问Tensorboard摘要标量误差仅为一个标量

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cross_entropy = -tf.reduce_sum(y * tf.log(logits))
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_)
cross_entropy = tf.reduce_mean(cross_entropy) * 100

correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

learning_rate = 0.0001 + tf.train.exponential_decay(0.003, step, 2000, 1 / math.e)
train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)

tf.summary.scalar("cross_entropy", cross_entropy)
tf.summary.scalar("training_accuracy", accuracy)
tf.summary.scalar("learning_rate", learning_rate)
tf.summary.image("input", x_image, 3)

with tf.Session() as sess:
    sess.run(initializer)
    merged_summary = tf.summary.merge_all()
    writer = tf.summary.FileWriter("./visualization/1")
    writer.add_graph(sess.graph)

    for i in range(1000):
        batch_x, batch_y = mnist.train.next_batch(100)
        if i % 5 == 0:
            s = sess.run(merged_summary, feed_dict={X: batch_x, Y_: batch_y})
            writer.add_summary(s, i)
        sess.run(train_step, feed_dict={X: batch_x, Y_: batch_y, step: i})
        print(sess.run(accuracy, feed_dict={X: mnist.test.images, Y_: mnist.test.labels}))

tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype int32

tf.summary.scalar("learning_rate", learning_rate)