DBN python代码

以下是使用Python实现DBN（深度置信网络）的示例代码：

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

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# 定义网络参数
n_visible = 784
n_hidden_1 = 500
n_hidden_2 = 200

# 定义网络输入
x = tf.placeholder(tf.float32, [None, n_visible])

# 定义权重和偏置
W_1 = tf.Variable(tf.truncated_normal([n_visible, n_hidden_1], stddev=0.1))
b_1 = tf.Variable(tf.zeros([n_hidden_1]))
W_2 = tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2], stddev=0.1))
b_2 = tf.Variable(tf.zeros([n_hidden_2]))
W_3 = tf.Variable(tf.zeros([n_hidden_2, 10]))
b_3 = tf.Variable(tf.zeros([10]))

# 定义网络模型
def network(input):
    hidden_1 = tf.nn.sigmoid(tf.matmul(input, W_1) + b_1)
    hidden_2 = tf.nn.sigmoid(tf.matmul(hidden_1, W_2) + b_2)
    output = tf.nn.softmax(tf.matmul(hidden_2, W_3) + b_3)
    return output

# 定义损失函数和优化器
y = network(x)
y_ = tf.placeholder(tf.float32, [None, 10])
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)

# 定义评估模型的准确率
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 训练模型
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
    if i % 100 == 0:
        print("Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

# 评估模型
print("Final accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

这个示例代码实现了一个两层的DBN，用于MNIST手写数字分类。代码使用TensorFlow实现，读取MNIST数据集，定义网络结构、损失函数、优化器和评估指标，并进行训练和测试。