class Seqgan(Gan):
def __init__(self, oracle=None):
super().__init__()
def init_metric(self):
nll = Nll(data_loader=self.oracle_data_loader, rnn=self.oracle, sess=self.sess)
self.add_metric(nll)
# inll = Nll(data_loader=self.gen_data_loader, rnn=self.generator, sess=self.sess)
# inll.set_name('nll-test')
# self.add_metric(inll)
# from utils.metrics.DocEmbSim import DocEmbSim
# docsim = DocEmbSim(oracle_file=self.oracle_file, generator_file=self.generator_file, num_vocabulary=self.vocab_size)
# self.add_metric(docsim)
def train_discriminator(self):
self.generate_samples()
self.dis_data_loader.load_train_data(self.oracle_file, self.generator_file)
for _ in range(3):
self.dis_data_loader.next_batch()
x_batch, y_batch = self.dis_data_loader.next_batch()
feed = {
self.discriminator.input_x: x_batch,
self.discriminator.input_y: y_batch,
}
loss,_ = self.sess.run([self.discriminator.d_loss, self.discriminator.train_op], feed)
def train_new_discriminator(self, epoch, stage=None):
if stage == 'pre':
#valid dataset
self.dis_valid_data_loader.next_batch()
x_batch, y_batch = self.dis_valid_data_loader.next_batch()
feed = {
self.discriminator_new.input_x: x_batch,
self.discriminator_new.input_y: y_batch,
}
merge_summary_valid = self.sess.run(self.discriminator_new.merge_summary_valid, feed)
# tensoboard
self.writer.add_summary(merge_summary_valid, epoch)
#train dataset
for i in range(3):
self.dis_train_data_loader.next_batch()
x_batch, y_batch = self.dis_train_data_loader.next_batch()
feed = {
self.discriminator_new.input_x: x_batch,
self.discriminator_new.input_y: y_batch,
}
if i == 2:
merge_summary_train, _ = self.sess.run(
[self.discriminator_new.merge_summary_train_ad, self.discriminator_new.train_op], feed)
# tensoboard
self.writer.add_summary(merge_summary_train, epoch)
else:
self.sess.run(self.discriminator_new.train_op, feed)
else:
# valid
self.dis_valid_data_loader.next_batch()
x_batch, y_batch = self.dis_valid_data_loader.next_batch()
feed = {
self.discriminator_new.input_x: x_batch,
self.discriminator_new.input_y: y_batch,
}
merge_summary_valid_ad = self.sess.run(self.discriminator_new.merge_summary_valid_ad, feed)
# tensoboard
self.writer.add_summary(merge_summary_valid_ad, epoch)
# train
for i in range(3):
self.dis_train_data_loader.next_batch()
x_batch, y_batch = self.dis_train_data_loader.next_batch()
feed = {
self.discriminator_new.input_x: x_batch,
self.discriminator_new.input_y: y_batch,
}
if i==2:
merge_summary_train_ad, _ = self.sess.run([self.discriminator_new.merge_summary_train_ad, self.discriminator_new.train_op], feed)
# tensoboard
self.writer.add_summary(merge_summary_train_ad, epoch)
else:
self.sess.run(self.discriminator_new.train_op,feed)
def init_oracle_trainng(self, oracle=None):
if oracle is None:
oracle = OracleLstm(num_vocabulary=self.vocab_size, batch_size=self.batch_size, emb_dim=self.emb_dim,
hidden_dim=self.hidden_dim, sequence_length=self.sequence_length,
start_token=self.start_token)
self.set_oracle(oracle)
generator = Generator(num_vocabulary=self.vocab_size, batch_size=self.batch_size, emb_dim=self.emb_dim,
hidden_dim=self.hidden_dim, sequence_length=self.sequence_length,
start_token=self.start_token)
self.set_generator(generator)
discriminator = Discriminator(sequence_length=self.sequence_length, num_classes=2, vocab_size=self.vocab_size,
emd_dim=self.emb_dim, filter_sizes=self.filter_size, num_filters=self.num_filters,
l2_reg_lambda=self.l2_reg_lambda, splited_steps=self.splited_steps)
self.set_discriminator(discriminator)
gen_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
oracle_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
dis_dataloader = DisDataloader(batch_size=self.batch_size, seq_length=self.sequence_length)
self.set_data_loader(gen_loader=gen_dataloader, dis_loader=dis_dataloader, oracle_loader=oracle_dataloader)
def train_oracle(self):
self.init_oracle_trainng()
self.init_metric()
self.sess.run(tf.global_variables_initializer())
# self.pre_epoch_num = 80
# self.adversarial_epoch_num = 100
# self.log = open('experiment-log-seqgan.csv', 'w')
generate_samples(self.sess, self.oracle, self.batch_size, self.generated_num, self.oracle_file)
generate_samples(self.sess, self.generator, self.batch_size, self.generated_num, self.generator_file)
self.gen_data_loader.create_batches(self.oracle_file)
self.oracle_data_loader.create_batches(self.generator_file)
print('start pre-train generator:')
for epoch in range(self.pre_epoch_num):
start = time()
loss = pre_train_epoch(self.sess, self.generator, self.gen_data_loader)
end = time()
print('epoch:' + str(self.epoch) + '\t time:' + str(end - start))
self.add_epoch()
if epoch % self.ntest_pre == 0:
generate_samples(self.sess, self.generator, self.batch_size, self.generated_num, self.generator_file)
self.evaluate()
print('start pre-train discriminator:')
# self.reset_epoch()
for epoch in range(self.pre_epoch_num):
print('epoch:' + str(epoch))
self.train_discriminator()
# self.reset_epoch()
print('adversarial training:')
self.reward = Reward(self.generator, .8)
for epoch in range(self.adversarial_epoch_num):
# print('epoch:' + str(epoch))
start = time()
for index in range(1):
samples = self.generator.generate(self.sess)
rewards = self.reward.get_reward(self.sess, samples, 16, self.discriminator)
feed = {
self.generator.x: samples,
self.generator.rewards: rewards
}
_ = self.sess.run(self.generator.g_updates, feed_dict=feed)
end = time()
self.add_epoch()
print('epoch:' + str(self.epoch) + '\t time:' + str(end - start))
if epoch % self.ntest == 0 or epoch == self.adversarial_epoch_num - 1:
generate_samples(self.sess, self.generator, self.batch_size, self.generated_num, self.generator_file)
self.evaluate()
self.reward.update_params()
for _ in range(15):
self.train_discriminator()
def init_cfg_training(self, grammar=None):
oracle = OracleCfg(sequence_length=self.sequence_length, cfg_grammar=grammar)
self.set_oracle(oracle)
self.oracle.generate_oracle()
self.vocab_size = self.oracle.vocab_size + 1
generator = Generator(num_vocabulary=self.vocab_size, batch_size=self.batch_size, emb_dim=self.emb_dim,
hidden_dim=self.hidden_dim, sequence_length=self.sequence_length,
start_token=self.start_token)
self.set_generator(generator)
discriminator = Discriminator(sequence_length=self.sequence_length, num_classes=2, vocab_size=self.vocab_size,
emd_dim=self.emb_dim, filter_sizes=self.filter_size, num_filters=self.num_filters,
l2_reg_lambda=self.l2_reg_lambda)
self.set_discriminator(discriminator)
gen_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
oracle_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
dis_dataloader = DisDataloader(batch_size=self.batch_size, seq_length=self.sequence_length)
self.set_data_loader(gen_loader=gen_dataloader, dis_loader=dis_dataloader, oracle_loader=oracle_dataloader)
return oracle.wi_dict, oracle.iw_dict
def init_cfg_metric(self, grammar=None):
cfg = Cfg(test_file=self.test_file, cfg_grammar=grammar)
self.add_metric(cfg)
def train_cfg(self):
cfg_grammar = """
S -> S PLUS x | S SUB x | S PROD x | S DIV x | x | '(' S ')'
PLUS -> '+'
SUB -> '-'
PROD -> '*'
DIV -> '/'
x -> 'x' | 'y'
"""
wi_dict_loc, iw_dict_loc = self.init_cfg_training(cfg_grammar)
with open(iw_dict_loc, 'r') as file:
iw_dict = json.load(file)
def get_cfg_test_file(dict=iw_dict):
with open(self.generator_file, 'r') as file:
codes = get_tokenlized(self.generator_file)
with open(self.test_file, 'w') as outfile:
outfile.write(code_to_text(codes=codes, dictionary=dict))
self.init_cfg_metric(grammar=cfg_grammar)
self.sess.run(tf.global_variables_initializer())
self.pre_epoch_num = 80
self.adversarial_epoch_num = 100
self.log = open('experiment-log-seqgan-cfg.csv', 'w')
generate_samples(self.sess, self.generator, self.batch_size, self.generated_num, self.generator_file)
self.gen_data_loader.create_batches(self.oracle_file)
self.oracle_data_loader.create_batches(self.generator_file)
print('start pre-train generator:')
for epoch in range(self.pre_epoch_num):
start = time()
loss = pre_train_epoch(self.sess, self.generator, self.gen_data_loader)
end = time()
print('epoch:' + str(self.epoch) + '\t time:' + str(end - start))
self.add_epoch()
if epoch % 5 == 0:
generate_samples(self.sess, self.generator, self.batch_size, self.generated_num, self.generator_file)
get_cfg_test_file()
self.evaluate()
print('start pre-train discriminator:')
self.reset_epoch()
for epoch in range(self.pre_epoch_num * 3):
print('epoch:' + str(epoch))
self.train_discriminator()
self.reset_epoch()
print('adversarial training:')
self.reward = Reward(self.generator, .8)
for epoch in range(self.adversarial_epoch_num):
# print('epoch:' + str(epoch))
start = time()
for index in range(1):
samples = self.generator.generate(self.sess)
rewards = self.reward.get_reward(self.sess, samples, 16, self.discriminator)
feed = {
self.generator.x: samples,
self.generator.rewards: rewards
}
loss, _ = self.sess.run([self.generator.g_loss, self.generator.g_updates], feed_dict=feed)
print(loss)
end = time()
self.add_epoch()
print('epoch:' + str(self.epoch) + '\t time:' + str(end - start))
if epoch % 5 == 0 or epoch == self.adversarial_epoch_num - 1:
generate_samples(self.sess, self.generator, self.batch_size, self.generated_num, self.generator_file)
get_cfg_test_file()
self.evaluate()
self.reward.update_params()
for _ in range(15):
self.train_discriminator()
return
def init_real_trainng(self, data_loc=None):
from utils.text_process import text_precess, text_to_code
from utils.text_process import get_tokenlized, get_word_list, get_dict
if data_loc is None:
data_loc = 'data/image_coco.txt'
#self.sequence_length, self.vocab_size = text_precess(data_loc)
generator = Generator(num_vocabulary=self.vocab_size, batch_size=self.batch_size, emb_dim=self.emb_dim,
hidden_dim=self.hidden_dim, sequence_length=self.sequence_length,
start_token=self.start_token)
self.set_generator(generator)
discriminator = Discriminator(sequence_length=self.sequence_length, num_classes=2, vocab_size=self.vocab_size,
emd_dim=self.emb_dim, filter_sizes=self.filter_size, num_filters=self.num_filters,
l2_reg_lambda=self.l2_reg_lambda, splited_steps=self.splited_steps)
discriminator_new = DiscriminatorNew(sequence_length=self.sequence_length, num_classes=2, vocab_size=self.vocab_size,
emd_dim=self.emb_dim, filter_sizes=self.filter_size, num_filters=self.num_filters,
l2_reg_lambda=self.l2_reg_lambda, splited_steps=self.splited_steps)
self.set_discriminator(discriminator,discriminator_new=discriminator_new)
gen_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
oracle_dataloader = None
dis_dataloader = DisDataloader(batch_size=self.batch_size, seq_length=self.sequence_length)
dis_valid_dataloader = BalanceDisDataloader(batch_size=self.batch_size, seq_length=self.sequence_length)
dis_train_dataloader = BalanceDisDataloader(batch_size=self.batch_size, seq_length=self.sequence_length)
valid_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
test_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
fake_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
self.set_data_loader(gen_loader=gen_dataloader, dis_loader=dis_dataloader, dis_train_loader=dis_train_dataloader, dis_valid_loader=dis_valid_dataloader,oracle_loader=oracle_dataloader, fake_loader=fake_dataloader, valid_loader=valid_dataloader, test_loader=test_dataloader)
tokens = get_tokenlized(data_loc)
with open(self.oracle_file, 'w') as outfile:
outfile.write(text_to_code(tokens, self.wi_dict, self.sequence_length))
def train_real(self, data_loc=None):
self.init_real_trainng(data_loc)
self.init_real_metric()
self.gen_data_loader.create_batches(self.oracle_file)
self.valid_data_loader.create_batches(self.valid_file)
self.sess.run(tf.global_variables_initializer())
#++ Saver
saver_variables = tf.global_variables()
saver = tf.train.Saver(saver_variables)
#++ ====================
# summary writer
self.writer = self.save_summary()
if self.restore:
restore_from = tf.train.latest_checkpoint(self.save_path)
saver.restore(self.sess, restore_from)
print(f"{Fore.BLUE}Restore from : {restore_from}{Fore.RESET}")
self.epoch = self.pre_epoch_num
else:
print('start pre-train generator:')
for epoch in range(self.pre_epoch_num):
start = time()
loss = pre_train_epoch(self.sess, self.generator, self.gen_data_loader)
end = time()
print('epoch:' + str(self.epoch) + '\t time:' + str(end - start))
self.add_epoch()
if epoch % self.ntest_pre == 0:
self.evaluate_real()
print('start pre-train discriminator:')
for epoch in range(self.pre_epoch_num):
print('epoch:' + str(epoch))
self.train_discriminator()
print('start pre-train new discriminator')
self.generate_samples()
self.dis_valid_data_loader.load_train_data(self.valid_file, self.generator_file)
self.dis_train_data_loader.load_train_data(self.oracle_file, self.generator_file)
for epoch in range(self.pre_new_dis_num):
print('evaluate epoch:' + str(epoch))
self.train_new_discriminator(epoch, 'pre')
self.total_distance_for_seqgan()
# save pre_train
saver.save(self.sess, os.path.join(self.save_path, 'pre_train'))
# stop after pretrain
if self.pretrain:
self.evaluate_real()
exit()
print('adversarial training:')
self.reward = Reward(self.generator, .8)
for epoch in range(self.adversarial_epoch_num):
# print('epoch:' + str(epoch))
start = time()
for index in range(1):
samples = self.generator.generate(self.sess)
rewards = self.reward.get_reward(self.sess, samples, 16, self.discriminator)
feed = {
self.generator.x: samples,
self.generator.rewards: rewards
}
loss, _ = self.sess.run([self.generator.g_loss, self.generator.g_updates], feed_dict=feed)
end = time()
self.add_epoch()
print('epoch:' + str(self.epoch) + '\t time:' + str(end - start))
if epoch % self.ntest == 0 or epoch == self.adversarial_epoch_num - 1:
self.evaluate_real()
self.reward.update_params()
for _ in range(15):
self.train_discriminator()
print('new discriminator evaluate')
self.dis_valid_data_loader.load_train_data(self.valid_file, self.generator_file)
self.dis_train_data_loader.load_train_data(self.oracle_file, self.generator_file)
for epoch in range(self.pre_new_dis_num):
print('evaluate epoch:' + str(epoch))
self.train_new_discriminator(epoch)
# save all
saver.save(self.sess, os.path.join(self.save_path, 'all_train'))
class Seqgan(Gan):
def __init__(self, oracle=None):
super().__init__()
# you can change parameters, generator here
self.vocab_size = 20
self.emb_dim = 32
self.hidden_dim = 32
self.sequence_length = 20
self.filter_size = [2, 3]
self.num_filters = [100, 200]
self.l2_reg_lambda = 0.2
self.dropout_keep_prob = 0.75
self.batch_size = 64
self.generate_num = 128
self.start_token = 0
self.oracle_file = 'save/oracle.txt'
self.generator_file = 'save/generator.txt'
self.test_file = 'save/test_file.txt'
def init_metric(self):
nll = Nll(data_loader=self.oracle_data_loader,
rnn=self.oracle,
sess=self.sess)
self.add_metric(nll)
inll = Nll(data_loader=self.gen_data_loader,
rnn=self.generator,
sess=self.sess)
inll.set_name('nll-test')
self.add_metric(inll)
from utils.metrics.DocEmbSim import DocEmbSim
docsim = DocEmbSim(oracle_file=self.oracle_file,
generator_file=self.generator_file,
num_vocabulary=self.vocab_size)
self.add_metric(docsim)
def train_discriminator(self):
generate_samples(self.sess, self.generator, self.batch_size,
self.generate_num, self.generator_file)
self.dis_data_loader.load_train_data(self.oracle_file,
self.generator_file)
for _ in range(3):
self.dis_data_loader.next_batch()
x_batch, y_batch = self.dis_data_loader.next_batch()
feed = {
self.discriminator.input_x: x_batch,
self.discriminator.input_y: y_batch,
}
loss, _ = self.sess.run(
[self.discriminator.d_loss, self.discriminator.train_op], feed)
# print(loss)
def evaluate(self):
generate_samples(self.sess, self.generator, self.batch_size,
self.generate_num, self.generator_file)
if self.oracle_data_loader is not None:
self.oracle_data_loader.create_batches(self.generator_file)
if self.log is not None:
if self.epoch == 0 or self.epoch == 1:
self.log.write('epochs, ')
for metric in self.metrics:
self.log.write(metric.get_name() + ',')
self.log.write('\n')
scores = super().evaluate()
for score in scores:
self.log.write(str(score) + ',')
self.log.write('\n')
return scores
return super().evaluate()
def init_oracle_trainng(self, oracle=None):
if oracle is None:
oracle = OracleLstm(num_vocabulary=self.vocab_size,
batch_size=self.batch_size,
emb_dim=self.emb_dim,
hidden_dim=self.hidden_dim,
sequence_length=self.sequence_length,
start_token=self.start_token)
self.set_oracle(oracle)
generator = Generator(num_vocabulary=self.vocab_size,
batch_size=self.batch_size,
emb_dim=self.emb_dim,
hidden_dim=self.hidden_dim,
sequence_length=self.sequence_length,
start_token=self.start_token)
self.set_generator(generator)
discriminator = Discriminator(sequence_length=self.sequence_length,
num_classes=2,
vocab_size=self.vocab_size,
emd_dim=self.emb_dim,
filter_sizes=self.filter_size,
num_filters=self.num_filters,
l2_reg_lambda=self.l2_reg_lambda)
self.set_discriminator(discriminator)
gen_dataloader = DataLoader(batch_size=self.batch_size,
seq_length=self.sequence_length)
oracle_dataloader = DataLoader(batch_size=self.batch_size,
seq_length=self.sequence_length)
dis_dataloader = DisDataloader(batch_size=self.batch_size,
seq_length=self.sequence_length)
self.set_data_loader(gen_loader=gen_dataloader,
dis_loader=dis_dataloader,
oracle_loader=oracle_dataloader)
def train_oracle(self):
self.init_oracle_trainng()
self.init_metric()
self.sess.run(tf.global_variables_initializer())
self.pre_epoch_num = 80
self.adversarial_epoch_num = 100
self.log = open('experiment-log-seqgan.csv', 'w')
generate_samples(self.sess, self.oracle, self.batch_size,
self.generate_num, self.oracle_file)
generate_samples(self.sess, self.generator, self.batch_size,
self.generate_num, self.generator_file)
self.gen_data_loader.create_batches(self.oracle_file)
self.oracle_data_loader.create_batches(self.generator_file)
# rollout = Reward(generator, update_rate)
print('start pre-train generator:')
for epoch in range(self.pre_epoch_num):
start = time()
loss = pre_train_epoch(self.sess, self.generator,
self.gen_data_loader)
end = time()
print('epoch:' + str(epoch) + '\t time:' + str(end - start))
self.add_epoch()
if epoch % 5 == 0:
generate_samples(self.sess, self.generator, self.batch_size,
self.generate_num, self.generator_file)
self.evaluate()
print('start pre-train discriminator:')
self.reset_epoch()
for epoch in range(self.pre_epoch_num):
print('epoch:' + str(epoch))
self.train_discriminator()
self.reset_epoch()
print('adversarial training:')
self.reward = Reward(self.generator, .8)
for epoch in range(self.adversarial_epoch_num):
# print('epoch:' + str(epoch))
start = time()
for index in range(1):
samples = self.generator.generate(self.sess)
rewards = self.reward.get_reward(self.sess, samples, 16,
self.discriminator)
feed = {
self.generator.x: samples,
self.generator.rewards: rewards
}
_ = self.sess.run(self.generator.g_updates, feed_dict=feed)
end = time()
self.add_epoch()
print('epoch:' + str(epoch) + '\t time:' + str(end - start))
if epoch % 5 == 0 or epoch == self.adversarial_epoch_num - 1:
generate_samples(self.sess, self.generator, self.batch_size,
self.generate_num, self.generator_file)
self.evaluate()
self.reward.update_params()
for _ in range(15):
self.train_discriminator()
def init_cfg_training(self, grammar=None):
oracle = OracleCfg(sequence_length=self.sequence_length,
cfg_grammar=grammar)
self.set_oracle(oracle)
self.oracle.generate_oracle()
self.vocab_size = self.oracle.vocab_size + 1
generator = Generator(num_vocabulary=self.vocab_size,
batch_size=self.batch_size,
emb_dim=self.emb_dim,
hidden_dim=self.hidden_dim,
sequence_length=self.sequence_length,
start_token=self.start_token)
self.set_generator(generator)
discriminator = Discriminator(sequence_length=self.sequence_length,
num_classes=2,
vocab_size=self.vocab_size,
emd_dim=self.emb_dim,
filter_sizes=self.filter_size,
num_filters=self.num_filters,
l2_reg_lambda=self.l2_reg_lambda)
self.set_discriminator(discriminator)
gen_dataloader = DataLoader(batch_size=self.batch_size,
seq_length=self.sequence_length)
oracle_dataloader = DataLoader(batch_size=self.batch_size,
seq_length=self.sequence_length)
dis_dataloader = DisDataloader(batch_size=self.batch_size,
seq_length=self.sequence_length)
self.set_data_loader(gen_loader=gen_dataloader,
dis_loader=dis_dataloader,
oracle_loader=oracle_dataloader)
return oracle.wi_dict, oracle.iw_dict
def init_cfg_metric(self, grammar=None):
cfg = Cfg(test_file=self.test_file, cfg_grammar=grammar)
self.add_metric(cfg)
def train_cfg(self):
cfg_grammar = """
S -> S PLUS x | S SUB x | S PROD x | S DIV x | x | '(' S ')'
PLUS -> '+'
SUB -> '-'
PROD -> '*'
DIV -> '/'
x -> 'x' | 'y'
"""
wi_dict_loc, iw_dict_loc = self.init_cfg_training(cfg_grammar)
with open(iw_dict_loc, 'r') as file:
iw_dict = json.load(file)
def get_cfg_test_file(dict=iw_dict):
with open(self.generator_file, 'r') as file:
codes = get_tokenlized(self.generator_file)
with open(self.test_file, 'w') as outfile:
outfile.write(code_to_text(codes=codes, dictionary=dict))
self.init_cfg_metric(grammar=cfg_grammar)
self.sess.run(tf.global_variables_initializer())
self.pre_epoch_num = 80
self.adversarial_epoch_num = 100
self.log = open('experiment-log-seqgan-cfg.csv', 'w')
# generate_samples(self.sess, self.oracle, self.batch_size, self.generate_num, self.oracle_file)
generate_samples(self.sess, self.generator, self.batch_size,
self.generate_num, self.generator_file)
self.gen_data_loader.create_batches(self.oracle_file)
self.oracle_data_loader.create_batches(self.generator_file)
print('start pre-train generator:')
for epoch in range(self.pre_epoch_num):
start = time()
loss = pre_train_epoch(self.sess, self.generator,
self.gen_data_loader)
end = time()
print('epoch:' + str(epoch) + '\t time:' + str(end - start))
self.add_epoch()
if epoch % 5 == 0:
generate_samples(self.sess, self.generator, self.batch_size,
self.generate_num, self.generator_file)
get_cfg_test_file()
self.evaluate()
print('start pre-train discriminator:')
self.reset_epoch()
for epoch in range(self.pre_epoch_num * 3):
print('epoch:' + str(epoch))
self.train_discriminator()
self.reset_epoch()
print('adversarial training:')
self.reward = Reward(self.generator, .8)
for epoch in range(self.adversarial_epoch_num):
# print('epoch:' + str(epoch))
start = time()
for index in range(1):
samples = self.generator.generate(self.sess)
rewards = self.reward.get_reward(self.sess, samples, 16,
self.discriminator)
feed = {
self.generator.x: samples,
self.generator.rewards: rewards
}
loss, _ = self.sess.run(
[self.generator.g_loss, self.generator.g_updates],
feed_dict=feed)
print(loss)
end = time()
self.add_epoch()
print('epoch:' + str(epoch) + '\t time:' + str(end - start))
if epoch % 5 == 0 or epoch == self.adversarial_epoch_num - 1:
generate_samples(self.sess, self.generator, self.batch_size,
self.generate_num, self.generator_file)
get_cfg_test_file()
self.evaluate()
self.reward.update_params()
for _ in range(15):
self.train_discriminator()
return
def init_real_trainng(self, data_loc=None):
from utils.text_process import text_precess, text_to_code
from utils.text_process import get_tokenlized, get_word_list, get_dict
if data_loc is None:
data_loc = 'data/image_coco.txt'
self.sequence_length, self.vocab_size = text_precess(data_loc)
generator = Generator(num_vocabulary=self.vocab_size,
batch_size=self.batch_size,
emb_dim=self.emb_dim,
hidden_dim=self.hidden_dim,
sequence_length=self.sequence_length,
start_token=self.start_token)
self.set_generator(generator)
discriminator = Discriminator(sequence_length=self.sequence_length,
num_classes=2,
vocab_size=self.vocab_size,
emd_dim=self.emb_dim,
filter_sizes=self.filter_size,
num_filters=self.num_filters,
l2_reg_lambda=self.l2_reg_lambda)
self.set_discriminator(discriminator)
gen_dataloader = DataLoader(batch_size=self.batch_size,
seq_length=self.sequence_length)
oracle_dataloader = None
dis_dataloader = DisDataloader(batch_size=self.batch_size,
seq_length=self.sequence_length)
self.set_data_loader(gen_loader=gen_dataloader,
dis_loader=dis_dataloader,
oracle_loader=oracle_dataloader)
tokens = get_tokenlized(data_loc)
word_set = get_word_list(tokens)
[word_index_dict, index_word_dict] = get_dict(word_set)
with open(self.oracle_file, 'w') as outfile:
outfile.write(
text_to_code(tokens, word_index_dict, self.sequence_length))
return word_index_dict, index_word_dict
def init_real_metric(self):
from utils.metrics.DocEmbSim import DocEmbSim
docsim = DocEmbSim(oracle_file=self.oracle_file,
generator_file=self.generator_file,
num_vocabulary=self.vocab_size)
self.add_metric(docsim)
inll = Nll(data_loader=self.gen_data_loader,
rnn=self.generator,
sess=self.sess)
inll.set_name('nll-test')
self.add_metric(inll)
def train_real(self, data_loc=None):
from utils.text_process import code_to_text
from utils.text_process import get_tokenlized
wi_dict, iw_dict = self.init_real_trainng(data_loc)
self.init_real_metric()
def get_real_test_file(dict=iw_dict):
with open(self.generator_file, 'r') as file:
codes = get_tokenlized(self.generator_file)
with open(self.test_file, 'w') as outfile:
outfile.write(code_to_text(codes=codes, dictionary=dict))
self.sess.run(tf.global_variables_initializer())
self.pre_epoch_num = 80
self.adversarial_epoch_num = 100
self.log = open('experiment-log-seqgan-real.csv', 'w')
generate_samples(self.sess, self.generator, self.batch_size,
self.generate_num, self.generator_file)
self.gen_data_loader.create_batches(self.oracle_file)
print('start pre-train generator:')
for epoch in range(self.pre_epoch_num):
start = time()
loss = pre_train_epoch(self.sess, self.generator,
self.gen_data_loader)
end = time()
print('epoch:' + str(epoch) + '\t time:' + str(end - start))
self.add_epoch()
if epoch % 5 == 0:
generate_samples(self.sess, self.generator, self.batch_size,
self.generate_num, self.generator_file)
get_real_test_file()
self.evaluate()
print('start pre-train discriminator:')
self.reset_epoch()
for epoch in range(self.pre_epoch_num):
print('epoch:' + str(epoch))
self.train_discriminator()
self.reset_epoch()
print('adversarial training:')
self.reward = Reward(self.generator, .8)
for epoch in range(self.adversarial_epoch_num):
# print('epoch:' + str(epoch))
start = time()
for index in range(1):
samples = self.generator.generate(self.sess)
rewards = self.reward.get_reward(self.sess, samples, 16,
self.discriminator)
feed = {
self.generator.x: samples,
self.generator.rewards: rewards
}
loss, _ = self.sess.run(
[self.generator.g_loss, self.generator.g_updates],
feed_dict=feed)
print(loss)
end = time()
self.add_epoch()
print('epoch:' + str(epoch) + '\t time:' + str(end - start))
if epoch % 5 == 0 or epoch == self.adversarial_epoch_num - 1:
generate_samples(self.sess, self.generator, self.batch_size,
self.generate_num, self.generator_file)
get_real_test_file()
self.evaluate()
self.reward.update_params()
for _ in range(15):
self.train_discriminator()
class Seqgan(Gan):
def __init__(self, oracle=None):
# super().__init__()
super(Seqgan, self).__init__()
# you can change parameters, generator here
self.vocab_size = 409
self.emb_dim = 32
self.hidden_dim = 32
self.sequence_length = 20
self.filter_size = [2, 3]
self.num_filters = [100, 200]
self.l2_reg_lambda = 0.2
self.dropout_keep_prob = 0.75
self.batch_size = 64
self.generate_num = 128
self.start_token = 0
self.pre_epoch_num = 50
self.adversarial_epoch_num = 80
self.temps = [0.001,0.5,1.0,1.5,2.0]
self.generate_temp_size = 200
self.name = 'seqgan'
self.generate_temp_dataset = 'emnlp'
self.oracle_file = 'save/seqgan/oracle.txt'
self.generator_file = 'save/seqgan/generator.txt'
self.test_file = 'save/seqgan/test_file.txt'
self.real_file = ''
self.result_file = 'save/result_file.txt'
def init_metric(self):
nll = Nll(data_loader=self.oracle_data_loader, rnn=self.oracle, sess=self.sess)
self.add_metric(nll)
inll = Nll(data_loader=self.gen_data_loader, rnn=self.generator, sess=self.sess)
inll.set_name('nll-test')
self.add_metric(inll)
# from utils.metrics.DocEmbSim import DocEmbSim
# docsim = DocEmbSim(oracle_file=self.oracle_file, generator_file=self.generator_file, num_vocabulary=self.vocab_size)
# self.add_metric(docsim)
def train_discriminator(self):
generate_samples(self.sess, self.generator, self.batch_size, self.generate_num, self.generator_file)
self.dis_data_loader.load_train_data(self.oracle_file, self.generator_file)
for _ in range(3):
self.dis_data_loader.next_batch()
x_batch, y_batch = self.dis_data_loader.next_batch()
feed = {
self.discriminator.input_x: x_batch,
self.discriminator.input_y: y_batch,
}
loss,_ = self.sess.run([self.discriminator.d_loss, self.discriminator.train_op], feed)
# print(loss)
def evaluate(self):
generate_samples(self.sess, self.generator, self.batch_size, self.generate_num, self.generator_file)
if self.oracle_data_loader is not None:
self.oracle_data_loader.create_batches(self.generator_file)
if self.log is not None:
if self.epoch == 0 or self.epoch == 1:
self.log.write('epochs, ')
for metric in self.metrics:
print('metric.get_name', metric.get_name())
self.log.write(metric.get_name() + ',')
self.log.write('\n')
scores = super(Seqgan, self).evaluate()
for score in scores:
# print(str(score) + ',')
self.log.write(str(score) + ',')
self.log.write('\n')
return scores
return super().evaluate()
def init_oracle_trainng(self, oracle=None):
if oracle is None:
oracle = OracleLstm(num_vocabulary=self.vocab_size, batch_size=self.batch_size, emb_dim=self.emb_dim,
hidden_dim=self.hidden_dim, sequence_length=self.sequence_length,
start_token=self.start_token)
self.set_oracle(oracle)
generator = Generator(num_vocabulary=self.vocab_size, batch_size=self.batch_size, emb_dim=self.emb_dim,
hidden_dim=self.hidden_dim, sequence_length=self.sequence_length,
start_token=self.start_token)
self.set_generator(generator)
discriminator = Discriminator(sequence_length=self.sequence_length, num_classes=2, vocab_size=self.vocab_size,
emd_dim=self.emb_dim, filter_sizes=self.filter_size, num_filters=self.num_filters,
l2_reg_lambda=self.l2_reg_lambda)
self.set_discriminator(discriminator)
gen_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
oracle_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
dis_dataloader = DisDataloader(batch_size=self.batch_size, seq_length=self.sequence_length)
self.set_data_loader(gen_loader=gen_dataloader, dis_loader=dis_dataloader, oracle_loader=oracle_dataloader)
def train_oracle(self):
self.init_oracle_trainng()
self.init_metric()
self.sess.run(tf.global_variables_initializer())
self.log = open('experiment-log-seqgan.csv', 'w')
generate_samples(self.sess, self.oracle, self.batch_size, self.generate_num, self.oracle_file)
generate_samples(self.sess, self.generator, self.batch_size, self.generate_num, self.generator_file)
self.gen_data_loader.create_batches(self.oracle_file)
self.oracle_data_loader.create_batches(self.generator_file)
print('start pre-train generator:')
for epoch in range(self.pre_epoch_num):
start = time()
loss = pre_train_epoch(self.sess, self.generator, self.gen_data_loader)
end = time()
print('epoch:' + str(self.epoch) + '\t time:' + str(end - start))
self.add_epoch()
if epoch % 5 == 0:
generate_samples(self.sess, self.generator, self.batch_size, self.generate_num, self.generator_file)
self.evaluate()
print('start pre-train discriminator:')
self.reset_epoch()
for epoch in range(self.pre_epoch_num):
# print('epoch:' + str(epoch))
self.train_discriminator()
self.reset_epoch()
print('adversarial training:')
self.reward = Reward(self.generator, .8)
for epoch in range(self.adversarial_epoch_num):
# print('epoch:' + str(epoch))
start = time()
for index in range(1):
samples = self.generator.generate(self.sess)
rewards = self.reward.get_reward(self.sess, samples, 16, self.discriminator)
feed = {
self.generator.x: samples,
self.generator.rewards: rewards
}
_ = self.sess.run(self.generator.g_updates, feed_dict=feed)
end = time()
self.add_epoch()
# print('epoch:' + str(self.epoch) + '\t time:' + str(end - start))
if epoch % 5 == 0 or epoch == self.adversarial_epoch_num - 1:
generate_samples(self.sess, self.generator, self.batch_size, self.generate_num, self.generator_file)
self.evaluate()
self.reward.update_params()
for _ in range(15):
self.train_discriminator()
def init_cfg_training(self, grammar=None):
oracle = OracleCfg(sequence_length=self.sequence_length, cfg_grammar=grammar)
self.set_oracle(oracle)
self.oracle.generate_oracle()
self.vocab_size = self.oracle.vocab_size + 1
generator = Generator(num_vocabulary=self.vocab_size, batch_size=self.batch_size, emb_dim=self.emb_dim,
hidden_dim=self.hidden_dim, sequence_length=self.sequence_length,
start_token=self.start_token)
self.set_generator(generator)
discriminator = Discriminator(sequence_length=self.sequence_length, num_classes=2, vocab_size=self.vocab_size,
emd_dim=self.emb_dim, filter_sizes=self.filter_size, num_filters=self.num_filters,
l2_reg_lambda=self.l2_reg_lambda)
self.set_discriminator(discriminator)
gen_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
oracle_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
dis_dataloader = DisDataloader(batch_size=self.batch_size, seq_length=self.sequence_length)
self.set_data_loader(gen_loader=gen_dataloader, dis_loader=dis_dataloader, oracle_loader=oracle_dataloader)
return oracle.wi_dict, oracle.iw_dict
def init_cfg_metric(self, grammar=None):
cfg = Cfg(test_file=self.test_file, cfg_grammar=grammar)
self.add_metric(cfg)
def train_cfg(self):
cfg_grammar = """
S -> S PLUS x | S SUB x | S PROD x | S DIV x | x | '(' S ')'
PLUS -> '+'
SUB -> '-'
PROD -> '*'
DIV -> '/'
x -> 'x' | 'y'
"""
wi_dict_loc, iw_dict_loc = self.init_cfg_training(cfg_grammar)
with open(iw_dict_loc, 'r') as file:
iw_dict = json.load(file)
def get_cfg_test_file(dict=iw_dict):
with open(self.generator_file, 'r') as file:
codes = get_tokenlized(self.generator_file)
with open(self.test_file, 'w') as outfile:
outfile.write(code_to_text(codes=codes, dictionary=dict))
self.init_cfg_metric(grammar=cfg_grammar)
self.sess.run(tf.global_variables_initializer())
self.log = open('experiment-log-seqgan-cfg.csv', 'w')
generate_samples(self.sess, self.generator, self.batch_size, self.generate_num, self.generator_file)
self.gen_data_loader.create_batches(self.oracle_file)
self.oracle_data_loader.create_batches(self.generator_file)
print('start pre-train generator:')
for epoch in range(self.pre_epoch_num):
start = time()
loss = pre_train_epoch(self.sess, self.generator, self.gen_data_loader)
end = time()
# print('epoch:' + str(self.epoch) + '\t time:' + str(end - start))
self.add_epoch()
if epoch % 5 == 0:
generate_samples(self.sess, self.generator, self.batch_size, self.generate_num, self.generator_file)
get_cfg_test_file()
self.evaluate()
print('start pre-train discriminator:')
self.reset_epoch()
for epoch in range(self.pre_epoch_num * 3):
# print('epoch:' + str(epoch))
self.train_discriminator()
self.reset_epoch()
print('adversarial training:')
self.reward = Reward(self.generator, .8)
for epoch in range(self.adversarial_epoch_num):
# print('epoch:' + str(epoch))
start = time()
for index in range(1):
samples = self.generator.generate(self.sess)
rewards = self.reward.get_reward(self.sess, samples, 16, self.discriminator)
feed = {
self.generator.x: samples,
self.generator.rewards: rewards
}
loss, _ = self.sess.run([self.generator.g_loss, self.generator.g_updates], feed_dict=feed)
# print(loss)
end = time()
self.add_epoch()
# print('epoch:' + str(self.epoch) + '\t time:' + str(end - start))
if epoch % 5 == 0 or epoch == self.adversarial_epoch_num - 1:
generate_samples(self.sess, self.generator, self.batch_size, self.generate_num, self.generator_file)
get_cfg_test_file()
self.evaluate()
self.reward.update_params()
for _ in range(15):
self.train_discriminator()
return
def init_real_trainng(self, data_loc=None):
from utils.text_process import text_precess, text_to_code
from utils.text_process import get_tokenlized, get_word_list, get_dict
if data_loc is None:
data_loc = 'data/image_coco.txt'
self.sequence_length, self.vocab_size = text_precess(data_loc)
print('self.sequence_length, self.vocab_size', self.sequence_length, self.vocab_size)
generator = Generator(num_vocabulary=self.vocab_size, batch_size=self.batch_size, emb_dim=self.emb_dim,
hidden_dim=self.hidden_dim, sequence_length=self.sequence_length,
start_token=self.start_token)
self.set_generator(generator)
discriminator = Discriminator(sequence_length=self.sequence_length, num_classes=2, vocab_size=self.vocab_size,
emd_dim=self.emb_dim, filter_sizes=self.filter_size, num_filters=self.num_filters,
l2_reg_lambda=self.l2_reg_lambda)
self.set_discriminator(discriminator)
gen_dataloader = DataLoader(batch_size=self.batch_size, seq_length=self.sequence_length)
oracle_dataloader = None
dis_dataloader = DisDataloader(batch_size=self.batch_size, seq_length=self.sequence_length)
self.set_data_loader(gen_loader=gen_dataloader, dis_loader=dis_dataloader, oracle_loader=oracle_dataloader)
tokens = get_tokenlized(data_loc)
word_set = get_word_list(tokens)
[word_index_dict, index_word_dict] = get_dict(word_set)
with open(self.oracle_file, 'w') as outfile:
outfile.write(text_to_code(tokens, word_index_dict, self.sequence_length))
return word_index_dict, index_word_dict
def init_real_metric(self):
# from utils.metrics.DocEmbSim import DocEmbSim
# docsim = DocEmbSim(oracle_file=self.oracle_file, generator_file=self.generator_file, num_vocabulary=self.vocab_size)
# self.add_metric(docsim)
inll = Nll(data_loader=self.gen_data_loader, rnn=self.generator, sess=self.sess)
inll.set_name('nll-test')
self.add_metric(inll)
def train_real(self, size,itr, data_loc=None):
sizes = [200, 400, 600, 800, 1000]
# size_files = ['1_200.txt','2_200.txt','3_200.txt']
from utils.text_process import code_to_text
from utils.text_process import get_tokenlized
data_loc = 'train_data/coco/'+str(itr)+'_'+str(size)+'.txt'
# data_loc = 'train/'+size_files[cv_itr]
wi_dict, iw_dict = self.init_real_trainng(data_loc)
self.init_real_metric()
def get_real_test_file(dict=iw_dict):
with open(self.generator_file, 'r') as file:
codes = get_tokenlized(self.generator_file)
with open(self.test_file, 'w') as outfile:
outfile.write(code_to_text(codes=codes, dictionary=dict))
def get_real_test_file_temp(write_file, dict=iw_dict):
with open(self.generator_file, 'r') as file:
codes = get_tokenlized(self.generator_file)
with open(write_file, 'w') as outfile:
outfile.write(code_to_text(codes=codes, dictionary=dict))
self.sess.run(tf.global_variables_initializer())
# print(self.pre_epoch_num)
self.log = open('experiment-log-seqgan-real.csv', 'w')
generate_samples(self.sess, self.generator, self.batch_size, self.generate_num, self.generator_file)
self.gen_data_loader.create_batches(self.oracle_file)
print('start pre-train generator:')
for epoch in range(self.pre_epoch_num):
start = time()
loss = pre_train_epoch(self.sess, self.generator, self.gen_data_loader)
end = time()
# print('epoch:' + str(self.epoch) + '\t time:' + str(end - start))
self.add_epoch()
if epoch % 5 == 0:
# write generate.txt in indexes of the gen sentences
generate_samples(self.sess, self.generator, self.batch_size, self.generate_num, self.generator_file)
# write ^ in words
get_real_test_file()
self.evaluate()
print('start pre-train discriminator:')
self.reset_epoch()
for epoch in range(self.pre_epoch_num):
# print('epoch:' + str(epoch))
self.train_discriminator()
self.reset_epoch()
print('adversarial training:')
self.reward = Reward(self.generator, .8)
for epoch in range(self.adversarial_epoch_num):
# print('epoch:' + str(epoch))
start = time()
for index in range(1):
samples = self.generator.generate(self.sess)
rewards = self.reward.get_reward(self.sess, samples, 16, self.discriminator)
feed = {
self.generator.x: samples,
self.generator.rewards: rewards
}
loss, _ = self.sess.run([self.generator.g_loss, self.generator.g_updates], feed_dict=feed)
# print(loss)
end = time()
self.add_epoch()
# print('epoch:' + str(self.epoch) + '\t time:' + str(end - start))
if epoch % 5 == 0 or epoch == self.adversarial_epoch_num - 1:
generate_samples(self.sess, self.generator, self.batch_size, self.generate_num, self.generator_file)
get_real_test_file()
self.evaluate()
self.reward.update_params()
for _ in range(15):
self.train_discriminator()
# bleu = Bleu( self.test_file, self.real_file, self.sess)
# sbleu = SelfBleu(self.test_file, self.sess)
# scorefile = open(self.result_file, 'a+')
for alpha in self.temps:
gen_file= 'gen_data/seqgan/coco/'+str(itr)+'_'+str(size)+'_'+str(alpha)+'.txt'
# gen_file= 'gen_data/seqgan/coco/'+'ds.txt'
print('alpha', alpha, gen_file)
generate_samples_temp(self.sess, self.generator, self.batch_size, self.generate_num, alpha, self.generator_file)
get_real_test_file_temp(gen_file)
