train and eval the model

@@ -11,7 +11,7 @@ class Solver(object):
 
     def __init__(self, model, batch_size=100, pretrain_iter=20000, train_iter=2000, sample_iter=100, 
                  svhn_dir='svhn', mnist_dir='mnist', log_dir='logs', sample_save_path='sample', 
-                 model_save_path='model', pretrained_model='model/svhn_model-20000', test_model='model/dtn-600'):
+                 model_save_path='model', pretrained_model='model/svhn_model-20000', test_model='model/dtn-1800'):
         
         self.model = model
         self.batch_size = batch_size
@@ -121,11 +121,9 @@ class Solver(object):
             tf.global_variables_initializer().run()
             # restore variables of F
             print ('loading pretrained model F..')
-            #variables_to_restore = slim.get_model_variables(scope='content_extractor')
-            #restorer = tf.train.Saver(variables_to_restore)
-            #restorer.restore(sess, self.pretrained_model)
-            restorer = tf.train.Saver()
-            restorer.restore(sess, 'model/dtn-1600')
+            variables_to_restore = slim.get_model_variables(scope='content_extractor')
+            restorer = tf.train.Saver(variables_to_restore)
+            restorer.restore(sess, self.pretrained_model)
             summary_writer = tf.summary.FileWriter(logdir=self.log_dir, graph=tf.get_default_graph())
             saver = tf.train.Saver()