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