import tensorflow as tf

      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, 10]), 1.0, 0.0)
      logits = tf.get_collection("logits")[0]
      cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits,
                                                              onehot_labels,
                                                              name="xentropy")
      loss = tf.reduce_mean(cross_entropy, name="xentropy_mean")

      tf.scalar_summary(loss.op.name, loss)
      # Creates the gradient descent optimizer with the given learning rate.
