Merge changes from github.

Change: 147897309

6 files changed, 11 insertions, 10 deletions

diff --git a/tensorflow/examples/learn/boston.py b/tensorflow/examples/learn/boston.py
index 5d5ddff564..2986ff9106 100644
--- a/tensorflow/examples/learn/boston.py
+++ b/tensorflow/examples/learn/boston.py
@@ -43,11 +43,12 @@ def main(unused_argv):
 
   # Fit
   regressor.fit(x_train, y_train, steps=5000, batch_size=1)
-
+  
+  # Transform
+  x_transformed = scaler.transform(x_test)
+  
   # Predict and score
-  y_predicted = list(
-      regressor.predict(
-          scaler.transform(x_test), as_iterable=True))
+  y_predicted = list(regressor.predict(x_transformed, as_iterable=True))
   score = metrics.mean_squared_error(y_predicted, y_test)
 
   print('MSE: {0:f}'.format(score))
diff --git a/tensorflow/examples/learn/iris_custom_model.py b/tensorflow/examples/learn/iris_custom_model.py
index 31fb88954b..fbc50716c9 100644
--- a/tensorflow/examples/learn/iris_custom_model.py
+++ b/tensorflow/examples/learn/iris_custom_model.py
@@ -43,7 +43,7 @@ def my_model(features, target):
 
   # Compute logits (1 per class) and compute loss.
   logits = layers.fully_connected(features, 3, activation_fn=None)
-  loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
+  loss = tf.losses.softmax_cross_entropy(target, logits)
 
   # Create a tensor for training op.
   train_op = tf.contrib.layers.optimize_loss(
diff --git a/tensorflow/examples/learn/mnist.py b/tensorflow/examples/learn/mnist.py
index 4b3f1835e2..6e5fe7891b 100644
--- a/tensorflow/examples/learn/mnist.py
+++ b/tensorflow/examples/learn/mnist.py
@@ -67,7 +67,7 @@ def conv_model(feature, target, mode):
 
   # Compute logits (1 per class) and compute loss.
   logits = layers.fully_connected(h_fc1, 10, activation_fn=None)
-  loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
+  loss = tf.losses.softmax_cross_entropy(target, logits)
 
   # Create a tensor for training op.
   train_op = layers.optimize_loss(
diff --git a/tensorflow/examples/learn/multiple_gpu.py b/tensorflow/examples/learn/multiple_gpu.py
index a395d94151..df58906b39 100644
--- a/tensorflow/examples/learn/multiple_gpu.py
+++ b/tensorflow/examples/learn/multiple_gpu.py
@@ -60,7 +60,7 @@ def my_model(features, target):
   with tf.device('/gpu:2'):
     # Compute logits (1 per class) and compute loss.
     logits = layers.fully_connected(features, 3, activation_fn=None)
-    loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
+    loss = tf.losses.softmax_cross_entropy(target, logits)
 
     # Create a tensor for training op.
     train_op = tf.contrib.layers.optimize_loss(
diff --git a/tensorflow/examples/learn/resnet.py b/tensorflow/examples/learn/resnet.py
index 49d89ef660..f822903208 100755
--- a/tensorflow/examples/learn/resnet.py
+++ b/tensorflow/examples/learn/resnet.py
@@ -144,7 +144,7 @@ def res_net(x, y, activation=tf.nn.relu):
 
   target = tf.one_hot(y, depth=10, dtype=tf.float32)
   logits = tf.contrib.layers.fully_connected(net, 10, activation_fn=None)
-  loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
+  loss = tf.losses.softmax_cross_entropy(target, logits)
   return tf.softmax(logits), loss
 
 
diff --git a/tensorflow/examples/learn/text_classification_character_cnn.py b/tensorflow/examples/learn/text_classification_character_cnn.py
index 143af4f664..0c96976146 100644
--- a/tensorflow/examples/learn/text_classification_character_cnn.py
+++ b/tensorflow/examples/learn/text_classification_character_cnn.py
@@ -49,7 +49,7 @@ def char_cnn_model(features, target):
   """Character level convolutional neural network model to predict classes."""
   target = tf.one_hot(target, 15, 1, 0)
   byte_list = tf.reshape(
-      tf.one_hot(features, 256, 1, 0), [-1, MAX_DOCUMENT_LENGTH, 256, 1])
+      tf.one_hot(features, 256), [-1, MAX_DOCUMENT_LENGTH, 256, 1])
   with tf.variable_scope('CNN_Layer1'):
     # Apply Convolution filtering on input sequence.
     conv1 = tf.contrib.layers.convolution2d(
@@ -73,7 +73,7 @@ def char_cnn_model(features, target):
 
   # Apply regular WX + B and classification.
   logits = tf.contrib.layers.fully_connected(pool2, 15, activation_fn=None)
-  loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
+  loss = tf.losses.softmax_cross_entropy(target, logits)
 
   train_op = tf.contrib.layers.optimize_loss(
       loss,