def exo8():
print("\n\n>>EXERCICE 8 MNIST")
Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest = utils.readMNISTfile()
default_h = 30
maxIter = 1
neuralNetwork = NeuralNetwork(len(Xtrain[0]),
default_h,
utils.getClassCount(ytrain),
K=100)
neuralNetworkEfficient = NeuralNetworkEfficient(
len(Xtrain[0]), default_h, utils.getClassCount(ytrain), K=100)
neuralNetworkEfficient._w1 = neuralNetwork._w1
neuralNetworkEfficient._w2 = neuralNetwork._w2
print("--- Reseau de depart ---")
t1 = datetime.now()
neuralNetwork.train(Xtrain, ytrain, maxIter)
t2 = datetime.now()
delta = t2 - t1
print("Cela a mis : " + str(delta.total_seconds()) + " secondes")
print("--- Reseau optimise ---")
t1 = datetime.now()
neuralNetworkEfficient.train(Xtrain, ytrain, maxIter)
t2 = datetime.now()
delta = t2 - t1
print("Cela a mis : " + str(delta.total_seconds()) + " secondes")
def exo9_10():
print("\n\n>>EXERCICE 9-10")
print("Train Err;Valid Err;Test Err;Avg Cost Train;Avg Cost Valid;Avg Cost Test")
h = 300
wd = 0.0001
maxIter = 500
K = 50
Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest = utils.readMNISTfile()
neuralNetwork = NeuralNetworkEfficient(len(Xtrain[0]), h, utils.getClassCount(ytrain), K, wd)
neuralNetwork.setDataSets(Xtrain, Xvalid, Xtest, ytrain, yvalid, ytest)
neuralNetwork.train(Xtrain, ytrain, maxIter)
epochsData = ""
for d in neuralNetwork.epochData:
epochsData += d+"\n"
f = open('no9.txt', 'w')
f.write(epochsData)
f.close()
x = range(1, maxIter+1, 10)
title = "Taux d'erreur - "+str(maxIter)+" epoques"
name = str(h) + "_" + str(wd)+ "_" + str(h)+ "_" + "taux_erreur"
utils.plotCourbeApprentissage(neuralNetwork.trainError, neuralNetwork.validError, neuralNetwork.testError, x, title, name)
title = "Cout moyen - "+str(maxIter)+" epoques"
name = str(h) + "_" + str(wd)+ "_" + str(h)+ "_" +"cout_moyen"
utils.plotCourbeApprentissage(neuralNetwork.trainSumL, neuralNetwork.validSumL, neuralNetwork.testSumL, x, title, name)
def exo8():
print("\n\n>>EXERCICE 8 MNIST")
Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest = utils.readMNISTfile()
default_h = 30
maxIter = 1
neuralNetwork = NeuralNetwork(len(Xtrain[0]), default_h, utils.getClassCount(ytrain),K=100)
neuralNetworkEfficient = NeuralNetworkEfficient(len(Xtrain[0]), default_h, utils.getClassCount(ytrain),K=100)
neuralNetworkEfficient._w1 = neuralNetwork._w1
neuralNetworkEfficient._w2 = neuralNetwork._w2
print("--- Reseau de depart ---")
t1 = datetime.now()
neuralNetwork.train(Xtrain, ytrain, maxIter)
t2 = datetime.now()
delta = t2 - t1
print("Cela a mis : " + str(delta.total_seconds()) + " secondes")
print("--- Reseau optimise ---")
t1 = datetime.now()
neuralNetworkEfficient.train(Xtrain, ytrain, maxIter)
t2 = datetime.now()
delta = t2 - t1
print("Cela a mis : " + str(delta.total_seconds()) + " secondes")
def exo9_10():
print("\n\n>>EXERCICE 9-10")
print(
"Train Err;Valid Err;Test Err;Avg Cost Train;Avg Cost Valid;Avg Cost Test"
)
h = 300
wd = 0.0001
maxIter = 500
K = 50
Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest = utils.readMNISTfile()
neuralNetwork = NeuralNetworkEfficient(len(Xtrain[0]), h,
utils.getClassCount(ytrain), K, wd)
neuralNetwork.setDataSets(Xtrain, Xvalid, Xtest, ytrain, yvalid, ytest)
neuralNetwork.train(Xtrain, ytrain, maxIter)
epochsData = ""
for d in neuralNetwork.epochData:
epochsData += d + "\n"
f = open('no9.txt', 'w')
f.write(epochsData)
f.close()
x = range(1, maxIter + 1, 10)
title = "Taux d'erreur - " + str(maxIter) + " epoques"
name = str(h) + "_" + str(wd) + "_" + str(h) + "_" + "taux_erreur"
utils.plotCourbeApprentissage(neuralNetwork.trainError,
neuralNetwork.validError,
neuralNetwork.testError, x, title, name)
title = "Cout moyen - " + str(maxIter) + " epoques"
name = str(h) + "_" + str(wd) + "_" + str(h) + "_" + "cout_moyen"
utils.plotCourbeApprentissage(neuralNetwork.trainSumL,
neuralNetwork.validSumL,
neuralNetwork.testSumL, x, title, name)