def test(choice, X_test, y_test):
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
#Teacher Model
if choice['teacher'] == 'lnin':
model = LNIN(img_size, img_class, sess, choice)
elif choice['teacher'] == 'lnin_ae':
model = DNIN(img_size, img_class, sess, choice)
ae = AE(img_size, img_class, sess, choice)
elif choice['teacher'] == 'lnin_ssae':
model = DNIN(img_size, img_class, sess, choice)
ae = AE(img_size, img_class, sess, choice)
ssae = SSAE(img_size, img_class, sess, choice)
elif choice['teacher'] == 'dnin':
model = DNIN(img_size, img_class, sess, choice)
elif choice['teacher'] == 'tnin':
model = TNIN(img_size, img_class, sess, choice)
elif choice['teacher'] == 'mnin':
model = MNIN(img_size, img_class, sess, choice)
else:
model = SN(img_size, img_class, sess, choice)
#Student Model
if choice['student'] == 'lsn':
model = LSN(img_size, img_class, sess, choice)
elif choice['student'] == 'dsn':
model = DSN(img_size, img_class, sess, choice)
elif choice['student'] == 'msn':
model = MSN(img_size, img_class, sess, choice)
elif choice['student'] == 'ssn':
model = SSN(img_size, img_class, sess, choice)
if not model.save:
#sess.run(tf.initialize_all_variables())
print "No Model to test"
return
a = 0
for i in range(100):
if choice['student'] == 'none':
if choice['teacher'] == 'lnin_ssae':
hidden = model.get_hidden(X_test[i * 100:(i + 1) * 100])
hidden = ae.get_embedding(hidden)
a += ssae.test(hidden, y_test[i * 100:(i + 1) * 100])
else:
a += model.test(X_test[i * 100:(i + 1) * 100],
y_test[i * 100:(i + 1) * 100])
else:
a += model.test(X_test[i * 100:(i + 1) * 100],
y_test[i * 100:(i + 1) * 100])
print " Test Average Accuracy: ", 1. * a / 100
return a
def train(choice, X_train, y_train, X_test, y_test):
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
student_model = None
teacher_model = None
with tf.Session(config=config) as sess:
#Teacher Model
global ssae, ae
if choice['teacher'] == 'lnin':
teacher_model = LNIN(img_size, img_class, sess, choice)
elif choice['teacher'] == 'lnin_ae':
teacher_model = DNIN(img_size, img_class, sess, choice)
ae = AE(img_size, img_class, sess, choice)
elif choice['teacher'] == 'lnin_ssae':
teacher_model = DNIN(img_size, img_class, sess, choice)
ae = AE(img_size, img_class, sess, choice)
ssae = SSAE(img_size, img_class, sess, choice)
elif choice['teacher'] == 'dnin':
teacher_model = DNIN(img_size, img_class, sess, choice)
elif choice['teacher'] == 'mnin':
teacher_model = MNIN(img_size, img_class, sess, choice)
elif choice['teacher'] == 'tnin':
teacher_model = TNIN(img_size, img_class, sess, choice)
else:
teacher_model = SN(img_size, img_class, sess, choice)
#Student Model
if choice['student'] == 'lsn':
student_model = LSN(img_size, img_class, sess, choice)
elif choice['student'] == 'dsn':
student_model = DSN(img_size, img_class, sess, choice)
elif choice['student'] == 'msn':
student_model = MSN(img_size, img_class, sess, choice)
if choice['student'] != 'none':
if not student_model.save:
sess.run(tf.initialize_all_variables())
print "Initialize"
else:
if choice['teacher'] == 'lnin_ssae':
if not ssae.save:
sess.run(tf.initialize_all_variables())
print "Initialize"
elif choice['teacher'] == 'lnin_ae':
if not ae.save:
sess.run(tf.initialize_all_variables())
print "Initialize"
else:
if not teacher_model.save:
sess.run(tf.initialize_all_variables())
print "Initialize"
max_epochs = choice['epochs']
l = 0
a = 0
test_accuracy = []
step = 0
epoch = 0
loss = [
] #np.load("loss_"+choice['teacher'] +'_'+choice['student']+"_run"+str(choice['run'])+".npy").tolist()
accuracy = [
] #np.load("accuracy_"+choice['teacher']+'_'+choice['student']+"_run"+str(choice['run'])+".npy").tolist()
while epoch <= choice['epochs']:
if step * batch_size > X_train.shape[0] or step == 0:
s = np.arange(X_train.shape[0])
np.random.shuffle(s)
X_train = X_train[s]
y_train = y_train[s]
step = 0
print "Epoch:%d, loss: %f, accuracy: %f" % (epoch, l, a)
save_results(choice, teacher_model, student_model, loss,
accuracy)
epoch += 1
X_batch = X_train[step *
batch_size:min(X_train.shape[0], (step + 1) *
batch_size), :]
y_batch = y_train[step *
batch_size:min(y_train.shape[0], (step + 1) *
batch_size), :]
if choice['student'] != 'none':
s = np.random.choice(X_batch.shape[0], X_batch.shape[0])
X_batch2 = X_batch[s]
eps = np.random.rand(X_batch.shape[0])[:, None, None, None]
eps = np.tile(eps, [1, 32, 32, 3])
X_batch3 = eps * X_batch + (1 - eps) * X_batch2
X_batch = np.concatenate(
(X_batch[:X_batch.shape[0]], X_batch3[X_batch3.shape[0]:]))
if choice['student'] != 'none':
if choice['teacher'] == 'lnin_ssae':
#X_batch, y_batch = teacher_model.correct(X_batch, y_batch)
hidden = teacher_model.get_hidden(X_batch)
hidden = ae.get_embedding(hidden)
labels = ssae.get_embedding(hidden)
elif choice['teacher'] == 'dnin':
labels = teacher_model.predict(X_batch)
#labels = y_batch
elif choice['teacher'] == 'tnin':
labels = teacher_model.get_embedding(X_batch)
labels[labels >= 0.5] = 1
labels[labels < 0.5] = 0
elif choice['teacher'] == 'mnin':
labels = teacher_model.get_sub_prediction(X_batch)
elif choice['teacher'] == 'lnin':
labels = teacher_model.get_logits(X_batch)
l, a = student_model.train(X_batch, y_batch, labels)
else:
if choice['teacher'] == 'lnin_ae':
#X_batch, y_batch = teacher_model.correct(X_batch, y_batch)
hidden = teacher_model.get_hidden(X_batch)
l, a = ae.train(hidden)
elif choice['teacher'] == 'lnin_ssae':
#X_batch, y_batch = teacher_model.correct(X_batch, y_batch)
hidden = teacher_model.get_hidden(X_batch)
hidden = ae.get_embedding(hidden)
l, a = ssae.train(hidden, y_batch)
else:
l, a = teacher_model.train(X_batch, y_batch)
loss.append(l)
accuracy.append(a)
if epoch == choice['epochs']:
a = 0
for i in range(100):
if choice['student'] != 'none':
a += student_model.test(X_test[i * 100:(i + 1) * 100],
y_test[i * 100:(i + 1) * 100])
else:
a += teacher_model.test(X_test[i * 100:(i + 1) * 100],
y_test[i * 100:(i + 1) * 100])
a = 1. * a / 100
print "Test Accuracy ", a
test_accuracy.append(a)
if step == 100:
np.save("test_accuracy_"+choice['teacher']+'_'+choice['student']\
+"_run"+str(choice['run'])+".npy",np.asarray(test_accuracy))
print "Student Done"
break
step += 1