Change Python print to be compatible with Python3 (tensorflow#8470)

tensorflow/docs_src/get_started/mnist/mechanics.md

@@ -351,7 +351,7 @@ training.
 
 ```python
 if step % 100 == 0:
-    print 'Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration)
+    print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration))
 ```
 
 #### Visualize the Status
@@ -421,19 +421,19 @@ the training and test datasets.  The `do_eval()` function is called thrice, for
 the training, validation, and test datasets.
 
 ```python
-print 'Training Data Eval:'
+print('Training Data Eval:')
 do_eval(sess,
         eval_correct,
         images_placeholder,
         labels_placeholder,
         data_sets.train)
-print 'Validation Data Eval:'
+print('Validation Data Eval:')
 do_eval(sess,
         eval_correct,
         images_placeholder,
         labels_placeholder,
         data_sets.validation)
-print 'Test Data Eval:'
+print('Test Data Eval:')
 do_eval(sess,
         eval_correct,
         images_placeholder,

tensorflow/docs_src/programmers_guide/faq.md

@@ -92,12 +92,12 @@ two following snippets of code are equivalent:
 # Using `Session.run()`.
 sess = tf.Session()
 c = tf.constant(5.0)
-print sess.run(c)
+print(sess.run(c))
 
 # Using `Tensor.eval()`.
 c = tf.constant(5.0)
 with tf.Session():
-  print c.eval()
+  print(c.eval())
 ```
 
 In the second example, the session acts as a

tensorflow/docs_src/tutorials/linear.md

@@ -217,7 +217,7 @@ results = e.evaluate(input_fn=input_fn_test, steps=1)
 
 # Print the stats for the evaluation.
 for key in sorted(results):
-    print "%s: %s" % (key, results[key])
+    print("%s: %s" % (key, results[key]))
 ```
 
 ### Wide and deep learning

tensorflow/docs_src/tutorials/using_gpu.md

@@ -28,7 +28,7 @@ c = tf.matmul(a, b)
 # Creates a session with log_device_placement set to True.
 sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
 # Runs the op.
-print sess.run(c)
+print(sess.run(c))
 ```
 
 You should see the following output:
@@ -61,7 +61,7 @@ with tf.device('/cpu:0'):
 # Creates a session with log_device_placement set to True.
 sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
 # Runs the op.
-print sess.run(c)
+print(sess.run(c))
 ```
 
 You will see that now `a` and `b` are assigned to `cpu:0`.
@@ -131,7 +131,7 @@ with tf.device('/gpu:2'):
 # Creates a session with log_device_placement set to True.
 sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
 # Runs the op.
-print sess.run(c)
+print(sess.run(c))
 ```
 
 If the device you have specified does not exist, you will get
@@ -160,7 +160,7 @@ with tf.device('/gpu:2'):
 sess = tf.Session(config=tf.ConfigProto(
       allow_soft_placement=True, log_device_placement=True))
 # Runs the op.
-print sess.run(c)
+print(sess.run(c))
 ```
 
 ## Using multiple GPUs
@@ -182,7 +182,7 @@ with tf.device('/cpu:0'):
 # Creates a session with log_device_placement set to True.
 sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
 # Runs the op.
-print sess.run(sum)
+print(sess.run(sum))
 ```
 
 You will see the following output.

tensorflow/docs_src/tutorials/wide.md

@@ -379,7 +379,7 @@ the labels of the holdout data:
 ```python
 results = m.evaluate(input_fn=eval_input_fn, steps=1)
 for key in sorted(results):
-    print "%s: %s" % (key, results[key])
+    print("%s: %s" % (key, results[key]))
 ```
 
 The first line of the output should be something like `accuracy: 0.83557522`,

tensorflow/docs_src/tutorials/wide_and_deep.md

@@ -255,7 +255,7 @@ After reading in the data, you can train and evaluate the model:
 m.fit(input_fn=train_input_fn, steps=200)
 results = m.evaluate(input_fn=eval_input_fn, steps=1)
 for key in sorted(results):
-    print "%s: %s" % (key, results[key])
+    print("%s: %s" % (key, results[key]))
 ```
 
 The first line of the output should be something like `accuracy: 0.84429705`. We