之前一直被误导，以为sparse_softmax_cross_entropy_with_logit的便签也是one-hot形式。于是一直报错如下： ValueError: Rank mismatch: Rank of labels (received 2) should equal rank of logits minus 1 (received 1).
后来看了 stackoverflow后才明白，原来sparse_softmax_cross_entropy_with_logits输入的就是putu Both functions computes the same results and sparse_softmax_cross_entropy_with_logits computes the cross entropy directly on the sparse labels instead of converting them with one-hot encoding.
You can verify this by running the following program:
import tensorflow as tf from random import randint
dims = 8 pos = randint(0, dims - 1)
logits = tf.random_uniform([dims], maxval=3, dtype=tf.float32) labels = tf.one_hot(pos, dims)
res1 = tf.nn.softmax_cross_entropy_with_logits( logits=logits, labels=labels) res2 = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=tf.constant(pos))
with tf.Session() as sess: a, b = sess.run([res1, res2]) print a, b print a == b