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

class CapsNet(models.Model):
    def __init__(self, input_shape, n_class, routings):
        super(CapsNet, self).__init__()
        self.input_shape = input_shape
        self.n_class = n_class
        self.routings = routings

        # Layer 1: Just a conventional Conv1D layer
        self.conv1 = layers.Conv1D(filters=256, kernel_size=9, strides=1, padding='valid', activation='relu', name='conv1')

        # Layer 2: Conv1D layer with `squash` activation, then reshape to [None, num_capsule, dim_capsule]
        self.primarycaps = layers.Conv1D(filters=256, kernel_size=9, strides=2, padding='valid', name='primarycaps')
        self.activation = layers.Lambda(squash, name='activation')

        # Layer 3: Capsule layer. Routing algorithm works here.
        self.digitcaps = layers.CapsuleLayer(num_capsule=n_class, dim_capsule=16, routings=routings,
                                             name='digitcaps')

        # Layer 4: This is an auxiliary layer to replace each capsule with its length. Just to match the true label's shape.
        self.out_caps = layers.Lambda(lambda x: K.sqrt(K.sum(K.square(x), 2)), name='out_caps')

    def call(self, inputs, training=None):
        # Inputs
        x = self.conv1(inputs)

        x = self.primarycaps(x)
        x = self.activation(x)

        # Digit Capsule
        x = self.digitcaps(x)
        # Output
        out_caps = self.out_caps(x)

        return out_caps

    def model(self):
        x = layers.Input(shape=self.input_shape)

        return models.Model(inputs=[x], outputs=self.call(x))

# Squash activation function
def squash(vectors, axis=-1):
    s_squared_norm = K.sum(K.square(vectors), axis, keepdims=True)
    scale = s_squared_norm / (1 + s_squared_norm) / K.sqrt(s_squared_norm + K.epsilon())
    return scale * vectors

以上代码实现了一个包含卷积层、胶囊层和长度层的一维胶囊网络。你可以根据自己的数据和实际情况调整模型的参数。