如何添加tensorflow损失函数？

import tensorflow as tf

# 定义输入
x = tf.placeholder(tf.float32, shape=[None, input_size], name='input')
# 定义输出
y_true = tf.placeholder(tf.float32, shape=[None, output_size], name='output')

# 定义模型的权重和偏置
weights = tf.Variable(tf.zeros([input_size, output_size]), name='weights')
biases = tf.Variable(tf.zeros([output_size]), name='biases')

# 定义模型的输出
y_pred = tf.matmul(x, weights) + biases

# 定义交叉熵损失函数
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y_pred))

# 定义优化器
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
# 定义训练操作
train_op = optimizer.minimize(cross_entropy)

with tf.Session() as sess:
    # 初始化变量
    sess.run(tf.global_variables_initializer())
    
    # 执行训练迭代
    for i in range(num_iterations):
        # 获取批量数据
        batch_x, batch_y = ...
        
        # 执行训练操作
        sess.run(train_op, feed_dict={x: batch_x, y_true: batch_y})