train and eval the model

@@ -9,9 +9,9 @@ import scipy.misc
 
 class Solver(object):
 
-    def __init__(self, model, batch_size=100, pretrain_iter=10000, train_iter=2000, sample_iter=100, 
+    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-10000', test_model='model/dtn-2000'):
+                 model_save_path='model', pretrained_model='model/svhn_model-20000', test_model='model/dtn-600'):
         
         self.model = model
         self.batch_size = batch_size
@@ -111,7 +111,7 @@ class Solver(object):
         model = self.model
         model.build_model()
 
-        # make log directory if not exists
+        # make directory if not exists
         if tf.gfile.Exists(self.log_dir):
             tf.gfile.DeleteRecursively(self.log_dir)
         tf.gfile.MakeDirs(self.log_dir)
@@ -121,13 +121,16 @@ 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)
+            #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')
             summary_writer = tf.summary.FileWriter(logdir=self.log_dir, graph=tf.get_default_graph())
             saver = tf.train.Saver()
 
             print ('start training..!')
+            f_interval = 15
             for step in range(self.train_iter+1):
                 
                 i = step % int(svhn_images.shape[0] / self.batch_size)
@@ -143,7 +146,10 @@ class Solver(object):
                 sess.run([model.g_train_op_src], feed_dict) 
                 sess.run([model.g_train_op_src], feed_dict)
                 
-                if i % 15 == 0:
+                if step > 1600:
+                    f_interval = 30
+                
+                if i % f_interval == 0:
                     sess.run(model.f_train_op_src, feed_dict)
                 
                 if (step+1) % 10 == 0: