E2D495BBD6473C8BE490881BDB11DD23E884219BE7753C3BE202ABD3E897F543E3BEE0B3EBD92B6BE0DAF033D5A50903ED62D9F3EF1264C3EFEDF94BE4AD20BBF645E01BFCAAD173F1D5F0E3F27EC0E3E9E20853EB275D83D82187E3EF48972BECBF3D1BE503CAF3DAABD8F3EF43996BE96950C3ECFDF063F1A506B3E630477BE8B3264BE68C540BE11C9223F541003BE2B2B5C3E27EA30BC8AA2D0BE7750B63E7014B3BDF846EBBEB40ACD3E9DA4B8BEBFCF0FBB98E906BF5E70063F0DA99F3ED3ABBB3DF9CBB1BEDFE76BBCEB0C433DA0DD33BF6466833E74B0FCBEF2C066BE6456C73DD91CB83E98C1543EC401C2BE0A88403D13B1AB3EFF2982BEA656083F781B70BE09BD5CBED8CA19BD583D1E3FDBA9CC3E8F5C31BF280BE93EC81FC6BE\"> : tensor<64x12xf32>} : () -> tensor<64x12xf32>\n  %cst_13 = \"std.constant\"() {value = dense<\"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\"> : tensor<32x64xf32>} : () -> tensor<32x64xf32>\n  %cst_14 = \"std.constant\"() {value = dense<\"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\"> : tensor<32x32xf32>} : () -> tensor<32x32xf32>\n  %cst_15 = \"std.constant\"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>\n  %cst_16 = \"std.constant\"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>\n  %cst_17 = \"std.constant\"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>\n  %cst_18 = \"std.constant\"() {value = dense<[-0.18137075, -0.190813139, -0.044286862, 0.211237565, -1.746690e-01, -0.151737764, 0.16834183, 0.119328104, 0.105832867, -0.0240005907, -0.0751447379, 0.037534222, -0.342453718, 0.186597332, -0.310402393, 0.018641986, 0.161040291, 0.232865468, -0.081227988, 0.160159558, -0.437950075, -0.137296706, -0.329482079, -0.239970639, -0.0312041733, 0.190941975, -0.402803183, 0.483502597, 0.173778772, 0.298825443, 0.334926784, 0.363431662, 0.524393857, -0.133963794, 0.304172069, -0.42725718, 0.16676645, 0.0580276921, 0.193012163, 0.0510975271, 0.813377559, 0.0750992373, 0.161859617, -0.254672736, 0.190442935, -0.226609036, -0.343025893, -0.335672051, -0.34258005, -0.254799277, 0.181894705, -0.34304902, 0.131500974, 0.0329634733, 0.0467517935, 0.325228602, -0.512234688, 0.171455756, -0.0636215433, -0.16159603, 0.178863093, -0.261178225, -0.211183608, 0.135612398]> : tensor<64xf32>} : () -> tensor<64xf32>\n  %cst_19 = \"std.constant\"() {value = dense<[0.360518038, -0.168143243, 0.11197973, 0.127239093, 0.0691332668, -0.266665071, 0.0290160123, 0.169769675, 0.236813396, 0.418850154, -0.415096372, -0.178710625, 0.493837386, -0.269553035, 0.242321983, 0.225369349, -0.294833422, -0.269634217, 0.524404645, 0.0572571382, -0.173829466, -0.321533293, 0.339223772, -0.0190662649, 0.203185797, -0.0887959301, 0.0538560525, -0.392667502, 0.318463176, -0.404079944, 0.492643982, 0.248903751]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %cst_20 = \"std.constant\"() {value = dense<[-0.274599046, 0.2693699, -0.0384969115, 0.547718585, 0.44997403, -0.281009167, -0.0743118376, 0.0901606306, 0.306658685, -0.00219570659, -0.0720264911, -0.340246856, -0.0986638963, 0.286379397, -0.0639733076, 0.49492836, 0.157945499, -0.232450366, 0.11595349, 0.357020527, -0.306564212, -0.223095223, 0.32178998, 0.656092644, 0.211648986, 0.0336364061, -0.346420258, -0.214254037, 0.199852765, -0.188247502, 0.677568316, 0.299702734]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %0 = \"tfl.shape\"(%arg0) : (tensor<?x8160xf32>) -> tensor<2xi32>\n  %1 = \"tfl.strided_slice\"(%0, %cst_15, %cst_17, %cst_17) {begin_mask = 0 : i32, ellipsis_mask = 0 : i32, end_mask = 0 : i32, new_axis_mask = 0 : i32, shrink_axis_mask = 1 : i32} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %2 = \"tfl.reshape\"(%1, %cst_17) : (tensor<i32>, tensor<1xi32>) -> tensor<1xi32>\n  %3 = \"tfl.concatenation\"(%2, %cst_8) {axis = 0 : i32, fused_activation_function = \"NONE\"} : (tensor<1xi32>, tensor<1xi32>) -> tensor<2xi32>\n  %4 = \"tfl.fill\"(%3, %cst_10) : (tensor<2xi32>, tensor<f32>) -> tensor<?x?xf32>\n  %5:5 = \"tfl.while\"(%cst_9, %cst_9, %4, %1, %arg0) ( {\n  ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<?x?xf32>):  // no predecessors\n    %25 = \"std.call\"(%arg1, %arg2, %arg3, %1, %arg0) {callee = @\"sequential/chroma/map/while_cond\"} : (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> tensor<i1>\n    \"tfl.yield\"(%25) : (tensor<i1>) -> ()\n  },  {\n  ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<?x?xf32>):  // no predecessors\n    %25:5 = \"std.call\"(%arg1, %arg2, %arg3, %1, %arg0) {callee = @\"sequential/chroma/map/while_body\"} : (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n    \"tfl.yield\"(%25#0, %25#1, %25#2, %25#3, %25#4) : (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> ()\n  }) {is_stateless = true} : (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n  %6 = \"tfl.shape\"(%5#2) : (tensor<?x?xf32>) -> tensor<2xi32>\n  %7 = \"tfl.strided_slice\"(%6, %cst_15, %cst_17, %cst_17) {begin_mask = 0 : i32, ellipsis_mask = 0 : i32, end_mask = 0 : i32, new_axis_mask = 0 : i32, shrink_axis_mask = 1 : i32} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %8 = \"tfl.pack\"(%7) {axis = 0 : i32, values_count = 1 : i32} : (tensor<i32>) -> tensor<1xi32>\n  %9 = \"tfl.fill\"(%8, %cst_3) : (tensor<1xi32>, tensor<f32>) -> tensor<?xf32>\n  %10 = \"tfl.fill\"(%8, %cst_10) : (tensor<1xi32>, tensor<f32>) -> tensor<?xf32>\n  %11 = \"tfl.strided_slice\"(%6, %cst_17, %cst_16, %cst_17) {begin_mask = 0 : i32, ellipsis_mask = 0 : i32, end_mask = 0 : i32, new_axis_mask = 0 : i32, shrink_axis_mask = 1 : i32} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %12 = \"tfl.pack\"(%cst_7, %7, %11, %cst_7) {axis = 0 : i32, values_count = 4 : i32} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<4xi32>\n  %13 = \"tfl.reshape\"(%5#2, %12) : (tensor<?x?xf32>, tensor<4xi32>) -> tensor<1x?x?x1xf32>\n  %y, %batch_mean, %batch_variance, %reserve_space_1, %reserve_space_2, %reserve_space_3 = \"tf.FusedBatchNormV3\"(%13, %9, %10, %cst_4, %cst_4) {data_format = \"NCHW\", device = \"\", epsilon = 1.000000e-03 : f32, exponential_avg_factor = 1.000000e+00 : f32, is_training = true} : (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<0xf32>, tensor<0xf32>) -> (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<*xf32>)\n  %14 = \"tfl.reshape\"(%y, %6) : (tensor<1x?x?x1xf32>, tensor<2xi32>) -> tensor<?x?xf32>\n  %15 = \"tfl.mul\"(%14, %cst_6) {fused_activation_function = \"NONE\"} : (tensor<?x?xf32>, tensor<12xf32>) -> tensor<?x12xf32>\n  %16 = \"tfl.add\"(%15, %cst_5) {fused_activation_function = \"NONE\"} : (tensor<?x12xf32>, tensor<12xf32>) -> tensor<?x12xf32>\n  %17 = \"tfl.fully_connected\"(%16, %cst_12, %cst_0) {fused_activation_function = \"NONE\", keep_num_dims = false, weights_format = \"DEFAULT\"} : (tensor<?x12xf32>, tensor<64x12xf32>, tensor<64xf32>) -> tensor<?x64xf32>\n  %18 = \"tfl.prelu\"(%17, %cst_18) : (tensor<?x64xf32>, tensor<64xf32>) -> tensor<?x64xf32>\n  %19 = \"tfl.fully_connected\"(%18, %cst_13, %cst_1) {fused_activation_function = \"NONE\", keep_num_dims = false, weights_format = \"DEFAULT\"} : (tensor<?x64xf32>, tensor<32x64xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %20 = \"tfl.prelu\"(%19, %cst_19) : (tensor<?x32xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %21 = \"tfl.fully_connected\"(%20, %cst_14, %cst_2) {fused_activation_function = \"NONE\", keep_num_dims = false, weights_format = \"DEFAULT\"} : (tensor<?x32xf32>, tensor<32x32xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %22 = \"tfl.prelu\"(%21, %cst_20) : (tensor<?x32xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %23 = \"tfl.fully_connected\"(%22, %cst_11, %cst) {fused_activation_function = \"NONE\", keep_num_dims = false, weights_format = \"DEFAULT\"} : (tensor<?x32xf32>, tensor<25x32xf32>, tensor<25xf32>) -> tensor<?x25xf32>\n  %24 = \"tfl.softmax\"(%23) {beta = 1.000000e+00 : f32} : (tensor<?x25xf32>) -> tensor<?x25xf32>\n  \"std.return\"(%24) : (tensor<?x25xf32>) -> ()\n}) {sym_name = \"main\", tf.entry_function = {control_outputs = \"\", inputs = \"chroma_input\", outputs = \"Identity\"}, type = (tensor<?x8160xf32>) -> tensor<?x25xf32>} : () -> ()\n",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mConverterError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-3a990714fb94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mconverter1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_spec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msupported_ops\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpsSet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTFLITE_BUILTINS\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtflite_model1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconverter1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'model_builtins.tflite'\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/lite/python/lite.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    835\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    836\u001b[0m     return super(TFLiteKerasModelConverterV2,\n\u001b[0;32m--> 837\u001b[0;31m                  self).convert(graph_def, input_tensors, output_tensors)\n\u001b[0m\u001b[1;32m    838\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    839\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/lite/python/lite.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(self, graph_def, input_tensors, output_tensors)\u001b[0m\n\u001b[1;32m    637\u001b[0m         \u001b[0minput_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_tensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    638\u001b[0m         \u001b[0moutput_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_tensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 639\u001b[0;31m         **converter_kwargs)\n\u001b[0m\u001b[1;32m    640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m     calibrate_and_quantize, flags = quant_mode.quantizer_flags(\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/lite/python/convert.py\u001b[0m in \u001b[0;36mtoco_convert_impl\u001b[0;34m(input_data, input_tensors, output_tensors, enable_mlir_converter, *args, **kwargs)\u001b[0m\n\u001b[1;32m    572\u001b[0m       \u001b[0minput_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSerializeToString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m       \u001b[0mdebug_info_str\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug_info_str\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 574\u001b[0;31m       enable_mlir_converter=enable_mlir_converter)\n\u001b[0m\u001b[1;32m    575\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/lite/python/convert.py\u001b[0m in \u001b[0;36mtoco_convert_protos\u001b[0;34m(model_flags_str, toco_flags_str, input_data_str, debug_info_str, enable_mlir_converter)\u001b[0m\n\u001b[1;32m    200\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_str\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 202\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mConverterError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mdistutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspawn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_executable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_toco_from_proto_bin\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mConverterError\u001b[0m: /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py:1089:0: error: 'tf.FusedBatchNormV3' op is neither a custom op nor a flex op\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py:1183:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/lite/python/lite.py:810:0: note: called from\n<ipython-input-20-3a990714fb94>:5:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2963:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2903:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2785:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2662:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/ipykernel/zmqshell.py:537:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/ipykernel/ipkernel.py:208:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py:1089:0: note: see current operation: %y, %batch_mean, %batch_variance, %reserve_space_1, %reserve_space_2, %reserve_space_3 = \"tf.FusedBatchNormV3\"(%13, %9, %10, %cst_4, %cst_4) {data_format = \"NCHW\", device = \"\", epsilon = 1.000000e-03 : f32, exponential_avg_factor = 1.000000e+00 : f32, is_training = true} : (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<0xf32>, tensor<0xf32>) -> (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<*xf32>)\n<unknown>:0: error: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Pad@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): 'tf.Pad' op is neither a custom op nor a flex op\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n<unknown>:0: note: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Pad@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): see current operation: %4 = \"tf.Pad\"(%3, %cst_6) {device = \"\"} : (tensor<8160xcomplex<f32>>, tensor<1x2xi32>) -> tensor<8192xcomplex<f32>>\n<unknown>:0: error: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/FFT@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): 'tf.FFT' op is neither a custom op nor a flex op\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n<unknown>:0: note: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/FFT@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): see current operation: %5 = \"tf.FFT\"(%4) {device = \"\"} : (tensor<8192xcomplex<f32>>) -> tensor<8192xcomplex<f32>>\n<unknown>:0: error: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Tensordot/MatMul@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): 'tf.MatMul' op is neither a custom op nor a flex op\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n<unknown>:0: note: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Tensordot/MatMul@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): see current operation: %7 = \"tf.MatMul\"(%6, %cst_10) {transpose_a = false, transpose_b = true} : (tensor<1x8192xcomplex<f32>>, tensor<48x8192xcomplex<f32>>) -> tensor<1x48xcomplex<f32>>\n<unknown>:0: error: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Abs@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): 'tf.ComplexAbs' op is neither a custom op nor a flex op\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n<unknown>:0: note: loc(callsite(callsite(\"sequential/chroma/map/while/PartitionedCall/Abs@sequential_chroma_map_while_body_18617\" at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0)))))))))) at callsite(\"sequential/chroma/map/while\"(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":507:0) at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py\":574:0 at callsite(\"<ipython-input-7-cf0e73ae6b83>\":12:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py\":617:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":504:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py\":382:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\":372:0 at callsite(\"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\":985:0 at \"/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py\":134:0))))))))))): see current operation: %9 = \"tf.ComplexAbs\"(%8) {device = \"\"} : (tensor<48xcomplex<f32>>) -> tensor<48xf32>\n<unknown>:0: error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):\n\ttf.ComplexAbs {device = \"\"}\n\ttf.FFT {device = \"\"}\n\ttf.FusedBatchNormV3 {data_format = \"NCHW\", device = \"\", epsilon = 1.000000e-03 : f32, exponential_avg_factor = 1.000000e+00 : f32, is_training = true}\n\ttf.MatMul {transpose_a = false, transpose_b = true}\n\ttf.Pad {device = \"\"}\n<unknown>:0: note: see current operation: \"func\"() ( {\n^bb0(%arg0: tensor<?x8160xf32>):  // no predecessors\n  %cst = \"std.constant\"() {value = dense<[0.147737071, -0.112439565, 0.0381680913, -0.117878117, -0.0436722748, 0.00939085335, -0.0897184237, 0.128659829, -0.047683455, 0.0467018858, 0.0466206186, -0.00430281786, -0.0359039567, -0.0798944235, 0.0561503395, -0.0470165387, -0.0136118941, 0.0447785147, -0.148503736, 0.0860366448, 0.0881774276, 0.0692368522, 0.0460185111, 0.00844638515, -0.117945939]> : tensor<25xf32>} : () -> tensor<25xf32>\n  %cst_0 = \"std.constant\"() {value = dense<[0.168076098, 0.189250961, 0.331452399, 0.241827965, -0.0550747886, 0.221319988, 0.300828785, 0.148949519, 0.393287301, 0.196752965, 0.175839543, 0.161021888, 0.168520689, 0.117292024, 0.17293328, 0.16789341, 0.036989145, 0.27923131, -0.149837464, 0.264275849, 0.172219113, 0.10350284, 0.303251654, 0.00179201632, 0.126206487, 0.303781778, 0.357120544, 0.222722366, 0.31719318, 0.279861689, 0.141150013, 0.0332527235, 0.254058361, 0.220147535, 0.245440096, 0.136673525, 0.112707727, 0.185189515, 0.24833253, 2.750830e-01, 0.176150858, 0.268469721, 0.143250883, 0.167769864, 0.238804296, 0.0973271057, 0.224579096, 0.321996093, 0.202449262, 0.0528840162, 0.172480404, -0.106782943, 0.150560513, 0.241444588, 0.0187574979, 0.157111406, 0.200682238, 0.0350796692, 0.208003864, -0.00685206195, 0.140565231, 0.0904081687, 0.189968288, 0.0431946218]> : tensor<64xf32>} : () -> tensor<64xf32>\n  %cst_1 = \"std.constant\"() {value = dense<[0.0549458079, 0.238936529, 0.103667565, 0.0921238735, 0.143788829, 0.0911116451, 0.175867394, 0.21835795, -0.0401271246, 0.0155029483, 0.122010075, 0.223504156, 0.100209974, -0.0316341706, 0.0610777251, 0.0399796627, 0.204122216, 0.131453395, 0.252934813, 0.182648122, 0.104780763, 0.126945794, 0.0563390441, 0.0932675302, -0.00845822785, 0.171583086, 0.182540447, 0.0219219904, 0.075256735, -0.0730319172, 0.0945926234, 0.03456036]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %cst_2 = \"std.constant\"() {value = dense<[-0.0118571837, 0.0800256952, 0.170635894, 0.028398335, 0.259022981, 0.0898712798, 0.175803497, 0.161798835, 0.0338865407, -3.69851245E-4, 0.0425762795, 0.212983623, -0.0411456116, 0.0527183078, 0.119301379, 0.065352641, 0.0161737688, 0.0426695272, -0.00505993469, 0.085072346, 0.0391224585, 0.0117550725, 0.039498996, -0.00897677056, -8.358640e-02, 0.179748133, -0.0227445941, 0.0897767916, 0.0937709659, 0.091977097, -0.112039283, 0.133670092]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %cst_3 = \"std.constant\"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>\n  %cst_4 = \"std.constant\"() {value = dense<> : tensor<0xf32>} : () -> tensor<0xf32>\n  %cst_5 = \"std.constant\"() {value = dense<[0.00725550949, -0.155836254, 0.117213838, -7.415890e-02, -0.22646898, -0.17580685, 0.145644963, -0.127022445, -0.0325459577, -0.229648903, -0.162604809, -0.104097113]> : tensor<12xf32>} : () -> tensor<12xf32>\n  %cst_6 = \"std.constant\"() {value = dense<[1.40223753, 1.42622852, 1.53603494, 1.36610556, 1.69401383, 1.55997336, 1.62918568, 1.57092464, 1.45249867, 1.6583848, 1.62247539, 1.52738059]> : tensor<12xf32>} : () -> tensor<12xf32>\n  %cst_7 = \"std.constant\"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>\n  %cst_8 = \"std.constant\"() {value = dense<12> : tensor<1xi32>} : () -> tensor<1xi32>\n  %cst_9 = \"std.constant\"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>\n  %cst_10 = \"std.constant\"() {value = dense<0.000000e+00> : tensor<f32>} : () -> tensor<f32>\n  %cst_11 = \"std.constant\"() {value = dense<\"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tensor<25x32xf32>} : () -> tensor<25x32xf32>\n  %cst_12 = \"std.constant\"() {value = dense<\"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tensor<64x12xf32>} : () -> tensor<64x12xf32>\n  %cst_13 = \"std.constant\"() {value = dense<\"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\"> : tensor<32x64xf32>} : () -> tensor<32x64xf32>\n  %cst_14 = \"std.constant\"() {value = dense<\"0x10F218BE0FCC233BE20E4CBE5A643C3E055DD3BEFCD0E6BB1349B73E8687D8BE0CF6AE3EAD07E8BD38E9A6BE91FA7CBEB64137BC2093AFBE91CC07BE44E3D23E159E5CBE001BE73D9B50E0BE6D7A9ABED1ED95BEBC41A7BE61A7823E7510733E2E11A7BE312E0BBFF33B003F1D462F3E534B86BE4539363E015D533E15069EBE23B3743E6BEC2ABEEA2B943EAFD5D33EC7D001BDB210003EA606DC3E51E42EBF1FD64CBEBCDAA43E2C7AF2BDC4D1CB3E9A1DDD3B04EE25BFA9EB12BEF65C933E1DEC7B3E283C123FEFC8C0BDB626783EFEF273BDE78F133F59749F3DFC590C3FF0C7E1BE912EB4BE0DDBA8BB3A370C3E90A40DBEA4603ABECEF4B23E46A9D2BE16E1663E0EDC8BBC005F8F3E6A6E78BE918C87BE2110903ED3AD5C3E51BD7A3E18884C3ED3F10BBE88CD5E3EC99D9C3E1DA6843EA635233F087721BEB83E3FBEE1D9813EB415E5BD87ED89BD2C22AABD3927553E15D6DBBE9095B53EBE673F3FB7EE91BEA90A38BE7DAEC23E100B07BF6A9CB23EB6170C3E43E4C73E92961FBEB04CDBBEB04156BD7EACE63E808E163FD54FFA3ED1A0913EF7191BBE3C6CBBBDF74BB83E233DD93D885F403EEA7D4ABEED74FB3EFB99943E324A86BEE313423E0603243EB2F1A53DBF8BBBBD44CF3C3D2630A53E7052D23E9158CD3E9F7CB4BDA6A2F13D398454BE63C8ECBDA03184BDAFC9CEBE156510BE28BC983D2117263D4DA4C1BD06E7C13EBC04063F904125BE347D16BFE692223EC9BFD13E245F1DBE0204E0BE46FE50BE0C0D203F17E5EE3DD351EDBE13D5F8BEFE8D003FE07A89BC57D11A3ED28C22BE4CEFEA3ECD89963D84975F3E8C7A36BE86CCCDBDFCB3A53DE13A97BCED9AAABD767FC5BE5F83943D8F81DF3ED6F7C23E761A4FBD60A1D0BE0FED0C3F9D002E3E544AA03DD00C34BE02B1A23EF845133FD2814A3F5416C53EDE5C2B3F02DB7D3E41EDD63C407CE13D1DD1A1BEB4A59A3D73DDAE3D3809AF3EC0F5BFBE9BD9263E2B6991BEBA2DFDBE5521CD3D900284BEC9D5D63EA97CBFBE616CF3BE2598BB3D0671023F09A7B63CA95E343EB642A13DCC55B63E2AC8E3BE745308BE412E2BBF907279BE5107F8BE798FB9BE8BF77C3E0FFD223EAFC1623E46529F3DB69DABBD3FB0883D0E37393E0E4ACA3DDE4E08BF084688BE7D0195BEBD0A203F46DBEE3CEEBACF3C1D9F973E6842A33E688E89BD5624833E61B0BCBBBAC94CBD7E64023F595DDE3EA5D782BE49DF81BD7CF2393E3FCB123EBEF015BDFE03E73D26E188BEB5EB00BE5C1BAABEAB2573BFCA6CE43E7E422C3F1EDCC53EF71031BEE332233EC99CD23E3A659ABE737584BE36D5F5BEB430FE3CB13CDEBE8E59953E218F603E5B42573C6E26A63E2482C13E899A01BFD324AFBDA8A69B3D3A584C3DEA25D73E6B43FABD4BE7CB3EA5951E3D965AE23D6F2C95BD436CA73E5D7C8ABE77B009BED420F23D8E28243E27C8023F41CBDE3ECE950DBE61E8D53E494A22BDFF67833DA387AA3EC78A5EBD4A6DBD3E5B1F213FF79DB33E2E0500BEC094ADBE5312B8BEE6EEE03DEA4B183EAAC0AE3E7DC614BE90499DBD9E7A773EF912FE3DC48FE43E3237D53EAA8C3B3EA81E06BFEBFE07BDD2EB17BD87B31E3FA955AD3E7A47F9BE1CFB36BE17B9B63C5F09F8BE921BA33DDF2C7C3E13D89E3E96D662BDBA544DBEE888063D58BFEF3E8023A6BDF2163EBE35E39D3E5A80BABE0577D23E42C87B3E0014063DAE83813EED3F103ECE03FEBE8E53CC3DF3AB80BE9DD0863EAB14053F3D2CF03E5751B7BDE54C513E716AB9BD25649DBE2AB93BBE7980B4BDE53479BD25DFD83E3896D03CB728983ECE43FABDDB4034BE9DDAB8BC90D8A2BDB547FDBEBE89B13D4241903D8BE5033F6B4BDB3EF6A6693D046B1BBF0E28B6BCCE825E3E2B3544BE94FA873E84F71ABE4864813EEC1B403EAB4DCB3EC08942BD82C9A33E62F2133D4EC5E5BEC781BC3E34C70A3FE33AD1BCE7BED5BD581A8FBE6D8A22BEC5E6DCBDEDD3ACBD6C0B9DBED393FC3EFD03853E7817EA3D4AF1B2BE3B2F97BDEDDBA4BE3994833EF5257C3E79AC30BFA8F1AEBE67F14BBDB7F9883E635AC43EF80CB93EB1D5143F518A19BE7929133FDE6AC7BEE8EF5BBE4F3645BDBFE0D93E851C2F3EC679F03EC0FFA3BEF058A3BEE1DEEFBDAE94E63CC2FFC83E174B003C2ECEAC3EA24C73BEDCF5543EBC420CBED5B523BD8AC7B8BE3B7A1E3EC2877C3E46E1DF3E464284BE5BE58BBEAC6C99BDD1D10ABDFAD2ECBCC5179BBDCF34E8BC985664BEC925B4BE08DFDF3EC6F168BE4650AE3E3783873E7294BFBD0BCC783CE556D6BDBAF80CBE6B27973E6DBCEC3E5517603E3EF9993EB6E7043F6F750DBE013B2ABCD1D4E2BD33173FBFB94CC53E1DF5A4BE7B72933EB30CD8BEB390663EDB850D3FD888943DCB4377BE6A83F93DAC7F033E98F9ABBEA83073BD83B987BE9F548B3D238DAEBEA2FEF73EBDC65ABEC108B4BE95F3B5BD7E63E13E6D0241BACAC561BEA5CBC33E94D934BE128834BEABAEE0BE35FA5B3C6784C1BE4EAABC3EA8A8043FCB02063F2A4891BE4D739C3EECFB62BE7A98C03DD61C25BE5E9B55BE3CBDDEBD2722403EE6470BBE01BDB03E23C0943EEDE2843E6511B1BD0BAE353E9C16D63EBE3B5CBDA9557E3EBB15393EAB61423D6C53A6BE2C57AD3E61D8B0BE4551D6BCF89BAB3E0EE1EEBEDFC42B3EAF7547BE42F7553E0A480F3F7419B73D5256193EB2E6D9BDAC7D1DBF554EBABE412597BE81F9473E873D6DBEB92CE6BD425A843E7F6ACB3E277C2EBDF001E93EA876C33D967C843EB6DA96BCD1E058BE0820DA3C00E09EBC0FB4593E827B6E3ACE16673EE83C523E60A04B3E17EB3FBD7F88973ED3F735BEB0F6C3BE68F1DCBE8360953EECA8723D9700BDBD89E417BEEEA7D43D79814F3E02A1363E4E4D043FF26D1D3F0F35213C052BEBBD21603CBD5CF29E3A38785EBE79E3A63E514D533E3CC6873EFCFBFFBCB48EEFBD8FF656BD1B71B23EB225A0BD88D8E53E28E3953D81039D3C71350DBE486BFD3E35C9483D880EB83DEAA6003EC2B43DBD611F2B3D0EA8BEBCBB63EA3B357001BC961A0D3F7A14D43D66CAA23D58D9C3BE6C9E573EABBD1BBFF2050A3D261D87BD5F0B203E6203E93DC2A735BE96602DBE1C266F3EAC03AF3E7BC90D3FCD6EFDBEDE62243E64511EBEDF8F09BF867B77BCD54CAEBDC78AD6BE018AD53E669DEE3E79A18FBE2D8CE23E73430A3F7396D4BE7C521BBE0C9A5D3E5F1DF73E500D94BEAA04C43EEBDEC93E4256443D30CA42BFEF9B893E83F4CE3E4606E7BE561FF93D8412DDBE5122083FF2FE0CBE6BA582BD17018CBEC1477F3AA01F8C3D1118C0BE7916A1BC38055A3EE9FF033E3F64CE3ED039073F753943BE992D64BEE87C90BE6B65DCBE21B9C8BE88A424BCCF02183E2A18AABDAFD7DF3E62FB903DAC89C3BE83BA883E41765B3DB03AAB3E92C5AD3E1392F03CDA72ABBEB507333E9A42E43E0580EF3E608222BF1C31A33E2D357ABEA250C6BD5466473E76DF273ECE18103E8E101A3EE2F85E3E9C2ECCBEBAC3473E1E685ABEE230CBBED3971EBD18C020BED3E2D23E37E1FB3E9C03953E18F3F53E1927953EB13BDA3E0DC4B1BEF4B9B43E0F78B7BEA5BC0C3E43F5A9BDDD5FC93E98AC5F3EFE7599BD98A057BEC38BDBBD813DF53E73C601BEF62386BD7794F0BE49A0BEBE41F591BE894DB63E5EC6173CB5E2A23BC1DEB2BE566BCBBEE0D9263E65F3F83DEF5792BE7D3EA93E308D67BE507FA03E59BA613ED83A503D39745CBE8BF4603BFA4BF73BAF6A983D5F43D03E5BC006BE9623B9BC67BF8EBC23C17DBDE63F7F3E353B7F3E87C43E3D952471BE93867BBE7D6B353ECD6D983E2EFBAEBCCE13A33E2253FD3DDF9BA13E7CF9593E3E7DDA3E352CD3BEE96C093F8B66493BB20B5A3E0F709F3DB652A6BEB7FA92BA0D61CE3CB6BD4FBE301D0F3F6287C03C5A90C0BADB260CBFCC5690BEA757A8BEBF8DC2BEF030183FB56AF13DF2F2DF3E644E8BBE4915473E39FD78BEEED6F33EF66099BE6B59993D5F87CB3E003E1C3EDFFCFC3E8E6BE33EC074003EA46F563DB0A6B23D945DDC3E01C2F6BEB94E7D3EF0F0A9BD668D2C3FDD381BBDCB9D0B3EA49C65BE9DAAEBBDB0B468BE7997D63DB644D1BE41D28B3E6840A1BC2087E23DCB1C593E588546BE6490D23EDE1BED3EAE00F13E191E773E371EFBBEC14DF7BD59F69A3E64BB88BEA713AA3C24A8913ECF5F7BBD212C4BBEE68375BE38CF193E0961333EBBBD173EA1E0033F77D6843E32AD22BE5D0789BDDDB5E63E0FD46F3DC9964B3E1A09BCBECCF2B43E17AAA33E79049FBDF6641C3E40DBC43EFAE9E4BED42B8DBD425ACB3D1A5E943E7B8087BD724C5DBE8ADB3D3EE16A7A3E1B2EBFBED99DF23E6A8549BD366160BE7E76343E2AF96F3E03438E3EA999E03E0F00B3BEC61A6F3D1CAE743EAEB0873C68D1873DD7DAA53E13C983BEA7D847BE02F4273EBF6B32BEF002A53D89F3EC3EA863FF3E71CCA83E31F4E63C94B9D13E78CF60BE0D1B143EE466943EA70342BD682180BED8B0D0BE26BC49BE54BA0EBDAAC651BE189A22BE8BF95B3C2DDCD63E454B0CBEB4AE323EC641393D9906F63EBE1E0B3F5778BE3E446F28BE7430213FC01E95BDF3DF983E768890BEC0A0773D366984BE04732ABECF00903E583388BCCA578F3E32FA223F9673A73D52BC73BEDB8DDCBB17F804BFB6BD89BEB3F09DBDFC5C86BEA92F1ABED447F5BEA12C193E1080AA3DC398963D99C3703D43FC3EBD63511CBE675BB0BEE7CB0A3F100187BD7F3EF93DC527153FA296683E9E012CBF2BC40D3E01B488BE1CDDB7BE850CF3BE03FD673EE234863E3C034C3E53F647BE4501A53E3BD55CBEC9FE6A3D80AEA5BEDFEF113DBE1AB13EA323683DAD1637BDB92386BE53E746BE531DBBBE800DA9BE0F59CDBE677CC7BDC81EDE3E0CFDCB3EF96D553E139F6F3E032E51BE292BA1BE2552233FE978F33E5F31A03E25E1263EBD638ABE24C6F83E7B610FBD7B37ED3D2D0AB5BD2F44C3BE6C9FD23CC35BA73EC303DFBD775DBDBDAC27A53E635530BEA1F9A4BEDFE7F63D2B4A293D891D92BD754842BE309EAABE3A618ABEFE2CAF3EDDA5BFBD736189BEB155F53EE482FD3E778E9CBC366E8C3D17198C3DFE125ABEAC5E873DC7856A3ED184E93E7C687CBD767A583E323EDE3C4A7C243EA8ADAD3E5E70963E22A5EA3E8CE5A0BEF2A1C5BE4BF9013EDF04823C4668F93D3D76AE3E23D408BF69052B3EB390EDBD597FF43E4FDCDCBD3E72C03E0BF4993CF04DE03EB011A43EE9B8F9BEBF2CEFBDB8A7CD3B5444953E04DC61BE881EEC3ECC02F7BD021ED93DA0F0E6BD0A57923E072B2F3E1B05A4BDF83E013EC2DEFA3CE760C2BD5360453DBF42A63E4A71A73E8253C9BE9D1B54BE2313D2BD6ACA5DBD2DE1333D692D7F3D491B3DBF05D4953E55FFD0BE4384303F21BD2ABEEACE2FBE3007D23EE1EA1F3FB6A3C6BDD361463E81EDCFBDD00A063FD843F2BEEF3D253DAEE1A3BE2FCEE4BB5C95093E2C7D6FBC4AB7B4BEB623BB3E3EFDC33D3D648E3EC491C63B22F98FBE7817133F6968F8BEE138EABECC1EC63DA4D1763E7A24233FBF8D7B3E99C2433EBD4F113FD0A48EBED10FF4BE44C9A73E76D0A4BE2EF8D1BE19A4D7BD8C4DF7BE0FE4A43EF364FA3D2E4BBA3C6BD6413DE7D18BBE9A53E13E23DC253E1C9FC43D9ADA0C3FD9E49D3E1F78C3BE5841C03D8E5ADD3EA0C5BA3DFB5DDFBE210582BE9E5B073F6ADA2ABD01B16DBECD98A7BEA4BBB6BE2DF2FA3DCEDDE7BE\"> : tensor<32x32xf32>} : () -> tensor<32x32xf32>\n  %cst_15 = \"std.constant\"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>\n  %cst_16 = \"std.constant\"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>\n  %cst_17 = \"std.constant\"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>\n  %cst_18 = \"std.constant\"() {value = dense<[-0.18137075, -0.190813139, -0.044286862, 0.211237565, -1.746690e-01, -0.151737764, 0.16834183, 0.119328104, 0.105832867, -0.0240005907, -0.0751447379, 0.037534222, -0.342453718, 0.186597332, -0.310402393, 0.018641986, 0.161040291, 0.232865468, -0.081227988, 0.160159558, -0.437950075, -0.137296706, -0.329482079, -0.239970639, -0.0312041733, 0.190941975, -0.402803183, 0.483502597, 0.173778772, 0.298825443, 0.334926784, 0.363431662, 0.524393857, -0.133963794, 0.304172069, -0.42725718, 0.16676645, 0.0580276921, 0.193012163, 0.0510975271, 0.813377559, 0.0750992373, 0.161859617, -0.254672736, 0.190442935, -0.226609036, -0.343025893, -0.335672051, -0.34258005, -0.254799277, 0.181894705, -0.34304902, 0.131500974, 0.0329634733, 0.0467517935, 0.325228602, -0.512234688, 0.171455756, -0.0636215433, -0.16159603, 0.178863093, -0.261178225, -0.211183608, 0.135612398]> : tensor<64xf32>} : () -> tensor<64xf32>\n  %cst_19 = \"std.constant\"() {value = dense<[0.360518038, -0.168143243, 0.11197973, 0.127239093, 0.0691332668, -0.266665071, 0.0290160123, 0.169769675, 0.236813396, 0.418850154, -0.415096372, -0.178710625, 0.493837386, -0.269553035, 0.242321983, 0.225369349, -0.294833422, -0.269634217, 0.524404645, 0.0572571382, -0.173829466, -0.321533293, 0.339223772, -0.0190662649, 0.203185797, -0.0887959301, 0.0538560525, -0.392667502, 0.318463176, -0.404079944, 0.492643982, 0.248903751]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %cst_20 = \"std.constant\"() {value = dense<[-0.274599046, 0.2693699, -0.0384969115, 0.547718585, 0.44997403, -0.281009167, -0.0743118376, 0.0901606306, 0.306658685, -0.00219570659, -0.0720264911, -0.340246856, -0.0986638963, 0.286379397, -0.0639733076, 0.49492836, 0.157945499, -0.232450366, 0.11595349, 0.357020527, -0.306564212, -0.223095223, 0.32178998, 0.656092644, 0.211648986, 0.0336364061, -0.346420258, -0.214254037, 0.199852765, -0.188247502, 0.677568316, 0.299702734]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %0 = \"tfl.shape\"(%arg0) : (tensor<?x8160xf32>) -> tensor<2xi32>\n  %1 = \"tfl.strided_slice\"(%0, %cst_15, %cst_17, %cst_17) {begin_mask = 0 : i32, ellipsis_mask = 0 : i32, end_mask = 0 : i32, new_axis_mask = 0 : i32, shrink_axis_mask = 1 : i32} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %2 = \"tfl.reshape\"(%1, %cst_17) : (tensor<i32>, tensor<1xi32>) -> tensor<1xi32>\n  %3 = \"tfl.concatenation\"(%2, %cst_8) {axis = 0 : i32, fused_activation_function = \"NONE\"} : (tensor<1xi32>, tensor<1xi32>) -> tensor<2xi32>\n  %4 = \"tfl.fill\"(%3, %cst_10) : (tensor<2xi32>, tensor<f32>) -> tensor<?x?xf32>\n  %5:5 = \"tfl.while\"(%cst_9, %cst_9, %4, %1, %arg0) ( {\n  ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<?x?xf32>):  // no predecessors\n    %25 = \"std.call\"(%arg1, %arg2, %arg3, %1, %arg0) {callee = @\"sequential/chroma/map/while_cond\"} : (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> tensor<i1>\n    \"tfl.yield\"(%25) : (tensor<i1>) -> ()\n  },  {\n  ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<?x?xf32>):  // no predecessors\n    %25:5 = \"std.call\"(%arg1, %arg2, %arg3, %1, %arg0) {callee = @\"sequential/chroma/map/while_body\"} : (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n    \"tfl.yield\"(%25#0, %25#1, %25#2, %25#3, %25#4) : (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> ()\n  }) {is_stateless = true} : (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n  %6 = \"tfl.shape\"(%5#2) : (tensor<?x?xf32>) -> tensor<2xi32>\n  %7 = \"tfl.strided_slice\"(%6, %cst_15, %cst_17, %cst_17) {begin_mask = 0 : i32, ellipsis_mask = 0 : i32, end_mask = 0 : i32, new_axis_mask = 0 : i32, shrink_axis_mask = 1 : i32} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %8 = \"tfl.pack\"(%7) {axis = 0 : i32, values_count = 1 : i32} : (tensor<i32>) -> tensor<1xi32>\n  %9 = \"tfl.fill\"(%8, %cst_3) : (tensor<1xi32>, tensor<f32>) -> tensor<?xf32>\n  %10 = \"tfl.fill\"(%8, %cst_10) : (tensor<1xi32>, tensor<f32>) -> tensor<?xf32>\n  %11 = \"tfl.strided_slice\"(%6, %cst_17, %cst_16, %cst_17) {begin_mask = 0 : i32, ellipsis_mask = 0 : i32, end_mask = 0 : i32, new_axis_mask = 0 : i32, shrink_axis_mask = 1 : i32} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %12 = \"tfl.pack\"(%cst_7, %7, %11, %cst_7) {axis = 0 : i32, values_count = 4 : i32} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<4xi32>\n  %13 = \"tfl.reshape\"(%5#2, %12) : (tensor<?x?xf32>, tensor<4xi32>) -> tensor<1x?x?x1xf32>\n  %y, %batch_mean, %batch_variance, %reserve_space_1, %reserve_space_2, %reserve_space_3 = \"tf.FusedBatchNormV3\"(%13, %9, %10, %cst_4, %cst_4) {data_format = \"NCHW\", device = \"\", epsilon = 1.000000e-03 : f32, exponential_avg_factor = 1.000000e+00 : f32, is_training = true} : (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<0xf32>, tensor<0xf32>) -> (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<*xf32>)\n  %14 = \"tfl.reshape\"(%y, %6) : (tensor<1x?x?x1xf32>, tensor<2xi32>) -> tensor<?x?xf32>\n  %15 = \"tfl.mul\"(%14, %cst_6) {fused_activation_function = \"NONE\"} : (tensor<?x?xf32>, tensor<12xf32>) -> tensor<?x12xf32>\n  %16 = \"tfl.add\"(%15, %cst_5) {fused_activation_function = \"NONE\"} : (tensor<?x12xf32>, tensor<12xf32>) -> tensor<?x12xf32>\n  %17 = \"tfl.fully_connected\"(%16, %cst_12, %cst_0) {fused_activation_function = \"NONE\", keep_num_dims = false, weights_format = \"DEFAULT\"} : (tensor<?x12xf32>, tensor<64x12xf32>, tensor<64xf32>) -> tensor<?x64xf32>\n  %18 = \"tfl.prelu\"(%17, %cst_18) : (tensor<?x64xf32>, tensor<64xf32>) -> tensor<?x64xf32>\n  %19 = \"tfl.fully_connected\"(%18, %cst_13, %cst_1) {fused_activation_function = \"NONE\", keep_num_dims = false, weights_format = \"DEFAULT\"} : (tensor<?x64xf32>, tensor<32x64xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %20 = \"tfl.prelu\"(%19, %cst_19) : (tensor<?x32xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %21 = \"tfl.fully_connected\"(%20, %cst_14, %cst_2) {fused_activation_function = \"NONE\", keep_num_dims = false, weights_format = \"DEFAULT\"} : (tensor<?x32xf32>, tensor<32x32xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %22 = \"tfl.prelu\"(%21, %cst_20) : (tensor<?x32xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %23 = \"tfl.fully_connected\"(%22, %cst_11, %cst) {fused_activation_function = \"NONE\", keep_num_dims = false, weights_format = \"DEFAULT\"} : (tensor<?x32xf32>, tensor<25x32xf32>, tensor<25xf32>) -> tensor<?x25xf32>\n  %24 = \"tfl.softmax\"(%23) {beta = 1.000000e+00 : f32} : (tensor<?x25xf32>) -> tensor<?x25xf32>\n  \"std.return\"(%24) : (tensor<?x25xf32>) -> ()\n}) {sym_name = \"main\", tf.entry_function = {control_outputs = \"\", inputs = \"chroma_input\", outputs = \"Identity\"}, type = (tensor<?x8160xf32>) -> tensor<?x25xf32>} : () -> ()\n"
     ]
    }
   ],
   "source": [
    "converter1 = tf.lite.TFLiteConverter.from_keras_model(model_best)\n",
    "converter1.experimental_new_converter = True\n",
    "converter1.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]\n",
    "\n",
    "tflite_model1 = converter1.convert()\n",
    "\n",
    "file = open('model_builtins.tflite' , 'wb' ) \n",
    "file.write(tflite_model1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmpxia9jzyn/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmpxia9jzyn/assets\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3179200"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "converter2 = tf.lite.TFLiteConverter.from_keras_model(model_best)\n",
    "converter2.experimental_new_converter = True\n",
    "converter2.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]\n",
    "\n",
    "tflite_model2 = converter2.convert()\n",
    "\n",
    "file = open('model_both.tflite' , 'wb' ) \n",
    "file.write(tflite_model2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmpllqut9oj/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmpllqut9oj/assets\n"
     ]
    },
    {
     "ename": "ConverterError",
     "evalue": "/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: error: 'tfl.while' op is a TFLite builtin op but builtin emission is not enabled\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: see current operation: %26:6 = \"tfl.while\"(%19, %22, %19, %25, %22, %arg0) ( {\n^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<?x?xf32>):  // no predecessors\n  %79 = \"std.call\"(%arg1, %arg2, %arg3, %arg4, %22, %arg0) {callee = @\"sequential/chroma/map/while_cond\"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> tensor<i1>\n  \"tfl.yield\"(%79) : (tensor<i1>) -> ()\n},  {\n^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<?x?xf32>):  // no predecessors\n  %79:6 = \"std.call\"(%arg1, %arg2, %arg3, %arg4, %22, %arg0) {callee = @\"sequential/chroma/map/while_body\"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n  \"tfl.yield\"(%79#0, %79#1, %79#2, %79#3, %79#4, %79#5) : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> ()\n}) {is_stateless = true} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: error: 'tfl.cast' op is a TFLite builtin op but builtin emission is not enabled\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: see current operation: %39 = \"tfl.cast\"(%38) : (tensor<?x12xf32>) -> tensor<?x?xf32>\n<unknown>:0: error: failed while converting: 'main': \n<unknown>:0: note: see current operation: \"func\"() ( {\n^bb0(%arg0: tensor<?x8160xf32>):  // no predecessors\n  %0 = \"tf.Const\"() {value = dense<[0.147737071, -0.112439565, 0.0381680913, -0.117878117, -0.0436722748, 0.00939085335, -0.0897184237, 0.128659829, -0.047683455, 0.0467018858, 0.0466206186, -0.00430281786, -0.0359039567, -0.0798944235, 0.0561503395, -0.0470165387, -0.0136118941, 0.0447785147, -0.148503736, 0.0860366448, 0.0881774276, 0.0692368522, 0.0460185111, 0.00844638515, -0.117945939]> : tensor<25xf32>} : () -> tensor<25xf32>\n  %1 = \"tf.Const\"() {value = dense<\"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\"> : tensor<32x25xf32>} : () -> tensor<32x25xf32>\n  %2 = \"tf.Const\"() {value = dense<[0.168076098, 0.189250961, 0.331452399, 0.241827965, -0.0550747886, 0.221319988, 0.300828785, 0.148949519, 0.393287301, 0.196752965, 0.175839543, 0.161021888, 0.168520689, 0.117292024, 0.17293328, 0.16789341, 0.036989145, 0.27923131, -0.149837464, 0.264275849, 0.172219113, 0.10350284, 0.303251654, 0.00179201632, 0.126206487, 0.303781778, 0.357120544, 0.222722366, 0.31719318, 0.279861689, 0.141150013, 0.0332527235, 0.254058361, 0.220147535, 0.245440096, 0.136673525, 0.112707727, 0.185189515, 0.24833253, 2.750830e-01, 0.176150858, 0.268469721, 0.143250883, 0.167769864, 0.238804296, 0.0973271057, 0.224579096, 0.321996093, 0.202449262, 0.0528840162, 0.172480404, -0.106782943, 0.150560513, 0.241444588, 0.0187574979, 0.157111406, 0.200682238, 0.0350796692, 0.208003864, -0.00685206195, 0.140565231, 0.0904081687, 0.189968288, 0.0431946218]> : tensor<64xf32>} : () -> tensor<64xf32>\n  %3 = \"tf.Const\"() {value = dense<\"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\"> : tensor<12x64xf32>} : () -> tensor<12x64xf32>\n  %4 = \"tf.Const\"() {value = dense<[0.0549458079, 0.238936529, 0.103667565, 0.0921238735, 0.143788829, 0.0911116451, 0.175867394, 0.21835795, -0.0401271246, 0.0155029483, 0.122010075, 0.223504156, 0.100209974, -0.0316341706, 0.0610777251, 0.0399796627, 0.204122216, 0.131453395, 0.252934813, 0.182648122, 0.104780763, 0.126945794, 0.0563390441, 0.0932675302, -0.00845822785, 0.171583086, 0.182540447, 0.0219219904, 0.075256735, -0.0730319172, 0.0945926234, 0.03456036]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %5 = \"tf.Const\"() {value = 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\"> : tensor<64x32xf32>} : () -> tensor<64x32xf32>\n  %6 = \"tf.Const\"() {value = dense<[-0.0118571837, 0.0800256952, 0.170635894, 0.028398335, 0.259022981, 0.0898712798, 0.175803497, 0.161798835, 0.0338865407, -3.69851245E-4, 0.0425762795, 0.212983623, -0.0411456116, 0.0527183078, 0.119301379, 0.065352641, 0.0161737688, 0.0426695272, -0.00505993469, 0.085072346, 0.0391224585, 0.0117550725, 0.039498996, -0.00897677056, -8.358640e-02, 0.179748133, -0.0227445941, 0.0897767916, 0.0937709659, 0.091977097, -0.112039283, 0.133670092]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %7 = \"tf.Const\"() {value = dense<\"0x10F218BE23B3743E16E1663EB04CDBBE4DA4C1BD0FED0C3F745308BEFE03E73D5D7C8ABEA955AD3E7980B4BD581A8FBEC2FFC83EB6E7043F6784C1BE0A480F3F9700BDBD357001BC500D94BEAC89C3BEF4B9B43E5F43D03EA757A8BE2087E23DFAE9E4BE78CF60BEDB8DDCBBDFEF113DA1F9A4BE23D408BF692D7F3D99C2433E0FCC233B6BEC2ABE0EDC8BBCB04156BD06E7C13E9D002E3E412E2BBF26E188BE77B009BE7A47F9BEE53479BD6D8A22BE174B003C6F750DBE4EAABC3E7419B73D89E417BE961A0D3FAA04C43E83BA883E0F78B7BE5BC006BEBF8DC2BECB1C593ED42B8DBD0D1B143E17F804BFBE1AB13EDFE7F63D69052B3E491B3DBFBD4F113FE20E4CBEEA2B943E005F8F3E7EACE63EBC04063F544AA03D907279BEB5EB00BED420F23D1CFB36BE25DFD83EC5E6DCBD2ECEAC3E013B2ABCA8A8043F5256193EEEA7D43D7A14D43DEBDEC93E41765B3DA5BC0C3E9623B9BCF030183F588546BE425ACB3DE466943EB6BD89BEA323683D2B4A293DB390EDBD05D4953ED0A48EBE5A643C3EAFD5D33E6A6E78BE808E163F904125BED00C34BE5107F8BE5C1BAABE8E28243E17B9B63C3896D03CEDD3ACBDA24C73BED1D4E2BDCB02063FB2E6D9BD79814F3E66CAA23D4256443DB03AAB3E43F5A9BD67BF8EBCB56AF13D6490D23E1A5E943EA70342BDB3F09DBDAD1637BD891D92BD597FF43E55FFD0BED10FF4BE055DD3BEC7D001BD918C87BED54FFA3E347D16BF02B1A23E798FB9BEAB2573BF27C8023F5F09F8BEB728983E6C0B9DBEDCF5543E33173FBF2A4891BEAC7D1DBF02A1363E58D9C3BE30CA42BF92C5AD3EDD5FC93E23C17DBDF2F2DF3EDE1BED3E7B8087BD682180BEFC5C86BEB92386BE754842BE4FDCDCBD4384303F44C9A73EFCD0E6BBB210003E2110903ED1A0913EE692223EF845133F8BF77C3ECA6CE43E41CBDE3E921BA33DCE43FABDD393FC3EBC420CBEB94CC53E4D739C3E554EBABE4E4D043F6C9E573EEF9B893E1392F03C98AC5F3EE63F7F3E644E8BBEAE00F13E724C5DBED8B0D0BEA92F1ABE53E746BE309EAABE3E72C03E21BD2ABE76D0A4BE1349B73EA606DC3ED3AD5C3EF7191BBEC9BFD13ED2814A3F0FFD223E7E422C3FCE950DBEDF2C7C3EDB4034BEFD03853ED5B523BD1DF5A4BEECFB62BE412597BEF26D1D3FABBD1BBF83F4CE3EDA72ABBEFE7599BD353B7F3E4915473E191E773E8ADB3D3E26BC49BED447F5BE531DBBBE3A618ABE0BF4993CEACE2FBE2EF8D1BE8687D8BE51E42EBF51BD7A3E3C6CBBBD245F1DBE5416C53EAFC1623E1EDCC53E61E8D53E13D89E3E9DDAB8BC7817EA3D8AC7B8BE7B72933E7A98C03D81F9473E0F35213CF2050A3D4606E7BEB507333E98A057BE87C43E3D39FD78BE371EFBBEE16A7A3E54BA0EBDA12C193E800DA9BEFE2CAF3EF04DE03E3007D23E19A4D7BD0CF6AE3E1FD64CBE18884C3EF74BB83E0204E0BEDE5C2B3F46529F3DF71031BE494A22BD96D662BD90D8A2BD4AF1B2BE3B7A1E3EB30CD8BED61C25BE873D6DBE052BEBBD261D87BD561FF93D9A42E43EC38BDBBD952471BEEED6F33EC14DF7BD1B2EBFBEAAC651BE1080AA3D0F59CDBEDDA5BFBDB011A43EE1EA1F3F8C4DF7BEAD07E8BDBCDAA43ED3F10BBE233DD93D46FE50BE02DB7D3EB69DABBDE332233EFF67833DBA544DBEB547FDBE3B2F97BDC2877C3EB390663E5E9B55BEB92CE6BD21603CBD5F0B203E8412DDBE0580EF3E813DF53E93867BBEF66099BE59F69A3ED99DF23E189A22BEC398963D677CC7BD736189BEE9B8F9BEB6A3C6BD0FE4A43E38E9A6BE2C7AF2BD88CD5E3E885F403E0C0D203F41EDD63C3FB0883DC99CD23EA387AA3EE888063DBE89B13DEDDBA4BE46E1DF3EDB850D3F3CBDDEBD425A843E5CF29E3A6203E93D5122083F608222BF73C601BE7D6B353E6B59993D64BB88BE6A8549BD8BF95B3C99C3703DC81EDE3EB155F53EBF2CEFBDD361463EF364FA3D91FA7CBEC4D1CB3EC99D9C3EEA7D4ABE17E5EE3D407CE13D0E37393E3A659ABEC78A5EBD58BFEF3E4241903D3994833E464284BED888943D2722403E7F6ACB3E38785EBEC2A735BEF2FE0CBE1C31A33EF62386BDCD6D983E5F87CB3EA713AA3C366160BE2DDCD63E43FC3EBD0CFDCB3EE482FD3EB8A7CD3B81EDCFBD2E4BBA3CB64137BC9A1DDD3B1DA6843EED74FB3ED351EDBE1DD1A1BE0E4ACA3D737584BE4A6DBD3E8023A6BD8BE5033FF5257C3E5BE58BBECB4377BEE6470BBE277C2EBD79E3A63E96602DBE6BA582BD2D357ABE7794F0BE2EFBAEBC003E1C3E24A8913E7E76343E454B0CBE63511CBEF96D553E778E9CBC5444953ED00A063F6BD6413D2093AFBE04EE25BFA635233FFB99943E13D5F8BEB4A59A3DDE4E08BF36D5F5BE5B1F213FF2163EBE6B4BDB3E79AC30BFAC6C99BD6A83F93D01BDB03EF001E93E514D533E1C266F3E17018CBEA250C6BD49A0BEBECE13A33EDFFCFC3ECF5F7BBD2AF96F3EB4AE323E675BB0BE139F6F3E366E8C3D04DC61BED843F2BEE7D18BBE91CC07BEA9EB12BE087721BE324A86BEFE8D003F73DDAE3D084688BEB430FE3CF79DB33E35E39D3EF6A6693DA8F1AEBED1D10ABDAC7F033E23C0943EA876C33D3CC6873EAC03AF3EC1477F3A5466473E41F591BE2253FD3D8E6BE33E212C4BBE03438E3EC641393DE7CB0A3F032E51BE17198C3D881EEC3EEF3D253D9A53E13E44E3D23EF65C933EB83E3FBEE313423EE07A89BC3809AF3E7D0195BEB13CDEBE2E0500BE5A80BABE046B1BBF67F14BBDFAD2ECBC98F9ABBEEDE2843E967C843EFCFBFFBC7BC90D3FA01F8C3D76DF273E894DB63EDF9BA13EC074003EE68375BEA999E03E9906F63E100187BD292BA1BEFE125ABECC02F7BDAEE1A3BE23DC253E159E5CBE1DEC7B3EE1D9813E0603243E57D11A3EC0F5BFBEBD0A203F8E59953EC094ADBE0577D23E0E28B6BCB7F9883EC5179BBDA83073BD6511B1BDB6DA96BCB48EEFBDCD6EFDBE1118C0BECE18103E5EC6173C7CF9593EA46F563D38CF193E0F00B3BEBE1E0B3F7F3EF93D2552233FAC5E873D021ED93D2FCEE4BB1C9FC43D001BE73D283C123FB415E5BDB2F1A53DD28C22BE9BD9263E46DBEE3C218F603E5312B8BE42C87B3ECE825E3E635AC43ECF34E8BC83B987BE0BAE353ED1E058BE8FF656BDDE62243E7916A1BC8E101A3EB5E2A23B3E7DDA3EB0A6B23D0961333EC61A6F3D5778BE3EC527153FE978F33EC7856A3EA0F0E6BD5C95093E9ADA0C3F9B50E0BEEFC8C0BD87ED89BDBF8BBBBD4CEFEA3E2B6991BEEEBACF3C5B42573CE6EEE03D0014063D2B3544BEF80CB93E985664BE9F548B3D9C16D63E0820DA3C1B71B23E64511EBE38055A3EE2F85E3EC1DEB2BE352CD3BE945DDC3EBBBD173E1CAE743E446F28BEA296683E5F31A03ED184E93E0A57923E2C7D6FBCD9E49D3E6D7A9ABEB626783E2C22AABD44CF3C3DCD89963DBA2DFDBE1D9F973E6E26A63EEA4B183EAE83813E94FA873EB1D5143FC925B4BE238DAEBEBE3B5CBD00E09EBCB225A0BDDF8F09BFE9FF033E9C2ECCBE566BCBBEE96C093F01C2F6BEA1E0033FAEB0873C7430213F9E012CBF25E1263E7C687CBD072B2F3E4AB7B4BE1F78C3BED1ED95BEFEF273BD3927553E2630A53E84975F3E5521CD3D6842A33E2482C13EAAC0AE3EED3F103E84F71ABE518A19BE08DFDF3EA2FEF73EA9557E3E0FB4593E88D8E53E867B77BC3F64CE3EBAC3473EE0D9263E8B66493BB94E7D3E77D6843E68D1873DC01E95BD2BC40D3EBD638ABE767A583E1B05A4BDB623BB3E5841C03DBC41A7BEE78F133F15D6DBBE7052D23E8C7A36BE900284BE688E89BD899A01BF7DC614BECE03FEBE4864813E7929133FC6F168BEBDC65ABEBB15393E827B6E3A28E3953DD54CAEBDD039073F1E685ABE65F3F83DB20B5A3EF0F0A9BD32AD22BED7DAA53EF3DF983E01B488BE24C6F83E323EDE3CF83E013E3EFDC33D8E5ADD3E61A7823E59749F3D9095B53E9158CD3E86CCCDBDC9D5D63E5624833ED324AFBD90499DBD8E53CC3DEC1B403EDE6AC7BE4650AE3EC108B4BEAB61423DCE16673E81039D3CC78AD6BE753943BEE230CBBEEF5792BE0F709F3D668D2C3F5D0789BD13C983BE768890BE1CDDB7BE7B610FBD4A7C243EC2DEFA3C3D648E3EA0C5BA3D7510733EFC590C3FBE673F3F9F7CB4BDFCB3A53DA97CBFBE61B0BCBBA8A69B3D9E7A773EF3AB80BEAB4DCB3EE8EF5BBE3783873E95F3B5BD6C53A6BEE83C523E71350DBE018AD53E992D64BED3971EBD7D3EA93EB652A6BEDD381BBDDDB5E63EA7D847BEC0A0773D850CF3BE7B37ED3DA8ADAD3EE760C2BDC491C63BFB5DDFBE2E11A7BEF0C7E1BEB7EE91BEA6A2F13DE13A97BC616CF3BEBAC94CBD3A584C3DF912FE3D9DD0863EC08942BD4F3645BD7294BFBD7E63E13E2C57AD3E60A04B3E486BFD3E669DEE3EE87C90BE18C020BE308D67BEB7FA92BACB9D0B3E0FD46F3D02F4273E366984BE03FD673E2D0AB5BD5E70963E5360453D22F98FBE210582BE312E0BBF912EB4BEA90A38BE398454BEED9AAABD2598BB3D7E64023FEA25D73EC48FE43EAB14053F82C9A33EBFE0D93E0BCC783C6D0241BA61D8B0BE17EB3FBD35C9483D79A18FBE6B65DCBED3E2D23E507FA03E0D61CE3CA49C65BEC9964B3EBF6B32BE04732ABEE234863E2F44C3BE22A5EA3EBF42A63E7817133F9E5B073FF33B003F0DDBA8BB7DAEC23E63C8ECBD767FC5BE0671023F595DDE3E6B43FABD3237D53E3D2CF03E62F2133D851C2F3EE556D6BDCAC561BE4551D6BC7F88973E880EB83D2D8CE23E21B9C8BE37E1FB3E59BA613EB6BD4FBE9DAAEBBD1A09BCBEF002A53DCF00903E3C034C3E6C9FD23C8CE5A0BE4A71A73E6968F8BE6ADA2ABD1D462F3E3A370C3E100B07BFA03184BD5F83943D09A7B63CA5D782BE4BE7CB3EAA8C3B3E5751B7BD4EC5E5BEC679F03EBAF80CBEA5CBC33EF89BAB3ED3F735BEEAA6003E73430A3F88A424BC9C03953ED83A503D301D0F3FB0B468BECCF2B43E89F3EC3E583388BC53F647BEC35BA73EF2A1C5BE8253C9BEE138EABE01B16DBE534B86BE90A40DBE6A9CB23EAFC9CEBE8F81DF3EA95E343E49DF81BDA5951E3DA81E06BFE54C513EC781BC3EC0FFA3BE6B27973E94D934BE0EE1EEBEB0F6C3BEC2B43DBD7396D4BECF02183E18F3F53E39745CBE6287C03C7997D63D17AAA33EA863FF3ECA578F3E4501A53EC303DFBD4BF9013E9D1B54BECC1EC63DCD98A7BE4539363EA4603ABEB6170C3E156510BED6F7C23EB642A13D7CF2393E965AE23DEBFE07BD716AB9BD34C70A3FF058A3BE6DBCEC3E128834BEDFC42B3E68F1DCBE611F2B3D7C521BBE2A18AABD1927953E8BF4603B5A90C0BAB644D1BE79049FBD71CCA83E32FA223F3BD55CBE775DBDBDDF04823C2313D2BDA4D1763EA4BBB6BE015D533ECEF4B23E43E4C73E28BC983D761A4FBDCC55B63E3FCB123E6F2C95BDD2EB17BD25649DBEE33AD1BCE1DEEFBD5517603EABAEE0BEAF7547BE8360953E0EA8BEBC0C9A5D3EAFD7DF3EB13BDA3EFA4BF73BDB260CBF41D28B3EF6641C3E31F4E63C9673A73DC9FE6A3DAC27A53E4668F93D6ACA5DBD7A24233F2DF2FA3D15069EBE46A9D2BE92961FBE2117263D60A1D0BE2AC8E3BEBEF015BD436CA73E87B31E3F2AB93BBEE7BED5BDAE94E63C3EF9993E35FA5B3C42F7553EECA8723DBB63EA3B5F1DF73E62FB903D0DC4B1BEAF6A983DCC5690BE6840A1BC40DBC43E94B9D13E52BC73BE80AEA5BE635530BE3D76AE3E2DE1333DBF8D7B3ECEDDE7BE\"> : tensor<32x32xf32>} : () -> tensor<32x32xf32>\n  %8 = \"tf.Const\"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>\n  %9 = \"tf.Const\"() {value = dense<0.000000e+00> : tensor<f32>} : () -> tensor<f32>\n  %10 = \"tf.Const\"() {value = dense<> : tensor<0xf32>} : () -> tensor<0xf32>\n  %11 = \"tf.Const\"() {value = dense<[0.00725550949, -0.155836254, 0.117213838, -7.415890e-02, -0.22646898, -0.17580685, 0.145644963, -0.127022445, -0.0325459577, -0.229648903, -0.162604809, -0.104097113]> : tensor<12xf32>} : () -> tensor<12xf32>\n  %12 = \"tf.Const\"() {value = dense<[1.40223753, 1.42622852, 1.53603494, 1.36610556, 1.69401383, 1.55997336, 1.62918568, 1.57092464, 1.45249867, 1.6583848, 1.62247539, 1.52738059]> : tensor<12xf32>} : () -> tensor<12xf32>\n  %13 = \"tf.Const\"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>\n  %14 = \"tf.Const\"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>\n  %15 = \"tf.Const\"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>\n  %16 = \"tf.Const\"() {value = dense<[-0.18137075, -0.190813139, -0.044286862, 0.211237565, -1.746690e-01, -0.151737764, 0.16834183, 0.119328104, 0.105832867, -0.0240005907, -0.0751447379, 0.037534222, -0.342453718, 0.186597332, -0.310402393, 0.018641986, 0.161040291, 0.232865468, -0.081227988, 0.160159558, -0.437950075, -0.137296706, -0.329482079, -0.239970639, -0.0312041733, 0.190941975, -0.402803183, 0.483502597, 0.173778772, 0.298825443, 0.334926784, 0.363431662, 0.524393857, -0.133963794, 0.304172069, -0.42725718, 0.16676645, 0.0580276921, 0.193012163, 0.0510975271, 0.813377559, 0.0750992373, 0.161859617, -0.254672736, 0.190442935, -0.226609036, -0.343025893, -0.335672051, -0.34258005, -0.254799277, 0.181894705, -0.34304902, 0.131500974, 0.0329634733, 0.0467517935, 0.325228602, -0.512234688, 0.171455756, -0.0636215433, -0.16159603, 0.178863093, -0.261178225, -0.211183608, 0.135612398]> : tensor<64xf32>} : () -> tensor<64xf32>\n  %17 = \"tf.Const\"() {value = dense<[0.360518038, -0.168143243, 0.11197973, 0.127239093, 0.0691332668, -0.266665071, 0.0290160123, 0.169769675, 0.236813396, 0.418850154, -0.415096372, -0.178710625, 0.493837386, -0.269553035, 0.242321983, 0.225369349, -0.294833422, -0.269634217, 0.524404645, 0.0572571382, -0.173829466, -0.321533293, 0.339223772, -0.0190662649, 0.203185797, -0.0887959301, 0.0538560525, -0.392667502, 0.318463176, -0.404079944, 0.492643982, 0.248903751]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %18 = \"tf.Const\"() {value = dense<[-0.274599046, 0.2693699, -0.0384969115, 0.547718585, 0.44997403, -0.281009167, -0.0743118376, 0.0901606306, 0.306658685, -0.00219570659, -0.0720264911, -0.340246856, -0.0986638963, 0.286379397, -0.0639733076, 0.49492836, 0.157945499, -0.232450366, 0.11595349, 0.357020527, -0.306564212, -0.223095223, 0.32178998, 0.656092644, 0.211648986, 0.0336364061, -0.346420258, -0.214254037, 0.199852765, -0.188247502, 0.677568316, 0.299702734]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %19 = \"tf.Const\"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>\n  %20 = \"tf.Const\"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>\n  %cst = \"std.constant\"() {value = dense<12> : tensor<1xi32>} : () -> tensor<1xi32>\n  %cst_0 = \"std.constant\"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>\n  %cst_1 = \"std.constant\"() {value = dense<0.000000e+00> : tensor<f32>} : () -> tensor<f32>\n  %21 = \"tf.Shape\"(%arg0) {device = \"\"} : (tensor<?x8160xf32>) -> tensor<2xi32>\n  %22 = \"tf.StridedSlice\"(%21, %13, %15, %15) {begin_mask = 0 : i64, device = \"\", ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 1 : i64} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %23 = \"tf.ExpandDims\"(%22, %cst_0) : (tensor<i32>, tensor<i32>) -> tensor<1xi32>\n  %24 = \"tf.ConcatV2\"(%23, %cst, %cst_0) : (tensor<1xi32>, tensor<1xi32>, tensor<i32>) -> tensor<2xi32>\n  %25 = \"tf.Fill\"(%24, %cst_1) : (tensor<2xi32>, tensor<f32>) -> tensor<?x?xf32>\n  %26:6 = \"tfl.while\"(%19, %22, %19, %25, %22, %arg0) ( {\n  ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<?x?xf32>):  // no predecessors\n    %79 = \"std.call\"(%arg1, %arg2, %arg3, %arg4, %22, %arg0) {callee = @\"sequential/chroma/map/while_cond\"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> tensor<i1>\n    \"tfl.yield\"(%79) : (tensor<i1>) -> ()\n  },  {\n  ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<?x?xf32>):  // no predecessors\n    %79:6 = \"std.call\"(%arg1, %arg2, %arg3, %arg4, %22, %arg0) {callee = @\"sequential/chroma/map/while_body\"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n    \"tfl.yield\"(%79#0, %79#1, %79#2, %79#3, %79#4, %79#5) : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> ()\n  }) {is_stateless = true} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n  %27 = \"tf.Shape\"(%26#3) {device = \"\"} : (tensor<?x?xf32>) -> tensor<2xi32>\n  %28 = \"tf.Identity\"(%0) {device = \"\"} : (tensor<25xf32>) -> tensor<25xf32>\n  %29 = \"tf.Identity\"(%1) {device = \"\"} : (tensor<32x25xf32>) -> tensor<32x25xf32>\n  %30 = \"tf.Identity\"(%2) {device = \"\"} : (tensor<64xf32>) -> tensor<64xf32>\n  %31 = \"tf.Identity\"(%3) {device = \"\"} : (tensor<12x64xf32>) -> tensor<12x64xf32>\n  %32 = \"tf.Identity\"(%4) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %33 = \"tf.Identity\"(%5) {device = \"\"} : (tensor<64x32xf32>) -> tensor<64x32xf32>\n  %34 = \"tf.Identity\"(%6) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %35 = \"tf.Identity\"(%7) {device = \"\"} : (tensor<32x32xf32>) -> tensor<32x32xf32>\n  %36 = \"tf.Identity\"(%11) {device = \"\"} : (tensor<12xf32>) -> tensor<12xf32>\n  %37 = \"tf.Identity\"(%12) {device = \"\"} : (tensor<12xf32>) -> tensor<12xf32>\n  %38 = \"tf.StridedSlice\"(%27, %13, %15, %15) {begin_mask = 0 : i64, device = \"\", ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 1 : i64} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %39 = \"tf.Pack\"(%38) {axis = 0 : i64, device = \"\"} : (tensor<i32>) -> tensor<1xi32>\n  %40 = \"tf.Fill\"(%39, %8) {device = \"\"} : (tensor<1xi32>, tensor<f32>) -> tensor<?xf32>\n  %41 = \"tf.Fill\"(%39, %9) {device = \"\"} : (tensor<1xi32>, tensor<f32>) -> tensor<?xf32>\n  %42 = \"tf.StridedSlice\"(%27, %15, %14, %15) {begin_mask = 0 : i64, device = \"\", ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 1 : i64} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %43 = \"tf.Pack\"(%20, %38, %42, %20) {axis = 0 : i64, device = \"\"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<4xi32>\n  %44 = \"tf.Reshape\"(%26#3, %43) : (tensor<?x?xf32>, tensor<4xi32>) -> tensor<1x?x?x1xf32>\n  %y, %batch_mean, %batch_variance, %reserve_space_1, %reserve_space_2, %reserve_space_3 = \"tf.FusedBatchNormV3\"(%44, %40, %41, %10, %10) {data_format = \"NCHW\", device = \"\", epsilon = 1.000000e-03 : f32, exponential_avg_factor = 1.000000e+00 : f32, is_training = true} : (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<0xf32>, tensor<0xf32>) -> (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<*xf32>)\n  %45 = \"tf.Reshape\"(%y, %27) {device = \"\"} : (tensor<1x?x?x1xf32>, tensor<2xi32>) -> tensor<?x?xf32>\n  %46 = \"tf.Mul\"(%45, %37) {device = \"\"} : (tensor<?x?xf32>, tensor<12xf32>) -> tensor<?x12xf32>\n  %47 = \"tf.AddV2\"(%46, %36) {device = \"\"} : (tensor<?x12xf32>, tensor<12xf32>) -> tensor<?x12xf32>\n  %48 = \"tf.MatMul\"(%47, %31) {device = \"\", transpose_a = false, transpose_b = false} : (tensor<?x12xf32>, tensor<12x64xf32>) -> tensor<?x64xf32>\n  %49 = \"tf.BiasAdd\"(%48, %30) {data_format = \"NHWC\", device = \"\"} : (tensor<?x64xf32>, tensor<64xf32>) -> tensor<?x64xf32>\n  %50 = \"tf.Neg\"(%49) {device = \"\"} : (tensor<?x64xf32>) -> tensor<?x64xf32>\n  %51 = \"tf.Relu\"(%50) {device = \"\"} : (tensor<?x64xf32>) -> tensor<?x64xf32>\n  %52 = \"tf.Relu\"(%49) {device = \"\"} : (tensor<?x64xf32>) -> tensor<?x64xf32>\n  %53 = \"tf.Identity\"(%16) {device = \"\"} : (tensor<64xf32>) -> tensor<64xf32>\n  %54 = \"tf.Neg\"(%53) {device = \"\"} : (tensor<64xf32>) -> tensor<64xf32>\n  %55 = \"tf.Mul\"(%54, %51) {device = \"\"} : (tensor<64xf32>, tensor<?x64xf32>) -> tensor<?x64xf32>\n  %56 = \"tf.AddV2\"(%52, %55) {device = \"\"} : (tensor<?x64xf32>, tensor<?x64xf32>) -> tensor<?x64xf32>\n  %57 = \"tf.MatMul\"(%56, %33) {device = \"\", transpose_a = false, transpose_b = false} : (tensor<?x64xf32>, tensor<64x32xf32>) -> tensor<?x32xf32>\n  %58 = \"tf.BiasAdd\"(%57, %32) {data_format = \"NHWC\", device = \"\"} : (tensor<?x32xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %59 = \"tf.Neg\"(%58) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %60 = \"tf.Relu\"(%59) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %61 = \"tf.Relu\"(%58) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %62 = \"tf.Identity\"(%17) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %63 = \"tf.Neg\"(%62) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %64 = \"tf.Mul\"(%63, %60) {device = \"\"} : (tensor<32xf32>, tensor<?x32xf32>) -> tensor<?x32xf32>\n  %65 = \"tf.AddV2\"(%61, %64) {device = \"\"} : (tensor<?x32xf32>, tensor<?x32xf32>) -> tensor<?x32xf32>\n  %66 = \"tf.MatMul\"(%65, %35) {device = \"\", transpose_a = false, transpose_b = false} : (tensor<?x32xf32>, tensor<32x32xf32>) -> tensor<?x32xf32>\n  %67 = \"tf.BiasAdd\"(%66, %34) {data_format = \"NHWC\", device = \"\"} : (tensor<?x32xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %68 = \"tf.Neg\"(%67) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %69 = \"tf.Relu\"(%68) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %70 = \"tf.Relu\"(%67) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %71 = \"tf.Identity\"(%18) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %72 = \"tf.Neg\"(%71) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %73 = \"tf.Mul\"(%72, %69) {device = \"\"} : (tensor<32xf32>, tensor<?x32xf32>) -> tensor<?x32xf32>\n  %74 = \"tf.AddV2\"(%70, %73) {device = \"\"} : (tensor<?x32xf32>, tensor<?x32xf32>) -> tensor<?x32xf32>\n  %75 = \"tf.MatMul\"(%74, %29) {device = \"\", transpose_a = false, transpose_b = false} : (tensor<?x32xf32>, tensor<32x25xf32>) -> tensor<?x25xf32>\n  %76 = \"tf.BiasAdd\"(%75, %28) {data_format = \"NHWC\", device = \"\"} : (tensor<?x25xf32>, tensor<25xf32>) -> tensor<?x25xf32>\n  %77 = \"tf.Softmax\"(%76) {device = \"\"} : (tensor<?x25xf32>) -> tensor<?x25xf32>\n  %78 = \"tf.Identity\"(%77) {device = \"\"} : (tensor<?x25xf32>) -> tensor<?x25xf32>\n  \"std.return\"(%78) : (tensor<?x25xf32>) -> ()\n}) {sym_name = \"main\", tf.entry_function = {control_outputs = \"\", inputs = \"chroma_input\", outputs = \"Identity\"}, type = (tensor<?x8160xf32>) -> tensor<?x25xf32>} : () -> ()\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/lite/python/convert.py\u001b[0m in \u001b[0;36mtoco_convert_protos\u001b[0;34m(model_flags_str, toco_flags_str, input_data_str, debug_info_str, enable_mlir_converter)\u001b[0m\n\u001b[1;32m    198\u001b[0m                                                  \u001b[0mdebug_info_str\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m                                                  enable_mlir_converter)\n\u001b[0m\u001b[1;32m    200\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_str\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/lite/python/wrap_toco.py\u001b[0m in \u001b[0;36mwrapped_toco_convert\u001b[0;34m(model_flags_str, toco_flags_str, input_data_str, debug_info_str, enable_mlir_converter)\u001b[0m\n\u001b[1;32m     37\u001b[0m       \u001b[0mdebug_info_str\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m       enable_mlir_converter)\n\u001b[0m\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mException\u001b[0m: /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: error: 'tfl.while' op is a TFLite builtin op but builtin emission is not enabled\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: see current operation: %26:6 = \"tfl.while\"(%19, %22, %19, %25, %22, %arg0) ( {\n^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<?x?xf32>):  // no predecessors\n  %79 = \"std.call\"(%arg1, %arg2, %arg3, %arg4, %22, %arg0) {callee = @\"sequential/chroma/map/while_cond\"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> tensor<i1>\n  \"tfl.yield\"(%79) : (tensor<i1>) -> ()\n},  {\n^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<?x?xf32>):  // no predecessors\n  %79:6 = \"std.call\"(%arg1, %arg2, %arg3, %arg4, %22, %arg0) {callee = @\"sequential/chroma/map/while_body\"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n  \"tfl.yield\"(%79#0, %79#1, %79#2, %79#3, %79#4, %79#5) : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> ()\n}) {is_stateless = true} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: error: 'tfl.cast' op is a TFLite builtin op but builtin emission is not enabled\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: see current operation: %39 = \"tfl.cast\"(%38) : (tensor<?x12xf32>) -> tensor<?x?xf32>\n<unknown>:0: error: failed while converting: 'main': \n<unknown>:0: note: see current operation: \"func\"() ( {\n^bb0(%arg0: tensor<?x8160xf32>):  // no predecessors\n  %0 = \"tf.Const\"() {value = dense<[0.147737071, -0.112439565, 0.0381680913, -0.117878117, -0.0436722748, 0.00939085335, -0.0897184237, 0.128659829, -0.047683455, 0.0467018858, 0.0466206186, -0.00430281786, -0.0359039567, -0.0798944235, 0.0561503395, -0.0470165387, -0.0136118941, 0.0447785147, -0.148503736, 0.0860366448, 0.0881774276, 0.0692368522, 0.0460185111, 0.00844638515, -0.117945939]> : tensor<25xf32>} : () -> tensor<25xf32>\n  %1 = \"tf.Const\"() {value = dense<\"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\"> : tensor<32x25xf32>} : () -> tensor<32x25xf32>\n  %2 = \"tf.Const\"() {value = dense<[0.168076098, 0.189250961, 0.331452399, 0.241827965, -0.0550747886, 0.221319988, 0.300828785, 0.148949519, 0.393287301, 0.196752965, 0.175839543, 0.161021888, 0.168520689, 0.117292024, 0.17293328, 0.16789341, 0.036989145, 0.27923131, -0.149837464, 0.264275849, 0.172219113, 0.10350284, 0.303251654, 0.00179201632, 0.126206487, 0.303781778, 0.357120544, 0.222722366, 0.31719318, 0.279861689, 0.141150013, 0.0332527235, 0.254058361, 0.220147535, 0.245440096, 0.136673525, 0.112707727, 0.185189515, 0.24833253, 2.750830e-01, 0.176150858, 0.268469721, 0.143250883, 0.167769864, 0.238804296, 0.0973271057, 0.224579096, 0.321996093, 0.202449262, 0.0528840162, 0.172480404, -0.106782943, 0.150560513, 0.241444588, 0.0187574979, 0.157111406, 0.200682238, 0.0350796692, 0.208003864, -0.00685206195, 0.140565231, 0.0904081687, 0.189968288, 0.0431946218]> : tensor<64xf32>} : () -> tensor<64xf32>\n  %3 = \"tf.Const\"() {value = dense<\"0x40AAFD3E8888E23E7FDEA43EFDC38C3C1799C53E58ECB43E12BD72BEA1969D3E1C140FBE8BE65CBE9D98953E6242E63E831BB23EB3F285BE73FD863CACEB12BF075809BE84BBD5BE38F6A33E19638A3D324F8E3E34EE703EEED3B4BE23DB2D3EF697E53C819F11BEE17230BE25819FBEFE8FDFBEE65C7D3E7B50A03ED9FF7CBE6E1A793EBB188A3E0E822B3C6A4C833D26CA633EF88EA73EB13FECBEBC4D6EBE76DD2CBEF05F33BEA0D17DBE181FBCBEA69B73BEE695D13CF2663DBE1A6004BFEA4934BE5204AB3EC502B9BE0A007F3EB1C98F3EFDA22E3EDBF520BF6604743E97237C3E18829CBD2D495BBDD62D9F3ECBF3D1BE2B2B5C3ED3ABBB3D0A88403D3D83163EDDCD0CBD5079B43E77C0733EE531F23B43468ABEB0C6493E4A9309BF399645BD7871FF3E77F2993CD60CFBBE8C90AC3EC1E739BE1F4B7CBEA94EFBBD30DD82BEB6BF87BDD630A5BEB08322BFDCCF993E0FCBEFBEDC8AB9BE8B3227BE3D3A72BDA63B83BDCDF3913D5C1A3E3E6101BDBE0D48B2BDE2BD8CBE8B125D3EA2EC1D3ED6FDC93D17B6ECBC83361F3EF77C763D79E2A4BD3D03CEBEC7E270BE4AC75ABE4BB6EFBEC16082BEC388663E688FAE3E653033BFE73310BF93900ABFC65BD13ECE40CDBC1388A7BD017F033F0FE3123DC2B7853E387CB43DA5B602BF555AD3BEE8BC323E6473C8BEF1264C3E503CAF3D27EA30BCF9CBB1BE13B1AB3EE94C2CBED55303BF04B84EBF8F2A8B3EB9BA20BEA4C410BE3A51AB3E173203BDEC3CBFBE668404BD8071803E944A3BBFE42E1CBF9A82473EB110C13E5CA0C73EA18518BE4FC0E63EC70C913E66AB143E48D9BBBE50C7BD3E413144BC75E88DBEEF5D953DD4C7C83EBC6D11BFDD10CE3EF380A239EFAE11BDADC506BE2B3EBF3EE0875D3E08EB49BE1CA224BE62598B3E431C0B3F5DB3833E65A07B3E3D65E7BED616903E4C7684BE0DEAE2BE666D003EA485423E8C09ABBF592C79BE9666813E69F03CBED41845BE90CCD03D839648BE4F0605BE2B5249BEBDAFD03EFFBE3E3DAE52783D78173E3E490881BDFEDF94BEAABD8F3E8AA2D0BEDFE76BBCFF2982BE7D0EFC3AC489E7BE22E398BE181A4EBE9215E7BEFA24A23EE0B15CBE766D88BE12BF04BFC2DC58BD69AAA3BD24443E3EE135CBBE732E933ED4D5C8BDC381443E2671EB3EB1F50A3CFD8AD63E0CF5B13E7A484EBD075E4CBE23D99CBBD1273BBE4AD3ACBC3E1A8FBEDC803EBF285AA6BCA40FDEBCE385B7BED1B9B53E7BABAEBE8CA3AE3E51E98B3CDF93B83DCA40D63CD5A0633D2AF884BEF90A2D3EDE855DBE78DFBEBD0D88953EEE35BABE2E60F9BE534B19BE985516BE81A9293F0A36333F6B5E363ECA64FABE653B3F3D14E125BEEB48543BEE34143E894C863E2765BE3EFA610DBF98C7C73EB11DD23E4AD20BBFF43996BE7750B63EEB0C433DA656083FEB638C3E18822ABEB191823C62B9FBBEBA0DA0BE2387B13D443190BE11D4033F00315A3E9770F9BEAE73743E7A0671BCF62FC0BEFD8EF73DB6DB6CBE49B3F6BC23F8223F2EA733BE81EE1FBE9FB2E43E242711BDB16DBF3D46BCCFBE159997BD5C80F73EC879233FA1D6A63E32FF12BF3DD5BFBE093CAABCBCCB713E5E37C03E5E3231BD496D243F599295BD60DE273D1ADA013E409F10BFD22BCABE2F59EB3EB30E28BF80210BBEDCBB9CBE4B690BBD032989BE9BFC123E55E3A1BEDF2E84BEBF04253F5EA8F4BEA6E392BEB5E998BED4D42CBE7B594C3EE6736BBE13B70C3F873EB3BE5EF0CD3E884219BE645E01BF96950C3E7014B3BDA0DD33BF781B70BE97A1E2BCBAB7A13E083076BE114D873DAEFAD13E77075FBC6A0C2DBEC032893D5F1AE13E0D52E2BDC788AEBE1B78513EE7AD21BE3BEC9E3E070751BEAEA3E23E9BDB6EBEA0FD9E3D62CCEBBB17EE883E23F8363E840B083FFA9FDFBE272C113FC518D53E87C6A93E542870BB12E5D83DD83A98BD771AE23E5FBEE8BE4959AB3EC2A915BF38EAE5BE784DB3BE331BF7BD915B0FBF76EC22BF0A57653DF639703E175AA2BCB42DFEBE72CB233F65EF4E3DDB7E80BD08D130BE08BEDBBE07AE5BBE279DF43D36E07FBE36D7E3BE3CE67A3D4581F9BE4E27A83E14BEDB3E823BC3BECEDB113E69D7923E7753C3BECAAD173FCFDF063FF846EBBE6466833E09BD5CBEBF9EA6BE5E6F363E73D15A3D3DA7EB3EBE1C433E417609BF04F86D3E93F9AEBE7A5E6BBEEDBF5E3E922797BE2117983E6C6D363FF3A7E23ED43206BEFDB045BEA756EE3BDF6BCE3E30B968BB73C6003FB63D1C3F015B49BEA5486D3D8CA5D63E502325BE77DD02BFFD8E0B3D8B04B53E61CB29BB7D7602BF6F527A3D154F163E1434B43E080E05BF0425253C207A1ABFBD3A42BE1BE21C3FD46B943DD595D13D2713D23ED405D53D42A7CFBE8B799FBE99A396BEADFF783D87548F3E59CB5D3E677F88BEC159F83E7D2320BF31BA563E399B4C3F8C890DBFC757B53DE8A4683EC7DEC93E036E233F202ABD3E1D5F0E3F1A506B3EB40ACD3E74B0FCBED8CA19BD11709DBEDD009ABEFEA75F3E6108043FFD361EBE273038BE090B863D8EB300BDE9B6E3BDA98CF4BE7DD2E7BEA55C7CBEBE1D65BD265093BEAC178F3EB8BAC3BDC075813EE219093F50D3F03D35D328BFF053DB3D99D51E3FFDDE1C3E9DF259BD4F61D1BE1119E5BE413FA4BEF0D608BF52DF32BE91FCD8BE50C2AE3EAAFC03BF2103D23EB197DC3DCA7916BF2BCD87BE85D27F3D305B6D3DB6CBB5BD4C131CBF70E2E6BE3AEC76BE0FF2E7BEFB04DEBEDC055DBEC493033E4BF6B3BE31267E3E25D593BE1EA0973E32AB263F983F0FBF7FE5E73DEEE6923B4544313F711E2D3ECAEF253D2B8EDCBE897F543E27EC0E3E630477BE9DA4B8BEF2C066BE583D1E3F73EA8ABE954510BFAE0E113D17D27C3DD47F493EDAFC9D3E906CFF3E9D875EBE1EEB093FB25EC93D8639553E71B8453E5ADC1CBED514DDBE50805C3C4A2DAD3D1334953E4A63ACBE109AECBE0EFFD3BEA9F7E53DF5FCA6BE2FDDFEBD278BB23E3E3F07BF0D2F853ECBB65CBDC5BC08BF4D81613E48C2F8BE1426803E946B02BE8070713C87DB4C3D56AE003F91C91C3FEA98A2BCC3651DBFBE1AFBBE09F0293E19ACDBBE3F41053E4A1DB73E1A531D3F4CA1063CF9FEAE3E71D3C63DF2C6AEBE957025BFBB9FA5BE081C383E8925913E3F519B3D63E1183FD1EEB1BD825E0E3F8B741B3EB167EDBE3BEE0B3E9E20853E8B3264BEBFCF0FBB6456C73DDBA9CC3EC78EC53D1D8FA93EEE1B723EE8E3A6BDB9FA533EA1B9ACBEDDCB233EF3BD2E3E4E8C71BED77F7DBEF6EC343F2C5A3B3E7973DBBEE96F1BBFA5C92ABE0199F9BE958F13BF1E8D80BDCC822DBEC976E3BD8F3591BEEDF20DBFA834D4BEDB9897BEECBC0D3EB23F8BBE3E83693EB2E4813E6119D3BEC710BC3E6A5A51BE9EB287BEFD4DE3BE31CECDBE38E53DBE8D63163F102FF7BE7D5FE03E43323A3EB0947ABEA2595D3BCE5115BCDA18AFBE56E300BDF026C4BE1B3C47BD926CD93EC0ED253A1B77573E60B7433E416606BF6187A9BEED8308BFD3953DBD8F71BCBE70D8C5BE274083BEDDBAF8BEBD92B6BEB275D83D68C540BE98E906BFD91CB83E8F5C31BF5DC3E13EC983833E9CFE95BEC65BF5BEB7EA4DBE27337CBE84B4E4BEAE8AB2BEC14044BE2068193FC2B334BE66D627BFE9B8AC3E96CDA73ED7250ABF22A5503E5E0C98BE3B0B1ABC9B3601BE34CE5EBEF9020DBFC0E283BC5979E83E19492CBD7D6CC03E4621B5BE69198FBEA5ED913D1B28003F80E6843D13565FBE20F9E13E6A1EBA3D5BFDB3BBE8167E3D5CE5B7BE6F6C1BBFEC08A5BE7E6D453E0E93EA3CD66E65BECD2D113F28D9ECBE696438BE7BCC2B3E2DE2F63E45FCADBE5A038C3E2BC50F3D5A2DA23C557ACD3DD477763EB88CB5BE57E05BBDDA77743E6CDE17BFA7CD63BE676E773E0DAF033D82187E3E11C9223F5E70063F98C1543E280BE93EAFB08DBEF787393E1E2155BF56B58ABE815A96BE1E9668BE164AF3BC591B49BD1F5D193ED5CFAEBEEA5EE4BE9675203E18DB97BE8CCE9A3CABE5D23DFB0416BFBC89E53E416010BF5CF3783E0578B43DE2B59FBEEEB203BE6F86D83E5ED7DF3E429485BEB5B2C73E9106693D563A90BE51BAF93E92ABCBBE5A12923DBE582C3E1B79EE3D063D2CBE8C71573E8B23503EF6BB0C3F07C1ED3DC0CD6C3E48DB713E00323F3E23B5E3BED2BA343CFA998EBE431CDCBE1AB96C3E262AAFBD994037BF1A2814BF10A298BE4B3AC73D52A82BBE55F13F3E1AB38EBEE1D900BE0C2BB63D8522873EA3FA5A3E5A50903EF48972BE541003BE0DA99F3EC401C2BEC81FC6BE\"> : tensor<12x64xf32>} : () -> tensor<12x64xf32>\n  %4 = \"tf.Const\"() {value = dense<[0.0549458079, 0.238936529, 0.103667565, 0.0921238735, 0.143788829, 0.0911116451, 0.175867394, 0.21835795, -0.0401271246, 0.0155029483, 0.122010075, 0.223504156, 0.100209974, -0.0316341706, 0.0610777251, 0.0399796627, 0.204122216, 0.131453395, 0.252934813, 0.182648122, 0.104780763, 0.126945794, 0.0563390441, 0.0932675302, -0.00845822785, 0.171583086, 0.182540447, 0.0219219904, 0.075256735, -0.0730319172, 0.0945926234, 0.03456036]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %5 = \"tf.Const\"() {value = 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\"> : tensor<64x32xf32>} : () -> tensor<64x32xf32>\n  %6 = \"tf.Const\"() {value = dense<[-0.0118571837, 0.0800256952, 0.170635894, 0.028398335, 0.259022981, 0.0898712798, 0.175803497, 0.161798835, 0.0338865407, -3.69851245E-4, 0.0425762795, 0.212983623, -0.0411456116, 0.0527183078, 0.119301379, 0.065352641, 0.0161737688, 0.0426695272, -0.00505993469, 0.085072346, 0.0391224585, 0.0117550725, 0.039498996, -0.00897677056, -8.358640e-02, 0.179748133, -0.0227445941, 0.0897767916, 0.0937709659, 0.091977097, -0.112039283, 0.133670092]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %7 = \"tf.Const\"() {value = dense<\"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\"> : tensor<32x32xf32>} : () -> tensor<32x32xf32>\n  %8 = \"tf.Const\"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>\n  %9 = \"tf.Const\"() {value = dense<0.000000e+00> : tensor<f32>} : () -> tensor<f32>\n  %10 = \"tf.Const\"() {value = dense<> : tensor<0xf32>} : () -> tensor<0xf32>\n  %11 = \"tf.Const\"() {value = dense<[0.00725550949, -0.155836254, 0.117213838, -7.415890e-02, -0.22646898, -0.17580685, 0.145644963, -0.127022445, -0.0325459577, -0.229648903, -0.162604809, -0.104097113]> : tensor<12xf32>} : () -> tensor<12xf32>\n  %12 = \"tf.Const\"() {value = dense<[1.40223753, 1.42622852, 1.53603494, 1.36610556, 1.69401383, 1.55997336, 1.62918568, 1.57092464, 1.45249867, 1.6583848, 1.62247539, 1.52738059]> : tensor<12xf32>} : () -> tensor<12xf32>\n  %13 = \"tf.Const\"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>\n  %14 = \"tf.Const\"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>\n  %15 = \"tf.Const\"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>\n  %16 = \"tf.Const\"() {value = dense<[-0.18137075, -0.190813139, -0.044286862, 0.211237565, -1.746690e-01, -0.151737764, 0.16834183, 0.119328104, 0.105832867, -0.0240005907, -0.0751447379, 0.037534222, -0.342453718, 0.186597332, -0.310402393, 0.018641986, 0.161040291, 0.232865468, -0.081227988, 0.160159558, -0.437950075, -0.137296706, -0.329482079, -0.239970639, -0.0312041733, 0.190941975, -0.402803183, 0.483502597, 0.173778772, 0.298825443, 0.334926784, 0.363431662, 0.524393857, -0.133963794, 0.304172069, -0.42725718, 0.16676645, 0.0580276921, 0.193012163, 0.0510975271, 0.813377559, 0.0750992373, 0.161859617, -0.254672736, 0.190442935, -0.226609036, -0.343025893, -0.335672051, -0.34258005, -0.254799277, 0.181894705, -0.34304902, 0.131500974, 0.0329634733, 0.0467517935, 0.325228602, -0.512234688, 0.171455756, -0.0636215433, -0.16159603, 0.178863093, -0.261178225, -0.211183608, 0.135612398]> : tensor<64xf32>} : () -> tensor<64xf32>\n  %17 = \"tf.Const\"() {value = dense<[0.360518038, -0.168143243, 0.11197973, 0.127239093, 0.0691332668, -0.266665071, 0.0290160123, 0.169769675, 0.236813396, 0.418850154, -0.415096372, -0.178710625, 0.493837386, -0.269553035, 0.242321983, 0.225369349, -0.294833422, -0.269634217, 0.524404645, 0.0572571382, -0.173829466, -0.321533293, 0.339223772, -0.0190662649, 0.203185797, -0.0887959301, 0.0538560525, -0.392667502, 0.318463176, -0.404079944, 0.492643982, 0.248903751]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %18 = \"tf.Const\"() {value = dense<[-0.274599046, 0.2693699, -0.0384969115, 0.547718585, 0.44997403, -0.281009167, -0.0743118376, 0.0901606306, 0.306658685, -0.00219570659, -0.0720264911, -0.340246856, -0.0986638963, 0.286379397, -0.0639733076, 0.49492836, 0.157945499, -0.232450366, 0.11595349, 0.357020527, -0.306564212, -0.223095223, 0.32178998, 0.656092644, 0.211648986, 0.0336364061, -0.346420258, -0.214254037, 0.199852765, -0.188247502, 0.677568316, 0.299702734]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %19 = \"tf.Const\"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>\n  %20 = \"tf.Const\"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>\n  %cst = \"std.constant\"() {value = dense<12> : tensor<1xi32>} : () -> tensor<1xi32>\n  %cst_0 = \"std.constant\"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>\n  %cst_1 = \"std.constant\"() {value = dense<0.000000e+00> : tensor<f32>} : () -> tensor<f32>\n  %21 = \"tf.Shape\"(%arg0) {device = \"\"} : (tensor<?x8160xf32>) -> tensor<2xi32>\n  %22 = \"tf.StridedSlice\"(%21, %13, %15, %15) {begin_mask = 0 : i64, device = \"\", ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 1 : i64} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %23 = \"tf.ExpandDims\"(%22, %cst_0) : (tensor<i32>, tensor<i32>) -> tensor<1xi32>\n  %24 = \"tf.ConcatV2\"(%23, %cst, %cst_0) : (tensor<1xi32>, tensor<1xi32>, tensor<i32>) -> tensor<2xi32>\n  %25 = \"tf.Fill\"(%24, %cst_1) : (tensor<2xi32>, tensor<f32>) -> tensor<?x?xf32>\n  %26:6 = \"tfl.while\"(%19, %22, %19, %25, %22, %arg0) ( {\n  ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<?x?xf32>):  // no predecessors\n    %79 = \"std.call\"(%arg1, %arg2, %arg3, %arg4, %22, %arg0) {callee = @\"sequential/chroma/map/while_cond\"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> tensor<i1>\n    \"tfl.yield\"(%79) : (tensor<i1>) -> ()\n  },  {\n  ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<?x?xf32>):  // no predecessors\n    %79:6 = \"std.call\"(%arg1, %arg2, %arg3, %arg4, %22, %arg0) {callee = @\"sequential/chroma/map/while_body\"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n    \"tfl.yield\"(%79#0, %79#1, %79#2, %79#3, %79#4, %79#5) : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> ()\n  }) {is_stateless = true} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n  %27 = \"tf.Shape\"(%26#3) {device = \"\"} : (tensor<?x?xf32>) -> tensor<2xi32>\n  %28 = \"tf.Identity\"(%0) {device = \"\"} : (tensor<25xf32>) -> tensor<25xf32>\n  %29 = \"tf.Identity\"(%1) {device = \"\"} : (tensor<32x25xf32>) -> tensor<32x25xf32>\n  %30 = \"tf.Identity\"(%2) {device = \"\"} : (tensor<64xf32>) -> tensor<64xf32>\n  %31 = \"tf.Identity\"(%3) {device = \"\"} : (tensor<12x64xf32>) -> tensor<12x64xf32>\n  %32 = \"tf.Identity\"(%4) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %33 = \"tf.Identity\"(%5) {device = \"\"} : (tensor<64x32xf32>) -> tensor<64x32xf32>\n  %34 = \"tf.Identity\"(%6) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %35 = \"tf.Identity\"(%7) {device = \"\"} : (tensor<32x32xf32>) -> tensor<32x32xf32>\n  %36 = \"tf.Identity\"(%11) {device = \"\"} : (tensor<12xf32>) -> tensor<12xf32>\n  %37 = \"tf.Identity\"(%12) {device = \"\"} : (tensor<12xf32>) -> tensor<12xf32>\n  %38 = \"tf.StridedSlice\"(%27, %13, %15, %15) {begin_mask = 0 : i64, device = \"\", ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 1 : i64} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %39 = \"tf.Pack\"(%38) {axis = 0 : i64, device = \"\"} : (tensor<i32>) -> tensor<1xi32>\n  %40 = \"tf.Fill\"(%39, %8) {device = \"\"} : (tensor<1xi32>, tensor<f32>) -> tensor<?xf32>\n  %41 = \"tf.Fill\"(%39, %9) {device = \"\"} : (tensor<1xi32>, tensor<f32>) -> tensor<?xf32>\n  %42 = \"tf.StridedSlice\"(%27, %15, %14, %15) {begin_mask = 0 : i64, device = \"\", ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 1 : i64} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %43 = \"tf.Pack\"(%20, %38, %42, %20) {axis = 0 : i64, device = \"\"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<4xi32>\n  %44 = \"tf.Reshape\"(%26#3, %43) : (tensor<?x?xf32>, tensor<4xi32>) -> tensor<1x?x?x1xf32>\n  %y, %batch_mean, %batch_variance, %reserve_space_1, %reserve_space_2, %reserve_space_3 = \"tf.FusedBatchNormV3\"(%44, %40, %41, %10, %10) {data_format = \"NCHW\", device = \"\", epsilon = 1.000000e-03 : f32, exponential_avg_factor = 1.000000e+00 : f32, is_training = true} : (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<0xf32>, tensor<0xf32>) -> (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<*xf32>)\n  %45 = \"tf.Reshape\"(%y, %27) {device = \"\"} : (tensor<1x?x?x1xf32>, tensor<2xi32>) -> tensor<?x?xf32>\n  %46 = \"tf.Mul\"(%45, %37) {device = \"\"} : (tensor<?x?xf32>, tensor<12xf32>) -> tensor<?x12xf32>\n  %47 = \"tf.AddV2\"(%46, %36) {device = \"\"} : (tensor<?x12xf32>, tensor<12xf32>) -> tensor<?x12xf32>\n  %48 = \"tf.MatMul\"(%47, %31) {device = \"\", transpose_a = false, transpose_b = false} : (tensor<?x12xf32>, tensor<12x64xf32>) -> tensor<?x64xf32>\n  %49 = \"tf.BiasAdd\"(%48, %30) {data_format = \"NHWC\", device = \"\"} : (tensor<?x64xf32>, tensor<64xf32>) -> tensor<?x64xf32>\n  %50 = \"tf.Neg\"(%49) {device = \"\"} : (tensor<?x64xf32>) -> tensor<?x64xf32>\n  %51 = \"tf.Relu\"(%50) {device = \"\"} : (tensor<?x64xf32>) -> tensor<?x64xf32>\n  %52 = \"tf.Relu\"(%49) {device = \"\"} : (tensor<?x64xf32>) -> tensor<?x64xf32>\n  %53 = \"tf.Identity\"(%16) {device = \"\"} : (tensor<64xf32>) -> tensor<64xf32>\n  %54 = \"tf.Neg\"(%53) {device = \"\"} : (tensor<64xf32>) -> tensor<64xf32>\n  %55 = \"tf.Mul\"(%54, %51) {device = \"\"} : (tensor<64xf32>, tensor<?x64xf32>) -> tensor<?x64xf32>\n  %56 = \"tf.AddV2\"(%52, %55) {device = \"\"} : (tensor<?x64xf32>, tensor<?x64xf32>) -> tensor<?x64xf32>\n  %57 = \"tf.MatMul\"(%56, %33) {device = \"\", transpose_a = false, transpose_b = false} : (tensor<?x64xf32>, tensor<64x32xf32>) -> tensor<?x32xf32>\n  %58 = \"tf.BiasAdd\"(%57, %32) {data_format = \"NHWC\", device = \"\"} : (tensor<?x32xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %59 = \"tf.Neg\"(%58) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %60 = \"tf.Relu\"(%59) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %61 = \"tf.Relu\"(%58) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %62 = \"tf.Identity\"(%17) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %63 = \"tf.Neg\"(%62) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %64 = \"tf.Mul\"(%63, %60) {device = \"\"} : (tensor<32xf32>, tensor<?x32xf32>) -> tensor<?x32xf32>\n  %65 = \"tf.AddV2\"(%61, %64) {device = \"\"} : (tensor<?x32xf32>, tensor<?x32xf32>) -> tensor<?x32xf32>\n  %66 = \"tf.MatMul\"(%65, %35) {device = \"\", transpose_a = false, transpose_b = false} : (tensor<?x32xf32>, tensor<32x32xf32>) -> tensor<?x32xf32>\n  %67 = \"tf.BiasAdd\"(%66, %34) {data_format = \"NHWC\", device = \"\"} : (tensor<?x32xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %68 = \"tf.Neg\"(%67) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %69 = \"tf.Relu\"(%68) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %70 = \"tf.Relu\"(%67) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %71 = \"tf.Identity\"(%18) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %72 = \"tf.Neg\"(%71) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %73 = \"tf.Mul\"(%72, %69) {device = \"\"} : (tensor<32xf32>, tensor<?x32xf32>) -> tensor<?x32xf32>\n  %74 = \"tf.AddV2\"(%70, %73) {device = \"\"} : (tensor<?x32xf32>, tensor<?x32xf32>) -> tensor<?x32xf32>\n  %75 = \"tf.MatMul\"(%74, %29) {device = \"\", transpose_a = false, transpose_b = false} : (tensor<?x32xf32>, tensor<32x25xf32>) -> tensor<?x25xf32>\n  %76 = \"tf.BiasAdd\"(%75, %28) {data_format = \"NHWC\", device = \"\"} : (tensor<?x25xf32>, tensor<25xf32>) -> tensor<?x25xf32>\n  %77 = \"tf.Softmax\"(%76) {device = \"\"} : (tensor<?x25xf32>) -> tensor<?x25xf32>\n  %78 = \"tf.Identity\"(%77) {device = \"\"} : (tensor<?x25xf32>) -> tensor<?x25xf32>\n  \"std.return\"(%78) : (tensor<?x25xf32>) -> ()\n}) {sym_name = \"main\", tf.entry_function = {control_outputs = \"\", inputs = \"chroma_input\", outputs = \"Identity\"}, type = (tensor<?x8160xf32>) -> tensor<?x25xf32>} : () -> ()\n",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mConverterError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-c964b7e0a385>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mconverter3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_spec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msupported_ops\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpsSet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSELECT_TF_OPS\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtflite_model3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconverter3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'model_select.tflite'\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/lite/python/lite.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    835\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    836\u001b[0m     return super(TFLiteKerasModelConverterV2,\n\u001b[0;32m--> 837\u001b[0;31m                  self).convert(graph_def, input_tensors, output_tensors)\n\u001b[0m\u001b[1;32m    838\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    839\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/lite/python/lite.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(self, graph_def, input_tensors, output_tensors)\u001b[0m\n\u001b[1;32m    637\u001b[0m         \u001b[0minput_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_tensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    638\u001b[0m         \u001b[0moutput_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_tensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 639\u001b[0;31m         **converter_kwargs)\n\u001b[0m\u001b[1;32m    640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m     calibrate_and_quantize, flags = quant_mode.quantizer_flags(\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/lite/python/convert.py\u001b[0m in \u001b[0;36mtoco_convert_impl\u001b[0;34m(input_data, input_tensors, output_tensors, enable_mlir_converter, *args, **kwargs)\u001b[0m\n\u001b[1;32m    572\u001b[0m       \u001b[0minput_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSerializeToString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m       \u001b[0mdebug_info_str\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug_info_str\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 574\u001b[0;31m       enable_mlir_converter=enable_mlir_converter)\n\u001b[0m\u001b[1;32m    575\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/lite/python/convert.py\u001b[0m in \u001b[0;36mtoco_convert_protos\u001b[0;34m(model_flags_str, toco_flags_str, input_data_str, debug_info_str, enable_mlir_converter)\u001b[0m\n\u001b[1;32m    200\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_str\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 202\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mConverterError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mdistutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspawn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_executable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_toco_from_proto_bin\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mConverterError\u001b[0m: /home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: error: 'tfl.while' op is a TFLite builtin op but builtin emission is not enabled\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: see current operation: %26:6 = \"tfl.while\"(%19, %22, %19, %25, %22, %arg0) ( {\n^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<?x?xf32>):  // no predecessors\n  %79 = \"std.call\"(%arg1, %arg2, %arg3, %arg4, %22, %arg0) {callee = @\"sequential/chroma/map/while_cond\"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> tensor<i1>\n  \"tfl.yield\"(%79) : (tensor<i1>) -> ()\n},  {\n^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<?x?xf32>):  // no predecessors\n  %79:6 = \"std.call\"(%arg1, %arg2, %arg3, %arg4, %22, %arg0) {callee = @\"sequential/chroma/map/while_body\"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n  \"tfl.yield\"(%79#0, %79#1, %79#2, %79#3, %79#4, %79#5) : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> ()\n}) {is_stateless = true} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: error: 'tfl.cast' op is a TFLite builtin op but builtin emission is not enabled\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:574:0: note: called from\n<ipython-input-7-cf0e73ae6b83>:12:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/autograph/impl/api.py:617:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:504:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py:382:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py:372:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:985:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/keras/saving/saving_utils.py:134:0: note: called from\n/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:507:0: note: see current operation: %39 = \"tfl.cast\"(%38) : (tensor<?x12xf32>) -> tensor<?x?xf32>\n<unknown>:0: error: failed while converting: 'main': \n<unknown>:0: note: see current operation: \"func\"() ( {\n^bb0(%arg0: tensor<?x8160xf32>):  // no predecessors\n  %0 = \"tf.Const\"() {value = dense<[0.147737071, -0.112439565, 0.0381680913, -0.117878117, -0.0436722748, 0.00939085335, -0.0897184237, 0.128659829, -0.047683455, 0.0467018858, 0.0466206186, -0.00430281786, -0.0359039567, -0.0798944235, 0.0561503395, -0.0470165387, -0.0136118941, 0.0447785147, -0.148503736, 0.0860366448, 0.0881774276, 0.0692368522, 0.0460185111, 0.00844638515, -0.117945939]> : tensor<25xf32>} : () -> tensor<25xf32>\n  %1 = \"tf.Const\"() {value = dense<\"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\"> : tensor<32x25xf32>} : () -> tensor<32x25xf32>\n  %2 = \"tf.Const\"() {value = dense<[0.168076098, 0.189250961, 0.331452399, 0.241827965, -0.0550747886, 0.221319988, 0.300828785, 0.148949519, 0.393287301, 0.196752965, 0.175839543, 0.161021888, 0.168520689, 0.117292024, 0.17293328, 0.16789341, 0.036989145, 0.27923131, -0.149837464, 0.264275849, 0.172219113, 0.10350284, 0.303251654, 0.00179201632, 0.126206487, 0.303781778, 0.357120544, 0.222722366, 0.31719318, 0.279861689, 0.141150013, 0.0332527235, 0.254058361, 0.220147535, 0.245440096, 0.136673525, 0.112707727, 0.185189515, 0.24833253, 2.750830e-01, 0.176150858, 0.268469721, 0.143250883, 0.167769864, 0.238804296, 0.0973271057, 0.224579096, 0.321996093, 0.202449262, 0.0528840162, 0.172480404, -0.106782943, 0.150560513, 0.241444588, 0.0187574979, 0.157111406, 0.200682238, 0.0350796692, 0.208003864, -0.00685206195, 0.140565231, 0.0904081687, 0.189968288, 0.0431946218]> : tensor<64xf32>} : () -> tensor<64xf32>\n  %3 = \"tf.Const\"() {value = dense<\"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\"> : tensor<12x64xf32>} : () -> tensor<12x64xf32>\n  %4 = \"tf.Const\"() {value = dense<[0.0549458079, 0.238936529, 0.103667565, 0.0921238735, 0.143788829, 0.0911116451, 0.175867394, 0.21835795, -0.0401271246, 0.0155029483, 0.122010075, 0.223504156, 0.100209974, -0.0316341706, 0.0610777251, 0.0399796627, 0.204122216, 0.131453395, 0.252934813, 0.182648122, 0.104780763, 0.126945794, 0.0563390441, 0.0932675302, -0.00845822785, 0.171583086, 0.182540447, 0.0219219904, 0.075256735, -0.0730319172, 0.0945926234, 0.03456036]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %5 = \"tf.Const\"() {value = dense<\"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\"> : tensor<64x32xf32>} : () -> tensor<64x32xf32>\n  %6 = \"tf.Const\"() {value = dense<[-0.0118571837, 0.0800256952, 0.170635894, 0.028398335, 0.259022981, 0.0898712798, 0.175803497, 0.161798835, 0.0338865407, -3.69851245E-4, 0.0425762795, 0.212983623, -0.0411456116, 0.0527183078, 0.119301379, 0.065352641, 0.0161737688, 0.0426695272, -0.00505993469, 0.085072346, 0.0391224585, 0.0117550725, 0.039498996, -0.00897677056, -8.358640e-02, 0.179748133, -0.0227445941, 0.0897767916, 0.0937709659, 0.091977097, -0.112039283, 0.133670092]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %7 = \"tf.Const\"() {value = dense<\"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\"> : tensor<32x32xf32>} : () -> tensor<32x32xf32>\n  %8 = \"tf.Const\"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>\n  %9 = \"tf.Const\"() {value = dense<0.000000e+00> : tensor<f32>} : () -> tensor<f32>\n  %10 = \"tf.Const\"() {value = dense<> : tensor<0xf32>} : () -> tensor<0xf32>\n  %11 = \"tf.Const\"() {value = dense<[0.00725550949, -0.155836254, 0.117213838, -7.415890e-02, -0.22646898, -0.17580685, 0.145644963, -0.127022445, -0.0325459577, -0.229648903, -0.162604809, -0.104097113]> : tensor<12xf32>} : () -> tensor<12xf32>\n  %12 = \"tf.Const\"() {value = dense<[1.40223753, 1.42622852, 1.53603494, 1.36610556, 1.69401383, 1.55997336, 1.62918568, 1.57092464, 1.45249867, 1.6583848, 1.62247539, 1.52738059]> : tensor<12xf32>} : () -> tensor<12xf32>\n  %13 = \"tf.Const\"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>\n  %14 = \"tf.Const\"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>\n  %15 = \"tf.Const\"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>\n  %16 = \"tf.Const\"() {value = dense<[-0.18137075, -0.190813139, -0.044286862, 0.211237565, -1.746690e-01, -0.151737764, 0.16834183, 0.119328104, 0.105832867, -0.0240005907, -0.0751447379, 0.037534222, -0.342453718, 0.186597332, -0.310402393, 0.018641986, 0.161040291, 0.232865468, -0.081227988, 0.160159558, -0.437950075, -0.137296706, -0.329482079, -0.239970639, -0.0312041733, 0.190941975, -0.402803183, 0.483502597, 0.173778772, 0.298825443, 0.334926784, 0.363431662, 0.524393857, -0.133963794, 0.304172069, -0.42725718, 0.16676645, 0.0580276921, 0.193012163, 0.0510975271, 0.813377559, 0.0750992373, 0.161859617, -0.254672736, 0.190442935, -0.226609036, -0.343025893, -0.335672051, -0.34258005, -0.254799277, 0.181894705, -0.34304902, 0.131500974, 0.0329634733, 0.0467517935, 0.325228602, -0.512234688, 0.171455756, -0.0636215433, -0.16159603, 0.178863093, -0.261178225, -0.211183608, 0.135612398]> : tensor<64xf32>} : () -> tensor<64xf32>\n  %17 = \"tf.Const\"() {value = dense<[0.360518038, -0.168143243, 0.11197973, 0.127239093, 0.0691332668, -0.266665071, 0.0290160123, 0.169769675, 0.236813396, 0.418850154, -0.415096372, -0.178710625, 0.493837386, -0.269553035, 0.242321983, 0.225369349, -0.294833422, -0.269634217, 0.524404645, 0.0572571382, -0.173829466, -0.321533293, 0.339223772, -0.0190662649, 0.203185797, -0.0887959301, 0.0538560525, -0.392667502, 0.318463176, -0.404079944, 0.492643982, 0.248903751]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %18 = \"tf.Const\"() {value = dense<[-0.274599046, 0.2693699, -0.0384969115, 0.547718585, 0.44997403, -0.281009167, -0.0743118376, 0.0901606306, 0.306658685, -0.00219570659, -0.0720264911, -0.340246856, -0.0986638963, 0.286379397, -0.0639733076, 0.49492836, 0.157945499, -0.232450366, 0.11595349, 0.357020527, -0.306564212, -0.223095223, 0.32178998, 0.656092644, 0.211648986, 0.0336364061, -0.346420258, -0.214254037, 0.199852765, -0.188247502, 0.677568316, 0.299702734]> : tensor<32xf32>} : () -> tensor<32xf32>\n  %19 = \"tf.Const\"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>\n  %20 = \"tf.Const\"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>\n  %cst = \"std.constant\"() {value = dense<12> : tensor<1xi32>} : () -> tensor<1xi32>\n  %cst_0 = \"std.constant\"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>\n  %cst_1 = \"std.constant\"() {value = dense<0.000000e+00> : tensor<f32>} : () -> tensor<f32>\n  %21 = \"tf.Shape\"(%arg0) {device = \"\"} : (tensor<?x8160xf32>) -> tensor<2xi32>\n  %22 = \"tf.StridedSlice\"(%21, %13, %15, %15) {begin_mask = 0 : i64, device = \"\", ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 1 : i64} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %23 = \"tf.ExpandDims\"(%22, %cst_0) : (tensor<i32>, tensor<i32>) -> tensor<1xi32>\n  %24 = \"tf.ConcatV2\"(%23, %cst, %cst_0) : (tensor<1xi32>, tensor<1xi32>, tensor<i32>) -> tensor<2xi32>\n  %25 = \"tf.Fill\"(%24, %cst_1) : (tensor<2xi32>, tensor<f32>) -> tensor<?x?xf32>\n  %26:6 = \"tfl.while\"(%19, %22, %19, %25, %22, %arg0) ( {\n  ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<?x?xf32>):  // no predecessors\n    %79 = \"std.call\"(%arg1, %arg2, %arg3, %arg4, %22, %arg0) {callee = @\"sequential/chroma/map/while_cond\"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> tensor<i1>\n    \"tfl.yield\"(%79) : (tensor<i1>) -> ()\n  },  {\n  ^bb0(%arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<?x?xf32>):  // no predecessors\n    %79:6 = \"std.call\"(%arg1, %arg2, %arg3, %arg4, %22, %arg0) {callee = @\"sequential/chroma/map/while_body\"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n    \"tfl.yield\"(%79#0, %79#1, %79#2, %79#3, %79#4, %79#5) : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> ()\n  }) {is_stateless = true} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>) -> (tensor<i32>, tensor<i32>, tensor<i32>, tensor<?x?xf32>, tensor<i32>, tensor<?x8160xf32>)\n  %27 = \"tf.Shape\"(%26#3) {device = \"\"} : (tensor<?x?xf32>) -> tensor<2xi32>\n  %28 = \"tf.Identity\"(%0) {device = \"\"} : (tensor<25xf32>) -> tensor<25xf32>\n  %29 = \"tf.Identity\"(%1) {device = \"\"} : (tensor<32x25xf32>) -> tensor<32x25xf32>\n  %30 = \"tf.Identity\"(%2) {device = \"\"} : (tensor<64xf32>) -> tensor<64xf32>\n  %31 = \"tf.Identity\"(%3) {device = \"\"} : (tensor<12x64xf32>) -> tensor<12x64xf32>\n  %32 = \"tf.Identity\"(%4) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %33 = \"tf.Identity\"(%5) {device = \"\"} : (tensor<64x32xf32>) -> tensor<64x32xf32>\n  %34 = \"tf.Identity\"(%6) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %35 = \"tf.Identity\"(%7) {device = \"\"} : (tensor<32x32xf32>) -> tensor<32x32xf32>\n  %36 = \"tf.Identity\"(%11) {device = \"\"} : (tensor<12xf32>) -> tensor<12xf32>\n  %37 = \"tf.Identity\"(%12) {device = \"\"} : (tensor<12xf32>) -> tensor<12xf32>\n  %38 = \"tf.StridedSlice\"(%27, %13, %15, %15) {begin_mask = 0 : i64, device = \"\", ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 1 : i64} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %39 = \"tf.Pack\"(%38) {axis = 0 : i64, device = \"\"} : (tensor<i32>) -> tensor<1xi32>\n  %40 = \"tf.Fill\"(%39, %8) {device = \"\"} : (tensor<1xi32>, tensor<f32>) -> tensor<?xf32>\n  %41 = \"tf.Fill\"(%39, %9) {device = \"\"} : (tensor<1xi32>, tensor<f32>) -> tensor<?xf32>\n  %42 = \"tf.StridedSlice\"(%27, %15, %14, %15) {begin_mask = 0 : i64, device = \"\", ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 1 : i64} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<i32>\n  %43 = \"tf.Pack\"(%20, %38, %42, %20) {axis = 0 : i64, device = \"\"} : (tensor<i32>, tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<4xi32>\n  %44 = \"tf.Reshape\"(%26#3, %43) : (tensor<?x?xf32>, tensor<4xi32>) -> tensor<1x?x?x1xf32>\n  %y, %batch_mean, %batch_variance, %reserve_space_1, %reserve_space_2, %reserve_space_3 = \"tf.FusedBatchNormV3\"(%44, %40, %41, %10, %10) {data_format = \"NCHW\", device = \"\", epsilon = 1.000000e-03 : f32, exponential_avg_factor = 1.000000e+00 : f32, is_training = true} : (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<0xf32>, tensor<0xf32>) -> (tensor<1x?x?x1xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<*xf32>)\n  %45 = \"tf.Reshape\"(%y, %27) {device = \"\"} : (tensor<1x?x?x1xf32>, tensor<2xi32>) -> tensor<?x?xf32>\n  %46 = \"tf.Mul\"(%45, %37) {device = \"\"} : (tensor<?x?xf32>, tensor<12xf32>) -> tensor<?x12xf32>\n  %47 = \"tf.AddV2\"(%46, %36) {device = \"\"} : (tensor<?x12xf32>, tensor<12xf32>) -> tensor<?x12xf32>\n  %48 = \"tf.MatMul\"(%47, %31) {device = \"\", transpose_a = false, transpose_b = false} : (tensor<?x12xf32>, tensor<12x64xf32>) -> tensor<?x64xf32>\n  %49 = \"tf.BiasAdd\"(%48, %30) {data_format = \"NHWC\", device = \"\"} : (tensor<?x64xf32>, tensor<64xf32>) -> tensor<?x64xf32>\n  %50 = \"tf.Neg\"(%49) {device = \"\"} : (tensor<?x64xf32>) -> tensor<?x64xf32>\n  %51 = \"tf.Relu\"(%50) {device = \"\"} : (tensor<?x64xf32>) -> tensor<?x64xf32>\n  %52 = \"tf.Relu\"(%49) {device = \"\"} : (tensor<?x64xf32>) -> tensor<?x64xf32>\n  %53 = \"tf.Identity\"(%16) {device = \"\"} : (tensor<64xf32>) -> tensor<64xf32>\n  %54 = \"tf.Neg\"(%53) {device = \"\"} : (tensor<64xf32>) -> tensor<64xf32>\n  %55 = \"tf.Mul\"(%54, %51) {device = \"\"} : (tensor<64xf32>, tensor<?x64xf32>) -> tensor<?x64xf32>\n  %56 = \"tf.AddV2\"(%52, %55) {device = \"\"} : (tensor<?x64xf32>, tensor<?x64xf32>) -> tensor<?x64xf32>\n  %57 = \"tf.MatMul\"(%56, %33) {device = \"\", transpose_a = false, transpose_b = false} : (tensor<?x64xf32>, tensor<64x32xf32>) -> tensor<?x32xf32>\n  %58 = \"tf.BiasAdd\"(%57, %32) {data_format = \"NHWC\", device = \"\"} : (tensor<?x32xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %59 = \"tf.Neg\"(%58) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %60 = \"tf.Relu\"(%59) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %61 = \"tf.Relu\"(%58) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %62 = \"tf.Identity\"(%17) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %63 = \"tf.Neg\"(%62) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %64 = \"tf.Mul\"(%63, %60) {device = \"\"} : (tensor<32xf32>, tensor<?x32xf32>) -> tensor<?x32xf32>\n  %65 = \"tf.AddV2\"(%61, %64) {device = \"\"} : (tensor<?x32xf32>, tensor<?x32xf32>) -> tensor<?x32xf32>\n  %66 = \"tf.MatMul\"(%65, %35) {device = \"\", transpose_a = false, transpose_b = false} : (tensor<?x32xf32>, tensor<32x32xf32>) -> tensor<?x32xf32>\n  %67 = \"tf.BiasAdd\"(%66, %34) {data_format = \"NHWC\", device = \"\"} : (tensor<?x32xf32>, tensor<32xf32>) -> tensor<?x32xf32>\n  %68 = \"tf.Neg\"(%67) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %69 = \"tf.Relu\"(%68) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %70 = \"tf.Relu\"(%67) {device = \"\"} : (tensor<?x32xf32>) -> tensor<?x32xf32>\n  %71 = \"tf.Identity\"(%18) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %72 = \"tf.Neg\"(%71) {device = \"\"} : (tensor<32xf32>) -> tensor<32xf32>\n  %73 = \"tf.Mul\"(%72, %69) {device = \"\"} : (tensor<32xf32>, tensor<?x32xf32>) -> tensor<?x32xf32>\n  %74 = \"tf.AddV2\"(%70, %73) {device = \"\"} : (tensor<?x32xf32>, tensor<?x32xf32>) -> tensor<?x32xf32>\n  %75 = \"tf.MatMul\"(%74, %29) {device = \"\", transpose_a = false, transpose_b = false} : (tensor<?x32xf32>, tensor<32x25xf32>) -> tensor<?x25xf32>\n  %76 = \"tf.BiasAdd\"(%75, %28) {data_format = \"NHWC\", device = \"\"} : (tensor<?x25xf32>, tensor<25xf32>) -> tensor<?x25xf32>\n  %77 = \"tf.Softmax\"(%76) {device = \"\"} : (tensor<?x25xf32>) -> tensor<?x25xf32>\n  %78 = \"tf.Identity\"(%77) {device = \"\"} : (tensor<?x25xf32>) -> tensor<?x25xf32>\n  \"std.return\"(%78) : (tensor<?x25xf32>) -> ()\n}) {sym_name = \"main\", tf.entry_function = {control_outputs = \"\", inputs = \"chroma_input\", outputs = \"Identity\"}, type = (tensor<?x8160xf32>) -> tensor<?x25xf32>} : () -> ()\n"
     ]
    }
   ],
   "source": [
    "converter3 = tf.lite.TFLiteConverter.from_keras_model(model_best)\n",
    "converter3.experimental_new_converter = True\n",
    "converter3.target_spec.supported_ops = [tf.lite.OpsSet.SELECT_TF_OPS]\n",
    "\n",
    "tflite_model3 = converter3.convert()\n",
    "\n",
    "file = open('model_select.tflite' , 'wb' ) \n",
    "file.write(tflite_model2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Archive]  Alternative model.save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# need model.save ....\n",
    "model.save('./model2')\n",
    "model = tf.keras.models.load_model('./model2', custom_objects={'AudioToChromaLayerLARGE': AudioToChromaLayerLARGE})\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "tflite_model = converter.convert()\n",
    "# file = open('model.tflite' , 'wb' ) \n",
    "# file.write(tflite_model)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Archive] Failed Models (using Librosa.feature.chroma_stft)\n",
    "Works for one layer, ie I pass in the input (8160,) and get the correct chromagram output of (12,)\n",
    "But throws building, saving errors when put into the whole Model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## TRY 1:\n",
    "class AudioToChromagram(tf.keras.layers.Layer):\n",
    "    \"\"\"input is raw audio of size (*, 8160), output chromagram of size (*, 12).\"\"\"\n",
    "\n",
    "    def __init__(self): # SAVE CONFIG IN MEMBER_VARIABLES\n",
    "        super(AudioToChromagram, self).__init__(trainable=False)\n",
    "        \n",
    "    def chroma_stft(self, clip):\n",
    "        return librosa.feature.chroma_stft(clip, sr=48000, hop_length=512)\n",
    "\n",
    "    def call(self, audio): # LOGIC\n",
    "        # print('layer input ', audio.shape)\n",
    "        print('is model eager? ', tf.executing_eagerly())\n",
    "        audio_numpy = audio.numpy()\n",
    "        # print('audio_numpy ', audio_numpy.shape)\n",
    "        if audio_numpy.ndim == 1:\n",
    "            chroma = self.chroma_stft(audio_numpy)\n",
    "            chroma_ave = np.mean(chroma.T,axis=0)\n",
    "        elif audio_numpy.ndim == 2: # call stft on each sample\n",
    "            chroma = np.apply_along_axis(self.chroma_stft, 1, audio_numpy)\n",
    "            chroma_ave = np.mean(chroma, axis=2)\n",
    "        else:\n",
    "            raise Exception('audio_numpy should be (*, 8160) or (8160,)')\n",
    "        return tf.convert_to_tensor(chroma_ave)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## COMPARE WITH LIBROSA LIBRARY - works well!! Checked inputs and outputs to this layer\n",
    "\n",
    "filepath1 = './audioData/A Test2 32446.0-35746.0 iter0.wav'\n",
    "audio1, sr1 = librosa.load(filepath1, sr=None)\n",
    "\n",
    "filepath2 = './audioData/C# Test3 3827-11140 iter514.wav'\n",
    "audio2, sr2 = librosa.load(filepath2, sr=None)\n",
    "\n",
    "# BATCHED samples\n",
    "sample_data = np.array([audio1, audio2])\n",
    "layer = AudioToChromagram()\n",
    "output = layer(sample_data)\n",
    "print(output)\n",
    "\n",
    "## SINGLE sample\n",
    "layer = AudioToChromagram()\n",
    "output = layer(audio1)\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we run just the layer, `tf.executing_eagerly() == True` . But when we put it in the whole model, `tf.executing_eagerly() == False` so we run into the Numpy-Tensor conversion issues and the model can't build.\n",
    "\n",
    "Have tried using all sorts of ways to fix this: TF 2 (eager by default), \n",
    "```import tensorflow as tf; tf.enable_eager_execution()``` or \n",
    "```model.compile(run_eagerly=True)```\n",
    "but seems to be a known bug in 2020. That a Layer can run eagerly but is not eager when put into a Sequential Model\n",
    "https://github.com/tensorflow/tensorflow/issues/38038\n",
    "https://github.com/tensorflow/tensorflow/issues/38775"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inputs = Input(shape=(8160,))\n",
    "# chroma = AudioToChromagram()\n",
    "# x = chroma(inputs)\n",
    "# dense1 = Dense(64, activation='relu')\n",
    "# x = dense1(x)\n",
    "# dense2 = Dense(64, activation=\"relu\")\n",
    "# x = dense2(x)\n",
    "# outputs = Dense(num_labels)(x)\n",
    "# model = tf.keras.Model(inputs=inputs, outputs=outputs, name=\"audio_model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TRY 2: Different way of writing the same model - can build, train and \"save\"\n",
    "But turns out it's only building/saving weights without the model, so the actual model can't save and be converted to tflite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "num_labels = 25\n",
    "\n",
    "## TRY 2\n",
    "\n",
    "class ChordModel(tf.keras.Model):\n",
    "    \n",
    "    def chroma_stft(self, clip):\n",
    "        return librosa.feature.chroma_stft(clip, sr=48000, hop_length=512)\n",
    "    \n",
    "    def audio_to_chroma(self, audio):\n",
    "        # print(tf.executing_eagerly())\n",
    "        audio_numpy = audio.numpy()\n",
    "        if audio_numpy.ndim == 1:\n",
    "            chroma = self.chroma_stft(audio_numpy)\n",
    "            chroma_ave = np.mean(chroma.T,axis=0)\n",
    "        elif audio_numpy.ndim == 2: # call stft on each sample\n",
    "            chroma = np.apply_along_axis(self.chroma_stft, 1, audio_numpy)\n",
    "            chroma_ave = np.mean(chroma, axis=2)\n",
    "        else:\n",
    "            raise Exception('audio_numpy should be (*, 8160) or (8160,)')\n",
    "        return tf.convert_to_tensor(chroma_ave)\n",
    "    \n",
    "    def __init__(self, inputs, **kwargs):\n",
    "        super(ChordModel, self).__init__(inputs, **kwargs)\n",
    "        self.chromagram = Lambda(self.audio_to_chroma)\n",
    "        self.dense1 = Dense(64, activation='relu')\n",
    "        self.drop1 = Dropout(0.25)\n",
    "        self.dense2 = Dense(128, activation='relu')\n",
    "        self.drop2 = Dropout(0.25)\n",
    "        self.dense3 = Dense(64, activation='relu')\n",
    "        self.dense4 = Dense(num_labels, activation='softmax')\n",
    "        self.activate = Activation('softmax')\n",
    "        \n",
    "    def call(self, inputs):\n",
    "#         print('model: inputs shape ', inputs.shape)\n",
    "        x = self.chromagram(inputs)\n",
    "        x = self.dense1(x)\n",
    "        x = self.drop1(x)\n",
    "        x = self.dense2(x)\n",
    "        x = self.drop2(x)\n",
    "        x = self.dense3(x)\n",
    "        x = self.dense4(x)\n",
    "        return self.activate(x)\n",
    "\n",
    "inp = Input(shape=(8160,))\n",
    "model = ChordModel(inputs=inp, dynamic=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Reference]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CHORDS = [\n",
    "          'Noise', 'A', 'Am', 'Bb', 'Bbm', 'B', \n",
    "          'Bm', 'C', 'Cm', 'C#', 'C#m', 'D',\n",
    "          'Dm', 'D#', 'D#m','E', 'Em', 'F',\n",
    "          'Fm', 'F#', 'F#m', 'G', 'Gm', 'G#',\n",
    "          'G#m']\n",
    "\n",
    "chord_templates = {'A':   [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0],\n",
    " 'Am':  [1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],\n",
    " 'A#':  [0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],\n",
    " 'A#m': [0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],\n",
    " 'B':   [0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0],\n",
    " 'Bm':  [0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0],\n",
    " 'C':   [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0],\n",
    " 'Cm':  [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0],\n",
    " 'C#':  [0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1],\n",
    " 'C#m': [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1],\n",
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    " 'Dm':  [1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],\n",
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    " 'D#m': [0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0],\n",
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    " 'Em':  [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0],\n",
    " 'F':   [1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0],\n",
    " 'Fm':  [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1],\n",
    " 'F#':  [0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n",
    " 'F#m': [1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0],\n",
    " 'G':   [0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0],\n",
    " 'Gm':  [0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0],\n",
    " 'G#':  [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1],\n",
    " 'G#m': [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1]}"
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