def eval_all(self, method_cal_final, val_phase=["train", "val"]):
print("EVALUATE MODEL NNGOHR ON THIS DATASET ON TRAIN AND VAL")
print()
data_train = DataLoader_cipher_binary(self.X_train_nn_binaire,
self.Y_train_nn_binaire,
self.device)
dataloader_train = DataLoader(data_train,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.args.num_workers)
data_val = DataLoader_cipher_binary(self.X_val_nn_binaire,
self.Y_val_nn_binaire, self.device)
dataloader_val = DataLoader(data_val,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.args.num_workers)
if len(val_phase) > 1:
self.dataloaders = {
'train': dataloader_train,
'val': dataloader_val
}
else:
self.dataloaders = {'val': dataloader_val}
self.load_general_train()
self.eval(method_cal_final, val_phase)
def create_data(self):
self.X_deltaout_train, self.Y_tf, _, _, _, _= self.creator_data_binary.make_data(
self.args.nbre_generate_data_train_val);
self.X_eval, self.Y_eval, _, _, _, _ = self.creator_data_binary.make_data(
self.args.nbre_generate_data_train_val);
data_train = DataLoader_cipher_binary(self.X_deltaout_train, self.Y_tf, self.device)
self.dataloader_train = DataLoader(data_train, batch_size=self.args.batch_size,
shuffle=False, num_workers=self.args.num_workers)
data_val = DataLoader_cipher_binary(self.X_eval, self.Y_eval, self.device)
self.dataloader_val = DataLoader(data_val, batch_size=self.args.batch_size,
shuffle=False, num_workers=self.args.num_workers)
def train_from_scractch(self, name_input):
data_train = DataLoader_cipher_binary(self.X_train_nn_binaire,
self.Y_train_nn_binaire,
self.device)
dataloader_train = DataLoader(data_train,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.args.num_workers)
data_val = DataLoader_cipher_binary(self.X_val_nn_binaire,
self.Y_val_nn_binaire, self.device)
dataloader_val = DataLoader(data_val,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.args.num_workers)
self.dataloaders = {'train': dataloader_train, 'val': dataloader_val}
self.load_general_train()
self.train(name_input)
def __init__(self, X, Y, device, old_net, catgeorie, args, train=True):
"""
"""
self.args = args
self.categorie_1 = []
self.categorie_2 = []
self.categorie_3 = []
self.old_net = old_net
self.X, self.Y = X, Y
self.data_val_c = DataLoader_cipher_binary(self.X, self.Y, device)
self.dataloader_val_c = DataLoader(self.data_val_c,
batch_size=self.args.batch_size,
shuffle=False,
num_workers=self.args.num_workers)
self.device = device
self.train = train
self.catgeorie = catgeorie
self.t = Variable(torch.Tensor([0.5]))
if self.train:
print("START PREPROCESSING")
self.oder_input()
self.categorie_22 = self.categorie_1 + self.categorie_2
print(filenames)
nn_model_ref = NN_Model_Ref(args, writer, device, rng, path_save_model, cipher, creator_data_binary, path_save_model_train)
args.load_nn_path = filenames
nn_model_ref.load_nn()
all_models_trained[filenames] = nn_model_ref.net.eval()
del nn_model_ref
all_models_trained["coef"] = [1, 1, 1, 1, 1, 1, 1, 1]
#[0.125,0.008, 0.06, 0.5, 0.03, 0.0125, 0.008, 0.25]
nn_model_ref = NN_Model_Ref(args, writer, device, rng, path_save_model, cipher, creator_data_binary, path_save_model_train)
data_train = DataLoader_cipher_binary(nn_model_ref.X_train_nn_binaire, nn_model_ref.Y_train_nn_binaire, nn_model_ref.device)
dataloader_train = DataLoader(data_train, batch_size=nn_model_ref.batch_size,
shuffle=False, num_workers=nn_model_ref.args.num_workers)
data_val = DataLoader_cipher_binary(nn_model_ref.X_val_nn_binaire, nn_model_ref.Y_val_nn_binaire, nn_model_ref.device)
dataloader_val = DataLoader(data_val, batch_size=nn_model_ref.batch_size,
shuffle=False, num_workers=nn_model_ref.args.num_workers)
nn_model_ref.dataloaders = {'train': dataloader_train, 'val': dataloader_val}
nn_model_ref.load_general_train()
import time
import torch
val_phase = ['train', 'val']
since = time.time()
n_batches = nn_model_ref.batch_size
pourcentage = 3