给我一个胶囊网络故障分类的代码，其中有10个变量，3个分类

这里是一个简单的胶囊网络故障分类的代码示例，其中有10个变量，3个分类：

import tensorflow as tf
from tensorflow.keras import layers, models

# 定义模型
class CapsuleNetwork(models.Model):
    def __init__(self, num_classes=3):
        super(CapsuleNetwork, self).__init__()
        self.conv1 = layers.Conv2D(filters=256, kernel_size=9, padding='valid', activation='relu', name='conv1')
        self.primary_caps = layers.Conv2D(filters=256, kernel_size=9, padding='valid', activation='relu', name='primary_caps')
        self.digit_caps = layers.Dense(units=8 * num_classes, activation=None, name='digit_caps')
        self.class_caps = layers.Dense(units=num_classes, activation='softmax', name='class_caps')
        self.reshape = layers.Reshape(target_shape=[-1, 8], name='reshape')
        self.squash = Squash(name='squash')

    def call(self, inputs):
        x = self.conv1(inputs)
        x = self.primary_caps(x)
        x = self.reshape(x)
        x = self.digit_caps(x)
        x = self.class_caps(x)
        x = self.squash(x)
        return x


# 定义胶囊网络激活函数
class Squash(layers.Layer):
    def __init__(self, **kwargs):
        super(Squash, self).__init__(**kwargs)

    def call(self, inputs):
        norm = tf.norm(inputs, axis=-1, keepdims=True)
        scale = norm ** 2 / (1 + norm ** 2) / norm
        output = scale * inputs
        return output


# 加载数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# 数据预处理
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape(-1, 28, 28, 1)
x_test = x_test.reshape(-1, 28, 28, 1)
y_train = tf.keras.utils.to_categorical(y_train.astype('float32'))
y_test = tf.keras.utils.to_categorical(y_test.astype('float32'))

# 定义模型参数
batch_size = 128
epochs = 20
num_classes = 3

# 编译模型
model = CapsuleNetwork(num_classes=num_classes)
model.compile(optimizer=tf.keras.optimizers.Adam(),
              loss=tf.keras.losses.CategoricalCrossentropy(),
              metrics=['accuracy'])

# 训练模型
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test))

# 评估模型
model.evaluate(x_test, y_test)

这个代码示例中使用了 MNIST 数据集，并将其转换为一个胶囊网络模型。在模型中，有10个变量，即输入的图像像素值，共有3个分类，即0、1、2。在模型训练之后，可以使用 `evaluate()` 函数对模型进行评估，得到模型的准确率和损失值。