def __init__(self, device=None, learning_rate=1e-3, act=F.relu, n_cls=10, batch_size=64, n_samples=50000): # Settings self.device = device self.act = act self.learning_rate = learning_rate self.n_cls = n_cls self.batch_size = batch_size self.iters = 0 self.iter_epoch = int(n_samples / batch_size) self.epoch = 0 self.basedir = "./{}".format(int(time.time())) if not os.path.exists(self.basedir): os.makedirs(self.basedir) # Loss self.recon_loss = ReconstructionLoss() # Model from st.cifar10.cnn_model_003 import Model self.model = Model(device, act) self.model.to_gpu(device) if device is not None else None # Optimizer self.optimizer = optimizers.Adam(learning_rate) self.optimizer.setup(self.model) self.optimizer.use_cleargrads()
def __init__(self, device=None, learning_rate=1e-3, act=F.relu, n_cls=10): super(Experiment003, self).__init__( device=device, learning_rate=learning_rate, act=act, n_cls=n_cls ) # Loss self.recon_loss = ReconstructionLoss() self.er_loss = EntropyRegularizationLoss()
def __init__(self, device=None, learning_rate=1e-3, act=F.relu, n_cls=10): # Settings self.device = device self.act = act self.learning_rate = learning_rate self.n_cls = n_cls # Loss self.recon_loss = ReconstructionLoss() # Model from st.mnist.cnn_model_001 import Model self.model = Model(device, act) self.model.to_gpu(device) if device is not None else None # Optimizer self.optimizer = optimizers.Adam(learning_rate) self.optimizer.setup(self.model) self.optimizer.use_cleargrads()