Tear Model Autoencoder (10-CV + Timing).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ca820db0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import math\n",
    "import time\n",
    "from sklearn import model_selection\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ba03a50f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Declare Cross-Fold Validation Split / Model Params\n",
    "N_SPLIT = 10\n",
    "EPOCHS = 650\n",
    "LAYERSIZE = 40\n",
    "AUTO_DIM = 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f771b06b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read Data\n",
    "xdf = pd.read_csv('ML_inputs_tear.csv')\n",
    "ydf = pd.read_csv('U_tear.csv')\n",
    "\n",
    "# Adding Constant Parameters to X data\n",
    "xdf.insert(0, 'c', 6)\n",
    "xdf.insert(1, 'D', .05)\n",
    "xdf.insert(2, 'k1', 15)\n",
    "xdf.insert(3, 'k2', 15)\n",
    "xdf.insert(4, 'N', 2)\n",
    "xdf.insert(5, 'kappa_1', .25)\n",
    "xdf.insert(6, 'kappa_2', .25)\n",
    "xdf.insert(7, 'kappa_3', .25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "547428c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to Array\n",
    "x = xdf.to_numpy()\n",
    "y = ydf.T.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d5f9f0c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Autoencoder(tf.keras.models.Model):\n",
    "    def __init__(self, dim):\n",
    "        super(Autoencoder, self).__init__()\n",
    "        self.dim = dim\n",
    "        self.encoder = tf.keras.Sequential([\n",
    "            tf.keras.layers.Dense(len(y[0])),\n",
    "            tf.keras.layers.Dense(500, 'relu'),\n",
    "            tf.keras.layers.Dense(500, 'relu'),\n",
    "            tf.keras.layers.Dense(dim)\n",
    "        ])\n",
    "        self.decoder = tf.keras.Sequential([\n",
    "            tf.keras.layers.Dense(500, 'relu'),\n",
    "            tf.keras.layers.Dense(500, 'relu'),\n",
    "            tf.keras.layers.Dense(len(y[0]))\n",
    "        ])\n",
    "    def call(self, x):\n",
    "        encoded = self.encoder(x)\n",
    "        decoded = self.decoder(encoded)\n",
    "        return decoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2c793ef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder = Autoencoder(AUTO_DIM)\n",
    "autoencoder.compile(optimizer = 'adamax', loss = 'mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c1cda58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "8/8 [==============================] - 0s 6ms/step - loss: 2.0292e-04\n",
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      "Epoch 405/500\n",
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      "8/8 [==============================] - 0s 5ms/step - loss: 2.6384e-05\n",
      "Epoch 410/500\n",
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      "Epoch 411/500\n",
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      "Epoch 412/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.0730e-05\n",
      "Epoch 413/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.3492e-05\n",
      "Epoch 414/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 4.2978e-05\n",
      "Epoch 415/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.3404e-04\n",
      "Epoch 416/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 8.9720e-04\n",
      "Epoch 417/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 5.2961e-04\n",
      "Epoch 418/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.7092e-04\n",
      "Epoch 419/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.5990e-04\n",
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      "8/8 [==============================] - 0s 5ms/step - loss: 1.0686e-04\n",
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      "8/8 [==============================] - 0s 5ms/step - loss: 6.2722e-05\n",
      "Epoch 422/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 4.6356e-05\n",
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      "8/8 [==============================] - 0s 5ms/step - loss: 2.6350e-05\n",
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      "8/8 [==============================] - 0s 5ms/step - loss: 2.0367e-05\n",
      "Epoch 425/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.6750e-05\n",
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      "8/8 [==============================] - 0s 5ms/step - loss: 1.8772e-05\n",
      "Epoch 427/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 1.9904e-05\n",
      "Epoch 428/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 3.1742e-05\n",
      "Epoch 429/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 2.3534e-05\n",
      "Epoch 430/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 1.7120e-05\n",
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      "8/8 [==============================] - 0s 5ms/step - loss: 2.1254e-05\n",
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      "8/8 [==============================] - 0s 6ms/step - loss: 2.1905e-05\n",
      "Epoch 433/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 3.2142e-05\n",
      "Epoch 434/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 1.7280e-05\n",
      "Epoch 435/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.1114e-05\n",
      "Epoch 436/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.3404e-05\n",
      "Epoch 437/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.8414e-05\n",
      "Epoch 438/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.6537e-05\n",
      "Epoch 439/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.7906e-05\n",
      "Epoch 440/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.8606e-05\n",
      "Epoch 441/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.7792e-05\n",
      "Epoch 442/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 4.7163e-05\n",
      "Epoch 443/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.2864e-04\n",
      "Epoch 444/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.3759e-04\n",
      "Epoch 445/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.6588e-04\n",
      "Epoch 446/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.9722e-04\n",
      "Epoch 447/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.8392e-04\n",
      "Epoch 448/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 2.1247e-04\n",
      "Epoch 449/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 1.2410e-04\n",
      "Epoch 450/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 7.1281e-05\n",
      "Epoch 451/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 6.1578e-05\n",
      "Epoch 452/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 3.8895e-05\n",
      "Epoch 453/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 3.5896e-05\n",
      "Epoch 454/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 2.8804e-05\n",
      "Epoch 455/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.9274e-05\n",
      "Epoch 456/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 1.5452e-05\n",
      "Epoch 457/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 1.4820e-05\n",
      "Epoch 458/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 1.6944e-05\n",
      "Epoch 459/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 2.5472e-05\n",
      "Epoch 460/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.4965e-05\n",
      "Epoch 461/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 4.1658e-05\n",
      "Epoch 462/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.8925e-05\n",
      "Epoch 463/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 1.9153e-05\n",
      "Epoch 464/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.4051e-05\n",
      "Epoch 465/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.1230e-05\n",
      "Epoch 466/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 1.9124e-05\n",
      "Epoch 467/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 3.1657e-05\n",
      "Epoch 468/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.0545e-04\n",
      "Epoch 469/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.2907e-04\n",
      "Epoch 470/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.3327e-04\n",
      "Epoch 471/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.7245e-04\n",
      "Epoch 472/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.4245e-04\n",
      "Epoch 473/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.3890e-04\n",
      "Epoch 474/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.0465e-04\n",
      "Epoch 475/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 4.7272e-05\n",
      "Epoch 476/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 3.5103e-05\n",
      "Epoch 477/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.9155e-05\n",
      "Epoch 478/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.5095e-05\n",
      "Epoch 479/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.2283e-05\n",
      "Epoch 480/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.8061e-05\n",
      "Epoch 481/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.4149e-05\n",
      "Epoch 482/500\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 1.5156e-05\n",
      "Epoch 483/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.3201e-05\n",
      "Epoch 484/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.6249e-05\n",
      "Epoch 485/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.3728e-05\n",
      "Epoch 486/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.1969e-05\n",
      "Epoch 487/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.2063e-05\n",
      "Epoch 488/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.5896e-05\n",
      "Epoch 489/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.3972e-05\n",
      "Epoch 490/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.7455e-04\n",
      "Epoch 491/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.4842e-04\n",
      "Epoch 492/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.5598e-04\n",
      "Epoch 493/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.3214e-04\n",
      "Epoch 494/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 9.4188e-05\n",
      "Epoch 495/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.0818e-04\n",
      "Epoch 496/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.6591e-04\n",
      "Epoch 497/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 2.4441e-04\n",
      "Epoch 498/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 1.9390e-04\n",
      "Epoch 499/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 9.4232e-05\n",
      "Epoch 500/500\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 4.9861e-05\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "autoencoder.fit(y, y, epochs = 500)\n",
    "end = time.time()\n",
    "\n",
    "auto_train_time = (end-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e6f437e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 80)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded = autoencoder.encoder.predict(y)\n",
    "encoded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78d7bfc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define Model\n",
    "\n",
    "def create_model():\n",
    "    \n",
    "    model = tf.keras.Sequential([\n",
    "        tf.keras.layers.Dense(12),\n",
    "        tf.keras.layers.Dense(LAYERSIZE, 'sigmoid'),\n",
    "        tf.keras.layers.Dense(LAYERSIZE, 'sigmoid'),\n",
    "        tf.keras.layers.Dense(AUTO_DIM)\n",
    "    ])\n",
    "    \n",
    "    model.compile(optimizer = 'adam', loss = 'mse')\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4402ae8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/650\n",
      "7/7 - 0s - loss: 25.5691\n",
      "Epoch 2/650\n",
      "7/7 - 0s - loss: 24.2148\n",
      "Epoch 3/650\n",
      "7/7 - 0s - loss: 22.9399\n",
      "Epoch 4/650\n",
      "7/7 - 0s - loss: 21.6506\n",
      "Epoch 5/650\n",
      "7/7 - 0s - loss: 20.3267\n",
      "Epoch 6/650\n",
      "7/7 - 0s - loss: 18.9671\n",
      "Epoch 7/650\n",
      "7/7 - 0s - loss: 17.5968\n",
      "Epoch 8/650\n",
      "7/7 - 0s - loss: 16.2102\n",
      "Epoch 9/650\n",
      "7/7 - 0s - loss: 14.8504\n",
      "Epoch 10/650\n",
      "7/7 - 0s - loss: 13.5399\n",
      "Epoch 11/650\n",
      "7/7 - 0s - loss: 12.3076\n",
      "Epoch 12/650\n",
      "7/7 - 0s - loss: 11.1750\n",
      "Epoch 13/650\n",
      "7/7 - 0s - loss: 10.0992\n",
      "Epoch 14/650\n",
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      "Epoch 15/650\n",
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      "Epoch 16/650\n",
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      "Epoch 17/650\n",
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      "Epoch 18/650\n",
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      "Epoch 20/650\n",
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      "Epoch 21/650\n",
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      "Epoch 22/650\n",
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      "Epoch 23/650\n",
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      "Epoch 25/650\n",
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      "Epoch 26/650\n",
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      "Epoch 28/650\n",
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      "Epoch 29/650\n",
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      "Epoch 30/650\n",
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      "Epoch 31/650\n",
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      "Epoch 32/650\n",
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      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_test_function.<locals>.test_function at 0x7f741f012dc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
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      "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_test_function.<locals>.test_function at 0x7f741f043f70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
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      "WARNING:tensorflow:7 out of the last 7 calls to <function Model.make_test_function.<locals>.test_function at 0x7f741ec0bee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
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      "WARNING:tensorflow:8 out of the last 8 calls to <function Model.make_test_function.<locals>.test_function at 0x7f741820bca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
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      "WARNING:tensorflow:9 out of the last 9 calls to <function Model.make_test_function.<locals>.test_function at 0x7f741820b9d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
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      "Epoch 650/650\n",
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      "WARNING:tensorflow:10 out of the last 10 calls to <function Model.make_test_function.<locals>.test_function at 0x7f7ac843dc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "1/1 [==============================] - 0s 64ms/step - loss: 0.0088\n"
     ]
    }
   ],
   "source": [
    "train_time = []\n",
    "annmodels = []\n",
    "test_val = []\n",
    "acc = []\n",
    "train_val = []\n",
    "\n",
    "for train_index, test_index in model_selection.KFold(N_SPLIT, shuffle=True).split(x):\n",
    "    xtrain, xtest = x[train_index], x[test_index]\n",
    "    ytrain, ytest = encoded[train_index], encoded[test_index]\n",
    "    \n",
    "    start = time.time()\n",
    "    model = create_model()\n",
    "    model.fit(xtrain, ytrain, epochs = EPOCHS, verbose = 2)\n",
    "    end = time.time()\n",
    "    \n",
    "    train_time.append(end-start)\n",
    "    \n",
    "    annmodels.append(model)\n",
    "    test_val.append((xtest, ytest))\n",
    "    train_val.append((xtrain, ytrain))\n",
    "    acc.append(model.evaluate(xtest, ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ade6c6f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Timing Calculations\n",
    "modelTime = []\n",
    "\n",
    "for model in annmodels:\n",
    "    \n",
    "    # Perform 50 timed predictions, average result\n",
    "    tmpTime = []\n",
    "    for i in range(50):\n",
    "        start = time.time()\n",
    "        pred = model.predict(x)\n",
    "        auto_pred = autoencoder.decoder.predict(pred)\n",
    "        end = time.time()\n",
    "        tmpTime.append(end - start)\n",
    "        \n",
    "    modelTime.append(np.mean(tmpTime))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cda7de19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Error Calculations\n",
    "modelError = []\n",
    "\n",
    "for model in annmodels:\n",
    "    # find the relative frobenius error of the prediction\n",
    "    pred = model.predict(x)\n",
    "    auto_pred = autoencoder.decoder.predict(pred)\n",
    "    \n",
    "    toterr = 0\n",
    "    for i in range(len(x)):\n",
    "        Norm1 = np.linalg.norm(y[i] - auto_pred[i], 2)\n",
    "        Norm2 = np.linalg.norm(y[i], 2)\n",
    "        toterr = toterr + ((Norm1**2)/(Norm2**2))\n",
    "        \n",
    "    modelError.append(math.sqrt(toterr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "403af8a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "export = pd.DataFrame({'offline time': train_time,'online time': modelTime, 'error': modelError})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "785d2552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>offline time</th>\n",
       "      <th>online time</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.647209</td>\n",
       "      <td>0.069298</td>\n",
       "      <td>0.353105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.868989</td>\n",
       "      <td>0.075624</td>\n",
       "      <td>0.369713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.818835</td>\n",
       "      <td>0.067846</td>\n",
       "      <td>0.443558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.741733</td>\n",
       "      <td>0.068229</td>\n",
       "      <td>0.382096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.868367</td>\n",
       "      <td>0.066681</td>\n",
       "      <td>0.380527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.797615</td>\n",
       "      <td>0.069561</td>\n",
       "      <td>0.364571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.833052</td>\n",
       "      <td>0.068066</td>\n",
       "      <td>0.403021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.697266</td>\n",
       "      <td>0.077392</td>\n",
       "      <td>0.366526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.680925</td>\n",
       "      <td>0.069988</td>\n",
       "      <td>0.365165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.666751</td>\n",
       "      <td>0.070302</td>\n",
       "      <td>0.363349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   offline time  online time     error\n",
       "0      4.647209     0.069298  0.353105\n",
       "1      4.868989     0.075624  0.369713\n",
       "2      4.818835     0.067846  0.443558\n",
       "3      4.741733     0.068229  0.382096\n",
       "4      4.868367     0.066681  0.380527\n",
       "5      4.797615     0.069561  0.364571\n",
       "6      4.833052     0.068066  0.403021\n",
       "7      4.697266     0.077392  0.366526\n",
       "8      4.680925     0.069988  0.365165\n",
       "9      4.666751     0.070302  0.363349"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e8358df7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.829305171966553"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_train_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7428ff6e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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