def exo67():
print("\n\n>>EXERCICE 6 et 7 : Calcul matriciel")
print(" --- K=1 ---")
#Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest = utils.readMoonFile()
Xtrain = [[30, 20, 40, 50], [25, 15, 35, 45]]
ytrain = [0,0]
default_h = 2
nn = NeuralNetwork(len(Xtrain[0]), default_h, utils.getClassCount(ytrain), K=1, wd=0)
nne = NeuralNetworkEfficient(len(Xtrain[0]), default_h, utils.getClassCount(ytrain), K=1, wd=0)
nne._w1 = nn._w1 # trick pour que l'aleatoire soit egale
nne._w2 = nn._w2
nn.train(Xtrain,ytrain,1)
nne.train(Xtrain,ytrain,1)
utils.compareNN(nn,nne)
print(" --- K=10 ---")
Xtrain = [[30, 20, 40, 50], [25, 15, 35, 45],[30, 76, 45, 44],[89, 27, 42, 52],[30, 24, 44, 53],[89, 25, 45, 50],[30, 20, 40, 50],[30, 65, 47, 50],[30, 34, 40, 50],[39, 20, 29, 58]]
ytrain = [0,0,0,0,0,0,0,0,0,0]
default_h = 2
nn = NeuralNetwork(len(Xtrain[0]), default_h, utils.getClassCount(ytrain), K=10, wd=0)
nne = NeuralNetworkEfficient(len(Xtrain[0]), default_h, utils.getClassCount(ytrain), K=10, wd=0)
nne._w1 = nn._w1 # trick pour que l'aleatoire soit egale
nne._w2 = nn._w2
nn.train(Xtrain,ytrain,1)
nne.train(Xtrain,ytrain,1)
utils.compareNN(nn,nne,10)
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 exo67():
print("\n\n>>EXERCICE 6 et 7 : Calcul matriciel")
print(" --- K=1 ---")
#Xtrain, ytrain, Xvalid, yvalid, Xtest, ytest = utils.readMoonFile()
Xtrain = [[30, 20, 40, 50], [25, 15, 35, 45]]
ytrain = [0, 0]
default_h = 2
nn = NeuralNetwork(len(Xtrain[0]),
default_h,
utils.getClassCount(ytrain),
K=1,
wd=0)
nne = NeuralNetworkEfficient(len(Xtrain[0]),
default_h,
utils.getClassCount(ytrain),
K=1,
wd=0)
nne._w1 = nn._w1 # trick pour que l'aleatoire soit egale
nne._w2 = nn._w2
nn.train(Xtrain, ytrain, 1)
nne.train(Xtrain, ytrain, 1)
utils.compareNN(nn, nne)
print(" --- K=10 ---")
Xtrain = [[30, 20, 40, 50], [25, 15, 35, 45], [30, 76, 45, 44],
[89, 27, 42, 52], [30, 24, 44, 53], [89, 25, 45, 50],
[30, 20, 40, 50], [30, 65, 47, 50], [30, 34, 40, 50],
[39, 20, 29, 58]]
ytrain = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
default_h = 2
nn = NeuralNetwork(len(Xtrain[0]),
default_h,
utils.getClassCount(ytrain),
K=10,
wd=0)
nne = NeuralNetworkEfficient(len(Xtrain[0]),
default_h,
utils.getClassCount(ytrain),
K=10,
wd=0)
nne._w1 = nn._w1 # trick pour que l'aleatoire soit egale
nne._w2 = nn._w2
nn.train(Xtrain, ytrain, 1)
nne.train(Xtrain, ytrain, 1)
utils.compareNN(nn, nne, 10)
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")
