layers3.graphConvLayer

class layers3.graphConvLayer(outsize, adj_mtx=None, adj_fn=None, values=None, usebias=True)

Basic graph convolution layer

__init__(outsize, adj_mtx=None, adj_fn=None, values=None, usebias=True)

Parameters:

• outsize (int) – Output unit number
• adj_mtx (np.array) – A matrix that indicates the affinity.
• adj_fn (callable) – A function to infer the affinity matrix.
• values (list[np.array]) – If the param ‘values’ is set, the layer will be initialized with the list of numpy array.
• usebias (bool) – Whether to use bias in this layer.

build(input_shape)

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters:input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).

call(x)

Parameters:x – Input tensor or numpy array. The object will be automatically converted to tensor if the input is np.array. Note that other arrays in args or kwargs will not be auto-converted.