如何使用tensorflow softmax_cross_entropy_with_logits缩放和重新规格化输出以解决类不平衡问题

import tensorflow as tf

# 定义输入和输出的占位符
x = tf.placeholder(tf.float32, [None, input_dim])
y = tf.placeholder(tf.float32, [None, num_classes])

# 定义模型参数
W = tf.Variable(tf.zeros([input_dim, num_classes]))
b = tf.Variable(tf.zeros([num_classes]))

# 定义模型的输出
logits = tf.matmul(x, W) + b

# 对输出进行缩放和重新规范化
scaled_logits = tf.nn.softmax(logits)

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

# 定义优化器
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(num_epochs):
        # 执行优化器和损失函数
        _, curr_loss = sess.run([optimizer, loss], feed_dict={x: train_x, y: train_y})
        # 打印损失函数
        print("Epoch: %d, Loss: %f" % (epoch+1, curr_loss))
    
    # 进行预测
    predictions = sess.run(scaled_logits, feed_dict={x: test_x})