readme source url and import optimization (#7084)

* restored missing function

* missing import

* missing imports

* updated tutorial link

* recovered _print_download_progress func

* change default train with float16 instead of float32 accuracy

* test disable func call

* redundant function call, currently data is pulled automatically in

* optimized imports

* optimized imports

tutorials/image/cifar10/README.md

@@ -10,4 +10,4 @@ Code in this directory demonstrates how to use TensorFlow to train and evaluate
 
 Detailed instructions on how to get started available at:
 
-http://tensorflow.org/tutorials/deep_cnn/
+https://www.tensorflow.org/tutorials/images/deep_cnn

tutorials/image/cifar10/cifar10.py

@@ -46,7 +46,7 @@ FLAGS = tf.app.flags.FLAGS
 # Basic model parameters.
 tf.app.flags.DEFINE_integer('batch_size', 128,
                             """Number of images to process in a batch.""")
-tf.app.flags.DEFINE_boolean('use_fp16', False,
+tf.app.flags.DEFINE_boolean('use_fp16', True,
                             """Train the model using fp16.""")
 
 # Global constants describing the CIFAR-10 data set.
@@ -146,7 +146,6 @@ def distorted_inputs():
 
 def inputs(eval_data):
   """Construct input for CIFAR evaluation using the Reader ops.
-
   Args:
     eval_data: bool, indicating if one should use the train or eval data set.
 
@@ -154,8 +153,7 @@ def inputs(eval_data):
     images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
     labels: Labels. 1D tensor of [batch_size] size.
   """
-  images, labels = cifar10_input.inputs(eval_data=eval_data,
-                                        batch_size=FLAGS.batch_size)
+  images, labels = cifar10_input.inputs(eval_data=eval_data, batch_size=FLAGS.batch_size)
   if FLAGS.use_fp16:
     images = tf.cast(images, tf.float16)
     labels = tf.cast(labels, tf.float16)

tutorials/image/cifar10/cifar10_multi_gpu_train.py

@@ -39,14 +39,15 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from datetime import datetime
 import os.path
 import re
 import time
+from datetime import datetime
 
 import numpy as np
-from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
+from six.moves import xrange  # pylint: disable=redefined-builtin
+
 import cifar10
 
 FLAGS = tf.app.flags.FLAGS
@@ -266,7 +267,6 @@ def train():
 
 
 def main(argv=None):  # pylint: disable=unused-argument
-  cifar10.maybe_download_and_extract()
   if tf.gfile.Exists(FLAGS.train_dir):
     tf.gfile.DeleteRecursively(FLAGS.train_dir)
   tf.gfile.MakeDirs(FLAGS.train_dir)