写一个七层CNN网络，使用MINST数据集，给出其代码

好的，以下是一个基于 TensorFlow 的七层 CNN 网络，用于 MNIST 数据集的分类：

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

mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

n_classes = 10
batch_size = 128

x = tf.placeholder('float', [None, 784])
y = tf.placeholder('float')

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def maxpool2d(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
                          padding='SAME')

def convolutional_neural_network(x):
    weights = {'W_conv1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
               'W_conv2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
               'W_fc1': tf.Variable(tf.random_normal([7 * 7 * 64, 1024])),
               'out': tf.Variable(tf.random_normal([1024, n_classes]))}

    biases = {'b_conv1': tf.Variable(tf.random_normal([32])),
              'b_conv2': tf.Variable(tf.random_normal([64])),
              'b_fc1': tf.Variable(tf.random_normal([1024])),
              'out': tf.Variable(tf.random_normal([n_classes]))}

    x = tf.reshape(x, shape=[-1, 28, 28, 1])

    conv1 = tf.nn.relu(conv2d(x, weights['W_conv1']) + biases['b_conv1'])
    conv1 = maxpool2d(conv1)

    conv2 = tf.nn.relu(conv2d(conv1, weights['W_conv2']) + biases['b_conv2'])
    conv2 = maxpool2d(conv2)

    fc1 = tf.reshape(conv2, [-1, 7 * 7 * 64])
    fc1 = tf.nn.relu(tf.matmul(fc1, weights['W_fc1']) + biases['b_fc1'])

    output = tf.matmul(fc1, weights['out']) + biases['out']

    return output

def train_neural_network(x):
    prediction = convolutional_neural_network(x)
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
    optimizer = tf.train.AdamOptimizer().minimize(cost)

    hm_epochs = 10

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        for epoch in range(hm_epochs):
            epoch_loss = 0
            for _ in range(int(mnist.train.num_examples/batch_size)):
                epoch_x, epoch_y = mnist.train.next_batch(batch_size)
                _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
                epoch_loss += c

            print('Epoch', epoch, 'completed out of', hm_epochs,'loss:', epoch_loss)

        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
        print('Accuracy:', accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))

train_neural_network(x)

这个网络包含两个卷积层和三个全连接层。如果需要更深的网络，可以添加更多的卷积层和全连接层。