def main(run_load_from_file=False):
# load MNIST images
images, labels = dataset.load_train_images()
# config
opt = Operation()
config = Config()
opt.check_dir(config.ckpt_dir, is_restart=False)
opt.check_dir(config.log_dir, is_restart=True)
# setting
max_epoch = 510
num_trains_per_epoch = 500
batch_size_u = 100
# training
with tf.device(config.device):
h = build_graph()
sess_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True)
sess_config.gpu_options.allow_growth = True
sess_config.gpu_options.per_process_gpu_memory_fraction = 0.9
saver = tf.train.Saver(max_to_keep=2)
with tf.Session(config=sess_config) as sess:
'''
Load from checkpoint or start a new session
'''
if run_load_from_file:
saver.restore(sess, tf.train.latest_checkpoint(config.ckpt_dir))
training_epoch_loss, _ = pickle.load(
open(config.ckpt_dir + '/pickle.pkl', 'rb'))
else:
sess.run(tf.global_variables_initializer())
training_epoch_loss = []
# Recording loss per epoch
process = Process()
for epoch in range(max_epoch):
process.start_epoch(epoch, max_epoch)
'''
Learning rate generator
'''
learning_rate = opt.ladder_learning_rate(epoch +
len(training_epoch_loss))
# Recording loss per iteration
training_loss_set = []
sum_loss_reconstruction = 0
sum_loss_discrminator = 0
sum_loss_generator = 0
sum_loss_cluster_head = 0
process_iteration = Process()
for i in range(num_trains_per_epoch):
process_iteration.start_epoch(i, num_trains_per_epoch)
# sample from data distribution
images_u = dataset.sample_unlabeled_data(images, batch_size_u)
# additional cost function that penalizes the euclidean between of every two of cluster
if epoch == 0:
for j in range(5):
starting_labels, ending_labels = dataset.cluster_create_dataset(
config.ndim_y)
_, loss_transform = sess.run(
[h.opt_t, h.loss_t],
feed_dict={
h.g_s: starting_labels,
h.g_e: ending_labels,
h.lr: learning_rate
})
# reconstruction_phase
_, loss_reconstruction = sess.run([h.opt_r, h.loss_r],
feed_dict={
h.x: images_u,
h.lr: learning_rate
})
z_true_u = sampler.gaussian(batch_size_u,
config.ndim_z,
mean=0,
var=1)
y_true_u = sampler.onehot_categorical(batch_size_u,
config.ndim_y)
# adversarial phase for discriminator
_, loss_discriminator_y = sess.run([h.opt_dy, h.loss_dy],
feed_dict={
h.x: images_u,
h.y: y_true_u,
h.lr: learning_rate
})
_, loss_discriminator_z = sess.run([h.opt_dz, h.loss_dz],
feed_dict={
h.x: images_u,
h.z: z_true_u,
h.lr: learning_rate
})
loss_discriminator = loss_discriminator_y + loss_discriminator_z
# adversarial phase for generator
_, loss_generator_y, loss_generator_z = sess.run(
[h.opt_e, h.loss_gy, h.loss_gz],
feed_dict={
h.x: images_u,
h.lr: learning_rate
})
loss_generator = loss_generator_y + loss_generator_z
training_loss_set.append([
loss_reconstruction, loss_discriminator,
loss_discriminator_y, loss_discriminator_z, loss_generator,
loss_generator_z, loss_generator_y, loss_transform
])
sum_loss_reconstruction += loss_reconstruction
sum_loss_discrminator += loss_discriminator
sum_loss_generator += loss_generator
sum_loss_cluster_head += loss_transform
if i % 1000 == 0:
process_iteration.show_table_2d(
i, num_trains_per_epoch, {
'reconstruction': sum_loss_reconstruction /
(i + 1),
'discriminator': sum_loss_discrminator / (i + 1),
'generator': sum_loss_generator / (i + 1),
'cluster_head': sum_loss_cluster_head / (i + 1)
})
# In end of epoch, summary the loss
average_training_loss_per_epoch = np.mean(
np.array(training_loss_set), axis=0)
training_epoch_loss.append(average_training_loss_per_epoch)
loss_name_per_epoch = [
'reconstruction',
'discriminator',
'discriminator_y',
'discriminator_z',
'generator',
'generator_z',
'generator_y',
'transform',
]
if epoch % 1 == 0:
process.show_bar(
epoch, max_epoch, {
'loss_r': average_training_loss_per_epoch[0],
'loss_d': average_training_loss_per_epoch[1],
'loss_g': average_training_loss_per_epoch[4],
})
if epoch % 10 == 0:
saver.save(sess,
os.path.join(config.ckpt_dir, 'model_ckptpoint'),
global_step=epoch)
pickle.dump((training_epoch_loss, loss_name_per_epoch),
open(config.ckpt_dir + '/pickle.pkl', 'wb'))
plt.plot_double_scale_trend(config.ckpt_dir)
def main(run_load_from_file=False):
# load MNIST images
images, labels = dataset.load_train_images()
# config
opt = Operation()
config = Config()
opt.check_dir(config.ckpt_dir, is_restart=False)
opt.check_dir(config.log_dir, is_restart=True)
# setting
max_epoch = 510
num_trains_per_epoch = 500
batch_size_l = 100
batch_size_u = 100
# create semi-supervised split
num_validation_data = 10000
num_labeled_data = 100
num_types_of_label = 10
training_images_l, training_labels_l, training_images_u, validation_images, validation_labels = dataset.create_semisupervised(
images, labels, num_validation_data, num_labeled_data,
num_types_of_label)
# training
with tf.device(config.device):
h = build_graph()
sess_config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True)
sess_config.gpu_options.allow_growth = True
sess_config.gpu_options.per_process_gpu_memory_fraction = 0.9
saver = tf.train.Saver(max_to_keep=2)
with tf.Session(config=sess_config) as sess:
'''
Load from checkpoint or start a new session
'''
if run_load_from_file:
saver.restore(sess, tf.train.latest_checkpoint(config.ckpt_dir))
training_epoch_loss, _ = pickle.load(
open(config.ckpt_dir + '/pickle.pkl', 'rb'))
else:
sess.run(tf.global_variables_initializer())
training_epoch_loss = []
# Recording loss per epoch
process = Process()
for epoch in range(max_epoch):
process.start_epoch(epoch, max_epoch)
'''
Learning rate generator
'''
learning_rate = opt.ladder_learning_rate(epoch +
len(training_epoch_loss))
# Recording loss per iteration
training_loss_set = []
sum_loss_reconstruction = 0
sum_loss_supervised = 0
sum_loss_discrminator = 0
sum_loss_generator = 0
process_iteration = Process()
for i in range(num_trains_per_epoch):
process_iteration.start_epoch(i, num_trains_per_epoch)
# sample from data distribution
images_l, label_onehot_l, label_id_l = dataset.sample_labeled_data(
training_images_l, training_labels_l, batch_size_l)
images_u = dataset.sample_unlabeled_data(
training_images_u, batch_size_u)
# reconstruction_phase
_, loss_reconstruction = sess.run([h.opt_r, h.loss_r],
feed_dict={
h.x: images_u,
h.lr: learning_rate
})
z_true_u = sampler.gaussian(batch_size_u,
config.ndim_z,
mean=0,
var=1)
y_true_u = sampler.onehot_categorical(batch_size_u,
config.ndim_y)
# adversarial phase for discriminator
_, loss_discriminator_y = sess.run([h.opt_dy, h.loss_dy],
feed_dict={
h.x: images_u,
h.y: y_true_u,
h.lr: learning_rate
})
_, loss_discriminator_z = sess.run([h.opt_dz, h.loss_dz],
feed_dict={
h.x: images_u,
h.z: z_true_u,
h.lr: learning_rate
})
loss_discriminator = loss_discriminator_y + loss_discriminator_z
# adversarial phase for generator
_, loss_generator_y, loss_generator_z = sess.run(
[h.opt_e, h.loss_gy, h.loss_gz],
feed_dict={
h.x: images_u,
h.lr: learning_rate
})
loss_generator = loss_generator_y + loss_generator_z
# supervised phase
_, loss_generator_supervised = sess.run([h.opt_ey, h.loss_ey],
feed_dict={
h.x: images_l,
h.y_s: label_id_l,
h.lr: learning_rate
})
training_loss_set.append([
loss_reconstruction,
loss_discriminator,
loss_discriminator_y,
loss_discriminator_z,
loss_generator,
loss_generator_z,
loss_generator_y,
loss_generator_supervised,
])
sum_loss_reconstruction += loss_reconstruction
sum_loss_discrminator += loss_discriminator
sum_loss_generator += loss_generator
sum_loss_supervised += loss_generator_supervised
if i % 1000 == 0:
process_iteration.show_table_2d(
i, num_trains_per_epoch, {
'reconstruction': sum_loss_reconstruction /
(i + 1),
'discriminator': sum_loss_discrminator / (i + 1),
'generator': sum_loss_generator / (i + 1),
'supervise': sum_loss_supervised / (i + 1),
})
# In end of epoch, summary the loss
average_training_loss_per_epoch = np.mean(
np.array(training_loss_set), axis=0)
# validation phase
images_v_segments = np.split(validation_images,
num_validation_data // 1000)
labels_v_segments = np.split(validation_labels,
num_validation_data // 1000)
sum_accuracy = 0
for images_v, labels_v in zip(images_v_segments,
labels_v_segments):
y_v = sess.run(h.y_r, feed_dict={h.x: images_v})
accuracy = opt.compute_accuracy(y_v, labels_v)
sum_accuracy += accuracy
validation_accuracy = sum_accuracy / len(images_v_segments)
# append validation accuracy to the training loss
average_loss_per_epoch = np.append(average_training_loss_per_epoch,
validation_accuracy)
training_epoch_loss.append(average_loss_per_epoch)
loss_name_per_epoch = [
'reconstruction', 'discriminator', 'discriminator_y',
'discriminator_z', 'generator', 'generator_z', 'generator_y',
'supervised', 'validation_accuracy'
]
if epoch % 1 == 0:
process.show_bar(
epoch, max_epoch, {
'loss_r': average_loss_per_epoch[0],
'loss_d': average_loss_per_epoch[1],
'loss_g': average_loss_per_epoch[4],
'loss_v': average_loss_per_epoch[8],
})
plt.tile_images(sess.run(h.x_, feed_dict={h.x: images_u}),
dir=config.log_dir,
filename='x_rec_epoch_{}'.format(
str(epoch).zfill(3)))
if epoch % 10 == 0:
saver.save(sess,
os.path.join(config.ckpt_dir, 'model_ckptpoint'),
global_step=epoch)
pickle.dump((training_epoch_loss, loss_name_per_epoch),
open(config.ckpt_dir + '/pickle.pkl', 'wb'))
plt.plot_double_scale_trend(config.ckpt_dir)
def main(run_load_from_file=False):
# config
opt = Operation()
opt.check_dir(config.ckpt_dir, is_restart=False)
opt.check_dir(config.log_dir, is_restart=True)
max_epoch = 510
num_trains_per_epoch = 500
batch_size_l = 100
batch_size_u = 100
# create semi-supervised split
# Load minist images
images, labels = dataset.load_train_images()
num_labeled_data = 10000
num_types_of_label = 11 # additional label corresponds to unlabeled data
training_images_l, training_labels_l, training_images_u, _, _ = dataset.create_semisupervised(images, labels, 0, num_labeled_data, num_types_of_label)
# training
with tf.device(config.device):
h = build_graph()
sess_config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)
sess_config.gpu_options.allow_growth = True
sess_config.gpu_options.per_process_gpu_memory_fraction = 0.9
saver = tf.train.Saver(max_to_keep=2)
with tf.Session(config=sess_config) as sess:
'''
Load from checkpoint or start a new session
'''
if run_load_from_file:
saver.restore(sess, tf.train.latest_checkpoint(config.ckpt_dir))
training_epoch_loss, _ = pickle.load(open(config.ckpt_dir + '/pickle.pkl', 'rb'))
else:
sess.run(tf.global_variables_initializer())
training_epoch_loss = []
# Recording loss per epoch
process = Process()
for epoch in range(max_epoch):
process.start_epoch(epoch, max_epoch)
'''
Learning rate generator
'''
learning_rate = opt.ladder_learning_rate(epoch + len(training_epoch_loss))
# Recording loss per iteration
sum_loss_reconstruction = 0
sum_loss_discrminator = 0
sum_loss_generator = 0
process_iteration = Process()
for i in range(num_trains_per_epoch):
process_iteration.start_epoch(i, num_trains_per_epoch)
# Inputs
'''
_l -> labeled
_u -> unlabeled
'''
images_l, label_onehot_l, label_id_l = dataset.sample_labeled_data(training_images_l, training_labels_l, batch_size_l, ndim_y=num_types_of_label)
images_u = dataset.sample_unlabeled_data(training_images_u, batch_size_u)
onehot = np.zeros((1, num_types_of_label), dtype=np.float32)
onehot[-1] = 1
label_onehot_u = np.repeat(onehot, batch_size_u, axis=0)
z_true_l = sampler.supervised_swiss_roll(batch_size_l, config.ndim_z, label_id_l, num_types_of_label - 1)
z_true_u = sampler.swiss_roll(batch_size_u, config.ndim_z, num_types_of_label - 1)
# z_true_l = sampler.supervised_gaussian_mixture(batch_size_l, config.ndim_z, label_id_l, num_types_of_label - 1)
# z_true_u = sampler.gaussian_mixture(batch_size_u, config.ndim_z, num_types_of_label - 1)
# reconstruction_phase
_, loss_reconstruction = sess.run([h.opt_r, h.loss_r], feed_dict={
h.x: images_u,
h.lr: learning_rate
})
# adversarial phase for discriminator
_, loss_discriminator_l = sess.run([h.opt_d, h.loss_d], feed_dict={
h.x: images_l,
h.label: label_onehot_l,
h.z: z_true_l,
h.lr: learning_rate
})
_, loss_discriminator_u = sess.run([h.opt_d, h.loss_d], feed_dict={
h.x: images_u,
h.label: label_onehot_u,
h.z: z_true_u,
h.lr: learning_rate
})
loss_discriminator = loss_discriminator_l + loss_discriminator_u
# adversarial phase for generator
_, loss_generator_l= sess.run([h.opt_e, h.loss_e,], feed_dict={
h.x: images_l,
h.label: label_onehot_l,
h.lr: learning_rate
})
_, loss_generator_u = sess.run([h.opt_e, h.loss_e], feed_dict={
h.x: images_u,
h.label: label_onehot_u,
h.lr: learning_rate
})
loss_generator = loss_generator_l + loss_generator_u
sum_loss_reconstruction += loss_reconstruction / batch_size_u
sum_loss_discrminator += loss_discriminator
sum_loss_generator += loss_generator
if i % 1000 == 0:
process_iteration.show_table_2d(i, num_trains_per_epoch, {
'reconstruction': sum_loss_reconstruction / (i + 1),
'discriminator': sum_loss_discrminator / (i + 1),
'generator': sum_loss_generator / (i + 1),
})
average_loss_per_epoch = [
sum_loss_reconstruction / num_trains_per_epoch,
sum_loss_discrminator / num_trains_per_epoch,
sum_loss_generator / num_trains_per_epoch,
]
training_epoch_loss.append(average_loss_per_epoch)
training_loss_name = [
'reconstruction',
'discriminator',
'generator'
]
if epoch % 1 == 0:
process.show_bar(epoch, max_epoch, {
'loss_r': average_loss_per_epoch[0],
'loss_d': average_loss_per_epoch[1],
'loss_g': average_loss_per_epoch[2]
})
plt.tile_images(sess.run(h.x_, feed_dict={h.x: images_u}),
dir=config.log_dir,
filename='x_rec_epoch_{}'.format(str(epoch).zfill(3)))
plt.scatter_labeled_z(sess.run(h.z_r, feed_dict={h.x: images[:1000]}), [int(var) for var in labels[:1000]],
dir=config.log_dir,
filename='z_representation-{}'.format(epoch))
if epoch % 10 == 0:
saver.save(sess, os.path.join(config.ckpt_dir, 'model_ckptpoint'), global_step=epoch)
pickle.dump((training_epoch_loss, training_loss_name), open(config.ckpt_dir + '/pickle.pkl', 'wb'))
plt.plot_double_scale_trend(config.ckpt_dir)