{"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":"3.12.12"},"kaggle":{"accelerator":"none","dataSources":[{"sourceType":"datasetVersion","sourceId":9621147,"datasetId":3695918,"databundleVersionId":9845199},{"sourceType":"datasetVersion","sourceId":14413364,"datasetId":9205632,"databundleVersionId":15229372}],"dockerImageVersionId":31286,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false},"papermill":{"default_parameters":{},"duration":25.276263,"end_time":"2026-02-23T17:34:10.61247","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2026-02-23T17:33:45.336207","version":"2.6.0"}},"nbformat_minor":5,"nbformat":4,"cells":[{"id":"aac2638f","cell_type":"markdown","source":"# Deep learning 2 - Classification (FR) v2.1\n\n```python\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport pandas as pd\n\nimport plotly.express as px\n\nfrom sklearn.metrics import *\nfrom sklearn.model_selection import train_test_split, cross_val_score\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\nfrom sklearn.pipeline import Pipeline\n\nimport torch\nimport torch.nn as nn\n\nfrom torch.utils.data import TensorDataset, DataLoader\n```\n","metadata":{"papermill":{"duration":0.009515,"end_time":"2026-02-23T17:33:48.385881","exception":false,"start_time":"2026-02-23T17:33:48.376366","status":"completed"},"tags":[]}},{"id":"d9e1df7f-593f-4516-977b-ccb79549d7af","cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\n\nimport pandas as pd\n\nimport plotly.express as px\n\nfrom sklearn.metrics import *\nfrom sklearn.model_selection import train_test_split, cross_val_score\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\nfrom sklearn.pipeline import Pipeline\n\nimport torch\nimport torch.nn as nn\n\nfrom torch.utils.data import TensorDataset, DataLoader","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-14T00:15:33.226599Z","iopub.execute_input":"2026-05-14T00:15:33.227000Z","iopub.status.idle":"2026-05-14T00:15:41.782354Z","shell.execute_reply.started":"2026-05-14T00:15:33.226960Z","shell.execute_reply":"2026-05-14T00:15:41.781516Z"}},"outputs":[],"execution_count":1},{"id":"811d6cb0","cell_type":"markdown","source":"## Classification binaire et neurone logistique\n\nEn classification binaire, la variable cible prend deux valeurs :\n$$\ny \\in {0,1}\n$$\n\nL’objectif n’est pas de prédire directement une classe,\nmais d’estimer une **probabilité conditionnelle** :\n$$\nP(y = 1 \\mid X)\n$$\n\nLa décision finale (classe 0 ou 1) sera prise ensuite,\npar exemple à l’aide d’un seuil.\n\n\n### Modélisation probabiliste : loi de Bernoulli\n\nUne variable binaire est naturellement modélisée par une **loi de Bernoulli**.\nPour un paramètre $p \\in [0,1]$ :\n$$\nP(y = 1) = p,\n\\qquad\nP(y = 0) = 1 - p\n$$\n\nUne écriture compacte est :\n$$\nP(y \\mid p) = p^y (1-p)^{1-y}\n$$\n\nIci, $p$ est un paramètre inconnu que le modèle doit apprendre.\n\n\n### De la probabilité à la vraisemblance\n\nDans un cadre d’apprentissage supervisé, les données $(X_i, y_i)$ sont **observées et fixées**.\nLa probabilité devient alors une fonction des paramètres du modèle.\nOn parle de **vraisemblance**.\n\nPour une observation :\n$$\n\\mathcal{L}(p \\mid y) = P(y \\mid p)\n$$\n\nPour un jeu de données supposé composé d’observations indépendantes :\n$$\n\\mathcal{L} = \\prod_{i=1}^{n} P(y_i \\mid p_i)\n$$\n\noù $p_i$ est la probabilité attribuée par le modèle à l’exemple $i$.\n\n\n\n### Prédire la probabilité avec un neurone\n\nComme en régression, on commence par une combinaison affine :\n$$\nz = Xw + b\n$$\n\nCette quantité n’est pas une probabilité.\nOn applique donc une **fonction logistique** :\n$$\n\\sigma(z) = \\frac{1}{1 + e^{-z}}\n$$\n\net on pose :\n$$\np = P(y = 1 \\mid X) = \\sigma(Xw + b)\n$$\n\nLe neurone logistique est donc un **estimateur de probabilité**.\n\n\n### Vraisemblance du modèle\n\nEn remplaçant $p_i$ par la sortie du neurone, la vraisemblance devient :\n$$\n\\mathcal{L}(w,b)\n= \\prod_{i=1}^{n}\n\\left[\\sigma(X_i w + b)\\right]^{y_i}\n\\left[1-\\sigma(X_i w + b)\\right]^{1-y_i}\n$$\n\nL’apprentissage consiste à choisir $w$ et $b$ qui rendent les données observées\nles plus vraisemblables possibles.\nC’est le principe du **maximum de vraisemblance**.\n\n\n### Pourquoi utiliser le logarithme ?\n\nLa vraisemblance est un produit de probabilités,\ndonc de nombres compris entre 0 et 1,\nce qui conduit rapidement à des valeurs numériquement très petites.\n\nOn maximise donc la **log-vraisemblance** :\n$$\n\\log \\mathcal{L}(w,b)\n= \\sum_{i=1}^{n}\n\\left[\ny_i \\log(p_i) + (1-y_i)\\log(1-p_i)\n\\right]\n$$\n\nLe logarithme ne change pas la position du maximum,\nmais transforme le produit en somme.\n\n\n### Fonction de coût : cross-entropy binaire\n\nEn pratique, on minimise l’opposé de la log-vraisemblance :\n$$\n\\mathcal{L}_{\\text{CE}}\n= - \\sum_{i=1}^{n}\n\\left[\ny_i \\log(p_i) + (1-y_i)\\log(1-p_i)\n\\right]\n$$\n\nCette fonction est appelée :\n\n* log-loss\n* binary cross-entropy\n* negative log-likelihood de la Bernoulli\n\n\n### Lien avec le perceptron\n\nSans fonction logistique, on pourrait décider :\n$$\n\\hat{y} =\n\\begin{cases}\n1 & \\text{si } Xw + b \\ge 0 \\\\\n0 & \\text{sinon}\n\\end{cases}\n$$\n\nC’est le **perceptron** :\n\n* décision dure\n* pas de probabilité\n* non différentiable\n\nLa fonction logistique est une **version lisse et probabiliste** de ce seuil,\ncompatible avec la descente de gradient.\n\n","metadata":{"papermill":{"duration":0.007829,"end_time":"2026-02-23T17:33:56.320595","exception":false,"start_time":"2026-02-23T17:33:56.312766","status":"completed"},"tags":[]}},{"id":"27a0b59e","cell_type":"markdown","source":"![LogisticNeuron.png](attachment:8c04fa7f-402c-44a7-9a7d-69a60d8cb4ab.png)","metadata":{"papermill":{"duration":0.007975,"end_time":"2026-02-23T17:33:56.33649","exception":false,"start_time":"2026-02-23T17:33:56.328515","status":"completed"},"tags":[]},"attachments":{"8c04fa7f-402c-44a7-9a7d-69a60d8cb4ab.png":{"image/png":"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IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYFwFKuO6cfsmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsFEFWq1WMjExsSvW95ZKpfKmuE/FfWu/35+M57PxfCaeH43rT2ma/uLAgQNPb8S9CDNvxK9iTQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmMrcPjw4bdFSPnrEVh+/VUixPD+jxYXF++OEPTyVb4zkmG1kcxiEgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEhgocOnTolggwH41B74/70LGZzhhe+dDmzZu/FvXfZPoKbQozF8pvcgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKbNnW73XeFw88ilLz5hXokSbLlhb67Xu8JM6+XrN8lQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgcBUCnU7n1ggxn4ihawWZn+r3+7+KMX+Ley/uW+M+HeNvj+dLAeaoXbiK6UY6RJh5pNwmI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIPC8QJzI/LJo/TSuG5+vXnp6PE3Tz/Z6vftbrVZ6qXrxYXZ2trp79+491Wr1rii9PMb9Pjum6Hal6AWYnwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMC4CkSY+ftxuvJHsvuPU5YfXlxcfGeEmP+e7StTW5i5TF/LWgkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBK4bgQgyvyaCzE/EhqorNxVB5ofiek+z2Tyzsl7G56SMi7ZmAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmUXiMDyp2IPlwWZo306TmR+x/UQZB58H2HmgYI/AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAiMUmJ+fn0iS5JM5U97barXO5tRLWaqVctUWTYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDEAtu3b78rlj+1cgtxUnMa1zdX1sr+7GTmsn9B6ydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECidQJzKPJOz6J83m80nc+qlLQkzl/bTWTgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECJBd6Ys/Yf5tRKXRJmLvXns3gCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGSCrwhu+40TR/P1srerpV9A9ZPgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoEwC7Xb7pljv1uyal5aW/h9mXlhYmK7X62+P/jsqlcp0v9/fFvcb434h7n+N+x8HV5Ikv200Go9kf2cjtSsbaTHWQoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQOB6F+h0OrdXq9UHM/s8HyczfzzCyp+J645M35rNCDV/69SpU184evTouTUHFdiRFDi3qQkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiMnUCElW/I2fRknLT8vWsJMg9+I8Z/etu2bSe73e5VB6Bz5l63kjDzutH6YQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKrBSK0vGV19UVVpiPUfH+73d7xon5lHV6urcNv+kkCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBNYQSNN0SwSa1+h9rtzv95+OgPIvY+zJuD8T1yuj53VRn4nnm58bddn/yVqt9qWozF1WLbghzFzwBzA9AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAeAlEkPkla+04wso/iesbzWbzgRjTz46bn5+f2LFjx6EINH8u+ior+6P2sYWFhXsOHjz46Mp6kc/CzEXqm5sAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDsBOK05aXsycwRYD4b9Xfv37//18NAjh071ov+z3e73VMRXv5KZmy1Xq9/NGrNTL2w5vDzpwtblokJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIXJ8CEUI+m91Z1J69UpB55TunT58+FgHoMytrF59fm1MrrCTMXBi9iQkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBMZU4Fx23xFMnszWhrWPHDnyn+g/kTNmZ06tsJIwc2H0JiZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBhHgbyTmcPhhtnZ2eo1ejyRM/7mnFphJWHmwuhNTIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMI4CaZo+m913BJxru3btelW2Pqwd7yxl+6P232ytyLYwc5H65iZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBg7gV6v91jepqvV6s68+lq1fr+/KrgctVUB57XeH0VdmHkUyuYgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgcFGg1Wo9E49P5YDM5NTWLMUpzDfldP4lp1ZYSZi5MHoTEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIjLHAw9m9Rzj5zmxtWDtOYc4LPz8y7J1R9wkzj1rcfAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmMvkKbpyRyEO9vt9qtz6qtKnU7nzRF+3pPtiICzk5mzKNoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIExkkgwswnsvuNcHKtXq/vy9Zz2pUkSb6crUeQeWl5efm+bL3IdqXIyc1NgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYFwFut3uQxFgnsnuP0LJH963b98PsvVBe+/evS+dmpr6Trz3gZz+rzYajS/m1Asr1Qqb2cQECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIExlggQsvH88LMUTsRQec90f/jeP5DBJv/vbCwMB2nNs9EuxFkt+Ww/fP8+fPtnHqhJSczF8pvcgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXEVmJubq09PT98X+3/rEIN+9C3GNTlkzKYIPs9F6Pnbw8YU0ZcUMak5CRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECIy7wPHjxy+kafrBcHhsiMXgcONhQeZz0f+JjRhkHuxJmHmg4I8AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAAQLNZvPM0tLSe+Nk5Qevdfp453cRht7daDTuvdZ3RzV+kMT2R4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAwQLdbvd9lUqlHcu47QpL+XOEmL/b6/XuabVay1cYW2i3MHOh/CYnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgcLlAp9N5RVRuSZJkOsLNOwe9EV7+Vzz/I05jPhmnOT85qPkjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIA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Calcul du gradient\n\nOn considère un exemple $(X_i, y_i)$ avec $y_i \\in {0,1}$.\n\nLe neurone logistique est composé de **trois blocs successifs** :\n\n$$\nw ;\\longrightarrow; z_i ;\\longrightarrow; u_i ;\\longrightarrow; E_i\n$$\n\navec :\n\n$$\nz_i = X_i w + b\n$$\n\n$$\nu_i = \\sigma(z_i) = \\frac{1}{1 + e^{-z_i}}\n$$\n\n$$\nE_i = -\\left[y_i \\log(u_i) + (1-y_i)\\log(1-u_i)\\right]\n$$\n\n\n### Chain rule \n\nPour un poids $w_j$, la dérivée s’écrit :\n\n$$\n\\frac{\\partial E_i}{\\partial w_j} =\n\\frac{\\partial E_i}{\\partial u_i}\n\\cdot\n\\frac{\\partial u_i}{\\partial z_i}\n\\cdot\n\\frac{\\partial z_i}{\\partial w_j}\n$$\n\nIl y a **trois dérivées élémentaires**, correspondant  à :\n\n1. la loss par rapport à la sortie\n2. l’activation par rapport à l’entrée\n3. le module linéaire par rapport au poids\n\n\n### Version vectorielle et batch\n\nPour un vecteur de poids $w$ :\n\n$$\n\\frac{\\partial E_i}{\\partial w} = (u_i - y_i) X_i\n$$\n\nEt pour $n$ exemples (moyenne batch) :\n\n$$\n\\frac{\\partial E}{\\partial w} = \n\\frac{1}{n}\\sum_{i=1}^{n}(u_i - y_i)X_i\n$$\n\n$$\n\\frac{\\partial E}{\\partial b} =\n\\frac{1}{n}\\sum_{i=1}^{n}(u_i - y_i)\n$$\n\n\n\n\n","metadata":{"papermill":{"duration":0.00787,"end_time":"2026-02-23T17:33:56.352548","exception":false,"start_time":"2026-02-23T17:33:56.344678","status":"completed"},"tags":[]}},{"id":"b3e80c48","cell_type":"markdown","source":"> #### Exercice\n>\n> On considère :\n> $$\n z_i = X_i w + b\n $$\n> $$\n u_i = \\sigma(z_i) = \\frac{1}{1 + e^{-z_i}}\n $$\n> $$\n E_i = -\\left[y_i \\log(u_i) + (1-y_i)\\log(1-u_i)\\right]\n $$\n>\n> 1. Calculer $\\frac{\\partial E_i}{\\partial u_i}$.\n> 2. Calculer $\\frac{\\partial u_i}{\\partial z_i}$.\n> 3. Calculer $\\frac{\\partial z_i}{\\partial w_j}$.\n> 4. En appliquant la chain rule, en déduire $\\frac{\\partial E_i}{\\partial w_j}$.\n> 5. Écrire le gradient $\\frac{\\partial E}{\\partial w}$ en version batch.\n>\n> ### Rappels de dérivées utiles\n>\n> $\\log(v)' = \\dfrac{v'}{v}$\n>\n> $\\exp(v)' = v'\\exp(v)$\n>\n> $(1-v)' = -v'$\n>\n> $\\dfrac{1}{v}' = -\\dfrac{v'}{v^2}$","metadata":{"papermill":{"duration":0.008206,"end_time":"2026-02-23T17:33:56.368678","exception":false,"start_time":"2026-02-23T17:33:56.360472","status":"completed"},"tags":[]}},{"id":"4682dda7","cell_type":"markdown","source":"### Correction\n\nOn repart des définitions :\n$$\nz_i = X_i w + b\n$$\n$$\nu_i = \\sigma(z_i) = \\frac{1}{1+e^{-z_i}}\n$$\n$$\nE_i = -\\left[y_i \\log(u_i) + (1-y_i)\\log(1-u_i)\\right]\n$$\n\n\n\n#### 1. Calcul de $\\frac{\\partial E_i}{\\partial u_i}$\n\nOn dérive terme à terme :\n$$\nE_i = -y_i \\log(u_i) - (1-y_i)\\log(1-u_i)\n$$\n\nDérivée du premier terme :\n$$\n\\frac{\\partial}{\\partial u_i}\\left(-y_i \\log(u_i)\\right) = -y_i \\frac{1}{u_i}\n$$\n\nPour le second terme, on utilise $\\log(1-u_i)' = \\frac{-(1)}{1-u_i}$ :\n$$\n\\frac{\\partial}{\\partial u_i}\\left(-(1-y_i)\\log(1-u_i)\\right)\n= -(1-y_i)\\left(\\frac{-1}{1-u_i}\\right)\n= \\frac{1-y_i}{1-u_i}\n$$\n\nDonc :\n$$\n\\frac{\\partial E_i}{\\partial u_i} =\n-\\frac{y_i}{u_i} + \\frac{1-y_i}{1-u_i}\n$$\n\n\n\n#### 2. Calcul de $\\frac{\\partial u_i}{\\partial z_i}$\n\nOn sait que :\n$$\nu_i = \\sigma(z_i)\n$$\net la dérivée classique de la sigmoïde est :\n$$\n\\frac{\\partial u_i}{\\partial z_i} = u_i(1-u_i)\n$$\n\n\n\n#### 3. Calcul de $\\frac{\\partial z_i}{\\partial w_j}$\n\nOn écrit :\n$$\nz_i = \\sum_{j=1}^{m} x_{ij} w_j + b\n$$\n\nDonc :\n$$\n\\frac{\\partial z_i}{\\partial w_j} = x_{ij}\n$$\n\nEt :\n$$\n\\frac{\\partial z_i}{\\partial b} = 1\n$$\n\n\n\n#### 4. Chain rule (3 termes) pour $\\frac{\\partial E_i}{\\partial w_j}$\n\nLa règle de dérivation en chaîne donne :\n$$\n\\frac{\\partial E_i}{\\partial w_j}=\n\\frac{\\partial E_i}{\\partial u_i}\n\\cdot\n\\frac{\\partial u_i}{\\partial z_i}\n\\cdot\n\\frac{\\partial z_i}{\\partial w_j}\n$$\n\nOn remplace :\n$$\n\\frac{\\partial E_i}{\\partial w_j} =\n\\left(-\\frac{y_i}{u_i} + \\frac{1-y_i}{1-u_i}\\right)\n\\cdot\nu_i(1-u_i)\n\\cdot\nx_{ij}\n$$\n\nOn simplifie le facteur entre parenthèses :\n\n$$\n\\left(-\\frac{y_i}{u_i} + \\frac{1-y_i}{1-u_i}\\right)u_i(1-u_i) =\n-y_i(1-u_i) + (1-y_i)u_i\n$$\n\nDéveloppons :\n$$\n-y_i + y_i u_i + u_i - y_i u_i = u_i - y_i\n$$\n\nDonc :\n$$\n\\frac{\\partial E_i}{\\partial w_j} = (u_i - y_i)x_{ij}\n$$\n\n\n\n#### 5. Gradient vectoriel et version batch\n\nEn écriture vectorielle, pour un exemple :\n$$\n\\frac{\\partial E_i}{\\partial w} = (u_i - y_i)X_i\n$$\n\nVersion batch (moyenne sur $n$ exemples) :\n$$\nE = \\frac{1}{n}\\sum_{i=1}^{n}E_i\n$$\n\nDonc :\n$$\n\\frac{\\partial E}{\\partial w} =\n\\frac{1}{n}\\sum_{i=1}^{n}\\frac{\\partial E_i}{\\partial w} =\n\\frac{1}{n}\\sum_{i=1}^{n}(u_i - y_i)X_i\n$$\n\nPour le biais :\n$$\n\\frac{\\partial E_i}{\\partial b} =\n\\frac{\\partial E_i}{\\partial u_i}\n\\cdot\n\\frac{\\partial u_i}{\\partial z_i}\n\\cdot\n\\frac{\\partial z_i}{\\partial b} =\n(u_i - y_i)\\cdot 1\n$$\n\nDonc :\n$$\n\\frac{\\partial E}{\\partial b} = \\frac{1}{n}\\sum_{i=1}^{n}(u_i - y_i)\n$$\n\n\n\n#### Résultat final\n\n$$\n\\frac{\\partial E}{\\partial w} = \\frac{1}{n}X^T(u-y)\n$$\n$$\n\\frac{\\partial E}{\\partial b} = \\frac{1}{n}\\sum_{i=1}^{n}(u_i-y_i)\n$$\n\noù $u = \\sigma(Xw+b)$.\n","metadata":{"papermill":{"duration":0.007959,"end_time":"2026-02-23T17:33:56.384593","exception":false,"start_time":"2026-02-23T17:33:56.376634","status":"completed"},"tags":[]}},{"id":"0d252830","cell_type":"markdown","source":"### Similarité avec le cas du neurone linéaire\n\nLe gradient obtenu pour le neurone logistique est :\n$$\n\\frac{\\partial E}{\\partial w} = \\frac{1}{n}X^T(u - y)\n$$\n$$\n\\frac{\\partial E}{\\partial b} = \\frac{1}{n}\\sum_{i=1}^{n}(u_i - y_i)\n$$\n\nCette expression est **formellement identique** à celle obtenue en régression linéaire,\nà une différence près :\n\n* en régression linéaire : $u = Xw + b$\n* en classification logistique : $u = \\sigma(Xw + b)$\n\nAutrement dit, le terme $(u - y)$ joue exactement le même rôle\nque dans le cas linéaire.\n\n\n### Conséquence algorithmique\n\nDans une implémentation *from scratch* par descente de gradient :\n\n* la **structure de la boucle d’entraînement** est inchangée\n* les **formules de mise à jour** de $w$ et $b$ sont identiques\n* seule la définition de la sortie $u$ change\n\nEn pratique :\n\n* neurone linéaire :\n  $$\n  u = Xw + b\n  $$\n\n* neurone logistique :\n  $$\n  u = \\sigma(Xw + b)\n  $$\n\nTout le reste (calcul du gradient, descente de gradient batch, minibatch, SGD)\nreste strictement le même.\n\n","metadata":{"papermill":{"duration":0.008203,"end_time":"2026-02-23T17:33:56.400644","exception":false,"start_time":"2026-02-23T17:33:56.392441","status":"completed"},"tags":[]}},{"id":"4da45699-17c9-4a62-9261-7b2df20cdf56","cell_type":"markdown","source":"### 💡 Syntaxe à reprendre\n\n_Repère syntaxique pour aborder l'exercice ci-dessous._\n\n```python\nclass LogisticNeuron:\n\n    def __init__(self):\n        self.w = None\n        self.b = 0.0\n        self.history = []\n\n    def forward(self, X):\n        z = X @ self.w + self.b\n        return 1.0 / (1.0 + np.exp(-z))\n\n    def predict(self, X, threshold=0.5):\n        u = self.forward(X)\n        return (u >= threshold).astype(int)\n\n    def fit(self, X, y, lr=0.1, epochs=100, mode='batch', batch_size=32):\n        (n, m) = X.shape\n        if self.w is None:\n            self.w = np.random.uniform(size=m)\n        self.history = []\n        for _ in range(epochs):\n            if mode == 'batch':\n                idx = np.arange(n)\n            elif mode == 'sgd':\n                idx = np.array([np.random.randint(0, n)])\n            elif mode == 'minibatch':\n                B = min(batch_size, n)\n                idx = np.random.choice(n, size=B)\n            else:\n                raise ValueError(\"mode must be 'batch', 'sgd', or 'minibatch'\")\n            Xb = X[idx]\n            yb = y[idx]\n            u = self.forward(Xb)\n            eps = 1e-12\n            loss = -np.mean(yb * np.log(u + eps) + (1 - yb) * np.log(1 - u + eps))\n            self.history.append(loss)\n            grad_w = Xb.T @ (u - yb) / len(idx)\n            grad_b = (u - yb).mean()\n            self.w -= lr * grad_w\n            self.b -= lr * grad_b\n        return self\n```\n","metadata":{}},{"id":"f56aa4f3","cell_type":"markdown","source":"> #### Exercice  \n> On repart du code du neurone linéaire `LinearNeuron` (régression)\n> implémenté *from scratch*.\n>\n> L’objectif est de le transformer en **neurone logistique**\n> pour faire de la **classification binaire**.\n>\n> On considère maintenant :\n> $$\n z = Xw + b\n $$\n> $$\n u = \\sigma(z) = \\frac{1}{1 + e^{-z}}\n $$\n> où $u$ représente une estimation de la probabilité $P(y=1 \\mid X)$.\n>\n> 1. Modifier la méthode `forward(X)`  \n>    Au lieu de retourner $u = Xw + b$, retourner :\n>    $$\n    u = \\sigma(Xw + b)\n    $$\n>    Indication NumPy :\n>    $$\n    \\sigma(z) = \\frac{1}{1+\\exp(-z)}\n    $$\n>\n> 2. Modifier la fonction de coût dans `fit`  \n>    Remplacer la MSE par la **cross-entropy binaire** :\n>    $$\n    E = -\\frac{1}{n}\\sum_{i=1}^{n}\\left[y_i\\log(u_i) + (1-y_i)\\log(1-u_i)\\right]\n    $$\n>    Indication : pour éviter les problèmes numériques, on pourra utiliser une petite constante $\\epsilon$ :\n>    $$\n    \\log(u_i) \\to \\log(u_i + \\epsilon)\n    \\qquad\n    \\log(1-u_i) \\to \\log(1-u_i + \\epsilon)\n    $$\n>\n> 3. Conserver la même forme de gradient  \n>    Utiliser exactement les mêmes formules que dans le neurone linéaire,\n>    en remplaçant simplement $u = Xw+b$ par $u=\\sigma(Xw+b)$ :\n>    $$\n    \\frac{\\partial E}{\\partial w} = \\frac{1}{n}X^T(u-y)\n    \\qquad\n    \\frac{\\partial E}{\\partial b} = \\frac{1}{n}\\sum_{i=1}^{n}(u_i-y_i)\n    $$\n>\n> 4. Ajouter une méthode de prédiction de classe  \n>    Écrire une méthode `predict(X, threshold=0.5)` qui renvoie une classe binaire :\n>    $$\n    \\hat{y} =\n    \\begin{cases}\n    1 & \\text{si } u \\ge \\text{threshold} \\\\\n    0 & \\text{sinon}\n    \\end{cases}\n    $$\n>\n> 5. Tester sur le dataset `cancer_mini`  \n>    - séparer train/test avec `random_state=42`  \n>    - normaliser les entrées avec `StandardScaler` (fit sur train, transform sur test)  \n>    - entraîner le neurone logistique\n>    - évaluer les performances sur le jeu de test\n>\n\n","metadata":{"papermill":{"duration":0.007938,"end_time":"2026-02-23T17:33:56.416473","exception":false,"start_time":"2026-02-23T17:33:56.408535","status":"completed"},"tags":[]}},{"id":"9502d38c-eca8-4cb4-bb30-874616a50dac","cell_type":"code","source":"class NeuroneLogistique:\n    def __init__(self):\n        self.b = np.random.uniform()\n        self.history = []\n\n    def predict(self, X):\n        z = X@self.w + self.b\n        return 1 / (1 + np.exp(-z))\n\n    def fit(self, X, y, learning_rate=0.1, epochs=100):\n        n = X.shape[0]\n        m = X.shape[1]\n        self.w = np.random.uniform(size=m)\n        grad_w = np.zeros(m)\n        for i in range(epochs):\n            u = self.predict(X)\n            for j in range(m):\n                grad_w[j] = np.mean((u-y)*X[:,j])\n                self.w[j] -= learning_rate*grad_w[j]\n            grad_b = np.mean(u-y)\n            self.b = self.b - learning_rate*grad_b\n            self.history.append(np.mean(-(y*np.log(u) + (1-y)*np.log(1-u))))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-14T00:15:41.784214Z","iopub.execute_input":"2026-05-14T00:15:41.784835Z","iopub.status.idle":"2026-05-14T00:15:41.794339Z","shell.execute_reply.started":"2026-05-14T00:15:41.784800Z","shell.execute_reply":"2026-05-14T00:15:41.793128Z"}},"outputs":[],"execution_count":2},{"id":"3c8a502e-a7e4-45c1-b88f-d8532d0eaf65","cell_type":"code","source":"df = pd.read_csv('/kaggle/input/datasets/pyim59/mini-datasets/cancer_mini.csv')\n\nX = df.drop(columns='diagnosis').to_numpy()\ny = df['diagnosis'].to_numpy()\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n\nmodel = NeuroneLogistique()\nmodel.fit(X_train,y_train)\ny_hat = (model.predict(X_test)>0.5).astype(int)\n\nplt.plot(model.history)\nplt.show()\n\naccuracy_score(y_test, y_hat)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-14T00:15:41.795674Z","iopub.execute_input":"2026-05-14T00:15:41.796032Z","iopub.status.idle":"2026-05-14T00:15:42.068646Z","shell.execute_reply.started":"2026-05-14T00:15:41.796003Z","shell.execute_reply":"2026-05-14T00:15:42.067682Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{}},{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"0.8771929824561403"},"metadata":{}}],"execution_count":3},{"id":"d0af7fdb-8046-4a1e-9495-958fbe5ddb21","cell_type":"code","source":"y_hat","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-14T00:15:42.069749Z","iopub.execute_input":"2026-05-14T00:15:42.070011Z","iopub.status.idle":"2026-05-14T00:15:42.076619Z","shell.execute_reply.started":"2026-05-14T00:15:42.069986Z","shell.execute_reply":"2026-05-14T00:15:42.075649Z"}},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"array([0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0,\n       0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0,\n       1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n       1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0,\n       0, 0, 1, 1])"},"metadata":{}}],"execution_count":4},{"id":"9eca1409","cell_type":"markdown","source":"## Version PyTorch du neurone logistique\n\nL’objectif est de reproduire le neurone logistique *from scratch*, mais en laissant PyTorch gérer automatiquement le calcul des gradients et les mises à jour.\n\nOn cherche toujours à modéliser :\n$$\nP(y=1\\mid X)=\\sigma(Xw+b)\n$$\n\nEn PyTorch, on ne code pas explicitement $w$ et $b$ : on utilise une couche `nn.Linear`, puis une loss adaptée à la Bernoulli.\n\n\n### Deux façons de faire en PyTorch\n\n#### Option 1 : `nn.Linear` + `nn.Sigmoid` + `nn.BCELoss`\n\nOn calcule explicitement la probabilité $u=\\sigma(z)$, puis on applique la binary cross-entropy.\n\n```python\nimport torch\nimport torch.nn as nn\n\nmodel = nn.Sequential(\n    nn.Linear(m, 1),\n    nn.Sigmoid()\n)\n\ncriterion = nn.BCELoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n```\n\nIci :\n\n* `model(X)` renvoie directement $u\\in(0,1)$\n* `criterion(u, y)` calcule la cross-entropy binaire\n\n\n\n#### Option 2 : recommandé, `nn.Linear` + `nn.BCEWithLogitsLoss`\n\nCette option est la plus standard en pratique.\n\nOn ne met **pas** de sigmoïde dans le modèle.\nLe modèle renvoie :\n$$\nz = Xw + b\n$$\n\nLa loss `BCEWithLogitsLoss` applique **en interne** la sigmoïde et la cross-entropy, de manière numériquement stable.\n\n```python\nmodel = nn.Linear(m, 1)\n\ncriterion = nn.BCEWithLogitsLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n```\n\nDans ce cas :\n\n* `model(X)` renvoie les logits $z$\n* `criterion(z, y)` calcule :\n  $$\n  -\\left[y\\log(\\sigma(z))+(1-y)\\log(1-\\sigma(z))\\right]\n  $$\n  sans instabilités numériques\n\n\n\n### Préparation des données\n\nComme en régression, on convertit en tenseurs `float32`.\nLes cibles doivent être de forme `(n,1)`.\n\n```python\nX_train = torch.tensor(X_train, dtype=torch.float32)\ny_train = torch.tensor(y_train, dtype=torch.float32).view(-1, 1)\n\nX_test = torch.tensor(X_test, dtype=torch.float32)\ny_test = torch.tensor(y_test, dtype=torch.float32).view(-1, 1)\n```\n\n\n### Entraînement (batch ou minibatch)\n\nLe schéma est identique à celui de la régression.\n\n```python\nepochs = 200\nloss_history = []\n\nfor _ in range(epochs):\n    optimizer.zero_grad()\n\n    logits = model(X_train)\n    loss = criterion(logits, y_train)\n\n    loss.backward()\n    optimizer.step()\n\n    loss_history.append(loss.item())\n```\n\n\n### Probabilités et prédiction de classe\n\nSi on utilise `BCEWithLogitsLoss`, le modèle renvoie des logits.\nOn convertit en probabilité avec la sigmoïde :\n\n```python\nmodel.eval()\nwith torch.no_grad():\n    logits = model(X_test)\n    proba = torch.sigmoid(logits)\n    y_hat = (proba >= 0.5).float()\n```\n\nOn obtient :\n\n* `proba` de forme `(n,1)` dans $(0,1)$\n* `y_hat` binaire (0 ou 1)\n\n\n### Évaluation avec `sklearn`\n\nOn convertit en NumPy 1D :\n\n```python\ny_hat_np = y_hat.cpu().numpy().reshape(-1)\ny_test_np = y_test.cpu().numpy().reshape(-1)\n```\n\nPuis on calcule des métriques :\n\n```python\nfrom sklearn.metrics import accuracy_score, confusion_matrix\n\nprint(\"Accuracy =\", accuracy_score(y_test_np, y_hat_np))\nprint(\"Confusion matrix:\\n\", confusion_matrix(y_test_np, y_hat_np))\n```\n\n\n### Correspondance avec le neurone *from scratch*\n\n* `forward` :\n\n  * from scratch : $u=\\sigma(Xw+b)$\n  * PyTorch : `model(X)` puis `torch.sigmoid`\n\n* gradient :\n\n  * from scratch : calcul manuel\n  * PyTorch : `loss.backward()`\n\n* mise à jour :\n\n  * from scratch : $w \\leftarrow w-lr\\nabla_w$\n  * PyTorch : `optimizer.step()`\n\n\n","metadata":{"papermill":{"duration":0.008626,"end_time":"2026-02-23T17:33:56.747034","exception":false,"start_time":"2026-02-23T17:33:56.738408","status":"completed"},"tags":[]}},{"id":"93e634a7-8a81-4c1d-9f48-468dc3616e37","cell_type":"code","source":"df = pd.read_csv('/kaggle/input/datasets/pyim59/mini-datasets/cancer_mini.csv')\n\nX = df.drop(columns='diagnosis').to_numpy()\ny = df['diagnosis'].to_numpy()\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n\nX_train_t = torch.tensor(X_train, dtype=torch.float32)\ny_train_t = torch.tensor(y_train, dtype=torch.float32).view(-1, 1)\n\nX_test_t = torch.tensor(X_test, dtype=torch.float32)\ny_test_t = torch.tensor(y_test, dtype=torch.float32).view(-1, 1)\n\nprint(X_train.shape)\nprint(y_train.shape)\n\ntrain_dataset = TensorDataset(X_train_t, y_train_t)\n\nbatch_size = 32\n\ntrain_loader = DataLoader(\n    train_dataset,\n    batch_size=batch_size,\n    shuffle=True\n)\n\nmodel = nn.Sequential(\n    nn.Linear(X_train_t.shape[1], 3),\n    nn.ReLU(),\n    nn.Linear(3, 1)\n    )\n\ncriterion = nn.BCEWithLogitsLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n\nepochs = 100\nloss_history = []\nfor _ in range(epochs):\n    nb=0\n    epoch_loss=0\n    for Xb, yb in train_loader:\n        optimizer.zero_grad()\n        u = model(Xb)\n        loss = criterion(u, yb)\n        loss.backward()\n        optimizer.step()\n        epoch_loss += loss.item()\n        nb += 1\n    loss_history.append(epoch_loss/nb)\n\nplt.plot(loss_history)\nplt.show()\n\nmodel.eval()\nwith torch.no_grad():\n    logits = model(X_test_t)\n    proba = torch.sigmoid(logits)\n    y_hat = (proba >= 0.5).int()\n    \n\ny_hat = y_hat.cpu().numpy().reshape(-1)\n\naccuracy_score(y_test, y_hat)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-14T00:15:42.078518Z","iopub.execute_input":"2026-05-14T00:15:42.079283Z","iopub.status.idle":"2026-05-14T00:15:46.563519Z","shell.execute_reply.started":"2026-05-14T00:15:42.079237Z","shell.execute_reply":"2026-05-14T00:15:46.562499Z"}},"outputs":[{"name":"stdout","text":"(455, 4)\n(455,)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAATZxJREFUeJzt3Xl4U1X+BvD3JmmSrindVyh7KVuhhVpWlSoqLog6jKJgRxkXdNT+ZkYZR1BHLerIOCqKw4g6ooIL7gpCWRQpFFooW2lZu9KdNl2TNrm/P9KkLV1o2iS3y/t5njzPkN6k314Z8vac7zlHEEVRBBEREZFEZFIXQERERAMbwwgRERFJimGEiIiIJMUwQkRERJJiGCEiIiJJMYwQERGRpBhGiIiISFIMI0RERCQphdQFdIXRaERBQQHc3d0hCILU5RAREVEXiKKIqqoqBAUFQSbrePyjT4SRgoIChIaGSl0GERERdUNubi5CQkI6/HqfCCPu7u4ATD+Mh4eHxNUQERFRV2i1WoSGhlo+xzvSJ8KIeWrGw8ODYYSIiKiPuVyLBRtYiYiISFIMI0RERCQphhEiIiKSFMMIERERSYphhIiIiCTFMEJERESSYhghIiIiSTGMEBERkaQYRoiIiEhSDCNEREQkKYYRIiIikhTDCBEREUlqQIeR9387h799dRSni6ulLoWIiGjAGtBh5Nv0AnyyPwdnShhGiIiIpDKgw4ivmwoAUFylk7gSIiKigWtAhxE/D1MYKWEYISIikszADiPuagBASVW9xJUQERENXAM6jPi6N03TaDkyQkREJJUBHUb8msJISTXDCBERkVQGdBjhyAgREZH0BnQYMfeMlFbrYDSKEldDREQ0MA3oMOLtpoQgAI1GERdr9VKXQ0RENCAN6DDiJJfBy0UJgHuNEBERSWVAhxGguW+Ee40QERFJg2HEnbuwEhERSWnAh5Hmjc8YRoiIiKQw4MNI88gId2ElIiKSwoAPI37sGSEiIpLUgA8j7BkhIiKS1oAPIxwZISIiktaADyNc2ktERCStAR9G/DxMq2mqdY2o1TdKXA0REdHAM+DDiJtKARelHABHR4iIiKQw4MMIwCZWIiIiKTGMoLmJtVjLMEJERORoDCNo2cTKjc+IiIgcjWEEzVvCc5qGiIjI8RhGwOW9REREUmIYARtYiYiIpMQwAo6MEBERSYlhBC1W0zCMEBERORzDCJobWMtqdGg0GCWuhoiIaGBhGAHg5aqETABEESiv0UtdDhER0YDSrTCyZs0ahIWFQa1WIyYmBikpKR1ee+WVV0IQhDaPefPmdbtoW5PLBPi4caqGiIhIClaHkU2bNiEhIQErV65EWloaJk6ciLlz56K4uLjd6zdv3owLFy5YHseOHYNcLscdd9zR4+JtiU2sRERE0rA6jKxevRpLly5FfHw8IiIisHbtWri4uGD9+vXtXu/l5YWAgADLY9u2bXBxcel1YaS5iZW7sBIRETmSVWFEr9cjNTUVcXFxzW8gkyEuLg7Jycldeo/33nsPv//97+Hq6mpdpXbmy/NpiIiIJKGw5uLS0lIYDAb4+/u3et7f3x8nT5687OtTUlJw7NgxvPfee51ep9PpoNM1hwKtVmtNmd1iXlFTUs0wQkRE5EgOXU3z3nvvYfz48Zg6dWqn1yUmJkKj0VgeoaGhdq/Nz4MjI0RERFKwKoz4+PhALpejqKio1fNFRUUICAjo9LU1NTXYuHEj7rvvvst+n+XLl6OystLyyM3NtabMbvFtWk3DkREiIiLHsiqMKJVKREVFISkpyfKc0WhEUlISYmNjO33t559/Dp1Oh7vvvvuy30elUsHDw6PVw94sIyNsYCUiInIoq3pGACAhIQFLlixBdHQ0pk6ditdffx01NTWIj48HACxevBjBwcFITExs9br33nsP8+fPh7e3t20qtzFft6aekSodRFGEIAgSV0RERDQwWB1GFi5ciJKSEqxYsQKFhYWIjIzEli1bLE2tOTk5kMlaD7hkZmZiz549+Pnnn21TtR2YV9PUNxhRpWuEh9pJ4oqIiIgGBkEURVHqIi5Hq9VCo9GgsrLSrlM241duRZWuEdsTZmOEn5vdvg8REdFA0NXPb55N04KvB3dhJSIicjSGkRZ83djESkRE5GgMIy34eTQ3sRIREZFjMIy04MfD8oiIiByOYaQFy/k0DCNEREQOwzDSAkdGiIiIHI9hpIXmkRE2sBIRETkKw0gLlpN7OTJCRETkMAwjLZhHRi7WNkDfaJS4GiIiooGBYaQFT2cnOMlNZ9KU8vReIiIih2AYaUEmE+DjxiZWIiIiR2IYuYQfl/cSERE5FMPIJXy5vJeIiMihGEYuweW9REREjsUwcglfLu8lIiJyKIaRS3BLeCIiIsdiGLkEt4QnIiJyLIaRS7CBlYiIyLEYRi7RcmREFEWJqyEiIur/GEYuYd70TG8wQlvXKHE1RERE/R/DyCXUTnJ4qBUAuLyXiIjIERhG2uHnweW9REREjsIw0g5fNy7vJSIichSGkXb4eXBFDRERkaMwjLSjeWSEPSNERET2xjDSDo6MEBEROQ7DSDu4JTwREZHjMIy0w9eNq2mIiIgchWGkHZZpmmqGESIiIntjGGmHuYG1orYBukaDxNUQERH1bwwj7fB0cYKTXAAAlFbrJa6GiIiof2MYaYcgCM3Le7Vc3ktERGRPDCMd8OWW8ERERA7BMNIBbglPRETkGAwjHeDGZ0RERI7BMNIBjowQERE5BsNIB8y7sHJkhIiIyL4YRjrg586Nz4iIiByBYaQDlpERLu0lIiKyK4aRDviZl/ZW6yCKosTVEBER9V8MIx3wcVMCABoMIipqGySuhoiIqP9iGOmASiGHp4sTAPaNEBER2RPDSCeat4RnGCEiIrIXhpFOWJpYq9nESkREZC8MI50wL+/lyAgREZH9MIx0ghufERER2R/DSCf83JuX9xIREZF9MIx0wpfTNERERHbHMNIJbglPRERkfwwjnWgeGeFqGiIiInthGOmEOYxo6xtR32CQuBoiIqL+iWGkExpnJyjlplvEFTVERET2wTDSCUEQWmx8xjBCRERkD90KI2vWrEFYWBjUajViYmKQkpLS6fUVFRVYtmwZAgMDoVKpMGrUKPz444/dKtjRuKKGiIjIvhTWvmDTpk1ISEjA2rVrERMTg9dffx1z585FZmYm/Pz82lyv1+txzTXXwM/PD1988QWCg4ORnZ0NT09PW9Rvd80bn7GJlYiIyB6sDiOrV6/G0qVLER8fDwBYu3YtfvjhB6xfvx5PPfVUm+vXr1+P8vJy7N27F05OplNww8LCela1AwVpTBufXahkGCEiIrIHq6Zp9Ho9UlNTERcX1/wGMhni4uKQnJzc7mu+/fZbxMbGYtmyZfD398e4cePw0ksvwWDoeHWKTqeDVqtt9ZBKkKczAKCgok6yGoiIiPozq8JIaWkpDAYD/P39Wz3v7++PwsLCdl9z9uxZfPHFFzAYDPjxxx/xzDPP4LXXXsMLL7zQ4fdJTEyERqOxPEJDQ60p06aawwhHRoiIiOzB7qtpjEYj/Pz88J///AdRUVFYuHAhnn76aaxdu7bD1yxfvhyVlZWWR25urr3L7FCQp2maJp8jI0RERHZhVc+Ij48P5HI5ioqKWj1fVFSEgICAdl8TGBgIJycnyOVyy3NjxoxBYWEh9Ho9lEplm9eoVCqoVCprSrMb88hIobYeBqMIuUyQuCIiIqL+xaqREaVSiaioKCQlJVmeMxqNSEpKQmxsbLuvmT59Ok6fPg2j0Wh5LisrC4GBge0Gkd7Gz10NuUyAwSiimCtqiIiIbM7qaZqEhASsW7cOH374ITIyMvDQQw+hpqbGsrpm8eLFWL58ueX6hx56COXl5XjssceQlZWFH374AS+99BKWLVtmu5/CjuQyAQEepqkaNrESERHZntVLexcuXIiSkhKsWLEChYWFiIyMxJYtWyxNrTk5OZDJmjNOaGgotm7diieeeAITJkxAcHAwHnvsMTz55JO2+ynsLNjTGfkVdSioqEfUEKmrISIi6l8EURRFqYu4HK1WC41Gg8rKSnh4eDj8+z++8RC+PlyA5deH44HZwx3+/YmIiPqirn5+82yaLuBeI0RERPbDMNIF5jCSz71GiIiIbI5hpAvMe41wZISIiMj2GEa6wDJNU8kwQkREZGsMI11gDiMVtQ2o1TdKXA0REVH/wjDSBR5qJ7irTKugeUYNERGRbTGMdBFX1BAREdkHw0gXsYmViIjIPhhGuogjI0RERPbBMNJF3GuEiIjIPhhGuojTNERERPbBMNJFQRrTyMgF7jVCRERkUwwjXdS88Vk9jMZef7YgERFRn8Ew0kUBGjUEAdA3GlFWo5e6HCIion6DYaSLnOQy+Luzb4SIiMjWGEaswCZWIiIi22MYsULz8l6GESIiIlthGLGCOYxcqOReI0RERLbCMGKFIA2naYiIiGyNYcQK3BKeiIjI9hhGrMAt4YmIiGyPYcQKwU1hpLRah/oGg8TVEBER9Q8MI1bwdHGCs5McAFDIJlYiIiKbYBixgiAI3GuEiIjIxhhGrNTyjBoiIiLqOYYRK5lP7+XICBERkW0wjFiJy3uJiIhsi2HESuaeEW4JT0REZBsMI1YK5sgIERGRTTGMWKl5mqYeoihKXA0REVHfxzBipUBPNQQBqGswoLxGL3U5REREfR7DiJVUCjn83U19I3kXOVVDRETUUwwj3RAyyDRVwzBCRETUcwwj3dAcRmolroSIiKjvYxjphpBBLgCAXIYRIiKiHmMY6QZO0xAREdkOw0g3hHqZRkYYRoiIiHqOYaQbWvaMcK8RIiKinmEY6YZAjTMEAahvMKK0mnuNEBER9QTDSDcoFTIEeJj3GmETKxERUU8wjHRT6CD2jRAREdkCw0g3cUUNERGRbTCMdBM3PiMiIrINhpFuat74jCMjREREPcEw0k0cGSEiIrINhpFuMm98ln+xjnuNEBER9QDDSDcFaNSQCYCu0YiSap3U5RAREfVZDCPd5CSXIVBjmqrJLWffCBERUXcxjPRAMPtGiIiIeoxhpAe48RkREVHPMYz0ADc+IyIi6jmGkR7g8l4iIqKe61YYWbNmDcLCwqBWqxETE4OUlJQOr/3ggw8gCEKrh1qt7nbBvUkIp2mIiIh6zOowsmnTJiQkJGDlypVIS0vDxIkTMXfuXBQXF3f4Gg8PD1y4cMHyyM7O7lHRvYV5ZCT/Yh2MRu41QkRE1B1Wh5HVq1dj6dKliI+PR0REBNauXQsXFxesX7++w9cIgoCAgADLw9/fv0dF9xaBGjXkMgF6A/caISIi6i6rwoher0dqairi4uKa30AmQ1xcHJKTkzt8XXV1NYYMGYLQ0FDccsstOH78ePcr7kUUchkCNaYpJ/aNEBERdY9VYaS0tBQGg6HNyIa/vz8KCwvbfc3o0aOxfv16fPPNN9iwYQOMRiOmTZuGvLy8Dr+PTqeDVqtt9eitzFM13PiMiIioe+y+miY2NhaLFy9GZGQkZs+ejc2bN8PX1xfvvvtuh69JTEyERqOxPEJDQ+1dZrc1N7FyZISIiKg7rAojPj4+kMvlKCoqavV8UVERAgICuvQeTk5OmDRpEk6fPt3hNcuXL0dlZaXlkZuba02ZDsWNz4iIiHrGqjCiVCoRFRWFpKQky3NGoxFJSUmIjY3t0nsYDAYcPXoUgYGBHV6jUqng4eHR6tFbceMzIiKinlFY+4KEhAQsWbIE0dHRmDp1Kl5//XXU1NQgPj4eALB48WIEBwcjMTERAPD888/jiiuuwIgRI1BRUYFXX30V2dnZuP/++237k0jE0jPCaRoiIqJusTqMLFy4ECUlJVixYgUKCwsRGRmJLVu2WJpac3JyIJM1D7hcvHgRS5cuRWFhIQYNGoSoqCjs3bsXERERtvspJBTiZZqmKaiog8EoQi4TJK6IiIiobxFEUez1u3VptVpoNBpUVlb2uikbg1HE6L//hEajiOTlVyNQ4yx1SURERL1CVz+/eTZND8llAoI82TdCRETUXQwjNsAD84iIiLqPYcQGzGEkp4wjI0RERNZiGLGB4b5uAICs4iqJKyEiIup7GEZsYEygqSkno6D3bltPRETUWzGM2IA5jJwrq0GtvlHiaoiIiPoWhhEb8HVXwc9dBVEEThZyqoaIiMgaDCM2Yh4dOcGpGiIiIqswjNhIRFBTGLnAMEJERGQNhhEbiTA3sTKMEBERWYVhxEbM0zQnL1TBYOz1O+wTERH1GgwjNjLUxxVqJxnqGgw4X1YjdTlERER9BsOIjchlAsID2MRKRERkLYYRGzI3sbJvhIiIqOsYRmzIsryXYYSIiKjLGEZsKIJ7jRAREVmNYcSGwgPcIQhAcZUOpdU6qcshIiLqExhGbMhVpUCYtysA9o0QERF1FcOIjXGqhoiIyDoMIzbGFTVERETWYRixsQiuqCEiIrIKw4iNmZf3nimpQX2DQeJqiIiIej+GERvz91DBy1UJg1FEVlGV1OUQERH1egwjNiYIAk/wJSIisgLDiB2MCXQHwBU1REREXcEwYgfmFTVsYiUiIro8hhE7iAjUAAAyLlTBaBQlroaIiKh3Yxixg2G+rlDKZajWNSK/ok7qcoiIiHo1hhE7cJLLMMzXtC08V9QQERF1jmHETkb5m5pYs4qqJa6EiIiod2MYsZNR/m4AODJCRER0OQwjdtI8MsIwQkRE1BmGETsxh5HTxdUwcEUNERFRhxhG7CTUywUqhQy6RiNyymulLoeIiKjXYhixE7lMwEj2jRAREV0Ww4gdjfIzTdWcYhghIiLqEMOIHY1s6hvJ5PJeIiKiDjGM2NHoANM0DUdGiIiIOsYwYkcjm6ZpzpbUoMFglLgaIiKi3olhxI6CPZ3hopRDbzAiu6xG6nKIiIh6JYYRO5LJBEvfCLeFJyIiah/DiJ2N8uPyXiIios4wjNgZt4UnIiLqHMOInY0K4DQNERFRZxhG7Mx8eu/50hroGg0SV0NERNT7MIzYWYCHGu4qBRqNIs6VckUNERHRpRhG7EwQBE7VEBERdYJhxAHMUzXciZWIiKgthhEHMO/EmlnIMEJERHQphhEHGN00TXOqmNM0REREl2IYcYCRTdM02WU1qG/gihoiIqKWGEYcwNdNBU8XJxhF4DRHR4iIiFphGHEAQRAsO7GeKmbfCBERUUvdCiNr1qxBWFgY1Go1YmJikJKS0qXXbdy4EYIgYP78+d35tn2aeUVNZiFHRoiIiFqyOoxs2rQJCQkJWLlyJdLS0jBx4kTMnTsXxcXFnb7u/Pnz+POf/4yZM2d2u9i+bEKIJwDgy7Q81OgapS2GiIioF7E6jKxevRpLly5FfHw8IiIisHbtWri4uGD9+vUdvsZgMGDRokV47rnnMGzYsB4V3FfdEhmEwV4uKKnSYe3uM1KXQ0RE1GtYFUb0ej1SU1MRFxfX/AYyGeLi4pCcnNzh655//nn4+fnhvvvu69L30el00Gq1rR59nUohx/LrwwEA//nlLPIr6iSuiIiIqHewKoyUlpbCYDDA39+/1fP+/v4oLCxs9zV79uzBe++9h3Xr1nX5+yQmJkKj0VgeoaGh1pTZa103LgBTh3pB12jEq1tOSl0OERFRr2DX1TRVVVW45557sG7dOvj4+HT5dcuXL0dlZaXlkZuba8cqHUcQBDwzLwIA8PXhAhzOrZC2ICIiol5AYc3FPj4+kMvlKCoqavV8UVERAgIC2lx/5swZnD9/HjfddJPlOaPRaPrGCgUyMzMxfPjwNq9TqVRQqVTWlNZnjA/RYMHkYGxOy8c/vj+BLx6MhSAIUpdFREQkGatGRpRKJaKiopCUlGR5zmg0IikpCbGxsW2uDw8Px9GjR3H48GHL4+abb8ZVV12Fw4cP95vpF2v9dW44nJ3kSM2+iO+PXJC6HCIiIklZNTICAAkJCViyZAmio6MxdepUvP7666ipqUF8fDwAYPHixQgODkZiYiLUajXGjRvX6vWenp4A0Ob5gSRAo8YDs4fh9e2nsOqnk7h2rD9UCrnUZREREUnC6jCycOFClJSUYMWKFSgsLERkZCS2bNliaWrNycmBTMaNXS/nj7OGYcO+bORX1CHlXDlmjvSVuiQiIiJJCKIoilIXcTlarRYajQaVlZXw8PCQuhybeXzjIXx9uACPzRmJJ64ZJXU5RERENtXVz28OYUgoOswLAHAwu1ziSoiIiKTDMCKhKU1h5FBOBRoNRomrISIikgbDiIRG+rnBQ61Ard6AjAs8zZeIiAYmhhEJyWQCooYMAgAcOM+pGiIiGpgYRiTGvhEiIhroGEYkFm0ZGbmIPrCwiYiIyOYYRiQ2MdQTTnIBJVU65JbzJF8iIhp4GEYkpnaSY3ywBgD7RoiIaGBiGOkFprBvhIiIBjCGkV7AvKLm4PmLEldCRETkeAwjvYA5jJwqrsbFGr3E1RARETkWw0gv4O2mwnBfVwBAajZHR4iIaGBhGOklmvtGGEaIiGhgYRjpJZr7RtjESkREAwvDSC9hHhk5kleJ+gaDxNUQERE5DsNILzHE2wU+biroDUYcy6+UuhwiIiKHYRjpJQRBwJSw5q3hiYiIBgqGkV7E3Dey82Qxz6khIqIBg2GkF5k7NgBKuQwp58ux7USR1OUQERE5BMNILxLq5YL7Zw4FALzwQwYbWYmIaEBgGOllll01Av4eKuSU1+K9PeekLoeIiMjuGEZ6GVeVAsuvHwMAeGvHaVyorJO4IiIiIvtiGOmFbokMQtSQQahrMGDVTyelLoeIiMiuGEZ6IUEQ8NzNYyEIwDeHC3CAu7ISEVE/ppC6AGrfuGANfj8lFJ+m5GL55qOYPtwbBZX1uFBZh5IqHeZPCrZM5xAREfVlDCO92J+vHY3vj1zA6eJqnC6ubvW1//56DvdNHwo/D7VE1REREdkGw0gv5u2mwpq7JuO79AL4uqsQ6OmMQA813txxCul5ldh8KB8Pzh4udZlEREQ9wjDSy80a5YtZo3xbPVdarUN63lF8djAXD8waBkEQJKqOiIio59jA2gfNmxAIZyc5zpbUIC2nQupyiIiIeoRhpA9yVzvhhvGBAIDPD+ZKXA0REVHPMIz0Ub+LDgEAfJdegFp9o8TVEBERdR/DSB81dagXhni7oEZvwE9HC6Uuh4iIqNsYRvooQRBwR5RpdOQzTtUQEVEfxjDSh90WFQJBAPafK8f50hqpyyEiIuoWhpE+LFDjjJkjTct+v0jNk7gaIiKi7uE+I33c76JD8EtWCb5My8NV4X44VVSFrKJqnCquwvhgDf4ydzT3ISEiol6NYaSPuybCH54uTrhQWY/b3tnb6mu/niqFl6sS988cJlF1REREl8dpmj5OpZDj/hlDAQB+7irMHOmDP0wfisWxQwAAiT+dRPKZMilLJCIi6pQgiqIodRGXo9VqodFoUFlZCQ8PD6nL6ZX0jUYoFc3ZUhRFJHyWjq8O5cPbVYnv/zQDgRpnCSskIqKBpquf3xwZ6SdaBhHAtPT3pVvHIyLQA2U1ejy4IQ26RoNE1REREXWMPSP9mLNSjnfvicKNb+5Bem4FVn5zHPHThyK7rAbZZbXILq/BYC8XLJ3Jw/aIiEg6DCP9XKiXC964cxLufT8FGw/kYuOBthukNRhELLtqhATVERERcZpmQJg9yhd/u34MAMBDrcD4YA3mTQjEgsnBAIBXt2Zi63FuKU9ERNJgA+sAcmmTKwCs/OYYPkzOhotSjs8fjMXYII1E1RERUX/DBlZq49IgAgDP3BiBmSN9UKs3YOmHB1FcVS9BZURENJAxjAxwCrkMb905GcN8XFFQWY8HPkpFfQNX3RARkeMwjBA0Lk54794p0Dg74VBOBRJ/zJC6JCIiGkAYRggAMNTHFW/cOQkA8NG+bBzLr5S4IiIiGigYRshi9ihf3DghEEYRWPntcXS1t7myrgEp58pRWFnf5dcQERGZcZ8RauXpeWOw42QxUrMv4qtD+VgwOaTT6wsq6nDnun3ILqsFALgq5Rjm64Zhvq5YGB2KaSN8HFE2ERH1YRwZoVYCNc549OqRAICXfjyJqvqGDq8tqKjD7/9jCiIuSjnkMgE1egOO5lfim8MFWPZJGpthiYjoshhGqI0/zAjDMB9XlFbr8O/tp9q9Jr8piOSU12Kwlwu2J8xGxvPXYXvCLLx7TxQCNWpcrG3AD0cuOLh6IiLqaxhGqA2VQo6VN48FALy/9zyyiqpafd0URJKRU16LId4u2PjHKxDk6QylQoYRfu6YOzYAd18xBADwv33ZDq+fiIj6lm6FkTVr1iAsLAxqtRoxMTFISUnp8NrNmzcjOjoanp6ecHV1RWRkJD766KNuF0yOMXuUL66N8IfBKOKxjYfx96+P4vGNh3D/hwex4O3fkFte1yqIXGrhlFAo5TKk51YgPbfC8T8AERH1GVaHkU2bNiEhIQErV65EWloaJk6ciLlz56K4uLjd6728vPD0008jOTkZR44cQXx8POLj47F169YeF0/29cyNEVApZMi4oMWGfTn4+nABtmcUoUirQ1hTEAnUtA0iAODjpsIN4wMAAP9L5ugIERF1zOqzaWJiYjBlyhS89dZbAACj0YjQ0FA8+uijeOqpp7r0HpMnT8a8efPwj3/8o0vX82wa6ezOKsGvWSVwUSngppLDTeUED2cFZo3yhYfaqdPXpmZfxG3v7IVSIcO+5XPg5ap0UNVERNQbdPXz26qlvXq9HqmpqVi+fLnlOZlMhri4OCQnJ1/29aIoYseOHcjMzMTLL7/c4XU6nQ46nc7yZ61Wa02ZZEOzR/li9ijfbr128mBPjA3ywPECLT47mIsHZw+3cXUm+RV1uFijx7hgHvJHRNQXWTVNU1paCoPBAH9//1bP+/v7o7Cw4yPoKysr4ebmBqVSiXnz5uHNN9/ENddc0+H1iYmJ0Gg0lkdoaKg1ZVIvIQgCFseaGlk37MuGwWj9hmiiKHa6kZooirhr3T7c+vZvyGna64SIiPoWh6ymcXd3x+HDh3HgwAG8+OKLSEhIwK5duzq8fvny5aisrLQ8cnNzHVEm2cHNE4OhcXZC3sU67Mpsv6+oI1lFVbju9V8x74090Dca273meIEW2WW1aDCISD5baouSiYjIwawKIz4+PpDL5SgqKmr1fFFREQICAjr+JjIZRowYgcjISPzf//0fbr/9diQmJnZ4vUqlgoeHR6sH9U3OSjnuiDLt4mpNI+sPRy5g/prfkFlUhRMXtNh3tqzd63aebA44adkVPaqViIikYVUYUSqViIqKQlJSkuU5o9GIpKQkxMbGdvl9jEZjq54Q6t/Me47szirBoZyLnV7baDAi8acMLPskDbV6A5yd5ACArcfbnwbc2WK0JfUy701ERL2T1dM0CQkJWLduHT788ENkZGTgoYceQk1NDeLj4wEAixcvbtXgmpiYiG3btuHs2bPIyMjAa6+9ho8++gh333237X4K6tXCfFwxJ9wPAHD72mQk/pSBOn3bbeLPlFTj3vcP4N3dZwEAf5w1DGsWmU4S3naiCMZLek7Ka/Q41GIPk9PF1aio1dvppyAiInux+qC8hQsXoqSkBCtWrEBhYSEiIyOxZcsWS1NrTk4OZLLmjFNTU4OHH34YeXl5cHZ2Rnh4ODZs2ICFCxfa7qegXu+V2yfg718fw0/HCvHu7rP44cgFvDB/HEYHuOO79AJ8m16AY/mmVVMuSjleuX0CbpwQBH2jEe4qBYqrdDiUW4GoIYMs7/nrqRKIIhAe4A59oxFnS2twKKcCVzUFHyIi6hus3mdECtxnpP/YfqIIK745hoLK+jZfU8gEzBrliyevC8foAHfL83/69BC+TS/AA7OGYfkNYyzPP77xEL4+XICHrhyOYq0OX6bl4dGrR+D/rh3tkJ+FiIg619XPb55NQw4VF+GPbQmzcd+MoZAJpuemhnnhhfnjkPJ0HNbfO6VVEAGAuWNNzdFbjxdalvkajCJ2Z5UAAK4a7WcZMUnNZt8IEVFfY/U0DVFPuaoUeObGCDw4eziMogh/D3Wn11852hdKhQzny2qRVVSN0QHuSM+rwMXaBrirFZg82BMaZ9NusIdzK9BoMEIhZ84mIuor+C82ScbXXXXZIAKYwsvMET4AmlfVmJf0zhrlC4VchpF+bnBXKVCrNyDzklOGiYiod2MYoT6h5VQN0Lyk96rRpmZVmUxA5GBPAEAap2qIiPoUhhHqE+aM8YNMMO24mpZz0bLy5srRzefmsG+EiKhvYhihPsHbTYUpYV4AgGe+PgYAmBiigY+bynKNOYyk5VS0ef3Pxwvx9FdHUV7DfUiIiHobhhHqM8xTNccLzKMirfcTiQz1hCAAOeW1KK5qXjqcU1aLRz89hI/35yD+gwOo1Tc6rmgiIroshhHqM64d2/q06Es3N3NXO2G0v2lZcMtzap7//jh0TQftpedW4OGP09BgaP/gPXv521dHseDt31CtYxAiIroUwwj1GSGDXDAu2LRpjrerEhOCNW2umdw0VWM+AycpowjbM4rhJBfwzzsmQu0kw67MEjz55RE4ar+/izV6fJqSg7ScCmw51v4ZO0REAxnDCPUpN04IAgBcE+EPmXnXtBYmD25uYq1vMODZ744DAO6bMQy3R4Xg7UWTIZcJ2JyWj1VbTl72+xmMIhp7OIqy72wZzLnnu/SCHr0XEVF/xDBCfcr9M4birbsm4W/zxrT7dXMT65H8SryRdAq55XUI1Kjx6NUjAABXh/tj1YLxAIB3d5/Fh3vPd/i9Kmr1mL5qB+a//Ruq6hu6XfPeM2WW//3b6VI20RIRXYJhhPoUhVyGGycEwUPt1O7Xw7xd4OWqhL7RiLd3nQEA/H1eBFxVzZsN3xEdir9eZzq/5tWtmR32cXyRmodCbT2O5WvxxKb0NqcGd9XeM6UAAKVchkajyKkaIqJLMIxQvyIIAiY3bX4GANNHeOOG8QFtrntw1nAM83FFta4RXx3Kb/N1URTxSUqO5c/bM4rw+vYsq+sp0tbjTEkNBAG4b+ZQAMD3RzhVQ0TUEsMI9TvmJlYnuYDnbh4HQWjbWyKTCbj7iiEAgI+Sz7dpZt13thxnS2rgqpTjH7eMBQC8seM0fjp6wapakpumaMYFaXDX1MFN713WaukxEQCUVetQ32CQugwiSTCMUL9zS2QwwgPc8fQNYzDCz63D626LCoGLUo6somrsP1fe6msf7882vdekYNwTG4b7Z5hGNf7v83RkXNB2uRbzFM204d4I9XLBpMGeMIrAT0c5VUPNThdXI3bVDvzliyNSl0I9YDSK+P5IAUqrdT16nwaDEX//+ig2thid7e8YRqjfCfZ0xpbHZ+He6UM7vU7j7IT5k4IBAP9LPm95vrRaZzkDxzya8dT14Zg50ge1egOW/u8gyrrwj40oivjttGlkZFrTQX/m1UBcVUMt7coshr7RiO0nimDoZm8SSe+zg7l45JNDeOmHjB69z86TxdiwLwfPfXeix6v5+gqGERrQFseapmq2Hi9CYaVp6uSL1Dw0GERMDPXEuKa9TBRyGd68cxIGe7kg72Idbn17L7Iuczpwbnkd8ivqoJAJmBJmmjqaNz4QggAczL6Igoo6O/5kfV+1rhHr95zDR8nnseVYIdJyLiK3vNbhG9Y5wpG8SgBAXYMBZ0qqJa6GuuvXU6aR0NScnp2PtaPpVPK6BgNOFg6MU8gZRmhACw/wwNQwLxiMpoZVo1HEJ/tNQ6OLmkZFzDxdlHg/fgpCBjkjp7wWC97ei6SMog7f2zxFM2mwJ1yUptU8ARq15YydH45Y138y0Ly7+wye//4EnvnmOB7ckIoFb+/FzFd24spXd/VoqXVvdCSvwvK/03MrOryOei9RFJFy3jTdm11W2+3dlkVRtIQRAEjrYbDpKxhGaMC7p2l05NOUHOzOKkFOeS3c1QrcODGwzbXDfd3w7SMzEDPUC9W6Rtz/v4N4Z9eZdndzNe8vEjvcp9XzN000TdVwVU3ntp0wBb1Jgz0RGeqJYE9nyGUC8ivqWu3d0tdV1OpxvqzW8uej+ZUSVkPdlVNei5Kq5unbzG6OaBwv0KK4xfukDZBTyBlGaMCbOzYAvu4qlFTpLA2ECyYFW0YzLuXlqsRH98XgrpjBEEXg5S0n8efPj7Tah0QURcsH5vTh3q1ef/24AMgEID2vEtllNR3WJYoi9pwqRd7F2g6v6asaDUYcyavocEv+vIu1OFlYBZkArF8yBV8vm47fnrra0sOT3I/CiHmKxiw9r2+HkdzyWlTUDryN/VIuaYK3ptG9paQM06iIj5sSQM+nfPoKhhEa8JQKGe5s+pAzd8HfFTPksq95cf44PH/LWMhlAr5My8PLW5u3lz9dXI3Sah3UTjJEttj3BAB83FSY3tTQ2lkj66cpubj7vf24+7/7+10T26qfTuLmt37D/5Kz2/26eZg6asggDHJVWp6f1hTs+lcYqQAATAz1BGD6ENM39s3/3pmFVZizejfuXLffYWc/9RYHmqZonOSmrQS6G0Z2nDSNCD44ezgEwdR71nLEpb9iGCECsChmMBRNZ91EDxmE0QHul32NIAhYHBuG1+6YCMC0vfymA6Z+k99Om/pFpoR5QaWQt3mtearmnV1n2v1H62ShFs81natzvqwWP3Syv4mu0dCn/uHX1jdYNpT7Xzt7vADNvx3OGdP6pOaYYaYwkllU1W/+gTaPhNw0IRAeagX0jcbLNkf3Vm/uOAV9oxEZF7TILut/I3qdOXDeNIIxb7xperc7YaSkSmf5+3DzxCCM8ms6hXwAjI4wjBAB8PdQW5b53j+z8yXBl5o/KRiPzRkJAHj6q2PYe7q0Rb+Id7uvuXVSMKYN90aN3oD7PzzY6oO1Vt+IRz45BF2jER5q01TRmp2n292OvqCiDrNe2YnZr+6yBKDe7ouDeajVmzb3OlNSg8OXNGzW6BotIx9zwv1afc3LVYkxgaaTm/ed7R+jIy1HRiaEeDY91/emak4XV7cKzb/2kb+PtlBcVY9zpaadls2bKZ4srLL6CImdmaYQPj5YAz8PNSYP8QTQszCSXVbT7VEaR2IYIWry4q3jsD1hNq4b17Zx9XIejxuJmycGodEo4sENqZYwMu2S5lUzJ7kMby+ajKE+rsivqMMDHx207L75/HcncLq4Gn7uKnzzyAy4qxTIKqrG9ktW7oiiiBXfHEORVoec8los+u9+LN98tNVKk6r6Bny0Lxu3rPkN9394UPLpHqNRxEf7TFMzg1xM5wt9nprX6prfTpdCbzAi1Mu53U3rLFM1/SCMFFbWo0irg0wAxgZ5YEKIaSn50fwKaQvrhrd3noYoms5gAoA9p0okrshxDjaNioz2d0dkqCeUChlq9QbklFs3OrSjaUTw6qYQPqnpFPLuNrHW6htx69t7cfNbe3r9aBvDCFETlULe6Y6tnREEAa/cPgGTB3tCW9+Ial0j3NUKjAvy6PA1ni5K/HdJNDzUCqTlVGD55qP4Nr0AGw/kQhCA1xdGYqiPq2W1z5pLVu1sOVaI7RnFcJILWNA0qvNpSg7m/usXfH4wF099eQQxLyXhma+PIT23AtszirDpYG63fj5b+fV0Kc6V1sBdrcArt5umt75LL2i1Dbq5X2ROuH+7W/nHDus/fSPpTaMio/zd4aJUWMJIem7fGhnJLqvBN039T8/cFAHAtJpMivBrMIoOn7Y0N69OHeoFhVyGUf6mf0dOFnZ9RELfaMSvTQFuzhhTGLGcQp5X2a0+op+OFqK8Ro8Gg4hXt2Za/XpHYhghshG1kxz/WRyNkEHOAICYpn+YOjPc1w1vL4qCXCbgq0P5eGLTYQDAI1eNsOza+ocZQ6FSyJCeW2HZ0VVb34CV35p6Sh6cPRyrF0bi06VXYLCXCwoq6/GXL45g44Fc1OoNGO7rinkTTKM9/9p2CjXd3P/AFv639zwA4I6oUMwJ90OwpzOq6hvxc9MyXqNRRJI5jIzxa/c9pg7zgkwAzpXW4EKl/TaOMxhFnCqqgq7RuvNiNuzLxj3v7cfNb+3B7Fd3YuJzP2PU33/Cu7vPtLnWMkXTND1jnqbJKqrqU+fUvLPrDAxGEbNH+eKuqYOhcXZCVX0jjjh4mfKx/EpEPvczrn5tN9b9chYXaxyzqsfcvGreQ2hMgOmXkBMXuj4akXKuHDV6A3zcVBgXZAqlw3xc4eniBF1TH461Pk9t/uVj24kipGaXd3K1tBhGiGzIx02FD/8wFQsmB+PxuFFdes2MkT549mbTYXwGo4gpYYMsPSjm9zSv9lmz8zQA4NUtmSiu0mGojyuWXTUCgKk/ZcvjMxE/PQyeLk64JTIIm/54BbYnzMa/fheJId4uKK3W4b+/nrPlj4xibX2XPjhzymqxo2lO/J7YIZDJBNw22TSi83nTiM2xgkqUVOngqpRj6lCvdt/HQ+2E8U0749pzdOSVLSdxzb9+wYRnf8Zd6/bhzaRTOHi+vNMdYA/nVuDvXx/Dr6dKcSSvEtlltaisa4C+0Yi3dpxuEwTNvSETQk0/T6BGDR83JRqNIk5048Mnq6gKbySdwhObDuO+Dw7g9nf24prVuzHrlZ146ssjOHi+3OajBvkVdfgyzTTV9qc5IyCXCZaptD2nHNc3YjCKpmlKXSPOldbgxR8zEJOYhCc2HbZrA2hVfYMlKJj/zpr7mqwJEElNq2iuDveFrKmZXhAETGpaZZVq5VRNdlkN9p0thyA0T/u8/FNmr212ZxghsrHhvm5Y/btIy1byXXHPFUPw1+tGY+ZIH/z795PajKj8cdYwKGQCks+W4b+/nsWGpoP8Xpw/Dmqn5tU6LkoFVt40FodXXIt//34SYoZ5QxAEKBUy/GXuaADAu7+csclKlKr6Bjz77XHEJCYh5qUkJP6U0emeKB/tOw9RBGaP8sVQH1cApsMKAWDP6VJcqKyzrKKZOdK33VVIZuaN5OwVRoq09Xi/aRRH12jE3jNleG1bFm5fm4wb/v1ru/toGI0iVn5zDAAQN8YP/10cjS8ejMW2J2ZhqI8rqnSN+OpQvuV6URQtYcQ8MiIIgmV05GgXm1hPFmqx+udMxK3ejWv/9QtWb8vCV4fykXSyGAezL+JUcTVyymux8UAubl+bjKtf2423dpxCkdY2J0ev3XUGDQYR04Z7I2qI6cN4xkjTfx9HhpGP92fjaH4l3NUKrLwpAmODPKBvNOKrQ/lY8PZeu50HlZp9EUYRGOzlAn8PNQAgPNC0CqarYaTlrqtXh7deQTbZ3DdiZaD6oqkXa+ZIX7x063ioFDKknC+3NMn2NgwjRL3Ew1eOwEf3xSDI07nN14I8nbGgaRThhR8yIIrAbZNDLFM5XTFvfCAmhmhQqzfgjaRTPap16/FCXLP6F3yw1xQwKusa8O7us5j1yk48tCEV+8+WtfoNrE5vwKYDptGPe6eFWZ4f4u2KqUO9IIrA5rT85n+QO5iiMTP/5r33TJldftN7Z9cZ6BuNmBI2CNsTZuEft4zFDeMD4K5W4FRxNf78+ZE23/fz1Fyk51XCTaXAS7eOR1yEP6LDvDDS3x33NK2waLmU2TxqolTIWi0lN4/6pLfYIr492voGLPskDde9/ive2HEap4uroZTLMCfcD09dH45VC8bjnUWT8fH9Mfggfgpubzql+lxpDf75cxbiVu9GTg+X3xZp6y19SI9e3TyaN3OELwDTB2h3t0W3RrG2Hq9uMfVE/GXuaMRPH4rvH52Bb5ZNx3VjAwAAz3xzDMVVtglgLZmnaKKbzp8CgIimkZG8i3XQduHogrOlNcguq4WTXLAEOTNz38ihnIo2rztdXI3cdppkDUbREkbuiApBgEaNe6eHAQBe2ZLZKw9jZBgh6iMeunIEmkZvMcjFCU/PG2PV6wVBwFPXm17zSUoOzlp5IFtlXQNSs8vxx/8dxAMfpaJQW48h3i748A9TsW5xNKaP8IZRBH46VoiF/9mHq/65C28knUJueS2+PpwPbX0jBnu5YPYo31bve3vT6MhHyabfbAUBuGp052EkOmwQnOSmreFzy23bN1KsrcenTfugPDZnFEb4ueOe2DC8vSgKny69Akq5DNszivDenubprsraBrzc9GH4eNxI+DX9hmx2e7QpCGQVVVtGc8xhIyLQA04tRsIsK2o6GRk5kleBG9/Ygx+OXIBCJuCaCH/8a+FEHHwmDu/dOwUPzh6O308djOvHB2L6CB9cOdoP/7xjIg48HYd/3jERI/zcUFXfaNnLpjtE0dQUaQ5tVwxrnlYb7O2CwV4uaDSK2O+AVU8v/JCBKl0jJoRosKhpw0JBEDAx1BNv3jUJY4M8UFHbgL9tPmrz8GreX2RqWPPP7+miRKDG9HfgZBf6RsyraK4Y5g03VeudnyeGekImmKbDzId5AsD+s2WY+/oviFu9GycKWo/AmEYa66FxdsI1EaaRlodnj4CHWoGThVX45nA+ehuGEaI+YqiPK26bbPrgfvbmsfBqsTNpV8UO98bV4X4wGJu767PLavDRvmw88NFB3PzWHix8Nxl/+OAAln2ShoTPDmPhu8mIfmE7Jj73M257Jxk/nyiCQibg4SuHY+vjszB7lC+uifDHx/dfga2Pz8KdU0PhopTjfFktVm/LwsxXduL5704AMJ2SbJ4PN5s3PhAuSjkKm6YNJoZ4wtdd1enP4aJUILJpLj35rG2nAtbuPgtdoxFRQwZh+ojW+8SMC9bgmRtNgW7VTyctQ+ert2WivEaPEX5uWNJi5MfMQ+1k+W/3QdP0j3nFzMSQ1tN545v+fLqkuk2PiSiKWL/nHG57Zy9yymsRMsgZXzw0DesWR+PWSSHwUDt1+rO5qhS4PSoEa++eDCe5gKSTxZYzgC5VWFmP5A5GnkRRxD++z7D89v1E3Kg2K5/Mv+H/auepmj2nSvFtegEEAXhh/jjIL/n75SSX4bXfTYSTXMD2jGJsTrPdB7Gu0WDZJ2fKJT1O5r6Ry62oya+ow/u/mYJteyHcVaXA6KaGWPPft+Kqejzy6SEYjCJ0jUY8/HFqqxEYcw/W/MggyzSuxsUJD11p6i977ecsqxuz7Y1hhKgPeWnBeOx96mrcEhnc7fd48rpwyATTCMaMl3dg9qu78MzXx7D1eBGO5FVi/7ly7DhZjB+OXMDmtHzsP1du2SY/wEONayL88f2fZuCv14W36lcBgNEB7khcMAEHno7D6t9NxPQR3hAE01Hozk5y3BEV2qYeV5UC17fY2+XSjc46Yu4bseWhecVV9fi4qR/nsTkj211afPcVQzBvQiAajSIe/eQQks+UWfZOee7msa1GOVpaMs30G/v2jCLkXaxtsw28mZ+7GoEaNUTRtDrErL7BgIc2pOH570+gwSDiurEB+OFPMy2hzBoj/Nxx34xhAIBnvz2OOn3rD6asoirc8MavuHPdPix5/wDyK5pHn8xBZH3TB2jigvHtThfObHpujx03P9M1GrCiqU9n8RVDLP02lwoP8LA0lD/73fFWIww9cbRpya2PmxLDmvqgzMZ0oW+kSFuPu9btQ0FlfatfNi4VZd78LPsiGg1G/OnTQyip0mGknxuCPZ1xvqwWf22aOqyo1ePn46aAeUd06/+/3TstDP4eKuRX1OHjfTnd/bHtov2TwIioV3KSy9rtKbHG6AB33B4Vgs8O5iHvYh2c5AImDx6EmSN9EB7ggfpGA2p1BtToG1HXYECAhxoj/NwwzNetzRByR1xVCiyYHIIFk0NQUFGHrccLMSbQAxqX9n9zvz0qxLIi43L9Imaxw7zxRtIpy2/v7QUHwLTF9gd7z2GUvztumhDUZmSmpXebRkUmDfbEzJHt9+MIgoBVC8bjeH4lzpfV4u739sMomkZ4pnfSwzPCzx3TR3jjt9Nl+HDveRwraFpJ084H6IQQDS5U1uNofiVihnlD12jAgxtSsSuzBEq5DE/PG4PFsUM6/Jm74k9zRuDbw/nIr6jDmp2n8eemBuezJdW4a91+lDcti/0lqwTXrt6N5TeMwV1TB+OFH1oHEfNKr0tNG+4DmWDqa7hQWYdATc/+3l5KFEWs3paFs6U18HVX4f+a6u/IA7OG4efjhUjPq8STXx7BB/FTenT/ACDF3C8yxKvNe5lHRjpa3ltarcNd6/Yhu6wWoV7O+GRpTIf//5g8eBA27MtBWs5FrN6WhX1ny+GqlGPtPVGoqm/EHWv3YsvxQry35xyc5DLoDUZEBHq0aaJ3VsrxeNwoLN98FG/sOIXbokKgce58NM1RGEaIBqCVN43FKH93DPN1RcxQb7h2MWR0R5CnM+Knd77FfsxQL9weFQKZ0Nz8dzmTBntCpZChuEqHMyU1bTasazQY8fH+HPzz50xU1ZumO9b/dh4rboywNAW2VFKlu+yoiJm72glv3TUZC97ZC32jEc5OcvytCz08S2LD8NvpMnyw9zwaDCLcVYo2v1EDpoCy9XgR0vMq0WAw4tFPDmFXZgmcneT4IH6K5YyennBRKrDipgg8uCEN//nlLBZMDoaTXIZF/92P0modwgPc8eKt4/HSjxlIzb6Iv399DO/+csbSo9NZEAFM0wLjQzyRnluBPadK2/yW3hNF2nr89Ysj2J1l2iTs7/PGXHaKStE0XXPDG3uwO6sE7/92HvHTw7oVSIxGEYXaevzS9P0vnaIBTKMxAJBZqIXBKLaaPrpYo8fd/92PMyU1CNSo8cn9V3Qa1swratLzKpHW1Mi66rYJGO5r+ju/4sYIPPPNcST+dBJ+TVOcv4tuf5TljqgQrN9zDqeKq7Fm52n87Qbres/shdM0RAOQq0qB+2cOw9Xh/nYNIl0lkwn45x0T8crtE7v84aB2kltCxbfpBci7WGvZ7yQ1uxw3v/UbVn57HFX1jRjt7w5XpRzpuRW47Z29+NOnh5Bbbrq+Tm9Aja4R7+w6g/oGIyaGerZpsm3PuGCNpUfhyetGI7gLI1ZzxvgjZJAzGgyi5T3aG6mxrKjJrcATmw7j5xNFUCpk+O+SaJsEEbO5YwMwe5Qv9AYjnvzyCO5ctw8XKusx0s8NH98fg6ghg/DZA7FYeVMEnJ3kliDy0q2dBxEze0zVfJdegGv/9Qt2Z5VAqZBhxY0RuLnp4MnLGeHnjj9fa5quef77E7jhjT346lBep3vHAKbVKT8evYD7PzyAq1/bhfBntmDaqh3Yd9a82VnbcDvUxxVqJxnqG4w4X1ZjeV5b34DF61NwsrAKvu4qfLL0CoR6uXT6/Yd4u8DLVWlZBXPvtDDLYZuAaerw5olBMBhFXKish1Iu63AqVyGXWYLzB7+db3c1jhSk/1eIiKibpg33xt4zZXgj6ZRlubKbSmFZTqpxdsJf5o7GnVMHo6xGh9e2ZuGz1Fx8m16AbzvYd+Lxy4yKtPS76FDcNjmkTdNkR+QyAfdcMQSJP50E0LzZ2aXMK2pyymuRU25a8vnu3VGdTgN1hyAIeO7msbj29V8sq0LCvF3w8f0x8HZTWWqOnz4UV4f74Z1dZzBjpA9unNC1D/8ZI33w1s7T+O10KYxGsdMpssvJr6hD4o8Z+P6I6TC+ccEe+NfvIjHS//InbLd034xhuFjbgA/3nkfGBS2e2JSOV7dk4t7pYYgd5oOR/m6WXihdowFfpeXj3V/O4lxpTav3UcgEhAxyxvQRPpYdU1uSywSM9ndHel4lMi5oMdzXDbpGAx74XyqO5lfCy1WJT+6Psey50xlBEBA1ZBC2nShCZKhnm9EMQRCQuGA8TlzQ4nRxNeIi/DCokwb3K0f5YsYIH+w5XYqXt5zEW3dN7sqtsytB7K3bsbWg1Wqh0WhQWVkJD4+uDeESUf9XWFmPJzYdRnZZDUqr9dC3+A13YXQo/nrdaMuHqtnxgkq88H1GuwftXTXaF+vv7XkvQWcu1uhxRWISdI1GvL1oMm4Y3/7BjLNe2Ymc8lrIZQLW3DUZ140LsFtN/9qWhX8nnULIIGd89kBsj/uSzPSNRkQ+/zNq9QasWxyNWaN8Ot3Mrj2ni6vwzq6z+OZwPhqbpjuWXTUCj149osNm4a6oqNVjw75sfLD3PEqrmzexkwmmUY1R/u5Izb6I4qYNAjXOTlgcOwRXDPPGYC8XBGrUlz3u4akvTccyPHLVCCRcMwqPbTqM79IL4KqUY9MDsVZtjJhVVIXPDuTij7OGtVk6bpZbXov3fzuPP8wIQ8igzkdbThRoMe/NX017/Dw8zTIVZGtd/fxmGCGifkEURWjrG1FarYOzk/yyH6g1ukaIMH34yAQBggCrPyi767ODudh3pgwvLRjfZkWS2StbTmL9b+fw8m0TerR6qiuMRhG7s0owMdSzW0vGOxP/fgp2Zpp6KxQyASP83BAR6IHwQHeM9He3rAgxB8A6vQF5F2txvqwWX6bmYeuJQpg/paYN98aT14W3WYHUE/UNBnx1KB/fpRcg44IWF2tbb1IW4KHG/TOH4s6pg62e0vxw73ms/PY45oT7YaiPK/675xwUMgHvx0/BzJGXnwq0t798no7PU/MQNWQQvngw1i4hnGGEiKiP0zUaHBaQ7OVIXgVe3nISxwu0qKhtfzdSV6UcoV4uKKvRt3tUwTUR/nj4yuGYZKff3s1EUURJlQ4ZhVXILNTCz12N68cHdPu/Qcq5cvzu3WQoZAIam/o9Xl8YifmT7Bsuu6qwsh5X/XMX6hoMnY7S9QTDCBER9RqiaGquPFGgxYkLWmQWVeFUURXOltRYPqjN3FUKhHq5YHywBvfNHIpRVvaF9Bba+gZMePZny5+XXx+OB2YPl7CitlZvy8IbSacw2MsF2xNmQ6mw7bqWrn5+s4GViIjsThAEBHk6I8jTGXERzYfBNRiMOF9ag9yLtfBxU2Gwlws0zk527dtxFA+1E4b5uOJsaQ3ip4fhj7OGSV1SGw/MGoZPU3KQU16Lrw/l43dTbLcE2xocGSEiIrKTY/mm1TS3TQ7p0Woie/r+SAFq9QarVoZ1FUdGiIiIJDYuWGPVqhkpdHWptj1x0zMiIiKSFMMIERERSYphhIiIiCTFMEJERESSYhghIiIiSTGMEBERkaQYRoiIiEhSDCNEREQkKYYRIiIiklS3wsiaNWsQFhYGtVqNmJgYpKSkdHjtunXrMHPmTAwaNAiDBg1CXFxcp9cTERHRwGJ1GNm0aRMSEhKwcuVKpKWlYeLEiZg7dy6Ki4vbvX7Xrl248847sXPnTiQnJyM0NBTXXnst8vPze1w8ERER9X1WH5QXExODKVOm4K233gIAGI1GhIaG4tFHH8VTTz112dcbDAYMGjQIb731FhYvXtyl78mD8oiIiPqern5+WzUyotfrkZqairi4uOY3kMkQFxeH5OTkLr1HbW0tGhoa4OXl1eE1Op0OWq221YOIiIj6J6tO7S0tLYXBYIC/v3+r5/39/XHy5MkuvceTTz6JoKCgVoHmUomJiXjuuefaPM9QQkRE1HeYP7cvNwljVRjpqVWrVmHjxo3YtWsX1Gp1h9ctX74cCQkJlj/n5+cjIiICoaGhjiiTiIiIbKiqqgoajabDr1sVRnx8fCCXy1FUVNTq+aKiIgQEBHT62n/+859YtWoVtm/fjgkTJnR6rUqlgkqlsvzZzc0Nubm5cHd3hyAI1pTcKa1Wi9DQUOTm5rIXxc54rx2H99qxeL8dh/facWx1r0VRRFVVFYKCgjq9zqowolQqERUVhaSkJMyfPx+AqYE1KSkJjzzySIeve+WVV/Diiy9i69atiI6OtuZbAjD1pYSEhFj9uq7y8PDgX2wH4b12HN5rx+L9dhzea8exxb3ubETEzOppmoSEBCxZsgTR0dGYOnUqXn/9ddTU1CA+Ph4AsHjxYgQHByMxMREA8PLLL2PFihX45JNPEBYWhsLCQgCm0Q43Nzdrvz0RERH1M1aHkYULF6KkpAQrVqxAYWEhIiMjsWXLFktTa05ODmSy5kU677zzDvR6PW6//fZW77Ny5Uo8++yzPaueiIiI+rxuNbA+8sgjHU7L7Nq1q9Wfz58/351v4RAqlQorV65s1Z9C9sF77Ti8147F++04vNeO4+h7bfWmZ0RERES2xIPyiIiISFIMI0RERCQphhEiIiKSFMMIERERSWpAh5E1a9YgLCwMarUaMTExSElJkbqkPi8xMRFTpkyBu7s7/Pz8MH/+fGRmZra6pr6+HsuWLYO3tzfc3Nxw2223tdnVl6yzatUqCIKAxx9/3PIc77Nt5efn4+6774a3tzecnZ0xfvx4HDx40PJ1URSxYsUKBAYGwtnZGXFxcTh16pSEFfdNBoMBzzzzDIYOHQpnZ2cMHz4c//jHP1qdbcJ73T2//PILbrrpJgQFBUEQBHz99detvt6V+1peXo5FixbBw8MDnp6euO+++1BdXd3z4sQBauPGjaJSqRTXr18vHj9+XFy6dKno6ekpFhUVSV1anzZ37lzx/fffF48dOyYePnxYvOGGG8TBgweL1dXVlmsefPBBMTQ0VExKShIPHjwoXnHFFeK0adMkrLpvS0lJEcPCwsQJEyaIjz32mOV53mfbKS8vF4cMGSLee++94v79+8WzZ8+KW7duFU+fPm25ZtWqVaJGoxG//vprMT09Xbz55pvFoUOHinV1dRJW3ve8+OKLore3t/j999+L586dEz///HPRzc1N/Pe//225hve6e3788Ufx6aefFjdv3iwCEL/66qtWX+/Kfb3uuuvEiRMnivv27RN//fVXccSIEeKdd97Z49oGbBiZOnWquGzZMsufDQaDGBQUJCYmJkpYVf9TXFwsAhB3794tiqIoVlRUiE5OTuLnn39uuSYjI0MEICYnJ0tVZp9VVVUljhw5Uty2bZs4e/ZsSxjhfbatJ598UpwxY0aHXzcajWJAQID46quvWp6rqKgQVSqV+OmnnzqixH5j3rx54h/+8IdWzy1YsEBctGiRKIq817ZyaRjpyn09ceKECEA8cOCA5ZqffvpJFARBzM/P71E9A3KaRq/XIzU1FXFxcZbnZDIZ4uLikJycLGFl/U9lZSUAwMvLCwCQmpqKhoaGVvc+PDwcgwcP5r3vhmXLlmHevHmt7ifA+2xr3377LaKjo3HHHXfAz88PkyZNwrp16yxfP3fuHAoLC1vdb41Gg5iYGN5vK02bNg1JSUnIysoCAKSnp2PPnj24/vrrAfBe20tX7mtycjI8PT1bnTEXFxcHmUyG/fv39+j7d2sH1r6utLQUBoPBsoW9mb+/P06ePClRVf2P0WjE448/junTp2PcuHEAgMLCQiiVSnh6era61t/f33JuEXXNxo0bkZaWhgMHDrT5Gu+zbZ09exbvvPMOEhIS8Le//Q0HDhzAn/70JyiVSixZssRyT9v7N4X32zpPPfUUtFotwsPDIZfLYTAY8OKLL2LRokUAwHttJ125r4WFhfDz82v1dYVCAS8vrx7f+wEZRsgxli1bhmPHjmHPnj1Sl9Lv5Obm4rHHHsO2bdugVqulLqffMxqNiI6OxksvvQQAmDRpEo4dO4a1a9diyZIlElfXv3z22Wf4+OOP8cknn2Ds2LE4fPgwHn/8cQQFBfFe92MDcprGx8cHcrm8zcqCoqIiBAQESFRV//LII4/g+++/x86dOxESEmJ5PiAgAHq9HhUVFa2u5723TmpqKoqLizF58mQoFAooFArs3r0bb7zxBhQKBfz9/XmfbSgwMBARERGtnhszZgxycnIAwHJP+W9Kz/3lL3/BU089hd///vcYP3487rnnHjzxxBOWk+B5r+2jK/c1ICAAxcXFrb7e2NiI8vLyHt/7ARlGlEoloqKikJSUZHnOaDQiKSkJsbGxElbW94miiEceeQRfffUVduzYgaFDh7b6elRUFJycnFrd+8zMTOTk5PDeW2HOnDk4evQoDh8+bHlER0dj0aJFlv/N+2w706dPb7NEPSsrC0OGDAEADB06FAEBAa3ut1arxf79+3m/rVRbW9vq5HcAkMvlMBqNAHiv7aUr9zU2NhYVFRVITU21XLNjxw4YjUbExMT0rIAetb/2YRs3bhRVKpX4wQcfiCdOnBD/+Mc/ip6enmJhYaHUpfVpDz30kKjRaMRdu3aJFy5csDxqa2st1zz44IPi4MGDxR07dogHDx4UY2NjxdjYWAmr7h9arqYRRd5nW0pJSREVCoX44osviqdOnRI//vhj0cXFRdywYYPlmlWrVomenp7iN998Ix45ckS85ZZbuNy0G5YsWSIGBwdblvZu3rxZ9PHxEf/6179aruG97p6qqirx0KFD4qFDh0QA4urVq8VDhw6J2dnZoih27b5ed9114qRJk8T9+/eLe/bsEUeOHMmlvT315ptvioMHDxaVSqU4depUcd++fVKX1OcBaPfx/vvvW66pq6sTH374YXHQoEGii4uLeOutt4oXLlyQruh+4tIwwvtsW9999504btw4UaVSieHh4eJ//vOfVl83Go3iM888I/r7+4sqlUqcM2eOmJmZKVG1fZdWqxUfe+wxcfDgwaJarRaHDRsmPv3006JOp7Ncw3vdPTt37mz33+clS5aIoti1+1pWVibeeeedopubm+jh4SHGx8eLVVVVPa5NEMUW29oREREROdiA7BkhIiKi3oNhhIiIiCTFMEJERESSYhghIiIiSTGMEBERkaQYRoiIiEhSDCNEREQkKYYRIiIikhTDCBEREUmKYYSIiIgkxTBCREREkmIYISIiIkn9P4v1CrBbhuN3AAAAAElFTkSuQmCC\n"},"metadata":{}},{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"0.8859649122807017"},"metadata":{}}],"execution_count":5},{"id":"e657971b-42bb-4ddc-a6c9-ff72ac66e805","cell_type":"markdown","source":"### 💡 Syntaxe à reprendre\n\n_Repère syntaxique pour aborder l'exercice ci-dessous._\n\n```python\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score\nimport torch\nimport torch.nn as nn\nif y.dtype.kind in {'U', 'S', 'O'}:\n    y = (y == 'M').astype(np.int64)\nelse:\n    y = y.astype(np.int64)\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train).astype(np.float32)\nX_test = scaler.transform(X_test).astype(np.float32)\nX_train_t = torch.tensor(X_train, dtype=torch.float32)\ny_train_t = torch.tensor(y_train, dtype=torch.float32).view(-1, 1)\nX_test_t = torch.tensor(X_test, dtype=torch.float32)\ny_test_t = torch.tensor(y_test, dtype=torch.float32).view(-1, 1)\ntorch.manual_seed(0)\nm = X_train_t.shape[1]\nmodel = nn.Linear(m, 1)\ncriterion = nn.BCEWithLogitsLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=0.1)\nepochs = 200\nloss_history = []\nmodel.train()\nfor _ in range(epochs):\n    optimizer.zero_grad()\n    logits = model(X_train_t)\n    loss = criterion(logits, y_train_t)\n    loss.backward()\n    optimizer.step()\n    loss_history.append(loss.item())\nmodel.eval()\nwith torch.no_grad():\n    logits_test = model(X_test_t)\n    proba_test = torch.sigmoid(logits_test)\ny_true = y_test_t.cpu().numpy().reshape(-1)\nfor thr in [0.3, 0.5, 0.7]:\n    y_hat = (proba_test >= thr).int().cpu().numpy().reshape(-1)\n    acc = accuracy_score(y_true, y_hat)\n    prec = precision_score(y_true, y_hat, zero_division=0)\n    rec = recall_score(y_true, y_hat, zero_division=0)\n    cm = confusion_matrix(y_true, y_hat)\n    print(f'Threshold = {thr}')\n    print('Accuracy  =', acc)\n    print('Precision =', prec)\n    print('Recall    =', rec)\n    print('Confusion matrix:\\n', cm)\n    print()\n```\n","metadata":{}},{"id":"47bc06a6","cell_type":"markdown","source":"> #### Exercice  \n> On considère maintenant la **classification binaire** sur le dataset `cancer_mini`.\n> L’objectif est d’entraîner un **neurone logistique PyTorch** et d’évaluer ses performances.\n>\n> 1. Charger `cancer_mini`, construire :\n>    - $X$ matrice des variables explicatives\n>    - $y$ vecteur binaire de labels dans $\\{0,1\\}$\n>\n> 2. Séparer en train / test avec :\n>    - `test_size=0.2`\n>    - `random_state=42`\n>    - `stratify=y`\n>\n> 3. Normaliser les entrées avec `StandardScaler` :\n>    - `fit_transform` sur `X_train`\n>    - `transform` sur `X_test`\n>\n> 4. Convertir en tenseurs PyTorch `float32` et mettre $y$ au format `(n,1)` :\n>    ```python\n>    X_train = torch.tensor(X_train, dtype=torch.float32)\n>    y_train = torch.tensor(y_train, dtype=torch.float32).view(-1, 1)\n>    X_test  = torch.tensor(X_test, dtype=torch.float32)\n>    y_test  = torch.tensor(y_test, dtype=torch.float32).view(-1, 1)\n>    ```\n>\n> 5. Définir un modèle PyTorch correspondant à un neurone logistique\n>    en utilisant un seul `nn.Linear(m,1)` et la loss `nn.BCEWithLogitsLoss`.\n>\n> 6. Entraîner le modèle par descente de gradient pendant un nombre d’epochs fixé.\n>    Stocker la loss dans une liste `loss_history` et tracer la courbe de convergence.\n>\n> 7. Évaluer sur le jeu de test :\n>    - calculer les logits $z = Xw + b$\n>    - convertir en probabilité $u=\\sigma(z)$ avec `torch.sigmoid`\n>    - prédire une classe avec un seuil $0.5$\n>\n> 8. Calculer au moins :\n>    - l’accuracy\n>    - la matrice de confusion\n>    - éventuellement précision et rappel\n","metadata":{"papermill":{"duration":0.008515,"end_time":"2026-02-23T17:33:56.763926","exception":false,"start_time":"2026-02-23T17:33:56.755411","status":"completed"},"tags":[]}},{"id":"ce82697e-0479-49e0-a9f6-4bb634fa091a","cell_type":"code","source":"df = pd.read_csv('/kaggle/input/datasets/pyim59/mini-datasets/iris_mini.csv')\ndf.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-14T00:17:04.704358Z","iopub.execute_input":"2026-05-14T00:17:04.704747Z","iopub.status.idle":"2026-05-14T00:17:04.742662Z","shell.execute_reply.started":"2026-05-14T00:17:04.704716Z","shell.execute_reply":"2026-05-14T00:17:04.741730Z"}},"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"   petal_length  petal_width  species\n0           1.4          0.2        0\n1           1.4          0.2        0\n2           1.3          0.2        0\n3           1.5          0.2        0\n4           1.4          0.2        0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":6},{"id":"71473094-6f27-47d9-860d-637d229bbf06","cell_type":"code","source":"import seaborn as sns\n\nsns.pairplot(df, hue='species')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-14T00:17:49.810713Z","iopub.execute_input":"2026-05-14T00:17:49.811146Z","iopub.status.idle":"2026-05-14T00:17:50.944146Z","shell.execute_reply.started":"2026-05-14T00:17:49.811105Z","shell.execute_reply":"2026-05-14T00:17:50.943162Z"}},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"<seaborn.axisgrid.PairGrid at 0x79207c7c83b0>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 562.736x500 with 6 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\n"},"metadata":{}}],"execution_count":9},{"id":"24da73ea","cell_type":"markdown","source":"## Réseaux multicouches en PyTorch (classification)\n\nEn PyTorch, un réseau multicouches pour la classification\nest construit exactement comme en régression,\nen empilant des couches `nn.Linear` séparées par des **activations non linéaires**.\n\nConceptuellement, on calcule :\n$$\nX ;\\xrightarrow{;\\text{Linear + ReLU};}; h_1\n;\\xrightarrow{;\\text{Linear + ReLU};}; h_2\n;\\xrightarrow{;\\text{Linear};}; z\n$$\n\nLa sortie $z$ est un **logit**.\nLa probabilité est obtenue via :\n$$\nP(y=1\\mid X) = \\sigma(z)\n$$\n\n\n\n### Implémentation PyTorch typique\n\n```python\nmodel = nn.Sequential(\n    nn.Linear(m, 64),\n    nn.ReLU(),\n    nn.Linear(64, 32),\n    nn.ReLU(),\n    nn.Linear(32, 1)   # logits\n)\n```\n\n* chaque `nn.Linear` correspond à une couche de neurones\n* `ReLU` introduit la non-linéarité\n* la dernière couche **n’a pas d’activation**\n\nLa conversion en probabilité est faite :\n\n* soit explicitement avec `torch.sigmoid`\n* soit implicitement via `nn.BCEWithLogitsLoss`\n\n\n\n### Fonction de coût\n\nEn classification binaire, on utilise :\n\n```python\ncriterion = nn.BCEWithLogitsLoss()\n```\n\nCette loss :\n\n* applique la sigmoïde en interne\n* calcule la cross-entropy Bernoulli\n* est plus stable numériquement qu’un `Sigmoid + BCELoss`\n\n\n\n### Lien avec ce qui précède\n\n* la boucle d’entraînement est identique au cas linéaire\n* seules changent :\n\n  * la **structure du modèle**\n  * la **fonction de perte**\n* le calcul des gradients et la rétropropagation\n  sont entièrement gérés par PyTorch\n","metadata":{"papermill":{"duration":0.008705,"end_time":"2026-02-23T17:34:03.363259","exception":false,"start_time":"2026-02-23T17:34:03.354554","status":"completed"},"tags":[]}},{"id":"d28d664e-210d-42db-ab75-9c3d065edc33","cell_type":"markdown","source":"### 💡 Syntaxe à reprendre\n\n_Repère syntaxique pour aborder l'exercice ci-dessous._\n\n```python\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score\nimport torch\nimport torch.nn as nn\nif y.dtype.kind in {'U', 'S', 'O'}:\n    y = (y == 'M').astype(np.int64)\nelse:\n    y = y.astype(np.int64)\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train).astype(np.float32)\nX_test = scaler.transform(X_test).astype(np.float32)\nX_train_t = torch.tensor(X_train, dtype=torch.float32)\ny_train_t = torch.tensor(y_train, dtype=torch.float32).view(-1, 1)\nX_test_t = torch.tensor(X_test, dtype=torch.float32)\ny_test_t = torch.tensor(y_test, dtype=torch.float32).view(-1, 1)\ntorch.manual_seed(0)\nm = X_train_t.shape[1]\nmodel = nn.Sequential(nn.Linear(m, 64), nn.ReLU(), nn.Linear(64, 32), nn.ReLU(), nn.Linear(32, 1))\ncriterion = nn.BCEWithLogitsLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=0.1)\nepochs = 200\nloss_history = []\nmodel.train()\nfor _ in range(epochs):\n    optimizer.zero_grad()\n    logits = model(X_train_t)\n    loss = criterion(logits, y_train_t)\n    loss.backward()\n    optimizer.step()\n    loss_history.append(loss.item())\nmodel.eval()\nwith torch.no_grad():\n    logits_test = model(X_test_t)\n    proba_test = torch.sigmoid(logits_test)\ny_true = y_test_t.cpu().numpy().reshape(-1)\nfor thr in [0.3, 0.5, 0.7]:\n    y_hat = (proba_test >= thr).int().cpu().numpy().reshape(-1)\n    acc = accuracy_score(y_true, y_hat)\n    prec = precision_score(y_true, y_hat, zero_division=0)\n    rec = recall_score(y_true, y_hat, zero_division=0)\n    cm = confusion_matrix(y_true, y_hat)\n    print(f'Threshold = {thr}')\n    print('Accuracy  =', acc)\n    print('Precision =', prec)\n    print('Recall    =', rec)\n    print('Confusion matrix:\\n', cm)\n    print()\n```\n","metadata":{}},{"id":"c706ac44","cell_type":"markdown","source":"> #### Exercice  \n>\n> Tester plusieurs couches pour cancer_mini","metadata":{"papermill":{"duration":0.008772,"end_time":"2026-02-23T17:34:03.380888","exception":false,"start_time":"2026-02-23T17:34:03.372116","status":"completed"},"tags":[]}},{"id":"161e8683-4dc8-45c9-b590-871e8921d40d","cell_type":"code","source":"tab = np.random.uniform(size=3)\ntab","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-14T00:26:23.880175Z","iopub.execute_input":"2026-05-14T00:26:23.881078Z","iopub.status.idle":"2026-05-14T00:26:23.886759Z","shell.execute_reply.started":"2026-05-14T00:26:23.881020Z","shell.execute_reply":"2026-05-14T00:26:23.885968Z"}},"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"array([0.21240762, 0.96828401, 0.66787555])"},"metadata":{}}],"execution_count":16},{"id":"e7823de8-d93b-40ca-b8b6-27b4a138cf59","cell_type":"code","source":"np.argmax(tab)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-14T00:26:25.713184Z","iopub.execute_input":"2026-05-14T00:26:25.713516Z","iopub.status.idle":"2026-05-14T00:26:25.719958Z","shell.execute_reply.started":"2026-05-14T00:26:25.713489Z","shell.execute_reply":"2026-05-14T00:26:25.718845Z"}},"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"np.int64(1)"},"metadata":{}}],"execution_count":17},{"id":"15d22edc","cell_type":"markdown","source":"## Classification multiclasse en PyTorch\n\nEn classification multiclasse, la variable cible prend plus de deux valeurs :\n$$\ny \\in {0,1,\\dots,C-1}\n$$\n\nChaque observation appartient à **une seule classe parmi $C$**.\n\nL’objectif est d’estimer, pour chaque entrée $X$, la distribution de probabilité :\n$$\nP(y=c \\mid X), \\quad c=0,\\dots,C-1\n$$\n\n\n### Sortie du réseau : logits multiclasse\n\nLe réseau de neurones calcule d’abord un vecteur de scores réels :\n$$\nz = (z_0, z_1, \\dots, z_{C-1})\n$$\n\nEn PyTorch, cela correspond à une dernière couche :\n\n```python\nnn.Linear(h, C)\n```\n\nLa sortie du modèle a donc la forme :\n$$\n\\text{logits} \\in \\mathbb{R}^{n \\times C}\n$$\n\nCes valeurs sont appelées **logits** :\n\n* elles ne sont pas bornées\n* elles ne sont pas encore des probabilités\n* une composante correspond à chaque classe\n\n\n### Cible et compatibilité des dimensions\n\nLa cible $y$ **n’a pas la même forme que la sortie du réseau**.\n\nEn PyTorch avec `nn.CrossEntropyLoss` :\n\n* $y$ est un vecteur de taille $(n,)$\n* $y_i \\in {0,\\dots,C-1}$\n* $y$ est de type `torch.long`\n\nOn **ne** code **pas** $y$ en one-hot.\n\nPour un exemple $i$ :\n\n* le réseau produit un vecteur $(z_{i,0},\\dots,z_{i,C-1})$\n* la cible $y_i$ indique **quelle composante correspond à la bonne classe**\n\n\n\n### Softmax et cross-entropy\n\nPour transformer les logits en probabilités, on utilise la **softmax** :\n$$\nP(y=c \\mid X) = \\frac{e^{z_c}}{\\sum_{k=0}^{C-1} e^{z_k}}\n$$\n\nEn pratique, on n’applique **pas** la softmax dans le modèle.\n\nOn utilise directement :\n\n```python\ncriterion = nn.CrossEntropyLoss()\n```\n\nCette loss applique en interne :\n\n* une `LogSoftmax`\n* puis la cross-entropy\n\nPour chaque observation, la loss est :\n$$\n\\mathcal{L}_i = -\\log P(y=y_i \\mid X_i)\n$$\n\nAutrement dit, elle sélectionne automatiquement\nla probabilité associée à la classe correcte $y_i$.\n\n\n\n### Modèle multiclasses typique en PyTorch\n\n```python\nmodel = nn.Sequential(\n    nn.Linear(m, 64),\n    nn.ReLU(),\n    nn.Linear(64, 32),\n    nn.ReLU(),\n    nn.Linear(32, C)   # logits multiclasse\n)\n```\n\nLa dernière couche a $C$ neurones, un par classe.\n\n\n\n### Prédiction de classe\n\nAprès l’entraînement, on prédit la classe par :\n\n```python\nwith torch.no_grad():\n    logits = model(X_test)\n    y_hat = torch.argmax(logits, dim=1)\n```\n\nCela correspond à :\n$$\n\\hat{y} = \\arg\\max_c z_c\n$$\n\n","metadata":{"papermill":{"duration":0.009322,"end_time":"2026-02-23T17:34:03.795564","exception":false,"start_time":"2026-02-23T17:34:03.786242","status":"completed"},"tags":[]}},{"id":"b14273a6-0796-45ba-8e49-79dc4d1def9c","cell_type":"markdown","source":"### 💡 Syntaxe à reprendre\n\n_Repère syntaxique pour aborder l'exercice ci-dessous._\n\n```python\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder\nfrom sklearn.metrics import accuracy_score, confusion_matrix\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import TensorDataset, DataLoader\ndf['sex'] = df['sex'].fillna('male')\ndf['sex'] = df['sex'].map({'male': 0, 'female': 1})\ndf['species'] = df['species'].map({'Adelie': 0, 'Gentoo': 1, 'Chinstrap': 2})\nC = 3\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train).astype(np.float32)\nX_test = scaler.transform(X_test).astype(np.float32)\nX_train_t = torch.tensor(X_train, dtype=torch.float32)\ny_train_t = torch.tensor(y_train, dtype=torch.long)\nX_test_t = torch.tensor(X_test, dtype=torch.float32)\ny_test_t = torch.tensor(y_test, dtype=torch.long)\nbatch_size = 32\ntrain_loader = DataLoader(TensorDataset(X_train_t, y_train_t), batch_size=batch_size, shuffle=True)\ntorch.manual_seed(0)\nm = X_train_t.shape[1]\nuse_mlp = True\nif use_mlp:\n    model = nn.Sequential(nn.Linear(m, 64), nn.ReLU(), nn.Linear(64, 32), nn.ReLU(), nn.Linear(32, C))\nelse:\n    model = nn.Linear(m, C)\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=0.001)\nepochs = 300\nloss_history = []\nmodel.train()\nfor _ in range(epochs):\n    epoch_loss = 0.0\n    nb_batches = 0\n    for (Xb, yb) in train_loader:\n        optimizer.zero_grad()\n        logits = model(Xb)\n        loss = criterion(logits, yb)\n        loss.backward()\n        optimizer.step()\n        epoch_loss += loss.item()\n        nb_batches += 1\n    loss_history.append(epoch_loss / nb_batches)\nmodel.eval()\nwith torch.no_grad():\n    logits_test = model(X_test_t)\n    y_hat = torch.argmax(logits_test, dim=1)\ny_hat_np = y_hat.cpu().numpy()\ny_test_np = y_test_t.cpu().numpy()\nacc = accuracy_score(y_test_np, y_hat_np)\ncm = confusion_matrix(y_test_np, y_hat_np)\n```\n","metadata":{}},{"id":"5695e2d1","cell_type":"markdown","source":"> #### Exercice — Classification multiclasse avec PyTorch (`penguins_mini`)\n>\n> On considère maintenant le dataset `penguins`, dans lequel la variable cible\n> correspond à **l’espèce de manchot** (classification multiclasse).\n>\n> L’objectif est d’entraîner un **réseau de neurones multicouches PyTorch**\n> pour prédire l’espèce à partir des variables morphologiques.\n>\n> 1. Charger le dataset `penguins_mini` et construire :\n>    - $X$ : matrice des variables explicatives (numériques)\n>    - $y$ : vecteur de labels correspondant aux espèces\n>\n> 2. Encoder la variable cible sous forme d’indices de classes\n>    $y \\in \\{0,1,\\dots,C-1\\}$  \n>    Indication : utiliser `LabelEncoder` de `sklearn`.\n>\n> 3. Séparer les données en train / test avec :\n>    - `test_size=0.2`\n>    - `random_state=42`\n>    - `stratify=y`\n>\n> 4. Normaliser les entrées avec `StandardScaler` :\n>    - `fit_transform` sur `X_train`\n>    - `transform` sur `X_test`\n>\n> 5. Convertir les données en tenseurs PyTorch :\n>    - `X_train`, `X_test` en `torch.float32`\n>    - `y_train`, `y_test` en `torch.long` (format requis par `CrossEntropyLoss`)\n>\n> 6. Définir un modèle PyTorch multiclasses, par exemple :\n>    - une ou deux couches cachées `Linear + ReLU`\n>    - une couche de sortie `nn.Linear(h, C)`  \n>    où $C$ est le nombre de classes.\n>\n> 7. Utiliser comme fonction de coût :\n>    ```python\n>    criterion = nn.CrossEntropyLoss()\n>    ```\n>    Rappel : **ne pas** appliquer de softmax dans le modèle.\n>\n> 8. Entraîner le réseau pendant un nombre d’epochs fixé\n>    (batch ou minibatch), et stocker la loss dans `loss_history`.\n>\n> 9. Évaluer le modèle sur le jeu de test :\n>    - prédire les logits\n>    - obtenir la classe prédite avec `argmax`\n>    - calculer l’accuracy\n>    - afficher la matrice de confusion","metadata":{"papermill":{"duration":0.009101,"end_time":"2026-02-23T17:34:03.813948","exception":false,"start_time":"2026-02-23T17:34:03.804847","status":"completed"},"tags":[]}},{"id":"7144ee16-38c1-414a-8a0a-f5f5a26f25f9","cell_type":"code","source":"df = pd.read_csv('/kaggle/input/datasets/pyim59/images-in-csv-datasets/mnist_small.csv')\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"f41dc3d9-bd02-4217-afb1-7b649fd7c2bb","cell_type":"code","source":"df = pd.read_csv('/kaggle/input/datasets/pyim59/images-in-csv-datasets/mnist_small.csv')\n\nX = df.drop(columns='label').to_numpy()\ny = df['label'].to_numpy()\n\nX = X/255\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\n\nX_train_t = torch.tensor(X_train, dtype=torch.float32)\ny_train_t = torch.tensor(y_train, dtype=torch.long)\n\nX_test_t = torch.tensor(X_test, dtype=torch.float32)\ny_test_t = torch.tensor(y_test, dtype=torch.long)\n\nprint(X_train.shape)\nprint(y_train.shape)\n\ntrain_dataset = TensorDataset(X_train_t, y_train_t)\n\nbatch_size = 32\n\ntrain_loader = DataLoader(\n    train_dataset,\n    batch_size=batch_size,\n    shuffle=True\n)\n\nmodel = nn.Sequential(\n    nn.Linear(X_train_t.shape[1], 32),\n    nn.ReLU(),\n    nn.Linear(32, 16),\n    nn.ReLU(),\n    nn.Linear(16, 10)\n    )\n\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n\nepochs = 30\nloss_history = []\nfor epoch in range(epochs):\n    nb=0\n    epoch_loss=0\n    for Xb, yb in train_loader:\n        optimizer.zero_grad()\n        u = model(Xb)\n        loss = criterion(u, yb)\n        loss.backward()\n        optimizer.step()\n        epoch_loss += loss.item()\n        nb += 1\n    loss_history.append(epoch_loss/nb)\n    print(f\"Epoch n° {epoch} ; loss : {epoch_loss/nb}\")\n\nplt.plot(loss_history)\nplt.show()\n\nmodel.eval()\nwith torch.no_grad():\n    logits = model(X_test_t)\n    proba = torch.softmax(logits, dim=1)\n    y_hat = torch.argmax(proba, dim=1)\n    \n\ny_hat = y_hat.cpu().numpy().reshape(-1)\n\naccuracy_score(y_test, y_hat)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-14T00:49:39.668652Z","iopub.execute_input":"2026-05-14T00:49:39.668987Z","iopub.status.idle":"2026-05-14T00:49:47.997580Z","shell.execute_reply.started":"2026-05-14T00:49:39.668957Z","shell.execute_reply":"2026-05-14T00:49:47.996773Z"}},"outputs":[{"name":"stdout","text":"(8000, 784)\n(8000,)\nEpoch n° 0 ; loss : 1.1060710649490357\nEpoch n° 1 ; loss : 0.41409162947535516\nEpoch n° 2 ; loss : 0.3306646644771099\nEpoch n° 3 ; loss : 0.26638228215277193\nEpoch n° 4 ; loss : 0.23227354818582535\nEpoch n° 5 ; loss : 0.19519098871946336\nEpoch n° 6 ; loss : 0.16198920932784677\nEpoch n° 7 ; loss : 0.1396678661517799\nEpoch n° 8 ; loss : 0.11785509077459574\nEpoch n° 9 ; loss : 0.10243260344490408\nEpoch n° 10 ; loss : 0.091633170645684\nEpoch n° 11 ; loss : 0.07898095662705601\nEpoch n° 12 ; loss : 0.06958943640254438\nEpoch n° 13 ; loss : 0.0636892796298489\nEpoch n° 14 ; loss : 0.04983181860810146\nEpoch n° 15 ; loss : 0.0412267120142933\nEpoch n° 16 ; loss : 0.0317433624737896\nEpoch n° 17 ; loss : 0.02986732005979866\nEpoch n° 18 ; loss : 0.023042030696757136\nEpoch n° 19 ; loss : 0.01831699238275178\nEpoch n° 20 ; loss : 0.01982229492254555\nEpoch n° 21 ; loss : 0.013381378218065947\nEpoch n° 22 ; loss : 0.01021671708940994\nEpoch n° 23 ; loss : 0.00799814644480648\nEpoch n° 24 ; loss : 0.0066945186557713894\nEpoch n° 25 ; loss : 0.006134124599600909\nEpoch n° 26 ; loss : 0.005214725899626501\nEpoch n° 27 ; loss : 0.0047042955945362334\nEpoch n° 28 ; loss : 0.004048931080265902\nEpoch n° 29 ; loss : 0.003693564271379728\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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\n"},"metadata":{}},{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"0.949"},"metadata":{}}],"execution_count":24},{"id":"0a679bc6-59c5-427a-92e1-414cbfdceeeb","cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}