@@ -29,9 +29,9 @@ def VGG16(x, n_classes, is_pretrain=True):
2929 x = tools .pool ('pool3' , x , kernel = [1 ,2 ,2 ,1 ], stride = [1 ,2 ,2 ,1 ], is_max_pool = True )
3030
3131 x = tools .FC_layer ('fc6' , x , out_nodes = 4096 )
32- x = tools .batch_norm (x )
32+ # x = tools.batch_norm(x)
3333 x = tools .FC_layer ('fc7' , x , out_nodes = 4096 )
34- x = tools .batch_norm (x )
34+ # x = tools.batch_norm(x)
3535 x = tools .FC_layer ('fc8' , x , out_nodes = n_classes )
3636
3737 return x
@@ -80,11 +80,11 @@ def VGG16N(x, n_classes, is_pretrain=True):
8080
8181
8282 x = tools .FC_layer ('fc6' , x , out_nodes = 4096 )
83- with tf .name_scope ('batch_norm1' ):
84- x = tools .batch_norm (x )
83+ # with tf.name_scope('batch_norm1'):
84+ # x = tools.batch_norm(x)
8585 x = tools .FC_layer ('fc7' , x , out_nodes = 4096 )
86- with tf .name_scope ('batch_norm2' ):
87- x = tools .batch_norm (x )
86+ # with tf.name_scope('batch_norm2'):
87+ # x = tools.batch_norm(x)
8888 x = tools .FC_layer ('fc8' , x , out_nodes = n_classes )
8989
9090 return x
@@ -99,4 +99,4 @@ def VGG16N(x, n_classes, is_pretrain=True):
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101101
102- 102+
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