{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example demonstrates the whole process from initial atomic structure to training, evaluation and prediction. It includes:\n",
    "\n",
    "\n",
    "1. Read input atomic structures (saved as extxyz files) and create descriptors and their derivatives.\n",
    "\n",
    "2. Read inputs and outputs into a Data object.\n",
    "\n",
    "3. Create tensorflow dataset for training.\n",
    "\n",
    "4. Train the potential and apply it for prediction.\n",
    "\n",
    "5. Save the trained model and then load it for retraining or prediction.\n",
    "\n",
    "\n",
    "The code has been tested on Tensorflow 2.5 and 2.6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import atomdnn\n",
    "\n",
    "# 'float64' is used for reading data and train by default\n",
    "atomdnn.data_type = 'float64'\n",
    "\n",
    "# force and stress are evaluated by default, \n",
    "# if one only need to compute potential energy, then set compute_force to false\n",
    "atomdnn.compute_force = True\n",
    "\n",
    "# default value is for converting ev/A^3 to GPa\n",
    "# note that: the predicted positive stress means tension and negative stress means compression\n",
    "stress_unit_convert = 160.2176 \n",
    "\n",
    "import numpy as np\n",
    "import pickle\n",
    "import tensorflow as tf\n",
    "from atomdnn import data\n",
    "from atomdnn.data import Data\n",
    "from atomdnn.data import *\n",
    "from atomdnn.io import *\n",
    "from atomdnn import network\n",
    "from atomdnn.network import Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create descriptors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Read input atomic structures (saved as extxyz files) and create descriptors and their derivatives**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "descriptor = {'name': 'acsf', \n",
    "              'cutoff': 6.5,\n",
    "              'etaG2':[0.01,0.05,0.1,0.5,1,5,10], \n",
    "              'etaG4': [0.01], \n",
    "              'zeta': [0.08,0.2,1.0,5.0,10.0,50.0,100.0],\n",
    "              'lambda': [1.0, -1.0]}\n",
    "\n",
    "# define lammps excutable (serial or mpi) \n",
    "# LAMMPS has to be compiled with the added compute and dump_local subrutines (inside atomdnn/lammps)\n",
    "lmpexe = 'lmp_serial' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start creating fingerprints ...\n",
      "  so far finished for 10 images ...\n",
      "  so far finished for 20 images ...\n",
      "  so far finished for 30 images ...\n",
      "  so far finished for 40 images ...\n",
      "  so far finished for 50 images ...\n",
      "Finish creating descriptors and their derivatives from total 50 images.\n",
      "It took 5.02 seconds.\n"
     ]
    }
   ],
   "source": [
    "xyzfile_path = './extxyz' \n",
    "xyzfile_name = 'example_extxyz.*' # a serials of files like example_extxyz.1, example_extxyz.2, ...example_extxyz.n\n",
    "descriptors_path = './descriptors'\n",
    "descriptor_filename = 'dump_fp' # a serials of dump_fp.* files will be created\n",
    "der_filename ='dump_der'\n",
    "\n",
    "# this will create a serials of files for descriptors and their derivatives inside descriptors_path\n",
    "# by default, descriptor files are saved as 'dump_fp.*' and derivatives are saved as 'dump_der.*'\n",
    "create_descriptors(xyzfile_path = xyzfile_path, \\\n",
    "                   xyzfile_name = xyzfile_name, \\\n",
    "                   lmpexe = lmpexe, \\\n",
    "                   descriptors_path = descriptors_path, \\\n",
    "                   descriptor = descriptor, \\\n",
    "                   descriptor_filename = descriptor_filename, \\\n",
    "                   der_filename = der_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read inputs&outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Read inputs and outputs into a Data object** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading fingerprints data from LAMMPS dump files ./descriptors/dump_fp.i\n",
      "  so far read 50 images ...\n",
      "  Finish reading fingerprints from total 50 images.\n",
      "\n",
      "  image number = 50\n",
      "  max number of atom = 4\n",
      "  number of fingerprints = 22\n",
      "  type of atoms = 1\n",
      "\n",
      "Reading derivative data from a series of files ./descriptors/dump_der.i\n",
      "This may take a while for large data set ...\n",
      "  so far read 50 images ...\n",
      "  Finish reading dGdr derivatives from total 50 images.\n",
      "\n",
      "  Pad zeros to derivatives data if needed ...\n",
      "  Pading finished: 48 images derivatives have been padded with zeros.\n",
      "\n",
      "  image number = 50\n",
      "  max number of derivative pairs = 200\n",
      "  number of fingerprints = 22\n",
      "\n",
      "  It took 0.90 seconds to read the derivatives data.\n"
     ]
    }
   ],
   "source": [
    "# create a Data object\n",
    "grdata = Data()\n",
    "\n",
    "# read inputs: descriptors and their derivatives\n",
    "fp_filename = descriptors_path + '/dump_fp.*'\n",
    "der_filename = descriptors_path + '/dump_der.*'\n",
    "\n",
    "grdata.read_inputdata(fp_filename = fp_filename,der_filename = der_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading outputs from extxyz files ...\n",
      "  so far read 50 images ...\n",
      "  Finish reading outputs from total 50 images.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# read outputs: potential energy, force and stress from extxyz files\n",
    "grdata.read_outputdata(xyzfile_path=xyzfile_path, xyzfile_name=xyzfile_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create TFdataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create tensorflow dataset for training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conversion may take a while for large datasets...\n",
      "It took 0.2547 second.\n"
     ]
    }
   ],
   "source": [
    "# convert data to tensors\n",
    "grdata.convert_data_to_tensor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create tensorflow dataset\n",
    "tf_dataset = tf.data.Dataset.from_tensor_slices((grdata.input_dict,grdata.output_dict))\n",
    "\n",
    "dataset_path = './example_tfdataset'\n",
    "\n",
    "# save the dataset\n",
    "tf.data.experimental.save(tf_dataset, dataset_path)\n",
    "\n",
    "# save the element_spec to disk for future loading, this is only needed for tensorflow lower than 2.6\n",
    "with open(dataset_path + '/element_spec', 'wb') as out_: \n",
    "    pickle.dump(tf_dataset.element_spec, out_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note: The above three steps just need to be done once for one data set, the training only uses the saved tensorflow dataset.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load the dataset and train the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load tensorflow dataset, for Tensorflow version lower than 2.6, need to specify element_spec.\n",
    "\n",
    "with open(dataset_path + '/element_spec', 'rb') as in_:\n",
    "    element_spec = pickle.load(in_)\n",
    "\n",
    "dataset = tf.data.experimental.load(dataset_path,element_spec=element_spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traning data: 35 images\n",
      "Validation data: 10 images\n",
      "Test data: 5 images\n"
     ]
    }
   ],
   "source": [
    "# split the data to training, validation and testing sets\n",
    "\n",
    "train_dataset, val_dataset, test_dataset = split_dataset(dataset,0.7,0.2,0.1,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the network\n",
    "# See section 'Training' for detailed description on Network object.\n",
    "\n",
    "elements = ['C']\n",
    "act_fun = 'relu' # activation function\n",
    "nfp = get_fingerprints_num(dataset) # number of fingerprints (or descriptors)\n",
    "arch = [10,10] # NN layers\n",
    "\n",
    "model = Network(elements = elements,\\\n",
    "                num_fingerprints = nfp,\\\n",
    "                arch = arch,\\\n",
    "                activation_function = act_fun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forces are used for training.\n",
      "Stresses are used for training.\n",
      "Scaling factors are computed using training dataset.\n",
      "Training dataset are standardized.\n",
      "Validation dataset are standardized.\n",
      "Training dataset will be shuffled during training.\n",
      "\n",
      "===> Epoch 1/50 - 0.242s/epoch\n",
      "     training_loss    - pe_loss: 63.060 - force_loss: 340.074 - stress_loss: 7355.740 - total_loss: 1138.708\n",
      "     validation_loss  - pe_loss: 47.981 - force_loss: 340.109 - stress_loss: 11641.806 - total_loss: 1552.271\n",
      "\n",
      "===> Epoch 2/50 - 0.202s/epoch\n",
      "     training_loss    - pe_loss: 41.839 - force_loss: 338.547 - stress_loss: 6488.671 - total_loss: 1029.253\n",
      "     validation_loss  - pe_loss: 37.360 - force_loss: 302.898 - stress_loss: 11096.109 - total_loss: 1449.869\n",
      "\n",
      "===> Epoch 3/50 - 0.229s/epoch\n",
      "     training_loss    - pe_loss: 30.148 - force_loss: 295.398 - stress_loss: 6031.364 - total_loss: 928.682\n",
      "     validation_loss  - pe_loss: 27.729 - force_loss: 257.073 - stress_loss: 6611.963 - total_loss: 945.998\n",
      "\n",
      "===> Epoch 4/50 - 0.221s/epoch\n",
      "     training_loss    - pe_loss: 25.499 - force_loss: 264.563 - stress_loss: 5426.328 - total_loss: 832.694\n",
      "     validation_loss  - pe_loss: 21.732 - force_loss: 222.970 - stress_loss: 4001.949 - total_loss: 644.896\n",
      "\n",
      "===> Epoch 5/50 - 0.223s/epoch\n",
      "     training_loss    - pe_loss: 19.913 - force_loss: 256.813 - stress_loss: 4623.454 - total_loss: 739.071\n",
      "     validation_loss  - pe_loss: 18.560 - force_loss: 204.918 - stress_loss: 3570.819 - total_loss: 580.560\n",
      "\n",
      "===> Epoch 6/50 - 0.216s/epoch\n",
      "     training_loss    - pe_loss: 20.351 - force_loss: 214.047 - stress_loss: 4919.543 - total_loss: 726.352\n",
      "     validation_loss  - pe_loss: 16.724 - force_loss: 187.974 - stress_loss: 2774.870 - total_loss: 482.185\n",
      "\n",
      "===> Epoch 7/50 - 0.228s/epoch\n",
      "     training_loss    - pe_loss: 16.538 - force_loss: 212.149 - stress_loss: 4363.531 - total_loss: 665.040\n",
      "     validation_loss  - pe_loss: 14.973 - force_loss: 169.384 - stress_loss: 2824.495 - total_loss: 466.807\n",
      "\n",
      "===> Epoch 8/50 - 0.204s/epoch\n",
      "     training_loss    - pe_loss: 11.135 - force_loss: 173.746 - stress_loss: 3679.028 - total_loss: 552.783\n",
      "     validation_loss  - pe_loss: 14.269 - force_loss: 155.110 - stress_loss: 2688.537 - total_loss: 438.233\n",
      "\n",
      "===> Epoch 9/50 - 0.217s/epoch\n",
      "     training_loss    - pe_loss: 13.449 - force_loss: 152.360 - stress_loss: 3108.144 - total_loss: 476.624\n",
      "     validation_loss  - pe_loss: 14.968 - force_loss: 141.976 - stress_loss: 3357.161 - total_loss: 492.661\n",
      "\n",
      "===> Epoch 10/50 - 0.205s/epoch\n",
      "     training_loss    - pe_loss: 17.161 - force_loss: 144.477 - stress_loss: 3740.302 - total_loss: 535.668\n",
      "     validation_loss  - pe_loss: 16.087 - force_loss: 129.152 - stress_loss: 3363.140 - total_loss: 481.553\n",
      "\n",
      "===> Epoch 11/50 - 0.211s/epoch\n",
      "     training_loss    - pe_loss: 17.185 - force_loss: 121.094 - stress_loss: 3050.336 - total_loss: 443.312\n",
      "     validation_loss  - pe_loss: 17.087 - force_loss: 113.933 - stress_loss: 2551.663 - total_loss: 386.186\n",
      "\n",
      "===> Epoch 12/50 - 0.224s/epoch\n",
      "     training_loss    - pe_loss: 16.637 - force_loss: 109.219 - stress_loss: 2370.110 - total_loss: 362.866\n",
      "     validation_loss  - pe_loss: 17.689 - force_loss: 97.074 - stress_loss: 2602.237 - total_loss: 374.987\n",
      "\n",
      "===> Epoch 13/50 - 0.217s/epoch\n",
      "     training_loss    - pe_loss: 16.613 - force_loss: 105.366 - stress_loss: 2287.128 - total_loss: 350.692\n",
      "     validation_loss  - pe_loss: 17.418 - force_loss: 91.997 - stress_loss: 2337.202 - total_loss: 343.135\n",
      "\n",
      "===> Epoch 14/50 - 0.215s/epoch\n",
      "     training_loss    - pe_loss: 19.297 - force_loss: 87.292 - stress_loss: 1404.290 - total_loss: 247.018\n",
      "     validation_loss  - pe_loss: 16.794 - force_loss: 88.163 - stress_loss: 2260.646 - total_loss: 331.022\n",
      "\n",
      "===> Epoch 15/50 - 0.190s/epoch\n",
      "     training_loss    - pe_loss: 16.452 - force_loss: 93.070 - stress_loss: 1363.711 - total_loss: 245.893\n",
      "     validation_loss  - pe_loss: 16.137 - force_loss: 82.981 - stress_loss: 2126.521 - total_loss: 311.770\n",
      "\n",
      "===> Epoch 16/50 - 0.193s/epoch\n",
      "     training_loss    - pe_loss: 16.693 - force_loss: 79.436 - stress_loss: 1505.886 - total_loss: 246.717\n",
      "     validation_loss  - pe_loss: 15.632 - force_loss: 78.440 - stress_loss: 1899.606 - total_loss: 284.033\n",
      "\n",
      "===> Epoch 17/50 - 0.203s/epoch\n",
      "     training_loss    - pe_loss: 16.359 - force_loss: 84.692 - stress_loss: 1567.637 - total_loss: 257.815\n",
      "     validation_loss  - pe_loss: 15.098 - force_loss: 72.142 - stress_loss: 1882.750 - total_loss: 275.515\n",
      "\n",
      "===> Epoch 18/50 - 0.247s/epoch\n",
      "     training_loss    - pe_loss: 17.538 - force_loss: 70.511 - stress_loss: 1223.243 - total_loss: 210.374\n",
      "     validation_loss  - pe_loss: 14.630 - force_loss: 67.023 - stress_loss: 2512.499 - total_loss: 332.903\n",
      "\n",
      "===> Epoch 19/50 - 0.191s/epoch\n",
      "     training_loss    - pe_loss: 14.991 - force_loss: 68.985 - stress_loss: 1350.129 - total_loss: 218.989\n",
      "     validation_loss  - pe_loss: 14.003 - force_loss: 63.077 - stress_loss: 2543.449 - total_loss: 331.425\n",
      "\n",
      "===> Epoch 20/50 - 0.200s/epoch\n",
      "     training_loss    - pe_loss: 15.257 - force_loss: 59.828 - stress_loss: 1157.593 - total_loss: 190.844\n",
      "     validation_loss  - pe_loss: 13.590 - force_loss: 59.145 - stress_loss: 2209.569 - total_loss: 293.691\n",
      "\n",
      "===> Epoch 21/50 - 0.228s/epoch\n",
      "     training_loss    - pe_loss: 13.402 - force_loss: 67.119 - stress_loss: 1015.503 - total_loss: 182.071\n",
      "     validation_loss  - pe_loss: 13.357 - force_loss: 53.518 - stress_loss: 1958.299 - total_loss: 262.704\n",
      "\n",
      "===> Epoch 22/50 - 0.193s/epoch\n",
      "     training_loss    - pe_loss: 13.518 - force_loss: 56.822 - stress_loss: 839.241 - total_loss: 154.264\n",
      "     validation_loss  - pe_loss: 13.425 - force_loss: 48.992 - stress_loss: 1616.169 - total_loss: 224.034\n",
      "\n",
      "===> Epoch 23/50 - 0.253s/epoch\n",
      "     training_loss    - pe_loss: 13.768 - force_loss: 56.009 - stress_loss: 877.928 - total_loss: 157.570\n",
      "     validation_loss  - pe_loss: 13.435 - force_loss: 48.059 - stress_loss: 1219.728 - total_loss: 183.467\n",
      "\n",
      "===> Epoch 24/50 - 0.231s/epoch\n",
      "     training_loss    - pe_loss: 13.434 - force_loss: 58.265 - stress_loss: 1123.461 - total_loss: 184.045\n",
      "     validation_loss  - pe_loss: 13.149 - force_loss: 42.599 - stress_loss: 1004.809 - total_loss: 156.228\n",
      "\n",
      "===> Epoch 25/50 - 0.207s/epoch\n",
      "     training_loss    - pe_loss: 11.494 - force_loss: 49.842 - stress_loss: 948.868 - total_loss: 156.223\n",
      "     validation_loss  - pe_loss: 13.072 - force_loss: 36.523 - stress_loss: 985.632 - total_loss: 148.158\n",
      "\n",
      "===> Epoch 26/50 - 0.217s/epoch\n",
      "     training_loss    - pe_loss: 13.035 - force_loss: 46.092 - stress_loss: 821.157 - total_loss: 141.243\n",
      "     validation_loss  - pe_loss: 13.018 - force_loss: 34.420 - stress_loss: 879.450 - total_loss: 135.384\n",
      "\n",
      "===> Epoch 27/50 - 0.199s/epoch\n",
      "     training_loss    - pe_loss: 14.402 - force_loss: 39.259 - stress_loss: 422.067 - total_loss: 95.868\n",
      "     validation_loss  - pe_loss: 12.979 - force_loss: 32.963 - stress_loss: 743.509 - total_loss: 120.293\n",
      "\n",
      "===> Epoch 28/50 - 0.207s/epoch\n",
      "     training_loss    - pe_loss: 13.238 - force_loss: 41.517 - stress_loss: 430.548 - total_loss: 97.810\n",
      "     validation_loss  - pe_loss: 12.728 - force_loss: 29.243 - stress_loss: 581.828 - total_loss: 100.154\n",
      "\n",
      "===> Epoch 29/50 - 0.205s/epoch\n",
      "     training_loss    - pe_loss: 12.872 - force_loss: 36.499 - stress_loss: 506.248 - total_loss: 99.996\n",
      "     validation_loss  - pe_loss: 12.348 - force_loss: 27.912 - stress_loss: 696.936 - total_loss: 109.954\n",
      "\n",
      "===> Epoch 30/50 - 0.232s/epoch\n",
      "     training_loss    - pe_loss: 12.664 - force_loss: 44.302 - stress_loss: 485.721 - total_loss: 105.538\n",
      "     validation_loss  - pe_loss: 12.160 - force_loss: 28.085 - stress_loss: 690.873 - total_loss: 109.333\n",
      "\n",
      "===> Epoch 31/50 - 0.224s/epoch\n",
      "     training_loss    - pe_loss: 12.688 - force_loss: 35.669 - stress_loss: 385.926 - total_loss: 86.950\n",
      "     validation_loss  - pe_loss: 12.399 - force_loss: 26.697 - stress_loss: 563.213 - total_loss: 95.418\n",
      "\n",
      "===> Epoch 32/50 - 0.210s/epoch\n",
      "     training_loss    - pe_loss: 12.791 - force_loss: 30.050 - stress_loss: 371.575 - total_loss: 79.998\n",
      "     validation_loss  - pe_loss: 12.590 - force_loss: 26.551 - stress_loss: 511.294 - total_loss: 90.270\n",
      "\n",
      "===> Epoch 33/50 - 0.238s/epoch\n",
      "     training_loss    - pe_loss: 13.013 - force_loss: 33.084 - stress_loss: 518.474 - total_loss: 97.944\n",
      "     validation_loss  - pe_loss: 12.531 - force_loss: 26.636 - stress_loss: 437.782 - total_loss: 82.945\n",
      "\n",
      "===> Epoch 34/50 - 0.175s/epoch\n",
      "     training_loss    - pe_loss: 12.870 - force_loss: 27.200 - stress_loss: 465.778 - total_loss: 86.648\n",
      "     validation_loss  - pe_loss: 12.371 - force_loss: 26.732 - stress_loss: 472.396 - total_loss: 86.342\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===> Epoch 35/50 - 0.230s/epoch\n",
      "     training_loss    - pe_loss: 13.072 - force_loss: 28.979 - stress_loss: 457.129 - total_loss: 87.764\n",
      "     validation_loss  - pe_loss: 12.162 - force_loss: 27.584 - stress_loss: 405.666 - total_loss: 80.313\n",
      "\n",
      "===> Epoch 36/50 - 0.177s/epoch\n",
      "     training_loss    - pe_loss: 12.871 - force_loss: 28.323 - stress_loss: 359.149 - total_loss: 77.109\n",
      "     validation_loss  - pe_loss: 11.897 - force_loss: 25.966 - stress_loss: 308.571 - total_loss: 68.720\n",
      "\n",
      "===> Epoch 37/50 - 0.216s/epoch\n",
      "     training_loss    - pe_loss: 12.214 - force_loss: 30.554 - stress_loss: 343.573 - total_loss: 77.126\n",
      "     validation_loss  - pe_loss: 11.583 - force_loss: 27.246 - stress_loss: 341.293 - total_loss: 72.958\n",
      "\n",
      "===> Epoch 38/50 - 0.205s/epoch\n",
      "     training_loss    - pe_loss: 11.326 - force_loss: 27.573 - stress_loss: 374.659 - total_loss: 76.365\n",
      "     validation_loss  - pe_loss: 10.775 - force_loss: 27.324 - stress_loss: 264.535 - total_loss: 64.553\n",
      "\n",
      "===> Epoch 39/50 - 0.225s/epoch\n",
      "     training_loss    - pe_loss: 10.716 - force_loss: 25.122 - stress_loss: 391.479 - total_loss: 74.985\n",
      "     validation_loss  - pe_loss: 9.601 - force_loss: 25.424 - stress_loss: 377.649 - total_loss: 72.790\n",
      "\n",
      "===> Epoch 40/50 - 0.196s/epoch\n",
      "     training_loss    - pe_loss: 9.577 - force_loss: 25.639 - stress_loss: 416.391 - total_loss: 76.855\n",
      "     validation_loss  - pe_loss: 8.494 - force_loss: 26.807 - stress_loss: 416.565 - total_loss: 76.958\n",
      "\n",
      "===> Epoch 41/50 - 0.198s/epoch\n",
      "     training_loss    - pe_loss: 8.368 - force_loss: 23.657 - stress_loss: 287.868 - total_loss: 60.812\n",
      "     validation_loss  - pe_loss: 7.708 - force_loss: 26.754 - stress_loss: 422.066 - total_loss: 76.668\n",
      "\n",
      "===> Epoch 42/50 - 0.230s/epoch\n",
      "     training_loss    - pe_loss: 8.452 - force_loss: 24.731 - stress_loss: 315.003 - total_loss: 64.683\n",
      "     validation_loss  - pe_loss: 7.105 - force_loss: 28.294 - stress_loss: 299.194 - total_loss: 65.318\n",
      "\n",
      "===> Epoch 43/50 - 0.229s/epoch\n",
      "     training_loss    - pe_loss: 8.083 - force_loss: 24.970 - stress_loss: 254.668 - total_loss: 58.519\n",
      "     validation_loss  - pe_loss: 6.269 - force_loss: 28.521 - stress_loss: 253.969 - total_loss: 60.187\n",
      "\n",
      "===> Epoch 44/50 - 0.183s/epoch\n",
      "     training_loss    - pe_loss: 6.112 - force_loss: 23.348 - stress_loss: 235.360 - total_loss: 52.995\n",
      "     validation_loss  - pe_loss: 5.358 - force_loss: 29.082 - stress_loss: 208.927 - total_loss: 55.332\n",
      "\n",
      "===> Epoch 45/50 - 0.226s/epoch\n",
      "     training_loss    - pe_loss: 6.441 - force_loss: 26.129 - stress_loss: 156.721 - total_loss: 48.242\n",
      "     validation_loss  - pe_loss: 4.450 - force_loss: 28.374 - stress_loss: 186.346 - total_loss: 51.459\n",
      "\n",
      "===> Epoch 46/50 - 0.221s/epoch\n",
      "     training_loss    - pe_loss: 4.000 - force_loss: 23.461 - stress_loss: 151.348 - total_loss: 42.597\n",
      "     validation_loss  - pe_loss: 3.738 - force_loss: 27.473 - stress_loss: 168.654 - total_loss: 48.076\n",
      "\n",
      "===> Epoch 47/50 - 0.185s/epoch\n",
      "     training_loss    - pe_loss: 4.507 - force_loss: 23.344 - stress_loss: 174.907 - total_loss: 45.341\n",
      "     validation_loss  - pe_loss: 3.287 - force_loss: 27.115 - stress_loss: 141.923 - total_loss: 44.594\n",
      "\n",
      "===> Epoch 48/50 - 0.183s/epoch\n",
      "     training_loss    - pe_loss: 3.173 - force_loss: 24.408 - stress_loss: 139.998 - total_loss: 41.581\n",
      "     validation_loss  - pe_loss: 2.883 - force_loss: 26.482 - stress_loss: 162.336 - total_loss: 45.598\n",
      "\n",
      "===> Epoch 49/50 - 0.231s/epoch\n",
      "     training_loss    - pe_loss: 3.309 - force_loss: 21.386 - stress_loss: 93.648 - total_loss: 34.059\n",
      "     validation_loss  - pe_loss: 2.527 - force_loss: 24.680 - stress_loss: 203.034 - total_loss: 47.511\n",
      "\n",
      "===> Epoch 50/50 - 0.209s/epoch\n",
      "     training_loss    - pe_loss: 2.896 - force_loss: 23.390 - stress_loss: 137.304 - total_loss: 40.017\n",
      "     validation_loss  - pe_loss: 2.308 - force_loss: 23.773 - stress_loss: 195.185 - total_loss: 45.600\n",
      "\n",
      "End of training, elapsed time:  00:00:10\n"
     ]
    }
   ],
   "source": [
    "# Train the model \n",
    "\n",
    "opt = 'Adam' # optimizer\n",
    "loss_fun = 'mae' # loss function\n",
    "scaling = 'std' # scaling the traning data with standardization\n",
    "lr = 0.02 # learning rate\n",
    "loss_weights = {'pe' : 1, 'force' : 1, 'stress': 0.1} # the weights in loss function\n",
    "\n",
    "model.train(train_dataset, val_dataset, \\\n",
    "            optimizer=opt, \\\n",
    "            loss_fun = loss_fun, \\\n",
    "            batch_size=30, \\\n",
    "            lr=lr, \\\n",
    "            epochs=50, \\\n",
    "            scaling=scaling, \\\n",
    "            loss_weights=loss_weights, \\\n",
    "            compute_all_loss=True, \\\n",
    "            shuffle=True, \\\n",
    "            append_loss=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mzQt3CIfsvPPOcyeeeOJ+ZSeeeKKrWLGiW7t27QGPy8nJcZmZme6hhx5yTZo02Vv+5ZdfOsD9/vvve8sAd/zxx+93/Nlnn+06dep00HE2atTI3XnnnXs/d+rUyXXr1m2/Oo8++qiLiopyK1ascM45165dO/fMM88csM1KlSq5999//6BjyHUo/92BmS6P30TdkogAcbFxDOw1kJEfjAx3KCIipUbHjh2pXbv2fmW7du1i2LBhNG/enLi4OGJjYxk8eDBLly7d71ZAXrp3777f57Zt27Jy5coixZadnc0vv/xCv3799ivv378/OTk5zJjhPVLfoUMHHn/8cZ577jkWLVr0t3Y6dOjAoEGDGD9+PMuXLy9SLKGihCFCXHXcVcxaPotf/vol3KGISDlgFr4tVIKTBYB7772XJ554gmuvvZaPPvqIn376iSFDhgBeMpGfatWq7fe5QoUKBR5zIBs3biQzM/NvMeZ+3rx5MwBjxozhnHPOYeTIkbRq1YoWLVrwxhtv7K0/YcIEUlNTuf3222nUqBEdOnTg888/L1JMh0oJQ4SoGFuRvh378tXCr8IdioiUA86FbwuVvGY0nDhxIjfffDP33HMPp556KqmpqcTElPxwvZo1axIbG/u3NR3WrVsHQPXq1QEvSXnmmWdYu3Yts2fPplOnTlx88cXMmzcPgHr16jF+/Hg2bdrEjBkzqFOnDmeddRabNm0q2S+EEoaIUi+pHqu3rA53GCIiEaUwf+lnZGQQFxe393N2dvZ+f7GXlOjoaDp27MjEiRP3K3/zzTeJioqiS5cufzvm8MMP5/HHHycnJ+dvAxujoqLo3Lkzw4YNY+fOnfz111/FGn9eSjztMrPmwN1AF6Ad8I1zrls+9Z8EbgNGOefuCtrXFhjtt5UOvASMcM5lB9QxYBBwA1AT+Am4xTn3a6i+U6ikVE1h1vJZ4Q5DRCSitG7dmvfee493332X+vXrk5KScsC6p512Gs8++yzNmzenevXqPPvss4f8aGRRjRgxgh49enDFFVdwwQUX8PvvvzN06FCuueYa6tevD8Bxxx1Hnz59aN++PWbGiy++SKVKlTjmmGPYsmULPXr04LLLLqNly5bs3r2bUaNGUadOHdq0aVPi3yccj1W2A04Hvgdi86voJwRXAVvz2JcETAXmAWcDzYBReL0mQwKqDgSG4iUpC4A7gKlm1t45t/ZQv0wopVRLYXW6ehhERALdeOONzJo1iyuvvJK0tDSGDRt2wLqjR4/m+uuv56abbiI+Pp7LL7+cPn36cO2115ZgxJ7u3bvzxhtv8OCDD/Laa69Rq1Yt7rzzTkaM2DdZX5cuXRg/fjzLli0jOjqaI488kilTplC/fn12797NYYcdxtNPP82KFStISEigc+fOfPrpp2GZdMpcKG8oHcwJzaKcczn++7eAmgfqYTCzz4HvgEuBtwJ7GMxsEHAP0Mg5t9UvuwcYDtRxzm01s4rAOrzeiZF+nUrAMuAF51xgYvE3qampbubMmYfwbQtn4dqF9B7dm0UP/X2krIhIUcyfPz8sf41KeB3Kf3cz+9k5lxpcXuJjGHKThYKYWV+gNfDIAar0Aj7JTRZ8bwDxwIn+565AIvBmwPl3AJP94yNK3ap1NYZBREQiUkQOejSzeLzbCwP9H/i8tMa7xbCXc245sNPfl1snG1gcdOz8gDoRo0rFKgBs27UtzJGIiEig7OxssrKy8twOdg2L0i4iEwa8QYprgP/mUycJb6BjsDR/X26d7YGDIAPqJJhZhUOMM6TMjJSqGscgIhJpmjVrRmxsbJ5beVn1MuLWkjCzJsBdwEmupAdYeOe/FrgWoGHDhiV9+r0DH1vVaVXi5xYRkbxNnjz5gE9bBD7GWZZFXMKAN2ZhCrDQzKr5ZVFAnP95i59IpAFV8zg+yd+H/1rZzKKDehmSgJ3OuT3BBzvnxgHjwBv0eOhfp3D0pISISOQ57LDDwh1C2EXiLYlWwLl4P/a5WwPgH/77en69BQSNQzCzBkAC+8Y2LACigeZB5/jb+IdIkVItRQMfRUQk4kRiwnA1cFLQtg7vSYeTgA1+vSlADzOrEnBsfyADmOZ//g5vDoe9q3+YWQLQ2z8+4mgMg4iIRKJwzPSYgDdxE3i9BYn+I5QAHznn/jbxgZntAlY4574KKB4L3AJMMrNHgaZ4czD8M/dRS+fcLjN7BBhqZmnsm7gpCm+GyIhTt2pdflj6Q7jDEBER2U84xjDUAiYGleV+boI3qVKBnHNpZnYKMAZvXoV04Em8pCHQI3gJwiCgBjATOM05t67woRe/lGoprNmyJtxhiIiI7KfEEwbn3DKgUAucOucaH6B8HnByAcc64CF/i3ga9CgiIpEoEscwlGu5sz2G4YlSERGRA1LCEGEqV6xMbHQsWzK2hDsUEZEyY/jw4dSsWfOg63fr1o2+ffsWXLEcUcIQgfSkhIiIRBolDBFI4xhERCTSKGGIQHWr1lXCICICjB8/ngoVKpCenr5f+dy5czEzpk6dyocffshpp51GrVq1SExMpHPnznz66achj+WLL76gU6dOVKxYkdq1a3PjjTeyffv2vfszMzO56667aNiwIXFxcaSkpNCnTx/27PEmFU5PT+fqq68mJSWFihUr0rBhQ6655pqQx1lclDBEID1aKSLiOeecczAz3nnnnf3KJ0yYQO3atTnppJNYunQpvXv35j//+Q9vv/02Xbt2pVevXnz77bchi2Pu3Ln07NmTmjVr8vbbbzNixAhef/31/cY5PPzww7z22ms88MADfPbZZzz11FNUrVp172qWd9xxB9OnT+fJJ5/kk08+4f/+7/8wK9RDg2EViWtJlHsp1VJYunFpuMMQEQm7atWq0bNnTyZMmMAVV1yxt3zChAn07duX6Oho/vGPf+wtz8nJ4aSTTmLu3Lm8/PLLHHvssSGJ44EHHqBRo0a8//77REdHA1C9enX69+/PjBkz6NKlCz/++CMXXXQRl19++d7jzj///L3vf/zxR2666Sb69++/t+ySSy4JSXwlQQlDBEqpmsK3f4QuMxYRCWbXhO8vW/di4R4b79+/P5dffjmbNm2iRo0a/PrrryxatIiXXnoJgJUrVzJ48GCmTp3KmjVr9j6WHqpkAbwf+9wEJdd5551HTEwM06dPp0uXLnTo0IHnn3+e2rVr07NnTw477LD9ehA6dOjA448/TnR0NKeeeiotW7YMWXwlQQlDBNKgRxEpboX90Q6ns846i9jYWN5++22uvfZaJkyYQP369TnuuOPIycnhrLPOYtu2bYwcOZLmzZtTqVIl7r//ftavXx+yGNasWUPt2rX3K4uOjqZGjRps3rwZgCFDhhAVFcVzzz3HvffeS7169bj77ru59dZbARgzZgz3338/I0eO5KabbqJ58+Y88MADXHDBBSGLszhpDEMEUsIgIrJP5cqVOeOMM5gwYQIAb775Jv369cPM+OOPP5g1axajR4/mqquu4sQTTyQ1NZWMjIyQxlC3bt2/JSDZ2dls2rSJ6tWrA1CxYkVGjhzJsmXLWLRoEf379+e2227j448/BrzbK8888wxr165l9uzZdOrUiYsvvph58+aFNNbiooQhAmm2RxGR/V1wwQVMmzaNyZMn8+eff+79qzw3MYiLi9tb96+//grpgEeATp068c477+wdwAgwadIksrKyOO644/5Wv0WLFjzxxBPExcXlmRAcfvjhPP744+Tk5LBgwYKQxlpcdEsiAsVXiCehQgKbd2ymRuUa4Q5HRCTsTj/9dBISErjuuuto0qQJxxxzDACtW7emfv363HnnnTzwwANs27aNYcOGUa9evZCef8iQIRx55JGcc8453HDDDaxcuZJ7772XHj160KVLFwD69OlDx44dOfLII4mPj+ett94iKyuLE044AYDjjjuOPn360L59e8yMF198kUqVKu39LpFOPQwRKqWqHq0UEckVHx/PWWedxZo1a/Z7yiAuLo5JkyYRExND3759GTp0KIMGDeLEE08M6fnbtWvHlClTWL9+Peeeey5Dhgzhwgsv5K233tpbp2vXrrz77rtcdNFFnH322fz888+8/fbbpKamAtClSxfGjx9P3759Of/889m4cSNTpkyhfv36IY21uJi6vQ8sNTXVzZw5MyznPu2fp3F3j7vp3q57WM4vImXD/PnzadOmTbjDkBJ2KP/dzexn51xqcLl6GCKUBj6KiEgk0RiGCKWEQUSkeGRnZ+c7qDwmRj+NeVEPQ4RKqZrC6i1KGEREQu2UU04hNjb2gJvkTWlUhKpbrS5fLvwy3GGIiJQ5L7zwAtu2bQt3GKWOEoYIlVJVtyRERIpDq1atwh1CqaRbEhFKK1aKiEgkKfGEwcyam9kLZvabmWWb2VdB++ua2eNmNtvMtpvZCjN7xcxS8mirnpm9Y2bbzGyjmY0xs4Q86l1jZovNbJeZ/WxmpxTjVwyJulXrsnbrWnJycsIdioiUcnp8vnwprv/e4ehhaAecDiwEFuWxvyPQB/gf0Bu4G+gEfGdmlXMrmVks8AnQCLgAuBXoB4wLbMzMLgTGAq8CvYC5wAdm1j6k3yrE4mLjqFKxCpt2bAp3KCJSisXGxoZ8XQWJbBkZGcUyeDMcYxgmO+feAzCzt4CaQfunA62dc1m5BWb2C16CcR7wil/cF2gDNHfOLfXrZQJvmNkI59xiv95w4BXn3AN+nWnAkcBAIKIXIs8dx5BcJTncoYhIKVWrVi1WrVpFvXr1iI+P32+5ZSlbnHNkZGSwatWqv62sGQolnjA45/LtY3fOpedRtsjMdgKBtyV6AT/lJgu+d4E9QE9gsZk1BVri9T7sPb+ZTQwsi1S5czEc0eCIcIciIqVUYmIiAKtXryYzMzPM0Uhxi42NpXbt2nv/u4dSqXhKwswOBxLY/xZGa2C/JcCcc3vMbIm/j4DX4KXA5gPVzSzZObehGEIOibpV6+pJCRE5ZImJicXyAyLlS8Q/JWFmUcDTwGLg/YBdSUB6Hoek+fsIeA2ulxa0PyKlVNPkTSIiEhkiPmEAHga6AJc654q9P83MrjWzmWY2c8OG8HY+6NFKERGJFBGdMJjZjXhPSVzunPshaHcaUDWPw5LY14OQ+xpcLylo/17OuXHOuVTnXGpycngHG2ryJhERiRQRmzCY2XnAaOAe59yEPKosYN8YhdxjKgBN2TdmIfd1v3r+582RPH4BtACViIhEjohMGMysG/AaMNo598QBqk0BjjazRgFlZwFxwMcAzrk/8QZK9gtoO8r/PCXkgYeYEgYREYkUJf6UhD8T4+n+x3pAopn19T9/hDcR07t4vQMTzKxzwOEbnHNL/PdvAYOBSWY2FO+2w5PA6wFzMIA3D8N/zWwZ8C1wOdACuCi03yz0aifWZt22dWTnZBMdFR3ucEREpBwLx2OVtYCJQWW5n5vgzepYFTgC+C6o3ivAAADnXKaZ9QTGAG8Cu4E38MY87OWc+58/Q+S9wFC8mR7PdM7NCdH3KTYVYiqQlJDEhm0bqFO1TrjDERGRciwcEzctA/Kbamy8vx1MWyuBcw6i3ovAiwfTZqTJHfiohEFERMIpIscwyD56tFJERCKBEoYIp4GPIiISCZQwlBDn4IQTYPnywh2nhEFERCKBEoYSYgbHHANPPlm441KqanpoEREJPyUMJej22+GVV2Dz5oM/pm41LUAlIiLhp4ShBNWrB+ecA88+e/DHaHpoERGJBEoYStjdd8OYMbBz58HV14qVIiISCZQwlLA2baBLF/j3vw+ufu3E2mzavoms7KziDUxERCQfShjC4N574YknIOsgcoCY6BhqVK7B+m3riz8wERGRA1DCEAZdukCDBjAxeILsA9A4BhERCTclDGFy773w6KPe/AwF0VwMIiISbkoYwuT00yE7Gz79tOC6davq0UoREQkvJQxhYgb33OP1MhRET0qIiEi4KWEIowsugCVL4Kef8q+nWxIiIhJuShjCKDYW7rij4F6GlKpasVJERMJLCUOYXX01fP01LFp04DrqYRARkXBTwhBmlSrBDTd48zIciBIGEREJNyUMEeDmm+Gtt2DNAe46JFdJZvPOzWRmZZZsYCIiIj4lDBGgZk24+GJ4+um890dHRVOrSi3Wbl1bsoGJiIj4SjxhMLPmZvaCmf1mZtlm9lUedczM7jOzFWaWYWZfm1mHPOq1NbPPzWynma02s5FmFl2UtsLtzjvhxRdhy5a892u2RxERCadw9DC0A04HFgIHGuo3EBgKPAr0BrYDU82sTm4FM0sCpgIOOBsYCdwJjChsW5GgcWPo1Queey7v/RrHICIi4RSOhGGyc66Bc64fMDd4p5lVxPuRf9g5N8Y5NxXoh5cY/COg6vVAPHCuc+4z59xYvGThDjNLLGRbEWHwYHjySdi69e/7Uqrp0UoREQmfEk8YnHM5BVTpCiQCbwYcswOYDPQKqNcL+MQ5F/jz+gZeEnFiIduKCG3aQPfuMHr03/eph0FERMIpEgc9tgaygcVB5fP9fYH1FgRWcM4tB3YG1DvYtiLG/ffDU0/9fSxDSlVNDy0iIuETiQlDErDdOZcdVJ4GJJhZhYB66Xkcn+bvK0xbEaNlS29hquAnJupW0wJUIiISPpGYMISVmV1rZjPNbOaGDRvCEsPQofDMM5Cevq9MT0mIiEg4RWLCkAZUDn48Eq+3YKdzbk9Avap5HJ/k7ytMW3s558Y551Kdc6nJyclF/hKHonlzOOssbwBkLq1YKSIi4RSJCcMCIBpoHlQePGZhAUHjEMysAZAQUO9g24o4Q4bAmDGwebP3uWblmmzN2MruzN3hDUxERMqlSEwYvgO24j3+CICZJeDNoTAloN4UoIeZVQko6w9kANMK2VbEadoUzj0XRo3yPkdFRVGnah3N9igiImERU9In9H+wT/c/1gMSzayv//kj59xOM3sEGGpmaXg9AXfgJTeBDxyOBW4BJpnZo0BTYDjwz9xHLZ1zuw6yrYg0eDB07Ai33+5NH507jqFRjUbhDk1ERMqZEk8YgFrAxKCy3M9NgGXAI3g/6oOAGsBM4DTn3LrcA5xzaWZ2CjAGb16FdOBJvKQhUIFtRarGjaFfP28ly0ce0VwMIiISPiWeMDjnlgFWQB0HPORv+dWbB5wcirYi1X33QYcOcMcdULeqHq0UEZHwiMQxDBKgYUO48EJ4/HE9KSEiIuGjhKEUGDQIXn4ZKlkKq9JWhTscEREph5QwlAL168Mll8C3Hzfhz41/hjscEREph8Ix6FGKYOBAaHtUO+g3F+ccZvkOAxEREQkp9TCUEikpcMWFyezZFauBjyIiUuKUMJQiAwdC5vr2fD5rbrhDERGRckYJQylSuzZ0aNyO0f9VwiAiIiVLCUMpc1HPdsxZOYe5yhlERKQEKWEoZVKbtaNWq7kMGRLuSEREpDxRwlDKtKvXjjQ3j59mOr7/PtzRiIhIeaGEoZSpXqk6lStW5uaBKxg0CJwLd0QiIlIeKGEohdqltKPtsXNYvRo++yzc0YiISHmghKEUapfSjgXr5vLgg9600Tk54Y5IRETKOiUMpVD7eu2Zu2ou550HZvD22+GOSEREyjolDKVQu5R2zF09l6goePhhGDwYMjPDHZWIiJRlShhKobZ12zJvzTxycnI49VRvcarx48MdlYiIlGWFShjMLMbM4oLKupvZbWZ2VGhDkwOpmlCV6pWqs2zTMsy8XoaRIyEjI9yRiYhIWVXYHoYJwPO5H8zsFuBj4GHgezM7M4SxST7a12vP3NXedI+dOsHRR8Ozz4Y5KBERKbMKmzB0Bj4K+Hw3MMo5Fw+8BAwOVWCSv9xxDLkefBAeewzS08MXk4iIlF2FTRhqAGsBzOwwIAUY6++bCLQNXWiSn3Yp7Zizas7ez23bwhlnwBNPhDEoEREpswqbMKwDGvvvewJ/OeeW+J/jgZDNCGBmF5jZL2a23cxWmdmrZpYSVMfM7D4zW2FmGWb2tZl1yKOttmb2uZntNLPVZjbSzKJDFWs4tE9pv18PA8Dw4fD887BmTXhiEhGRsquwCcNE4FEzexy4F3g1YN+RwOJQBGVmZwH/A74DzvbPdQLwoZkFxjwQGAo8CvQGtgNTzaxOQFtJwFTA+W2NBO4ERoQi1nBpU7cNC9ctJDsne29Zo0YwYACMKNXfTEREIlFMIesPBLYCR+MNfvy/gH0d8QZFhsJFwC/OuX/kFpjZVuA9oBUw38wq+vE87Jwb49eZASwD/gHkrud4PV7vx7nOua3AZ2aWCAw3s8f8slKncsXK1K5SmyXrl9CyTsu95ffdB61bw+23Q6tWYQxQRETKlEL1MDjnspxzI51zvZ1zQ51zewL2neucGxWiuGKBLUFl6f6r+a9dgUTgzYAYdgCTgV4Bx/UCPglKDN7ASyJODFG8YRE88BGgRg246y5vymgREZFQKew8DLXMrEnAZzOza83sKTPrHcK4/gUcb2aXmVmimbUEHgS+cM7N8+u0BrL5+22Q+f4+AuotCKzgnFsO7AyqV+oEPloZ6JZbYOZM+O67MAQlIiJlUmHHMIwHbg/4PBJ4Dm8A5DtmNiAUQTnnPgQGAOPwehoWAtHAeQHVkoDtzrnsoMPTgAQzqxBQLz2P06T5+0qtvHoYAOLjvYmc7rlHy1+LiEhoFDZhOAr4AsAffHg9cJ9zrjXwEHBbKIIys5PwHtd8GjgJuACojpeUFOvTDX6PyUwzm7lhw4biPNUha5fSjjmr5+S579JLYetWeP/9Eg5KRETKpMImDFWBTf77jng/4q/5n78AmocorlHA+865e51zXznnJgDnAN3wnnQAr4egch4JRBKwM2B8RZofd7Akf99+nHPjnHOpzrnU5OTkQ/8mxah1ndb8sf4PMrP+vvJUdDQ88ggMHAhZWWEITkREypTCJgwr2Tc50xnAAufcKv9zVWBXiOJqDfwaWOCcWwhkAM38ogV4tymCk5TgMQsLCBqrYGYNgISgeqVOQlwC9ZPq88eGP/Lc36sX1KkD//pXCQcmIiJlTmEThn8Bj5nZROAevDEGuTrjDTgMhb/wbn/sZWZt8J5sWOYXfYf3iGe/gDoJePMxTAk4dArQw8yqBJT1x0s+poUo3rA50DgGADNvuugRI2DHjhIOTEREypTCPlb5MHAz3vTQNwPPBOyujreeRCiMBfqb2SgzO9XMLgbexUsWPvJj2QU8AtxnZjeZ2Sl4E0tFAaOD2toNTPLbuhYYDvyztM7BECh4iuhgRx8Nxx8PTz5ZgkGJiEiZU9iJm3DOvcr+Mzzmll8fkog8zwB7gBvwBlamA9OBQf5cC7kewUsQBuGtczETOM05ty4grjQ/mRiDN0dDOvAkXtJQ6rVLacc7s97Jt85DD3krWl53HUT4sAwREYlQ5gr53J2ZxeA93ngcXq/CZuAbYJJzrkwNr0tNTXUzZ84Mdxj5+m3lb1ww7gLmjZyXb71bbvFen3km32oiIlLOmdnPzrnU4PJCT9yE91f8//AGPTb1X98AfjIz/f1awlrVbsXSjUvZnbk733pDh8Lrr8OSJflWExERyVNhBz3+E6/rv7NzrqlzrotzrinQyS//Z6gDlPzFxcbRqEYjFq1blG+95GS47TYYPLhk4hIRkbKlsAnD6cC9zrkfAwudcz/hjSM4I1SBycHLa6nrvNx+O0yfDpdfDq+8AsuWFX9sIiJSNhQ2YYgDth1g3zagwgH2STHK79HKQJUqwYwZ0KULTJkCnTtD48bektjjxyuBEBGRAytswvA9cK+ZVQos9D/f6++XElbQo5WBGjSA66+HN96ANWvg44+9xCH3tXFjGDeuwGZERKScKexjlXcCXwIrzOxTYB1QC+iBt+x0t5BGJwflYHsYgplB69bedv313kJVs2fD6adD/freq4iICBR+4qZfgZZ4MzwmA6fhJQxjgRbOudmhDlAK1qJ2C1akrWBX5qHNzG0GHTrA2297tynm5f+kpoiIlCNFmbhpAzCwGGKRIqoQU4Fmyc1YsGYBHRp2OOT2unSBJ56A3r3hhx+gZs1Dj1FEREq3AhMGM/sJOOjZnZxzxxxSRFIkuUtdhyJhALjsMpg7F/r2hU8/hQoazioiUq4dTA/DXAqRMEh4FHUcQ37+7/+gTx+4+WYYO9a7ZSEiIuVTgQmDc25ACcQhh6h9vfaM/258SNuMjobXXoOuXWHMGC9xEBGR8qnQYxgkMhVHDwNAlSrw/vte0tCqFXTvHvJTiIhIKVDYeRgkQjVLbsbq9NXs2L2j4MqF1KQJTJgAl1wCCxeGvHkRESkFlDCUETHRMbSq3Yr5a+YXS/snnAAPP+w9OZGWViynEBGRCKaEoQwprtsSua66Cs4801uLQkREyhclDGVIcScMAI8+CrNmwS+/FOtpREQkwihhKEPa1Tv4NSWKKjYWbr0VRo0q1tOIiEiE0VMSZUj7lPb8vup3nHNYMU6acM010LQpLF8ODRsW22lERCSCqIehDGlSswn1k+rz6MePFut5qlaFK66Ap58u1tOIiEgEUcJQhkRFRTHxuok88/kzfDr302I91623wr//DenpxXoaERGJEBGbMJhZjJkNNLPFZrbbzFaa2ZNBdczM7jOzFWaWYWZfm1mHPNpqa2afm9lOM1ttZiPNLLrEvkwJql+9Pm9c+waXvnwpSzcsLbbzNGjgLX89blyxnUJERCJIxCYMwHjgFuAJoDveCpkZQXUGAkOBR4HewHZgqpnVya1gZknAVLz1MM4GRgJ3AiOKN/zwOaHlCdx3+n2c+/y57Ny9s9jOc+ed3m2JPXuK7RQiIhIhzLnIW1fKzHoCk4EjnHPzDlCnIrAOGOWcG+mXVQKWAS8454b4ZYOAe4BGzrmtftk9wHCgTm5ZXlJTU93MmTND9bVKlHOOS166hOioaF658pViGwR56qneypaXXVYszYuISAkzs5+dc6nB5ZHaw3Al8MWBkgVfVyAReDO3wDm3Ay/R6BVQrxfwSVBi8AYQD5wYsogjjJnx4mUv8tvK3xjzxZhiO89dd8ETT0AE5p0iIhJCkZowdAIWmdkYM9vqjz2YZGYpAXVaA9nA4qBj5/v7AustCKzgnFsO7AyqV+YkxCUw6cZJPPjhg3yz6JtiOUePHl6y8NlnxdK8iIhEiEhNGOoAA4AOwAXAFUBH4B3b17eeBGx3zmUHHZsGJJhZhYB66XmcI83ftx8zu9bMZprZzA0bNhzi1wi/pslNeeXKV7jgxQtYlbYq5O2b7etlEBGRsitSEwbzt7Odcx855yYAlwLHACcX54mdc+Occ6nOudTk5OTiPFWJ6dm+Jzd2u5G+Y/uyO3N3yNu/8EKYOxdmzw550yIiEiEiNWFIA353zm0KKJsO7AHaBtSpnMfjkUnATufcnoB6VfM4R5K/r1wY1GsQdRLrcNuE20LedoUKcMstmi5aRKQsi9SEYT5eD0MwA3L89wuAaKB5UJ3gMQsLCBqrYGYNgISgemVaVFQUr1z5Cp/P/5zJsyeHvP3rroMPPoCVK0PetIiIRIBITRg+AA4zs5oBZScAsUBux/d3wFagX24FM0vAm49hSsBxU4AeZlYloKw/3pwO00IfeuRKjE/kiX5PMOTdIeTk5BR8QCFUq+Yte/3MMyFtVkREIkSkJgzjgE3AZDPrbWYXAf8BpjrnpgM453YBjwD3mdlNZnYKMBHvO40OaGsssBuYZGanmtm1eHMw/DO/ORjKqt5H9CYuJo6JP08Medu33govvwxby91VFREp+yIyYfB/yE/GG2PwBvAs8DlwflDVR4CHgEF4vRKJwGnOuXUBbaUBp+DdvpiMN8Pjk8Cw4v0WkcnMePCcBxn2/jCysrNC2nbjxtC9O7z0UkibFRGRCBCRMz1GitI802N+nHN0e6IbVx57JZd3vTykbc+cCeeeC4sXQ1xcSJsWEZESUNpmepRiZGY8ePaDDH9/OHuyQrsQRGoqHHUUPPhgSJsVEZEwU8JQTh3f8nha1WnFv6b/K+RtP/88vPAC/PxzyJsWEZEwUcJQjj1w9gM8+OGDZOwJXgT00NStC//8JwwYALuLME/UtGkwdmxIQxIRkUOkhKEcO7rJ0Rzd+GjGTgv9r/PFF0PTpvDAA4U7bs4c6NcP7rsPliwJeVgiIlJEShjKuZFnj+TRjx9l+67tIW3XzOslePFFbyDkwVi7Fs480+uduP12L2kQEZHIoIShnDus/mGc3Ppknvk89DMuFebWxI4d0Ls3XHklXHIJ3HEHTJ8OP/wQ8rBERKQIlDAIw3sP58mpT5K+Mz3kbV90ETRvDiNHHrhOdrZ3C6NtWxg61CurVMk75q67vOWzRUQkvJQwCC3rtKT3Eb0Z9WnoV4/KvTXx0ksHvjVx992wZYt3+8ICVhAZMADS0+G990IeloiIFJISBgHg/jPv57mvnmPDtg0hb7tOHXjyybxvTTz7LEyZApMmeateBoqOhsceg3vvhczMkIclIiKFoJke81FWZ3o8kJteu4n4CvE80e+JkLftnDcDZJs2UOvkp/hzw5/EbenMK493ZsanTWjWLK/FSb3jTjvNO/bGG0MeloiIBDnQTI9KGPJR3hKG1emraT+sPXNGzCGlWkrI21+3Dpqf+yjJx77KWa0H8PxbP5DYYgYWnUnnpp3p1KQTnZt25ujGR5MYn7j3uFmzoFcvWLQIEhPzOYGIiBwyTQ0tBUqplsL1J15P54c7c8v/bmHqvKkhnTr63QUvkHDUC0R98ilvD7ub1658iw1Pr+KXIb9wWZfLSNuZxrD3h1H3rrrc8N8b9h535JHeolaPPRayUEREpJDUw5CP8tbDAN7CVL+v+p3JsyczefZkFqxdQI92Peh9RG96te9Fjco1itTuGz++wZ0T72TaXV/z8H3NOOwwuO22vOtu27WNVkNaMfnmyXRs1BGA5cu9xOG336BevSJ+ORERKZBuSRRBeUwYgq3dspYPf/uQyb9N5suFX3JE/SM4P/V8rjn+GuJiD245yg9/+5Arx1/J1Dumclj9ww7qmOe/ep53Zr3Dp7d/urds4EBYvx7+FfrlL0RExKeEoQiUMOwvY08GXyz4grHTxjJ39Vwe6/sY5x11HmZ5D1gE+HrR1/Qd25f3b3qfzs06H/S5MrMyaXN/G1649AVOaXMK4D162bIlfPYZHH74IX8dERHJg8YwyCGLrxDPGYefweSbJ/PiZS/y4AcPcvxjx/Pj0h/zrP/zXz/Td2xf/nfN/wqVLADExsTy4DkPMmjSIHKT2qpVYfBguOeeQ/4qIiJSSEoYpEhOaXMKPw/9mauOu4o+z/Xh4hcvZvmm5Xv3L1izgDNHn8kLl+zrISis81PPJysni0m/TNpbdv313qJUn312yF9BREQKQQmDFFl0VDRXHHsFCx9YSPNazTnygSMZ/M5g5qyaQ/enuvPIuY/Q56g+RW4/KiqKh/s8zOB3B5OVnQV4kzs9/LA3ZXRWVqi+iYiIFEQJgxyyyhUrM+LsEcy+fzYr01Zy+IjDufO0O7m86+WH3Hb3dt1JqZrCv7/9996y886DmjXhmdCvlyUiIgdQKgY9mlk9YCFQCajinNvulxswCLgBqAn8BNzinPs16Pi2wGigC5AOvASMcM5l53deDXosmo3bNlKzSs2Qtffj0h8597lzWfzQYuIrxAOweDF06eKtT9G4cchOJSJS7pX2QY+PA9vzKB8IDAUeBXr7daaaWZ3cCmaWBEwFHHA2MBK4ExhRzDGXW6FMFgCOaXIMnZp2YvQXo/eWtWjhLYF9441azVJEpCREfMJgZicAPYEngsor4iUMDzvnxjjnpgL98BKDfwRUvR6IB851zn3mnBuLlyzcYWaaaLiUeOich3j8k8dJ25G2t+yuu2DFCnjzzTAGJiJSTkR0wmBm0Xi3EkYCG4N2dwUSgb0/F865HcBkoFdAvV7AJ865rQFlb+AlEScWQ9hSDFrXbc05R57DY5/smx+6QgUYNw5uvx3S0vI5WEREDllEJwx4vQNxwLN57GsNZAOLg8rn+/sC6y0IrOCcWw7sDKonEW7YmcMY9/U4Vqev3lvWpYu3kqXmZhARKV4RmzCYWQ3gAeAO51xmHlWSgO15DFxMAxLMrEJAvfQ8jk/z90kpUb96fa467ipGTh65X/n//R98/DFMmxamwEREyoGITRiAh4DvnXMfleRJzexaM5tpZjM3bNhQkqeWgzCw10De/uVtFq1dtLcsMRFGj4brroNdu8IYnIhIGRaRCYOZtQOuBEaaWTUzqwYk+Lurmlk8Xg9BZX+cQ6AkYKdzLndd5jSgah6nSfL37cc5N845l+qcS01OTg7Bt5FQql6pOnecdgd3vHkHi9ct3jtt9DnnQNu23qROIiISejHhDuAAWgCxwIw89q0EXgZeB6KB5nhzNOQKHrOwgKCxCmbWAC8B2W9sg5QOt55yK/PXzOeUf57Czj076dykM52bdqb/7V248fyj6d8/kbZtwx2liEjZEqkJw3TgpKCynsC9wOnAn8BfwFa8RykfBDCzBLz5GMYFHDcFuNvMqjjntvll/YEMQHe9S6GEuARevepVAFalreKHpT8wY8kMxswdzvazZnH0w0246NQu3HbqrbSr1y7M0YqIlA2lYqZHADMbAPyb/Wd6HIQ3cdPdeL0FdwCdgHbOuXV+nSRgHjAHb4KnpsA/gaecc0PyO6dmeix9dmdmcnSP2TQ7YSrTt4zi2hOuZcgZQ/bOECkiIvkr7TM9HsgjeIMjBwEf4M3LcFpusgDgnEsDTsG7fTEZb9KmJ4FhJR6tFLu42FhefyaV6c8O5O5ms/n5j8UcNvwwps6bGu7QRERKtVLTwxAO6mEovT78ECZMgG++gc2VPiTrmJtoU/14/u/sUZzcpRYxkXozTkQkzMpqD4NIns44A159FZYuhTlTzmD0SXPJ2lqH019uT+WOL3Na9xy+/jrcUYqIlB7qYciHehjKnl+X/8pV469jy+Y4Mn8YxpwvulAlPqHgA0VEyokD9TCoY1bKlQ4NO/DjkO8YO+0FBm4YQvJtv3Fk48M5vsXxHN/ieI5tfizVK1UPd5giIhFHPQz5UA9D2fbRR3D3oJ0888YPfLdkOt8s/obv//yehtUbcnyL4zmlzSmcefiZVIytGJb4VqWt4u637iYrO4uKsRWJi42jYsz+r5UqVOLSLpeSXEWTjIlIaByoh0EJQz6UMJRtzsHRR8PgwdCnj1eWlZ3Fryt+5ZvF3/Dh7x/y64pf6Z/anyuOvYKOjTpiZiUS25adWzj+sePp0a4HqY1T2ZW5i91Zu9mduZtdWbvYnbmb3Vm7WbF5BZ8v+Jz/XvVfTmylxVdF5NApYSgCJQxl33vvwfDh8MsvkFcu8Nemv3h1xquM/248CRUSGNB1AJd0voTaibWLLaY9WXvo9XQv2tRtw+gLRxeYpHw852OuGH8FN5x4A4PPGEx0VPBs6SIiB08JQxEoYSj7nIMjj4QHHoDevQ9cLycnh28Wf8P478bz7q/vckKLE7jyuCvpfXhvoqJC97BRTk4Ol758KRmZGUy8fuJB//ivTl/NJS9dAsBrV79G3Wp1QxaTiJQveqxSJA9mMHSolzDklztHRUVxYqsT+fcV/2b5o8s558hzePCDB0l9KJXP5n0WsngGTRrEsk3LeO3q1wrVU5BSLYXP7viMbq260fHBjnwy55OQxSQiAuphyJd6GMqHnBw4/HB44gno2fPgj3PO8fYvbzNo0iCa1mzKo+c9SoeGHYocx5gvxjDmyzF8e++31Khco8jtfLXwKy556RIu7XIpI88aSWxMbJHbEpHyRz0MIgcQFQVDhsDIkfn3MgQzM/p27Mu8EfM4q8NZ9Hy6J5e+fCl/bfqr0DFM+mUSD095mCm3TDmkZAGgW6tuzLp/Fr+u+JVuT3Rj2cZlh9SeiAgoYRABoF8/2LwZvvii8MfGxsRy00k3sejBRTSp2YSjHjiKuyfeTdqOtIM6/ts/vuW6/1zH5H9Mpklyk8IHkIfkKsl8ePOH9DmyD6kPpfLIlEfYk7UnJG2LSPmkWxL50C2J8uU//4GXXoJph7jo+Zr0NQyfPJxJv0yiR7setEtpt3drUrPJfoMkF6xZwImPn8grV75Cz/aFuB9SCH9u+JOb/3czf274k+cufo6TWgevHC8iso+ekigCJQzlS1YWtG4NL78MJ4ZgSoM/1v/BN4u/Yd7qecxdPZe5q+eycftGWtVptTeBeGHaCwzrPYwBxw449BPmwznHe7++x61v3MrxLY7niX5PUKdqnWI9p4iUTkoYikAJQ/nzr3/B66/D1GJaDXvbrm17E4h5a+bRuk5rrj7+6uI5WR527N7BAx88wMvTX2ZY72Hc0O0GzdsgIvtRwlAEShjKn8xMaNHCSxq6dg13NMVn3up53PjajWzdtZWxl4zlmCbHhDskEYkQekpC5CDExsKgQd68DMXFOfjzTy8p+eqr4jtPftqmtOXLu77k9lNvp/fo3oz6dBT640FE8qOEQSTIgAEwZw78+GNo2tu2zXv64v/+D846C2rXhuOOg7fegosugn//OzTnKSwz49Iul/LDfT8w4acJ9HmuD+k708MTjIhEPC1vLRIkLg4GDoQLL4T27aFCBW+Ljd3/fWysN+lTZqY3YDJ427MHFi6EJUu86ac7dYJLL4Vnn4X69b1ZJhctgu7dIT0dbr89PN+3cc3GTL93OndNvIuOD3bkzevepGOjjuEJRkQilhIGkTxcfz20bAk7dngJwZ493hb8PjoaYmK8LTZ23/vcrVkzbxbJChXyPk/LlvDNN3Daad48ECNH5r0IVnGrEFOBZy58huNbHE/Pp3oy8uyRXH/i9SW2OqeIRD4NesyHBj1KSdmwwZuWuksXeOYZb/bJcFm0dhH9XujnPfZ56QtUqVglfMGISIkrVYMezayfmb1vZqvMbLuZ/WxmF+ZR7xozW2xmu/w6p+RRp56ZvWNm28xso5mNMbOEkvkmIgcnOdkb5/D773DZZV7vRbi0rNOS7wd9T6W4Shz90NHMWTUnfMGISMSIyIQBuAPYDtwOnAV8CbxuZjfnVvATiLHAq0AvYC7wgZm1D6gTC3wCNAIuAG4F+gHjSuZriBy8qlXh44+98QznngsZGfnXz8mB1asLt/7FwYqvEM+Ll73IoF6DOOmJk/jfD/8L/UlEpFSJyFsSZlbTObcxqOx1oItzron/eSHwrXPuSv9zFDAbmO2cu8QvuxD4L9DcObfULzsfeANo5ZxbnF8cuiUh4ZCZCZdf7iUD778PVarAypXekxtz53rbnDkwf743TqJtW2+lzeKaN2L2itmc+/y59DmyD4+c+wgx0Rr6JFKWlapbEsHJgm8WkAJgZk2BlsCbAcfkABPxehty9QJ+yk0WfO8Ce4Dimbhf5BDFxsJ//+slAq1aQbVqcMwxMGqUlzh07QpPP+2937zZG6DZvz/07Qt//BH6eI5ocAQ/Df6J31f+To+nerBxW17/e4pIWReRCcMBdAEW+e9b+68LgurMB6qbWXJAvf3qOOf2AEsC2hCJOFFR3uOXH38MS5fCmjXedNVPPQXXXOMlDdWqefUuu8x7PLNjR+jcGW69FTaG+De9eqXqfHTrRxzT5BhSH0rll79+Ce0JRCTilYqEwR/MeA4wyi9K8l/Tg6qmBe1PyqNObr2kPMoxs2vNbKaZzdywYUMRIxY5dGZwxBFQvXrBdePjvRkq582D7Gxo0wYeewx27QpdPNFR0Tx87sM83vdxejzVg//M+E/oGheRiBfxCYOZNQZeB95zzo0v7vM558Y551Kdc6nJyckFHyASQWrVgjFjYPp0+O47b/XNESNg0iRYvNhLJg5Vv9R+fHnXl4z8YCS3vXEbmVlhfKRDREpMRCcMZlYdmAL8BVwcsCu3J6Fq0CFJQfvT8qiTWy8tj3KRMqFVK3j3XW8sxO7d3vTT3btDYqJ36+Lyy+Hxx2HKFG8OiMJqX689P973I4vWLeK0J09jxeYVB6z7ww/w0UfF8zSHiJSciE0Y/LkSPgAqAGc653YG7M4dlxA8DqE1sNk5tyGg3n51zKwC0JS/j38QKXOOO85bw2LyZG8sxNq18NxzcMIJ3lMYo0Z5ycW993qPcxZGUqUkJt88mVPanEKHkR24e+LdbN6xee/+X3+F3r2hXz9vqu3jjvN6PUSkdIrIhMHMYvCeeGgB9HTOrQ/c75z7E28AZL+AY6L8z1MCqk4BjjazRgFlZwFxwMfFE71I5KpSxVvT4qqr4MknvYGUv//uPW3RsqVXtnv3wbcXHRXN0DOH8vvw39m6ayuthrTizlcf4bz+O+nZ05vyetEimDULrrsOLrjAm2Ni4cLi+44iUjwiMmEAngNOBx4AaphZ54Atzq8zHLjCzIaY2UnAv/ASjEcC2nkLrydhkpmd7s/LMAZ4vaA5GETKi3r14MUX4csvvdkmW7eG//3PmxjqYKVUS2HQ8S9wfPp0xrzxM1OTWnLfq+O48aYsKlb01ty47DIvUejc2ettuPFGWLeu+L6XiIRWpCYM3f3Xp4EZQVtdAOfc/4DrgQF4vQWH49262DuPrXMuE2++hRV4czaMAd4Gri2JLyFSmrRr5926+Pe/vZ6GY47xEogDyc6GTZu8JzP+8Q9vbETbeq1Y9/pEpt77Du/9PoF2w9rx1s9vkTtBXHw83HMPLFgAFSt6c02MHAnbt5fQlxSRIovImR4jhWZ6lPLKOZg40XtUs0EDSEqCtDRvnENamrft2OFNZ12tGpxzjjdOoVatwDYcn837jIGTBrJu6zrapbSjTd02tKnThtZ1W9Ombht2bKzFkCHGp5/C6afDGWdAjx5emyISHgea6VEJQz6UMEh5t2ePNz11VJT3I56U5G3VqnlPXBzMqprOOZZuXMqCtQuYv2b+3tf5a+bjnKN13dY0TmxP7ObjWfHDScycVp+jjoIzz/QSiNatw7Pkt0h5pYShCJQwiBQf5xwbt29k/pr5zF4xm2mLpjFt0TQSK1alWUI3sleexPwvuhGfU48zz4ShQ6FmzXBHLVL2KWEoAiUMIiUrJyeHeWvm8eWCL/lq0VdMWziNSjFJVN7ejbTfevHJv07jsNZVwh2mSJmmhKEIlDCIhFdOTg5zV8/liwVfMPajj1iY9h3HNO7CRcedyRmHnUGzWs3CHaJImaOEoQiUMIhEltcnbuOGB6fS5fwPmJ32EdXiq3Hm4Wdy5uFncmzzY7X0tkgIlKrlrUVE8nJRvyp8+GwfZj3zMo8cvor/XPUfKsVV4o4376DhvQ0Z8s4Qlm5YWnBDIlJo6mHIh3oYRCLTvHneY5g33ODN62AGc1fN5cVvXuS/P/yXoxoexbUnXMtZR5xFhZgK+ba1Y/cOZq+Yzfw188l22URbNNFR0cREx+x9n/v5sHqH0TS5aQl9S5Hw0C2JIlDCIBK5Vq3ykoYTToCnnvJmkwTYlbmLSb9MYtzX45i/Zj6Xd72cq4+7mpZ1WrJ5x2ZmLZ/FL8t/YdbyWcxaPou/Nv9F27ptaZ/SntiYWDKzstm+I5tt27PZvjOLHTuy2ZGRzc5de9gaN5MaiZU5/YjudG/bnZNanUTVhLzWtxMpvZQwFIESBpHItmUL9OnjzQ3xyCOwaxfs3OltGRnwx4ZFfLLsJb5Z+wq4KPbk7CA5ugPV3ZEkZh5Jws4jid7WlowdsWzb5i3ItXGjNwFV/fr7b7Vrw9dfO96c+ju1jvyUuGaf8lfGDI5ocATd23oJRMdGHYmNiS3Sd1m7Fj780DtXp06avErCRwlDEShhEIl8u3d7tyamTfOmnk5I2Lflfo6L30NO/ErqVm5M5cpRVKrEfltCgrcwV0oK1KkDMfmMndy1y5tC+9VX4etvM0jt/Q1J7T9l0Y5Pmb92PjUr1ySlagop1fwt4H29avVoXqs5lStWBryEZ9IkeP11mDnTW4J83Tr4+Wdo2BC6dPG2zp2hTZuDmyhL5FApYSgCJQwikp/16+GNN+A///FukXTvmUXtxuuoWmc1cdXXYAmrSc9czep0b1uRtoIl65cQF1WVmO0t2LKiBS1qt6Bnl+ac36sFbes3JTM7kw1b0/hx9mZ+mJXGrwvSWPhnGtsy06jbKI3ESglUzGpI3J5GxO5pSHRGA7L3VCQz05uZs1Ilb12PY47xtgYNNFOmFI4ShiJQwiAiB2vePJg+HZYs2X+LiYFmzbwtNhY++DCHNh1X07XnYuq1Xczq7YtZvH4xf6z/gz83/klcTBxJCUkkJSRRvVJ1732lJCqQxNYNSaRv20Fa1nI271nOxl1/sWnXSqpUSKJ2pYbUrdKQ5LgWxGzqwsZ5XZn9Q02c8xKHo4/2Xjt08Bb+yk9UlBd3TIw3NiQqKrRJx649e8ByMAwzI8qi9nuV8FLCUARKGETkUDjnjYnITR62bYPevb0lxUMlJyeHdVvX8demv1i+eTnz1sxjxpIZfL/0e+ok1uGIOsdSI6sre1Z25c9fWvP7b1FkZQXEGJWBi9uAi9tATsX1uAqbIKMWbnNbsrbVIzvLcG7/BCI2FuLi/r5FxW8mq8o89sT9wY6c9WS4DexiI3uiNpAZs4HsWO88RO/CiCYqKgeLcjgcOS5n76qmZkaj6o3o0qwLXZt1pUuzLhxe7/Aijw+RwlHCUARKGESktMrOyWbu6rl8+8e3fLfkO75b8h1pO9Lo0KADO/bsYMO2DWzYtoHM7EySqySTXCWZWlVqUaNSDdZsWcO8NfPIyMygbd22tKnblta129KqVjtaJLfF5cQwd6W3gNjCdfNYvHE+f26ex66sDBpWaUPdyi1IrlTbazMxmTrVkklJSqZejWTq10ymSlwiX35pvPsuvPeeN8i0Tx9v1dMjjnA4cli0bhEzlsxgxp8zmLFkBss2LaNjo450adqFTk26cET9I2lQva6SiGKghKEIlDCISFmyJn0Nv6/6ncSKif6PeS0qx1U+4G2ATds3MW/1POat8ba5q+cyd/VcsnOy9y5V3jalLW3qtqFt3bakVEsp9C2F7Gz4/nt491145x3v8znneANQN26ETZu817Wbt7A68wc2Rc9gZ5UZkPQ7xK8nNieJSlEpVK+YQt3EFBrWTKFFvbq0a5JC41p1qZNYh9qJtYmLjTv0C1hOKGEoAiUMIiIlxzmYO9dLHtLTvdVJa9TY95r7vnp1r/7yFdnMXrSBOX+uZtHK1Sxbv4Y16avZmLGa7TmrsUpriaq0hqzY9VSwKiTG1KFmpbrUqVqHhjXr0Kh2Mg2Sa1Kzcg1qVq5Jjco1qFGpBtUrVS/X04wrYSgCJQwiIqWTc17vxMqVsHxFDov/2syilWtYtn4tqzavZf32NWzds5HM6I1USNxETKWNuLiNZMVsYg/pVIyuQtW4GiRVSiI5MYna1ZKoXskbgJo7KDWpUhLVE6p7r/4A1SoVqxS6l8U5x67MXezcs5OMPRns3LPTe5+ZQXZONvEV4kmokEBChQTiY7338RXiiY6KLpZrd6CEofymUCIiUmaZeb0RNWtChw5RQE1/O2y/ert2eXNfrF3rbevWweo12Sxbk8aKFZtZtSGN2WlpbM9Mo2qtNKrUSCO+2npiKy0kJ9Yr35G5mYycNHaxmRx2E51djajM6rCnGlHROUTHZBIVk4lFe5uLysRZJo5MMt0u9mTvokJ0hb2JQGBiEBUVRcaeDDIyM/ZLKDIyM4iNjqVOYl3+enRZiVxTJQwiIlJuVawIjRp52z7R7EswPLt2eXNtrFwJK1Z4r5s3Q5Ua3qRfiYneFl95DxaXRnZMGllR6WTsjGLHtli2b41l2xZv25Iey5a0WNI3xbJ6RUX+WBhPUrUoWrZkv61FC6/NFStg+fKAbRUsX+FYvnI3GbG74NGSuVblImEws7bAaKALkA68BIxwzmWHMy4RESkdKlbcN59G/ioAtf3t4OTkeMnIokX7ti+/9F63bfNm/czdmjaFbt2gYUOjYcOKJCcXMKlGCJX5hMHMkoCpwDzgbKAZMApvae8hYQxNRESEqChvRs4GDeCUU8IdzYGV+YQBuB6IB851zm0FPjOzRGC4mT3ml4mIiEg+ysNSJr2AT4ISgzfwkogTwxOSiIhI6VIeEobWwILAAufccmCnv09EREQKUB4ShiS8gY7B0vx9IiIiUoDykDAUiplda2YzzWzmhg0bwh2OiIhIRCgPCUMaUDWP8iR/336cc+Occ6nOudTk5ORiD05ERKQ0KA8JwwKCxiqYWQMggaCxDSIiIpK38pAwTAF6mFmVgLL+QAYwLTwhiYiIlC7lIWEYC+wGJpnZqWZ2LTAc+KfmYBARETk4ZX7iJudcmpmdAowBJuM9MfEkXtIgIiIiB6HMJwwAzrl5wMnhjkNERKS0MudcuGOIWGa2AfirEIfUBDYWUzjlja5l6Ohaho6uZejoWoZGcVzHRs65vz0mqIQhhMxspnMuNdxxlAW6lqGjaxk6upaho2sZGiV5HcvDoEcRERE5REoYREREpEBKGEJrXLgDKEN0LUNH1zJ0dC1DR9cyNErsOmoMg4iIiBRIPQwiIiJSICUMIWBmbc3sczPbaWarzWykmUWHO65IZmbNzewFM/vNzLLN7Ks86piZ3WdmK8wsw8y+NrMOJR9tZDOzfmb2vpmtMrPtZvazmV2YR71rzGyxme3y65wSjngjlZn1NbPvzGyTf40WmtkQM6sQUEf/JovAzOr5/zadmVUOKNf1LICZDfCvW/B2fUCdErmOShgOkZklAVMBB5wNjATuBEaEM65SoB1wOrAQWHSAOgOBocCjQG9gOzDVzOqUSISlxx141+Z24CzgS+B1M7s5t4KfQIwFXgV6AXOBD8ysfcmHG7FqAF8AV+Ndo38Bg4F/BtTRv8mieRzvWgXT9Tx4JwNdArZJAftK5jo657QdwgYMwlsmOzGg7B5gZ2CZtr9dt6iA928BXwXtrwhsAe4PKKsEbAAeDHf8kbQBNfMoex1YGvB5IfCvwOsP/A78N9zxR/IGPIQ3nbzp32SRr+EJwGbgLrw/rCr75bqeB3f9BgRetzz2l9h1VA/DoesFfOL2X8jqDSAeODE8IUU+51xOAVW6AonAmwHH7MBbD6RXMYZW6jjn8prlbRaQAmBmTYGW7H8tc4CJ6FoWZBOQe0tC/yYLyb81Oxqv5zX436muZ2iU2HVUwnDoWgMLAgucc8vxehhahyWisqE1kA0sDiqfj67rwejCvls9uddrQVCd+UB1M/vbFLDlmZlFm1mCmR0H3AI877w/2/RvsvCuB+KAZ/PYp+tZOEvMLMsfW3NdQHmJXcdysfhUMUvC67IMlubvk6JJArY757KDytOABDOr4JzbE4a4Ip4/mPEc4Eq/KPffYXpQ1bSA/RuKPbDSYwfejxx4Yz7u9t/r32QhmFkN4AHgEudcppkFV9H1PDhr8MYn/AhEAxcAY80swTn3JCV4HZUwiJQhZtYYb/zCe8658eGNptTqCiQAxwD3A2OAG8MaUen0EPC9c+6jcAdSmjnnPgE+CSiaYmYVgSFm9nRJxqKE4dClAVXzKE9i319wUnhpQGUziw7KnJOAnfrL4+/MrDowBW+F1YsDduX+O6zK/r0MSUH7BXDO/eK/nW5mG4FXzGwU+jd50MysHV4P1wlmVs0vTvBfq5pZNrqeh+It4HygMSV4HTWG4dAtIOg+kZk1wPufI/iesRy8BXjdb82Dyv82ZkTAzBKAD/AG6J3pnNsZsDv3egXfz2wNbHbO6XbEgeUmD03Qv8nCaAHEAjPwftDS2DeOYSXeQEhdz6JzAa8ldh2VMBy6KUAPM6sSUNYfyACmhSekMuE7YCvQL7fA/1HsjXfNxWdmMXhPPLQAejrn1gfud879iTcAMvBaRvmfdS3zd6z/uhT9myyM6cBJQduj/r7T8eZl0PUsur54T538RQleR92SOHRj8UZSTzKzR4GmwHDgn0GPWkoA/x/06f7HekCimfX1P3/knNtpZo8AQ80sDS9TvgMvyR1d4gFHtufwruWtQA1/sFmuWc653Xj/Jv9rZsuAb4HL8RKMi0o21MhlZh/jTcI2F2/U+bF4k7BNcM4t8evo3+RB8B/1/SqwzB9fA/CNc267X6brWQAzextvwONveD0J/f3tFv/x6F0ldh3DPSlFWdiAtngzxGXgjWh9AIgOd1yRvOHde3MH2Br7dQxvpr2V/rX9Bjgy3LFH2gYsK+ha+vWuAf4AduN1tZ8S7tgjafP/v52DN0teun+NbgZiA+ro32TRr+8AgiYg0vU8qOv2f3gTr+30r9HPwKVBdUrkOmq1ShERESmQxjCIiIhIgZQwiIiISIGUMIiIiEiBlDCIiIhIgZQwiIiISIGUMIiIiEiBlDCISJlhZt3MzJlZ+3DHIlLWKGEQERGRAilhEBERkQIpYRCRQ2Zmx5vZNDPbaWabzOzF3AXZzGyAf5vgaDP7xswyzGyRmfXJo51/mNliM9ttZn+Y2e151DnczCabWbqZbTezH83stKBqNc1sor//TzO7sZi+uki5oYRBRA6JmR2Lt2jTWrxV9G7DWwzr30FVJwDvAecCvwMTzeyIgHauwVss5328lfYmAqPMbGBAndZ4i2fVBa4H+gDvAA2CzvUiMNvf/xXwrJkdc8hfVqQc01oSInJIzOwbIMs5d1JA2cnA58BhQCpe8jDYOfd//v4oYB7wq3PuAv/zCuBT59wVAe08B1wM1HbO7TKz/wHHAy2ccxl5xNIN+BJ4wDl3v18WC6wGXnbODQw+RkQOjnoYRKTI/GXKuwBvmllM7gZMBzKBjgHV38l947xled8Dcv/qrw+k4PUqBJoAJOIlHgAn4y03/bdkIcinAefKBBb75xCRIlLCICKHIgmIBp7DSxByt91ALPvfKlgfdOx6vFsLBLyuC6qT+7m6/1oDbwn5gqQHfd4DVDyI40TkAGLCHYCIlGrpgAOGAx/lsX810N1/XwvYFLCvFvt+/NcElAWq7b9u9l83sS+5EJESpB4GESky59wO4HuglXNuZh7b6oDqe5+K8McsnA386BetxEsu+gWd4nxgK94gSfDGRZxvZuotEClh6mEQkUN1D/C5meUAbwHbgIbAGcDggHpXm9keYA5wNdAcuBC8MQ1mNhx4wcw2AZ8BJwI3APc553b5bYwAfgK+NrNReD0ORwKbnHP/KtZvKVLOqYdBRA6Jc246cAKQDPwHmIyXRKxg/zEJF+D1MrwLHAH0d87NCmjnReBWv84HeMnEnc65RwLqLASOAzYCL+ENpOwL/FU8305EcumxShEpVmY2AO+xyirOue1hDkdEikg9DCIiIlIgJQwiIiJSIN2SEBERkQKph0FEREQKpIRBRERECqSEQURERAqkhEFEREQKpIRBRERECqSEQURERAr0/90TnHpaDerZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the training loss\n",
    "\n",
    "model.plot_loss(start_epoch=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation loss is:\n",
      "        pe_loss:       1.5027e+00\n",
      "     force_loss:       3.3879e+01\n",
      "    stress_loss:       2.5975e+02\n",
      "     total_loss:       6.1357e+01\n",
      "The total loss is computed using the loss weights - pe: 1.00 - force: 1.00 - stress: 0.10\n"
     ]
    }
   ],
   "source": [
    "# Evaluate using the first 5 data in test dataset\n",
    "\n",
    "model.evaluate(test_dataset.take(5),return_prediction=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pe': array([-24.43516537, -27.25794917, -26.44558567, -29.71721262,\n",
       "        -26.18328476]),\n",
       " 'force': array([[[-14.98369464,  60.16402837, -11.24057251],\n",
       "         [ 27.58750673, -46.4019775 ,  11.68806955],\n",
       "         [ 50.03849536,  47.02174987, -46.65503074],\n",
       "         [-62.64230727, -60.78380053,  46.2075337 ]],\n",
       " \n",
       "        [[ 66.39126588,  -0.918724  ,  31.74912699],\n",
       "         [  2.26208116,  17.5541579 , -31.09016267],\n",
       "         [-42.02774142,  45.56440882, -12.25110425],\n",
       "         [-26.62560584, -62.19984267,  11.59213992]],\n",
       " \n",
       "        [[-70.52058765,  80.59492525, -15.88633072],\n",
       "         [ 95.63537577, -90.82699739,  12.29866236],\n",
       "         [ 40.98061171, -82.91953395,  15.95529539],\n",
       "         [-66.09539983,  93.15160611, -12.36762705]],\n",
       " \n",
       "        [[ 35.92992587, -28.45139903,  43.08669821],\n",
       "         [-42.52183304,  13.74105695, -30.8883209 ],\n",
       "         [ 28.8808097 ,  16.15299698,  12.92609446],\n",
       "         [-22.28890239,  -1.4426549 , -25.12447177]],\n",
       " \n",
       "        [[ 10.62635586, -23.71130926,   3.13159382],\n",
       "         [-24.0703802 ,  40.75223825,   3.90257854],\n",
       "         [ -6.31185408, -37.11107881,  26.76910415],\n",
       "         [ 19.75587847,  20.07014987, -33.80327653]]]),\n",
       " 'stress': array([[-4.74467792e+02,  4.50541291e+01,  7.48435275e-01,\n",
       "          4.50541293e+01, -4.45066829e+02, -1.39581531e+00,\n",
       "          7.48433858e-01, -1.39581685e+00,  8.08017409e-01],\n",
       "        [ 1.46181081e+03,  3.81848270e+01, -3.52670752e+00,\n",
       "          3.81848270e+01,  1.34280176e+03,  4.23490518e+00,\n",
       "         -3.52670589e+00,  4.23490481e+00, -1.47902890e+00],\n",
       "        [-1.00130692e+03, -1.73574190e+01,  3.24464109e+00,\n",
       "         -1.73574189e+01, -1.03593198e+03, -3.93371377e+00,\n",
       "          3.24464110e+00, -3.93371389e+00,  5.60459398e-01],\n",
       "        [-6.92777205e+02, -3.50395507e+01,  3.72795564e-01,\n",
       "         -3.50395507e+01, -6.08529653e+02, -4.44634887e-01,\n",
       "          3.72794506e-01, -4.44634859e-01,  6.23967448e-01],\n",
       "        [ 9.13840620e+01, -5.63079683e+01, -4.92513182e-01,\n",
       "         -5.63079682e+01,  4.23616309e+02,  9.50404198e-01,\n",
       "         -4.92513625e-01,  9.50403883e-01, -5.03840202e-02]])}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prediction using the first 5 data in test dataset\n",
    "\n",
    "input_dict = get_input_dict(test_dataset.take(5))\n",
    "model.predict(input_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save/load model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**save the trained model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: example.tfdnn/assets\n",
      "Network signatures and descriptor are written to example.tfdnn/parameters for LAMMPS simulation.\n"
     ]
    }
   ],
   "source": [
    "# we re-write the descriptor here to empasize that it should be the same one defined above\n",
    "descriptor = {'name': 'acsf', \n",
    "              'cutoff': 6.5,\n",
    "              'etaG2':[0.01,0.05,0.1,0.5,1,5,10], \n",
    "              'etaG4': [0.01], \n",
    "              'zeta': [0.08,0.2,1.0,5.0,10.0,50.0,100.0],\n",
    "              'lambda': [1.0, -1.0]}\n",
    "\n",
    "save_dir = 'example.tfdnn'\n",
    "network.save(model,save_dir,descriptor=descriptor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load the trained model for continuous training and prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network has been inflated! self.built: True\n",
      "Forces are used for training.\n",
      "Stresses are used for training.\n",
      "Scaling factors are computed using training dataset.\n",
      "Training dataset are standardized.\n",
      "Validation dataset are standardized.\n",
      "Training dataset will be shuffled during training.\n",
      "\n",
      "===> Epoch 1/5 - 0.209s/epoch\n",
      "     training_loss    - pe_loss: 3.176 - force_loss: 25.337 - stress_loss: 287.488 - total_loss: 57.262\n",
      "     validation_loss  - pe_loss: 4.905 - force_loss: 25.092 - stress_loss: 710.237 - total_loss: 101.021\n",
      "\n",
      "===> Epoch 2/5 - 0.182s/epoch\n",
      "     training_loss    - pe_loss: 4.932 - force_loss: 24.058 - stress_loss: 421.647 - total_loss: 71.155\n",
      "     validation_loss  - pe_loss: 5.630 - force_loss: 22.020 - stress_loss: 581.018 - total_loss: 85.751\n",
      "\n",
      "===> Epoch 3/5 - 0.214s/epoch\n",
      "     training_loss    - pe_loss: 5.663 - force_loss: 20.711 - stress_loss: 462.624 - total_loss: 72.636\n",
      "     validation_loss  - pe_loss: 7.609 - force_loss: 21.834 - stress_loss: 472.273 - total_loss: 76.671\n",
      "\n",
      "===> Epoch 4/5 - 0.204s/epoch\n",
      "     training_loss    - pe_loss: 7.579 - force_loss: 18.940 - stress_loss: 349.679 - total_loss: 61.486\n",
      "     validation_loss  - pe_loss: 9.569 - force_loss: 19.086 - stress_loss: 308.439 - total_loss: 59.499\n",
      "\n",
      "===> Epoch 5/5 - 0.196s/epoch\n",
      "     training_loss    - pe_loss: 8.719 - force_loss: 20.088 - stress_loss: 184.540 - total_loss: 47.261\n",
      "     validation_loss  - pe_loss: 9.957 - force_loss: 19.737 - stress_loss: 293.991 - total_loss: 59.094\n",
      "\n",
      "End of training, elapsed time:  00:00:01\n"
     ]
    }
   ],
   "source": [
    "imported_model = network.load(save_dir)\n",
    "\n",
    "# Re-train the model \n",
    "loss_weights = {'pe' : 1, 'force' : 1, 'stress': 0.1}\n",
    "\n",
    "opt = 'Adam'\n",
    "loss_fun = 'rmse'\n",
    "scaling = 'std'\n",
    "\n",
    "model.train(train_dataset, val_dataset, \\\n",
    "            optimizer=opt, \\\n",
    "            loss_fun = loss_fun, \\\n",
    "            batch_size=30, \\\n",
    "            lr=0.02, \\\n",
    "            epochs=5, \\\n",
    "            scaling=scaling, \\\n",
    "            loss_weights=loss_weights, \\\n",
    "            compute_all_loss=True, \\\n",
    "            shuffle=True, \\\n",
    "            append_loss=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation loss is:\n",
      "        pe_loss:       1.5027e+00\n",
      "     force_loss:       3.3879e+01\n",
      "    stress_loss:       2.5975e+02\n",
      "     total_loss:       6.1357e+01\n",
      "The total loss is computed using the loss weights - pe: 1.00 - force: 1.00 - stress: 0.10\n"
     ]
    }
   ],
   "source": [
    "imported_model.evaluate(test_dataset.take(5),return_prediction=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pe': array([-24.43516537, -27.25794917, -26.44558567, -29.71721262,\n",
       "        -26.18328476]),\n",
       " 'force': array([[[-14.98369464,  60.16402837, -11.24057251],\n",
       "         [ 27.58750673, -46.4019775 ,  11.68806955],\n",
       "         [ 50.03849536,  47.02174987, -46.65503074],\n",
       "         [-62.64230727, -60.78380053,  46.2075337 ]],\n",
       " \n",
       "        [[ 66.39126588,  -0.918724  ,  31.74912699],\n",
       "         [  2.26208116,  17.5541579 , -31.09016267],\n",
       "         [-42.02774142,  45.56440882, -12.25110425],\n",
       "         [-26.62560584, -62.19984267,  11.59213992]],\n",
       " \n",
       "        [[-70.52058765,  80.59492525, -15.88633072],\n",
       "         [ 95.63537577, -90.82699739,  12.29866236],\n",
       "         [ 40.98061171, -82.91953395,  15.95529539],\n",
       "         [-66.09539983,  93.15160611, -12.36762705]],\n",
       " \n",
       "        [[ 35.92992587, -28.45139903,  43.08669821],\n",
       "         [-42.52183304,  13.74105695, -30.8883209 ],\n",
       "         [ 28.8808097 ,  16.15299698,  12.92609446],\n",
       "         [-22.28890239,  -1.4426549 , -25.12447177]],\n",
       " \n",
       "        [[ 10.62635586, -23.71130926,   3.13159382],\n",
       "         [-24.0703802 ,  40.75223825,   3.90257854],\n",
       "         [ -6.31185408, -37.11107881,  26.76910415],\n",
       "         [ 19.75587847,  20.07014987, -33.80327653]]]),\n",
       " 'stress': array([[-4.74467792e+02,  4.50541291e+01,  7.48435275e-01,\n",
       "          4.50541293e+01, -4.45066829e+02, -1.39581531e+00,\n",
       "          7.48433858e-01, -1.39581685e+00,  8.08017409e-01],\n",
       "        [ 1.46181081e+03,  3.81848270e+01, -3.52670752e+00,\n",
       "          3.81848270e+01,  1.34280176e+03,  4.23490518e+00,\n",
       "         -3.52670589e+00,  4.23490481e+00, -1.47902890e+00],\n",
       "        [-1.00130692e+03, -1.73574190e+01,  3.24464109e+00,\n",
       "         -1.73574189e+01, -1.03593198e+03, -3.93371377e+00,\n",
       "          3.24464110e+00, -3.93371389e+00,  5.60459398e-01],\n",
       "        [-6.92777205e+02, -3.50395507e+01,  3.72795564e-01,\n",
       "         -3.50395507e+01, -6.08529653e+02, -4.44634887e-01,\n",
       "          3.72794506e-01, -4.44634859e-01,  6.23967448e-01],\n",
       "        [ 9.13840620e+01, -5.63079683e+01, -4.92513182e-01,\n",
       "         -5.63079682e+01,  4.23616309e+02,  9.50404198e-01,\n",
       "         -4.92513625e-01,  9.50403883e-01, -5.03840202e-02]])}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_dict = get_input_dict(test_dataset.take(5))\n",
    "imported_model.predict(input_dict)"
   ]
  }
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