Added comments
This commit is contained in:
parent
c58aa1ec51
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3 changed files with 206 additions and 7 deletions
77
ann.ipynb
77
ann.ipynb
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@ -1,5 +1,13 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "4d162f18",
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"metadata": {},
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"source": [
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"## Import du dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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@ -23,6 +31,14 @@
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"from sklearn.neural_network import MLPClassifier"
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]
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},
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{
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"cell_type": "markdown",
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"id": "95e0ce45",
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"metadata": {},
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"source": [
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"## Echantillonnage du dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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@ -34,10 +50,15 @@
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"data = mnist.data.values[echantillon]\n",
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"target = mnist.target[echantillon]\n",
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"trainSize = 17500/25000\n",
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"xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=trainSize)\n",
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"#trainSize = 49000/len(data)\n",
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"#xtrain, xtest, ytrain, ytest = train_test_split(data, target, \n",
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"#train_size=trainSize)"
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"xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=trainSize)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f35a2a10",
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"metadata": {},
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"source": [
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"## Premier entraînement et scores"
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]
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},
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{
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@ -110,6 +131,14 @@
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"precision_score(target.values, clf.predict(data), average='micro')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d5d7d4c1",
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"metadata": {},
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"source": [
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"## Nombre de couches cachées optimal"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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@ -149,6 +178,14 @@
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e4b13f21",
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"metadata": {},
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"source": [
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"## Nombre de couches cachées et de neurones par couche optimaux"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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@ -231,6 +268,14 @@
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "11adbe03",
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"metadata": {},
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"source": [
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"## Algorithme d'optimisation optimal"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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@ -370,6 +415,14 @@
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cf7095a5",
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"metadata": {},
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"source": [
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"## Fonction d'activation optimale"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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@ -517,6 +570,14 @@
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "569f6393",
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"metadata": {},
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"source": [
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"## Valeur optimale d'alpha"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 62,
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@ -631,6 +692,14 @@
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c46d042a",
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"metadata": {},
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"source": [
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"## Matrice de confusion"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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92
knn.ipynb
92
knn.ipynb
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@ -1,9 +1,17 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "87214e92",
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"metadata": {},
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"source": [
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"## Import du dataset MNIST"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "b4978985",
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"execution_count": null,
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"id": "c2e6fa5e",
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"metadata": {},
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"outputs": [],
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"source": [
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@ -11,6 +19,14 @@
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"mnist = fetch_openml('mnist_784') "
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]
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},
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{
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"cell_type": "markdown",
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"id": "a36d996c",
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"metadata": {},
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"source": [
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"## Manpulation du jeu de données"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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@ -703,6 +719,14 @@
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"print(mnist.target[4])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "73194d9b",
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"metadata": {},
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"source": [
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"## Premier entraînement"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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@ -747,6 +771,14 @@
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"clf.fit(xtrain, ytrain)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d7d80cd2",
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"metadata": {},
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"source": [
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"## Vérification de la prédiction faite avec l'algorithme entraîné"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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@ -781,6 +813,14 @@
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"print(clf.predict([data[4]]))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8eb362f6",
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"metadata": {},
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"source": [
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"## Scores obtenus"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 71,
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@ -805,6 +845,14 @@
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"print(clf.score(xtrain, ytrain))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aac45aac",
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"metadata": {},
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"source": [
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"## Nombre de voisins optimal"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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@ -860,6 +908,14 @@
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "411f6841",
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"metadata": {},
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"source": [
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"## Séparation optimale entre l'entraînement et le test "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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@ -924,6 +980,14 @@
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b3563ad5",
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"metadata": {},
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"source": [
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"## Echantillonnage optimal du dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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@ -985,6 +1049,14 @@
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e387a386",
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"metadata": {},
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"source": [
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"## Distance optimale"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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@ -1055,6 +1127,14 @@
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c863ff41",
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"metadata": {},
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"source": [
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"## Meilleure valeur de n_jobs "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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@ -1129,6 +1209,14 @@
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"print(f'Temps d\\'exécution : {elapsed:.4}ms')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1a26da41",
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"metadata": {},
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"source": [
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"## Matrice de confusion"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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44
svm.ipynb
44
svm.ipynb
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@ -1,5 +1,13 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "557db042",
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"metadata": {},
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"source": [
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"## Import du dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"mnist = fetch_openml('mnist_784')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2295fc5b",
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"metadata": {},
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"source": [
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"## Premier entraînement et scores"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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@ -72,6 +88,14 @@
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"print(classifier.score(xtest, ytest))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "32caafa6",
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"metadata": {},
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"source": [
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"## Meilleur kernel à utiliser"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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@ -196,6 +220,8 @@
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}
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],
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"source": [
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"## Il est nécessaire de donner à l'algorithme en entrée une matrice carrée. \n",
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"## Ne sachant pas comment faire nous avons décidé de ne pas prendre en compte ce type. \n",
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"start = time.time()\n",
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"clsvm = SVC(kernel='precomputed')\n",
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"clsvm.fit(xtrain, ytrain) \n",
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@ -243,6 +269,14 @@
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "218f2c18",
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"metadata": {},
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"source": [
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"## Valeur optimale du paramètre C"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2ddd41c7",
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"metadata": {},
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"source": [
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"## Matrice de confusion"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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{
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"cell_type": "code",
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"execution_count": 58,
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"id": "14cd493b",
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"id": "7ea957d9",
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"metadata": {},
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"outputs": [
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{
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