给一个卷积神经网路模型的事例和代码事例

我可以回答这个问题。一个常见的卷积神经网络模型是LeNet-5，它由Yann LeCun在1998年提出，用于手写数字识别。以下是一个使用TensorFlow实现LeNet-5的代码示例：

import tensorflow as tf

# 定义LeNet-5模型
def LeNet5(input):
    # 第一层卷积层
    conv1 = tf.layers.conv2d(inputs=input, filters=6, kernel_size=[5, 5], padding="same", activation=tf.nn.relu)
    # 第一层池化层
    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
    # 第二层卷积层
    conv2 = tf.layers.conv2d(inputs=pool1, filters=16, kernel_size=[5, 5], padding="same", activation=tf.nn.relu)
    # 第二层池化层
    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
    # 将卷积层的输出展平
    flatten = tf.layers.flatten(pool2)
    # 第一层全连接层
    fc1 = tf.layers.dense(inputs=flatten, units=120, activation=tf.nn.relu)
    # 第二层全连接层
    fc2 = tf.layers.dense(inputs=fc1, units=84, activation=tf.nn.relu)
    # 输出层
    output = tf.layers.dense(inputs=fc2, units=10)
    return output

# 定义输入数据
x = tf.placeholder(tf.float32, [None, 28, 28, 1])
y = tf.placeholder(tf.float32, [None, 10])

# 构建LeNet-5模型
logits = LeNet5(x)

# 定义损失函数和优化器
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
optimizer = tf.train.AdamOptimizer().minimize(loss)

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(num_epochs):
        batch_x, batch_y = mnist.train.next_batch(batch_size)
        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
        if i % display_step == 0:
            acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
            print("Step " + str(i) + ", Testing Accuracy= " + str(acc))