Modify flag name for the checkpoint path

change flag name to checkpoint_dir according to the variable name
used by the checkpoint_utils within tensorflow python framework.

The important point is that when run the run_eval script, an error
occurs due to the different flag name.
Signed-off-by: MyungSung Kwak <yesmung@gmail.com>

research/learning_unsupervised_learning/run_eval.py

@@ -35,13 +35,13 @@ import sonnet as snt
 
 from tensorflow.contrib.framework.python.framework import checkpoint_utils
 
-flags.DEFINE_string("checkpoint", None, "Dir to load pretrained update rule from")
+flags.DEFINE_string("checkpoint_dir", None, "Dir to load pretrained update rule from")
 flags.DEFINE_string("train_log_dir", None, "Training log directory")
 
 FLAGS = flags.FLAGS
 
 
-def train(train_log_dir, checkpoint, eval_every_n_steps=10, num_steps=3000):
+def train(train_log_dir, checkpoint_dir, eval_every_n_steps=10, num_steps=3000):
   dataset_fn = datasets.mnist.TinyMnist
   w_learner_fn = architectures.more_local_weight_update.MoreLocalWeightUpdateWLearner
   theta_process_fn = architectures.more_local_weight_update.MoreLocalWeightUpdateProcess
@@ -77,8 +77,8 @@ def train(train_log_dir, checkpoint, eval_every_n_steps=10, num_steps=3000):
   summary_op = tf.summary.merge_all()
 
   file_writer = summary_utils.LoggingFileWriter(train_log_dir, regexes=[".*"])
-  if checkpoint:
-    str_var_list = checkpoint_utils.list_variables(checkpoint)
+  if checkpoint_dir:
+    str_var_list = checkpoint_utils.list_variables(checkpoint_dir)
     name_to_v_map = {v.op.name: v for v in tf.all_variables()}
     var_list = [
         name_to_v_map[vn] for vn, _ in str_var_list if vn in name_to_v_map
@@ -99,9 +99,9 @@ def train(train_log_dir, checkpoint, eval_every_n_steps=10, num_steps=3000):
     # global step should be restored from the evals job checkpoint or zero for fresh.
     step = sess.run(global_step)
 
-    if step == 0 and checkpoint:
+    if step == 0 and checkpoint_dir:
       tf.logging.info("force restore")
-      saver.restore(sess, checkpoint)
+      saver.restore(sess, checkpoint_dir)
       tf.logging.info("force restore done")
       sess.run(reset_global_step)
       step = sess.run(global_step)
@@ -115,7 +115,7 @@ def train(train_log_dir, checkpoint, eval_every_n_steps=10, num_steps=3000):
 
 
 def main(argv):
-  train(FLAGS.train_log_dir, FLAGS.checkpoint)
+  train(FLAGS.train_log_dir, FLAGS.checkpoint_dir)
 
 
 if __name__ == "__main__":