c56dc1933bd601a4630e963d4f0d3142fdc9c0d9,tutorials/mnist.py,,loss,#Any#Any#,82

Before Change

  // be a 1.0 in the entry corresponding to the label).
  batch_size = tf.size(labels)
  labels = tf.expand_dims(labels, 1)
  indices = tf.expand_dims(tf.range(0, batch_size), 1)
  concated = tf.concat(1, [indices, labels])
  onehot_labels = tf.sparse_to_dense(
      concated, tf.pack([batch_size, NUM_CLASSES]), 1.0, 0.0)
  cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits,
                                                          onehot_labels,

After Change

    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
        logits, onehot_labels, name="xentropy"
    )
    return tf.reduce_mean(cross_entropy, name="xentropy_mean")


def convert_labels_to_onehot(labels):
    

Instances