Minor update the resnet runloop. (#4113)

1. trainning hooks and train/eval function does not need to be
declared several times.
2. change to use tf.logging.info instead of print
3. Show the current index and total count of training cycle, which
give user some hint about where they are in the whole process.

official/resnet/resnet_run_loop.py

@@ -401,28 +401,29 @@ def resnet_main(flags, model_function, input_function, shape=None):
   benchmark_logger = logger.config_benchmark_logger(flags.benchmark_log_dir)
   benchmark_logger.log_run_info('resnet')
 
-  for _ in range(flags.train_epochs // flags.epochs_between_evals):
   train_hooks = hooks_helper.get_train_hooks(
       flags.hooks,
       batch_size=flags.batch_size,
       benchmark_log_dir=flags.benchmark_log_dir)
 
-    print('Starting a training cycle.')
-
   def input_fn_train():
     return input_function(True, flags.data_dir, flags.batch_size,
                           flags.epochs_between_evals,
                           flags.num_parallel_calls, flags.multi_gpu)
 
-    classifier.train(input_fn=input_fn_train, hooks=train_hooks,
-                     max_steps=flags.max_train_steps)
-
-    print('Starting to evaluate.')
-    # Evaluate the model and print results
   def input_fn_eval():
     return input_function(False, flags.data_dir, flags.batch_size,
                           1, flags.num_parallel_calls, flags.multi_gpu)
 
+  total_training_cycle = flags.train_epochs // flags.epochs_between_evals
+  for cycle_index in range(total_training_cycle):
+    tf.logging.info('Starting a training cycle: %d/%d',
+                    cycle_index, total_training_cycle)
+
+    classifier.train(input_fn=input_fn_train, hooks=train_hooks,
+                     max_steps=flags.max_train_steps)
+
+    tf.logging.info('Starting to evaluate.')
     # flags.max_train_steps is generally associated with testing and profiling.
     # As a result it is frequently called with synthetic data, which will
     # iterate forever. Passing steps=flags.max_train_steps allows the eval