如何在tensorflow中使用softmax

import tensorflow as tf

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

W = tf.Variable(tf.zeros([input_size, num_classes]))
b = tf.Variable(tf.zeros([num_classes]))

logits = tf.matmul(x, W) + b
y_pred = tf.nn.softmax(logits)

y_true = tf.placeholder(tf.float32, [None, num_classes])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_true * tf.log(y_pred), reduction_indices=[1]))

optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)

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
        _, loss = sess.run([optimizer, cross_entropy], feed_dict={x: train_data, y_true: train_labels})
        # 打印训练过程中的损失值
        print("Epoch:", epoch, "Loss:", loss)