def __init__(self,
out_dim,
hidden_dim=16,
n_layers=4,
n_atom_types=MAX_ATOMIC_NUM,
concat_hidden=False,
weight_tying=True,
activation=functions.identity,
num_edge_type=4):
super(GGNN, self).__init__()
n_readout_layer = n_layers if concat_hidden else 1
n_message_layer = 1 if weight_tying else n_layers
with self.init_scope():
# Update
self.embed = EmbedAtomID(out_size=hidden_dim, in_size=n_atom_types)
self.update_layers = chainer.ChainList(*[
GGNNUpdate(hidden_dim=hidden_dim, num_edge_type=num_edge_type)
for _ in range(n_message_layer)
])
# Readout
self.readout_layers = chainer.ChainList(*[
GGNNReadout(out_dim=out_dim,
hidden_dim=hidden_dim,
activation=activation,
activation_agg=activation)
for _ in range(n_readout_layer)
])
self.out_dim = out_dim
self.hidden_dim = hidden_dim
self.n_layers = n_layers
self.num_edge_type = num_edge_type
self.activation = activation
self.concat_hidden = concat_hidden
self.weight_tying = weight_tying
def __init__(
self,
out_dim, # type: int
hidden_channels=16, # type: int
n_update_layers=4, # type: int
n_atom_types=MAX_ATOMIC_NUM, # type: int
concat_hidden=False, # type: bool
weight_tying=True, # type: bool
n_edge_types=4, # type: int
nn=None, # type: Optional[chainer.Link]
message_func='edgenet', # type: str
readout_func='set2set', # type: str
):
# type: (...) -> None
super(MPNN, self).__init__()
if message_func not in ('edgenet', 'ggnn'):
raise ValueError(
'Invalid message function: {}'.format(message_func))
if readout_func not in ('set2set', 'ggnn'):
raise ValueError(
'Invalid readout function: {}'.format(readout_func))
n_readout_layer = n_update_layers if concat_hidden else 1
n_message_layer = 1 if weight_tying else n_update_layers
with self.init_scope():
# Update
self.embed = EmbedAtomID(out_size=hidden_channels,
in_size=n_atom_types)
if message_func == 'ggnn':
self.update_layers = chainer.ChainList(*[
GGNNUpdate(hidden_channels=hidden_channels,
n_edge_types=n_edge_types)
for _ in range(n_message_layer)
])
else:
self.update_layers = chainer.ChainList(*[
MPNNUpdate(hidden_channels=hidden_channels, nn=nn)
for _ in range(n_message_layer)
])
# Readout
if readout_func == 'ggnn':
self.readout_layers = chainer.ChainList(*[
GGNNReadout(out_dim=out_dim,
in_channels=hidden_channels * 2)
for _ in range(n_readout_layer)
])
else:
self.readout_layers = chainer.ChainList(*[
MPNNReadout(out_dim=out_dim,
in_channels=hidden_channels,
n_layers=1) for _ in range(n_readout_layer)
])
self.out_dim = out_dim
self.hidden_channels = hidden_channels
self.n_update_layers = n_update_layers
self.n_edge_types = n_edge_types
self.concat_hidden = concat_hidden
self.weight_tying = weight_tying
self.message_func = message_func
self.readout_func = readout_func
def __init__(self,
out_dim,
hidden_dim=16,
hidden_dim_super=16,
n_layers=4,
n_heads=8,
n_atom_types=MAX_ATOMIC_NUM,
n_super_feature=2 + 2 * 4 + MAX_ATOMIC_NUM * 2,
dropout_ratio=0.5,
concat_hidden=False,
weight_tying=True,
activation=functions.identity,
num_edge_type=4):
super(GGNN_GWM, self).__init__()
n_readout_layer = n_layers if concat_hidden else 1
n_message_layer = 1 if weight_tying else n_layers
with self.init_scope():
# Update
self.embed = EmbedAtomID(out_size=hidden_dim, in_size=n_atom_types)
self.update_layers = chainer.ChainList(*[
GGNNUpdate(hidden_dim=hidden_dim, num_edge_type=num_edge_type)
for _ in range(n_message_layer)
])
# GWM
self.embed_super = links.Linear(in_size=n_super_feature,
out_size=hidden_dim_super)
self.gwm = GWM(hidden_dim=hidden_dim,
hidden_dim_super=hidden_dim_super,
n_layers=n_message_layer,
n_heads=n_heads,
dropout_ratio=dropout_ratio,
tying_flag=weight_tying,
gpu=-1)
# Readout
self.readout_layers = chainer.ChainList(*[
GGNNReadout(out_dim=out_dim,
hidden_dim=hidden_dim,
activation=activation,
activation_agg=activation)
for _ in range(n_readout_layer)
])
self.linear_for_concat_super = links.Linear(in_size=None,
out_size=out_dim)
self.out_dim = out_dim
self.hidden_dim = hidden_dim
self.hidden_dim_super = hidden_dim_super
self.n_layers = n_layers
self.n_heads = n_heads
self.dropout_ratio = dropout_ratio
self.num_edge_type = num_edge_type
self.activation = activation
self.concat_hidden = concat_hidden
self.weight_tying = weight_tying
def update():
return GGNNUpdate(hidden_dim=hidden_dim, num_edge_type=num_edge_type)
def update():
return GGNNUpdate(in_channels=in_channels,
hidden_channels=hidden_channels,
n_edge_types=n_edge_types)