Source code for tsl.nn.blocks.encoders.recurrent.gcrnn

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" <>`_ (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)