mnist example now uses sparse_softmax_cross_entropy_with_logits.

Change: 115636069

tensorflow/examples/tutorials/mnist/mnist.py

@@ -93,19 +93,9 @@ def loss(logits, labels):
   Returns:
     loss: Loss tensor of type float.
   """
-  # Convert from sparse integer labels in the range [0, NUM_CLASSES)
-  # to 1-hot dense float vectors (that is we will have batch_size vectors,
-  # each with NUM_CLASSES values, all of which are 0.0 except there will
-  # 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,
-                                                          name='xentropy')
+  labels = tf.to_int64(labels)
+  cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
+      logits, labels, name='xentropy')
   loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
   return loss