1341 lines
345 KiB
Text
1341 lines
345 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "3eb7a65b",
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"metadata": {},
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"outputs": [],
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"source": [
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"####### Import #######\n",
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"from sklearn.datasets import fetch_openml\n",
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"import sklearn\n",
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"from matplotlib import pyplot as plt\n",
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"from sklearn import model_selection\n",
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"from sklearn import neural_network\n",
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"from sklearn import metrics\n",
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"import numpy as np\n",
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"import time\n",
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"import warnings\n",
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "a8812842",
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"metadata": {},
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"outputs": [],
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"source": [
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"####### Loading data #######\n",
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"mnist = fetch_openml('mnist_784',as_frame=False)\n",
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"# images = mnist.data.reshape((-1, 28, 28))\n",
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"# plt.imshow(images[0],cmap=plt.cm.gray_r,interpolation=\"nearest\")\n",
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"# plt.show()\n",
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"# print(\"Classe : \", mnist.target[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "6ec263be",
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"metadata": {},
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"outputs": [],
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"source": [
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"####### Division des données pour train/test #######\n",
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"# xtrain data set d'entraînement et ytrain étiquettes de xtrain\n",
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"# xtest dataset de prédiction et ytest étiquettes de xtest\n",
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"xtrain, xtest, ytrain, ytest = model_selection.train_test_split(mnist.data, mnist.target,train_size=0.7)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "3b1a54ef",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training...\n",
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"Predicting...\n",
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"Score échantillon de test : 0.9493333333333334\n",
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"Classe image 4 : 9\n",
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"Classe prédite image 4 : 9\n",
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"Précision pour chaque classe : \n",
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" [0.97175682 0.97969543 0.95804541 0.93567251 0.94288528 0.95647383\n",
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" 0.94714286 0.95737855 0.89422181 0.94911067]\n",
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"Matrice de confusion :\n",
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" [[2030 0 10 2 2 5 17 1 27 1]\n",
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" [ 0 2316 6 9 9 6 3 6 22 2]\n",
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" [ 17 10 1941 15 9 5 21 28 48 1]\n",
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" [ 2 0 25 2080 1 22 1 13 43 7]\n",
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" [ 7 8 9 2 1915 4 21 4 13 43]\n",
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" [ 5 4 1 45 2 1736 29 1 29 16]\n",
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" [ 8 2 5 0 10 7 1989 0 18 0]\n",
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" [ 8 4 18 10 8 1 1 2089 4 28]\n",
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" [ 2 12 9 20 13 19 15 5 1919 5]\n",
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" [ 10 8 2 40 62 10 3 35 23 1921]]\n",
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"Zero-one classification loss :\n",
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" 0.05066666666666664\n"
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]
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}
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],
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"source": [
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"####### Premier modèle de Classifier #######\n",
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"\n",
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"#Entraîne le classifier\n",
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"clf = neural_network.MLPClassifier(random_state=1, max_iter=100, hidden_layer_sizes=(50))\n",
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"print(\"Training...\")\n",
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"clf.fit(xtrain, ytrain)\n",
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"\n",
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"#Prédiction sur le jeu de tests\n",
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"print(\"Predicting...\")\n",
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"pred = clf.predict(xtest)\n",
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"#print(\"Prédiction : \", pred)\n",
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"# Probabilités des prédictions sur xtest\n",
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"pred_proba = clf.predict_proba(xtest)\n",
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"#print(\"Probabilités : \", pred_proba)\n",
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"# On calcule le score obtenu sur xtest avec les étiquettes ytest\n",
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"score = clf.score(xtest, ytest)\n",
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"print(\"Score échantillon de test : \", score)\n",
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"\n",
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"#Infos image 4\n",
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"print(\"Classe image 4 : \", ytest[3])\n",
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"print(\"Classe prédite image 4 : \", pred[3])\n",
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"\n",
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"#Calcul de différentes metrics\n",
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"print(\"Précision pour chaque classe : \\n\", metrics.precision_score(ytest, pred,average=None))\n",
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"print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))\n",
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"print(\"Zero-one classification loss :\\n\", metrics.zero_one_loss(ytest, pred))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "5a4a5485",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Computing for 1 hidden layers...\n",
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"Computing for 10 hidden layers...\n",
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"Computing for 19 hidden layers...\n",
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"Computing for 28 hidden layers...\n",
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"Computing for 37 hidden layers...\n",
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"Computing for 46 hidden layers...\n",
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"Computing for 55 hidden layers...\n",
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"Computing for 64 hidden layers...\n",
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"Computing for 73 hidden layers...\n",
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"Computing for 82 hidden layers...\n",
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"Computing for 91 hidden layers...\n",
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"Computing for 100 hidden layers...\n",
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"Done\n"
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]
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}
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],
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"source": [
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"####### Variations du nombres de couches de 1 à 100 couches cachées #######\n",
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"\n",
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"list_training_times_k = []\n",
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"list_precision_scores_k = []\n",
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"list_zero_one_loss_k = []\n",
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" \n",
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"for i in range(1, 101, 9):\n",
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" print(\"Computing for \", i, \" hidden layers...\")\n",
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" #Entraîne le classifier\n",
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" clf = neural_network.MLPClassifier(random_state=1, max_iter=25, hidden_layer_sizes=((50,) * i))\n",
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" t1 = round(time.time(),5)\n",
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" clf.fit(xtrain, ytrain)\n",
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" t2 = round(time.time(),5)\n",
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" #Prédiction sur le jeu de tests\n",
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" pred = clf.predict(xtest)\n",
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" # Probabilités des prédictions sur xtest\n",
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" pred_proba = clf.predict_proba(xtest)\n",
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" # On sauvegarde le temps de calcul, la précision et \n",
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" # les taux d'erreurs par classe\n",
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" list_training_times_k.append(t2-t1)\n",
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" list_precision_scores_k.append(clf.score(xtest, ytest))\n",
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" list_zero_one_loss_k.append(metrics.zero_one_loss(ytest, pred))\n",
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" \n",
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"print(\"Done\")\n",
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"# print(\"Liste des scores : \\n\", list_precision_scores)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "9b961ed8",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Text(36.0, 0.5, 'Zero-one loss')"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 504x720 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"##### x coordinates\n",
|
|
"x = np.arange(1,101,9)\n",
|
|
"training_times = list_training_times_k\n",
|
|
"precision_scores = list_precision_scores_k\n",
|
|
"zero_one_loss = list_zero_one_loss_k\n",
|
|
" \n",
|
|
"training_times = [round(i,2) for i in training_times]\n",
|
|
"precision_scores = [round(i,2) for i in precision_scores]\n",
|
|
"zero_one_loss = [round(i,2) for i in zero_one_loss]\n",
|
|
"\n",
|
|
"### Create plot\n",
|
|
"fig, figs = plt.subplots(nrows=3, ncols=1, figsize=(7,10))\n",
|
|
"fig.tight_layout(pad=3.0)\n",
|
|
"figs[0].plot(x,training_times, marker='o', color='r')\n",
|
|
"figs[1].plot(x,precision_scores, marker='o', color='b')\n",
|
|
"figs[2].plot(x,zero_one_loss, marker='o', color='g')\n",
|
|
"\n",
|
|
"### Add every x coordinates\n",
|
|
"figs[0].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
|
|
"figs[1].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
|
|
"figs[2].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
|
|
"\n",
|
|
"for i in range(len(x)):\n",
|
|
" figs[0].annotate(training_times[i], # this is the text\n",
|
|
" (x[i],training_times[i]), # these are the coordinates to position the label\n",
|
|
" textcoords=\"offset points\", # how to position the text\n",
|
|
" xytext=(12,3), # distance from text to points (x,y)\n",
|
|
" ha='center') # horizontal alignment can be left, right or center\n",
|
|
" figs[1].annotate(precision_scores[i], # this is the text\n",
|
|
" (x[i],precision_scores[i]), # these are the coordinates to position the label\n",
|
|
" textcoords=\"offset points\", # how to position the text\n",
|
|
" xytext=(12,3), # distance from text to points (x,y)\n",
|
|
" ha='center') # horizontal alignment can be left, right or center\n",
|
|
" figs[2].annotate(zero_one_loss[i], # this is the text\n",
|
|
" (x[i],zero_one_loss[i]), # these are the coordinates to position the label\n",
|
|
" textcoords=\"offset points\", # how to position the text\n",
|
|
" xytext=(12,3), # distance from text to points (x,y)\n",
|
|
" ha='center') # horizontal alignment can be left, right or center\n",
|
|
"\n",
|
|
"figs[0].set_xticks(x)\n",
|
|
"figs[1].set_xticks(x)\n",
|
|
"figs[2].set_xticks(x)\n",
|
|
" \n",
|
|
"### Add title and axis names\n",
|
|
"figs[0].title.set_text('Training times for k hidden layers (layer_size=50)')\n",
|
|
"figs[1].title.set_text('Precision score for k hidden layers (layer_size=50)')\n",
|
|
"figs[2].title.set_text('Zero-one loss metrics for k hidden layers (layer_size=50)')\n",
|
|
"figs[0].set_xlabel('n_hidden_layer')\n",
|
|
"figs[1].set_xlabel('n_hidden_layer')\n",
|
|
"figs[2].set_xlabel('n_hidden_layer')\n",
|
|
"figs[0].set_ylabel('Training times (in seconds)')\n",
|
|
"figs[1].set_ylabel('Precision score')\n",
|
|
"figs[2].set_ylabel('Zero-one loss')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "16283951",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[0.214, 0.114, 0.114, 0.215, 0.377, 0.634, 0.617, 0.531, 0.492, 0.765, 0.706, 0.66, 0.738, 0.838, 0.782, 0.879, 0.846, 0.897, 0.911, 0.87, 0.905, 0.919, 0.879, 0.91, 0.913, 0.922, 0.922, 0.922, 0.918, 0.916, 0.917, 0.919, 0.924, 0.928, 0.929, 0.935, 0.927, 0.923, 0.929, 0.929, 0.931, 0.936, 0.934, 0.927, 0.936, 0.929, 0.935, 0.931, 0.937, 0.93, 0.938, 0.935, 0.938, 0.937, 0.939, 0.938, 0.941, 0.939, 0.941, 0.943, 0.945, 0.941, 0.944, 0.943, 0.941, 0.941, 0.941, 0.945, 0.942, 0.947, 0.945, 0.944, 0.948, 0.939, 0.947, 0.943, 0.94, 0.946, 0.95, 0.945, 0.951, 0.949, 0.947, 0.947, 0.944, 0.95, 0.951, 0.946, 0.951, 0.949, 0.952, 0.948, 0.947, 0.949, 0.95, 0.949, 0.947, 0.946, 0.95, 0.954]\n",
|
|
"0.954\n",
|
|
"100\n",
|
|
"0.954\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# list_rounded_scores = [round(i,3) for i in list_scores]\n",
|
|
"# print(list_rounded_scores)\n",
|
|
"\n",
|
|
"# n = 1\n",
|
|
"# max_score = 0\n",
|
|
"# max_index = 1\n",
|
|
"# for i in list_rounded_scores:\n",
|
|
"# if i > max_score:\n",
|
|
"# max_score = i\n",
|
|
"# max_index = n\n",
|
|
"# n += 1\n",
|
|
"# else:\n",
|
|
"# n += 1\n",
|
|
"# print(max_score)\n",
|
|
"# print(max_index)\n",
|
|
"# print(list_rounded_scores[max_index-1])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "5726fcb1",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Nombres de couches cachées : [7 2 8 8 7]\n",
|
|
"Taille des couches cachées : [ 80 54 283 82 174]\n",
|
|
"Computing for 7 hidden layers of size 80 ...\n",
|
|
"Computing for 2 hidden layers of size 54 ...\n",
|
|
"Computing for 8 hidden layers of size 283 ...\n",
|
|
"Computing for 8 hidden layers of size 82 ...\n",
|
|
"Computing for 7 hidden layers of size 174 ...\n",
|
|
"Done\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"####### Construction de cinq modèles #######\n",
|
|
"\n",
|
|
"rand_nb_couches_cachees = np.random.randint(low=1, high=10, size=5)\n",
|
|
"print(\"Nombres de couches cachées : \", rand_nb_couches_cachees)\n",
|
|
"rand_taille_couches = np.random.randint(low=10, high=300, size=5)\n",
|
|
"print(\"Taille des couches cachées : \", rand_taille_couches)\n",
|
|
"\n",
|
|
"#Liste des tuples utilisés comme arguments pour hidden_layer_sizes\n",
|
|
"list_args = []\n",
|
|
"for i in range(5):\n",
|
|
" list_args += [((rand_taille_couches[i],) * rand_nb_couches_cachees[i])]\n",
|
|
"\n",
|
|
"list_training_times_models = []\n",
|
|
"list_precision_scores_models = []\n",
|
|
"list_zero_one_loss_models = []\n",
|
|
"\n",
|
|
"for i in range(5):\n",
|
|
" print(\"Computing for \", rand_nb_couches_cachees[i], \" hidden layers of size \", rand_taille_couches[i], \"...\")\n",
|
|
" #Entraîne le classifier\n",
|
|
" clf = neural_network.MLPClassifier(random_state=1, max_iter=25, hidden_layer_sizes=list_args[i])\n",
|
|
" t1 = round(time.time(),5)\n",
|
|
" clf.fit(xtrain, ytrain)\n",
|
|
" t2 = round(time.time(),5)\n",
|
|
" #Prédiction sur le jeu de tests\n",
|
|
" pred = clf.predict(xtest)\n",
|
|
" # Probabilités des prédictions sur xtest\n",
|
|
" pred_proba = clf.predict_proba(xtest)\n",
|
|
" # On sauvegarde le temps de calcul, la précision et \n",
|
|
" # les taux d'erreurs par classe\n",
|
|
" list_training_times_models.append(t2-t1)\n",
|
|
" list_precision_scores_models.append(clf.score(xtest, ytest))\n",
|
|
" list_zero_one_loss_models.append(metrics.zero_one_loss(ytest, pred))\n",
|
|
"print(\"Done\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "741f82ca",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(45.0, 0.5, 'Zero-one loss')"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
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"text/plain": [
|
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"<Figure size 576x720 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
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},
|
|
"output_type": "display_data"
|
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}
|
|
],
|
|
"source": [
|
|
"##### x coordinates\n",
|
|
"x = []\n",
|
|
"for i in range(len(rand_nb_couches_cachees)):\n",
|
|
" x.append(\"nb_hidden_layers=\"+str(rand_nb_couches_cachees[i])+\",\\nsize_layer=\"+str(rand_taille_couches[i]))\n",
|
|
"training_times = list_training_times_models\n",
|
|
"precision_scores = list_precision_scores_models\n",
|
|
"zero_one_loss = list_zero_one_loss_models\n",
|
|
" \n",
|
|
"training_times = [round(i,2) for i in training_times]\n",
|
|
"precision_scores = [round(i,2) for i in precision_scores]\n",
|
|
"zero_one_loss = [round(i,2) for i in zero_one_loss]\n",
|
|
"\n",
|
|
"### Create plot\n",
|
|
"fig, figs = plt.subplots(nrows=3, ncols=1, figsize=(8,10))\n",
|
|
"fig.tight_layout(pad=4.0)\n",
|
|
"figs[0].plot(x,training_times, marker='o', color='r')\n",
|
|
"figs[1].plot(x,precision_scores, marker='o', color='b')\n",
|
|
"figs[2].plot(x,zero_one_loss, marker='o', color='g')\n",
|
|
"\n",
|
|
"### Add every x coordinates\n",
|
|
"figs[0].tick_params(axis='both', which='both', labelsize=9, labelbottom=True)\n",
|
|
"figs[1].tick_params(axis='both', which='both', labelsize=9, labelbottom=True)\n",
|
|
"figs[2].tick_params(axis='both', which='both', labelsize=9, labelbottom=True)\n",
|
|
"\n",
|
|
"for i in range(len(x)):\n",
|
|
" figs[0].annotate(training_times[i], # this is the text\n",
|
|
" (x[i],training_times[i]), # these are the coordinates to position the label\n",
|
|
" textcoords=\"offset points\", # how to position the text\n",
|
|
" xytext=(12,3), # distance from text to points (x,y)\n",
|
|
" ha='center') # horizontal alignment can be left, right or center\n",
|
|
" figs[1].annotate(precision_scores[i], # this is the text\n",
|
|
" (x[i],precision_scores[i]), # these are the coordinates to position the label\n",
|
|
" textcoords=\"offset points\", # how to position the text\n",
|
|
" xytext=(12,3), # distance from text to points (x,y)\n",
|
|
" ha='center') # horizontal alignment can be left, right or center\n",
|
|
" figs[2].annotate(zero_one_loss[i], # this is the text\n",
|
|
" (x[i],zero_one_loss[i]), # these are the coordinates to position the label\n",
|
|
" textcoords=\"offset points\", # how to position the text\n",
|
|
" xytext=(12,3), # distance from text to points (x,y)\n",
|
|
" ha='center') # horizontal alignment can be left, right or center\n",
|
|
"\n",
|
|
"figs[0].set_xticks(x)\n",
|
|
"figs[1].set_xticks(x)\n",
|
|
"figs[2].set_xticks(x)\n",
|
|
" \n",
|
|
"### Add title and axis names\n",
|
|
"figs[0].title.set_text('Training times for each models')\n",
|
|
"figs[1].title.set_text('Precision score for each models')\n",
|
|
"figs[2].title.set_text('Zero-one loss metrics for each models')\n",
|
|
"figs[0].set_xlabel('model')\n",
|
|
"figs[1].set_xlabel('model')\n",
|
|
"figs[2].set_xlabel('model')\n",
|
|
"figs[0].set_ylabel('Training times (in seconds)')\n",
|
|
"figs[1].set_ylabel('Precision score')\n",
|
|
"figs[2].set_ylabel('Zero-one loss')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 68,
|
|
"id": "c32eeb4e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Computing for solver = adam ...\n",
|
|
"Iteration 1, loss = 0.85650213\n",
|
|
"Iteration 2, loss = 0.26988524\n",
|
|
"Iteration 3, loss = 0.20729628\n",
|
|
"Iteration 4, loss = 0.16877153\n",
|
|
"Iteration 5, loss = 0.15256668\n",
|
|
"Iteration 6, loss = 0.13732062\n",
|
|
"Iteration 7, loss = 0.11812157\n",
|
|
"Iteration 8, loss = 0.11218156\n",
|
|
"Iteration 9, loss = 0.10456389\n",
|
|
"Iteration 10, loss = 0.09150458\n",
|
|
"Iteration 11, loss = 0.08853537\n",
|
|
"Iteration 12, loss = 0.08494664\n",
|
|
"Iteration 13, loss = 0.07498355\n",
|
|
"Iteration 14, loss = 0.06693150\n",
|
|
"Iteration 15, loss = 0.06702981\n",
|
|
"Iteration 16, loss = 0.06717589\n",
|
|
"Iteration 17, loss = 0.06056735\n",
|
|
"Iteration 18, loss = 0.05594405\n",
|
|
"Iteration 19, loss = 0.05904609\n",
|
|
"Iteration 20, loss = 0.05234987\n",
|
|
"Iteration 21, loss = 0.05235762\n",
|
|
"Iteration 22, loss = 0.04459628\n",
|
|
"Iteration 23, loss = 0.04813990\n",
|
|
"Iteration 24, loss = 0.04149658\n",
|
|
"Iteration 25, loss = 0.04235831\n",
|
|
"Computing for solver = lbfgs ...\n",
|
|
"Computing for solver = sgd ...\n",
|
|
"Iteration 1, loss = 1.07441605\n",
|
|
"Iteration 2, loss = 0.39002201\n",
|
|
"Iteration 3, loss = 0.30063719\n",
|
|
"Iteration 4, loss = 0.25510758\n",
|
|
"Iteration 5, loss = 0.22466352\n",
|
|
"Iteration 6, loss = 0.20441535\n",
|
|
"Iteration 7, loss = 0.18772771\n",
|
|
"Iteration 8, loss = 0.17393574\n",
|
|
"Iteration 9, loss = 0.16292551\n",
|
|
"Iteration 10, loss = 0.15469196\n",
|
|
"Iteration 11, loss = 0.14460615\n",
|
|
"Iteration 12, loss = 0.13736591\n",
|
|
"Iteration 13, loss = 0.12975363\n",
|
|
"Iteration 14, loss = 0.12368437\n",
|
|
"Iteration 15, loss = 0.11724675\n",
|
|
"Iteration 16, loss = 0.11199104\n",
|
|
"Iteration 17, loss = 0.10762864\n",
|
|
"Iteration 18, loss = 0.10318293\n",
|
|
"Iteration 19, loss = 0.09936798\n",
|
|
"Iteration 20, loss = 0.09514227\n",
|
|
"Iteration 21, loss = 0.09172820\n",
|
|
"Iteration 22, loss = 0.08829583\n",
|
|
"Iteration 23, loss = 0.08419505\n",
|
|
"Iteration 24, loss = 0.08035413\n",
|
|
"Iteration 25, loss = 0.07649316\n",
|
|
"Done\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"####### Etude de la convergence des algos d'optimisations #######\n",
|
|
"\n",
|
|
"list_training_times_opti = []\n",
|
|
"list_precision_scores_opti = []\n",
|
|
"list_zero_one_loss_opti = []\n",
|
|
"\n",
|
|
"solvers = [\"adam\",\"lbfgs\",\"sgd\"]\n",
|
|
"\n",
|
|
"for i in solvers:\n",
|
|
" print(\"Computing for solver = \", i, \"...\")\n",
|
|
" #Entraîne le classifier\n",
|
|
" clf = neural_network.MLPClassifier(random_state=1, max_iter=25, hidden_layer_sizes=(50,)*10, verbose=True, solver=i)\n",
|
|
" t1 = round(time.time(),5)\n",
|
|
" clf.fit(xtrain, ytrain)\n",
|
|
" t2 = round(time.time(),5)\n",
|
|
" #Prédiction sur le jeu de tests\n",
|
|
" pred = clf.predict(xtest)\n",
|
|
" # Probabilités des prédictions sur xtest\n",
|
|
" pred_proba = clf.predict_proba(xtest)\n",
|
|
" # On sauvegarde le temps de calcul, la précision et \n",
|
|
" # les taux d'erreurs par classe\n",
|
|
" list_training_times_opti.append(t2-t1)\n",
|
|
" list_precision_scores_opti.append(clf.score(xtest, ytest))\n",
|
|
" list_zero_one_loss_opti.append(metrics.zero_one_loss(ytest, pred))\n",
|
|
"print(\"Done\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 79,
|
|
"id": "35f5c30d",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(18.0, 0.5, 'Zero-one loss')"
|
|
]
|
|
},
|
|
"execution_count": 79,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
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\n",
|
|
"text/plain": [
|
|
"<Figure size 360x720 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
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"source": [
|
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"##### x coordinates\n",
|
|
"x = solvers\n",
|
|
"training_times = list_training_times_opti\n",
|
|
"precision_scores = list_precision_scores_opti\n",
|
|
"zero_one_loss = list_zero_one_loss_opti\n",
|
|
" \n",
|
|
"training_times = [round(i,2) for i in training_times]\n",
|
|
"precision_scores = [round(i,2) for i in precision_scores]\n",
|
|
"zero_one_loss = [round(i,2) for i in zero_one_loss]\n",
|
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"\n",
|
|
"### Create plot\n",
|
|
"fig, figs = plt.subplots(nrows=3, ncols=1, figsize=(5,10))\n",
|
|
"fig.tight_layout(pad=4.0)\n",
|
|
"figs[0].plot(x,training_times, marker='o', color='r')\n",
|
|
"figs[1].plot(x,precision_scores, marker='o', color='b')\n",
|
|
"figs[2].plot(x,zero_one_loss, marker='o', color='g')\n",
|
|
"\n",
|
|
"### Add every x coordinates\n",
|
|
"figs[0].tick_params(axis='both', which='both', labelsize=9, labelbottom=True)\n",
|
|
"figs[1].tick_params(axis='both', which='both', labelsize=9, labelbottom=True)\n",
|
|
"figs[2].tick_params(axis='both', which='both', labelsize=9, labelbottom=True)\n",
|
|
"\n",
|
|
"for i in range(len(x)):\n",
|
|
" figs[0].annotate(training_times[i], # this is the text\n",
|
|
" (x[i],training_times[i]), # these are the coordinates to position the label\n",
|
|
" textcoords=\"offset points\", # how to position the text\n",
|
|
" xytext=(17,-2), # distance from text to points (x,y)\n",
|
|
" ha='center') # horizontal alignment can be left, right or center\n",
|
|
" figs[1].annotate(precision_scores[i], # this is the text\n",
|
|
" (x[i],precision_scores[i]), # these are the coordinates to position the label\n",
|
|
" textcoords=\"offset points\", # how to position the text\n",
|
|
" xytext=(17,-2), # distance from text to points (x,y)\n",
|
|
" ha='center') # horizontal alignment can be left, right or center\n",
|
|
" figs[2].annotate(zero_one_loss[i], # this is the text\n",
|
|
" (x[i],zero_one_loss[i]), # these are the coordinates to position the label\n",
|
|
" textcoords=\"offset points\", # how to position the text\n",
|
|
" xytext=(17,-2), # distance from text to points (x,y)\n",
|
|
" ha='center') # horizontal alignment can be left, right or center\n",
|
|
"\n",
|
|
"figs[0].set_xticks(x)\n",
|
|
"figs[1].set_xticks(x)\n",
|
|
"figs[2].set_xticks(x)\n",
|
|
" \n",
|
|
"### Add title and axis names\n",
|
|
"figs[0].title.set_text('Training times for each solvers (n_layers = 10,layer_size=50)')\n",
|
|
"figs[1].title.set_text('Precision score for each solvers (n_layers = 10,layer_size=50)')\n",
|
|
"figs[2].title.set_text('Zero-one loss metrics for each solvers (n_layers = 10,layer_size=50)')\n",
|
|
"figs[0].set_xlabel('solver')\n",
|
|
"figs[1].set_xlabel('solver')\n",
|
|
"figs[2].set_xlabel('solver')\n",
|
|
"figs[0].set_ylabel('Training times (in seconds)')\n",
|
|
"figs[1].set_ylabel('Precision score')\n",
|
|
"figs[2].set_ylabel('Zero-one loss')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "b5c53e81",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Computing for activations = identity ...\n",
|
|
"Iteration 1, loss = 3.11224909\n",
|
|
"Iteration 2, loss = 0.70854568\n",
|
|
"Iteration 3, loss = 0.52376870\n",
|
|
"Iteration 4, loss = 0.43536739\n",
|
|
"Iteration 5, loss = 0.39083398\n",
|
|
"Iteration 6, loss = 0.36212214\n",
|
|
"Iteration 7, loss = 0.34711959\n",
|
|
"Iteration 8, loss = 0.33529051\n",
|
|
"Iteration 9, loss = 0.32645959\n",
|
|
"Iteration 10, loss = 0.31210041\n",
|
|
"Iteration 11, loss = 0.30752402\n",
|
|
"Iteration 12, loss = 0.30330625\n",
|
|
"Iteration 13, loss = 0.29551273\n",
|
|
"Iteration 14, loss = 0.29731196\n",
|
|
"Iteration 15, loss = 0.29336812\n",
|
|
"Iteration 16, loss = 0.28803723\n",
|
|
"Iteration 17, loss = 0.29129394\n",
|
|
"Iteration 18, loss = 0.28711416\n",
|
|
"Iteration 19, loss = 0.28879544\n",
|
|
"Iteration 20, loss = 0.28476943\n",
|
|
"Iteration 21, loss = 0.28758300\n",
|
|
"Iteration 22, loss = 0.29131686\n",
|
|
"Iteration 23, loss = 0.28409160\n",
|
|
"Iteration 24, loss = 0.28754863\n",
|
|
"Iteration 25, loss = 0.28786580\n",
|
|
"Iteration 26, loss = 0.28697806\n",
|
|
"Iteration 27, loss = 0.28521567\n",
|
|
"Iteration 28, loss = 0.28971331\n",
|
|
"Iteration 29, loss = 0.28413101\n",
|
|
"Iteration 30, loss = 0.28784417\n",
|
|
"Iteration 31, loss = 0.28586074\n",
|
|
"Iteration 32, loss = 0.29405241\n",
|
|
"Iteration 33, loss = 0.28681966\n",
|
|
"Iteration 34, loss = 0.28580153\n",
|
|
"Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
|
|
"Computing for activations = logistic ...\n",
|
|
"Iteration 1, loss = 2.30769833\n",
|
|
"Iteration 2, loss = 2.30291040\n",
|
|
"Iteration 3, loss = 2.30244898\n",
|
|
"Iteration 4, loss = 2.30268244\n",
|
|
"Iteration 5, loss = 2.30239578\n",
|
|
"Iteration 6, loss = 2.30232797\n",
|
|
"Iteration 7, loss = 2.30225384\n",
|
|
"Iteration 8, loss = 2.30250679\n",
|
|
"Iteration 9, loss = 2.30217561\n",
|
|
"Iteration 10, loss = 2.30191053\n",
|
|
"Iteration 11, loss = 2.30200626\n",
|
|
"Iteration 12, loss = 2.30190266\n",
|
|
"Iteration 13, loss = 2.30192900\n",
|
|
"Iteration 14, loss = 2.30175066\n",
|
|
"Iteration 15, loss = 2.30167723\n",
|
|
"Iteration 16, loss = 2.30164233\n",
|
|
"Iteration 17, loss = 2.30159434\n",
|
|
"Iteration 18, loss = 2.30156877\n",
|
|
"Iteration 19, loss = 2.30152622\n",
|
|
"Iteration 20, loss = 2.30154849\n",
|
|
"Iteration 21, loss = 2.30150245\n",
|
|
"Iteration 22, loss = 2.30148105\n",
|
|
"Iteration 23, loss = 2.30142407\n",
|
|
"Iteration 24, loss = 2.30137533\n",
|
|
"Iteration 25, loss = 2.30152046\n",
|
|
"Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
|
|
"Computing for activations = tanh ...\n",
|
|
"Iteration 1, loss = 0.90876300\n",
|
|
"Iteration 2, loss = 0.54644437\n",
|
|
"Iteration 3, loss = 0.46970528\n",
|
|
"Iteration 4, loss = 0.41632441\n",
|
|
"Iteration 5, loss = 0.40556014\n",
|
|
"Iteration 6, loss = 0.39304098\n",
|
|
"Iteration 7, loss = 0.34769367\n",
|
|
"Iteration 8, loss = 0.33746557\n",
|
|
"Iteration 9, loss = 0.31966629\n",
|
|
"Iteration 10, loss = 0.30298759\n",
|
|
"Iteration 11, loss = 0.31346061\n",
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"Iteration 12, loss = 0.31918136\n",
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"Iteration 13, loss = 0.33433773\n",
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"Iteration 14, loss = 0.31077716\n",
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"Iteration 15, loss = 0.31443855\n",
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"Iteration 16, loss = 0.29622440\n",
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"Iteration 17, loss = 0.29625351\n",
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"Iteration 18, loss = 0.28699997\n",
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"Iteration 19, loss = 0.30362581\n",
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"Iteration 20, loss = 0.30475983\n",
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"Iteration 21, loss = 0.28076258\n",
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"Iteration 22, loss = 0.28172739\n",
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"Iteration 23, loss = 0.28994075\n",
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"Iteration 24, loss = 0.27289827\n",
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"Iteration 25, loss = 0.26683419\n",
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"Iteration 26, loss = 0.25457182\n",
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"Iteration 27, loss = 0.25186019\n",
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"Iteration 28, loss = 0.24638283\n",
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"Iteration 29, loss = 0.24811218\n",
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"Iteration 30, loss = 0.24545728\n",
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"Iteration 31, loss = 0.23533057\n",
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"Iteration 32, loss = 0.23147359\n",
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"Iteration 33, loss = 0.23779321\n",
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"Iteration 34, loss = 0.23702320\n",
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"Iteration 35, loss = 0.23076596\n",
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"Iteration 36, loss = 0.23144501\n",
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"Iteration 37, loss = 0.23887763\n",
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"Iteration 38, loss = 0.24003808\n",
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"Iteration 39, loss = 0.23919613\n",
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"Iteration 40, loss = 0.24155683\n",
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"Iteration 41, loss = 0.23472365\n",
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"Iteration 42, loss = 0.24448571\n",
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"Iteration 43, loss = 0.23936428\n",
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"Iteration 44, loss = 0.24179928\n",
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"Iteration 45, loss = 0.22778094\n",
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"Iteration 46, loss = 0.22838648\n",
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"Iteration 47, loss = 0.22280802\n",
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"Iteration 48, loss = 0.22293833\n",
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"Iteration 49, loss = 0.21873897\n",
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"Iteration 50, loss = 0.22255140\n",
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"Iteration 51, loss = 0.22840063\n",
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"Iteration 52, loss = 0.21844829\n",
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"Iteration 53, loss = 0.20518849\n",
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"Iteration 54, loss = 0.21499200\n",
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"Iteration 55, loss = 0.20790650\n",
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"Iteration 56, loss = 0.21104213\n",
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"Iteration 57, loss = 0.21095536\n",
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"Iteration 58, loss = 0.21809221\n",
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"Iteration 59, loss = 0.21593479\n",
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"Iteration 60, loss = 0.21037508\n",
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"Iteration 61, loss = 0.20032046\n",
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"Iteration 62, loss = 0.20149654\n",
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"Iteration 63, loss = 0.20030395\n",
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"Iteration 64, loss = 0.19530838\n",
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"Iteration 65, loss = 0.20230686\n",
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"Iteration 66, loss = 0.18727727\n",
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"Iteration 67, loss = 0.18870452\n",
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"Iteration 68, loss = 0.19615807\n",
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"Iteration 69, loss = 0.20960995\n",
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"Iteration 70, loss = 0.20044529\n",
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"Iteration 71, loss = 0.19594940\n",
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"Iteration 72, loss = 0.21108270\n",
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"Iteration 73, loss = 0.20002339\n",
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"Iteration 74, loss = 0.19700233\n",
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"Iteration 75, loss = 0.18615323\n",
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"Iteration 76, loss = 0.18832837\n",
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"Iteration 77, loss = 0.19898041\n",
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"Iteration 78, loss = 0.20418693\n",
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"Iteration 79, loss = 0.19368883\n",
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"Iteration 80, loss = 0.18468801\n",
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"Iteration 81, loss = 0.18402290\n",
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"Iteration 82, loss = 0.18080945\n",
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"Iteration 83, loss = 0.18818585\n",
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"Iteration 84, loss = 0.18884275\n",
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"Iteration 85, loss = 0.19623093\n",
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"Iteration 86, loss = 0.18967099\n",
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"Iteration 87, loss = 0.18097206\n",
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"Iteration 88, loss = 0.18184130\n",
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"Iteration 89, loss = 0.18070406\n",
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"Iteration 90, loss = 0.18038370\n",
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"Iteration 91, loss = 0.17927189\n",
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"Iteration 92, loss = 0.18369377\n",
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"Iteration 93, loss = 0.17680643\n",
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"Iteration 94, loss = 0.17613015\n",
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"Iteration 95, loss = 0.18412622\n",
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"Iteration 96, loss = 0.18176041\n",
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"Iteration 97, loss = 0.17843724\n",
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"Iteration 98, loss = 0.17323722\n",
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"Iteration 99, loss = 0.15801558\n",
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"Iteration 100, loss = 0.16873188\n",
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"Computing for activations = relu ...\n",
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"Iteration 1, loss = 0.84846145\n",
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"Iteration 2, loss = 0.25873061\n",
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"Iteration 3, loss = 0.19454472\n",
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"Iteration 4, loss = 0.16307308\n",
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"Iteration 5, loss = 0.14511879\n",
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"Iteration 6, loss = 0.12548967\n",
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"Iteration 7, loss = 0.11394716\n",
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"Iteration 8, loss = 0.10319434\n",
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"Iteration 9, loss = 0.09340300\n",
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"Iteration 10, loss = 0.08821125\n",
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"Iteration 11, loss = 0.08199792\n",
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"Iteration 12, loss = 0.07829423\n",
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"Iteration 13, loss = 0.07068076\n",
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"Iteration 14, loss = 0.06754605\n",
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"Iteration 15, loss = 0.06630167\n",
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"Iteration 16, loss = 0.05935105\n",
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"Iteration 17, loss = 0.05915074\n",
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"Iteration 18, loss = 0.05527145\n",
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"Iteration 19, loss = 0.05223425\n",
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"Iteration 20, loss = 0.05321723\n",
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"Iteration 21, loss = 0.04984139\n",
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"Iteration 22, loss = 0.04480437\n",
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"Iteration 23, loss = 0.04665587\n",
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"Iteration 24, loss = 0.04289580\n",
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"Iteration 25, loss = 0.03622594\n",
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"Iteration 26, loss = 0.03530255\n",
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"Iteration 27, loss = 0.04065954\n",
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"Iteration 28, loss = 0.04278904\n",
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"Iteration 29, loss = 0.03540820\n",
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"Iteration 30, loss = 0.03756356\n",
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"Iteration 31, loss = 0.02881830\n",
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"Iteration 32, loss = 0.03197044\n",
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"Iteration 33, loss = 0.03519754\n",
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"Iteration 34, loss = 0.03226369\n",
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"Iteration 35, loss = 0.03484656\n",
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"Iteration 36, loss = 0.02892709\n",
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"Iteration 37, loss = 0.02495425\n",
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"Iteration 38, loss = 0.02849610\n",
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"Iteration 39, loss = 0.02382546\n",
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"Iteration 40, loss = 0.02260332\n",
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"Iteration 41, loss = 0.02632971\n",
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"Iteration 42, loss = 0.03400246\n",
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"Iteration 43, loss = 0.02602642\n",
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"Iteration 44, loss = 0.02628543\n",
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"Iteration 45, loss = 0.02091241\n",
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"Iteration 46, loss = 0.02698089\n",
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"Iteration 47, loss = 0.02287099\n",
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"Iteration 48, loss = 0.01953821\n",
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"Iteration 49, loss = 0.02287816\n",
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"Iteration 50, loss = 0.01786992\n",
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"Iteration 51, loss = 0.01614434\n",
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"Iteration 52, loss = 0.02288893\n",
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"Iteration 53, loss = 0.01831700\n",
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"Iteration 54, loss = 0.02733430\n",
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"Iteration 55, loss = 0.01667890\n",
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"Iteration 56, loss = 0.01619622\n",
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"Iteration 57, loss = 0.02049603\n",
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"Iteration 58, loss = 0.02224142\n",
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"Iteration 59, loss = 0.01578972\n",
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"Iteration 60, loss = 0.01984688\n",
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"Iteration 61, loss = 0.02051338\n",
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"Iteration 62, loss = 0.01760285\n",
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"Iteration 63, loss = 0.02048740\n",
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"Iteration 64, loss = 0.01581426\n",
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"Iteration 65, loss = 0.01006159\n",
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"Iteration 66, loss = 0.01263444\n",
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"Iteration 67, loss = 0.01597941\n",
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"Iteration 68, loss = 0.01564173\n",
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"Iteration 69, loss = 0.01664765\n",
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"Iteration 70, loss = 0.01238414\n",
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"Iteration 71, loss = 0.01315483\n",
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"Iteration 72, loss = 0.01670207\n",
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"Iteration 73, loss = 0.01893887\n",
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"Iteration 74, loss = 0.01745178\n",
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"Iteration 75, loss = 0.01212984\n",
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"Iteration 76, loss = 0.01667099\n",
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"Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
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"Done\n"
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]
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}
|
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],
|
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"source": [
|
|
"####### Variations des fonctions d'activations #######\n",
|
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"\n",
|
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"list_training_times_acti = []\n",
|
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"list_precision_scores_acti = []\n",
|
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"list_zero_one_loss_acti = []\n",
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"\n",
|
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"activations = [\"identity\", \"logistic\", \"tanh\", \"relu\"]\n",
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"\n",
|
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"for i in activations:\n",
|
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" print(\"Computing for activations = \", i, \"...\")\n",
|
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" #Entraîne le classifier\n",
|
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" clf = neural_network.MLPClassifier(random_state=1, max_iter=100, hidden_layer_sizes=(50,)*10, verbose=True, activation=i)\n",
|
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" t1 = round(time.time(),5)\n",
|
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" clf.fit(xtrain, ytrain)\n",
|
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" t2 = round(time.time(),5)\n",
|
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" #Prédiction sur le jeu de tests\n",
|
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" pred = clf.predict(xtest)\n",
|
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" # Probabilités des prédictions sur xtest\n",
|
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" pred_proba = clf.predict_proba(xtest)\n",
|
|
" # On sauvegarde le temps de calcul, la précision et \n",
|
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" # les taux d'erreurs par classe\n",
|
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" list_training_times_acti.append(t2-t1)\n",
|
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" list_precision_scores_acti.append(clf.score(xtest, ytest))\n",
|
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" list_zero_one_loss_acti.append(metrics.zero_one_loss(ytest, pred))\n",
|
|
"print(\"Done\")"
|
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]
|
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},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
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"id": "c7afbbdc",
|
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"metadata": {},
|
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"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
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"x : ['identity', 'logistic', 'tanh', 'relu']\n",
|
|
"training_times : [34.288330078125, 33.1211998462677, 171.96871995925903, 106.40883994102478]\n",
|
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"precision_scores : [0.9074285714285715, 0.11142857142857143, 0.9351904761904762, 0.9683333333333334]\n",
|
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"zero_one_loss : [0.09257142857142853, 0.8885714285714286, 0.06480952380952376, 0.03166666666666662]\n"
|
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]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(36.0, 0.5, 'Zero-one loss')"
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
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{
|
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"data": {
|
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"image/png": 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\n",
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"text/plain": [
|
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"<Figure size 504x720 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
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"needs_background": "light"
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},
|
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"output_type": "display_data"
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}
|
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],
|
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"source": [
|
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"##### x coordinates\n",
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"x = activations\n",
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"training_times = list_training_times_acti\n",
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"precision_scores = list_precision_scores_acti\n",
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"zero_one_loss = list_zero_one_loss_acti\n",
|
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"print(\"x : \",x)\n",
|
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"print(\"training_times : \",training_times)\n",
|
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"print(\"precision_scores : \",precision_scores)\n",
|
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"print(\"zero_one_loss : \",zero_one_loss)\n",
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" \n",
|
|
"training_times = [round(i,2) for i in training_times]\n",
|
|
"precision_scores = [round(i,2) for i in precision_scores]\n",
|
|
"zero_one_loss = [round(i,2) for i in zero_one_loss]\n",
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"\n",
|
|
"### Create plot\n",
|
|
"fig, figs = plt.subplots(nrows=3, ncols=1, figsize=(7,10))\n",
|
|
"fig.tight_layout(pad=4.0)\n",
|
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"figs[0].plot(x,training_times, marker='o', color='r')\n",
|
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"figs[1].plot(x,precision_scores, marker='o', color='b')\n",
|
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"figs[2].plot(x,zero_one_loss, marker='o', color='g')\n",
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"\n",
|
|
"### Add every x coordinates\n",
|
|
"figs[0].tick_params(axis='both', which='both', labelsize=9, labelbottom=True)\n",
|
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"figs[1].tick_params(axis='both', which='both', labelsize=9, labelbottom=True)\n",
|
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"figs[2].tick_params(axis='both', which='both', labelsize=9, labelbottom=True)\n",
|
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"\n",
|
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"for i in range(len(x)):\n",
|
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" figs[0].annotate(training_times[i], # this is the text\n",
|
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" (x[i],training_times[i]), # these are the coordinates to position the label\n",
|
|
" textcoords=\"offset points\", # how to position the text\n",
|
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" xytext=(17,-2), # distance from text to points (x,y)\n",
|
|
" ha='center') # horizontal alignment can be left, right or center\n",
|
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" figs[1].annotate(precision_scores[i], # this is the text\n",
|
|
" (x[i],precision_scores[i]), # these are the coordinates to position the label\n",
|
|
" textcoords=\"offset points\", # how to position the text\n",
|
|
" xytext=(17,-2), # distance from text to points (x,y)\n",
|
|
" ha='center') # horizontal alignment can be left, right or center\n",
|
|
" figs[2].annotate(zero_one_loss[i], # this is the text\n",
|
|
" (x[i],zero_one_loss[i]), # these are the coordinates to position the label\n",
|
|
" textcoords=\"offset points\", # how to position the text\n",
|
|
" xytext=(17,-2), # distance from text to points (x,y)\n",
|
|
" ha='center') # horizontal alignment can be left, right or center\n",
|
|
"\n",
|
|
"figs[0].set_xticks(x)\n",
|
|
"figs[1].set_xticks(x)\n",
|
|
"figs[2].set_xticks(x)\n",
|
|
" \n",
|
|
"### Add title and axis names\n",
|
|
"figs[0].title.set_text('Training times for each activations functions (n_layers = 10,layer_size=50)')\n",
|
|
"figs[1].title.set_text('Precision score for each activations functions (n_layers = 10,layer_size=50)')\n",
|
|
"figs[2].title.set_text('Zero-one loss metrics for each activations functions (n_layers = 10,layer_size=50)')\n",
|
|
"figs[0].set_xlabel('activation')\n",
|
|
"figs[1].set_xlabel('activation')\n",
|
|
"figs[2].set_xlabel('activation')\n",
|
|
"figs[0].set_ylabel('Training times (in seconds)')\n",
|
|
"figs[1].set_ylabel('Precision score')\n",
|
|
"figs[2].set_ylabel('Zero-one loss')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 90,
|
|
"id": "df66b8c2",
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Iteration 1, loss = 0.86420278\n",
|
|
"Iteration 2, loss = 0.26996549\n",
|
|
"Iteration 3, loss = 0.21207782\n",
|
|
"Iteration 4, loss = 0.16897296\n",
|
|
"Iteration 5, loss = 0.15104859\n",
|
|
"Iteration 6, loss = 0.13411933\n",
|
|
"Iteration 7, loss = 0.11642768\n",
|
|
"Iteration 8, loss = 0.10925475\n",
|
|
"Iteration 9, loss = 0.09696347\n",
|
|
"Iteration 10, loss = 0.08712179\n",
|
|
"Iteration 11, loss = 0.07829793\n",
|
|
"Iteration 12, loss = 0.07869050\n",
|
|
"Iteration 13, loss = 0.07164445\n",
|
|
"Iteration 14, loss = 0.06658763\n",
|
|
"Iteration 15, loss = 0.06688489\n",
|
|
"Iteration 16, loss = 0.06271908\n",
|
|
"Iteration 17, loss = 0.05857527\n",
|
|
"Iteration 18, loss = 0.05716217\n",
|
|
"Iteration 19, loss = 0.05741046\n",
|
|
"Iteration 20, loss = 0.05091226\n",
|
|
"Iteration 21, loss = 0.04827472\n",
|
|
"Iteration 22, loss = 0.04322551\n",
|
|
"Iteration 23, loss = 0.04547947\n",
|
|
"Iteration 24, loss = 0.03996680\n",
|
|
"Iteration 25, loss = 0.04363192\n",
|
|
"Iteration 1, loss = 0.85650213\n",
|
|
"Iteration 2, loss = 0.26988524\n",
|
|
"Iteration 3, loss = 0.20729628\n",
|
|
"Iteration 4, loss = 0.16877153\n",
|
|
"Iteration 5, loss = 0.15256668\n",
|
|
"Iteration 6, loss = 0.13732062\n",
|
|
"Iteration 7, loss = 0.11812157\n",
|
|
"Iteration 8, loss = 0.11218156\n",
|
|
"Iteration 9, loss = 0.10456389\n",
|
|
"Iteration 10, loss = 0.09150458\n",
|
|
"Iteration 11, loss = 0.08853537\n",
|
|
"Iteration 12, loss = 0.08494664\n",
|
|
"Iteration 13, loss = 0.07498355\n",
|
|
"Iteration 14, loss = 0.06693150\n",
|
|
"Iteration 15, loss = 0.06702981\n",
|
|
"Iteration 16, loss = 0.06717589\n",
|
|
"Iteration 17, loss = 0.06056735\n",
|
|
"Iteration 18, loss = 0.05594405\n",
|
|
"Iteration 19, loss = 0.05904609\n",
|
|
"Iteration 20, loss = 0.05234987\n",
|
|
"Iteration 21, loss = 0.05235762\n",
|
|
"Iteration 22, loss = 0.04459628\n",
|
|
"Iteration 23, loss = 0.04813990\n",
|
|
"Iteration 24, loss = 0.04149658\n",
|
|
"Iteration 25, loss = 0.04235831\n",
|
|
"Iteration 1, loss = 0.99073368\n",
|
|
"Iteration 2, loss = 0.38153682\n",
|
|
"Iteration 3, loss = 0.30651351\n",
|
|
"Iteration 4, loss = 0.26536057\n",
|
|
"Iteration 5, loss = 0.23897365\n",
|
|
"Iteration 6, loss = 0.21996450\n",
|
|
"Iteration 7, loss = 0.20446358\n",
|
|
"Iteration 8, loss = 0.19323276\n",
|
|
"Iteration 9, loss = 0.18014261\n",
|
|
"Iteration 10, loss = 0.17444642\n",
|
|
"Iteration 11, loss = 0.16320506\n",
|
|
"Iteration 12, loss = 0.15742091\n",
|
|
"Iteration 13, loss = 0.14963168\n",
|
|
"Iteration 14, loss = 0.14896737\n",
|
|
"Iteration 15, loss = 0.13821782\n",
|
|
"Iteration 16, loss = 0.13751191\n",
|
|
"Iteration 17, loss = 0.13376591\n",
|
|
"Iteration 18, loss = 0.13146977\n",
|
|
"Iteration 19, loss = 0.12623878\n",
|
|
"Iteration 20, loss = 0.12124028\n",
|
|
"Iteration 21, loss = 0.12095598\n",
|
|
"Iteration 22, loss = 0.12042183\n",
|
|
"Iteration 23, loss = 0.11731234\n",
|
|
"Iteration 24, loss = 0.11140033\n",
|
|
"Iteration 25, loss = 0.11018915\n",
|
|
"Iteration 1, loss = 48.19378286\n",
|
|
"Iteration 2, loss = 5.23712791\n",
|
|
"Iteration 3, loss = 2.64171954\n",
|
|
"Iteration 4, loss = 2.36863422\n",
|
|
"Iteration 5, loss = 2.31669450\n",
|
|
"Iteration 6, loss = 2.30503551\n",
|
|
"Iteration 7, loss = 2.30223175\n",
|
|
"Iteration 8, loss = 2.30153684\n",
|
|
"Iteration 9, loss = 2.30132250\n",
|
|
"Iteration 10, loss = 2.30130248\n",
|
|
"Iteration 11, loss = 2.30130867\n",
|
|
"Iteration 12, loss = 2.30132698\n",
|
|
"Iteration 13, loss = 2.30132090\n",
|
|
"Iteration 14, loss = 2.30131409\n",
|
|
"Iteration 15, loss = 2.30132247\n",
|
|
"Iteration 16, loss = 2.30134203\n",
|
|
"Iteration 17, loss = 2.30132345\n",
|
|
"Iteration 18, loss = 2.30130656\n",
|
|
"Iteration 19, loss = 2.30131641\n",
|
|
"Iteration 20, loss = 2.30131388\n",
|
|
"Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
|
|
"Done\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"####### Variations de la régularisation L2 #######\n",
|
|
"\n",
|
|
"list_training_times_alpha = []\n",
|
|
"list_precision_scores_alpha = []\n",
|
|
"list_zero_one_loss_alpha = []\n",
|
|
"\n",
|
|
"alphas = [0.0000001, 0.0001, 0.1,100]\n",
|
|
"\n",
|
|
"for i in alphas:\n",
|
|
" #Entraîne le classifier\n",
|
|
" clf = neural_network.MLPClassifier(random_state=1, max_iter=25, hidden_layer_sizes=(50,)*10, verbose=True, alpha=i)\n",
|
|
" t1 = round(time.time(),5)\n",
|
|
" clf.fit(xtrain, ytrain)\n",
|
|
" t2 = round(time.time(),5)\n",
|
|
" #Prédiction sur le jeu de tests\n",
|
|
" pred = clf.predict(xtest)\n",
|
|
" # Probabilités des prédictions sur xtest\n",
|
|
" pred_proba = clf.predict_proba(xtest)\n",
|
|
" # On sauvegarde le temps de calcul, la précision et \n",
|
|
" # les taux d'erreurs par classe\n",
|
|
" list_training_times_alpha.append(t2-t1)\n",
|
|
" list_precision_scores_alpha.append(clf.score(xtest, ytest))\n",
|
|
" list_zero_one_loss_alpha.append(metrics.zero_one_loss(ytest, pred))\n",
|
|
"print(\"Done\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 92,
|
|
"id": "e8f41050",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"x : ['1e-07', '0.0001', '0.1', '100']\n",
|
|
"training_times : [17.083820104599, 17.386620044708252, 17.509379863739014, 12.109119892120361]\n",
|
|
"precision_scores : [0.9652380952380952, 0.9637619047619047, 0.9543809523809523, 0.11223809523809523]\n",
|
|
"zero_one_loss : [0.03476190476190477, 0.03623809523809529, 0.04561904761904767, 0.8877619047619048]\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(36.0, 0.5, 'Zero-one loss')"
|
|
]
|
|
},
|
|
"execution_count": 92,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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vqBhu/fr1HHDAAXz77becf/75tG3bFggVOV588UU6derEs88+y5w5SQ+PN0svURRVQlqf7iM3C1ChAlSuvPmrSpXN+6tXh+2223K6VNOmO+zYY+GnFM+qbto0ZZhvvvkme+yxB02apH484++//86GDRuoWbMmv//+O6+//jr//Oc/C2cbuVLHE6Qr+XKvU+UWxc2eDeecE9733DMMf+ONzZLHyH//mx7NmsHdd6dMNOX/+INP9tmHpc2b03noUD5/9132rliRx1auZOCZZ3L92rWcXLUqlX7/HRo02DTvmjWF85mk9BJKzZqFm5DyGla+fOF8rkzddtvm+xagcmV6bL89E9u1Y9GiRTRp0oTrrruOfv36MWrUqC2KV3/66SfOOeccXnnlFX755Rc6d+4MhLPNM844g+OPPx6AsWPHcuGFF7Jw4UJOOOEEWrduzWuvvVZkH9UVPyWqsfKcnBzz+5NKOTNYtgx+/RUWLw6v3O7k99zu//0PNmxIubgfgBMJT4HNtQ7YAZgObHaeUbFiykRx3ZIlVK9Uib83b77Z8K9Xr6bX++8z9fTTCz8hVawYkqTbvHSgQoXwmjED8jj7d2WXpOlmllPwlOnxM0iXHWbhqD+vpJbfsPyKBWvXhnr1QlHedttBixbw7bepp5Xgscfguutg+PCNyefNqVPZ49FHaTJu3OZJqVyo1L1w4UIqVqxInTp1WLVqFW8eeyyXXXYZnHgiCxYsoGHDhmzYsIEb+vRhwK23Qt++WdiAbqOePTcVl8+eDQccAJ07wwcfQHSt2Lls8ATpCrZ6dXqJLnlcfpUyqlfflOjq1YN99tnUnfie2F23bjh7SDZlSvjjTNKjalUmXnFFKIbr1m1TMdydd9KjTx9o1GjjtInFcD///DNnnXUW69evZ8OGDXTt2pUTTzwRCDUkH3jgAQBOPfVUzj777G3atC5DzZrBqFFw3HHQr1/o9jNtlyVexFqWrFsHS5akdxaX+P7773kvs1KlLRNZquSWOKxu3VCsWFiSr0ECVKsGjzySb0UdV4Lddhtcdhncfjv8/e9xR+OKCS9iLWR9+/bl5ZdfpmHDhnz+ebhS1a1bN7766isAli5dSp06dfjkk0+2mHf8+PEMGjSI9evXc84553D55ZcD8MknnzBgwAD++OMPKlSowJAhQzjwwAMLL+gNG+C33zIrtly8OMyTl/LlQxLLTWQ77gj77ltwwqtWLf4j+NwkmEEtVlfCXXIJ/Pe/IUnutx8cdVTcEblSqMyfQU6aNIkaNWrQu3fvjQkyUe7N5MlVwdevX89uu+3GG2+8QZMmTWjTpg0jR45kr7324thjj+X//u//6NChA6+88gq33XYbE1Pd1GwGK1ZkVmy5eHE4C8yjUgoQztDSOZtLHFez5sZrcM6VCMuXw0EHwS+/wPTpofjVlWl+Brm18rhPbmtvJp86dSq77LILO++8MwDdTzuNcU89xV49eqAlS1j2xhswZw6/vfUWjX/5JVwvSZXw1q7NO+YaNTZPbs2aFZzw6taNr0q+c0WpZk0YOxbatIHTToPJk6Fq1bijcqVI2glSUhOgO3AY0BhYRag9/x/gVTPL55QmZqnuk+vfP3QnF8OtXbsxiU1+6y0aVa7Mru++C+PGbZbg5n35JTv+9FMoily8mCarVvEhwC23cA9w3LRp/B3YAEypXDkc7eYmsj33LPgMr169cH3POZe33XaDp56CTp3gr38NtZbjLvJ3pUZaCVLS44Rbx14GbgUWAFWA3YDjgaskXW5mk/KY/zVgf+BfZnaDpDOAKEPRCJhlZqdt0yfJz1VXpW7PsX9/GDYstNTx9ddQq1ZIZJGRQA/YVI2/QoWNCczMwhHsUUeFYbNno4UL4fzzefDpp7n70EM5rWtXRk+cSL8nnuDNN9/M2sdzrkw7+WT45z/h+uuhbVsYMCDuiFwpkdY1SEl7m9mWF+g2ja8ENDWzlDekRWefRwNNzOyGpHFDgElmNqqgOLb6GmS5cuF6XyoHHcQPVapw4scf83mfPhvP5tbVrs0OF1zA9BEjaLLXXuGMrmbNjUen77//Ptdee+3GljZuvvlmAK644gpq167N0qVLkYSZUbt2bX/qg3PZtGEDnHRSaDHpnXegXbu4I3IxKOxrkGnVykiVHCXVldQqGr8mr+QYjZ+barikikAHYFx64W6lPNptpFkzeP99ePxxaNIE7rkH/vEP+OtfeXO77dijVSuadOwYnkFXq9ZmRTdt2rThm2++4fvvv2fNmjWMGjWKk08+GQiNIb/zzjsAvPXWW3m29+mcKyTlyoXGIJo2Ddcj58+POyJXCmRUbVHSREm1JNUDZgCPS7prG9bfgXD2uGobllGwG28MtyMkqlYNbryRHj160K5dO7766iuaNGnC0KFDAfJs07Fjx44AVKhQgfvvv5/jjjuOPffck65du9KyZUsAHn30US6++GL23XdfrrzySn/mnHNFoW5dGDMm3M50+umF1y6uK7Myus1D0sdmtp+kc4AdzewaSZ+aWas05u1DUhGrpNHAo2b2Rj7z9Se6Xtm0adMDZqdoMSUtGT7twTlXQo0aBT16wIUXwr33xh2NK0KxFLEmqCBpe6ArocLOVpNUCzgAmJDfdGb2iJnlmFlOgwYNtn6FPXvCDz+EaxU//ODJ0bnSqnt3+Nvf4L77Qg1X57ZSpvdBXg+8BrxrZv+VtDPwTUEzSXoUOBioLCnHzE4BugAvFOvbQ5xzJdOtt8JHH4Wa6nvvHVrbcS5DZb4lHedcKbVgQXjyR4UKMG1aqKHuSrVYWtKRdB+QZyY1s4GFFZBzzhWKhg3h+efhsMPgjDPglVe8lSmXkXSvQU4jPF+2CuGG/2+iV2sgn4f3OedcjA48EIYMgddfD7dwOZeBtM4gzWwYbKyJ+mczWxv1PwS8nrXonHNuW/XrB1Onws03Q04OnHpq3BG5EiLTWqyNgZoJ/TWiYc45V3zde29ohu6ss+CLL+KOxpUQmSbIW4CPJT0h6QngI+CmQo/KOecKU+XK8NxzoYGQzp3Bm350acgoQZrZ40BbYGz0apdb/Oqcc8VakyYwejR8+y307p3/M1WdI/MzSIDywEJgCbCbpMMLNyTnnMuSI46AO+8Mj6+75Za4o3HFXEYNBUi6FegGzCQ86hDC7R8pH3PlnHPFzsCBodLO1VfD/vvD8cfHHZErpjJtSecUYHczW52FWJxzLvskePRRmDkz3B85bRrsvHPcUbliKNMi1v8BFbMRiHPOFZlq1cKTP8zCbR/JD1R3jszPIFcCn0iaAGw8i/SWdJxzJc7OO8PIkdCxI5x7bnieZMIzX53LNEG+GL2cc67kO/54GDw4XI888EAYNCjuiFwxklGCNLNhkioBu0WDvsptVcc550qkK66A//4XLr4YWrcONV2dI8NrkJLaE9pgfQAYAnztt3k450q0cuXgySehRQvo2hXmzYs7IldMZFpJ507gWDM7wswOB44D7i78sJxzrgjVqgUvvBAq65x2Gqz2ivou8wRZ0cy+yu0xs6/xWq3OudJgzz1h2DD48EO/FumAzBPkNElDJbWPXo8SHoPlnHMl36mnwuWXw8MPw9ChcUfjYpZpgvwLoRWdgcAgYBYwoLCDcs652NxwAxxzDPz1r6HFHVdmyczSn1iqDvxhZuuj/vJAZTMrkrtsc3JybNq0aUWxKudcWbZ4MRxwAKxfD9OnQ8OGcUfk0iBpupnlFNbyMj2DnABUTeivCrxZWME451yxsN12MHYsLFoE3brBunVxR+RikGmCrGJmK3J7ou5qBc0k6TVJCyVdnTCst6QJkt6WdEaGcTjnXHbttx888ghMnAiXXRZ3NC4Gmbak87uk/c3sIwBJBwCr0pivH3A00CSar2XUf7RlUsbrnHNF6cwzw3XIu+6CNm2ge/e4I3JFKNMEeRHwrKSfov7tCY+/ypeZzdXmbRx2AX4HXpe0ArjQzOZmGItzzmXfnXfCJ59Av37QsiXss0/cEbkiklERq5n9F9iDUJv1r8CeZrY1t3k0BuoDxwJDgTvymlBSf0nTJE1buHDhVqzKOee2QaVK8OyzULs2dO4MS5bEHZErIpk2NVcNuAwYZGafAc0lnbgV6/0VeC0qXn0NyPOQzMweMbMcM8tp0KDBVqzKOee20Z/+BM8/Dz/+CL16wYYNBc/jSrxMK+k8DqwB2kX9c4EbtmK9E4HcqrgHAN9txTKcc67otGsH//oXvPIKXHdd3NG4IpDpNcgWZtZNUg8AM1slFfwAtajFnYOBypJygM7A8ZImEpJ0/wzjcM65ojdgQKi0c/31kJMDJ50Ud0QuizJNkGskVQUMQFILEh6cnBczOzfF4P/LcN3OORcvCYYMgc8+C0Wt//0v7LZbwfO5EinTItZrgPHAjpJGEBoOuLTQo3LOueKqatVwPbJSpVBpZ8WKgudxJVKmtVjfAE4F+gAjgRwzm1j4YTnnXDHWrBmMGgVffgl9+4Lfzl0qZVqL9RBCW6z/AeoAV0pqlo3AnHOuWDvqKLjllnALyB153qnmSrBMi1gfBFZK2he4BJgNPFnoUTnnXEnw97/D6aeHR2RNmBB3NK6QZZog10X3LnYC7jWzfwE1Cz8s55wrASR47DHYY4/QqPns2XFH5ApRpglyuaQrgF7Af6LHXVUs/LCcc66EqFEjPPlj7drwwOVV6TRP7UqCTBNkN8JtHf3MbD6wA3B7oUflnHMlyW67wfDh8NFH4UHLXmmnVMi0Fut8M7vLzCZH/T+amV+DdM65k06Cf/4TnngCHnoo7mhcIcj0DNI551xerrkGOnaEQYNgypS4o3HbyBOkc84VlnLlQlFr06bQpQv8/HPcEblt4AnSOecKU926odLOb7+FW0DWrIk7IreVMm4oQNIbkr6W9D9J30v6X7aCc865EmmffWDoUHjvPbj44rijcVsp08bKhxIaGZ8OrC/8cJxzrpTo3j00Zn7XXdCmDfTuHXdELkOZJsjfzOzVrETinHOlza23hls/zjsP9t4b9t8/7ohcBjK9Bvm2pNsltZO0f+4rK5E551xJV6ECPPMM1K8fGhFYvDjuiFwGMj2DbBu95yQMM+DIwgnHOedKmYYNYcwYOPRQ6NEDXn0VypePOyqXhowSpJn9OVuBOOdcqdWmTXjQ8jnnwNVXw803xx2RS0NaCVJSLzMbLulvqcab2V2FG5ZzzpUy/fqFSju33AI5OXDaaXFH5AqQ7hlk9ejdn9zhnHNb61//ghkzoE8f2HNP2GuvuCNy+ZCVoEZ1c3JybNq0aXGH4ZxzW2/evFCbtU4dmDoVateOO6JSQ9J0M8speMr0pFWLVdLVkurlM/5ISSfmM/41SQslXR31t5f0s6SJ0euAzEN3zrkSaIcd4Nln4bvv4KyzYMOGuCNyeUi3iPUz4CVJfwAfAQuBKsCuQGvgTeCmfObvBxwNNEkY9h8zOyfTgJ1zrsQ7/HC480646KJQYeeqq+KOyKWQVoI0s3HAOEm7AocA2wPLgOFAfzPL9wmhZjZXUvLg4yRNBj4BLi1oGc45V6oMHBgq7fzjH6HItUOHuCNySYrsGqSkPkATM7tBUk1grZn9IelG4A8zG5zHfP2B/gBNmzY9YPbs2UUSr3POZd3KlXDwwTB7NkybBi1axB1RiRbLNcjCZmbLzeyPqHcEmzc8kDztI2aWY2Y5DRo0KJoAnXOuKFSrFhoRkEJLOytXxh2RSxBLgpSUWG3rSOCrOOJwzrnY7bwzPP00fPYZnHsulKA7C0q7TJua2yqSHgUOBipLygFel9QXWAksAvoWRRzOOVcsHX883HBDqKxz4IEwaFDcETkyTJCSbgNuAFYB44F9gYvMbHh+85nZuSkGD8lk3c45V6pdfnmotHPxxdC6NRxxRNwRlXmZFrEea2bLgBOBucBuwCWFHpVzzpU15crBsGGwyy7QtSvMnRt3RGVepgmyYvTeERhpZr8WcjzOOVd21aoFY8eGyjpdusDq1XFHVKZlmiBfkvQlodbpBEkNgD8KmMc551y69twznEl++GG4V9LFJqMEaWaXA+2AHDNbS6hk0ykbgTnnXJl16qlwxRXwyCPw73/HHU2ZlVGClFQNOB94MBrUmHzuYXTOObeVBg+GY4+F888PjZq7IpdpEevjwBrCLRsQKurcUKgROeecg/Llw/2RjRuHZ0cuWBB3RGVOpgmyhZndBqwFiNpP3aKRVeecc4Vgu+1CSzuLFkG3brBuXdwRlSmZJsg1kqoCBiCpBeDVrJxzLlv22y9ci5w4ES67LO5oypRMW9K5htBAwI6SRhCe7NGnsINyzjmX4MwzQyMCd90FOTnQo0fcEZUJGSVIM3tD0kfAQYSi1UFmtigrkTnnnNvkzjvh44+hXz9o2RJatYo7olJvaxor3wEoD1QCDpd0auGG5JxzbgsVK8Kzz0KdOuE2kCVL4o6o1Mu0LdbHgFbATGBDNNiAMYUcl3POuWR/+hM8/3xop7VXL3jppdBEncuKTK9BHmRme2UlEueccwVr1w7uvRf+8he47rrwclmR6aHH+5I8QTrnXJzOOw/OPhuuvx5efDHuaEqtTM8ghxGS5HzC7R0CzMz8arFzzhUVCYYMCQ9Zzq3huttucUdV6mSaIB8DzgQ+Y9M1SOecc0WtSpVwPfKAA6BzZ/jgA6hZM+6oSpVMi1h/NLMXzex7M5ud+8pKZM455/LXtCk88wx8+SX07QtmcUdUqmSaIL+U9LSkHpJOzX1lJTLnnHMFO/JIuPVWeO45uOOOuKMpVTItYq1KuPZ4bMIwv83DOefidPHF4Trk5ZeHpumOPjruiEqFTFvSOTtbgTjnnNtKEgwdCjNnQvfuMH06NGsWd1QlXlpFrJIujd7vk3Rv8iuN+V+TtFDS1UnD+0pau3WhO+ec26hGDRg7Njzx49RTYdWquCMq8dI9g/wiep+2levpBxwNNMkdIKkKcCowZyuX6ZxzLtGuu8Lw4XDSSaEhgccfD2eXbqukdQZpZi9FnSvNbFjiC1iZxvxzUwweCDyE3y7inHOF58QT4ZprYNgwePDBuKMp0TKtxXpFmsPyJakucLiZvZzGtP0lTZM0beHChZmuyjnnyp5//hNOOAEGDYIpU+KOpsRKq4hVUgegI7BD0jXHWsDWPOL6CuC2dCY0s0eARwBycnL8Jh/nnCtIuXKhqDUnB047DT76CLbfPu6oSpx0zyB/Ilx//AOYnvB6EThuK9a7G3ClpPHA9pKe2YplOOecy0udOvDCC7BsGZx+OqxZE3dEJU5aZ5BmNgOYIelpM8u41qmkR4GDgcqScszslIRx35pZt0yX6ZxzrgB77w2PPRZu/bj4YrjvvrgjKlEyvQ9yq27JMLNz8xm3y9Ys0znnXBq6dQuNCNx5J7RpA717xx1RieFP2nTOudLullvgz38Oj8n66KO4oykxPEE651xpV6FCaNS8QYPQiMCiRXFHVCJkVMQq6SVC26uJfiNU4HnYzP4orMCcc84VogYNwuOxDjsMevSA8eOhfPm4oyrWMj2D/B+wAng0ei0DfiHUSn20cENzzjlXqNq0CQ9afvNNuOqquKMp9jJ9msd+ZnZ4Qv9LkiaZ2eGSZhZmYM4557Kgb1+YOjU8IqtNm3CfpEsp0zPIBpKa5vZE3fWjXr/JxjnnSoJ//QsOOgj69IFZs+KOptjKNEFeDLwr6W1JE4HJwCWSqgPDCjs455xzWVC5cnjAcvXq0Lkz/PZb3BEVS5neB/mKpF2BPQABXyZUzLmnkGNzzjmXLTvsAM8+C0ceCWedBWPGhCbq3EZbszUOAFoCrYCukvyuU+ecK4kOOyw0IDBuHNx0U9zRFDuZ3ubxFNAC+ARYHw024MnCDcs551yRuPDC0NLOP/8JBxwAHTrEHVGxkWkt1hxgLzPzp2o451xpIMHDD8Nnn8EZZ8C0adCiRdxRFQuZFrF+DvwpG4E455yLSbVq4RqkFCrt/P573BEVC5kmyPrALEmvSXox95WNwJxzzhWhnXeGkSPh88+hf3/wgsKMi1ivzUYQzjnnioHjjoMbbgit7LRpAxddFHdEscr0No93shWIc865YuCKK8J1yL//HfbbD444Iu6IYpNWEaukd6P35ZKWJbyWS1qW3RCdc84VGQmeeAJ23RW6doW5c+OOKDZpJUgzOzR6r2lmtRJeNc2sVnZDdM45V6Rq1YKxY2HVqtBW6+rVcUcUi4wbCpBUXlJjSU1zX9kIzDnnXIz22AOGDQsNm194YdzRxCKjBCnpQsLjrd4A/hO9Xs5CXM455+LWuXO4Jvnoo+FVxmRai3UQsLuZLc5GMM4554qZwYNh+nS44ALYd1848MC4IyoymRaxzgEybvY9um9yoaSro/4jJL0n6Z3oySA7ZrpM55xzRaB8eXj6aWjcOFyPXLAg7oiKTKZnkP8DJkr6D7Dxqq2Z3VXAfP2Ao4EmUf/7ZnYIgKS+wEDgkgxjcc45VxS22y5U2mnXLtRsffNNqJBp+ih5Mj2D/JFw/bESUDPhlS8zm5vUn/hw5VrApxnG4Zxzrii1bh2uQ77zDlx6adzRFIlMGwq4rrBWLOkE4DpCguyYz3T9gf4ATZt6hVnnnItNr17hyR933x1a2unRI+6IskrpPJhD0j1mdpGklwiPt9qMmZ2cxjL6AE3M7Iak4V2BLmbWtaBl5OTk2LRp0wqM1znnXJasXQtHHRVa2/ngA2jVKu6INpI03cxyCmt56Z5BPhW931EYK5VUxcz+iHqXAisLY7nOOeeyrGJFGD06PDuyc+eQKOvWjTuqrEgrQZrZ9Oh9q9pilfQocDBQWVIO8LKkM4ENwBqiIlTnnHMlwJ/+BM89F9pp7dkTXnop1HYtZTK6BilpV+BmYC+gSu5wM9s5v/nM7NwUg/+dybqdc84VI+3awb33wl/+AtddB9dfH3dEhS7TWqyPAw8C64A/A0+yqfjVOedcWXLeedC3b2hM4MXS92jgTBNkVTObQKjcM9vMrgWOLPywnHPOFXsSPPAA5OTAmWfCV1/FHVGhyjRB/iGpHPCNpAskdQYaZiEu55xzJUGVKvD881CpEpx6KixfHndEhSbTBHkRUI3Q8s0BQC/grEKOyTnnXEnStCk88wx8+WUock3j9sGSIO0EKak80NXMVpjZXDM728xOM7MPshifc865kuDII+HWW0Pt1ttvjzuaQpFWgpRUwczWAwdIUpZjcs45VxJdfHFoq/WKK+CNN+KOZpule5vHVGB/4GNgnKRngd9zR5rZmCzE5pxzriSRYOhQmDUrNEM3bRo0bx53VFst02uQ9YDFhJqrJwInRe/OOecc1KgBY8bAunXh8VirVsUd0VZLN0E2lPQ34HPgs+h9ZvT+eZZic845VxLtuisMHw4ffRQaEiihlXbSTZDlgRrRq2ZCd+7LOeec2+TEE+Haa2HYMHjwwbij2SrpXoP82cxKXztCzjnnsucf/wjXIQcNgn33hUMOiTuijKR7Buk1V51zzmWmXDl46qlQUadLF/j557gjyki6CfKorEbhnHOudKpTB8aOhWXLQpJcsybuiNKWVoI0s1+zHYhzzrlSau+94fHHYcoU+Nvf4o4mbZne5uGcc85lrmtX+PvfQ+Pmw4blOVnfvn1p2LAhe++998Zhl1xyCXvssQetWrWic+fOLF26NK/Zm0taIGmzuysknS5ppqQN0TOJ0+IJ0jnnXNG4+ebQJN2AAeEWkBT69OnD+PHjNxt2zDHH8Pnnn/Ppp5+y2267cfPNN+e1hkXA8SmGfw6cCkzKJFxPkM4554pGhQowahQ0aBCe/LFo0RaTHH744dSrV2+zYcceeywVKoSbLg466CDmzp2b1xpWAFtcEjSzL8ws42dxeYJ0zjlXdBo0CC3tzJ8P7dtDs2ahtmvz5jBiRIGzP/bYY3To0CHrYUL690E655xzhSMnB3r3hkcf3TRs9mzo3z9053G/5I033kiFChXo2bNnEQTpZ5DOOefi8PrrWw5buRKuuirl5MOGDePll19mxIgRFNVDpYokQUp6TdJCSVdH/b0lfShpkqRRkioXRRzOOeeKiR9/THv4+PHjufXWW3nxxRepVq1algPbpKjOIPsBlyT0vwscbGaHAz8CvYooDuecc8VB06YpB/eoWpV27drx1Vdf0aRJE4YOHcoFF1zA8uXLOeaYY2jdujUDBgwA4KeffqJjx46Js+8EvA/sLmmupH4AkjpLmgu0A/4j6bV0QpQVUSvrkvoATczshqThNwDfmFneN8ZEcnJybNq0aVmK0DnnXJEZMSJcc1y5ctOwatXgkUdgK68xSppuZmnf51iQWK9BStoT6Ag8k880/SVNkzRt4cKFRRecc8657OnZMyTDZs3Cg5abNdum5JgNsZ1BSmoCPA+cYWbfpbMMP4N0zjmXl1JxBimpPiE5/iXd5Oicc84VpSK5D1LSo8DBQOWoHby5wA7AXVF13afMbGhRxOKcc86lo8iKWAuDpIXA7G1cTH1Ce32udPL9W3r5vi29CmvfNjOzBoWwHKCEJcjCIGlaYZZRu+LF92/p5fu29Cqu+9Zb0nHOOedS8ATpnHPOpVAWE+QjcQfgssr3b+nl+7b0Kpb7tsxdg3TOOefSURbPIJ1zzrkCeYJ0zjnnUijxCTL5UVppzlNF0ghJk6P3KtHwJyVNjF5LJJ2UvchdXiT1kTRF0nuS9k8al9e+ay7prWieKxOmP17S+9HruGhYzah/qSR/kkwxU8D+P1jSZ5L+iJqrdCVAikceStJ90e/4ZUn1ouH1ov7J0fiiefBjHkp8gmTLR2mlow/wpZkdBnwV9WNmvc2sPXAssBR4o7CCdOmRVBcYCLQnPAbt3qRJ+pBi3wG3ANeY2SHAkZL2kFQeuA3oEL1uj4atAjoD92Tzs7jMpbH/ZxIeWfRB0UbmtlHy//RxQLXodzwauDQafinwTDS8ejRdbEp8gjSzuYn9kmpLGi1pQnRGsUuK2doDL0fdLwGHJ40/EZhgZn8UesCuIG2ByWa2xsy+B2okPVC7Pan3XWszmxx1/ycavivwvZktNbOlwPdACzNbZ2bzs/w53NbJd/+b2W9mtiK+8NzWSP6fJu/fcV7DY1HiE2QKVwBjzOwo4P8IZxbJ6gFLou6lwHZJ43sBI7IVoMtX4r4B+C0almr8Ujbtu8Tvcu7w5GUlTu+Kp4L2vysdkn/Hufu4btSfOzzW32uRNFZexPYBjpA0IOpfF51F/jvqPwf4FagT9deO+gGQVAdoBbxTFMG6LSTuG0jaP+S97zakmKegZbnix/dZ2ZD8O85Nlkui/qUUg31fGs8gZwK3mVn76HpiRzP7NrffzL4lJL+O0fQd2TwZdgWeN7MNuDh8CBwqqaKkpsAKM1udMD6vfTdD0sFRdwdgEvANsJOkWpJqATsB32b9E7htUdD+d6VDXr/j/P6bi1yJbygg8VFawOfAWcBDQCNAwMtmdmfSPFWBx4AmhEdvnZ17vVHSJOB8M/usyD6E24ykvoQzfQMGAeuAY8zs9rz2naSdgaFAJeDVhAdzdwT+ES16sJm9Eg1/CWgJrATeNbMBuGKhgP2/GzAEOIDwe3/azB6MLViXlhT/06cC9xFK65YBvc1ssaTtgCeBWsCnwIVxnqyU+ATpnHPOZUNpLGJ1zjnntpknSOeccy4FT5DOOedcCp4gnXPOuRQ8QTrnnHMpeIJ0rhiT9IOk+ts6jXMuc54gnXPOuRQ8QTpXTEh6QdJ0STMl9U8a11zSl5KGSfpU0nOSqiVMcqGkj6JHQe0RzXNg9Nioj6P33Yv0AzlXwnmCdK746GtmBwA5wMCoVZFEuwOPmFlu6yN/TRi3yMz2Bx4E/h4N+xI43Mz2A/4J3JTV6J0rZTxBOld8DJQ0g/Cswx0Jj+tKNMfM3ou6hwOHJowbE71PB5pH3bWBZyV9DtxNaFrPOZcmT5DOFQOS2gNHA+3MbF/gY6BK0mTJ7UIm9uc26L2eTU/pGQy8bWZ7AyelWJ5zLh+eIJ0rHmoDS8xsZXQN8aAU0zSV1C7q7gG8m8Yy50XdfQolSufKEE+QzhUP44EKkj4lnPl9kGKaL4CzomnqEa435uc24GZJ7wHlCzNY58oCf5qHcyWApOaER7ftHXcszpUVfgbpnHPOpeBnkM4551wKfgbpnHPOpeAJ0jnnnEvBE6RzzjmXgidI55xzLgVPkM4551wKniCdc865FDxBOueccyl4gnTOOedS8ATpnHPOpeAJ0jnnnEvBE6RzzjmXgidI55xzLgVPkM4551wKniCdc865FDxBFiOSekp6PY3pHpL0j6KIKW6SbpC0SNL8IljXtZKGF/a0aS5vL0nTtnEZ7SXNLayYSitJEyWdE3cc6ZC0QtLOccdREEljJB0fdxyFzRNkmiT9IGlV9IX9RdLjkmoU5jrMbISZHZvGdAPMbHBhrrs4krQjcDGwl5n9Ke54smwwcEfcQRRXkrpKmiJppaSJKca3ljQ9Gj9dUuuij7LwmVkNM/tfUawr6T9uRfLBuqQzJM2W9LukFyTVSxh9C3BjUcRZlDxBZuYkM6sB7A+0Aa5OnkBShSKPqoTYim3TDFhsZguKYF2xkbQ98GfghZhDyVgRbudfgXsIf8TJMVQCxgHDgbrAMGBcNLxYKEHfx5OipFwj8WBdUkvgYeBMoBGwEhiSO97MpgK1JOUUdcDZ5AlyK5jZPOBVYG8ASSbpfEnfAN9Ew06U9ImkpdGRb6vc+SXtGBVJLJS0WNL90fA+kt6NuiXpbkkLJP0m6VNJuet7QtINCcs7V9K3kn6V9KKkxgnjTNIASd9IWiLpAUlK9bkkHShpmqRl0VnyXQnjDo0+x1JJcyT1iYbXlvRk9FlmS7paUrmEz/Ne9Dl+Ba6VVFnSHZJ+jNbxkKSqKWI5GngDaBwdzT4RDT9Z0swojomS9kyY5wdJl0n6FPg91Z+SpH9F8S+LzjQOy2NbNI+2XX9JP0n6WdLFSZNVij778iimnIT5L5f0XTRulqTOqdYTOQb4yMz+SPosf4/2+2+SnpFUJZ9lpPoMKWOI9sGvkvZJmLahwtlDg6g/v+/vFts56p8XresrSUdlEmtBzOxNMxsN/JRidHugAnCPma02s3sBAUcWtFxJLSS9Ff0OF0kaIalONO4SSc8nTX+fpHui7tqShkbfjXkKlwPKR+O2+O7nE8Mukt6J9vMiSc8kjLNofO7vIPe1UpIlTNdX0hcKv/HXJDUr6LNnqCfwkplNMrMVwD+AUyXVTJhmInBCIa83XmbmrzRewA/A0VH3jsBMYHDUb4Q/83pAVcIZ5gKgLVAeOCuav3LUPwO4G6gOVAEOjZbTB3g36j4OmA7UIfzY9wS2j8Y9AdwQdR8JLIrWWRm4D5iUELcBL0fLaQosBI7P4zO+D5wZddcADoq6mwLLgR5ARWA7oHU07knC0XtNoDnwNdAv4fOsAy4k/IFVJZwFvBhtq5rAS8DNecTTHpib0L8b8DshoVQELgW+BSol7KNPov1TNY9l9orir0Aovp0PVInGXQsMj7qbR9tuZLSf9om23dEJ0/4BdIz26c3ABwnrOR1oTDgI7RbFvX0eMd0OPJDi+zY1WkY94AtgQAHf0eTtlWcMhKP/WxOmHUT4A4R8vr+ptjOwOzAHaJyw7VrkEePlwNK8Xmn8Ds8BJiYN+z/g1aRhLwMX57GMicA5Ufcu0fepMtAAmERItADbR9usTtRfIdouB0T9LxDOqqoDDaP9dV5e3/18PtNI4KpoP238P0j4/e6SYp4RwMio+xTC72DPaF1XA1MSpv00n20+JOk79wvhe/46sG/CuHHAZUkxrMjdFlH/34Ax2/pfW5xesQdQUl7Rl2dF9KWaTfiDqRqNM+DIhGkfJEqeCcO+Ao4A2kVfwAop1tGHTQnySEKyOQgolzTdE2xKkEOB2xLG1QDWAs0TYkv8wY0GLs/jM04CrgPqJw2/AhibYvrywGrCNcLcYecR/YFFn+fHhHEi/OG0SBjWDvg+j3jas/kf/j+A0Qn95YB5QPuEfdQ3w/26JPePgNQJco+EaW8DhiZM+2bCuL2AVfms5xOgUx7jHgVuSfF965W07ocK+Cybba/8YiAkvzm53y1gGtC1oO9vqu1MSDILgKOBioXxe8vnM6RKkP8ARiUNGwFcm8cyJhIlyBTjTgE+Tuh/FTg36j4RmBV1N4q++1UTpu0BvJ3qu1/AZ3oSeARokmLcFgkSuIxw8Fw1IcZ+Sb+LlUCzDLftIYQDnmqE3/x8Nh0cTCDpAI2E317Ufy7wVjb3f1G/vIg1M6eYWR0za2ZmfzWzVQnj5iR0NwMujoqnlkpaSjjabhy9zzazdfmtyMzeAu4HHgB+kfSIpFopJm1MSNi5860AFgM7JEyTWAN0JSGJptKPcJb2paT/SjoxGr4j8F2K6esDlRLXH3UnrjtxuzQg/PimJ2yX8dHwdCR/1g3R8vNa3xYkXRwVRf0Wrb929Dnykri82VEMuZK3a5XcYl1JvROKKJcSiuPzWs8Swtl0snT3W0r5xWBmHxIOVo6QtAchyb0YzZrf9zfXxu1iZt8CFxEOGhZIGqWEYv4isAJI/m3UIpR65CsqWh4VFZEuI1zHTNxPwwilDkTvT0XdzQilGD8nbKOHCWeSufL9Lia4lHDwODUqqu+bT7wdCGf7pyT8/zQD/pUQx6/R8nZIvZTUzOw9M1tlZivN7GbCyUDuJYh0tnHNaJ5SwxNk4bGE7jnAjVEyzX1VM7OR0bimSuOivZnda2YHAC0JieuSFJP9RPiBACCpOqEIcV7GH8DsGzPrQfiR3wo8Fy1vDtAixSyLCGerzRKGNU1atyVNvwpombBdaluo+JSO5M8qwh93XuvbjML1xsuArkBdM6sD/Eb4M8nLjgndTUl9DSx5Pc0IZ4UXANtF6/k8n/V8Sti/hSbNGHL//M8EnrNN10Dz+/7m2mw7m9nTZnYoYf8Y4fuTKq4rk66lbfbayo87E2gVfR9ytYqGF+TmKN5WZlaLsD0Sl/NCtOy9CWeQI6LhcwhnkPUTtlEtM2uZMG+e38VEZjbfzM41s8aEEpghknZJnk7S7oR91tXMEpPvHELRbuL+qmpmU6L5ZuazzR/KL7SEbTET2Dchlp0JxdJfJ0y/J+HyUanhCTI7HgUGSGqroLqkE6IL2lOBn4FbouFVJB2SvABJbaL5KxKO9P8A1qdY19PA2QrV3CsDNwEfmtkPmQYtqZekBtGZ2dJo8HrCn8LRClXtK0jaTlJrM1tPKLK9UVLN6E/5b4Sj8C1Ey30UuFtSw2idO0g6Ls0QRwMnSDoq2i4XE/6kpqQ5f03CdaGFQAVJ/2TLo+Jk/5BUTaEW39nAMwVMD+GalEXrQdLZRBW68vAGsL8yrIRTCDE8BXQmJIUnE4bn9/3dgqTdJR0Zff/+IBwEpfquYmY32aZaklu88vowkspH26cCUC763VSMRk+M1jdQoQLSBdHwt6J52yuhQkuSmkSXTiTtQNJBaHTQ8BzhdzbVzH6Mhv9MuE53p6RaksopVPg5Iq/PkM9nO11Sk6h3CWG/rU+aphbhOuDVZvZu0iIeAq6IvqO5lYdOT/gMLfPZ5gOieZpKOkRSpWjbXkI4k34vWswI4CRJh0UHzdcTrjcmnkEeQSjuLTU8QWaBmU0jlMffT/jCf0u4JkGUVE4iFGn9CMwlVKBIVovwR7WEULS3mBT3yZnZBMI1mOcJibcF0H0rQz8emBkdyf8L6G5mf0R/Ch0JCelXwrWs3KPJCwkJ/H/Au4Q/ksfyWcdlhO3xQVSk9SahkkeBzOwrwp/5fYSz0ZMI1dLXpPn5XiP8gL8mbNM/KLgY7J0o3gnAHWZWYEMOZjYLuJNQ6ekXQgWf9/KZ/hfCn3mngj9CetKJwczmAh8R/pAnJwzP8/ubh8qE2y8WEYqFGwJXFs4n2ehMQuJ9kFDst4rw+yDa/6cAvQkHdn0JRZC534sdCdshlesIlZJ+A/4DjEkxzTDC9nsqaXhvwiWGWYTt9ByhYk+m2gAfRr+7F4FBZvZ90jT7E34ndyWfcZvZWMIZ+6joN/U50CHDGGoStu0SQonM8UAHM1scrWMmMICQKBdE0/81d2ZJbYDfLdzuUWrILK1SAOfKFEnNge8JlU7yvV5cSOvbi/BHfKAV4Y9S0mPAT2a2xT29pYWkfwPPmtlrWzl/U+BL4E9mtqxQgyslFG6HGWpmr8QdS2HyBOlcCkWdIOMQfcZPgP1SnLE4QOGe3ruAWmaWZ+UZVzp5EatzJUg+lVwyuvYjaTChKO52T46pRdfalhHuk7xmG5f10FZUknEx8zNI55xzLgU/g3TOOedSyFoDupJeI9S8+peZ3ZA0rgqhBZimhJqc/RLuwcpT/fr1rXnz5lmI1jnnXEk3ffr0RWaWbsMjBcpmC/P9CE1PNUkxrg/wpZn1jO5F60O4lydfzZs3Z9q0bXpknnPOuVJK0uyCp0pf1opYo3us8tKe0JgwhMaqD89WHJkaP348u+++O7vssgu33LLFk3VYsmQJnTt3plWrVhx44IF8/vnnAHz11Ve0bt1646tWrVrcc889RRy9c865whLXM8rqEW5IhXBj73Z5TSipP9AfoGnTplkNav369Zx//vm88cYbNGnShDZt2nDyySez1157bZzmpptuonXr1owdO5Yvv/yS888/nwkTJrD77rvzySefbFzODjvsQOfO+T3hyDnnXHEWVyWdXwmPX4LQWPSveU1oZo+YWY6Z5TRoUGhFyylNnTqVXXbZhZ133plKlSrRvXt3xo0bt9k0s2bN4qijwqPu9thjD3744Qd++eWXzaaZMGECLVq0oFmzZrjiY2tLBwCWLl1Kly5d2GOPPdhzzz15//28GmZxzpUWcSXIdwhNlxG9v5PtFY4YAc2bQ7ly4X3EiC2nmTdvHjvuuKlt6iZNmjBv3uZtfu+7776MGRNao5o6dSqzZ89m7tzNS5NHjRpFjx49CvsjuG2QWzrw6quvMmvWLEaOHMmsWbM2mya3dODTTz/lySefZNCgQRvHDRo0iOOPP54vv/ySGTNmsOeeeyavwjlXymQtQUp6lNDwbx9JL0SNaec2BPwEsI+kyYQ2Dp/IVhwQkmH//jB7NpiF9/79t0ySqe4JlTZ/AMPll1/OkiVLaN26Nffddx/77bcfFSpsKqles2YNL774IqeffnryolyMtqV0YNmyZUyaNIl+/foBUKlSJerUqVPUH8HlY1tKB5o3b84+++xD69atycnJKcqwXTGXtWuQZnZuisGfRONWER4uWiSuugpWrtx82MqVcM45MGoUVKgQXkuXNmHmzDn06RP6P/tsLuXKNWbQoE3TVKhQi6ZNH2ennaB8eeM//9mJ//xnJyZODOM///xVGjXan1dfbZQwT3iVL59/fzrTJPcn5e8ya8SIsJ9//BGaNoUbb4SePTeNT1U68OGHH262jNzSgUMPPXSz0oHy5cvToEEDzj77bGbMmMEBBxzAv/71L6pXr15UH8/lY1vqDuR6++23qV8/v8eCurIorko6RerHH1MP/+MPmDcP1q2D9eth7do2LFz4DW+88T2wAwsWjKJ69af54ovEaZayfn01QiP+/wYO56qrEp+YNBLowdlnZ/tTBeXKZZ5Us5WsszVPQdM89xycf/6mg6DcEgLYlCTTLR0YNGgQrVu3Zp999tlYOrB27Vo++ugj7rvvPtq2bcugQYO45ZZbGDx4cDZ3rYsUdPCTWDoAbCwdSEyQs2bN4oorrgA2Lx1o1KhRkX4WV7KUiQTZtGn400zWrBl89FHikAq88sr9XHTRcaxfv55rr+3LVVe15KGHwi2aAwYM4P33v6B3796UL1+ePfbYiwceGErNmiGBLl++kv32e4PJkx+mWrVNSXXduk2v5P50psn2PGvWbPtyi5uVK+HMM+HCC0MS3bChCStWzGHChND/229zkRozeXJi0q1FhQqPs9128MsvxsyZO3H55TshraRy5Sb8619tKV8eFi/uwuef38KiRZkfEBTGQUUmyyzpJQy5l0fyO/jZltKBRo0aIYljjz0WSZx33nn0z12BK/PKRIK88cbNf2QA1aqF4ck6duxIx44dNxs2YMCAjd3t2rXjm2++SbmeevWq8euviwsl5pLEDDZsKNwEn0myvuSSvOPq1Sv3IKANzzzzDfvt9z2VKu3A+PGjaNPmaapX37S8VauWYlaNNWsqMW/ev6lW7XDmz6/F+vW1MNuRKVO+oly53Vm8eALr1+/FmDF5x1VclCtX+Ek3G4k8r/6//S315ZGrriqc0gGA9957j8aNG7NgwQKOOeYY9thjDw4/vNjcmu1iVCYSZO4PKb9iGrf1pPDHVr48VK5c9Ou///68SwjuvTe3rwKnnrqpdOCSS/IvHWjdei+GDh1K3bph7k8+uY9zzunJmjVraNVqZx5//PGN45IlHzAUVSlBtpaxZk1IStuyzMKWeNmkSZMmzJmz6bnXc+fOpXHjxptNX6tWLR5//PFo/xg77bQTO+20E8DGaRs2bEjnzp2ZOnWqJ0gHlLCneeTk5Jg3NeeSJRfDQSgheOQRPwgqDrblgOGkk2D+/C2X2awZ/PBD6F63bh277bYbEyZMYIcddqBNmzY8/fTTtGzZcuP0S5cupVq1alSqVIlHH32UyZMn8+STT/L777+zYcMGatasye+//84xxxzDP//5T44//vii2TiuUEmabmY5hbW8MnEG6Uo3LyEo3ralhOGOO7Y8+KlcefPLIxUqVOD+++/nuONC6UDfvn1p2XLz0oEvvthUOrDXXqF0AOCXX37Z2OLVunXrOOOMMzw5uo38DNI5V6wl1mItXz6UDsycCU2axB2ZK24K+wzSnwfpnCvWevYMxakbNsCMGeG9c2dYtSruyFxp5wnSOVdi7LUXDB8O06bBeeeF65vOZYsnSOdcidKpE1x3HTz1FPgT5Vw2eYJ0zpU4V18diln//nd44424o3GllSdI51yJU64cDBsWily7dYPvvos7IlcaeYJ0zpVINWvCCy+E7lNOgRUr4ozGlUaeIJ1zJVaLFjB6NMyaBb17hxquzhUWT5DOuRLt6KPh9tth7Fi44Ya4o3GliSdI51yJ93//F57ecs01kPQcbOe2midI51yJJ8HDD0NOTniCy6xZcUfkSgNPkM65UqFq1VDMWr16uFdyyZK4I3IlnSdI51yp0aQJPP98ePxZ9+7F69mcruTxBOmcK1UOOQQeeABefx2uuCLuaFxJ5o+7cs6VOueeCx9/HGq3tm4NZ5wRd0SuJPIzSOdcqXTPPXDYYdCvH0yfHnc0riTyBOmcK5UqVYLnnoMGDUK7rQsWxB2RK2k8QTrnSq2GDUNzdAsXQpcusGZN3BG5ksQTpHOuVNt/f3jsMZg8GS66KO5oXEnilXScc6Vejx6bV9rp3z/uiFxJ4GeQzrky4eab4bjj4IIL4L334o7GlQRZTZCS+kiaIuk9SfsnjdtZ0iRJEyW9LalJNmNxzpVt5cvDyJHQrBmcdhrMnRt3RK64y1qClFQXGAi0B3oB9yZN8ldgqJm1B4YBF2YrFuecA6hbNzRm/vvvoWbrqlVxR+SKs2yeQbYFJpvZGjP7HqghqXLC+JlAnai7HuCVsJ1zWbfXXjB8OEybFq5FmsUdkSuuspkg6wGJzQX/Fg3L9SZwnqRPgfOAf6daiKT+kqZJmrZw4cKsBeucKzs6dYLrrguJ8u67447GFVfZTJC/sukMEaB2NCzXrcDVZtYKuBa4KdVCzOwRM8sxs5wGDRpkKVTnXFlz9dVw6qlwySXwxhtxR+OKo2wmyA+BQyVVlNQUWGFmqxPGC1gUdS9g87NL55zLqnLlYNiwUOTarRt8913cEbniJmv3QZrZEklDgHcAAwZJag0cY2a3AzcAD0taB1QkFLM651yRqVEjtLTTpk0odn3/fahZM+6oXHEhK0FXqHNycmzatGlxh+GcK2XefDPcI9mpU2i/tZzfIV4iSZpuZjmFtTz/Gjjnyryjj4Y77oCxY+GGG+KOxhUXniCdc47QTuuZZ8I114R7JZ3zBOmcc4AEDz8MOTnQqxfMnBl3RC5uniCdcy5StWooZq1eHU45BZYsKXAWV4qlnSAlVc9mIM45Vxw0aQJjxsDs2dC9O6xfH3dELi4FJkhJB0uaBXwR9e8b3b7hnHOl0sEHwwMPwOuvwxVXxB2Ni0s6Z5B3A8cBiwHMbAZweDaDcs65uJ17LvzlL+EZkk8/HXc0Lg5pFbGa2ZykQV7o4Jwr9e65Bw4/HPr1g+nT447GFbV0EuQcSQcDJqmSpL8TFbc651xpVqkSPPssNGgQHo+1wJ85VKakkyAHAOcDOwBzgdZRv3POlXoNG4bm6BYtgi5dYM2auCNyRSXfBCmpPHCPmfU0s0Zm1tDMepnZ4iKKzznnYrf//jB0KEyeHBoUcGVDvo2Vm9l6SQ0kVTIzP25yzpVZPXrAJ5/AbbdB69bhYcuudEvnaR4/AO9JehH4PXegmd2VraCcc644uukm+PRTuOACaNkSDjkk7ohcNqVzDfIn4OVo2poJL+ecK1PKlw+3fDRrBqedBnPnxh2Ry6YCzyDN7DoASTVDr63IelTOOVdM1a0bGjNv2zbUbJ00KTRR50qfdFrS2VvSx8DnwExJ0yW1zH5ozjlXPO21F4wYAdOmhWuRJeixui4D6RSxPgL8zcyamVkz4GLg0eyG5ZxzxdvJJ8P118Pw4XD33XFH47IhnQRZ3czezu0xs4mAN1zunCvzrroKTj0VLrkE3ngj7mhcYUsnQf5P0j8kNY9eVwPfZzsw55wr7sqVg2HDQpFrt27w3XdxR+QKUzoJsi/QABgTveoDZ2czKOecKylq1AiVdiTo1AmWL487IldY0qnFugQYWASxOOdcibTzzvDMM3DccXDWWfDcc+Hs0pVs6dRifUNSnYT+upJey2pUzjlXwhx9NNxxB4wdCzfcEHc0rjCkc4xT38yW5vZEZ5QNsxaRc86VUBddBGeeCddcE4pdXcmWToLcIKlpbo+kZoDf9eOcc0kkePhhaNMGevWCmTPjjshti3QS5FXAu5KekvQUMAm4IrthOedcyVS1KowZA9WrwymnwJIlcUfktlaBCdLMxgP7A88Ao4EDzMyvQTrnXB6aNAlJcvZs6N4d1q+POyK3NdKppHMIsMrMXgZqA1dGxazOOefycPDB8MAD8PrrcIWXuZVI6RSxPgislLQvcAkwG3gynYVL6iNpiqT3JO2fYvxlkiZImijpyIwid865Yu7cc+Gvf4Xbbw9PAXElSzrPg1xnZiapE3CvmQ2VdFZBM0mqS7h/8iBgB+Ap4NCE8R2A2mZ21NaF7pxzxd8998Dnn0O/frD77nDAAXFH5NKVzhnkcklXAL2A/0gqD1RMY762wGQzW2Nm3wM1JFVOGN8VqBKdQT4lqXbG0TvnXDFXsSI8+yw0aBAej7VgQdwRuXSlkyC7AauBfmY2n3A2eHsa89UDEutv/RYNy9UY2BCdQX5IHjVjJfWXNE3StIULF6axWuecK14aNoQXXoBFi6BLF1izJu6IXDrSqcU638zuMrPJUf+PZpbONchfgToJ/bWjYYnjx0fd44FWeaz/ETPLMbOcBg0apLFa55wrfvbfH4YOhcmTQ4MCrvjLZmuBHwKHSqoYNTSwwsxWJ4yfCORE3TnAt1mMxTnnYtejB1x6KTz4IDzySNzRuIKkU0lnq5jZEklDgHcILe8MktQaOMbMbgeeAB6V9DawFuidrVicc664uOkm+PRTuOACaNkSDjkk7ohcXmRWclqNy8nJsWnTpsUdhnPObZMlS6BtW1i2DKZNCw0LuG0nabqZ5RQ8ZXrSaiggeqLH15L+J+l7Sf8rrACcc66sqVs3NGa+cmWo2bpqVdwRuVTSuQY5FLiLcA9jG8L1wjbZDMo550q7PfeE4cPDGWT//lCCCvPKjHQS5G9m9qqZLTCzxbmvrEfmnHOl3Mknw/XXh0R5991xR+OSpVNJ521JtwNjCPdDAmBmH2UtKuecKyOuugpmzIBLLoF99oFjjok7IpcrnQTZNnpPvPBpgLed6pxz26hcOXjiCfj6a+jWDf77X2jRIu6oHKSRIM3sz0URiHPOlVU1aoSWdtq0gU6d4P33oWbNuKNy6dRirS3prtzm3iTd6e2mOudc4dp5Z3jmGfjiCzjrLNiwIe6IXDqVdB4DlhMaF+8KLAMez2ZQzjlXFh19NNx5J4wdCzfcEHc0Lp1rkC3M7LSE/uskfZKleJxzrkwbNAg++QSuuQb23TcUubp4pHMGuUpS4nMcDwH8tlbnnMsCCR56KFyP7NULZs6MO6KyK50E+RfgAUk/SJoN3A8MyG5YzjlXdlWpAmPGQPXqcMopoWk6V/TSedzVJ2a2L+FxVPuY2X5mNiP7oTnnXNnVpElIkrNnQ/fusH593BGVPXleg5TUy8yGS/pb0nAAzOyuLMfmnHNl2sEHw5AhcO65cMUVcNttcUdUtuRXSad69O534zjnXEzOOQc+/hhuvz1U2unZM+6Iyg5/3JVzzhVza9eGW0CmToV334UDDog7ouIpjsdd3SaplqSKkiZIWiSpV2EF4JxzLn8VK8Kzz0LDhuHxWAsWxB1R2ZBOLdZjzWwZcCIwF9gNuCSrUTnnnNtMw4ahObpFi6BLF1izJu6ISr90EmTF6L0jMNLMfs1iPM455/Kw334wdChMnhwaFHDZlU5LOi9J+pLQOMBfJTUA/shuWM4551Lp0SO0tHPbbSFh9u8fd0SlVzr3QV4OtANyzGwt8DvgjR8551xMbroJjj8eLrgA3nsv7mhKr/zugzzSzN6SdGrCsMRJxmQzMOecc6mVLw8jR8KBB8Jpp4VnSO64Y9xRlT75FbEeAbwFnJRinOEJ0jnnYlOnDowbB23bhpqtkydD1apxR1W65Jkgzeya6P3sogvHOedcuvbcE4YPD0/86N8fnnwyNHbuCkc690HeJKlOQn9dSf6kMuecKwZOPhkGDw6J8u67446mdEnnNo8OZrY0t8fMlhBu+XDOOVcMXHVVuBZ5ySXwxhtxR1N6pJMgy0uqnNsjqSpQOZ/pnXPOFSEJnngCWraEbt3gu+/ijqh0SCdBDgcmSOonqS/wBjAsu2E555zLRI0aoaUdKVyTXL487ohKvnTug7wNuAHYE2gJDI6GFUhSH0lTJL0naf88prlO0reZBO2cc25LO+8Mo0fDl1/CWWfBhg1xR1SypXMGCfAFMN7MLgYmSyrwEViS6gIDgfZAL+DeFNM0IrTt6pxzrhAcdRTccQeMHQs3eHXKbZJOLdZzgeeAh6NBOwAvpLHstsBkM1tjZt8DNRKvZUb+AdycfrjOOecKMmhQOIO85ppQ7Oq2TjpnkOcDhwDLAMzsG6BhGvPVA5Yk9P8WDQNA0q5ADTP7NL+FSOovaZqkaQsXLkxjtc45V7ZJ8NBD0KYNnHkmzJwZd0QlUzoJcrWZbXywiqQKhJZ0CvIrUCehv3Y0LNe1wOCCFmJmj5hZjpnlNGjQII3VOuecq1IlFLPWqAGnnAJLlhQ4i0uSToJ8R9KVQFVJxwDPAi+lMd+HwKHRg5abAivMbHXC+J2BBySNB7aXtMU1Suecc1tvhx3g+edh9mzo3h3WrYs7opIlnQR5GbAQ+Aw4D3gFuLqgmaIGBYYA7wAjgYsktZZ0STS+nZkdb2bHAz+b2cCt/AzOOefycPDBMGQIvP46XHFF3NGULPk+D1JSOeBTM9sbeDTThZvZY8BjSYM/STHdLpku2znnXHrOOSc8Q/KOO6B1a+jZM+6ISoZ8zyDNbAMwIyoidc45V0LdfTcccURIltOnxx1NyZBOEev2wExJEyS9mPvKdmDOOecKT8WK8Oyz0LBhqLTzyy9xR1T85VvEGrku61E455zLugYNwn2RhxwCXbrAhAlQqVLcURVfeZ5BSqoi6SLgdGAP4D0zeyf3VVQBOuecKzz77QePPQbvvhsaFHB5y+8MchiwFpgMdAD2AnxzOudcCde9e6i0c+utIWH27x93RMVTfglyLzPbB0DSUGBq0YTknHMu2268ET79FC64APbaCw49NO6Iip/8Kumsze0wM7+91DnnSpHy5eHpp6F58/Cw5Tlz4o6o+MkvQe4raVn0Wg60yu2WtKyoAnTOOZcdderAuHGwahV07hze3SZ5JkgzK29mtaJXTTOrkNBdqyiDdM45lx177gkjRsBHH4VrkZZOS9tlRLrPg3TOOVdKnXQSXH89DB8eGhRwgSdI55xzXHVVuBZ5ySWh3VbnCdI55xzhGZJPPAEtW4bbQL77Lu6I4ucJ0jnnHBCeHTluXEiWnTrB8uVxRxQvT5DOOec22mknGD0avvwSzjoLNmyIO6L4eIJ0zjm3maOOCo/GGjsWBg+OO5r4eIJ0zjm3hUGDwhnktdeGBs7LIk+QzjnntiDBQw/BgQfCmWfCzJlxR1T0PEE655xLqUoVGDMmVN7p1Al+/TXuiIqWJ0jnnHN52mGHkCTnzIEePWBdGWqZ2xOkc865fLVrB0OGhAYErrgi7miKTn6Pu3LOOecA6NcPPv441G5t3Rp69ow7ouzzM0jnnHNpuftuOOIIOOccmD497miyzxOkc865tFSsCM8+Cw0bwimnwC+/xB1RdnmCdM45l7YGDUJzdIsXQ5cusGZN3BFljydI55xzGWndGh5/HN59NzQoUFp5JR3nnHMZ69YtVNq59daQMM87L+6ICp+fQTrnnNsqN94IHTrABReEs8nSJqsJUlIfSVMkvSdp/6Rxl0r6MBp3nyRlMxbnnHOFq3x5ePpp2Hnn8LDlOXPijqhwZS1BSqoLDATaA72Ae5MmGWtmbc3sEKARcGS2YnHOOZcddeqExsxXrYLOncN7aZHNM8i2wGQzW2Nm3wM1JFXOHWlm3yRMuwYoQw0YOedc6bHnnjBiBHz0EfTvD2ZxR1Q4spkg6wFLEvp/i4ZtRlJ7YHtgUqqFSOovaZqkaQsXLsxCmM4557bVSSeFZ0cOHw533RV3NIUjmwnyV6BOQn/taNhGkloBNwPdzFIfc5jZI2aWY2Y5DRo0yFaszjnnttGVV4Z7Iy+9NLTbWtJlM0F+CBwqqaKkpsAKM1udO1LSLsBjQHczW5TFOJxzzhUBKdwf2bIldO8O330Xd0TbJmsJ0syWAEOAd4CRwEWSWku6JJrkHsIZ5jBJEyWdkK1YnHPOFY0aNUJLO1J4huTy5XFHtPWUR8lmsZSTk2PTpk2LOwznnHMFmDABjjsuXJt8/nkoVwR33UuabmY5hbU8byjAOedcoTvqKLjzznALyODBcUezdTxBOuecy4qBA+Gss+Daa0OiLGk8QTrnnMsKCR56CA48EM48E2bOjDuizHiCdM45lzVVqsCYMaHyTqdO8OuvBc9TXHiCdM45l1U77BCS5Jw50KMHrCsh7aZ5gnTOOZd17drBkCGhAYErrkh/vvHjx7P77ruzyy67cMstt2wx/ssvv6Rdu3ZUrlwZQrveG0l6TNICSZ9vTcyeIJ1zzhWJfv3g/PPhjjtC260FWb9+Peeffz6vvvoqs2bNYuTIkcyaNWuzaerVq8e9997L3//+91SLeAI4fmvj9QTpnHOuyNx9NxxxBJxzDkyfnv+0U6dOZZdddmHnnXemUqVKdO/enXHjxm02TcOGDWnTpg0VK1bcYn4zm0RSE6eZ8ATpnHOuyFSsCM8+C40awTHHwI47hkYEmjff8qxy3rx57Ljjjhv7mzRpwrx584osVk+QzjnnilSDBuGxWEuWwNy54fFYs2eHYYlJMlVLb5KKLE5PkM4554rcI49sOWzlSrjqqk39TZo0Yc6cORv7586dS+PGjYsgusATpHPOuSL3448FD2/Tpg3ffPMN33//PWvWrGHUqFGcfPLJRRMgUKHI1uScc85FmjYNxaqphueqUKEC999/P8cddxzr16+nb9++tGzZkoceegiAAQMGMH/+fHJycli2bBnA9pLmAnuZ2TJJI4H2QP1o+DVmNjTdGP1pHs4554rciBHhmuPKlZuGVasWil579ty6ZfrTPJxzzpV4PXuGZNisWWiztVmzbUuO2eBFrM4552LRs2fxSojJ/AzSOeecS8ETpHPOOZdCiaqkI2khkKLeU0bqA4sKIRxXPPn+Lb1835ZehbVvm5lZg0JYDlDCEmRhkDStMGs5ueLF92/p5fu29Cqu+9aLWJ1zzrkUPEE655xzKZTFBJmiBUBXivj+Lb1835ZexXLflrlrkM4551w6yuIZpHPOOVcgT5DOOedcCiU+QUp6TdJCSVdnME8VSSMkTY7eq0TDn5Q0MXotkXRS9iJ3eZHUR9IUSe9J2j9pXF77rrmkt6J5rkyY/nhJ70ev46JhNaP+pZJ6Fe2ncwUpYP8fLOkzSX9IahJXjC4zyf/TCu6LfscvS6oXDa8X9U+Oxhfd05FTKPEJEugHXJLhPH2AL83sMOCrqB8z621m7YFjgaXAG4UVpEuPpLrAQMIjanoB9yZN0ocU+w64hfAom0OAIyXtIak8cBvQIXrdHg1bBXQG7snmZ3GZS2P/zwTaAR8UbWRuGyX/Tx8HVIt+x6OBS6PhlwLPRMOrR9PFpsQnSDObm9gvqbak0ZImRGcUu6SYrT3wctT9EnB40vgTgQlm9kehB+wK0haYbGZrzOx7oIakygnj25N637U2s8lR93+i4bsC35vZUjNbCnwPtDCzdWY2P8ufw22dfPe/mf1mZiviC89tjeT/afL+Hec1PBYlPkGmcAUwxsyOAv6PcGaRrB6wJOpeCmyXNL4XMCJbAbp8Je4bgN+iYanGL2XTvkv8LucOT15W4vSueCpo/7vSIfl3nLuP60b9ucNj/b2Wxsdd7QMcIWlA1L8uOov8d9R/DvArUCfqrx31AyCpDtAKeKcognVbSNw3kLR/yHvfbUgxT0HLcsWP77OyIfl3nJssl0T9SykG+740nkHOBG4zs/bR9cSOZvZtbr+ZfUtIfh2j6TuyeTLsCjxvZhtwcfgQOFRSRUlNgRVmtjphfF77boakg6PuDsAk4BtgJ0m1JNUCdgK+zfoncNuioP3vSoe8fsf5/TcXuRLfUICkR4GDgcrA58BZwENAI0DAy2Z2Z9I8VYHHgCbAXODs3OuNkiYB55vZZ0X2IdxmJPUlnOkbMAhYBxxjZrfnte8k7QwMBSoBr5rZDdGyOgL/iBY92MxeiYa/BLQEVgLvmtkAXLFQwP7fDRgCHED4vT9tZg/GFqxLS4r/6VOB+wildcuA3ma2WNJ2wJNALeBT4MI4T1ZKfIJ0zjnnsqE0FrE655xz28wTpHPOOZeCJ0jnnHMuBU+QzjnnXAqeIJ1zzrkUPEE6V4xJ+kFS/W2dxjmXOU+QzjnnXAqeIJ0rJiS9IGm6pJmS+ieNay7pS0nDJH0q6TlJ1RImuVDSR9GjoPaI5jkwemzUx9H77kX6gZwr4TxBOld89DWzA4AcYGDUqkii3YFHzCy39ZG/JoxbZGb7Aw8Cf4+GfQkcbmb7Af8Ebspq9M6VMp4gnSs+BkqaQXjW4Y6Ex3UlmmNm70Xdw4FDE8aNid6nA82j7trAs5I+B+4mNK3nnEuTJ0jnigFJ7YGjgXZmti/wMVAlabLkdiET+3Mb9F7Ppqf0DAbeNrO9gZNSLM85lw9PkM4VD7WBJWa2MrqGeFCKaZpKahd19wDeTWOZ86LuPoUSpXNliCdI54qH8UAFSZ8Szvw+SDHNF8BZ0TT1CNcb83MbcLOk94DyhRmsc2WBP83DuRJAUnPCo9v2jjsW58oKP4N0zjnnUvAzSOeccy4FP4N0zjnnUvAE6ZxzzqXgCdI555xLwROkc845l4InSOeccy4FT5DOOedcCp4gnXPOuRQ8QTrnnHMpeIJ0zjnnUvAE6ZxzzqXgCdI555xLwROkc845l4InSOeccy4FT5DOOedcCp4gSzBJfSS9G3cc2SLpMElfFfIyO0uaI2mFpP0Kc9kp1tVe0tzCnjbN5VWWNEvSn7ZxOSZpl8KKqzSSdK2k4XHHkQ5Jr0o6K+44CiJpoKRb4o6j1CVIST2jP7/kl0n6Z9zxuSCdP14zm2xmuxfyqu8ALjCzGmb2cSEvuzjpD0wys/lxB1IcSdpb0muSFkna4qG4kupJGivpd0mzJZ0RR5yFzcw6mNmwoliXpCckrUn6Hy6fML61pOmSVkbvrRNmfwToJalhUcSal1KXIM1sRPTnt/EFXAT8Ajya6fIkVSjsGF3BsrjdmwEzt2bGxB93CXAe8FTcQWyNItrOa4HRQL88xj8ArAEaAT2BByW1LIK40lKC/pduS/o/Xg8gqRIwDhgO1AWGAeOi4ZjZH8CrQO+Y4gZKYYJMFhWj3Q10N7Ofo2G1JQ2V9LOkeZJuyP1RRsWW70m6W9KvwLXR9E9KWhgdTV4tKc9tFxVv3SPpp+h1j6TK0bj2kuZKuljSgiiGs5PmvUPSj5J+kfSQpKppftaDJf1X0m/R+8EJ4/pI+p+k5ZK+l9QzGr6LpHeieRZJeiaPZTePzvrOjoool0gaIKmNpE8lLZV0f9I8fSV9EU37mqRm0fBJ0SQzoqPKbgnb5TJJ84HHlVTsKGlHSWOi/bA4d33pfIZou64Aykfr/S4avqekiVH8MyWdnDDPE5IelPSKpN+BP6dY7tnRZ1webd/z8tk/P0i6QqHoc4mkxyVVSZomr+/FCZI+lrQs2v7X5rOepkAL4MOkz/KApP9EsX4oqUVey8hjuXnGEC33wqTpP5V0StS9h6Q3JP0q6StJXZNi22w7S+oYbaflCr/Rv2cSa0HM7CszG0qKgyVJ1YHTgH+Y2Qozexd4ETgznWVLelbS/Oj7OElRYo1+K78oIblJOk3SJ1F3OUmXS/ou+n6PllQvGpf7++sn6UfgrXzWX0XS8GgZSxX+CxpF4yZKOifqzv39JZaytY/GHSRpSjT/jNzhhag9UAG4x8xWm9m9gIAjE6aZCJxQyOvNjJmV2hdQB/gOuCxp+AvAw0B1oCEwFTgvGtcHWAdcSNiBVYEnCUc7NYHmwNdAv3zWez3wQbTsBsAUYHA0rn20/OuBikBHYCVQNxp/D+HHWC9a30vAzXmspw/wbtRdD1hC+BFXAHpE/dtFn3MZsHs07fZAy6h7JHAV4WCpCnBoHutqDhjwUDTdscAf0bZsCOwALACOiKY/BfgW2DOK52pgSsLyDNgloT93u9wKVI62e3tgbjS+PDCDcLBTPTHWdD9D8nqj7f8tcCVQifDjXJ6wnZ4AfgMOyV12iuWdQEhGAo6I9uX+CZ9pbsK0PwCfAztG++s94IY0vxftgX2iOFoRSkROyeMzngDMTBr2BPArcGC0P0YAo9L4DSVurzxjALoCHybMty+wONqu1YE5wNnRuvcHFrHpO7jFdgZ+Bg6LxtfN3aYp4jsUWJrPK8/vQjT/LoAlDdsPWJU07O/AS3ks41pgeEJ/X8JvtzLh9/xJwrhZQIeE/rHAxVH3RYT/jSbRvA8DI5N+f09G27NqPp/pPML/RjXC7+YAoFY0biJwTop5+gNfArUIv+XFhO9gOeCYqL9BNO2QfLb3pym+c78C04HTEsb9H/BqUgwv526LqH9/4NeCvqPZfMW24qx/sPCHNS56KWF4I2B14heMkEzejrr7AD8mjCsfTb9X0hdwYj7r/g7omNB/HPBD1N0eWAVUSBi/ADgoivl3oEXCuHbA93mspw+bEuSZwNSk8e9H01SPvrynJf+woh/cI0CTArZn7g90h4Rhi4FuCf3PAxdF3a+ScBAR/dBWAs2i/lQJcg0JSYjNE2Q7YGHidsv0MySvFzgMmA+USxg/Erg26n4CeDLD790LwKDk+KP+H4ABCf0dge8K+l7ksZ57gLvzGNcT+CBp2BPAv5PW/WUm2yu/GAh/6L8Cu0b9dwBDou5uwOSkeR8GrslrOwM/En5ntTLZ/pm+SJ0gDwPmJw07lzx+8yQlyKRxdaJtWDvqvwwYEXXXi34T20f9XwBHJcy7PaEouAKbfn87p/GZ+hIOylulGDeRpARJOMhYAOyWEONTSdO8BpyV4bbdn3CAXiH6vi0HDonG/YOkAzTCQdu1Cf27Auuzuf8LepXmItbLgL0JO9UShjcjHKH/HBUfLCX8WBMvBs9J6K5POAqenTBsNuEoC4Ui0Nwiiiuj8Y1TTN84oX+xma1L6F8J1CCcbVYDpifENj4aXpDkdW6M08x+J/xJDYg+938k7RFNcykhMU+Nihj7FrCeXxK6V6XorxF1NwP+lfA5fo3Ws0M+y15o4dpDKjsCs5O2W65MP0OuxsAcM9uQMGzjvo3MIR+SOkj6ICo6XEr4I6ifzyyJy0v3e4GktpLeVihe/o2wL/NazxLCGUyyxAo7G5edrvxiMLPVhGt6vRQuP/Rg0zXQZkDb3O9CtJ16Aok1bJO382mEbTlbofi8XSaxbqMVhDOpRLUIf/D5klRe0i1RMekywkERbNpXw4GTJNUgnHVPtujSD2E7jU3YRl8A6wkH9bny/T5GniIktFEKl3huk1Qxj3h3JOy3s8zs64Q4Tk/aX4cSEnbazOwjM1tsZuvM7BVCAjw1Gp3ONq5JKFmITalMkFF5+VVAFzNbmjR6DuGMsL6Z1Yletcws8QJ8YkJdRDiKa5YwrCkwD8DMBtimC9A3ReN/SjH9T2mEvoiQZFomxFbbQkWjgiSvMznO18zsGMKX/EuiCktmNt/MzjWzxoQj9iEqnGr9cwjF1nUSXlXNbEo+81g+4+YATZWicsI2fIafgB21+fXkjdusoJgUris/TzhbamRmdYBXCMk6LzsmrSud7wXA04Si9x3NrDahqDuv9XwK7JxqW22jgmIYRkh8RwErzez9aPgc4J2k70INM/tLwrybbWcz+6+ZdSIcuL5A+BPfgsKtQKlqree+DtuKz/k1UEHSrgnD9iW9yl1nAJ2Ao4HahDM/iLaTmc0jlOx0JpT6JFakmkMofk3cTlWieXLl9xshWsdaM7vOzPYCDgZOJEVlF4W6DS8QrgO+mhTHU0lxVDezW6L5Hspne+e3jYxN35eZQCtJid+fVmy+jfckXFaJTalLkJK2B0YRivo+Th4fHa29DtwpqVZ0YbyFpCNSLc9CravRwI2SaipUNPkb4UgwLyOBqyU1kFQf+GcB0+euawMhcd2tqHqzpB0kHVfQvIQ/5t0knSGpgqRuwF7Ay5IaSTpZofLBasLRW25tstMlNYmWsYTwJV6fxvoK8hBwhTZVUKgt6fSE8b8AO2ewvKmE61K3SKoeVUQ4JFr21n6GDwlF2pdKqhgdWJ1E+P6koxKhaHEhsE5SB8K12fycL6mJQuWLK4GUlaJSqEm4HvOHpAMJf8Qpmdlc4BvC9cbClG8MUULcANzJ5n/8LxO+m2dG27miQoWVPVOtRFIlhdu1apvZWsL185T708KtQDXyeU3OYx1SqCBVKeqvEh3wEJW4jAGuj75rhxCS3lMJ82+s0JJiG60mXH6oBtyUYponCaUe+xCuQeZ6iPA/0yxaRwNJnVLFnx9Jf5a0j0LFw2WEA/xU2+8xQjH7bUnDc89yj4vOiKsoVJhrAlucFCS/Np5oSOoiqUb0H3ss0ItwgAWhqHc9MFChAt0F0fDEykdHEC7VxKbUJUjCtYJGhOK95KObh6JpehN+GLMIf6jPkX/xwYWEP9L/Ae8SjqQfy2f6G4BphCP5z4CPomHpuIxQceSDqIjmTaDAewHNbDHhSPFiwo/zUuBEM1tE2M8XE85WfiV88f4azdoG+FChhueLhOtn36cZa37xjCVUuBkVfY7PgQ4Jk1wLDIuKcLqmWETy8tYTktcuhOtTcwnFxlv9GcxsDXByFNciQuWD3mb2ZZqfcTkwkHAAtYSQMF7Md6bw3Xmd8F36H+l/L/5K+MNeTjjgSnlGleBh0qx1mYF0YniS8Me/8YAw2k7HAt0J38H5bKqMlZczgR+i784Awp9rYWpGKK3JPWNZBSQ2SvFXQkWxBYQD3r+Y2UyAKFGsIPy2kz1JKDqfR/h/+SDFNGOj9Y+NknGufxG+P69H2/gDoO1WfLY/Ef7TlhGKad8h9QF6d6Bz8hm3mc0hHBBcSTj4mwNcQub5YhBhOywFbgfONbOJsPG3dwrhv3gp4brpKdFwooOXjoRSidho88tzzrlskfQDoYLEm0WwrsrAx4RKHz8XNH0hrrc30N/MDi2qdRY1Sb0Il0Gu2IZlfEe4BJH170JJpHDL0I5mdmmccZSUm02dcxmIKs3sVZTrlFSNcOY1pCjXW9TMbJualZN0GuEyQJ73MpZ1ZnZf3DFA6Sxidc6lKb9KLhku5zhCcdwvhGJkl4KkicCDwPlJtaczXU5eTWpuVStRLjUvYnXOOedS8DNI55xzLoUSdQ2yfv361rx587jDcM45VwxNnz59kZml07BKWkpUgmzevDnTpk2LOwznnHPFkKTk1sS2iRexOuecK7bGjx/P7rvvzi677MItt2z5DOXffvuNk046iX333RegpTZ/Cs4gSZ8rNEF5Uabr9gTpnHOuWFq/fj3nn38+r776KrNmzWLkyJHMmjVrs2keeOAB9tprL2bMmAGhsYc7o9aY9iY0HHMgoanAE7V584EF8gTpnHOuWJo6dSq77LILO++8M5UqVaJ79+6MGzdus2kksXz58twngJQjtBa2jtCW6wdmtjJ6CMA7hDZw0+YJ0jnnXCxGfDaC5vc0p9x15Wh+T3NGfDZis/Hz5s1jxx03te/fpEkT5s2bt9k0F1xwAV988QWNGzcGaEloanIDoXnLwyVtFzVi0ZHNHxZQIE+QzjnnityIz0bQ/6X+zP5tNoYx+7fZ9H+p/2ZJMtV9+tLmD7F57bXXaN26NT/99BOE9m/vl1TLzL4gtPn7BuGxgTMIZ5Zp8wTpnHOuyF014SpWrl252bCVa1dy1YSrNvY3adKEOXM2PQJz7ty5uWeKGz3++OOceuqpuYlzNfA9sAeAmQ01s/3N7HBC0es3mcToCdI551yR+/G3Hwsc3qZNG7755hu+//571qxZw6hRozj55JM3m75p06ZMmDAht7cC4elH/wNIeGxgU8LDmkdmEmOJug/SOedcybduwzoqV6jMH+v+2GJc09pNN3ZXqFCB+++/n+OOO47169fTt29fWrZsyUMPhScXDhgwgH/84x/06dOHffbZB0JyPDd6zB/A85K2IzwT83wzW5JJnCWqLdacnBzzhgKcc65ku/i1i7nrg7uoVL4Sa9av2Ti8WsVqPHLSI/Tcp+dWLVfSdDPLKaw4vYjVOedckXlqxlPc9cFdDDxwII91eoxmtZshRLPazbYpOWaDF7E655wrEv+d91/Ofelc/tz8z9xx7B1ULF+xWCXEZH4G6ZxzLuvmr5hP52c6s33N7Rl9+mgqlq8Yd0gF8jNI55xzWbVm/Rq6jO7Ckj+WMKXvFOpXqx93SGnxBOmccy5rzIwLXrmA9+a8x+guo9n3T/vGHVLavIjVOedc1jw07SEe/ehRrjz0Sk5veXrc4WTEE6RzzrmsmDR7EgPHD+SEXU9g8JGD4w4nY54gnXPOFboff/uRLqO70KJuC0acOoJyKnnppuRF7JxzrlhbuXYlnZ/pzOr1qxnXfRy1q9SOO6StktUEKamPpCmS3pO0f9K4nSVNkjRR0tuSmmQzFuecc9lnZpzz4jl8/PPHjDxtJLvX3z3ukLZa1hKkpLrAQKA90Au4N2mSvwJDzaw9MAy4MFuxOOecKxp3TLmDkZ+P5MYjb6Tjrh3jDmebZPMMsi0w2czWmNn3QA1JlRPGzwTqRN31gAVZjMU551yWjf92PJdPuJyuLbty+aGXxx3ONsvmfZD1gMSW03+Lhv0c9b8JvCapH1AZODDVQiT1B/pDeKyJc8654uebxd/Q/bnu7NNwHx47+bEtHmxcEmXzDPJXNp0hAtSOhuW6FbjazFoB1wI3pVqImT1iZjlmltOgQYMsheqcc25rLVu9jE6jOlGxfEVe6P4C1StVjzukQpHNBPkhcKikitHDKleY2eqE8QJyn9m1gHB26ZxzrgTZYBs4c+yZfL34a549/Vma12ked0iFJmtFrGa2RNIQ4B3AgEGSWgPHmNntwA3Aw5LWARWB87IVi3POuey4buJ1vPjVi9x7/L20b94+7nAKVVbbYjWzx4DHkgZ/Eo2bCRyazfU755zLnjFfjOH6SdfTt3VfLjjwgrjDKXTeUIBzzrmMffbLZ/Qe25uDmhzEkBOGlIpKOck8QTrnnMvIr6t+5ZRnTqFW5Vo83/V5KleoXPBMJZA/7so551za1m1YR7fnujF32Vwm9ZlE45qN4w4pazxBOuecS9ulb1zKm/97k8dOfoy2TdrGHU5WeRGrc865tDw540nu/uBuBh44kLP3OzvucLLOE6RzzrkC/Xfef+n/Un/+3PzP3HHsHXGHUyQ8QTrnnMvX/BXz6fxMZ7avuT2jTx9NxfIV4w6pSPg1SOecc3lavW41p40+jSV/LGFK3ynUr1Y/7pCKjCdI55xzKZkZF756IVPmTGF0l9Hs+6d94w6pSHkRq3POuZQemvYQj370KFceeiWntzw97nCKnCdI55xzW5g0exIDxw/khF1PYPCRg+MOJxaeIJ1zzm3mx99+pMvoLrSo24IRp46gnMpmqiibn9o551xKK9eu5JRRp7B6/WrGdR9H7Sq14w4pNl5JxznnHBAq5Zzz4jl8Mv8TXj7jZXavv3vcIcXKE6RzzjkA7phyByM/H8lNR95Ex107xh1O7LyI1TnnHOO/Hc9lb15Gt5bduPzQy+MOp1jwBOmcc2XcN4u/oftz3WnVqBVDTx5aKp/tuDU8QTrnXBm2bPUyOo3qRMXyFXmh+wtUr1Q97pCKDb8G6ZxzZdQG28CZY8/k68Vf82bvN2lep3ncIRUrniCdc66Munbitbz41Yvc1+E+2jdvH3c4xY4XsTrnXBk05osxDJ40mL6t+3J+m/PjDqdY8gTpnHNlzGe/fEbvsb05qMlBDDlhiFfKyUNGCVJSXUmtshWMc8657Fq8cjGdRnWiVuVaPN/1eSpXqBx3SMVWgQlS0kRJtSTVA2YAj0u6K/uhOeecK0zrNqyj23PdmLd8HmO7jaVxzcZxh1SspXMGWdvMlgGnAo+b2QHA0dkNyznnXGG79I1LmfD9BB4+8WHaNmkbdzjFXjoJsoKk7YGuwMtZjsc551wWPDnjSe7+4G4GHjiQPq37xB1OiZBOgrweeA341sz+K2ln4JvshuWcc66w/Hfef+n/Un/+3PzP3HHsHXGHU2IUmCDN7Fkza2Vmf436/2dmp6WzcEl9JE2R9J6k/VOMv0zShOg655GZh++ccy4/81fMp/Mzndm+5vaMPn00FctXjDukEiOdSjq3RZV0KkbJbJGkXmnMVxcYCLQHegH3Jo3vQLi+eZSZtTezt7buIzjnnEtl9brVnDb6NJb8sYRx3cdRv1r9uEMqUdIpYj02qqRzIjAX2A24JI352gKTzWyNmX0P1JCUWJ+4K1AlSrpPSSq7T+V0zrlCZmZc+OqFTJkzhSc6PUGrRn6HXqbSSZC55+MdgZFm9muay64HLEno/y0alqsxsMHMjgI+BK5ItRBJ/SVNkzRt4cKFaa7aOefKtoemPcSjHz3KlYdeyektT487nBIpnQT5kqQvgRxggqQGwB9pzPcrUCehv3Y0LHH8+Kh7PJDy8MbMHjGzHDPLadCgQRqrdc65sm3S7EkMHD+QE3c7kcFHDo47nBIrnUo6lwPtgBwzWwv8DnRKY9kfAodG1y6bAivMbHXC+ImEpEv0/m0mgTvnnNvSj7/9SJfRXWhRtwXDOw+nnLxF0a1V4NM8JFUEzgQOj9rrewd4qKD5zGyJpCHR9AYMktQaOMbMbgeeAB6V9DawFui9lZ/BOeccsHLtSk4ZdQqr169mXPdx1K7iVTu2RTqPu3qQcB1ySNR/ZjTsnIJmNLPHgMeSBn8SjVuNJ0XnnCsUZsY5L57DJ/M/4eUzXmb3+rvHHVKJl06CbGNm+yb0vyVpRrYCcs45l7nbp9zOyM9HcvNRN9Nx145xh1MqpFM4vV5Si9yeqCWd9dkLyTnnXCbGfzuey9+8nG4tu3HZIZfFHU6pkc4Z5CXA25L+BwhoBpyd1aicc86l5ZvF39D9ue60atSKoScP9Wc7FqICE6SZTZC0K7A7IUF+mVQb1TnnXAyWrV5Gp1GdqFi+Ii90f4HqlarHHVKpkmeClHRqHqNaSMLMxmQpJueccwXYYBvoNaYXXy/+mjd7v0nzOs3jDqnUye8M8qR8xhngCdI552Jy7cRreenrl7ivw320b94+7nBKpTwTpJn5dUbnnCuGxnwxhsGTBtO3dV/Ob3N+3OGUWt7EgnPOlSCf/fIZvcf25qAmBzHkhCFeKSeLPEE651wJsXjlYjqN6kStyrV4vuvzVK5QueCZ3FZL5zYP55xzMVu3YR3dnuvGvOXzmNRnEo1rNo47pFIvnQcmV5P0D0mPRv27Sjox+6E555zLdekblzLh+wk8fOLDtG3SNu5wyoR0ilgfB1YTnugB4aHJN2QtIuecc5t5csaT3P3B3Qw8cCB9WveJO5wyI50E2cLMbiM8cQMzW0VoMMA551yWTZ03lf4v9efPzf/MHcfeEXc4ZUo6CXKNpKqEex+J2mX1lnSccy7L5q+Yz6nPnMr2Nbdn9OmjqVi+YtwhlSnpVNK5BhgP7ChpBHAI0CebQTnnXFm3et1qTht9Gkv+WML7/d6nfrX6cYdU5qTTFusbkj4CDiIUrQ4ys0VZj8w558ooM+OCVy5gypwpjO4ymlaNWsUdUpmU7m0eVYAl0fR7RW2xTspeWM45V3Y9OO1B/v3xv7ny0Cs5veXpcYdTZhWYICXdCnQDZgIbosEGeIJ0zrlCNmn2JAaNH8SJu53I4CMHxx1OmZbOGeQpwO7+iCvnnMuuH3/7kS6ju9CibguGdx5OOXljZ3FKZ+v/D/CqU845l0Ur167klFGnsHr9asZ1H0ftKrXjDqnMS+cMciXwiaQJJNzeYWYDsxaVc86VIWZGvxf78cn8T3j5jJfZvf7ucYfkSC9Bvhi9nHPOZcHtU25n1OejuPmom+m4a8e4w3GRdG7zGCapErBbNOgrM1ub3bCcc65sGP/teC5/83K6tezGZYdcFnc4LkE6tVjbA8OAHwj3Qe4o6Sy/zcM557bN14u/pvtz3WnVqBVDTx7qz3YsZtIpYr0TONbMvgKQtBswEjggm4E551xptmz1Mk4ZdQoVy1fkhe4vUL1S9bhDcknSSZAVc5MjgJl9LclrtTrn3FbaYBvoNaYXXy/+mjd7v0nzOs3jDsmlkE6CnCZpKPBU1N8TmJ69kJxzrnS7duK1vPT1S9zX4T7aN28fdzguD+ncB/kXQis6A4FBwCxgQDoLl9RH0hRJ70naP49prpP0bboBO+dcSfb8rOcZPGkwfVv35fw258cdjstHOrVYVwN3AXdJ2t7Mfk5nwZLqEpLqQcAOhDPQQ5OmacSm2rHOOVeqffbLZ5z1wlkc1OQghpwwxCvlFHOZtmP0nwymbQtMNrM1ZvY9UENS5aRp/gHcnGEMzjlX4ixeuZhOozpRu0ptxnQdQ+UKyX+HrrjJNEFmcrhTj/AEkFy/RcPCgqRdgRpm9mm+K5T6S5omadrChQszCtY554qDdRvW0e25bsxbPo8xXcewfc3t4w7JpSHTBPloBtP+CtRJ6K8dDct1LVBgU/Vm9oiZ5ZhZToMGDTJYvXPOFQ+XvnEpE76fwMMnPkzbJm3jDselKa0EKamRpBOBHyU1THPZHwKHSqooqSmwIumJIDsDD0gaD2wv6d6MInfOuRLgyRlPcvcHdzPwwIH0ad0n7nBcBtJpSacrcDswkVDEep+kS8zsufzmM7MlkoYA7xCeHzlIUmvgGDO73czaJazjW2/83DlX2kydN5X+L/XnyJ2O5I5j74g7HJchmVn+E0gzCEltQdTfAHjTzPYtgvg2k5OTY9OmTSvq1TrnXMbmr5hPziM5VCxfkf+e+1/qV6sfd0ilnqTpZpZTWMtLp6GAcrnJMbKYzK9dOudcmbF63WpOG30aS/5Ywvv93vfkWEKlkyDHS3qN0P4qQDfgleyF5JxzJZeZccErFzBlzhRGdxlNq0at4g7JbaV8E6TCXaz3Am0IN/kLeMTMxhZBbM45V+I8OO1B/v3xv7nqsKs4veXpcYfjtkG+CdLMTNILZnYAMKaIYnLOuRJp0uxJDBo/iBN3O5Hr/3x93OG4bZTOtcQPJLXJeiTOOVeCzV46my6ju9CibguGdx5OOXlVjZIunWuQfwYGSPoB+J1QzGpm5gXrzjkHrFy7ks7PdGb1+tWM6z6O2lVqxx2SKwTpJMgOWY/COedKKDOj34v9+GT+J7x8xsvsXn/3uENyhaTAMgAzmw3sCBwZda9MZz7nnCsLbp9yO6M+H8VNR91Ex107xh2OK0QFJjpJ1wCXAVdEgyoCw7MZlHPOlQSvfvMql795Od1aduOyQy6LOxxXyNI5E+wMnEy4/oiZ/QTUzGZQzjlX3H29+Gt6PN+DVo1aMfTkof5sx1IonQS5xkJ7dAYgqXp2Q3LOueJt2eplnDLqFCqWr8gL3V+geiX/WyyN0kmQoyU9DNSRdC7wJvDv7IblnHPF0wbbQK8xvfh68dc8e/qzNK/TPO6QXJYUWIvVzO6QdAywDNgd+KeZvZH1yJxzrhi65u1reOnrl7ivw320b94+7nBcFuWZICUdZ2avAUQJ8Y2Ecaeb2bNFEJ9zzhUbz896nhsm30Df1n05v835cYfjsiy/ItZXJL0taYcU465IMcw550qtz375jLNeOIuDmhzEkBOGeKWcMiC/BPkp8DShqbnkFnf9m+GcKzMWr1xMp1GdqF2lNmO6jqFyhcpxh+SKQH4J0szsUeAo4FJJj0uqljsu+6E551z81m1YR9fnujJv+TzGdB3D9jW3jzskV0TSaUnna6Ad8AvwsaS2WY/KOeeKiUtev4S3vn+Lh098mLZN/O+vLMmvFuvGYlQzWwdcLmk84cHJDbIdmHPOxe3JGU9yz4f3MKjtIPq07hN3OK6I5Zcgr0seYGYTJR0AnJe9kJxzLn5T502l/0v9OXKnI7nj2DviDsfFIM8EaWYv5DF8CXBLtgJyzrm4/bz8Zzo/05nta27PM12eoUK5dB585Eob3+vOOZdg9brVnDb6NJb+sZT3+71P/Wr14w7JxcQTpHPORcyMC165gPfnvs/oLqNp1cifC1+W+XMdnXMu8uC0B/n3x//mqsOu4vSWybd/u7LGE6RzzgHv/PAOg8YP4sTdTuT6P18fdziuGPAE6Zwr82YvnU2XZ7vQom4LhnceTjn5X6PzBOmcK+NWrl1J52c6s3b9WsZ1H0ftKrXjDskVE1lNkJL6SJoi6T1J/9/e3UdXVZ15HP8+CQEngqEWX1IiiUyEasqLQgSVIsqImiKIWkBliQVLdVilzFpjWY4zulbr21BdgxVQM8XWWiiUojC8xJeCCKU0EVGxUiEo8pIijSgEQWhu8swfObnehEsCITc39+b3WSsrd5+9z+E57NzzZJ+Tu/cl9ep+bGbFQd1Tppl/RaSFuTsT/28i73zyDvNunkfPLj3jHZK0IjFLkGb2NWAKMAQYB/y8XpOX3H2Au18BnANcHatYRESimb5uOvP/Mp9Hhj5CwQUF8Q5HWplYjiAHAGvd/R/uvh3oaGbhKfDdvTSi7T+AUAxjERGpo6i0iPtW3seYvDFMu2JavMORViiWCfJM4POI8oFgWx1mNgTIBNZEO4iZTTKzDWa2oby8PAZhikhbs3XfVm5ddCt9zu3DnBFztLajRBXLBPkZ0DminBFsCzOz3sCjwBh3j7qElrsXunt/d+9/1lmaI11ETk3F0QpGzh9JWmoai8cs5vT2p8c7JGmlYpkgi4FBZpZmZt2AL9z9aG2lmeUCzwFj3f3TGMYhIgJAtVcz7sVxlO4rZeF3F5LdOTveIUkrFrMEGUxqPht4g5olsqaaWV8zuzdoMoOaEebzZrbazL4Tq1hERAAefP1Blm5dyozrZjAkZ0i8w5FWLqZzsbr7c9SMEiO9E9QNj+W/LSISadHmRTy09iEmXjyRyfmT4x2OJABNFCAiSW/T3k2MXzyey7IuY1bBLP1RjpwQJUgRSWr7Du/jxvk3knFaBotGL6JDuw6N7ySClrsSkSQWqg4x+vejKTtYxpo715DZKTPeIUkCUYIUkaR176v3smr7Kn418lcMyBoQ73AkwegWq4gkpeffeZ4ZxTP40YAfMb7v+HiHIwlICVJEkk5JWQk/WPYDrj7/ah4f9ni8w5EEpQQpIkllz8E9jFowisxOmSy4ZQHtUvQkSZpGPzkikjSOho5y8+9uZv+R/ayfuJ4u6V3iHZIkMCVIEUkK7s7kFZNZv3s9C7+7kN7n9I53SJLgdItVRJLC7DdnM+ftOdz/7fu55aJb4h2OJAElSBFJeG98/AZTX5nK8B7D+clVP4l3OJIklCBFJKHt2L+DWxbeQu6Zufxm1G9IMV3WpHnoJ0lEEtbhysPcuOBGKqsqWTJ2CRmnZcQ7JEki+iMdEUlI7s6EJRN495N3WXbbMnp8vUe8Q5IkowQpIglp+rrpLHh/AY8OfZSCCwriHY4kId1iFZGEU1RaxH0r72NM3himXTEt3uFIklKCFJGEsnXfVm5ddCt9zu3DcyOfw8x4+eWX6dmzJ7m5uTz22GPH7OPuTJkyhdzcXHr37s3GjRvDdTk5OfTq1Yu+ffvSv3//ljwVaeV0i1VEEkbF0QpGzh9JWmoai8csJj0tnaqqKiZPnsxrr71GVlYW+fn5jBgxgosuuii8X1FREaWlpZSWllJcXMw999xDcXFxuP7111+nSxfNuiN1aQQpbcapjDIAqqqquPjiixk+fHhLhSwRqr2acS+Oo3RfKQu/u5DsztkAlJSUkJubS/fu3Wnfvj1jx45lyZIldfZdsmQJd9xxB2bGwIED2b9/P3v27InHaUgCUYKUNqF2lFFUVMTmzZv57W9/y+bNm+u0iRxlFBYWcs8999Spf/LJJ7nwwgtbMmwB5r43l5wZOaT+JJWlW5cyrtc4huQMCdeXlZVx3nnnhctZWVmUlZXVOUZDbcyMYcOG0a9fPwoLC2N7MpJQlCDraeoo48iRI1x66aX06dOHvLw8HnzwwZYOXRpwqqOM3bt3s3z5cu666654hN9mzX1vLpOWTmLHgR3hbQv/upC5780Nl939mP3MrE65oTbr1q1j48aNFBUVMWvWLNasWdNc4UuC0zPICKfyLKNDhw6sWrWKjh07UllZyaBBg7j++usZOHBgHM+o7Zj73lzuX3k/Ow/spFtGNx4e+jC397o9XB9tBBH5DOp4bcrKysjMzGTq1KlMnz6dgwcPxv5kAu5OtVcTqg4Rqg5R5VXh16HqEFXV9cr16k+kTbMdw4/fvin/bm19xdEKnLrJ7XDlYe5feX+4f7Oysti1a1e4fvfu3XzjG9+os09DbWq/n3322YwaNYqSkhIGDx4ckz6VxNJmEmRjF1CoO8oAwqOMyAR5vFFGZmYmHTt2BKCyspLKyspjfouVhjU1ISzdupRH1jzCkaojAOw4sIOJSyby9p63GZw9mFB1iHU71/HhZx/ywrsvEKoOsXb7Wj76+0fMLJkZPt62fdt4/p3nWRlaSag6xIeffciTxU9ycMVBPjryEXM+mcPOd3eyae8mxi8eH5OEUL9Na5FiKbRLaRf+SrXUuuWU1Abr26W0o31qe9LT0hs+htXdZ0bxjKjx7DywM/w6Pz+f0tJStm/fTteuXZk/fz7z5s2r037EiBHMnDmTsWPHUlxcTEZGBpmZmRw6dIjq6mo6derEoUOHePXVV3nggQdi+V8pCaRNJMja2zSHKw8DNRfQSUsnATTrKKOqqop+/fqxbds2Jk+ezIABAxqMq7GE0KIjgJMYIcQyruZytOooT6x/gifWP1GzYRewCVYvXl1TXlvz7Y2iN77aqRK2rN4C+2oSQvWuavbu2kv1n6upfLuS9//0PoTAjziLHlpE5vjMBpNFZEI4bkKx6AmmqUmosTZN/Xfi9cveSx+8VOf2aq1uGd3Cr9u1a8fMmTO59tprqaqqYsKECeTl5fHMM88AcPfdd1NQUMCKFSvIzc0lPT2dX/7ylwDs3buXUaNGARAKhbjtttu47rrrWuDMJBFYtHvzrVX//v19w4YNJ71fzoycqG+yMzqcwaRLJoUvzlvWbGHHWzu4fPLlVHkVpatKKS8tp/f3eocv5sXTi8kenk2nCzoRqg6xafomut7clQ7ndQi3OfrFUf72i7/R+abOpJyT0iIJ4VTVjhBiflG25j/mTQtuOuY2HIBhbJi0oWZF+Wr4zmXf4YWXXqBr166MHDqSp597mry8vPBxXnv5NZ6d/SzLVyznzZI3mTJlCiUlJXWOuXr1ah5//HGWLVvWUl3TptX/5RYgPS2dwhsKj7kDJGJmb7l7s32YtU2MICNvx0SqOFrBrDdnhS+QftA5vOMwhz46RLuUdlRsryA1LZUtn24Jt0nJSKGivILTc0+nfWp7QvtDnHvuuXQ8o+NXF+9zU9ncbzPt/96evlf0bdJv9s0xOjjR0UNqSmpCr4DQLaPbcUcZl2ReEi4/O/tZJo2ZFB5lXJl/ZZ1Rxo033MgfXvkDPS7oUWeUIfFTmwQbezwiEgttegSZnZHNx1M/DpdDoRA9evRg5cqVdO3alfz8fObNm0deXl64zfLly5k5cyYrVqyguLg4PMooLy8nLS2Nzp078+WXXzJs2DCmTZumz8y1AI0yRASafwQZ02GDmd1pZn8ys3Vmdkm9utPMbK6ZrQ2+nxarOB4e+jDpael1tqWnpfPw0IfrbIt8lnHhhRcyevTo8LOM2pFGQUEB3bt3Jzc3l+9///vMnj0bgD179nDVVVfRu3dv8vPzueaaa5QcW8jtvW6n8IZCsjOyMYzsjGwlRxE5ZTEbQZrZ14CVwECgK/CCuw+KqL8bOMvdf2pmDwB/d/dnGjpmU0eQcGJ/xSoiIokrkZ5BDgDWuvs/gO1m1tHMOrj70aB+CPDfweulwL1AgwnyVNze63YlRBEROWGxvMV6JvB5RPlAsC1a/X7g6zGMRURE5KTEMkF+BnSOKGcE26LV168LM7NJZrbBzDaUl5fHIEwREZFjxTJBFgODzCzNzLoBX0TcXgV4A6hdBrwgKB/D3Qvdvb+79z/rrLNiGK6IiMhXYvoxDzObANwFOPAjIARc4+4/M7N/Ap4DsoDdwPfc/UgjxysHjv28xsnpAnx6iseQ1kv9m7zUt8mrufo2292bbSSVUJ+DbA5mtqE5/8pJWhf1b/JS3yav1tq3iTt9ioiISAwpQYqIiETRFhOklgxPburf5KW+TV6tsm/b3DNIERGRE9EWR5AiIiKNSvgEaWavmFm5mf3nSewTdaJ0M/u1ma0Ovj43sxtiF7kcT1MmuTezHDNbFezzHxHtrzOz9cHXtcG2TkF5v5mNa9mzk8Y00v+Xm9l7ZnbEzLLiFaOcnPrXaavxVPA+XmZmZwbbzwzKa4P6+KzUHUj4BAlMpGYe15NxJ/CBu38b2BKUcfc73H0IMIya6e9ea64g5cQEk9xPoWau3nHAz+s1uZMofQc8Bjzo7lcAV5vZN80sFZgOXB98/SzY9iUwCpgRy3ORk3cC/f8+cBnw55aNTE5R/ev0tUB68D7+HfDjYPuPgQXB9tODdnGT8AnS3XdHls0sw8x+Z2YrgxFFbpTdhgC1S8IvBQbXqx8OrGxs4gKJifAk9+6+HehoZh0i6ocQve/6uvva4PXyYPsFwHZ33+/u+4HtwD+7e8jdP4nxeUjTNNj/7n7A3b+IX3jSFPWv0xz/fXy87XGR8AkyivuAF919KPBv1Iws6mtsovRxwNxYBSgNauok95E/y7Xb6x8rsr20To31vySH+u/j2j7+WlCu3R7X92ssl7uKl17AlcF6kwChYBT5i6B8Fw1MlG5mnYHeHGduWIm5pk5yXx1ln8aOJa2P+qxtqP8+rk2Wnwfl/bSCvk/GEeT7wHR3HxI8Tyxw9221ZXffRsMTpY8GFrl7NRIPTZ3k/l0zuzx4fT2wBigFzjezM8zsDOB8YFvMz0BORWP9L8nheO/jE1rEoqUk/Ocgzex/gcuBDsBfgPHULLx8DmDAMnd/ot4+x50o3czWAJPd/b0WOwmpoymT3JtZd2AO0B4ocveHgmMVAP8VHPqn7r4i2L4UyAMOA39097uRVqGR/u8BzAb6UfN+n+fuT8ctWDkhUa7TNwFPUXO3rgK4w933mdnXgV8DZwCbgB/Gc7CS8AlSREQkFpLxFquIiMgpU4IUERGJQglSREQkCiVIERGRKJQgRUREolCCFGnFzOxjM+tyqm1E5OQpQYqIiEShBCnSSpjZYjN7y8zeN7NJ9epyzOwDM3vezDaZ2e/NLD2iyQ/NbGOwFNQ3g30uDZaNejv43rNFT0gkwSlBirQeE9y9H9AfmBLMKhKpJ1Do7rWzj/xrRN2n7n4J8DTw78G2D4DB7n4x8ADwSEyjF0kySpAirccUM3uXmrUOz6Nmua5Iu9x9XfD6N8CgiLoXg+9vATnB6wxgoZn9BfgfaqbWE5ETpAQp0gqY2RDgX4DL3L0P8DZwWr1m9eeFjCzXTuhdxVer9PwUeN3dvwXcEOV4ItIAJUiR1iED+NzdDwfPEAdGadPNzC4LXt8K/PEEjlkWvL6zWaIUaUOUIEVah5eBdma2iZqR35+jtPkrMD5ocyY1zxsbMh141MzWAanNGaxIW6DVPEQSgJnlULN027fiHYtIW6ERpIiISBQaQYqIiEShEaSIiEgUSpAiIiJRKEGKiIhEoQQpIiIShRKkiIhIFEqQIiIiUfw/lx6FudBE8aMAAAAASUVORK5CYII=\n",
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"text/plain": [
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"<Figure size 504x720 with 3 Axes>"
|
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]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
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},
|
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"output_type": "display_data"
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}
|
|
],
|
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"source": [
|
|
"##### x coordinates\n",
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"x = [str(i) for i in alphas]\n",
|
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"training_times = list_training_times_alpha\n",
|
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"precision_scores = list_precision_scores_alpha\n",
|
|
"zero_one_loss = list_zero_one_loss_alpha\n",
|
|
"print(\"x : \",x)\n",
|
|
"print(\"training_times : \",training_times)\n",
|
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"print(\"precision_scores : \",precision_scores)\n",
|
|
"print(\"zero_one_loss : \",zero_one_loss)\n",
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" \n",
|
|
"training_times = [round(i,2) for i in training_times]\n",
|
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"precision_scores = [round(i,2) for i in precision_scores]\n",
|
|
"zero_one_loss = [round(i,2) for i in zero_one_loss]\n",
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"\n",
|
|
"### Create plot\n",
|
|
"fig, figs = plt.subplots(nrows=3, ncols=1, figsize=(7,10))\n",
|
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"fig.tight_layout(pad=4.0)\n",
|
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"figs[0].plot(x,training_times, marker='o', color='r')\n",
|
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"figs[1].plot(x,precision_scores, marker='o', color='b')\n",
|
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"figs[2].plot(x,zero_one_loss, marker='o', color='g')\n",
|
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"\n",
|
|
"### Add every x coordinates\n",
|
|
"figs[0].tick_params(axis='both', which='both', labelsize=9, labelbottom=True)\n",
|
|
"figs[1].tick_params(axis='both', which='both', labelsize=9, labelbottom=True)\n",
|
|
"figs[2].tick_params(axis='both', which='both', labelsize=9, labelbottom=True)\n",
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"\n",
|
|
"for i in range(len(x)):\n",
|
|
" figs[0].annotate(training_times[i], # this is the text\n",
|
|
" (x[i],training_times[i]), # these are the coordinates to position the label\n",
|
|
" textcoords=\"offset points\", # how to position the text\n",
|
|
" xytext=(17,-2), # distance from text to points (x,y)\n",
|
|
" ha='center') # horizontal alignment can be left, right or center\n",
|
|
" figs[1].annotate(precision_scores[i], # this is the text\n",
|
|
" (x[i],precision_scores[i]), # these are the coordinates to position the label\n",
|
|
" textcoords=\"offset points\", # how to position the text\n",
|
|
" xytext=(17,-2), # distance from text to points (x,y)\n",
|
|
" ha='center') # horizontal alignment can be left, right or center\n",
|
|
" figs[2].annotate(zero_one_loss[i], # this is the text\n",
|
|
" (x[i],zero_one_loss[i]), # these are the coordinates to position the label\n",
|
|
" textcoords=\"offset points\", # how to position the text\n",
|
|
" xytext=(17,-2), # distance from text to points (x,y)\n",
|
|
" ha='center') # horizontal alignment can be left, right or center\n",
|
|
"\n",
|
|
"figs[0].set_xticks(x)\n",
|
|
"figs[1].set_xticks(x)\n",
|
|
"figs[2].set_xticks(x)\n",
|
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" \n",
|
|
"### Add title and axis names\n",
|
|
"figs[0].title.set_text('Training times for alpha (n_layers = 10,layer_size=50)')\n",
|
|
"figs[1].title.set_text('Precision score for alpha (n_layers = 10,layer_size=50)')\n",
|
|
"figs[2].title.set_text('Zero-one loss metrics for alpha (n_layers = 10,layer_size=50)')\n",
|
|
"figs[0].set_xlabel('alpha')\n",
|
|
"figs[1].set_xlabel('alpha')\n",
|
|
"figs[2].set_xlabel('alpha')\n",
|
|
"figs[0].set_ylabel('Training times (in seconds)')\n",
|
|
"figs[1].set_ylabel('Precision score')\n",
|
|
"figs[2].set_ylabel('Zero-one loss')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "abb0fcf1",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Matrice de confusion A-NN :\n",
|
|
" [[59 0 0 0 0 0 0 0 0 0]\n",
|
|
" [ 0 60 0 0 0 0 0 1 0 0]\n",
|
|
" [ 0 0 42 0 0 1 2 1 2 0]\n",
|
|
" [ 0 0 1 44 0 1 0 0 0 0]\n",
|
|
" [ 0 0 0 0 46 0 1 0 0 4]\n",
|
|
" [ 0 0 0 0 0 31 0 0 1 0]\n",
|
|
" [ 0 0 0 0 0 0 48 0 0 0]\n",
|
|
" [ 1 0 0 1 0 0 0 49 0 0]\n",
|
|
" [ 0 1 1 5 0 1 0 0 48 0]\n",
|
|
" [ 2 0 0 1 1 2 0 0 2 40]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"### Create vector of 5000 random indexes\n",
|
|
"rand_indexes = np.random.randint(70000, size=5000)\n",
|
|
"### Load data with the previous vector\n",
|
|
"data = mnist.data[rand_indexes]\n",
|
|
"# print(\"Dataset : \", data)\n",
|
|
"target = mnist.target[rand_indexes]\n",
|
|
"\n",
|
|
"# Split the dataset\n",
|
|
"xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
|
|
"\n",
|
|
"best_training_time = 0\n",
|
|
"best_precision_score = 0\n",
|
|
"best_zero_one_loss = 0\n",
|
|
"\n",
|
|
"r = 1\n",
|
|
"max_i = 300\n",
|
|
"nb_hl = 15\n",
|
|
"hl_size = 85\n",
|
|
"hl = ((hl_size,)*nb_hl)\n",
|
|
"sol = \"adam\"\n",
|
|
"act = \"relu\"\n",
|
|
"a = 0.0000001\n",
|
|
"\n",
|
|
"#Entraîne le classifier\n",
|
|
"clf = neural_network.MLPClassifier(random_state=r, max_iter=max_i, hidden_layer_sizes=hl, solver=sol, activation=act, alpha=a, verbose=False)\n",
|
|
"t1 = round(time.time(),5)\n",
|
|
"clf.fit(xtrain, ytrain)\n",
|
|
"t2 = round(time.time(),5)\n",
|
|
"#Prédiction sur le jeu de tests\n",
|
|
"pred = clf.predict(xtest)\n",
|
|
"# Probabilités des prédictions sur xtest\n",
|
|
"pred_proba = clf.predict_proba(xtest)\n",
|
|
"# On sauvegarde le temps de calcul, la précision et \n",
|
|
"# les taux d'erreurs par classe\n",
|
|
"best_training_time = t2-t1\n",
|
|
"best_precision_score = clf.score(xtest, ytest)\n",
|
|
"best_zero_one_loss = metrics.zero_one_loss(ytest, pred)\n",
|
|
"\n",
|
|
"# print(\"Paramètre :\\n\")\n",
|
|
"# print(\"random_state = \", r)\n",
|
|
"# print(\"max_iter = \", max_i)\n",
|
|
"# print(\"nb_hidden_layer = \", nb_hl)\n",
|
|
"# print(\"hidden_layer_size = \", hl_size)\n",
|
|
"# print(\"solver = \", sol)\n",
|
|
"# print(\"activation = \", act)\n",
|
|
"# print(\"alpha = \", a)\n",
|
|
"# print(\"Temps d'entraînement : \", best_training_time)\n",
|
|
"# print(\"Score : \", best_precision_score)\n",
|
|
"# print(\"Zero-one loss : \", best_zero_one_loss)\n",
|
|
"print(\"Matrice de confusion A-NN :\\n\", metrics.confusion_matrix(ytest, pred))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "be9bc965",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.8"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|