from tsl.nn.layers.recurrent import GraphConvGRUCell, GraphConvLSTMCell
from .base import RNNBase
[docs]class GraphConvRNN(RNNBase):
r"""The Graph Convolutional Recurrent Network based on the paper
`"Structured Sequence Modeling with Graph Convolutional Recurrent Networks"
<https://arxiv.org/abs/1612.07659>`_ (Seo et al., ICONIP 2017), using
:class:`~tsl.nn.layers.graph_convs.GraphConv` as graph convolution.
Args:
input_size (int): Size of the input.
hidden_size (int): Number of units in the hidden state.
n_layers (int): Number of hidden layers.
(default: ``1``)
cat_states_layers (bool): If :obj:`True`, then the states of each layer
are concatenated along the feature dimension.
(default: :obj:`False`)
return_only_last_state (bool): If :obj:`True`, then the ``forward()``
method returns only the state at the end of the processing, instead
of the full sequence of states.
(default: :obj:`False`)
cell (str): Type of graph recurrent cell that should be use
(options: ``'gru'``, ``'lstm'``).
(default: ``'gru'``)
bias (bool): If :obj:`False`, then the layer will not learn an additive
bias vector for each gate.
(default: :obj:`True`)
norm (str): The normalization used for edges and edge weights. If
:obj:`'mean'`, then edge weights are normalized as
:math:`a_{j \rightarrow i} = \frac{a_{j \rightarrow i}} {deg_{i}}`,
other available options are: :obj:`'gcn'`, :obj:`'asym'` and
:obj:`'none'`.
(default: :obj:`'mean'`)
root_weight (bool): If :obj:`True`, then add a filter (with different
weights) for the root node itself.
(default :obj:`True`)
activation (str, optional): Activation function to be used, :obj:`None`
for identity function (i.e., no activation).
(default: :obj:`None`)
cached (bool): If :obj:`True`, then cached the normalized edge weights
computed in the first call.
(default :obj:`False`)
**kwargs (optional): Additional arguments of
:class:`torch_geometric.nn.conv.MessagePassing`.
"""
def __init__(self,
input_size: int,
hidden_size: int,
n_layers: int = 1,
cat_states_layers: bool = False,
return_only_last_state: bool = False,
cell: str = 'gru',
bias: bool = True,
norm: str = 'mean',
root_weight: bool = True,
activation: str = None,
cached: bool = False,
**kwargs):
self.input_size = input_size
self.hidden_size = hidden_size
if cell == 'gru':
cell = GraphConvGRUCell
elif cell == 'lstm':
cell = GraphConvLSTMCell
else:
raise NotImplementedError(f'"{cell}" cell not implemented.')
rnn_cells = [
cell(input_size if i == 0 else hidden_size,
hidden_size,
norm=norm,
root_weight=root_weight,
activation=activation,
bias=bias,
cached=cached,
**kwargs) for i in range(n_layers)
]
super(GraphConvRNN, self).__init__(rnn_cells, cat_states_layers,
return_only_last_state)