diff --git a/DeepSpeech.py b/DeepSpeech.py
index dcfe1b85..ea654345 100755
--- a/DeepSpeech.py
+++ b/DeepSpeech.py
@@ -350,8 +350,12 @@ class SampleIndex:
         return self.index
 
 
-def try_loading(session, saver, checkpoint_path, caption):
+def try_loading(session, saver, checkpoint_filename, caption):
     try:
+        checkpoint = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir, checkpoint_filename)
+        if not checkpoint:
+            return False
+        checkpoint_path = checkpoint.model_checkpoint_path
         saver.restore(session, checkpoint_path)
         log_info('Restored model from %s checkpoint at %s' % (caption, checkpoint_path))
         return True
@@ -448,9 +452,13 @@ def train():
 
     # Checkpointing
     checkpoint_saver = tf.train.Saver(max_to_keep=FLAGS.max_to_keep)
-    checkpoint_path = FLAGS.checkpoint_dir
+    checkpoint_path = os.path.join(FLAGS.checkpoint_dir, 'train')
+    checkpoint_filename = 'checkpoint'
+
     best_dev_saver = tf.train.Saver(max_to_keep=1)
-    best_dev_path = os.path.join(FLAGS.checkpoint_dir, 'best_dev.ckpt')
+    best_dev_path = os.path.join(FLAGS.checkpoint_dir, 'best_dev')
+    best_dev_filename = 'best_dev_checkpoint'
+
     initializer = tf.global_variables_initializer()
 
     with tf.Session(config=Config.session_config) as session:
@@ -460,9 +468,9 @@ def train():
         # Loading or initializing
         loaded = False
         if FLAGS.load in ['auto', 'last']:
-            loaded = try_loading(session, checkpoint_saver, checkpoint_path, 'most recent epoch')
+            loaded = try_loading(session, checkpoint_saver, checkpoint_filename, 'most recent epoch')
         if not loaded and FLAGS.load in ['auto', 'best']:
-            loaded = try_loading(session, best_dev_saver, best_dev_path, 'best validation')
+            loaded = try_loading(session, best_dev_saver, best_dev_filename, 'best validation')
         if not loaded:
             if FLAGS.load in ['auto', 'init']:
                 log_info('Initializing...')
@@ -513,8 +521,8 @@ def train():
                 step_summary_writer.add_summary(step_summary, current_step)
                 if FLAGS.show_progressbar:
                     pbar.update(step_index + 1, force=True)
-                if FLAGS.checkpoint_secs > 0 and time.time() - checkpoint_time > FLAGS.checkpoint_secs:
-                    checkpoint_saver.save(session, checkpoint_path)
+                if is_train and FLAGS.checkpoint_secs > 0 and time.time() - checkpoint_time > FLAGS.checkpoint_secs:
+                    checkpoint_saver.save(session, checkpoint_path, global_step=current_step)
                     checkpoint_time = time.time()
             if FLAGS.show_progressbar:
                 pbar.finish()
@@ -537,7 +545,7 @@ def train():
                     log_info('Training epoch %d ...' % current_epoch)
                     train_loss = run_set('train')
                     log_info('Finished training epoch %d - loss: %f' % (current_epoch, train_loss))
-                    checkpoint_saver.save(session, checkpoint_path)
+                    checkpoint_saver.save(session, checkpoint_path, global_step=global_step)
                     steps_trained = 0
                     # Validation
                     log_info('Validating epoch %d ...' % current_epoch)
@@ -546,7 +554,7 @@ def train():
                     log_info('Finished validating epoch %d - loss: %f' % (current_epoch, dev_loss))
                     if dev_loss < best_dev_loss:
                         best_dev_loss = dev_loss
-                        save_path = best_dev_saver.save(session, best_dev_path)
+                        save_path = best_dev_saver.save(session, best_dev_path, latest_filename=best_dev_filename)
                         log_info("Saved new best validating model with loss %f to: %s" % (best_dev_loss, save_path))
                     # Early stopping
                     if FLAGS.early_stop and len(dev_losses) >= FLAGS.es_steps: