01_federatedcustomerjourney.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default_exp federatedcustomerjourney\n",
    "%load_ext lab_black\n",
    "# nb_black if running in jupyter\n",
    "%load_ext autoreload\n",
    "# automatically reload python modules if there are changes in the\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hide\n",
    "from nbdev.showdoc import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FederatedCustomerJourney\r\n",
    "\r\n",
    "> Learning to predict customer journey with a federated setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***input***: data + tff conversion function from CustomClientData\n",
    "\n",
    "***output***: TFF model for predicting customer paths \n",
    "\n",
    "***description:***\n",
    "\n",
    "Simulating federated learning on predicting customer paths."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import relevant modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# uncomment this cell to get the newest version of tff\n",
    "\n",
    "# tensorflow_federated_nightly also bring in tf_nightly, which\n",
    "# can causes a duplicate tensorboard install, leading to errors.\n",
    "#!pip uninstall --yes tensorboard tb-nightly\n",
    "\n",
    "#!pip install --quiet --upgrade tensorflow-federated-nightly\n",
    "#!pip install --quiet --upgrade nest-asyncio\n",
    "#!pip install --quiet --upgrade tb-nightly  # or tensorboard, but not both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "2021-12-23 12:20:40.264164: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n"
    }
   ],
   "source": [
    "import collections\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()\n",
    "\n",
    "from pathlib import Path\n",
    "from pyarrow import feather\n",
    "import pandas as pd\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow_federated as tff\n",
    "\n",
    "\n",
    "from ml_federated_customer_journey.customclientdata import (\n",
    "    create_tff_client_data_from_df,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_test_client_split = tff.simulation.datasets.ClientData.train_test_client_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also view the results using tensorboard:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "2021-12-23 12:20:42.695475: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n2021-12-23 12:20:42.929745: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n2021-12-23 12:20:42.929813: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (ml-nuutti-1a-m-mem): /proc/driver/nvidia/version does not exist\n2021-12-23 12:20:42.930471: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2021-12-23 12:20:42.956578: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n2021-12-23 12:20:42.964904: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2095190000 Hz\n"
    },
    {
     "data": {
      "text/plain": "b'Hello, World!'"
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tff.federated_computation(lambda: \"Hello, World!\")()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define notebook parameters\r\n",
    "\r\n",
    "You can easily try different setups by running the notebook with papermill using different parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this cell is tagged with 'parameters'\n",
    "seed = 0\n",
    "data_filepath_parts = (\"data\", \"preprocessed_data\", \"data.f\")  # for pathlib\n",
    "test_split = 0.2\n",
    "\n",
    "NUM_EPOCHS = 20\n",
    "BATCH_SIZE = 32\n",
    "SHUFFLE_BUFFER = 100\n",
    "FEDERATED_UPDATES = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make immediate derivations from the parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(seed)\n",
    "tf.random.set_seed(seed)\n",
    "data_filepath = Path.cwd() / Path(*data_filepath_parts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>client_id</th>\n      <th>x</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>[0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, ...</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>[1, 1, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, ...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>[2, 1, 2, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>[0, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, ...</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>[1, 3, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, ...</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "   client_id                                                  x   y\n0          0  [0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, ...   3\n1          0  [1, 1, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, ...   1\n2          0  [2, 1, 2, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  15\n3          1  [0, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, ...   3\n4          1  [1, 3, 2, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, ...   1"
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = feather.read_feather(data_filepath)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(21488, 3)"
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "20"
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NUM_FEATURES = df.x[0].shape[0]\n",
    "NUM_FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUNIQUE_LABELS = df.y.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert into tff ClientData (training + testing datasets):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client_data = create_tff_client_data_from_df(df, sample_size=1)\n",
    "train_data, test_data = train_test_client_split(\n",
    "    client_data, int(df.client_id.nunique() * test_split)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test and train dataset size (number of clients in each set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "2141"
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data.client_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "998"
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_data.client_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "OrderedDict([('x', TensorSpec(shape=(None, 20), dtype=tf.int64, name=None)),\n             ('y', TensorSpec(shape=(None,), dtype=tf.int32, name=None))])"
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ELEMENT_SPEC = train_data.element_type_structure\n",
    "ELEMENT_SPEC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Federeted ML Process with TFF\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input layer\n",
    "def create_keras_model():\n",
    "    \"\"\"\n",
    "    Return new keras model instance\n",
    "    \"\"\"\n",
    "    visible = tf.keras.layers.Input(shape=(NUM_FEATURES,))\n",
    "\n",
    "    hidden1 = tf.keras.layers.Dense(\n",
    "        48,\n",
    "        activation=None,\n",
    "        name=\"l1relu\",\n",
    "    )(visible)\n",
    "    output = tf.keras.layers.Dense(\n",
    "        NUNIQUE_LABELS + 1,\n",
    "        activation=\"softmax\",\n",
    "        name=\"l3softmax\",\n",
    "    )(hidden1)\n",
    "    model = tf.keras.Model(inputs=visible, outputs=output)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_fn():\n",
    "    \"\"\"\n",
    "    Create tff model (keras model + data format + loss & metrics)\n",
    "    \"\"\"\n",
    "    # We _must_ create a new model here, and _not_ capture it from an external\n",
    "    # scope. TFF will call this within different graph contexts.\n",
    "    keras_model = create_keras_model()\n",
    "    return tff.learning.from_keras_model(\n",
    "        keras_model,\n",
    "        input_spec=collections.OrderedDict(\n",
    "            x=ELEMENT_SPEC[\"x\"],\n",
    "            y=ELEMENT_SPEC[\"y\"],\n",
    "        ),\n",
    "        loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "        metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create federated averaging process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iterative_process = tff.learning.build_federated_averaging_process(\n",
    "    model_fn,\n",
    "    client_optimizer_fn=lambda: tf.keras.optimizers.RMSprop(learning_rate=0.02),\n",
    "    server_optimizer_fn=lambda: tf.keras.optimizers.RMSprop(learning_rate=1.0),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize federated averaging process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "WARNING:tensorflow:From /anaconda/envs/customerjourney/lib/python3.8/site-packages/tensorflow_federated/python/core/impl/compiler/tensorflow_computation_transformations.py:59: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse `tf.compat.v1.graph_util.extract_sub_graph`\n"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "WARNING:tensorflow:From /anaconda/envs/customerjourney/lib/python3.8/site-packages/tensorflow_federated/python/core/impl/compiler/tensorflow_computation_transformations.py:59: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse `tf.compat.v1.graph_util.extract_sub_graph`\n"
    }
   ],
   "source": [
    "state = iterative_process.initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create federated evaluation process (validation):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluation = tff.learning.build_federated_evaluation(model_fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function for loading client data in batches:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def batch_client_data(client_data, batch_size=BATCH_SIZE):\n",
    "    batch = [\n",
    "        client_data.create_tf_dataset_for_client(client_data.client_ids[idx])\n",
    "        for idx in np.random.choice(\n",
    "            np.arange(len(client_data.client_ids)), size=BATCH_SIZE\n",
    "        )\n",
    "    ]\n",
    "    return batch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Federated training & evaluation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "2021-12-23 12:21:27.136377: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:27.656336: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:27.748344: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:33.631581: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:35.353653: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:35.392388: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:36.808093: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:36.908555: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:36.981553: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:38.516368: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:41.828356: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:43.524509: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:43.530324: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:45.048431: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:45.100342: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:48.485201: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:48.509787: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:51.612459: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:51.612554: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:54.912343: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:54.972350: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:21:57.984340: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:01.244655: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:01.255567: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:01.265025: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:01.276832: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:02.636345: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:02.729645: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:06.080092: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:06.196943: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:07.739155: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:07.764423: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:07.775073: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:09.325119: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:09.340700: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:10.832119: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:10.837680: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:12.590122: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:14.121172: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:14.140444: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:14.146665: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:17.100732: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:18.959558: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:18.995023: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:22.011549: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:22.020355: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:22.025446: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:22.047956: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:23.838793: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:23.841719: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:25.348562: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:25.391140: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:25.399491: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:25.399578: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:26.892338: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:26.903450: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:28.312364: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:28.376336: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:28.378961: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:28.413330: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:28.418083: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:28.438301: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:30.176286: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:30.210613: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:31.728869: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:31.791241: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:34.796362: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:34.870061: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:36.608354: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:36.609238: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:37.998275: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:38.128351: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:38.169969: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:39.537369: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:39.617985: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:42.934535: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:44.518924: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:44.536359: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:49.264395: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:50.889295: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:53.864860: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:55.628354: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:57.070208: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:58.596341: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:58.638823: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:22:58.639441: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:00.108343: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:00.110461: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:00.169729: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:01.936760: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:04.959681: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:04.966119: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:04.981693: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:06.482261: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:06.553552: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:08.320345: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:08.339202: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:11.368930: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:11.425565: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:11.428588: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:11.430548: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:12.919752: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:14.824729: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:16.393254: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:17.890446: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:17.902777: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:19.475934: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:25.901253: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:27.525006: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:32.094540: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:32.112477: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:32.144562: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:33.941747: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:35.410229: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:35.424451: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:35.479841: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:36.872498: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:36.971176: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:38.447056: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:38.525556: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:40.268701: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:41.746859: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:41.751637: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:44.912351: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:46.676215: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:49.799873: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:53.104417: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:53.140458: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:56.161489: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:57.488345: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:23:59.472940: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:24:01.010235: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:24:01.032257: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:24:02.489017: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:24:04.062348: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n2021-12-23 12:24:07.927882: W tensorflow/core/kernels/data/model_dataset_op.cc:205] Optimization loop failed: Cancelled: Operation was cancelled\n"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "federated_update  0, loss=2.30, accuracy=0.33\nfederated_update  1, loss=14.91, accuracy=0.07\nfederated_update  2, loss=12.05, accuracy=0.25\nfederated_update  3, loss=11.01, accuracy=0.32\nfederated_update  4, loss=14.65, accuracy=0.09\nfederated_update  5, loss=9.02, accuracy=0.44\nfederated_update  6, loss=12.66, accuracy=0.21\nfederated_update  7, loss=9.11, accuracy=0.43\nfederated_update  8, loss=11.26, accuracy=0.30\nfederated_update  9, loss=11.19, accuracy=0.30\nfederated_update  10, loss=10.50, accuracy=0.34\nfederated_update  11, loss=11.39, accuracy=0.29\nfederated_update  12, loss=8.87, accuracy=0.44\nfederated_update  13, loss=9.65, accuracy=0.39\nfederated_update  14, loss=9.11, accuracy=0.43\nfederated_update  15, loss=8.27, accuracy=0.48\nfederated_update  16, loss=13.42, accuracy=0.16\nfederated_update  17, loss=6.81, accuracy=0.57\nfederated_update  18, loss=10.34, accuracy=0.35\nfederated_update  19, loss=9.33, accuracy=0.42\nfederated_update  20, loss=9.08, accuracy=0.43\nfederated_update  21, loss=8.00, accuracy=0.50\nfederated_update  22, loss=11.33, accuracy=0.29\nfederated_update  23, loss=10.83, accuracy=0.32\nfederated_update  24, loss=11.91, accuracy=0.26\nfederated_update  25, loss=9.72, accuracy=0.39\nfederated_update  26, loss=11.46, accuracy=0.29\nfederated_update  27, loss=7.77, accuracy=0.51\nfederated_update  28, loss=10.68, accuracy=0.33\nfederated_update  29, loss=8.59, accuracy=0.46\nfederated_update  30, loss=10.04, accuracy=0.37\nfederated_update  31, loss=9.78, accuracy=0.38\nfederated_update  32, loss=9.94, accuracy=0.38\nfederated_update  33, loss=12.65, accuracy=0.21\nfederated_update  34, loss=7.63, accuracy=0.52\nfederated_update  35, loss=11.50, accuracy=0.28\nfederated_update  36, loss=8.16, accuracy=0.49\nfederated_update  37, loss=10.84, accuracy=0.32\nfederated_update  38, loss=7.17, accuracy=0.54\nfederated_update  39, loss=12.02, accuracy=0.24\nfederated_update  40, loss=9.21, accuracy=0.43\nfederated_update  41, loss=10.20, accuracy=0.36\nfederated_update  42, loss=9.85, accuracy=0.39\nfederated_update  43, loss=10.54, accuracy=0.34\nfederated_update  44, loss=9.02, accuracy=0.42\nfederated_update  45, loss=10.84, accuracy=0.32\nfederated_update  46, loss=9.34, accuracy=0.42\nfederated_update  47, loss=12.42, accuracy=0.22\nfederated_update  48, loss=9.63, accuracy=0.40\nfederated_update  49, loss=10.28, accuracy=0.35\nfederated_update  50, loss=10.26, accuracy=0.36\nCPU times: user 6min 14s, sys: 14.5 s, total: 6min 28s\nWall time: 3min 1s\n"
    }
   ],
   "source": [
    "%%time\n",
    "# first iteration\n",
    "state, train_metrics = iterative_process.next(state, batch_client_data(train_data))\n",
    "test_metrics = evaluation(state.model, batch_client_data(test_data))\n",
    "\n",
    "# see progress\n",
    "print(\"federated_update  {}, loss={:.2f}, accuracy={:.2f}\".format(0, train_metrics['train']['loss'], train_metrics['train']['sparse_categorical_accuracy']))\n",
    "\n",
    "# save results\n",
    "metrics_df = pd.DataFrame(\n",
    "    {\n",
    "        \"federated_update\": [0],\n",
    "        \"train_loss\": [train_metrics[\"train\"][\"loss\"]],\n",
    "        \"train_accuracy\": [train_metrics[\"train\"][\"sparse_categorical_accuracy\"]],\n",
    "        \"train_size\": [train_metrics[\"stat\"][\"num_examples\"]],\n",
    "        \"test_loss\": [test_metrics[\"loss\"]],\n",
    "        \"test_accuracy\": [test_metrics[\"sparse_categorical_accuracy\"]],\n",
    "    }\n",
    ")  # , 'test_loss': float, 'test_size':int})\n",
    "\n",
    "# run federated update cycles\n",
    "for i in range(FEDERATED_UPDATES):\n",
    "    # update, get train metrics\n",
    "    state, train_metrics = iterative_process.next(state, batch_client_data(train_data))\n",
    "    # evaluate\n",
    "    test_metrics = evaluation(state.model, batch_client_data(test_data))\n",
    "    # save results\n",
    "    metrics_df = pd.concat(\n",
    "        (\n",
    "            metrics_df,\n",
    "            pd.DataFrame(\n",
    "                {\n",
    "                    \"federated_update\": [i + 1],\n",
    "                    \"train_loss\": [train_metrics[\"train\"][\"loss\"]],\n",
    "                    \"train_accuracy\": [\n",
    "                        train_metrics[\"train\"][\"sparse_categorical_accuracy\"]\n",
    "                    ],\n",
    "                    \"train_size\": [train_metrics[\"stat\"][\"num_examples\"]],\n",
    "                    \"test_loss\": [test_metrics[\"loss\"]],\n",
    "                    \"test_accuracy\": [test_metrics[\"sparse_categorical_accuracy\"]],\n",
    "                }\n",
    "            ),\n",
    "        ),\n",
    "        axis=0,\n",
    "    )\n",
    "    # see progress\n",
    "    print(\"federated_update  {}, loss={:.2f}, accuracy={:.2f}\".format(i + 1, train_metrics['train']['loss'], train_metrics['train']['sparse_categorical_accuracy']))\n",
    "metrics_df.set_index(\"federated_update\", drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create baseline (centralized model)\r\n",
    "\r\n",
    "Compare federated learning to running same in a centralized setup.\r\n",
    "The learning speed is not directly comparable, because with centralized model we only do epochs,\r\n",
    "whereas with federated learning there are decentralized epochs and federated updates alternating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "WARNING:tensorflow:AutoGraph could not transform <function <lambda> at 0x7ff4d59c4f70> and will run it as-is.\nCause: could not parse the source code of <function <lambda> at 0x7ff4d59c4f70>: no matching AST found\nTo silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\nWARNING: AutoGraph could not transform <function <lambda> at 0x7ff4d59c4f70> and will run it as-is.\nCause: could not parse the source code of <function <lambda> at 0x7ff4d59c4f70>: no matching AST found\nTo silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\nWARNING:tensorflow:AutoGraph could not transform <function <lambda> at 0x7ff4d4001670> and will run it as-is.\nCause: could not parse the source code of <function <lambda> at 0x7ff4d4001670>: no matching AST found\nTo silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\nWARNING: AutoGraph could not transform <function <lambda> at 0x7ff4d4001670> and will run it as-is.\nCause: could not parse the source code of <function <lambda> at 0x7ff4d4001670>: no matching AST found\nTo silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\nEpoch 1/20\n210/210 [==============================] - 10s 46ms/step - loss: 1.8254 - sparse_categorical_accuracy: 0.4806 - val_loss: 1.4598 - val_sparse_categorical_accuracy: 0.6456\nEpoch 2/20\n210/210 [==============================] - 10s 46ms/step - loss: 1.4857 - sparse_categorical_accuracy: 0.6144 - val_loss: 1.2795 - val_sparse_categorical_accuracy: 0.6621\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 3/20\n210/210 [==============================] - 9s 45ms/step - loss: 1.5108 - sparse_categorical_accuracy: 0.5861 - val_loss: 1.3949 - val_sparse_categorical_accuracy: 0.6128\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 4/20\n210/210 [==============================] - 10s 46ms/step - loss: 1.3662 - sparse_categorical_accuracy: 0.6013 - val_loss: 1.2798 - val_sparse_categorical_accuracy: 0.6761\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 5/20\n210/210 [==============================] - 10s 45ms/step - loss: 1.2101 - sparse_categorical_accuracy: 0.6661 - val_loss: 1.1067 - val_sparse_categorical_accuracy: 0.6978\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 6/20\n210/210 [==============================] - 9s 44ms/step - loss: 1.3494 - sparse_categorical_accuracy: 0.6034 - val_loss: 1.3121 - val_sparse_categorical_accuracy: 0.6304\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 7/20\n210/210 [==============================] - 10s 46ms/step - loss: 1.2088 - sparse_categorical_accuracy: 0.6489 - val_loss: 1.0820 - val_sparse_categorical_accuracy: 0.6790\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 8/20\n210/210 [==============================] - 10s 46ms/step - loss: 1.1442 - sparse_categorical_accuracy: 0.6738 - val_loss: 1.1076 - val_sparse_categorical_accuracy: 0.6650\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 9/20\n210/210 [==============================] - 10s 47ms/step - loss: 1.2855 - sparse_categorical_accuracy: 0.6379 - val_loss: 1.1851 - val_sparse_categorical_accuracy: 0.6738\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 10/20\n210/210 [==============================] - 10s 45ms/step - loss: 1.4220 - sparse_categorical_accuracy: 0.5843 - val_loss: 1.3356 - val_sparse_categorical_accuracy: 0.6245\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 11/20\n210/210 [==============================] - 10s 46ms/step - loss: 1.3806 - sparse_categorical_accuracy: 0.6065 - val_loss: 1.3161 - val_sparse_categorical_accuracy: 0.6449\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 12/20\n210/210 [==============================] - 10s 46ms/step - loss: 1.2324 - sparse_categorical_accuracy: 0.6366 - val_loss: 1.1956 - val_sparse_categorical_accuracy: 0.6534\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 13/20\n210/210 [==============================] - 10s 46ms/step - loss: 1.3406 - sparse_categorical_accuracy: 0.6060 - val_loss: 1.2209 - val_sparse_categorical_accuracy: 0.6310\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 14/20\n210/210 [==============================] - 10s 45ms/step - loss: 1.3025 - sparse_categorical_accuracy: 0.6123 - val_loss: 1.2510 - val_sparse_categorical_accuracy: 0.6388\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 15/20\n210/210 [==============================] - 10s 46ms/step - loss: 1.2434 - sparse_categorical_accuracy: 0.6559 - val_loss: 1.2067 - val_sparse_categorical_accuracy: 0.6654\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 16/20\n210/210 [==============================] - 10s 46ms/step - loss: 1.4265 - sparse_categorical_accuracy: 0.6081 - val_loss: 1.3274 - val_sparse_categorical_accuracy: 0.6702\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 17/20\n210/210 [==============================] - 10s 45ms/step - loss: 1.2236 - sparse_categorical_accuracy: 0.6579 - val_loss: 1.1487 - val_sparse_categorical_accuracy: 0.6907\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 18/20\n210/210 [==============================] - 10s 45ms/step - loss: 0.9367 - sparse_categorical_accuracy: 0.7356 - val_loss: 0.8296 - val_sparse_categorical_accuracy: 0.7944\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 19/20\n210/210 [==============================] - 10s 45ms/step - loss: 1.3789 - sparse_categorical_accuracy: 0.5812 - val_loss: 1.3323 - val_sparse_categorical_accuracy: 0.6054\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nEpoch 20/20\n210/210 [==============================] - 10s 45ms/step - loss: 1.3409 - sparse_categorical_accuracy: 0.6163 - val_loss: 1.2852 - val_sparse_categorical_accuracy: 0.6177\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\nCPU times: user 4min 18s, sys: 14.3 s, total: 4min 32s\nWall time: 3min 48s\n"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "WARNING:tensorflow:AutoGraph could not transform <function <lambda> at 0x7ff4d59c4f70> and will run it as-is.\nCause: could not parse the source code of <function <lambda> at 0x7ff4d59c4f70>: no matching AST found\nTo silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\nWARNING:tensorflow:AutoGraph could not transform <function <lambda> at 0x7ff4d4001670> and will run it as-is.\nCause: could not parse the source code of <function <lambda> at 0x7ff4d4001670>: no matching AST found\nTo silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
    },
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# create train and test data for centralized setup\n",
    "# (use the same train and test data, although it is passed to the models in different ways)\n",
    "train_data_centralized = (\n",
    "    train_data.create_tf_dataset_from_all_clients()\n",
    "    .map(lambda x: (x[\"x\"][0], x[\"y\"][0]))\n",
    "    .shuffle(SHUFFLE_BUFFER)\n",
    "    .batch(BATCH_SIZE)\n",
    "    .prefetch(tf.data.AUTOTUNE)\n",
    ")\n",
    "\n",
    "test_data_centralized = (\n",
    "    test_data.create_tf_dataset_from_all_clients()\n",
    "    .map(lambda x: (x[\"x\"][0], x[\"y\"][0]))\n",
    "    .shuffle(SHUFFLE_BUFFER)\n",
    "    .batch(BATCH_SIZE)\n",
    "    .prefetch(tf.data.AUTOTUNE)\n",
    ")\n",
    "\n",
    "# create centralized model\n",
    "\n",
    "centralized_model = create_keras_model()\n",
    "\n",
    "centralized_model.compile(\n",
    "    loss=\"sparse_categorical_crossentropy\",\n",
    "    metrics=[\"sparse_categorical_accuracy\"],\n",
    "    optimizer=\"RMSprop\",\n",
    ")\n",
    "\n",
    "# fit and evaluate\n",
    "history = centralized_model.fit(\n",
    "    train_data_centralized,\n",
    "    validation_data=test_data_centralized,\n",
    "    epochs=NUM_EPOCHS,\n",
    "    batch_size=BATCH_SIZE,\n",
    ")\n",
    "\n",
    "# view results\n",
    "pd.DataFrame(history.history).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(0.0, 16.483655858039857)"
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbgAAAEoCAYAAAAqrOTwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAACOSUlEQVR4nO2dd3hcxdWH39nVqnfJam6Se7dxoxiH3gk9QCgJgUB6IIUEklDS+RJCCCkkJJSEDqYHQjcd29jY4I6L5Kbe+9b5/ph7t2mbZK3WkuZ9nn2kbffO3t07v3vOnCKklGg0Go1GM9KwJHoAGo1Go9HEAy1wGo1GoxmRaIHTaDQazYhEC5xGo9FoRiRa4DQajUYzItECp9FoNJoRSVKiBxArp556qnz55ZcTPQyNRqPRHFqIcE8MGwuusbEx0UPQaDQazTBi2AicRqPRaDT9QQucRqPRaEYkcRU4IcR9Qoh6IcSmoMe/I4TYJoTYLIT4XTzHoNFoNJrRSbwtuAeAU/0fEEIcB5wNzJdSzgZuj/MYNBqNRjMKiavASSnfAZqDHv4GcJuU0m68pj6eY9BoNBrN6CQRa3DTgOVCiNVCiLeFEEvCvVAIcY0QYq0QYm1DQ8MQDlGj0Wg0w51ECFwSkA8cAVwPPCGECJnHIKW8R0q5WEq5eMyYMUM5Ro1Go9EMcxIhcPuBp6ViDeABCodkz1KCyzUku9JoNBpNYkmEwD0LHAcghJgGJAPxz+KuqoLMTHj00bjvSqPRaDSJJ66luoQQjwLHAoVCiP3ALcB9wH1G6oAD+LIcirbiJSXQ3Q2VlXHflUaj0WgST1wFTkr5xTBPXRbP/YYkNRXKyrTAaTQazShhdFUyKS9XrkqNRqPRjHhGl8BVVGgLTqPRaEYJo0/g9u0DpzPRI9FoNBpNnBldAldeDh4P7N+f6JFoNBqNJs6MLoGrqFB/B9NNuXkzbN06eNvTaDQazaAwbDp6DwrxELirroL0dHjzzcHbpkaj0WgOmtElcOPGgcUyeJGUUirrrbR0cLan0Wg0mkFjdAmczQbjxw+eBVdfD+3tYLUOzvY0Go1GM2iMrjU4GNxUgc8+U39bWsDhGJxtajQajWZQ0AJn4PFI+l0xbMcO3/+6nY9Go9EcUow+gSsvh5oa6O31PiSl5NKbq3n+3c7+bcu04EC5KzUaTWzs2we/+pVax9Zo4sToEzgzknLPHu9DLjfUNbv5dIe9f9vSAqfRDIwHH4SbbtI5qZq4MnoFzs9NaXeoq8g9Nf2scLJjB8ycqf7XAqfRxI4ZydzSktBhaEY2o0/gysvVX79UAbtTCdy+eidud4wuE49HCdzRR6v7IQTO6ZL0OjwHMViNZoRiXmA2Nyd2HJoRzegTuLIySE4OsOBMEXK6oLoxxo7f+/aB3Q6LF0NKSkiB+/vTLfzwT9qy6zerVsHs2dDRkeiRaOKFeYGpBe7Q4I474P77Ez2KQWf0CZzFAhMnBgicw+mz2mJ2U5oRlNOmQVFRSIH7bK+DvbW6sHO/WbUKtmzRrY1GKm63bw1cuygPDf72N7j77kSPYtAZfQIHah3Ob/LsdfgJXKyCZAaYRBC42iY3nT0SV6xuT42iqSnwr2ZkUVPj6+ihLbjEIyVUV6s5bYRFtcYkcEIIixDiMCHEGUKI44UQRfEeWFwpLw+04BwDsOA++wwyMlSZrhACZ3d4aGpzA9DWqdfh+oUpbHryG5n456Hq7zjxtLdDTw+0tY24YLmIpbqEEJOBHwMnAjuABiAVmCaE6Ab+AfxbSjm8ZvCKCmhshM5OyMyk13BRZqVbqIrVgtuxA6ZOBSGUwG3aFPB0XbPb+39rh5uCHF3OK2a0wI1s/F3P+jtOPNXVvv+3b4fi4sSNZZCJZsH9CngImCylPEVKeZmU8gIp5TzgLCAHuDzegxx0glIFzDSBaROS2Vvrwu2JwUz/7DPlngSfBedn3tc0+YJVWrUF1z+0i3JkY1pwFRV6De5QoKbG979/bu8IIKLASSm/KKV8R4aoYSWlrJdS3iml/Hf8hhcnglIF/AXO4ZQB1ldInE51kvoLnN0eEPVX6xeN2doRZXuaQBob1V99dT8yqapSrv2yMv0dHwoEW3AjiFjX4NYJIb4lhMjrz8aFEPcJIeqFEJtCPPcDIYQUQhT2Z5uDQrAF5/QJHMSwDldZqSLBpk5V94uMJUk//3VNkwsh1P9a4PqJtuBGNpWV6iIzL08L3KGAacFNmDC6LDg/LgLKgI+EEI8JIU4Rwpy+I/IAcGrwg0KI8cDJwN5YBzqojBmjmpR6XZTKhRizwPlHUILPZ+0ncLVNLsaOScJi0S7KfqPX4EY2VVXqIjM/X3/HhwI1NSpgbvHi0WnBSSl3Sil/CkwDHgHuA/YIIX4uhMiP8L53gFC/4D8CPwISE5MqhLqCNF2UhgVXkGOlIMcaPVUgWOBCWXCNLsoKk8jJtOgoyv7Q06NuoC24kYjLpYoklJcrgdNrcImnulq5jKdNg1271Hc0Qog5D04IMQ/4A/B74CngC0A78GZ/diiEOBs4IKX8JIbXXiOEWCuEWNsw2O1o/Nrm2B0SIcCWBOWlNqqiWXA7dqiTM9/Q9hACV9vkpqQgidxMKy3aRRk7/qKmr+5HHvv3K/e+acG1t/ty4jSJoaZGrYdOn67EbbD6ZR4CxLwGh7K6PgLmSSm/K6VcLaX8A7A71p0JIdKBnwA3x/J6KeU9UsrFUsrFY8aMiXU3sWEKnJTYnZIUm0AIwcSSJPbUOiP3hvOPoATl8gSvwHX2eOjo9lCiLbj+Ywpcbq4WuJGIfwRlnrGk39qasOFoCLTgYES5KWO14L4gpTxBSvmIlDKgp4yU8rx+7G8yUAF8IoSoAsYBHwshSvqxjcGhokJdPba2YndIUpLVkuLEUhu9dkl9SwSrK1jgkpPVhFxXB/giKEsLksjLsuogk/5gCty0aer/EVZZYdRj5sCZLkrQbspE42/BwYgKNIlV4NqEEHcJIT42Iir/JIQo6O/OpJQbpZRFUspyKWU5sB9YKKWs7e+2DhozVaCy0mvBgRI4iBBo0t2t3CxmBKWJXzUTMweu1LDgtMD1AzNFYOpUlXphrsdpRgaVlaoe7PjxPoHTlnri6OiAri5lwRUUqNsotOAeQ1UxOR+4wPj/8WhvEkI8CnwITBdC7BdCXDXQgQ46fqkCARZciSFw4QJNdu5Uf/0tOAgQuFpD4EoKrORmWXU9yv7gb8H539eMDKqqYOxY5fUwXZRa4BKHmQNXVqb+Tps2Ki24UinlL6WUlcbtV0DUei5GoniplNImpRwnpbw36PlyKWXjQAZ+0PgLnJ8Fl5NpJS/LEt6CC46gNPG34BpdZKQKstIt5GaqQ6zX4WIkWOD05DeyqKz0nXvagks8Zg5caan6O336qLTgXhVCXGwUXbYIIS4EXonnwOJObi7k5EBVVYAFB8qKCxtJaQrclCmBjxcXB1hwJYVJCCHIzVI1KLWbMkaamiAzE0pKfPc1I4eqKt/ygF6DSzymBWcK3LRpSvTa2xM3pkEkVoG7GpX/5jBujwFfE0J0CCGG75EwIil7HR6vBQdqHS5sJOWOHcqcz8wMfLyoSE3GLhc1RooAQI5hwelk7xhpavKtBYC+uh9J2O1w4IDPgsvNVX/1d5w4TAvOdFGagSZmv8thTqyJ3llSSouUMsm4WYzHsqSU2fEeZNwwBM7ulKQk+w7FxFIbXT3S2+4mgOAISpOiIpAS2dhIXZOLUkPgcjO1BdcvTIEL477yeCTvrO/GE0tBbM2hxb59KirWtOCsVuVF0QKXOKqrIS0Nso1p3BS4EeKm7E+i91lCiNuN25nxHNSQYTQ+tduDLDhvoEmIjP7PPusbQQneZO/2ylp6HdJrweVmGRacFrjYCBa4IBfl+s/s3PrPRjbttod4s+aQxj8HzmQ4lOtyu0dUdY8AzBQBs/Li5Mnq/xESaBJrovdtwLXAFuN2rRDit/Ec2JBQXg49PaS2NgSuwYVLFWhpUWHs4Sw4oHWXMvlLC5XAZaVbdD3K/tDYCIWF6qoyLa3P5NfQoiaa1g59PIcd/jlwJsOhXNeVV8L55yd6FPGhpsa3/gaQmqq+nxFiwUVseOrH6cACs7GpEOLfwHrgxngNbEgwriRzG/eSYpvgfTgvy0J2RohIStMvHUHgOiprgdmUFCjXpMUiyMnQ1UxixrTgQE1+QRZciyFs7V36eA47KishKUmlCZgMBwvuk0+UOEvps3RGCtXVMH9+4GMjKFUgZhclkOv3f84gjyMxGAKX17wvwIITQjCx1Na3u7f5pUdwUfbuVznrposSIDcrRD3Kzs4R8yMaNFwuVbbJFLiCgj6TX3O7Oo4d3Vrghh1VVSrBO8nvuno4tMyprYW2NrWGONIwXZT+TJ+u5qYRUEUoVoH7DbBeCPGAYb2tA34dv2ENERMnAlDYvDdA4ADKS2zsqQmKpNyxQ1VhmDSp77ZycyEpCVdNPXlZFtJSfIc2ZD3K225T7SlGqm9/IJiuKn8LLmjyazEErr1Tr2kOO/xz4EwOdQvO7Qaz0PvGjYkdy2DT2akqmfi7KEFZcJ2dgY1QhylRBU4IYQE8wBHA06hOAkdKKaNWMjnkycxEjhlDSfs+Um2BAjex1EZ7lydw7eyzz5R/OiWl77YsFigqQjTUB1hvoCIp+wSZbNqkflx7E9MS75DEdEf6W3B9XJQjxIKrrYWHHkr0KIYW/xw4E3MN7lC1FurrwWP81j79NLFjGWyCUwRMRlBNyqgCZ6y7/UhKWSOlfN64DX3tyDjhmVhBScc+kpP7ChwEBZqEi6A0KSoiqamBksIggcsKUY/SLPk1An5Eg0awwIW4um9uHyFrcH/6E1x++Yi4So6Jnh4l6sEWXF6e8mJ0dsa2HacTfvazoQtMqfWb6kaaBRec5G0yglIFYnVRvi6E+KEQYrwQIt+8xXVkQ4Rr/ERKOvaTmhx4KCaWKJHyCpyUykUZKsDEQI4pIr290ZsDZ9KnHqXHoxoLwohJqBwUzELLwUEmflf3LSNlDW7tWvV306bEjmOoCBVBCf0v1/XRR/DrX8Pzzw/WyCJjWjljxow8gQsu02UydqyKYI7nxXdXlzrf42y5xypwFwHfAt5Brb+tA9bGa1BDiWPsRIo7DpBsCZwwC3KsZKQJX6BJXZ1yKUYQuN7cQnK6G/u4KHOC61EeOAC9vep/LXA+TAuusFD9LShQV+xdXQC43NJruXUMZwtOSp/Abd6c2LEMFabAhVqDg9gtMnNSNrcXb0wL7qSTYNs2cDiGZr9DQTgXpcWi5rl4WnAvv6wuGjZsiN8+iF3gZkopK/xvwKx4Dmyo6B1Xjs3jJKct0Ouqmp/afBZcpAhKg47MQvJ6mrw5cCZ5wfUoTfekEFrg/AnlovR73Fx/swhoH84W3O7dviafo8WCC5XkDf234MxJeai6Tpv7O+kk5Urdtm1o9jsUVFereAKzZJo/8Ra43Uaf7FABe4NIrAL3QYyPDTt6SlUkZVZD32CPcqMmJRA5B86gJbWANFc3pam9AY/3qUdpCtzhh+s1OH+amsBm89X5DKpH2WKsv5UWJg3vNTjTeisqGl0WXEqKr4i2SX9b5pgW1VAJXG2tEoDFi9X9keSmDK5i4s/06eoYx8ti3b1bXdzkxDfjLKLACSFKhBCLgDQhxGFCiIXG7VggPa4jGyK6ipXAZdTu6fPcxFIbLe0e2jrdSoiSk2HChD6vM6m3qQm5yBV4svapR7ljhzrZjz9enfhBP6JNu+xc+4c6HM5++qffeAOOOcYnoMMNM8nbPOGCru5NC25CiQ2HU2J3DFORW7tWff/nnacEzjNMP0d/qKxUaTmWoCknhAXX2Oril/c10t0b4rgMtYuypkaJ8vTp6uJrJAlcdXXf9TeT6dMDYwUGm1ApI3EgmgV3CnA7MA64A/iDcfs+8JP4Dm1oaB8zHg+C9JqqPs+ZNSn31jqVwE2erArEhqHaoq5Gk5oaAh7vU49y505lmps/oqCr0Y+29LBxl519dWFa9gTj8cBvfgMnnwzvvAMvvhjb+w41/KuYgO9/00VpBJiYEa7D1k25di0sWACHHaaiB0dDqkioFAEIuQb34cYeVq7tZvueENaDKXD796v12XhTW6tEwGaDmTNHlsCFSvI2MT1V8fIw7d4dd/ckRBE4KeW/pZTHAVdIKY/zu50lpXw67qMbAuwimaaMYlKr+04y3lSBWlfUCEqAvdKYkI2+cCZ96lHu3KnW8sz1vKB1uBqjI3hNYwxJ4C0tcPbZ8NOfwoUXqgCN4XoSBgtc0NW9WcXEjHAdloEmHg+sW6dcXrNnq8dGg5sy3BV7WpqyZv0suN3VSrjM7zsA00Xp8QxNZZHaWp9bde7ckZULF8mCM+e6eKzDud3qgifRAufHf4UQlwghfiKEuNm8xXVkQ0SvQ1KbNY7k/VV9nivKs5KWIthzoFeJUhSB2+0w1hOCBC6gHqXHo7Y1ZYpP4IKukmqb1IltCl1Y1q+HRYvglVfgrrvgkUdg3rzhK3CNjaEFzhtk4iE1RTAmbxgL3I4dKhrXX+BGeqBJR4f6DkNZcNAn37HKELg+5e1AWR1ms+F4r8NJGViMeO5cZTke6sWhY6G7WzU1DSdwublqjfizz+js8QxuN5TqamV9HwIuSpPngLMBF9Dldxv2OJyS2uzxJO2t6vOcEIIJJTbatlSpZo0RIigdTkmlK1fdCRI48KtHWVOjkl6nTFGTeV5eXwvOsNyqI1lw994LRx6pfijvvAPf+Y5au5ozZ/iu6zQ1+VIEQF3ZZ2T4BZm4yc+2kp2ufrbD0kX50Ufq7+LFahIZO3bkW3DhUgRM/AROSkml14IL+n7dbnVuHXWUuh9vgevsVEJgWnDz5qm/I+GCJFyKgD/Tp8P27fzxkWZ+cndD+Nf1lyGKoITYBW6clPIiKeXvpJR/MG9xHdkQ0evwUJM1Hkt1aJ9+eakN92fRIyhrm104klJxpWeFFDhvPUozAGTqVCVIU6cGCJzD6Wu0GtJF6XTCVVfBV78Ky5fDxx/DEUf4np87V+WNDdUi/GAhZV8XJQR0FGhud3s7PcAwrWaydi2kp8OMGer+nDkjY8KMRLgkbxO/ljkt7R7v99oS7KJsaFAXbosXq7XweP/GgxOh585Vf0eCmzJcFRN/DIHbsc/Bzn0OX6GKg+UQFLgPhBBz4zqSBGE3XJQijE9/YomN7AOGKEUSOEOM3IVjQltwZj1KU8xMN0uQwNU2q+1YLL5tBvDcc3DfffDjH/uSJf0xT8Lh5qbs6FB5RsEC59dRoKXDQ16WlSxD4Iali3LtWhVcYlbUnzMHtm5V1slIJVwOnIlfR4Hd1SqwxCJCrMGZgjNunOpKEG8LzlzvMy24sWOV1T3czq1QxGLBTZsGDQ2072/C5YYD9YNUGL6yUk1wESLSB4tYBe5oYJ0QYrsQ4lMhxEYhRNTLGCHEfUKIeiHEJr/Hfi+E2GZs5xkhRO4Axz4o2J2Supzx6k6IE2ZiqY1xrZW4MzL75vD4Ya6XWYqLwrgojXqUO3eqiKzxxj6nTlVRdD09gE/UZkxMpqbJhccTdNW0Zo1KV/jFL0JHdM4y8u+Hm1UQnORt4ue+aml3k5dtJTVZYEs6RFyUvb2x11F0udS6qZlTBWodrrfXd1UbC21t/RtjoqmqUlZr8MWYid93bLonp09MDi9wpaVKLOMtcMEWnBDqAnIkCFysFhxQ1qJ+m1XB/TEHyu7dav6z2QZnexGIVeBOA6YCJwOfB840/kbjAeDUoMdeA+ZIKecBn5Hgpql2h6Qp37iSeP11+OADFeW2aRPs2MEkdw3lzZ/ROXZyxGaHtY0ubEmQVFqsynoFYdaj9OwwUgRMcTKtQiPfxBTKhTNScbqgKfgkX7dOnWTJyaEHkpWlTv7+noQXXghPPtm/9wwmkQSuqclbpis/24oQgqx0Cx1dh4DV841vwLHHxvbabdvUmo6/wM2Zo/7GekGyYYM6RqtX92eUiaWyUrknw50/fi7KqmoneVkWKsbaogtcf12Uv/kNfPhh7K8PtuDAF8R1qHY/iJWaGjWHmIFcoTDmpvGt6kJiUAVuCNyTEKPASSn3AOOB443/u2N5r5TyHaA56LFXpZSmrbsKlWOXMOwOSWd+maqecdttsGyZmoDmzoVp0yg+bDKLDrxP3ZjJEbdT0+SiOD8JEcaCM6uZeD7bGRisEpQqYArl7EmqJU/AOpyUas1t0aLIH2ru3P5ZcPv3K3F7PIEdkMIJnOGiNCPq8oycwqwM66FRcHnDBnXRsXVr9NeaFUz8BW7mTPU31kCT//1PuTP7M1EnmnA5cCb5+coKdjiorHFSUWYjP9tKW4cHt78HwxSc4mK1PTNgKxY6O1UqzX33xT7umhplZfiLwNy5yp2+RxWG6Orx0NAyDHs6mikCkTqUT5qEx2JlfOsu8rIsVFYPUlWTIUryBkiK/hIQQtwCLAamA/cDNuAhYNlB7v9KIKF95exOSVKqTS0cV1WpaMmg2zOvNvF6wXLudElsSaF/ELVNblWD0l3sWwz3q9qQl2UFKbHs3gknHud7Y5DA1TS5KClIosyoZ1nb6GKesVxHZaWqYRhN4ObMgZdeUhVSwll6/pjWQCIXz81OAv5RlOB1X7UYgTd52cryzU63HBpBJqYVsWIF3HRT5NeuXasupPzXcjMz1cke6wXJW2+pv8Mp8rKyUl04hsMo1+VpaqaqxsnpR2WSn23FI6Gtw0N+juHtqKlRr01N9U2Qe/f62rtEwrwA6Y9b08yB8xcB/zXu8nLufb6VVZt6eOSXY2Pf7qGAf/pDOJKTaRszgUldlcysSAlsHTZQurvVcT2ULDjgXOAsjNQAKWU1kHUwOxZC/BSVdvBwhNdcI4RYK4RY29AwiGGqftgdUjU7raiA446DU09VidMXXqj6dX31q4y79btsFeNYuTZ8ZkStIUwUFSlxC6qtl5NpIb+7Hkt3ly/ABFQttjFjvLlwtU1uSguSlDUoglIF1q1TfxcujPyh5s7tX2HYVavU35071Q8wEUSy4Fwu2mpaAcg3BS7jEBC41lZf0eQVK6K/fu1adXESXK5q9uwAweru9fDS+51911+dTnjvPfX/li0DHvaQ0tqq1gyjWXBAY2UDvXbpteAgKBfOf1I2txerYJnHqz9uTf8kbxPTpWwsAVQ3uKhtcqtyfsOJSFVM/DiQP5mJ7ZWUl9rYX+/C6TpI16z5fR1iAueQUkpAAgghMg5mp0KIK1DreJca2w2JlPIeKeViKeXiMeEWqA+SXoenT7PTYBbPTKWizMYTr3cQarjdvSq0udQUOOjjpszNtDK2zah36S9woK7oTRelIZTJNsGYXGugi/Ljj5XLxLyKDEd/13VWrVKTrpSJswyamtSVsll818SY/LoOqAsc04LLyrAkPorSnCyXL1fWb6SyRk6ncmcuWdL3uTlzVMUII03l0Vfbuf3hZrZUBrmE1q5VFyBlZep7Gg7rQNEiKMH7HdfuVN+xv8AFrMP5C465vVgFzvxd790be8RqKCsnO1uJqyFw5hr5oK1PDRWRqpj4sSujgqKmSsqLrbg9sL/+ID9nLL+HQSRWgXtCCPEPIFcIcTXwOvDPgexQCHEq8CPgLCllgswFHw6n7NPsNBghBBeckMXuaifrtvX2ed4UoZLCCAKXZWFsm/HlBieMG6kCXT1KKM2O4KWFSdQ0+Z2M69apyTAlJfKH6k9hWKdTbfeMM9T9RLkpm5pUCHZwZKhh0fXUKAvPuwaXbkn8GpwpcD/4gfobyYrbvFm5vP3X30zmzFHfw44d2B0eXnhXRWVurbIHvs50T159tbKKzKCLweCTT0KuHR800XLgwHtR01ypBK681EZetvqeAwTOX3BKS5X7PVaLzLTgnM7Yu6iHsuAgoGRXs+E6HxT33VDR06Ms6ygWXHuXm13pFdgcPUxBBc6ZVWYGzBDmwEHsQSa3AyuAp1DrcDdLKf8c7X1CiEeBD4HpQoj9QoirgL+g3JuvCSE2CCH+PuDRDwK9DkmKLbIFB3DC4gzysy08+UZHn+dqjcjH0gJrWIHLSrcwrn0PHmtS3/yPqVOhpoa6va3GdpTAlRQk+Sw4KZUQRXNPghK3GTNiE7iNG9UP/uKLVdWQRIVAh0ryBu/VvaOuibQUQVqK+slmZ1jodcj+d1wYTMzJ9eijVVWZSAIXKsDExK9k1xsfddPe5cGWBFuDLbi33lKT6zHHqPuDYW3b7fC976niz9/73sFvL5h+WHBt+xopKbCSnmohP8u04IyLmOCyWRaL6k7QHwvO9ALF8h6XS62lh7Jy5s6F7dtxd/d668sOqgXn8cCrr8bPQg/XyTuIfXUu9uUqIRrbshuLGITPuXu3mmfi5JELJiaBM1ySb0opr0dZbmlCiKhJDFLKL0opS6WUNinlOCnlvVLKKVLK8VLKBcbt6wf5GQ4Kh1OSEsVFCZBsE5xzTBYfbentE01khvaXRHBRWiyC8q49tI2Z6EvyNTGCDtrWqzUzs2FqWWESTW1u1RZm7161rhctwMQk1goZZoDJUUep9yTSgosgcO76Jq97EtQFA5BYK66yUqVl5OfDBReoHLdw7UXWrlUWaqgr1xkzwGJBbtzIUys7mDzOxrJ56YEWnMOh1t+OPdaX63iw63BbtqiehHfeqY79mjUHt71QVFUpt16w69kf4zvurm6i3ChwnpZqIS1F+Cy49naVL+g/KceaC2dW9jntNN+YolFfrwQmnAXndtO+brNXgwZV4B5/HE45BZ6OUz37mAXOyX5D4Gy7d1A2JungP6cZQRkpenMQidVF+Q6QIoQYC7wMXI7KcRv2xGrBAXx+eSYpNtHHiqttdJGWIlQJqfx8dXUZIhduXFsl9YUhrmQNl2XvZrWGU1KgJnJT6Gqb3L4Ak1gFbu5cFcrc3h75datWKVGeOFHl+Hz6aWLWdsIJnPGYp6nJ654E/Mp1JXBx3wx/FwLOP189Fs6K++gjZb2FOrFTU2HKFFpXb6Sy2sn5x2UxsyKZuma3b4I319+OPVZ9XwUFA7fgpIS771a/pQMH4IUX4PvfV0FGg51EHi0HDiAnBykEroZmKsp8Ub/52Vbf5zcnZX/BiTUXzoygPPVUNY5YRDGSCBg1KXvXqYvB7AyLrzHyYPDoo+rvw2Hj7w4O00UbxUW5r85Fa2YRMjMTtm+nvNQ2OBbcELknIXaBE8Z62XnA3VLKLwCz4zesoSNWCw4gJ9PKqUdm8MZHXV7fO0CNEfkohFBrSIWFfdczpKSopYqa3PK+GzaDTnbsICNVeK0TU+BqGl1K4KzW6AEmJubrollxq1erWpZmlYampsFd24mVxsa+KQLgvfK3tDR7Aw9A5cFBgutR+ud3TZwIS5eGFrjeXuX6DeWeNJkzB9cnm8jLsnD84gxmTFQTvdeKM9ffPvc59V3NmjUwC66xEc45B775TeXq3LgRzjzT5/resKH/24xEVVX0gAKLBU9OLpm9rUwa63MM5WVbvekhIQWnvFx9Hr9KMp/s6KXHHvSbMI/TYYepcluxiGKoJG+TqVMhORnPJ8qdf9j0VF9j5IOlpUWV4EtLU30dzSjdwaQfFlxZkQ1h1KQsL7NxoME18GUBKQ9dgRNCHAlcCpjdNMN3/hxG9MeCA7jg+CxcbnjmbZ8VV9vk8gaGACoRNVjg6utJtXexL3Ni341mZEBZGclVuygpNIQSn8BVN7pUBOXs2eqHHwuxRFK2tKjoPbNYs1ktPRHrcOEsuORkyMoiqa1Z5RIaZCe6HqWUPuvE5IILlKUVPIFu3KiCGyIIXHv5DPIbKjnncBVBO3VCMhYLbDPX4cz1N/MiwEwt8LO2XW7J3U+1hO8j+MYb6jt++WX44x9VrqQ5gZsCZ3oKBoNQxygM9sw8suytXhclQH62pa8FF+yiBK9F1trh5vt31vPf94JKp23erNalp0xRY+mPBRdK4Izmp7at6txaOF0FfQ2Km/LZZ9Vv5fe/V27peLgpzQT2UOebH/vqXYwvtik39ttvM13UqTZ8sTZiDqahQXkhhiiCEmIXuOtQJbWekVJuFkJMAlbGbVRDSH8sOICxRTaWzUvj+Xc66bF7kFJ6k7O9FIWoZmJ0EdiVFqbA6LRp5FTvDNhOXpaF1GRBTaMR6RirexKURZGVFVmszDWXww9Xf8NUS3/i9Xbe3RDHgFe7Xa2ThDnhZH4+qR0th9YaXEuLqmjhf7KGc1NGCjAxeN8zGav0cNaYAwCkJluYNNbGtj0ONdG9/35gSbBZs9TVvWlpAJ/utPPkGx28sz7Ed9XTA2edpfIu16yB664LzMcrKlJFjD/+OPpnj5WmJvW9xjChdaXlkm1vVROqQZ6/izKURWVu17igqGl0IaVyrQWwZYuKLE5KUgJ3sBYcwNy5ZOxULuKF01OBQYqkfPxx9bm++U0lyI88cvDbDKa6Wn2u4HxMP9weSXWDk/FFSfCjH4GUzH30d8BBCPkQR1BC7FGUbxtdvP9PCGEBGqWU343z2OKOlLLfFhzAhSdm09Ht4ZVVXbR1eui1SxVBaRJK4Iw8t51p5SHbTsgpUxjTWOmNoASVnlBSkET3jr3q6ieWCErfm5UVF0ngVq1SrzNzs/LzlQvHT+B27nPw96dbWREienTQCJfkbeDKzSfL3kp+dqg1uAQJXKjw90mT1HdkCNynO3u59/lWPB99pCyvMNXTO7s9PNeknsvZ60vOn1mewrYqO541H/nW30xCdANft1WlsNSGapRrruH97ncwf37oz7Rw4eAKnGkpxWDBtSZlU+BpJ9nvXMzPVvVbHU4jgjI1VQm0SVCyd53RieNAQ9AEvHmz73hVVKiuISFaYwVQW6vc4+FScubNI6OpmjJLG2VjkkhLEQdvwTU0qHq4F12kzstLLoE33xz8JYMYqpjUNrlwulAXHBMnwte/Ttbj/2Z8e+XIEzghxCNCiGwjmnITsEUIcX18hxZ/nMY80B8LDmD2pGRmliez4s0Ob6WRABdlGAvOY7VSlzlW9YULomfiFHJ6mhmfEuheKS1MIm3zenUnigXX3evhF/9q5PaHmnjs1XZqS2fg/nQTDkcYEVi9Wp34WX5FaYI6gt/7fCsAuw44+lbWGCyiCJwjK4/s3tYAF2VaisBqSaAFFy6/6wtfUMd1717++24nD7/cTuPra5CLwgSYAP/7sJPdaeVImy3ApTyzPJmuXknbi2+qBz73Od+bQkRSmjmaIQXOrF3p3zswmIULVfWbrkHqZRyt0akfjZYcch2BAS4B1UzMSdn/GI4Zo7oUGPupbVbW3oEGv89vRlCax6u8XIXh798feUDRRMDwdszp3oEQgomDEYDx9NMqCf3ii9X9L35RuXkHu0ZsdXVMASYA44uNee0nP0GkpPD1T+4c+OfsxwXPYBGri3KWlLIdOAf4H1CBiqQc1vQaE39/LTghBF84MZvqBhdPrVSWTWmwi9IMazbZuRN76UTcVlvI9u9NY9RVzcTOqoDHSwuTyN/1CdJiCX/lbbB+ey9vfdzNuxt6uOfZVp6sn4i1pYnLrlnHZbdU85O/1bP7gLGmI6UvwMSfefPUpOl0snFnL6s391JeaqO7V3onkEEnisD1ZhgC5+eiFEJFrSZsDS5cfpfppnzqKQ40uMgRvRTs38aa9Nkhq+C43ZKnV3Ywa3qWWsz3s8hmlivrwf3GW4Hrb6DWefPzva9v63SzY5/6bsMK3JQpkfOPFi5Uv4vBCjQxK7tEmdB67B7qRTYZva0BjwdUMwmVdC1EQKqAacE1tLh9gRDbtqnP5G/BQfR1uHBJ3iaGwE1v3w6o5PSDjqR87DHlSjXXwmfMUN/JYEdTxmDBmetsXpdxcTFcdx1HbnoWz/oNA9vv7t3qmKanD+z9AyBWgbMZeW/nAM9LKZ0YZbuGM3bjJEiJUskkFMvnp1FSYGXlWrXe0UfgQLkcTHbswFmhoiVbQ1hwB3LViVfSFNgXrKwwiUk1n+KZPjPqD2NrlQOrBZ74TRnP3z6Os791JABXTdrP9AnJbKl08PN/Naq8up07VV6duf5mMm8eOJ3Ibdv413Nt5Gdb+O5FKpJx575BqiYeTBSB60xXAQj+AgdqHS5hPeGqqpS7LDc38PGpU9WFyIoVHGhwcWFRJVbp5oWeGfzz2dY+Ivf+pz3UNbs5//gsNQn7WXDji5PISXaS8+mqvi15giIp12/vVfP4pGRqm9yB+5FSCdyRR0b+TKYLfDDclO+9p9rTLFmi8uAisKfGSXtKLimdrcq6Msj3r2YSblL2CxqpM4RdSr8uHKaF62/BQfR1uGgiUFZGZ2oO5Y3KpVxeaju4SMrqanj7bWW9+Vupl16q3MuRysD1h95edd5HseD217nISrd4u6AA8MMfYs/I4cyX/29gkZRDHEEJsQvcP4AqIAN4RwgxEYiSYHXoY3eoLym1ny5KAKtVcP5xyrWXk2khLTVowR58bkoplaAY6QChLLjKtPF4EOTVBgpcSYGVqY2b6Ji5IOqYtlTamTIumZRkC5npFiacqCasUzIruemqQm66qpB9dS7ufb7NV2A52IIzrkx3/W8dG3fZufy0HGaUJ2MR+Kw/f370I/ja16KOLSLhOgkYdKTkKoELqoCanWlNXB5cpBYwF1wAH3xAcl010+vVeuaEzx/JY6918NDLgafN0ys7KCmwctS8NLVmWlnpDXu3WAQniG3Y7N2he875RVKu3dZLRprgmIXp2J2Slg4/4a+qUnmZ0QSurExdqR+swJml38aNUzl2UaiscdKRkoPweFTgjoHXRdnuCS84frlw9c1ub4kv7zqcfwQlqEabVmtkC07KqBacBHYVzKSsRgnoRCP6c8DuuxUr1H4vuijwcXM9zsyNO1jM4JkYLLjxxb6IbgDy8jhwxfc4cs8b1P/3nf7vewjb5JjEGmRyl5RyrJTydKnYAxwX9Y2HOKbAJffTRWly2lGZZKSJwAhK8Amcmezd2Ajt7dhmhBe4Ax1JNGaPxVa5M+Dxca56CrobqC+fF3Esbo9k2x4HMyv82uMUFqqT1FhTWzQjlbM/l8lTKztofOV91arF7EdmMn060mZjx4vrKC2wcvqyTFKTLYwvTmLn/qCT1+GAv/8d/vUvlTA8UKJYcC3JuVilhzR74PqkanqaQBdlOIH7whcAWF75P8bu3QClpXz167M4aWk697/Qxoo3lch9ttfBpzvtnHtsFlaL8LnR/HrLHdGgLkR6jzi6735mzYKWFmRtLeu29rJgWipjx6iJNsBNaa6/RRM4IQ4+0GTzZlWFIy9PBUwUF0d9S1W1k550o9KJXxeOXGPNtbWpW0WthhKc8nKVnN7SQl2zi8OmqYhG7zrcli2qUpDZPTopSQlvJAuuvV1FnUYQgY5uD7vzppO/bxt4PN70hgFHUj72mPKeBJ+PY8eqi5tHHomtAENXV+TXxZoDZ6YIBGG57rs0p40h41c/619BCIdDBfccihacECJHCHGH2bpGCPEHlDU3rDFdlAOx4ADSUy3cdGUhV5+TG/iEeVKbFpwRQZkycyoWEdpFWdvooqlokve1JiV7lAVQWRpZ4KqqnfTapXfdxktQ89NrzsmlpCCJ9jc/xL14ad/ixsnJdJVPJ69qM1ecmevtfzdpXDI79wdZcO++q664PR74z38iji8iTU3K/ZqaGvLp5qQc3+v8yM5IkItSysgJzNOn0zl5Fsfseom8z9bD4sVYLIIfXV7A8gVp/G1FKy++38nTKztISxGcdlSmel+I3MXJuz5gV/4MdnSHcPMZgtj4/qfUNbtZPCPVWwWnj8BlZPi2H4mFC5VIxdpI1J9du+Ckk5SYvP66spZiYPcBJ2mlxsWNn8DZktQ6q31PhEnZ+A66t+yiq1cybUIyGWmCalPg/CMo/d8TyYKLliKAqpG5u2AGtp5O2LOHojzrwCMp9+xR35EZXBLMJZcoF2W0C4+PP1Zzz+23h39NDFVMuno8NLW5VYpAEGPLc3lk8bfJ+/g99R3Hyt69ap44FAUOuA/oAC40bu2oxqfDml7DgutvkIk/S2enefNgvAS7KI0cOMv0aeRkWkJGUdY0uekcP1n9kP2ujJI/XY8HwdacmX3e48/WKiU+s/wtOFCT2ubN3hYhaakWbrgwjQl1W1iX3Vc03W7JJ6nTmNa6jeOX+Nb8poxLpr7ZHRi1+N//qjDqJUtUp+SBlvgKl+Rt0CAMgQvqsZcwC87M74oQPLHriLOYU/MRth3bvPlvVqvgZ1cWsnR2Knc80swbH3Vx6pEZZKYZp+GkSUrkzUATh4PcT1fxydgjVD5cMMa6Us276iJo0cxUSvJ9jXK9fPihqrISXAM1FAsXqt9Kf5P99+2DE05QV+qvv963JVQEqmqc5Iw3vv+WloDn8rKtuA8Yk3IEgWvfolz7xflJjB1jU9HN3d1KyMz1N5MQuXAH6p2+6kSRkrwNmtvcVOYbjVY//fTgIimfeEL9DXZPmpx/vrpoiJQT19gI556rfpe33x4Y4GZQWe2gp8rwtESw4MyWOONCWHC2JMEnx32Jlvzx8JOfxH7OD3GbHJNYBW6ylPIWKeVu4/ZzYGilOA44TIEboAUXlowMZZH4C5zFAuXl5GRaA5s4otyL9c0uXBVTlbvFXJMCWLeO+qLJ7O0Mbd2YbK20k51hoWxM0CQ2d666Gt/tW9ub27aFJI+L5x1z+GhL4JX6y6u62JQxnfz2Gqxtrd7HJxsllHaZVpyUan3lhBNUUurOnSoZeSBEEbg6DOslWOAyLPTY5cE3YewvMYQ7fzTrTCxIhJQBCd62JMHPry5k3pQUJHDusX4pGlarclGZFtzatYjubqqmHdW3swCoCTgvj971myjOtzJ2TBJpqRZyMy2+iNfubtUKJ5p70sRMRemPm7KuDk48UYnTK6/0tZgi0NbppqnNTX65Ed0Z9B3nZ1sQdRHWjYzvoGebKnJtHocDDa6+EZQmFRXKkrH7ilnf+LcG/rrCENcY1qma/AXOuBgYcCTl44+ri8Rw1k1eHpx+unJjhupl53Ip66+uDv7v/9S8EySGLrfku3+o4+OVu9XvLEI0bZ8UgSDGjc/kiaO/p4Jfnn02po+YiBw4iF3geoQQ3kUAIcQyYAA+jEOLXmecBA4Cc+F27lTJksnJ5Gb1teAaWty4PWCdbvSJ83dTfvwxjZMWUB0q9NuPLVUOZpYnBy4KQ+iSXUYHgfY5i7n9oWY6DavM4ZT858U2XLMCuxaDsuDAT+A++0y5pM48UwVVZGTA/QM06qMIXLUnjIsyUdVMYsjv+iR5MrXFxvcZlL+Ykmzh/75dxAM3lzKuKOgq2bS4AVaqYkHuZZ/r2xsOQAjkzFmk797Gopmp3u++2L/N0tq1agKMVeAmTFDpB7EKXHMznHyysuBefLF/1XbwBWWUTA0ncFas9RFchnl5kJODZ5e66CguSKJsTBK1TS7cm4zjGMqCk1K5zVD5o/vrXb4SVDFYcE3tbnqSM/GUVwQIXL8jKXfuVEE54aw3k0suUaL8Tojgjp/8RJVh+9vf4PrrVRTvHXcEWFfb9zjo6pH0VFYjo1Qx2VfnxCLwrucGU15qY0XZ2Ximz4Cf/Sy2BrK7d6uyezF0ER9MYhW4rwN/FUJUCSGqUD3dDjJ0LvGYCdADSROIir/A7djh7RiQm2ntE2Rirpekz53uez2oK7IDB+iafRiNLe6wlkpnj4e9tU5mVYSoujB7tgoe8Hc5rVoF5eV841szaGp3e69cn3ung4ZWN5/7shFZ6VfRJD/HSl6WxRdo8t//qr9nnKGCVS68ULlaBpIkHEHgnC5JtQzjokxUNRNT4CaGqCtqUN3g4tMzvq6OT4hAi2Sb6CtuoL6v/ftVGS6j/mT5vNLAzgJ+tEyYwfjGz1g83ffdlxYkeXPCYkrwBu54pFnVcDQDTWKtSXnttcpSeu451Revn1QaDTTHzTDc+iEELrWpTuWBmq7/YCoqsO7bQ4pNkJupvBgeD3Sv3ajcssENhoNy4UyRNdteUVurJuMILX5a2t2kJgvEvLnKJfuHPzDNbAraHzelmcR94YWRX3fmmeo8C86Je+IJVbfy61+HK69U39/3vqcukl57zfuy9duVyzK7o56uvAj5fSgLrrggKWzwXXmpDbdIovbaW1QQTyzlxCor1fkSvOYfZ6LO7EIIK3C5lHI+MA+YJ6U8TEqZoMZhg8dgrMGFxRQ4KZVgGWsSOVmWPgJnnlh58yerH4CZ82JOMgsX4pFQ3xzaitu+x4GUBEZQmqSnw+TJgQK3ejUcfjgzy1P44snZvLKqi9fXdPHIK+0smpHK3OUV6io+qCbl5HHJ7DJTBV54QUV9meWnvvIVFd4eqelnOMJ1EkBFnHakhA8yAegY6lSBykqv5RCKrh4PLR0emi76qu9CIFZMi3v9em/9STNwKJQVtzNrCjn2Vhbm+9IPSgqs1DW7VOWZDz9UE3yY4wtq3fXlDzt56QMjSnXhQvV7cUTJe+zogKeegq9+VQWXDIDKaieZaYLC4gxVSDzEGlxOR506n8JNjuXlpNfsoSjfihCCsYab3rUpKILS7/WA90Jl9wElSF09UnkzzBSBCC1+mtrc5GVbET/9qRLMH/6QBSdN485nLkD87a8h22WF5LHH1IVBtICc9HS1xrZihc+1ummTErUjj4Q//cn32osvVuP/4x+9D63/rJeJJUmM6a6jLiVys9F99U4mhHFPAt6I0U0LzlC/lVtvDchfDEkCcuAgBoGTUrqBo43/242KJiMC+1C4KJub1bqaIXB5Waq+nn89ytpGFxYBxcVp6mQxLTjDTZR+pMpnqw5TJX5LpR0hYEZwBKWJf/PTmhrlmjGu6L90eg6Txtr4zQNNtHV6uOqsHHViB5XsApgyzsaeGifOhmaVyHvmmb4njz5afcb+uindbjWphUsR6PDgsSThyszpc3WfbbbMSYSLMoJ70vyexgavh8aCuV70wANq7fTYY5k63hbYWcCPNWIyANlVvhqWJQVJOF3Q1OaKKcH7QKMLl1sl8tsdHuVmdDqj95t75hk1xksv7ddH9Keq2klFmeFaz88PacHldzfgLIyQblBRQU7jXorz1O/BdK3Ztm8JvR5YVqZEz7Dg/BsY1zS5Yqr00dzmpiDHqoJ3jERs+YtfkONoZd6dP1T7OPlkdT6E82ps3qzOy2juSZNLLoG2Np66/gmqdzQowcvKUqKX7Hdxm5IC3/626hqxeTMOp2TTLjtLZ6dRZG9gl7swbNk9j0eyvy50ioDJ2KIkkqxQVedWfQR3746+/n6oCpzBeiHE80KIy4UQ55m3uI5sCLDH04IzW+aY1phpwRmVAfzX4WqaXBTmWUmyCnW1bQrcunUwbRol5arjcbg2KFsr7UwoTvJF4wUzd67aZm+vr4O3UcHEliS48csFJFlh+YI0n0iaAud3ZTZ5XDJOFzQ/8ZISJn+BEwKuuEJVYwjX1ToUra3Kyg0jcKZbzpOX18eC83YUSISLMkKAiZmDNSCBmzBBuaIee0zdP+YYUozOAsEWXFePh3cdxjj8alKabZaa1u9Uv8EoArfXcKm53Co3L+aKJg8/rIQ+1vW9IKSUVFY7KC8zJtOQAmchv7ue3oIIbrWKClKcPZQntXjfk22xk1FT1Xf9DZQlOGGC14KrrHaSmqLmgJpGV/QyXag1OP/i30ydirjpJn7343f43Y9Wwo03qkn9yiuV2H3rWyrYx5/HH1drYRdcEHFfXk44ga6sQgpffBTnFy9X43/yydDrWl/7mrKI77yTzbvtOF2wsMJCRmcTB2xFbN8b2jpvaHVjd8qwASYASVbB+GIbVdUOOPtsZV1GKifW2qouYoc4ghJiF7hUoAk4Hvi8cTsz4juGAXanxGKBpHi4hYuK1FWw2SrFbw0OApO9a42Gqd7X7dihJv1162DhQgpyrNiSQguclJKtVQ5mhlp/M5k7VwnS1q1q/c1mU80fDSaPS+a+m0r5yRUFge/p6grIF5psBJq4X/ivcnktXRq4ny9/WQndAw9EOzo+oiV5m+tOISY/cw1uSINMzBy4CAJXbYRZlxUOQOAsFjUpOxzqIsM4LjPLU9i+J7Dg9YYdvTSkFuHKzg2wtoqN35Lj3dgSvP0j/zZXOtSVdnZ25HW42lq19nTJJZG7dUegsdVNZ49kUkSBs1LQXU9XbngLzjlWuckr7CoEXgjBYbJSRbCGi+g0cuGUyDpZMlNFKdc1x2jBtRsWXBATS22sFlPgV79S5/G77yoRuPdeWLBAeU7uu0+dW489ppK4o4ipyXubnbw28XSO2f0/Jq57Be68M/y6Z2EhfOlL8OCDbF2zD4sF5maqC4CWjCLe/yR0jGCfGpRhKC9V3hwyM1UD3SefDO/SNueQQ9WCk1J+JcTtyngPLt7YjVY5fSIPBwNzQfyDD3xFYYHcLHXI/ZO9axr9+slNm6Z+/Bs3qsi0RYuwWATF+Um+RXA/qhtdtHV6QgeYmPhHUq5erU60oKTqcUW2wGAbs+Cr3zrc+KIkUiwu8t9/VQVPBK+JjBun3DL//ndskVUQs8BZxxT0mfwyUgUWS2xBJlJK/vF0i7JQDob6euWWi2LB5WcHlW/rD+b35Veey+ws4N/rbN3WXlKSLVhmB3b3NnPhkteuUhNQlATvPbVOxuSq8Potu+1KZA87LLIF9/jjyro/CPekGWDiteDy8vqsweVnCvK6G2nLDBNgAjTmK4Er6/J1CJjda3gRQllw4M2FU1GPHuZNSSEjVVBX26PWhCOIjt3hoatHBnSY927WP5JSCCVA//mPioD84x9VlZSrrlIenh07wid3B9Ha4eaPjzazdbkKRnl5+gXsPe/qyG+67jqw28l56J/MmJhMerOKRs2eOpb3Pwnd39GXIhBd4Gqa3Kp7+qWXqnPz5ZdDvzhBKQIQeyWTfwshcv3u5wkh7ovbqIYIu1MOuIpJVEyBe/995Q4x+koFW3AOp6Spze11K3kjvszoKsNdVFaYFNKCM/OjZpaHCDAxmTpV7X/DBvjoo6gRdUDI6EurVXCCayOpnS2B7kl/vvIVJcxvvhl9HxBV4Jo7PKo1TmFBHxelEEIVXI5B4JrbPTz+eocv12mgxJAicKDBFTbEOiZCClzfQJN123qZNzUFy5zA7t7JNkFBjpXcTauVlR0lcm1vrYsJJTZmVSSzudKuCjUvWqRcaq4w6SkPP6wulIJLS/UDr8CVhrfgsnuasUo3zenhAyOqM8YBUNS61/vY5NYduCxJuCeHSTgvL4e6Oqp2tQJQMTaZksIkOqtiSRFQv7f8EBZcebialPn5SnA2b1ah/ueeq8L5ze4TEZBScudjzXT2eLj4ZyfR9M46/nDMbby/sW8ydwAzZuA+7XSOevd+FpfjTX+YtGgie2pd7A3RmXtfnZP0VBHofg2BWXtzb61TBRgVFvZxU368rZdr/1DH8/9RF8m/ez+b2x9q4i9PNHPvc608/HJbn5zgwSbWS8x5UspW846UsgU4LPzLhwd2hxxwHcqomAK3b19AVQevBWd8sbVGZGRJQZDAmWswhsCVhhO4KjupKcI3SYQiKUlNRE8+qazD4A4CocjIUOMOiqRcfuBNXJYkZLioubPPVhX2Yw02MUUrTJRfS4dbXSkX9LXggJhb5pjHbuNOO5t3h8gpi5VwfeD8ONDg6ptw3x/OPluFjZ94oveh8cVJZKQJb8Wa+mYX++pcLJ6ZqqyUpqaA7hUTsuyM2bs5qnvS45HsrXMysSSJWZNSaGn3UNvkVr+73l6VAhDMjh3qQukgrDdQIlCQYyXHuOgLJXAWI8m7IS28BVdjT6M1NZ+cBp/AldZ/xv6cchq6woi7cYFSv0FZF5PKbJQUJGHfFz3J26x4UhDCgpsYrSalELB8Oc1/eYBHbnubztTcsPsxeXNtN++s7+GKM3OpKEumYPlCJldkhLXC/Pnsgm+T19PIcZ895xW4ucvLAfgghJtyX52L8UW2qF4t0+quqnGqJY+LLoLnn1cWKtDQ6uIX9zZS2+Qiv2EPXWm5bGpMY/XmXl5Z3cWjr7Vz7/NtcV9eiFXgLEIIb1KIECIfiHoGCyHuE0LUCyE2+T2WL4R4TQixw/gbPtkkzgyJBQcBeThZ6ZaAepRmSSVvR/AJE1RE1O7dKrzfaMdSWphEZ4/s84PYUulgxoRkrNYon2POHCW2EJsFB2odLkjgZm56jU9KD6dRhilFmpqq1mWeeUYtLkfDrNoSzkVphGOTn6/cV0Guz6x0S0wniRnZmGSFx149iEDgKFVMeuyqjt+4EHX8YmbSJGXB+zWitVgEMyameC04s7npohmpIbt7L2jfiNXjjipw9S1ueu2SCSU25kxSVuLm3XZfoEmodbhHHlET9Re/ONBPCKjuFBVlfhdmeXnK/etfZsqoKlJjC2/B1TW7qM0eT1qNT+By921jT95UX03KYIzvr3PLLvKyLORmWSkpSEKatRoj1qE0BC6EBVeUZyU9NXpNyn8938q/nm/ja7fVsn1P+AuupjY3dz3ewqyKZC480fd7WDYvja1VDl95sTC8nXM4uwtmMv7Rv6iC6BYLY6aXMXW8jfdCCOS+emfEABOTsYVJ2JJUFCygLnZ6e+GZZ3B7JLf9uwmHU/L7a4s4OruWjFmT+c+tZTz527G88IfxvPbn8bz8p/GMO5gLwRiIVeD+AHwohPilEOKXwAfA72J43wPAqUGP3QC8IaWcCrxh3E8Idocnfhacv0XiZ8FZLCKgHqW5rubtCG61KmED3ySDLzLO34pzOCW79juYEWn9zcRog0NhYey+8HnzVKWFbuNEqKwku2orqyaewK59EU7gr3xF/dhNKzQSTU3KwgzTM6ylw01elkUJoJQq5cKP7AxLTC1zahpdCAEXnJDN+5/2DLzqe1WVOoaZmSGfrj6YCMoozChPZvcBJ70OD2u39ZKfbVECEaK794xqtX7mXhLZWt9rBJhMLLVRXmYjLUWwudKu1oIzMvquw0mpXFHHHgtjx9Jj94TsjhENt0eyp9YVKHD5Klo4YB3OsDr2W8Ln8dU3u2gpGI+oMi4+enpI2VdJVd40X9ucYAwLzrmjyjuG0oIkstoNKzhKmS6gT39CUG7ziSWRa1LWNbt4bXUXR8xJxe2WfOf2Op5e2dGnV6CUkj88rITix18qUB0nDI6al4aU8MHGyAWl1n9mZ9XJX8eyeZM6H4uLwWrl6PnpbK1yeD8LqAbQ9c3uqOtvoJYrxhf7fc4jjlDzysMP88TrHazfbufbX8hjQrEtZJscIQTJNoHFEqf51yDWIJP/AOcBdcbtPCnlg+bz4awwKeU7QLBf6Wzg38b//0Y1UU0IyoKLQxUTUJO2aZUEFZ71r0dZ2+jClhTk7jAtPr+yR2aUpb/A7djnwOWGWZHW30xMgTv88Nij3ubNUxOaaRkYScsflp/Qt7OAP4sWKYsxFjdlU5Oa2MKMqaXDo1yU5uQXIlUgFguuptHFmFwrF56QRYpN8PjrA7TiYkwRKDuYNbgwzCxPxuNRofwfb+tl4QyjPFdZmUo697PgxleuZV/OJBqsuRG3aUZQTiyxYbUIZpQnq0ATq1WtsQUL3Nq1ykVpuCf/8HAzF/7kAHc/1eIt+RYLNY0uHE7pCzAB33fs76Y0BK5SRqhV2uymq2Siqsrv8cD27QiPhwOFU31tc4IpLkampGDbX0XFWHX+lBRYyeuu9z4fjuY2NxYL5GaGnjsmRqlJ+aTx27v24nz+cWMJi2em8pcnW/j5vxrp7PEdw5c/7GLVpl6uPie3j+hMGmujpMDKB5+Gd1O2d7nZud+JvMhI/N6925tSsGy+EsgP/QTyQH3kGpTBlPsXlxYCLrkE+cYbPPPoZ3zusDROOypDeVyqqhISYAKxW3BIKbdIKf9i3LYEPf1GP/ZZLKU0VnKpBcL+koQQ15gtehr8u2MPEnFdgwPfSRJUKsi/HmVNk4vi/KTAK5kQAlcSwoLbUqlcGxFTBEzMqMj+5CyZomi6Kf/7X9UvbvIUdh2IYAEJofJ/1qyJniwcpUxXe5fH56KEEKkC1pjX4EoLk8jNsnLaURm8vqaLhpbI9T1DEqkPHD4L7qDW4MJg5ij+971O2jo9LJ5hRMIGdfdGSgq2fMTmkoWBbXNCsLfWSXaGxdt7bfakFHYdcKrouIULVUUV/yoVDz+sXOjnn0+P3cP7n/RQmGtlxZsdXHZLNc++3YHbLcPsTdHZ7eHV1Sr5uaI0usDZ07Op70kOm5xc1+zCOb5chanX1HiPQ/fkmeEFzmLBNX4iY1r3edMUSguTKOiux5GTH5g4HURTu5u8LGtY6yNSTcrWDjcvvt/FiUszKM5PIifTyq++PoavnZvL+5/08LXfKpdlbZOLv65oYcHUFM45pq+3QAjBUfPSWbetl57e0L//T3bYkRLmz8lRid/gtUwrymyUFiYFrOPFmiLg/znrmt3e/fdc8EWEx8MZ+1/kB5cWqIuv6mr1vRzqAheFAamEVDZ52LNBSnmPlHKxlHLxmAjVrwdKXNfgQK3DCdHny/WvR1nb5BdBaXL00epk96tCn5lmITvDEiBwWysdFOdbQ64F9GHsWHjpJfjOd2If/6RJKonz009VWaa33oLPf54p42y+osvhuOwyZcVGs+IiCJx5jLwuSghRzcRCV29gZZhQVBsCB3Dhidl4JKx4syPie/rg8SgrIaIF5yQ30xI+6f4gyM+2Upxv5c2P1KS00BQ48HX3Bti9G1tzA1uKF4ZMLfFnT62LiSW+CW1WRYoygvYYCd9dXb5iBS6XcnOdeSbk5vLRll7sTsn1lxXw9xtKmFRm467HW/jqr2tYvbknwOXW1ePh9TVd/PTuBs6/YT8P/a+dqeNtTBrrJyRm7Ud/F2VtLY6CYtye0PmObrekodWNqChXD1RWquNgtWKZPo2acAIHdBRNoKRjn9eKLMlPIr+7ge4IOXcQPgfOJGwkJfDUyg4cLskXT/a55C0WwUUnZXPn94u9Lssb/lKPlHD95QVhhfTo+Wk4XfDR1tDRlOu395KaLJg+MdmX+G3UTxVCcPT8ND7e3ku3IVBmisDYGNePvZ/TsFb/tKGYz8bM4Qt1z3uLMCSqTY7JYJ2F/elXUieEKAUw/tYP0hj6jd0hSY6nwI0bpybDoJwz/3qUtU2uvh3BzzlHBV8YASYmpYVJAeW6tlbZ+zY4jcRpp4Vd6wqJxaKsuI0bVVKvwwFnnsnkcckcaHCFvXIEVDuOM89UV/yRcuIiCFxLhxGOHcFFmR1Dsrfd4QlIxSgpSOK4Ren8973O/kVx1dWpOoBRUgTiYb2ZzCxPwSPV5FKY67efWbPUb6ahwVtgeWvJwsC+cEFIKdlb6/RG/oGvn2BAoInppnzzTXUMDPfkuxu6yc6wMG9KClPHJ/OH64r45dcKcbnhxr828OO/NPC/Dzu5+R8NnPfj/fzmgSZ27HNw9uey+Ov1xfz9hpJAD0oYC85VpAI+QhWbbmxz4/FAyjTjIrKqSllwU6dSUpbBgQZXn7Utk4bcCZR07PdO1GmpFop6G2jLCh+xCcpFGSmMPlwkZVePh2ff7mD5gjQmlPS1kmZPSvG6LPfWufjG+Xl9L379mDs5hewMCx98Gnodbv1nduZOSVFNiwsLVQ7sLbd4n19mCOSaLUog99U5Kcq3xrxs4x9J+cZHXby6uovWsy4mfePHvouiBObAweAJXH94Hviy8f+XgecSMAZACVxqPF2Uv/2tqrIehFmPsq3TTXuXx1fFxJ8Qa1JlhUlel1NTm5u6ZnfoAsuDiRlJ+cILSnCPOorJ42xICburowRqXHihioJbtSr8a5qawqYINPsv5oex4PqU63r/fV/XYoPaJrUd/8oiF5+UTY9d8tzb/bDiYugDV93gikuAiYn5fS+aGdQf0D+S8sMPISuLzskzI7ooWzs9tHd5mFDiG292hpUJxUlK4GbNUhdnpsA9/LBa6zv9dBxOyaqNPRw9P80bwSuEYNn8dO67qZRvXpDLtio7v3+wmS2Vds48OpO7flDMY78q45sX5DGzIqVvKHq4NTjDrdbc3vdixOyakDXTr0PA5s0waxZjxyRhN/JMQ7EvdSw5vS2kOXy1Igt6G2hMjewtamp3h8yBMwkXSfn8u5109UguOSV0kW7A67J84JZSzjw6dCCTidUqOHx2Kqs29fRxCze3udlT4+SwaX6/k7lzA/rAzZ6UQk6mxeumNFMEYqW0UHUcWLWxhz8+2szsScksuuUKNXeZOXG7d6sLZbMo+xATVxelEOJR4ENguhBivxDiKuA24CQhxA7gRON+QrA7h8CCM9ex/DDrUW43ujSXxFjSqaTA6HPlkWw11t8iVjAZDObNUyL05JNw6qlgszF5bFBvuDC4Tj0dmZwMTz8d+gVSKqsjrAVnuiitPms2jAXX3uVR60VHH63csUuXqnJJn35KjRFJ5381PHlcMktnpfL0yg5VYDgWouTA2R0e6lvcjO3HJNFfFkxLRQg4am5a4BP+kZRGB+/iwhSvuIfCrEE5MciamDUphS2VDqTVqr7/jz9WkbRPP63qJqam8vH2Xrp6JUcvSO+zXVuS4ILjs3noF2Xc/eNiHv/NWL5zYT5zJqdEjprLylKTob/A1daSNNYQuBBCVWd8vqKyTCWE27apWqizZ3st6XDrcNstRg1H83uVktyOemqTwwuc2y1p7fCEzIEzCRVJaXd4WPFmO4tnpjJtQuSLUotFqOjDGFg2P532Lg8bdwWmGqz/TFllh00PPz9YLYKj5qaxalMPTpeMOUXA//0TipN4d0MPQsBPv1KIdfxYOP54JXBSqguOceMirmnGk4gCZ+Sshb35vfSEUO+XUn5RSlkqpbRJKcdJKe+VUjZJKU+QUk6VUp4opeybvTtE9MbbgguDWc1kmyFw3hy4KJQWJuH2qAapW6scJFlh6vg4/3DM4JTOTm/1kuJ8K5lpgl37w1twUkpuftTO2rFH41zxdOjW9l1dyu0ZpUxXXrZFRfXl5oa34Lo9KtFUCOWGsVrh5pth/nzmnjyTb793C+M3vhUQMHHxydm0dnp4eVWMPeyiCFyNMdnG04KbOj6ZFbeN5bDpQRbc2LHK/bxmjbK4jzySUj+LPxRVtaEFbvakFNq7VBNQFi5UAvf88+o3YLgn39vQTUaqYGHwOPzIzrAyfWJKQHh7RCyWwHJdHR3Q1UXKBCVEoapemBZccZ5VuY5ffVV9x7NmeS80QuXCOZySLRgCZ1rmbW0kuezstxSEDWhp7fQgZegqJv4ER1K+/GEXLe0eLjmlH0sEMbBkZiq2JHg/yE25/rNeMtIEU6LMD8vmp9HVI1m5tovuXhlzgImJ6ab83hfzfUstl16qLjLWrElYFwGTaBbcOmCt8Tf4ttZ8USJFaqB4PBKHU8anVU4UzGom24yk3T5rcGEo84uk3FppZ/K45PhGgYLPArVYlAWHukIN6A0Xgvc/6WHVpl7eqzgZ294qmt8LkTAcrUxXu5v0VOFbEyjoW64ry78n3IsvqjSIW29VVkx1NfzrX9SPn83pWx8j6+xTVRK6sSY4f2oKM8qTeeL16JF/gBK4oiIVeBMCM+cqngIHhkUbjBlJ+dRT6vMdeSTF+VYa29w4nKE/295aJ2kpgjF5gdsz1+G2VBrrcG1t8JvfKBE95hjcbsl7n/RwxNy0wf/9+VczMVIEbONLSbGJkGtwdc0u8rIsqo5qRYWvcMDs2RTnWbFaQltwe2qdVGcaPdjMCxdjfw0pY8K6Nc3HQ9Wh9Mc/ktLlljz+ejuzKpKZP3VwPS5pqRYWzUjlg0+6A9Ya12+3s2BqatSLi0UzUklNFjz2mnLV98eCA7joxGx+cGk+xy/2K/xw3nmqNOAjjxzaAielrJBSTjL+Bt8SN+pBwOEye8EN/TKkacFtrXKQniq8brZomC62Aw0utu11eCeiuJKfr5oxHnVUgBBNHqeSjt0hrnR77B7+sqKFSWU2Pv+7S3ALC+/f/HBAjg8QvdByhydwMg9Vq9DoCde7v1aVjzrjDN+TJSVw1VXce9W/+f6Nm+CXv1QVQr71LZASIQRfPDmbmkYX76yPXvYo1hSBeLooIzJ7trKyAI44gtLCJKQkbDrE3loXE4r7lmWaWGIjI02webfDl6qycaOqXGKx8OlOO+1dHpaHcE8eNP7fsVHFRJSVkZ9tCSNwboqM4tLe78ZqhWnTsFoFpYVJIQWustpJW2o+nrR0nwVn7K85oyis5est0xXFgvOPpHxzbTe1TW4uOSU7LoXdj5qXTk2T21vbs7bJRU2jq6+VH4KUZAuLZ6Z63an9teAmj0vmjGVBa4U5Ob4As9rahEVQQj/W4IwCy0uFEJ8zb/EcWLyJay+4KJgWXFunh5KCpJh/9EV5ViwW+ODTbnrtMv7rbyaPPQb33BPw0JRxNnodMuTk8fDL7dQ3u/nuxXlMWTSerkVHMefT/3HrPQ04XX6CGEMngYBqESHqUWakCiwCct97VT3gL3AGNY0uCsqy4Wc/U326/vEP+OlPAVXyaHxxEo++1h422s5LlEan++tdZGdYfCHSQ425Djd9OuTne7sKhEsV2FPrDAgwMbFYBLMqUlTC9+zZvo7YhnvynQ3dpNgES2ZFn0D7jb+LssZX+Dgv20pLmCCT4nzjN2J+N1OmeIublxUmhaxmUnnAgc0mEJMqfBacKXDpY7zu5mCa2mOz4MxIyqpqJ4++2s6kMhtHzEmL+J6BctTcNITwuSljWX/z5+j5alwpNsGY3EHqHXbppb7z+1C14EyEEF8F3gFeAX5u/L01fsOKP16BS4CL0qxHCYSOoAyD1SoozrPykRHWOyOWCiaDwVFH9akab/aG2x0UaLK3zskTr7dz8uEZzJuiJsDsyy6gonk7dau38fuHmnxCEtWCCwrHzs/v46K0WASZ6RZKV72iqjQsWBDwvJTSm+QNwK9/rXKCfvtbuP12lYN0YjY79zlZtSlCdfYYcuCq45wiEBUzktJI5jeDl0IFmnT1eGhsdQekCPgzqyKFyhonXR6bqno/ezbMn4/HI3lvQw9LZ6eSlhIHIQ/hoqS0lLxsax8LTkpJfbOb4mALzq9FTtmYJGpCpApUVjuZUGJDlJf7LDhjf03pESy4GAXOjKR8amUHe2qcfPGU7LiVpcrPsTKzPNlbPHn99l5yMy2RC7D7cficNCwWGFecNHhjPP10X2DYoS5wwLXAEmCPlPI4VCeB1ngNaiiwG+sScU30DoNZjxJij6A0MQNNcjItA2uoOUio0k6w0y/QRErJnx9vISVZcM25ub4Xn3MOANelvs3ra7q573mjnqS5XhIuTaA9yEUZpqNAXqqLCZ+8qU6qIGu4pcNDr0P6BE4I+OtfVQrD9dfDvfdy4tIMSgus3PT3Bn73YBP1zSEmt+pq1cA2SpJ3vNffIjJ/vrJcjC4EhblqDSrUZL03TICJyexJyUhptOd56CFVPFsIb/3CuLgnoa+LMjkZ8vLIDyFwrZ0e7E7pEzjTgvNrcjp2TBJdvdJbOcikstqpalAafeG8+0tJIXVMXsjOHaBclFnplqhrj2Yk5f56dXF17MI4HS+Do+alsX2vg4YWF+u32zlsemrMnqGcTCunHZkxuN9pSoqvU/kwELheKWUvgBAiRUq5DZgev2HFH9OCi3uQRhjMFiH9seDAtw43szw5Po1aYyTZpk5g/1SBt9f3sG5bL1d+PjfwCnfiRFi0iIVbXuKMZRk8/Eo7z7/T4bPGzPwnP5wu1TkhwEWZn686FAT1KFtQt5aU3s6QPerMiSogYdZqhQcfVEEz11xD8vNP87cfl3D+8Vm88VEXl99azd+fbgks4hwlgtLhVNZEQgWupERZmZdcAqgw7qIwjXLNCL9QCcegEsqFUN0qmD7dWz7u3Q3dJFnhiOA0hcHC/I49HmVRlZSAEORnW2nr9ARUrKkzPlexGYVcXq5cz1/6kvc1Zl8+f1d6R7eHhlY3k8qSlSi2tSm3qJFzVzrG5t12ME1tkXPg/DGt44tPyore7eMgWTZPidPjr3fQ1OaOaf3Nnx9cWsCXTg+fnzcgfv5zVcmoKHLifDyJVeD2Gw1PnwVeE0I8B+yJ16CGgkRacOBbhyuJMUXAxCtwQ7X+FoHJ42xeC66718PfVrQwZZyNs5aHSFA97zzE6tVct7yHw2enctfjLdRsr1ML0kl9RcGs9JIfLHDQpw3Pwl1v4LQmwwl9s1VMgetj7SYnw4oVqgr6JZeQ8+GbfOP8PP59SxnHL87gyTc6uPTmah55pY1ehydqo9PaZhceGZ8iy/2iuDjAii0tsIacrPfWOrElhTguBhlpysXl3ztPSsm767tZOCM1LqXIALUGZ3aN8EvyNl3V/qkCdc3qf3OtEYtF5T761X4tK+qbC1dZrS7Kyk0LDtT3W1sLJSWU5FvDrls2t7spiNIM1GTZvDTmT03hlCMiJ2wPBhNKkhhXlOQtXHDYtMTPD5SVwRVXJHQIsXYTOFdK2SqlvBW4CbiXBHYBGAwSbcGZkZSRSvGEwrwiHbIAkwhMGZ9MU5ub1g43D77URmOrm2svzg99tXruuQBYX3iOm68qZOr4ZLZ9XIM7L3yKABh1KE3MtbqgdbhZW15n68QjQ7awMQUuZCpGRoYqID1jhhrfmjWUFCTx4y8V8M+flDB3cgr/eq6Ny2+pYd8qo/RQmIoM1fXxa5NzMJQUhLPgXIwrskW0LGZPSmFLpd2bE7Zrv5OaJjefi5d7EgKrmQQInDpf/ANNvDlw+eGPeUl+EhYB1X6BJpVGofBJZTbfBUtlpXd/JYVJNLS4Q9Y3bY5SxcSfZfPT+eP3iodkjhFCsGxeGm6PWv9L6FrwIUSsQSZHCCGyAKSUbwNvMcw7eifagssxLbgIJ2cojpqXxk+uKDgkrtDMiiZvfNTFijc7OO3IDGZPCjOumTOVkDz9NGmpFm76aiGZPS3UW0K7RcyJrI+LEgLX4XbtoqB2J6smHBdyO9WNLgpzreEnmbw8eOUV5Ua54ALvtieNTeY33yzizu8XkZkm2PHuZ8iSElWwNgRDlQPXX0oKkmhp9/Sp1qIiKCNbm7Mqkunqkew1ivC+s6Ebi1C/wbjh/x0bFhX4BM5/Ha6u2UV6qiAjLfw5nGwTFOVbgyw4JxlpRv5fCAuutCAJj1QFFfyRUpX9ihZgkiiWzVcXHv1ZfxvpxOpnuBvo9LvfaTw2bDFP+ERZcMcvSufik7NJS+2fq8eWJDhxaUbcGwXGwuRxaoL8xzOtpKdauPqc3MhvOPdcePttaGqirDCJibZ29rmy2R0iYbwllIsyVD3KF18E4J2y40MmawdEUIajpASeeEJNcF/9akDVlXlTUvnaeXnkNu2jbczEsJs40OAiI80XPHSoYFqu/pGUDqekttHFxBApAv7M8u/wDby7oYd5U1O8rXXigtlRoK5OBSEZFlye14ILdFEW50dPsxk7xtZH4CaVGWvYeXmqAsz27cozUFLiPWbBlm9nj8Tpip4DlyhmViRz2pEZUWtYjiZiPRuF9IuzlVJ6gEPrUrWf+Cy4xExIc6ekck00QTjEycm0UphrxeWGq87KiT7xnXeeqrLxwgsAFLpb6crI555nWvu8NKSLMlRHgRdfpGP8VKpzJvZNJEcJXEzRposXw223qWjBv/894Kmls1IZ17WfHZaysLlyqshy36TpROMVOL/I0H11TjwyfICJyfiiJLIzLGzZbWdvrZM9Nc74RU+amN/x1q3qb5CLMtiC8+bARaBsTJI3CV9Kye5qh6/RqhDKilu92rs/7zELiqSMtYpJorBaBNdfXhDeizIKiXV23y2E+K4QwmbcrgV2x3Ng8aY3gYneI4mF01OZOzmFM2K5aly0SFVFMYovW5oaGTujhDVbelm7NbCWXotRpiug0kywBdfZCW+9RfNyVUKsPajxqcMpaWwL0W8vHNddp1oKfe97viavgMXjprCjms8sZSqqMAQH4txFYKCYQUz+k3W0FAETIQSzKpLZvNvOuxtUpZejF8TRPQk+gTObtxouymSbIDMtsFxXndEsOBplhUm0d3no6Fa5f1090tvkFFDrcBs3evdnFlQItuBirWKiOXSIVeC+DhwFHAD2A4cD18RrUEOBWZ8vEYneI4kffymfO75XFFtBXSGUm/LVV5VIdXQwaV4JJQVW/vFMa0DZr5YOT98r5ZwctQ3TgnvjDXA46D7hNKBvT7jaZhdS9iMVw2KBBx5QbquLL1bFoAGqq7G4XbQUjOPplX3b67jckpqmBCd5hyE/24otKXCy3lPrxCJiK8s0e1IKe+tcvLKqi1kVyYzJjfNnNF2UZvNWw4IDjFw49R139Xjo7JExCZx54VHd4PS2eKoY6/fZy8t9RbhLS7FaBUV5faNPY03y1hw6xBpFWS+lvFhKWSSlLJZSXiKlTFij0sGgN4GVTEYSQojYq8WDclPa7aoQK5BUVMhXz85l134nr6/xVfVvbnf3LSpsVps3LbgXX1TrJ8uOBvpacCFz4KJRVKQSm7dtUxYdeCtdTDxyGm+v7+6TCF7X5MLjOfQCTEAVFSjOD+wqsKfWRYnRyysaZrTu/npX/N2ToNI3MjL6uCiBgGomvgjK6GIz1i9VwIygrCjzqwLkn/phWIyhok+9LkptwQ0borXL+ZHx989CiLuCb0MzxPjgcEisFkiKcwKmJoijj1aVS/75T3W/oIDjFqUzozyZe583cs4w61CG+HmaHQWkVAJ38slk5amk1mCBM9dd+l3x5YQTVM3Kf/1LFWc2cuCOOHMmSHjunc6Al5sBDIeiwAFG2xyfa29vrZMJMVaNnzEx2VtWbvlhQyBwoNyUnZ3KWvdLEs7PtnqDTMwcuOIYrHP/IuW7qx0U5loD64X6J+8XFwNm78XAKMrmdjcpNkFGqp4zhgvRLDjjMipsy5xhS2+CWuWMeqxWOPts3xpXQQFCCL5+Xi6NrW6eelO5AFs6gqqYmJilnDZsUOWzzjjDr6t34IRU0+gixSZCC2U0br1V1XS85hpYuRKEYMz8SSybn8aL73d6hRj8BS7BSd5hMBvlgmrYub/eGXOdwrRUC1PHJzNlvG3oSsOZ63CFhb5Cz4Sz4KKPKTXZQmGuleoGF1XVzsD1N/BZcH77Ky1Ioimo1ZBZxeRQCyTShCdau5wXhBBWYK6U8t/BtyEaY1xwOLTAJQwj6RvwBo7Mm5LKsvlpPPpqOw0tLjq6PeSHiso061Ea6QGcdhqZ6RaE6LsGZ6YIDGhCstmUG1UI+Pe/VVWGlBTOOy6L9i4Pr6/xtdc50OAiNWWAQjoElBSoIIvuXg/VjS6crugRlP7c9NVCfnFN+C7Xg465DufnngRVzaS7V9Lr8FDX5MKWFBRlG4GxY5JUJGitUYPSH9OC89tfqOjT5vag4t+aQ56o35aU0g0sG4KxDCm9Do+OoEwUJ5wAWVnqf79Cy9eck4vdIbnzMdUuJawF19SkBG7JEiguxmoRZKZZQq7B9bdSTADl5cpNaf4PzJuSwpRxNp5e2eFNGag2iiwfqlf2Zsf42iZXzBGU/pQVJsXclHdQMC24PgLnq2ZS1+ymKC/26vdjxySxfY8Dp4u+ApedrfZprL+Bz63pH33a3ObWEZTDjFgvRzYIIZ4XQlwuhDjPvMV1ZHHG7pQJaXaqAVJTfX3b/FrljC+2cebyTD7cqFIGQl6dFxQo1+Tq1QG937IyLAEWnJSS6qaDFDhQ1U1+9zvVJBUVVHP+8VlU1Tj5eLtKgD5UUwRMiv0Sl80iy+P7IXBDjilwfoIDgblwsebAmZSNUdVJACrGhmgz9aUvebtegK+As3+gSVP7oVvFRBOaWM/KVKAJON7vMQk8PegjGiLsDqktuERy883KAksPDFz48uk5vLa6i+5eGXoyyc8Hh5GL5i9w6RY6/Cy41k4PvXY5OOtG118fcPe4RRnc80wrT73ZzoJpKdQ0ujh6/hAFYAwAM02irsnN3lpVuixuxZIHgygWnClwh8+OPSfPXB+1WMJYr3/8Y8DdAiO9wkwVsDs8dPVIbcENM2I6+6WUX4n3QIYau16DSywzZ/ZpogqQm2XlslNz+NfzraEDCMzJr7gYFi70PpydYaHNT+AGlCIQI8k2weeXZ/Kfl9pZv70Xl/vQjaAE1TswNVl4Lbj+uCcTQtg1OCUudc0umts9MUVQmpg5iuPGxJYeYaZXmJ29zfw7bcENL2IttjxOCPGMEKLeuD0lhBh3MDsWQnxPCLFZCLFJCPGoEKJ/DYwOErtTW3CHKhedlMVDPy8LnW9kujRPP11djhtkpQe6KOMpcABnLc8iyQp3P9UKHNoCJ4RQeV2Nag1uQpQalAknjIsyJ8uCRcD2PcqC75eL0vgdlAevv0WgtDDJuwanc+CGJ7H6Ke4HngfKjNsLxmMDQggxFvgusFhKOQewAhcPdHsDQVtwhy7mhBwSI08puLlpdkagi7La2yYnPhNSfo6V4xdnUGlUxjD7jh2qlBRY2bzbTo9d9iuCMiGEcVFaLYKcLAtbq0yBi/2YZ6RZOHFJOscvzoj5PSX5Sd4oSjM9oUBbcMOKWAVujJTyfimly7g9ABxs3HASkCaESALSgeqD3F6/0BbcMOXYY+G55wICAkAJXGePx1vuq6bRRUGONa7FtM87TkWCptjEIT/xlRQk0dapLgDKD3WBO+UUlYd4xBF9nsrPtnoT+PsjcAA/+Uohn+tHsnpJoTpmPb0ebcENU2I9+5uEEJcJIazG7TJU0MmAkFIeAG4H9gI1QJuU8tWBbm8g9DpkwnrBaQ4CiwXOOivAPQnKRSmlqlEIg5AiEAPTJiSzYGoK5WW2Q6J9UST8LeIJMSZ5J4ysLLjlloAkbxOzfJtFoPq5xZFSv0jK5nY3FsEh1w5JE5lYv60rgQuBWpQgXQAMOPBECJEHnA1UoFyeGYZoBr/uGiHEWiHE2oaGhoHuLiQOXclkRJGVoSYj001Z0+jyTlDx5OdfG8OvvzGESdADxBS47AwLucN4kjaDPApyrHEvs+ffF665zU1etrV/dVc1CSfWKMo9wFmDuN8TgUopZQOAEOJpVLeCh4L2ew9wD8DixYtDN+IaIDrRe2SRbZTrau/yMMYpaWjtR5ucgyCgpuEhjHksJpQcej3r+oNZSaQ/EZQDpcQvvaJJVzEZlsT0KwlTWLkNWCulfG4A+90LHCGESAd6gBNQ9S6HBI9HdebVid4jh6wMQ+C6PdQZbXLKDtHakInAnKyjdfE+1DGr2/QngnKg5GRaSE0RXguuMFevvw03Yp3hU4EFwA7jNg8YB1wlhLizvzuVUq4GVgAfAxuNcdzT3+0MFLObt7bgRg6mwHV0ebwpAkNWHHgYkJVu4dxjMznp8NijCA9F8r0CF//vVghBaYFKFWjWVUyGJbH+SuYBy4y6lAgh7gbeBY5GCVS/kVLeAtwykPceLLrZ6cgjJ8PnojQDTYbCRTmc+M6F+YkewkFjikxRnANMTIrzrVQ3umjt8OgIymFIrBZcHpDpdz8DyDcEzz7oo4oz3man2oIbMZilpzq6VcX8ZJvQayYjkEljbZSX2pg7JWVI9ldamMSeGiceqXPghiOxXuL+DlVw+S1AAJ8DfiOEyABej9PY4oZdW3AjDqtVkJEm6OhyU9/iHnibHM0hTU6mlftuKo3+wkGipMBXpFlbcMOPWKMo7xVCvAQsNR76iZTSTMy+PszbDlns2oIbkWSnq5Y5NU1DkyKgGfmU+kVr6jW44UestSgFKtJxvhE1mSSEWBrlbYcsXoHTFtyIIivDqgSu0aUDTDSDgn+CvO4kMPyIdZHib8CRwBeN+x3AX+MyoiFAuyhHJlnpFvY3uOjulTrARDMolBRqC244E6vAHS6l/BbQCyClbAFCdA0cHtgdKsounnUKNUNPdqbFW6dQC5xmMMhMs5CVrm6xtNnRHFrEOgs4hRBWVJNThBBjAE/ktxy6mC5K/YMdWWT7VRXRAqcZLEoKrN7UIs3wItZZ4C7gGaBICPFrVC3Km+I2qjhjuih1seWRhZnsDVrgNIPH6csytcANU2KNonxYCLEOFWgigHOklFvjOrI4oi24kYlZFzI/26Ldz5pB4+zPZSV6CJoBEmstygellJcD20I8NuwwE721BTeyyDYsOG29aTQaiD3IZLb/HWM9btHgD2docOhalCMS04LTAqfRaCCKwAkhbhRCdADzhBDtQogO4349MJAuAocEvQ5JklVVv9CMHLKNnnA6B06j0UAUgZNS/lZKmQX8XkqZLaXMMm4FUsobh2iMg47dKbX1NgIxE3HHF+s2ORqNJvYgkxuNLtxTUa1zzMffidfA4ond4dFJ3iOQ0sIk/nJ9MdMnDtsUTY1GM4jEGmTyVeBaVA+4DcARwIfA8XEbWRzRFtzIZVbF0FSZ12g0hz6xBplcCywB9kgpjwMOA1rjNah4Y3dI3c1bo9FoRjixzvK9UspeACFEipRyGzA9fsOKL9qC02g0mpFPrOFm+4UQucCzwGtCiBZgT7wGFW+UBacFTqPRaEYysQaZnGv8e6sQYiWQA7wct1HFGbtTBtQt1Gg0Gs3II9Z+cEcIIbIApJRvA2+h1uGGJXaH1GW6NBqNZoQTqxlzN9Dpd7/TeGxYYndKXaZLo9FoRjixCpyQUnrLaUspPcS+fhd6g0LkCiFWCCG2CSG2CiGOPJjt9Qe7Q5KsBU6j0WhGNLEK3G4hxHeFEDbjdi2w+yD3/SfgZSnlDGA+MGTdCewOD6naRanRaDQjmlgF7uvAUcABYD9wOHDNQHcqhMgBPgfcCyCldEgpWwe6vf5id0qSdR6cRqPRjGhimuWllPVSyoullEVSymIp5SVSynrzeSFEf+tSVgANwP1CiPVCiH8JITL6uY0B4fZInC7dKkej0WhGOoNlxnyhn69PAhYCd0spDwO6gBuCXySEuEYIsVYIsbahoWEQhgkO3exUo9FoRgWDJXD9VYv9wH4p5Wrj/gqU4AUgpbxHSrlYSrl4zJgxBztGQLknQVtwGo1GM9IZLIGT0V/i92Ipa4F9Qgiz3NcJwJZBGktEzG7eulSXRqPRjGwGqzPkQNTiO8DDQohkVETmVwZpLBGxmwKnLTiNRqMZ0QyWwD3Z3zdIKTcAiwdp/zFjuii1BafRaDQjm1hLdU0TQrwhhNhk3J8nhPiZ+byU8jfxGuBgY3d4AG3BaTQazUgn1jW4fwI3Ak4AKeWnwMXxGlQ80RacRqPRjA5iFbh0KeWaoMdcgz2YocC3BqcTvTUajWYkE+ss3yiEmIwRLSmEuACoiduo4oi24DQajWZ0EGuQybeAe4AZQogDQCVwWdxGFUd0FKVGo9GMDmJteLobONEop2WRUnbEd1jxw2vBaYHTaDSaEU2sUZTXCiGygW7gj0KIj4UQJ8d3aPHBTPTW3QQ0Go1mZBPrGtyVUsp24GSgALgcuC1uo4ojDsOC0/3gNBqNZmQTc8NT4+/pwH+klJsZWPWShNPrkNiSwGoZlsPXaDQaTYzEKnDrhBCvogTuFSFEFuCJ37Dih8Ph0Z0ENBqNZhQQNchECCGAm4ExwG4pZbcQooAhqh052PQ6Jak6B06j0WhGPFEFTkophRAvSSnn+j3WBDTFdWRxwuGQ2oLTaDSaUUCspszHQoglcR3JENHrkLoXnEaj0YwCYk30Phy4VAixB9V9W6CMu3lxG1mccDilzoHTaDSaUUCsAndKXEcxhPQ6pC7TpdFoNKOAmFyUUso9Uso9QA+qHqV5G3bYtQWn0Wg0o4JYK5mcJYTYgapB+TZQBfwvjuOKG3ZtwWk0Gs2oINYgk18CRwCfSSkrgBOAVXEbVRyxOzzagtNoNJpRQKwC5zRSAyxCCIuUciWwOI7jiht2p7bgNBqNZjQQa5BJqxAiE3gXeFgIUY+Kphx22B1SNzvVaDSaUUCsM/3ZqACT64CXgV3A5+M0priiLTiNRqMZHcTaD65LCFECLAWagVcMl+Wwwu2WuNzoRG+NRqMZBcQaRflVYA1wHnABsEoIceXB7lwIYRVCrBdC/PdgtxULZrNTXapLo9FoRj6xrsFdDxxmWm1GseUPgPsOcv/XAluB7IPcTkx4m51qC06j0WhGPLGuwTUBHX73OzjIYstCiHHAGcC/DmY7/UE3O9VoNJrRQ6wW3E5gtRDiOVQFk7OBT4UQ3weQUt4xgH3fCfwIyAr3AiHENcA1ABMmTBjALgLxWnDaRanRaDQjnlgtuF3As/jKcz2HqmqSRQSBCocQ4kygXkq5LtLrpJT3SCkXSykXjxkzpr+76YO24DQajWb0EGsU5c/N/4UQFiBTStl+EPtdBpwlhDgdSAWyhRAPSSkvO4htRqXXoZqQ64anGo1GM/KJNYryESFEthAiA9gEbBFCXD/QnUopb5RSjpNSlgMXA2/GW9xAJXkDOg9Oo9FoRgGxmjKzDIvtHFSR5Qrg8ngNKl54BU67KDUajWbEE6vA2YQQNpTAPS+ldDJI7XKklG9JKc8cjG1Fw8yD0xacRqPRjHxijaL8B6pFzifAO0KIicDBrMElBG3BaRKF0+lk//799Pb2JnooGs2wJDU1lXHjxmGz2WJ+T6xBJncBd5n3hRB7geP6PcIEoy04TaLYv38/WVlZlJeXI4T+/Wk0/UFKSVNTE/v376eioiLm90V0UQohLjOiJoN3JqWULiHEZCHE0QMYb0LQFpwmUfT29lJQUKDFTaMZAEIICgoK+u0BiWbBFQDrhRDrgHVAAyqsfwpwDNAI3ND/4SYGbcFpEokWN41m4Azk/IkocFLKPwkh/gIcj8pdm4dqm7MVuFxKuXcA40wYdocHWxJYLHqi0Wg0mpFO1ChKKaVbSvmalPJWKeXXpJTXSSn/MdzEDZQFp5O8NaOR1tZW/va3v/X7faeffjqtra39ft8VV1zBihUr+v0+jWYwGVWzvd0hdasczagknMC5XK6I73vppZfIzc2N06g0mvgyqgSu1yF1qxzNqOSGG25g165dLFiwgCVLlrB8+XLOOussZs2aBcA555zDokWLmD17Nvfcc4/3feXl5TQ2NlJVVcXMmTO5+uqrmT17NieffDI9PT0x7fuNN97gsMMOY+7cuVx55ZXY7XbvmGbNmsW8efP44Q9/CMCTTz7JnDlzmD9/Pp/73OcG+ShoRhux5sGNCOxOqQNMNAnnL0+2sGu/Y1C3OXlcMt/+Ql7Y52+77TY2bdrEhg0beOuttzjjjDPYtGmTN+T6vvvuIz8/n56eHpYsWcL5559PQUFBwDZ27NjBo48+yj//+U8uvPBCnnrqKS67LHKFvd7eXq644greeOMNpk2bxpe+9CXuvvtuLr/8cp555hm2bduGEMLrBv3FL37BK6+8wtixYwfkGtVo/Im1FuW1Ri1KIYS4VwjxsRDi5HgPbrCxO6ROEdBogKVLlwbkE911113Mnz+fI444gn379rFjx44+76moqGDBggUALFq0iKqqqqj72b59OxUVFUybNg2AL3/5y7zzzjvk5OSQmprKVVddxdNPP016ejoAy5Yt44orruCf//wnbrf74D+oZlQTqwV3pRFReQqQh6pD+SDwatxGFge0Bac5FIhkaQ0VGRkZ3v/feustXn/9dT788EPS09M59thjQ+YbpaSkeP+3Wq0xuyhDkZSUxJo1a3jjjTdYsWIFf/nLX3jzzTf5+9//zurVq3nxxRdZtGgR69at62NJajSxEqvAmapwOvCglHKzGIZJPXaHJC9rVC07ajQAZGVl0dHREfK5trY28vLySE9PZ9u2baxatWrQ9jt9+nSqqqrYuXMnU6ZM4cEHH+SYY46hs7OT7u5uTj/9dJYtW8akSZMA2LVrF4cffjiHH344//vf/9i3b58WOM2AiVXg1gkhXkV1EbhRCJEFeOI3rPhgd2oXpWZ0UlBQwLJly5gzZw5paWkUFxd7nzv11FP5+9//zsyZM5k+fTpHHHHEoO03NTWV+++/ny984Qu4XC6WLFnC17/+dZqbmzn77LPp7e1FSskdd9wBwPXXX8+OHTuQUnLCCScwf/78QRuLZvQhpIzeFMAo17UA2C2lbBVC5APjpJSfxnl8XhYvXizXrl17UNu45KYDzJ2cwo1XFA7SqDSa2Ni6dSszZ85M9DA0mmFNmPMorNUSq7/uSGC7IW6XAT8D2gY2xMShLDjtotRoNJrRQKyz/d1AtxBiPvADYBfwn7iNKk7oKEqNZnD51re+xYIFCwJu999/f6KHpdEAsa/BuaSUUghxNvAXKeW9Qoir4jmweGB36ChKjWYw+etf/5roIWg0YYlV4DqEEDei0gOWG2tysXedOwRwuSVuj26Vo9FoNKOFWF2UFwF2VD5cLTAO+H3cRhUHvL3gtAWn0Wg0o4KYBM4QtYeBHCHEmUCvlHJYrcHpZqcajUYzuoi1VNeFwBrgC8CFwGohxAXxHNhgY7HA5w5LY1zRsPKsajQajWaAxOqi/CmwREr5ZSnll4ClwE0D3akQYrwQYqUQYosQYrMQ4tqBbitWcrOs3Hr1GBbNSI33rjSaQ46B9oMDuPPOO+nu7o74GrPrgEZzKBGrwFmklPV+95v68d5QuIAfSClnAUcA3xJCzDqI7Wk0mgjEW+A0mkORWKMoXxZCvAI8aty/CHhpoDuVUtYANcb/HUKIrcBYYMtAt6nRDBuuuw42bBjcbS5YAHfeGfZp/35wJ510EkVFRTzxxBPY7XbOPfdcfv7zn9PV1cWFF17I/v37cbvd3HTTTdTV1VFdXc1xxx1HYWEhK1eujDqUO+64g/vuuw+Ar371q1x33XUht33RRRdxww038Pzzz5OUlMTJJ5/M7bffPkgHRKOJUeCklNcLIc4HlhkP3SOlfGYwBiCEKAcOA1YPxvY0Gk1f/PvBvfrqq6xYsYI1a9YgpeSss87inXfeoaGhgbKyMl588UVAFWHOycnhjjvuYOXKlRQWRi9xt27dOu6//35Wr16NlJLDDz+cY445ht27d/fZdlNTU8iecBrNYBFzw1Mp5VPAU4O5cyFEprHN66SU7SGevwa4BmDChAmDuWuNJnFEsLSGgldffZVXX32Vww47DIDOzk527NjB8uXL+cEPfsCPf/xjzjzzTJYvX97vbb/33nuce+653nY85513Hu+++y6nnnpqn227XC5vT7gzzzyTM888c1A/p0YTcR1NCNEhhGgPcesQQvQRpP4ghLChxO1hKeXToV4jpbxHSrlYSrl4zJgxB7M7jUZjIKXkxhtvZMOGDWzYsIGdO3dy1VVXMW3aND7++GPmzp3Lz372M37xi18M2j5DbdvsCXfBBRfw3//+l1NPPXXQ9qfRQBSBk1JmSSmzQ9yypJTZA92p0UvuXmCrlPKOgW5Ho9HEhn8/uFNOOYX77ruPzs5OAA4cOEB9fT3V1dWkp6dz2WWXcf311/Pxxx/3eW80li9fzrPPPkt3dzddXV0888wzLF++POS2Ozs7aWtr4/TTT+ePf/wjn3zySXw+vGbUErOLcpBZhir7tVEIscF47CdSygEHrmg0mvD494M77bTTuOSSSzjyyCMByMzM5KGHHmLnzp1cf/31WCwWbDYbd999NwDXXHMNp556KmVlZVGDTBYuXMgVV1zB0qVLARVkcthhh/HKK6/02XZHR0fInnAazWARUz+4Q4HB6Aen0SQK3Q9Oozl44tUPTqPRaDSaYUWiXJQajWYYcvjhh2O32wMee/DBB5k7d26CRqTRhEcLnEajiZnVq3W6qmb4oF2UGo1GoxmRDBsLbvv27Rx77LEBj1144YV885vfpLu7m9NPP73Pe6644gquuOIKGhsbueCCvs0PvvGNb3DRRRexb98+Lr/88j7P/+AHP+Dzn/8827dv52tf+1qf53/2s59x4oknsmHDBq677ro+z//mN7/hqKOO4oMPPuAnP/lJn+fvvPNOFixYwOuvv86vfvWrPs//4x//YPr06bzwwgv84Q9/6PP8gw8+yPjx43n88ce9EW/+rFixgsLCQh544AEeeOCBPs+/9NJLpKen87e//Y0nnniiz/NvvfUWALfffjv//e9/A55LS0vjf//7HwC//OUveeONNwKeLygo4KmnVF2AG2+8kQ8//DDg+XHjxvHQQw8BcN1117EhqHTVtGnTuOeeewAVxffZZ58FPL9gwQLuNBKmL7vsMvbv3x/w/JFHHslvf/tbAM4//3yampoCnj/hhBO46SZVL/y0006jp6cn4PkzzzyTH/7whwB9fnfQ/9/eLbfcgsXiu54cM2YM+fn5OBwOKisr+7y/uLiY3Nxcent72bNnT5/nS0tLyc7Opru7m3379vV5fuzYsWRmZtLZ2cmBAwf6PD9+/HjS09Npb2+npqamz/MTJ04kNTWV1tZW6urq+jxfUVFBcnIyzc3NNDQ09Hl+0qRJ2Gw2Ghsb+xx7gClTpmC1Wqmvr6elpaXP89OnTwegtraWtra2gOeEEEybNg2A6urqPikMVquVKVOmALB//366uroCnrfZbEyaNAmAvXv39vnuU1JSKC8vB6CqqqqPSzYtLc1beGL37t04nc6A5zMyMhg3bhwAO3fuxO12BzyflZVFWVkZAJ999hnBgX45OTmUlJQAat4LJi8vj6KiItxuNzt37uzzfEFBAYWFhTidTnbv3t3n+eH826utrSUzMzNg3jPnqVBoC06j0Wg0IxMp5bC4LVq0SGo0w5UtW7YkdP+VlZVy9uzZcdv+ypUr5RlnnCGllPK5556Tv/3tbwdlux6PRx533HGyra1tULYnpZQtLS3yr3/964DeO3HiRNnQ0CCllPLII4886LHcf//98lvf+tZBbyde3H///fLAgQP9ft1VV10lN2/ePKB9/vnPf5b33ntvyOfCnEdhdUNbcBqNZlA566yzuOGGGwZlWy+99BLz588nO3vAhZP6EKl1kMvlink7H3zwwWAN6ZDlgQceoLq6ut+v+9e//sWsWQPrgHbllVfy5z//eUDvDUYLnEYzSnC5XFx66aXMnDmTCy64wNvj7Re/+AVLlixhzpw5XHPNNd41obvuuotZs2Yxb948Lr74YgC6urq48sorWbp0KYcddhjPPfdcn/088MADfPvb3wbUWuR3v/tdjjrqKCZNmsSKFSu8r/v973/PkiVLmDdvHrfcckvIMT/88MOcffbZ3vv/+c9/mDdvHvPnz/eumzc0NHD++eezZMkSlixZwvvvvw/ArbfeypVXXsmxxx7LpEmTuOuuu4DA1kHXX389b731FsuXL+ess87yTsrnnHMOixYtYvbs2d614GAyMzMBuPnmm1mwYAELFixg7NixfOUrXwHgoYceYunSpSxYsICvfe1r3rW4+++/n2nTprF06VLvWIPp7OzkK1/5CnPnzmXevHne9exHH32UuXPnMmfOHH784x8HjOX6669n9uzZnHjiiaxZs8b7uZ9//nnv93L22Wdz7LHHMnXqVH7+858Dap1xzpw53m3dfvvt3HrrraxYsYK1a9dy6aWXsmDBAnp6ekL+VkK97thjj8UszBFpzD/96U+ZP38+RxxxhHe9LT09nfLyctasWRPy2PSLSObdoXTTLkrNcCbYtXLMMcf0uZlus66urpDP33///VJKKRsaGvo8F43KykoJyPfee09KKeVXvvIV+fvf/15KKWVTU5P3dZdddpl8/vnnpZRSlpaWyt7eXimlcutJKeWNN94oH3zwQe9jU6dOlZ2dnQEuSn+325e//GV5wQUXSLfbLTdv3iwnT54spZTylVdekVdffbX0eDzS7XbLM844Q7799tt9xj1hwgTZ3t4upZRy06ZNcurUqV4XoTnuL37xi/Ldd9+VUkq5Z88eOWPGDCmllLfccos88sgjZW9vr2xoaJD5+fnS4XD0cdeuXLlSpqeny927d3sfM7fd3d0tZ8+eLRsbG6WUgS7KjIyMgLG2tLTIOXPmyLVr18otW7bIM888UzocDimllN/4xjfkv//9b1ldXS3Hjx8v6+vrpd1ul0cddVRIF+WPfvQjee2113rvNzc3ywMHDnjf63Q65XHHHSefeeYZKaWUgHzppZeklFKec8458qSTTpIOh0Nu2LBBzp8/3/u9lJSUyMbGRu/n+uijj/ocj9///vfylltukVKq3+lHH33U57hIGfhbCX6deT/amM33X3/99fKXv/yl9/2/+tWv5O23397nuGgXpUajCcn48eNZtky1dLzssst47733AFi5ciWHH344c+fO5c0332Tz5s0AzJs3j0svvZSHHnqIpCQVcP3qq69y2223sWDBAo499lh6e3vZu3dvxP2ec845WCwWZs2a5b1K92/Zs3DhQrZt28aOHTv6vLe5uZmsrCwA3nzzTb7whS94+9Ll5+cD8Prrr/Ptb3+bBQsWcNZZZ9He3u4tJH3GGWeQkpJCYWEhRUVFISNCAZYuXUpFRYX3/l133eW1LPbt2xdybP5IKbnsssv4/ve/z6JFi3jjjTdYt24dS5YsYcGCBbzxxhvs3r2b1atXc+yxxzJmzBiSk5O56KKLQm7v9ddf51vf+pb3fl5eHh999JH3vUlJSVx66aW88847ACQnJ3u7McydO5djjjkGm83G3Llzqaqq8m7npJNOoqCggLS0NM477zzvbyBWwv1WwhFtzGaLpEWLFgWMs6ioKCbXaDSGTZqARjOSiBTanJ6eHvH5wsLCiM+HQzXxCLzf29vLN7/5TdauXcv48eO59dZb6e3tBeDFF1/knXfe4YUXXuDXv/41GzduRErJU0895Q3jNwknHKDC7k2k4f6URsueUOk3/iQlJeHxeAJSLILxeDysWrWK1NTUiPu2Wq1h19jM/nWgvpvXX3+dDz/8kPT0dK+QR+LWW29l3LhxXveklJIvf/nL3lQVk2effTbidgaKzWbzfr8Wi8X7uS0WS8BnDvUbMI+xSbjPGum3crBjDv5uent7SUtLG/C2TbQFp9GMEvbu3evNR3zkkUc4+uijvRNUYWEhnZ2d3jUyj8fDvn37OO644/i///s/2tra6Ozs5JRTTuHPf/6zV6jWr18/oLGEa9kTzPTp0725XMcffzxPPvmkN6+uubkZgJNPPjkgKCE4pzKYaO1/2trayMvLIz09nW3btrFq1aqI23vhhRd4/fXXvWt8oPIsV6xY4f1Mzc3N7Nmzh8MPP5y3336bpqYmnE4nTz75ZMhtnnTSSfz1r3/13m9paWHp0qW8/fbbNDY24na7efTRRznmmGMiji2Y1157jebmZnp6enj22WdZtmwZxcXF1NfX09TUhN1uD8h59T9W4X4rwa/zZ6Bj/uyzzwLWBQeKFjiNZpQwffp0/vrXvzJz5kxaWlr4xje+QW5uLldffTVz5szhlFNOYcmSJQC43W4uu+wy5s6dy2GHHcZ3v/tdcnNzuemmm3A6ncybN4/Zs2d7k+X7y8knn+xt2TN37lwuuOCCkBPkGWec4bVWZ8+ezU9/+lOOOeYY5s+fz/e//31AuRPXrl3LvHnzmDVrFn//+98j7tu/ddD111/f5/lTTz0Vl8vFzJkzueGGGzjiiCMibu+OO+7gwIED3oCSm2++mVmzZvGrX/2Kk08+mXnz5nHSSSdRU1NDaWkpt956K0ceeSTLli0L22HiZz/7GS0tLcyZM4f58+ezcuVKSktLue222zjuuOOYP38+ixYtCgjAiYWlS5dy/vnnM2/ePM4//3wWL16MzWbj5ptvZunSpZx00knMmDHD+/orrriCr3/96yxYsICUlJSQv5Xg1/knzg90zO+//z4nnXRSvz5bKHS7HI1mCNDtcgZGTU0NX/rSl3jttdcSPZRhzwMPPMDatWv5y1/+kuihRGT9+vXccccdPPjgg32e0+1yNBrNiKG0tJSrr76a9vb2RA9FM0Q0Njbyy1/+clC2pS04jWYI0BacRnPwaAtOo9FoNBq0wGk0Q8Zw8ZZoNIciAzl/tMBpNENAamoqTU1NWuQ0mgEgpaSpqSlkrmMkdKK3RjMEjBs3jv3794fsnabRaKKTmprq7bMXKwkTOCHEqcCfACvwLynlbYkai0YTb2w2W0ApKI1GE38S4qIUQliBvwKnAbOALwohBtZbQaPRaDSaECRqDW4psFNKuVtK6QAeA/qXkq/RaDQaTQQSJXBjgX1+9/cbj2k0Go1GMygc0kEmQohrgGuMu51CiO2DsNlCoHEQtjMS0ccmPPrYhEcfm/DoYxOewTo2L0spTw31RKIE7gAw3u/+OOOxAKSU9wCh2+kOECHEWinl4sHc5khBH5vw6GMTHn1swqOPTXiG4tgkykX5ETBVCFEhhEgGLgaeT9BYNBqNRjMCSYgFJ6V0CSG+DbyCShO4T0oZuTWsRqPRaDT9IGFrcFLKl4CXErDrQXV5jjD0sQmPPjbh0ccmPPrYhCfux2bYdBPQaDQajaY/6FqUGo1GoxmRjBqBE0KcKoTYLoTYKYS4IdHjSTRCiPuEEPVCiE1+j+ULIV4TQuww/uYlcoyJQAgxXgixUgixRQixWQhxrfH4qD82AEKIVCHEGiHEJ8bx+bnxeIUQYrVxfj1uBI+NOoQQViHEeiHEf437+rgYCCGqhBAbhRAbhBBrjcfiel6NCoHTpcFC8gAQnDtyA/CGlHIq8IZxf7ThAn4gpZwFHAF8y/it6GOjsAPHSynnAwuAU4UQRwD/B/xRSjkFaAGuStwQE8q1wFa/+/q4BHKclHKBX3pAXM+rUSFw6NJgfZBSvgM0Bz18NvBv4/9/A+cM5ZgOBaSUNVLKj43/O1CT1Vj0sQFAKjqNuzbjJoHjgRXG46Py+AghxgFnAP8y7gv0cYlGXM+r0SJwujRYbBRLKWuM/2uB4kQOJtEIIcqBw4DV6GPjxXDDbQDqgdeAXUCrlNJlvGS0nl93Aj8CPMb9AvRx8UcCrwoh1hlVqiDO59UhXapLkziklFIIMWpDbIUQmcBTwHVSynZ1Ma4Y7cdGSukGFgghcoFngBmJHVHiEUKcCdRLKdcJIY5N8HAOVY6WUh4QQhQBrwkhtvk/GY/zarRYcDGVBtNQJ4QoBTD+1id4PAlBCGFDidvDUsqnjYf1sQlCStkKrASOBHKFEOYF82g8v5YBZwkhqlBLIMej+l2O9uPiRUp5wPhbj7owWkqcz6vRInC6NFhsPA982fj/y8BzCRxLQjDWTe4Ftkop7/B7atQfGwAhxBjDckMIkQachFqnXAlcYLxs1B0fKeWNUspxUspy1PzyppTyUkb5cTERQmQIIbLM/4GTgU3E+bwaNYneQojTUT5yszTYrxM7osQihHgUOBZV0bsOuAV4FngCmADsAS6UUgYHooxohBBHA+8CG/GtpfwEtQ43qo8NgBBiHioYwIq6QH5CSvkLIcQklOWSD6wHLpNS2hM30sRhuCh/KKU8Ux8XhXEcnjHuJgGPSCl/LYQoII7n1agROI1Go9GMLkaLi1Kj0Wg0owwtcBqNRqMZkWiB02g0Gs2IRAucRqPRaEYkWuA0Go1GMyLRAqfRaDSaEYkWOI0GEEJ8VwixVQjxcIyvf0sIsTj6K2Pa1jkD6W4hhOiM/qqBE237QohcIcQ34zkGjeZg0AKn0Si+CZxkVJ8YdIyWTeE4B9XGabiRizpuGs0hiRY4zahHCPF3YBLwPyHET41msGuMxpVnG69JE0I8Zlh5zwBpfu8/WQjxoRDiYyHEk0ahZrPB4/8JIT4GviCEuFoI8ZHRLPQpIUS6EOIo4Czg90YjyMnG7WWj6vq7QogZxvYqjP1sFEL8KspnOtZsumnc/4sQ4gq/cf3O2M4aIcSUSNsXQmQKId4wPt9G85gAtwGTjXH/3njt9cZn/FQYzVA1mkShBU4z6pFSfh2oBo4DMlB1BJca939v1M77BtAtpZyJKmu2CEAIUQj8DDhRSrkQWAt832/zTVLKhVLKx4CnpZRLjGahW4GrpJQfoOrxXW80gtwF3AN8R0q5CPgh8DdjW38C7pZSzgVqODjajO38BVXCLtL2e4Fzjc93HPAHo2bnDcAuY9zXCyFOBqaiiuguABYJIT53kOPUaAaMbpej0QRyMqoq/A+N+6moOnmfA+4CkFJ+KoT41Hj+CJR78X2jpU4y8KHf9h73+3+OYRnlApnAK8E7N6y/o4An/Vr0pBh/lwHnG/8/iOoWPVAe9fv7xyjbF8BvDLHyoHqaherbdbJxW2/cz0QJ3jsHMU6NZsBogdNoAhHA+VLK7QEP+vWDC/H616SUXwzzfJff/w8A50gpPzHchceGeL0F1SRzQZjtxVo81kWghyY1wnbC/W9yKTAGWCSldBotYYK3B+pY/FZK+Y8Yx6jRxBXtotRoAnkF+I7hgkMIcZjx+DvAJcZjc4B5xuOrgGV+61gZQohpYbadBdQY/eb8g1k6jOeQUrYDlUKILxjbE0KI+cbr3ke1YiHo/aHYA8wSQqQY7W1OCHr+Ir+/psUZbvs5qGaeTiHEccDE4HEbvAJc6bcGOVao5pYaTULQAqfRBPJLwAZ8KoTYbNwHuBvIFEJsBX4BrAOQUjYAVwCPGm7LDwnf4fomVNud9wH/bsaPAdcbQS2TUeJylRDiE2AzYAZ1XAt8SwixEeUmDIuUch+qDckm4+/6oJfkGeO9FvhelO0/DCw2Hv+SOXYpZRPKNbtJCPF7KeWrwCPAh8ZrVxAogBrNkKLb5Wg0owzDxbhYStmY6LFoNPFEW3AajUajGZFoC06jGcYIIeaiIh79sUspD0/EeDSaQwktcBqNRqMZkWgXpUaj0WhGJFrgNBqNRjMi0QKn0Wg0mhGJFjiNRqPRjEi0wGk0Go1mRPL/va8pY1muArIAAAAASUVORK5CYII=\n",
      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, constrained_layout=True)\n",
    "# federated results\n",
    "metrics_df.plot(ax=ax, y=[\"train_loss\", \"test_loss\"], color=[\"royalblue\", \"red\"])\n",
    "# centralized baseline\n",
    "ax.axhline(\n",
    "    history.history[\"val_loss\"][-1],\n",
    "    linestyle=\"--\",\n",
    "    color=\"black\",\n",
    "    label=\"baseline (centralized computation)\",\n",
    ")\n",
    "ax.set_ylabel(\"loss (sparse_categorical_crossentropy)\")\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.legend()\n",
    "ax.set_ylim(ymin=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, constrained_layout=True)\n",
    "# plot federated learning results\n",
    "metrics_df.plot(\n",
    "    ax=ax, y=[\"train_accuracy\", \"test_accuracy\"], color=[\"royalblue\", \"red\"]\n",
    ")\n",
    "# centralized baseline\n",
    "ax.axhline(\n",
    "    history.history[\"val_sparse_categorical_accuracy\"][-1],\n",
    "    linestyle=\"--\",\n",
    "    color=\"black\",\n",
    "    label=\"baseline (centralized computation)\",\n",
    ")\n",
    "# random guessing baseline\n",
    "ax.axhline(\n",
    "    1 / NUNIQUE_LABELS, linestyle=\"-.\", color=\"grey\", label=\"baseline (random quessing)\"\n",
    ")\n",
    "ax.legend()\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.set_ylim(0, 1)\n",
    "plt.savefig(Path.cwd() / \"results\" / \"accuracy.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the current setup, we can reach test accuracy of 50% and slightly above (depending on the run). However, at times the model falls back to the level of random quessing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can do federated learning of customer paths even with quite little data\r\n",
    "- It's doing significantly better than random guessing\r\n",
    "- However, cetralized model stil outperforms the federated computation. More work is required for optimization.\r\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fcj38",
   "language": "python",
   "name": "fcj38"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}