17_deep_learning/02_how_to_use_tensorflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "# How to use Tensorflow 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Keras was designed as a high-level or meta API to accelerate the iterative workflow when designing and training deep neural networks with computational backends like TensorFlow, Theano, or CNTK. It has been integrated into TensorFlow in 2017 and is set to become the principal TensorFlow interface with the 2.0 release. You can also combine code from both libraries to leverage Keras’ high-level abstractions as well as customized TensorFlow graph operations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Please follow the installations instructions in `Installation Guide.md` in the root folder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Imports & Settings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:17.624718Z",
     "start_time": "2021-02-23T05:36:17.619204Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:19.513260Z",
     "start_time": "2021-02-23T05:36:17.629034Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pathlib import Path\n",
    "from copy import deepcopy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import sklearn\n",
    "from sklearn.datasets import make_circles # To generate the dataset\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from mpl_toolkits.mplot3d import Axes3D  # 3D plots\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:19.516729Z",
     "start_time": "2021-02-23T05:36:19.514455Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "# plotting style\n",
    "sns.set_style('white')\n",
    "# for reproducibility\n",
    "np.random.seed(seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:19.537150Z",
     "start_time": "2021-02-23T05:36:19.518978Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using CPU\n"
     ]
    }
   ],
   "source": [
    "gpu_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpu_devices:\n",
    "    print('Using GPU')\n",
    "    tf.config.experimental.set_memory_growth(gpu_devices[0], True)\n",
    "else:\n",
    "    print('Using CPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:19.548033Z",
     "start_time": "2021-02-23T05:36:19.538461Z"
    }
   },
   "outputs": [],
   "source": [
    "results_path = Path('results')\n",
    "if not results_path.exists():\n",
    "    results_path.mkdir()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Input Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "### Generate random data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "The target `y` represents two classes generated by two circular distribution that are not linearly separable because class 0 surrounds class 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:19.568104Z",
     "start_time": "2021-02-23T05:36:19.549515Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "# dataset params\n",
    "N = 50000\n",
    "factor = 0.1\n",
    "noise = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:19.594677Z",
     "start_time": "2021-02-23T05:36:19.569366Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "# generate data\n",
    "X, y = make_circles(\n",
    "    n_samples=N,\n",
    "    shuffle=True,\n",
    "    factor=factor,\n",
    "    noise=noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:19.599490Z",
     "start_time": "2021-02-23T05:36:19.595980Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "# define outcome matrix\n",
    "Y = np.zeros((N, 2))\n",
    "for c in [0, 1]:\n",
    "    Y[y == c, c] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:19.611989Z",
     "start_time": "2021-02-23T05:36:19.601424Z"
    },
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Shape of: X: (50000, 2) | Y: (50000, 2) | y: (50000,)'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'Shape of: X: {X.shape} | Y: {Y.shape} | y: {y.shape}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "### Visualize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:20.390419Z",
     "start_time": "2021-02-23T05:36:19.614061Z"
    },
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=X[:, 0], \n",
    "                y=X[:, 1], \n",
    "                hue=y,\n",
    "               style=y,\n",
    "               markers=['_', '+']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Build Keras Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Keras supports both a slightly simpler Sequential and more flexible Functional API. We will introduce the former at this point and use the Functional API in more complex examples in the following chapters.\n",
    "\n",
    "To create a model, we just need to instantiate a Sequential object and provide a list with the sequence of standard layers and their configurations, including the number of units, type of activation function, or name. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "### Define Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:20.423170Z",
     "start_time": "2021-02-23T05:36:20.391860Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Dense(units=3, input_shape=(2,), name='hidden'),\n",
    "    Activation('sigmoid', name='logistic'),\n",
    "    Dense(2, name='output'),\n",
    "    Activation('softmax', name='softmax'),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "The first hidden layer needs information about the number of features in the matrix it receives from the input layer via the input_shape argument. In our simple case, these are just two. Keras infers the number of rows it needs to process during training, through the batch_size argument that we will pass to the fit method below. \n",
    "\n",
    "Keras infers the sizes of the inputs received by other layers from the previous layer’s units argument."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Keras provides numerous standard building blocks, including recurrent and convolutional layers, various options for regularization, a range of loss functions and optimizers, and also preprocessing, visualization and logging (see documentation on GitHub for reference). It is also extensible.\n",
    "\n",
    "The model’s summary method produces a concise description of the network architecture, including a list of the layer types and shapes, and the number of parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:20.436691Z",
     "start_time": "2021-02-23T05:36:20.429238Z"
    },
    "pycharm": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "hidden (Dense)               (None, 3)                 9         \n",
      "_________________________________________________________________\n",
      "logistic (Activation)        (None, 3)                 0         \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 2)                 8         \n",
      "_________________________________________________________________\n",
      "softmax (Activation)         (None, 2)                 0         \n",
      "=================================================================\n",
      "Total params: 17\n",
      "Trainable params: 17\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Compile Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Next, we compile the Sequential model to configure the learning process. To this end, we define the optimizer, the loss function, and one or several performance metrics to monitor during training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:20.457076Z",
     "start_time": "2021-02-23T05:36:20.442862Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='rmsprop',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Tensorboard Callback"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Keras uses callbacks to enable certain functionality during training, such as logging information for interactive display in TensorBoard (see next section):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:20.461183Z",
     "start_time": "2021-02-23T05:36:20.458377Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "tb_callback = TensorBoard(log_dir=results_path / 'tensorboard', \n",
    "                          histogram_freq=1, \n",
    "                          write_graph=True, \n",
    "                          write_images=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Train Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "To train the model, we call its fit method and pass several parameters in addition to the training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:33.342752Z",
     "start_time": "2021-02-23T05:36:20.462355Z"
    },
    "pycharm": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "  1/313 [..............................] - ETA: 0s - loss: 0.7017 - accuracy: 0.3125WARNING:tensorflow:From /home/stefan/.pyenv/versions/miniconda3-latest/envs/ml4t-dl/lib/python3.8/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
      "Instructions for updating:\n",
      "use `tf.profiler.experimental.stop` instead.\n",
      "WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0010s vs `on_train_batch_end` time: 0.0070s). Check your callbacks.\n",
      "313/313 [==============================] - 0s 1ms/step - loss: 0.6932 - accuracy: 0.5534 - val_loss: 0.6929 - val_accuracy: 0.6943\n",
      "Epoch 2/50\n",
      "313/313 [==============================] - 0s 739us/step - loss: 0.6917 - accuracy: 0.6453 - val_loss: 0.6908 - val_accuracy: 0.6600\n",
      "Epoch 3/50\n",
      "313/313 [==============================] - 0s 707us/step - loss: 0.6888 - accuracy: 0.6600 - val_loss: 0.6863 - val_accuracy: 0.7242\n",
      "Epoch 4/50\n",
      "313/313 [==============================] - 0s 729us/step - loss: 0.6815 - accuracy: 0.7295 - val_loss: 0.6760 - val_accuracy: 0.7016\n",
      "Epoch 5/50\n",
      "313/313 [==============================] - 0s 746us/step - loss: 0.6663 - accuracy: 0.7814 - val_loss: 0.6559 - val_accuracy: 0.8286\n",
      "Epoch 6/50\n",
      "313/313 [==============================] - 0s 739us/step - loss: 0.6414 - accuracy: 0.8174 - val_loss: 0.6264 - val_accuracy: 0.8431\n",
      "Epoch 7/50\n",
      "313/313 [==============================] - 0s 787us/step - loss: 0.6068 - accuracy: 0.8352 - val_loss: 0.5882 - val_accuracy: 0.8432\n",
      "Epoch 8/50\n",
      "313/313 [==============================] - 0s 704us/step - loss: 0.5656 - accuracy: 0.8475 - val_loss: 0.5454 - val_accuracy: 0.8508\n",
      "Epoch 9/50\n",
      "313/313 [==============================] - 0s 689us/step - loss: 0.5219 - accuracy: 0.8569 - val_loss: 0.5027 - val_accuracy: 0.8514\n",
      "Epoch 10/50\n",
      "313/313 [==============================] - 0s 748us/step - loss: 0.4796 - accuracy: 0.8629 - val_loss: 0.4613 - val_accuracy: 0.8613\n",
      "Epoch 11/50\n",
      "313/313 [==============================] - 0s 737us/step - loss: 0.4398 - accuracy: 0.8694 - val_loss: 0.4241 - val_accuracy: 0.8689\n",
      "Epoch 12/50\n",
      "313/313 [==============================] - 0s 838us/step - loss: 0.4053 - accuracy: 0.8749 - val_loss: 0.3917 - val_accuracy: 0.8731\n",
      "Epoch 13/50\n",
      "313/313 [==============================] - 0s 692us/step - loss: 0.3754 - accuracy: 0.8803 - val_loss: 0.3648 - val_accuracy: 0.8759\n",
      "Epoch 14/50\n",
      "313/313 [==============================] - 0s 780us/step - loss: 0.3503 - accuracy: 0.8846 - val_loss: 0.3408 - val_accuracy: 0.8844\n",
      "Epoch 15/50\n",
      "313/313 [==============================] - 0s 688us/step - loss: 0.3271 - accuracy: 0.8888 - val_loss: 0.3176 - val_accuracy: 0.8893\n",
      "Epoch 16/50\n",
      "313/313 [==============================] - 0s 723us/step - loss: 0.3037 - accuracy: 0.8945 - val_loss: 0.2934 - val_accuracy: 0.8946\n",
      "Epoch 17/50\n",
      "313/313 [==============================] - 0s 741us/step - loss: 0.2778 - accuracy: 0.9010 - val_loss: 0.2656 - val_accuracy: 0.9020\n",
      "Epoch 18/50\n",
      "313/313 [==============================] - 0s 734us/step - loss: 0.2475 - accuracy: 0.9105 - val_loss: 0.2334 - val_accuracy: 0.9145\n",
      "Epoch 19/50\n",
      "313/313 [==============================] - 0s 722us/step - loss: 0.2146 - accuracy: 0.9255 - val_loss: 0.1997 - val_accuracy: 0.9318\n",
      "Epoch 20/50\n",
      "313/313 [==============================] - 0s 788us/step - loss: 0.1806 - accuracy: 0.9448 - val_loss: 0.1655 - val_accuracy: 0.9545\n",
      "Epoch 21/50\n",
      "313/313 [==============================] - 0s 770us/step - loss: 0.1478 - accuracy: 0.9721 - val_loss: 0.1337 - val_accuracy: 0.9872\n",
      "Epoch 22/50\n",
      "313/313 [==============================] - 0s 807us/step - loss: 0.1176 - accuracy: 0.9942 - val_loss: 0.1048 - val_accuracy: 0.9982\n",
      "Epoch 23/50\n",
      "313/313 [==============================] - 0s 820us/step - loss: 0.0915 - accuracy: 0.9990 - val_loss: 0.0808 - val_accuracy: 0.9994\n",
      "Epoch 24/50\n",
      "313/313 [==============================] - 0s 904us/step - loss: 0.0701 - accuracy: 0.9995 - val_loss: 0.0613 - val_accuracy: 0.9998\n",
      "Epoch 25/50\n",
      "313/313 [==============================] - 0s 930us/step - loss: 0.0530 - accuracy: 0.9998 - val_loss: 0.0463 - val_accuracy: 0.9999\n",
      "Epoch 26/50\n",
      "313/313 [==============================] - 0s 920us/step - loss: 0.0399 - accuracy: 0.9999 - val_loss: 0.0347 - val_accuracy: 0.9999\n",
      "Epoch 27/50\n",
      "313/313 [==============================] - 0s 794us/step - loss: 0.0299 - accuracy: 0.9999 - val_loss: 0.0260 - val_accuracy: 1.0000\n",
      "Epoch 28/50\n",
      "313/313 [==============================] - 0s 731us/step - loss: 0.0225 - accuracy: 0.9999 - val_loss: 0.0196 - val_accuracy: 1.0000\n",
      "Epoch 29/50\n",
      "313/313 [==============================] - 0s 762us/step - loss: 0.0170 - accuracy: 1.0000 - val_loss: 0.0148 - val_accuracy: 1.0000\n",
      "Epoch 30/50\n",
      "313/313 [==============================] - 0s 732us/step - loss: 0.0129 - accuracy: 1.0000 - val_loss: 0.0113 - val_accuracy: 1.0000\n",
      "Epoch 31/50\n",
      "313/313 [==============================] - 0s 749us/step - loss: 0.0099 - accuracy: 1.0000 - val_loss: 0.0087 - val_accuracy: 1.0000\n",
      "Epoch 32/50\n",
      "313/313 [==============================] - 0s 800us/step - loss: 0.0077 - accuracy: 1.0000 - val_loss: 0.0068 - val_accuracy: 1.0000\n",
      "Epoch 33/50\n",
      "313/313 [==============================] - 0s 1ms/step - loss: 0.0060 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 1.0000\n",
      "Epoch 34/50\n",
      "313/313 [==============================] - 0s 894us/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.0043 - val_accuracy: 1.0000\n",
      "Epoch 35/50\n",
      "313/313 [==============================] - 0s 626us/step - loss: 0.0039 - accuracy: 0.9999 - val_loss: 0.0035 - val_accuracy: 1.0000\n",
      "Epoch 36/50\n",
      "313/313 [==============================] - 0s 712us/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000\n",
      "Epoch 37/50\n",
      "313/313 [==============================] - 0s 643us/step - loss: 0.0026 - accuracy: 0.9999 - val_loss: 0.0024 - val_accuracy: 1.0000\n",
      "Epoch 38/50\n",
      "313/313 [==============================] - 0s 689us/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000\n",
      "Epoch 39/50\n",
      "313/313 [==============================] - 0s 852us/step - loss: 0.0019 - accuracy: 0.9999 - val_loss: 0.0017 - val_accuracy: 1.0000\n",
      "Epoch 40/50\n",
      "313/313 [==============================] - 0s 682us/step - loss: 0.0016 - accuracy: 0.9999 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
      "Epoch 41/50\n",
      "313/313 [==============================] - 0s 687us/step - loss: 0.0014 - accuracy: 0.9999 - val_loss: 0.0013 - val_accuracy: 1.0000\n",
      "Epoch 42/50\n",
      "313/313 [==============================] - 0s 656us/step - loss: 0.0013 - accuracy: 0.9999 - val_loss: 0.0012 - val_accuracy: 1.0000\n",
      "Epoch 43/50\n",
      "313/313 [==============================] - 0s 979us/step - loss: 0.0011 - accuracy: 0.9999 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
      "Epoch 44/50\n",
      "313/313 [==============================] - 0s 900us/step - loss: 0.0010 - accuracy: 0.9999 - val_loss: 9.5180e-04 - val_accuracy: 1.0000\n",
      "Epoch 45/50\n",
      "313/313 [==============================] - 0s 757us/step - loss: 9.3114e-04 - accuracy: 0.9999 - val_loss: 8.5783e-04 - val_accuracy: 1.0000\n",
      "Epoch 46/50\n",
      "313/313 [==============================] - 0s 777us/step - loss: 8.5770e-04 - accuracy: 0.9999 - val_loss: 7.9056e-04 - val_accuracy: 1.0000\n",
      "Epoch 47/50\n",
      "313/313 [==============================] - 0s 803us/step - loss: 7.8891e-04 - accuracy: 0.9999 - val_loss: 7.2263e-04 - val_accuracy: 1.0000\n",
      "Epoch 48/50\n",
      "313/313 [==============================] - 0s 765us/step - loss: 7.4050e-04 - accuracy: 0.9999 - val_loss: 6.7238e-04 - val_accuracy: 1.0000\n",
      "Epoch 49/50\n",
      "313/313 [==============================] - 0s 743us/step - loss: 6.9102e-04 - accuracy: 0.9999 - val_loss: 6.3254e-04 - val_accuracy: 1.0000\n",
      "Epoch 50/50\n",
      "313/313 [==============================] - 0s 703us/step - loss: 6.4889e-04 - accuracy: 0.9999 - val_loss: 5.9569e-04 - val_accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "training=model.fit(X, \n",
    "          Y, \n",
    "          epochs=50,\n",
    "          validation_split=.2,\n",
    "          batch_size=128, \n",
    "          verbose=1, \n",
    "          callbacks=[tb_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:33.782363Z",
     "start_time": "2021-02-23T05:36:33.344163Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VySQeOMmrMZHcEOBldDoREfmnam0h5mNI+Q2+HcGINmG8FhNJwh9HuWvab2Rm5xmdUERErsHlutuBAwfo3r17Eaa5NBX0UiA/P9/oCHI5tnz4aQIE1of6MRxIP8M7S3bRsU4g0ZGVjU4nIiKXUr83dH0ddsyHBU8yoEUo/zegCRv2pTNo6hrST+canVBEREqZ4ruK+6ZY2DijYF+zyWBoPPCSD7/55ptUqVKFQYMGAfD+++9jMplYu3YtGRkZ5Ofn88gjj3Drrbde8a1Onz7N6NGjL/q8OXPm8Mknn2AymahduzZvvvkmx44d46WXXiIlJQWAl19+mcDAQO6//37mz58PwCeffMKZM2d46KGHGDJkCE2aNGHDhg1ERUVRrVo1Jk2aRF5eHn5+frz11ltUrFiR06dP88orr7BlyxYAHnzwQTIyMvjjjz947rnnAPj666/Zs2cPzz777L//bOXSNn8JaXtgwJc4TCZemrsVgLG96ut85yIixV3L+yDjEKz6H/hUpkf7J/FyMzNq5gaGfLqGb+6/SYcpiUjZZkBvg4Ltbn+Xk5PDyy+/zJYtWzCbzTzzzDO0atWKP/74g2effZa8vDzsdjvvv/8+gYGBPProo6SmpmK32xk9ejTdunX71782FOeCboDo6GgmTJhw7g/5hx9+YOrUqQwbNgxvb2/S0tK444476Nix4xWLlbu7Ox9++OEFz9u9ezeTJk0iNjYWf39/Tpw4AcArr7xCixYt+PDDD7HZbJw5c4aTJ09e9j0yMjKYMcP5l+HkyZN8/fXXmEwmZs+ezdSpU3nmmWeYOHEi3t7ezJs379w4V1dXevbsyZNPPomrqytxcXGMHTv2Oj89uaj8HFjxBlRtBrW7sWhrKst2HOG5bnUIqaCp7SIiJcKtL0NmKvz4CpSvTMcmg/nwzqbcO30dY+dt49WYSKMTioiUOQXZ3f5u5syZAMybN489e/YwfPhwFi1axFdffcXQoUPp2bMnubm52O12VqxYQWBgIFOmTAEgMzPzun+v4lvQGw+84rcmBa1evXocP34cq9VKeno6Pj4+VKpUiVdffZW1a9fi4uKC1Wrl2LFjVKpU6bKv5XA4eOeddy543q+//kqXLl3w9/cHwM/PD4Bff/2VN954AwCz2Uz58uWvWND//u1Mamoqjz32GEePHiU3N5eQEOcpu1avXs0777xzbpyvry8ArVq1Yvny5dSoUYO8vDxq1659bR+WXJ31n8PJFOj5f5zKtfHy99uoE1yeu9tUNzqZiIhcLZMJer4Pp4/A9w9DuUA61buN+zvU5KMVe7ixegVub6JTZYpIGWVAb4OC7W5/t379egYPHgxAzZo1qVKlCklJSTRu3JiPPvqI1NRUbrvtNqpVq0atWrV4/fXXefPNN7nlllto3rz5df9eOgb9Hzp37syiRYtYsGAB0dHRzJs3j7S0NOLi4pg7dy4VK1YkJyfniq9zqec5HI6rzmKxWLDb/zqdyz/f19PT89zPr7zyCoMGDWLevHmMGzeO3FzncXEOh+Oi3xj169ePuLg44uLiiImJuepMcg1yz8DKtyCsLdS4hbcX78Samc2EmEhczfqrJyJSoljcoP90CG4As++CA+t54rZa3Fjdn+fitrDLev17TURE5NoUVHf7u0v1tR49ejBp0iQ8PDwYPnw4q1evpnr16sTFxVGrVi3efvttPvjgg+v+ndQS/iE6OpoFCxawaNEiOnfuTGZmJgEBAbi6uvLrr79y8ODBq3qdSz2vdevWLFy4kPT0dIBzU9xbt27Nl19+CYDNZuPUqVMEBARw/Phx0tPTyc3NZfny5Zd9v6CgIMB5jPuf2rRpc24aPHBur3yjRo1ITU1l/vz5xWbFwlLntylwygodX+T3gxl8/ksyg1reQNMbKhidTERE/g338nDnbChXCb7sh+VEEh8MbEI5dzOjZ27gdI4WbRURKUoF1d3+rkWLFucOD05KSuLw4cPUqFGDlJQUQkNDGTp0KFFRUezcuROr1Yqnpye9evVi+PDhbNu27bp/JxX0f4iIiOD06dMEBgYSGBhIjx492LJlCzExMcybN48aNWpc1etc6nkRERHcf//9DBkyhJ49e/Laa68B8Pzzz7NmzRp69OhBTEwMf/zxB66urjzwwAP079+f++6777Lv/eCDD/LII49w5513nps2DzBq1CgyMjLo3r07PXv2ZM2aNece69q1K02bNj037V0KUPZJ54JC4Z3Ir3ojz36XSIC3O092rmN0MhERuR7lg2DId86fYwcQ6GHn/wY0Ye/RUzz/3e/XNFNORESuT0F1t7+78847sdvt9OjRg8cee4xXX30VNzc3FixYQPfu3enVqxd79+6ld+/e7Nq1i759+9KrVy8mTZrEqFGjrvt3MjkM2pLExMQQFxd33n3bt2+nbt26RsQpk+677z6GDRtG69atLzlGfyb/0pIxsOo9GLmCT/f6Mm7+Nt4f2IQejaoYnUxEpMBcbFteZuxdDtN7Qcv7oevrvL/sD95esovxtzdgUMswo9OJiBQqdYRrcy2fl/agl0EZGRl07twZd3f3y5Zz+Zd2L4NV/wdNh3K4XG3eXryT9rUq0b2hznkuIlJq1LjZWc7XfAR7l/PALeF0qFWJsd9vY8vByy/yKiIicinFdxX3EmLnzp089dRT593n5ubG7NmzDUp0ZT4+PixatMjoGKVTxmGIGwmV6kCX13l51lby7Q5e6dVA5zwXESltOr7k/FJ2zmhcRv3Cu3c0Jvr/VjJq5nrmP9QOX09XoxOKiMjflITuVuwK+qVWHS+uateuzdy5c42OUSh0HN01suXDtyMg7wz0/5yluzNZtNXKk51rc0OAznkuIlLquHlBzGSY2gl+eBr/mMl8cGdT7pi8midnb2bykGYl6t80IiLXoqT1NjCmu11rpypWU9w9PDw4fvy4imEx4HA4OH78OB4eHkZHKTlWvA77fobodzjtU5OXvt9KrSBv7m137YtTiIhICVG1GbR/EhK/gm1zaRZWgWe71WXxNitTVyYZnU5EpFCot12df9OpitUe9JCQEA4cOMDRo0eNjiI4/+KFhIQYHaNk2PMTJLwJjQfjaDSA/8b9zsETWXxzf2vcLMXqezARKYMSEhIYP348drudfv36MXLkyAvGrFmzhgkTJpCfn0+FChXOO0WnXEH7J+CPRTDvUQhtxT1tqrEuOY3XFu6gaVgFmoXp9JoiUrqot129a+1Uxaqgu7q6Ur16daNjiFybzFSIuxcq1YZubzBx+R6+WpvCqJtr0ryav9HpRKSMs9lsjBs3jmnTphEUFETfvn2JiooiPDz83JiMjAzGjh3L1KlTqVKlCsePHzcwcQlkdoXbJ8Pk9vD9Q5junMXrfRuS+L+VPPXNZhY80g53i9nolCIiBUa9rfBo157I9bDbnMed556Gfp8zZ+sJ3ly0k56NqvDkbbWNTiciQmJiImFhYYSGhuLm5kZ0dDTLli07b8y8efPo1KkTVao4TwUZEBBgRNSSrVJtuPVl5570DdPx8XBl/O0N2HP0NBN/2mN0OhERKSFU0EWux4rXIXkldHuLXzIq8uQ3m2lVw583+zXExaVkLZohIqWT1WolODj43O2goCCsVut5Y5KTk8nIyGDIkCHExMQwZ86cIk5ZStx4H1RvD4ueg7Qkbq4dSO/GVZi4fDe7rJlGpxMRkRJABV3k39q7HFa8AY3uZEflHtz3xXqqVyzH5CHNNZVRRIqNiy3g889Vd202G1u3bmXy5MlMnTqViRMnkpSkBc6umYsL9JoIJheYMwrsNl7sXg9vdwvPfJuI3a7FlERE5PJU0EX+jUwrfHsvVKxFarv/cve0tXi5m5l29406762IFCvBwcGkpqaeu221WgkMDLxgTLt27fDy8sLf35/mzZuzY8eOoo5aOviFQtc3YP9qWP0BAd7uvNi9Hhv2n2DGmn1GpxMRkWJOBV3kWtny4dvhkJPJ6V5TGTZjG5nZ+Xw6rAVV/TyNTicicp7IyEiSk5NJSUkhNzeX+Ph4oqKizhvTsWNH1q1bR35+PllZWSQmJlKzZk2DEpcCjQZA3R7w4ytg3crtTarSLqIibyzcyaETWUanExGRYkwFXeRa2G0wdzQkryS/y5vctyiL3UdOMWlwU+pX8TU6nYjIBSwWC2PGjGHEiBF069aNrl27EhERQWxsLLGxsQDUrFmTdu3a0bNnT/r160ffvn2pVauWwclLMJMJuv8P3MvDvEcxORyM7x1Jvt3OmLlbdN5gERG5JJPDoK1ETEwMcXFxRry1yL9jt8P3D8KmmThueYH/WDsRt+Egb/ZtSL/moUanExEpctqWX8GmWJhzP/R4D5oN4+OEvYxfsJ0P72xKdMPKRqcTEZFiSHvQRa6G3Q7zH4VNM6HDM7yT05O4DQd57NZaKuciInJxjQZAWFtY8hKcOsrdbaoRWdWXl77fyskzeUanExGRYkgFXeRKHA5Y8ARs+BxHuyd4J/d23v9xN3c0D+XhjuFGpxMRkeLKZILu70DuaVjyIhazC6/GRJJ+JpcJC7YbnU5ERIohFXSRy3E44IenYd0n2Fo/zBPHevB/P+2hf/MQxt/e4IJTFYmIiJynUm1o8zBsjoWkBBpU9WVEu+rMWpfCL3uOGZ1ORESKGRV0kUtxOGDxC/DbZHJb3M9d+6P5dqNzWvvrfRpiMeuvj4iIXIV2T4BfGMT/B/JzebRjLcICvHgu7ney82xGpxMRkWJEDUPkYhwOWPoyrP6A042H03NXN35NSuONvg155NYI7TkXEZGr5+YF3d6CY7vgl//D083MhNsjST5+hveW/WF0OhERKUZU0EUu5qfxsOp/nKg3hFu3dSMlPYtPh7WgvxaEExGRf6PWbVCvFyS8CWlJtAmvSL9mIUxJ2MvO1Eyj04mISDGhgi7yd3Y7LBsHCW9iDe9P+23R2Bzw9f2taV+rktHpRESkJOvyGrhYnAuPOhw8260u5T0svKhzo4uIyFkq6CJ/yrTCjNth5dskh/Wl3fZeBPl48d0DbahfxdfodCIiUtL5VIGoF2D3Utg2F/9ybjzVuQ6/JaUxd9Mho9OJiEgxoIIuArB7GXzUBsf+NSwOf4Gbd95OkxsC+Ob+m6jq52l0OhERKS1a3AvBkbDwGcjOYECLUBqF+vFK/HYysnVudBGRsk4FXco2Wx4seQlmxJBh9mOwy+uM3FKP25uEMH34jfh6uRqdUEREShOzBbr/DzJTYfmruLiY+G+v+hw/ncO7S3YZnU5ERAymgi5lV3oyTOsKq/7HQo+utDjyPOnlavDliJa8e0dj3C1moxOKiEhpFNIcmt8Daz6Cw5tpGOLHnTfewOe/JLPtUIbR6URExEBXVdATEhLo3LkznTp1YsqUKRc8fvLkSR544AF69OhB37592bVL3wBLMbd1DvZJ7cg6tI3RuQ8zxjaCV/o2Z95DbbkpvKLR6UREpLTrOAa8AmDeo2C38WTn2vh5uTFm7hbsdi0YJyJSVl2xoNtsNsaNG8fUqVOJj49n/vz57N69+7wxH330EXXr1mXevHm8/vrrjB8/vtACi1yXnFPkzHkEZt/F79mV6JH3GrWihrD8yZvp1zwUs4vOby4iIkXA0w86vwqHNsD6afh5ufFMlzqs25dO3MaDRqcTERGDXLGgJyYmEhYWRmhoKG5ubkRHR7Ns2bLzxuzZs4dWrVoBULNmTQ4ePMixY8cKJ7HIv5G+j5NznyL7jdq4b/qMj/J7EBv5MTOf7M+jt9bCy81idEIRESlrIvtC9Q6wdBxkWunbLIQmN/jx6oLtnMzSgnEiImXRFQu61WolODj43O2goCCsVut5Y+rUqcOSJUsAZ6E/dOgQqampBRxV5Bo5HOTsXsnByX2wv9eYchs+ZmluA16p/D7tRn/Ia/2aEeTjYXRKEREpq0wmiH4b8rNg8QtnF4xrQPqZXN5ZvNPodCIiYoAr7jZ0OC48DspkOn8a8MiRIxk/fjy9evWiVq1a1K1bF4tFeyTFGI68bFJWzsS89iOqZu2inKMcsa69yWt6D53bNKe7r06bJiIixUTFCGj7GKx4HZoMokGNmxncKowvft1Hv+ahNKjqa3RCEREpQlds0cHBweftDbdarQQGBp43xtvbm1dffRVwFvqOHTsSEhJSwFFFLsPh4Njejexf+SXV9s3mBscJdjuqMrvyf7jhlnsYGFEVFx1fLiIixVHbxyHxa4j/D4z6hf/cVpv4xMO8OHcL395/k7ZfIiJlyBWnuEdGRpKcnExKSgq5ubnEx8cTFRV13piMjAxyc3MBmD17Ns2bN8fb27twEov8yW4jfdtytnz6IKmv1KHiF7fQNPlj9rpG8GOLyQQ+s4l+94+hZe0Q/eNGRESKL1cPiH4Lju+GVe/h6+nKs93qsnH/Cb5Zf8DodCIiUoSuuAfdYrEwZswYRowYgc1mo0+fPkRERBAbGwvAwIED2bNnD08//TQuLi6Eh4drFXcpPHlZnNy6hKNrvyXw8E9UsJ/Ey2Fhs2sjtoXfQ422fWlerabRKUVERK5N+K1Q/3ZIeAsa9CGmSQ2++m0/ry3cwW31g/DzcjM6oYiIFAGT42IHmReBmJgY4uLijHhrKWFs6Smkrp9H/s5FBB1bjYcjhwyHF+tcm5NVswt12t5OzdAqRscUESlztC0vYBmH4YMWENoCBsex7XAm3d9fyYAbb2DC7ZFGpxMRkSKgldyk2MnNySFp00+c3vIDlVJXEJqXRFXggKMii9yiyIvoRsO20URVCTA6qoiISMHxqQwdX4QfnoKt31GvQQzDbqrOp6uS6NM0hGZhFYxOKCIihUwFXQyXk28jce9h0tfOxufAMuqdWU9t0xnyHGa2WOrxe+XReNTtQp3IFvSq4GV0XBERkcLTYgRsmgkLn4Xwjjx+Wy1+2HKY57/7nXkPtcXVfMXlg0REpARTQZcil2ezk3jgJL/uPc66Pw5Q+8A3DDfNpYUpg+Mmf3ZX7AgRnQhrEU2TgIo0MTqwiIhIUXExQ/d34eOO8ON4vLu9wcs963PfF+v55Ock7u+gdVZEREozFXQpdA6Hg62HMli1+xi/7DnOuuQ0bLlnGGReyjtu8VRwOcGxwJs41fEZAmq1J8CkFddFRKQMq9rMuSd97cfQeCCd6zehU70g/rd0F9GRlQn112wyEZHSSgVdCoXd7mD9/nQWbkll4ZZUDp7IAqBeRQtvhqyk4/FY3HOOQ/WbocMzVAxrbWheEZHSLCEhgfHjx2O32+nXrx8jR4487/E1a9YwevRoQkJCAOjUqRMPPvigEVHlT1EvwLa5MP8xGLGMsT3rc+s7KxgzdwufDmuBSV9mi4iUSiroUmDybHZ+3XuchVtSWbzNytHMHNzMLrSNqMjjN4dwW9YCyq/7AA4dhRo3Q4dnQMVcRKRQ2Ww2xo0bx7Rp0wgKCqJv375ERUURHh5+3rjmzZszefJkg1LKBTz9oMur8O1wWPsJVVqO5PFOtXglfjsLfk8lumFloxOKiEghUEGX65KdZ2PlH8f4Ycthlm0/QmZWDrXdjjGiaibtI44TbjqA6/FdsHQX5GdD9Q5w8zMQdpPR0UVEyoTExETCwsIIDQ0FIDo6mmXLll1Q0KUYatAHNn4By8ZBnWiG3VSN7zYeZOy8rbSrVREfD1ejE4qISAFTQZdrdiY3n592HGXh7wc4vHMtjWxbudU1iSfcUwkypWC258JhnBffUKhUG6q3hzrdtcdcRKSIWa1WgoODz90OCgoiMTHxgnGbNm2iZ8+eBAYG8vTTTxMREVGUMeViTCbngnETb4IFT2IZMJMJt0fSe+Iq3lq0k3G9GhidUERECpgKulyVjOw8ftp6iK0bVmLe/wvN2cYE807Ku5wBF3D4hmIKrAeVoqFSnbOXWuBe3ujoIiJlmsPhuOC+fx6/XL9+fX788UfKlSvHihUreOCBB1i8eHFRRZTL8a/hnHm29CXY/j2N6vXirtbV+Hx1MjFNQ2gc6md0QhERKUAq6HJJ2Xk2EjZuI331dCofX0OUaRe9TFlghiyf6riH94dqbaFaG0w+VYyOKyIiFxEcHExqauq521arlcDAwPPGeHt7n/u5Q4cOjB07lrS0NPz9/Yssp1xG6wdhyzew4Emo3oH/nD03+rNxvzPvwTZYdG50EZFSQwVdzuNwOPj94EmW/7ySqjum0d2RgLspj6Ne1ciq1ody9aNwqdYGz/LBV34xERExXGRkJMnJyaSkpBAUFER8fDxvv/32eWOOHj1KxYoVMZlMJCYmYrfbqVChgkGJ5QJmC/R8Hz6OgqUvUb7He7zcoz6jZm5g2qpk7m1fw+iEIiJSQFTQBYCjmTnM3XiA3Wvi6ZzxDQ+bN5NrcuN4RD8COz1GpcBaRkcUEZF/wWKxMGbMGEaMGIHNZqNPnz5EREQQGxsLwMCBA1m0aBGxsbGYzWY8PDx45513dBqv4qZKE2g1GlZ/AJH96dLgJjrWCeSdJbvoGhlMSAWdG11EpDQwOS52cFoRiImJIS4uzoi3lrNy8m38tOMo361Lwmf3HO5xWUBdl/1kuQVgajkSj1b3QrkAo2OKiEgxpW15Ecs9DRNbgdkd7v+ZA6fsdHongTbhAXw8tLm+VBERKQW0B72MsdkdrEk6ztyNh1iyZT8xeQt4xXUBlSzp5FSoDe0+wDOyH7h6GB1VRERE/s6tnHNV9xl9YOXbhEQ9z2OdIpiwYAc/bEmlW6TOjS4iUtKpoJcBDoeDrYcymLvpIN9vPoQ1I5tubptZ5D6TShzEXv1maPMQ7jU7Ok/pIiIiIsVT+K0Q2R9+fhcaxHBPm9rM23yYF+dsoVWNAPzLuRmdUEREroMKeil2NDOH2N/2M3fTQfYcPY2r2cQd1bJ5uMKnBFpXgk8EdP0Wl/BbjY4qIiIiV6vLq7B7KXz/MJZ7FvFmv4b0eP9nXvp+K+8PbGJ0OhERuQ4q6KVQVq6NqSv38tGKPZzOtdGyuj/3t6xEjxNf4LHhY3D1gs4T4MaRYHY1Oq6IiIhci3IVndvxOffDuk+oc+O9PBQVwTtLdhEdWZkuDXSmFRGRkkoFvRSx2R3EbTjA24t3kZqRTef6QTzVuRY1D8yFZWPh9DFoMhg6vgTelYyOKyIiIv9WowGQOAuWjoXa3Rh1c00WbknlhTlbaFndnwqa6i4iUiKpoJdUDgcc2ginrJCXxc6DR1m8OZkTJzN4wNdM1I3lqVoO+O5nOLwJQlvCoNnO07SIiIhIyWYyOReMm9gaFjyB64AveatfI3p+8DPj5m/j3TsaG51QRET+BRX0kiY/F7Z+5zwPamriubtrn73gCpwBEgGLJ/hUhpiPIbKfFoATEREpTfyrwy3PwpIxsOVb6kX25YFbwnlv2R9ER1bm1npBRicUEZFrpIJeUmSlw/rPYM1kyDyMLaAWc6s+yfQkH1zcPOnfqha3t6yJu0c5ZzG3uKuQi4iIlHatHoDt82H+4xB6Iw/cEs6irak8993vtKjmj6+X1poRESlJXIwOIFeQthcWPAXv1IelL0PFWuzpNI2oM6/xZFJTmra+lU+fuosBXTrgXiEEPCs4z2Guci4iIlL6mS0QMwUcNvjuftxcHLzVrxHHT+cybv42o9OJiMg10h50I+TnwK6FkJcFDrvzeHKH3Xnhbz/vXe78VtzFApF9sbcczUe7vHgnfhdBPia+vq8VzcL8jf5tRERExEj+1aHbmzBnFKx6jwbtHmdUh5p88NNuujeszC11Ao1OKCIiV0kFvaidPg6zBsH+1Vce6+EHbR+DG0dyhAo89vUmVu1OITqyMhNiIvH11LQ1ERERARoNhF2L4KfxUPMWHurYkMXbUnk27ncWP94eHw/9m0FEpCRQQS9Kx/6Amf0g4xD0/ghCbwSTy9mLyXmN6a/bnhXA4s6PO6w8MXslWbk2Xu8TSf/moZg0hV1ERET+9Oeq7gfWwrcjcL8vgTf7NuL2iasYP387r/dtaHRCERG5CiroRSUpAWYNBhdXGDbfWc6vICffxmvztjJtVTJ1K/vw/sAmhAd6F0FYERERKXG8/OH2j+DznrDoeRr1+B/3dajJpOV76NawMh1qVTI6oYiIXIEWiSsKG2fAF7eDdzDcu+yqyvneo6e4/cNfmLYqmbvbVOO70TepnIuIiMjlVW8PNz0E66fBjgU80jGC8EBvnv4mkbTTuUanExGRK1BBL0x2OywdC3MfgGptYfhiqFDtik/bfeQU/T5aTWpGNp/c1ZyXetTHw9Vc+HlFRESk5It6AYIbwvcP4pF9jP/d0Zi0M7k8OmsTdrvD6HQiInIZKuiFJS8Lvrkbfn4Hmt4Fg74BT78rPm3/8TMMmvorJhPMvr81HesGFX5WERERKT0s7tBnKuSehrmjaVDFh5d61CNh11E++Gm30elEROQyVNALw6kj8Fl32DYXOv0XerwH5iuvnnr4ZBZ3Tv2V7Dw7M0a0pGYlTWkXERGRf6FSbbjtFdi9FH6bwp033sDtTary7tJd/PzHMaPTiYjIJaigF4YZfcC6FfpPhzYPO1dWvYKjmTkM+ngNJ87kMf2eG6kT7FMEQUVERKTUajECIjrD4hcxHdnO+NsbEF7Jm0e+2kjqyWyj04mIyEWooBe03NOQmug8f3m9nlf1lBNnchnyyRoOn8xm2t0taBTqV7gZRUREpPQzmaDXB+DhA98Ox4scJg1uSlaejQe/3ECezW50QhER+QcV9IKWvs95HVDzqoZnZudx16e/sffoaT4e2pwW1fwLMZyIiIiUKd6BcPtkOLoD5owivJI3r8ZEsm5fOm8u2ml0OhER+QcV9IKWnuy8rlD9ikPP5OZzz2dr2Xoog4mDmtI2omLhZhMREZGyJ7wj3DrWuTbOyrfo1bgqQ1qFMSVhL4u2phqdTkRE/kYFvaCdK+jVLjssO8/GfV+sZ/2+dP43oDG31tNq7SIiIlJIbnoIIvvDj6/Azh94oXtdGob48sTszew7ftrodCIicpYKekFLTwJ3H/C69FT1PJudB7/cyMo/jvF6n4Z0b1ilCAOKiIhImWMyQc//g8qN4dt7cU/fzYd3NsXFZGL0zA1k59mMTigiIqigF7z0ZKgQdtmV279cs5+l262M7Vmffs1Diy6biIiIlF2unjDgS+d17EBCPXN4p38jth7KYOy8bUanExERVNALXnryZae359nsTEnYS7OwCgxtHVZksUREpOxKSEigc+fOdOrUiSlTplxyXGJiInXr1mXhwoVFmE6KlG9VuOMLOLEfvhlOx9oVGXVzTWJ/28/X61KMTiciUuapoBcku925ivtlCvq8zYc4eCKL0TfXxHQV50cXERG5HjabjXHjxjF16lTi4+OZP38+u3fvvui4t956i7Zt2xqQUorUDa0g+i3YswyWvsx/OtWiTXgAz8X9zso/jhqdTkSkTFNBL0inUsGWc8kV3O12B5OW76F2UHluqR1YxOFERKQsSkxMJCwsjNDQUNzc3IiOjmbZsmUXjPviiy/o3LkzAQEBBqSUItdsGLQYAb/8H5at3zBpcDPCA70ZNWMD2w5lGJ1ORKTMUkEvSGlJzutL7EFftuMIfxw5xaiba+Lior3nIiJS+KxWK8HBweduBwUFYbVaLxizdOlSBgwYUNTxxEhdXoOwNvD9Q/gc/51pd7fA293C3Z/9xqETWUanExEpk1TQC9JlTrHmcDiYuHw3IRU86d6wcpHGEhGRssvhcFxw3z8PsRo/fjxPPPEEZrO5qGJJcWB2hf7ToVwlmDWYyi4ZfHZPC87k2Bg27TdOZuUZnVBEpMy5qoJ+pcVlMjMzuf/+++nZsyfR0dF8++23BR60REhPBpML+F64MvuapDQ27j/Bfe1rYDHrexERESkawcHBpKamnrtttVoJDDz/MKstW7bw+OOPExUVxaJFixg7dixLly4t6qhihHIVnSu7Z6XDjD7U8cnnoyHNSDp2mvu/WE9Ovk6/JiJSlK7YFK9mcZmZM2dSs2ZNvv/+e7744gtef/11cnNzCy10sZWeDL4hYHG74KGJy/dQ0dtNp1UTEZEiFRkZSXJyMikpKeTm5hIfH09UVNR5Y3788cdzl86dO/PSSy9x6623GpRYilzlhnDHDDi2C6b3ok1VM6/3acjqvcd5+pvEi87CEBGRwnHFgn41i8uYTCZOnz6Nw+Hg9OnT+Pr6YrFYCi10sZWedNHp7VsOniRh11HublMdD1dNHxQRkaJjsVgYM2YMI0aMoFu3bnTt2pWIiAhiY2OJjY01Op4UF+EdYcBMOLoDvridmLrePNm5NnM2HeLNRTuNTiciUmZcsUVfbHGZxMTE88YMGjSIUaNG0a5dO06fPs27776Li0sZnMadngy1u15w96QVeyjvbmGIznsuIiIG6NChAx06dDjvvoEDB1507GuvvVYUkaQ4iujkPCZ91hCY0YfRQ+I4kJ7FxOV7qOLnyeBW+neMiEhhu2KLvprFZX7++Wfq1q3LypUrmTNnDuPGjePUqVMFl7IkyDkFp49esAc96dhpfvj9MINaheHj4WpMNhEREZGrUbsr9PsMDm/CNLM//+0aRlSdQMbM3cLSbdYrPl1ERK7PFQv61SwuExcXx2233YbJZCIsLIyQkBD27t1b8GmLsxP7nNf/OAf6lIQ9WMwu3NO2WtFnEhEREblWdbtDn0/gwFosXw3g/T61aFDVlwdjN/DL7mNGpxMRKdWuWNCvZnGZypUrs3r1agCOHTtGUlISISEhhZO4uLrIOdCtGdl8u/4g/ZqFEFjew5hcIiIiIteqfm/o8zHsX025bwfxyZ31ucHfi2GfreXHHdqTLiJSWK5Y0K9mcZnRo0ezceNGevTowbBhw3jiiSfw9/cv9PDFykXOgf7Jz0nk2+3c176mIZFERERE/rUGfeD2yZD8M5XmD+OruxtTK8ibkdPXE5942Oh0IiKl0lUttX6lxWWCgoL49NNPCzZZSZOeDO6+4FkBgJNn8pj56z66N6zCDQFexmYTERER+Tca9gd7PswZjf/8e/hy2DTumbGFh2I3kJXXiL7NytiMSRGRQlYGl1ovJOnJUCEMzi6gN311MqdzbYy6WXvPRUREpARrfCf0/D/YvQyf2XcwfVBtbqpZkSdmb+aL1clGpxMRKVVU0AtKehL4OxeIy8q1Me2XZG6pXYm6lX0MDiYiIiJynZoOhb7OheO8ZvZkakwIt9YN5MW5W/loxR6j04mIlBoq6AXBboMT+88dfz5r7X7STucy+pZwY3OJiIiIFJQGfWDQ15CWhMf0rkzqVoHuDSvz2g87eGfxzouemldERK6NCnpByDwMtlyoUA273cHUn5NoHlaBFtXK2EJ5IiIiUrrVjIJh8yD3FK6fdeG9Di70bx7C//24m1fit6uki4hcJxX0gvC3Fdw3HzjBgfQs7mx5g6GRRERERApF1WZwzyKweGD+vDuvNTnBsJuq8cnPSTwb9zv5NrvRCUVESiwV9IJw7hzo1flhSyquZhMd6wYZm0lERESksFSMgOGLwTcEly/78lL4bh68JZyv1qYw8ov1nMnNNzqhiEiJpIJeENKTwWTG4VOVBb8fpm14RXw9XY1OJSIiIlJ4fKrA3QugShNMs4fxRMAv/Ld3A5bvPMKAKb9yNDPH6IQiIiWOCnpBSE8G3xC2pGZxID2LrpGVjU4kIiIiUvi8/GHIHAjvBPMfZUjOLCYPbsYuayZ9Jv3C3qOnjE4oIlKiqKAXhPRkqFCNBVsOY3ExcVs9TW8XERGRMsLNCwbMhEYD4afxdNr7KrHDm3M6J58+k35h/b40oxOKiJQYKugFIT0JR4Xq/PD7YVrXDMDPy83oRCIiIiJFx+wKvSdBuydg/Wc0WfUAccMb4evpyp0fr2HhllSjE4qIlAgq6NcrOwPOHOeoa2WSj5+hawNNbxcREZEyyGSCji9C93dh9xLC5vcnbmg4dSv7MGrmej7/JdnohCIixZ4K+vU6sQ+ANenlcTHBbfU1vV1ERETKsOb3wIBYOLoT/9hovoqpSMc6Qbz0/VZeXbAdu13nShcRuRQV9Ot19hzoCw560LJ6ABW93Y3NIyIiImK02l1g2HzIPY3H9C5Mvjmfwa1uYHLCXh6ZtYmcfJvRCUVEiiUV9Ot19hzoq46Xp1tksMFhRERERIqJqs1gxBLwrID5i178t9Zenu5Sh3mbD3HXp79xMivP6IQiIsWOCvr1Sk8m2+JDpqkcneuroIuIiIic418Dhi+B4EhMX9/FKM+lvHtHI9bvS6f/R6s5dCLL6IQiIsWKCvr1Sk9mvyOQFmH+BPp4GJ1GREREpHgpFwBDv4c60fDDU9x+bAqfDWvBwRNZxEz8hR2pGUYnFBEpNlTQr1Pusb3syg2gSwPtPRcRERG5KDcv6D8dmg+HVe/RZtvLfH1vCxw46PfRan7Zc8zohCIixYIK+vWw2zBnpLDfEaSCLiIiInI5LmaIfhs6PAMbZ1Bv5YPEjWxKsI8Hwz5dy/ebDxmdUETEcCro1yPjIGZHPlSoRhU/T6PTiIiIXFRCQgKdO3emU6dOTJky5YLHly5dSo8ePejVqxcxMTGsW7fOgJRSJphMcMuz0PVN2LmAqvMG883dDWh8gx8Px25kSsIeHA6dhk1Eyi6L0QFKMuu+nQQBYTXrGR1FRETkomw2G+PGjWPatGkEBQXRt29foqKiCA8PPzemdevWdOzYEZPJxI4dO3j00UdZuHChgaml1Gs5Erz84bv78f2qF9MHfM1/fnBnwoIdHD6ZzYvR9XBxMRmdUkSkyGkP+nXYvi0RgCaNmhicRERE5OISExMJCwsjNDQUNzc3oqOjWbZs2XljypUrh8nkLENZWVnnfhYpVJF94c5ZkLYHj+ndeL9zBe5pU51pq5J57rvfsdu1J11Eyh4V9OtwdP8O8jFT5YbwKw8WERExgNVqJTj4r3VSgoKCsFqtF4xbsmQJXbp04b777mPChAlFGVHKsvCOcNc8yD6By7TOvNjCxoO3hPPV2hSe/jYRm0q6iJQxKuj/0sETWXic2s9pj8pg1pECIiJSPF3seN6L7SHv1KkTCxcu5MMPP+S9994rimgiTiHN4Z5F4GLBNC2a/9Q+xiMdI5i9/gBPfrNZJV1EyhQV9H9p4ZZUQk1HcKtUw+goIiIilxQcHExqauq521arlcDAwEuOb9GiBfv37yctLa0o4ok4VartLOnegZhmxPBYxBEe71SLuA0HefzrTeTb7EYnFBEpEiro/9IPvx+muvkYnoE1jY4iIiJySZGRkSQnJ5OSkkJubi7x8fFERUWdN2bfvn3n9rRv3bqVvLw8KlSoYERcKcv8QuGeheAXBl8O4OE6GTzZuTZzNx3i0Vkq6SJSNmhu9r+QejKbnfsO4uuRARWqGR1HRETkkiwWC2PGjGHEiBHYbDb69OlDREQEsbGxAAwcOJBFixYxd+5cLBYLHh4evPvuu1ooToxRriIMnQOfdoEZfXhg2AIsLnV49Ycd2B0O3hvQBFez9i+JSOmlgv4vLNqayg2mI84b/tWNDSMiInIFHTp0oEOHDufdN3DgwHM/jxw5kpEjRxZ1LJGL86kCQ+fCtK4wvRf33bMQs0tdXonfjs2+gfcHNsXNopIuIqWT/u/2L/yw5TAtK2Q4b2gPuoiIiEjB8q/uLOkOG0zvxYiGbrzcox6LtloZPXM9Ofk2oxOKiBQKFfRrdDQzh9+S0ri50hnnHSroIiIiIgWvUm0Y8h1kZ8D0XgxrVI7/9qrP0u1HeDh2o1Z3F5FSSQX9Gi3elordAZHl0sCzAnj4Gh1JREREpHSq3AgGfQ0Zh2B6b4Y08mFMd+ee9FfitxmdTkSkwKmgX6OEXUcJqeCJX/ZBqKDjz0VEREQK1Q2tYMCXcPwPmNmPe1pU5J421Zm2KplPf04yOp2ISIFSQb8GDoeD35LSaFUjAFN6sqa3i4iIiBSFmrdAv8/g0EaIHcjzt1Wjc/0g/hu/jYVbUo1OJyJSYFTQ/+7kQXinHuyIv+jDfxw5RfqZPFqF+cDJFBV0ERERkaJSJxpu/wiSf8b83b38r38jGoX48chXG9mwP93odCIiBUIF/e/+WAQZB+G7UZB24ZSpNXuPA9C6Yg7Y81XQRURERIpSw/7QeQLsmI/nL28y9a7mBPl4MOLzdew7ftrodCIi100F/e/2rgCvimACvrkb8nPOe3hNUhqVfT2o4jg7lUrnQBcREREpWq1GQePBsOJ1Ku5bwGd3t8DucDBs2lrST+canU5E5LqooP/JboekFVCrM/Sa6DzGafGL5x52OBysSUrjxur+mE4kO+/UHnQRERGRomUyQfd3IORGmDOaGvl7mTq0OQdPZHHv9HVk5+kc6SJScqmg/yk1EbLSoXoHqNsdWo2G3ybDtrkAJB8/w9HMHFpWD4D0ZHCxgE9VYzOLiIiIlEUWd7hjhvOUt1/dSfOKNt7t35h1+9L5z+zN2HWOdBEpoVTQ/5S0wnldo4Pz+taxULUZzH0Q0pLOHX9+Y3V/5/HpfjeAi9mgsCIiIiJlXPkgGDATTh+Fr4cSXS+AZ7vWIT7xMK8v2mF0OhGRf0UF/U97l0OlulA+2Hnb4gZ9pzmnUc0exvo9qVT0dqNmpXLOPeg6B7qIiIiIsao0gV4fwv5f4IcnGdmuOoNb3cDkFXv5em2K0elERK6ZCjpAXjbsW/3X3vM/VQiD3pPg8CZa7n7Xefy5yXS2oFczIqmIiIiI/F1kX2j7GKz/DNO6T3i5R33ahlfkhTlb2JRywuh0IiLXRAUd4MBvkJ8FNW6+8LE60WQ2uY++tgX091zvPE49+4QKuoiIiEhxEfUi1OoCPzyNZf/PvD+wCYE+7tz/xXqOZGYbnU5E5KqpoIPz9GomM4S1uejDS6rcz0Z7OO22j4U9PznvVEEXERERKR5czBDzMQSEw9d3USH3EFOGNOdEVi4PzNxAbr7d6IQiIlflqgp6QkICnTt3plOnTkyZMuWCx6dOnUqvXr3o1asX3bt3p27dupw4caKgsxaevcshpDl4+Fz04TX7TvGc+XFczBaY+4DzTp0DXURERKT48PCBgbHgsEPsQOoFuPB6n4asTU7nlfhtRqcTEbkqVyzoNpuNcePGMXXqVOLj45k/fz67d+8+b8yIESOYO3cuc+fO5fHHH6dFixb4+fkVVuaClXUCDm1wnl7tEtYkHSekem1Mt38EeWecd/qFFU0+EREREbk6ATWh3zQ4ugO+f5Bejapwb7vqTF+9j6/XadE4ESn+rljQExMTCQsLIzQ0FDc3N6Kjo1m2bNklx8fHx9O9e/cCDVmo9q1yftN6sePPAWtGNsnHz9Cyuj/U7godnoGQGy+5t11EREREDFQzCjqOga3fweoPebpLHdqEB/DCnC1s1qJxIlLMXbGgW61WgoODz90OCgrCarVedGxWVhYrV67ktttuK7iEhW3vcnD1gpAWF314TVIaAC2rBzjvuOVZGLGkiMKJiIiIyDVr8yjU6Q5LxmBJ+YX3Bzalkrc7932xnqOZOUanExG5pCsWdIfDccF9JpPpomN/+uknmjZtWnKmt4OzoIfd5Dzv+UWs2Xscb3cLdSuXL9pcIiIiIvLvmEzOU+X6V4fZw/C3HWPK0GbOReO+3ECeTYvGiUjxdMWCHhwcTGpq6rnbVquVwMDAi46Nj48nOjq64NJdC+s2+HYE5J65+udkHIJjuy45vR3gt6Q0mlergMWsBe9FRERESgwPH7hjpvPfhl/fRf1AT17v05DfktIYH7/d6HQiIhd1xdYZGRlJcnIyKSkp5ObmEh8fT1RU1AXjMjMzWbt2LR07diyUoFeUlwW/z4b1067+OXtXOK8vUdCPn8rhjyOnuLG6//XnExEREZGiFVgHen0AB36Dxc/Tq3FVRrStzme/JDNbi8aJSDFkueIAi4UxY8YwYsQIbDYbffr0ISIigtjYWAAGDhwIwJIlS2jTpg1eXl6Fm/hSQppB9faw6j1ofg+4el75OXuXg1dFCKx/0YfXJv/j+HMRERERKVkaxMDB9bD6A6janGe69mPb4QxemLOFupV9aFDV1+iEIiLnmBwXO8i8CMTExBAXF1ewL5r8M3wWDV1eh1b3X36swwFv13Eef97v4nvdX/5+K1+t3U/iS51xs2iKu4iIyN8VyrZcpDDY8mB6Lzi4AUYs4bh3Lbq//zMWs4n5D7bD18vV6IQiIsBVTHEvUaq1hbC2sOp/kJd9+bHHdsGp1Csef94srILKuYiIlGgJCQl07tyZTp06MWXKlAse//777+nRowc9evRgwIAB7Nixw4CUIoXI7Ar9PgNPP5g1mADzGT4c1JTUk9k89vUm7HZD9leJiFyg9DXPm5+GzMOwYfrlx+1d7ry+REE/eSaP7akZ3FhN09tFRKTkstlsjBs3jqlTpxIfH8/8+fPZvXv3eWNCQkKYMWMG8+bNY9SoUbz44osGpRUpRN6B0O9zOHkAvrufpiG+vNi9Hj/uOMLE5buv/HwRkSJQ+gp6tXZww03w87uQf5nzXO5dDhWqQ4Wwiz68bl8aDge0rKEF4kREpORKTEwkLCyM0NBQ3NzciI6OZtmyZeeNadq0Kb6+zuNwGzdufN7ZW0RKlRtaQudXYddCWPkWQ1qF0btxFd5esouVfxw1Op2ISCks6CYTdHgKMg9dei+6Ld95vHqNDpd8mTVJabiZXWgc6lc4OUVERIqA1WolODj43O2goCCsVuslx3/zzTe0b9++KKKJGOPGe6HhHfDTBEx7fmRCTCS1AsvzcOxGDp7IMjqdiJRxpa+gg3PaemjLS+9FP7QRcjIue/z5mr3HaRzqh4erudBiioiIFLaLrQVrMpkuOvbXX3/lm2++4YknnijsWCLGMZmg+7tQqQ58OwKvrFQmDW5Kvs3B6JkbyMm3GZ1QRMqw0lnQTSbo8DRkHIRNMy98fO9ywATVLr6H4FROPlsOZWh6u4iIlHjBwcHnTVm3Wq0EBgZeMG7Hjh288MILTJw4kQoVKhRlRJGi51YO7vjCubr713dRo4Ibb/ZrxOaUE7wyf7vR6USkDCudBR2gZhRUbQ4r34H83PMfS1oBwZFQ7uILwK3fl47N7uDG6iroIiJSskVGRpKcnExKSgq5ubnEx8cTFRV13phDhw7x0EMP8cYbb1C9enWDkooUsYoR0OsDOLgOFr9AlwbB3Ne+Bl/8uo/vNh4wOp2IlFEWowMUGpMJbn4GZvaFzV9Cs2HO+3NPQ8oaaHnp86T/lnQci4uJZmHagyAiIiWbxWJhzJgxjBgxApvNRp8+fYiIiCA2NhaAgQMH8uGHH3LixAnGjh0LgNls1vnNpWyo3xtSRsOvEyH0Rp7sHMPmAyd4Nu536lb2oU6wj9EJRaSMMTkudnBaEYiJiSn8jb/DAR9HwZlj8NAG5zkwdy+FGX1gcByEd7zo0/pO+oV8u4M5D7Qp3HwiIiIlWJFsy0UKmy0PPouG1C0w8ieOelQj+v9W4uVm5vuH2uLj4Wp0QhEpQ0rvFHf4ay/6if2w+SvnfXuXg9kNbmh90adk59nYfOCEjj8XERERKQvMrtDvM3D1hFlDqOSWx8RBTTmQnsXjszZhtxuyL0tEyqjSXdABIm6Dyo1h5VvOb0j3rnCu8O7mddHhG/ank2dz0FLHn4uIiIiUDT5VoO8ncPwPmPcIzcMq8GL3eizdfoT3lv1hdDoRKUNKf0H/c0X39GT4dRKkJl72/Oe/JaVhMkHzairoIiIiImVGjZvhludgyzewdipDW4fRt1kI7y37g8VbU6/4dBGRglD6CzpA7a4Q3BCWORe/ocYtlxy6Zm8a9Sr76HgjERERkbKm7X8gojMsfBbTwfW80rsBjUJ8eWzWJnYfyTQ6nYiUAWWjoP+5F92eD+4+zinvF5GTb2PD/nRaVr/46ddEREREpBRzcYHbPwKfyvD1XXjknuCjIc3wdDMzcvp6MrLzjE4oIqVc2SjoALW7Oc+LXrsrmC88u1xWro2HYzeSk2+nfa2KBgQUEREREcN5+UO/z+H0EfhmGJW9LUwc1Iz9aWd49CstGicihavsFHQXF7hnIfSedMFDRzNzGDBlNYu3WRnTvR431w40IKCIiIiIFAtVm0KP9yApARY+y43V/XmpZ31+3HGEd5fuMjqdiJRiF+5KLs3MFx5X/oc1k2HT1pJ2OpfJg5txW/1gA4KJiIiISLHS+E44sg1+eR8C6zC45XC2HDjJ+z/upn4VH7o0qGx0QhEphcpWQf+HVbuPcf+M9Xi4mpl1XysahvgZHUlEREREiotbx8LRnbDgKUwBEYzr3Zad1kwe/3ozNSp5UyuovNEJRaSUKTtT3P/h63Up3PXpb1T29eC70TepnIuIiIjI+VzM0OcTqBgBXw/F/WQyHw1uRjl3CyOnr+PkGS0aJyIFq8wVdIfDwVuLdvLUN4m0qhHAN6NuIqSCl9GxRERERKQ48vCBgbHOswLFDiTYPYdJg5py8EQWj8zaiE2LxolIASpTBT0n38YjX23ig592c0fzUKbd3ULnOxcRERGRy/OvAf2nQ9oe+GY4zW/wZWzPBizfeZRx87bicKiki0jBKFMF/alvEvl+8yGe7Fyb1/pE4mouU7++iIiIiPxb1dtDtzdh9xJYMoY7W97Ave2q8/nqfXzyc5LR6USklCgzi8Q5HA5+3HGEO5qH8sAt4UbHEREREZGSpvk9cGQ7rP4AAuvybNdBHDyRxfgF26ni50m3SK3sLiLXp8wU9APpWWRm59Mw1NfoKCIiIiJSUnV+FY7tgnmP4hIQzjv9W2DNWMOjszYR5ONOszB/oxOKSAlWZuZ4bz+cAUDdyj4GJxERERGREstsgX6fgd8N8NUgPDL38fHQ5lT182TE5+tIOnba6IQiUoKVoYKeickEdYJ1vkoRERERuQ6eFeDOWeCwwRcx+NvT+ezuFphMJoZN+43jp3KMTigiJVSZKejbDp+kekA5vNzKzKx+ERERESksFSNg0Ddwygoz+xBWLp+pdzUn9WQ2I6avIzvPZnRCESmBykxB3344U9PbRURERKTghDSHO75wLhz31SCaVvbkvQFN2JRygke+0jnSReTalYmCnpmdx/60M9SrooIuIiIiIgUo/Fbo/REkr4S4EXSpV4kXo+uxaKuV8fHbjU4nIiVMmZjvvSM1E4C6lXX8uYiIiIgUsIb94MwxWPgMxD/OPd3/x4H0LD5dlUQVPw9GtKthdEIRKSHKREHXCu4iIiIiUqhajYLTR2Hl21CuEs9HP8/hk1m8Er8di4uJYW2qG51QREqAMlHQtx3KoIKXK8E+HkZHEREREZHSKupFZ0lPeBNzuUq8N+BebF9u4OV527A74J62KukicnlloqBvP5xB3co+mEwmo6OIiIiISGllMkH0u3AmDX54GjevAD4cFMNDX25k3Pxt2B0OTXcXkcsq9YvE5dvs7EjVCu4iIlJ2JSQk0LlzZzp16sSUKVMueHzPnj3ccccdNGjQgE8++cSAhCKliNkCfT6BsDbw3f24Jv3I+3c2oWuDYF6J387HCXuNTigixVipL+jJx0+Tk29XQRcRkTLJZrMxbtw4pk6dSnx8PPPnz2f37t3njfHz8+P5559n+PDhBqUUKWVcPWDglxBYB2LvxHXnfP5vYBOiIyszfsF2Jq/YY3RCESmmSn1B33bYuYJ7PRV0EREpgxITEwkLCyM0NBQ3Nzeio6NZtmzZeWMCAgJo2LAhFkuZOPJNpGh4+MLQ76FyI5h9F64bP+O9AY3p0agKr/6wg0nLVdJF5EKlfku8/XAGrmYT4YHeRkcREREpclarleDg4HO3g4KCSExMNDCRSBni5Q9D58LsYTD/MSynjvJuvycwAa8v3IHd4eCBW8KNTikixUipL+jbDmVQs5I3bpZSP1lARETkAg6H44L7tGiqSBFy84IBM+H7h2H5BCynj/JO31dxMcGbi3bicDh4MCrC6JQiUkyU+oK+/XAGbSMqGh1DRETEEMHBwaSmpp67bbVaCQwMNDCRSBlkdoXeE8G7Eqx6D8vpo7wdMxkXk4m3Fu8i/Uwez3atg8WsHUoiZV2pLujHTuVwJDNHx5+LiEiZFRkZSXJyMikpKQQFBREfH8/bb79tdCyRssdkgk7joFwlWPwC5qw03uw/Ax9PVz75OYld1kzeH9gEPy83o5OKiIFKdUHffjgDQCu4i4hImWWxWBgzZgwjRozAZrPRp08fIiIiiI2NBWDgwIEcPXqUPn36cOrUKVxcXPj8889ZsGAB3t5av0WkwN30EJQLhLmjMX/enZcHf0vdyuV5Yc4Wen24io+HNqdWUHmjU4qIQVTQRURESrkOHTrQoUOH8+4bOHDguZ8rVapEQkJCUccSKbsa3eFcQO7rofDJbdwxaDbhI1tx3xcbuP3DVbx7R2Nuqx985dcRkVLnqg50SUhIoHPnznTq1IkpU6ZcdMyaNWvo1asX0dHRDB48uEBD/lvbD2cS7OOBfzlNFRIRERGRYiSik/M0bDkZMLkDzU4sZt5DbQgP9GbkF+v5v2V/YLdfuMijiJRuV9yDbrPZGDduHNOmTSMoKIi+ffsSFRVFePhfp4TIyMhg7NixTJ06lSpVqnD8+PFCDX21th3KoG5lTRESERERkWIotAXc/zN8Mxy+u4/KjROYdfdrPDd/L+8s2cX2wxm81a8R5dxL9aRXEfmbK+5BT0xMJCwsjNDQUNzc3IiOjmbZsmXnjZk3bx6dOnWiSpUqAAQEBBRO2muQk29jz9FT1Kui6e0iIiIiUkz5VIG75kH7p2DTl3hMu5W3b3blhei6LNqaSp9Jv7D/+BmjU4pIEbliQbdarQQH/3UMTFBQEFar9bwxycnJZGRkMGTIEGJiYpgzZ06BB71Wf1hPkW936PhzERERESnezBaIeh6GzoGsdEwfRzHCayWf392Cwyez6f7+Sr5ZfwCHQ1PeRUq7Kxb0i/2PwGQynXfbZrOxdetWJk+ezNSpU5k4cSJJSUkFl/Jf2KYF4kRERESkJKlxM4xaBTe0gnkP0y7xWeaNbEitoPI8MXszQz/9jZQ07U0XKc2uWNCDg4NJTU09d9tqtRIYGHjBmHbt2uHl5YW/vz/Nmzdnx44dBZ/2Gmw/nIGnq5lqAeUMzSEiIiIictW8A2FwHES9AFvjuGF2V77u6cm4XvXZsC+dzv9L4LNVSVpATqSUumJBj4yMJDk5mZSUFHJzc4mPjycqKuq8MR07dmTdunXk5+eTlZVFYmIiNWvWLLTQV2P74QxqB5fH7GK68mARERERkeLCxQztn4Rh8ZCXjcsntzL0xEcsHhVJi2r+vDxvG/0mr2b3kUyjk4pIAbtiQbdYLIwZM4YRI0bQrVs3unbtSkREBLGxscTGxgJQs2ZN2rVrR8+ePenXrx99+/alVq1ahR7+UhwOx9kV3DW9XURERERKqLCbnFPemw6F3yZTdXobPqu/kXf71mPP0VN0e+9n3l/2B3k2u9FJRaSAmBwGrTYRExNDXFxcobz2wRNZtHntR/7buwFDWoUVynuIiIiUdYW5LReRf7BuhYXPQtIKqFibE+3H8vyWIOITD1MnuDyv9G5A82r+RqcUket0xT3oJdH2Q84F4urpHOgiIiIiUhoE1Yehc2HgV2DPxy9uAB/aJzCjlx9pp3Pp+9FqhnyyhvX70oxOKiLXoVQW9D9XcK8drCnuIiIiIlJKmExQuyuM/hVuGw8pv9F2cQ9WNfyBsbcGse1QBn0mqaiLlGSlsqBvP5xBtQAvvN0tRkcRERERESlYFje46UF4eAM0vxvXDZ9y16/R/Bo5jzc6uLH98F9FfV2yirpISVJqC7oWiBMRERGRUq1cRYh+G0avgYZ34LplFv3X9GXNDZOY1DKd7YdO0vej1Qyeuoa1yWkYtPSUiFyDUlfQT+Xkk3z8jAq6iIiIiJQNlWpBz/+Dx7bCLS9gPrKFrpsf4LcKL/BF4+0kHT5Kv49W0/W9lXzycxLHT+UYnVhELqHUFfSdqX8uEKeCLiIiIiJlSLmK0OFJePR3uH0yLhZ32u34Lz+7Pcy8ej9Rg0P8d/42Wr26jPu+WMfSbVbydYo2kWKl1B2kve1wJgB1q6igi4iIiEgZZHGHRgOg4R2wbxWm1ROJ3DmViXxMTpXa/OrRjg+S6zNiq5WK3u7ENK1Kv2YhRATpDEgiRit9Bf1QBj4eFqr4ehgdRURERETEOCYTVGvrvGQcgm3f475tLh32f0IHHJwKrMlyl9ZMXBXJlIQQ6lfx5ebalehQK5AmN/jhai51k21Fir1SV9D/XCDOZDIZHUVEREREpHjwqQKt7ndeMlNh+zy8t82l+74v6e5q54RvGCtyWvB9Qg2m/1QL3H24KTyA9rUq0aFWJUIqeBn9G4iUCaWqoNvsDnamZjLgxlCjo4iIiIiIFE/lg+HGe52XU0dhx3z8ts2hV/Jcernm4XB14ZBHOD8n1+bH7eG8Za+Nf6XKtK9ViZbV/WkY4kdlXw/tEBMpBKWqoCcfP01Wnk0ruIuIiIiIXA3vStD8buclLwsOrMWUvIqq+1bR/8AS7nCbB8CB7GqsWFuLZb9W4317GCfK1aBuaCCNQnxpFOpHwxBf/LzcDP5lREq+UlXQtx/WCu4iIiIiIv+KqydUb++8AKb8HDi0EfatIiR5FXem/MKg3MUA2PLNHNhXmcTdIfxqv4HPHDeQ6Vub4NBwwgPLUzOwHDUqelO9Yjk83cxG/lYiJUqpK+gWFxPhgd5GRxERERERKdks7nBDK+el3X8w2W2QlgTWLZitWwmzbiU0dQs9Tv7qHJ8Fmbu8SN4RyH5HIAmOIGY4AjnjFYK5YnV8g2tQPdCXsIByVPHzoLKvJ+XcS1UdEblupepvxLZDGdSs5I2Hq76lExEREREpUC5mqBjuvNTv7bwLIDsDjmwH6xbKH9lOvbQkah1LwpK5EbM9D/KAw2A7ZOKQoyKHCGC7w4/ljgpkWALI9wrEVD4YV7/KeAeE4B9QiUo+HviXc6Oitzv+5dy0oryUGaWqoG8/nEmrGv5GxxARESlWEhISGD9+PHa7nX79+jFy5MjzHnc4HIwfP54VK1bg4eHBa6+9Rv369Q1KKyIljocP3NDSeQHMZy/Y7ZB5GNKTID0Zl7QkKh7Zg196CvVPH8Q9axOu9mw4g/Nidb5ctsOVdMpzwuHNHoc36/HmjNmHHFc/bB6+ODz8MXn5Y/b0wdXLF7dyvrh7++FVvgLeXuXw9XLFx8OVcu4WPF3NuLhoMTspOUpNQU87nUtqRjb1quj4cxERkT/ZbDbGjRvHtGnTCAoKom/fvkRFRREeHn5uTEJCAsnJySxevJjNmzfz8ssvM3v2bANTi0ip4OICvlWdl2ptMQGe/xyTkwmZVjiVCpmp5J48TPbxg1gyj+F/Jo2ArDQs2Udxy/sDz7yTmPPskHnpt8x1mDmFJ6ccnqThQRbu5JrcyXXxIN/FnXyzFzaLB3azJw5XTzC747C4g8UDk6sHLq7Oa7PFHbObJy5uHphd3TFb3LC4uWN2dcfVzR2LqycWN7ezP7vj5uqKxeyCq8UFVxcXXM0mzC4mrXQv16zUFPQ/F4jTCu4iIiJ/SUxMJCwsjNBQ5ylIo6OjWbZs2XkFfdmyZfTu3RuTyUTjxo3JyMjgyJEjBAYGGhVbRMoK9/LOS0Xn/5Pczl4uyuGAnAw4kwbZJyAnk9zTJ8k+fYKcUyfJPXOC/DMZ2LMzIDsD97wzeORn4ZKfhdmWidl2FFdbNq75Objbs3Enp0B/lTyHGRsu5GEhBxfyMGPDjA0LdpMLNlywYcZhMp+9dt62m8w4cMFhcl7sZx/D5ILd5ILDZAaTy3ljwAVMprO3zWAywdnngPNn52uYMJ0d7zCZnOPOPhfT367hr9uY/na/82fT2WvH32+7/PX4X89x/nzui4k/M8D54/4+5txrcvb1Ofd+/O15JpPz8X8+79zPcN4XIudlOPc6Z1/7r6dw9kCNsz/+7flnB3j6BVK3+S1X+V/B9Ss1BT0jKw9PVzP1q/gaHUVERKTYsFqtBAcHn7sdFBREYmLiZccEBwdjtVpV0EWkeDGZwMPXeTnrsoX+Sux2sOWCLQfycyA/G1tuNrm5WeRmZ5GXfYb83Czy8/Kw5eWcu9jzc7Dn52LPz8WRn43Dlg+2fBy2fBz2PLDngy0Phz0f09mfTQ6782eHDZPDhsmRj8lux8WRj9mejwmH837ycXHYwOHAxW7DhB0Xhx0TNkwOBybsmHCcvc+BC3bnfY4/f3bgggPOXpuwn7ttPvsc4OxYcDE5ru/PpIw4WmUzlapUK5L3KjUF/bb6wfz8tD/+5XT+RRERkT85HBf+4+ufUy6vZoyISKnj4gIuHuDqce4uM85p+BdMxS/NHA5w2P+6xoHDYcdhP3vtsGO3O3Bgdz6Mw/kYdux2OzjsOBwO57bkz3GOs9sWh9359cDZ+/lznMOBAwecHefcCv31GGe3S+eNPZvV8ee1Mwz8+Wy746+fz23W/vY68NdzHOff/8+f//YCePlWpHIRlXMoRQXd7GIiwNvd6BgiIiLFSnBwMKmpqeduX2zP+D/HpKamau+5iEhZYTKB6fyzYJ2djC4G0PkKRERESrHIyEiSk5NJSUkhNzeX+Ph4oqKizhsTFRXFnDlzcDgcbNq0ifLly6ugi4iIGKDU7EEXERGRC1ksFsaMGcOIESOw2Wz06dOHiIgIYmNjARg4cCAdOnRgxYoVdOrUCU9PTyZMmGBwahERkbLJ5LjYgWdFICYmhri4OCPeWkRERAqAtuUiIiIFS1PcRURERERERIoBFXQRERERERGRYkAFXURERERERKQYUEEXERERERERKQZU0EVERERERESKARV0ERERERERkWLAsNOstWzZkqpVqxrx1iIiIiVahQoV+OSTT4yOoW25iIjIv3SpbblhBV1ERERERERE/qIp7iIiIiIiIiLFgAq6iIiIiIiISDGggi4iIiIiIiJSDKigi4iIiIiIiBQDKugiIiIiIiIixYAKuoiIiIiIiEgxYDE6QEFJSEhg/Pjx2O12+vXrx8iRI42OVCI9++yzLF++nICAAObPnw/AiRMneOyxxzh48CBVq1blf//7H76+vgYnLVkOHz7MU089xbFjx3BxcaF///7cdddd+myvU05ODoMGDSI3NxebzUbnzp15+OGH9bkWEJvNRp8+fQgKCmLy5Mn6XAtIVFQU5cqVw8XFBbPZTFxcnD5btB0vSNqWFw5tywuHtuWFS9vywlGY2/JSsQfdZrMxbtw4pk6dSnx8PPPnz2f37t1GxyqRYmJimDp16nn3TZkyhdatW7N48WJat27NlClTDEpXcpnNZp555hl++OEHZs2axZdffsnu3bv12V4nNzc3Pv/8c77//nvmzJnDypUr2bRpkz7XAjJ9+nRq1qx57rY+14Lz+eefM3fuXOLi4gB9ttqOFyxtywuHtuWFQ9vywqVteeEprG15qSjoiYmJhIWFERoaipubG9HR0SxbtszoWCVSixYtLvimZ9myZfTu3RuA3r17s3TpUgOSlWyBgYHUr18fAG9vb2rUqIHVatVne51MJhPlypUDID8/n/z8fEwmkz7XApCamsry5cvp27fvufv0uRaesv7ZajtesLQtLxzalhcObcsLj7blRaugPttSUdCtVivBwcHnbgcFBWG1Wg1MVLocP36cwMBAwLlxSktLMzhRyXbgwAG2b99Oo0aN9NkWAJvNRq9evbjpppu46aab9LkWkAkTJvDkk0/i4vLXZkKfa8EZPnw4MTExzJo1C9Bnq+144Svr/40VNG3LC5a25YVD2/LCVVjb8lJxDLrD4bjgPpPJZEASkcs7ffo0Dz/8MM899xze3t5GxykVzGYzc+fOJSMjgwceeIBdu3YZHanE++mnn/D396dBgwasWbPG6DilTmxsLEFBQRw/fpy7776bGjVqGB3JcNqOS0mibXnB07a84GlbXrgKc1teKgp6cHAwqamp525brdZz317I9QsICODIkSMEBgZy5MgR/P39jY5UIuXl5fHwww/To0cPbrvtNkCfbUHy8fGhZcuWrFy5Up/rddqwYQM//vgjCQkJ5OTkcOrUKZ544gl9rgUkKCgIcP7979SpE4mJiWX+s9V2vPCV9f/GCoq25YVL2/KCo2154SrMbXmpmOIeGRlJcnIyKSkp5ObmEh8fT1RUlNGxSo2oqCjmzJkDwJw5c+jYsaOxgUogh8PB888/T40aNbj77rvP3a/P9vqkpaWRkZEBQHZ2Nr/88gs1atTQ53qd/vOf/5CQkMCPP/7IO++8Q6tWrXjrrbf0uRaAM2fOcOrUqXM/r1q1ioiIiDL/2Wo7XvjK+n9jBUHb8sKhbXnh0La88BT2ttzkuNi8shJoxYoVTJgw4dypBEaNGmV0pBLp8ccf57fffiM9PZ2AgAAeeughbr31Vh599FEOHz5M5cqVee+99/Dz8zM6aomybt06Bg0aRK1atc4dB/T444/TsGFDfbbXYceOHTzzzDPYbDYcDgddunThwQcfJD09XZ9rAVmzZg2ffvopkydP1udaAFJSUnjggQcA5zGX3bt3Z9SoUfps0Xa8IGlbXji0LS8c2pYXPm3LC1Zhb8tLTUEXERERERERKclKxRR3ERERERERkZJOBV1ERERERESkGFBBFxERERERESkGVNBFREREREREigEVdBEREREREZFiQAVdREREREREpBhQQRcREREREREpBv4fNpStR2msvNMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(14,4))\n",
    "pd.DataFrame(training.history)[['accuracy', 'val_accuracy']].plot(ax=axes[0])\n",
    "pd.DataFrame(training.history)[['loss', 'val_loss']].plot(ax=axes[1])\n",
    "sns.despine()\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Get Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:33.785849Z",
     "start_time": "2021-02-23T05:36:33.783501Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "hidden = model.get_layer('hidden').get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:33.798988Z",
     "start_time": "2021-02-23T05:36:33.787003Z"
    },
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2, 3), (3,)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[t.shape for t in hidden]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Plot Decision Boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "The visualization of the decision boundary resembles the result from the manual network implementation. The training with Keras runs a multiple faster, though."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:33.814244Z",
     "start_time": "2021-02-23T05:36:33.800246Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "n_vals = 200\n",
    "x1 = np.linspace(-1.5, 1.5, num=n_vals)\n",
    "x2 = np.linspace(-1.5, 1.5, num=n_vals)\n",
    "xx, yy = np.meshgrid(x1, x2)  # create the grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:33.828866Z",
     "start_time": "2021-02-23T05:36:33.816709Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "X_ = np.array([xx.ravel(), yy.ravel()]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:34.179574Z",
     "start_time": "2021-02-23T05:36:33.830190Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "y_hat = np.argmax(model.predict(X_), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:34.797077Z",
     "start_time": "2021-02-23T05:36:34.180638Z"
    },
    "pycharm": {},
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a color map to show the classification colors of each grid point\n",
    "cmap = ListedColormap([sns.xkcd_rgb[\"pale red\"],\n",
    "                       sns.xkcd_rgb[\"denim blue\"]])\n",
    "\n",
    "# Plot the classification plane with decision boundary and input samples\n",
    "plt.contourf(xx, yy, y_hat.reshape(n_vals, -1), cmap=cmap, alpha=.25)\n",
    "\n",
    "# Plot both classes on the x1, x2 plane\n",
    "data = pd.DataFrame(X, columns=['$x_1$', '$x_2$']).assign(Class=pd.Series(y).map({0:'negative', 1:'positive'}))\n",
    "sns.scatterplot(x='$x_1$', y='$x_2$', hue='Class', data=data, style=y, markers=['_', '+'], legend=False)\n",
    "sns.despine()\n",
    "plt.title('Decision Boundary');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:34.803123Z",
     "start_time": "2021-02-23T05:36:34.799485Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:36:37.341569Z",
     "start_time": "2021-02-23T05:36:34.805327Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-8cd46a7d63884e90\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-8cd46a7d63884e90\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%tensorboard --logdir results/tensorboard/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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  "toc": {
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   "title_cell": "Table of Contents",
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    "height": "calc(100% - 180px)",
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    "width": "254px"
   },
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   "toc_window_display": true
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 },
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 "nbformat_minor": 4
}