def __init__(self,
para=Parameters.gtn_net(),
debug_mode=False,
device='cuda'):
# Initialize Parameters
self.para = para
Programclass.Program.__init__(self,
device=device,
dtype=para['dtype'],
debug_mode=debug_mode)
MPSclass.MPS.__init__(self)
MLclass.MachineLearning.__init__(self,
self.para,
debug_mode=debug_mode)
tc.nn.Module.__init__(self)
self.initialize_parameters_gtn_net()
self.name_md5_generate()
self.debug_mode = debug_mode
self.update_info = dict()
self.data_info = dict()
# Initialize MPS and update info
self.weight = None
self.tensor_shape = None
if not debug_mode:
self.load_weight()
if self.weight is None:
self.initialize_dataset()
self.generate_tensor_info()
self.generate_update_info()
self.initialize_mps_gtn_net()
self.initialize_tensor_shape()
self.weight = tc.nn.Parameter(
tc.empty((self.tensor_info['n_length'],
self.tensor_info['tensor_initialize_bond'],
self.tensor_info['physical_bond'],
self.tensor_info['tensor_initialize_bond']),
device=self.device,
dtype=self.dtype))
self.initialize_weight()
self.tensor_input = None
self.opt = None
self.initialize_opt()
self.fun = tc.nn.LogSoftmax(dim=0)
from library import TNMLclass from library import Parameters as Pa pa = Pa.gtn_net() A = TNMLclass.GTN_Net(para=pa, device='cuda:0', debug_mode=False) A.start_learning()
