paperSource/Automatic_subj_9_training-onlyVel_v1.1-sliced-noVal-seed1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ccc04654",
   "metadata": {},
   "source": [
    "## Var name setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d887cfd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input file:  inputFeatures9_sorted.mat\n"
     ]
    }
   ],
   "source": [
    "subjectID='9'\n",
    "noEpochs=950\n",
    "noLSTMUnits=100\n",
    "\n",
    "# Change to true if input file 's features are sorted by time (False by default) \n",
    "orderedInput=True\n",
    "\n",
    "# Change at least one of these 2 variable to true for reading inputFeatures (Can be both)\n",
    "fourInputFeats=False\n",
    "onlyVel=True\n",
    "\n",
    "HRInputFeats=False\n",
    "assert onlyVel==True or fourInputFeats == True or HRInputFeats==True, \"Both condition are false!! Need at least one of these 2 variables to true\"\n",
    "\n",
    "# Change number of steps according to Matlab file for testing input data\n",
    "test_noTimeSteps=50 \n",
    "\n",
    "# Change to 1 if want to export to CSV O2 precict against test (0 by default)\n",
    "exportCSV=1\n",
    "\n",
    "# Chabge to 1 if want to save the training model (0 by default)\n",
    "savedModel=1\n",
    "\n",
    "# Set seed value for uniform (NOT RECOMMEND TO CHANGE, by default 12321)\n",
    "seed_value = 1\n",
    "\n",
    "# Getting input file name\n",
    "if orderedInput:\n",
    "    orderedPrefix = 'order-'\n",
    "    sortedPrefix = '_sored'\n",
    "    fileName='inputFeatures'+subjectID+'_sorted.mat'\n",
    "else:    \n",
    "    # Unordered input features\n",
    "    orderedPrefix = ''\n",
    "    sortedPrefix = ''\n",
    "    fileName='inputFeatures'+subjectID+'.mat'\n",
    "\n",
    "## For testinng purpose\n",
    "#fileName=\"inputFeatures29_sorted.mat\"\n",
    "#fileName='inputFeatures9_sorted_99.mat'\n",
    "print(\"Input file: \",fileName)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d64db4a1",
   "metadata": {},
   "source": [
    "## Include lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f273d91",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "import scipy.io as sio \n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import random\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from tensorflow.keras.models import Model\n",
    "from keras.models import Sequential\n",
    "from keras.optimizers import Adam\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Input, Bidirectional\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.python.client import device_lib\n",
    "from datetime import datetime as dt\n",
    "from keras import backend as K\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "#from comet_ml import Experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d232b155",
   "metadata": {},
   "source": [
    "## Set seed for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d846f5ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['PYTHONHASHSEED']=str(seed_value)\n",
    "random.seed(seed_value)\n",
    "np.random.seed(seed_value)\n",
    "tf.random.set_seed(seed_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48fdef95",
   "metadata": {},
   "source": [
    "## Defilne parser function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e95b0608",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_test=[]\n",
    "\n",
    "\"\"\"\n",
    "The function take number of feature according to data \n",
    "and return a list normalized input features data\n",
    "\"\"\"\n",
    "def parse_input(noFeatures):\n",
    "    global list_test\n",
    "    print(\"Number of features: \",noFeatures)\n",
    "    if noFeatures==5:\n",
    "        print(\"Keep all data\")\n",
    "        for i in range(len(test)):\n",
    "            temp_trainData=np.transpose(np.array(test[i]))\n",
    "            list_test.append(temp_trainData)\n",
    "        for j in range(len(test2)):  \n",
    "            temp_testData=np.transpose(np.array(test2[j]))\n",
    "            list_test.append(temp_testData)\n",
    "    elif noFeatures==4:\n",
    "        print(\"Remove HR data\")\n",
    "        for i in range(len(test)):\n",
    "            temp_trainData=np.transpose(np.array(test[i]))\n",
    "            list_test.append(temp_trainData[:,:4])\n",
    "        for j in range(len(test2)):  \n",
    "            temp_testData=np.transpose(np.array(test2[j]))\n",
    "            list_test.append(temp_testData[:,:4])\n",
    "    elif noFeatures==1 and onlyVel==1:\n",
    "        print(\"Only vel feature\")\n",
    "        for i in range(len(test)):\n",
    "            temp_trainData=np.transpose(np.array(test[i]))\n",
    "            list_test.append(temp_trainData[:,[1]])\n",
    "        for j in range(len(test2)):  \n",
    "            temp_testData=np.transpose(np.array(test2[j]))\n",
    "            list_test.append(temp_testData[:,[1]])\n",
    "    elif noFeatures==1 and onlyVel==0:\n",
    "        print(\"Only HR data\")\n",
    "        for i in range(len(test)):\n",
    "            temp_trainData=np.transpose(np.array(test[i]))\n",
    "            list_test.append(temp_trainData[:,[-1]])\n",
    "        for j in range(len(test2)):  \n",
    "            temp_testData=np.transpose(np.array(test2[j]))\n",
    "            list_test.append(temp_testData[:,[-11]])\n",
    "    else:\n",
    "        # Code will runinto this part\n",
    "        print(\"Unknown number of features\")\n",
    "        \n",
    "    print(\"---------\")\n",
    "    print(np.shape(list_test))    \n",
    "    list_test=np.vstack(list_test)\n",
    "    print(np.shape(list_test))\n",
    "    scaler = StandardScaler()\n",
    "    scaler=scaler.fit(list_test)\n",
    "    scaled_list_test=scaler.transform(list_test)\n",
    "    \n",
    "    return scaled_list_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9410f08",
   "metadata": {},
   "source": [
    "## Read input from Matlab file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9be7b2e8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 50)\n",
      "(5, 50)\n",
      "The time step now should be  50\n",
      "1 input feture included: speed\n"
     ]
    }
   ],
   "source": [
    "gpu_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "for device in gpu_devices:\n",
    "    tf.config.experimental.set_memory_growth(device, True)\n",
    "\n",
    "i=sio.loadmat(fileName)\n",
    "\n",
    "TTest=i['TTest']\n",
    "TTrain=i['TTrain']\n",
    "XTrain=i['XTrain']\n",
    "XTest=i['XTest']\n",
    "\n",
    "XTrain=XTrain.reshape(-1)\n",
    "TTrain=TTrain.reshape(-1)\n",
    "XTest=XTest.reshape(-1)\n",
    "TTest=TTest.reshape(-1)\n",
    "\n",
    "test=np.transpose(XTrain).reshape(-1)\n",
    "test2=np.transpose(XTest).reshape(-1)\n",
    "\n",
    "print(np.shape(test[0]))\n",
    "print(np.shape(test2[0]))\n",
    "\n",
    "# Get number of features and number of number of time steps\n",
    "#noFeatures = np.shape(test[0])[0]\n",
    "noTimeSteps = np.shape(test[0])[1]\n",
    "#print(noFeatures)\n",
    "\n",
    "# Validate the input data's number of feature and number of time step\n",
    "assert noTimeSteps == test_noTimeSteps, \"The number of time steps does not match our configuration!! Check either of them\"\n",
    "print(\"The time step now should be \",noTimeSteps)\n",
    "\n",
    "if fourInputFeats and HRInputFeats:\n",
    "    #assert noFeatures==5, \"The number of input features is not 5! Check setting again\"\n",
    "    print(\"5 input features included: speed, speedChange, stepDuration, vertOscillation_dist_amp, heart rate\")\n",
    "    confPrefix=\"withHR_\"\n",
    "    noFeatures=5\n",
    "elif fourInputFeats:\n",
    "    #assert noFeatures==4, \"The number of input features is not 4! Check setting again\"\n",
    "    print(\"4 input features included: speed, speedChange, stepDuration, vertOscillation_dist_amp\")\n",
    "    confPrefix=\"NoHR_\"\n",
    "    noFeatures=4\n",
    "elif HRInputFeats:\n",
    "    print(\"1 input feture included: heart rate\")\n",
    "    confPrefix=\"onlyHR_\"\n",
    "elif onlyVel:\n",
    "    #assert noFeatures==1, \"The number of input features is not 1! Check setting again\"\n",
    "    print(\"1 input feture included: speed\")\n",
    "    confPrefix=\"onlyVel_\"\n",
    "    noFeatures=1\n",
    "else:\n",
    "    ## Both conditions are false, code will not run into this section\n",
    "    print(\"Both condition are false!! Need at least \")\n",
    "    \n",
    "# Automatically setting name of output files based on configuration    \n",
    "modelSaveName=confPrefix+'Subj-'+subjectID+'-'+str(noEpochs)+'pochs-'+orderedPrefix+str(noLSTMUnits)+'-LSTM_u.h5'\n",
    "figPredictName=confPrefix+'Subj'+subjectID+'-'+str(noEpochs)+'pochs-'+orderedPrefix+str(noLSTMUnits)+'-LSTM_u.jpg'\n",
    "figRMSEName=confPrefix+'RMSE_Subj'+subjectID+'-'+str(noEpochs)+'pochs-'+orderedPrefix+str(noLSTMUnits)+'-LSTM_u.jpg'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4c78ae0",
   "metadata": {},
   "source": [
    "## Call parser function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d2a27fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features:  1\n",
      "Only vel feature\n",
      "---------\n",
      "(1344, 50, 1)\n",
      "(67200, 1)\n"
     ]
    }
   ],
   "source": [
    "scaled_list_test = parse_input(noFeatures)\n",
    "\n",
    "# Return back input data with scaled value\n",
    "XT_s_stack=list()\n",
    "i=0\n",
    "timeSteps=noTimeSteps\n",
    "while i+timeSteps <= (len(scaled_list_test)):\n",
    "    XT_s_stack.append(scaled_list_test[i:i+timeSteps])\n",
    "    i=i+timeSteps\n",
    "\n",
    "XT_s_stack=np.stack(XT_s_stack)\n",
    "cutPoint=np.shape(TTest.reshape(-1))[0]\n",
    "\n",
    "\n",
    "XTrain_s=XT_s_stack[:-cutPoint,:,:]\n",
    "XTest_s=XT_s_stack[-cutPoint:,:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55305abc",
   "metadata": {},
   "source": [
    "## Configure the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d6ba2216",
   "metadata": {},
   "outputs": [],
   "source": [
    "es = EarlyStopping(monitor='val_loss',mode='min', verbose=1,patience=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "91546b25",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 100)               40800     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 101       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 40,901\n",
      "Trainable params: 40,901\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "length = noTimeSteps\n",
    "n_features = noFeatures\n",
    "model = Sequential()\n",
    "model.add(LSTM(noLSTMUnits,input_shape=(length,n_features)))\n",
    "model.add(Dense(1))\n",
    "opt=Adam(learning_rate=0.005)\n",
    "model.compile(optimizer=opt, loss='mean_squared_logarithmic_error')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66f31e4d",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b3c4d1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(np.shape(XTrain_s))\n",
    "#print(np.isnan(XTrain_s))\n",
    "#print(XTrain_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d6fcf1ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(np.shape(TTrain))\n",
    "# TTrain[np.isnan(TTrain)]=0\n",
    "# print(TTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a62b3fc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/950\n",
      "34/34 - 3s - loss: 2.9238 - 3s/epoch - 81ms/step\n",
      "Epoch 2/950\n",
      "34/34 - 1s - loss: 1.6034 - 819ms/epoch - 24ms/step\n",
      "Epoch 3/950\n",
      "34/34 - 1s - loss: 1.5906 - 875ms/epoch - 26ms/step\n",
      "Epoch 4/950\n",
      "34/34 - 1s - loss: 1.5895 - 781ms/epoch - 23ms/step\n",
      "Epoch 5/950\n",
      "34/34 - 1s - loss: 1.5894 - 797ms/epoch - 23ms/step\n",
      "Epoch 6/950\n",
      "34/34 - 1s - loss: 1.5894 - 810ms/epoch - 24ms/step\n",
      "Epoch 7/950\n",
      "34/34 - 1s - loss: 1.5894 - 899ms/epoch - 26ms/step\n",
      "Epoch 8/950\n",
      "34/34 - 1s - loss: 1.5894 - 858ms/epoch - 25ms/step\n",
      "Epoch 9/950\n",
      "34/34 - 1s - loss: 1.5895 - 914ms/epoch - 27ms/step\n",
      "Epoch 10/950\n",
      "34/34 - 1s - loss: 1.5896 - 785ms/epoch - 23ms/step\n",
      "Epoch 11/950\n",
      "34/34 - 1s - loss: 1.5894 - 817ms/epoch - 24ms/step\n",
      "Epoch 12/950\n",
      "34/34 - 1s - loss: 1.5896 - 762ms/epoch - 22ms/step\n",
      "Epoch 13/950\n",
      "34/34 - 1s - loss: 1.5897 - 798ms/epoch - 23ms/step\n",
      "Epoch 14/950\n",
      "34/34 - 1s - loss: 1.5896 - 784ms/epoch - 23ms/step\n",
      "Epoch 15/950\n",
      "34/34 - 1s - loss: 1.5896 - 800ms/epoch - 24ms/step\n",
      "Epoch 16/950\n",
      "34/34 - 1s - loss: 1.5896 - 792ms/epoch - 23ms/step\n",
      "Epoch 17/950\n",
      "34/34 - 1s - loss: 1.5896 - 809ms/epoch - 24ms/step\n",
      "Epoch 18/950\n",
      "34/34 - 1s - loss: 1.5895 - 785ms/epoch - 23ms/step\n",
      "Epoch 19/950\n",
      "34/34 - 1s - loss: 1.5893 - 830ms/epoch - 24ms/step\n",
      "Epoch 20/950\n",
      "34/34 - 1s - loss: 1.5893 - 772ms/epoch - 23ms/step\n",
      "Epoch 21/950\n",
      "34/34 - 1s - loss: 1.5892 - 816ms/epoch - 24ms/step\n",
      "Epoch 22/950\n",
      "34/34 - 1s - loss: 1.5892 - 851ms/epoch - 25ms/step\n",
      "Epoch 23/950\n",
      "34/34 - 1s - loss: 1.5893 - 908ms/epoch - 27ms/step\n",
      "Epoch 24/950\n",
      "34/34 - 1s - loss: 1.5892 - 790ms/epoch - 23ms/step\n",
      "Epoch 25/950\n",
      "34/34 - 1s - loss: 1.5892 - 798ms/epoch - 23ms/step\n",
      "Epoch 26/950\n",
      "34/34 - 1s - loss: 1.5894 - 867ms/epoch - 26ms/step\n",
      "Epoch 27/950\n",
      "34/34 - 1s - loss: 1.5888 - 866ms/epoch - 25ms/step\n",
      "Epoch 28/950\n",
      "34/34 - 1s - loss: 1.5888 - 911ms/epoch - 27ms/step\n",
      "Epoch 29/950\n",
      "34/34 - 1s - loss: 1.5887 - 829ms/epoch - 24ms/step\n",
      "Epoch 30/950\n",
      "34/34 - 1s - loss: 1.5882 - 768ms/epoch - 23ms/step\n",
      "Epoch 31/950\n",
      "34/34 - 1s - loss: 1.5880 - 806ms/epoch - 24ms/step\n",
      "Epoch 32/950\n",
      "34/34 - 1s - loss: 1.5876 - 787ms/epoch - 23ms/step\n",
      "Epoch 33/950\n",
      "34/34 - 1s - loss: 1.5872 - 981ms/epoch - 29ms/step\n",
      "Epoch 34/950\n",
      "34/34 - 1s - loss: 1.5865 - 960ms/epoch - 28ms/step\n",
      "Epoch 35/950\n",
      "34/34 - 1s - loss: 1.5854 - 803ms/epoch - 24ms/step\n",
      "Epoch 36/950\n",
      "34/34 - 1s - loss: 1.5844 - 805ms/epoch - 24ms/step\n",
      "Epoch 37/950\n",
      "34/34 - 1s - loss: 1.5827 - 785ms/epoch - 23ms/step\n",
      "Epoch 38/950\n",
      "34/34 - 1s - loss: 1.5793 - 786ms/epoch - 23ms/step\n",
      "Epoch 39/950\n",
      "34/34 - 1s - loss: 1.5766 - 787ms/epoch - 23ms/step\n",
      "Epoch 40/950\n",
      "34/34 - 1s - loss: 1.5752 - 793ms/epoch - 23ms/step\n",
      "Epoch 41/950\n",
      "34/34 - 1s - loss: 1.5880 - 798ms/epoch - 23ms/step\n",
      "Epoch 42/950\n",
      "34/34 - 1s - loss: 1.6002 - 771ms/epoch - 23ms/step\n",
      "Epoch 43/950\n",
      "34/34 - 1s - loss: 1.5953 - 810ms/epoch - 24ms/step\n",
      "Epoch 44/950\n",
      "34/34 - 1s - loss: 1.5817 - 765ms/epoch - 23ms/step\n",
      "Epoch 45/950\n",
      "34/34 - 1s - loss: 1.5912 - 861ms/epoch - 25ms/step\n",
      "Epoch 46/950\n",
      "34/34 - 1s - loss: 1.5795 - 850ms/epoch - 25ms/step\n",
      "Epoch 47/950\n",
      "34/34 - 1s - loss: 1.6013 - 895ms/epoch - 26ms/step\n",
      "Epoch 48/950\n",
      "34/34 - 1s - loss: 1.6007 - 924ms/epoch - 27ms/step\n",
      "Epoch 49/950\n",
      "34/34 - 1s - loss: 1.5864 - 1s/epoch - 32ms/step\n",
      "Epoch 50/950\n",
      "34/34 - 1s - loss: 1.5701 - 1s/epoch - 30ms/step\n",
      "Epoch 51/950\n",
      "34/34 - 1s - loss: 1.5721 - 1s/epoch - 30ms/step\n",
      "Epoch 52/950\n",
      "34/34 - 1s - loss: 1.5700 - 941ms/epoch - 28ms/step\n",
      "Epoch 53/950\n",
      "34/34 - 1s - loss: 1.5749 - 860ms/epoch - 25ms/step\n",
      "Epoch 54/950\n",
      "34/34 - 1s - loss: 1.5916 - 793ms/epoch - 23ms/step\n",
      "Epoch 55/950\n",
      "34/34 - 1s - loss: 1.5767 - 777ms/epoch - 23ms/step\n",
      "Epoch 56/950\n",
      "34/34 - 1s - loss: 1.5980 - 810ms/epoch - 24ms/step\n",
      "Epoch 57/950\n",
      "34/34 - 1s - loss: 1.5832 - 792ms/epoch - 23ms/step\n",
      "Epoch 58/950\n",
      "34/34 - 1s - loss: 1.5736 - 801ms/epoch - 24ms/step\n",
      "Epoch 59/950\n",
      "34/34 - 1s - loss: 1.5723 - 795ms/epoch - 23ms/step\n",
      "Epoch 60/950\n",
      "34/34 - 1s - loss: 1.5717 - 816ms/epoch - 24ms/step\n",
      "Epoch 61/950\n",
      "34/34 - 1s - loss: 1.5715 - 797ms/epoch - 23ms/step\n",
      "Epoch 62/950\n",
      "34/34 - 1s - loss: 1.5714 - 807ms/epoch - 24ms/step\n",
      "Epoch 63/950\n",
      "34/34 - 1s - loss: 1.5708 - 778ms/epoch - 23ms/step\n",
      "Epoch 64/950\n",
      "34/34 - 1s - loss: 1.5708 - 1s/epoch - 29ms/step\n",
      "Epoch 65/950\n",
      "34/34 - 1s - loss: 1.5706 - 948ms/epoch - 28ms/step\n",
      "Epoch 66/950\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 127/950\n",
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      "Epoch 250/950\n",
      "34/34 - 1s - loss: 0.0283 - 829ms/epoch - 24ms/step\n",
      "Epoch 251/950\n",
      "34/34 - 1s - loss: 0.0254 - 796ms/epoch - 23ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 252/950\n",
      "34/34 - 1s - loss: 0.0239 - 849ms/epoch - 25ms/step\n",
      "Epoch 253/950\n",
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      "Epoch 254/950\n",
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      "Epoch 255/950\n",
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      "Epoch 256/950\n",
      "34/34 - 1s - loss: 0.0327 - 1s/epoch - 31ms/step\n",
      "Epoch 257/950\n",
      "34/34 - 1s - loss: 0.0384 - 907ms/epoch - 27ms/step\n",
      "Epoch 258/950\n",
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      "Epoch 259/950\n",
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      "Epoch 260/950\n",
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      "Epoch 261/950\n",
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      "Epoch 262/950\n",
      "34/34 - 1s - loss: 0.0210 - 781ms/epoch - 23ms/step\n",
      "Epoch 263/950\n",
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      "Epoch 265/950\n",
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      "Epoch 266/950\n",
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      "Epoch 267/950\n",
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      "Epoch 268/950\n",
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      "Epoch 269/950\n",
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      "Epoch 270/950\n",
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      "Epoch 271/950\n",
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      "Epoch 272/950\n",
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      "Epoch 273/950\n",
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      "Epoch 274/950\n",
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      "Epoch 275/950\n",
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      "Epoch 276/950\n",
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      "Epoch 277/950\n",
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      "Epoch 278/950\n",
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      "Epoch 279/950\n",
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      "Epoch 280/950\n",
      "34/34 - 1s - loss: 0.0168 - 812ms/epoch - 24ms/step\n",
      "Epoch 281/950\n",
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      "Epoch 282/950\n",
      "34/34 - 1s - loss: 0.0194 - 803ms/epoch - 24ms/step\n",
      "Epoch 283/950\n",
      "34/34 - 1s - loss: 0.0169 - 829ms/epoch - 24ms/step\n",
      "Epoch 284/950\n",
      "34/34 - 1s - loss: 0.0136 - 790ms/epoch - 23ms/step\n",
      "Epoch 285/950\n",
      "34/34 - 1s - loss: 0.0161 - 841ms/epoch - 25ms/step\n",
      "Epoch 286/950\n",
      "34/34 - 1s - loss: 0.0141 - 833ms/epoch - 24ms/step\n",
      "Epoch 287/950\n",
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      "Epoch 288/950\n",
      "34/34 - 1s - loss: 0.0112 - 821ms/epoch - 24ms/step\n",
      "Epoch 289/950\n",
      "34/34 - 1s - loss: 0.0106 - 828ms/epoch - 24ms/step\n",
      "Epoch 290/950\n",
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      "Epoch 291/950\n",
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      "Epoch 292/950\n",
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      "Epoch 293/950\n",
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      "Epoch 294/950\n",
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      "Epoch 295/950\n",
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      "Epoch 296/950\n",
      "34/34 - 1s - loss: 0.0112 - 917ms/epoch - 27ms/step\n",
      "Epoch 297/950\n",
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      "Epoch 298/950\n",
      "34/34 - 1s - loss: 0.0118 - 787ms/epoch - 23ms/step\n",
      "Epoch 299/950\n",
      "34/34 - 1s - loss: 0.0099 - 827ms/epoch - 24ms/step\n",
      "Epoch 300/950\n",
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      "Epoch 301/950\n",
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      "Epoch 302/950\n",
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      "Epoch 303/950\n",
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      "Epoch 304/950\n",
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      "Epoch 305/950\n",
      "34/34 - 1s - loss: 0.0087 - 794ms/epoch - 23ms/step\n",
      "Epoch 306/950\n",
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      "Epoch 307/950\n",
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      "Epoch 308/950\n",
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      "Epoch 309/950\n",
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      "Epoch 310/950\n",
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      "Epoch 311/950\n",
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      "Epoch 312/950\n",
      "34/34 - 1s - loss: 0.0119 - 835ms/epoch - 25ms/step\n",
      "Epoch 313/950\n",
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      "Epoch 314/950\n",
      "34/34 - 1s - loss: 0.0161 - 918ms/epoch - 27ms/step\n",
      "Epoch 315/950\n",
      "34/34 - 1s - loss: 0.0107 - 878ms/epoch - 26ms/step\n",
      "Epoch 316/950\n",
      "34/34 - 1s - loss: 0.0127 - 916ms/epoch - 27ms/step\n",
      "Epoch 317/950\n",
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      "Epoch 318/950\n",
      "34/34 - 1s - loss: 0.0101 - 838ms/epoch - 25ms/step\n",
      "Epoch 319/950\n",
      "34/34 - 1s - loss: 0.0089 - 815ms/epoch - 24ms/step\n",
      "Epoch 320/950\n",
      "34/34 - 1s - loss: 0.0079 - 824ms/epoch - 24ms/step\n",
      "Epoch 321/950\n",
      "34/34 - 1s - loss: 0.0084 - 811ms/epoch - 24ms/step\n",
      "Epoch 322/950\n",
      "34/34 - 1s - loss: 0.0088 - 831ms/epoch - 24ms/step\n",
      "Epoch 323/950\n",
      "34/34 - 1s - loss: 0.0108 - 825ms/epoch - 24ms/step\n",
      "Epoch 324/950\n",
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      "Epoch 325/950\n",
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      "Epoch 326/950\n",
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      "Epoch 327/950\n",
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      "Epoch 328/950\n",
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      "Epoch 329/950\n",
      "34/34 - 1s - loss: 0.0084 - 796ms/epoch - 23ms/step\n",
      "Epoch 330/950\n",
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      "Epoch 331/950\n",
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      "Epoch 332/950\n",
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      "Epoch 333/950\n",
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      "Epoch 334/950\n",
      "34/34 - 1s - loss: 0.0060 - 916ms/epoch - 27ms/step\n",
      "Epoch 335/950\n",
      "34/34 - 1s - loss: 0.0060 - 939ms/epoch - 28ms/step\n",
      "Epoch 336/950\n",
      "34/34 - 1s - loss: 0.0058 - 801ms/epoch - 24ms/step\n",
      "Epoch 337/950\n",
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      "Epoch 338/950\n",
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      "Epoch 339/950\n",
      "34/34 - 1s - loss: 0.0074 - 824ms/epoch - 24ms/step\n",
      "Epoch 340/950\n",
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      "Epoch 341/950\n",
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      "Epoch 342/950\n",
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      "Epoch 343/950\n",
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      "Epoch 344/950\n",
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      "Epoch 345/950\n",
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      "Epoch 346/950\n",
      "34/34 - 1s - loss: 0.0111 - 792ms/epoch - 23ms/step\n",
      "Epoch 347/950\n",
      "34/34 - 1s - loss: 0.0084 - 847ms/epoch - 25ms/step\n",
      "Epoch 348/950\n",
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      "Epoch 349/950\n",
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      "Epoch 350/950\n",
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      "Epoch 351/950\n",
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      "Epoch 352/950\n",
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      "Epoch 353/950\n",
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      "Epoch 354/950\n",
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      "Epoch 355/950\n",
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      "Epoch 356/950\n",
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      "Epoch 357/950\n",
      "34/34 - 1s - loss: 0.0059 - 837ms/epoch - 25ms/step\n",
      "Epoch 358/950\n",
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      "Epoch 359/950\n",
      "34/34 - 1s - loss: 0.0056 - 824ms/epoch - 24ms/step\n",
      "Epoch 360/950\n",
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      "Epoch 361/950\n",
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      "Epoch 362/950\n",
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      "Epoch 363/950\n",
      "34/34 - 1s - loss: 0.0168 - 796ms/epoch - 23ms/step\n",
      "Epoch 364/950\n",
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      "Epoch 365/950\n",
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      "Epoch 366/950\n",
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      "Epoch 367/950\n",
      "34/34 - 1s - loss: 0.0184 - 807ms/epoch - 24ms/step\n",
      "Epoch 368/950\n",
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      "Epoch 369/950\n",
      "34/34 - 1s - loss: 0.0102 - 832ms/epoch - 24ms/step\n",
      "Epoch 370/950\n",
      "34/34 - 1s - loss: 0.0074 - 827ms/epoch - 24ms/step\n",
      "Epoch 371/950\n",
      "34/34 - 1s - loss: 0.0059 - 900ms/epoch - 26ms/step\n",
      "Epoch 372/950\n",
      "34/34 - 1s - loss: 0.0054 - 911ms/epoch - 27ms/step\n",
      "Epoch 373/950\n",
      "34/34 - 1s - loss: 0.0069 - 907ms/epoch - 27ms/step\n",
      "Epoch 374/950\n",
      "34/34 - 1s - loss: 0.0053 - 849ms/epoch - 25ms/step\n",
      "Epoch 375/950\n",
      "34/34 - 1s - loss: 0.0049 - 803ms/epoch - 24ms/step\n",
      "Epoch 376/950\n",
      "34/34 - 1s - loss: 0.0046 - 826ms/epoch - 24ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 377/950\n",
      "34/34 - 1s - loss: 0.0056 - 799ms/epoch - 23ms/step\n",
      "Epoch 378/950\n",
      "34/34 - 1s - loss: 0.0050 - 844ms/epoch - 25ms/step\n",
      "Epoch 379/950\n",
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      "Epoch 380/950\n",
      "34/34 - 1s - loss: 0.0042 - 825ms/epoch - 24ms/step\n",
      "Epoch 381/950\n",
      "34/34 - 1s - loss: 0.0042 - 800ms/epoch - 24ms/step\n",
      "Epoch 382/950\n",
      "34/34 - 1s - loss: 0.0041 - 822ms/epoch - 24ms/step\n",
      "Epoch 383/950\n",
      "34/34 - 1s - loss: 0.0039 - 804ms/epoch - 24ms/step\n",
      "Epoch 384/950\n",
      "34/34 - 1s - loss: 0.0038 - 829ms/epoch - 24ms/step\n",
      "Epoch 385/950\n",
      "34/34 - 1s - loss: 0.0037 - 798ms/epoch - 23ms/step\n",
      "Epoch 386/950\n",
      "34/34 - 1s - loss: 0.0037 - 832ms/epoch - 24ms/step\n",
      "Epoch 387/950\n",
      "34/34 - 1s - loss: 0.0038 - 781ms/epoch - 23ms/step\n",
      "Epoch 388/950\n",
      "34/34 - 1s - loss: 0.0037 - 840ms/epoch - 25ms/step\n",
      "Epoch 389/950\n",
      "34/34 - 1s - loss: 0.0036 - 803ms/epoch - 24ms/step\n",
      "Epoch 390/950\n",
      "34/34 - 1s - loss: 0.0035 - 922ms/epoch - 27ms/step\n",
      "Epoch 391/950\n",
      "34/34 - 1s - loss: 0.0034 - 882ms/epoch - 26ms/step\n",
      "Epoch 392/950\n",
      "34/34 - 1s - loss: 0.0036 - 930ms/epoch - 27ms/step\n",
      "Epoch 393/950\n",
      "34/34 - 1s - loss: 0.0036 - 867ms/epoch - 26ms/step\n",
      "Epoch 394/950\n",
      "34/34 - 1s - loss: 0.0035 - 794ms/epoch - 23ms/step\n",
      "Epoch 395/950\n",
      "34/34 - 1s - loss: 0.0042 - 810ms/epoch - 24ms/step\n",
      "Epoch 396/950\n",
      "34/34 - 1s - loss: 0.0037 - 843ms/epoch - 25ms/step\n",
      "Epoch 397/950\n",
      "34/34 - 1s - loss: 0.0031 - 828ms/epoch - 24ms/step\n",
      "Epoch 398/950\n",
      "34/34 - 1s - loss: 0.0033 - 793ms/epoch - 23ms/step\n",
      "Epoch 399/950\n",
      "34/34 - 1s - loss: 0.0033 - 798ms/epoch - 23ms/step\n",
      "Epoch 400/950\n",
      "34/34 - 1s - loss: 0.0035 - 837ms/epoch - 25ms/step\n",
      "Epoch 401/950\n",
      "34/34 - 1s - loss: 0.0032 - 827ms/epoch - 24ms/step\n",
      "Epoch 402/950\n",
      "34/34 - 1s - loss: 0.0037 - 806ms/epoch - 24ms/step\n",
      "Epoch 403/950\n",
      "34/34 - 1s - loss: 0.0061 - 793ms/epoch - 23ms/step\n",
      "Epoch 404/950\n",
      "34/34 - 1s - loss: 0.0167 - 831ms/epoch - 24ms/step\n",
      "Epoch 405/950\n",
      "34/34 - 1s - loss: 0.0173 - 828ms/epoch - 24ms/step\n",
      "Epoch 406/950\n",
      "34/34 - 1s - loss: 0.0284 - 792ms/epoch - 23ms/step\n",
      "Epoch 407/950\n",
      "34/34 - 1s - loss: 0.0118 - 880ms/epoch - 26ms/step\n",
      "Epoch 408/950\n",
      "34/34 - 1s - loss: 0.0099 - 984ms/epoch - 29ms/step\n",
      "Epoch 409/950\n",
      "34/34 - 1s - loss: 0.0087 - 909ms/epoch - 27ms/step\n",
      "Epoch 410/950\n",
      "34/34 - 1s - loss: 0.0060 - 884ms/epoch - 26ms/step\n",
      "Epoch 411/950\n",
      "34/34 - 1s - loss: 0.0070 - 918ms/epoch - 27ms/step\n",
      "Epoch 412/950\n",
      "34/34 - 1s - loss: 0.0088 - 848ms/epoch - 25ms/step\n",
      "Epoch 413/950\n",
      "34/34 - 1s - loss: 0.0111 - 822ms/epoch - 24ms/step\n",
      "Epoch 414/950\n",
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      "Epoch 415/950\n",
      "34/34 - 1s - loss: 0.0050 - 830ms/epoch - 24ms/step\n",
      "Epoch 416/950\n",
      "34/34 - 1s - loss: 0.0039 - 832ms/epoch - 24ms/step\n",
      "Epoch 417/950\n",
      "34/34 - 1s - loss: 0.0036 - 819ms/epoch - 24ms/step\n",
      "Epoch 418/950\n",
      "34/34 - 1s - loss: 0.0026 - 796ms/epoch - 23ms/step\n",
      "Epoch 419/950\n",
      "34/34 - 1s - loss: 0.0025 - 837ms/epoch - 25ms/step\n",
      "Epoch 420/950\n",
      "34/34 - 1s - loss: 0.0022 - 807ms/epoch - 24ms/step\n",
      "Epoch 421/950\n",
      "34/34 - 1s - loss: 0.0026 - 820ms/epoch - 24ms/step\n",
      "Epoch 422/950\n",
      "34/34 - 1s - loss: 0.0029 - 801ms/epoch - 24ms/step\n",
      "Epoch 423/950\n",
      "34/34 - 1s - loss: 0.0035 - 906ms/epoch - 27ms/step\n",
      "Epoch 424/950\n",
      "34/34 - 1s - loss: 0.0034 - 843ms/epoch - 25ms/step\n",
      "Epoch 425/950\n",
      "34/34 - 1s - loss: 0.0031 - 796ms/epoch - 23ms/step\n",
      "Epoch 426/950\n",
      "34/34 - 1s - loss: 0.0026 - 803ms/epoch - 24ms/step\n",
      "Epoch 427/950\n",
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      "Epoch 428/950\n",
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      "Epoch 429/950\n",
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      "Epoch 430/950\n",
      "34/34 - 1s - loss: 0.0018 - 910ms/epoch - 27ms/step\n",
      "Epoch 431/950\n",
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      "Epoch 432/950\n",
      "34/34 - 1s - loss: 0.0020 - 856ms/epoch - 25ms/step\n",
      "Epoch 433/950\n",
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      "Epoch 434/950\n",
      "34/34 - 1s - loss: 0.0022 - 793ms/epoch - 23ms/step\n",
      "Epoch 435/950\n",
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      "Epoch 436/950\n",
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      "Epoch 437/950\n",
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      "Epoch 438/950\n",
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      "Epoch 439/950\n",
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      "Epoch 440/950\n",
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      "Epoch 441/950\n",
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      "Epoch 442/950\n",
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      "Epoch 443/950\n",
      "34/34 - 1s - loss: 0.0017 - 849ms/epoch - 25ms/step\n",
      "Epoch 444/950\n",
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      "Epoch 445/950\n",
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      "Epoch 446/950\n",
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      "Epoch 447/950\n",
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      "Epoch 449/950\n",
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      "Epoch 450/950\n",
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      "Epoch 451/950\n",
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      "Epoch 455/950\n",
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      "Epoch 456/950\n",
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      "Epoch 457/950\n",
      "34/34 - 1s - loss: 0.0045 - 811ms/epoch - 24ms/step\n",
      "Epoch 458/950\n",
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      "Epoch 459/950\n",
      "34/34 - 1s - loss: 0.0029 - 835ms/epoch - 25ms/step\n",
      "Epoch 460/950\n",
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      "Epoch 461/950\n",
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      "Epoch 462/950\n",
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      "Epoch 463/950\n",
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      "Epoch 464/950\n",
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      "Epoch 465/950\n",
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      "Epoch 467/950\n",
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      "Epoch 468/950\n",
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      "Epoch 469/950\n",
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      "Epoch 470/950\n",
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      "Epoch 471/950\n",
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      "Epoch 472/950\n",
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      "Epoch 473/950\n",
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      "Epoch 474/950\n",
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      "Epoch 475/950\n",
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      "Epoch 476/950\n",
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      "Epoch 477/950\n",
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      "Epoch 478/950\n",
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      "Epoch 479/950\n",
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      "Epoch 480/950\n",
      "34/34 - 1s - loss: 5.0592e-04 - 820ms/epoch - 24ms/step\n",
      "Epoch 481/950\n",
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      "Epoch 482/950\n",
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      "Epoch 483/950\n",
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      "Epoch 484/950\n",
      "34/34 - 1s - loss: 0.0012 - 799ms/epoch - 23ms/step\n",
      "Epoch 485/950\n",
      "34/34 - 1s - loss: 0.0043 - 936ms/epoch - 28ms/step\n",
      "Epoch 486/950\n",
      "34/34 - 1s - loss: 0.0366 - 913ms/epoch - 27ms/step\n",
      "Epoch 487/950\n",
      "34/34 - 1s - loss: 0.0786 - 906ms/epoch - 27ms/step\n",
      "Epoch 488/950\n",
      "34/34 - 1s - loss: 0.0562 - 856ms/epoch - 25ms/step\n",
      "Epoch 489/950\n",
      "34/34 - 1s - loss: 0.0520 - 884ms/epoch - 26ms/step\n",
      "Epoch 490/950\n",
      "34/34 - 1s - loss: 0.0373 - 904ms/epoch - 27ms/step\n",
      "Epoch 491/950\n",
      "34/34 - 1s - loss: 0.0181 - 812ms/epoch - 24ms/step\n",
      "Epoch 492/950\n",
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      "Epoch 493/950\n",
      "34/34 - 1s - loss: 0.0110 - 833ms/epoch - 24ms/step\n",
      "Epoch 494/950\n",
      "34/34 - 1s - loss: 0.0082 - 839ms/epoch - 25ms/step\n",
      "Epoch 495/950\n",
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      "Epoch 496/950\n",
      "34/34 - 1s - loss: 0.0066 - 809ms/epoch - 24ms/step\n",
      "Epoch 497/950\n",
      "34/34 - 1s - loss: 0.0050 - 844ms/epoch - 25ms/step\n",
      "Epoch 498/950\n",
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      "Epoch 499/950\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 500/950\n",
      "34/34 - 1s - loss: 0.0037 - 859ms/epoch - 25ms/step\n",
      "Epoch 501/950\n",
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      "Epoch 502/950\n",
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      "Epoch 503/950\n",
      "34/34 - 1s - loss: 0.0017 - 820ms/epoch - 24ms/step\n",
      "Epoch 504/950\n",
      "34/34 - 1s - loss: 0.0015 - 942ms/epoch - 28ms/step\n",
      "Epoch 505/950\n",
      "34/34 - 1s - loss: 0.0014 - 918ms/epoch - 27ms/step\n",
      "Epoch 506/950\n",
      "34/34 - 1s - loss: 0.0014 - 912ms/epoch - 27ms/step\n",
      "Epoch 507/950\n",
      "34/34 - 1s - loss: 0.0012 - 828ms/epoch - 24ms/step\n",
      "Epoch 508/950\n",
      "34/34 - 1s - loss: 0.0064 - 832ms/epoch - 24ms/step\n",
      "Epoch 509/950\n",
      "34/34 - 1s - loss: 0.0051 - 839ms/epoch - 25ms/step\n",
      "Epoch 510/950\n",
      "34/34 - 1s - loss: 0.0024 - 804ms/epoch - 24ms/step\n",
      "Epoch 511/950\n",
      "34/34 - 1s - loss: 0.0057 - 817ms/epoch - 24ms/step\n",
      "Epoch 512/950\n",
      "34/34 - 1s - loss: 0.0054 - 830ms/epoch - 24ms/step\n",
      "Epoch 513/950\n",
      "34/34 - 1s - loss: 0.0071 - 833ms/epoch - 25ms/step\n",
      "Epoch 514/950\n",
      "34/34 - 1s - loss: 0.0136 - 808ms/epoch - 24ms/step\n",
      "Epoch 515/950\n",
      "34/34 - 1s - loss: 0.0183 - 803ms/epoch - 24ms/step\n",
      "Epoch 516/950\n",
      "34/34 - 1s - loss: 0.0112 - 844ms/epoch - 25ms/step\n",
      "Epoch 517/950\n",
      "34/34 - 1s - loss: 0.0075 - 830ms/epoch - 24ms/step\n",
      "Epoch 518/950\n",
      "34/34 - 1s - loss: 0.0051 - 816ms/epoch - 24ms/step\n",
      "Epoch 519/950\n",
      "34/34 - 1s - loss: 0.0048 - 806ms/epoch - 24ms/step\n",
      "Epoch 520/950\n",
      "34/34 - 1s - loss: 0.0031 - 838ms/epoch - 25ms/step\n",
      "Epoch 521/950\n",
      "34/34 - 1s - loss: 0.0022 - 839ms/epoch - 25ms/step\n",
      "Epoch 522/950\n",
      "34/34 - 1s - loss: 0.0021 - 809ms/epoch - 24ms/step\n",
      "Epoch 523/950\n",
      "34/34 - 1s - loss: 0.0013 - 902ms/epoch - 27ms/step\n",
      "Epoch 524/950\n",
      "34/34 - 1s - loss: 0.0014 - 922ms/epoch - 27ms/step\n",
      "Epoch 525/950\n",
      "34/34 - 1s - loss: 0.0011 - 933ms/epoch - 27ms/step\n",
      "Epoch 526/950\n",
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      "Epoch 527/950\n",
      "34/34 - 1s - loss: 0.0012 - 844ms/epoch - 25ms/step\n",
      "Epoch 528/950\n",
      "34/34 - 1s - loss: 9.0451e-04 - 820ms/epoch - 24ms/step\n",
      "Epoch 529/950\n",
      "34/34 - 1s - loss: 7.9980e-04 - 813ms/epoch - 24ms/step\n",
      "Epoch 530/950\n",
      "34/34 - 1s - loss: 7.4034e-04 - 815ms/epoch - 24ms/step\n",
      "Epoch 531/950\n",
      "34/34 - 1s - loss: 8.1169e-04 - 837ms/epoch - 25ms/step\n",
      "Epoch 532/950\n",
      "34/34 - 1s - loss: 8.2942e-04 - 823ms/epoch - 24ms/step\n",
      "Epoch 533/950\n",
      "34/34 - 1s - loss: 7.2850e-04 - 827ms/epoch - 24ms/step\n",
      "Epoch 534/950\n",
      "34/34 - 1s - loss: 6.6908e-04 - 800ms/epoch - 24ms/step\n",
      "Epoch 535/950\n",
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      "Epoch 536/950\n",
      "34/34 - 1s - loss: 6.5354e-04 - 827ms/epoch - 24ms/step\n",
      "Epoch 537/950\n",
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      "Epoch 538/950\n",
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      "Epoch 539/950\n",
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      "Epoch 540/950\n",
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      "Epoch 541/950\n",
      "34/34 - 1s - loss: 6.0266e-04 - 794ms/epoch - 23ms/step\n",
      "Epoch 542/950\n",
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      "Epoch 543/950\n",
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      "Epoch 544/950\n",
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      "Epoch 545/950\n",
      "34/34 - 1s - loss: 5.5996e-04 - 831ms/epoch - 24ms/step\n",
      "Epoch 546/950\n",
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      "Epoch 547/950\n",
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      "Epoch 548/950\n",
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      "Epoch 549/950\n",
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      "Epoch 550/950\n",
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      "Epoch 551/950\n",
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      "Epoch 552/950\n",
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      "Epoch 553/950\n",
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      "Epoch 554/950\n",
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      "Epoch 555/950\n",
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      "Epoch 556/950\n",
      "34/34 - 1s - loss: 0.0063 - 828ms/epoch - 24ms/step\n",
      "Epoch 557/950\n",
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      "Epoch 558/950\n",
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      "Epoch 559/950\n",
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      "Epoch 560/950\n",
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      "Epoch 561/950\n",
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      "Epoch 562/950\n",
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      "Epoch 563/950\n",
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      "Epoch 564/950\n",
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      "Epoch 565/950\n",
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      "Epoch 566/950\n",
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      "Epoch 567/950\n",
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      "Epoch 568/950\n",
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      "Epoch 569/950\n",
      "34/34 - 1s - loss: 0.0044 - 795ms/epoch - 23ms/step\n",
      "Epoch 570/950\n",
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      "Epoch 571/950\n",
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      "Epoch 572/950\n",
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      "Epoch 575/950\n",
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      "Epoch 576/950\n",
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      "Epoch 577/950\n",
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      "Epoch 578/950\n",
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      "Epoch 579/950\n",
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      "Epoch 580/950\n",
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      "Epoch 581/950\n",
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      "Epoch 582/950\n",
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      "Epoch 583/950\n",
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      "Epoch 584/950\n",
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      "Epoch 585/950\n",
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      "Epoch 586/950\n",
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      "Epoch 587/950\n",
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      "Epoch 588/950\n",
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      "Epoch 589/950\n",
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      "Epoch 590/950\n",
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      "Epoch 591/950\n",
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      "Epoch 592/950\n",
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      "Epoch 593/950\n",
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      "Epoch 594/950\n",
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      "Epoch 595/950\n",
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      "Epoch 596/950\n",
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      "Epoch 597/950\n",
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      "Epoch 598/950\n",
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      "Epoch 599/950\n",
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      "Epoch 600/950\n",
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      "Epoch 601/950\n",
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      "Epoch 602/950\n",
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      "Epoch 603/950\n",
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      "Epoch 604/950\n",
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      "Epoch 605/950\n",
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      "Epoch 606/950\n",
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      "Epoch 607/950\n",
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      "Epoch 608/950\n",
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      "Epoch 609/950\n",
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      "Epoch 610/950\n",
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      "Epoch 611/950\n",
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      "Epoch 612/950\n",
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      "Epoch 614/950\n",
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      "Epoch 615/950\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 621/950\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 741/950\n",
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      "Epoch 787/950\n",
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      "Epoch 788/950\n",
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      "Epoch 789/950\n",
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      "Epoch 790/950\n",
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      "Epoch 791/950\n",
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      "Epoch 792/950\n",
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      "Epoch 793/950\n",
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      "Epoch 794/950\n",
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      "Epoch 795/950\n",
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      "Epoch 796/950\n",
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      "Epoch 797/950\n",
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      "Epoch 798/950\n",
      "34/34 - 1s - loss: 2.3048e-04 - 945ms/epoch - 28ms/step\n",
      "Epoch 799/950\n",
      "34/34 - 1s - loss: 2.3977e-04 - 922ms/epoch - 27ms/step\n",
      "Epoch 800/950\n",
      "34/34 - 1s - loss: 2.3383e-04 - 932ms/epoch - 27ms/step\n",
      "Epoch 801/950\n",
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      "Epoch 802/950\n",
      "34/34 - 1s - loss: 0.0061 - 849ms/epoch - 25ms/step\n",
      "Epoch 803/950\n",
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      "Epoch 804/950\n",
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      "Epoch 805/950\n",
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      "Epoch 806/950\n",
      "34/34 - 1s - loss: 0.0027 - 851ms/epoch - 25ms/step\n",
      "Epoch 807/950\n",
      "34/34 - 1s - loss: 0.0015 - 828ms/epoch - 24ms/step\n",
      "Epoch 808/950\n",
      "34/34 - 1s - loss: 8.9465e-04 - 823ms/epoch - 24ms/step\n",
      "Epoch 809/950\n",
      "34/34 - 1s - loss: 7.6183e-04 - 847ms/epoch - 25ms/step\n",
      "Epoch 810/950\n",
      "34/34 - 1s - loss: 8.1356e-04 - 856ms/epoch - 25ms/step\n",
      "Epoch 811/950\n",
      "34/34 - 1s - loss: 6.6574e-04 - 1s/epoch - 30ms/step\n",
      "Epoch 812/950\n",
      "34/34 - 1s - loss: 5.6685e-04 - 881ms/epoch - 26ms/step\n",
      "Epoch 813/950\n",
      "34/34 - 1s - loss: 5.0835e-04 - 837ms/epoch - 25ms/step\n",
      "Epoch 814/950\n",
      "34/34 - 1s - loss: 4.3249e-04 - 839ms/epoch - 25ms/step\n",
      "Epoch 815/950\n",
      "34/34 - 1s - loss: 4.2322e-04 - 819ms/epoch - 24ms/step\n",
      "Epoch 816/950\n",
      "34/34 - 1s - loss: 3.8032e-04 - 906ms/epoch - 27ms/step\n",
      "Epoch 817/950\n",
      "34/34 - 1s - loss: 3.8627e-04 - 931ms/epoch - 27ms/step\n",
      "Epoch 818/950\n",
      "34/34 - 1s - loss: 4.1304e-04 - 895ms/epoch - 26ms/step\n",
      "Epoch 819/950\n",
      "34/34 - 1s - loss: 3.9000e-04 - 879ms/epoch - 26ms/step\n",
      "Epoch 820/950\n",
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      "Epoch 821/950\n",
      "34/34 - 1s - loss: 3.3547e-04 - 853ms/epoch - 25ms/step\n",
      "Epoch 822/950\n",
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      "Epoch 823/950\n",
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      "Epoch 824/950\n",
      "34/34 - 1s - loss: 3.1740e-04 - 865ms/epoch - 25ms/step\n",
      "Epoch 825/950\n",
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      "Epoch 826/950\n",
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      "Epoch 827/950\n",
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      "Epoch 828/950\n",
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      "Epoch 829/950\n",
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      "Epoch 830/950\n",
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      "Epoch 831/950\n",
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      "Epoch 832/950\n",
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      "Epoch 833/950\n",
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      "Epoch 834/950\n",
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      "Epoch 835/950\n",
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      "Epoch 836/950\n",
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      "Epoch 837/950\n",
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      "Epoch 838/950\n",
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      "Epoch 839/950\n",
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      "Epoch 840/950\n",
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      "Epoch 841/950\n",
      "34/34 - 1s - loss: 2.4044e-04 - 806ms/epoch - 24ms/step\n",
      "Epoch 842/950\n",
      "34/34 - 1s - loss: 2.3154e-04 - 822ms/epoch - 24ms/step\n",
      "Epoch 843/950\n",
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      "Epoch 844/950\n",
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      "Epoch 845/950\n",
      "34/34 - 1s - loss: 2.1207e-04 - 859ms/epoch - 25ms/step\n",
      "Epoch 846/950\n",
      "34/34 - 1s - loss: 2.9170e-04 - 834ms/epoch - 25ms/step\n",
      "Epoch 847/950\n",
      "34/34 - 1s - loss: 2.6403e-04 - 897ms/epoch - 26ms/step\n",
      "Epoch 848/950\n",
      "34/34 - 1s - loss: 2.4594e-04 - 831ms/epoch - 24ms/step\n",
      "Epoch 849/950\n",
      "34/34 - 1s - loss: 2.2938e-04 - 818ms/epoch - 24ms/step\n",
      "Epoch 850/950\n",
      "34/34 - 1s - loss: 2.0907e-04 - 831ms/epoch - 24ms/step\n",
      "Epoch 851/950\n",
      "34/34 - 1s - loss: 1.9762e-04 - 834ms/epoch - 25ms/step\n",
      "Epoch 852/950\n",
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      "Epoch 853/950\n",
      "34/34 - 1s - loss: 2.0041e-04 - 841ms/epoch - 25ms/step\n",
      "Epoch 854/950\n",
      "34/34 - 1s - loss: 1.9929e-04 - 948ms/epoch - 28ms/step\n",
      "Epoch 855/950\n",
      "34/34 - 1s - loss: 2.1438e-04 - 938ms/epoch - 28ms/step\n",
      "Epoch 856/950\n",
      "34/34 - 1s - loss: 2.4563e-04 - 902ms/epoch - 27ms/step\n",
      "Epoch 857/950\n",
      "34/34 - 1s - loss: 2.2320e-04 - 815ms/epoch - 24ms/step\n",
      "Epoch 858/950\n",
      "34/34 - 1s - loss: 2.1699e-04 - 862ms/epoch - 25ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 859/950\n",
      "34/34 - 1s - loss: 2.3478e-04 - 851ms/epoch - 25ms/step\n",
      "Epoch 860/950\n",
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      "Epoch 861/950\n",
      "34/34 - 1s - loss: 2.1928e-04 - 1s/epoch - 36ms/step\n",
      "Epoch 862/950\n",
      "34/34 - 1s - loss: 1.9392e-04 - 991ms/epoch - 29ms/step\n",
      "Epoch 863/950\n",
      "34/34 - 1s - loss: 1.9154e-04 - 831ms/epoch - 24ms/step\n",
      "Epoch 864/950\n",
      "34/34 - 1s - loss: 1.8255e-04 - 829ms/epoch - 24ms/step\n",
      "Epoch 865/950\n",
      "34/34 - 1s - loss: 1.9708e-04 - 872ms/epoch - 26ms/step\n",
      "Epoch 866/950\n",
      "34/34 - 1s - loss: 1.9465e-04 - 840ms/epoch - 25ms/step\n",
      "Epoch 867/950\n",
      "34/34 - 1s - loss: 1.8968e-04 - 821ms/epoch - 24ms/step\n",
      "Epoch 868/950\n",
      "34/34 - 1s - loss: 1.9794e-04 - 820ms/epoch - 24ms/step\n",
      "Epoch 869/950\n",
      "34/34 - 1s - loss: 1.9364e-04 - 835ms/epoch - 25ms/step\n",
      "Epoch 870/950\n",
      "34/34 - 1s - loss: 1.8435e-04 - 864ms/epoch - 25ms/step\n",
      "Epoch 871/950\n",
      "34/34 - 1s - loss: 1.6748e-04 - 842ms/epoch - 25ms/step\n",
      "Epoch 872/950\n",
      "34/34 - 1s - loss: 1.6503e-04 - 908ms/epoch - 27ms/step\n",
      "Epoch 873/950\n",
      "34/34 - 1s - loss: 1.6966e-04 - 934ms/epoch - 27ms/step\n",
      "Epoch 874/950\n",
      "34/34 - 1s - loss: 1.6921e-04 - 937ms/epoch - 28ms/step\n",
      "Epoch 875/950\n",
      "34/34 - 1s - loss: 1.7084e-04 - 817ms/epoch - 24ms/step\n",
      "Epoch 876/950\n",
      "34/34 - 1s - loss: 2.1530e-04 - 840ms/epoch - 25ms/step\n",
      "Epoch 877/950\n",
      "34/34 - 1s - loss: 1.9707e-04 - 854ms/epoch - 25ms/step\n",
      "Epoch 878/950\n",
      "34/34 - 1s - loss: 1.8229e-04 - 844ms/epoch - 25ms/step\n",
      "Epoch 879/950\n",
      "34/34 - 1s - loss: 1.7854e-04 - 834ms/epoch - 25ms/step\n",
      "Epoch 880/950\n",
      "34/34 - 1s - loss: 2.8311e-04 - 855ms/epoch - 25ms/step\n",
      "Epoch 881/950\n",
      "34/34 - 1s - loss: 2.1972e-04 - 858ms/epoch - 25ms/step\n",
      "Epoch 882/950\n",
      "34/34 - 1s - loss: 2.0491e-04 - 844ms/epoch - 25ms/step\n",
      "Epoch 883/950\n",
      "34/34 - 1s - loss: 2.1440e-04 - 812ms/epoch - 24ms/step\n",
      "Epoch 884/950\n",
      "34/34 - 1s - loss: 1.9145e-04 - 854ms/epoch - 25ms/step\n",
      "Epoch 885/950\n",
      "34/34 - 1s - loss: 1.6436e-04 - 850ms/epoch - 25ms/step\n",
      "Epoch 886/950\n",
      "34/34 - 1s - loss: 1.5149e-04 - 831ms/epoch - 24ms/step\n",
      "Epoch 887/950\n",
      "34/34 - 1s - loss: 2.1547e-04 - 817ms/epoch - 24ms/step\n",
      "Epoch 888/950\n",
      "34/34 - 1s - loss: 0.0014 - 857ms/epoch - 25ms/step\n",
      "Epoch 889/950\n",
      "34/34 - 1s - loss: 0.0064 - 854ms/epoch - 25ms/step\n",
      "Epoch 890/950\n",
      "34/34 - 1s - loss: 0.0067 - 876ms/epoch - 26ms/step\n",
      "Epoch 891/950\n",
      "34/34 - 1s - loss: 0.0259 - 920ms/epoch - 27ms/step\n",
      "Epoch 892/950\n",
      "34/34 - 1s - loss: 0.0365 - 940ms/epoch - 28ms/step\n",
      "Epoch 893/950\n",
      "34/34 - 1s - loss: 0.0353 - 929ms/epoch - 27ms/step\n",
      "Epoch 894/950\n",
      "34/34 - 1s - loss: 0.0256 - 815ms/epoch - 24ms/step\n",
      "Epoch 895/950\n",
      "34/34 - 1s - loss: 0.0278 - 834ms/epoch - 25ms/step\n",
      "Epoch 896/950\n",
      "34/34 - 1s - loss: 0.0224 - 838ms/epoch - 25ms/step\n",
      "Epoch 897/950\n",
      "34/34 - 1s - loss: 0.0181 - 847ms/epoch - 25ms/step\n",
      "Epoch 898/950\n",
      "34/34 - 1s - loss: 0.0150 - 824ms/epoch - 24ms/step\n",
      "Epoch 899/950\n",
      "34/34 - 1s - loss: 0.0119 - 851ms/epoch - 25ms/step\n",
      "Epoch 900/950\n",
      "34/34 - 1s - loss: 0.0137 - 866ms/epoch - 25ms/step\n",
      "Epoch 901/950\n",
      "34/34 - 1s - loss: 0.0146 - 828ms/epoch - 24ms/step\n",
      "Epoch 902/950\n",
      "34/34 - 1s - loss: 0.0349 - 828ms/epoch - 24ms/step\n",
      "Epoch 903/950\n",
      "34/34 - 1s - loss: 0.0291 - 855ms/epoch - 25ms/step\n",
      "Epoch 904/950\n",
      "34/34 - 1s - loss: 0.0260 - 841ms/epoch - 25ms/step\n",
      "Epoch 905/950\n",
      "34/34 - 1s - loss: 0.0225 - 832ms/epoch - 24ms/step\n",
      "Epoch 906/950\n",
      "34/34 - 1s - loss: 0.0175 - 802ms/epoch - 24ms/step\n",
      "Epoch 907/950\n",
      "34/34 - 1s - loss: 0.0162 - 851ms/epoch - 25ms/step\n",
      "Epoch 908/950\n",
      "34/34 - 1s - loss: 0.0147 - 849ms/epoch - 25ms/step\n",
      "Epoch 909/950\n",
      "34/34 - 1s - loss: 0.0131 - 908ms/epoch - 27ms/step\n",
      "Epoch 910/950\n",
      "34/34 - 1s - loss: 0.0120 - 909ms/epoch - 27ms/step\n",
      "Epoch 911/950\n",
      "34/34 - 1s - loss: 0.0106 - 953ms/epoch - 28ms/step\n",
      "Epoch 912/950\n",
      "34/34 - 1s - loss: 0.0202 - 923ms/epoch - 27ms/step\n",
      "Epoch 913/950\n",
      "34/34 - 1s - loss: 0.0130 - 818ms/epoch - 24ms/step\n",
      "Epoch 914/950\n",
      "34/34 - 1s - loss: 0.0103 - 848ms/epoch - 25ms/step\n",
      "Epoch 915/950\n",
      "34/34 - 1s - loss: 0.0086 - 877ms/epoch - 26ms/step\n",
      "Epoch 916/950\n",
      "34/34 - 1s - loss: 0.0095 - 851ms/epoch - 25ms/step\n",
      "Epoch 917/950\n",
      "34/34 - 1s - loss: 0.0071 - 877ms/epoch - 26ms/step\n",
      "Epoch 918/950\n",
      "34/34 - 1s - loss: 0.0061 - 950ms/epoch - 28ms/step\n",
      "Epoch 919/950\n",
      "34/34 - 1s - loss: 0.0064 - 914ms/epoch - 27ms/step\n",
      "Epoch 920/950\n",
      "34/34 - 1s - loss: 0.0181 - 1s/epoch - 30ms/step\n",
      "Epoch 921/950\n",
      "34/34 - 1s - loss: 0.0124 - 831ms/epoch - 24ms/step\n",
      "Epoch 922/950\n",
      "34/34 - 1s - loss: 0.0093 - 874ms/epoch - 26ms/step\n",
      "Epoch 923/950\n",
      "34/34 - 1s - loss: 0.0081 - 871ms/epoch - 26ms/step\n",
      "Epoch 924/950\n",
      "34/34 - 1s - loss: 0.0054 - 826ms/epoch - 24ms/step\n",
      "Epoch 925/950\n",
      "34/34 - 1s - loss: 0.0119 - 841ms/epoch - 25ms/step\n",
      "Epoch 926/950\n",
      "34/34 - 1s - loss: 0.0067 - 836ms/epoch - 25ms/step\n",
      "Epoch 927/950\n",
      "34/34 - 1s - loss: 0.0043 - 937ms/epoch - 28ms/step\n",
      "Epoch 928/950\n",
      "34/34 - 1s - loss: 0.0037 - 918ms/epoch - 27ms/step\n",
      "Epoch 929/950\n",
      "34/34 - 1s - loss: 0.0032 - 942ms/epoch - 28ms/step\n",
      "Epoch 930/950\n",
      "34/34 - 1s - loss: 0.0030 - 891ms/epoch - 26ms/step\n",
      "Epoch 931/950\n",
      "34/34 - 1s - loss: 0.0028 - 814ms/epoch - 24ms/step\n",
      "Epoch 932/950\n",
      "34/34 - 1s - loss: 0.0027 - 840ms/epoch - 25ms/step\n",
      "Epoch 933/950\n",
      "34/34 - 1s - loss: 0.0026 - 848ms/epoch - 25ms/step\n",
      "Epoch 934/950\n",
      "34/34 - 1s - loss: 0.0023 - 896ms/epoch - 26ms/step\n",
      "Epoch 935/950\n",
      "34/34 - 1s - loss: 0.0023 - 819ms/epoch - 24ms/step\n",
      "Epoch 936/950\n",
      "34/34 - 1s - loss: 0.0022 - 827ms/epoch - 24ms/step\n",
      "Epoch 937/950\n",
      "34/34 - 1s - loss: 0.0025 - 863ms/epoch - 25ms/step\n",
      "Epoch 938/950\n",
      "34/34 - 1s - loss: 0.0248 - 829ms/epoch - 24ms/step\n",
      "Epoch 939/950\n",
      "34/34 - 1s - loss: 0.0084 - 843ms/epoch - 25ms/step\n",
      "Epoch 940/950\n",
      "34/34 - 1s - loss: 0.0052 - 828ms/epoch - 24ms/step\n",
      "Epoch 941/950\n",
      "34/34 - 1s - loss: 0.0080 - 846ms/epoch - 25ms/step\n",
      "Epoch 942/950\n",
      "34/34 - 1s - loss: 0.0099 - 867ms/epoch - 25ms/step\n",
      "Epoch 943/950\n",
      "34/34 - 1s - loss: 0.0063 - 819ms/epoch - 24ms/step\n",
      "Epoch 944/950\n",
      "34/34 - 1s - loss: 0.0059 - 852ms/epoch - 25ms/step\n",
      "Epoch 945/950\n",
      "34/34 - 1s - loss: 0.0039 - 869ms/epoch - 26ms/step\n",
      "Epoch 946/950\n",
      "34/34 - 1s - loss: 0.0043 - 953ms/epoch - 28ms/step\n",
      "Epoch 947/950\n",
      "34/34 - 1s - loss: 0.0081 - 921ms/epoch - 27ms/step\n",
      "Epoch 948/950\n",
      "34/34 - 1s - loss: 0.0083 - 950ms/epoch - 28ms/step\n",
      "Epoch 949/950\n",
      "34/34 - 1s - loss: 0.0043 - 868ms/epoch - 26ms/step\n",
      "Epoch 950/950\n",
      "34/34 - 1s - loss: 0.0040 - 826ms/epoch - 24ms/step\n"
     ]
    }
   ],
   "source": [
    "#TTrain[np.isnan(TTrain)]=0\n",
    "with tf.device('/gpu:0'):\n",
    "    start = dt.now()\n",
    "    hist=model.fit(XTrain_s,np.array(TTrain),epochs=noEpochs,verbose=2)\n",
    "    trainTime=(dt.now() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c494eda6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "# from matplotlib.pyplot import figure\n",
    "# plt.figure(figsize=(35,15))\n",
    "# plt.plot(hist.history['loss'])\n",
    "# plt.plot(hist.history['val_loss'])\n",
    "# plt.title('model train vs validation loss')\n",
    "# plt.ylabel('loss')\n",
    "# plt.xlabel('epochs')\n",
    "# plt.legend(['train', 'validation'], loc='upper right',prop={'size': 10})\n",
    "# fig = plt.gcf()\n",
    "# plt.yscale(\"log\")\n",
    "# fig.set_size_inches(20.5, 10.5)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a852559c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train time:  0:13:31.567656 s\n"
     ]
    }
   ],
   "source": [
    "print(\"Train time: \",trainTime,\"s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e24d20bf",
   "metadata": {},
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5df4584d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 10ms/step\n"
     ]
    }
   ],
   "source": [
    "with tf.device('/cpu:0'):\n",
    "    startInf = dt.now()\n",
    "    Y_hat=model.predict(XTest_s)\n",
    "    inferenceTime=(dt.now() - startInf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eda1f3f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference time:  0:00:00.527340 s\n"
     ]
    }
   ],
   "source": [
    "print(\"Inference time: \",inferenceTime,\"s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0edfc195",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1075, 50, 1)\n",
      "(1075,)\n"
     ]
    }
   ],
   "source": [
    "print(XTrain_s.shape)\n",
    "print(TTrain.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d28eda96",
   "metadata": {},
   "source": [
    "## Plot prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e0d91afc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1476x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "plt.figure(figsize=(15,7))\n",
    "plt.plot(TTest,color='blue',label='Actual',alpha=0.5,linewidth=6.0)\n",
    "plt.plot(Y_hat,color='red',label='Predicted')\n",
    "plt.xlabel('Steps',fontsize=25,backgroundcolor='w')\n",
    "plt.ylabel(\"Oxygen uptake\\n\"+\"$(mlxmin^{-1}xkg^{-1})$\",fontsize=25,backgroundcolor='w')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.yticks(fontsize=25)\n",
    "plt.xticks(fontsize=25)\n",
    "\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.gca().spines['left'].set_linewidth(2)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['bottom'].set_linewidth(2)\n",
    "\n",
    "plt.ylim(0)\n",
    "plt.xlim(0)\n",
    "\n",
    "plt.legend(prop={'size': 25},loc='upper left')\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(20.5, 10.5)\n",
    "fig.savefig(figPredictName)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f8ef8bc",
   "metadata": {},
   "source": [
    "## Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "95627e67",
   "metadata": {},
   "outputs": [],
   "source": [
    "if savedModel:\n",
    "    model.save(modelSaveName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4e33285e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(269, 1)\n",
      "(269,)\n",
      "9/9 [==============================] - 1s 10ms/step - loss: 0.0103\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(Y_hat))\n",
    "print(np.shape(TTest))\n",
    "#Y_hat=np.expand_dims(Y_hat,axis=0)\n",
    "#TTest=np.expand_dims(TTest,axis=0)\n",
    "#print(np.shape(XTrain_s))\n",
    "#print(np.shape(TTrain))\n",
    "results = model.evaluate(XTest_s,TTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7441ce70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1476x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Y_hat=np.transpose(Y_hat)\n",
    "TTest=TTest.reshape(len(TTest),1)\n",
    "#print(Y_hat)\n",
    "#diff=np.concatenate((Y_hat,TTest),axis=1)\n",
    "diff=np.subtract(Y_hat,TTest)\n",
    "#print(diff)\n",
    "plt.figure(figsize=(20.5,15.5))\n",
    "plt.plot(diff,color='red')\n",
    "plt.xlabel('Number')\n",
    "plt.ylabel('Diff')\n",
    "plt.title('Difference between grounded and predicted value.')\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(20.5, 10.5)\n",
    "#fig.savefig(figPredictName+'L_'+str(results)+'.jpg', dpi=150)\n",
    "plt.show()\n",
    "#model.save('1st-stage-subj-1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bb05838c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Error:  2.3114461289962143\n"
     ]
    }
   ],
   "source": [
    "rmse=np.sqrt(np.square(np.subtract(TTest,Y_hat)).mean())\n",
    "print(\"RMSE Error: \",rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5d4a8537",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 7.63092041  5.8028      1.82812041  6.71686021]\n",
      " [ 6.90461922  5.8028      1.10181922  6.35370961]\n",
      " [ 6.94997311  5.8028      1.14717311  6.37638655]\n",
      " ...\n",
      " [34.75290298 37.526      -2.77309702 36.13945149]\n",
      " [34.25860596 37.526      -3.26739404 35.89230298]\n",
      " [33.20853043 34.956      -1.74746957 34.08226521]]\n",
      "Store to oVel\n"
     ]
    }
   ],
   "source": [
    "res=np.concatenate((Y_hat,TTest,Y_hat-TTest,(Y_hat+TTest)/2),axis=1)\n",
    "print(res)\n",
    "\n",
    "if exportCSV:\n",
    "    if HRInputFeats and fourInputFeats:\n",
    "        pd.DataFrame(res).to_csv('Subj-'+subjectID+'-predict-wHR.csv')\n",
    "    elif HRInputFeats:        \n",
    "        pd.DataFrame(res).to_csv('Subj-'+subjectID+'-predict-oHR.csv')\n",
    "    elif fourInputFeats:\n",
    "        pd.DataFrame(res).to_csv('Subj-'+subjectID+'-predict-noHR.csv')\n",
    "    elif onlyVel:\n",
    "        print(\"Store to oVel\")\n",
    "        pd.DataFrame(res).to_csv('Subj-'+subjectID+'-predict-oVel.csv')"
   ]
  }
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