中文手写数字识别运用至少一种神经网络代码

以下是一个使用卷积神经网络（CNN）进行中文手写数字识别的代码，同样使用的是Python语言和TensorFlow框架：

import tensorflow as tf
import numpy as np

# 加载手写数字数据集
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# 定义输入和输出的占位符
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])

# 将输入数据转化为图片格式
x_image = tf.reshape(x, [-1,28,28,1])

# 定义卷积层和池化层函数
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

# 定义第一层卷积层和池化层的权重和偏置变量
W_conv1 = tf.Variable(tf.truncated_normal([5, 5, 1, 32], stddev=0.1))
b_conv1 = tf.Variable(tf.constant(0.1, shape=[32]))

# 定义第一层卷积层和池化层
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

# 定义第二层卷积层和池化层的权重和偏置变量
W_conv2 = tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.1))
b_conv2 = tf.Variable(tf.constant(0.1, shape=[64]))

# 定义第二层卷积层和池化层
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

# 将池化层输出的数据展开
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])

# 定义全连接层的权重和偏置变量
W_fc1 = tf.Variable(tf.truncated_normal([7 * 7 * 64, 1024], stddev=0.1))
b_fc1 = tf.Variable(tf.constant(0.1, shape=[1024]))

# 定义全连接层并使用dropout防止过拟合
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# 定义输出层的权重和偏置变量
W_fc2 = tf.Variable(tf.truncated_normal([1024, 10], stddev=0.1))
b_fc2 = tf.Variable(tf.constant(0.1, shape=[10]))

# 定义输出层
y_hat = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# 定义交叉熵损失函数
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(y_hat), reduction_indices=[1]))

# 定义优化器
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

# 定义会话并初始化变量
sess = tf.Session()
sess.run(tf.global_variables_initializer())

# 训练模型
for i in range(20000):
    batch = mnist.train.next_batch(50)
    if i % 100 == 0:
        train_accuracy = sess.run(accuracy, feed_dict={x: batch[0], y: batch[1], keep_prob: 1.0})
        print("step %d, training accuracy %g"%(i, train_accuracy))
    sess.run(train_step, feed_dict={x: batch[0], y: batch[1], keep_prob: 0.5})

# 测试模型
correct_prediction = tf.equal(tf.argmax(y_hat,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels, keep_prob: 1.0}))

这段代码使用了一个包含了两个卷积层和一个全连接层的CNN模型，对手写数字数据集进行训练和测试，并输出了模型的准确率。你可以根据自己的需要进行修改和优化。