class graph_bilinear_interdependence_layer(layer):
def __init__(
self,
m: int, n: int,
width: int = 1,
name: str = 'graph_bilinear_interdependence_layer',
channel_num: int = 1,
# graph structure parameters
graph: graph_class = None,
graph_file_path: str = None,
nodes: list = None,
links: list = None,
directed: bool = False,
# graph interdependence function parameters
with_multihop: bool = False, h: int = 1, accumulative: bool = False,
with_pagerank: bool = False, c: float = 0.15,
require_data: bool = False,
require_parameters: bool = False,
# adj matrix processing parameters
normalization: bool = False,
normalization_mode: str = 'column',
self_dependence: bool = True,
# bilinear interdependence function parameters
with_dual_lphm_interdependence: bool = False,
with_lorr_interdependence: bool = False, r_interdependence: int = 3,
# parameter reconciliation and remainder functions
with_dual_lphm: bool = False,
with_lorr: bool = False, r: int = 3,
with_residual: bool = False,
enable_bias: bool = False,
# output processing parameters
with_batch_norm: bool = False,
with_relu: bool = True,
with_softmax: bool = True,
with_dropout: bool = True, p: float = 0.5,
# other parameters
parameters_init_method: str = 'xavier_normal',
device: str = 'cpu', *args, **kwargs
):
print('* graph_bilinear_interdependence_layer, width:', width)
heads = [
graph_bilinear_interdependence_head(
m=m, n=n,
channel_num=channel_num,
# -------------------
graph=graph,
graph_file_path=graph_file_path,
nodes=nodes,
links=links,
directed=directed,
# -------------------
with_multihop=with_multihop, h=h, accumulative=accumulative,
with_pagerank=with_pagerank, c=c,
require_data=require_data,
require_parameters=require_parameters,
# -------------------
normalization=normalization,
normalization_mode=normalization_mode,
self_dependence=self_dependence,
# -------------------
with_dual_lphm_interdependence=with_dual_lphm_interdependence,
with_lorr_interdependence=with_lorr_interdependence, r_interdependence=r_interdependence,
# -------------------
with_dual_lphm=with_dual_lphm,
with_lorr=with_lorr, r=r,
with_residual=with_residual,
enable_bias=enable_bias,
# -------------------
with_batch_norm=with_batch_norm,
with_relu=with_relu,
with_softmax=with_softmax,
with_dropout=with_dropout, p=p,
# -------------------
parameters_init_method=parameters_init_method,
device=device, *args, **kwargs
)
] * width
print('--------------------------')
super().__init__(name=name, m=m, n=n, heads=heads, device=device, *args, **kwargs)