def lr_schedule(epoch):
lr = FLAGS.lr
if epoch > epochs_inter[1]:
lr *= 1e-2
elif epoch > epochs_inter[0]:
lr *= 1e-1
print('Learning rate: ', lr)
return lr
model_input = Input(shape=input_shape)
#dim of logtis is batchsize x dim_means
if version == 2:
original_model, _, _, _, final_features = resnet_v2(input=model_input,
depth=depth,
num_classes=num_class,
use_BN=FLAGS.use_BN)
else:
original_model, _, _, _, final_features = resnet_v1(input=model_input,
depth=depth,
num_classes=num_class,
use_BN=FLAGS.use_BN)
if FLAGS.use_BN == True:
BN_name = '_withBN'
print('Use BN in the model')
else:
BN_name = '_noBN'
print('Do not use BN in the model')
#Whether use target attack for adversarial training
# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_class)
y_test = keras.utils.to_categorical(y_test, num_class)
y_test_target = keras.utils.to_categorical(y_test_target, num_class)
# Define input TF placeholder
y_place = tf.placeholder(tf.float32, shape=(None, num_class))
y_target = tf.placeholder(tf.float32, shape=(None, num_class))
sess = tf.Session()
keras.backend.set_session(sess)
model_input = Input(shape=input_shape)
#dim of logtis is batchsize x dim_means
if version == 2:
original_model,_,_,_,final_features = resnet_v2(input=model_input, depth=depth, num_classes=num_class, \
use_BN=FLAGS.use_BN, use_dense=FLAGS.use_dense, use_leaky=FLAGS.use_leaky)
else:
original_model,_,_,_,final_features = resnet_v1(input=model_input, depth=depth, num_classes=num_class, \
use_BN=FLAGS.use_BN, use_dense=FLAGS.use_dense, use_leaky=FLAGS.use_leaky)
if FLAGS.use_BN == True:
BN_name = '_withBN'
print('Use BN in the model')
else:
BN_name = '_noBN'
print('Do not use BN in the model')
#Whether use target attack for adversarial training
if FLAGS.use_target == False:
is_target = ''
else: