graph_convolution.py 文件源码

def __init__(self, 
                 filters, 
                 num_neighbors,
                 neighbors_ix_mat, 
                 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,                 
                 **kwargs):

        if K.backend() != 'theano':
            raise Exception("GraphConv Requires Theano Backend.")

        if 'input_shape' not in kwargs and 'input_dim' in kwargs:
           kwargs['input_shape'] = (kwargs.pop('input_dim'),)

        super(GraphConv, self).__init__(**kwargs)        

        self.filters = filters     
        self.num_neighbors = num_neighbors
        self.neighbors_ix_mat = neighbors_ix_mat

        self.activation = activations.get(activation)
        self.use_bias = use_bias
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.bias_initializer = initializers.get(bias_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.bias_constraint = constraints.get(bias_constraint)
        self.input_spec = InputSpec(ndim=3)