tf.app.flags.DEFINE_string('dis_model_ckpt', None, '')
tf.app.flags.DEFINE_string('gen_figure_data', None, '')
# gen model
tf.app.flags.DEFINE_float('kd_lamda', 0.3, '')
tf.app.flags.DEFINE_float('gen_weight_decay', 0.001, 'l2 coefficient')
tf.app.flags.DEFINE_float('temperature', 3.0, '')
tf.app.flags.DEFINE_string('gen_model_ckpt', None, '')
tf.app.flags.DEFINE_integer('num_gen_epoch', 5, '')
# tch model
tf.app.flags.DEFINE_float('tch_weight_decay', 0.00001, 'l2 coefficient')
tf.app.flags.DEFINE_integer('embedding_size', 10, '')
tf.app.flags.DEFINE_string('tch_model_ckpt', None, '')
tf.app.flags.DEFINE_integer('num_tch_epoch', 5, '')
flags = tf.app.flags.FLAGS
train_data_size = utils.get_train_data_size(flags.dataset)
valid_data_size = utils.get_valid_data_size(flags.dataset)
num_batch_t = int(flags.num_epoch * train_data_size / flags.batch_size)
num_batch_v = int(valid_data_size / config.valid_batch_size)
eval_interval = int(train_data_size / flags.batch_size)
print('tn:\t#batch=%d\nvd:\t#batch=%d\neval:\t#interval=%d' %
(num_batch_t, num_batch_v, eval_interval))
def main(_):
gen_t = GEN(flags, is_training=True)
scope = tf.get_variable_scope()
scope.reuse_variables()
gen_v = GEN(flags, is_training=False)
tf.summary.scalar(gen_t.learning_rate.name, gen_t.learning_rate)
def __init__(self, flags, is_training=True):
self.is_training = is_training
# None = batch_size
self.image_ph = tf.placeholder(tf.float32,
shape=(None, flags.feature_size))
self.text_ph = tf.placeholder(tf.int64, shape=(None, None))
self.hard_label_ph = tf.placeholder(tf.float32,
shape=(None, flags.num_label))
#self.soft_label_ph = tf.placeholder(tf.float32, shape=(None, flags.num_label))
# None = batch_size * sample_size
self.sample_ph = tf.placeholder(tf.int32, shape=(None, 2))
self.reward_ph = tf.placeholder(tf.float32, shape=(None, ))
tch_scope = 'tch'
vocab_size = utils.get_vocab_size(flags.dataset)
model_scope = nets_factory.arg_scopes_map[flags.model_name]
# initializer = tf.random_uniform([vocab_size, flags.embedding_size], -0.1, 0.1)
with tf.variable_scope(tch_scope) as scope:
"""
with slim.arg_scope([slim.fully_connected],
weights_regularizer=slim.l2_regularizer(flags.tch_weight_decay)):
word_embedding = slim.variable('word_embedding',
shape=[vocab_size, flags.embedding_size],
# regularizer=slim.l2_regularizer(flags.tch_weight_decay),
initializer=tf.random_uniform_initializer(-0.1, 0.1))
# word_embedding = tf.get_variable('word_embedding', initializer=initializer)
text_embedding = tf.nn.embedding_lookup(word_embedding, self.text_ph)
text_embedding = tf.reduce_mean(text_embedding, axis=-2)
"""
with slim.arg_scope(
model_scope(weight_decay=flags.gen_weight_decay)):
net = self.image_ph
net = slim.dropout(net,
flags.dropout_keep_prob,
is_training=is_training)
#combined_logits = tf.concat([net, text_embedding], 1)
#"""
self.logits = slim.fully_connected(net,
flags.num_label,
activation_fn=None)
self.labels = tf.nn.softmax(self.logits)
if not is_training:
return
save_dict = {}
for variable in tf.trainable_variables():
if not variable.name.startswith(tch_scope):
continue
print('%-50s added to TCH saver' % variable.name)
save_dict[variable.name] = variable
self.saver = tf.train.Saver(save_dict)
global_step = tf.Variable(0, trainable=False)
train_data_size = utils.get_train_data_size(flags.dataset)
self.learning_rate = utils.get_lr(flags, global_step, train_data_size,
flags.learning_rate,
flags.learning_rate_decay_factor,
flags.num_epochs_per_decay,
tch_scope)
# pre train
pre_losses = []
pre_losses.append(
tf.losses.sigmoid_cross_entropy(self.hard_label_ph, self.logits))
pre_losses.extend(tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES))
self.pre_loss = tf.add_n(pre_losses, name='%s_pre_loss' % tch_scope)
pre_optimizer = tf.train.AdamOptimizer(self.learning_rate)
#pre_optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)
self.pre_update = pre_optimizer.minimize(self.pre_loss,
global_step=global_step)
# kdgan train
sample_logits = tf.gather_nd(self.logits, self.sample_ph)
kdgan_losses = [
tf.losses.sigmoid_cross_entropy(self.reward_ph, sample_logits)
]
self.kdgan_loss = tf.add_n(kdgan_losses,
name='%s_kdgan_loss' % tch_scope)
kdgan_optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)
self.kdgan_update = kdgan_optimizer.minimize(self.kdgan_loss,
global_step=global_step)
def __init__(self, flags, is_training=True):
self.is_training = is_training
# None = batch_size
self.image_ph = tf.placeholder(tf.float32,
shape=(None, flags.feature_size))
self.hard_label_ph = tf.placeholder(tf.float32,
shape=(None, flags.num_label))
self.soft_label_ph = tf.placeholder(tf.float32,
shape=(None, flags.num_label))
# None = batch_size * sample_size
self.sample_ph = tf.placeholder(tf.int32, shape=(None, 2))
self.reward_ph = tf.placeholder(tf.float32, shape=(None, ))
gen_scope = 'gen'
model_scope = nets_factory.arg_scopes_map[flags.model_name]
with tf.variable_scope(gen_scope) as scope:
with slim.arg_scope(
model_scope(weight_decay=flags.gen_weight_decay)):
net = self.image_ph
net = slim.dropout(net,
flags.dropout_keep_prob,
is_training=is_training)
net = slim.fully_connected(net,
flags.num_label,
activation_fn=None)
self.logits = net
self.labels = tf.nn.softmax(self.logits)
if not is_training:
return
save_dict = {}
for variable in tf.trainable_variables():
if not variable.name.startswith(gen_scope):
continue
print('%-50s added to GEN saver' % variable.name)
save_dict[variable.name] = variable
self.saver = tf.train.Saver(save_dict)
global_step = tf.Variable(0, trainable=False)
train_data_size = utils.get_train_data_size(flags.dataset)
self.learning_rate = utils.get_lr(flags, global_step, train_data_size,
flags.learning_rate,
flags.learning_rate_decay_factor,
flags.num_epochs_per_decay,
gen_scope)
# pre train
pre_losses = []
pre_losses.append(
tf.losses.sigmoid_cross_entropy(self.hard_label_ph, self.logits))
pre_losses.extend(tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES))
self.pre_loss = tf.add_n(pre_losses, name='%s_pre_loss' % gen_scope)
pre_optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)
self.pre_update = pre_optimizer.minimize(self.pre_loss,
global_step=global_step)
# kd train
kd_losses = self.get_kd_losses(flags)
self.kd_loss = tf.add_n(kd_losses, name='%s_kd_loss' % gen_scope)
kd_optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)
self.kd_update = kd_optimizer.minimize(self.kd_loss,
global_step=global_step)
# gan train
gan_losses = self.get_gan_losses(flags)
gan_losses.extend(tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES))
self.gan_loss = tf.add_n(gan_losses, name='%s_gan_loss' % gen_scope)
gan_optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)
self.gan_update = gan_optimizer.minimize(self.gan_loss,
global_step=global_step)
# kdgan train
kdgan_losses = self.get_kd_losses(flags) + self.get_gan_losses(flags)
self.kdgan_loss = tf.add_n(kdgan_losses,
name='%s_kdgan_loss' % gen_scope)
kdgan_optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)
self.kdgan_update = kdgan_optimizer.minimize(self.kdgan_loss,
global_step=global_step)
