用tensorflow框架，写一个LeNet分类网络

你好，我可以回答你的问题。以下是使用 TensorFlow 框架编写 LeNet 分类网络的代码示例：

import tensorflow as tf

# 定义 LeNet 网络结构
def LeNet(x):
    # 第一层卷积层
    conv1 = tf.layers.conv2d(inputs=x, filters=6, kernel_size=[5, 5], padding="valid", 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="valid", activation=tf.nn.relu)
    # 第二层池化层
    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

    # 将卷积层输出的特征图转换为一维向量
    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
    logits = tf.layers.dense(inputs=fc2, units=10)

    return logits

# 定义输入占位符
x = tf.placeholder(tf.float32, [None, 32, 32, 1])
y = tf.placeholder(tf.int64, [None])

# 加载 MNIST 数据集
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)

# 将数据集转换为 LeNet 所需的格式
x_train = mnist.train.images.reshape(-1, 28, 28, 1)
y_train = mnist.train.labels
x_test = mnist.test.images.reshape(-1, 28, 28, 1)
y_test = mnist.test.labels

# 定义损失函数和优化器
logits = LeNet(x)
loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=logits)
train_op = tf.train.AdamOptimizer().minimize(loss)

# 定义评估指标
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, axis=1), y), tf.float32))

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(100):
        _, loss_val, acc_val = sess.run([train_op, loss, accuracy], feed_dict={x: x_train, y: y_train})
        if i % 100 == :
            print("Step %d, loss = %.4f, accuracy = %.2f%%" % (i, loss_val, acc_val * 100))
    test_acc = sess.run(accuracy, feed_dict={x: x_test, y: y_test})
    print("Test accuracy = %.2f%%" % (test_acc * 100))

希望这个代码示例能够帮助到你。如果你有其他问题，欢迎继续提问。