def np_two_class_mnist_model(model_dir):
with tf.Graph().as_default():
x_input = tf.placeholder(tf.float32, (None, 28, 28, 1))
sess = tf.Session()
model = mnist_convnet.Model(model_dir, sess)
two_class_model = TwoClassWrapper(model)
logits = two_class_model(x_input)
def np_model(x):
return sess.run(logits, {x_input: x})
return np_model
def train_mnist(model_dir,
next_batch_fn,
total_batches,
train_mode,
save_every=1000,
print_every=100):
x_input = tf.placeholder(tf.float32, (None, 28, 28, 1))
y_input = tf.placeholder(tf.float32, [None, 10])
model = mnist_convnet.Model()
logits = model(x_input)
loss = tf.nn.softmax_cross_entropy_with_logits(labels=y_input,
logits=logits)
loss = tf.reduce_mean(loss)
accuracy = tf.reduce_mean(
tf.cast(tf.equal(tf.argmax(logits, axis=1), tf.argmax(y_input,
axis=1)),
dtype=tf.float32))
global_step = tf.contrib.framework.get_or_create_global_step()
train_step = tf.train.AdamOptimizer(1e-4).minimize(loss,
global_step=global_step)
saver = tf.train.Saver(max_to_keep=3)
a = tf.summary.scalar('accuracy adv train', accuracy)
b = tf.summary.scalar('xent adv train', loss)
c = tf.summary.image('images adv train', x_input)
adv_summaries = tf.summary.merge([a, b, c])
a = tf.summary.scalar('accuracy nat train', accuracy)
b = tf.summary.scalar('xent nat train', loss)
c = tf.summary.image('images nat train', x_input)
nat_summaries = tf.summary.merge([a, b, c])
with tf.Session() as sess:
attack = MadryEtAl(model, sess=sess)
summary_writer = tf.summary.FileWriter(model_dir, sess.graph)
sess.run(tf.global_variables_initializer())
for batch_num in range(total_batches):
x_batch, y_batch = next_batch_fn()
x_batch = np.reshape(x_batch, (-1, 28, 28, 1))
if train_mode == "adversarial" and batch_num > 1000:
x_batch_adv = attack.generate_np(x_batch,
y=y_batch,
eps=.3,
nb_iter=40,
eps_iter=.01,
rand_init=True,
clip_min=0,
clip_max=1)
else:
x_batch_adv = x_batch
nat_dict = {x_input: x_batch, y_input: y_batch}
adv_dict = {x_input: x_batch_adv, y_input: y_batch}
if batch_num % print_every == 0:
a, l, s = sess.run((accuracy, loss, nat_summaries), nat_dict)
summary_writer.add_summary(s, sess.run(global_step))
print(batch_num, "Clean accuracy", a, "loss", l)
if train_mode == "adversarial":
a, l, s = sess.run((accuracy, loss, adv_summaries),
adv_dict)
summary_writer.add_summary(s, sess.run(global_step))
print(batch_num, "Adv accuracy", a, "loss", l)
if batch_num % save_every == 0:
saver.save(sess,
os.path.join(model_dir, "checkpoint"),
global_step=global_step)
sess.run(train_step, nat_dict)
sess.run(train_step, adv_dict)