如何在Tensorflow中指定深度神经网络的体系结构？

import tensorflow as tf

# 定义神经网络模型
def deep_neural_network(input_data):
    # 定义网络的层级结构
    hidden_layer1 = {'weights': tf.Variable(tf.random_normal([input_size, n_nodes_hl1])),
                     'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))}
    hidden_layer2 = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                     'biases': tf.Variable(tf.random_normal([n_nodes_hl2]))}
    output_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl2, n_classes])),
                    'biases': tf.Variable(tf.random_normal([n_classes]))}

    # 构建网络的计算图
    l1 = tf.add(tf.matmul(input_data, hidden_layer1['weights']), hidden_layer1['biases'])
    l1 = tf.nn.relu(l1)

    l2 = tf.add(tf.matmul(l1, hidden_layer2['weights']), hidden_layer2['biases'])
    l2 = tf.nn.relu(l2)

    output = tf.matmul(l2, output_layer['weights']) + output_layer['biases']

    return output

# 定义输入数据和参数
input_size = 784  # 输入数据的大小
n_nodes_hl1 = 500  # 第一个隐藏层的节点数
n_nodes_hl2 = 500  # 第二个隐藏层的节点数
n_classes = 10  # 输出的类别数

# 定义输入数据的占位符
x = tf.placeholder('float', [None, input_size])
y = tf.placeholder('float')

# 构建模型
model = deep_neural_network(x)

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

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for epoch in range(num_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', num_epochs, 'loss:', epoch_loss)

    # 在测试集上评估模型
    correct = tf.equal(tf.argmax(model, 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}))