def __init__(self, config):
self.config = config # model configuration
self.share_layer_A = torch.nn.Linear(config['latent_dim'],
config['latent_dim'])
self.share_layer_B = torch.nn.Linear(config['latent_dim'],
config['latent_dim'])
self.metric_layer_A = torch.nn.Linear(config['latent_dim'],
config['latent_dim'])
self.metric_layer_B = torch.nn.Linear(config['latent_dim'],
config['latent_dim'])
self.modelA = MLP(config)
self.modelB = MLP(config)
self.sharelayer = ShareLayer(config)
if config['use_cuda'] is True:
self.modelA.cuda()
self.modelB.cuda()
self.sharelayer.cuda()
self.optA = use_optimizer(self.modelA, config)
self.optB = use_optimizer(self.modelB, config)
self.optshare = torch.optim.SGD(self.sharelayer.parameters(), lr=1e-1)
self.optmetric_A = torch.optim.SGD(self.metric_layer_A.parameters(),
lr=1e-1)
self.optmetric_B = torch.optim.SGD(self.metric_layer_B.parameters(),
lr=1e-1)
self.crit = torch.nn.MSELoss()
def __init__(self, config):
self.config = config # model configuration
self.modelA = MLP(config)
self.modelB = MLP(config)
if config['use_cuda'] is True:
use_cuda(True, config['device_id'])
self.modelA.cuda()
self.modelB.cuda()
print(self.modelA)
if config['pretrain']:
self.model.load_pretrain_weights()
self.optA = use_optimizer(self.modelA, config)
self.optB = use_optimizer(self.modelB, config)
self.crit = torch.nn.MSELoss()
self.alpha = config['alpha']
def __init__(self):
self._writer = SummaryWriter(log_dir=Config["normal_config"]
["model_log_dir"]) # tensorboard writer
# self._writer.add_text('config', str(config), 0)
self.opt = use_optimizer(self.model)
self.scheduler = use_scheduler(self.opt)
self.crit = nn.BCELoss()
def __init__(self, config):
self.config = config # model configuration
self.model = MLP(config)
if config['use_cuda'] is True:
self.model.cuda()
self.opt = use_optimizer(self.model, config)
self.crit = torch.nn.MSELoss()
def __init__(self, config):
self.config = config # model configuration
self._metron = MetronAtK(top_k=10)
self._writer = SummaryWriter(log_dir='runs/{}'.format(config['alias'])) # tensorboard writer
self._writer.add_text('config', str(config), 0)
self.opt = use_optimizer(self.model, config)
self.crit = torch.nn.MSELoss()
def __init__(self, config):
self.config = config
self.opt = use_optimizer(self.model, config)
self.crit = torch.nn.BCELoss()
torch.autograd.set_detect_anomaly(True)
if torch.cuda.is_available():
self.crit = self.crit.cuda()
def __init__(self, config):
self.config = config # model configuration
self._metron = MetronAtK(top_k=10)
self.opt = use_optimizer(self.model, config)
self.model_name = config['model']
# explicit feedback
# self.crit = torch.nn.MSELoss()
# implicit feedback
if self.model_name == 'MF':
self.crit = torch.nn.MSELoss()
else:
self.crit = torch.nn.BCELoss()
def __init__(self, config):
self.config = config # model configuration
self._metron = MetronAtK()
self._writer = SummaryWriter(log_dir='runs/{}'.format(
config['alias'])) # tensorboard writer
self._writer.add_text('config', str(config), 0)
self.opt = use_optimizer(self.model, config)
if not config['implicit']:
# explicit feedback
self.crit = torch.nn.MSELoss()
else:
# implicit feedback
self.crit = torch.nn.BCEWithLogitsLoss()
def __init__(self, config):
self.config = config # model configuration
self._metron = MetronAtK(top_k=10)
self._writer = SummaryWriter(log_dir='runs/{}'.format(
config['alias'])) # tensorboard writer
self._writer.add_text('config', str(config), 0)
self.opt = use_optimizer(self.model, config)
self.crit = torch.nn.BCELoss()
self.mse = torch.nn.MSELoss()
self.sparse = False
if config['friend_item_matrix'].split(".")[-1] == "npz":
self.friend_item_matrix = scipy.sparse.load_npz(
config['friend_item_matrix'])
self.sparse = True
else:
self.friend_item_matrix = np.load(config['friend_item_matrix'])
def __init__(self, config):
"""
Function to initialize the engine
:param config: configuration dictionary
"""
self.config = config # model configuration
self._metron = MetronAtK(top_k=10) # Metrics for Top-10
self._writer = SummaryWriter(log_dir='runs/{}'.format(
config['alias'])) # Tensorboard Writer
self._writer.add_text('config', str(config),
0) # String output for Tensorboard Writer
self.opt = use_optimizer(self.model, config) # set optimizer
# self.crit = torch.nn.MSELoss() # mean squared error loss for explicit feedback
self.crit = torch.nn.BCELoss(
) # binary cross entropy loss for implicit feedback
def __init__(self, args):
self.opt = utils.use_optimizer(self.model, args)
self.device = self.set_device()
w_pos = torch.tensor([args.pos_weight]).to(self.device)
self.loss = nn.BCEWithLogitsLoss(pos_weight=w_pos)
