max_lbfgs_iter=max_lbfgs_iter,
num_classes=num_classes,
batch_size=batch_size,
data_sets=data_sets,
initial_learning_rate=initial_learning_rate,
keep_probs=keep_probs,
decay_epochs=decay_epochs,
mini_batch=False,
train_dir='output',
log_dir='log',
model_name='%s_inception_onlytop' % dataset_name)
inception_model.train()
inception_predicted_loss_diffs = inception_model.get_influence_on_test_loss(
[test_idx],
np.arange(len(inception_model.data_sets.train.labels)),
force_refresh=True)
x_test = X_test[test_idx, :]
y_test = Y_test[test_idx]
distances = dataset.find_distances(x_test, X_train)
flipped_idx = Y_train != y_test
rbf_margins_test = rbf_model.sess.run(rbf_model.margin,
feed_dict=rbf_model.all_test_feed_dict)
rbf_margins_train = rbf_model.sess.run(rbf_model.margin,
feed_dict=rbf_model.all_train_feed_dict)
inception_Y_pred_correct = get_Y_pred_correct_inception(inception_model)
np.savez('output/rbf_results',
test_idx=test_idx,
test_idx = 8
actual_loss_diffs, predicted_loss_diffs_cg, indices_to_remove = experiments.test_retraining(
tf_model,
test_idx,
iter_to_load=0,
force_refresh=False,
num_to_remove=500,
remove_type='maxinf',
random_seed=0)
# LiSSA
np.random.seed(17)
predicted_loss_diffs_lissa = tf_model.get_influence_on_test_loss(
[test_idx],
indices_to_remove,
approx_type='cg',
approx_params={
'scale': 25,
'recursion_depth': 5000,
'damping': 0,
'batch_size': 1,
'num_samples': 10
},
force_refresh=True)
np.savez('output/spam_logreg_lbfgs_retraining-500.npz',
actual_loss_diffs=actual_loss_diffs,
predicted_loss_diffs_cg=predicted_loss_diffs_cg,
predicted_loss_diffs_lissa=predicted_loss_diffs_lissa,
indices_to_remove=indices_to_remove)
# plt.title("centroid 9")
# plt.show()
mask_neg = Y_test == -1
k = -1
n = -1
while k < closest_7_idx:
n = n + 1
if mask_neg[n]:
k = k + 1
test_idx_7 = n
num_train = len(tf_model.data_sets.train.labels)
influences_7 = tf_model.get_influence_on_test_loss(
[test_idx_7],
np.arange(len(tf_model.data_sets.train.labels)),
force_refresh=True) * num_train
fittedKmeans_1 = KMeans(n_clusters=numClusters,
random_state=randomState).fit(class_wise_1)
clusters_1 = fittedKmeans_1.predict(class_wise_1)
centroids_1 = fittedKmeans_1.cluster_centers_
#pcaVis(class_wise_1, clusters_1, centroids_1, "for fours")
distanceScoresByCluster_1, minDist_1, maxDist_1 = getDistancesbyCluster(
class_wise_1, clusters_1, centroids_1)
closest_1_idx = getClosest(distanceScoresByCluster_1)[0]
# plt.figure(2)
# plt.imshow(class_wise_1[closest_1_idx, :].reshape(28,-1))
# plt.title("centroid 4")
# plt.show()
# top_model.update_train_x(X_train)
# top_model.train()
# if attack_success:
# break
### Try attacking all test examples
step_size = 0.005
test_indices = np.arange(num_test)
test_description = 'all_%s' % dataset_name
## Find idx to poison?
# Use top model to quickly generate inverse HVP
with top_graph.as_default():
top_model.get_influence_on_test_loss(test_indices, [0],
test_description=test_description,
force_refresh=True)
copyfile(
'output/%s-cg-normal_loss-test-%s.npz' %
(top_model_name, test_description),
'output/%s-cg-normal_loss-test-%s.npz' %
(full_model_name, test_description))
with full_graph.as_default():
grad_influence_wrt_input_val = full_model.get_grad_of_influence_wrt_input(
np.arange(num_train),
test_indices,
force_refresh=False,
test_description=test_description,
loss_type='normal_loss')
indices_to_poison = select_examples_to_attack(full_model,
data_sets=lr_data_sets,
initial_learning_rate=initial_learning_rate,
keep_probs=keep_probs,
decay_epochs=decay_epochs,
mini_batch=False,
train_dir='output',
log_dir='log',
model_name='mnist-17_logreg')
tf_model.train()
test_idx = 30
num_train = len(tf_model.data_sets.train.labels)
influences = tf_model.get_influence_on_test_loss(
[test_idx],
np.arange(len(tf_model.data_sets.train.labels)),
force_refresh=True) * num_train
influences_without_train_error = tf_model.get_influence_on_test_loss(
[test_idx],
np.arange(len(tf_model.data_sets.train.labels)),
force_refresh=False,
ignore_training_error=True) * num_train
influences_without_hessian = tf_model.get_influence_on_test_loss(
[test_idx],
np.arange(len(tf_model.data_sets.train.labels)),
force_refresh=False,
ignore_hessian=True) * num_train
influences_without_both = tf_model.get_influence_on_test_loss(