145170ca9bbd89aa01d8a40841e3c039d3683af8,stellargraph/layer/graph_attention.py,GraphAttention,call,#GraphAttention#Any#,211

Before Change

        if kwargs.get("add_self_loops", False):
            // get the number of nodes from inputs[1] directly
            N = K.int_shape(inputs[1])[-1]
            if N is not None:
                // create self-loops
                A = tf.linalg.set_diag(A, K.cast(np.ones((N,)), dtype="float"))
            else:
                raise ValueError(
                    "{}: need to know number of nodes to add self-loops; obtained None instead".format(
                        type(self).__name__
                    )
                )

        outputs = []
        for head in range(self.attn_heads):
            kernel = self.kernels[head]  // W in the paper (F x F")
            attention_kernel = self.attn_kernels[

After Change

        print(">>", X.shape, A.shape, out_indices.shape)

        batch_dim, n_nodes, _ = K.int_shape(X)
        if batch_dim != 1:
            raise ValueError(
                "Currently full-batch methods only support a batch dimension of one"
            )

        // Remove singleton batch dimension
        A = K.squeeze(A, 0)
        X = K.squeeze(X, 0)
        out_indices = K.squeeze(out_indices, 0)

Instances