# coding=utf-8
import tensorflow as tf

labels = ''
XXX = ''
biases = ''

# logits = tf.nn.softmax(XXX)
# loss = tf.reduce_mean(-tf.reduce_sum(labels * tf.log(logits)))

logits = tf.matmul(XXX, XXX) + biases
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits, name='losses')

ad161cbfa54a73a08f5599e6c4ba1df2e4d68c7c
<mask token>

<mask token>
labels = ''
XXX = ''
biases = ''
logits = tf.matmul(XXX, XXX) + biases
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits,
    name='losses')

import tensorflow as tf
labels = ''
XXX = ''
biases = ''
logits = tf.matmul(XXX, XXX) + biases
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits,
    name='losses')

# coding=utf-8
import tensorflow as tf

labels = ''
XXX = ''
biases = ''

# logits = tf.nn.softmax(XXX)
# loss = tf.reduce_mean(-tf.reduce_sum(labels * tf.log(logits)))

logits = tf.matmul(XXX, XXX) + biases
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits, name='losses')
