clean_batch_x = x_train[index[i*batch_size:(i*batch_size + num_clean_samples)]]
train_feed[xx] = np.concatenate([clean_batch_x, adv_batch])
train_feed[yy] = clean_batch_y
else:
train_feed[xx] = clean_batch_x
train_feed[yy] = clean_batch_y
results = sess.run(train_calc, feed_dict=train_feed)
performance.add_loss(results[1])
#performance.progress_bar(i+1., (len(x_train) // batch_size), metric/(i+1))
predictions = nntrainer.get_activations(sess, valid, 'output')
print(metrics.accuracy(valid['targets'], predictions))
if print_adv_test and epoch >= num_clean_epochs:
adv_test['inputs'] = perturb(test['inputs'], test['targets'], sess, nnmodel, train_feed, grad_tensor)
# adv_test['inputs'] = test['inputs']
predictions = nntrainer.get_activations(sess, adv_test, 'output')
roc, roc_curves = metrics.roc(test['targets'], predictions)
print('Adversarial Accuracy')
print(metrics.accuracy(test['targets'], predictions))
# save cross-validcation metrics
loss, mean_vals, error_vals = nntrainer.test_model(sess, valid,
name="valid",
batch_size=batch_size,
verbose=verbose)
# save cross-validcation metrics
loss, mean_vals, error_vals = nntrainer.test_model(
sess, valid, name="valid", batch_size=batch_size, verbose=verbose)
for eps in eps_list:
res_dict[eps] = []
for idx in range(num_trials):
# get performance metrics
noisy_train = {
'inputs':
train['inputs'] + np.random.uniform(
low=-eps, high=eps, size=train['inputs'].shape),
'targets':
train['targets']
}
predictions = nntrainer.get_activations(sess, noisy_train,
'output')
acc = metrics.accuracy(train['targets'], predictions)
print('Epsilon: ' + str(eps))
print('Trial: ' + str(idx + 1))
print(acc[0])
res_dict[eps].append(acc)
print('Mean for eps=' + str(eps))
print(np.mean(res_dict[eps]))
with open(os.path.join(results_path, model_name + '_acc.pickle'),
'wb') as f:
cPickle.dump(res_dict, f, protocol=cPickle.HIGHEST_PROTOCOL)
print(res_dict)