{ "cells": [ { "cell_type": "code", "execution_count": 16, "id": "3eb7a65b", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Classe : 5\n" ] } ], "source": [ "from sklearn.datasets import fetch_openml\n", "import sklearn\n", "import matplotlib.pyplot as plt\n", "mnist = fetch_openml('mnist_784',as_frame=False)\n", "images = mnist.data.reshape((-1, 28, 28))\n", "plt.imshow(images[0],cmap=plt.cm.gray_r,interpolation=\"nearest\")\n", "plt.show()\n", "print(\"Classe : \", mnist.target[0])\n", "\n", "from sklearn import model_selection\n", "from sklearn import neural_network\n", "from sklearn import metrics" ] }, { "cell_type": "code", "execution_count": 23, "id": "3b1a54ef", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Score échantillon de test : 0.9461904761904761\n", "Classe image 4 : 1\n", "Classe prédite image 4 : 1\n", "Précision pour chaque classe : [0.98069307 0.96283925 0.9399428 0.92090657 0.95114943 0.94722671\n", " 0.9645803 0.95956383 0.9084195 0.9247724 ]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/insa/anaconda/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n" ] } ], "source": [ "# xtrain data set d'entraînement et ytrain étiquettes de xtrain\n", "# xtest dataset de prédiction et ytest étiquettes de xtest\n", "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(mnist.data, mnist.target,train_size=0.7)\n", "\n", "#Entraîne le classifier\n", "clf = neural_network.MLPClassifier(random_state=1, max_iter=100, hidden_layer_sizes=(50))\n", "clf.fit(xtrain, ytrain)\n", "\n", "#Prédiction sur le jeu de tests\n", "pred = clf.predict(xtest)\n", "#print(\"Prédiction : \", pred)\n", "# Probabilités des prédictions sur xtest\n", "pred_proba = clf.predict_proba(xtest)\n", "#print(\"Probabilités : \", pred_proba)\n", "# On calcule le score obtenu sur xtest avec les étiquettes ytest\n", "score = clf.score(xtest, ytest)\n", "print(\"Score échantillon de test : \", score)\n", "\n", "#Infos image 4\n", "print(\"Classe image 4 : \", ytest[3])\n", "print(\"Classe prédite image 4 : \", pred[3])\n", "\n", "#Calcul de la précision avec metrics.precision_score\n", "print(\"Précision pour chaque classe : \", metrics.precision_score(ytest, pred,average=None))" ] }, { "cell_type": "code", "execution_count": 26, "id": "6068ca09", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Précision pour chaque classe :\n", " [[1981 2 18 8 6 12 6 0 29 8]\n", " [ 0 2306 9 5 4 3 0 4 12 2]\n", " [ 6 20 1972 31 19 2 16 23 52 3]\n", " [ 1 6 17 1991 0 20 0 14 24 12]\n", " [ 3 8 5 3 1986 0 11 9 6 40]\n", " [ 3 1 1 59 5 1759 20 7 20 17]\n", " [ 7 4 5 0 6 24 1988 0 8 0]\n", " [ 1 14 44 14 5 3 1 2112 9 25]\n", " [ 4 28 24 21 11 21 19 8 1845 50]\n", " [ 14 6 3 30 46 13 0 24 26 1930]]\n" ] } ], "source": [ "print(\"Précision pour chaque classe :\\n\", metrics.confusion_matrix(ytest, pred))" ] }, { "cell_type": "code", "execution_count": null, "id": "5a4a5485", "metadata": {}, "outputs": [], "source": [ "# xtrain data set d'entraînement et ytrain étiquettes de xtrain\n", "# xtest dataset de prédiction et ytest étiquettes de xtest\n", "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(mnist.data, mnist.target,train_size=0.7)\n", "\n", "list_scores = []\n", "\n", "for i in range(1, 101):\n", " #Entraîne le classifier\n", " clf = neural_network.MLPClassifier(random_state=1, max_iter=300, hidden_layer_sizes=(50))\n", " clf.fit(xtrain, ytrain)\n", "\n", " #Prédiction sur le jeu de tests\n", " pred = clf.predict(xtest)\n", " #print(\"Prédiction : \", pred)\n", " # Probabilités des prédictions sur xtest\n", " pred_proba = clf.predict_proba(xtest)\n", " #print(\"Probabilités : \", pred_proba)\n", " # On calcule le score obtenu sur xtest avec les étiquettes ytest\n", " score = clf.score(xtest, ytest)\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }