Improving experiment implementation

- fixing typos
- reusing variables

tutorials/image/cifar10_estimator/cifar10_main.py

@@ -71,11 +71,10 @@ tf.flags.DEFINE_float('weight_decay', 1e-4, 'Weight decay for convolutions.')
 
 tf.flags.DEFINE_boolean('use_distortion_for_training', True,
                         'If doing image distortion for training.')
-
 tf.flags.DEFINE_boolean('run_experiment', False,
-                        "If True will run an experiment," 
-                        "otherwise will run training and evaluatio"
-                        "using the estimator's methods")
+                        "If True will run an experiment,"
+                        "otherwise will run training and evaluation"
+                        "using the estimator interface")
 
 # Perf flags
 tf.flags.DEFINE_integer('num_intra_threads', 1,
@@ -365,17 +364,19 @@ def input_fn(subset, num_shards):
     return feature_shards, label_shards
 
 # create experiment
-def experiment_fn(run_config, hparams):    
-	# create estimator
-	classifier = tf.estimator.Estimator(model_fn=_resnet_model_fn,
-									                    config=run_config)
-	return tf.contrib.learn.Experiment(
-		classifier,
-		train_input_fn=train_input_fn,
-		eval_input_fn=test_input_fn,
-    train_steps=FLAGS.train_steps,
-    eval_steps=num_eval_examples // FLAGS.eval_batch_size
-	)
+def get_experiment_fn(train_input_fn, eval_input_fn, train_steps, eval_steps):
+  def _experiment_fn(run_config, hparams):
+    # create estimator
+    classifier = tf.estimator.Estimator(model_fn=_resnet_model_fn,
+                                        config=run_config)
+    return tf.contrib.learn.Experiment(
+        classifier,
+        train_input_fn=train_input_fn,
+        eval_input_fn=eval_input_fn,
+        train_steps=train_steps,
+        eval_steps=eval_steps
+    )
+  return _experiment_fn
 
 
 def main(unused_argv):
@@ -408,8 +409,19 @@ def main(unused_argv):
   sess_config.gpu_options.force_gpu_compatible = FLAGS.force_gpu_compatible
   config = config.replace(session_config=sess_config)
 
+  train_input_fn = functools.partial(input_fn, subset='train',
+                                     num_shards=FLAGS.num_gpus)
+
+  eval_input_fn = functools.partial(input_fn, subset='eval',
+                                   num_shards=FLAGS.num_gpus)
+
+  train_steps = FLAGS.train_steps
+  eval_steps = num_eval_examples // FLAGS.eval_batch_size
+
   if FLAGS.run_experiment:
-    tf.contrib.learn.learn_runner.run(experiment_fn, run_config=run_config)
+    tf.contrib.learn.learn_runner.run(
+        get_experiment_fn(train_input_fn, eval_input_fn,
+                          train_steps, eval_steps), run_config=config)
   else:
     classifier = tf.estimator.Estimator(
         model_fn=_resnet_model_fn, config=config)
@@ -419,17 +431,14 @@ def main(unused_argv):
         tensors=tensors_to_log, every_n_iter=100)
 
     print('Starting to train...')
-    classifier.train(
-        input_fn=functools.partial(
-            input_fn, subset='train', num_shards=FLAGS.num_gpus),
-        steps=FLAGS.train_steps,
-        hooks=[logging_hook])
+    classifier.train(input_fn=train_input_fn,
+                     steps=train_steps,
+                     hooks=[logging_hook])
 
     print('Starting to evaluate...')
     eval_results = classifier.evaluate(
-        input_fn=functools.partial(
-            input_fn, subset='eval', num_shards=FLAGS.num_gpus),
-        steps=num_eval_examples // FLAGS.eval_batch_size)
+        input_fn=eval_input_fn,
+        steps=eval_steps)
     print(eval_results)