mnist中文手写数字识别代码

以下是使用Python和TensorFlow实现的MNIST中文手写数字识别代码：

# 导入所需的库
import tensorflow as tf
import numpy as np
import os

# 设置参数
learning_rate = 0.001
training_epochs = 20
batch_size = 100
display_step = 1

# 创建输入占位符
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])

# 创建模型
def multilayer_perceptron(x, weights, biases):
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)
    out_layer = tf.matmul(layer_1, weights['out']) + biases['out']
    return out_layer

# 定义权重和偏置项
weights = {
    'h1': tf.Variable(tf.random_normal([784, 256])),
    'out': tf.Variable(tf.random_normal([256, 10]))
}
biases = {
    'b1': tf.Variable(tf.random_normal([256])),
    'out': tf.Variable(tf.random_normal([10]))
}

# 构建模型
pred = multilayer_perceptron(x, weights, biases)

# 定义损失函数和优化器
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)

# 初始化变量
init = tf.global_variables_initializer()

# 加载数据
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)

# 执行训练
with tf.Session() as sess:
    sess.run(init)
    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}))

这是一个简单的多层感知机（MLP）模型，使用ReLU作为激活函数。它使用Adam优化器进行训练，并使用softmax交叉熵作为损失函数。在训练完成后，它计算测试数据集的准确度并输出结果。