写一段用于故障诊断的一维胶囊网络代码keras

好的，以下是一段用于故障诊断的一维胶囊网络代码keras：

from keras import layers, models

input_shape = (None, 1)

# 定义胶囊层
class CapsuleLayer(layers.Layer):
    def __init__(self, num_capsules, capsule_dim, routings=3):
        super(CapsuleLayer, self).__init__()
        self.num_capsules = num_capsules
        self.capsule_dim = capsule_dim
        self.routings = routings
        self.W = self.add_weight(shape=[1, input_shape[1], self.num_capsules * self.capsule_dim],
                                 initializer='random_normal', trainable=True)

    def call(self, inputs, **kwargs):
        # 转换输入形状为[batch_size, input_num_capsule, input_dim_capsule]
        inputs_expand = tf.expand_dims(inputs, axis=-1)
        inputs_tiled = tf.tile(inputs_expand, [1, 1, self.num_capsules, self.capsule_dim])
        inputs_hat = tf.keras.backend.batch_dot(inputs_tiled, self.W)
        # 动态路由过程
        b = tf.zeros(shape=[tf.shape(inputs_hat)[0], input_shape[1], self.num_capsules, 1])
        assert self.routings > 0
        for i in range(self.routings):
            c = tf.nn.softmax(b, axis=2)
            outputs = tf.keras.backend.batch_dot(tf.transpose(inputs_hat, perm=[0, 2, 1, 3]), c)
            if i < self.routings - 1:
                b += tf.keras.backend.batch_dot(outputs, inputs_hat, [3, 2])
        # 返回胶囊输出
        return tf.squeeze(outputs, axis=1)

# 定义模型
def get_model():
    x_input = layers.Input(shape=input_shape)
    conv1 = layers.Conv1D(filters=64, kernel_size=3, strides=1, padding='valid', activation='relu', name='conv1')(x_input)
    conv2 = layers.Conv1D(filters=64, kernel_size=3, strides=2, padding='valid', activation='relu', name='conv2')(conv1)
    primary_capsule = CapsuleLayer(num_capsules=8, capsule_dim=16, routings=3, name='primary_capsule')(conv2)
    output_capsule = CapsuleLayer(num_capsules=1, capsule_dim=16, routings=3, name='output_capsule')(primary_capsule)
    model = models.Model(inputs=x_input, outputs=output_capsule)
    return model

这段代码定义了一个一维胶囊网络，用于故障诊断。模型包括两个胶囊层，其中第一个胶囊层是主胶囊层，第二个胶囊层是输出胶囊层。主胶囊层的输出被传递到输出胶囊层，输出胶囊层输出故障诊断结果。