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main-website/ruty/WhykIA/Mon premier neurone.ipynb
T
2024-03-11 00:54:46 +01:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1048,
"metadata": {},
"outputs": [],
"source": [
"#Neurone artificiel\n",
"#importation\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.metrics import *\n",
"from sklearn.datasets import make_blobs"
]
},
{
"cell_type": "code",
"execution_count": 1049,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dimension de X: (100, 2)\n",
"dimension de y: (100, 1)\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Génération du dataset\n",
"X, y=make_blobs(n_samples= 100, n_features=2, centers=2, random_state=0)\n",
"y = y.reshape((y.shape[0], 1))\n",
"\n",
"#Affichage des dimension du dataset\n",
"print(\"dimension de X:\", X.shape)\n",
"print(\"dimension de y:\", y.shape)\n",
"\n",
"#Mise en graphique du dataset\n",
"plt.scatter(X[:,0], X[:,1], c=y, cmap=\"summer\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 1050,
"metadata": {},
"outputs": [],
"source": [
"#Création de la fonction d'initialisation de la matrice \"W\" et de l'entier \"b\"\n",
"def Initialisation(X):\n",
" W = np.random.randn(X.shape[1], 1)\n",
" b = np.random.randn(1)\n",
" return (W, b)"
]
},
{
"cell_type": "code",
"execution_count": 1051,
"metadata": {},
"outputs": [],
"source": [
"#initialisation de la fonction de création du model linéaire\n",
"def Model(X, W, b):\n",
" Z = X.dot(W) + b\n",
" A = 1 / (1 + np.exp(-Z))\n",
" return A"
]
},
{
"cell_type": "code",
"execution_count": 1052,
"metadata": {},
"outputs": [],
"source": [
"#initialisation de la fonction cout (logloss) afin de determiner la marge d'erreur de notre model linéaire\n",
"def Log_loss(A, y):\n",
" return 1 / len(y) * np.sum(-y * np.log(A) - (1 - y) * np.log(1 - A))"
]
},
{
"cell_type": "code",
"execution_count": 1053,
"metadata": {},
"outputs": [],
"source": [
"#initialisation des valeurs des gradient de W et b\n",
"def Gradients(A, X, y):\n",
" dW = 1 / len(y) * np.dot(X.T, A - y)\n",
" db = 1 /len(y) * np.sum(A - y)\n",
" return (dW, db)"
]
},
{
"cell_type": "code",
"execution_count": 1054,
"metadata": {},
"outputs": [],
"source": [
"#initialisation de la fonction d'update servant a mettre a jour les valeur de W en fonction de dW et de b en fonction de db afin de miniùmiser la marge d'erreur\n",
"def Update(W, b, dW, db, LearningRate):\n",
" W = W - LearningRate * dW\n",
" b = b - LearningRate * db\n",
" return (W, b)"
]
},
{
"cell_type": "code",
"execution_count": 1055,
"metadata": {},
"outputs": [],
"source": [
"#initialisation de la fonction permettant de prédire à quelle classe appartiendrat un nouveaux cas selon \"x\" variable (2)\n",
"def Predict(X, W, b):\n",
" A = Model(X, W, b)\n",
" print(A)\n",
" return A >= 0.5"
]
},
{
"cell_type": "code",
"execution_count": 1056,
"metadata": {},
"outputs": [],
"source": [
"#initialisation de la fonction rassemblant toutes les autre pour former un neurone artificiel\n",
"def ArtificialNeuron(X, y, LearningRate = 0.1, NbIter = 100):\n",
" #initialisation W, b\n",
" W, b = Initialisation(X)\n",
"\n",
" #création d'une liste de suivit de la marge d'erreur du neurone en fonction du nombre d'itération\n",
" Loss = []\n",
"\n",
" #Aprentissage: Répétition de la descente de grdient pour minimiser la marge d'erreure \n",
" for i in range(NbIter):\n",
" A=Model(X, W, b)\n",
" Loss.append(Log_loss(A, y))\n",
" dW, db = Gradients(A, X, y)\n",
" W, b = Update(W, b, dW, db, LearningRate)\n",
"\n",
" #prédiction du dataset selon notre neurone après aprentissage\n",
" y_pred = Predict(X, W, b)\n",
"\n",
" #comparaison avec les vrai réponse\n",
" print(accuracy_score(y, y_pred))\n",
" \n",
" \n",
" \n",
" #affichage de la liste de suivit de la marge d'erreur\n",
" plt.plot(Loss)\n",
" plt.show()\n",
"\n",
" #renvoi des paramètre finaux après aprentissage\n",
" return (W, b)"
]
},
{
"cell_type": "code",
"execution_count": 1057,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[9.79688138e-01]\n",
" [6.22791607e-01]\n",
" [5.18082049e-03]\n",
" [8.88739676e-02]\n",
" [9.64849669e-01]\n",
" [3.30771238e-01]\n",
" [7.61475671e-02]\n",
" [9.67544074e-01]\n",
" [4.57105461e-02]\n",
" [7.73783334e-01]\n",
" [3.30105495e-02]\n",
" [8.15190806e-01]\n",
" [3.68744341e-02]\n",
" [1.60359382e-02]\n",
" [6.75235745e-01]\n",
" [9.89457701e-01]\n",
" [9.88236894e-01]\n",
" [3.19214556e-02]\n",
" [5.67615062e-01]\n",
" [5.53629690e-01]\n",
" [5.60677414e-02]\n",
" [3.78090404e-02]\n",
" [3.52313676e-01]\n",
" [5.30181359e-03]\n",
" [9.39871220e-01]\n",
" [3.78858770e-02]\n",
" [8.07734208e-01]\n",
" [1.39690586e-02]\n",
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},
{
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#execution de la fonction d'entrainement du neurone artificiel\n",
"W, b = ArtificialNeuron(X, y)"
]
},
{
"cell_type": "code",
"execution_count": 1058,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/q1/84vf8ynd79g502ds_gw52x580000gp/T/ipykernel_80509/3911570722.py:12: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
" plt.scatter(new_plant[0], new_plant[1], c='red', cmap=\"summer\")\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.83940979]\n"
]
},
{
"data": {
"text/plain": [
"array([ True])"
]
},
"execution_count": 1058,
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],
"source": [
"#création d'une nouvelle plante\n",
"new_plant = np.array([2,1])\n",
"\n",
"#calcul de la frontière de déscision\n",
"x0 = np.linspace(-2, 5, 100)\n",
"x1 = ( -W[0] * x0 -b) / W[1]\n",
"\n",
"#affichage du dataset de base\n",
"plt.scatter(X[:,0], X[:,1], c=y, cmap=\"summer\")\n",
"\n",
"#ajout de la nouvelle plante en rouge dans le dataset\n",
"plt.scatter(new_plant[0], new_plant[1], c='red', cmap=\"summer\")\n",
"\n",
"\n",
"#affichage de la frontière de déscision\n",
"plt.plot(x0, x1, 'b', lw=2)\n",
"plt.show()\n",
"\n",
"#prédiction sur le type de la nouvelle plante\n",
"Predict(new_plant, W, b)\n",
"\n"
]
}
],
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