DBN实现故障分类的代码

很抱歉，由于您没有提供更多详细信息，我不能提供完整的代码。但是，以下是一个简单的DBN实现分类问题的代码示例：

import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 载入数据集
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# 定义参数
learning_rate = 0.01
training_epochs = 10
batch_size = 100
display_step = 1

# 定义输入
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])

# 定义权重和偏置
W1 = tf.Variable(tf.random_normal([784, 256]))
b1 = tf.Variable(tf.random_normal([256]))

W2 = tf.Variable(tf.random_normal([256, 128]))
b2 = tf.Variable(tf.random_normal([128]))

W3 = tf.Variable(tf.random_normal([128, 64]))
b3 = tf.Variable(tf.random_normal([64]))

W4 = tf.Variable(tf.random_normal([64, 10]))
b4 = tf.Variable(tf.random_normal([10]))

# 定义网络结构：DBN
def model(x):
    hidden_layer1 = tf.nn.sigmoid(tf.add(tf.matmul(x, W1), b1))
    hidden_layer2 = tf.nn.sigmoid(tf.add(tf.matmul(hidden_layer1, W2), b2))
    hidden_layer3 = tf.nn.sigmoid(tf.add(tf.matmul(hidden_layer2, W3), b3))
    output_layer = tf.nn.softmax(tf.add(tf.matmul(hidden_layer3, W4), b4))
    return output_layer

# 定义损失函数和优化器
pred = model(x)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# 进行训练
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples/batch_size)
        for i in range(total_batch):
            batch_x, batch_y = mnist.train.next_batch(batch_size)
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
            avg_cost += c / total_batch
        if epoch % display_step == 0:
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
    print("Optimization Finished!")
    
    # 测试模型
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))