如何使用tensorflow创建自定义连接的神经网络？

import tensorflow as tf

input_layer = tf.placeholder(tf.float32, shape=[None, input_size])

weights = {
    'hidden': tf.Variable(tf.random_normal([input_size, hidden_size])),
    'output': tf.Variable(tf.random_normal([hidden_size, num_classes]))
}

biases = {
    'hidden': tf.Variable(tf.random_normal([hidden_size])),
    'output': tf.Variable(tf.random_normal([num_classes]))
}

hidden_layer = tf.add(tf.matmul(input_layer, weights['hidden']), biases['hidden'])
hidden_layer = tf.nn.relu(hidden_layer)

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

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=output_layer, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss)

correct_pred = tf.equal(tf.argmax(output_layer, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

init = tf.global_variables_initializer()

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

    # 训练模型
    for epoch in range(num_epochs):
        # 执行训练操作

    # 测试模型
    acc = sess.run(accuracy, feed_dict={input_layer: test_data, y: test_labels})
    print("Test Accuracy:", acc)