import matplotlib.pyplot as plt
from utilities import SDC_data, generate_data
from model import SDC_model_epoch
from attack import L2RegressionAttack
IMAGE_FILE = 'test_image.csv'
IMAGE_FOLDER = '/home/alesia/Documents/sdc/'
with tf.Session() as sess:
data, model = SDC_data(IMAGE_FILE, IMAGE_FOLDER), SDC_model_epoch("models/sdc", sess)
attack = L2RegressionAttack(sess, model, batch_size=1, max_iterations=1000, confidence=0)
inputs, targets = generate_data(data, samples=1, targeted=True, start=0)
adv = attack.attack(inputs, targets)
for i in range(len(adv)):
inp = model.model.predict(inputs[i:i+1])
print("Valid :",inp)
adver = model.model.predict(adv[i:i+1])
print("Classification:", adver)
print()
mse = (targets[i] - adver) * (targets[i] - adver)
mse_init = (targets[i] - inp) * (targets[i] - inp)
print('MSE', (targets[i] - adver) * (targets[i] - adver))
train_labels_600 = np.zeros(shape)
for i in range(0, NUM_ATTACKS, 2):
train_labels_600[i] = train_labels[int(i/2)]
for i in range(1, NUM_ATTACKS, 2):
train_labels_600[i] = train_labels[int(np.floor(i/2))]
predicted_probabilities_array = np.zeros(shape)
for k in range (NUM_INPUTS):
inputs, targets = generate_data(data, samples = 1, targeted=True, start=k, inception=False)
predicted_probabilities_array = np.zeros(shape)
probabilities_attack_array = np.zeros(shape)
probabilities_array_071 = np.zeros(shape)
probabilities_array_072 = np.zeros(shape)
probabilities_array_075 = np.zeros(shape)
#get prediction probas
with open(LABELS_FILE, 'r') as f:
predictions = f.readlines()
#get resulting distances
with open(DISTANCES_FILE, 'r') as f:
distances = f.read()
from utilities import generate_data
from seq2seq import Seq2seq
seq2seq = Seq2seq(lr=0.03, init_range=0.5)
epochs = 80
train_length = 1000
test_length = 100
# generating data
max_array_length = 2
input_train, output_train = generate_data(train_length, max_array_length)
input_test, output_test = generate_data(test_length, max_array_length)
# model training
for epoch in range(epochs):
cost = 0
for j in range(len(input_train)):
cost += seq2seq.train(input_train[j], output_train[j])
if epoch % 5 == 0:
print(f'Epoch: {epoch}')
print(f'loss: {cost}')
print('-' * 25)
# model evaluation
correct_test = 0
print('\ntest results:')
for l in range(test_length):
prediction = seq2seq.predict(input_test[l])
print(input_test[l], end=' ')
if prediction == output_test[l]: