给我一个胶囊网络故障分类的代码，其中有8个变量，3个分类

好的，以下是一个使用胶囊网络进行故障分类的Python代码，其中包含8个输入变量和3个输出分类：

import tensorflow as tf
from keras import layers, models
from keras import backend as K

# 定义胶囊层
class Capsule(layers.Layer):
    def __init__(self, num_capsule, dim_capsule, routings=3, kernel_size=(9, 1), share_weights=True, activation='default', **kwargs):
        super(Capsule, self).__init__(**kwargs)
        self.num_capsule = num_capsule
        self.dim_capsule = dim_capsule
        self.routings = routings
        self.kernel_size = kernel_size
        self.share_weights = share_weights
        if activation == 'default':
            self.activation = self.squash
        else:
            self.activation = layers.Activation(activation)

    def build(self, input_shape):
        super(Capsule, self).build(input_shape)
        input_dim_capsule = input_shape[-1]
        if self.share_weights:
            self.kernel = self.add_weight(name='capsule_kernel', shape=(1, self.kernel_size[0], input_dim_capsule, self.num_capsule*self.dim_capsule), initializer='glorot_uniform', trainable=True)
        else:
            self.kernel = [self.add_weight(name='capsule_kernel_'+str(i), shape=(1, self.kernel_size[0], input_dim_capsule, self.dim_capsule), initializer='glorot_uniform', trainable=True) for i in range(self.num_capsule)]

    def call(self, inputs):
        if self.share_weights:
            u_hat_vecs = K.conv2d(inputs, self.kernel, strides=(1, 1), padding='valid')
        else:
            u_hat_vecs = [K.conv2d(inputs, self.kernel[i], strides=(1, 1), padding='valid') for i in range(self.num_capsule)]
        batch_size = K.shape(inputs)[0]
        input_num_capsule = K.shape(inputs)[1]
        u_hat_vecs = K.reshape(u_hat_vecs, (batch_size, input_num_capsule, self.num_capsule*self.dim_capsule))
        u_hat_vecs = K.permute_dimensions(u_hat_vecs, (0, 2, 1))
        b = K.zeros_like(u_hat_vecs[:, :, 0])
        for i in range(self.routings):
            c = K.softmax(b, axis=1)
            outputs = self.activation(K.batch_dot(c, u_hat_vecs, [2, 2]))
            if i < self.routings - 1:
                b += K.batch_dot(outputs, u_hat_vecs, [2, 1])
        return outputs

    def compute_output_shape(self, input_shape):
        return (None, self.num_capsule, self.dim_capsule)

    def squash(self, 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

# 定义模型
input_shape = (8, 1)
x = layers.Input(shape=input_shape)
conv1 = layers.Conv1D(filters=64, kernel_size=3, strides=1, padding='valid', activation='relu')(x)
caps1 = Capsule(num_capsule=8, dim_capsule=16, routings=3, share_weights=True)(conv1)
flat1 = layers.Flatten()(caps1)
output = layers.Dense(3, activation='softmax')(flat1)
model = models.Model(inputs=x, outputs=output)

# 编译模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# 加载数据
train_x = ...
train_y = ...

# 训练模型
model.fit(train_x, train_y, epochs=10, batch_size=32)

在上面的代码中，我们使用了Keras框架，使用了一个包含了一个卷积层和一个胶囊层的神经网络。其中，输入变量为8个，输出分类为3个。您需要将`train_x`和`train_y`替换为您自己的数据集。