Merge commit for internal changes

6 files changed, 24 insertions, 31 deletions

diff --git a/tensorflow/examples/tutorials/layers/cnn_mnist.py b/tensorflow/examples/tutorials/layers/cnn_mnist.py
index 2124843fcb..1e8d7d05e1 100644
--- a/tensorflow/examples/tutorials/layers/cnn_mnist.py
+++ b/tensorflow/examples/tutorials/layers/cnn_mnist.py
@@ -97,9 +97,7 @@ def cnn_model_fn(features, labels, mode):
     return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
 
   # Calculate Loss (for both TRAIN and EVAL modes)
-  onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
-  loss = tf.losses.softmax_cross_entropy(
-      onehot_labels=onehot_labels, logits=logits)
+  loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
 
   # Configure the Training Op (for TRAIN mode)
   if mode == tf.estimator.ModeKeys.TRAIN:
diff --git a/tensorflow/examples/tutorials/mnist/mnist.py b/tensorflow/examples/tutorials/mnist/mnist.py
index 3585043a2a..7cedd0e264 100644
--- a/tensorflow/examples/tutorials/mnist/mnist.py
+++ b/tensorflow/examples/tutorials/mnist/mnist.py
@@ -94,9 +94,7 @@ def loss(logits, labels):
     loss: Loss tensor of type float.
   """
   labels = tf.to_int64(labels)
-  cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
-      labels=labels, logits=logits, name='xentropy')
-  return tf.reduce_mean(cross_entropy, name='xentropy_mean')
+  return tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
 
 
 def training(loss, learning_rate):
diff --git a/tensorflow/examples/tutorials/mnist/mnist_deep.py b/tensorflow/examples/tutorials/mnist/mnist_deep.py
index a4dbab5123..1e0294db27 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_deep.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_deep.py
@@ -125,27 +125,27 @@ def bias_variable(shape):
 
 def main(_):
   # Import data
-  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
+  mnist = input_data.read_data_sets(FLAGS.data_dir)
 
   # Create the model
   x = tf.placeholder(tf.float32, [None, 784])
 
   # Define loss and optimizer
-  y_ = tf.placeholder(tf.float32, [None, 10])
+  y_ = tf.placeholder(tf.int64, [None])
 
   # Build the graph for the deep net
   y_conv, keep_prob = deepnn(x)
 
   with tf.name_scope('loss'):
-    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,
-                                                            logits=y_conv)
+    cross_entropy = tf.losses.sparse_softmax_cross_entropy(
+        labels=y_, logits=y_conv)
   cross_entropy = tf.reduce_mean(cross_entropy)
 
   with tf.name_scope('adam_optimizer'):
     train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
 
   with tf.name_scope('accuracy'):
-    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
+    correct_prediction = tf.equal(tf.argmax(y_conv, 1), y_)
     correct_prediction = tf.cast(correct_prediction, tf.float32)
   accuracy = tf.reduce_mean(correct_prediction)
 
diff --git a/tensorflow/examples/tutorials/mnist/mnist_softmax.py b/tensorflow/examples/tutorials/mnist/mnist_softmax.py
index addd2d3810..fb3ac94203 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_softmax.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_softmax.py
@@ -34,7 +34,7 @@ FLAGS = None
 
 def main(_):
   # Import data
-  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
+  mnist = input_data.read_data_sets(FLAGS.data_dir)
 
   # Create the model
   x = tf.placeholder(tf.float32, [None, 784])
@@ -43,7 +43,7 @@ def main(_):
   y = tf.matmul(x, W) + b
 
   # Define loss and optimizer
-  y_ = tf.placeholder(tf.float32, [None, 10])
+  y_ = tf.placeholder(tf.int64, [None])
 
   # The raw formulation of cross-entropy,
   #
@@ -52,10 +52,9 @@ def main(_):
   #
   # can be numerically unstable.
   #
-  # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
+  # So here we use tf.losses.sparse_softmax_cross_entropy on the raw
   # outputs of 'y', and then average across the batch.
-  cross_entropy = tf.reduce_mean(
-      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
+  cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y)
   train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
 
   sess = tf.InteractiveSession()
@@ -66,7 +65,7 @@ def main(_):
     sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
 
   # Test trained model
-  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
+  correct_prediction = tf.equal(tf.argmax(y, 1), y_)
   accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
   print(sess.run(accuracy, feed_dict={x: mnist.test.images,
                                       y_: mnist.test.labels}))
diff --git a/tensorflow/examples/tutorials/mnist/mnist_softmax_xla.py b/tensorflow/examples/tutorials/mnist/mnist_softmax_xla.py
index eaff05913a..e89317494f 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_softmax_xla.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_softmax_xla.py
@@ -32,7 +32,7 @@ FLAGS = None
 
 def main(_):
   # Import data
-  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
+  mnist = input_data.read_data_sets(FLAGS.data_dir)
 
   # Create the model
   x = tf.placeholder(tf.float32, [None, 784])
@@ -41,7 +41,7 @@ def main(_):
   y = tf.matmul(x, w) + b
 
   # Define loss and optimizer
-  y_ = tf.placeholder(tf.float32, [None, 10])
+  y_ = tf.placeholder(tf.int64, [None])
 
   # The raw formulation of cross-entropy,
   #
@@ -50,10 +50,9 @@ def main(_):
   #
   # can be numerically unstable.
   #
-  # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
-  # outputs of 'y', and then average across the batch.
-  cross_entropy = tf.reduce_mean(
-      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
+  # So here we use tf.losses.sparse_softmax_cross_entropy on the raw
+  # logit outputs of 'y', and then average across the batch.
+  cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y)
   train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
 
   config = tf.ConfigProto()
@@ -86,7 +85,7 @@ def main(_):
       sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
 
   # Test trained model
-  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
+  correct_prediction = tf.equal(tf.argmax(y, 1), y_)
   accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
   print(sess.run(accuracy,
                  feed_dict={x: mnist.test.images,
diff --git a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
index c401d09df8..7967e22d6a 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
@@ -38,7 +38,6 @@ FLAGS = None
 def train():
   # Import data
   mnist = input_data.read_data_sets(FLAGS.data_dir,
-                                    one_hot=True,
                                     fake_data=FLAGS.fake_data)
 
   sess = tf.InteractiveSession()
@@ -47,7 +46,7 @@ def train():
   # Input placeholders
   with tf.name_scope('input'):
     x = tf.placeholder(tf.float32, [None, 784], name='x-input')
-    y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')
+    y_ = tf.placeholder(tf.int64, [None], name='y-input')
 
   with tf.name_scope('input_reshape'):
     image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
@@ -117,12 +116,12 @@ def train():
     #
     # can be numerically unstable.
     #
-    # So here we use tf.nn.softmax_cross_entropy_with_logits on the
-    # raw outputs of the nn_layer above, and then average across
+    # So here we use tf.losses.sparse_softmax_cross_entropy on the
+    # raw logit outputs of the nn_layer above, and then average across
     # the batch.
-    diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)
     with tf.name_scope('total'):
-      cross_entropy = tf.reduce_mean(diff)
+      cross_entropy = tf.losses.sparse_softmax_cross_entropy(
+          labels=y_, logits=y)
   tf.summary.scalar('cross_entropy', cross_entropy)
 
   with tf.name_scope('train'):
@@ -131,7 +130,7 @@ def train():
 
   with tf.name_scope('accuracy'):
     with tf.name_scope('correct_prediction'):
-      correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
+      correct_prediction = tf.equal(tf.argmax(y, 1), y_)
     with tf.name_scope('accuracy'):
       accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
   tf.summary.scalar('accuracy', accuracy)