写一维胶囊网络的代码keras

好的，以下是一维胶囊网络的Keras代码：

from keras import layers, models

class CapsuleLayer(layers.Layer):
    def __init__(self, num_capsules, capsule_dim, num_routing=3, **kwargs):
        super(CapsuleLayer, self).__init__(**kwargs)
        self.num_capsules = num_capsules
        self.capsule_dim = capsule_dim
        self.num_routing = num_routing

    def build(self, input_shape):
        self.input_num_capsules = input_shape[1]
        self.input_capsule_dim = input_shape[2]

        self.W = self.add_weight(shape=[self.input_num_capsules, self.num_capsules, self.input_capsule_dim, self.capsule_dim], initializer='glorot_uniform', name='W')

        self.built = True

    def call(self, inputs, **kwargs):
        inputs_expand = tf.expand_dims(inputs, 2)
        inputs_tiled = tf.tile(inputs_expand, [1, 1, self.num_capsules, 1])
        inputs_hat = tf.scan(lambda ac, x: tf.matmul(self.W, x), elems=inputs_tiled, initializer=tf.zeros([self.input_num_capsules, self.num_capsules, 1, self.capsule_dim]))
        b = tf.zeros([inputs.shape[0], self.input_num_capsules, self.num_capsules, 1, 1])

        for i in range(self.num_routing):
            c = tf.nn.softmax(b, dim=2)
            s = tf.reduce_sum(tf.multiply(c, inputs_hat), axis=1, keepdims=True)
            v = self.squash(s)
            b += tf.reduce_sum(tf.multiply(v, inputs_hat), axis=-1, keepdims=True)

        return tf.squeeze(v, axis=1)

    def squash(self, x):
        norm = tf.reduce_sum(tf.square(x), axis=-1, keepdims=True)
        scale = norm / (1 + norm) / tf.sqrt(norm + 1e-8)
        return scale * x

def build_capsule_network(input_shape, n_class, num_routing):
    x = layers.Input(shape=input_shape)

    conv1 = layers.Conv1D(filters=256, kernel_size=9, strides=1, padding='valid', activation='relu', name='conv1')(x)

    primary_caps = CapsuleLayer(num_capsules=8, capsule_dim=32, num_routing=num_routing, name='primary_caps')(conv1)

    digit_caps = CapsuleLayer(num_capsules=n_class, capsule_dim=16, num_routing=num_routing, name='digit_caps')(primary_caps)

    output = layers.Lambda(lambda x: tf.sqrt(tf.reduce_sum(tf.square(x), axis=-1)), name='output')(digit_caps)

    model = models.Model(inputs=x, outputs=output)

    return model

这段代码定义了一个包含两个胶囊层的一维胶囊网络，其中第一个胶囊层包含8个胶囊，每个胶囊的维度为32，第二个胶囊层包含n_class个胶囊，每个胶囊的维度为16。num_routing参数指定了动态路由的迭代次数。输出层使用了Lambda层来计算胶囊输出的范数。