assert (y_test_targets.argmax(axis=1) != y_test_sparse).all()
np.save(os.path.join(attack_dir, 'y_val_targets.npy') , y_val_targets)
np.save(os.path.join(attack_dir, 'y_test_targets.npy'), y_test_targets)
else:
y_val_targets = np.load(os.path.join(attack_dir, 'y_val_targets.npy'))
y_test_targets = np.load(os.path.join(attack_dir, 'y_test_targets.npy'))
# Use Image Parameters
img_rows, img_cols, nchannels = X_test.shape[1:4]
nb_classes = y_test.shape[1]
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, nchannels), name='x')
y = tf.placeholder(tf.float32, shape=(None, nb_classes), name='y')
model = DarkonReplica(scope=ARCH_NAME[FLAGS.dataset], nb_classes=feeder.num_classes, n=5, input_shape=[32, 32, 3])
preds = model.get_predicted_class(x)
logits = model.get_logits(x)
embeddings = model.get_embeddings(x)
loss = CrossEntropy(model, smoothing=LABEL_SMOOTHING[FLAGS.dataset])
regu_losses = WeightDecay(model)
full_loss = WeightedSum(model, [(1.0, loss), (weight_decay, regu_losses)])
# loading the checkpoint
saver = tf.train.Saver()
checkpoint_path = os.path.join(model_dir, 'best_model.ckpt')
saver.restore(sess, checkpoint_path)
# predict labels from trainset
if USE_TRAIN_MINI:
# Train a model
train_params = {
'nb_epochs': FLAGS.nb_epochs,
'batch_size': FLAGS.batch_size,
'learning_rate': 0.1,
'lr_factor': 0.9,
'lr_patience': 3,
'lr_cooldown': 2,
'best_model_path': os.path.join(model_dir, 'best_model.ckpt')
}
eval_params = {'batch_size': FLAGS.batch_size}
fgsm_params = {'eps': 0.3, 'clip_min': 0., 'clip_max': 1.}
model = DarkonReplica(scope=ARCH_NAME[FLAGS.dataset],
nb_classes=feeder.num_classes,
n=5,
input_shape=[32, 32, 3])
logits = model.get_logits(x)
loss = CrossEntropy(model, smoothing=label_smoothing[FLAGS.dataset])
regu_losses = WeightDecay(model)
full_loss = WeightedSum(model, [(1.0, loss), (weight_decay, regu_losses)])
def do_eval(preds, x_set, y_set, report_key, is_adv=None):
acc = model_eval(sess, x, y, preds, x_set, y_set, args=eval_params)
setattr(report, report_key, acc)
if is_adv is None:
report_text = None
elif is_adv:
report_text = 'adversarial'
else:
info_file = os.path.join(attack_dir, 'info.pkl')
print('loading info as pickle from {}'.format(info_file))
with open(info_file, 'rb') as handle:
info_old = pickle.load(handle)
assert info == info_old
# Use Image Parameters
img_rows, img_cols, nchannels = X_test.shape[1:4]
nb_classes = y_test.shape[1]
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, nchannels), name='x')
y = tf.placeholder(tf.float32, shape=(None, nb_classes), name='y')
model = DarkonReplica(scope=ARCH_NAME, nb_classes=feeder.num_classes, n=5, input_shape=[32, 32, 3])
preds = model.get_predicted_class(x)
logits = model.get_logits(x)
embeddings = model.get_embeddings(x)
# loading the checkpoint
saver = tf.train.Saver()
checkpoint_path = os.path.join(model_dir, 'best_model.ckpt')
saver.restore(sess, checkpoint_path)
# get noisy images
def get_noisy_samples(X, std):
""" Add Gaussian noise to the samples """
# std = STDEVS[subset][FLAGS.dataset][FLAGS.attack]
X_noisy = np.clip(X + rand_gen.normal(loc=0.0, scale=std, size=X.shape), 0, 1)
return X_noisy