from __future__ import absolute_import, division, print_function, unicode_literals
import sys
from os.path import abspath
sys.path.append(abspath('.'))
import numpy as np
import numpy.linalg as LA
from art.attacks.carlini import CarliniL2Method
from models.utils import load_mnist_vectorized
from old_models import neural_networks
# Read MNIST dataset
(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist_vectorized()
print(x_train.shape)
l1_reg_levels = [0.0001, 0.0002, 0.0003, 0.0004, 0.0005]
for l1_level in range(0, 5):
classifier = neural_networks.two_layer_dnn_art(x_train.shape[1:], 0,
l1_reg_levels[l1_level], 0)
classifier.fit(x_train, y_train, nb_epochs=10, batch_size=128)
# Evaluate the classifier on the test set
preds = np.argmax(classifier.predict(x_test), axis=1)
acc = np.sum(preds == np.argmax(y_test, axis=1)) / y_test.shape[0]
print("\nTest accuracy on L1 regularization level %.5f%%: %.2f%%" %
(l1_reg_levels[l1_level], acc * 100))
np.set_printoptions(threshold=sys.maxsize)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
x_train = None
y_train = None
x_test = None
y_test = None
min_ = None
max_ = None
if args.dataset == 'mnist':
if args.experiment_type in ["three_layer", "five_layer", "six_layer"]:
(x_train, y_train), (x_test,
y_test), min_, max_ = load_mnist_vectorized()
elif args.experiment_type in ["VGG", "leNet"]:
(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()
# Pad images to 32x32 size
x_train = np.pad(x_train, ((0, 0), (2, 2), (2, 2), (0, 0)), 'constant')
x_test = np.pad(x_test, ((0, 0), (2, 2), (2, 2), (0, 0)), 'constant')
elif args.dataset == 'cifar':
(x_train, y_train), (x_test, y_test), min_, max_ = load_cifar10()
else:
raise Exception("Invalid dataset!")
input_shape = x_train.shape[1:]
output_shape = len(y_train[0])
ones = np.where(np.argmax(y_test, axis=1) == 1)[0]
