GraphConvLSTM
GraphConvLSTM(output_dim, graph_conv_filters, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)
1D convolution layer (e.g. temporal convolution).
This layer creates a convolution kernel that is convolved
with the layer input over a single spatial (or temporal) dimension
to produce a tensor of outputs.
If use_bias
is True, a bias vector is created and added to the outputs.
Finally, if activation
is not None
,
it is applied to the outputs as well.
When using this layer as the first layer in a model,
provide an input_shape
argument
(tuple of integers or None
, e.g.
(10, 128)
for sequences of 10 vectors of 128-dimensional vectors,
or (None, 128)
for variable-length sequences of 128-dimensional vectors.
Arguments
- output_dim: Positive integer, dimensionality of each graph node output space (or dimension of graph node embedding).
- graph_conv_filters: 3D Tensor, the dimensionality of the output space (i.e. the number output of filters in the convolution).
- activation: Activation function to use
(see activations).
If you don't specify anything, no activation is applied
(ie. "linear" activation:
a(x) = x
). - use_bias: Boolean, whether the layer uses a bias vector.
- kernel_initializer: Initializer for the
kernel
weights matrix (see initializers). - bias_initializer: Initializer for the bias vector (see initializers).
- kernel_regularizer: Regularizer function applied to
the
kernel
weights matrix (see regularizer). - bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- kernel_constraint: Constraint function applied to the kernel matrix (see constraints).
- bias_constraint: Constraint function applied to the bias vector (see constraints).
Input shapes
- 4D tensor with shape:
(samples, timestep, num_graph_nodes, input_dim)
Output shape
- if
return_sequences
4D tensor with shape:(samples, timestep, num_graph_nodes, output_dim)
- else
4D tensor with shape:(samples, num_graph_nodes, output_dim)