class CapsuleLayer(layers.Layer):
    """
    The capsule layer. It is similar to Dense layer. Dense layer has `in_num` inputs, each is a scalar, the output of the
    neuron from the former layer, and it has `out_num` output neurons. CapsuleLayer just expand the output of the neuron
    from scalar to vector. So its input shape = [None, input_num_capsule, input_dim_capsule] and output shape = \
    [None, num_capsule, dim_capsule]. For Dense Layer, input_dim_capsule = dim_capsule = 1.

    :param num_capsule: number of capsules in this layer
    :param dim_capsule: dimension of the output vectors of the capsules in this layer
    :param routings: number of iterations for the routing algorithm
    """
    def __init__(self, num_capsule, dim_capsule, routings=3,
                 kernel_initializer='glorot_uniform',
                 **kwargs):
        super(CapsuleLayer, self).__init__(**kwargs)
        self.num_capsule = num_capsule
        self.dim_capsule = dim_capsule
        self.routings = routings
        self.kernel_initializer = initializers.get(kernel_initializer)

    def build(self, input_shape):
        assert len(input_shape) >= 3, "The input Tensor should have shape=[None, input_num_capsule, input_dim_capsule]"
        self.input_num_capsule = input_shape[1]
        self.input_dim_capsule = input_shape[2]

        # Transform matrix
        self.W = self.add_weight(shape=[self.num_capsule, self.input_num_capsule,
                                        self.dim_capsule, self.input_dim_capsule],
                                 initializer=self.kernel_initializer,
                                 name='W')

        self.built = True