diff --git a/example/tutorial_mnist_simple.py b/example/tutorial_mnist_simple.py
index 0b12f9579..c2957c614 100644
--- a/example/tutorial_mnist_simple.py
+++ b/example/tutorial_mnist_simple.py
@@ -33,7 +33,7 @@
 
 # define cost function and metric.
 y = network.outputs
-cost = tl.cost.cross_entropy(y, y_)
+cost = tl.cost.cross_entropy(y, y_, name='xentropy')
 correct_prediction = tf.equal(tf.argmax(y, 1), y_)
 acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 y_op = tf.argmax(tf.nn.softmax(y), 1)
diff --git a/tensorlayer/cost.py b/tensorlayer/cost.py
index 19d88088b..30b80f443 100644
--- a/tensorlayer/cost.py
+++ b/tensorlayer/cost.py
@@ -36,7 +36,7 @@ def cross_entropy(output, target, name=None):
     if tf.__version__ <= "0.12":
         return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=output, targets=target, name=name))
     else: # TF 1.0
-        return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target, logits=outputs, name=name))
+        return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target, logits=output, name=name))