使用tensorflow ValueError拟合线性二进制分类器时出错:没有为任何变量提供梯度，请检查图表

import tensorflow as tf

# 定义输入数据和标签
x = tf.placeholder(tf.float32, shape=[None, num_features])
y_true = tf.placeholder(tf.float32, shape=[None, 1])

# 定义模型参数
W = tf.Variable(tf.zeros([num_features, 1]))
b = tf.Variable(tf.zeros([1]))

# 定义模型输出
logits = tf.matmul(x, W) + b
y_pred = tf.sigmoid(logits)

# 定义损失函数和优化器
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true, logits=logits)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss)

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    
    for epoch in range(num_epochs):
        _, current_loss = sess.run([optimizer, loss], feed_dict={x: train_x, y_true: train_y})
        
        if epoch % 100 == 0:
            print("Epoch {}: Loss = {}".format(epoch, current_loss))