start = time.time()
model.fit_generator(generator=my_training_batch_generator,
epochs=5,
verbose=1,
shuffle=True,
validation_data=my_validation_batch_generator)
model.save_weights('/home/shayan/Codes/DenseNet-Keras-master/adversarial_weights_tf_' + str(ib) + '.h5')
end = time.time()
train_time = end - start
start = time.time()
score = model.evaluate(X_testNP, Y_test, verbose=0)
print(score[0])
print(score[1])
end = time.time()
test_time = end - start
f = open("/home/shayan/Codes/DenseNet-Keras-master/Stat_Knowledge_" + str(ib) + ".txt", "w")
f.write(str(['Test loss: ', score[0]]))
f.write('\n')
f.write(str(['Test accuracy: ', score[1]]))
f.write('\n')
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
X_test = np.array(X_test)
Y_test = np.array(Y_test)
Y_test = to_categorical(Y_test)
model.fit_generator(generator=my_training_batch_generator,
epochs=1,
verbose=1,
shuffle=True,
validation_data=my_validation_batch_generator)
score = model.evaluate(X_test, Y_test, verbose=0)
X_adv_fgsm = X_test
X_adv_jsma = X_test
X_adv_deepfool = X_test
X_adv_cw = X_test
for i in range(len(X_test)):
xorg, y0 = X_test[i], Y_test[i]
xorg = np.expand_dims(xorg, axis=0)
xadvs = [
make_fgsm(sess, env, xorg, eps=0, epochs=1),
make_jsma(sess, env, xorg, eps=0, epochs=1),