Why softmax cross entropy loss never gives a value of zero in tensorflow?

Question

Im doing a neural network in tensorflow and Im using softmax_cross_entropy to calculate the loss, I'm doing tests and note that it never gives a value of zero, even if I compare the same values, this is my code

labels=[1,0,1,1]


with tf.Session() as sess:
    onehot_labels=tf.one_hot(indices=labels,depth=2)
    logits=[[0.,1.],[1.,0.],[0.,1.],[0.,1.]]
    print(sess.run(onehot_labels))
    loss=tf.losses.softmax_cross_entropy(onehot_labels=onehot_labels,logits=logits)
    print(sess.run(loss))

I obtain this

[[0. 1.]
 [1. 0.]
 [0. 1.]
 [0. 1.]]
0.31326166

Why is not zero??

Answer 1

Matias's post is correct. The following code gives the same result as your code

labels=[1,0,1,1]

with tf.Session() as sess:
    onehot_labels=tf.one_hot(indices=labels,depth=2)
    logits=[[0.,1.],[1.,0.],[0.,1.],[0.,1.]]
    print(sess.run(onehot_labels))

    probabilities = tf.nn.softmax(logits=logits)
    # cross entropy
    loss = -tf.reduce_sum(onehot_labels * tf.log(probabilities)) / len(labels)

    print(sess.run(loss))