给出手写数字识别mnist的python代码

以下是手写数字识别mnist的Python代码：

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

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

# 定义输入和输出
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])

# 定义模型
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)

# 定义损失函数和优化器
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(.5).minimize(cross_entropy)

# 训练模型
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
for i in range(100):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

# 测试模型
correct_prediction = tf.equal(tf.argmax(y,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}))

注意：以上代码仅供参考，实际应用中可能需要进行调整和优化。