写出利用卷积神经网络进行手写数字识别的代码

好的，以下是基于Python和TensorFlow的手写数字识别代码：

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

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

# 定义输入输出变量
x = tf.placeholder(tf.float32, [None, 784])
y_actual = tf.placeholder(tf.float32, shape=[None, 10])

# 定义卷积神经网络
x_image = tf.reshape(x, [-1,28,28,1])

# 第一层卷积层
W_conv1 = tf.Variable(tf.truncated_normal([5, 5, 1, 32], stddev=0.1))
b_conv1 = tf.Variable(tf.constant(0.1, shape=[32]))
h_conv1 = tf.nn.relu(tf.nn.conv2d(x_image, W_conv1, strides=[1, 1, 1, 1], padding='SAME') + b_conv1)
h_pool1 = tf.nn.max_pool(h_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

# 第二层卷积层
W_conv2 = tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.1))
b_conv2 = tf.Variable(tf.constant(0.1, shape=[64]))
h_conv2 = tf.nn.relu(tf.nn.conv2d(h_pool1, W_conv2, strides=[1, 1, 1, 1], padding='SAME') + b_conv2)
h_pool2 = tf.nn.max_pool(h_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

# 全连接层
W_fc1 = tf.Variable(tf.truncated_normal([7 * 7 * 64, 1024], stddev=0.1))
b_fc1 = tf.Variable(tf.constant(0.1, shape=[1024]))
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

# Dropout层
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# 输出层
W_fc2 = tf.Variable(tf.truncated_normal([1024, 10], stddev=0.1))
b_fc2 = tf.Variable(tf.constant(0.1, shape=[10]))
y_predict = 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_sum(y_actual*tf.log(y_predict))

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

# 定义评价指标
correct_prediction = tf.equal(tf.argmax(y_predict,1), tf.argmax(y_actual,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

# 开始训练
with tf.Session() as sess:
    init = tf.initialize_all_variables()
    sess.run(init)
    for i in range(2000):
        batch_xs, batch_ys = mnist.train.next_batch(50)
        if i%100 == 0:
            train_accuracy = accuracy.eval(feed_dict={x:batch_xs, y_actual: batch_ys, keep_prob: 1.0})
            print("step %d, training accuracy %g"%(i, train_accuracy))
        sess.run(train_step, feed_dict={x: batch_xs, y_actual: batch_ys, keep_prob: 0.5})
    print("test accuracy %g"%accuracy.eval(feed_dict={x: mnist.test.images, y_actual: mnist.test.labels, keep_prob: 1.0}))

这段代码定义了一个有两个卷积层和一个全连接层的卷积神经网络。在训练过程中，使用Adam优化器进行优化，并且在训练过程中使用Dropout避免过拟合。最终输出测试集的准确率。