{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5rmpybwysXGV"
   },
   "source": [
    "##### Copyright 2020 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:51.438503Z",
     "iopub.status.busy": "2022-12-14T21:48:51.438035Z",
     "iopub.status.idle": "2022-12-14T21:48:51.441705Z",
     "shell.execute_reply": "2022-12-14T21:48:51.441192Z"
    },
    "id": "m8y3rGtQsYP2"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hrXv0rU9sIma"
   },
   "source": [
    "# TensorFlow 기본 사항"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7S0BwJ_8sLu7"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>     <a target=\"_blank\" href=\"https://www.tensorflow.org/guide/basics\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\">TensorFlow.org에서 보기</a>\n",
    "</td>\n",
    "  <td>     <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ko/guide/basics.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Google Colab에서 실행하기</a>\n",
    "</td>\n",
    "  <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ko/guide/basics.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">GitHub에서 소스 보기</a></td>\n",
    "  <td>     <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ko/guide/basics.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">노트북 다운로드하기</a>   </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iJyZUDbzBTIG"
   },
   "source": [
    "이 가이드에서는 *TensorFlow 기본 사항*에 대한 간략한 개요를 제공합니다. 이 문서의 각 섹션은 더 큰 주제의 개요입니다. 각 섹션의 끝에서 전체 가이드에 대한 링크를 확인할 수 있습니다.\n",
    "\n",
    "TensorFlow는 머신러닝 위한 엔드 투 엔드 플랫폼입니다. TensorFlow는 다음을 지원합니다.\n",
    "\n",
    "- 다차원 배열 기반 숫자 계산(<a href=\"https://numpy.org/\" class=\"external\">NumPy</a>과 유사)\n",
    "- GPU 및 분산 처리\n",
    "- 자동 미분\n",
    "- 모델 구성, 훈련 및 내보내기\n",
    "- 그 외"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gvLegMMvBZYg"
   },
   "source": [
    "## 텐서\n",
    "\n",
    "TensorFlow는 `tf.Tensor` 객체로 표현되는 다차원 배열 또는 *텐서*에서 작동합니다. 다음은 2차원 텐서입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:51.445217Z",
     "iopub.status.busy": "2022-12-14T21:48:51.444774Z",
     "iopub.status.idle": "2022-12-14T21:48:56.693660Z",
     "shell.execute_reply": "2022-12-14T21:48:56.692913Z"
    },
    "id": "6ZqX5RnbBS1f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-14 21:48:52.378691: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2022-12-14 21:48:52.378780: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2022-12-14 21:48:52.378789: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[1. 2. 3.]\n",
      " [4. 5. 6.]], shape=(2, 3), dtype=float32)\n",
      "(2, 3)\n",
      "<dtype: 'float32'>\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "x = tf.constant([[1., 2., 3.],\n",
    "                 [4., 5., 6.]])\n",
    "\n",
    "print(x)\n",
    "print(x.shape)\n",
    "print(x.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k-AOMqevQGN4"
   },
   "source": [
    "`tf.Tensor`의 가장 중요한 속성은 `shape`와 `dtype`입니다.\n",
    "\n",
    "- `Tensor.shape`: 각 축을 따라 텐서의 크기를 알려줍니다.\n",
    "- `Tensor.dtype`: 텐서에 있는 모든 요소의 유형을 알려줍니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bUkKeNWZCIJO"
   },
   "source": [
    "TensorFlow는 텐서에 대한 표준 수학 연산과 머신러닝에 특화된 많은 연산을 구현합니다.\n",
    "\n",
    "예제:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:56.697339Z",
     "iopub.status.busy": "2022-12-14T21:48:56.696877Z",
     "iopub.status.idle": "2022-12-14T21:48:56.705198Z",
     "shell.execute_reply": "2022-12-14T21:48:56.704587Z"
    },
    "id": "BM7xXNDsBfN5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
       "array([[ 2.,  4.,  6.],\n",
       "       [ 8., 10., 12.]], dtype=float32)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x + x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:56.708417Z",
     "iopub.status.busy": "2022-12-14T21:48:56.707789Z",
     "iopub.status.idle": "2022-12-14T21:48:56.714338Z",
     "shell.execute_reply": "2022-12-14T21:48:56.713765Z"
    },
    "id": "ZLGqscTxB61v"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
       "array([[ 5., 10., 15.],\n",
       "       [20., 25., 30.]], dtype=float32)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:56.717519Z",
     "iopub.status.busy": "2022-12-14T21:48:56.716913Z",
     "iopub.status.idle": "2022-12-14T21:48:57.036336Z",
     "shell.execute_reply": "2022-12-14T21:48:57.035690Z"
    },
    "id": "2ImJHd8VfnWq"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
       "array([[14., 32.],\n",
       "       [32., 77.]], dtype=float32)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x @ tf.transpose(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.039917Z",
     "iopub.status.busy": "2022-12-14T21:48:57.039670Z",
     "iopub.status.idle": "2022-12-14T21:48:57.046331Z",
     "shell.execute_reply": "2022-12-14T21:48:57.045753Z"
    },
    "id": "U9JZD6TYCZWu"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(6, 3), dtype=float32, numpy=\n",
       "array([[1., 2., 3.],\n",
       "       [4., 5., 6.],\n",
       "       [1., 2., 3.],\n",
       "       [4., 5., 6.],\n",
       "       [1., 2., 3.],\n",
       "       [4., 5., 6.]], dtype=float32)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.concat([x, x, x], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.049927Z",
     "iopub.status.busy": "2022-12-14T21:48:57.049412Z",
     "iopub.status.idle": "2022-12-14T21:48:57.055045Z",
     "shell.execute_reply": "2022-12-14T21:48:57.054423Z"
    },
    "id": "seGBLeD9P_PI"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
       "array([[0.09003057, 0.24472848, 0.6652409 ],\n",
       "       [0.09003057, 0.24472848, 0.6652409 ]], dtype=float32)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.nn.softmax(x, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.058293Z",
     "iopub.status.busy": "2022-12-14T21:48:57.057749Z",
     "iopub.status.idle": "2022-12-14T21:48:57.063313Z",
     "shell.execute_reply": "2022-12-14T21:48:57.062764Z"
    },
    "id": "YZNZRv1ECjf8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=21.0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.reduce_sum(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TNHnIjOVLJfA"
   },
   "source": [
    "참고: 일반적으로 TensorFlow 함수가 `Tensor`를 입력으로 받을 것을 예상하는 경우 이 함수는 `tf.convert_to_tensor`를 사용하여 `Tensor`로 변환할 수 있는 모든 항목을 허용하게 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.066217Z",
     "iopub.status.busy": "2022-12-14T21:48:57.065990Z",
     "iopub.status.idle": "2022-12-14T21:48:57.070833Z",
     "shell.execute_reply": "2022-12-14T21:48:57.070289Z"
    },
    "id": "i_XKgjDsL4GE"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 2, 3], dtype=int32)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.convert_to_tensor([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.073935Z",
     "iopub.status.busy": "2022-12-14T21:48:57.073462Z",
     "iopub.status.idle": "2022-12-14T21:48:57.080451Z",
     "shell.execute_reply": "2022-12-14T21:48:57.079917Z"
    },
    "id": "wTBt-JUqLJDJ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=int32, numpy=6>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.reduce_sum([1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8-mi5031DVxz"
   },
   "source": [
    "CPU에서 대규모 계산을 실행하면 속도가 느려질 수 있습니다. 적절하게 구성된 TensorFlow는 GPU와 같은 가속기 하드웨어를 사용하여 작업을 매우 빠르게 실행할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.083599Z",
     "iopub.status.busy": "2022-12-14T21:48:57.083057Z",
     "iopub.status.idle": "2022-12-14T21:48:57.086617Z",
     "shell.execute_reply": "2022-12-14T21:48:57.086045Z"
    },
    "id": "m97Gv5H6Dz0G"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow **IS** using the GPU\n"
     ]
    }
   ],
   "source": [
    "if tf.config.list_physical_devices('GPU'):\n",
    "  print(\"TensorFlow **IS** using the GPU\")\n",
    "else:\n",
    "  print(\"TensorFlow **IS NOT** using the GPU\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ln2FkLOqMX92"
   },
   "source": [
    "자세한 내용은 [텐서 가이드](tensor.ipynb)를 참고하세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oVbomvMyEIVF"
   },
   "source": [
    "## 변수\n",
    "\n",
    "일반 `tf.Tensor` 객체는 변경할 수 없습니다. TensorFlow에 모델 가중치(또는 기타 변경 가능한 상태)를 저장하려면 `tf.Variable`을 사용하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.089869Z",
     "iopub.status.busy": "2022-12-14T21:48:57.089346Z",
     "iopub.status.idle": "2022-12-14T21:48:57.095229Z",
     "shell.execute_reply": "2022-12-14T21:48:57.094672Z"
    },
    "id": "SO8_bP4UEzxS"
   },
   "outputs": [],
   "source": [
    "var = tf.Variable([0.0, 0.0, 0.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.098578Z",
     "iopub.status.busy": "2022-12-14T21:48:57.097949Z",
     "iopub.status.idle": "2022-12-14T21:48:57.104910Z",
     "shell.execute_reply": "2022-12-14T21:48:57.104300Z"
    },
    "id": "aDLYFvu5FAFa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'UnreadVariable' shape=(3,) dtype=float32, numpy=array([1., 2., 3.], dtype=float32)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var.assign([1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.107923Z",
     "iopub.status.busy": "2022-12-14T21:48:57.107302Z",
     "iopub.status.idle": "2022-12-14T21:48:57.113475Z",
     "shell.execute_reply": "2022-12-14T21:48:57.112898Z"
    },
    "id": "9EpiOmxXFDSS"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'UnreadVariable' shape=(3,) dtype=float32, numpy=array([2., 3., 4.], dtype=float32)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var.assign_add([1, 1, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tlvTpi1CMedC"
   },
   "source": [
    "자세한 내용은 [변수 가이드](variable.ipynb)를 참고하세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rG1Dhv2QFkV3"
   },
   "source": [
    "## 자동 미분\n",
    "\n",
    "<a href=\"https://en.wikipedia.org/wiki/Gradient_descent\" class=\"external\"><em>경사 하강</em></a> 및 관련 알고리즘은 최신 머신러닝의 초석입니다.\n",
    "\n",
    "이를 사용하기 위해 TensorFlow는 미분을 사용하여 그래디언트를 계산하는 자동 미분(autodiff)을 구현합니다. 일반적으로 이를 사용하여 가중치에 대한 모델의 *오류* 또는 *손실* 그래디언트를 계산합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.116901Z",
     "iopub.status.busy": "2022-12-14T21:48:57.116363Z",
     "iopub.status.idle": "2022-12-14T21:48:57.122065Z",
     "shell.execute_reply": "2022-12-14T21:48:57.121491Z"
    },
    "id": "cYKOi-z4GY9Y"
   },
   "outputs": [],
   "source": [
    "x = tf.Variable(1.0)\n",
    "\n",
    "def f(x):\n",
    "  y = x**2 + 2*x - 5\n",
    "  return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.125228Z",
     "iopub.status.busy": "2022-12-14T21:48:57.124676Z",
     "iopub.status.idle": "2022-12-14T21:48:57.132741Z",
     "shell.execute_reply": "2022-12-14T21:48:57.132182Z"
    },
    "id": "IQz99cxMGoF_"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=-2.0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ozLLop0cHeYl"
   },
   "source": [
    "`x = 1.0`, `y = f(x) = (1**2 + 2*1 - 5) = -2` 입니다.\n",
    "\n",
    "`y`의 도함수는 `y' = f'(x) = (2*x + 2) = 4`입니다. TensorFlow는 이를 다음과 같이 자동으로 계산할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.135979Z",
     "iopub.status.busy": "2022-12-14T21:48:57.135570Z",
     "iopub.status.idle": "2022-12-14T21:48:57.144442Z",
     "shell.execute_reply": "2022-12-14T21:48:57.143885Z"
    },
    "id": "N02NfWpHGvw8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=4.0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with tf.GradientTape() as tape:\n",
    "  y = f(x)\n",
    "\n",
    "g_x = tape.gradient(y, x)  # g(x) = dy/dx\n",
    "\n",
    "g_x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s-DVYJfcIRPd"
   },
   "source": [
    "이 간소화된 예제에서는 단일 스칼라(`x`)에 대한 도함수만 사용하지만 TensorFlow는 스칼라가 아닌 텐서의 개수에 관계없이 동시에 그래디언트를 계산할 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ECK3I9bUMk_r"
   },
   "source": [
    "자세한 내용은 [Autodiff 가이드](autodiff.ipynb)를 참고하세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VglUM4M3KhNz"
   },
   "source": [
    "## 그래프와 tf.function\n",
    "\n",
    "다른 Python 라이브러리처럼 TensorFlow를 대화형으로 사용할 수도 있으며, 또한 TensorFlow는 다음을 위한 도구도 제공합니다.\n",
    "\n",
    "- **성능 최적화**: 학습 및 추론 속도를 높입니다.\n",
    "- **내보내기**: 학습을 완료한 후 모델을 저장할 수 있습니다.\n",
    "\n",
    "이를 위해서는 `tf.function`을 사용하여 Python으로부터 순수 TensorFlow 코드를 분리해야 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.147744Z",
     "iopub.status.busy": "2022-12-14T21:48:57.147315Z",
     "iopub.status.idle": "2022-12-14T21:48:57.150657Z",
     "shell.execute_reply": "2022-12-14T21:48:57.150105Z"
    },
    "id": "VitACyZWKJD_"
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def my_func(x):\n",
    "  print('Tracing.\\n')\n",
    "  return tf.reduce_sum(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fBYDh-huNUBZ"
   },
   "source": [
    "`tf.function`을 처음 실행하면 Python에서 실행되지만 함수 내에서 수행된 TensorFlow 계산을 나타내는 완전하고 최적화된 그래프를 캡처합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.153793Z",
     "iopub.status.busy": "2022-12-14T21:48:57.153341Z",
     "iopub.status.idle": "2022-12-14T21:48:57.193336Z",
     "shell.execute_reply": "2022-12-14T21:48:57.192767Z"
    },
    "id": "vkOFSEkoM1bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tracing.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=int32, numpy=6>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = tf.constant([1, 2, 3])\n",
    "my_func(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a3aWzt-rNsBa"
   },
   "source": [
    "후속 호출에서 TensorFlow는 최적화된 그래프만 실행하고 TensorFlow가 아닌 단계는 건너뜁니다. 아래에서 `print`는 TensorFlow 함수가 아니라 Python 함수이므로 `my_func`는 *tracing*을 출력하지 않습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.196387Z",
     "iopub.status.busy": "2022-12-14T21:48:57.196167Z",
     "iopub.status.idle": "2022-12-14T21:48:57.201001Z",
     "shell.execute_reply": "2022-12-14T21:48:57.200458Z"
    },
    "id": "23dMHWwwNIoa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=int32, numpy=27>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = tf.constant([10, 9, 8])\n",
    "my_func(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nSeTti6zki0n"
   },
   "source": [
    "다른 *서명*(`shape` 및 `dtype`)이 있는 입력에는 그래프를 재사용할 수 없으므로 대신 다음과 같이 새 그래프가 생성됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.204396Z",
     "iopub.status.busy": "2022-12-14T21:48:57.203778Z",
     "iopub.status.idle": "2022-12-14T21:48:57.219101Z",
     "shell.execute_reply": "2022-12-14T21:48:57.218511Z"
    },
    "id": "OWffqyhqlVPf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tracing.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=27.3>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = tf.constant([10.0, 9.1, 8.2], dtype=tf.float32)\n",
    "my_func(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UWknAA_zNTOa"
   },
   "source": [
    "이렇게 캡처된 그래프는 두 가지 이점을 제공합니다.\n",
    "\n",
    "- 많은 경우 실행 속도를 크게 향상시킵니다(이 예제에서는 아님).\n",
    "- `tf.saved_model`을 사용하여 이 그래프를 내보내면 Python을 설치하지 않아도 [서버](https://www.tensorflow.org/tfx/serving/docker) 또는 [모바일 장치](https://www.tensorflow.org/lite/guide)와 같은 다른 시스템에서 실행할 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hLUJ6f2eMsA8"
   },
   "source": [
    "자세한 내용은 [그래프 소개](intro_to_graphs.ipynb)를 참고하세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t_36xPDPPBqp"
   },
   "source": [
    "## 모듈, 레이어 및 모델"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oDaT7kCpUgnJ"
   },
   "source": [
    "`tf.Module`은 `tf.Variable` 객체와 이 객체에서 작동하는 `tf.function` 객체를 관리하기 위한 클래스입니다. `tf.Module` 클래스는 다음 두 가지 중요한 기능을 지원하는 데 필요합니다.\n",
    "\n",
    "1. `tf.train.Checkpoint`를 사용하여 변수 값을 저장하고 복원할 수 있습니다. 이렇게 하면 모델의 상태를 빠르게 저장하고 복원할 수 있으므로 훈련을 진행할 때 유용합니다.\n",
    "2. `tf.saved_model`을 사용하여 `tf.Variable` 값과 `tf.function` 그래프  *모두*를 가져오고 내보낼 수 있습니다. 이를 통해 모델을 만든 Python 프로그램이 없이 독립적으로 모델을 실행할 수 있습니다.\n",
    "\n",
    "다음은 간단한 `tf.Module` 개체를 내보내는 전체 예제입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.222851Z",
     "iopub.status.busy": "2022-12-14T21:48:57.222293Z",
     "iopub.status.idle": "2022-12-14T21:48:57.226050Z",
     "shell.execute_reply": "2022-12-14T21:48:57.225440Z"
    },
    "id": "1MqEcZOqPBDV"
   },
   "outputs": [],
   "source": [
    "class MyModule(tf.Module):\n",
    "  def __init__(self, value):\n",
    "    self.weight = tf.Variable(value)\n",
    "\n",
    "  @tf.function\n",
    "  def multiply(self, x):\n",
    "    return x * self.weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.229333Z",
     "iopub.status.busy": "2022-12-14T21:48:57.228911Z",
     "iopub.status.idle": "2022-12-14T21:48:57.264292Z",
     "shell.execute_reply": "2022-12-14T21:48:57.263682Z"
    },
    "id": "la2G82HfVfU0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3,), dtype=int32, numpy=array([3, 6, 9], dtype=int32)>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = MyModule(3)\n",
    "mod.multiply(tf.constant([1, 2, 3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GaSJX7zQXCm4"
   },
   "source": [
    "`Module`을 저장합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.267512Z",
     "iopub.status.busy": "2022-12-14T21:48:57.266907Z",
     "iopub.status.idle": "2022-12-14T21:48:57.322271Z",
     "shell.execute_reply": "2022-12-14T21:48:57.321688Z"
    },
    "id": "1MlfbEMjVzG4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ./saved/assets\n"
     ]
    }
   ],
   "source": [
    "save_path = './saved'\n",
    "tf.saved_model.save(mod, save_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LgfoftD4XGJW"
   },
   "source": [
    "결과로 생성된 SavedModel은 생성한 코드와 무관합니다. Python, 기타 언어 바인딩 또는 [TensorFlow 서빙](https://www.tensorflow.org/tfx/serving/docker)에서 SavedModel을 로드할 수 있습니다. 또한 SavedModel이 [TensorFlow Lite](https://www.tensorflow.org/lite/guide) 또는 [TensorFlow JS](https://www.tensorflow.org/js/guide)와 함께 실행되도록 변환할 수도 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.325742Z",
     "iopub.status.busy": "2022-12-14T21:48:57.325326Z",
     "iopub.status.idle": "2022-12-14T21:48:57.357523Z",
     "shell.execute_reply": "2022-12-14T21:48:57.356926Z"
    },
    "id": "pWuLOIKBWZYG"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3,), dtype=int32, numpy=array([3, 6, 9], dtype=int32)>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reloaded = tf.saved_model.load(save_path)\n",
    "reloaded.multiply(tf.constant([1, 2, 3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nxU6P1RGwHyC"
   },
   "source": [
    "`tf.keras.layers.Layer` 및 `tf.keras.Model` 클래스는 `tf.Module`을 기반으로 빌드하며 모델 구축, 훈련 및 저장을 위한 추가 기능 및 편리한 방법을 제공합니다. 이러한 방법 중 일부는 다음 섹션에서 설명합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tQzt3yaWMzLf"
   },
   "source": [
    "자세한 내용은 [모듈 소개](intro_to_modules.ipynb)를 참고하세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rk1IEG5aav7X"
   },
   "source": [
    "## 훈련 루프\n",
    "\n",
    "이제 이 모든 기능을 결합하여 기본 모델을 빌드하고 처음부터 훈련을 진행해 봅니다.\n",
    "\n",
    "먼저 몇 가지 예제 데이터를 만듭니다. 다음 예제는 이차 곡선을 느슨하게 따르는 점 구름을 생성합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.360989Z",
     "iopub.status.busy": "2022-12-14T21:48:57.360428Z",
     "iopub.status.idle": "2022-12-14T21:48:57.702741Z",
     "shell.execute_reply": "2022-12-14T21:48:57.702045Z"
    },
    "id": "VcuFr7KPRPzn"
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "matplotlib.rcParams['figure.figsize'] = [9, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.706675Z",
     "iopub.status.busy": "2022-12-14T21:48:57.706150Z",
     "iopub.status.idle": "2022-12-14T21:48:57.929514Z",
     "shell.execute_reply": "2022-12-14T21:48:57.928618Z"
    },
    "id": "sXN9E_xf-GiP"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = tf.linspace(-2, 2, 201)\n",
    "x = tf.cast(x, tf.float32)\n",
    "\n",
    "def f(x):\n",
    "  y = x**2 + 2*x - 5\n",
    "  return y\n",
    "\n",
    "y = f(x) + tf.random.normal(shape=[201])\n",
    "\n",
    "plt.plot(x.numpy(), y.numpy(), '.', label='Data')\n",
    "plt.plot(x, f(x), label='Ground truth')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "De5LldboSWcW"
   },
   "source": [
    "무작위로 초기화된 가중치와 편향을 사용하여 이차 모델을 생성합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.933075Z",
     "iopub.status.busy": "2022-12-14T21:48:57.932538Z",
     "iopub.status.idle": "2022-12-14T21:48:57.937886Z",
     "shell.execute_reply": "2022-12-14T21:48:57.937256Z"
    },
    "id": "Pypd0GB4SRhf"
   },
   "outputs": [],
   "source": [
    "class Model(tf.Module):\n",
    "\n",
    "  def __init__(self):\n",
    "    # Randomly generate weight and bias terms\n",
    "    rand_init = tf.random.uniform(shape=[3], minval=0., maxval=5., seed=22)\n",
    "    # Initialize model parameters\n",
    "    self.w_q = tf.Variable(rand_init[0])\n",
    "    self.w_l = tf.Variable(rand_init[1])\n",
    "    self.b = tf.Variable(rand_init[2])\n",
    "  \n",
    "  @tf.function\n",
    "  def __call__(self, x):\n",
    "    # Quadratic Model : quadratic_weight * x^2 + linear_weight * x + bias\n",
    "    return self.w_q * (x**2) + self.w_l * x + self.b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "36o7VjaesScg"
   },
   "source": [
    "먼저, 훈련을 진행하기 전에 모델의 성능을 관찰합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.940967Z",
     "iopub.status.busy": "2022-12-14T21:48:57.940427Z",
     "iopub.status.idle": "2022-12-14T21:48:57.948195Z",
     "shell.execute_reply": "2022-12-14T21:48:57.947581Z"
    },
    "id": "GkwToC5BWV1c"
   },
   "outputs": [],
   "source": [
    "quad_model = Model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.951273Z",
     "iopub.status.busy": "2022-12-14T21:48:57.950667Z",
     "iopub.status.idle": "2022-12-14T21:48:57.954578Z",
     "shell.execute_reply": "2022-12-14T21:48:57.953945Z"
    },
    "id": "ReWhH40wTY5F"
   },
   "outputs": [],
   "source": [
    "def plot_preds(x, y, f, model, title):\n",
    "  plt.figure()\n",
    "  plt.plot(x, y, '.', label='Data')\n",
    "  plt.plot(x, f(x), label='Ground truth')\n",
    "  plt.plot(x, model(x), label='Predictions')\n",
    "  plt.title(title)\n",
    "  plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:57.957445Z",
     "iopub.status.busy": "2022-12-14T21:48:57.956920Z",
     "iopub.status.idle": "2022-12-14T21:48:58.197347Z",
     "shell.execute_reply": "2022-12-14T21:48:58.196649Z"
    },
    "id": "Y0JtXQat-nlk"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_preds(x, y, f, quad_model, 'Before training')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hLzwD0-ascGf"
   },
   "source": [
    "이제 모델의 손실을 정의합니다.\n",
    "\n",
    "이 모델이 연속 값을 예측하기 위한 것임을 감안할 때 평균 제곱 오차(MSE)는 손실 함수에 대응하기 위한 좋은 선택이 됩니다. 예측 벡터 $\\hat{y}$와 실제 목표 벡터 $y$가 제공되면 MSE는 예측 값과 실측 정보의 차이 값의 제곱의 평균으로 정의됩니다.\n",
    "\n",
    "$MSE = \\frac{1}{m}\\sum_{i=1}^{m}(\\hat{y}_i -y_i)^2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:58.200749Z",
     "iopub.status.busy": "2022-12-14T21:48:58.200505Z",
     "iopub.status.idle": "2022-12-14T21:48:58.203802Z",
     "shell.execute_reply": "2022-12-14T21:48:58.203195Z"
    },
    "id": "eCtJ1uuCseZd"
   },
   "outputs": [],
   "source": [
    "def mse_loss(y_pred, y):\n",
    "  return tf.reduce_mean(tf.square(y_pred - y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7EWyDu3zot2w"
   },
   "source": [
    "모델에 대한 기본 훈련 루프를 작성합니다. 루프는 모델의 매개변수를 반복적으로 업데이트하기 위해 입력에 대한 MSE 손실 함수 및 그래디언트를 사용합니다. 훈련에 미니 배치를 사용하면 메모리 효율성과 더 빠른 수렴이 모두 제공됩니다. `tf.data.Dataset` API에는 일괄 처리 및 셔플링에 유용한 함수가 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:58.207005Z",
     "iopub.status.busy": "2022-12-14T21:48:58.206420Z",
     "iopub.status.idle": "2022-12-14T21:48:58.216441Z",
     "shell.execute_reply": "2022-12-14T21:48:58.215881Z"
    },
    "id": "8kX_-zily2Ia"
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "dataset = tf.data.Dataset.from_tensor_slices((x, y))\n",
    "dataset = dataset.shuffle(buffer_size=x.shape[0]).batch(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:48:58.219915Z",
     "iopub.status.busy": "2022-12-14T21:48:58.219350Z",
     "iopub.status.idle": "2022-12-14T21:49:01.678800Z",
     "shell.execute_reply": "2022-12-14T21:49:01.678109Z"
    },
    "id": "nOaES5gyTDtG"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error for step 0: 57.032\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error for step 10: 10.153\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error for step 20: 4.238\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error for step 30: 2.218\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error for step 40: 1.496\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error for step 50: 1.238\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error for step 60: 1.149\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error for step 70: 1.121\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error for step 80: 1.109\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error for step 90: 1.107\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set training parameters\n",
    "epochs = 100\n",
    "learning_rate = 0.01\n",
    "losses = []\n",
    "\n",
    "# Format training loop\n",
    "for epoch in range(epochs):\n",
    "  for x_batch, y_batch in dataset:\n",
    "    with tf.GradientTape() as tape:\n",
    "      batch_loss = mse_loss(quad_model(x_batch), y_batch)\n",
    "    # Update parameters with respect to the gradient calculations\n",
    "    grads = tape.gradient(batch_loss, quad_model.variables)\n",
    "    for g,v in zip(grads, quad_model.variables):\n",
    "        v.assign_sub(learning_rate*g)\n",
    "  # Keep track of model loss per epoch\n",
    "  loss = mse_loss(quad_model(x), y)\n",
    "  losses.append(loss)\n",
    "  if epoch % 10 == 0:\n",
    "    print(f'Mean squared error for step {epoch}: {loss.numpy():0.3f}')\n",
    "\n",
    "# Plot model results\n",
    "print(\"\\n\")\n",
    "plt.plot(range(epochs), losses)\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Mean Squared Error (MSE)\")\n",
    "plt.title('MSE loss vs training iterations');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dW5B2TTRsvxE"
   },
   "source": [
    "이제 훈련 후 모델의 성능을 관찰합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:49:01.681919Z",
     "iopub.status.busy": "2022-12-14T21:49:01.681648Z",
     "iopub.status.idle": "2022-12-14T21:49:01.899999Z",
     "shell.execute_reply": "2022-12-14T21:49:01.899292Z"
    },
    "id": "Qcvzyg3eYLh8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YQHJyMh06dOCss87im2++4fnnn2fevHlYrVZ69+7N//3f//HUU0/Ro0cPZs+ezdSpU6scY/Dgwdx0002MHTuWhIQEnn76aRISEpg1axYffPAB3bt3Z9q0aUyfPr3Z3peIiIiIN/O2Co2jMUzTNI++mffKy8vDZrNht9uJioqq8lpRURFpaWmkpqYSEhLioQjFV+jzIiIiIofLtBc2e4VGhbpy3COp5EZEREREpAbJtlCvbZU/nEpuRERERER8mBJ6EREREREfpoReRERERMSHeVVCP23aNAzD4M477/R0KCIiIiIiPsFrEvpVq1bx6quv0qtXL0+HIiIiIiJ+JtNeyPJt2V672mtTeEVCf+jQIa644gpef/11YmJiPB2OiIiIiPiROasyGDJtMZe/vpIh0xYzZ1WGp0NyKa9I6CdMmMDIkSMZPnz4UbctLi4mLy+vykNEREREpCaZ9kImzV1Xueqr04TJc9f7VUu9xxP69957j59//rnaCqe1mTp1KjabrfKRkpLi5ghFRERExFelZedXJvMVHKZJenaBZwJyA48m9Dt27OCOO+5g9uzZ9V6Zc9KkSdjt9srHjh073BylNMSUKVPo06ePp8Ng6NChGlwtIiIipMaHYzGqPmc1DDrEh3kmIDfwaEK/evVq9u7dS9++fQkICCAgIIBvv/2W559/noCAABwOR7V9goODiYqKqvLwR1lZWdxxxx107tyZkJAQWrVqxZAhQ3jllVcoKPDdO8olS5ZgGAa5ubleeTwRERHxL8m2UKaO6YnVKM/qrYbBk2N6+MQKsPUV4MmTn3766axbt67Kc9deey3dunXj/vvvx2q1eigyz/rjjz8YMmQI0dHRPPnkk/Ts2ZPg4GDWrVvHa6+9Rps2bTj//PNr3Le0tJTAwMBmjtj1SkpKCAoK8nQYIiIi4gfGDmjHKV0SSM8uoEN8mF8l8+DhFvrIyEh69OhR5REeHk5cXBw9evTwZGgedcsttxAQEMBPP/3EJZdcwrHHHkvHjh0ZNWoUn3/+Oeedd17ltoZh8Morr3D++ecTHh7OE088AcArr7xCp06dCAoKomvXrvznP/+p3Cc9PR3DMFizZk3lc7m5uRiGwZIlS4C/Wr4XLVpE//79CQsLY/DgwWzevLlKrNOmTaNVq1ZERkZy/fXXU1RUVOv7Sk9PZ9iwYQDExMRgGAbjxo0Dyktkbr31Vu68807i4+MZMWLEUeOs63gATqeT++67j9jYWJKSkpgyZUp9vwUiIiLiZ5JtoQzqFOd3yTx4waDYZmWaUJLvmYdpHj0+ICcnh6+++ooJEyYQHh5e4zaGUbUQbMqUKVxwwQWsW7eO6667jo8++og77riDu+++m/Xr13PjjTdy7bXX8s033zT4kj3wwAP885//5KeffiIgIIDrrruu8rX333+fKVOm8OSTT/LTTz+RnJzMyy+/XOuxUlJS+PDDDwHYvHkzmZmZPPfcc5Wvv/322wQFBbFs2TJmzJhx1Njqc7zw8HBWrlzJ008/zaOPPsrChQsbfA1EREREvJlHS25qUtFC7BalBfBka/cdvy6Td0NQzQn64bZu3YppmnTt2rXK8/Hx8ZWt3xMmTOCpp56qfO3yyy/n2muvrfz6sssuY9y4cdxyyy0A3HXXXfzwww9Mnz69skW7vp544glOPfVUACZOnMjIkSMpKioiJCSEZ599luuvv57rr78egMcff5yvv/661lZ6q9VKbGwsAImJiURHR1d5/ZhjjuHpp5+u/Do9Pb3O2I52vF69evHwww9XHvvFF19k0aJFnHHGGfV67yIiIiK+oGW10PuwH3/8kTVr1nDcccdRXFxc5bX+/ftX+Xrjxo0MGTKkynNDhgxh48aNDT7v4Sv3JicnA7B3797K8wwcOLDK9oMGDWrwOSr069ev0fvW5MhVh5OTkytjFxEREfEXXtdC71aBYeUt5Z46dz107twZwzCq1ap37NgRgNDQ6nVftZXm1MZiKb+PMw8rAyotLa1x28MH2FaU+jidzgadr76OfB8NibMmRw4ONgzDbbGLiIiIeErLaqE3jPKyF088jqh7r01cXBxnnHEGL774Ivn5+Y16m8ceeyzLli2r8tyyZcvo3r07AAkJCQBkZmZWvn74wNOGnGflypVVnvvhhx/q3Kdi5pqapiQ9Un3ibMjxRERERPxRy2qh9xEvv/wyQ4YMoX///kyZMoVevXphsVhYtWoVmzZtOmppyr333ssll1zC8ccfz/Dhw/n000+ZO3cuX3/9NVDeyn/iiScybdo0UlNT2bt3Lw8++GCD47zjjjsYN24c/fv3Z8iQIcyePZsNGzZU9ibUpH379hiGwWeffcY555xDaGgoERERNW5bnzgbcjwRERERf9SyWuh9RKdOnfjll18YPnw4kyZNonfv3vTv358XXniBe+65h8cee6zO/UePHs1zzz3H9OnTOe6443j11VeZOXMmQ4cOrdzmrbfeoqysjH79+nHnnXfy+OOPNzjOsWPH8o9//IP77ruPfv36sX37dm6++eY692nTpg2PPPIIEydOpFWrVtx66611bn+0OBt6PBERERF/Y5hmPedT9FJ5eXnYbDbsdnu1VWOLiopIS0sjNTWVkJAQD0UovkKfFxEREfEWdeW4R1ILvYiIiIiID1NCLyIiIiLiw5TQi4iIiIj4MCX0IiIiIiI+TAm9iIiIiIgPU0IvIiIiIuLDlNCLiIiIiPgwJfQiIiIiIj5MCb2IiIiIiA9TQt/CjRs3jtGjR1d+PXToUO68884mHdMVxxARERHxhEx7Icu3ZZNpL/R0KPUW4OkApGbjxo3j7bffBiAwMJB27dpx9dVXM3nyZAIC3Pdtmzt3LoGBgfXadsmSJQwbNowDBw4QHR3dqGOIiIiIeIs5qzKYNHcdThMsBkwd05OxA9p5OqyjUgu9FzvrrLPIzMxky5Yt3H333UyZMoVnnnmm2nYlJSUuO2dsbCyRkZEeP4aIiIhIc8q0F1Ym8wBOEybPXe8TLfVK6L1YcHAwSUlJtG/fnptvvpnhw4fzySefVJbJPPHEE7Ru3ZquXbsCsGPHDi655BKio6OJjY1l1KhRpKenVx7P4XBw1113ER0dTVxcHPfddx+maVY555HlMsXFxdx///2kpKQQHBxM586defPNN0lPT2fYsGEAxMTEYBgG48aNq/EYBw4c4OqrryYmJoawsDDOPvtstmzZUvn6rFmziI6O5ssvv+TYY48lIiKi8mamwpIlSzjhhBMIDw8nOjqaIUOGsH37dhddaREREWnp0rLzK5P5Cg7TJD27wDMBNUCLSuhN06SgtMAjjyMT58YIDQ2tbI1ftGgRmzdvZuHChXz22WeUlpYyYsQIIiMj+f7771m2bFllYlyxzz//+U9mzZrFW2+9xdKlS9m/fz8fffRRnee8+uqreffdd3n++efZuHEjr776KhEREaSkpPDhhx8CsHnzZjIzM3nuuedqPMa4ceP46aef+OSTT1ixYgWmaXLOOedQWlpauU1BQQHTp0/nP//5D9999x0ZGRncc889AJSVlTF69GhOPfVUfv31V1asWMENN9yAYRhNvqYiIiIiAKnx4ViqpBYmwfGLCQzJ8VRI9daiaugLywoZ+M5Aj5x75eUrCQsMa9S+pmmyaNEivvzyS2677Tb27dtHeHg4b7zxBkFBQQD897//xel08sYbb1QmujNnziQ6OpolS5Zw5pln8uyzzzJp0iTGjBkDwIwZM/jyyy9rPe/vv//O+++/z8KFCxk+fDgAHTt2rHw9NjYWgMTExCo19IfbsmULn3zyCcuWLWPw4MEAzJ49m5SUFD7++GMuvvhiAEpLS5kxYwadOnUC4NZbb+XRRx8FIC8vD7vdzrnnnlv5+rHHHtvwCykiIiJSi2RbKFPH9GTy3PU4TJPg2KUEJXzF3Ut/4rMLPiMyyHvLiVtUC72v+eyzz4iIiCAkJISzzz6bsWPHMmXKFAB69uxZmcwDrF27lq1btxIZGUlERAQRERHExsZSVFTEtm3bsNvtZGZmMnDgXzc0AQEB9O/fv9bzr1mzBqvVyqmnntro97Bx40YCAgKqnDcuLo6uXbuycePGyufCwsIqk3WA5ORk9u7dC5TfOIwbN44RI0Zw3nnn8dxzz1UpxxERERFxhbED2rF04jDuG1NKcKsvALi+x/VencxDC2uhDw0IZeXlKz127oYaNmwYr7zyCkFBQbRu3brK7Dbh4eFVtj106BD9+vVj9uzZ1Y6TkJDQ8IApL/FpLkfOimMYRpUypZkzZ3L77bezYMEC5syZw4MPPsjChQs58cQTmy1GERER8X/7y/5g1pYnMTEZ2/Z0rup6madDOqoWldAbhtHoshdPCA8Pp3PnzvXatm/fvsyZM4fExESioqJq3CY5OZmVK1dyyimnAOW16atXr6Zv3741bt+zZ0+cTifffvttZcnN4Sp6CBwOR61xHXvssZSVlbFy5crKkpucnBw2b95M9+7d6/XeKhx//PEcf/zxTJo0iUGDBvHOO+8ooRcRERGXycrP4rZFt1FYVsiQuJ5MXPEeRtYuuHgWBIUfdX9PUcmNn7jiiiuIj49n1KhRfP/996SlpbFkyRJuv/12du7cCcAdd9zBtGnT+Pjjj9m0aRO33HILubm5tR6zQ4cOXHPNNVx33XV8/PHHlcd8//33AWjfvj2GYfDZZ5+xb98+Dh06VO0YxxxzDKNGjWL8+PEsXbqUtWvXcuWVV9KmTRtGjRpVr/eWlpbGpEmTWLFiBdu3b+err75iy5YtqqMXERERl8kvzWfCognsK9xH58j2TN+0ioCyQnA6wBp09AN4kBJ6PxEWFsZ3331Hu3btGDNmDMceeyzXX389RUVFlS32d999N1dddRXXXHMNgwYNIjIykgsuuKDO477yyitcdNFF3HLLLXTr1o3x48eTn58PQJs2bXjkkUeYOHEirVq14tZbb63xGDNnzqRfv36ce+65DBo0CNM0+eKLL+q9+FRYWBibNm3iwgsvpEuXLtxwww1MmDCBG2+8sQFXSERERKRmZc4y7v32Xn4/8DtxIbG8lJlFxKG90KpHeeu81bsXzDRMV8yn6EF5eXnYbDbsdnu1UpOioiLS0tJITU0lJCTEQxGKr9DnRUREpOXJtBcydeVUvsn8iBBrMDPLYuiR/iNEtoa/fQ22Nh6Jq64c90gtqoZeRERERKTCnFUZ/OObGQS3+hSAO4uS6LHzewiKhCve91gy31BK6EVERESkxcm0F/Lglx8Q3PYzAAZkt+GKg99jGlaMS2ZBUk/PBtgAqqEXERERkRbnu/S1BLd+B8MwaWtvy5sHVwDwx8DHoXP12f28mRJ6EREREfEamfZClm/LJtNe6LZz7C3Yy4zND2BYS4jIT+LDnJUYwMtlowkbdK3bzusuLaLkxsfH/Uoz0edERETEs+asymDS3HU4TbAYMHVMT8YOaOfScxSUFnDrolvJLtxLYkAi/9m3njDDwSeOwcSd/wjJtuZbWNNV/LqFvmJaxIKCAg9HIr6g4nNS3+k0RURExHUy7YWVyTyA04TJc9e7tKW+zFnG3d/ezcb9G4kNsjFrz05amwXYW53IgDvfZewJHVx2rubk1y30VquV6Oho9u7dC5TPZ24YhoejEm9jmiYFBQXs3buX6OhorFarp0MSERFpcdKy8yuT+QoO0yQ9u8AlreamafL4D4+zdNdSQqzBvHigiJS8LEjsjm3cHGyh0U0+h6f4dUIPkJSUBFCZ1IvUJjo6uvLzIiIiIs0rNT4ci0GVpN5qGHSID3PJ8d9c/yYfbvkQA4OnSsLpuXdN+VzzV3wAPpzMQwtI6A3DIDk5mcTEREpLSz0djnipwMBAtcyLiIi4WKa9kLTsfFLjw4/ayp5sC2XqmJ5Mnrseh2liNQyeHNPDJa3zn//xOc/9/BwAEwNac1raCgiOgiv/B7a2TT6+p/l9Ql/BarUqYRMRERFpJo0Z4Dp2QDtO6ZJAenYBHeLDXJLMr8paxT+W/QOAa0JTufy3b8EaBJfOhlbHNfn43sCvB8WKiIiISPNrygDXZFsogzrFuSSZ35a7jTu+uYNSZylnhKdy12/flr8w+hVIPaXJx/cWLaaFXkRERESah7sHuNaloswnKqKQu7+/hYMlB+kTnsLUDd+Wt2Sf+Tj0vMitMTQ3JfQiIiIi4lLuHuBam8oyH4oJ6/Aa1pDdtA9N5PlNqwg2gYE3w6Bb3RqDJ6jkRkRERERcqmKAq/XP6cJdOcC1Nn+V+TgIbfsO1pBdWMpCeSltCzFlJdB9FIx4EvxwCnO10IuIiIiIy7ljgGttMu2FfPbrbpymSXDSJwREbAZnAP/ak0P7Eju0GwwXvAYW/2zLVkIvIiIiIm6RbAt1SyJ/+HSY3/2+r3IAblDctwTFrAQT7ttTwmkl2ZTGHkPgpbMhMMTlcXgLJfQiIiIi4pVqmsf+8OkwK4pnTCAgag3BiQsAuDQ7iKuKtlIYnEDo1R9BWKxn3kAzUUIvIiIiIl6npnnsT+mSUGU6zIoxt9awPwhJ/gCAIbnhPHBoI86gCEKv/QiiUzzzBpqRfxYSiYiIiIjPqm0e+9XbD1SbDtMStJfQtv/GsDg45mAkLx/YiGkJxHLpbEjq2fzBe4ASehERERHxKrXNY8+frfUVjAA7oe3exLAWkVAYwTs5v2EBjNEvQ8ehzRmyRymhFxERERGvUjGP/eGshkG/DjF/TYdpKSAsZSaWQDvtAmP4cO8mQkwTzpoGvS7xTOAeooReRERERLxKXfPYjx3QjsX3DqbvgI+xhGSREBjJa2m/EeN0wkl3wYk3ezj65qdBsSIiIiLidWqbx97hdPDs2kfYkvcrEdYQXslIp01pKRx/FZz+UJVj1DRLjj/yeAv91KlTGTBgAJGRkSQmJjJ69Gg2b97s6bBERERExMOSbaEM6hRXmYybpsnUH6fydcbXBBoBPL8nm65F+dDtXDj32SqrwM5ZlcGQaYu5/PWVDJm2mDmrMjz0LtzP4wn9t99+y4QJE/jhhx9YuHAhpaWlnHnmmeTn53s6NBERERHxIq/9+hpzNs/BwGBqbgEDDu6H9kPgwjfA+lfhSW2z5GTaCz0UuXt5vORmwYIFVb6eNWsWiYmJrF69mlNOOcVDUYmIiIiIN/nw9w95cc2LAEwsgBH7s6BVD7j0HQisWk5T2yw56dkFfll64/EW+iPZ7XYAYmP9e0UvEREREamfJTuW8OgPjwIwvjSYy/dsh+j2cOWHEBpdbfvaZsnpEB/m/mA9wKsSeqfTyZ133smQIUPo0aNHjdsUFxeTl5dX5SEiIiIi/mnN3jXc++29OE0no8xwbtu5BcIT4KqPIDKpxn3qmiXHH3m85OZwEyZMYP369SxdurTWbaZOncojjzzSjFGJiIiIiCf8kfsHty6+lSJHESdbInl42waMoEi44n8Q16nOfWubJccfGaZpmkffzP1uvfVW5s2bx3fffUdqamqt2xUXF1NcXFz5dV5eHikpKdjtdqKiopojVBERERFxsz35e7hq/lVk5mfSyxrF69s2EGYJLC+zSfX/cZZ5eXnYbLZ65bgeb6E3TZPbbruNjz76iCVLltSZzAMEBwcTHBzcTNGJiIiISHPLK8njpq9vIjM/kw4Bkby47TfCTMpns2kByXxDeTyhnzBhAu+88w7z5s0jMjKSrKwsAGw2G6Gh/ts1IiIiIiLVFTuKuX3x7WzN3Uq8NYwZaZvKV4E991/QfZSnw/NKHi+5MQyjxudnzpzJuHHjjrp/Q7ojRERERMR7OZwO7v3uXhZuX0iEJZhZO9LpWlIKwx6AU+/zdHjNyudKbkRERESkeWXaC0nLzic1PtwrBoyapsljPzzGwu0LCTSsPLd7Z3kyf8KNcMq9ng7Pq3k8oRcRERGR5jVnVUblSqoWA6aO6cnYAe08GtNzPz/Hh1s+xILBtL3ZnFBYCL0vh7OmQS0VHVLOq+ahFxERERH3yrQXVibzAE4TJs9dT6a9sEnHXL4tu9HHmLV+Fm+ufxOAfxw4yJmHDkK3c+H8F8CidPVo1EIvIiIi0oKkZedXJvMVHKZJenZBo0pvmtra/9GWj/jn6n8CcGdeERfl7oeOw+Cit8CqVLU+dMsjIiIi0oKkxodjOaKCxWoYdIgPa/Cxmtra//X2r5myYgoA1+aXcX3OXmh7Alw6GwI0TXl9KaEXERERaUGSbaFMHdMT65916VbD4MkxPRrVOl9Xa//R/JD5A/d9dx9O08mYIvj73t3Qqidc8T4EhTc4lpZM/RgiIiIiLczYAe04pUsC6dkFdIgPq3cyf+TMOBWt/Ycn9fVp7V+fvZ47Ft9BqbOU4aUW/pGZjhHbCa6aC6ExTXlrLZISehEREZEWKNkW2qBW+dpq5aeO6cnkuetxmGa9Wvv/yP2Dm7++mYKyAgY6Anhq5x8ERLWFq+dBRKIr3lqLo4ReREREROpUW638KV0SGtTav/vQbsYvHE9ucS49zECe2/EHQWHxcPXHEJ3SPG/GDymhFxEREZE6HW1mnPq09ucU5nDDwhvYW7CXjgTxSsYfhAdFwVUfQfwxboze/2lQrIiIiIgfaeqc8DWpaWYcC5CTX1yv8xwqOcTNX9/M9rztJBtBvJqRRrQ1pHwAbHIvl8XZUimhFxEREfETc1ZlMGTaYi5/fSVDpi1mzqoMlxz3yJlxDMAEbn3nl6Oep6isiNsW38bG/RuJNQJ5LSOdJNMCY/8L7U50SXwtnRJ6ERERET/gjhVgDzd2QDuWThzGi5cdj2GUJ/RHO0+Zs4x7v7uXn/b8RIQRwIydGXRwOOGiN6Hz6S6JS5TQi4iIiPiFpswJX1/JtlBiI4LqdR6n6eShZQ+xZMcSgrHwwu5dHFtSCue/AN1HuSwmUUIvIiIi4hdcuQJsU89jmiZP/PAEn/7xKVYMpmftoX9RMZwzHY6/0qXxiBJ6EREREb/gyhVgm3Ie0zT550//5P3f38cApu7dx9DCQjjzCThhvEtjkXKGaZrm0TfzXnl5edhsNux2O1FRUZ4OR0RERMSjMu2FDV4B1pXneWXNK7y89mUAHt2XwwWH8uG0f8Ap97gtFn/UkBxX89CLiIiI+LhMeyFp2fmkxoc3eAXYxqrpPLPWz6pM5ifm7C9P5k+5V8m8mymhFxEREfFhc1ZlVM5uYzFg6piejB3Qrvnj2DSHf67+JwB37M/lirxDMPg2GPZAs8fS0qiGXkRERMRHHLlolLunqqyvT7Z9wuMrHwdgfG4ef7PnwQk3wBmPgWEcZW9pKrXQi4iIiPiAmlriU2LDap1CsjnKbgAWbl/IP5b9A4Ar8g5x24Fc6Hs1nPWUkvlmohZ6ERERES9XW0t8eJC1WaaqrM13O7/jvu/uw2k6GXOogPty9mP0GgvnPgsW16aZR/ZOyF+U0IuIiIh4udoWjSoocTbLVJU1+THzR+5achdlzjLOzi/ioX3ZWLqPhlEvg8V61P0bkqDPWZXBkGmLufz1lQyZtpg5qzKaFLu/3Ryo5EZERETEy1Us5nR4Ul/REj+oUxyndElolqkqK6zZu4ZbF99KsaOYoYXFPLF3L9au58CFb4D16OllQwby1tY7cUqXhEa9V28ZROxKaqEXERER8XJHW8wp2RbKoE5xzZLMb8zZyC1f30JhWSGDikqZvmcPgZ2Hw8WzwBpY634VreJrdxxo0EDe2non0rMLGhy7twwidjW10IuIiIj4gLED2jV7S/yRtuVu48aFN3Kw9CB9S8p4NiuL4PYnw9j/QkBwrfsd3ipuAEeualrXQN66eicaqq6bA09cT1dRC72IiIiIj2jOlvgj7cjbwfivxnOg+ADHlTh4cXcmYW0HwmXvQWDt8RzZKn5kMg91J+hH651oiIqbg/qe21eohV5ERERE6rTz4E6u++o69hXuo3OpgxmZmUS2GQBXfADBEXXuW1OrOFDZ6l6fBN1VvRMVNweT567HYZrNOojYnZTQi4iIiEitdh/azfVfXk9WfhapZQ5ez8wkunU/uPJDCIk66v61lczMvWUQBSXOeifoybZQlyTe3lC65GpK6EVERESkRln5WVz35XXszt9N+zInb+7OJD6pb72Teai9Vbx3Soybo687Jn9I5CsooRcRERGRavbk7+Ga+dexO38XKX8m8wlJfeGquRBia9Cx/LFV3JsooRcRERGRKvYV7OOST65hf8kukksdvJWZRUBk90Yl8xX8rVXcm2iWGxERERGplF2YzTXzr2N/yS5alTqYmZVFVml7Tt9zB5nFQZ4OT2qghF5EREREANhftJ+/ffk3dhxKJ6HMycysLPaVtOfqkknkmmGNWsxJ3E8JvYiIiIhwoOgAf/vqb2yzbyPB4WRWZhb7/0zm8wj3i/na/ZVq6EVERERaOHuxnRsW3sCWA1uILXPyVmYWuSUpXF06sTKZ94f52v2VEnoRERGRFsxebGf8V+PZtH8TMX+W2eSVpHBlySQOEcFLlx9P3/YxSua9mEpuRERERJoo017I8m3ZZNoLPR1KgxwsOchNC29i4/6NRDtM3sraQ35xW64smUQeETiB2PBgJfNeTi30IiIiIk0wZ1UGk+auw2mCxYCpY3oydkA7T4d1VIdKDnHT1zexPmc9NqfJG5lZFBW34cqSyeQRAaC6eR+hFnoRERGRRsq0F1Ym8wBOEybPXe/1LfX5pfncsugWft33K1FOk9czs+gadyxbRvyXQ0YkgOrmfYha6EVEREQaKS07vzKZr+AwTdKzC7wyEc60F/Jb1l5e+30yv+3/lUinyWuZWRyb0Auu/JALQmM4sUdnrejqY5TQi4iIiDRSanw4FoMqSb23lqnMWZXBpI9XEdL2LaxhGUQ6nbyWuZfjWvWDKz6AkChAK7r6IpXciIiIiDRSsi2UqWN6YjUMwHvLVDLthUz6+EdCUt7AGpZBlMPJG5l7OCaxP1z5IZnFgT45qFfKqYVeREREpAnGDmjHKV0SvLpMZX1mZnkyH7oLm8PJG1l72FvYjYMnv076r/t9clCv/EUt9CIiIiJNlGwLZVCnOK9M5nOLcnlp071YQ3cR7XDwZuYedhcex41l9xASFu6Tg3qlKiX0IiIiIn5qf9F+rv/qerbZfyfW4eCtzL1sL+rFhLK7mDKmL/kljloH9YrvUMmNiIiIiB/KKczhb1/9ja25W4kvc/Bm1h6SO5xNzgnTWZxoI9kWSqa90GcG9Urt1EIvIiIi4mGuXmk2uzCb67+8nq25W0koc/BW1h46HnshoZfO4sRjkipLg3xlUK/UTS30IiIiIh7k6pVm9xXs4/qvrifNnkZiWRlvZe6lfY9L4fznwWKttr0vDOqVuqmFXkRERMRDXL3S7J78PVz35XWk2dNIKitjVuZe2ve5Gs5/ocZkvoI3D+qVo1NCLyIiIuIhda00ezRHlulk5Wdx7ZfXkp6XTuvSMmZm7iGl399g5P+BRSmfP/OK7+5LL71Ehw4dCAkJYeDAgfz444+eDklERESk0epbE1+x0uzh6jModc6qDIZMW8zlr69kyLTFvLb8J65dcC07Du6gTWkZb2Xtoe0JE+CsaWAYdR7L1Vw9HkCOzuM19HPmzOGuu+5ixowZDBw4kGeffZYRI0awefNmEhMTPR2eiIiISIM0pCa+YlDq5LnrcZhmvQalHlmmYwbk8Nxv07AE5tK2tJS3MveSfPL9cOp9zZ7Mu3o8gNSPYZqmefTN3GfgwIEMGDCAF198EQCn00lKSgq33XYbEydOPOr+eXl52Gw27HY7UVFR7g5XREREpFaZ9kKGTFtcbRrIpROHHTVJr++g1OXbsrn89ZUAWIL2ENruDSyBB2lfWsobmXtJGv44DLrFJe+nIRr73qVmDclxPVpyU1JSwurVqxk+fHjlcxaLheHDh7NixQoPRiYiIiLScI2tiW/IoNSKMh1L8C7C2r+KJfAgnUtKmJm1j6SRz3kkmYemjQeQpvFoQp+dnY3D4aBVq1ZVnm/VqhVZWVk17lNcXExeXl6Vh4iIiIg3qKkm3gLk5Be7rKY82RbKhLMCCGv/OkZAAd2Li3kzK4eE0a9D36tcco7GaOx4AGk6rxgU2xBTp07FZrNVPlJSUjwdkoiIiAhQfaEmAzCBW9/5hSHTFjNnVUaTz7EqaxVzdv4Dw1pE36IiXt+bR+zYd6DHmCYfuym0SJXneLSGvqSkhLCwMP73v/8xevToyuevueYacnNzmTdvXrV9iouLKS4urvw6Ly+PlJQU1dCLiIiI18i0F7I6/QC3v/eLS2vKv9v5HXd983eKnSWcWFjIcweKCLvsPehwkosib7qGjAeQ2vlMDX1QUBD9+vVj0aJFlc85nU4WLVrEoEGDatwnODiYqKioKg8RERGRpnLldIvJtlBiI4JcWlO+cPtC7lh8B8XOEobmF/CivYywqz/xqmQetEiVJ3h82sq77rqLa665hv79+3PCCSfw7LPPkp+fz7XXXuvp0ERERKSFcMd0ixU15Ue20DempvzTbZ/y4NIHceLkrEP5PFkYSOC4T6FV9ybFKP7B4zX0Y8eOZfr06Tz00EP06dOHNWvWsGDBgmoDZUVERETc4ch53Z0mTJ67vskt9a6qKX9/8/s8sPQBnDgZffAQ00rDCbxuvpJ5qeTxeeibSvPQi4iISFMcPq/74d4dfyKDOsU1+fhNqSl/e8PbTP9pOgCX2Q8y0RKP5epPIKp1k+MS79aQHNfjJTciIiIinuTK0piaJNtCG5zIm6bJjF9n8PKalwG4LtfOncHtMK76GMLjXRKX+A+Pl9yIiIiIeELFIFjAq6ZbNE2Tf/38r8pk/rb9udwZ0Q1j3OdK5qVGaqEXERGRFqemQbBLJw7z+HSLDqeDJ1c+yfu/vw/AfTkHuKrVYLh4FgRpgSapmVroRUREpEWpbRAs4NHpFksdpdz/3f28//v7GKbJQ9k5XJV6Hlw62++TeVdOGdoSqYVeREREfFamvZC07HxS48PrnYinZefXOj+8p5L5gtIC/v7NnSzPXEGAaTJ1Xw5n9f4bnPEo/FkK5K/cMWVoS6OEXkRERHxSYxPBhg6CbcxNQ0PkFuUyYdHN/Jq9nlCnk2f3ZjP4pAdgyO0uP5e3qa235JQuCVqYqgFUciMiIiI+pylzxzdkfvg5qzIYMm0xl7++kiHTFjNnVYZL38ee/D2Mm381v2avx+Zw8PqebAaPeLZFJPNQd2+J1J9a6EVERMTnNLVsZuyAdpzSJaHOQbDubj3enredG778G7sLskgsK+PVfXl0HvNv6DKiycf2Fe6eMrSlUAu9iIiIVOPtgxQrEsHDNTQRTLaF1jkI1p2txxtzNnL151ewuyCL9qWl/Ht/IZ0vn9uiknlw3Wq6LZ1a6EVERKQKXxikWJEITp67HodpuiURdFfr8aqsVdy+6FYOlRVwbHEJLx+yEH/NfEg8tokR+6b69JZI3QzTNM2jb+a9GrIsroiIiNQt017IkGmLqyWxSycO88pEK9Ne6NZEcM6qjGo3DU25ufkm4xvu+fYuSpxl9Css4oXSKCKv+hiiUyq3cfUgXHcP6hX3aEiOqxZ6ERERqeSNUzrWJdkW6ta4XNl6PG/rPB5e9hAOnAzNL+CZwHaEXPU/CI+r3MbVvSO+0NsiTacaehEREankitp0f3O0Wvv6eHvD2zy47EEcODn/4CH+FdGLkKs/rZLMN2Xmnpq4+njivZTQi4iISCUNUnQt0zR5/ufnmP7TdACutufxWNJpBFw+B4Ijqmzr6kG4mhKy5VDJjYiIiFShQYquUeYs49HlU/ho2zwA7tify/U9rsc4/SGwVG9TdfUgXE0J2XKohV5ERESqcUWZSUtWUFrAHYsm8NG2eVhMk4eyD/C3k6ZgnDGlxmQeXN87ot6WlkOz3IiIiIi40P6i/dz61Y2sO7CJYKeTZ/YfYti5M6Dr2VW2q232mSNn7mnqLDXunglI3EOz3IiIiIh4wM6DO7lpwXVsL8jE5nDwor2UPpfOhTb9qmxX1+wzh8/c44pZatw9E5B4nkpuRERERFxgY85Grvx0LNsLMkkuK+PfBcH0uebLKsl8pr2QT9fuqtfsM5qlRupLLfQiIiIiTbR893L+vug2CpwldCku4RVLGxKv/aDWOeaPVNNc/762JoB4jlroRURERJrgs22fMWHhTRQ4SzihsIhZkceTeM3ndc4xf6SaZp/RmgBSX0roRURERBrBNE1mrXuLSUsnUYbJ2YfyeaXdKCLHzobAqi3oNbW2V6ht9hnNUiP1pZIbERERkQZyOB1M/3Ea/938HlC+YNTd/e7BMvhWMIxq29c0J7wFeOHy4+nbPqbWJF1rAkh9KKEXERERaYDCskImfXMXi3YvBeCeA3lcM/xf0GNMrftUtLZPnrseh2lWtraP7NX6qOfTLDVyNEroRUREROoppzCH278cz6/2LQQ5TZ6wF3LWBe9C+8FH3Vet7eIuSuhFRERE6iHNnsYtC65lZ1EONoeD5wuD6HvllxDfud7HUGu7uIMSehEREZGj+HnPz9y+8EbsjiLalpbysqUtqde+D2Gxng5NRLPciIiIiPfJtBeyfFu2VyyitOCPLxi/4FrsjiJ6FRXz3+hBpF79uZJ58RpqoRcRERGvcvgCTBYDpo7pydgB7Zo9DtM0eWvtDJ5d+zIAp+cXMPXYvxE6dGKNM9mIeIoSehEREfEaRy7A5DRh8tz1nNIloVlrz8ucZUxd+g/eT/sMgCvz8rnn1GlYe13SbDGI1JcSehEREfEaNS3A5DBN0rMLmi2hLygt4N6vbuS77DUYpsn9B0u4YtS70G5gs5xfpKGU0IuIiIjXqGkBJqth0CE+rFnOn5Wfxa1fXM3mgkxCnE6eLAzijCs+hdjUZjm/SGNoUKyIiIg0q7oGvFYswGT9s0a9YgGm5midX79vHZd9dD6bCzKJK3Nwx64I7tkxiTnbrG4/t0hTGKZpmkffzHvl5eVhs9mw2+1ERUV5OhwRERGpQ30HvGbaC5t1Aaavtn3OA0snU4STY0pKOG1XN54tuo4yArAaBksnDiPZFkqmvZC07HxS48M1n7y4VUNyXJXciIiIiMvVlPg2ZMBrcy3AZJomb65+luc2vAXAyQVFRO4ewfTSsyu3qajh/+73fV4x+47IkZTQi4iIiEvV1grvDQNeD1fqKGXK4jv4ZPf3AFyRX8y1Jz3PoHedVbazGgZhQRavmH1HpCaqoRcRERGXqa0VPtNeWDng9XDNOeD1cLlFuYyfN4ZPdn+P1TR5oDCAiWPn06rX2TXW8OeXOGq9GRHxNLXQi4iIiMvU1Qo/qFMcU8f0ZPLc9ThMs1kHvFaJMfcPbv38SjLKDhLhdDLd2pYh18yB0GgAxg5oxyldEqrU8GfaCz06+45IXZTQi4iIiMscbdrJmpLl5rRyx3f8ffHtHMRBm9IyXmx1Gp3PeQ6sVVOiI2v4K2bf8fTNiEhNlNCLiIiIy9Qn8W2uAa9Heu+XGUxb+xIOA3oVl/B87zuIG3hLvff39M2ISG2U0IuIiIhLeVviW+osZdqiv/P+7m/BgHMKS3nkjFcI6XRag4/lqZsRkboooRcRERGX85bE90DRAe7+9HJWFezEME3uKAvluks+xYjt4OnQRFxGCb2IiIj4pS3Zv3Hb/GvY5SwizOnkqZAuDL18NgRpIKv4F01bKSIiIn7nm81zufKzS9nlLKJtaRmz21/I0EvnKpkXv6QWehERET9V02qt/s40Td5c9ijPb/0A0zAYUFzG/500jejuozwdmojbKKEXERHxQ7Wt1urPisqKePjza/gi9zcwDMaWBnD/Bf8jMKGrp0MTcSuV3IiIiPiZulZr9VdZeTu5ds5wvsj9jQDT5EFrax686jsl89IiKKEXERHxM7Wt1vr5r5mNTuoz7YUs35btlTcFP6d9zdi5I1lfZsfmcPBq0nDGXj4fgiM9HZpIs1DJjYiIiJ+pabVWgMc/38iTX2xscPmNt5bvmKbJnB+e4qnN/6XMMDimtIzn+k0i5firPR2aSLNSC72IiIifqVit1WoY1V5raPmNt5bvlJQVM2XepTzx+2zKDIMRpVb+e+4HSualRVJC7ybe3DUpIiL+b+yAdiydOIwHRx5b7TWHaZKeXVCv49RWvlPf/d1hT2461747lLn237CYJn8PbMMzV35PWFIPj8Uk4kkeS+jT09O5/vrrSU1NJTQ0lE6dOvHwww9TUlLiqZBcZs6qDIZMW8zlr69kyLTFzFmV4emQRESkBUq2hTKyVzKWIxrqrYZBh/j6zcdeUb7T2P1d7ectnzH24/P51XmIKIeTV9qey3WXzccIUb28tFweS+g3bdqE0+nk1VdfZcOGDfzrX/9ixowZTJ482VMhuYS3dk2KiEjLdGT5jdUweHJMj3rPS9/U/V3FNE3e/+5hrl82kRzD5JgyJ+8NepzBw6dBDaVFIi2JYZqmefTNmsczzzzDK6+8wh9//FHvffLy8rDZbNjtdqKiotwYXf0s35bN5a+vrPb8u+NPZFCnOA9EJCIiUt7glJ5dQIf4sEYl403dvylKSot4ct6lfJi/DYARzhAeHTWHsNiOte7TEhfVEv/SkBzXq2a5sdvtxMbG1rlNcXExxcXFlV/n5eW5O6wGqWlmAU92TYqIiEB5S3tTEtum7t9YWfs2cvcXV/MrRRimyc0hnel9/EvYrbHU9pfVW2flEXEXrxkUu3XrVl544QVuvPHGOrebOnUqNput8pGSktJMEdaPt3RNioiI+LoVa2dxyWcX8ytFRDmc3B5+Ls+sHc/lM9fUOkZNpa/SErm85GbixIk89dRTdW6zceNGunXrVvn1rl27OPXUUxk6dChvvPFGnfvW1EKfkpLiNSU3FTzZNSkiIuLLnE4Hb3wxnhezf8Q0DI51GDw48BlG/7esWg/40onDqvydVemr+AuPltzcfffdjBs3rs5tOnb8q+Zt9+7dDBs2jMGDB/Paa68d9fjBwcEEBwc3NUy381TXpIiIiC/Ls+/ggU/GssR5EAyDMdY4Jl/0AauzDJxm1US9YvrMw//eqvRVWiKXJ/QJCQkkJCTUa9tdu3YxbNgw+vXrx8yZM7FYvKYCSERERJrZps2f8PdlD7DTCkGmyQOthzPmjH+BYZAaX1ivRL2i9HXy3PU4TFOlr9IieGyWm127djF06FDat2/P22+/jdVqrXwtKSmp3sfxtlluREREpIFMk3mL7uGxHV9SbDFo44D/GzSF7sdeWGWzOasyqiXqtQ12Vemr+DqfmOVm4cKFbN26la1bt9K2bdsqr3nRTJoiIiLiRiUF+5n20cV8ULYXLAYnGxFMHfMutugO1bYdO6Adp3RJqFeiXlvpq6azFH/kVfPQN4Za6EVERHzTrrQl3PPN7ay3mhimyS1x/bnhnDewWN3T3qjpLMWXNCTHVdG6iIiINC/TZPE3D3Lxkgmst5rYnCav9L6Dm86b5bZk3h3TWWbaC1m+LVtTYorHedXCUiIiIuLfSgtyePajsfy7bA9YLPQihGfOeYvWrXq69bxp2flVBtRCzbPk1Jda+8WbqIVeREREAPe3OGdu+4px7w4tT+aBq6N7MOvypW5P5uGv6SwP19jpLLV4lXgbtdCLiIiIe1ucTZNvF97H5J1fkBdgIdIJj/e+jdP63uCa49eDK6ezdHVrv0hTKaEXERFp4WprcT6lS0KTE9TSg3t44aNLmGnuB6uFHkYYz5w7k7YJ3V0QecM0ZJacumjxKvE2KrkRERFp4epqcW6KrE2fct2c08uTeeDK2D78+/LvPZLMV0i2hTKoU1yTblQqWvutRnkNjxavEk9TC72IiEgL5/IWZ0cZ33/1dyZnLiI30EqkCY8efyfDe1/vmoC9gKta+0VcQQm9iIhIC+fK+vKS7K08++mV/MeSD1Yr3a0RTD/nbVJiuxx1X19b9Km2xatEmpsSehEREXFJi/P2H1/h3rXPszGoPL24POEE7h7xCkHWoKPu64pBub52QyDiKkroRUREBGhCi3ORnU8/vobHC7dQEBRAtGnw2MAHGXrsJfXa3RWDcjUvvLRkGhQrIiIijZb/x7dM+vdJTC7eRoHFQv/gRP534YJ6J/PQ9EG5mhdeWjq10IuIiPgArysncZSx4etJ3LfjUzKCA7GYcHPHUYw/6RGsFmuDDtXUQbmaF15aOiX0IiIiXs7bykmc+//gPx9fybOWPMoCA0k2gnjqtOc4PuWkRh2vqYNyj3ZD4HU3QyIuZpimaR59M++Vl5eHzWbDbrcTFRXl6XBERERcKtNeyJBpi6slq0snDmv+5NQ0yf7pDf7x8z9ZGhIIwHBbN6ac/Qa2YFujDnl4sg00elDunFUZ1W4Ixg5o53U3QyL11ZAcVy30IiIiXuxo5STN1vqcn82Sj6/l4eJt7A8JJNiE+3rfwsV9bsL4c4GlhnJlsl3TLD3uXAFXxJsooRcREfFidZWTNFfrc8FvnzD9u/v5IDQArFaOCYzhqRGvcUxctyrbNeTmwh3J9pGz9Ki2XloKzXIjIiJ1yrQXsnxbtmYM8ZCK+nLrn63gFeUkgPtndik+yIa54xi77L7yZB64uv3ZvDt2YbVkfs6qDIZMW8zlr69kyLTFzFmVUeehmzqzTX1U3Awdrkkr4Ip4KbXQi4hIrVR/7B1qKidZvi3bra3PjvRlzFxwEy+FmJQFBZJoCeGJof/kxJRTqm3bmNb2ps5sUx+uXAFXxJspoRcRkRqp/ti7HF5OkmkvJOdQsXsS4rJidi2czOSMT/g5NAQwOCP+eB4e/kKtA18bU9rSXMm2K1bAFfF2SuhFRKRGqj/2Tof3mhiAYYBp4pKE2Mxcx2efXseTgQUcCgkhDCuTTrifUd0urXPga2Nb25sr2W70CrgiPkIJvYiI1Kg5SiKkYY7sNTEBiwkvXn48fdvHND5pdZSS++00ntj0NgvCQwELvSPaMfXMGaREphx196a0tivZFmk6JfQiIlIj1R97n5p6TZxAbHhw478vezbw3Sd/42GLnezwUKzAjd2vYXy/Owmw1D9NUGmLiOcooRcRkVopSfMuLu01cZRy6LuneWbDW8yNDAOspAbH8cTpz9MzoVej4lNru4hnKKEXEZE6KUnzHi7rNdnzG6s+uZ4HjVx2R4ZhAFd2vpDbB04kJCDELbGLiPsooRcREalFs63C2gBN6jVxlFH0/TM8t+51/hsVDgTQJsjGY0P/xYDkAW6LWUTcSwm9iIhIDbx5Dv5G9Zrs+Y1188Yz2cghPSocgAs7nMO9gx8iPDDcDVGKSHNRQi8iInIEv5qDv6yE0qX/ZMavr/FmVDgOI5CEgAgeOfUpTm5bfZEoEfE9SuhFRESOUNsc/J//msnIXsm+k9TvXM2Gz27mQaudrbYIAM5uO4wHTnqs1kWiRMT3WDwdgIiIiLepmE3mSI9/vpEh0xYzZ1VG8wfVECX5FM+/n3/NHcPlIQVsDQoiJiCM6adM5+nTn1cyL+JnlNCLiIgcoWI2GWsNq6NWlN9k2gtdft5MeyHLt2U37djbvmHNqydy8c55vGWLwmkYnJ1yGh9fOJ8RqSNcF6yIeA2V3IhIs/DG2UJE6lIxm8znv2by+Ocbq7zmME3Sswtc+llu8iDcwgMULpjECzvm89+oSEwjkPjASB486TFOb3e6y+I8kn62RTxPCb2IuJ03zxYiArUnpcm2UEb2SubJLza6ZjGnOs7f6EG4pgm/zWPVwvt4OMLCDlsUAOd3OIf7Tpzs1vIa/WyLeAeV3IiIW9WWqLijXEGkMeasymDItMVc/vrKGuvjjyy/afRiTnWobRBuenZB3Tvad1Lw7qU8sfhOrosJZkdgIK2CY3jp9Jd44tSn3JrM62dbxHuohV5E3KquREXd8+IqjS37qG/LeJMWc6qHikG4R/YChAVZWL4tu/r7cpTByhl8t+IZHo8OIzMqEoALO1/A3QPuJTIo0qXx1UQ/2yLeQwm9iLhVbYmKK8sVpGVrStlHQ5LSRi3mVE8VvQCT567HYZpYDYPRx7fmgpeXV39fO1eT/dltPO3IYn58eXlN69AEppz0BINaD3JLfDXRz7aI91DJjYi4VXOUK0jL1dSyj5qmp/RUUjp2QDuWThzGu+NPZO4tg/jol11V3teTc3/k0Nzb+ei98xkVlMv8iHAsGFzT/Wo+uuCzZk3mQT/bIt5ELfQi4nbuLleQlqupZR81tYx7Mimt6AVYvi37sPdlcq7lB64Nm83tewNYlRALwLHRnXn4pMc5Lu44j8QK+tkW8RZK6EWkWbizXEFaLleUfTRXUtqQOv+K99WGPTwcMJM/YtO5IdpGicUgxBLIhONv58ruVxJg8fyfcf1si3ie538TiIiINJKrWtjdnZQ2tM4/OcxgXs9lFKXP5MmEKLYGRQMwOPlE/jHoYdpGtnVbrO6guepF3MswTdM8+mbeKy8vD5vNht1uJyoqytPhiIiIB2TaC5u17KMhCWqmvZAh0xZX60VYOnFYzfv+/iX2BffxvGHng8gITMPAFhjJxBMnMzJ1JEYNq9d6M81VL9I4Dclx1UIvIiI+rznLPhqaoNa7zv9AOub8iXy6+zv+GRvNfmv51JPndzyPewbcS0xIjDvejls1acEsEak3JfQiIiL11JgE9ah1/qWFsOw5tv7wPI9Hh7M6IQ6AjlEdeHDQQwxIGuDOt+RWmqtepHkooRcREamnxiSoddb5b15Awfx7mWHk8Z+kWMoMgxBLEDf1uYWru19NoDWwGd6V+2iuepHmoYReRESknhqboFabSceRhTn7Ehbv+o6n4mLIDCivjx2WMoyJJ0ykdURrd76NZuNt04KK+Csl9CIi4pM8MXNKUxLUZFsoySFl8P1T7PxxBtNiIvi2VQIArcOSmHTiAwxNGermd9D8NFe9iPtplhsREfE5np45pcGz6jidsPYdChc9ylsBRcy0RVJssRBgBDCuxzhu6HUDoQFKdGujaS+lJdIsNyIi4re8YeaUBs2qk/ED5vz7WGj/nelxMWQG2AA4IWkADwx8kI7RHd0Yqe/z9M2biC9QQi8iIj7FZ2ZOyd1B4fwH2fnH5zwVF8PKP8trksOSuGfAvZzR/gyfm1O+uXnDzZuIL1BCLyIiTdacJRFeP3NKST4se479y57ntagQ3muThMMwCDACGd/rb1zb41qV19STz9y8iXiYEnoREWmS5i6J8NqZU5xO+HUOjsWP8bEzl+dax3LAagWgNO84CvedywWjLlQy3wBef/Mm4iW8IqEvLi5m4MCBrF27ll9++YU+ffp4OiSfpEFDItLcPFUS4XUzp/yxBL76Bz8f2MxTcdH8Fly+OJSjOJHiPefhyD8GQC3LDeS1N28iXsYrEvr77ruP1q1bs3btWk+H4rM0aEhEXKUhjQOeLIlo0MBUd9nzGyx8iB3pi/lXbAwLW7cCICwgnAO7hlGyfxBQ3kqvluXG8bqbNxEv5PGEfv78+Xz11Vd8+OGHzJ8/39Ph+CQNGhIRV2lo48DRSiL8tufwYBZ88wQH18zmdVsk/23bmlLDwIKFMV3GMKHPBBatL1DLsot4xc2biBfzaEK/Z88exo8fz8cff0xYWP1aLYqLiykuLq78Oi8vz13h+QwNGhIRV2hM40BdJRF+2XNYfAiWv0DZ8uf5X4iFl9smVdbJD0oexD0D7qFLTBcAxg5ALcsi0iw8ltCbpsm4ceO46aab6N+/P+np6fXab+rUqTzyyCPuDc7HaNCQiLhCYxsHaiqJ8LueQ0cp/PIfWDKN7x12pifG8EdQIACptlTu6X8PJ7c5udo0lGpZFpHmYHH1ASdOnIhhGHU+Nm3axAsvvMDBgweZNGlSg44/adIk7HZ75WPHjh2ufgs+p6KFzPrnHxJ17YpIY1Q0Dhyuvo0DybZQBnWKq/y9U9fNgU9xOmHd/+ClE9jy5X3cFO7klqRE/ggKJDo4mgcGPsCH53/IKW1P0ZzyIuIxhmma5tE3q799+/aRk5NT5zYdO3bkkksu4dNPP63yC9DhcGC1Wrniiit4++2363W+hiyL6+8avBS5iJfx9XprX48fymvojyyfaUyZTKa9kCHTFlfrOVw6cZhvXBvThK1fw6JHyNz3Gy/F2PgkIhzTMAiwBHDlsVcyvtd4ooJa9t8dEXGfhuS4Lk/o6ysjI6NK/fvu3bsZMWIE//vf/xg4cCBt27at13GU0Iv4B1+vt/b1+A/nqsYBV90cNLuMlbDoEew7VvCmLYrZtkhK/mx8OqP9Gfy9799JiUrxcJAi4u98IqE/Unp6OqmpqQ2eh14JvYjv8/XWXF+P3518qucwaz0sfoyiLV/yTlQEb0TbOGgpr0zt36o/f+/3d3ol9PJwkN7BH3qjRLxdQ3Jcj09bKSKu5Yt/aH19piZfj9+dfGJQaM42WDINx7oP+CQijJfaJrMnoPzPY2JwBx4+6d4aB7y2VP7UGyXiL7wmoe/QoQNe0lkg4rN89Q+tr8/U5Ovxt1j70+C7ZzDXvse3IUE816YVW4OCAHCW2ijedybpeX055uwBSub/5HezF4n4CZfPciMinlHbH9pMe6FnA6sHX5+pydfj95RMeyHLt2W79TNa4zkObId5t8KL/Vm56X9clRTPbUkJbA0KwnSEUrTnHPK33UOZvR8O0/CpmXncfU39ZvYiET/jNS30Ii2ZK8pkvKHsoynvw9eXd/f1+Jtbc/QmHXmO586O5zz7O/DLf1kTaOGFxFh+DA0BIMQawvkdL2bm56k4HX/1rPhST0tzXFP1Rol4JyX0Ih5W1x/hhiTInv5D64pkwifqrevg6/E3l+Yo2zj8HEnkMME6jxGLvuG3YAsvJsTwfVj5eQIsAVzc5WLG9xxPQlgCXQKrz8zjC9/T5iqFqWtlYBHxHCX0Ih5U1x/h737f16AE2ZN/aFVXKw3RHL1Jadn5JJnZ3BjwKZdavyEjyOD+mBi+Di+/wbUaVkZ3Hs0NvW6gdUTryv080dPiaz106o0S8T5K6D3MF2ckEdep7Y/w6vQDjUqQPfWH1hvKfcR3uL03KWcbfX5+hm+D32d3oMFDMTbmh4djGmBgcE7Hc7i59820j2pf4+7N2dPiqjKZ5u6hU2+UiHdRQu9BvjojibhObX+EOeI5qH+C7Ik/tJ4u9xHf4rbepD2/wff/hA1zyQqw8HpiNJ//mcgDdIscwtRh99A5pnPT34QLuLJnS6UwIi2bEnoPUYmCQO1/hPu1j/GpBFnJhDSUS3uTdv1cnshv+oytgYG8Fh/DgohwKn58esacyI29b+bU9n1dEruruLpnS6UwIi2XEnoPUYmCVKjtj7CvJcj+kEyoBK55Nbk3afsK+H46bP2azYGBvJYQz8KIsMpEfljKMG7sfSPHxR3nknhdzR09WyqFEWmZlNC7kC/NSCLepaY/wr6YIPtyMqESOB/hdMLv82HZ87DjBzYGBfJqYgKLwv/63A1vN5wbe99It9huHgz06NSzJSKuYpg+vjxrXl4eNpsNu91OVFSUx+JoTDIwZ1X16dGUQIg0v0x7IUOmLa52g7104jAlV96irBh+nVOeyOdsYW1wEG9ER7MkrHweeQODMzucyQ29bqBLTBcPB9swmfZCn7pxF5Hm0ZAcVy30LtDYenhfbIEV8UcqgfO8Wns4Cw/AT2/BylcxD+1heWgIb7Zuzarg8j9fFsPCWR3O4oZeN9ApupOHom8aX+7ZEhHvoITeBZqSDOgXefNRfbTURiVwnlVjD2cXC/zwCqyehaPkEAvDw3gzJYVNAeVT1gQYAZzb6Vyu63EdqbZUD78DERHPUkLvAkoGvJ/qo6UuqmX2nCN7OLvzB0GfvIwZ+AMlZhnzIiKYldyeHZbyDUIDQrmoy0Vc3f1qksKTPBh506iBQURcSQm9CygZ8G6aIlTqw9UlcC05YWvIe0/LzscwHZxt+YnrAuYzwPI7hwyDtyIj+E9MHDmGEzCxBdu4otsVXNbtMqJDopvlfbiLGhhExNWU0LuI6uG9l+qjpb5cVQLXkhO2Br33wgP0SJ/Fd8Gv0MbIZneAlWmRMfwvKopiiwk4aRXWinHHjWPMMWMIC/T9Xk81MIiIOyihdyHVw3snlURJc2rJCVu933v2Flg5A9a8Q1RpAekhQTwVlcSS8KA/V3U1SbWlcl2P6xiZOpJAa6An3o5beEsDQ0vuQRLxR0roxe+pJKpunvjD7s/JhLckbJ5Q53uPDIKti+DH12DrQhzA4rBQ/p2cyhqro3L74xMG8Lde4zipzUlYDEvzvoFm4A0NDEf2otx/Vjd6trX55c+jSEuhhF5aBJVE1cwTpSH+Xo7iDQmbp9T03hOMgxyX9iZ8+l/I3U6+YfBRVCT/jUtkF6WAgwBLAOeknsPV3a+ma2xXj8XfHDzdwFBTL8rU+ZsA//x5FGkptLBUM/Dn1kjxXZ5YTKmlLODUUhaNq+l3W/l7X0dvfueqgK85P3AlVmcpGQEBvBcTx8eR4Rw0ywCwBdu4pMslXNbtMhLCEjz5VpqdpxaTWr4tm8tfX1nr6/748yjiq7SwlBfxVGukbiLkaDxRGtJSylFaQo9Qjb/besUy1ljEmLavE7hvA05gaXAI7ya0Y6m1tHxHs4wOUR24qvtVnNfpPEID/O/a1IenxlzV1ItyOH/8eRRpCZTQu5GnBsf5e0lDc/KmGyNXx+KJ0pCWVI7iz4Pkj/zd1pXtFH8yC+fCFVhKDlJgMfg4Oob34hLY6SwCSjEwOKnNSVza7VK/rY/3BUeW/BzJX38eRfydEno38kRrZEuaYcPdybY33Ri5IxZP1PK645zedNPVUL4ae1p2PuFmAedbl3OJdQm9LX8AsJlA3m3Tns+DrRSZZeAsIjIokgs6X8DYrmNpF6WGBW9weA/Sr7tyeXr+Zk0YIOLjlNA3RVkJbPwEup4DQdVbNDzRGtlSShrcnWx7042RO2PxRGmIK8/pTTddDeWTsZsmZPxAn9Vv8WPwx4QaJRQZBnPDIngrshXbQ0sBE8wyusR04bJul3FO6jl+MX+8v6noQRrUKY7ze7f26/IwkZZACX1TbPkSPrwegiKhxwXQ5wpIGQiGAXimBbQllDQ0R7LtTTdGroqlttZgT5SGuOKcnrrpckWrujtid2tr/6F9sPZd+PnfkLOFMGBLUCBvRLRlYWQQpdYyoJQAI4DT25/OZd0uo29iX4w/fxeKd/Pn8jCRlkIJfRMUluYzO6kd5+7bRdLP/y7/YxfbEXpfDr0vheiUZm8B9fSUaM2hOZJtb7oxckUsPtkafBSeuOly1XV0dexu+f6WFcPvC2DtnPLGC2cZhYbBl7ZY/pfQmrWOvIoNaRWazCXdLmJ059EkhiU27bxH8NWyJBGR5qSEvgm+Dg/nuVB4oV1bBgVEc8GeDIbt/4Ogbx6Hb56A1FOgzxUkH3suyZ3imi2uum4i/OGPY3Mk2950Y9TUWLypfKgmjf1MNvdNlyuvY0Njr+saufT7a5qwc1V5a/z6uVCUC8DmwED+17Yrnwc6OegsBkceFsPKaSnDuKjLRQxqPcgtg1z98UZURMQdlNA3QVxIHP1a9WP1ntUsKzvAsrhIbImJnFsWwAW7f6dr2reQ9i18HgnHjYKel0CHk8BidXtsNXWh+ssfx+ZKtt3Zu9LQJLYpsXhT+dCRmvKZbO6bLldex4bEfrRrVFtcq9MPEBtRz8/YgXT49f3yRH5/+QDXXIuFLxLbMi86lt9Kc4FCcIKzJJbS3AE47P048dghDGnjnt8h3n4jKiLiTbSwlAtsz9vOvK3zmLd1HnsL91Y+f2xQLBfkHuCcfTuwOZ3lT0YkQY8LoedF0Pr4ynp7d/PHBX08tTBLUzX3jZW7v/eNbWF3VVzN9Tlwx3U8Wuz1OWdN2xiU/2qp8zNWsL98UP+v78P2ZQCUAssio/kkKZVvHLmUmQ4AAowABiWfwlc/pFKW3wmwuOT916W2BZDeHX8ig5qxx1NExFO0sFQzax/Vntv73s6EPhNYvns5H2/9mMU7FrOxZD8bw2B6hw6cFhjLeVnpDLJnEfjDS/DDSxDbCXpeXJ7cxx/j1hi9uZW2sXxxIJcnWh3d2ZLdlJsTV30mm+tz4I7reLTY63ONjozLAphQ82csuBQ2fwHrP4Rti8FZvmLr74FBzGvblc8Cythflg9lOQAcG3ssozqP4uzUs9m0y8kXi1bWGYurZNoLyTlU7DXjWI7GH0oZRcS3KaF3IavFysltT+bktidzoOgAX6R9wdwtc/n9wO8sKNnLgtgwbAndGFoWykV7t9J7/zaMb6fBt9MguXd5cn/cBWBr6/LYmlJvrD9WruOpGyt3lA819ebEmwYe11dzD3Kv7zU6PK6c/GJufeeXytdCKOY04xeCPpwFu5aAoxiAbKuFBW268ElEOBtLcgA7lEFsSCwjO45kVKdRdI3telgshc3y/Tr8JrGip8E08doB/v5Syigivk0lN25mmiYb92/k022f8tHvn5HvyK18LcESxUVmICN3bKB9aclfO7UdAN1HwbHnQ0x7l8UyZ1VGtdbFo/3h0R8r1/Kn0idXlEQ05jPZ0jT0GmXaCxk2bQFDjHWcZ13BGZbVhBvlSfxBw2BRq1S+iIlnZdFenJSXAgZYAhjadiijOo9iSJshBFoCXRJLQ9X082EBXrj8ePq2jwHwqsYFf/p5FhHv05AcVwl9Myn/xb8QI2wbgbZfCIjcgGH5K4nvGdKKkQXFjNixgXin468dWx//V3If18klcdS3dVF/rNzDX5JYX6uB9wW19YbV6xoVH4KtC2Hjp5RuWkBgWX750wZ8ZWvD4uQUvivZR4mztHKXXvG9OKfjOZyTeg4xITH1jtFd36+6bhIz9ud7XeOC6vxFxJ1UQ++FykstrJDfBUd+FzBKCIj8jQE90tiY+xPrivawzgJPt0+hf1hrRuQXcPqO9cTt/gV2/wJfT4Gknn8m96MgoUuj4mhIvbE/1t03hatKjzyxOqs7uKqm3BfHQrhDXb1htV6jgv3lc8Vv/BS2LqospzGApTGtmRubwnLjAPmOIijaDUBHW0dGdhzJ2R3OJiUqpcFxuvP7VVuJUViQxStnvPHFsjER8U9K6JtJtV/8ZhDmweP5v1PvIjAony/Tv+TzPz5nXfY6fizYyY8GPNGuLQPC23BmfiHDM9YRm7UOstbB4schrjN0PRu6ngNtTwCr67+V+mP1F1eXHvlLEusvNyee1qDxCHmZsPnz8iQ+7Xv4cyaaUmBlYgcWJqSwuDSH3NJD4MwEICk8ibNTz2Zk6ki6xHTx2hVca7tJzC9xeGXjgjetVyEiLZtKbppRfUotdh3axVfpX/FV+lesz1lf+bwFCwPCUxhRUMTpGb8SW1b8106hsdBlRHmC3+k0CI5slphbymBZlR6Ju9VZupEaA5lrylvif18AmWsrXy82YEVyVxbGJvFN8V4O/llmAxATHMOZHc7knNRz6JPYxy0LP7nLkWU93v4zqLIxEXEH1dB7sfr+4s+0F7Jq51bSCpezPGsxv+X8VvmaxbDQN7wdJxU5OX3nBjoUHvhrR2tQ+Qq1Xc+GY0ZAdMO71OsTc0saLKs6WXGlmm6Ej0xYwyjiFOt6/u/4LMLSF8GhPZX7FxoWlrc9jq+i4/m2aDf5ZYWVr8WFxDG8/XDOaH8G/Vr1I8DiP52w/jL2RESkvpTQNyN3tFLXlCwP7mbwVfpXfJn+JRv3b6yyfXBRDOeHRHHxwR10y06nSmd6QjfoPBw6nw7tBkNgSJPj8/bWMldrae9X3KfWG2HT5PNvl7Fq4QcMs/zMiZbfCDbKKvfLDonku5RefBMWzA/5Oyhy/NVDlxiWyBntz+CM9mfQJ6EP1mZYidpTfKUlvKX0XoqIeymhbybuaKWuT/L4y+5tjP3vG1gjfsMaloZh/LVxckg8w4ITOW1/Fn13/Eqg6fzrQIFh0OHkvxL8Rs6a0xJbrL2tdVAJg+858mc7inyGWH9jet8cwnd8C7nbK7c1ga1x7VmS3IUlliJ+Pbi9yrFah7eubInvldDLp8pp/F1L6r0UEffSLDfNwF0rftZnZpnCQhsl+0+C/SeBNZ+AiE0ERPxGWPRWMouyeacom3esEHFMVwZFtOfkojKG7NpAoj0TtnxZ/gCISS1P7FNPhQ4nQVhsvWJsiYNlvWnwpxIG35S2104ffudk6zpOtq6jj7GVAMMJ68pfL7UEsjqlN0tiElhSso9dhXshf3Pl/j3ienBqyqkMSxnm1QNbWzJPrAQtIgJK6BvNXVM61idZrrKNI5wyez/MvP7MvWIQ6flrWZyxmCU7lnCg+AALD/zGQoDYQLq1P4WTA2ycfGAPPbf/QsCBNFj1RvkDA5J7ldffp54K7QZBcESNMbbUmR28YWYaJQw+xOmEfRvLZ6JJ/54T075nbrC9yiZLLa3Z1rUHP4VaWHkwjcKyvWDfC0CwNZiByQMZmjKUU9ueSmJYoifehTSApvoVEU9RQt9I7mqlrk+yXNs2HWJj6BA7lKEpQ3E4HfyW8xvf7/qepbuWsj57PZsOprMJeB2I7NyFIZGpnFRqcmLWFlrt/b189ozMtbD8BbAEQJt+fyX4bQdUqb/3phbrlkQJgxdzOmHfJkhfCunfQfoyKNxf+bIFyA2K4g0jlSWhkewIz8MZlAtFm6CofJu4kDhOaXsKQ1OGcmLyiYQF+m+vlz9qib2XIuIdVEPfBO6sq67P4K+GDBDLKcxh+e7lfL/re5btWkZeSV6V11Mj2zEwpBUn5hcwYPcGog5kVD2ANah81dp2J5YPrk05od4lOr7AV2rSNUDXizgdsPc3yPgB0r6D7cugIKfKJo7AMDal9GFlTBLLzQJ+tm+h9LCVWgOMAPok9mFImyGc1OYkusR0cXs9vK981t3NXdfB28bbiIjv0qDYZuQrsy4czuF0sC57Hd/t/I4fMn9gQ84GnIcNnrUYFo6zdebEwFgGHsylz461BB82bV6lhGOh/SBoN4i9MX3YWhJLanw4QIP/UHoyyfC1mnQlDB5SZIedP8GOleWPnT9ByaEqmzgDw9jStg8/xibzo6WU1fatHCytuk2biDYMaT2EIW2GcELSCUQE1Vza5g6+9ll3F3dfB1/8uyAi3kcJvTSIvdjOT1k/8UPmD/yQ+QPpeelVXg+2BtM7ugv9Amz0PZRHr8xNhOVsrXac3WYsa52dWevsxBqzE+vNjvxjzICj/qH0ZJLhqy3eShjczDRh/x+wc1V58p6xsrw1nqq/Ls2gSNLa9OLHuGR+tDhYlbeV3OKqdfKRgZH0S+rHicknMqT1ENpHtffIgFZf/ay7mq6DiPgKzXIjDWILtnF6+9M5vf3pAGTlZ7EycyU/ZP7AysyV7Cvcx4856/jxz+0DbAF07zCCfkEJdD+YT+T6tZxgbqe1sZ/W1h8521q+pcM02PppWwq2n0xYhxPKa/ITu4P1r4+dpwd5+mpNujcM0PUbpgn2HbD7l/LHrp/LV2YtslfbtDSmPRtaH8eaiBh+ppC19m3sL94BOTsqtwkNCKVvq74MTBrICUkn0C22m1fMDe+rn3VX03UQEX+khF6qSQpPYlTnUYzqPArTNEmzp/HTnp9YvWc1q/esZk/BHn7dv5FfKV/gyuxgQHFfYguj6Vrk5KSSA5zu2E5bI4euxg5Y/075AyAgFJJ6lj+Se7GvrB2BZgnFBFWev64/rrWV5jS2ZEeD2FoY04S8XZC1rjxxr0jiC7Krb2sNxp7ck7WJHfklJJhfSg+w/sBmigvWQ8FfmwVZgjg+8XgGJA1gYPJAjos/jkBLYPO9p3rSZ72croOI+COV3EiDmKbJ7vzd/LznZ1bvWc3KzJ/YeWh79e3KwrAWJdGmKJS7OtkYYN+FbfdaKM6rtm2ZaWGb2ZrfzPb85mzPJjOVZ26/nKSktlW2q600p6klO6pJ936NumErKSifNjJrPezZ8OdjPRTlVt/WEkBp4rH83uoY1ofbWGeUsj5/J9vsf1TbNDo4muMTj698dI/rTpA1qPoxvZA+6+V0HUTEF6iGXprVmyvW8tQ38zFC0rGG7sASshvDUlZtuw5RHegV2YHjjFC6F+XTJXsHYXvWV5sZpFJ4AiR0g4Ru2CM7ceOCfDY723CA8u+z1TCYe8sgLnh5eZPrYX25Jt3fZy056g1bWTHkbIPs38sfFcn7/m1w+ErJFSwBOOOPIT2xK+uj4lhvdbK+MItNB36vMgNNhfZR7ekW3YvEoK4MbT+QAW26+vSiTod/1qHhA9j9hS//zItIy6CEXprd4X8cS52lLNu+Dru5lfRDG1m3bx0ZBzOq7WNg0MHWgW6RHWhfFkzKwQJOKMomKWczHEir9VzZZhRbnG3ZYrbhmO59eHW9QZqZxC4znrI/q8jeHX8igzrFue39egt/n7Xk8AGMURyis7GbzpZMHjrRSsTBNMjeDAfSa07cAcLiKWrVnW1x7dgcGsFmSxmbCvfxe+4WDh0x+wyUjyfpEdeDHvF/PRatL/DLa+zvnx0REV+nhF68Sqa9kHWZuyk00th0YANr925gd+FWcor21bh96/DWdI3uTOdAG52dBp0LDpKUvQP7H+tIMWreB8pLd3aYCWSQRP++/QlP6sL+kLakm61IbteZ5LiYesfrC62Wfjdbh9MJBzPLb+b2p8GBNPbt2MyuP36jvbGXGKN6Al4pOAoz/hiyY1PZEhnDpqBANjsL2Jy3nfS8dBymo9ouIdYQusd157j44+gZ35MecT1oG9m2Suu7313jP/nr+xIR8Sc+NcvN559/zqOPPsqvv/5KSEgIp556Kh9//LGnw2oRGpO4NnSfw1sBy9Okzph0xmLAA+e3pWu7g2zav4nfcn5j0/5N7Di4g935u9mdv5tvDjuO1bAS3e0YsrP7EV0SQmqJydWtgxkadIj83b8TkredEKOUVGMPqeyBNWsBiP3zAVAYHEdofAewpYCtLUS3O+z/KRASzZyfdvhMq6XPzdZRUlA+INW+E/J2V/1/7nY4sB0cxVV2SQASDltnKdOMZYuZTHKfXmRFx/FHgJU/nIVsK8jkD/sfHDz0I9SQ90cF2Wgd2onj4rvRL7kHXWK60Cm6EwGWun8F+tw1rid/fV8iIi2VRxP6Dz/8kPHjx/Pkk09y2mmnUVZWxvr16z0ZUovRmO72hu5z5JSUh+cPThOe/GQXSycO46Q2J1U+f7CkPMHfvH8zW3O3si13G9tyt3Gw9CA5JTsxosAOrPnzYS210jq1NUmh/Yg2bRwbEsZxFoN4ey7bflpHB2MP7Y29hBnFhBbnwK4c2LW6xnjNgFBOLI1kTmA0e81o9pnR7JgXQ27piUQntIOIRIhoVb5CrrXhs5i4uuXfK2brcDqh8ADk74VDeyF/X/mj4v+H9v6VuNc0GPVIlgCIbocZ3Z4D0W3JCLOxKLeEudtzsAcWYQbvJzg0m9KDy+BgDbsbFlIiU+ga05Vusd3oGtuVzRkRTP10N7tMg9UGHDumJ107Hf0mLdNeSM6hYs9fYzfwis+OG/lKL5uIiKt4rOSmrKyMDh068Mgjj3D99dc3+jgquWm4xnS3N2af5duyufz1lXXGUp9ad9M02VOwh22529iau7VKol9QVlD7fk4rztI4zJI4AkvDsZUF8LfuyfQPs5BceJCYvCyMvF2Qu6PmaQvrEhQJYTEQGlue4Ice/v9YCImCoAgIjoCgCBZsOcjjC3dw0Ayl0AjhsTHHu6Tlv8mzdTgdUJIPpQVV/y3MLZ+HvSj3z/8f+a+9/JrlZ0MN5Sy1CooEWxuIak1RZBJZ4TFkBYey02phB6XsKLGz49BOdhzcQX5pfq2HCbAE0CGqA6m2VDpFd6KjrSMdbR3pYOtAsDW4crvGlpZU61kyyme89KcZUfx1pheNDRARf+ETJTc///wzu3btwmKxcPzxx5OVlUWfPn145pln6NGjR637FRcXU1z8V7d8Xl71aRClbo3pbm/MPjW1Ah6uvi2ChmGQFJ5EUngSQ9oMqXzeNE32Fe5je952tudtJyMvg/S8dDLyMsg4uINSSrAG74XgvZhALjD9sLw9NCCUpMQkWnc8g9ahiUSUBTH/h53EljlJcpTS1lFIO2c+ozpZCSnMhkNZ5QksJpQcLH/kVh/sW5OzgLP+yjMp/iwQ56JILIHBYA0qfwQE1/yvxQoYYBjV/h2LwXm9neQXlxERaBL6hxO2lIGzDJyl4Kj4t7T839Kiqsm7o6Re8R9VaAyEJ5b3YoTHUxAWS05IBPsCg8kKCCTL4iTLUURmcQ578veQlZ/FAfvW8u6WOrQKa0VKZArtotqREplCqi2VjraOpESmHLVcBhr3ua2pZ8liwouXH0/f9jF+0+I7dkA7TumS4FczvXh6oToREU/xWEL/xx/l8ztPmTKF//u//6NDhw7885//ZOjQofz+++/ExsbWuN/UqVN55JFHmjNUv9OY7vbG7JNsC2XqmJ6VrYA1tXQ25Y+sYRgkhiWSGJbIgKQBlc9n2gvZti+PFdu38dryHyEwB2tQLr1TnQQG28k8lMnewr0UlhWSZk8jzX7YjDpJkAX8VnEOLLwWGk98fBsSQvsQHxJLdEAY0UYgNtMg2oQYhwNbWQnRJUVEFR3CWnQAig9C8SEoOURRvp38g7lEUESwUT4tYrBRCkX7oajRb79S2J+PJjEsEBgOQWEQGAah0RASDSE2zBAbh4IjyAsKIS8giDxrAHlWC/sNkxyzjBxHEdlF+8kpyiGnMIecot8oLCis12lDA0JJCk+idXhrUiJTqiTvbSLaEBIQ0qS31ZjPbU03AU4gNjzY75JCf1txWGMDRKSlcnlCP3HiRJ566qk6t9m4cSNOZ/k0cw888AAXXnghADNnzqRt27Z88MEH3HjjjTXuO2nSJO66667Kr/Py8khJSXFR9C3DkYl2fZLrxuwD1VsBAbe2CB7Z3X7/WefRq210tfOVOErIys9id/5uMg9lsuvQLvYV7mNfwT52H9rDvoJ9HCzNxcTJ3sK97C3cW6/zGxhEBkUSGRpJeFQE4YGtCDBCWbHlEE5nEBZHEAFOCyGmhZsGdyQ2OJAg0yAIkyATAk2ToD8fgU4HQaYD659lHwZgmADmn1+bGJWDjaHMsFBqQJnFQhkWyiwWDhSXsa+wlMjwIEJDAikyDAqBApwUVjycZRQ6SygsK6KgrICC0gLySvL+fGzjYO5BnLVNC1mHEGsIcaFxlb0rSWFJJIcn//V1eBJRQVFundO9MZ9bf68v92f63olIS+XyGvp9+/aRk1PLQkF/6tixI8uWLeO0007j+++/56ST/hoUOXDgQIYPH84TTzxRr/Ophr7xGrOwijcvxuLqqfjKnGXkFOaQXZhdnuwX7iO7MBt7sZ3c4lxyi3LL/y3OxV5sr3Fec38SbA0mKigKW7CNqKAoooOjiQuNIy40jviQ+Mr/x4WU/xsWENbsCzDVNhiyrs9tTfv4a315S6DvnYj4C4/W0CckJJCQkHDU7fr160dwcDCbN2+uTOhLS0tJT0+nffv2rg5LatCY7nZv7qJ3dXd7gCWAVuGtaBXeql7blzpKsZfYyS3K5VDpIfJL8ysfBWUFZB3MJeugncDAUjCKKSgroMRRQomzhFJHaeX/SxwlVf7vNJ2Yf84RZJpmtf9X3JMHWAIqHxYC2JtXCqYVEwuYVjCt9G4bjy0knLCAMEIDQqs9wgLCCA0MJSooqkryHhUcVWWwqTeqazBkbZ/b2vbxx/rylkLfOxFpiTxWQx8VFcVNN93Eww8/TEpKCu3bt+eZZ54B4OKLL/ZUWOLDPN3dHmgNJD40nvjQ+GY5X11qm2Ho9jP8cwXdxgyGPNo+3nzzKnXT905EWhrL0Tdxn2eeeYZLL72Uq666igEDBrB9+3YWL15MTEz9VvQUOVxFvbT1zzIPVwy89VUVNzeH8+da4rp6Z1y5z5Ey7YUs35ZNpr1+g4BFRETcwaMLSwUGBjJ9+nSmT5/uyTDEj6i7vVxjBzH7quaauelwmu9cRES8hccWlnIVDYoVqV1tg0H9cSXNxgyGbOwASlcPwBYRETmSTywsJVIbf0w2PaWmWmJ/bVluTO9MY3t0NN+5iIh4EyX0LYAvJcj+mmx6C39fSbO5Zm7y9ABsERGRw3l0UKxU5Y4BdnNWZTBk2mIuf30lQ6YtZs6qDJcd29VqSzY14NB1XDEQVBo+AFuDZ0VExJ3UQu8l3NEy7WutsSpjcD+1LLtOfct1fKXXyZd68kREpCq10HsBd7VM+1prbEubatETNLWnayXbQhnUKa7Bc917W0u9L/XkiYhIdWqh9wLuapluSGusN7TOtbSpFj1FU3s2H1/odfK1njwREalOCb0XcFcZRH0TZE+VBNR0E6Fks3loJc3m4QslTr5w0yEiInVTQu8FGtIy3dCW9KMlyJ5qnavrJkLJpvgLX+h18oWbDhERqZsSei9Rn5bpxrak15Uge6J1Tl38vs8bSrR8hbf3OvnCTYeIiNRNCb0XqSvxdlcS7InWOXXx+6aKJH7dLjtPzd/k9bO2eBNP9zod7QbM2286RESkbkrofYS7kmBPtM6pi9/3HN47dDj1rni/+vbsefqmQ0REGk8JvY9wZxLc3K1z6uL3LUf2Dh1JvSveS+VtIiItgxJ6H+HuJLi5W+fUxe87auodOpx6V7yXyttERFoGJfQ+xN+SYF/u4nfnoFBvG3BaU+9QBfWueDeVt4mItAxK6H2MLyfB/sKd8/Z7ak2AutTUO3TfWV3p1TbaJ28sve2GqTauiFPlbSIiLYNhmmYdneneLy8vD5vNht1uJyoqytPhiJ/LtBcyZNriai2eSycOa3KS5M5ju0KmvbBZe4fckXh74w1TTVwdZ3N/70REpOkakuOqhV6kAdxZk+zt9c7N2TvkjsTbVwaIuiNO9eyJiPg3i6cDEPElFTXJh3NVTbI7j+1LaktoM+2FTTpuXTdM3sRX4hQREe+hhF6kASpqkq1Geebtyppkdx7bl7grofWVGyZfiVNERLyHSm5EGsidsw3520xGjeGumVl8ZYCor8QpIiLeQ4NiRcTrzFmVUS2hddXgVV8ZIFpbnL4yS4+IiDRNQ3JcJfQi4pV8JfFuTr4yS4+IiDRdQ3Jc1dBLi5VpL2T5tuwmD7YU90i2hTKoU5yS+T+5a7CwiIj4PtXQS4uklk7xNbUNFv7810xG9krWjY+ISAumFnppcdTS2TL4Ww9MTbPfADz++UaGTFvMnFUZzR+Uj/G3z4SISAW10EuL4+0LOEnT+WMPzJGz3xzOWxfJ8ib++JkQEamgFnppcTTPt++rq6XVn3tgxg5ox9KJw3hw5LHVXtPiU7Xz58+EiAgooZcWSAs41Z83lijMWZXBkGmLufz1lTWWmvj7SqvJtlBG9krWTWkD+PtnQkREJTfSImkBp6PzxhKF2lpaDy81cdfCVN5Ei081TEv4TIhIy6aEXlqsZFuoEqBa1Cdx9oT6jH9oKcmubkrrr6V8JkSk5VJCLyLVeOvA4fq2tLaUZFc3pfXXUj4TItIyqYZevJo31nA3hK/G760Dhxsy/kELU8mR9JkQEX+lFnrxWt5Yw90Qvhy/N5coqKVVRESkKsM0j5jQ2Mfk5eVhs9mw2+1ERUV5OhxxkUx7IUOmLa5WWrF04jCfSOB8Pf4KmfZCJc4iIiIe0JAcVy304pW8tYa7vnw9/gqq0RYREfF+qqGXGnm69ttba7jry9fjFxEREd+hhN4HNHdyfbSFe5qDry/+5Ovxi7iLpxsLRET8kWrovVxzD6z0ttpvX6/h9rX4M+2FpGXnkxof7hPxim/x5YHiIiLNTTX0fsITi/t4W+23r9dw+1L8SrbEnbx1sTIREX+gkhsvVldy7S6q/W6Zaku2VBYhruKJ32ciIi2FEnov5onkWrXfLZOSLXE3NRaIiLiPSm68mKcW99HCPS1PRbJ15NgJJVviKt68WJmIiK/ToFgf4GsDK8U3zVmVUS3ZUg29uJp+n4mI1E9Dclwl9CJSScmWiIiId9AsNyLSKL40K4+IiIiU06BYEREREREfpoReRERERMSHKaEXEREREfFhSuhFXCDTXsjybdlaiElERESanUcT+t9//51Ro0YRHx9PVFQUJ510Et98840nQxJpsDmrMhgybTGXv76SIdMWM2dVhqdDEhERkRbEown9ueeeS1lZGYsXL2b16tX07t2bc889l6ysLE+GJVJvmfZCJs1dV7kgk9OEyXPXq6VeREREmo3HEvrs7Gy2bNnCxIkT6dWrF8cccwzTpk2joKCA9evXeyoskQZJy86vsroqgMM0Sc8u8ExAIiIi0uJ4LKGPi4uja9eu/Pvf/yY/P5+ysjJeffVVEhMT6devn6fCEmmQ1PhwLEbV56yGQYf4MM8EJCIiIi2OxxaWMgyDr7/+mtGjRxMZGYnFYiExMZEFCxYQExNT637FxcUUFxdXfp2Xl9cc4YrUKNkWytQxPZk8dz0O08RqGDw5pocWZxIREZFm4/IW+okTJ2IYRp2PTZs2YZomEyZMIDExke+//54ff/yR0aNHc95555GZmVnr8adOnYrNZqt8pKSkuPotiDTI2AHtWDpxGO+OP5GlE4cxdkA7T4ckIiIiLYhhmqZ59M3qb9++feTk5NS5TceOHfn+++8588wzOXDgAFFRUZWvHXPMMVx//fVMnDixxn1raqFPSUnBbrdXOY6IiIiIiK/Ky8vDZrPVK8d1eclNQkICCQkJR92uoKB80KDFUrWTwGKx4HQ6a90vODiY4ODgpgUpIiIiIuInPDYodtCgQcTExHDNNdewdu1afv/9d+69917S0tIYOXKkp8ISEREREfEpHkvo4+PjWbBgAYcOHeK0006jf//+LF26lHnz5tG7d29PhSUiIiIi4lNcXkPf3BpSXyTSHDLthaRl55MaH67ZbkRERKRRPFpDL9KSzVmVUblyrMWAqWN6atYbERERcSuPldyI+JtMe2FlMg/gNGHy3PVk2gs9G5iIiIj4NSX0Ii6Slp1fmcxXcJgm6dkFnglIREREWgQl9CIukhofjsWo+pzVMOgQH+aZgERERKRFUEIv4iLJtlCmjumJ1SjP6q2GwZNjemhgrIiIiLiVBsWKuNDYAe04pUsC6dkFdIgPUzIvIiIibqeEXsTFkm2hSuRFRESk2ajkRkRERETEhymhFxERERHxYUroRURERER8mBJ6EREREREfpoReRERERMSHKaEXEREREfFhSuhFRERERHyYEnoRERERER+mhF5ERERExIcpoRcRERER8WFK6EVEREREfJgSehERERERH6aEXkRERETEhymhFxERERHxYUroRURERER8mBJ6EREREREfpoReRERERMSHKaEXEREREfFhSuhFRERERHyYEnoRERERER+mhF5ERERExIcpoRcRERER8WFK6EVEREREfJgSehERERERH6aEXkTcItNeyPJt2WTaCz0dioiIiF8L8HQAIuJ/5qzKYNLcdThNsBgwdUxPxg5o5+mwRERE/JJa6EXEpTLthZXJPIDThMlz16ulXkRExE2U0IuIS6Vl51cm8xUcpkl6doFnAhIREfFzSuhFxKVS48OxGFWfsxoGHeLDPBOQiIiIn1NCLyIulWwLZeqYnliN8qzeahg8OaYHybZQD0cmIiLinzQoVkRcbuyAdpzSJYH07AI6xIcpmRcREXEjJfQi4hbJtlAl8iIiIs1AJTciIiIiIj5MCb2IiIiIiA9TQi8iIiIi4sOU0IuIiIiI+DAl9CIiIiIiPkwJvYiIiIiID1NCLyIiIiLiw5TQi4iIiIj4MCX0IiIiIiI+TAm9iIiIiIgPU0IvIiIiIuLD3JbQP/HEEwwePJiwsDCio6Nr3CYjI4ORI0cSFhZGYmIi9957L2VlZe4KSURERETE7wS468AlJSVcfPHFDBo0iDfffLPa6w6Hg5EjR5KUlMTy5cvJzMzk6quvJjAwkCeffNJdYYmIiIiI+BXDNE3TnSeYNWsWd955J7m5uVWenz9/Pueeey67d++mVatWAMyYMYP777+fffv2ERQUVK/j5+XlYbPZsNvtREVFuTp8EREREZFm15Ac12M19CtWrKBnz56VyTzAiBEjyMvLY8OGDZ4KS0RERETEp7it5OZosrKyqiTzQOXXWVlZte5XXFxMcXFx5dd2ux0ov4sREREREfEHFbltfYppGpTQT5w4kaeeeqrObTZu3Ei3bt0actgGmTp1Ko888ki151NSUtx2ThERERERTzh48CA2m63ObRqU0N99992MGzeuzm06duxYr2MlJSXx448/Vnluz549la/VZtKkSdx1112VXzudTvbv309cXByGYdTr3K6Ul5dHSkoKO3bsUA1/A+naNZ6uXePp2jWNrl/j6do1nq5d0+j6NZ4nr51pmhw8eJDWrVsfddsGJfQJCQkkJCQ0OrDDDRo0iCeeeIK9e/eSmJgIwMKFC4mKiqJ79+617hccHExwcHCV52qbFrM5RUVF6YekkXTtGk/XrvF07ZpG16/xdO0aT9euaXT9Gs9T1+5oLfMV3FZDn5GRwf79+8nIyMDhcLBmzRoAOnfuTEREBGeeeSbdu3fnqquu4umnnyYrK4sHH3yQCRMmVEvYRURERESkZm5L6B966CHefvvtyq+PP/54AL755huGDh2K1Wrls88+4+abb2bQoEGEh4dzzTXX8Oijj7orJBERERERv+O2hH7WrFnMmjWrzm3at2/PF1984a4QmkVwcDAPP/ywehUaQdeu8XTtGk/Xrml0/RpP167xdO2aRtev8Xzl2rl9YSkREREREXEfjy0sJSIiIiIiTaeEXkRERETEhymhFxERERHxYUroRURERER8mBL6BkhPT+f6668nNTWV0NBQOnXqxMMPP0xJSUmd+xUVFTFhwgTi4uKIiIjgwgsvrFwVt6V54oknGDx4MGFhYfVeEGzcuHEYhlHlcdZZZ7k3UC/UmGtnmiYPPfQQycnJhIaGMnz4cLZs2eLeQL3Q/v37ueKKK4iKiiI6Oprrr7+eQ4cO1bnP0KFDq33ubrrppmaK2LNeeuklOnToQEhICAMHDqy2qveRPvjgA7p160ZISAg9e/b0+dnLmqIh127WrFnVPmMhISHNGK33+O677zjvvPNo3bo1hmHw8ccfH3WfJUuW0LdvX4KDg+ncufNRZ9bzVw29dkuWLKn2uTMMg6ysrOYJ2ItMnTqVAQMGEBkZSWJiIqNHj2bz5s1H3c8bf+cpoW+ATZs24XQ6efXVV9mwYQP/+te/mDFjBpMnT65zv7///e98+umnfPDBB3z77bfs3r2bMWPGNFPU3qWkpISLL76Ym2++uUH7nXXWWWRmZlY+3n33XTdF6L0ac+2efvppnn/+eWbMmMHKlSsJDw9nxIgRFBUVuTFS73PFFVewYcMGFi5cyGeffcZ3333HDTfccNT9xo8fX+Vz9/TTTzdDtJ41Z84c7rrrLh5++GF+/vlnevfuzYgRI9i7d2+N2y9fvpzLLruM66+/nl9++YXRo0czevRo1q9f38yRe15Drx2Urz55+Gds+/btzRix98jPz6d379689NJL9do+LS2NkSNHMmzYMNasWcOdd97J3/72N7788ks3R+p9GnrtKmzevLnKZy8xMdFNEXqvb7/9lgkTJvDDDz+wcOFCSktLOfPMM8nPz691H6/9nWdKkzz99NNmampqra/n5uaagYGB5gcffFD53MaNG03AXLFiRXOE6JVmzpxp2my2em17zTXXmKNGjXJrPL6kvtfO6XSaSUlJ5jPPPFP5XG5urhkcHGy+++67bozQu/z2228mYK5ataryufnz55uGYZi7du2qdb9TTz3VvOOOO5ohQu9ywgknmBMmTKj82uFwmK1btzanTp1a4/aXXHKJOXLkyCrPDRw40LzxxhvdGqc3aui1a8jvwZYEMD/66KM6t7nvvvvM4447rspzY8eONUeMGOHGyLxffa7dN998YwLmgQMHmiUmX7J3714TML/99ttat/HW33lqoW8iu91ObGxsra+vXr2a0tJShg8fXvlct27daNeuHStWrGiOEP3CkiVLSExMpGvXrtx8883k5OR4OiSvl5aWRlZWVpXPns1mY+DAgS3qs7dixQqio6Pp379/5XPDhw/HYrGwcuXKOvedPXs28fHx9OjRg0mTJlFQUODucD2qpKSE1atXV/nMWCwWhg8fXutnZsWKFVW2BxgxYkSL+oxB464dwKFDh2jfvj0pKSmMGjWKDRs2NEe4Pk+fu6br06cPycnJnHHGGSxbtszT4XgFu90OUGde562fPbetFNsSbN26lRdeeIHp06fXuk1WVhZBQUHVap5btWrVIuvVGuOss85izJgxpKamsm3bNiZPnszZZ5/NihUrsFqtng7Pa1V8vlq1alXl+Zb22cvKyqrWlRwQEEBsbGyd1+Hyyy+nffv2tG7dml9//ZX777+fzZs3M3fuXHeH7DHZ2dk4HI4aPzObNm2qcZ+srKwW/xmDxl27rl278tZbb9GrVy/sdjvTp09n8ODBbNiwgbZt2zZH2D6rts9dXl4ehYWFhIaGeigy75ecnMyMGTPo378/xcXFvPHGGwwdOpSVK1fSt29fT4fnMU6nkzvvvJMhQ4bQo0ePWrfz1t95aqEHJk6cWOMAkcMfR/5C3rVrF2eddRYXX3wx48eP91Dk3qEx168hLr30Us4//3x69uzJ6NGj+eyzz1i1ahVLlixx3ZvwEHdfO3/m7mt3ww03MGLECHr27MkVV1zBv//9bz766CO2bdvmwnchLdmgQYO4+uqr6dOnD6eeeipz584lISGBV1991dOhiR/r2rUrN954I/369WPw4MG89dZbDB48mH/961+eDs2jJkyYwPr163nvvfc8HUqjqIUeuPvuuxk3blyd23Ts2LHy/7t372bYsGEMHjyY1157rc79kpKSKCkpITc3t0or/Z49e0hKSmpK2F6jodevqTp27Eh8fDxbt27l9NNPd9lxPcGd167i87Vnzx6Sk5Mrn9+zZw99+vRp1DG9SX2vXVJSUrVBiWVlZezfv79BP4MDBw4EynvmOnXq1OB4fUF8fDxWq7XaLFx1/b5KSkpq0Pb+qjHX7kiBgYEcf/zxbN261R0h+pXaPndRUVFqnW+EE044gaVLl3o6DI+59dZbKydMOFrvmLf+zlNCDyQkJJCQkFCvbXft2sWwYcPo168fM2fOxGKpu5OjX79+BAYGsmjRIi688EKgfGR5RkYGgwYNanLs3qAh188Vdu7cSU5OTpUk1Ve589qlpqaSlJTEokWLKhP4vLw8Vq5c2eBZhrxRfa/doEGDyM3NZfXq1fTr1w+AxYsX43Q6K5P0+lizZg2AX3zuahMUFES/fv1YtGgRo0ePBsq7oRctWsStt95a4z6DBg1i0aJF3HnnnZXPLVy40G9+v9VXY67dkRwOB+vWreOcc85xY6T+YdCgQdWmCmyJnztXWbNmjV//bquNaZrcdtttfPTRRyxZsoTU1NSj7uO1v/M8OiTXx+zcudPs3Lmzefrpp5s7d+40MzMzKx+Hb9O1a1dz5cqVlc/ddNNNZrt27czFixebP/30kzlo0CBz0KBBnngLHrd9+3bzl19+MR955BEzIiLC/OWXX8xffvnFPHjwYOU2Xbt2NefOnWuapmkePHjQvOeee8wVK1aYaWlp5tdff2327dvXPOaYY8yioiJPvQ2PaOi1M03TnDZtmhkdHW3OmzfP/PXXX81Ro0aZqampZmFhoSfegsecddZZ5vHH/3879w7SSBSGYXhWzUwI4iUg6RTipbFxG8tEECzs7EwRBgsFbbQJpBHRThAtxDqWYqWFoCBqoaDgpQgaxEvQysZChFh+WywOxnWLFJuzB98HpsmcwPl/Zs58w8D5qZOTEx0eHqqzs1OpVCo4//m+vb291dzcnE5PT1UsFrW5ual4PK5EImGqhKpZW1uT53laXV3V1dWVxsbG1NTUpKenJ0lSOp1WNpsNxh8dHamurk4LCwsqFAqamZlRKBRSPp83VYIxlfZudnZWOzs7uru709nZmYaHhxUOh3V5eWmqBGNeX1+DNc1xHC0uLuri4kIPDw+SpGw2q3Q6HYy/v79XJBJRJpNRoVDQysqKamtrtb29baoEYyrt3dLSkjY2NnRzc6N8Pq/JyUnV1NRod3fXVAnGjI+Pq7GxUQcHB2WZrlQqBWNsWfMI9BXI5XJyHOfL412xWJTjONrf3w9+e3t708TEhJqbmxWJRDQ0NFT2EvCd+L7/Zf8+9stxHOVyOUlSqVTSwMCAWlpaFAqF1NbWptHR0eAB+Z1U2jvp99aV09PTisVi8jxP/f39ur6+rv7kDXt+flYqlVJ9fb0aGho0MjJS9iL0+b59fHxUIpFQNBqV53nq6OhQJpPRy8uLoQqqa3l5Wa2trXJdV729vTo+Pg7OJZNJ+b5fNn59fV1dXV1yXVfd3d3a2tqq8oz/H5X0bmpqKhgbi8U0ODio8/NzA7M2730rxc/He79831cymfzjPz09PXJdV/F4vGzt+04q7d38/Lza29sVDocVjUbV19envb09M5M37G+Z7uO1ZMua90OS/uUXAAAAAAD/DrvcAAAAABYj0AMAAAAWI9ADAAAAFiPQAwAAABYj0AMAAAAWI9ADAAAAFiPQAwAAABYj0AMAAAAWI9ADAAAAFiPQAwAAABYj0AMAAAAWI9ADAAAAFvsFCusqIrSBRboAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_preds(x, y, f, quad_model, 'After training')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hbtmFJIXb6qm"
   },
   "source": [
    "잘 작동하지만 일반적인 훈련 유틸리티의 구현은 `tf.keras` 모듈에서 사용할 수 있다는 것을 명심해야 합니다. 따라서 직접 작성하기 전에 사용해보는 것이 좋습니다. 먼저 `Model.compile`와 `Model.fit` 메서드로 훈련 루프를 구현합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cjx23MiztFmT"
   },
   "source": [
    "`tf.keras.Sequential`을 사용하여 Keras의 순차 모델을 만드는 것으로 시작합니다. 가장 간단한 Keras 레이어 중 하나는 `tf.keras.layers.Dense`로 인스턴스화할 수 있는 덴스 레이어(dense layer)입니다. 덴스 레이어는 $\\mathrm{Y} = \\mathrm{W}\\mathrm{X} + \\vec{b}$ 형식의 다차원 선형 관계를 학습할 수 있습니다. $w_1x^2 + w_2x + b$ 형식의 비선형 방정식을 학습하려면 덴스 레이어의 입력은 $x^2$와 $x$를 특성으로 하는 데이터 행렬이어야 합니다. `tf.keras.layers.Lambda` 람다 레이어를 사용하여 이 스택 변환을 수행할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:49:01.903724Z",
     "iopub.status.busy": "2022-12-14T21:49:01.903069Z",
     "iopub.status.idle": "2022-12-14T21:49:01.915081Z",
     "shell.execute_reply": "2022-12-14T21:49:01.914468Z"
    },
    "id": "5rt8HP2TZhEM"
   },
   "outputs": [],
   "source": [
    "new_model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Lambda(lambda x: tf.stack([x, x**2], axis=1)),\n",
    "    tf.keras.layers.Dense(units=1, kernel_initializer=tf.random.normal)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:49:01.917993Z",
     "iopub.status.busy": "2022-12-14T21:49:01.917750Z",
     "iopub.status.idle": "2022-12-14T21:49:04.052831Z",
     "shell.execute_reply": "2022-12-14T21:49:04.052199Z"
    },
    "id": "73kCo1BtP3rQ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Unsupported signature for serialization: ((TensorSpec(shape=(2, 1), dtype=tf.float32, name='gradient'), <tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f08d04ae6a0>, 139675831163120), {}).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Unsupported signature for serialization: ((TensorSpec(shape=(1,), dtype=tf.float32, name='gradient'), <tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f08d0455a00>, 139675831205440), {}).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Unsupported signature for serialization: ((TensorSpec(shape=(2, 1), dtype=tf.float32, name='gradient'), <tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f08d04ae6a0>, 139675831163120), {}).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Unsupported signature for serialization: ((TensorSpec(shape=(1,), dtype=tf.float32, name='gradient'), <tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f08d0455a00>, 139675831205440), {}).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ./my_new_model/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: ./my_new_model/assets\n"
     ]
    }
   ],
   "source": [
    "new_model.compile(\n",
    "    loss=tf.keras.losses.MSE,\n",
    "    optimizer=tf.keras.optimizers.SGD(learning_rate=0.01))\n",
    "\n",
    "history = new_model.fit(x, y,\n",
    "                        epochs=100,\n",
    "                        batch_size=32,\n",
    "                        verbose=0)\n",
    "\n",
    "new_model.save('./my_new_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u3q5d1SzvzTq"
   },
   "source": [
    "훈련 후 Keras 모델의 성능을 관찰합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:49:04.056694Z",
     "iopub.status.busy": "2022-12-14T21:49:04.056189Z",
     "iopub.status.idle": "2022-12-14T21:49:04.209298Z",
     "shell.execute_reply": "2022-12-14T21:49:04.208665Z"
    },
    "id": "Mo7zRV7XZjv7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylim([0, max(plt.ylim())])\n",
    "plt.ylabel('Loss [Mean Squared Error]')\n",
    "plt.title('Keras training progress');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T21:49:04.212829Z",
     "iopub.status.busy": "2022-12-14T21:49:04.212061Z",
     "iopub.status.idle": "2022-12-14T21:49:04.434495Z",
     "shell.execute_reply": "2022-12-14T21:49:04.433848Z"
    },
    "id": "bB44a9YsvnfK"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_preds(x, y, f, new_model, 'After Training: Keras')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ng-BY_eGS0bn"
   },
   "source": [
    "자세한 내용은 [기본 훈련 루프](basic_training_loops.ipynb) 및 [Keras 가이드](https://www.tensorflow.org/guide/keras)를 참고하세요."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "basics.ipynb",
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}