def aiTest(images, shape):
"""
:param images: ndarray
:param shape: tuple, like (1000, 28, 28, 1)
:return:
"""
x_origin = np.reshape(images, (-1, ) + shape[1:])
x_origin = x_origin.astype(np.float32) / 255
print('Construction graph')
env = Dummy()
model = lenet5_tensorflow
with tf.variable_scope('model'):
env.x = tf.placeholder(tf.float32, (None, ) + shape[1:], name='x')
env.y = tf.placeholder(tf.float32, (None, N_CLASSES), name='y')
env.training = tf.placeholder_with_default(False, (), name='mode')
env.ybar, logits = model(env.x, logits=True, training=env.training)
with tf.variable_scope('acc'):
count = tf.equal(tf.argmax(env.y, axis=1),
tf.argmax(env.ybar, axis=1))
env.acc = tf.reduce_mean(tf.cast(count, tf.float32), name='acc')
with tf.variable_scope('loss'):
xent = tf.nn.softmax_cross_entropy_with_logits_v2(labels=env.y,
logits=logits)
env.loss = tf.reduce_mean(xent, name='loss')
with tf.variable_scope('train_op'):
optimizer = tf.train.AdamOptimizer()
env.train_op = optimizer.minimize(env.loss)
env.saver = tf.train.Saver()
with tf.variable_scope('model', reuse=True):
env.fgsm_eps = tf.placeholder(tf.float32, (), name='fgsm_eps')
env.fgsm_epochs = tf.placeholder(tf.int32, (), name='fgsm_epochs')
env.x_fgsm = fgm(model,
env.x,
epochs=env.fgsm_epochs,
eps=env.fgsm_eps)
print('Initializing graph')
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
print('Loading model')
env.saver.restore(sess, MODEL_PATH)
x_adv = make_fgsm(sess,
env,
x_origin,
eps=FGSM_EPSILON,
epochs=FGSM_EPOCH,
batch_size=FGSM_BATCH_SIZE)
return x_adv
with tf.variable_scope('loss'):
xent = tf.nn.softmax_cross_entropy_with_logits(labels=env.y,
logits=logits)
env.loss = tf.reduce_mean(xent, name='loss')
with tf.variable_scope('train_op'):
optimizer = tf.train.AdamOptimizer()
env.train_op = optimizer.minimize(env.loss)
env.saver = tf.train.Saver()
with tf.variable_scope('model', reuse=True):
env.fgsm_eps = tf.placeholder(tf.float32, (), name='fgsm_eps')
env.fgsm_epochs = tf.placeholder(tf.int32, (), name='fgsm_epochs')
env.x_fgsm = fgm(model, env.x, epochs=env.fgsm_epochs, eps=env.fgsm_eps)
print('\nInitializing graph')
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
def evaluate(sess, env, X_data, y_data, batch_size=128):
"""
Evaluate TF model by running env.loss and env.acc.
"""
print('Evaluating')
n_sample = X_data.shape[0]
norm_acc = tf.reduce_mean(tf.cast(correct_norm_discr, tf.float32))
adv_acc = tf.reduce_mean(tf.cast(correct_adv_discr, tf.float32))
comb_acc = (norm_acc + adv_acc) / 2
# create a saver
saver = tf.train.Saver()
# initialize graph
init = tf.global_variables_initializer()
# generating adversarial images
fgm_eps = tf.placeholder(tf.float32, ())
fgm_epochs = tf.placeholder(tf.float32, ())
adv_examples = fast_gradient.fgm(x_norm,
final_norm,
sm_norm,
eps=fgm_eps,
epochs=fgm_epochs)
with tf.Session() as sess:
sess.run(init)
y_norm_labels = np.squeeze(
np.stack([[np.array([1, 0])] for _ in range(batch_size)], axis=0))
y_adv_labels = np.squeeze(
np.stack([[np.array([0, 1])] for _ in range(batch_size)], axis=0))
for i in range(epochs):
print "EPOCH: " + str(i + 1)
for j in range(mnist.train.num_examples / batch_size):
print j
loss = alpha * tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=reg_output, labels=reg_y)) + (1 - alpha) * tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=adv_output, labels=adv_y)) - beta * cross_discr_adv
sm_norm = tf.nn.softmax(reg_output)
train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'reg')
#print(train_vars)
#train_vars.extend(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'adv'))
optimizer = tf.train.RMSPropOptimizer(learning_rate=1e-3).minimize(
loss, global_step=global_step, var_list=train_vars)
fgm_eps = tf.placeholder(tf.float32, ())
fgm_epochs = tf.placeholder(tf.float32, ())
adv_examples = fast_gradient.fgm(reg_x,
reg_output,
sm_norm,
eps=fgm_eps,
epochs=fgm_epochs)
reg_correct_prediction = tf.equal(reg_y_pred_cls, tf.argmax(reg_y, axis=1))
adv_correct_prediction = tf.equal(adv_y_pred_cls, tf.argmax(adv_y, axis=1))
accuracy_reg = tf.reduce_mean(tf.cast(reg_correct_prediction, tf.float32))
accuracy_adv = tf.reduce_mean(tf.cast(adv_correct_prediction, tf.float32))
accuracy = (accuracy_reg + accuracy_adv) / 2
tf.summary.scalar("Accuracy/class_reg_train", accuracy_reg)
tf.summary.scalar("Accuracy/class_adv_train", accuracy_adv)
tf.summary.scalar("Accuracy/discr_reg_train", discr_accuracy_reg)
tf.summary.scalar("Accuracy/discr_adv_train", discr_accuracy_adv)
merged = tf.summary.merge_all()
saver = tf.train.Saver()