Change arg order for {softmax,sparse_softmax,sigmoid}_cross_entropy_with_logits to be (labels, predictions), and force use of named args to avoid accidents.

Change: 143629623

7 files changed, 10 insertions, 10 deletions

diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index 0d5ba84c2d..c5518e2603 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -723,7 +723,7 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor):
 
   with tf.name_scope('cross_entropy'):
     cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
-      logits, ground_truth_input)
+        labels=ground_truth_input, logits=logits)
     with tf.name_scope('total'):
       cross_entropy_mean = tf.reduce_mean(cross_entropy)
   tf.summary.scalar('cross_entropy', cross_entropy_mean)
diff --git a/tensorflow/examples/tutorials/mnist/mnist.py b/tensorflow/examples/tutorials/mnist/mnist.py
index e97a6c48ef..d533697976 100644
--- a/tensorflow/examples/tutorials/mnist/mnist.py
+++ b/tensorflow/examples/tutorials/mnist/mnist.py
@@ -95,9 +95,8 @@ def loss(logits, labels):
   """
   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
+      labels=labels, logits=logits, name='xentropy')
+  return tf.reduce_mean(cross_entropy, name='xentropy_mean')
 
 
 def training(loss, learning_rate):
diff --git a/tensorflow/examples/tutorials/mnist/mnist_softmax.py b/tensorflow/examples/tutorials/mnist/mnist_softmax.py
index 42a406d386..4fa89ff246 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_softmax.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_softmax.py
@@ -54,7 +54,8 @@ def main(_):
   #
   # 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(y, y_))
+  cross_entropy = tf.reduce_mean(
+      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
   train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
 
   sess = tf.InteractiveSession()
diff --git a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
index 83879d0807..ff78f151c3 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
@@ -119,7 +119,7 @@ def train():
     # So here we use tf.nn.softmax_cross_entropy_with_logits on the
     # raw outputs of the nn_layer above, and then average across
     # the batch.
-    diff = tf.nn.softmax_cross_entropy_with_logits(y, y_)
+    diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)
     with tf.name_scope('total'):
       cross_entropy = tf.reduce_mean(diff)
   tf.summary.scalar('cross_entropy', cross_entropy)
diff --git a/tensorflow/examples/udacity/2_fullyconnected.ipynb b/tensorflow/examples/udacity/2_fullyconnected.ipynb
index 8a845171a4..a6a206307a 100644
--- a/tensorflow/examples/udacity/2_fullyconnected.ipynb
+++ b/tensorflow/examples/udacity/2_fullyconnected.ipynb
@@ -271,7 +271,7 @@
         "  # cross-entropy across all training examples: that's our loss.\n",
         "  logits = tf.matmul(tf_train_dataset, weights) + biases\n",
         "  loss = tf.reduce_mean(\n",
-        "    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n",
+        "    tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))\n",
         "  \n",
         "  # Optimizer.\n",
         "  # We are going to find the minimum of this loss using gradient descent.\n",
@@ -448,7 +448,7 @@
         "  # Training computation.\n",
         "  logits = tf.matmul(tf_train_dataset, weights) + biases\n",
         "  loss = tf.reduce_mean(\n",
-        "    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n",
+        "    tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))\n",
         "  \n",
         "  # Optimizer.\n",
         "  optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
diff --git a/tensorflow/examples/udacity/4_convolutions.ipynb b/tensorflow/examples/udacity/4_convolutions.ipynb
index 464d2c836e..d607dddbb2 100644
--- a/tensorflow/examples/udacity/4_convolutions.ipynb
+++ b/tensorflow/examples/udacity/4_convolutions.ipynb
@@ -286,7 +286,7 @@
         "  # Training computation.\n",
         "  logits = model(tf_train_dataset)\n",
         "  loss = tf.reduce_mean(\n",
-        "    tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n",
+        "    tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))\n",
         "    \n",
         "  # Optimizer.\n",
         "  optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)\n",
diff --git a/tensorflow/examples/udacity/6_lstm.ipynb b/tensorflow/examples/udacity/6_lstm.ipynb
index 64e913acf8..7e78c5328f 100644
--- a/tensorflow/examples/udacity/6_lstm.ipynb
+++ b/tensorflow/examples/udacity/6_lstm.ipynb
@@ -576,7 +576,7 @@
         "    logits = tf.nn.xw_plus_b(tf.concat_v2(outputs, 0), w, b)\n",
         "    loss = tf.reduce_mean(\n",
         "      tf.nn.softmax_cross_entropy_with_logits(\n",
-        "        logits, tf.concat_v2(train_labels, 0)))\n",
+        "        labels=tf.concat_v2(train_labels, 0), logits=logits))\n",
         "\n",
         "  # Optimizer.\n",
         "  global_step = tf.Variable(0)\n",