使用手写集数据集编写DBN的代码

以下是使用手写集数据集编写DBN的代码：

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DBN的参数
n_visible = 784  # 输入层神经元个数
n_hidden_1 = 500  # 隐藏层1神经元个数
n_hidden_2 = 200  # 隐藏层2神经元个数
n_hidden_3 = 50  # 隐藏层3神经元个数
n_classes = 10  # 输出层神经元个数

# 定义输入和输出的占位符
x = tf.placeholder(tf.float32, [None, n_visible])
y = tf.placeholder(tf.float32, [None, n_classes])

# 定义权重和偏置
weights = {
    'encoder_h1': tf.Variable(tf.random_normal([n_visible, n_hidden_1])),
    'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'encoder_h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3])),
    'decoder_h1': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_2])),
    'decoder_h2': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),
    'decoder_h3': tf.Variable(tf.random_normal([n_hidden_1, n_visible])),
    'out': tf.Variable(tf.random_normal([n_hidden_3, n_classes]))
}
biases = {
    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'encoder_b3': tf.Variable(tf.random_normal([n_hidden_3])),
    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_2])),
    'decoder_b2': tf.Variable(tf.random_normal([n_hidden_1])),
    'decoder_b3': tf.Variable(tf.random_normal([n_visible])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

# 定义编码器
def encoder(x):
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']), biases['encoder_b1']))
    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']), biases['encoder_b2']))
    layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['encoder_h3']), biases['encoder_b3']))
    return layer_3

# 定义解码器
def decoder(x):
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']), biases['decoder_b1']))
    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']), biases['decoder_b2']))
    layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['decoder_h3']), biases['decoder_b3']))
    return layer_3

# 定义分类器
def classifier(x):
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(encoder(x), weights['out']), biases['out']))
    return layer_1

# 定义损失函数和优化器
encoded = encoder(x)
decoded = decoder(encoded)
y_pred = classifier(x)
cost = tf.reduce_mean(tf.square(x - decoded)) + tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=y_pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.01).minimize(cost)

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

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(50):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples / 100)
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(100)
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs, y: batch_ys})
            avg_cost += c / total_batch
        print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
    print("Optimization Finished!")
    print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))

希望这个代码对你有帮助！