def __init__(self,
out_dim,
node_embedding=False,
hidden_channels=16,
out_channels=None,
n_update_layers=4,
n_atom_types=MAX_ATOMIC_NUM,
dropout_ratio=0.5,
concat_hidden=False,
weight_tying=False,
activation=functions.identity,
n_edge_types=4):
super(GIN, self).__init__()
n_message_layer = 1 if weight_tying else n_update_layers
n_readout_layer = n_update_layers if concat_hidden else 1
with self.init_scope():
# embedding
self.embed = EmbedAtomID(out_size=hidden_channels,
in_size=n_atom_types)
self.first_mlp = GINUpdate(hidden_channels=hidden_channels,
dropout_ratio=dropout_ratio,
out_channels=hidden_channels).graph_mlp
# two non-linear MLP part
if out_channels is None:
out_channels = hidden_channels
self.update_layers = chainer.ChainList(*[
GINUpdate(hidden_channels=hidden_channels,
dropout_ratio=dropout_ratio,
out_channels=(out_channels if i == n_message_layer -
1 else hidden_channels))
for i in range(n_message_layer)
])
# Readout
self.readout_layers = chainer.ChainList(*[
GGNNReadout(out_dim=out_dim,
in_channels=hidden_channels * 2,
activation=activation,
activation_agg=activation)
for _ in range(n_readout_layer)
])
# end with
self.node_embedding = node_embedding
self.out_dim = out_dim
self.hidden_channels = hidden_channels
self.n_update_layers = n_update_layers
self.n_message_layers = n_message_layer
self.n_readout_layer = n_readout_layer
self.dropout_ratio = dropout_ratio
self.concat_hidden = concat_hidden
self.weight_tying = weight_tying
self.n_edge_types = n_edge_types
def __init__(self, out_dim, hidden_dim=16,
n_layers=4, n_atom_types=MAX_ATOMIC_NUM,
dropout_ratio=0.5,
concat_hidden=False,
weight_tying=True,
activation=functions.identity):
super(GIN, self).__init__()
n_message_layer = 1 if weight_tying else n_layers
n_readout_layer = n_layers if concat_hidden else 1
with self.init_scope():
# embedding
self.embed = EmbedAtomID(out_size=hidden_dim, in_size=n_atom_types)
# two non-linear MLP part
self.update_layers = chainer.ChainList(*[GINUpdate(
hidden_dim=hidden_dim, dropout_ratio=dropout_ratio)
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)])
# end with
self.out_dim = out_dim
self.hidden_dim = hidden_dim
self.n_message_layers = n_message_layer
self.n_readout_layer = n_readout_layer
self.dropout_ratio = dropout_ratio
self.concat_hidden = concat_hidden
self.weight_tying = weight_tying
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 + MAX_ATOMIC_NUM * 2,
dropout_ratio=0.5,
concat_hidden=False,
weight_tying=True,
activation=functions.identity):
super(GIN_GWM, self).__init__()
n_message_layer = 1 if weight_tying else n_layers
n_readout_layer = n_layers if concat_hidden else 1
with self.init_scope():
# embedding
self.embed = EmbedAtomID(out_size=hidden_dim, in_size=n_atom_types)
# two non-linear MLP part
self.update_layers = chainer.ChainList(*[
GINUpdate(hidden_dim=hidden_dim, dropout_ratio=dropout_ratio)
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(*[
GINReadout(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)
# end with
self.out_dim = out_dim
self.hidden_dim = hidden_dim
self.hidden_dim_super = hidden_dim_super
self.n_message_layers = n_message_layer
self.n_readout_layer = n_readout_layer
self.dropout_ratio = dropout_ratio
self.concat_hidden = concat_hidden
self.weight_tying = weight_tying
def update():
# type: () -> GINUpdate
return GINUpdate(in_channels=in_channels,
hidden_channels=hidden_channels,
dropout_ratio=0)