def classificazione(directory,filename, version,exp_name,name, model,lr, epochs, momentum, batch_size, resize):
print("Classificazione")
#directory "Class"
directory =directory
version=version
lr=lr
epochs=epochs
momentum=momentum
batch_size = batch_size
resize=resize
controlFileCSVBase()
dataSetClass = DatasetClassi(resize)
dataSetClass.controlNormalize()
train = dataSetClass.dataset_train_norm
validation = dataSetClass.dataset_valid_norm
test = dataSetClass.dataset_test_norm
print("Numeri campioni",len(train))
createFolder(directory)
createFolder(directory+"\\"+version)
writeJsonModelInit1(directory,name,version)
money_train_loader = DataLoader(train, batch_size=batch_size, num_workers=0, shuffle=True)
money_test_loader = DataLoader(test, batch_size=batch_size, num_workers=0)
money_val_loader = DataLoader(validation , batch_size = batch_size, num_workers=0)
print("Numero di batch", len(money_train_loader))
modello ,f, last_loss_train, last_loss_val, last_acc_train, last_acc_val = train_class(directory,version, model, money_train_loader, money_val_loader,resize, batch_size, exp_name , lr=lr, epochs = epochs)
print("Time computing", f)
print("last_loss_train",last_loss_train)
print("last_loss_val",last_loss_val)
print("last_acc_train",last_acc_train)
print("last_acc_val",last_acc_val)
hyperparametr = {"indexEpoch":epochs-1,"lr":lr, "momentum" : momentum, "batchSize":batch_size }
contrastiveLoss = {"lossTrain": last_loss_train, "lossValid":last_loss_val}
accuracy = {"accuracyTrain":last_acc_train , "accuracyValid":last_acc_val }
time = {"training": f}
writeJsonModelClass(directory,name,version, hyperparametr,resize,batch_size, contrastiveLoss, accuracy ,time)
path = exp_name+".pth"
model_test = torch.load(path)
"""
def train_model_margine_dynamik(directory, filename, version, exp_name, name,
model, lr, epochs, momentum, batch_size,
resize):
dataSetPair = DataSetPairCreate(resize)
dataSetPair.controlNormalize()
pair_train = dataSetPair.pair_money_train
pair_test = dataSetPair.pair_money_test
pair_validation = dataSetPair.pair_money_val
pair_money_train_loader = DataLoader(pair_train,
batch_size=batch_size,
num_workers=0,
shuffle=True)
pair_money_test_loader = DataLoader(pair_test,
batch_size=batch_size,
num_workers=0)
pair_money_val_loader = DataLoader(pair_validation,
batch_size=batch_size,
num_workers=0)
#training
#modello, tempo di training, loss su train, loss su val
createFolder(directory + "\\" + version)
writeJsonModelInit1(directory, name, version)
print("Training...")
modello, f, last_loss_train, last_loss_val, last_acc_train, last_acc_val = train_margine_dynamik(
directory,
version,
model,
pair_money_train_loader,
pair_money_val_loader,
resize,
batch_size,
exp_name,
lr=lr,
epochs=epochs)
print("Time computing", f)
print("last_loss_train", last_loss_train)
print("last_loss_val", last_loss_val)
print("last_acc_train", last_acc_train)
print("last_acc_val", last_acc_val)
hyperparametr = {
"indexEpoch": epochs - 1,
"lr": lr,
"momentum": momentum,
"numSampleTrain": len(pair_train)
}
contrastiveLoss = {
"lossTrain": last_loss_train,
"lossValid": last_loss_val
}
accuracy = {"accuracyTrain": last_acc_train, "accuracyValid": last_acc_val}
time = {"training": f}
writeJsonModelClass(directory, name, version, hyperparametr, resize,
batch_size, contrastiveLoss, accuracy, time)
namep = exp_name + ".pth"
siamese_model = torch.load(namep)
print("Testing on Validation set")
timeVal, pair_prediction_val, pair_label_val = test_margine_dynamik(
siamese_model, pair_money_val_loader)
numSimilPredette = np.sum(pair_prediction_val == 0)
print("Num Simili predette", numSimilPredette)
numDissimilPredette = np.sum(pair_prediction_val == 1)
print("Num Dissimil predette", numDissimilPredette)
numSimilReali = np.sum(pair_label_val == 0)
print("Num Simili Reali", numSimilReali)
numDissimilReali = np.sum(pair_label_val == 1)
print("Num Dissimil Reali", numDissimilReali)
#calculate Accuracy
print(pair_prediction_val[0:10])
print(pair_label_val[0:10])
accuracyVal = accuracy_score(pair_label_val, pair_prediction_val)
print("Accuarcy di test: %0.4f" % accuracyVal)
#calculate Precision
precisionVal = precision_score(pair_label_val, pair_prediction_val)
print("Precision di test: %0.4f" % precisionVal)
#calculate Recall
recallVal = recall_score(pair_label_val, pair_prediction_val)
print("Recall di test: %0.4f" % recallVal)
#calculate F1 score
if recallVal != 0.0 and precisionVal != 0.0:
scores_testing_val = f1_score(pair_label_val,
pair_prediction_val,
average=None)
scores_testing_val = scores_testing_val.mean()
print("mF1 score di testing: %0.4f" % scores_testing_val)
else:
scores_testing_val = 0.000
print("mscoref1", scores_testing_val)
key = ["accuracy", "precision", "recall", "mf1_score", "time"]
entry = [
"accuracyVal", "precisionVal", "recallVal", "f1_score_Val", "testVal"
]
value = [accuracyVal, precisionVal, recallVal, scores_testing_val, timeVal]
addValueJsonModel(directory + "modelTrained.json", version, key[0],
entry[0], value[0])
addValueJsonModel(directory + "modelTrained.json", version, key[1],
entry[1], value[1])
addValueJsonModel(directory + "modelTrained.json", version, key[2],
entry[2], value[2])
addValueJsonModel(directory + "modelTrained.json", version, key[3],
entry[3], value[3])
addValueJsonModel(directory + "modelTrained.json", version, key[4],
entry[4], value[4])
def train_model_margine_double(directory,
filename,
version,
exp_name,
name,
model,
lr,
epochs,
momentum,
batch_size,
resize,
decay=None,
margin1=None,
margin2=None,
modeLoss=None):
# directory es "Model-6"
createFolder(directory)
dataSetPair = DataSetPairCreate(resize)
dataSetPair.controlNormalize()
pair_train = dataSetPair.pair_money_train
pair_test = dataSetPair.pair_money_test
pair_validation = dataSetPair.pair_money_val
pair_money_train_loader = DataLoader(pair_train,
batch_size=batch_size,
num_workers=0,
shuffle=True)
pair_money_test_loader = DataLoader(pair_test,
batch_size=batch_size,
num_workers=0)
pair_money_val_loader = DataLoader(pair_validation,
batch_size=batch_size,
num_workers=0)
siamese_money = model # modello
createFolder(directory + "\\" + version)
createFolder(directory + "\\" + version + "\\" + "Metod2")
writeJsonModelInit1(directory, name, version)
print("Training...")
modello, f, last_loss_train, last_loss_val, last_acc_train, last_acc_val = train_siamese_margin_double(
directory,
version,
siamese_money,
pair_money_train_loader,
pair_money_val_loader,
resize,
batch_size,
exp_name=exp_name,
lr=lr,
epochs=epochs,
momentum=momentum,
margin1=margin1,
margin2=margin2,
logdir='logs',
decay=decay,
modeLoss=modeLoss)
print("Time computing", f)
print("last_loss_train", last_loss_train)
print("last_loss_val", last_loss_val)
print("last_acc_train", last_acc_train)
print("last_acc_val", last_acc_val)
if not decay is None:
hyperparametr = {
"indexEpoch": epochs - 1,
"lr": lr,
"decay": decay,
"momentum": momentum,
"numSampleTrain": len(pair_train)
}
else:
hyperparametr = {
"indexEpoch": epochs - 1,
"lr": lr,
"momentum": momentum,
"numSampleTrain": len(pair_train)
}
contrastiveLoss = {
"lossTrain": last_loss_train,
"lossValid": last_loss_val
}
accuracy = {"accuracyTrain": last_acc_train, "accuracyValid": last_acc_val}
time = {"training": f}
writeJsonModelClass(directory, name, version, hyperparametr, resize,
resize, batch_size, contrastiveLoss, accuracy, time)