写一段一维Capsnet网络代码keras

好的，以下是一段使用Keras实现的一维CapsNet网络代码：

from keras import layers, models

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

    def build(self, input_shape):
        input_dim = input_shape[-1]
        self.W = self.add_weight(shape=[input_dim, self.num_capsules * self.capsule_dim],
                                 initializer='glorot_uniform',
                                 name='W')
        super(Capsule, self).build(input_shape)

    def call(self, inputs):
        inputs_expand = K.expand_dims(inputs, 2)
        inputs_tiled = K.tile(inputs_expand, [1, 1, self.num_capsules, 1])
        inputs_hat = K.map_fn(lambda x: K.batch_dot(x, self.W, [2, 1]), elems=inputs_tiled)
        b = tf.zeros(shape=[K.shape(inputs_hat)[0], self.num_capsules, inputs.shape[1], 1])
        for i in range(self.routings):
            c = tf.nn.softmax(b, axis=1)
            outputs = squash(K.batch_dot(c, inputs_hat, [2, 2]))
            if i != self.routings - 1:
                b += K.batch_dot(outputs, inputs_hat, [2, 3])
        return K.reshape(outputs, [-1, self.num_capsules * self.capsule_dim])

    def compute_output_shape(self, input_shape):
        return tuple([None, self.num_capsules * self.capsule_dim])

    def get_config(self):
        config = {'num_capsules': self.num_capsules,
                  'capsule_dim': self.capsule_dim,
                  'routings': self.routings}
        base_config = super(Capsule, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

def squash(x, axis=-1):
    s_squared_norm = K.sum(K.square(x), axis, keepdims=True)
    scale = K.sqrt(s_squared_norm + K.epsilon())
    return x / scale

def build_capsnet(input_shape, n_class, routings):
    x = layers.Input(shape=input_shape)

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

    primarycaps = layers.Conv1D(filters=256, kernel_size=9, strides=2, padding='valid', name='primarycaps')(conv1)
    primarycaps = layers.BatchNormalization()(primarycaps)
    primarycaps = layers.Activation('relu')(primarycaps)
    primarycaps = layers.Reshape(target_shape=[-1, 8], name='primarycaps_reshape')(primarycaps)

    digitcaps = Capsule(10, 16, routings=routings, name='digitcaps')(primarycaps)

    out_caps = layers.Length(name='out_caps')(digitcaps)

    y = layers.Input(shape=(n_class,))

    masked_by_y = Mask()([digitcaps, y])
    masked = Mask()(digitcaps)

    x_recon = layers.Dense(512, activation='relu')(masked)
    x_recon = layers.Dense(1024, activation='relu')(x_recon)
    x_recon = layers.Dense(np.prod(input_shape), activation='sigmoid')(x_recon)
    x_recon = layers.Reshape(target_shape=input_shape, name='out_recon')(x_recon)

    return models.Model([x, y], [out_caps, x_recon])

这是一个使用Keras搭建的一维CapsNet网络，包含了Capsule和build_capsnet两个函数。Capsule函数是定义了一个Capsule层，build_capsnet函数则是搭建了一个完整的CapsNet网络。