implementing the MLP algorithm

TP1/knn_vrai.py

@@ -43,6 +43,7 @@ score = clf.score(xtest, ytest)
     
 print("Prédiction : {}, Valeur : {}, Score : {}".format(prediction[4], ytest[4], score))
 
+
 #*********Taux d'erreur sur les données d'apprentissage *******
 
 xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.8, test_size=0.2)

TP2/MLP.py

@@ -0,0 +1,198 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Dec 15 18:26:31 2021
+
+@author: chouiya
+"""
+
+
+#********import***************
+from sklearn.datasets import fetch_openml
+from sklearn import datasets 
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+import numpy as np
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import precision_score
+from sklearn.metrics import zero_one_loss
+import time
+#*******************MLP pour une seule couche de 50 neurons*****************
+mnist = fetch_openml('mnist_784',as_frame=False)
+
+data=mnist.data
+target=mnist.target
+xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.7)
+
+clf = MLPClassifier(hidden_layer_sizes=(50))
+
+clf.fit(xtrain, ytrain)
+prediction = clf.predict(xtest)
+score = clf.score(xtest, ytest)
+precision = precision_score(ytest, prediction, average ='micro')
+loss1_0 = zero_one_loss(ytest, prediction)
+
+# Print & Test :------
+print("This MLP model, with one layer of 50, has a score of : ", score*100, "%.")
+print(" 4 th image : Prediction ",prediction[3], "Vs  Reel : ", ytest[3])
+
+# Showing the 4th image:
+images = xtest.reshape((-1, 28, 28))
+plt.imshow(images[3],cmap=plt.cm.gray_r,interpolation="nearest")
+plt.show()
+
+# Metrics :
+print ("This MLP model has a precision of :", precision*100, "%.")
+print ("This MLP model has a zero-one_loss of :",loss1_0*100, "%.")
+
+#*******************Variation de nombre de couche de 2 à 100*******
+hidden_layer =(50,)*100
+
+Scor = []
+Pred= []
+Loss= []
+
+for i in range (100):
+    clf = MLPClassifier(hidden_layer_sizes = hidden_layer[0:i])
+    clf.fit(xtrain, ytrain)
+    prediction = clf.predict(xtest)
+    score = clf.score(xtest, ytest)
+    precision = precision_score(ytest, prediction, average='micro')
+    loss0_1 = zero_one_loss(ytest, prediction)
+
+    Scor.append(score)
+    Pred.append(precision)
+    Loss.append(loss0_1)
+
+    print("For ", i, "hidden layer (s), The score = ", score *100, "%", ", Precision = ", precision*100, "% .." )
+
+
+fig, ax = plt.subplots(3, sharex=True, figsize=(10,10))
+ax[0].plot(range(100), Scor)
+ax[0].set_title('Number of hidden layers from 1 to 99')
+ax[0].set_ylabel('Score')
+ax[1].plot(range(100), Pred)
+ax[1].set_ylabel('Precision')
+ax[2].plot(range(100), Loss)
+ax[2].set_ylabel('Zero-to-one Loss')
+
+#**************les 5 modeles de classification*********
+clf1 = MLPClassifier(hidden_layer_sizes=(300))
+# 2 layers
+clf2 = MLPClassifier(hidden_layer_sizes=(20, 50))
+# 4 layers
+clf4 = MLPClassifier(hidden_layer_sizes=(20,50, 100, 150))
+# 6 layers
+clf6 = MLPClassifier(hidden_layer_sizes=( 20, 50, 150, 100, 50, 10))
+# 8 layers, increase neurals :
+clf8 = MLPClassifier(hidden_layer_sizes=(20, 40, 60, 120, 150, 180, 200, 250))
+
+ClassifierList = ("clf1", "clf2","clf4", "clf6", "clf8")
+
+Score =[]
+Precision = []
+Loss = []
+TimeTraining = []
+TimePrediction = []
+
+def clfs(clf, i):
+
+    #Training :
+    startTrain =time.time()
+    clf.fit(xtrain, ytrain)
+    endTrain = time.time()
+
+    #Prediction :
+    startpred= time.time()
+    predict = clf.predict(xtest)
+    endpred = time.time()
+
+    #Metrics :
+    score = clf.score(xtest,ytest)
+    precision =  precision_score(ytest, predict,  average='micro')
+    loss01 = zero_one_loss(ytest, predict)
+    timetrain = endTrain - startTrain
+    timePred = endpred - startpred
+
+    #Saving results
+    Score.append(score*100)
+    Precision.append(precision*100)
+    Loss.append(loss01)
+    TimePrediction.append(timePred)
+    TimeTraining.append(timetrain)
+
+    #Prints :
+    print("For the", i," model we have, score = ", score*100, "%, precision =",precision*100, "%." )
+    print("   Training's time = ", timetrain, " and prediction's time = ", timePred, "." )
+
+    
+#***********************plot*******
+fig, ax = plt.subplots(5, sharex=True, figsize=(10,10))
+ax[0].scatter(range(5), Score, c='orange')
+ax[0].set_title('The five classifiers with 1,2,4,6,8 hidden layers')
+ax[0].set_ylabel('Score (%)')
+ax[1].scatter(range(5), Precision, c='red')
+ax[1].set_ylabel('Precision (%)')
+ax[2].scatter(range(5), Loss, c='blue')
+ax[2].set_ylabel('Zero-to-one Loss')
+ax[3].scatter(range(5), TimeTraining, c='pink')
+ax[3].set_ylabel('Training Time (s)')
+ax[4].scatter(range(5), TimePrediction, c='purple')
+ax[4].set_ylabel('¨Prediction Time (s)')
+
+plt.show()
+
+#*****Etude de la convergence des algorithmes d'optimisation : adam, sgd, lbfgs***
+
+tab1=['adam','sgd','lbfgs']
+tab2=['relu','tanh','logistic','identity']
+for i in tab1:# solver
+  for j in tab2:
+    #activation function
+    clf = MLPClassifier(hidden_layer_sizes =50,activation=j,solver=i,verbose=False)
+    clf.fit(xtrain, ytrain)
+    prediction = clf.predict(xtest)
+    score = clf.score(xtest, ytest)
+    precision = precision_score(ytest, prediction, average='micro')
+    loss0_1 = zero_one_loss(ytest, prediction)
+    print('the result of the solver',i,'and the activation function',j)
+    Scor.append(score)
+    Pred.append(precision)
+    Loss.append(loss0_1)
+
+
+print('score :',Scor)
+print('prediction',Pred)
+print('loss',Loss)
+
+#**********Variation de alpha**************
+alphas = np.logspace(-5, 3, 5)
+ for i in alphas:
+   
+    clf = MLPClassifier(hidden_layer_sizes =50,activation='relu',solver='adam',alpha=i,verbose=False)
+    clf.fit(xtrain, ytrain)
+    prediction = clf.predict(xtest)
+    score = clf.score(xtest, ytest)
+    precision = precision_score(ytest, prediction, average='micro')
+    loss0_1 = zero_one_loss(ytest, prediction)
+    print('for alpha equal to: ',i)
+    Scor.append(score)
+    Pred.append(precision)
+    Loss.append(loss0_1)
+    
+print('score :',Scor)
+print('prediction',Pred)
+print('loss',Loss)
+#***************
+
+
+
+
+
+
+
+
+
+
+TP2_CNN.py
+Affichage de TP2_CNN.py en cours...