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 get_real_code():
text = get_tokenlized(self.oracle_file)
def toint_list(x):
return list(map(int, x))
codes = list(map(toint_list, text))
return codes
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 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 = 200
self.log = open('experiment-log-pepgan-real.csv', 'w')
generate_samples_gen(self.sess, self.generator, self.batch_size,
self.generate_num, self.generator_file)
self.gen_data_loader.create_batches(self.oracle_file)
for a in range(1):
g = self.sess.run(self.generator.gen_x,
feed_dict={
self.generator.drop_out: 1,
self.generator.train: 1
})
print(list(wi_dict))
self.reset_epoch()
print('adversarial training:')
self.reward = Reward(model=self.generator,
dis=self.discriminator,
sess=self.sess,
rollout_num=8)
for epoch in range(self.adversarial_epoch_num):
#for epoch_ in range(30):
print('epoch:' + str(epoch))
start = time()
for index in range(1):
samples = self.generator.generate(self.sess, 1)
rewards = self.reward.get_reward(samples)
feed = {
self.generator.x: samples,
self.generator.reward: rewards,
self.generator.drop_out: 1
}
_, _, g_loss, w_loss = self.sess.run([
self.generator.manager_updates,
self.generator.worker_updates,
self.generator.goal_loss,
self.generator.worker_loss,
],
feed_dict=feed)
print('epoch', str(epoch), 'g_loss', g_loss, 'w_loss', w_loss)
end = time()
self.add_epoch()
print('epoch:' + str(epoch) + '\t time:' + str(end - start))
with open("save/test_file_scores_ave.txt", 'a') as outfile:
outfile.write(str(np.mean(np.concatenate(rewards))) + "\n")
generate_samples_gen(self.sess, self.generator, self.batch_size,
self.generate_num, self.generator_file)
get_real_test_file()
self.evaluate()
with open(self.generator_file, 'r') as file:
codes = get_tokenlized(self.generator_file)
with open("save/test_file" + str(epoch) + ".txt", 'w') as outfile:
outfile.write(code_to_text(codes=codes, dictionary=iw_dict))
for _ in range(5):
self.train_discriminator()
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)
goal_out_size = sum(self.num_filters)
discriminator = Discriminator(sequence_length=self.sequence_length,
num_classes=2,
vocab_size=self.vocab_size,
dis_emb_dim=self.dis_embedding_dim,
filter_sizes=self.filter_size,
num_filters=self.num_filters,
batch_size=self.batch_size,
hidden_dim=self.hidden_dim,
start_token=self.start_token,
goal_out_size=goal_out_size,
step_size=4,
l2_reg_lambda=self.l2_reg_lambda)
self.set_discriminator(discriminator)
generator = Generator(num_classes=2,
num_vocabulary=self.vocab_size,
batch_size=self.batch_size,
emb_dim=self.emb_dim,
dis_emb_dim=self.dis_embedding_dim,
goal_size=self.goal_size,
hidden_dim=self.hidden_dim,
sequence_length=self.sequence_length,
filter_sizes=self.filter_size,
start_token=self.start_token,
num_filters=self.num_filters,
goal_out_size=goal_out_size,
D_model=discriminator,
step_size=4)
self.set_generator(generator)
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)#Original
[word_index_dict, index_word_dict] = [{
'b': '0',
'a': '1',
'r': '2',
'n': '3',
'd': '4',
'c': '5',
'q': '6',
'e': '7',
'g': '8',
'h': '9',
'i': '10',
'l': '11',
'k': '12',
'm': '13',
'f': '14',
'p': '15',
's': '16',
't': '17',
'w': '18',
'y': '19',
'v': '20'
}, {
'0': 'b',
'1': 'a',
'2': 'r',
'3': 'n',
'4': 'd',
'5': 'c',
'6': 'q',
'7': 'e',
'8': 'g',
'9': 'h',
'10': 'i',
'11': 'l',
'12': 'k',
'13': 'm',
'14': 'f',
'15': 'p',
'16': 's',
'17': 't',
'18': 'w',
'19': 'y',
'20': 'v'
}]
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 main():
print('program start')
from utils.text_process import text_precess, text_to_code # TODO: move?
from utils.text_process import get_tokenlized, get_word_list, get_dict
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
SEQ_LENGTH, vocab_size = text_precess(true_file, val_file)
gen_data_loader = Gen_Data_loader(BATCH_SIZE, SEQ_LENGTH)
val_data_loader = Gen_Data_loader(BATCH_SIZE, SEQ_LENGTH)
# Create training file and dicts
tokens = get_tokenlized(true_file)
val_tokens = get_tokenlized(val_file)
word_set = get_word_list(tokens + val_tokens)
[word_index_dict, index_word_dict] = get_dict(word_set)
with open(oracle_file, 'w') as outfile:
outfile.write(text_to_code(tokens, word_index_dict, SEQ_LENGTH))
with open(val_oracle_file, 'w') as outfile:
outfile.write(text_to_code(val_tokens, word_index_dict, SEQ_LENGTH))
generator = Generator(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM,
SEQ_LENGTH, START_TOKEN)
#target_params = pickle.load(open('save/target_params_py3.pkl', 'rb'))
#target_lstm = TARGET_LSTM(vocab_size, BATCH_SIZE, 32, 32, SEQ_LENGTH, START_TOKEN, target_params) # The oracle model
# replace target lstm with true data
mediator = Generator(vocab_size,
BATCH_SIZE * 2,
EMB_DIM * 2,
HIDDEN_DIM * 2,
SEQ_LENGTH,
START_TOKEN,
name="mediator",
dropout_rate=M_DROPOUT_RATE)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
gen_data_loader.create_batches(oracle_file)
val_data_loader.create_batches(val_oracle_file)
log = open('save/experiment-log.txt', 'w')
log_nll = open('save/experiment-log-nll.txt', 'w')
# pre-train generator (default 0 epochs)(not recommended)
print('Start pre-training...')
log.write('pre-training...\n')
for epoch in range(PRE_EPOCH_NUM):
loss = mle_epoch(sess, generator, gen_data_loader)
if epoch % 5 == 0:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
generator_file)
#get_real_test_file(index_word_dict, generator_file, test_file) # only needed in debugging
test_loss = target_loss(sess, generator, val_data_loader)
print('pre-train epoch ', epoch, 'nll_test ', test_loss)
buffer = 'epoch:\t' + str(epoch) + '\tnll_test:\t' + str(
test_loss) + '\n'
log_nll.write(buffer)
print(
'#########################################################################'
)
toc = time.time()
print('Start Cooperative Training...')
for iter_idx in range(TOTAL_BATCH):
print('iteration: ' + str(iter_idx) + '\ntime: ' +
str(time.time() - toc))
toc = time.time()
# Train the generator for one step
for it in range(1):
samples = generator.generate(sess)
rewards = mediator.get_reward(
sess, np.concatenate([samples, samples], axis=0))
feed = {
generator.x: samples,
generator.rewards: rewards[0:BATCH_SIZE]
}
loss, _ = sess.run([generator.g_loss, generator.g_updates],
feed_dict=feed)
# Test, removed oracle test
if iter_idx % gen_data_loader.num_batch == 0: # epochs instead of batches
test_loss = target_loss(sess, generator, val_data_loader)
print('epoch:\t', iter_idx // gen_data_loader.num_batch,
'nll_test ', test_loss)
buffer = 'epoch:\t' + str(
iter_idx // gen_data_loader.num_batch) + '\tnll_test:\t' + str(
test_loss) + '\n'
log_nll.write(buffer)
if iter_idx == TOTAL_BATCH - 1:
print('generating samples')
generate_samples(sess, generator, BATCH_SIZE, generated_num,
generator_file)
get_real_test_file(index_word_dict, generator_file, test_file)
# Train the mediator
for _ in range(1):
print('training mediator...')
bnll_ = []
collected_x = []
ratio = 2
for it in range(ratio):
if it % 2 == 0:
x_batch = gen_data_loader.next_batch()
else:
x_batch = generator.generate(sess)
collected_x.append(x_batch)
collected_x = np.reshape(collected_x, [-1, SEQ_LENGTH])
np.random.shuffle(collected_x)
collected_x = np.reshape(collected_x,
[-1, BATCH_SIZE * 2, SEQ_LENGTH])
for it in range(1):
feed = {
mediator.x: collected_x[it],
}
print('running bnll sess')
bnll = sess.run(mediator.likelihood_loss, feed)
bnll_.append(bnll)
print('running mediator and updating')
sess.run(mediator.dropout_on)
_ = sess.run(mediator.likelihood_updates, feed)
sess.run(mediator.dropout_off)
if iter_idx % 50 == 0:
bnll = np.mean(bnll_)
print("mediator cooptrain iter#%d, balanced_nll %f" %
(iter_idx, bnll))
log.write("%d\t%f\n" % (iter_idx, bnll))
log.close()
log_nll.close()