GraphEmbedding_sumAfter.py 文件源码

def __init__(self, output_dim, inner_dim, depth = 2, init_output='uniform', 
            activation_output='softmax', init_inner='identity',
            activation_inner='linear', scale_output=0.01, padding=False, **kwargs):
        if depth < 1:
            quit('Cannot use GraphFP with depth zero')
        self.init_output = initializations.get(init_output)
        self.activation_output = activations.get(activation_output)
        self.init_inner = initializations.get(init_inner)
        self.activation_inner = activations.get(activation_inner)
        self.output_dim = output_dim
        self.inner_dim = inner_dim
        self.depth = depth
        self.scale_output = scale_output
        self.padding = padding

        self.initial_weights = None
        self.input_dim = 4 # each entry is a 3D N_atom x N_atom x N_feature tensor
        if self.input_dim:
            kwargs['input_shape'] = (None, None, None,) # 3D tensor for each input
        #self.input = K.placeholder(ndim = 4)
        super(GraphFP, self).__init__(**kwargs)