mirror of
https://github.com/ArthurDanjou/ArtStudies.git
synced 2026-01-14 18:59:59 +01:00
Arthur DANJOU
d5a6bfd339
- Added missing commas in various print statements and function calls for better syntax. - Reformatted code to enhance clarity, including breaking long lines and aligning parameters. - Updated function signatures to use float type for sigma parameters instead of int for better precision. - Cleaned up comments and documentation strings for clarity and consistency. - Ensured consistent formatting in plotting functions and data handling.
808 lines
307 KiB
Plaintext
808 lines
307 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Séance 2 - Réseau de neurones convolutionnel\n",
|
|
"\n",
|
|
"On se propose de classifier les chiffres manuscrit du dataset [FashionMNIST](https://github.com/zalandoresearch/fashion-mnist) en définissant ses propres réseaux de neurones convolutionnel. L'objectif est de découvrir la manière d'entraîner ces algorithmes et observer en pratique les bases théoriques discutées en cours.\n",
|
|
"\n",
|
|
"## Exploration des données\n",
|
|
"\n",
|
|
"Commençons par importer les données."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 192,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"import pandas as pd\n",
|
|
"\n",
|
|
"%matplotlib inline\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import seaborn as sns\n",
|
|
"\n",
|
|
"sns.set(style=\"whitegrid\")\n",
|
|
"\n",
|
|
"from tensorflow import keras\n",
|
|
"\n",
|
|
"(X_train_full, y_train_full), (X_test, y_test) = (\n",
|
|
" keras.datasets.fashion_mnist.load_data()\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Consigne** : À l'aide de la fonction [`train_test_split`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html), séparer le jeu d'entraînement complet en un dataset d'entraînement et un dataset de validation. Afficher les tailles des datasets respectifs."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(48000, 28, 28) (48000,)\n",
|
|
"(12000, 28, 28) (12000,)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn.model_selection import train_test_split\n",
|
|
"\n",
|
|
"X_train, X_valid, y_train, y_valid = train_test_split(\n",
|
|
" X_train_full,\n",
|
|
" y_train_full,\n",
|
|
" test_size=0.2,\n",
|
|
" random_state=42,\n",
|
|
")\n",
|
|
"print(X_train.shape, y_train.shape)\n",
|
|
"print(X_valid.shape, y_valid.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Les classes sont encore des nombres, mais ils correspondent à une catégorie. Pour mieux visualiser, nous allons faire un dictionnaire."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"label_map = {\n",
|
|
" 0: \"t-shirt/top\",\n",
|
|
" 1: \"trouser\",\n",
|
|
" 2: \"pullover\",\n",
|
|
" 3: \"dress\",\n",
|
|
" 4: \"coat\",\n",
|
|
" 5: \"sandal\",\n",
|
|
" 6: \"shirt\",\n",
|
|
" 7: \"sneaker\",\n",
|
|
" 8: \"bag\",\n",
|
|
" 9: \"ankle boot\",\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Consigne** : Afficher plusieurs images du dataset d'entraînement aléatoirement. On pourra utiliser la fonction [`imshow`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html) et le dictionnaire."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAxoAAAMqCAYAAAAFOjf/AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQAA2rpJREFUeJzs3Qe8FNX9//+xIb33XgWkWhBFFDugRmOLGnsvsXzVxBJjiRpjNNZojEaNLWrsvWPBhiAKgvTee5Vmu//HZ37/5XH3nPcyh2Vu3dfz8cjX7x7m7s69e/bMnJ3zns9WRUVFRREAAAAApGjrNJ8MAAAAAAwTDQAAAACpY6IBAAAAIHVMNAAAAACkjokGAAAAgNQx0QAAAACQOiYaAAAAAFK3bchG33zzTWTlNrbbbrv09wAV0o8//hhttdVW0U477VTir0X/Q1n2P0MfRHH0P5Q1jsGoKP0vaKJhHYy6fiiuNPtDZex/6vdZtWpV4nbbbut/ZO3D7tp+++0Tt9tmm22iiqq0+0Nl7IPIH/2vZKxfv95r++mnnxJ/To2BNWrUiCozjsH/z4YNG+RJcHG//PKLt03t2rVLdL9+/vlnr2316tWJx2A1matSpYrXtvXWZbsgaXP6Q9BEI/OL9+jRI/+9QqUyZsyYUnutytj/3AHH/Pvf/04cMFu3bh00MPXt2zdxsGrQoIG3jZrIFHr/q6x9EPmj/23eSVbolxpDhw712mbMmJF40lirVi2v7aijjtrs/axIX8AU4jFYvV/Dhw/32iZPnpz1eO3atd425557blSSFixY4LW98847WY8bN27sbdOuXTuvrUOHDl5b/fr1o4rS/8hoAAAAAEgdEw0AAAAAqWOiAQAAACB1FWNBNlCB3XbbbV5bz549vbYddtjBa6tevXriukyVtVC5jRUrViSud23evLnXBgBp5xx++9vfBgW4L7zwwsRgrFovPmjQIK/t9NNPz3r8m9/8JijkqsLmCJPv3/O1115LzOuYlStXem2dOnXKevzKK69429x1111Bx80+ffpkPf7iiy+8bQYPHhyUtfj888+zHp9wwgneNp988onX9umnn3ptvXr1ynr8q1/9KiqvuKIBAAAAIHVMNAAAAACkjokGAAAAgNSR0QBSNmTIEK+qqqtLly5em1p73KJFi8R7yC9atMhra9KkidfWsWPHrMdr1qwJKphVtWpVrw1A4VHr7UPyGPvtt5/XdvLJJ3ttp556al77pcbTY445xmv7wx/+kFjs7aSTTvLayG2UPDdHMWvWLG+bHXfcMej4576v//d//xd0rHv++ee9tqZNm2Y9rlevnrfNDz/8ENQn27Ztm1jfo3Pnzl6bes1hw4ZlPW7ZsqW3TWlUjQ/BFQ0AAAAAqWOiAQAAACB1TDQAAAAApI6JBgAAAIDUEQYHUvbCCy8khiW//vrroAI/AwYMyHo8b968oIJZNWvW9Nrc/VBhxoULF3ptbdq08doAFB411my7rX8acemll26y6Fmu4Pe6desSi6ipcevHH38MuomFWzxVBcZV8bWGDRsm/i3UOE+IPPz3dQvvqTC1Ck9vvfXWiYVu1XFTFYc877zzEkPjqr+vWrUq6Fha1emT6m+j+oy6aUGrVq2yHk+dOtXbhjA4AAAAgEqLiQYAAACA1DHRAAAAAJA6JhoAAAAAUkcYHEhZrVq1sh7PmTPH22b58uVe2y677JIYHlPVw1Xb9ttvn7ifK1as8NpWr16d+HMoLG41ehW+LOnQ6+eff+61LV68OHG/1OdMVaRGFPSeqiCsMmrUqKzHb775ZtDPqbEspPK42kZVfnbH04MOOsjb5u677/babrzxxrzC4IUW/A61Zs2axHFG+emnn4JC3e6xzb2hQK6wtgqNu1W/1bG1WrVqefXJ77//3tumTp06XtuyZcsSX1M9V8hnoDRwRQMAAABA6phoAAAAAEgdEw0AAAAAqWOiAQAAACB1hMGBlO2+++5Zj7/99ltvm/Hjx3ttZ555pte2dOnSxNdTQbeQSrmqyqoKiKsAXmgoFBWfClmnVf13wYIFQWHcunXrJoZHv/zyS2+bFi1aeG0nnHBCYnBTBVND/g6FWAV8yJAhie9XaAA1JPgdKuSGGMcee6zXdu655+YdXEcYdZMGN7is3j91sxIVeHbfG/XZrVevXtA45h5f1eupfqs+P+udn1XBb7VfKrjuHuPV67lBdkMYHAAAAEClwEQDAAAAQOqYaAAAAABIHQutgS0wbdo0r+2AAw7IenzttdcGrXVu2bKl17Zo0aLENZ1qLataa+q2qeeaP39+0HraRo0aeW0oDEuWLPHaXn/9da9t9OjRiW2dOnXytunevbvXNnHiRK+tXbt2WY+vvvrqoCzU9OnTvbaOHTtmPabQWjhV5Kxp06aJP6fWlKeZ0Qh5D2vXrh2USQvZV1XgMN/9quxUPrBZs2ZZj2fNmpVYDNfUr18/8bipcgnquBlS9E7lPVROUh3jazn7H5LjyHUMdov4de3aNej8pHfv3lFp44oGAAAAgNQx0QAAAACQOiYaAAAAAFLHRAMAAABA6giDAymH2twQ2NixY71t9t5776CgmxuIUwFuFUQLKS42YcKEoDCmKv6HyimkUN0zzzzjbfPhhx8mFq40gwcPznrctm1bbxsVEA8J0KpgZatWrfJ6LgK74dasWZMY7C2LMHi+VBhc3STDvXmH+uyUh9+nPFKF5NxifP/85z+9bXbZZRev7c9//rPX9uabb2Y9rlatWlDoWh3r3CKjKkTesGHDoOdyqZC6urnCZ599lvi3aNy4sbfN4sWLo/KAKxoAAAAAUsdEAwAAAEDqmGgAAAAASB0TDQAAAACpIwwObAFVxTipUrjp0qVLUAjMDeKGBrNVmNWtXDt58mRvm2OOOcZrq1u3btBrojAcffTRXtt5551X6kFYN0w8Y8YMb5t169YFVRJ2qSrPhRYQD/191Y0nVMi1PHCD3qp6c5UqVYIqRleUcHtFuYmK22fUTU4efvhhr+2QQw7x2qpXr574eup9dn9O9RH1uVD9IyTwXl28nvrsqBu3uGFw1f9UW1ngigYAAACA1DHRAAAAAJA6JhoAAAAAUsdEAwAAAEDqCjYMrkK1KnwUwq0caZYtW+a1uRV1VejnkksuCXpNN+SzJaGzd999N+vxnnvu6W1To0aNvJ+/0L333nte2zvvvOO1jRkzJqgSqktVpFV92e0zp59+urcN4cXCFlJRvmnTpkHPpQLVblvI6+WyfPnyxOC3CmS2bt068bkLLfgd+v4pGzZs8NqmT58elUcq/I3Sp86Z3ONTnTp1vG1q1arltc2aNctra9OmTWKFbBXgrlevXuK5ogpwq/NJdSz92fkd1TijqoyrKuYLFixI3K9FixZF5QFXNAAAAACkjokGAAAAgNQx0QAAAACQukq5YDEkvxCaxxgyZEjW47/85S/eNh9++GFQQTZ3DbFag3jyySd7bQ0aNCjRtfRuAa6ePXt623z66aepvR6iaNq0aV5bq1atvDa3j7gFf3KtQ1eqVauW9Zg8BvKhMkFqrXFIziG0MJ7abtWqVYnFqTp06BDl+zu5tiRPUpmpteEhVMG08kC9z3PmzEk8xqvfh8KP4bke9/Os8gXqvenTp4/XtmTJksTjpnofli5dmvgeqrFC/Zw6LldzjsEqL6b2S+XP5s+fn9j/Qsa10sDICQAAACB1TDQAAAAApI6JBgAAAIDUMdEAAAAAkLpKGQYPCbk+9dRTXttll13mta1ZsyYxwL3bbrt5bU2aNEksuqLCTv379/farrzySq/tlFNOifLx4IMPem1uIGnevHl5PXchyjfsN2PGDK9NFUoMCaCGFgtyg2EEFQubCk+HjJ2hoeg0+5cKW7oFt1QoWd1IQyHo7fcHVdzuuuuu89q+/vprr23//fdPfD11/GvcuHFUkkaOHJn1eOedd/a2qVu3rtf2wAMPeG1uEbjjjz/e24bxNJz7GVyxYkVQuFkVuHOD0qpAoDqXU9wbB6lx0w155+rL85xzKxXybtGihde2/fbbe20LFy7c5HPn+jn3nLY0CjIzugIAAABIHRMNAAAAAKljogEAAAAgdUw0AAAAAFS8MHhoZUI3OLglVYufe+65rMfHHXdc0H7ttNNOXtuBBx64ycfmscce89pGjx7ttbm/kxsONzNnzvTaTj31VK/toosuynrcu3fvoFCbCgzVr18/MYT1zjvvZD2uV6+eDEAhCgpfuUEu06xZs8Sgt6por/qRCiHWqlUrcRuUHXcMDH1/8g1dh46xbvhRBafzrQweyg3eqkrCNialZfbs2V5b8fGuvFTczZfqMyH94a233vLaOnbs6LXNmjUr6/ETTzwRtF8q0O8ex1SIV42B06dP99rc/ejevXvQc6kbbrjPpcLgSqHdhEMd69RxzLV27dqgiuIq6O3eKEL1UbUPamxzPxcqYP3TTz9F+fTvbcUNF9RnQIW13TFR3TBDBeXdv02u508TVzQAAAAApI6JBgAAAIDUMdEAAAAAkDomGgAAAADKVxjcDcSpIE2aFVeffPJJr+2kk05K/Lmzzz7ba1MB8Y8//thr+/DDD7MeL1iwwNumSpUqXpsKDLnhIxUEUlXGVQDKrSipKk2r0JkKQLlBprlz53rbPPTQQ1mPzz33XMLggVS4UH0u3LC2eg9VCFW1qQC6G6IMDfWidOT7tw8Jl44ZM8bbZvLkyV7bkUce6bVtyY058jlmqBCoCpS6IWEVBl+yZInX9vnnnycGy0eMGOFtM3DgwM2uOF5eqTHDfZ9VaLRq1apBx9IJEyZkPW7ZsqW3Tdu2bb22qVOnem1vvPFG4vFW3cBkl1128dquuOKKxND/0KFDvbbdd989cV9VGLei95M0hIbr3bFAjTsq+K2eq2bNmonH1mXLlgWFukNuyKLGMVVB3LVo0SKvTd2gQJ1rzZkzJ/F8Uo1/6m/YpEmTqCRxRQMAAABA6phoAAAAAEgdEw0AAAAA5SujkW/+Yvny5V7biy++mPX46quvDioqctVVVyWuzRw3bpy3zY033ui1qfyFu9ZvyJAh3jb77ruv11a7du3ENYHuuuBc62LdfVDrkVUmROU2VOEr92fV33n16tVZjyt6warSpDI2av1pyDpPtVZTFWFUa02bN2++2eu0Uf6FjMNffvll0Dh8++23e22XXXZZVJr7qtZOh6whdo8hKitgxo8f77V17tw5MYvQqVOnnONhZfTJJ594bWoduDqOudmHHXfcMWhcVAXGTj755KzH7du3z7soqnt8/eKLL4LyHipn4GYy5s+fH5TRKLQcnMpc/fDDD15bo0aNEn9OHf9UAUe3YKTKcaisRUiuQm2jjrfq+Ws653Kqz6h9bdOmjdc2ceLExH1Qz6X6cknjigYAAACA1DHRAAAAAJA6JhoAAAAAUsdEAwAAAED5CoNPmjQp6/H//vc/bxtV/M0tNqeCqSoM3qVLF69NBb1ffvnlrMeffvqpt83hhx8eFCr6+uuvE4sFvf32217brrvumhikU8E6FZBT4R03CKRC5Or5VVGrpk2bZj1WQUe3OIwKHkELDWSp99C9qYAKa6s+o95D1R9Q8YUEHX/1q1952zzwwANe27Bhw7y2kSNHZj2+8847Uyv4pIoNumONmT59emL4u1WrVkGh0169eiWGwVWQuPg4qY5hFUnITR8++ugjr02N+4888khiCF8V1lX96IADDki82Yq60YpqUwUp99577yjJTTfd5LW9+uqrXpvbB0aNGhVUfK3QhIaPW7RokXgMU+doqhif+5rq51RYO6SQYGjoWoXGt3NeU93oR93YQI2v7s+qMUvdJEg9f0njigYAAACA1DHRAAAAAJA6JhoAAAAAUsdEAwAAAEDqgtOhFjh1g4LnnXdeYmBPVcFUYRo3nPbhhx8GVZVV1Y3dwLN6vY8//jio6qRbTVmFAFUlURUMc4PkKqiofh8VNHJ/JxUqUoE/Fcxyq7iq37FZs2aJIU5oKnSmqp4uWbIksS+rz5MKnamKo/lUaUbJUJ+fkIrBKqytbsIxduzYrMfvvfeet821117rtd1///1e26OPPpr1+N///re3jbp5h/p93Iq9Q4YM8bYZOnRoUBjSDX+rELl70wxzzDHHJI79qtp18dBpIVR3VmFqdZxxbyZiOnTokPj86oYpPXr08NoWLFiQWD1ccW9iYFq3bp31ePfdd/e2UW1qPHXH8A8++MDb5sQTT4wK3cqVK4Pa3JuaqPOXunXrBh1L3Z9VN0xR51VVq1b12tzzNDV2q9B1SEB8a3EMXrZsWWJQXgXcQwPpZXEjH840AAAAAKSOiQYAAACA1DHRAAAAAJA6JhoAAAAAyi4MbmEXN7z1ySefJFY2dquH5wpWvfTSS4lhbVXRUIVp3FCMCvgsXbrUa1MBPzcIpPYhNISjwt8hwSYVZAoJ6am/jWqrXr161uPly5d72xxyyCGb/BnkFhrUX7Rokde2ww47JD6/6h9u6FbdaIBK4Zsvn5sgqHElJEx86aWX5l3V1a2wrPqICjC6N/gwZ511VmLoVY0rTz31VOJ233zzjbfN+PHjg37vvn37Zj3u1KlTULDXrVqtQqYq4Fxo1I1JFFXB2X2/hg8f7m3zu9/9zmtT1Y3dG6t069Yt6POkAunuc/3mN78JCqmrG93Ur18/8e+gbvARGmavzGbMmJEYbm7btq23TaNGjbw2Nba5fVedj4VU7lbHanW+p47n6vPzi9OmKpar86+ePXsm3thA3Sjk8MMP99qoDA4AAACgUmCiAQAAACB1TDQAAAAApG7bLVmf7K43U+vn1HpEtTb82GOPzXp8/vnnBxWTaty4cWIWQq19T5Naz9u1a9fENXVuMbZc66ZD/q7quULWG6rnV7+PW7BvzJgx3jbQ3EKNud6HfNdOqrWmaq0zBfq2XEkWavvjH/+Y9Xjx4sXeNmpN76233uq1uRkq1R9UdqhGjRqJ4/W8efO8bS655BKv7aOPPvLa3P341a9+5W2j1marddHuuvk+ffoEFafq169fYgExVfyvsnvuuecSix326tUrqAhsSJ9xX880aNAgsR8NGjQocZtcBfvatWuX2BfUOKmK2LpFHdVnRxUeVgUjK7PihS43lXV76623Eos3qr+xysa4BTjV8Tak0J8Smn1Vv2OR06Z+H5Vhds8dVWG/Ll26BOVXVF8uaZx5AAAAAEgdEw0AAAAAqWOiAQAAACB1TDQAAAAAlG0Y3A1C1q5dO/FnVCBGhXfcwNA///lPbxvVpgI9bkjGDW3lKuKnwtMuFd5xC/eUBRUIVgElVUTG3a4kA6+Vjfp7uoGy0OKNKlQZQhXKVM8Vsl8hYbhC5r7f+QbsTzrpJK9typQpiUWa1Pvz1VdfeW1uMFr1BxUKVIU433///cSw4r777uu1TZ06NTEMrsYadYOPFi1aeG1u/33ssce8bVRBNvU7Dh06NOvxfvvtFxUa92Yr6niojqXqGOy+N2qcfPPNN4PeG3c/7r33Xm+byZMnB7W5heLUvqt9UP3UPRdQnzG3EHEhhsHVmKX6VsjxT92MQN0owr0JhLohi7rxjjpfDSlcqfqHuhHFWqe/qb+DurGPOl9wz6PVeYBqK4tiy1zRAAAAAJA6JhoAAAAAUsdEAwAAAEDqmGgAAAAAKNsweD5USEZVisyXCraURdilrIWGeAn7piskDOxWHTZt2rQJuklCiBUrVpRqkLlQWehw2rRpm6xIvMsuu8ifS6pQrMZF96YWuULKblhbVZlXocPly5d7bbfffnti/1UVe9VzNW3a1Gs76KCDEitG77DDDl6b+3dXlZ9VVWlVHfrAAw9MDFuqGyVUdp9++mli8FaFS1Xo1a06r4K3qk2ZPXt21uMvvvjC20btqzJu3Lisx3Xr1vW2Wbp0aVCw3B0/1efinXfeCQo9q5vTVBYqYK3O0dx+NH36dG+bgw8+OOj451amV2OwCnmrNnf/Q8+h1PH1J+d3DN0HdR695557Zj3+9ttvvW169+6d+LcpDZxpAAAAAEgdEw0AAAAAqWOiAQAAACB1TDQAAAAApK70UyFAgVHBUhVOU5Wa0wyD51PVHNmBPDfgN3HixMRgrApDtm7dOrEitgqgqoC4qnr78ccfbzKcm6vyuAqNDx48OOtxq1atvG1UQFcFXN2+6obWzaRJk4JCjc2aNUsMKnft2jXo/XCr8aptKpN//etfie+Xev9UwF+NW+74FlJZO9dYOWHChMRq3u+9917QTQUaNWqU+DuGBt7dvqxucqM+d6pa+PHHHx9VVuqYot7DDRs2ZD0+5JBDvG1atmzptS1cuNBrq1OnTuL7p/ZLBbjdvqyOmypgrZ5rvXMjALUP6jOgxiP3xiPDhg1L3PdcY3xJ44oGAAAAgNQx0QAAAACQOiYaAAAAAFJHRgMoYWotspLvuvBVq1YFrcN012uqNaRkNHKzdbjt27fPajv77LOzHs+cOdP7ueHDhycWRzM777xz4np4lcdR63Dd7MiMGTMSi5ep3IP55JNPNjt7kavwmdu/VJG90DX4bv9V/VkVBhs/frzX1qRJk6zHXbp0iQoto+EWDFW5CrXW/bvvvkvMG6mxbcGCBXmNP25xxVzF/9S+um2hxfPU38Jdq68+T+r5883UVVQhxyL1vqrCmvPnz/faVDbGza2pXEW+RfzUOKN+Tv3eG5wciqKeS+Wg+vfvn/X4zjvvDMoblUVBa65oAAAAAEgdEw0AAAAAqWOiAQAAACB1TDQAAAAApI4wOFDC8i3mExqEXL58edB+uIFMFYZTxd+QW6dOnTb52BxwwAFBz+WGoBctWhRUzEkFBY866qjEAKoKuLZo0SIxtKsChqr4nyrGV61atcTPhgr2qt/RDQ6rELkKZKqwpSoEVpntuOOOXtu0adMSbzKhbiqgxjL3765CsKof7bTTTok3NlA/pwLBKtTtjrHqc1G7du28Crmpz6YKpKtCgpWZCvi7BfXU59e9QYOZNWtW0GfcfZ9V/1PHP3d8UtuFFuBV7/06ZxxTP6eosa1hw4aJ45pSFjd84YoGAAAAgNQx0QAAAACQOiYaAAAAAFLHRAMAAABA6giDAyVMhdWmT5+eVyBVBdFUaFOF2lyhQTSUDrdia9u2baPywK2GXhZUeBT5u+OOOxKrr6uAdf369b22efPmeW3uz6owv6pCv3LlysTPhQp5q5tkhNwcoG7dukFh2UaNGiXeTEGN8+r5999//6iQqJtaqL7l/j3VjSk+/vjjxFC06g8qzK9C/+q9d4PkqrK2+n1UAH1750YXqs+om2Goz4XbJ9WNXNQxXp1DlDSuaAAAAABIHRMNAAAAAKljogEAAAAgdUw0AAAAAKSOMDhQwlQQsnPnzkHBLbeat7J48WKvrW/fvomVSkMriQKoXJo2beq11axZM7GycYMGDYICtDVq1NjkY7N69eqgtoULFyZWJ+/YsWPQTTI6dOiwyar3mxOgdUPpS5YsCarAXmi23XbboPC02/9UuFn1jzlz5uR1oxMVulZV593q8e5+5rpZgHquadOmJf5tVEBcBepdnTp18trUvrqh+9LAFQ0AAAAAqWOiAQAAACB1TDQAAAAApI6MBlDC1PpeVWBKFfhZv3594rrjyZMnB62ldl9TvR6AwvTEE09kPd5vv/28bbp16+a1LV26NHH9e69evYLW6Sv77rtv1uNLL700qKDjiBEjvDY337HLLrt42zRp0sRru/vuuxPX5auswCWXXBIVOlU4cf78+XkVmb3wwgvzOr6qXILKP6oshLv/qvifen6Vvxg8eHDiZ0D9XEjmRJ0bqEzLmjVrotLGFQ0AAAAAqWOiAQAAACB1TDQAAAAApI6JBgAAAIDUEQYHSpgqDKSKXKkQmFv0RwUVP/nkE6/tpZde8trcglJuwalcAbmyKPADoHT1798/6/E999zjbfPuu+96bTvttFNieHXZsmXeNqpgqCrsV79+/azHc+fODQqkq6Btq1atErdRN8k45phjEgulqiKsRxxxRFToWrRokViEMdfNB0KoY2lIsFxp06ZNVB5VC/h99t9//6CCfe3bt49KG1c0AAAAAKSOiQYAAACA1DHRAAAAAJC6rYrUomzH119/Ha/drlKlSvp7gArJCuLY2v2dd965xF+rovc/tRZ5+fLlQWtN3TXEahv1/Kooj5vJCC0yVOj9rzL0QaSrEPqfKgC2bt06r00VMMtnmy352dAcmVvcLXS/1BjrPtd2223nbVOvXr2o0I/BqticysG4p6KhBR3x/6xYsSJyqf6tclDqvCLN/hd0VkEYFKpPlFa/qOj9T33YVeXuNJ+/sg/Spdn/Mq8HFFL/q127dlAbykZFOQarCZhqw5apW7duVF77X9AVDQAAAADYHGQ0AAAAAKSOiQYAAACA1DHRAAAAAJA6JhoAAABACSgq8Cg0E40U7bffftGVV15Z1ruBCuLFF1+MOnfuHM2ZM6esdwVIjY2BNhaW9M+gcuC4ifImZDwKPX4/99xz0d/+9reokDHRAAAAAALts88+0f/+97+ocePGm9zu/vvvlzUuCknFqM4FAAAAlAP169eP/4cCuKIxduzY6JRTTol22WWXaKeddopOPfXUaNSoURsvf9njF154IRo4cGDUvXv36PDDD4+GDh2a9Rzz5s2LLr300mi33XaLevXqFT/fuHHjsraxy2OXX3551L9//6hbt27RHnvsET9WFZ4znn/++ahLly7Rfffdt7Ft0qRJ0TnnnBNXU7T//e53v4tmz5698d+//PLL+HLcM888E+27777xNp999lmKfzGUBass+89//jP+FsT62Pnnnx+tXLly47//4x//iA488MDo3nvvjfuh9bPMv9ul10MOOSTuv/bztm3xqrTLli2LLrvssmjPPfeMevToEffxl19+Oeu177zzzvhSsD2H/ff222+XFVtROWxqXMz0qSOPPDLq3bt31LNnz7jPvPXWW1nLAnbcccdo9OjR0bHHHhv3KxuPHn744azXsT561VVXxX22T58+0W233eZVUba++uCDD0aHHnpo/Fr2mscdd1w0bNiwUvhLoCKwseimm26K+9Cuu+4aXXHFFfG4FtpfzTfffBOdcMIJ8TY2Tj722GNxv2dZFvIZIzPjoJ072vh32GGHRR9//HHOpVPWz+z5rrvuuvi87eCDD44GDBgQzZ07N3rppZcKepl0hb6i8f3330dnnnlmtPvuu8cnX1YS3S5TnXHGGdFHH320sTMtWrQouuiii6KaNWtGd999d3ThhRfGk406derEg5kd9KpVqxZdc8018X9tgLIByyYKHTp0iNatWxedfPLJUb169eJOZFWXbVCzk8KqVatGN9xwg7dvb775Zvx8dkJpkwkzffr0+LXat28fr9n76aef4v09/vjjo1deeSWrWrQ995/+9Kdo/fr18YcAFZudgD3++OPReeedF0807CBpJ/vuhNcGMpsU2KVW658PPPBA/PjEE0+MT+jGjx8f9/X58+dHN998c/xzf/jDH6KlS5dGf/7zn+M+bn3JDtRNmzaNPxv//ve/o6effjpua9WqVXzyaM9p1Vntc4HKJWlcfPXVV+OTOhsH7SBrkwXrI7///e/jscb6jbEJw//93//FB2D7r42Ht956a7TDDjtEe+21V/zv9jp2ILW+ZZVpH3rooWjMmDFZywn+/ve/x/3PJsN2sF24cGH85cvFF18c74+NuShsNh7auHjLLbfEx2TrM1OmTImeffbZ+Eu3pP46derUuJ/aFyl33HFH/AWg/XfVqlXxlzTA5p472jHWviCxcap69erxMdOOlx988EHWuVpxX331VbT99tvH49vatWujZs2aRWeffXb8pY2dCyYts6qsKvREwwYiG1BsEmAzSGMn8bZubs2aNfHj1atXxzPP1q1bx4+tw9hJm32bZjNVm1TYSZ0dCFu0aBFvs/fee8ezUZuU3HPPPdGMGTPiwcwmB3aiZqyD2gnb8OHDvf368MMP46sd1sGKn8jZ5MEOqo8++mh8QmjsysgBBxwQH6DtYJ3x29/+Nho0aFCJ/v1QOuxg98QTT0SnnXZadMEFF8RtdqJmE+BPPvlk43Y28bQ+YN/oZfquXQWxb5Rt0mnsSoed0Nlje75OnTrFfdAms9aPjH27bNtUqVIlfmz/bgfgo446auO/Wz+0CTMqn6Rx0a6g2gHVDnwZNvbZN8YjR47ceGJmd0qxbY455pj4sZ3kvffee/GB2PqvfVnz7bffxid9NmZmxjM3RGn9/JJLLolOOumkjW12MLYTx4kTJ8bfQKOw2Zd4drXMjs+ZxzamWR8L6a/2hYyNZ3YczUxcrc/bF3tAPueO9kWKTRjsy+bMmJW56rH//vvL57VjuH3xnPmyxlSpUiVeYlXI41yFnmjYSZa9geeee258Um4HP1s+Yt/wZti/ZyYZJtMB7CqF+eKLL6KuXbtGTZo0iTuJ2XrrreMDp33zZ+zfn3rqqbjj2aRj5syZcUedNm3axp/J+O677+KrGTZztZlwcTa5sZM8uwqS+TmbcNiJ5eeff561rb0mKgcbmGxpgC09KW7w4MFZEw33fberZnZFy07civezzImcLamzz0Dfvn3jb2VsuZ99BuxybfFJq/27XT2xyav9rC0rsMk2KqekcTGzlMQmwDaG2XhmSzaNfbNXXPGrqZkDpn1Tl/n2zq6K2fNn2Imi9b8RI0ZsbMtcubNvqjOvZ1/GqNdDYbI+k5lkGBuntt1227gfhfRXO7baMbv41THru5kvD4HNPXe0yW5mkmFatmy58QvAXOwLvuKTDFSCiUaNGjWi//73v/ElL7v0arNRO4m39ZuZb4Ddy/JbbbVV/N/MOmK7mmEDl+UuFJuQ2HP85z//if71r3/F2zds2DD+htja3U5nGQw7kbNv/Wzfin+LZz9rkxD7n8sNFRUfdFGxZbIWNnAV16hRI9mnMzJ3qrArY4p9U2zskq71TfsMvPPOO/FEuV+/fvE3K3agtUvE9ryWVbIlCbaMywZa+4zYlTlULknj4oIFC6Jrr702/pLFJgr2TZ5lydT93u3nirO+ldnG+rUdWDNjaq5+bUupbFmf/dfGzI4dO0bNmzeXr4fC5PYZ62c2XtrkYtasWYn91SaxajmLHauBfM4d3XMw99wx1/Oikk00jA06duJkgUO7jG/r020ZVPGrGJtil1vtKoMtdVLsW7zXXnstXjtqs127XJuZFNgVCzt4FmczY7uMa0sFbI2oLWexdXqZ17ITQFvy4rJvb1A5ZSYYlqOw/pqRdMu72rVrx/+1yUHbtm1zHkStX1nftP/ZN35DhgyJl1zZyZ2tMbWDtmWO7H+2D5YDsYmJLV2xqyKZJVaoPHKNi/atnC0ltRM2y1zYFTQbe+wKrW2zuf3alh/Ya2yzzTayX2fWQls244033oj3y/qj9UGbFANqLLQ+ZX3L+ph90ZLUX+1b5CVLlnjP6465QFrnjiiQu069/fbb8Teyixcvjg90dqn0+uuvj0/QLFgbwiYZFtJu165dfGeBzP+s09nAZs9r60DtOe2AmZlk2Do+a3dnt5mTPwvu2s/a/hR/LRsgbbDMvI5dGbHMhq19RuVk/dK+LbH+Wlxm+UguFo60A6yFZ4v3TTvQ2iTW7mBhQVxbdpB5bhs8zzrrrHhCm/kM2DplC1Ma+9bPJss26bBvC+1EEJVL0rho493RRx+9sS+ZzJ34NvVtncvyGLak7/3339/YZktZit8lzya+dhJpa6HtSoZNMvJ9PVRe1meKLw+1Sag9tmNlSH+1u1XZMtQNGzZsfA5bSlqod/lByZ87htr6/x/zClmF/hrdQjw20FhozL71sMtWdhnMljMddNBBWbf4zMXCPTapsP+efvrp8TcotrTJ7nZhkwVjt9Ozma5d1bB19rZkxYJr9g2K3RlIsYyGXdWw5Suvv/56fGtHC7PZSZ/d3tbuNGXhIrtkZwdqC52jcrJ+ae/9XXfdFS8dsQHOvtFNmmhYX7TJrd2UwCYElrWwSYc9tsu4tnzArmbYt3k2kbBt7NsYu9OaPb/1s8xB+JFHHoknwTag2nPYUkCb+HIf8MpnU+OirUe2kzRbNmD9xg6sdoJmd0Qrnl0LnWjYzQlsqYF9c2zL9Ox5ii9jsS9wLIdmV9DsJNH+ZyeR9iXO5r4eKi874bMrrLbU2HKQ9kWKrZm3HJtd0U3qr7bW3o7bNl7acdy+RLFx0k7y3KV9QBrnjqFq164dT3rtpix2LukuRy0EFXqqZSfzdpcJO9m6+uqr4xMrC2NbMDZ07bmFwO32eXaQtBmtDVh2Ge0vf/lLPPkwRxxxRNwhrSPat8U2KbAAt00i7Ns6u7WeYpMJ61j2XHYZ2E4MbcC0gc+WatkdqWyAtTsbWOdG5WV9849//GP8TYrd4tbutlM8sJ2L3VbUwpB2xcv6nl3qtbv/PPnkkxvvGmV3M7Mle3ZgtYOsTYrt7laZ2yrbEj/r15bRsAOxTZjtBJHJbeWUNC7asjob96xfWf+yu+fZWmW7GmYB781hfc/uL299yZ7LTgZ/85vfbPx32wd7PVtLb/3Qxj37xtD6rx3cN/f1UDnZjSpscmpjlo1jv/rVr+K+ZcfKkP7apk2b+Ms/u6Jhx1XLrdl4adkP1s2jJM4dQ51++unxl9J25zT7ErAQbVVEGg8AAFRQmaB45tbgxq5q2BJSm9za0j0AZaNCL50CAACFzb6Ntqtql156aXwHSVtpYMtD7RtrW7YMoOww0QAAABWWLU+xGxHYslGr6Gy3JrUM2l//+ldyaEAZY+kUAAAAgNRV6DA4AAAAgPKJiQYAAACA1DHRAAAAAJA6JhoAAAAAyuauU998801ccMnuUw2YH3/8MS6mZJWmSxr9D2XZ/wx9EMXR/1DWOAajovS/oImGdTBuToXiSrM/lEb/+/nnn7Me//LLL/KD5frpp58S2+y2i661a9d6bVtv7V9g3H777bMeb7ut/5Fds2aN16b+Xttss03WY3XQqFatmtdmt4p0ub/T+vXrvW2qVKnitdWuXTtKQ2mPRxV9DHT7t1m9erXXpvqX20+sEFrSNqH7UadOnaB9KG/ofyhrle0YjIplc/pD0IieOYj06NEj/71CpTJmzJhSe63S6H8LFizIerx8+XJvm9mzZ3ttds9217Rp07Iejx071tvmrbfe8tqqVq3qtfXv3z/rcbNmzbxtnnvuuaATy8aNG2c97tq1q7fNgAEDvLbDDjvMaxs3bpxXmdelnv/CCy+MKlr/K6sxUL2H7mQx1Jw5c7y2t99+22tr2rSp19a5c+esx48//ri3TZcuXbw2NcFevHhx1uNLLrnE26Z9+/ZReVcI/Q/lW2U7BqPy9j8yGgAAAABSx0QDAAAAQOrK/2JYoBTMmjUr6/GVV17pbbNu3brEDIWZOHFi1uM99tjD22bw4MFe24cffui1derUKetx8+bNvW0GDhyYuExKLfP66quvvG2mTJnitT3zzDNeW8uWLbMez5w5M+hvg/ClUmqZlFpO9d///jcxT6TyGN9//73X9vzzz3ttHTp0yHr82GOPedu0bdvWa2vUqJHXtv/++yc+l9rXjh07em3nn39+VJrLzwAAm48rGgAAAABSx0QDAAAAQOqYaAAAAABIHRkNIIqi3XbbLevxXXfd5W1z/fXXB9WKcNeFW7GjkFvZHnPMMYm5jT59+njbfPLJJ17bzjvv7LXVqlUrMcdxwAEHeG2ff/6519avX7+sx7vuuqu3zU033eS1ITdVR8X1wAMPBNWdqFmzZmL9kh133NFr69WrV+ItadXtbevVq+e11a9fP7Hmi5uNylWbRtV3GT58+CY/w6F/UwBAyWEUBgAAAJA6JhoAAAAAUsdEAwAAAEDqmGgAAAAASB1hcCCKol9++SXrcc+ePb1t/vKXv3htBx98sNfWtGnTxNdbu3ZtUAi2Xbt2WY/Hjx/vbaOCvt27d/fa3n777U3+zmbkyJFeW7Nmzby2YcOGZT1+8803vW2webbaaqvEYnM//PBD0A0J3CB2UVGRt83UqVO9tjp16nhtLVq0SLyJwIQJE7y2FStWJBYSVIFxVfxR+fTTTxPD4O7fFABQuriiAQAAACB1TDQAAAAApI6JBgAAAIDUMdEAAAAAkDrC4ECgrl27em2//vWvE4PSdevW9bY55JBDvLYmTZp4bTvssENiRfG77747qDL4+vXrE8PGPXr0SKzmbC6//PIoiQqbU6k53Jw5c7y26tWre21z585NDF03atTI20YFsVu1auW1TZo0KTGQrvarefPmiUFvFVJ3w+dm5cqVXlvr1q03WcE81+8NAPlSY1bodluX4PFPHW9Dufs1a9asxBtr2M1rtt02bArBUR8AAABA6phoAAAAAEgdEw0AAAAAqWOiAQAAACB1hMGBLdCnTx+vbcqUKVmPL7jgAm+b+++/32urWbOm1+aGv7/99ltvm4YNGwYFuHffffesxx9++KG3zcKFC6MQIaE2FTYnDB5u3rx5QX1EVQafP39+1uM999zT22b48OFe27JlyxIrz7dv397bZsyYMXk9l7oBggoiLliwwGtzQ+nr1q3ztkHhUmPs448/nngzjyuuuCIqa998843X9swzz2Q9Pvjgg+UNHVCy3FD05mxX5ATELVCdb6jbfX51HAjl7pfqf4cffnjW4xUrVgQ/P0d9AAAAAKljogEAAAAgdUw0AAAAAKSOjMZmeuCBB7y2d955x2vr3Lmz17bjjjtmPR4wYEBiESqUjnyzAxMnTvTaVq9enfV4yJAhQT932GGHJRbZUxmKBg0aeG2jR49OzGh88skn3jbHH398UC7ALQi33Xbbedtss802XhvCLV26NOhvqrIwbs5BFdnbfvvtg4rljR07NrGQoCokqcYyt9+rPEbo79i4cePEgn2MpxWfu348dI28ymgsWbIk6/GDDz6YOH6bLl26JObz1DFfFW1z17obN2sxatQob5trr7028fOLcOq9UX3LfS/cAqa5jpEdOnTw2jo7fUQdN9P06aefBmXnVq1alfV48ODBqe4HVzQAAAAApI6JBgAAAIDUMdEAAAAAkDomGgAAAABSRxh8M6lCJiNHjvTaxo8f77W9/fbbWY8vu+wyb5t99tnHa9t1110T90uFMdeuXeu1LV++3Gs788wzsx536tQp8fWQO3TdtGnTxPdUFddRwa3zzz8/r6CsKkQ1ffr0rMdnnXWWt416fhVYc1+zpENthUiFDgcNGuS1zZw5M/Fz3q5dO2+bL774wmubO3duYtBRBazHjRuX2N9UgLVVq1ZBxflWrlyZuJ0aA1EY7rnnHq9N3TjDHZvdm1qYZ5991mtTxSDdn1V9tG3btnn1b/U5P/LIIxOLZCL9MLh7g4xhw4YFjZvqvPDcc8/NelynTp2gfVU3unCLsqptPvjgA6+tWbNmicd9dYOZLcEVDQAAAACpY6IBAAAAIHVMNAAAAACkjokGAAAAgNQRBt9MqsJut27dvDYVTHSDRvXq1fO2mTdvntd23333eW1uiEiFcVXoR+3/n//856zHTz75ZFRofvnll7wqhasgtluBW90YYNq0aV7bhx9+mPg+q+qzqpKt6lsrVqzIejx58uTEsKSZPXt2YvhNVUFVf5t8K7AXIvWZ/u6777y277//PrFPqHEltDL41KlTsx7PmDEj6LlUX3Urg7vPnYsatzp27JgY2EXFHodDx4wrrrgiaCzbsGFD4nOrALfifu7UeKduyKL2q1q1apusYI6y4waj69atGxQsVzfV+c1vfpP1+Ntvv/W2qVWrVlA/cm8gcMkll3jb7L333kE3Sdh225KdCnDUBwAAAJA6JhoAAAAAUsdEAwAAAEDqmGgAAAAASF1BhMFVUEdRVSFdixYt8tpq167ttdWvXz8xjBtafbFhw4ZemxtiU2HJZcuWeW3qNd1Q2+mnn+5t88gjjwTta6Fp1KhRYkXnCRMmJFZjz1XZs0aNGolVSc855xyv7Y033vDaPv7446zHxx13nLfN+vXrvTZVPffNN9/MenzhhRd622Dz/PTTT4kh/GOOOSavCsgq5K2oqsVuFfsddtjB20bd8GDKlCmJFWh33nlnb5sRI0Z4bSpo6+5rSQcaUfJCgt/XX3990M0vVNVl9/jduHFjbxsVxHY/m6E3b1i+fHni58msWrUq63H37t0TXw+lwz3+qfNJdTOM6tWre219+/ZNPDdQ/cMdN9Xzqz6jzgtVX1b7n+95tMIVDQAAAACpY6IBAAAAIHVMNAAAAACkriAWtYZkL3Jxi1Opte+HHnpoYmEqldFQhXtUwSK11s/NX6gCgSFrVFWxoNAiWpVJvoXk1Jp2d935rrvu6m3z6quvem1qHf1ee+2V9fibb75JzF6YX//6117bGWeckfX42muvDfo7NGnSJCiXFLJmGeHjlPobjxs3LjHHoz7TajxSa4Hbt2/vtY0ZMybr8fTp04MyFKpglZtfUjkORRXJcrNrqmCfWo9MlqN8UGu+1bF6zZo1mywwmys3pPqDe5xUa9NVJk2Ni2rdfMjnQo2L7nZdu3ZNfG6UDvd9VmOKOm9TeceaNWsmvp7qayF9UvVlldFQzxWS4SOjAQAAAKBcYaIBAAAAIHVMNAAAAACkjokGAAAAgNQVRCpOBXVCw7933nln1uNTTjklKCSjgthuGDw0KKaKEblhT1Uwa+bMmUG/txvafPDBB71toEPyquCTG8D69ttvvW322Wcfr23kyJGJ4UX1ng4aNMhru//++722/fffPzEMrAqouf021w0KsGXcoLcK8u22225e25w5cxIDz23btg3qb5MnT04MQ6rAuAqpqzHJHYtVYFyFeFXg3S1oqG6IocZ+lD51rAsJU5sDDjgg8SYq6ripCta6x2V17Fb7qrZz91+FbBU17roheFXoD2Vj3rx5iWNRaBi8Vq1aicFy1dfUdm6RRxU0d4sx5xpfmzVr5rWF7FcormgAAAAASB0TDQAAAACpY6IBAAAAIHVMNAAAAACkrsKHwUOCM6EVim+66SavbdasWZsM85jVq1cnBojMypUrEysuq1COCoYtWbIkMQipqumqgJIbGAoJBlU2bphLhebHjh2b+HPmnHPOyXp81llnedu88MILXlujRo0S+1aDBg2CQpVu8FtVm73yyiu9bVTQd/HixV6b6m/YMm5IVI01Kqz99ddfe21uYFEFs90bSpg2bdp4bd99913W42nTpgWFFXv27JlYZVyFyFXfUtW83fFTbaOC8qqPI10bNmxIrFqsHHPMMV6bW4nerQhv1q5dG9S/3c9YaIBbVSx3jxFuBfNcfVK1uSF19XlFuueJoTcEmjJlSmJfUAF/dV7Y1TkGq7HIPefM9ZrHHXdc4k0MVP9WY7W6eVGauKIBAAAAIHVMNAAAAACkjokGAAAAgNQx0QAAAABQtmFwN1CjAir5PI+iQrbq51SwKiT8/fbbbyeGHlVF3YULFwbtg6peWrt27cSA3IIFC7w29ZpueEcFuFWoUgWGZsyYkfV46NCh3jaqsnChUUH61q1be23vvPNO1uN+/fp523z55ZdBz9+iRYusx59//rm3TefOnYPe+/nz528yTJbr59Rn0Q2ZqX0nML553GC0qkCrKmmrPrho0aKsx/Xq1QsKHc6dOzfxphLt2rULGjtVcN0NTbZq1SrxRhe5+tLUqVM3+VlR1XMru9BK6OrY7f6sCo2GVLUODX/vu+++Qf3IvQGG2i9F7Wu+YfCQcxa1jeq36iYP7g031OcQ6d7cRVFjlhvyV88VGjZf7tzY5+abb/a2mT17dlCfcT9j6ufUDWbUTRJCx418cUUDAAAAQOqYaAAAAABIHRMNAAAAAOWrYJ+7rkutUwspdKOoImSh3PXJDz74YNAa+Z122ilxzahaH6p+b7cInqIKuqxbt85rU/kLN4ei1sTWrFkzaM2om9GYNGlSVGhC+uTIkSOD3sMzzzwz6/Ef//jHoP6t2tz3efDgwd42Krdx1VVXeW2PPvpoYvanR48eQX3ZXctKRmPLuTkKVShx+PDhQRkNt4DZiBEjgsYHVQzNfc0VK1ZEIXbYYYfEopejRo3ytqlfv35QDs7Nm4UUvKxs3L/Blhw33Z/dkudy80Z77bWXt406Zqkitm7xv9Bchdp/lVEMOZ6HZDTUflWvXt1rU0VX3WOwyk8h3WJ8bmbRzJw5M3HsUcc61ZdV/1vnnN+5xShzZfPcYs8qO6L6lToGq3NyNxfXsmXLoJ8LxRUNAAAAAKljogEAAAAgdUw0AAAAAKSOiQYAAACA8lWwL7QISj5++OEHr+3TTz8NCnWPGTMmMfSoAodff/2119a4cePEIJAbVssVAnPD8yr4rcI7qs3dfxU8Wrp0aVARI3dfX331VW+bm266KSokKtingluq6JQbvFVhfjesqwo6qv6mwsAquKUCu25f7tq1a9DP9e7dO7HwkPo5t9AbNm3evHmJhZXUWPCrX/3Ka3NvWrHjjjt626jAqQrL7rbbbokFIj/++OPEsKLqqx07dgwqWqrGt2HDhiWG59XYX5lsSWA7H+p9vu+++7y25557LvGGBap/qz4TEuBWNwtQ4VX3HCa0MHBIgUO1D+r5VbDXPQarv417PLfnLslzsHyEhOa3hHofQkLKakxRx3P1HrpjaejfXPXb7Zybu6hCquq8UAXE3Rt3qJu2qPNV1bfcGyilrXz1UgAAAACVAhMNAAAAAKljogEAAAAgdUw0AAAAAJRtGDwpBKPC2m7FQRV6NFOnTk0M9q1atSpoP93qoiqsrcJBKjjjhgnVc6kAlAqzq6riSa+nQry5nj8k+K1CyG5Qb+jQod42btDIfuctqRRZEangas+ePRNDtipMfeedd3ptl1xySeLnQu2DCvrOmTPHa+vXr1/W45NOOsnb5oILLgi6qYAbKFPBurZt23ptyM0Njqr3Wo0PbvDWXHrppVmPd999d28bVUm2e/fuiePuiy++GDQGqgCw25cefvjhxMCkmTJlitd2+OGHZz0++OCDg27wUZm478UDDzyQeOOGXO+Xe6MBFVpWgVMVXnWrwqtzB3UMU/vlBt7VcVTdvEOFcd3fUYW11XFN3YTBPRdQf4dWrVoFfe4mTZqU+Pdy//blMQxeFucE6n12K3yrsLPaVxUGd8cj1f/U+6Ce62dnX1V/V1XGVT91z+XUuV3IzX9yjRFpKl+9FAAAAEClwEQDAAAAQOqYaAAAAABIHRMNAAAAAGUbBnfdcccdWY+/+eYbb5s6deoEBVvcwFfTpk2DKhqq4Iwb6lZBFxWIUVVW586dmxj6Ca1W6QaBVHVyFd5RIfiQUJsKt6sKzo0aNUoMn7thTPtd1L5WFqpq8sKFC722Z599NrGKp6rq2bdvX69NVQnt0KFD1uMhQ4Z424wZM8Zr69Kli9e2evXqxIrSqpr32LFjE/ufCuuqqunIzQ1Kq7+7+sypm3C4FbhVwPqrr77y2r788kuvrVOnTlmPJ0+eHBRSVyFN93igArSqD6rf+5VXXsl6/N5773nb/OY3v4kqCwuOupWz//Of/yQGjWfPnh0Ung65WYkb8s71mu5YM27cOG8bdSxVxzG3H4WGcVXfcn9WHfPVsVsFbd33Qv2cqnTuBpXV30sd89u0aZO4TXmkxgH1d3f7pApKqxsJqZsWuOOdGp/Ue6o+F+5+qHNOta+qL2/rBMTVz6l9UOdt7t9VjfGhN1xQNzlKE1c0AAAAAKSOiQYAAACA1DHRAAAAAFB2GQ1b2+iu9fziiy8SMwFqjaLKbbjry9S6PlX8T62Xq127duL6TbWvaj1b+/btE7dR+6XWvLlr70KfS61JddfIq23UumbVVqtWrSiJWwDOch2VOaOh1veqDIUqhOautW/WrJm3zccffxxUeG3PPfdMLBzVrVs3r02t13SLC6rPgFrnfsIJJ3htH3zwQeKaW2we9zN89913e9sceuihQYWh3JyD6g8qI6bWO3ft2jVxbFN9KaQYlVrfrzJNKh8VsuZarZOuqGwdv5vJcI+TKiep/i4qw+Vmtm6//fagzMszzzwTlBvLN9uojvEu1SdVcUG3P4SOW2rdvNtP1T6oTIE67rvH0opS7NTNv6r8lvp9Q7K76pxG5bdUYVA386LOq9SYpc473fNHNaao84Vly5blNR79KPqaGuPdNvU5adiwYdB+7bffflFJ4ooGAAAAgNQx0QAAAACQOiYaAAAAAFLHRAMAAABA2YXBLTTVpEmTrLbDDjss6/GIESO8n5s2bZrXtmDBgsRgiwovqiCs4oZdVMBHhWtUaM4Ntan9UqFHFURzA18qqLPjjjt6baqAnvu3UMUM1e+o/obu36d3796JIdSQsF9FNnXqVK9N/Y07duzotX3yySd5FVNUofHXX389MSimCnKpsN2kSZMSQ3qq8J57cwVVLGrYsGHeNqeffrrXhtwuu+yyTT7OxS3kZXbbbbfEG2KooqXTp09PfK9VYFcFN1VA0u336jOlxmsVtHWLuj711FNRZWZ/g+HDh2e1zZ8/PzFErI5rasxwC0Sq4O2ZZ54Z5UMdi1Q/UuObe5xU/VbtqzpWu/uhnkv1ZTWGu8dSNZ6qsLkaT90biIwfPz7oc1HWJkyYkHjeo85z1Pvs3nhHvaeKei73HFC9D+qmE+rmRW6xPFUAWvUZdbON2s57r84dQ4oZqr+1+py7RU1zFWXdY489oiRbcmMNrmgAAAAASB0TDQAAAACpY6IBAAAAIHVMNAAAAACUXRjcgltumPmkk07Kejx48GDv51Sl0rfeestrmzFjRmJgXIULVTVEN6SlgrfquRT3udq1a+dt06lTJ6+te/fuidVYe/Xq5W3z2muveW0qZO9W81bVHlWASIWP3OqlLVq0iAqdCoqpkJaq8P3tt99mPW7evLm3jVVWDwkJumFZN/xpTjvtNK/t8ssv99r+/Oc/J34ujj/+eK/tySef9NpatmyZ9fijjz7ytkH6hgwZEhSUbtWqVdbjmTNnBoVlVTjWDaGqMcQ9NuRqcyv0qptTqH6p9uv999+PComNGY8//nhW2x/+8Iesx59++qn3cyqg6x4/zOjRo7Me16xZ09tGHbNUCDWkL6gbFKgx1g2Nq9C1Op6rQLUbMHZvKKC2yfVZCTmHUJ8VVaXa3U7dGMS9KYOFc9XfsDQtWrQo8WYE6v1S5yvu+6zC1G7F71x9xn1v1Bi5cuXKxOC3em/U+ZF6nxcuXOi1zXTGYXV+rG7IofbLDaCr/qg+w2q/zjvvvE3ehCbXDR1CcUUDAAAAQOqYaAAAAABIHRMNAAAAAKljogEAAAAgdZuV7nADL254R1WAPPDAA4Pa3OCyqsysqoyrkIwb8lEVNRs0aOC1qZDPDjvskBgeS5OqNP35558nBoFUuC80tOkGn3fdddeo0H311VdeW7169bw2FYK+6qqrEvvy3LlzgypvujdYUJ8Bt6KvOe6446IkkydPDqpMr2524IYA1edJhXpV+Bd6nFIhxzfeeCPoMx1y0wxV4VaFrt0bEqhwpxpj1X6545baRoWX1f6747X6TFX2G1vcdtttidu88847Xtstt9ySWOVZhaJVNW/VT902FRhXAWs1Brp9RlUx3nfffb22/fffP7H6tBumNw8++KDXpoLJSedDuX4f9fdyQ85qm7IOg9t51a233rrJG/vstNNO3s/16dPHa1OhcXc8Ujc+Cb3phNtn1Pugfk4dx9zg+ptvvultM2bMmKDj60IniK2Oh3Xq1PHaVH8IocZlFepWv3eauKIBAAAAIHVMNAAAAACkjokGAAAAgLLNaKg1iGlx12u6xe1ytVU2Xbt29dr++te/lsm+FDJ3jWeuYo1qjeUDDzyQ9bhNmzaJ695zrQNW6+FD1le6xbfUmte+ffsG5YGee+65xMKSd911l7fNmjVrvDYyGrmFrMNdvHhxUHFJN+eg1iOr9ydkjA8pkJVrO7dwlno99dlT2RF33b/KL1W2jEZIjsc1cODAoDaXypZ9/fXXiQVKVT5LZWzc/GOuDORBBx0UlWbGRY2nKq8Sst5eFc1Vz+9up3KS7nHEzWyUNMukuLmU7777LuvxrFmzggprugVf1d9PjWvq76mOkSH5GfVcqoif+z67BY5z5Y1UXuXcc89N/AxceeWVXpv6nLuvqfI6aixVn0X1t0gTVzQAAAAApI6JBgAAAIDUMdEAAAAAkDomGgAAAADKNgwOVFZuQZ+aNWt624SG0w444IDEYj4qPKsK9QwZMiRxvxo3bhy0r26o7e23304saGXOPvvsoCJgKHkqrKiKdbqhYVWwyg1m57pJgQoUhjyXCjCGPFcoN+A5bNgwb5u99torqkySwt+qQJcKiYaE/jt06BDUdswxx0RlTfUr9XuHFLhTYVz8v0D8FVdcsckitu5NQsyMGTO8tm+++Sbx9VRoWY0zIX1ZFY9VhevU58stYquOhyeeeKLX1qRJkygfRx11VNBNgtwij+p3VOO52s4tvJg2rmgAAAAASB0TDQAAAACpY6IBAAAAIHVMNAAAAACkjjA4EEXR0qVLsx4PHz7c2+bggw8OCla5odRmzZoF/ZwK+rphrunTpwdVRlVVn/fZZ5+sx1999VVQGG7SpEle27HHHpsY+Pvyyy+D/oYID7iqPqKqZrvhb/Vcqg+qmw24/UuFNFUgU7W5YWW1jVvVN1eb+3ur/lxoQiqFV0Yq2IuS5wajVVDaDS3nOr66x5mZM2d623z//fdemxqP1q5dm/W4d+/eQTc+ad26tdfWuXPnqKRsEOH2pk2bBlUe79atW9bjOXPmJAbZcz3X3nvvHZUkrmgAAAAASB0TDQAAAACpY6IBAAAAIHVMNAAAAACkjgQV8P9XPS2uS5cuQeHmFStWeG0tW7bMevyf//zH26Zfv35B+zV//vysxz179vS2UUFsFRB3K4EfccQR3jYqUKuqi7oV0du2bettU6jB1LSo4L8KcDdq1CgxDFmlSpWgELmqMu6+17Vr1/a2mTdvntemAuhuJWEV4nW3yRUCdT+z7n4CKHtqTNl///2D2iq77UWQ3T3mVwZc0QAAAACQOiYaAAAAAFLHRAMAAABA6shoAFEUjR07NuvxddddF7Rm/qGHHkp8blXwRxXUW7dundfmFi065JBDvG0GDBjgtU2YMMFr22WXXRIzJ2pdvcpauOtIVXbg6aef9toGDhzotUF75513ggrXuXkMs3jx4qzHW2+9dVAhLdW2bNmyrMc1atTwthk/fnxQ1sJdkzxlypSgPIbKCbn9Uq1tXrBgQVBBLABAyeCKBgAAAIDUMdEAAAAAkDomGgAAAABSx0QDAAAAQOoIgwNRFPXq1SsxKL1q1SqvrU+fPokh1TfeeMPb5pdffgkqoFanTp2sx+PGjfO2adeundfWpk2bxLD5yJEjvW1UEb9p06Z5bXvttVfW4yVLlnjbDB482GtDuF//+tdeW4cOHYJ+1u2rKmCtgt+qX7pB8tatW3vbHHbYYUFFI92bBrj921StWtVrU4X93CKEc+fODSpmCAAoPVzRAAAAAJA6JhoAAAAAUsdEAwAAAEDqtipSFaAcX3/9dVwoyl0Ti8Jl66+32mqraOeddy7x1yqL/rdhwwavzX7fkAJ3ruXLlwe9pnp+t039DVS248cff0x8PVVkTz2X+h1r1qyZ9fjnn38O+n3Seg9Ls/+VVR9Uf1OVe1DcrIXKXqi2ECovEfI5MO7hRhUSVP1GcbdT+1C3bt2oJBRC/0P5VtmPwag8/S8oDB468KNwWJ8orX5RFv3PrWCcS8jAq07eS5qqpOyqXbt2aq+nTj4rS//LvF5pUxXZQ95XlLxC6H8o3yr7MRiVp/8FXdEAAAAAgM1BRgMAAABA6phoAAAAAEgdEw0AAAAAqWOiAQAAACB1TDQqOLL8AACUDY7BKEtFFaD/FcRE48UXX4w6d+4czZkzR/67tdu/23Zb4h//+Ef8PKVl5MiR0dlnn11qr4fS7ZdASaL/obRwDEZZov+VrYKYaFRWzz33XDR16tSy3g0AAAoOx2CUpecqSP9jogEAAACgsCYa69evj26//fbooIMOirp37x6XOj/ttNOi8ePHb9zmyiuvjE499dTohRdeiAYOHBhvd/jhh0dDhw7N+byrVq2Kt9lvv/2iefPmyW2s/dJLL4122223qFevXtEpp5wSjRs3Lmi/33///XhfevToER1zzDHRF198kfXvixYtiq666qpowIABUc+ePaOjjz46GjJkSNY2GzZsiO67775o0KBB8fPY3+DBBx+Mfvnll42/90svvRTNnTs3lUt+KFn2vv3zn/+M9tlnn7g/nX/++dHKlSuzLrkeeOCB0b333hv3uf79+2/8d/vW4pBDDon7tv28bfvzzz9v/Nlly5ZFl112WbTnnnvGfcX69ssvv5z12nfeeWfc3+057L/2ufrxxx9L+a+AskL/Qz44BnMMLkv0v/sqRf/bNirHLr/88uirr76K3+zWrVtHM2fOjO6+++74oPbGG29sLH8+duzY+I276KKLopo1a8bbXHjhhXFHq1OnTtZzrlmzJjrrrLPijvbEE09EzZs399bt2YHzuOOOi6pVqxZdc8018X8fe+yx6IQTToief/75qEOHDpvc76uvvjrelxYtWkSPPvpo/HpPP/103FmWLFkSd6rtt98+uuSSS6J69erFHeR3v/tddOutt0aHHXZYHO4599xzo1GjRkUXXHBB1KVLl+jLL7+M7rrrrmj27NnRjTfeGJ8o2H5ax7eTA/v7oPy67bbboscffzw677zz4kHrrbfeigdQd2D7+OOP45OyFStWxH33gQceiB+feOKJ8cBkA6yd6M2fPz+6+eab45/7wx/+EC1dujT685//HPf/V155Jbriiiuipk2bRrvvvnv073//O+5/1taqVato9OjR8XNut912cT9F5Uf/Qz44BnMMLkv0v1GVo/8VlVMbNmwoOv3004veeOONrPZHHnmkaIcddihatGhR/PiKK66IH8+cOXPjNsOHD4/b3n777fjxCy+8ED+eMmVK0UknnVQ0YMCAolmzZm3cfvbs2fG/23bmjjvuKOrRo0fRnDlzsvZn//33L7rwwgtz7vM999wTP89bb721sW39+vVFe+6558afu/XWW4u6deuW9dzmlFNOibf7+eefiz766KP4eV5//fWsbe677764fdKkSRt/93333Xez/q4ofStXrozf89tuuy2r/YwzzojfT+t/mb4zYsSIjf++atWqop49exZde+21WT/37LPPZvWD7t27F91///0b/9360C233FI0cuTI+LF9jk477bSs53jiiSeKXn755RL5fVG+0P+QD47BHIPLEv1vh0rT/8rt0qkqVapEDz/8cHTwwQdHCxcujIYNGxY988wz0Ycffhj/+w8//LBx2/r162fN5uybNLNu3TpvdmyzQpvp2jdrudhlrq5du0ZNmjSJfvrpp/h/W2+9dbT33ntHn3/++Sb3276ls0tcGTZrtZ8bMWJE/Hj48OHRTjvtFM90i7NZ7OLFi6Np06bF22y77bbxJTN3m8xzoOKwbyVsmci+++6b1T548GBvW+t3Gd9880186dgu72b6of3PHpvPPvss/m/fvn3jb5ntGxRb5mLfmNi3x3aZOfPvtu1vf/vb6KGHHoqmTJkSf0Ntl45R+dH/kA+OwRyDyxL9b9tK0//K9dKpTz75JL48b3/4GjVqxJePqlev7t072C5rFZe5nJZZy5ZhnbVbt24b173Zcyq2bMAu0dm2inVe9zUz7DKYdcjiGjRoEF+mM7buWXXwhg0bxv+17Wwbe55tttkma5tGjRrF/129erV8bZRPmbXu9p6q97O44n3S+qHJdfs6u1RsbBnKv/71r3g5zDvvvBP3v379+kU33HBDPJideeaZ8fPaGta///3v8TKaTp06RX/605/ipS2o3Oh/yBfHYI7BZYn+t02l6H/ldqIxa9aseM3aAQccEK8TtjfGOs9///vfuPPlw9axWec48sgj44OjHeiUWrVqxQEgm/3mmmnnYh3APgCZjm7sGz6bcRtbL2izVlemzTqXbbN8+fI4cFm8o2UO7O4JA8q3zPtl69jbt2/vncjlUrt27fi/dnLWtm3bnAOT9VdbJ2//swHZQmUW/LU18xYes0HP1pba/2wfbB2+nRjatzr2TfOm+jMqPvof8sExmGNwWaL/La80/a/cLp2ycI+l7u3bNLsklnnTMh0sn2qIdmC0dL7docA6q4USFetg06dPj9q1axeHdzL/s5CjBYHcWaY707VLfMWDRx999FG8fMD06dMnXpJgdwoo7tVXX41nq23atIlf3y7Vvf322942Zpdddon/686aUT7ZZdKqVat672fmEnAuFtq1y7D2LUzxfmiXVO+44444wGb9yO5ckXluO5G04Jl9o5y5m4aF2m666aaN36zYIGsnffbNyffff19ivzfKB/of8sExmGNwWaL//VRp+l+5vaJhl6zsgGaX2U8//fR4PZ4l8+0NM2vXrs37uS3Fb5f5bTarbglmndA6lP3XXttmj2+++Wb07LPPxnde2RQ7MP/xj3+M75Jgdz+wb/RsnbPdIcDYrdmss9hz237UrVs3vhWkdUy7RJhZB2id0vbPDvJ2udDW5NndW4444oioY8eOG79xtJmyfUNo6wkbN26c998EJccuz9r7b3eMsG9TbLmIvWdJJ3rW72zZid1Bw07IrE9Yf7DHNuhav7BvXmw9qp3I2TY2INsAbc9/zjnnbBzYHnnkkXiQtZNOe47//Oc/8WCW+ZYFlRf9D/ngGMwxuCzR//pWnv5XVI5Zcv+QQw6J0//9+/cvuuCCC+K7CXTu3LnoySefzJm6d+8gkLnjgLVnfPjhh3Hbvffe621v7A4GF110UVGfPn3iO68cdthhRc8999wm99fuOLDXXnsVvfbaa/E+2d1YTjjhhKKxY8dmbWd3O7j44ouLdt1116JevXoVHXvssUXvv/9+1jZr166N79xiz2d3KBg4cGDRQw89FN+RIGPixIlFgwYNiv/9gQceyOtvjNLz+OOPx3etsH5hd7546qmnvLv+KNbXDz744Ph97tevX9Fll11WNHfu3I3/bnffuPLKK+PPiG1zwAEHxHcByvSVH3/8MX5+a7fX3mOPPYquvvrqomXLlpXa746yR//D5uIYzDG4LNH/bqkU/W8r+z9lPdkBAAAAULlUjAVeAAAAACoUJhoAAAAAUsdEAwAAAEDqmGgAAAAASB0TDQAAAACpY6IBAAAAoGwK9lkVQ7sLrhUiAcyPP/4YF+2yAlwljf6Hsux/hj6I4uh/KGscg1FR+l/QRMM6WHkot6H24eeff/barHS7+wdJ6zV/+eWXoP0K+XupD60qKa/K3bvbValSJSpNpdkfykv/y5fqM1blNGQ7t80+2C7VVrVq1aC+VVGVdn+oaH1wxYoVWY/Vvqt+s/322yeOp2o8sgq+LquGm/Saavy2SrnlHf0PZY1j8JZxxzWzatWqxGOwOo6qv406v6tdu3ZUWWxOfwiaaGT+YD169IjK0urVq7225cuXe22zZs3Kejxx4sSgkzrVgdwTQvV66qRxzZo1Xpt7gO7QoYO3TZ06dby2Jk2aeG1W2r640n5vxowZU2qvVV76X76WLVvmtY0ePTqof7sDnzoRVG39+/f32urXrx9VFqXZ/ypiH/zb3/6W9XjdunVBk4Pdd9/da5s6dWrW4xYtWnjbqDb7FtRVo0aNrMcLFizwtvn973/vtZW3b1LpfyhrHIO3zLRp07y2Z555JvG4XK1aNW+bDRs2eG2dOnXy2i6++OLE/Qr9UqisbU7/qzxfcQIAAAAoN5hoAAAAAEgdEw0AAAAAqQvKaJSFO++8M2j9XJcuXRIzGiqPoQLi7vphM3v27MTQtVrr3KhRo8TX/Oyzz4LyHp07d/bapkyZkvX4rLPO8rY57bTTvDaUvM8//zzr8VtvvRW0flP1LXcNe/Pmzb1t5s6d67U9/fTTXpt7d4jLL788ype7jrQ8riEtFEuXLvXa3LGlXr163jbDhg1L/DkzadKkxLB269atvbY5c+Z4bfvvv3/WY3XHEjU2l7eMBoDyISTT8OSTT3rbfPrpp15b27ZtEzOQ6jitcrTqZhjHHHNM1uPnnnsucd8rUm4jF65oAAAAAEgdEw0AAAAAqWOiAQAAAKDyZjTcwk1Dhgzxtundu3dQrqJx48abfO5cber+yG7WQtXaUEVemjVr5rV179496/HYsWOD1vU1aNDAa3PX6r/++uveNmQ08rclayLd/EzPnj29bR5//HGv7YwzzvDadtlll8SaGX379vXaBg4cmJjlUJ8BVYxNqUjrQyu7999/32t77bXXsh6fffbZ3jYHHXRQ0Fjm9huVeVPFrwYPHpyYQXPzH2bevHleW8eOHb02AIVFjTMqI/vxxx9nPX7zzTeD8hjqHNDNlSkfffRR0L7269cv6/Ef//hHb5ubb77Za1PHavV7l1dc0QAAAACQOiYaAAAAAFLHRAMAAABA6phoAAAAAEhduUmTDB06NDFMrUKIqliVG55WAev69et7bWvWrPHa1q5dGyVRxaRmzpzptbVq1Srx91m9enViuF2FgyZOnJi4nwi3JWFnt2DkgAEDvG1UATU3KGYWLVqUeDMCVeRRBcXcwoFu0DzXfoUE4yt6QaGKTBWLGjRoUOI45vatXMX4dtxxx8R+qm7KoQr2uWNerVq1gvaLMDhQWNQxJTQA/fzzzyduc/zxx3ttPXr0iPLRp0+foIKor776auLvE1qw1B1L1blBeVF+9wwAAABAhcVEAwAAAEDqmGgAAAAASB0TDQAAAACVNwy+ePHirMfVq1cPCn6vW7fOa+vUqVNiSEaFI1Ulx6pVq26yIrepXbu21zZ//nyvzQ0+quClCgepapUrV67cZGDTfPrpp15b//79vTakG1ibNm1aYpXuN954w2u78sorE0NgqkKouoGAqvDthr9VZXoVEFefRRfB77Kjxi33/VcBww0bNgSNW+6NM9TYqcZAVcW+SpUqieM3fQmAGrPc8cPMnj3ba3v33XezHt9yyy1BwW91g56QkLUag3fffXevbcqUKVmPX3jhBW+byZMne23q/M49FyAMDgAAAKCgMNEAAAAAkDomGgAAAABSx0QDAAAAQOUNg7sBWhXKWbFiRVDgsEWLFlmPx4wZExQ0UkFbNwi5fPlybxvV1rBhQ6/t448/Tgw2qd9bbedWg3ZD67mqhRMGD6OqyX/44YdeW506dby2c889N+vxkiVLvG0effTRoPfeDeyqkLcKa6tgWN++fRNvrjBixAivTd20oFevXl4byoZ6H92+pPqIGu/UGOtatWpVUIBRBSTdm120atUq8fUAFB51oxVl5MiRXpt7PnTEEUcEPVe+gWp1XFZOPPHErMe333570A05VBhcnS+UV1zRAAAAAJA6JhoAAAAAUsdEAwAAAEDlzWi4a4PdQmW58hiq7Yknnsh63LJly6B1xmp9Xtu2bbMeL1y4MGgtocqA1KxZM+tx06ZNvW2++OKLxOJ8as2eyhSoNdIIo7IKqq+pYopuP2rcuHHQ+nhVQM19TbUWVPU11b/dNZ1q3xs0aOC1qQJtbi6pXr163jYoHSpD07Vr16zH3bp187aZN2+e1/bdd995ba1bt856/NZbb3nbqL7kjp2mQ4cOWY87duzobaP2FUDl5p5HheYeVAbSHWfy3YeS1laMkaqIqVKeC/S5Ks6eAgAAAKgwmGgAAAAASB0TDQAAAACpY6IBAAAAoPKGwW+55Za8fk4FZ/bZZ5/E0LVqU4X33AB38+bNg4K9qmCfW2RPFeJTBaxOO+00r+3AAw/02pCeRYsWJRaCzBXgDgmxqZ+rXbt2YoBb9TXFDQOrUPeCBQsSXy9XCN79WcLgZadGjRqJbVtttVXQTQRU391hhx0Si/99/fXXXpsqIuqOeWocdsdcABWXOmapccYdo9zinrmsWbPGa5s5c+Zm7WOufShp24tj6/fffx/0s+qGSWm+H2niigYAAACA1DHRAAAAAJA6JhoAAAAAUsdEAwAAAEDlDYPnSwUOhw8fnvW4WbNm3ja77757UIXJxYsXJ1bprlWrVlBVbrdaswr9jB071msj+F3y3PdehbXV+6UC4m4fUYEvFbp2bxagflbtl6ouuu+++3ptp556atbjk08+OShEpz5jqhI9yk9lcHf8Uf1NhQlVGNK9sYUKcL/99tteW6NGjRL784oVK7xtVB9XlccBlC9lETR+6KGHgm6QUR6NHz8+6Nh63HHHpfaaJf1+KFzRAAAAAJA6JhoAAAAAUsdEAwAAAEDqmGgAAAAAqLxh8KKiorwqNKrt/ve//2U9vv/++71t5s6dm7gPqjrl1lv7czPVNn/+/MQKlmqb559/Pgqh9rWsq1xWZEOHDk2s7D5hwgSvTVXX7tu3b2LFeRUQV20h1VHVNvPmzfPali1blvV43bp1Qb9Px44dE6tKq/5I/ysdKtTthr9VH1SV6Bs0aJC4nVspXN00I9fNNdwbYixdutTbRgXECYNXPiU9ZjzwwANe2+zZs7MeX3XVVeUiSOzeaKRmzZpRRaSCxu7f3Fx66aWJ50fqxjjqphbqWO3eWMU9JqvXyxVmd29OEdpH1XlhTed9rVKlirfNokWLvLa99tor8VjdqVMnb5uWLVt6bXfccUfifqWNKxoAAAAAUsdEAwAAAEDqmGgAAAAAqLwZjTTXZrrZB7W+V63/UwWm3DXEaj28Wouniq6465Pr1avnbfPiiy96bVdeeWVefy/WzYfr0KFDYsHFLl26eG1nnHGG1/buu+8mvs/Vq1cPWh/q9hlVPG/hwoVe2/Tp0xPX8j/77LPeNqp/hxR2U38v9XNIn8r2uBka9V6o9cFKkyZNsh43b948aOxUa93d4qZqH0JySSh56vjh9qvQ90sdI0OPRX//+9+zHv/tb3/ztmnfvn1iJs2sXr066/Grr77qbXPdddd5bUcddVSUD5VPuOuuu7y2Tz/9NOvxf//736CsXEVwww03eG3Dhg3z2tq1a5dYGFQVzVV9yz2Wrl27Nqj/5VtwUD2XalvvnBeGFnsOya2pvNvXX38dlKd76aWXopLEFQ0AAAAAqWOiAQAAACB1TDQAAAAApI6JBgAAAIDUldvUXWgoRxUmcwPVKsSrAjGKW8hEhYpU2FcFc1zdu3f32u68806v7eSTT/ba3EBmviEm6OBWaMB/4MCBXtvMmTOzHvfu3dvb5ve//73XdvDBB3tt/fr12+x+lau4oNuXVcG2UaNGeW277bZbYgDUDVma+vXrB+0rtkzdunW9NjdkrQLjqkiTe/OBXGOL67vvvvPaVL93X1OFyNW+ovSpMGvoDQRCqKCqKib2+eefJx5vVYBWbeeOeapomyomd8kll3htbdq0SRxzVTG5OnXqeG1u8dSnn37a2+aaa66JKqLx48cHHRvcGw2E9jW13cqVKxML/aljvHou93OgbpKgzrVU21rn/NEtLJjr+dUY7O6rei514w51HlPSuKIBAAAAIHVMNAAAAACkjokGAAAAgNQx0QAAAABQOGFwFd5R4ZqHHnrIa2vWrFniz6kQogrTuOGx5cuX5x2ucSvldu7c2dumf//+XtvVV1/ttf3nP/8Jek2Ecd9XFSRU1ZVVoGzBggWbDKaZrl27BlVSdgNy6vVUm1s1VPVTFchTNyhQ+4/yHQZ3K8irYK8Kg6sKtKpf5hv8r169emLf5SYW4cdFNwStgqQqXK+2C6nUfeONN3ptKlDtVnlWNzlRNx5QN6hw+7fbt83kyZO9ttNPP91ru+KKK7Ie77fffkFhbfW3catUq+O5+uyo98M9/jz11FOVJgweeoMR9b6GjA3qRiTuOKNC3uq53OOtoj47inqft3VuoqJeT+2XGl/d51LHfDW+jhs3LiptXNEAAAAAkDomGgAAAABSx0QDAAAAQOqYaAAAAAAonDC4Ct4qU6ZM8dpq1aqVGN5xw0KqOqd6LhWumTVrltfWsmVLr82t6qxC5KqS6KRJk6IkBCjTDVmqPqNuFnDggQcmBmrnzJnjbXP88ccHVf90A4dbcjMF9zOlgmgdOnTw2lQ1cjcwqV4PZccNIoZWxm3cuLHX1rZt28TXc8dJtQ/qNVUgXYUaof9++VbqDgl+q5Dtgw8+6LWpsdINQavjuVtZO9cY6I5Tatxq3bq11/btt996bYsWLcp6/NZbb3nb7LPPPl5bixYtEoPrat9VUF4d992/z7Jly6KKaMaMGUHnVSH9TfUZdZ6jQuTue6Eqx6v3KyToHXqsU+cLNZzPRejnVz2X+zlQv4/6ubI4V+SKBgAAAIDUMdEAAAAAkDomGgAAAAAqb0bDXRsXsoY0V2E1t/hNq1atvG3Umj21Hj6kKIoqPKQK9bjr5RYuXBhUSPCzzz6L8pFvUaZC5L43ah2m+nuq9esjR45MfD1V+FGtnXTfr9CCfap/u2s61RpYVaxKZZDcLNHatWu9bVB23HFLUWt6VZ9w15SrMTB03a+7nlqNk6EFseAXdlO5BJWxCjn+TZ06NahwmMriuGvP1Tp91abGMvf5Vf9TWYhvvvnGaxs8eHDW4z322CNoXXvIcVP9PiqDpH5H93OhMp7uOGzvYcjnvDSpcxqVQ1CfcbdPqt8tNL8VUkRZvc/5npuqcVNt96NzDA4t9hxyLheaOVGZ0RdffDHr8ZFHHhmliSsaAAAAAFLHRAMAAABA6phoAAAAAEgdEw0AAAAAqStfSaKUuKEiFThUATkV0goJDKkgmgoyuUWtVBCobt26Qfs1YsSIrMd9+vTxtiH4Hc4Nf6uwpNK8eXOv7ZNPPkkMsKnnV23uz6qQugqBqXCu2ydV0Up1cwX1/C4K9pUv7lijipyp8Uf1m5AwuGpTIWFVXAth7D2cPHlyVtuXX36Z9fiDDz7wfm7x4sVBxzV3PGjQoEHQ+6yeyx0zQm8goMakDRs2JIauVV/r1KlTYl8eM2ZMUNFKVUDPfU01Tqq/vRrn3b/FkiVLvG3GjRvnBfPLWxjcLYiY671RBT5doUFpdZ4TctxUP6few5DjnwpdhxyrtxXvX+g+uGNp6A1s1A1fevXqFZUkrmgAAAAASB0TDQAAAACpY6IBAAAAIHVMNAAAAACkrtwkidwgiwqKqdC1CsC4VRpVZUr1c2o7N4itgm+hAcfatWsnViJXlaZVmOqrr75KDIMjnPs+qxCVCtSq8PTtt9+e9fjll19ODDjmCkyGhKzVNirw5QYfb7rpJm8bN1yaq+q3+xlTvw/Kjvv+qD6ixljVFlKNV93EQvWJVatWZT1u165d0M0v8P/eQzdc/Kc//Snr8d133+393NixY722Z5991mubMGFCYpVnFYpW44N7LFVVutUYq977Dh06JIZ4VcBaBW3dn1XHfPX7qHC2+1lR1aHVOKxuINK5c+fEiuJuUL483uwlpC/kGmfcMSrk3C7Xcdm9YYAai9Q+qP7n9i3VbxV13raV856F3hRG7Zf7XOrcVO2Dei733DRtjOgAAAAAUsdEAwAAAEDqmGgAAAAASB0TDQAAAACVNwweQlXzVhUT3bCLCk2FBrjd51JBnaZNm3ptajv3NVVYTQV6VDDMDe5hy4SErlWoTQX6r7rqqsT3WYXmVJ8JCfWqcJeqntumTZusx9dcc423zerVq7029ZpuAE+F9FB23Kq6IZWgc73XKlAYQoU0VZjYVR5DruWBfcbc8Px7772X+D7vvffeXtsNN9yQ1z4sX77ca5szZ47XNn/+/MQwbmi1ZndcVOOwqjStjvFuX1bHVvVz6vndn1Xh4jS9+OKLiaHy8ig0dO22hbx/uY6bITewUD+nPj9J+xkablfbhYbBVZ93Q+lqH9RxWf2O8+bNy3rcqFGjKE1c0QAAAACQOiYaAAAAAFLHRAMAAABAYWc01Hp4tS7NXeep1gqrdXZqLX1I8ShVlEetz3PXxqnnVpkTtd4wZC0h8qf+vqFZC7f4jSo0qfqt6qch6+ND+ppak9+4ceOg5woRWsQIpcMtMKbGmqlTpyZmO7YkM6HW5bvPr3J30Ow9bNCgQVbbcccdl/V46NCh3s+9/vrreWUbVQZAjRmq6GKzZs0S+5Bad66446LadzXeqTyJ2ydVIT41lqnPhdumxm/1XCHHFnVOsf/++2c9njFjRlTeuAUlc1HvjfobJxX83JxCdS7Vj0IykKGZE/Xe/+TkL0KfS31W3M+UOj9RbWq/QrJzW4IrGgAAAABSx0QDAAAAQOqYaAAAAABIHRMNAAAAAIUdBg8NrLiFXlQoURVFUSEcNxwUGtZW3GBOaKBbBYFUCBn5c/uDCvapAmf5hllDC++p/hZC9W/3ZgoqWDdq1Kigoj/u3yf0M4DS4fabkDDrltxkQhXXUn13wYIFqRQDLFTuuOGOGao4n2pT3JsDqPFBhX2//fbbxLFShV5Di+aqQGvIGKVe0y28p8Z0FVwPKcYXeoMZ1edr1KiR+Pwhr1fWVMFX9bdTY497DFFFZ1Wb+nu653zqeBj63rg/G3pMVttt4/wtVDA7pGilop5LFZpU7wdhcAAAAAAVDhMNAAAAAKljogEAAAAgdUw0AAAAAFTeMHhI2GrFihV5BbBCqx2HhMFDw9r5PpcK/ajAmhvsDQ0VQQv5W7kVv3OFxt1gotpG9XfVP9z+EBo4VNzXrFevXlBfW7Rokdfm/k7qhgsoO24F6QkTJnjbNGrUKLUwuAodqoCuG5D86quvvG369OmT1z4UgpIMAHfo0KHEnhuFIbTatroRQM2aNRPHIhUiV1XUXWvWrAnar5BjcOjxVj3Xds6YGFIlPrTNPSfM1abeo3xvOhOKKxoAAAAAUsdEAwAAAEDqmGgAAAAASB0TDQAAAACVNwweQlXDVuE4N5iqAjehlZmTKrHmCuqoUHe+YXD1/O7fQlV2dAOhCA9Kq/dZBbhvvvlmr+2kk05KDIOrvqCqkrohLRX6D73xgPv8S5Ys8bb5/PPPvba2bdtGSQiDly/umKH6rgr+qwB3iCZNmgRVkXb3q0uXLnm9HoCKQVXzVtzzNDVmqfFJHcfcG/SEhLxz3RTG/Vm1jToGq/POdc7fIvQ8VL2m26b+Nio8H1KxPG1c0QAAAACQOiYaAAAAAFLHRAMAAABAYWc05s+fH7Sdu449NKOhshDuz25JsRb3uVShv9B9ddvmzJnjbUNGI5ybMVAFyJYvX+61devWLbGAkFr/qN77kJxDSCYp13pNt039jqqvqTWd7n7ku7YfJcNdf6zeQ5XRCMnjKKowlCrK5e5Xvq8HoPxRxxSVL1B5RDe/oLId6vxLHf/cXGRIxiH0OKayHUq+BZOriHODGjVqeG3ueYXKb6p9DS2qmCauaAAAAABIHRMNAAAAAKljogEAAAAgdUw0AAAAABR2GHzlypVB4aB8QzgqEKMCNiFCi6jlW+jP/b2nT5/ubdOrV6+APUXoe6OK3/Ts2dNrq127dtbjFStW5BWwVqE2FXwLDYG5z6WCb/vuu6/X9u233yZ+xvL9nKB0xsrQgOHcuXNT2wc1Nrth0ZIOIQIoPeoGEKHHJ3csUDdRCT1uJj13Lqq4br7U/v/i7Ie6+c/q1auDzj3q1q2buA/q+dV+rVq1KipJXNEAAAAAkDomGgAAAABSx0QDAAAAQOqYaAAAAAAo7DD42rVr86o6qYJHIQEiJd+guQoCbUkQ0g1dqTA4wrmhqYULF3rb1K9f32sbNWqU1/bWW29lPT755JO9bSZOnBgUpHMDtSrIpULdajt1M4V8zZo1K+tx69atU3tubDkVKAwJaYYEDBUV0lTVf90+rkLqACrPuKPGgdDgcr5VuUPOrdQ2oQH0kJ8LOX/cPvBvo/bVDYiHnhuoMVdVYU8TVzQAAAAApI6JBgAAAIDUMdEAAAAAkDomGgAAAAAKOwweGmh0gzMqQKSCM99//31ipchq1arluaf6+V2LFy/22tT+u1V9Z8+enfd+wQ+izZw5M6hqttruoYceynp8zDHHBIWvQgJsqt+qCsxKzZo1E/tjixYtvDYVWJs0aVLW406dOgXtA8omlKluYqHCl+qGBCGaNm3qtS1btizvvgqg4lHnKvXq1QuqwO2OPSE3+tmSc8DQm/G4Y5a6kZBqCxnrthK/o/q5kKB86H41btw4r5uHbAmuaAAAAABIHRMNAAAAAKljogEAAAAgdRVqwawqnKLWuLmF/dT6vIYNGwatT3bXEqrnUgVQ1Jo6d32hWqfvZi9M+/btE/f/66+/9rZRz78lGZPKzF3nuWHDBm+b6tWre23XX3+91+bmZfbYYw9vmzZt2gStpwxZMx9SUEj1P/U7tm3b1mt75plnEp/bLR6EsuW+t6ofqQKOkydPzuv13MxOrtyGO36GrD0GUDE0atTIa5swYUJQDmHp0qWJGQp1vqcyGu54p7Idodld97i8JUWbNzjjstoH9fzqGO+2qb+pKtinijvPnz8/Kklc0QAAAACQOiYaAAAAAFLHRAMAAABA6phoAAAAACjsMPgtt9zitY0fP95rW758eWIxElUYT4WPQoquqCBsSPGUNWvWeNt07NgxKCDuFl8755xzvG0IfofbfffdEwP4Ktyqbirw2muvJb5e586do4qia9euiUV/mjVrVop7hCS77rpr4jipbhahCjaG6NOnT1DY3B1P6TdA5XHGGWd4bR06dPDaRo8e7bW5wWj3HCdXuFmdf7nnd+4NgtTr5QpduzesCA2pK9s5+69+H3XTGRVmd8/v1Dbqb6jOaY888sioJHFFAwAAAEDqmGgAAAAASB0TDQAAAACp26pIVTtxWDE420wVpitrquCTu4ZOralTbepPEbL2LuBPGPxzas2e2gc37xH6c2n+3e35d95556iklUb/cwszqrWa6vVVFqeyCfmMqXWfIfmmitD/yvsYqKxatWqT/Xtz+m69evUSt1F5DDXGuq9Zu3btUh230kL/Q1mrKMdglaFQxxT3fCh0fFLnUe7xO/QcTW2X7/mdspUztqmxLt+2kPPEXNwccMgYvDn9L+hMoDwP/Ay8ZcP6RGn1i9J4nZCQVqEqj5+x0ux/mderSNQJfEmqU6dOVEjofyhrFeUY7FbpztWGytv/gq5oAAAAAMDmqPzrPgAAAACUOiYaAAAAAFLHRAMAAABA6phoAEAlQuwOAFBeFPRE48UXX4w6d+4czZkzp6x3BQXuH//4R9wXM/bbb7/oyiuvLNN9QsUzZMiQ6Iorrijr3QDKBOMmNhd9puSV3I3uAQCl6tFHHy3rXQAAYKOCvqIBAAAAoGQUzETDqtT+85//jPbZZ5+oV69e0fnnn59VzdaWrhx44IHRvffeG+22225R//79N/77c889Fx1yyCFR9+7d45+3bYtXnly2bFl02WWXRXvuuWfUo0eP6PDDD49efvnlrNe+884740t09hz239tvv11W60XFY++nvb8333xz1KdPn6hv377R5ZdfHq1YsSL+95NOOin+X3FffvllvFTK/hti9erV0V//+tfogAMOiPvYoYceGj3//PMb//2aa66J+59bEfUvf/lLvD+ZvjZp0qTonHPOiat52v9+97vfRbNnz/b265lnnon23XffeJvPPvtsi/4+KB3Wx4YPHx7/L9O3cr2X9t/f/va30S677BL3Dxu/5s+fn3MpX4a12b9lvP7669Fhhx0W9ezZM9p9992j3//+99HChQuzfiZp/LRlC6ecckp03XXXxft48MEHe/0Y5dfYsWPj98/60k477RSdeuqp0ahRoza+t/b4hRdeiAYOHBj3ATs+Dh06NOs55s2bF1166aXxsdeOz/Z848aNy9rGljjbuGrH5m7dukV77LFH/Hj58uU5983GyC5dukT33XffxjbGQLjs+HjTTTfFx+9dd901Xn5q53XFx7Ajjzwy6t27dzzWWR9+6623sp7jm2++iU444YR4GxvnHnvssbjvX8myrMJZOnXbbbdFjz/+eHTeeefFA5l1EjvZdwe7jz/+OD5ptJNEq3b7wAMPxI9PPPHE6KqrrorGjx8fHyjtoGwnluYPf/hDtHTp0ujPf/5zVLNmzeiVV16JO2rTpk3jg++///3v6Omnn47bWrVqFY0ePTp+Tqs+fdFFF5XRXwRpeuqpp6I2bdrEkwEboKxvzZw5Mz5Yban169fHJ4XWx6y/tGjRInr//fejq6++OlqyZEl07rnnxgPfs88+Gx8k+/Xrt3GCa/3cTvKsr02fPj067rjjovbt20d/+9vfop9++im6//77o+OPPz7usw0aNNj4mjbh/tOf/hS/tp08oPyzE3UbizL/v/UX9V7alyA2Ftlk1U647ETtnnvuiY499tjopZdeyuoHmzJy5Mj4RM++tLED9IIFC+Jx1iYtTz75ZLxNyPhpvvrqq2j77bePTwjXrl0bbbPNNiXyN0K6vv/+++jMM8+Mj3P2vv7www/xmHLGGWdEH3300caJyKJFi+Kxy46Pd999d3ThhRfGkw07xtp4aeNStWrV4i9M7L92kmYnbTZR6NChQ7Ru3bro5JNPjurVqxf37Vq1asUndta3rcr0DTfc4O3bm2++GT+f9U+bTBjGQCh2nLTzwltuuSXuj3//+9+jKVOmxMdUO4bbJMT6rE2m7QtoO6ezL1WsX9h53tSpU+NJhU2k77jjjnhMtf+uWrUqPv4WuoKYaNib/cQTT0SnnXZadMEFF8Rte+21Vzz4ffLJJxu3s0HHDsA2o818i2xXQewAbAOOsW9T6tatGz+25+vUqVP8DaINZPZts7FvZWybKlWqxI/t360DHnXUURv/3QZTGyxROWy99dbRf/7zn43vaf369eM+Ubx/bclNC+xbOBvwMgc867/WX61/2oHTBkCbgNg3zJmJhk06Fi9eHE9CMgdO63e2jt8O+Ma+FbR++9BDD2WFiG1iM2jQoC3ed5Sejh07bnxf7Vu1zNWy4u+lTT7tIGrjWPEvWjJXEh5++OF48hA60bCTvLPPPnvjWGfj3pgxY+I7X9lJaMj4aawv28miHbRRcdjJmJ1U2STA+pCxk/j//e9/0Zo1azYeR20Ma926dfy4evXq8cRz2LBh8VUOm1TYF3v2ZZyNYWbvvfeO+6NNSmwSPGPGjLhv2OTAvqwzNrmxL+3s+Or68MMP435sfbP4l3mMgVBsAmtjn/XNzGM7fttk2K522cTZJqwZ1k/tCoeNgTaRsC9U7Nhvfcj6V+ZzYMdmFMhEwy7j2qUxuwxa3ODBg70Twa5du278/+0bE/s2w5bG2IEwwx4bu5xqB0pbemDf5tilXjsBHDBgQNaAZf9uB3UbuOxn7bKaDbSoPOx9LT5xtMfbbrttNGLEiC1+bjuQ2sDmfqtmS1bsGz872Fqfs8d2ZeX666+PT/zeeOONqG3btvE3NcYO7DbJtZPDTH+2g61NrD///POcnwNUbMXfS/tG1yafdtWhODsJtP6lTtpysasYdrXCrozYCaP1QZtI2H83Z/w0NvlgklHx2PtnX6rYVVU7Kbfjny3hzFxZM/bvmUmGybzPdpXCfPHFF3EfbdKkycZ+Yl/c2GTj1VdfjR/bv9vYZhNlm3TY1WKb5EybNi2rb5nvvvsuvprRuHHj6OKLL876N8ZAKDZmZSYZ7vE7s/TJvrC2/mZ9L/Mljl3By/Qr66+ZSYax8TQzcS50BTHRyGQtbJZaXKNGjbxta9SosfH/z6yxt29FFLsiYuxg+69//Su+/PbOO+/Eg6R9q2zf0FlHs0vL9ry2TtW+TbTlBTZA27d69q0MKj47SBZnfcD6W/EcUL7sOVRfbdiw4cYB0NiVC1sGYJNnO+C/++678Vrn4v3ZDsD2P5edDBRXfNBFxVb8vcyMaZm+U5y1ueviN8UOpA8++GD87bBdzbP/357DTjotLxI6frrjLioOe9/++9//xuOOHf/sSoadxNtYlLmKVfzky2y11Vbxf23SYKyf2Mmb5S4Um5DYc1gfs+OsbW/9zFYJWLtdMSnOrv7al3m2dMv2rXg+jjEQint8zRy/7dg6a9as6Nprr40nxLYE2a5UWO6neM0iW26llpyqcbYQFcREIzPBsDXL1kkyMgfCXGrXrh3/1yYH9s1wrk5k32TbNzj2P5vx2r3sbcmAZTbs4Gud1tab2v9sHywHYgOmrfmzb/Uyyw5QcbmBRAuzWpsdvOxbZDfcauvQQ9k6ZjsQu+yb6eL9u127dnFQzQ741udskLSrHBnWT20CbEtWXPbtDSo/u3JgLNuj+lOmL2VOBq3fZvISmaUwxdmE1v5nJ4P2rZ7l4Gw9s11FCx0/UbHZMdW+PLO+8u2338ZZB1sGVfwqxqbYuGRXGXIt2bPj42uvvRavn7djrC1ZyUwK7IqFLdUrzvqjLWW55JJL4nXytiyqWbNmG1+LMRAu91wwc/y28dC+KLEJhq0esKtc1k/sapr18+JX6dSY6p5zFqqCuOuUffNm37K8/fbb3jrOTbGDpXUwu4uK3ekn8z/raDaA2V0w5s6dG192yzy3daqzzjorHswsXG5snZ4dfI3Nem2gtEmHnQjaOmZUfLaWM3MZ1dhk0y7N2/pfuzRvQdnibG3n5ixRsX5mS1GKs2UF1j9tcpFh3yTaFQ1bNmVrpjPrmY0dzG2AtMEy05ftW0H7Rvq9997L8zdHeWITzE2xyah9e2dZnuJsHbItMc2ss8+sXy/eb90+a+vlLXdm3+rZN8u2NDWzZNTGvpDxExWbHffsqrxNUm1CasdaW7ppk8zM8S+JjUv2ZYz1zeL9xE7k7OTOntf6nj2nrQ7ITDJs4mvtmSsj7gTWbj5gP2v7U/y1GAPhsi98iy/Bs5Up9tj6ifXNo48+euPYZTJ3Tcv0PTtG23F3w4YNG5/Drg4zxv0/2xbK5V0L8tx1113xAdEGRruqkDTRsNmsDWwWSLMJgWUt7KBpj+0bP7t8Zt+Q2GzWJhK2jX2LY3fZsOe3O7pkOuEjjzwSD4A2ENtz2GVgG/Tcy7WomOwuOnZHMwtF2v9vJ1L2zZr1Gbsa8cEHH8R3pLK1n3aHneK3P05iE1Nbn2zhNAs2tmzZMn4+W4pnNzfIfHNsLEBp3/zZ0gC7O0tx9hmwSa/1S7vLit3lx5Y62B2sLHCJis/6gk1I7TK/+hLDJiJ2G1E7CbOchl3xsm/uLCRrV84y3/TalyfWX23JgAUhrU/bHaGKL3GycdTGMVvDbM9jOTgLQ9pVE/s3+2/S+ImKzSamdrJlY5N982v9w66o2nKmgw46KGics7v12KTC/nv66afHx10bv+yOP9ZPjX2ZYldJbGyzCa0tu7Pwrn2LbP1WsYyGXdWwJcw2sbYsEWMgFJso2woTW2ZnGSA7flvWyHK8dkXWluDZeZ6NrzahsCu3xXNGtlzU+qyNd9aH7UtkG+dsvN3q/786XNCKCsjjjz9etP/++xd179696KSTTip66qmninbYYYei2bNnF91zzz3x/688+eSTRQcffHBRt27divr161d02WWXFc2dO3fjvy9atKjoyiuvLOrfv3+8zQEHHFB0//33F/3888/xv//444/x81u7vfYee+xRdPXVVxctW7as1H53lJx999236NJLLy26/vrri3r37h33kZtvvrlo3bp18b//9NNPRbfddlvc3rNnz6IzzjijaOTIkXF/GzZsWLyN2//sOa+44oqNj5cuXVr0xz/+sWj33XeP+9Bhhx1W9Nxzz8n9Oeecc+JtVqxY4f3b2LFj49ffaaed4n39zW9+U/T+++9v/Hfbn+L7hYrliy++KNpnn33icejVV1/N+V6+/fbbRUcccUS8Xd++fYt+//vfF82bNy9rm5deeqnooIMOirex/vbpp58WDRw4MO6rGa+99lr8PNaXrE+deeaZRRMmTNis8dP6ufV3VEyjR48uOv3004t22223oh49ehQdeeSRRe+++27O99aOt9YvX3jhhY1tM2fOLLrooouK+vTpE4+R7vj2yy+/FN19991Fe++9d/wadiy98cYbi/73v//FzzVlyhQ5btox+Oijj47HzczxljEQxVmfueWWW+JzMusP1o/tWL5mzZr438ePH1904oknbvy33/72t0VDhw4tGjRoUNxnM0aMGFF0zDHHxMfeAQMGxOeXe+21V9xPC91W9n/KerIDVGR2lcKuTtm3bQAAoHBkguKZ0gjGrmrYEvrLL788XulQyApi6RQAAACQNrulsi29s2Wpdvc0C5dn6modeuihUaFjogEAAADkwXIZdjMYyxFZns1ujWyrHCznVp8cbsTSKQAAAACpK4jb2wIAAAAoXUw0AAAAAKSOiQYAAACAsgmDWwEoi3LY7bsAY8W5rBCNFSAsafQ/lGX/M/RBFEf/Q1njGIyK0v+CJhrWwciMb56ff/7Za9tmm22iyqI0+wP9D67S7g/0QRRH/0vnRMWV5omsen73b1ilSpWoouIYjLK0Of0haKKR+fD36NEjqix++uknr23bbdO72++iRYu8tsaNG0eVxZgxY0rttSpj/0PF6X+GPoji6H9b/sXbtGnTvLZOnTql9pqTJk3y2tasWZP1uKL+PQ3HYFSU/kdGAwAAAEDqmGgAAAAASB0TDQAAAACpSy+UUMHsueeeXptKz//rX/9KfK7jjjvOa5s7d67X9sknn2zWPgIAUJ4tXrzYa/v666+zHi9YsMDb5sorr/Ta9thjD6+tatWqWY/Xr1/vbaNC3S+88ILX1r9//6zH1113XVDIdeedd/ba6tSp47UhCvp72t2K8jFnzhyv7d13303MBJ111llRebB06dKsx08//bS3Tb9+/by2Xr16pXZzoV9++cVr23rrkr3mwBUNAAAAAKljogEAAAAgdUw0AAAAAKSuYDMaqqaFuu92iKlTp3ptu+66a17PBZRWMZ1818luybrPKVOmeG0TJkzIenzooYcGPT+AkvPSSy8FZQ/XrVuXOLao+gvnnXde0Gu6GQ23Fkaue/q3bds2MRei6l256+jN+PHjvbbatWtvMv+Rax8KTehxZvTo0VmPv/rqq6C+tsMOO3htd9xxR9bjxx9/3Nvm2GOP9doGDBjgtdWqVStxH4YPH+61ffjhh4n99OSTT/a2+fzzz7225557zmtr1apV1uN9993X26Zr166lnsdQuKIBAAAAIHVMNAAAAACkjokGAAAAgNQx0QAAAACQuoINg6vifKtWrfLa/vrXvyYGaVTgplu3blu8j0C+xY9CAnhuUaPN4T5/aOCvY8eOQeG38hBgAwrJ/fffn1iIr3Xr1l5b/fr1vbaffvopMUSubpjSsGHDxJDw8uXLvW0OPPDAoEK6a9eu3eRjFbLNtZ1bOPC9997ztvn1r3/ttTVq1CiqLMefkHFfBenfeOONxJ9r165dYgDf1KtXLzH0f/vtt3vbXHrppV7bjz/+mPia6lhUt25dr61Lly5e24UXXpj1eJdddvG2WbhwodfWvn17r23ZsmWJBSqVU0891Wtr2bJliRb14+gNAAAAIHVMNAAAAACkjokGAAAAgNQx0QAAAACQuoINg8+ZM8dr++ijj7y26tWrJz6XqhRZs2bNLdg7FKI0q3nPnj07MczVokWLqCStXLky6DP2ww8/lOh+AMg2dOhQr23+/Pl5VbVWn99tttkm6/G22/qnGgsWLPDaGjRo4LUNHjw48djqVm82S5Ys8do2bNgQJXGD7KZKlSp5nRuMHTvWa1MVnCuL6dOne20qpKxuluO+ryqYrY5/6n12A+L33nuvt426QYH6XLg3Athuu+2CbkagbpywzAlwz5gxI+g8QH1+mjRpkng8d1/P3HPPPV7b5ZdfnnhTBhUQD8UVDQAAAACpY6IBAAAAIHVMNAAAAACkjokGAAAAgNQVbBj8m2++8drq1KmTGARSQTEVnJk4ceIW7yPght9CK4Or0PWKFSuyHvft2zeoL6vQpksFNFUIcN68eV5bz549E58fQHq+/fZbr61x48aJwelq1ap5beqYGFJVWAVcVbDc3Y/vv/8+KBCsbL/99on7FRrGdZ/LrRRu1qxZ47WpkLMKGJdHSTcjeeWVV7y2rl27Bv2+7vusAvjq/VJ/dzcErarJq2reRxxxROJr/vzzz0E3PnEr2uf62ZC+prjnpuq51e/Yp08fr+2ZZ57JenzBBRd421AZHAAAAEC5wkQDAAAAQOqYaAAAAABIXUFkNFTRErVOskaNGl7bYYcdtsniQeboo4/22jp27Oi1TZgwIetxly5dNrHXqMxCsxZukRy1TlKtpa5du7bXNnPmzMS+rH5OrTN28x79+vXztlHPr4oYqXWk+RQzBBDltb5b5SOaN28e9FxqTbk7TqmxzS3ql2u7pOcO3Qc1jqgiZGofqlatmrj/qnCvm+PIdT7iFl+rKNwcgipiqHI96pjl5jZCC7mq8zb3vVB9bfXq1V7b0qVLvTZ3P1R2RFHbbReQxQkpgKn+1uoYqfJT6tx02rRpWY8XL17sbdOoUaMoX1zRAAAAAJA6JhoAAAAAUsdEAwAAAEDqmGgAAAAASF1BhMG//PLLxDCrOeGEE7y2q666KvH558yZ47XVqlXLa5s7d27WY8LgSCMErfqfCo+5QTRVZE8FKFWYzw1f7rzzzkE/p8J2KkSZT0gU5etGBiHcG2SYtm3bem3vv/++13bwwQenVlCqMlu1alVQuNktzKn+nq1atQq6sYrbH1ToOrQwmSs09KqC2Pnugwo5L1y4MHEcUz+nzj0qahh8/PjxiTf2UIXkQkLRqo+qoLS6WYn7/Kovq+dSfSakyGNIIT61nfpcKOp47vZd9blQfxsVxHc/10OHDvW2Oeqoo6J8MTIDAAAASB0TDQAAAACpY6IBAAAAIHVMNAAAAACkriDC4Cp8pUJaKnDoVp1UIe/58+d7bXXq1AkKzaHy25Kq1iEh2+XLl3ttKug9cuTIxMqoqjK4Cr+54UsV8u7WrZvX1qJFC6+Nqt/lm/v+pBnMf/vtt4MCmSq0e91112U9vvHGG/Pej0ceeSSxnw4cODCqiFauXBkUCG3Tpk3W42HDhgV9pt1QdK7wagg11riB2dBq3up464aE1XOpStNqXHT/Pg0aNIhCqHG3IrAx360g/f333yd+blQVevUZd98bdVwIrSbvtqmwtqrcHXIsCg1whzzXNuL3UZ8Bta/u76T6u3p+tf/uzQi+++67xJsq2XOH3HDBcEUDAAAAQOqYaAAAAABIHRMNAAAAAKljogEAAAAgdQURBt9hhx2Cqhar0NLdd9+dGLhp376916ZC4yhMoeFZFR5zg2Gq/6l+27BhQ69t4sSJiQHbkIriqhp5hw4dgkKPKjzGTRLKt5BgZWjo8B//+EfW4wcffNDb5txzzw26uYG7X4ceeqi3TaNGjbw2Vb3YPR48//zzlSYMPnPmzKCxxv0Mf/bZZ4mBUNOjR4/Eqsv5VgFX73NoBXi1nft7q/1SwVsVnp89e3bW4y5dugTddGb9+vVRRWTv6ejRozf5uQwNN6tK7iHVwtVzqeNrSB8JPS7ne2MDNf5tHdh386F+H/U3VfvgnkOoSvXuWKpuMpELVzQAAAAApI6JBgAAAIDUMdEAAAAAkLqCyGjMmDHDa5s+fbrXtuuuu3ptnTp1SlzfptbwqfXpqnAgsKl10+66y1WrVgWtBVVrqd01xSpHpJ4rpFjQqFGjvG32228/r80t8KTWkar10KGFgVDya4hD8xi33HJL4tp9tb6/fv36Xpvq925fUs+lismptczuc+21115RZaE+T+qY6H7GlixZ4m3TvHnzoOdft25dXpnFkCJnKleh+l9ILkRlNdV4p/rM+PHjsx63bt066O+liv9VBJY3OeKII7LaHnvsscS+EJKVUe9haDYiJGsYcmzN1ZbPNqFj6Y8inxhawNb9e6n9Cs0bueemffr0ScwijxkzJgrFFQ0AAAAAqWOiAQAAACB1TDQAAAAApI6JBgAAAIDUFUQYXIWwa9So4bWpQlRucR1VAKVq1apBhXpUMBGFKaQ4nzJ27FivTYXm5s+fn/iaoaFHN9ipPj9t2rRJfL1coW63+J8qMKaKbpYF93cKDQaWx/3Mt3jU448/HhQwVEWfrrnmmsRCj2ocVn3CDZarwpUqeKuCyW4YfHOKUZV3+++/v9e22267JRb07N69e1Cxw5deeslr23HHHROL1Kkgtjq+uqFX1T9CuX1GjW1qH9RYee211yb2md69e3ttzZo1iyqLXXbZJevx119/HXRzB1WwL+S4FhrgdgPiW1I8z93X0GO3es18g99qu5DPgQrKqxuyuM+vbmywJbiiAQAAACB1TDQAAAAApI6JBgAAAIDUMdEAAAAAkLqCCIO7YdNc4S43KKYCSSrMqkKIKgw+d+7coP1FxQn5hoa58uWGv1VF3xYtWnhtQ4cOTQxfqqCY+gyo39Ht8ypgqz4XITdhGD58eLkNg6dFBflCwomh/TJku+XLl3ttqtrrG2+8kdhH1L7XrFnTa7v00ksTb6Sh/jYqtOu+phrT1X6p/XefX91ApDJRn9ddd9018eeWLl3qta1ZsyavfVDjigoJu31Z9W0VHFbP744/quK8CrwvXrzYaxs4cGBU6NwbBnz33XfeNqFVuV3qvVHjhXqukAB3qJB9DQ2Db+scc9XPqWrhqn+745g6tqqfW7ZsmdfWsGHDxPPXLcEVDQAAAACpY6IBAAAAIHVMNAAAAACkjokGAAAAgNQVRBh8ypQpQdUqQwLiKuAaGmis7AHDQqSCYvkGy1evXu21zZ49OzE8pvrVN998k1glWVVzVv1WBencALqqwKzCcCqI5gZTp02bFpVXSe+l+luFtm3ua20qQOsGCm+//XZvG1XFt169el6bW/VdVTtWVZFVBVo3IL5o0aKgm2ao/uXe5EPdqCP07+reKEFVsi4eGC/pm0CUtHz7X4MGDYJC/ypwHzI+hIS61U0s1LFbhdTr1KmTGG5Xx3hVxRy+Tp06eW2TJk0KGmfcm1OoCu1qnHHfU9WPtqSafAjVl0PG/e3E76j6rdrOpcYs1W/V84fc2GBLxjyuaAAAAABIHRMNAAAAAKljogEAAAAgdQWR0VCFX1RxE7X2M+S5VPGj0DV7KB9C18On9VwLFy702kaPHp24nlLlKlRfa9KkSeJzKWqNtFoX26tXr6zHjRo18rZR6+/VZ8xdf69+zv172Zpbta8lLSl/o9571RZS4EkVGlUFG9Xfa9asWYnrd/fYY4+g9fbz5s3LenzDDTdEIdS+Xn311ZssFJXr91E5uI4dOyaurU/KWmxOXqp40baSXvNdkcY7dfxz/8YqP6PGLbVf7naqoJnKaCghRc7mz5/vtQ0aNCjo+Ss7971wxzGVfVXvs8oOuNk89Vzq+KHGBtVH0hJ6Hqeycy71uVBji/q93edXz6XGP3VuoDIzaY4ZXNEAAAAAkDomGgAAAABSx0QDAAAAQOqYaAAAAABIXUGEwVVIMKTQmnEDpyoM7ha0yhXCqegBwoooNLiVZjjSDRzOnDkzKHCo+pYb4FaBbhXg3XXXXb221157LfFvo0KV1atX99r22WefxCJrKgSogqNucE+F+9zPU3m9sYJ6f1RRsOnTp3tt7t9QBRpViFL9nd2/Ybdu3fIKpJvmzZtnPb7pppvyKihl7rvvvqzHl156adB+qYB406ZNE8f50JtyuH1c9dPy2ufKmgrtLlmyZJPvVWmMzSrg6n4W27dv720zdOjQvMPmlZ36bCa9z2psWLBgQWLBPhVaVu+D2qd8i+Yq7nOF9lu13c/OOWDoc6kwuHtMVH8bdfMY9yYaiho3k977TeGKBgAAAIDUMdEAAAAAkDomGgAAAABSx0QDAAAAQOoKIgyuwkgqrK0qDbuBTBXi7dy5c1DwW1VuRLh8Al5phrxVsEqFVN1KyirUq4Lfaju3n6qgdPGKxRkDBgzw2tyAqxtaN3Xr1vXaWrVq5bUtW7YssQqqComqz6K7HyqIq8Jp5aEPvvLKK1mPp06d6v1MnTp1vDZVkdgdf1QAUP0d1Pvo/g3V2KaC66pqthtAv//++71tbr755qB9/f777xN/7rzzzvPaWrZsmRgodT93qup8rgCm+7dYsWKFt83s2bM3/v+M5Zvuy24YPPTGA2q8dvu36stqbFZjpTtOqfMAdcOFlStXJobNVb9K8/hTEai/ufpcqvfe7Ucq3Kz+xuq9D/m5fN8b9XPq+KeO5784v3foPqjPj3ssVX1U/Zw6LiftZ67nCsUVDQAAAACpY6IBAAAAIHVMNAAAAACkjokGAAAAgNQVRBg8NECrQqhuqE09l6LCvqpqJsLlE95SoXw3kKqCVCrk7QagcwVx3RCb2kaFbkMCZSrUpkLDqhq5e9MCFapUIVj3M6BCtqHBPRWQcytPK26ATX2+SoMbiNt5550TQ8uzZs0K6ktuMFUFVVX1a7Wdu5/q51Sb6kvjxo1LDFgfcMABQf3Z3S9VUX7kyJF5BXTV+KD6swo1usHe1q1be9vUrFlzk+NKoQoJx6oxMN9wqfq5kBC56rvqs9OgQQOvrazGm8pg77339trUTSD22GOPxHOt0BsIuJ/P0HOHfAPiIVXAFTXeqhumqOOme+OWjz76yNumZ8+eXlvjxo0T91WdG2wJrmgAAAAASB0TDQAAAACpY6IBAAAAIHUFkdFo2LCh16YKaxVfg5urQJ9a16yo9YVq7SfCuesIR48enbgmd/ny5YnPo6i1oCEFeEILCIX+nLudypeodZ6q8J67blrllNQaVXf9uspkqJ9Tv4/KKc2ZMyfr8dixY71t9tlnn6is2TrcpOKC6vfr3bt30N/GLRoXkiUyq1evTnx+NW6pDIUqROd+XlS+RBUtVbkK1VddKu+jPttuIUm1tlkVp1IF5tyxX+1n8WyA6qOFSvVl971Qfa169eqJP6fGXTXWqDaVg3P7g9pGvfdq/xFmp5128tr+9re/eW1u8dPQInshWTD1XKotrULBodv9KM4p1Pin+qn7+VHj7SGHHBLlI+1Ck1zRAAAAAJA6JhoAAAAAUsdEAwAAAEDqmGgAAAAASF1BhME7dOjgtQ0ZMsRrU+HL3XffPevx8OHDvW1U0SkVKCMMnj8LRbtB2O+++y4x9K8KMoUUXVSBcdWWb/BbhVtVYNcNCCvqudTPuQE5FYpVfXT69OmJBaxUUb8pU6YEBd3cv6sqsuYGkO1n8i34lS8LyLn77xY2UkFp1UdCio6pAqLq/VHP5Y4/oTexUIWa3LClCoyHBjfdNhXkVMFH9ZkN+Z1U2FKN1yHjd/HwJeHgTY81bj9S/UONi6r/uduFhsFVm3uMDy1MFrJd2gHayqx9+/ZeW9++fbMez5gxI6jYnPqMqzHKFRoQd9/X0L4c8pobxLFB9TU1Hrk3uvntb38bdMMFta8lfSzligYAAACA1DHRAAAAAJA6JhoAAAAAUsdEAwAAAEDqCiIMrgJEKoSjQq+XXXZZYhVeFXpVFYJDQsjIzQ1l1a5dO+vxvHnzvJ9RAXEVtlKB6ny2CQ18qQCluhlBSAhRVdNt2bKl1+aG6T/99FNvm1mzZnltCxYs8NrcKqQq5K3CkerzE/I5cd/H0HBzSXPfn0aNGgX9nAowum3q5gMqhKz6lxuoVgFG1bdUKNAND6rXU/sa8jlTIUf1XOqmDu7fS/Ub1aZuguCGR1WYtHhbSD8uFGosc/tbaP8Iee9DbiiRazu3/+VbnRzhQsPH7mdObRNaNds99oSGtUP2NTT0r8bcrZ3nUv1KjYmqT7qfO7fq/ebIt/p5KK5oAAAAAEgdEw0AAAAAqWOiAQAAACB1TDQAAAAApK4gwuBuaDhX6Ee1uUE0VbU4NDgcGkiCDiu5f1M3SNWkSZOg8GxIKF+FjVUQTb2n7muGhnpVP3X7X926db1tZs+e7bV9+OGHXtvMmTM3GQ7PFcC76KKLvDa3auuIESO8bdS+qtd0f0f1/tSrVy/r8Zw5c6KKTIX7VBtQ3qmbA9SsWTPx50KqgIe+ngrvq3HXDdqq47S6WYB6LoQJDRa7oW51HFA3hQh5/tBzr5CbYahtVPB7S/Yj5NjgnkOE3qxGoTI4AAAAgAqHiQYAAACA1DHRAAAAAJC6gs1oqPX87rpztc5cFVNRhdZat24dtI4UYWz94ZgxY7LaHnnkkazHAwYM8H6ua9euXtvcuXMT10CqglyqH6m1me5aU5XHcAve5WqbOHFiYvZCFdRTGZPevXtnPT799NO9bQYNGhSFuPnmm7Mev/HGG942qnidWpftZm3cLAmA8kutDXePk2oMVNQafHddu1qvro6tKsvhjrGh+TnyUyWf0XDfi9CCjiGvqZ4rtIhp0nNvTlvI76PaQjIUaRfZSxNXNAAAAACkjokGAAAAgNQx0QAAAACQOiYaAAAAAFJXEGHwxo0bJwZQVVEwFeru16+ft83o0aODAuIq7IswFvbbaaedstoWLlyY9fiuu+7yfm633XYLCnW7oX8VyGrVqpXXtnz5cq/NLTSk+sI333wTFIR0g4mrVq3ytmnWrJnXdtNNN3lthx56aJSWKVOmZD2uU6dO0O+jAnhu2FMVzAJQPqnxrX79+qmFV5MKteYqEJhvQFeFwUNCwtDje+j77B4H1I13QsPg7n6EHHdy7WtISF09f0gRv5/F76N+ToXB3fPJ8txHuaIBAAAAIHVMNAAAAACkjokGAAAAgNQx0QAAAACQuoIIg7do0cJrU4EyFcJxw7cqeBsaUKIyeP4sDNWgQYOstpEjR2Y9vvjii72fe+WVV4Iqxrqhf1Xt9r333guqyl2rVq3E/hcSOjPz58/Pety5c2dvG1UtPF8qIKeCaG54XvXtKlWqBH3uqlevvsn3AkD5sG7dOq9tzZo1iTfEUAFrNa6EBGHVGKKeS40/7vHcHcdyjcMrV6702pBudeply5Zt8jiaq/+p99ltCz3fUwH0fG8yoPrkL87xVW2j9kFVpnd/x5Dq4bn2taRxRQMAAABA6phoAAAAAEgdEw0AAAAAqWOiAQAAACB1BREGb9iwYWIANVdgaNKkSYk/p4JuigoFIz1333231/bXv/7Va7vxxhu9NjdY/u2333rbLF68OCg87VbKXb16tbeNqn6tgm6HHHJI1uOnnnoqChFS9VSFwlRfVmG7CRMmbLJSuAr35QqOutXVx40b520DoGS54Vg1PqixTN0Iwg2vhoRsc3GfS4V4VVhW3fTDDXqr33FLKlJXdiF9JF916tTJety0aVNvm3nz5gU91/r16/OqDK7a3J9VfUEdN0NubLBB9FG1D/Xr1/fa3BsZhL4XW1K9PV9c0QAAAACQOiYaAAAAAFLHRAMAAABA6goioxGyhi+0bc6cOUHrQ9VzuUWMUPJUpkblNlyqYN/YsWO9NpVNmDlzZtbjJUuWeNu0b9/eaxs8eLDXFlK8LmQtaCjVl5Urrrgi6/E777wTtO/q/XDXh/bu3TtoHwCULlWg1C2Cp3JqKpOmMm+qSJu7jl0dW1UhXTUuujk4te9qH9R2SHf9/8cff5yYmVVZBdWmfjZf22yzTWJmMSTbobYLzWioz51bzHf27NneNieffHJUHnBFAwAAAEDqmGgAAAAASB0TDQAAAACpY6IBAAAAIHUFGwbv3LlzUDGiffbZJ+vxsGHDvG369u2bWEwlV2AN5ZMKfO28885BbaUtzWI7oc81YMCATT4GUPk0b97ca+vSpUti8TV1EwjVpm5GoUK1LhUQd/dBjW+qOJ8a+2vXrh3lQwV7871RR3mQdHzYkmPRoYcemvV4xYoVQTdpUX9jt2+pbUL33w1sp1kYb3tR7FLdNEj1U/d3DC0IXdLF+ZSK2+MBAAAAlFtMNAAAAACkjokGAAAAgNRtVRSwAPLrr7+O15uptYsV1cKFC702tY7PLQaj1oKuWbMmsciLadKkSVRZ2FpJW+tXGhmFytj/UHH6n6EPorhC7X/qGKmK2blrytW68NDibu52ah/U8VZlIULyHmofVFtZF+CtbMdgNwsRWgSvpHMIoa+Zj60C+5pqc4sSqhxHSfbRzel/25bX8EhJy/ekX4V3VOissrM+UVr9ojL2P1Sc/pd5PaDQ+586eVc3PkHJq2zHYHVuhfL799qc/hd0RQMAAAAANgcZDQAAAACpY6IBAAAAIHVMNAAAAACkrlJONIidAKWDzxpQWMrbZz5kf8rbPiN/5e29pP8V4ERjyJAh0RVXXFHWuwFs9OKLL0adO3eO5syZI//d2u3fbbst8Y9//CN+ntIycuTI6Oyzzy6110P5t99++0VXXnllWe8GyvHxNWSc+vLLL+Nt7L/57M/y5cujHXfcMb6N/eTJk6Pjjz9+i/YZ5QP9r2IKur1tRfLoo4+W9S4ABeG5556Lpk6dWta7AaCSHV+7desW/e9//4s6duyY1/588skn8c/abeyfffbZ6JtvvimhPUVpov9VTJVuogEAACqumjVrRr17987754cOHRoNGDAg1X1C4aD/patSLZ066aSTouHDh8f/y1z2sv8+88wz0b777htXMPzss8/ibe2/v/3tb6Nddtkl6tu3b3TZZZdF8+fPT7y8Zm32bxmvv/56dNhhh0U9e/aMdt999+j3v/+9V3Xcvvk95JBDou7du0f77LNP/PM///zzxn+3pQannHJKdN1118X7ePDBB2f9O8qGVYG//fbbo4MOOih+7+y9Oe2006Lx48dnvXennnpq9MILL0QDBw6Mtzv88MPjgSYXq6Rr29gyk3nz5sltrP3SSy+Ndtttt6hXr15x/xg3blzQfr///vvxvvTo0SM65phjoi+++CLr3xctWhRdddVV8UBo/fboo4+OLwG7VVrvu+++aNCgQfHz2N/gwQcf3Fit1X7vl156KZo7d24qy76Qv7Fjx8b9w8aynXbaKe6Po0aN2qz+GdLfbInf5ZdfHvXv3z/+xm+PPfaIH9sygVyef/75qEuXLnFfypg0aVJ0zjnnxJ8n+9/vfve7aPbs2Rv/fVPjNsrX8VWZNWtWdO6558bHVetLxx57bPTxxx9723300UfxsdPGF+ubL7/8cs6lK3bMPPDAA6N777037qPWB+1n1f7YGPXpp59Ge++9d/xz9jPusTtpfMv8vvb5+de//hX169cv/nydf/758ZiH0kf/26XC9r9KdUXDTtT/8Ic/bPz/ly5dGv//9kb/6U9/ik8c7UBsHcrW1R166KHxAc8OlPfcc0/cIe3kqUGDBsFr1O1Aa29+nz59ogULFkS33XZbPGl58skn420eeOCB6M4774xOPPHE+OTOTlKts9mk5uabb974XF999VVc6dE639q1a6NtttmmRP5GCGfvrb0vdgLWunXraObMmdHdd98dv79vvPHGxqqYdqJnJ+8XXXRR/E2IbXPhhRfGJ3Nu1fg1a9ZEZ511VjzZeOKJJ6LmzZt72Y1ly5ZFxx13XFStWrXommuuif/72GOPRSeccEJ84tahQ4dN7vfVV18d70uLFi3iS7v2ek8//XQ8oC1ZsiSeWFhfu+SSS6J69erFkwQ72bv11lvjwdOCazZQ28nqBRdcEJ8o2iB61113xSeEN954Y9znbT/tZNQ+X/b3Qen7/vvvozPPPDP+ksPGlR9++CG6//77ozPOOCM+kIb0z5D+tm7duujkk0+O+4uNrbVq1YqXA9h7X7Vq1eiGG27w9u3NN9+Mn8/6ivUvM3369Pi12rdvH/3tb3+Lfvrpp3h/bQ3zK6+8kjX2uuM2ytfxVS0rsRMlO6Y2btw4Hk+23Xbb6PHHH4/OO++86K233oratGmzcdtrr702+r//+79423//+9/xSZWNNfY/xSbDdsJox9MVK1bEJ25qf7799tv4izrrM61atYqPy9aPbSlM06ZNg8a3DPsCxvq89UP73eyLJzsBtPHfPicoPfS/Xyps/6tUEw17o+1AauyyV2aGaVcubOZo7M36+9//Hs9I7U3LyFxJePjhh+MTzNCJhh1kLRBbpUqVuK1u3brRmDFj4s5kJwH//Oc/4wmMdRRjr2vb2GP7drxTp05xux1w7WBtHRFlz07YbFJg75P1C2PfZNh7esstt8Qn7I0aNYrbV69eHZ+sZ062q1evHk8shw0bFn9TkmHfYtiAZ1e8bJLRsmVL+dp2kmcDmU0ObLJg7NsR2w87SbRJ8ab8+c9/3tjf7Vvn/fffPx5I7ef+85//xCeW77zzzsbntisb9q23Dcw2+bb1pZ9//nl0xx13xFfizJ577hn3dXt9O+G0flu/fv2432/JJWZsmSlTpsRflNh7YmOYsZN4O6hZ/w3pnyH9bcaMGfHYZJMDO3gam9yMHj06/kbP9eGHH8bjqI2NNsEpPnmwA6RNgDNjtfXRAw44IHrooYeygpXFx22Uv+OrYl/uTZs2LZ5cZpaO2FVTe99tTC3upptuivuZsb5p3xhbX8p1omfHSOsfu+6668Y2tT82gbZvgO0k0/ps5pia2cZOFkPGN2MTbPvsZPq8fbaOOOKI+MvKQg/4ljb6X1Rh+1+lWjqVS9euXTf+//aN2uLFi+MTquKso9kMVB00c7GrGNYR7Lls0mLffttEwmap9m23feNn38bZEhnrpJn/2WNTfDmATT6YZJQfdgJtk0472bKJgZ2U2VIOO4EyxQctO+Eu/o1+5n20vlGcnXjZ5Ne+Tc4MHIotdbI+a0GyTJ/Zeuut40HRBqhN2W677eLLsBl25cJ+bsSIEfFj69/WzzMnlBl2JcM+FzZI2zY2SLonebZN5jlQPmQmfPYNmX1D995770UNGzaMv2nL9MOk/hnS3+zfn3rqqbjf2KTDDpb2+bD+4h7Av/vuu+jiiy+Ovym0/xZnnyObsNtBNfNadrC2g7fbt4uP2yh/7Fvb4sc1+xLP+p6dENqVLDspe+211+J2u5qfOXnKKH7ClvnSxa70bkpIn7C+mTmBVDZnfLPJe/Gx2u4kZI8z4ynKDv2v4qhUVzRysW/wMuybO2Md0mVtoevgjZ2w2bo6+3bOvim2/9+eww76dnkr81q5bgFqyxkyatSosVm/E0qefbNvy9vsZMreH/umI9OXit8X272EmVlSVXy9pbEJi61tz6zNzPWeW7+xZVq2rWIniLkum9plVjtJLM6Wo2QG0JUrV8pJTubzYNvZNvY87vK94ldwUD5YH/rvf/8bLz+ypQF2JcNO4i2HkbmKmtQ/Q/ubjXG2Xti2t/5ieQ9rd/uDZTAsi2ZLt2zfbCzMsJ+1JVX2P5dNiHKN2yh/7Bvg4uvF7ZtWu9r7yCOPxP3RJr32zat9+WFXrOxKa/GlpMXf38yYlVRvIOk4ad9o2/LkTZ3obc74ZpNvl42n9hwoW/S/iqMgJhrF2ZUDY0tfXPaNrnWA4gdjmzVnOkRmKUJxe+21V/w/Oxjbt3W2HtAuyVkIqXbt2vE2tlSrbdu23s+qyQ7KBwuU2bpyG6AsZ2Mn59Yn7MTJJiD5yCwbOfLII+N1npkTQZetf7dvfXMt4css01NskLLBMtN/M309cxJnA631c1emzfq/bWPLcYr3/eIT48xnBOWDXU63bJi9X7Y+2LIOtgwqNDcT0t/sm0E7iNuVEuu/mf5kVyxsqWhxNh7aZ8YyQLY8wD5DzZo12/hatqzAlo267Fs+VBx2Mlf8alZmXLCTo+uvvz5etz5hwoTo7bffjpduZvI9JcnGZvtCKHPSpmzO+KZudGDjKZm0skf/qzgq3dIp99tcV7t27eJOYHeLKs5COBbOyaxzzqy9syBP8UxGcbZe+aijjopP7OwE0u6QklljbMEhm2zYbNq+ybYgbuZ/dkC1A3CuAm4oexagtUyFXY2yD3XmxD0zycin0qdNLC1AZnkIm7DY+nbFTvpsiZ/11eL9xk4gLVS2qRsFZCa8GTY5tm+W7Q4cmeV+tqTPvXPFq6++Gn8uLCxnr2+Xom2AdrcxdveLkM8aSp69R5aVsImi9Qu7ymoHWfuSI9cdzfLpbzb22XNa8DwzybC+Ze3ulbvMFyi2XMF+1van+GtZrsSWIGRex66M2FVh+wYS5Vvxz7yNZcX7iy0/sbHFJpI24bUx095nm3DusMMOwf0x3/3JLFuxie6mtgkd34z17+Ine3ZcsOO25YpQ+uh/Yytk/6t0Zwp2MLSDpq07Vuvt7E23uwjZ7cfs7kHWMezymn3DZjPNzDdtmSCRrXu2tcN2e0g7YBa/dGYHeFuPbHcrsLyFndDZ1Qy7amL/ZjNTOzBbwMfuKGD7ZK9lQSVbqpArdISyZ8tIbEJo3xTbe2vZDMtWZO7kY3cGy5dleOwbXrui8eOPP3r/bhMRO3mz/9oSE+s3tubUAuR2MrgpNrH94x//GH8Dbfts/c9yQtbnjPVv65/23HYiaf3fBmKbnNh/M2vzbWJi+2eXoa3/W/+1GxvY5enM3TXss2bfrthzFF8GiNJjX4xYX7Grb3ZbY+srNmbZla3iWZ1NCelvFqi08dSualjOyPqX3ZXK3n83i5RhGQ3rU/aZyXyxY/3QrhbanWFsf23ibp8ru4sK42HFOr6q5Ru2htyW7tnVMXtPra/Y1VtbTlL8xhglsT92QmZjtVu/ILOywPqgfaEYOr4Z69s2hlpftfHSxm47aXUznigd9L8LKmT/q3QTDTv42cmW3dLTTrAUu/Rvd1KxDmIHaDt42jeB9u1d5pKXHWDtioXNHu1bbVsSZbcds4NnhnUoWxZlJeatA9gExq5s2LaZJVp2+zSbiNi3dbZPduJqM1a7/a0tI0D5ZN/sW8DfrkbZnaLs5M3YyZd9U2LB/3xZH7Hns7Xslutx2aVfC55b8NYmt5b5sW9o/vKXv8Qng5ti3zbbBNoGV7vbj32jbH3NltcY69+2rMYmUjYptqUvdqtlG+Ts6pyx38+WvthtSO2bZuv/9u2L9e/it2S2z5Hto32Git+DHKXHxiO7W5ONJXZbYzuBty8/7Fa39mVHiJD+ZgdAe58tB2LjmI2fFqa0O+VZ7iJXhXi7M4pNUuy57EBskwm7mmd9zE4GrI/a1RjLLYVOjFA+jq+qVpDdfMJOnix4a++53WbZbtFp/cTGi5LcH+vD1q9sJUFx1q/sG287DtsNDELHN2N93FYq2GfL/s2+Sbbj+6aWr6Lk0P/2qJD9b6uifNaAAAAAVFKZmxjYl0tAaTupEvW/SndFAwAAAEDZY6IBAAAAIHUsnQIAAACQOq5oAAAAAEgdEw0AAAAAqWOiAQAAACB1TDQAAAAApG7bkI2srLtlxq0wCWCsorUVnrFChyWN/oey7H+GPoji6H8oaxyDUVH6X9BEwzoYN6dCcaXZH+h/cJV2fyiLPvjLL794bT/88IPXVrVq1agQrV27NutxtWrVvG3sQFgSCqH/KT///HPi+2A2bNiQ9VhVMlbvlzqRdT8HdoLjUs//008/Je6r2kZ9nmrUqBGVNxyDt8zy5cu9tnr16uX1XGvWrPHatt5666A+X1FtTn8ImmhkPvxWRh0wY8aMKbXXov+hLPtfWfXBJUuWeG0TJkzw2vr06eO1uSdjFembSHUyu80223htb775ZtbjffbZx9umevXqUUkohP6nzJ0712t75513vLYRI0ZkPe7WrZu3zaBBg7y2jh07em0rV67Mejxu3Dhvmx133NFrmz17ttf28ccfZz2eNm2at82AAQO8tqOPPjoqbzgGb5lrrrnGa7vxxhvzeq7nnnsuaNKy2267RZXF5vQ/MhoAAAAAUsdEAwAAAEDqgpZOAQBK18yZM7221157zWvr37+/11ZRlkqpdb5qmZTy/PPPZz1u0KCBt03fvn2jQqf+xiHZlT322MNrGzVqlNe2/fbbJz6/ynFceOGFUQh3rbvKLoXadtttE7MX//3vf4P29YYbbsh6fNZZZ+W9XyhZbmbI3HPPPV7bxIkTvbZnn3028flPPvlkr+2cc87x2g444ICoEHFFAwAAAEDqmGgAAAAASB0TDQAAAACpI6MBAOWQu57cvP32217btddem9d9/0NvI5sv9fzuevvQOheTJk3y2qZMmbLJ26Bi89x///1Zj4cNG+Zt07RpU6+tZcuWiW2qXoX7/plly5Z5be7Pqs+F6rdqO7e4mMp7qFv4qrbf/e53WY933nlnb5tddtnFa1OvqWouID0qR6Tem9dff91re+KJJ7Iev/HGG94269ev99q6d++ex55WTvRuAAAAAKljogEAAAAgdUw0AAAAAKSOiQYAAACA1FXKMLhboCg0cBjyXCrUpkJn6jWnT5+e9Xjy5MneNo0aNfLamjVr5rXVrFlzk4/TLvCkfm8AJUcFRBctWuS1DRo0yGsbOHBg1uM//elPeQe/Q8bTfAvvLViwwGv717/+5bW99dZbXtvChQsTg8QIP/69+eabiQHaH374wWtr0aKF1/b9999nPT7hhBO8bVTY/NFHH/XamjdvntjXli5d6rV17tzZazv//PMT+9qOO+4Y9Pzu3/WZZ54JChxvyfkI0tOxY0ev7bvvvvPa3LFTfS5atWrlte2zzz5bvI+VBVc0AAAAAKSOiQYAAACA1DHRAAAAAJA6JhoAAAAAUlcpw+Bphq3c51LPHRL8Ntddd13W43bt2gXtgwp616pVK+txtWrVgkLkqoprmzZtsh5Xr17d22a77bYL2lcA6QitdO0Gb83jjz+e9XjixIneNsccc4zXdthhh+U1nqptZsyYkRj2ffjhh4PGmoYNG+a1DwjnVl9XoVdV7f2TTz7x2qpUqZL1+Lzzzgt6v9T77N6IRN2YpHbt2kGVx4877rjEn6tRo4bXtm7dusR+qv4OCv20fFDnQqrPd+vWLevxihUrvG3mz58fFDYvVFzRAAAAAJA6JhoAAAAAUsdEAwAAAEDqmGgAAAAASF2lDIOXpNBqul988YXX1rVr18SK4qoC6cyZM722xo0bJ+7DrFmzvLaxY8d6bVWrVs16/OOPP3rb9OrVywvMuT8HID3r16/32lQQVqlTp07W46+++srb5sknn/TarrnmGq/t97//fWKA9n//+5/XdtlllyUGaJs2bRqF2LBhg9f2yy+/JFbnRfgNBGbPnp0YjFXHLPd9UO+XCnmrCt+K+1zqGKxC6u4NU9SxbdWqVYkV53M919Zbb5143ET5pW7Go95DNQ5j83BFAwAAAEDqmGgAAAAASB0TDQAAAACpI6OxmUKL7ai1fm7b4sWLvW322GOPoDWj7vpQtY1ah7tmzZrENbZqPXT9+vXLXTEad12uWrs7Z84cr+2DDz7w2k4++eSoMlHvs7u+313Hn8vatWsT+8wPP/wQtAZbFYN0C7u5WaZCpf5WavxRuQ33s6HWmO+6665e22233ea13X///VmP27dv720zYsQIr613796JGY2QcTLXZ9vtXyorAE0Vs3PHfVW4LvRz7h6fVBZCZfxC+ndoTlIdx0L2wS02qPZB7ceiRYuC9gvlg8oghRQmVsdDbBpXNAAAAACkjokGAAAAgNQx0QAAAACQOiYaAAAAAFJHGDyBGzB0Q265gmIqnL1y5cpNBqxz/Zzivmbz5s29bVQAVIWd3NdUoba+ffsmBs3LIxVSVYXK3OJiqpBY27ZtE99T1UdUSDW0gNW8efOyHs+fP9/bplOnTonFt9TPHnjggXkV31JtqoibCo5OnTo1MZj6zjvveNsccMABUaFRBaVUeFWNP26oVhWdUqHanj17Jr6Pqs+744P6uVxtIcXX1GfDLR7XoUOHxOfG/zNhwoTEv6c61ql+pLZzxwP1c+o9Xb16dWI/VWFc9blQz+9+LlQBwnXr1gX1SfdmGkuWLAkqwNugQQOvDaVPFUJWN+hx+586Ri5fvtxrGzNmjNfWo0ePqBBxRQMAAABA6phoAAAAAEgdEw0AAAAAqWOiAQAAACB1hMETwmNu0FJVDZ02bVpQCGzFihWJobDQaqlqX5NeL1eQyd0PFe7bYYcdEoNOJcl+XzdI6r4XX331lfdzt956a9DfxQ3Oz50719umWbNmXlv//v29Njcor15PBQ5VUNZ9TVWtV7W575cKs6u+oH7vJk2aJD6XCtGp90N9Llq3bp31+Prrr/e2KcQwuOpvqlq4eh9DAoxqXFFBW3esqVatmrdNvjeHUIF0NQaq/XJvbqD+XtC+/vrrvIL6qs8o7nFTVWFW773qW27fVcc+dZMTdRxzq86rz0Vo2Nz9W6ibMqgxcODAgV4bSt+bb77ptal+6r6v6jOwZs0ar+3ll1/22noQBgcAAACAdDDRAAAAAJA6JhoAAAAAUsdEAwAAAEDqCIMnhHxUyCyECo/ttddeiSHyWbNmBQUtW7RokRhiUvugKq+OHj0663HXrl2j8vjeJL0Xjz/+eNBzzZkzx2urWbNmYlX1yZMne20qAOi+N+oGAir0qCqyuxW4VYXdunXrem3qNd1w5Hfffedtoyqiq+cfNWpU1uPx48cHhT1VGM7dr2XLlnnb4P+ZPn2619a8efPEz74K+qpqx4r7/qhK8fkGh1UfUWOZek1VOR1h1FgWQoWn1VjjBrZVX1P9QwW4VVsIFRp321T/U/ulfu+QY8Enn3zitREGLx+GDRuWWO09dBxTx3MVBr/mmmuiQsQVDQAAAACpY6IBAAAAIHVMNAAAAACkjoxGwjpMd33o66+/7m3z0EMPeW2DBw/22r755pvEDIXSqlUrr80tTqXWh6o1qup3dNcldu7cOSqPktZKvvrqq15b7969vbYJEyYk/l1UQT21znjevHlem1u8rlGjRt42qk0VinILAak+o3IbKmsxceLExEyIW4jPzJ49OzGjoTItO+64o9e2YMGCxPd1n3328bZxMyBWeCvf/FRF5uZ/cvVVN+fgFirbkvXwoUXblJBciBqj1O/Yp0+fvPej0KmMxrbbbpt4TFFCcg6qX4XkHtRz5buN4v7OoZkT1ab2QRVGROlT4446btarV89rc/uu+lyo49+4cePy2NPKiSsaAAAAAFLHRAMAAABA6phoAAAAAEgdEw0AAAAAqSMMvplBSBVUfeGFF7y2IUOGJAaGVKGthg0bBhWwcoNoKsCmivOp52rQoMEmg+bl1ccff5z1eOXKlYmhZVO7du3EQLwKfKn3xg1+qwKL6rlUKFqFm90+qYo3qvdUFb1zw+AqMK4KRqpifO5rqr/NlClTvDYVSq5fv37W4xdffNHb5vDDD/f2vRDD4CqsP3bs2MTxIbTomQq0um0qGKt+Tm3nvmfq59TnRfWb6tWre20IM3/+/MT3Rr1/oe9DSH8LufnKllB9yw2uqyC7Coir4LD7t1B/B3X8QelT50JKyI0A1PikjkWqzxQF3ECgMuKKBgAAAIDUMdEAAAAAkDomGgAAAABSx0QDAAAAQOoKNgyuAj0qBOaGdi+99FJvm6eeesprGzRokNd24oknJgbfVBhXBfBq1KiRGNxTQTf1XAsXLkwMS5dHbkV2t4q2qVatWlCV4f3333+TVdzNhg0bgp6rZs2am9jr3Puqnst9v0ID0CoY796MQO2DqgKuAnJueF7tu+rfav/dIPncuXO9baZPn77JGxgUchhc9dWQ4K0aM0KC2OrmA+qzEXJzA7WNGrfyrWIOTd0swh0fVP+oWrVqUNVl933dksrgIQFa1W/Va7r7qvZd9T/1/O7+q76sQvcofXPmzMn7Z0PGmdCxaIMzTqrPU2XESA0AAAAgdUw0AAAAAKSOiQYAAACA1DHRAAAAAJC6gg2Dq8CX4lZ+7tSpk7fNs88+67W1bt3aa3Mrh6pqlSqQrsKXbiBOhXHVz6nwmxv2VeHf8ujJJ5/MenzYYYcFVU12g/Tmiy++SOwfKryoAoA//PBDXkFFFdR3f9YNbKowf6733t1/VWVc/T6qT7r9T22jnmvt2rWJwXVVsXzp0qV5BUkrkpDQa2glY/czrJ7L7ae5tmvevHnW4xkzZnjb9OrVy2ubMGFC4s0Z1PsYehMLFVYO2aZQqvFuLrcfqc+qOv6pG5i445YaC0Lev9DPemgYN+Q1Q88NQj6HSmgAHelZsmRJiT5/aP9b49yAhTA4AAAAAOSJiQYAAACA1DHRAAAAAJC6SpnRCFnrrNpef/31xLWTbmE3c++99yYW51PrBNW6Y7UGX63jc9cEqjWC6ndUhbXc4muqyF1Zs/3+/PPPN7mW/+677/Z+rl27dl5b7969vbZVq1ZtsohcrmyHysa465PV+6zW86oCeu5r9uvXz9vmueeey2stsuofKp+j+pb7O6rXUxkA9Xu7261YsSLxs6PWOVd0IdmBli1bem077bRTYkanUaNGQcXE1Bp89/1RY5T6TKnncvu4+vyov4N6TfU7wTdlypSg7dxjnfqMhR5L3fFAjQ+qLSSPEZqxCTkmqtcLLQ7p/mzoOv3x48d7bd27dw/6WeRH5RFD+5b7OdiSjNdCJ09ZKIVnuaIBAAAAIHVMNAAAAACkjokGAAAAgNQx0QAAAABQ2GFwFR5TgbWQwjmXX36517brrrt6bXPnzs16PGLECG+b4447LrHAmNovFThWoSUVEk567lw/p8JvbuDYDUabunXrRmXJike9++67WW0XX3xxYog4lBssVe+DKmClAuLuz6o+GlqAzN1u4sSJ3jaqHy1atCgxIKz6TGigMSSMrYLl6ufcNhUQLgTu30EV8Zo+fbrX9umnn3pt7k0rVPE81W/cIpimf//+WY8//PDDoAKR77zzjtd27rnnZj0eNWqUt02rVq2iEKooqouCffq9V9xjg/qs1q9f32tTBRzzLUAXGs4O+bmQ/qBuWFG7dm2vrVmzZonFYEOLr6kisoTBS5YqaquovhZaWDLEii04R6nIuKIBAAAAIHVMNAAAAACkjokGAAAAgNQx0QAAAABQvsPgJR28U88VEvxWAW4Vqjz55JO9tpkzZ2Y9PuWUU4KCtyEVH91qx2blypVBIeTtttsusZq3qgKuQt1uaFeF1dxAaGmz0N7o0aOz2m6++easx+6/51KzZs3E90IFmVXgVQUA3fdL9VsVOlN92X1N9+YEuahKyu7NAVRgM/Qz7O6/CmOq51d90v1Z9XOFEOANCdC6IW9z+OGHe22HHnpo4s0Nqlev7rUNHTrUazvwwAOzHr/++uuJrxe6nVthPteNNM4880yvbdKkSV4bfLNmzQrazv1Mq/6hjmvqxhYhAe6QiuKqTf1caBjc/Vn1eiogro6vIfulLFu2LGg7pEfd4EZRN0AICYO752O5rBTnd4WAKxoAAAAAUsdEAwAAAEDqmGgAAAAASB0TDQAAAABlGwZPCj+VdGBTVZFVlZkvvPDCrMddunTxtnn22We9ttNPP91rq1evXtbjNm3aeNs0adIkqCr37Nmz86rArQJ4blhZBdhUQEmFhN33rTxWr1y/fn30+eefb3K/VWBPUeHFkG1sH1wLFizw2tz3Qn0uVNhcBSjdkKPqC+r9Uu+9GywPrQIeEtpUz6WCviFjhPo7u5+nNKu1ViT77bef1/biiy96bQ8//HDiWLPXXnt5ba+++qrXttNOOyW+Xp8+fby2p556ymtz+8nXX3/tbbPbbrt5bY899ljQTR2SXq8QzZs3L+8grEvdVED9XGn/3UOD5SH7qcYfdTxwx9jQQPCcOXOCtkN61q1bF7SduqmA249CbliQyxpxXlgIGIUBAAAApI6JBgAAAIDUMdEAAAAAULYZjaRiN6rQ0urVq7222rVrJ+YXhgwZ4m2jnl+ty7/nnnsS8xhqrfMZZ5yRuK5dFWhTRcjUmmj376XyGGrdsVp/6q71U2tN1d9ZPb+7ln7+/PmJr2e/S2kWUbP1rx07dsxq6969e17rgtXf3V1fq4opqr6suIX31N9JZWpC1hmrtaCq0J/qk+7zh6xHzdXm9pnQQn/qNd013ipH5BZBLNSMxiWXXOK1LVy4MPHvrIq2vfnmm17bjBkzEvvSW2+9FbROf+LEiV7bn/70p6zHy5cv97b54IMPvLbGjRt7be3bt99kAU/oorOhVOZAjSvqsxhSfDJfapwPyZeEFuxTVLFWdz9UJk2hYF/pC8llhvYHtU1o//teZJwKAVc0AAAAAKSOiQYAAACA1DHRAAAAAJA6JhoAAAAAyjYM7nKLLc2dO9fbpn79+l7b+PHjE597//33Dyp0o0LXjzzySNbjzp07e9tceumlXtuUKVO8NjeArEJFqmib2s4NaIYWGVJBI/f3rlOnThRChfncQL0K24UWIyop1o9uvfXWrLaLL7446/GIESPkz4X0v6RilLmK7Knt3L9xaEhQhbrd90IF0lXwUhWyDN0PV0g/VSHv0DC4u/+qj7oFl9TzVDYrV64MKr6mijiGjCEdOnQIGn+mT5+eWAA1dL+aNWuW9Xjx4sXeNmr8UTdPGDt2rNcGn/obK+4xS900Q401alzJNygdIvQmFqofhYR91fijwuDueB06JhEGL33qPU0zDB56U4HvCYMDAAAAQDqYaAAAAABIHRMNAAAAAKljogEAAACg7MLgFhSbPHnyJoOp7dq1835OVX6tXr16YnhVVQb/7LPPvLZjjjkm8bkUFd5p2LBh4nYqlKhCwiqE7G6nnktV7lb76v4NVZBYBfdCfm9VSdYNFFrwrSSrv6ow18iRIzdZAb5ly5ZB1dHV3yokyKwCtSHBeXVjANWmQoJuCFrtV+hNBdz9Cq2krt5nN/gYWhk8JDS3fv16r60QK4O/++67QaFGNW65Y7G6mUO3bt28tuuuu85ru+222xJvpHHGGWd4bffdd5/XdtRRR2U9njRpkreNCpu7Nx4xo0aNynr8/vvve9sccMABUaELDcK641vVqlW9bdQxK3QcKY/U2BY6zrttoWHwQg0El6UVK1YEbafeQ/d93pJjzzrneF4oKu4IAQAAAKDcYqIBAAAAIHVMNAAAAACkjokGAAAAgLILg1uY5h//+McmQ2Y77rhjUPXZtm3bJgaw+vbt623Tr18/uV8u92dnzJgRVMlWhd/cEKUKa6tA2Zo1axIrUquKraqC6pIlSxLDsYsWLfr/2jsPICmqrg33R/hQkuQgWYKK5CxJMCIoFsaVoIhIEsUsiGLAjBJUQEEQAyUiCpIEDEQFDKAoAoJgAMlZUEnfX8+t6v1nuu/uNEtvmnmfqi2Yu3d6e7fPnHNPujfQaaa2Rjfv72Rr4J8xY0bU6759+zolS5Z0MgoasLyNiF458p5wntLvYsN2untYFChQIFCDpu1098GDB0e93r17dyD5szW8e5vYbHNsMmMb8zZ6264VtHHd+17bz+vUqVPCnQrdqFEj39j8+fMD6RpvU+PWrVt9c6pWreobGzFihG+sefPmUa8nTJjgm1OnTh3fWOvWrX1jNWrUiHq9du1a35zixYv7xpKSknxjXp23bt063xwRvFnbKzNB9VbQ08LT8/cJ0sQb9FpBm3296wVbo7yNRNjIIqth05FpfTZBm/5t/JWgGwEooyGEEEIIIYQIHTkaQgghhBBCiNCRoyGEEEIIIYTIvB4Naqu9/QreA5M2btzoe5+tbt5Wg+ut97f1aJQoUSJQfai3xtt2AJTtUDBb3feyZcti9kKsXr3aN2arifYecGirV7f1Pdjm2Q6iS8uhbba6wUKFCvnmFC5cOFMPaeKe+vfvHzXWp0+fqNfbt28PdECO7dkHqTG2jdl6Xrw9B7beH9szbdCgQcz7EonDxIkTfWPTp0/3jS1fvtw3Nm7cuJg9SP369fONLVmyxDfWu3fvmD1v7du3j/k+my6+/vrrfXNsnxdb70i3bt2iXrdt29Y3R9gPlLX9jb221FaLHvTAMa99sOnJtNbI264VtG7eO8+mh4Pqee/BwLa/jW39E+TAWJE5h1baCHI4bdC+m791YJ8QQgghhBBChIMcDSGEEEIIIUToyNEQQgghhBBChI4cDSGEEEIIIUToBO5KKlasmDNy5MiosfHjx0e9Xrhwoe9969ev941t2rQpZoPutGnTAjV+28a8B9DZGrJsh+vs2bMnZmOYranIdrCR7bCq8uXLR70uUqRIzIPxUmpeLlWqVMzmvqB4G91sDXJZ8ZAhb0N8kAZ5IbIq3sPrbBtitGnTxjc2dOhQ31jnzp1jNgS3aNHCN9a9e3ffWNeuXU/6MEho1qyZb8z7O/Xo0cM3p3Tp0r6xxYsX+8a8v9PMmTNjHhCY0mYX8YxtM5R58+b5xrx20mZ3bAfd2g4a9cqIzU7bCGJ7bI3fNvm2XSvIHNuhvN7NUGx/L9vfxnavZcqUiXlfIlyCbAAT9OBHm64LuhnB32oGF0IIIYQQQohwkKMhhBBCCCGECB05GkIIIYQQQojQkaMhhBBCCCGECJ2TOqLSe6p0z549U30NP/zwg29s6tSpvrHNmzfHfN++ffsCndi5f//+mO/znh6eUkOZ9xTzIUOG+Oa0atXKCYvZs2cHOqnU25C0YsWKQNe3NXJ6G9xtjXtNmzZNU/OTECIY3s0hbI2DVapU8Y3ZGqr/+OOPmE2v9erVC3QaeeXKlWOeDG7b2GLYsGExmzJ37NgRSEeNGjUq5iYcW7du9c05lU0y4oX69eunyQ7UqlUr0PXXrVsX89Tso0eP+ubYbIhtLMhGJLbfxybz3jHb+w4dOuQbO3DggG8sKSkp6vXcuXNjNhKfzN9VhIdNrmy6wbbW8q57bc/UtrmQdM//o4yGEEIIIYQQInTkaAghhBBCCCFCR46GEEIIIYQQInTkaAghhBBCCCEytxk8LdSsWTPQWJDmmr179/rGbCd1exu3tm3b5ptTtWrVQCeDe0/xLFGihJOeLFiwIE2nsW7fvj1QE523Sc/WIJc3b96YJ5HbmvWFEGnHuxmFrYHRtrHFnDlzfGO9evWKqU9t+s62GcWgQYOcWNhOTl66dGnMZvCOHTsG2qhjw4YNvrE1a9bEPPHbZh8SrUnTtoGA7W/gtZsdOnTwzZk8ebJvzGZfvfbIZrttDdY2O+Z9zjZZLlmyZCCZLF++fNTrihUr+ubY7J/tb7h+/fqYcmtrSK9UqZJvTKQvW7ZsCXRauPe0d5u8HT582DenYMGCga6/atUqJxFRRkMIIYQQQggROnI0hBBCCCGEEKEjR0MIIYQQQgiR/Xo00ort8DxbHWYQatSoEWhehQoVnMzG1kNh6yfxkt69I0KI9GXZsmWp1qbb6sJh9OjRvrFy5crFrDEfM2aMb2zKlCkx+8FsfRW2foxu3br5xvLnzx+zV8B2wFyXLl1i1rrbfp7t0MOGDRs6icTBgwcDjQX529nw9u+lNBZveA+ItPUD2bAdeCnSl6JFi/rGbD1dXv0U9Lnu3LnTN9amTRvfWLFixZxERBkNIYQQQgghROjI0RBCCCGEEEKEjhwNIYQQQgghROjI0RBCCCGEEEIkTjO4EEIkErNmzYp6vXLlSt+cY8eO+caaNGniGxs7dmzU6+PHj/vm2MZq167tGxs+fHjU6wEDBgTaqKNy5cq+sZw5c0a9Hjx4sG+O7aBC28Fn3sOvBg4c6JtTtmxZ39jUqVOdRKJatWq+sbZt28ZsEK9bt26g6x89etQ3liNHjpjPLyin8t5Yh9jaDrW1yV/u3Ll9Y9WrV4963a5du0DXatCgQeD7FeEwb968QPMmTZrkG2vatGmqhz7CokWLfGMtW7Y8qXuMZ5TREEIIIYQQQoSOHA0hhBBCCCFE6MjREEIIIYQQQoTOf/5nK1L0sGLFClPLaDtETyQmR44cMbWz9erVS/efJfkTmSl/GSWDu3fvTtMBYDYCqHXrHFs9vLfO3Nbb4e29SOla3rEg9xkU28/LlStXaAe/Zlf5s/X17Nq1yzfmfRa2Q86kg1P+XOzYsSPQ+woXLuwby5Mnz0n/fNng8Dl06FDMZ2PTKf/8849vzHYYaTxxMvKXK6ObsUR8gExklFxI/kRmyp/789Ib28JOZE2yk/zZFkaJcHJ3euNteM/ov6lscPjky5cvTe+Ld6fiVOUvUEZDCCGEEEIIIU4G9WgIIYQQQgghQkeOhhBCCCGEECJ05GgIIYQQQgghQkeOhhBCCCGEECJ05Ghkc9TLL0RikdU+82ndSleIjELyJ0TmkRCOxocffuicffbZzubNm63fZ5zvM+9UePnll811Mopvv/3W6dGjR4b9PBE+F154odO/f//Mvg2RTfjss8+cBx98MN311PLly80c/k3L/ezdu9epXr26s337dmf9+vXOjTfeeEr3LBILdCK68VTsusv777/vPPfccyHfoYgHgsqQODUCnaMhsiYo0F9++SWzb0MIkUFMmDAhQ37Oeeed57z33ntOlSpV0nQ/ixcvNu/lcLzJkyc7K1euTKc7FYlKq1atjIyWKFEi1XmjR492GjVqlGH3JYSIRo6GEEKIKPLnz+/UqVMnze9ftGiRc8EFF4R6T0JEUqRIEfMlhMjaZOnSKY51f/HFF51LL73UqVGjhjnq/JZbbnHWrFkTlWLt2rWr88EHHziXXXaZmXfVVVcZQ5cSBw4cMHNIzf7555/WOYzfc889JhJSu3Zt5+abb3Z++umnQPf96aefmnupWbOmc9111zlLly6N+v6OHTucAQMGGENcq1Yt59prrzUlCJH8+++/zsiRI502bdqY6/A3GDNmjHPixInk33vq1KnOli1bQin7EpnH0aNHnSeffNJp2LCh06BBA1OKsmfPnqjM1dVXX20WfsgLsvvxxx9HXYOIcadOncwcIn1vvvmm+VyoLCt+6NKli/PVV1+Zr9TKmn7//XenV69eTuPGjY3uuuGGG5yFCxf65i1YsMBp37690S/oq2nTpqVYOkW51SWXXOK88sorRic2b97cvNd2P+ioJUuWOC1btjTv4z3AHF4H0W/u74v8vvrqq07Tpk2d+vXrO3369DE6T2R/fvzxR2NXea5169Y1+uq7776LmoNdc20p8hYpx96yF2SF6z366KNmrdC2bVtjY5EXbKVKZBIbdMuoUaOMfUQvokv279+f/H2bjnO/jw1u166dWV/yfuYeP348+b3Y63vvvddp1qyZkVVsdKQ+PXHihDNs2DCz5uQa/MvaFtufCGTpjMYDDzzgfPPNN2bBX758eee3335zRowYYR7orFmzko8/R2GxeL/zzjtNJI45d9xxh3E2zjjjjKhrHjp0yLntttuMs/H22287Z555pk/5IDRJSUnO6aef7jzyyCPmXxZuLOSmTJniVK5cOdX7HjhwoLmXMmXKmNICft67775rBHDXrl3GsciTJ49z9913O4ULFzYK8/bbb3eef/55o0xpXGOhgNLt27evc8455xgjPnz4cOePP/5wBg8ebD4k3CfODx8M/j4ie4LTgOJ79tlnzTN94YUXnA0bNpiSk0mTJhknBHnGIKP4xo4d69x3333GOJcqVcqUz2GkUWBDhw419fH8i4yjHEV8wALq/vvvT/6/rawJg9azZ09TToI+yZUrl/PWW285vXv3NnJWoUKF5LmDBg1y7rrrLjMXmWKhhq7hK6XgCws9DOa+ffvMws12P6tWrTJGGPksV66cs23bNqM3KXNBXoPoNxcCMOjIhx9+2PxuGGccEPQ/ellkT/766y+ne/fuTpMmTcyi7ciRI6bE6dZbbzUOMGzdutU4n/369XPy5s1r5A67+vnnnztFixa1Xpf1ArYVJ/bw4cNO6dKlTR8j/ULYzFhlViJ+GTJkSLIuxN6iD9Enqek41o+vvfaaed25c2cTICbQjcwin08//bR5H3pw9+7dzuOPP27WoB999JEJGKLvmjRpYvQra0DG0Inff/+9uWbu3LmNTMc7WdbRQPHgFGBgiEwAXiYKigUZC/bixYub8YMHD5rFurvYRikhFMuWLTPREBeiaAgZDYo4GWXLlrX+bJwKhAzBwFkAonPcB07MSy+9lOq9I2xE6uD88893LrroIiNovO+NN94wi8m5c+cmX5uoCwtFFgZXXHGFqW/+8ssvzWLRXSjiKZ922mnm5990001O1apVTdr4v//97ymVOIjMh4XUuHHjjNy6r3E8cZRZeGF8MZIuyA0ZDjYDQD5QhAUKFHBef/315MXXWWedZZxlET+wkMeIQUqfeYzdxo0bjby4pUtkwQhGoFMjwYFFrwG6k2ge2YmUHI1jx44ZQ0nWzcV2P8gtGQicHAwtX5FzMORB9Bv8/fffRrdjnF257tChg4kWqsE8+0IghYAIz5rsg/tscUax+4BjicPgBvZwINysBzY1JRl94oknkmUOsJHYStnJxMUNLFMRQ3ADWrRoYQLUrLdS0nGsLcmCkBVmLQpkOgoVKmRecz10FXoTm33xxRcnr1WZg+wB3ycQeM011yR/H1uN3U4EsmzpFA+IxReLexwDnAaiu/PnzzffjzSaKJHIiL6rZDBS3gwJkTOiw67hskGp07nnnmsaGRE8vnLkyGGMMgYyNfBQKQNwQTnyvq+//jpZ4Ij0uU6GC5mMnTt3mkUCczDSrrMSOce9hogfWBC6TgaQVuX5IzNEmcleoCgxsERKJk6cGPUZ4LOBjEVGeG0yJuILsgaufuKLhVmxYsWMQ0ImFoM5Y8YMM04kzl28u0Q6DG7QBTlLDfRiLHAkXAfGxsnoNxahkbqayDSvXX0qsiduoIzMFpm1Tz75xMgukWHXfhNwiawecGWUxV9KsLiLdDKEAGwnZUqtW7eOGr/88stT1XGUJFPCj02O1LXujmhffPGF+ZcyVbIcZCcosyIQjv51nejGjRubuR07djQBQRxtguGUWCUCWTajAXiapKZYfOfLl89E2twFWeS+2N4UultSFVnvCzgs7Kbi1gZzTRtkMyjTYq4NHJiU0vYoR5ySSEjzugac0hebk4OSBeYxh+vkzJkzak5kBkfED+5zdUF+eP7IAvX2GGKcX5xYon5uxNn9DJAhs5USuDIl4hMyEJH9CkT6yfaOHz/elKGweCPyj9wQaSPTGllKGuncujor1nkDKenMyIwKpQWpORono98I9nhB1iNrq0X2AzkiYIKcUsJCJoOMFgsvN3IcKZ+p2XXvdYXw4uoL9E5qttcrQ6wFIaVjBMiIAGVQ9JIhy1SroE/J6pJdK1OmjCkT5Lr0ElMaTRkXzjayTmlVvJNlHQ0WWG4qitIQFucoGpRTZKrrZKB8AAeBshMEw1VoXkhnkdoiA2LDTYfZwEhirF2lCHi37u4YGHoyF17cMT4IzCGtTMQy0hi7Qu39sIjsjavMXHjuPH+eMwqOhSI17kRaiAQTDSGz4UIEDxmzLfpwTER8wiItMrPr6gUW54899pjpm1i7dq0zZ84cU7rJ9xlLT9DNOMI2A+5yMvqNeV6QdfWkZX/QTSy4kAP6etBplCvr2YqwcXWK1yZ6ba+XggULmn9xDipWrJhiMI81I9k4vgiM01tGyRXBnTFjxhjHgx5fvrgHsr44JlTXkOlIbU0ZD2TZ0ikavOmpYKGF4nEX7q6TkZaTPhEKGhip88RhoSHHBk7Gpk2bnEqVKpkGbvcLRciCzxuJ82Y7KGVxod6U5jZSZ8DOQqTjvDunTJ8+3RhnmjX5+aTnWCB45wBNweDNnIjsCYqG5+1CRITXOBbIIZsHIH84GeDuqOZG9pApPhd8XlzYJEA7rMQfkZ95dFmkfqK0BN1CJI2FGzoTGWLTiWrVqqW4w15Y9wMYUGqfU5sTVL8BfUiRzgZ2Abmm901kX3j2RHIJsGFPKfXEOWZhF7acyk4K5IuMmVfnuKX4KUHTOIE+qmEidS22mB4zdBFrOcqf3WvjyLABEHrYleWkpCTTE+dmZAl243RQtUDfcbyTZTMalC3xMIl4dOvWzUTuaAp0d6RgR4m0QjMQKS4yGrZtYXFEcCr4l5+NNzx79myzCxC1zqmBUD700ENmpywaJfFmqfFzm3lpHsKgcm3ug5pSyhtwTigTc3tBcEy4PwScCCF1y0QlKY9wd3dBKRPdw7izoNCOGtkTjC2RDXbT+fXXX40CozmW+lEiKTjFZC143jgU7JwR2YNEnTPySXoWeUV50VSLLEVm1kT2BxnAmaCUjn4F7656jGFQycYiUwRX6CujnInG2/S8H/QUTjNy7J0DM2fONIY7qH5zZRy5ZhMPgjZkonGa2DRDZF+oXSdQQtUCwUTKSrDJVATQ4xi5NeipgvwReEHG2BiBz4dILJAv1mDsbEdVC04u66ZYjgZrP/QP9hSHAL2FzuI1thXdRTYD+4wjwRwC4wREuD47ALrBQEpa0cc4PVyDjYEIuiTCWTBZ1tUnss/WYzwQjAx16sDOATxgtrFLKwga1/v555+NI+CF0gMaz6mtI8rCQo4I4VNPPWUchNRAaNh+192Kj2jNO++8k5yuI2tBehhHCsFk6z62SSPN5u5IwO9HuRheMNvjoojxlnFe3O3UAK+Ye0RZh6mYRcZCgxhRDp4jCuzKK680ZX7IAXKBPNIUzlakZOEomUGe3M8AnxU2TiCjgcwhe0RUkDXVLMcXRMEIZvB8bWcFsfkEBo36X/QVO5aRxqdWGH2RnveDzkRmcSYiYeFIFBAZRk6D6je3YZ0GTrYM53tkMnC0473UIN4hKEZTLIs0ni0LstWrV5uG2rBr1gm+EJDjs8ACUCQmyBhBYHQNa8p169aZhu1YYHfRXfS8oecIfpN1ZV3n7hqFvSaTi/1G3ljjEUjGpgPrPNaR9GjguNBLx+5VsXYwjRf+87+01CAJIbIMbqN45C5CZDVI3RLZTo9IthDpjZsZIbgkhBAie5JlS6eEEMEgEkhkhIgwmTIa3EjLEm1RiYkQQgghMgs5GkJkc9weJtK1lOGxLSS1n88880xC1H8KIYQQImui0ikhhBBCCCFE4jSDCyGEEEIIIbIvcjSEEEIIIYQQoSNHQwghhBBCCBE6cjSEEEIIIYQQoSNHQwghhBBCCBE6cjSEEEIIIYQQoSNHQwghhBBCCBE6cjSEEEIIIYQQTtj8H0NIplblEDdYAAAAAElFTkSuQmCC",
|
|
"text/plain": [
|
|
"<Figure size 1000x1000 with 25 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.figure(figsize=(10, 10))\n",
|
|
"for i in range(25):\n",
|
|
" plt.subplot(5, 5, i + 1)\n",
|
|
" plt.xticks([])\n",
|
|
" plt.yticks([])\n",
|
|
" plt.grid(False)\n",
|
|
" plt.imshow(X_train[i], cmap=plt.cm.binary)\n",
|
|
" plt.xlabel(label_map[y_train[i]])\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Consigne** : Standardiser les données en utilisant la classe [`StandardScaler`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html). On commencera par applatir les images en utilisant la méthode [`reshape`](https://numpy.org/doc/stable/reference/generated/numpy.reshape.html), puis on applique le pré-processing et on termine par reformer la matrice."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.preprocessing import StandardScaler\n",
|
|
"\n",
|
|
"scaler = StandardScaler()\n",
|
|
"X_train_flat = X_train.reshape(X_train.shape[0], -1).astype(np.float32)\n",
|
|
"X_valid_flat = X_valid.reshape(X_valid.shape[0], -1).astype(np.float32)\n",
|
|
"X_test_flat = X_test.reshape(X_test.shape[0], -1).astype(np.float32)\n",
|
|
"\n",
|
|
"X_train = scaler.fit_transform(X_train_flat).reshape(X_train.shape)\n",
|
|
"X_valid = scaler.transform(X_valid_flat).reshape(X_valid.shape)\n",
|
|
"X_test = scaler.transform(X_test_flat).reshape(X_test.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Modélisation\n",
|
|
"\n",
|
|
"On veut définir le réseau suivant:\n",
|
|
"* Deux convolutions avec 32 filtres 3x3 en conservant la taille. On utilisera la couche [`Conv2D`](https://keras.io/api/layers/convolution_layers/convolution2d/)\n",
|
|
"* Une couche max pooling avec un filtre 2x2 et 2 de stride. On utilisera la couche [`MaxPool2D`](https://keras.io/api/layers/pooling_layers/max_pooling2d/)\n",
|
|
"* Une couche [`Flatten`](https://keras.io/api/layers/reshaping_layers/flatten/) puis un réseau dense de 64 neurones\n",
|
|
"* Une couche de sortie à 10 neurones\n",
|
|
"\n",
|
|
"**Consigne** : Définir le réseau souhaité. On sélectionnera la fonction d'activation et la distribution initiale des poids adaptées."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = keras.models.Sequential(\n",
|
|
" [\n",
|
|
" keras.layers.Input(shape=(28, 28, 1)),\n",
|
|
" keras.layers.Conv2D(\n",
|
|
" filters=32,\n",
|
|
" kernel_size=3,\n",
|
|
" activation=\"relu\",\n",
|
|
" padding=\"same\",\n",
|
|
" ),\n",
|
|
" keras.layers.Conv2D(\n",
|
|
" filters=32,\n",
|
|
" kernel_size=3,\n",
|
|
" activation=\"relu\",\n",
|
|
" padding=\"same\",\n",
|
|
" ),\n",
|
|
" keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
|
|
" keras.layers.Flatten(),\n",
|
|
" keras.layers.Dense(units=64, activation=\"relu\"),\n",
|
|
" keras.layers.Dense(units=10, activation=\"softmax\"),\n",
|
|
" ],\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Consigne** : Calculer le nombre de paramètre du réseau de neurones à la main, puis vérifier avec la méthode [`summary`](https://keras.io/api/models/model/#summary-method)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_10\"</span>\n",
|
|
"</pre>\n"
|
|
],
|
|
"text/plain": [
|
|
"\u001b[1mModel: \"sequential_10\"\u001b[0m\n"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
|
|
"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
|
|
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
|
|
"│ conv2d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ conv2d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ max_pooling2d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ flatten_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6272</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ dense_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">401,472</span> │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ dense_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">650</span> │\n",
|
|
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
|
|
"</pre>\n"
|
|
],
|
|
"text/plain": [
|
|
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
|
|
"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
|
|
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
|
|
"│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m320\u001b[0m │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ max_pooling2d_10 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ flatten_10 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6272\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ dense_20 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m401,472\u001b[0m │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ dense_21 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n",
|
|
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">411,690</span> (1.57 MB)\n",
|
|
"</pre>\n"
|
|
],
|
|
"text/plain": [
|
|
"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m411,690\u001b[0m (1.57 MB)\n"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">411,690</span> (1.57 MB)\n",
|
|
"</pre>\n"
|
|
],
|
|
"text/plain": [
|
|
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m411,690\u001b[0m (1.57 MB)\n"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
|
|
"</pre>\n"
|
|
],
|
|
"text/plain": [
|
|
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.summary()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Consigne** : Lancer l'entraînement avec les paramètres adaptés sur quelques époque pour vérifier son fonctionnement."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1/5\n",
|
|
"750/750 - 17s - 22ms/step - accuracy: 0.8602 - loss: 0.3948 - val_accuracy: 0.8903 - val_loss: 0.2993\n",
|
|
"Epoch 2/5\n",
|
|
"750/750 - 15s - 20ms/step - accuracy: 0.9103 - loss: 0.2474 - val_accuracy: 0.9101 - val_loss: 0.2474\n",
|
|
"Epoch 3/5\n",
|
|
"750/750 - 15s - 20ms/step - accuracy: 0.9290 - loss: 0.1945 - val_accuracy: 0.9115 - val_loss: 0.2397\n",
|
|
"Epoch 4/5\n",
|
|
"750/750 - 17s - 23ms/step - accuracy: 0.9442 - loss: 0.1528 - val_accuracy: 0.9158 - val_loss: 0.2382\n",
|
|
"Epoch 5/5\n",
|
|
"750/750 - 17s - 22ms/step - accuracy: 0.9566 - loss: 0.1183 - val_accuracy: 0.9103 - val_loss: 0.2720\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model.compile(\n",
|
|
" optimizer=keras.optimizers.Adam(learning_rate=1e-3),\n",
|
|
" loss=\"sparse_categorical_crossentropy\",\n",
|
|
" metrics=[\"accuracy\"],\n",
|
|
")\n",
|
|
"\n",
|
|
"history = model.fit(\n",
|
|
" X_train,\n",
|
|
" y_train,\n",
|
|
" epochs=5,\n",
|
|
" batch_size=64,\n",
|
|
" validation_data=(X_valid, y_valid),\n",
|
|
" verbose=2,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Couche BatchNormalization\n",
|
|
"\n",
|
|
"On souhaite mesurer l'apport de la couche [`BatchNormalization`](https://keras.io/api/layers/normalization_layers/batch_normalization/) à un réseau de neurone. Pour cela, on se propose de faire une étude comparative sur le modèle que nous venons de définir. Nous nous proposons de placer la couche BatchNormalization uniquement entre les deux couches de convolution.\n",
|
|
"\n",
|
|
"**Consigne** : Définir une fonction `get_model` qui prend en paramètre:\n",
|
|
"* *normalization*: un booléen indiquant si la couche [`BatchNormalization`](https://keras.io/api/layers/normalization_layers/batch_normalization/) doit être présente dans le modèle\n",
|
|
"* *learning_rate*: un flottant correspondant au learning rate souhaité\n",
|
|
"\n",
|
|
"La fonction renvoie un modèle compilé."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def get_model(normalization: bool, learning_rate: float) -> keras.Model:\n",
|
|
" if normalization:\n",
|
|
" model = keras.models.Sequential(\n",
|
|
" [\n",
|
|
" keras.layers.Input(shape=(28, 28, 1)),\n",
|
|
" keras.layers.Conv2D(\n",
|
|
" filters=32,\n",
|
|
" kernel_size=3,\n",
|
|
" activation=\"relu\",\n",
|
|
" padding=\"same\",\n",
|
|
" ),\n",
|
|
" keras.layers.BatchNormalization(),\n",
|
|
" keras.layers.Conv2D(\n",
|
|
" filters=32,\n",
|
|
" kernel_size=3,\n",
|
|
" activation=\"relu\",\n",
|
|
" padding=\"same\",\n",
|
|
" ),\n",
|
|
" keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
|
|
" keras.layers.Flatten(),\n",
|
|
" keras.layers.Dense(units=64, activation=\"relu\"),\n",
|
|
" keras.layers.Dense(units=10, activation=\"softmax\"),\n",
|
|
" ],\n",
|
|
" )\n",
|
|
" else:\n",
|
|
" model = keras.models.Sequential(\n",
|
|
" [\n",
|
|
" keras.layers.Input(shape=(28, 28, 1)),\n",
|
|
" keras.layers.Conv2D(\n",
|
|
" filters=32,\n",
|
|
" kernel_size=3,\n",
|
|
" activation=\"relu\",\n",
|
|
" padding=\"same\",\n",
|
|
" ),\n",
|
|
" keras.layers.Conv2D(\n",
|
|
" filters=32,\n",
|
|
" kernel_size=3,\n",
|
|
" activation=\"relu\",\n",
|
|
" padding=\"same\",\n",
|
|
" ),\n",
|
|
" keras.layers.MaxPooling2D(pool_size=2, strides=2),\n",
|
|
" keras.layers.Flatten(),\n",
|
|
" keras.layers.Dense(units=64, activation=\"relu\"),\n",
|
|
" keras.layers.Dense(units=10, activation=\"softmax\"),\n",
|
|
" ],\n",
|
|
" )\n",
|
|
"\n",
|
|
" model.compile(\n",
|
|
" optimizer=keras.optimizers.Adam(learning_rate=learning_rate),\n",
|
|
" loss=\"sparse_categorical_crossentropy\",\n",
|
|
" metrics=[\"accuracy\"],\n",
|
|
" )\n",
|
|
" return model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Consigne** : Combien de paramètre un modèle avec la couche de [`BatchNormalization`](https://keras.io/api/layers/normalization_layers/batch_normalization/) a-t-il ? Est-ce équivalent à un modèle sans [`BatchNormalization`](https://keras.io/api/layers/normalization_layers/batch_normalization/) ? "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_11\"</span>\n",
|
|
"</pre>\n"
|
|
],
|
|
"text/plain": [
|
|
"\u001b[1mModel: \"sequential_11\"\u001b[0m\n"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
|
|
"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
|
|
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
|
|
"│ conv2d_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ batch_normalization_3 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
|
|
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>) │ │ │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ conv2d_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ max_pooling2d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ flatten_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6272</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ dense_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">401,472</span> │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ dense_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">650</span> │\n",
|
|
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
|
|
"</pre>\n"
|
|
],
|
|
"text/plain": [
|
|
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
|
|
"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
|
|
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
|
|
"│ conv2d_18 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m320\u001b[0m │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n",
|
|
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ conv2d_19 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ max_pooling2d_11 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ flatten_11 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6272\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ dense_22 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m401,472\u001b[0m │\n",
|
|
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
|
"│ dense_23 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n",
|
|
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">411,818</span> (1.57 MB)\n",
|
|
"</pre>\n"
|
|
],
|
|
"text/plain": [
|
|
"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m411,818\u001b[0m (1.57 MB)\n"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">411,754</span> (1.57 MB)\n",
|
|
"</pre>\n"
|
|
],
|
|
"text/plain": [
|
|
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m411,754\u001b[0m (1.57 MB)\n"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> (256.00 B)\n",
|
|
"</pre>\n"
|
|
],
|
|
"text/plain": [
|
|
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m64\u001b[0m (256.00 B)\n"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"model_normalized = get_model(normalization=True, learning_rate=1e-3)\n",
|
|
"\n",
|
|
"model_normalized.summary()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Pour s'affranchir un peu de l'aléatoire, nous proposons de lancer trois fois les deux types de modèles pour les comparer.\n",
|
|
"\n",
|
|
"**Consigne** : Écrire une boucle d'entraînement qui va stocker dans une liste les courbes d'apprentissage. Chaque élément de la liste correspondra à un dictionnaire avec pour clé:\n",
|
|
"* *type*: le type du réseau (avec ou sans BatchNormalization)\n",
|
|
"* *history*: l'historique d'apprentissage"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 25ms/step - accuracy: 0.8519 - loss: 0.4220 - val_accuracy: 0.8890 - val_loss: 0.3176\n",
|
|
"Epoch 2/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 25ms/step - accuracy: 0.9057 - loss: 0.2612 - val_accuracy: 0.9048 - val_loss: 0.2682\n",
|
|
"Epoch 3/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 25ms/step - accuracy: 0.9229 - loss: 0.2076 - val_accuracy: 0.9071 - val_loss: 0.2575\n",
|
|
"Epoch 4/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 24ms/step - accuracy: 0.9386 - loss: 0.1689 - val_accuracy: 0.9178 - val_loss: 0.2389\n",
|
|
"Epoch 5/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 24ms/step - accuracy: 0.9499 - loss: 0.1365 - val_accuracy: 0.9142 - val_loss: 0.2515\n",
|
|
"Epoch 1/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 25ms/step - accuracy: 0.8575 - loss: 0.4019 - val_accuracy: 0.8900 - val_loss: 0.2951\n",
|
|
"Epoch 2/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 24ms/step - accuracy: 0.9098 - loss: 0.2486 - val_accuracy: 0.9018 - val_loss: 0.2785\n",
|
|
"Epoch 3/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 24ms/step - accuracy: 0.9287 - loss: 0.1943 - val_accuracy: 0.9118 - val_loss: 0.2507\n",
|
|
"Epoch 4/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 23ms/step - accuracy: 0.9410 - loss: 0.1596 - val_accuracy: 0.9111 - val_loss: 0.2514\n",
|
|
"Epoch 5/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 27ms/step - accuracy: 0.9524 - loss: 0.1258 - val_accuracy: 0.9097 - val_loss: 0.2765\n",
|
|
"Epoch 1/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 26ms/step - accuracy: 0.8556 - loss: 0.4125 - val_accuracy: 0.8885 - val_loss: 0.3105\n",
|
|
"Epoch 2/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 24ms/step - accuracy: 0.9057 - loss: 0.2611 - val_accuracy: 0.9078 - val_loss: 0.2556\n",
|
|
"Epoch 3/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 24ms/step - accuracy: 0.9241 - loss: 0.2071 - val_accuracy: 0.9078 - val_loss: 0.2501\n",
|
|
"Epoch 4/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 26ms/step - accuracy: 0.9381 - loss: 0.1680 - val_accuracy: 0.9144 - val_loss: 0.2460\n",
|
|
"Epoch 5/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 25ms/step - accuracy: 0.9491 - loss: 0.1378 - val_accuracy: 0.9134 - val_loss: 0.2607\n",
|
|
"Epoch 1/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 23ms/step - accuracy: 0.8570 - loss: 0.4044 - val_accuracy: 0.8943 - val_loss: 0.2826\n",
|
|
"Epoch 2/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 28ms/step - accuracy: 0.9096 - loss: 0.2515 - val_accuracy: 0.9045 - val_loss: 0.2609\n",
|
|
"Epoch 3/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 26ms/step - accuracy: 0.9282 - loss: 0.1999 - val_accuracy: 0.9141 - val_loss: 0.2360\n",
|
|
"Epoch 4/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 23ms/step - accuracy: 0.9417 - loss: 0.1589 - val_accuracy: 0.9187 - val_loss: 0.2385\n",
|
|
"Epoch 5/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 21ms/step - accuracy: 0.9533 - loss: 0.1287 - val_accuracy: 0.9013 - val_loss: 0.2911\n",
|
|
"Epoch 1/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 23ms/step - accuracy: 0.8619 - loss: 0.3881 - val_accuracy: 0.8992 - val_loss: 0.2768\n",
|
|
"Epoch 2/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 25ms/step - accuracy: 0.9149 - loss: 0.2365 - val_accuracy: 0.8944 - val_loss: 0.2771\n",
|
|
"Epoch 3/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 23ms/step - accuracy: 0.9308 - loss: 0.1882 - val_accuracy: 0.9162 - val_loss: 0.2368\n",
|
|
"Epoch 4/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 24ms/step - accuracy: 0.9457 - loss: 0.1482 - val_accuracy: 0.9101 - val_loss: 0.2530\n",
|
|
"Epoch 5/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 25ms/step - accuracy: 0.9575 - loss: 0.1167 - val_accuracy: 0.9150 - val_loss: 0.2544\n",
|
|
"Epoch 1/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 23ms/step - accuracy: 0.8632 - loss: 0.3900 - val_accuracy: 0.8923 - val_loss: 0.2962\n",
|
|
"Epoch 2/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 23ms/step - accuracy: 0.9131 - loss: 0.2449 - val_accuracy: 0.9139 - val_loss: 0.2346\n",
|
|
"Epoch 3/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 23ms/step - accuracy: 0.9310 - loss: 0.1897 - val_accuracy: 0.9169 - val_loss: 0.2320\n",
|
|
"Epoch 4/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 22ms/step - accuracy: 0.9446 - loss: 0.1501 - val_accuracy: 0.9151 - val_loss: 0.2353\n",
|
|
"Epoch 5/5\n",
|
|
"\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 22ms/step - accuracy: 0.9560 - loss: 0.1180 - val_accuracy: 0.9145 - val_loss: 0.2734\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"training_curves = []\n",
|
|
"\n",
|
|
"num_trainings = 3\n",
|
|
"epochs = 5\n",
|
|
"batch_size = 64\n",
|
|
"\n",
|
|
"for normalized in [True, False]:\n",
|
|
" for _ in range(num_trainings):\n",
|
|
" model = get_model(normalization=normalized, learning_rate=1e-3)\n",
|
|
" history = model.fit(\n",
|
|
" X_train,\n",
|
|
" y_train,\n",
|
|
" epochs=epochs,\n",
|
|
" batch_size=batch_size,\n",
|
|
" validation_data=(X_valid, y_valid),\n",
|
|
" )\n",
|
|
" training_curves.append({\"history\": history, \"normalization\": normalized})"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Il faut maintenant visualiser les résultats. Commençons par préparer les données.\n",
|
|
"\n",
|
|
"**Consigne** : Définir une fonction `agregate_result` qui prend en paramètre:\n",
|
|
"* *results*: le dictionnaire de résultat, au format décrit précédemment\n",
|
|
"* *network_type*: chaîne de caractère identifiant le type de réseau\n",
|
|
"* *metric_name*: le nom de la métrique d'intérêt\n",
|
|
"\n",
|
|
"La fonction renverra deux matrices de tailles (nombre de comparaisons, nombre d'époque) : une pour le dataset d'entraînement et une pour le dataset de validation. On concatène donc les différentes courbes d'apprentissage."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 126,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def agregate_result(\n",
|
|
" results: list,\n",
|
|
" normalized: bool,\n",
|
|
" metric_name: str = \"accuracy\",\n",
|
|
") -> pd.DataFrame:\n",
|
|
" train_curves = []\n",
|
|
" val_curves = []\n",
|
|
"\n",
|
|
" for res in results:\n",
|
|
" if res.get(\"normalization\") == normalized:\n",
|
|
" hist_obj = res.get(\"history\")\n",
|
|
" train_curves.append(hist_obj.history[metric_name])\n",
|
|
" val_curves.append(hist_obj.history[f\"val_{metric_name}\"])\n",
|
|
"\n",
|
|
" return np.array(train_curves).flatten(), np.array(val_curves).flatten()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Consigne** : Visualiser les courbes d'apprentissage en faisant apparaître des intervals de confiance. On prendra exemple sur la fonction `show_results` du TP précédent. Commenter."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 191,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 1400x500 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, axs = plt.subplots(1, 2, figsize=(14, 5))\n",
|
|
"metrics = [\"loss\", \"accuracy\"]\n",
|
|
"colors = sns.color_palette(\"husl\", 2)\n",
|
|
"\n",
|
|
"for idx, metric in enumerate(metrics):\n",
|
|
" ax = axs[idx]\n",
|
|
" for normalized in [True, False]:\n",
|
|
" train, val = agregate_result(\n",
|
|
" training_curves,\n",
|
|
" normalized=normalized,\n",
|
|
" metric_name=metric,\n",
|
|
" )\n",
|
|
" train_runs = train.reshape(-1, epochs)\n",
|
|
" val_runs = val.reshape(-1, epochs)\n",
|
|
"\n",
|
|
" mean_train = np.mean(train_runs, axis=0)\n",
|
|
" std_train = np.std(train_runs, axis=0)\n",
|
|
" mean_val = np.mean(val_runs, axis=0)\n",
|
|
" std_val = np.std(val_runs, axis=0)\n",
|
|
"\n",
|
|
" color = colors[0] if normalized else colors[1]\n",
|
|
" label_prefix = \"With BN\" if normalized else \"Without BN\"\n",
|
|
"\n",
|
|
" ax.plot(mean_train, label=label_prefix, color=color, linestyle=\"-\")\n",
|
|
" ax.fill_between(\n",
|
|
" range(epochs),\n",
|
|
" mean_train - std_train,\n",
|
|
" mean_train + std_train,\n",
|
|
" color=color,\n",
|
|
" alpha=0.2,\n",
|
|
" )\n",
|
|
"\n",
|
|
" ax.plot(mean_val, color=color, linestyle=\"--\")\n",
|
|
" ax.fill_between(\n",
|
|
" range(epochs),\n",
|
|
" mean_val - std_val,\n",
|
|
" mean_val + std_val,\n",
|
|
" color=color,\n",
|
|
" alpha=0.2,\n",
|
|
" )\n",
|
|
"\n",
|
|
" ax.set_title(f\"Training and Validation {metric.capitalize()}\")\n",
|
|
" ax.set_xlabel(\"Epochs\")\n",
|
|
" ax.set_ylabel(metric.capitalize())\n",
|
|
" ax.legend()\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Pour continuer\n",
|
|
"\n",
|
|
"Choisir une ou plusieurs pistes de recherche parmi les suivantes. Il est possible de choisir une autre direction, mais elle doit être validé auparavant.\n",
|
|
"\n",
|
|
"1. Nous avons utilisé la couche [`MaxPool2D`](https://keras.io/api/layers/pooling_layers/max_pooling2d/), mais on peut se poser la question de l'utilisation de la couche [`AveragePooling2D`](https://keras.io/api/layers/pooling_layers/average_pooling2d/) voire l'absence de couche de pooling.\n",
|
|
"2. Nous avons vu en cours qu'une agencement particulier de couches permet d'avoir les meilleurs performance pour la compétition ImageNet: les ResNet. Comment écrire un réseau résiduel à la main ?\n",
|
|
"3. Dans un [billet de blog](https://www.rpisoni.dev/posts/cossim-convolution/) est proposée une alternative à la couche convolutionnelle traditionnelle. On se propose de l'implémenter et d'explorer ses capacités."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "studies",
|
|
"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": "3.13.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|