def main():
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
vocab_size = 5000
dis_data_loader = Dis_dataloader(BATCH_SIZE)
target_lstm = TARGET_LSTM(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM,
SEQ_LENGTH, START_TOKEN, target_params)
print(gen_data_loader)
def pretrain_generator(sess, generator, gen_data_loader, gold_generator, log):
# pre-train generator
print('Start pre-training...')
log.write('pre-training...\n')
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
for epoch in range(PRE_EPOCH_NUM):
pre_train_epoch(sess, generator, gen_data_loader)
if epoch % 5 == 0:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
eval_file)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, gold_generator,
likelihood_data_loader)
print('pre-train epoch ', epoch, 'test_loss ', test_loss)
buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(
test_loss) + '\n'
log.write(buffer)
def train_generator(sess, generator, gold_generator, rollout, discriminator,
total_batch, log):
for it in range(1):
samples = generator.generate(sess)
rewards = rollout.get_reward(sess, samples, 16, discriminator)
feed = {generator.x: samples, generator.rewards: rewards}
_ = sess.run(generator.g_updates, feed_dict=feed)
# Test
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
if total_batch % 5 == 0 or total_batch == TOTAL_BATCH - 1:
generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, gold_generator, likelihood_data_loader)
buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(
test_loss) + '\n'
print('total_batch: ', total_batch, 'test_loss: ', test_loss)
log.write(buffer)
def main():
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
vocab_size = 5000 # why not a constant?
log = open('save/experiment-log.txt', 'w')
#likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
dis_data_loader = Dis_dataloader(BATCH_SIZE)
generator = construct_generator(vocab_size)
target_lstm = construct_gold_generator(vocab_size)
discriminator = construct_discriminator(vocab_size)
sess = initialize_session()
# First, use the oracle model to provide the positive examples,
# which are sampled from the oracle data distribution
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num,
positive_file)
gen_data_loader.create_batches(positive_file)
# pre-train generator
print('Start pre-training...')
pretrain_generator(sess, generator, gen_data_loader, target_lstm, log)
print('Start pre-training discriminator...')
# Train 3 epoch on the generated data and do this for 50 times
pretrain_discriminator(sess, discriminator, dis_data_loader, generator)
rollout = ROLLOUT(generator, 0.8)
print(
'#########################################################################'
)
print('Start Adversarial Training...')
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
train_generator(sess, generator, target_lstm, rollout, discriminator,
total_batch, log)
rollout.update_params()
train_discriminator(sess, discriminator, dis_data_loader, generator)
log.close()
def main():
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
vocab_size = 4839
dis_data_loader = Dis_dataloader(re_batch_size)
generator = Generator(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM,
SEQ_LENGTH, START_TOKEN, MID_LAYER_G)
rewarder = Rewarder(vocab_size,
BATCH_SIZE,
EMB_DIM * 2,
HIDDEN_DIM * 2,
SEQ_LENGTH,
START_TOKEN,
MID_LAYER_R,
l2_reg_lambda=re_l2_reg_lambda)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
gen_data_loader.create_batches(positive_file)
log = open('save/experiment-log-' + str(ent_w) + '.txt', 'w')
# pre-train generator
if restore is False:
print 'Start pre-training...'
log.write('pre-training...\n')
for epoch in xrange(PRE_EPOCH_NUM):
loss = pre_train_epoch(sess, generator, gen_data_loader)
if epoch % 5 == 0:
print 'pre-train epoch ', epoch, 'test_loss ', loss
buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(
loss) + '\n'
log.write(buffer)
if epoch % 20 == 0 and epoch > 0:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
pretrain_file_prefix + str(epoch))
print 'Start pre-training rewarder...'
start = time.time()
for _ in range(1):
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(1):
dis_data_loader.reset_pointer()
r_losses = []
for it in xrange(dis_data_loader.num_batch):
x_text = dis_data_loader.next_batch()
_, r_loss = rewarder.reward_train_step(
sess, x_text, np.ones(BATCH_SIZE), 1.0,
re_dropout_keep_prob, 0.01)
r_losses.append(r_loss)
print 'reward_loss', np.mean(r_losses)
speed = time.time() - start
print 'Reward pre_training Speed:{:.3f}'.format(speed)
checkpoint_path = os.path.join('save', 'exper_40.ckpt')
saver.save(sess, checkpoint_path)
else:
print 'Restore pretrained model ...'
log.write('Restore pre-trained model...\n')
ckpt = tf.train.get_checkpoint_state('save')
saver.restore(sess, ckpt.model_checkpoint_path)
# by setting the parameters to 0.0 and 1.0, we didn't use the mixed policy RL training in SeqGAN
rollout = ROLLOUT(generator, 0.0, 1.0)
print '#########################################################################'
print 'Start Adversarial Training...'
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
if total_batch % 5 == 0 or total_batch == TOTAL_BATCH - 1:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
eval_file_prefix + str(total_batch))
# Train the generator for one step
start = time.time()
g_losses = []
off_samples, off_probs = off_policy_samples(sess, rollout, BATCH_SIZE,
off_num)
avg_reward = []
for it in range(off_num // BATCH_SIZE):
rewards = rollout.get_reward(sess, off_samples[it], 4, rewarder)
avg_reward.append(rewards)
baseline = np.zeros(SEQ_LENGTH)
for it in range(1):
for it2 in range(off_num // BATCH_SIZE):
_, g_loss = generator.rl_train_step(sess, off_samples[it2],
avg_reward[it2], baseline,
off_probs[it2], ent_w)
g_losses.append(g_loss)
speed = time.time() - start
print 'MaxentPolicy Gradient {} round, Speed:{:.3f}, Loss:{:.3f}'.format(
total_batch, speed, np.mean(g_losses))
# Update roll-out parameters
rollout.update_params()
# Train the rewarder
start = time.time()
r_loss_list = []
for _ in range(8):
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_text = dis_data_loader.next_batch()
weights = rewarder.reward_weight(sess, x_text, generator)
_, r_loss = rewarder.reward_train_step(
sess, x_text, weights, 1, re_dropout_keep_prob,
R_rate * np.exp(-(total_batch // R_decay)))
r_loss_list.append(r_loss)
avg_loss = np.mean(r_loss_list)
speed = time.time() - start
print 'Reward training {} round, Speed:{:.3f}, Loss:{:.3f}'.format(
total_batch, speed, avg_loss)
log.close()
def init_data_loader(positive_file):
dis_data_loader = Dis_dataloader(BATCH_SIZE, SEQ_LENGTH)
gen_data_loader = Gen_Data_loader(BATCH_SIZE, SEQ_LENGTH)
gen_data_loader.create_batches(positive_file)
return gen_data_loader, dis_data_loader
def main(unused_argv):
config_train = training_config()
config_gen = generator_config()
config_dis = discriminator_config()
np.random.seed(config_train.seed)
assert config_train.start_token == 0
#Build dataloader for generaotr, testing and discriminator
gen_data_loader = Gen_Data_loader(config_gen.gen_batch_size)
likelihood_data_loader = Gen_Data_loader(config_gen.gen_batch_size)
dis_data_loader = Dis_dataloader(config_dis.dis_batch_size)
#Build generator and its rollout
generator = Generator(config=config_gen)
generator.build()
rollout_gen = rollout(config=config_gen)
#Build target LSTM
target_params = cPickle.load(StrToBytes(open('save/target_params.pkl')),
encoding='bytes')
target_lstm = TARGET_LSTM(config=config_gen,
params=target_params) # The oracle model
#Build discriminator
discriminator = Discriminator(config=config_dis)
discriminator.build_discriminator()
#Build optimizer op for pretraining
pretrained_optimizer = tf.train.AdamOptimizer(
config_train.gen_learning_rate)
var_pretrained = [
v for v in tf.trainable_variables() if 'teller' in v.name
] #Using name 'teller' here to prevent name collision of target LSTM
gradients, variables = zip(*pretrained_optimizer.compute_gradients(
generator.pretrained_loss, var_list=var_pretrained))
gradients, _ = tf.clip_by_global_norm(gradients, config_train.grad_clip)
gen_pre_upate = pretrained_optimizer.apply_gradients(
zip(gradients, variables))
#Initialize all variables
sess = tf.Session(config=config_hardware)
sess.run(tf.global_variables_initializer())
#Initalize data loader of generator
# generate_samples(sess, target_lstm, config_train.batch_size, config_train.generated_num, config_train.positive_file)
gen_data_loader.create_batches(config_train.positive_file)
#Start pretraining
log = open('save/experiment-log.txt', 'w')
print('Start pre-training generator...')
log.write('pre-training...\n')
for epoch in range(config_train.pretrained_epoch_num):
gen_data_loader.reset_pointer()
for it in range(gen_data_loader.num_batch):
batch = gen_data_loader.next_batch()
_, g_loss = sess.run([gen_pre_upate, generator.pretrained_loss], feed_dict={generator.input_seqs_pre:batch,\
generator.input_seqs_mask:np.ones_like(batch)})
if epoch % config_train.test_per_epoch == 0:
# generate_samples(sess, generator, config_train.batch_size, config_train.generated_num, config_train.eval_file)
likelihood_data_loader.create_batches(config_train.eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print('pre-train epoch ', epoch, 'test_loss ', test_loss)
buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(
test_loss) + '\n'
log.write(buffer)
print('Start pre-training discriminator...')
for t in range(config_train.dis_update_time_pre):
print("Times: " + str(t))
generate_samples(sess, generator, config_train.batch_size,
config_train.generated_num,
config_train.negative_file)
dis_data_loader.load_train_data(config_train.positive_file,
config_train.negative_file)
for _ in range(config_train.dis_update_epoch_pre):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x:
x_batch,
discriminator.input_y:
y_batch,
discriminator.dropout_keep_prob:
config_dis.dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
#Build optimizer op for adversarial training
train_adv_opt = tf.train.AdamOptimizer(config_train.gen_learning_rate)
gradients, variables = zip(*train_adv_opt.compute_gradients(
generator.gen_loss_adv, var_list=var_pretrained))
gradients, _ = tf.clip_by_global_norm(gradients, config_train.grad_clip)
train_adv_update = train_adv_opt.apply_gradients(zip(gradients, variables))
#Initialize global variables of optimizer for adversarial training
uninitialized_var = [
e for e in tf.global_variables() if e not in tf.trainable_variables()
]
init_vars_uninit_op = tf.variables_initializer(uninitialized_var)
sess.run(init_vars_uninit_op)
#Start adversarial training
for total_batch in range(config_train.total_batch):
for iter_gen in range(config_train.gen_update_time):
samples = sess.run(generator.sample_word_list_reshape)
feed = {"pred_seq_rollout:0": samples}
reward_rollout = []
#calcuate the reward given in the specific stpe t by roll out
for iter_roll in range(config_train.rollout_num):
rollout_list = sess.run(rollout_gen.sample_rollout_step,
feed_dict=feed)
rollout_list_stack = np.vstack(
rollout_list
) #shape: #batch_size * #rollout_step, #sequence length
reward_rollout_seq = sess.run(
discriminator.ypred_for_auc,
feed_dict={
discriminator.input_x: rollout_list_stack,
discriminator.dropout_keep_prob: 1.0
})
reward_last_tok = sess.run(discriminator.ypred_for_auc,
feed_dict={
discriminator.input_x: samples,
discriminator.dropout_keep_prob:
1.0
})
reward_allseq = np.concatenate(
(reward_rollout_seq, reward_last_tok), axis=0)[:, 1]
reward_tmp = []
for r in range(config_gen.gen_batch_size):
reward_tmp.append(reward_allseq[range(
r,
config_gen.gen_batch_size * config_gen.sequence_length,
config_gen.gen_batch_size)])
reward_rollout.append(np.array(reward_tmp))
rewards = np.sum(reward_rollout, axis=0) / config_train.rollout_num
_, gen_loss = sess.run([train_adv_update, generator.gen_loss_adv], feed_dict={generator.input_seqs_adv:samples,\
generator.rewards:rewards})
if total_batch % config_train.test_per_epoch == 0 or total_batch == config_train.total_batch - 1:
generate_samples(sess, generator, config_train.batch_size,
config_train.generated_num,
config_train.eval_file)
likelihood_data_loader.create_batches(config_train.eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(
test_loss) + '\n'
print('total_batch: ', total_batch, 'test_loss: ', test_loss)
log.write(buffer)
for _ in range(config_train.dis_update_time_adv):
generate_samples(sess, generator, config_train.batch_size,
config_train.generated_num,
config_train.negative_file)
dis_data_loader.load_train_data(config_train.positive_file,
config_train.negative_file)
for _ in range(config_train.dis_update_epoch_adv):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x:
x_batch,
discriminator.input_y:
y_batch,
discriminator.dropout_keep_prob:
config_dis.dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
log.close()
def main():
random.seed(SEED)
np.random.seed(SEED)
# TODO: I changed this. Why was this asserted? Was it just to ensure the replication
# of results? Or is zero important otherwise?
# Changed because 0 is a bad start token for our data. (cannot have home label=0)
# assert START_TOKEN == 0
# set up logging
log_fpath = logger.get_experiment_log_filepath()
gen_data_loader = Gen_Data_loader(BATCH_SIZE, SEQ_LENGTH)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE, SEQ_LENGTH) # For testing
vocab_size = VOCAB_SIZE
dis_data_loader = Dis_dataloader(BATCH_SIZE, SEQ_LENGTH)
generator = Generator(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN)
discriminator = Discriminator(sequence_length=SEQ_LENGTH, num_classes=2, vocab_size=vocab_size,
embedding_size=dis_embedding_dim, filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters, l2_reg_lambda=dis_l2_reg_lambda)
if not USE_GPU:
# Prevent the environment from seeing the available GPUs (to avoid error on matlaber cluster)
import os
os.environ["CUDA_VISIBLE_DEVICES"]="-1"
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(real_file)
# pre-train generator
logger.write_log(log_fpath, 'pre-training generator...')
for epoch in xrange(PRE_EPOCH_NUM):
loss = pre_train_epoch(sess, generator, gen_data_loader)
if epoch % 5 == 0:
logger.write_log(log_fpath, 'generator loss:')
logger.log_progress(log_fpath, epoch, loss)
generate_samples(sess, generator, BATCH_SIZE, eval_generated_num, eval_file.format('pretrain'))
logger.write_log(log_fpath, 'Start pre-training discriminator...')
# Train 3 epoch on the generated data and do this for 50 times
for i in range(50):
generate_samples(sess, generator, BATCH_SIZE, generated_num, fake_file)
dis_data_loader.load_train_data(real_file, fake_file)
# dis_data_loader.load_train_data(positive_file, negative_file)
logger.write_log(log_fpath, 'epoch iterator: %s / 50' % i)
for j in range(3):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_d_train_output = sess.run(discriminator.train_op, feed)
logger.write_log(log_fpath, 'finished pre-training discriminator')
rollout = ROLLOUT(generator, 0.8)
logger.write_log(log_fpath, 'Start Adversarial Training...')
g_steps = 1
d_steps = 1
k = 10
for batch in range(TOTAL_BATCH):
buff = 'batch %s/%s' % (batch, TOTAL_BATCH)
logger.write_log(log_fpath, buff)
# Train the generator for one step
for it in range(g_steps):
samples = generator.generate(sess)
rollout_num = 16 # TODO: experiment with this value
rewards = rollout.get_reward(sess, samples, rollout_num, discriminator)
feed = {generator.x: samples, generator.rewards: rewards}
_ = sess.run(generator.g_updates, feed_dict=feed)
# Test
if batch % 5 == 0 or batch == TOTAL_BATCH - 1:
generate_samples(sess, generator, BATCH_SIZE, eval_generated_num, eval_file.format(batch))
logger.write_log(log_fpath, 'generated some more eval samples...')
# Update roll-out parameters
rollout.update_params()
# Train the discriminator
for _ in range(d_steps):
generate_samples(sess, generator, BATCH_SIZE, generated_num, fake_file)
dis_data_loader.load_train_data(real_file, fake_file)
for _ in range(k):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
logger.write_log(log_fpath, 'I\'M DONE')
def main():
random.seed(SEED)
np.random.seed(SEED)
# prepare data
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
dis_data_loader = Dis_Data_loader(BATCH_SIZE)
generator = Generator(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN)
# target_params's size: [15 * 5000 * 32]
target_params = pickle.load(open('./save/target_params_py3.pkl', 'rb'))
# The oracle model
target_lstm = TARGET_LSTM(5000, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, 20, 0, target_params)
discriminator = Discriminator(sequence_length=20, num_classes=2, vocab_size=vocab_size,
embedding_size=dis_embedding_dim,
filter_sizes=dis_filter_sizes, num_filters=dis_num_filters,
l2_reg_lambda=dis_l2_reg_lambda)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
generate_samples_from_target(sess, target_lstm, BATCH_SIZE, generated_num, positive_file)
gen_data_loader.create_batches(positive_file)
# print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++")
#
# likelihood_data_loader.create_batches(positive_file)
# for i in range(100):
# test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
# print('my step ', i, 'test_loss ', test_loss)
# input("next:")
# input("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++")
log = open('save/experiment-log.txt', 'w')
# pre-train generator
print('Start pre-training...')
log.write('pre-training...\n')
ans_file = open("learning_cure.txt", 'w')
for epoch in range(120): # 120
loss = pre_train_epoch(sess, generator, gen_data_loader)
if epoch % 1 == 0:
generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print('pre-train epoch ', epoch, 'test_loss ', test_loss)
buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(test_loss) + '\n'
log.write(buffer)
ans_file.write("%s\n" % str(test_loss))
buffer = 'Start pre-training discriminator...'
print(buffer)
log.write(buffer)
for _ in range(10): # 10
generate_samples(sess, generator, BATCH_SIZE, generated_num, negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob,
}
d_loss, d_acc, _ = sess.run([discriminator.loss, discriminator.accuracy, discriminator.train_op], feed)
buffer = "discriminator loss %f acc %f\n" % (d_loss, d_acc)
print(buffer)
log.write(buffer)
ans_file.write("==========\n")
print("Start Adversarial Training...")
log.write('adversarial training...')
for total_batch in range(TOTAL_BATCH):
# Train the generator
for it in range(1):
samples = generator.generate(sess)
rewards = generator.get_reward(sess, samples, 16, discriminator, START_TOKEN)
a = str(samples[0])
b = str(rewards[0])
buffer = "%s\n%s\n\n" % (a, b)
# print(buffer)
log.write(buffer)
rewards_loss = generator.update_with_rewards(sess, samples, rewards, START_TOKEN)
# good rewards
# good_samples = gen_data_loader.next_batch()
# rewards = np.array([[1.0] * SEQ_LENGTH] * BATCH_SIZE)
# a = str(good_samples[0])
# b = str(rewards[0])
# buffer = "%s\n%s\n\n" % (a, b)
# print(buffer)
# log.write(buffer)
# rewards_loss = generator.update_with_rewards(sess, good_samples, rewards, START_TOKEN)
# little1 good reward
# litter1_samples = gen_data_loader.next_batch()
# rewards = generator.get_reward(sess, litter1_samples, 16, discriminator, START_TOKEN)
# a = str(little1 good reward[0])
# b = str(rewards[0])
# buffer = "%s\n%s\n\n" % (a, b)
# print(buffer)
# log.write(buffer)
# rewards_loss = generator.update_with_rewards(sess, litter1_samples, rewards, START_TOKEN)
# Test
if total_batch % 1 == 0 or total_batch == TOTAL_BATCH - 1:
generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
buffer = 'reward-train epoch %s train loss %s test_loss %s\n' % (str(total_batch), str(rewards_loss), str(test_loss))
print(buffer)
log.write(buffer)
ans_file.write("%s\n" % str(test_loss))
# Train the discriminator
for _ in range(1):
generate_samples(sess, generator, BATCH_SIZE, generated_num, negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob,
}
d_loss, d_acc, _ = sess.run([discriminator.loss, discriminator.accuracy, discriminator.train_op], feed)
if total_batch % 5 == 0 or total_batch == TOTAL_BATCH - 1:
buffer = "discriminator loss %f acc %f\n" % (d_loss, d_acc)
print(buffer)
log.write(buffer)
def main():
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
gen_data_loader = Gen_Data_loader(BATCH_SIZE,SEQ_LENGTH)
vocab_size = 4839
dis_data_loader = Dis_dataloader(BATCH_SIZE,SEQ_LENGTH)
discriminator = Discriminator(SEQ_LENGTH,num_classes=2,vocab_size=vocab_size,dis_emb_dim=dis_embedding_dim,filter_sizes=dis_filter_sizes,num_filters=dis_num_filters,
batch_size=BATCH_SIZE,hidden_dim=HIDDEN_DIM,start_token=START_TOKEN,goal_out_size=GOAL_OUT_SIZE,step_size=4)
leakgan = LeakGAN(SEQ_LENGTH,num_classes=2,vocab_size=vocab_size,emb_dim=EMB_DIM,dis_emb_dim=dis_embedding_dim,filter_sizes=dis_filter_sizes,num_filters=dis_num_filters,
batch_size=BATCH_SIZE,hidden_dim=HIDDEN_DIM,start_token=START_TOKEN,goal_out_size=GOAL_OUT_SIZE,goal_size=GOAL_SIZE,step_size=4,D_model=discriminator)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.5
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
for a in range(1):
g = sess.run(leakgan.gen_x,feed_dict={leakgan.drop_out:0.8,leakgan.train:1})
print g
print "epoch:",a," "
log = open('save/experiment-log.txt', 'w')
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, negative_file, 0)
gen_data_loader.create_batches(positive_file)
saver_variables = tf.global_variables()
saver = tf.train.Saver(saver_variables)
model = tf.train.latest_checkpoint(model_path)
print model
if FLAGS.restore and model:
# model = tf.train.latest_checkpoint(model_path)
# if model and FLAGS.restore:
if model_path+'/' + FLAGS.model:
print model_path+'/' + FLAGS.model
saver.restore(sess, model_path+'/' + FLAGS.model)
else:
saver.restore(sess, model)
else:
if FLAGS.resD and model_path + '/' + FLAGS.model:
print model_path + '/' + FLAGS.model
saver.restore(sess, model_path + '/' + FLAGS.model)
print 'Start pre-training...'
log.write('pre-training...\n')
for epoch in xrange(PRE_EPOCH_NUM):
loss = pre_train_epoch(sess, leakgan, gen_data_loader)
if epoch % 5 == 0:
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, negative_file)
buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(loss) + '\n'
log.write(buffer)
saver.save(sess, model_path + '/leakgan_pre')
else:
print 'Start pre-training discriminator...'
# Train 3 epoch on the generated data and do this for 50 times
for i in range(16):
for _ in range(5):
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, negative_file,0)
# gen_data_loader.create_batches(positive_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.D_input_x: x_batch,
discriminator.D_input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
D_loss,_ = sess.run([discriminator.D_loss,discriminator.D_train_op], feed)
# print 'D_loss ', D_loss
buffer = str(D_loss) + '\n'
log.write(buffer)
leakgan.update_feature_function(discriminator)
saver.save(sess, model_path + '/leakgan_preD')
# saver.save(sess, model_path + '/leakgan')
# pre-train generator
print 'Start pre-training...'
log.write('pre-training...\n')
for epoch in xrange(PRE_EPOCH_NUM/16):
loss = pre_train_epoch(sess, leakgan, gen_data_loader)
if epoch % 5 == 0:
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, negative_file,0)
print 'pre-train epoch ', epoch, 'test_loss ', loss
buffer = 'epoch:\t'+ str(epoch) + '\tnll:\t' + str(loss) + '\n'
log.write(buffer)
saver.save(sess, model_path + '/leakgan_pre')
gencircle = 1
#
print '#########################################################################'
print 'Start Adversarial Training...'
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
# Train the generator for one step
for it in range(1):
for gi in range(gencircle):
samples = leakgan.generate(sess,1.0,1)
rewards = get_reward(leakgan, discriminator,sess, samples, 4, dis_dropout_keep_prob,total_batch,gen_data_loader)
feed = {leakgan.x: samples, leakgan.reward: rewards,leakgan.drop_out:1.0}
_,_,g_loss,w_loss = sess.run([leakgan.manager_updates,leakgan.worker_updates,leakgan.goal_loss,leakgan.worker_loss], feed_dict=feed)
print 'total_batch: ', total_batch, " ",g_loss," ", w_loss
# Test
if total_batch % 10 == 1 or total_batch == TOTAL_BATCH - 1:
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, "./save/coco_" + str(total_batch) + ".txt", 0)
saver.save(sess, model_path + '/leakgan', global_step=total_batch)
if total_batch % 15 == 0:
for epoch in xrange(1):
loss = pre_train_epoch(sess, leakgan, gen_data_loader)
# Train the discriminator
for _ in range(5):
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, negative_file,0)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.D_input_x: x_batch,
discriminator.D_input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
D_loss, _ = sess.run([discriminator.D_loss, discriminator.D_train_op], feed)
# print 'D_loss ', D_loss
leakgan.update_feature_function(discriminator)
log.close()
def main():
# load embedding info
vocab_dict, vocab_size, vocab_list = load_emb_data(emb_dict_file)
# prepare data
pre_train_data_loader = Gen_Data_loader(BATCH_SIZE, vocab_dict)
pre_train_data_loader.create_batches(
[imdb_file_id, sst_pos_file_id, sst_neg_file_id])
gen_data_loader = Gen_Data_loader(BATCH_SIZE, vocab_dict)
gen_data_loader.create_batches([sst_pos_file_id, sst_neg_file_id])
dis_data_loader = Dis_Data_loader(BATCH_SIZE, vocab_dict, MAX_SEQ_LENGTH)
# build model
# num_emb, vocab_dict, batch_size, emb_dim, num_units, sequence_length
generator = Generator(vocab_size, vocab_dict, BATCH_SIZE, EMB_DIM,
HIDDEN_DIM, MAX_SEQ_LENGTH)
discriminator = Discriminator(sequence_length=MAX_SEQ_LENGTH,
num_classes=2,
vocab_size=vocab_size,
embedding_size=dis_embedding_dim,
filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters,
l2_reg_lambda=dis_l2_reg_lambda)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
log = open('save/experiment-log.txt', 'w')
buffer = 'Start pre-training generator...'
print(buffer)
log.write(buffer + '\n')
for epoch in range(150): #120
train_loss = pre_train_epoch(sess, generator, pre_train_data_loader)
if epoch % 5 == 0:
generate_samples(sess,
generator,
1,
eval_file,
vocab_list,
if_log=True,
epoch=epoch)
print(' pre-train epoch ', epoch, 'train_loss ', train_loss)
buffer = ' epoch:\t' + str(epoch) + '\tnll:\t' + str(
train_loss) + '\n'
log.write(buffer)
buffer = 'Start pre-training discriminator...'
print(buffer)
log.write(buffer)
for _ in range(10): # 10
generate_samples(sess, generator, 70, negative_file, vocab_list)
dis_data_loader.load_train_data([sst_pos_file_id, sst_neg_file_id],
[negative_file])
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob,
}
d_loss, d_acc, _ = sess.run([
discriminator.loss, discriminator.accuracy,
discriminator.train_op
], feed)
buffer = "discriminator loss %f acc %f" % (d_loss, d_acc)
print(buffer)
log.write(buffer + '\n')
print("Start Adversarial Training...")
log.write('adversarial training...')
for total_batch in range(TOTAL_BATCH):
# Train the generator
for it in range(2):
# print("1")
samples = generator.generate(sess)
samples = produce_samples(samples)
# print("2")
rewards = generator.get_reward(sess, samples, 16, discriminator)
# print("3")
a = str(samples[0])
b = str(rewards[0])
# rewards = change_rewards(rewards)
# c = str(rewards[0])
d = build_from_ids(samples[0], vocab_list)
buffer = "%s\n%s\n%s\n\n" % (d, a, b)
print(buffer)
log.write(buffer)
# print("4")
rewards_loss = generator.update_with_rewards(
sess, samples, rewards)
# print("5")
# good rewards
# good_samples = gen_data_loader.next_batch()
# rewards = np.array([[0.0001] * SEQ_LENGTH] * BATCH_SIZE)
# a = str(good_samples[0])
# b = str(rewards[0])
# buffer = "%s\n%s\n\n" % (a, b)
# print(buffer)
# log.write(buffer)
# rewards_loss = generator.update_with_rewards(sess, good_samples, rewards, START_TOKEN)
# little1 good reward
little1_samples = gen_data_loader.next_batch()
rewards = generator.get_reward(sess, little1_samples, 16,
discriminator)
a = str(little1_samples[0])
b = str(rewards[0])
buffer = "%s\n%s\n\n" % (a, b)
# print(buffer)
log.write(buffer)
rewards_loss = generator.update_with_rewards(
sess, little1_samples, rewards)
# generate_infer(sess, generator, epoch, vocab_list)
# Test
if total_batch % 5 == 0 or total_batch == TOTAL_BATCH - 1:
generate_samples(sess,
generator,
120,
eval_file,
vocab_list,
if_log=True)
generate_infer(sess, generator, total_batch, vocab_list)
# generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
# likelihood_data_loader.create_batches(eval_file)
# test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
buffer = 'reward-train epoch %s train loss %s' % (
str(total_batch), str(rewards_loss))
print(buffer)
log.write(buffer + '\n')
generator.save_model(sess)
# Train the discriminator
begin = True
for _ in range(1):
generate_samples(sess, generator, 70, negative_file, vocab_list)
dis_data_loader.load_train_data([sst_pos_file_id, sst_neg_file_id],
[negative_file])
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob,
}
d_loss, d_acc, _ = sess.run([
discriminator.loss, discriminator.accuracy,
discriminator.train_op
], feed)
if (total_batch % 5 == 0
or total_batch == TOTAL_BATCH - 1) and begin:
buffer = "discriminator loss %f acc %f\n" % (d_loss,
d_acc)
print(buffer)
log.write(buffer)
begin = False
# pretrain
for _ in range(10):
train_loss = pre_train_epoch(sess, generator,
pre_train_data_loader)
sample_result = []
for i in range(len(sample_vocab)):
sample_result.append(type_str[i] + ' ' + ' '.join(sample_vocab[i]))
return sample_result
################################## main() #########################################
# 시간측정
start_time = time.time()
tf.reset_default_graph()
random.seed(SEED)
np.random.seed(SEED)
gen_data_loader = Gen_Data_loader(BATCH_SIZE, SEQ_LENGTH)
vocab_size = len(vocab_to_int) # 6390
print(vocab_size)
dis_data_loader = Dis_dataloader(BATCH_SIZE, SEQ_LENGTH)
generator = Generator(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN, TYPE_SIZE)
discriminator = Discriminator(sequence_length=SEQ_LENGTH, batch_size=BATCH_SIZE, num_classes=2,
word_embedding_matrix=word_embedding_matrix,
embedding_size=dis_embedding_dim, filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters, type_size=TYPE_SIZE, l2_reg_lambda=dis_l2_reg_lambda)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
saver = tf.train.Saver()
def main():
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
#
# Declare data loader
# ----------------------------------------------------------------------------
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
vocab_size = 5000
dis_data_loader = Dis_dataloader(BATCH_SIZE)
# ----------------------------------------------------------------------------
#
# Declare Generator & Discriminator
# ----------------------------------------------------------------------------
# declare: generator
generator = Generator(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN)
target_params = cPickle.load(open('save/target_params.pkl'))
target_lstm = TARGET_LSTM(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN, target_params) # The oracle model
# declare: discriminator
discriminator = Discriminator(sequence_length=20, num_classes=2,
vocab_size=vocab_size, embedding_size=dis_embedding_dim,
filter_sizes=dis_filter_sizes, num_filters=dis_num_filters,
l2_reg_lambda=dis_l2_reg_lambda)
# ----------------------------------------------------------------------------
#
# Set the session <sess>
# ----------------------------------------------------------------------------
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
# ----------------------------------------------------------------------------
# First, use the oracle model to provide the positive examples, which are sampled from the oracle data distribution
# generate samples by using <target_lstm> and write the samples to file <positive_file>
#generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, positive_file)
gen_data_loader.create_batches(positive_file)
log = open('save/experiment-log.txt', 'w')
#
# Pre-train <generator> by using <gen_data_loader>,
# and then compute the <test_loss> of <target_lstm> and <likelihood_data_loader>
# ----------------------------------------------------------------------------
print('Start pre-training...')
log.write('pre-training...\n')
for epoch in range(PRE_EPOCH_NUM):
loss = pre_train_epoch(sess, generator, gen_data_loader)
if epoch % 5 == 0:
# generate samples by using <generator> and write the samples to file <eval_file>
generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
# load samples from file <eval_file>
likelihood_data_loader.create_batches(eval_file)
# compute <test_loss> of <target_lstm>, with input <likelihood_data_loader>
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print('pre-train epoch ', epoch, 'test_loss ', test_loss)
buffer = 'epoch:\t'+ str(epoch) + '\tnll:\t' + str(test_loss) + '\n'
log.write(buffer)
# ----------------------------------------------------------------------------
#
# Pre-train <discriminator> by using <generator>
# ----------------------------------------------------------------------------
print('Start pre-training discriminator...')
# Generate data and train 3 epoch on the generated data, which will be done for 50 times
for _ in range(50):
# generate samples by using <generator> and write the samples to file <negative_file>
generate_samples(sess, generator, BATCH_SIZE, generated_num, negative_file)
# load samples from file <negative_file>
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob}
_ = sess.run(discriminator.train_op, feed_dict=feed)
# ----------------------------------------------------------------------------
rollout = ROLLOUT(generator, 0.8)
#
# Start seqGAN, train <discriminator> and <generator>
# ----------------------------------------------------------------------------
print('#########################################################################')
print('Start Adversarial Training...')
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
# ----- Train the generator for one step -----------------
for it in range(G_STEPS):
samples = generator.generate(sess)
rewards = rollout.get_reward(sess, samples, ROLLOUT_NUM, discriminator, SEQ_LENGTH)
feed = {generator.x: samples,
generator.rewards: rewards}
_ = sess.run(generator.g_updates, feed_dict=feed)
# --------------------------------------------------------
# Update roll-out parameters
rollout.update_params()
# ----- Train the discriminator -------------------------
for _ in range(D_STEPS):
generate_samples(sess, generator, BATCH_SIZE, generated_num, negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob}
_ = sess.run(discriminator.train_op, feed_dict=feed)
# --------------------------------------------------------
# ----------------------------------------------------------------------------
log.close()
def main(FLAGS):
#########################################################################################
# Generator Hyper-parameters
######################################################################################
EMB_DIM = FLAGS.gen_emb_dim # 32 # embedding dimension
HIDDEN_DIM = FLAGS.gen_hidden_dim # 32 # hidden state dimension of lstm cell
SEQ_LENGTH = FLAGS.seq_len # 20 # sequence length
START_TOKEN = 0
PRE_EPOCH_NUM = FLAGS.pretrain_epoch_num # 80 # supervise (maximum likelihood estimation) epochs for generator(X1) & descriminator(X5)
SEED = 88
BATCH_SIZE = FLAGS.batch_size #64
LEARNING_RATE = 0.01
GOAL_SIZE = 16
STEP_SIZE = 4
#########################################################################################
# Discriminator Hyper-parameters
#########################################################################################
dis_embedding_dim = FLAGS.dis_emb_dim # 64
dis_filter_sizes = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20]
dis_num_filters = [
100, 200, 200, 200, 200, 100, 100, 100, 100, 100, 160, 160
]
if FLAGS.seq_len == 20:
dis_filter_sizes = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20]
dis_num_filters = [
100, 200, 200, 200, 200, 100, 100, 100, 100, 100, 160, 160
]
LEARNING_RATE = 0.0015
# EMB_DIM = 32 # embedding dimension
# HIDDEN_DIM = 32 # hidden state dimension of lstm cell
elif FLAGS.seq_len == 40:
dis_filter_sizes = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40]
dis_num_filters = [
100, 200, 200, 200, 200, 100, 100, 100, 100, 100, 160, 160, 160
]
LEARNING_RATE = 0.0005
# EMB_DIM = 64
# HIDDEN_DIM = 64
else:
exit(0)
print(SEQ_LENGTH)
GOAL_OUT_SIZE = sum(dis_num_filters)
# dis_dropout_keep_prob = 0.75
dis_dropout_keep_prob = 1.0
dis_l2_reg_lambda = 0.2
dis_batch_size = FLAGS.batch_size #64
#########################################################################################
# Basic Training Parameters
#########################################################################################
EXPERIMENT_NAME = FLAGS.experiment_name
TOTAL_BATCH = FLAGS.num_epochs # 800 #num of adversarial epochs
positive_file = 'save/real_data_%0s.txt' % EXPERIMENT_NAME
negative_file = 'save/generator_sample_%0s.txt' % EXPERIMENT_NAME
eval_file = "save/eval_file_%0s" % EXPERIMENT_NAME
generated_num = 10000 # 10000
model_path = './ckpts'
#########################################################################################
# Data configurations
#########################################################################################
use_real_world_data = True
real_data_file_path = './data/text8'
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
if use_real_world_data:
vocab_size = 27
# split to train-valid-test
real_data_train_file = real_data_file_path + '-train'
real_data_valid_file = real_data_file_path + '-valid'
real_data_test_file = real_data_file_path + '-test'
real_data_dict_file = real_data_file_path + '-dict.json'
if not os.path.exists(real_data_train_file):
split_text8(real_data_file_path)
charmap, inv_charmap = create_real_data_dict(real_data_train_file,
real_data_dict_file)
gen_data_loader = Gen_Data_loader_text8(BATCH_SIZE,
charmap,
inv_charmap,
seq_len=SEQ_LENGTH)
dis_data_loader = Dis_dataloader_text8(BATCH_SIZE,
charmap,
inv_charmap,
seq_len=SEQ_LENGTH)
#TODO
else:
gen_data_loader = Gen_Data_loader(BATCH_SIZE, FLAGS.length)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE,
FLAGS.length) # For testing
vocab_size = 5000
file = open('save/target_params.pkl', 'rb')
target_params = pickle.load(file)
dis_data_loader = Dis_dataloader(BATCH_SIZE, SEQ_LENGTH)
discriminator = Discriminator(SEQ_LENGTH,
num_classes=2,
vocab_size=vocab_size,
dis_emb_dim=dis_embedding_dim,
filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters,
batch_size=BATCH_SIZE,
hidden_dim=HIDDEN_DIM,
start_token=START_TOKEN,
goal_out_size=GOAL_OUT_SIZE,
step_size=4)
leakgan = LeakGAN(SEQ_LENGTH,
num_classes=2,
vocab_size=vocab_size,
emb_dim=EMB_DIM,
dis_emb_dim=dis_embedding_dim,
filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters,
batch_size=BATCH_SIZE,
hidden_dim=HIDDEN_DIM,
start_token=START_TOKEN,
goal_out_size=GOAL_OUT_SIZE,
goal_size=GOAL_SIZE,
step_size=4,
D_model=discriminator,
learning_rate=LEARNING_RATE)
if not use_real_world_data:
if SEQ_LENGTH == 40:
target_lstm = TARGET_LSTM(vocab_size, BATCH_SIZE, EMB_DIM,
HIDDEN_DIM, SEQ_LENGTH,
START_TOKEN) # The oracle model
else:
target_lstm = TARGET_LSTM20(vocab_size, BATCH_SIZE, EMB_DIM,
HIDDEN_DIM, SEQ_LENGTH, START_TOKEN,
target_params)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# config.gpu_options.per_process_gpu_memory_fraction = 0.3
sess = tf.Session(config=config)
saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=999999)
sess.run(tf.global_variables_initializer())
if use_real_world_data:
# gen_data_loader.create_batches(real_data_train_file)
gen_data_loader.create_batches(real_data_train_file,
limit_num_samples=generated_num)
pass
else:
# First, use the oracle model to provide the positive examples, which are sampled from the oracle data distribution
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num,
positive_file, 0)
gen_data_loader.create_batches(positive_file)
for a in range(1):
g = sess.run(leakgan.gen_x,
feed_dict={
leakgan.drop_out: 0.8,
leakgan.train: 1
})
print(g)
print("epoch:", a, " ")
log = open('save/experiment-log.txt', 'w')
saver_variables = tf.global_variables()
saver = tf.train.Saver(saver_variables)
model = tf.train.latest_checkpoint(model_path)
print(model)
if FLAGS.restore and model:
# model = tf.train.latest_checkpoint(model_path)
# if model and FLAGS.restore:
if model_path + '/' + FLAGS.model:
print(model_path + '/' + FLAGS.model)
saver.restore(sess, model_path + '/' + FLAGS.model)
else:
saver.restore(sess, model)
else:
# if FLAGS.resD and model_path + '/' + FLAGS.model:
if False: #default of resD
print(model_path + '/' + FLAGS.model)
saver.restore(sess, model_path + '/' + FLAGS.model)
print('Start pre-training...')
log.write('pre-training...\n')
for epoch in range(PRE_EPOCH_NUM):
loss = pre_train_epoch(sess, leakgan, gen_data_loader)
if epoch % 5 == 0:
if use_real_world_data:
generate_real_data_samples(
sess, leakgan, BATCH_SIZE, generated_num,
eval_file + "_epoch_%0d.txt" % epoch, inv_charmap)
test_loss = 0 # FIXME - TEMP
else:
generate_samples(sess, leakgan, BATCH_SIZE,
generated_num, eval_file, 0)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm,
likelihood_data_loader)
print('pre-train epoch ', epoch, 'test_loss ', test_loss)
buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(
test_loss) + '\n'
log.write(buffer)
if use_real_world_data:
test_loss = 0 # FIXME - TEMP
else:
generate_samples(sess, target_lstm, BATCH_SIZE,
generated_num, eval_file, 0)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm,
likelihood_data_loader)
print("Groud-Truth:", test_loss)
saver.save(sess, model_path + '/leakgan_pre')
else:
print('Start pre-training discriminator...')
# Train 3 epoch on the generated data and do this for 50 times
for i in range(10):
for _ in range(5):
if use_real_world_data:
generate_real_data_samples(sess, leakgan, BATCH_SIZE,
generated_num,
negative_file, inv_charmap)
dis_data_loader.load_train_data(
real_data_train_file, negative_file)
else:
generate_samples(sess, leakgan, BATCH_SIZE,
generated_num, negative_file, 0)
generate_samples(sess, target_lstm, BATCH_SIZE,
generated_num, positive_file, 0)
# gen_data_loader.create_batches(positive_file)
dis_data_loader.load_train_data(
positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.D_input_x:
x_batch,
discriminator.D_input_y:
y_batch,
discriminator.dropout_keep_prob:
dis_dropout_keep_prob
}
D_loss, _ = sess.run([
discriminator.D_loss, discriminator.D_train_op
], feed)
# # print 'D_loss ', D_loss
# buffer = str(D_loss) + '\n'
# log.write(buffer)
leakgan.update_feature_function(discriminator)
saver.save(sess, model_path + '/leakgan_preD')
# saver.save(sess, model_path + '/leakgan')
# pre-train generator
print('Start pre-training...')
log.write('pre-training...\n')
for epoch in range(PRE_EPOCH_NUM // 10):
loss = pre_train_epoch(sess, leakgan, gen_data_loader)
if epoch % 5 == 0:
if use_real_world_data:
generate_real_data_samples(
sess, leakgan, BATCH_SIZE, generated_num,
eval_file + "_epoch_%0d.txt" % epoch,
inv_charmap)
test_loss = 0 # FIXME - TEMP
else:
generate_samples(sess, leakgan, BATCH_SIZE,
generated_num, eval_file, 0)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm,
likelihood_data_loader)
print('pre-train epoch ', epoch, 'test_loss ',
test_loss)
buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(
test_loss) + '\n'
log.write(buffer)
if use_real_world_data:
test_loss = 0 # FIXME - TEMP
else:
generate_samples(sess, target_lstm, BATCH_SIZE,
generated_num, eval_file, 0)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm,
likelihood_data_loader)
print("Groud-Truth:", test_loss)
saver.save(sess, model_path + '/leakgan_pre')
gencircle = 1
#
print(
'#########################################################################'
)
print('Start Adversarial Training...')
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
# Train the generator for one step
print("start epoch %0d" % total_batch)
if total_batch % FLAGS.save_each_epochs == 0:
print(
'#########################################################################'
)
print('saving model...')
save_file = os.path.join(
'.', 'ckp', EXPERIMENT_NAME + '_epoch_%0d' % total_batch,
EXPERIMENT_NAME + '_epoch_%0d' % total_batch)
saver.save(sess, save_file)
for it in range(1):
for gi in range(gencircle):
samples = leakgan.generate(sess, 1.0, 1)
rewards = get_reward(leakgan, discriminator, sess, samples, 4,
dis_dropout_keep_prob)
feed = {
leakgan.x: samples,
leakgan.reward: rewards,
leakgan.drop_out: 1.0
}
_, _, g_loss, w_loss = sess.run([
leakgan.manager_updates, leakgan.worker_updates,
leakgan.goal_loss, leakgan.worker_loss
],
feed_dict=feed)
print('total_batch: ', total_batch, " ", g_loss, " ", w_loss)
# Test
if total_batch % 5 == 0 or total_batch == TOTAL_BATCH - 1:
if not use_real_world_data:
generate_samples(sess, leakgan, BATCH_SIZE, generated_num,
eval_file, 0)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm,
likelihood_data_loader)
buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(
test_loss) + '\n'
print('total_batch: ', total_batch, 'test_loss: ', test_loss)
log.write(buffer)
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num,
eval_file, 0)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm,
likelihood_data_loader)
print("Groud-Truth:", test_loss)
# Train the discriminator
for _ in range(5):
if use_real_world_data:
generate_real_data_samples(sess, leakgan, BATCH_SIZE,
generated_num, negative_file,
inv_charmap)
dis_data_loader.load_train_data(real_data_train_file,
negative_file)
else:
generate_samples(sess, leakgan, BATCH_SIZE, generated_num,
negative_file, 0)
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num,
positive_file, 0)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.D_input_x: x_batch,
discriminator.D_input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
D_loss, _ = sess.run(
[discriminator.D_loss, discriminator.D_train_op], feed)
# print 'D_loss ', D_loss
leakgan.update_feature_function(discriminator)
print(
'#########################################################################'
)
print('saving model...')
save_file = os.path.join('.', 'ckp', EXPERIMENT_NAME, EXPERIMENT_NAME)
saver.save(sess, save_file)
#
# print '#########################################################################'
# print 'Start Language Model Evaluation...'
# test_data_loader = Gen_Data_loader_text8(BATCH_SIZE,charmap,inv_charmap)
# test_data_loader.create_batches(real_data_test_file)
# language_model_evaluation(sess,generator, test_data_loader)
log.close()
def main():
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
gen_data_loader = Gen_Data_loader(BATCH_SIZE, FLAGS.length)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE, FLAGS.length) # For testing
vocab_size = 5000
file = open('save/target_params.pkl', 'rb')
target_params = cPickle.load(file)
dis_data_loader = Dis_dataloader(BATCH_SIZE, SEQ_LENGTH)
discriminator = Discriminator(SEQ_LENGTH,
num_classes=2,
vocab_size=vocab_size,
dis_emb_dim=dis_embedding_dim,
filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters,
batch_size=BATCH_SIZE,
hidden_dim=HIDDEN_DIM,
start_token=START_TOKEN,
goal_out_size=GOAL_OUT_SIZE,
step_size=4)
leakgan = LeakGAN(SEQ_LENGTH,
num_classes=2,
vocab_size=vocab_size,
emb_dim=EMB_DIM,
dis_emb_dim=dis_embedding_dim,
filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters,
batch_size=BATCH_SIZE,
hidden_dim=HIDDEN_DIM,
start_token=START_TOKEN,
goal_out_size=GOAL_OUT_SIZE,
goal_size=GOAL_SIZE,
step_size=4,
D_model=discriminator,
learning_rate=LEARNING_RATE)
if SEQ_LENGTH == 40:
target_lstm = TARGET_LSTM(vocab_size,
BATCH_SIZE,
EMB_DIM,
HIDDEN_DIM,
SEQ_LENGTH,
START_TOKEN) # The oracle model
else:
target_lstm = TARGET_LSTM20(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN, target_params)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.5
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, positive_file, 0)
for a in range(1):
g = sess.run(leakgan.gen_x, feed_dict={leakgan.drop_out: 0.8, leakgan.train: 1})
print(g)
print("epoch:", a, " ")
log = open('save/experiment-log.txt', 'w')
gen_data_loader.create_batches(positive_file)
saver_variables = tf.global_variables()
saver = tf.train.Saver(saver_variables)
model = tf.train.latest_checkpoint(model_path)
print(model)
if FLAGS.restore and model:
# model = tf.train.latest_checkpoint(model_path)
# if model and FLAGS.restore:
if model_path + '/' + FLAGS.model:
print(model_path + '/' + FLAGS.model)
saver.restore(sess, model_path + '/' + FLAGS.model)
else:
saver.restore(sess, model)
else:
if FLAGS.resD and model_path + '/' + FLAGS.model:
print(model_path + '/' + FLAGS.model)
saver.restore(sess, model_path + '/' + FLAGS.model)
print('Start pre-training...')
log.write('pre-training...\n')
for epoch in range(PRE_EPOCH_NUM):
loss = pre_train_epoch(sess, leakgan, gen_data_loader)
if epoch % 5 == 0:
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, eval_file, 0)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print('pre-train epoch ', epoch, 'test_loss ', test_loss)
buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(test_loss) + '\n'
log.write(buffer)
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, eval_file, 0)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print("Groud-Truth:", test_loss)
saver.save(sess, model_path + '/leakgan_pre')
else:
print('Start pre-training discriminator...')
for i in range(10):
for _ in range(5):
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, negative_file, 0)
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, positive_file, 0)
# gen_data_loader.create_batches(positive_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.D_input_x: x_batch,
discriminator.D_input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
D_loss, _ = sess.run([discriminator.D_loss, discriminator.D_train_op], feed)
print ("D_loss: ", D_loss)
leakgan.update_feature_function(discriminator) ## todo: is important
saver.save(sess, model_path + '/leakgan_preD')
print('Start pre-training generator...')
log.write('pre-training...\n')
for epoch in range(PRE_EPOCH_NUM / 10):
loss = pre_train_epoch(sess, leakgan, gen_data_loader)
if epoch % 5 == 0:
print ("MLE Generator Loss: ", loss)
# generate_samples(sess, leakgan, BATCH_SIZE, generated_num, eval_file, 0)
# likelihood_data_loader.create_batches(eval_file)
# test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
# print('pre-train epoch ', epoch, 'test_loss ', test_loss)
# buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(test_loss) + '\n'
# log.write(buffer)
# generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, eval_file, 0)
# likelihood_data_loader.create_batches(eval_file)
# test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
# print("Groud-Truth:", test_loss)
saver.save(sess, model_path + '/leakgan_pre')
gencircle = 1
#
print('#########################################################################')
print('Start Adversarial Training...')
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
# Train the generator for one step
for it in range(1):
for gi in range(gencircle):
samples = leakgan.generate(sess, 1.0, 1)
rewards = get_reward(leakgan,
discriminator,
sess,
samples,
4,
dis_dropout_keep_prob)
feed = {leakgan.x: samples,
leakgan.reward: rewards,
leakgan.drop_out: 0.5}
_, _, g_loss, w_loss = sess.run(
[leakgan.manager_updates, leakgan.worker_updates, leakgan.goal_loss, leakgan.worker_loss],
feed_dict=feed)
print('total_batch: ', total_batch, " ", g_loss, " ", w_loss)
# Test
# if total_batch % 5 == 0 or total_batch == TOTAL_BATCH - 1:
# generate_samples(sess, leakgan, BATCH_SIZE, generated_num, eval_file, 0)
# likelihood_data_loader.create_batches(eval_file)
# test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
# buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(test_loss) + '\n'
# print('total_batch: ', total_batch, 'test_loss: ', test_loss)
# log.write(buffer)
# generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, eval_file, 0)
# likelihood_data_loader.create_batches(eval_file)
# test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
# print("Groud-Truth:", test_loss)
# Train the discriminator
for _ in range(5):
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, negative_file, 0)
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, positive_file, 0)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.D_input_x: x_batch,
discriminator.D_input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
D_loss, _ = sess.run([discriminator.D_loss, discriminator.D_train_op], feed)
leakgan.update_feature_function(discriminator)
log.close()
def main():
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
with open(true_file, 'r') as f_pos:
file_contents = f_pos.read().splitlines()
file_contents = [content.split() for content in file_contents]
tokens = set([item for sublist in file_contents for item in sublist])
# tokens = set(file_contents)
pad_idx = len(tokens)
vocab_size = pad_idx + 1
token2idx = dict((token, i) for i, token in enumerate(tokens))
idx2token = dict((i, token) for i, token in enumerate(tokens))
idx2token[pad_idx] = " "
load_positive(true_file, positive_file, token2idx, pad_idx)
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
dis_data_loader = Dis_dataloader(BATCH_SIZE)
generator = Generator(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM,
SEQ_LENGTH, START_TOKEN)
target_params = cPickle.load(open('save/target_params.pkl', 'rb'),
encoding='latin1')
target_params[0] = np.random.random([vocab_size, 32]).astype(np.float32)
target_params[13] = np.random.random([32, vocab_size]).astype(np.float32)
target_params[14] = np.random.random([
vocab_size,
]).astype(np.float32)
target_lstm = TARGET_LSTM(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM,
SEQ_LENGTH, START_TOKEN,
target_params) # The oracle model
discriminator = Discriminator(sequence_length=SEQ_LENGTH,
num_classes=2,
vocab_size=vocab_size,
embedding_size=dis_embedding_dim,
filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters,
l2_reg_lambda=dis_l2_reg_lambda)
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(positive_file, SEQ_LENGTH)
# log file that stores progress
log = open('save/experiment-log.txt', 'w')
# pre-train generator
print('Start pre-training...')
log.write('pre-training...\n')
all_pre_train_losses = []
for epoch in range(PRE_EPOCH_NUM):
loss = pre_train_epoch(sess, generator, gen_data_loader)
all_pre_train_losses.append(loss)
plt.plot(all_pre_train_losses)
plt.savefig('pre_train_losses_plot.png')
gen_outfile = 'save/generated_by_generator_after_' + str(
PRE_EPOCH_NUM) + '_' + str(datetime.datetime.now()) + '_epochs.txt'
generate_samples(sess, generator, BATCH_SIZE, generated_num, gen_outfile,
idx2token)
checksyntax.check_code(log, gen_outfile)
# if epoch % 5 == 0:
# generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
# likelihood_data_loader.create_batches(eval_file, SEQ_LENGTH)
# test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
# print('pre-train epoch ', epoch, 'test_loss ', test_loss)
# buffer = 'epoch:\t'+ str(epoch) + '\tnll:\t' + str(test_loss) + '\n'
# log.write(buffer)
print('Start pre-training discriminator...')
# Train 3 epoch on the generated data and do this for 50 times
for i in range(50):
print("discriminator pre train epoch : ", i)
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
dis_data_loader.load_train_data(positive_file, negative_file,
SEQ_LENGTH)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
gen_outfile = 'save/generated_by_generator_after_discriminator_training_' + str(
datetime.datetime.now) + '.txt'
generate_samples(sess, generator, BATCH_SIZE, generated_num, gen_outfile,
idx2token)
checksyntax.check_code(log, gen_outfile)
rollout = ROLLOUT(generator, 0.8)
print(
'#########################################################################'
)
print('Start Adversarial Training...')
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
print("total_batch : ", total_batch)
if total_batch % 20 == 0:
file_name = 'save/output_batch_' + str(total_batch) + '.txt'
generate_samples(sess, generator, BATCH_SIZE, generated_num,
file_name, idx2token)
checksyntax.check_code(log, file_name)
# Train the generator for one step
for it in range(1):
samples = generator.generate(sess)
rewards = rollout.get_reward(sess, samples, 16, discriminator)
feed = {generator.x: samples, generator.rewards: rewards}
_ = sess.run(generator.g_updates, feed_dict=feed)
# Test
# if total_batch % 5 == 0 or total_batch == TOTAL_BATCH - 1:
# generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
# likelihood_data_loader.create_batches(eval_file)
# test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
# buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(test_loss) + '\n'
# print('total_batch: ', total_batch, 'test_loss: ', test_loss)
# log.write(buffer)
#
# Update roll-out parameters
rollout.update_params()
#
# Train the discriminator
for _ in range(1):
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
dis_data_loader.load_train_data(positive_file, negative_file,
SEQ_LENGTH)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
final_gen_file = 'save/final_output.txt'
generate_samples(sess, generator, BATCH_SIZE, generated_num,
final_gen_file, idx2token)
checksyntax.check_code(log, final_gen_file)
# with open('save/output.txt','r') as f:
# with open('save/output_word.txt','w') as fout:
# for line in f:
# line = line.strip()
# line = line.split()
# word_line = ''.join([idx2token[int(x)] for x in line])
# fout.write(word_line + '\n')
#
log.close()
def main(FLAGS):
#########################################################################################
# Generator Hyper-parameters
######################################################################################
EMB_DIM = FLAGS.gen_emb_dim # 32 # embedding dimension
HIDDEN_DIM = FLAGS.gen_hidden_dim # 32 # hidden state dimension of lstm cell
SEQ_LENGTH = FLAGS.seq_len # 20 # sequence length
START_TOKEN = 0
PRE_EPOCH_NUM = FLAGS.gen_pretrain_epoch_num # 120 # supervise (maximum likelihood estimation) epochs for generator
DISC_PRE_EPOCH_NUM = FLAGS.dis_pretrain_epoch_num # 50 # supervise (maximum likelihood estimation) epochs for descriminator
SEED = 88
BATCH_SIZE = FLAGS.batch_size #64
gen_dropout_keep_prob = FLAGS.gen_dropout_keep_prob # 0.75
gen_num_recurrent_layers = FLAGS.gen_num_recurrent_layers # 1
gen_learning_rate = FLAGS.gen_learning_rate
#########################################################################################
# Discriminator Hyper-parameters
#########################################################################################
dis_embedding_dim = FLAGS.dis_emb_dim # 64
dis_filter_sizes = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20]
dis_num_filters = [
100, 200, 200, 200, 200, 100, 100, 100, 100, 100, 160, 160
]
dis_dropout_keep_prob = 0.75
dis_l2_reg_lambda = 0.2
dis_batch_size = FLAGS.batch_size #64
#########################################################################################
# Basic Training Parameters
#########################################################################################
EXPERIMENT_NAME = FLAGS.experiment_name
TOTAL_BATCH = FLAGS.num_epochs # 200 #num of adversarial epochs
positive_file = 'save/real_data_%0s.txt' % EXPERIMENT_NAME
negative_file = 'save/generator_sample_%0s.txt' % EXPERIMENT_NAME
eval_file = "save/eval_file_%0s" % EXPERIMENT_NAME
generated_num = 10000 # 10000
#########################################################################################
# Data configurations
#########################################################################################
use_real_world_data = True
real_data_file_path = FLAGS.dataset_path # './data/text8/text8'
dataset_name = os.path.basename(real_data_file_path)
base_token = FLAGS.base_token # 'char'
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
if use_real_world_data:
real_data_train_file = real_data_file_path + '-train'
real_data_valid_file = real_data_file_path + '-valid'
real_data_test_file = real_data_file_path + '-test'
real_data_dict_file = real_data_file_path + '-{}-dict.json'.format(
base_token)
if not os.path.exists(real_data_train_file):
split_text8(real_data_file_path)
map, inv_map = create_real_data_dict(real_data_train_file,
real_data_dict_file, base_token)
vocab_size = len(map)
if dataset_name == 'text8' and base_token == 'char':
assert vocab_size == 27 # SORRY FOR THE HARD CODING
elif dataset_name == 'ptb' and base_token == 'word':
assert vocab_size == 10001 # SORRY FOR THE HARD CODING
elif dataset_name == 'toy' and base_token == 'word':
assert vocab_size == 8 # SORRY FOR THE HARD CODING
elif dataset_name == 'wt2' and base_token == 'word':
assert vocab_size == 33279 # SORRY FOR THE HARD CODING
else:
raise TypeError
gen_data_loader = Gen_Data_loader_text(BATCH_SIZE,
map,
inv_map,
seq_len=SEQ_LENGTH,
token_type=base_token)
dis_data_loader = Dis_dataloader_text(BATCH_SIZE,
map,
inv_map,
seq_len=SEQ_LENGTH,
token_type=base_token)
else:
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
vocab_size = 5000
dis_data_loader = Dis_dataloader(BATCH_SIZE)
generator = Generator(vocab_size,
BATCH_SIZE,
EMB_DIM,
HIDDEN_DIM,
SEQ_LENGTH,
START_TOKEN,
dropout_keep_prob=gen_dropout_keep_prob,
num_recurrent_layers=gen_num_recurrent_layers)
if not use_real_world_data:
target_params = pickle.load(open('save/target_params.pkl'))
target_lstm = TARGET_LSTM(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM,
SEQ_LENGTH, START_TOKEN,
target_params) # The oracle model
discriminator = Discriminator(sequence_length=SEQ_LENGTH,
num_classes=2,
vocab_size=vocab_size,
embedding_size=dis_embedding_dim,
filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters,
l2_reg_lambda=dis_l2_reg_lambda)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.3
sess = tf.Session(config=config)
saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=999999)
sess.run(tf.global_variables_initializer())
if use_real_world_data:
# gen_data_loader.create_batches(real_data_train_file)
pass
else:
# First, use the oracle model to provide the positive examples, which are sampled from the oracle data distribution
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num,
positive_file)
gen_data_loader.create_batches(positive_file)
log = open('save/experiment-log.txt', 'w')
# pre-train generator
print('Start pre-training...')
log.write('pre-training...\n')
for epoch in range(PRE_EPOCH_NUM):
print("start epoch %0d" % epoch)
# update learning rate
if epoch > 5:
gen_learning_rate /= FLAGS.gen_learning_decay * 1.
if epoch % FLAGS.save_each_epochs == 0:
print(
'#########################################################################'
)
print('saving model...')
save_file = os.path.join(
'.', 'ckp', EXPERIMENT_NAME + '_pretrain_epoch_%0d' % epoch,
EXPERIMENT_NAME + '_pretrain_epoch_%0d' % epoch)
saver.save(sess, save_file)
if use_real_world_data:
gen_data_loader.create_batches(real_data_train_file,
limit_num_samples=generated_num)
loss = pre_train_epoch(sess, generator, gen_data_loader,
gen_learning_rate)
if epoch % 1 == 0:
if use_real_world_data:
generate_real_data_samples(
sess, generator, BATCH_SIZE, generated_num,
eval_file + "_epoch_%0d.txt" % epoch, inv_map, base_token)
test_loss = 0 # FIXME - TEMP
else:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
eval_file)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm,
likelihood_data_loader)
print('pre-train epoch ', epoch, 'test_loss ', test_loss)
buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(
test_loss) + '\n'
log.write(buffer)
print('Start pre-training discriminator...')
# Train 3 epoch on the generated data and do this for 50 times
for epoch in range(DISC_PRE_EPOCH_NUM):
print("start epoch %0d" % epoch)
if use_real_world_data:
generate_real_data_samples(sess, generator, BATCH_SIZE,
generated_num, negative_file, inv_map,
base_token)
dis_data_loader.load_train_data(real_data_train_file,
negative_file)
else:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
rollout = ROLLOUT(generator, 0.8)
print(
'#########################################################################'
)
print('Start Adversarial Training...')
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
# Train the generator for one step
print("start epoch %0d" % total_batch)
if total_batch % FLAGS.save_each_epochs == 0:
print(
'#########################################################################'
)
print('saving model...')
save_file = os.path.join(
'.', 'ckp', EXPERIMENT_NAME + '_epoch_%0d' % total_batch,
EXPERIMENT_NAME + '_epoch_%0d' % total_batch)
saver.save(sess, save_file)
for it in range(1):
samples = generator.generate(sess)
rewards = rollout.get_reward(sess, samples, 16, discriminator)
feed = {
generator.x: samples,
generator.rewards: rewards,
generator.learning_rate: 0.01
}
_ = sess.run(generator.g_updates, feed_dict=feed)
# Test
if total_batch % 5 == 0 or total_batch == TOTAL_BATCH - 1:
if not use_real_world_data:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
eval_file)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm,
likelihood_data_loader)
buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(
test_loss) + '\n'
print('total_batch: ', total_batch, 'test_loss: ', test_loss)
log.write(buffer)
# Update roll-out parameters
rollout.update_params()
# Train the discriminator
for _ in range(5):
if use_real_world_data:
generate_real_data_samples(sess, generator, BATCH_SIZE,
generated_num, negative_file,
inv_map, base_token)
dis_data_loader.load_train_data(real_data_train_file,
negative_file)
else:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
print(
'#########################################################################'
)
print('saving model...')
save_file = os.path.join('.', 'ckp', EXPERIMENT_NAME, EXPERIMENT_NAME)
saver.save(sess, save_file)
#
# print '#########################################################################'
# print 'Start Language Model Evaluation...'
# test_data_loader = Gen_Data_loader_text(BATCH_SIZE,map,inv_map)
# test_data_loader.create_batches(real_data_test_file)
# language_model_evaluation(sess,generator, test_data_loader)
log.close()
def main(source_file, wordVocab, vocab_size):
tf.reset_default_graph()
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
dis_data_loader = Dis_dataloader(BATCH_SIZE)
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
# todo: print ("starting generating positive samples...")
generated_num = gen_data_loader.transform_positive_file_2(
train_dir + source_file, train_dir + positive_file, wordVocab,
SEQ_LENGTH)
print("generated_num: ", generated_num)
if generated_num < 100: return
gen_data_loader.create_batches(train_dir + positive_file)
with tf.variable_scope("Train", reuse=None):
generator = Generator(wordVocab,
vocab_size,
BATCH_SIZE,
EMB_DIM,
HIDDEN_DIM,
SEQ_LENGTH,
START_TOKEN,
learning_rate=g_lrn)
discriminator = Discriminator(word_vocab=wordVocab,
sequence_length=SEQ_LENGTH,
num_classes=2,
embedding_size=dis_embedding_dim,
filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters,
l2_reg_lambda=dis_l2_reg_lambda,
learning_rate=d_lrn)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
# todo: 1.##############pre-train generator##############
print 'Start pre-training generator with MLE...'
log.write('pre-training...\n')
for epoch in xrange(PRE_EPOCH_NUM_generator):
loss = pre_train_epoch(sess, generator, gen_data_loader)
if epoch % 5 == 0:
buffer = 'epoch:\t' + str(epoch) + '\tloss:\t' + str(loss)
print(buffer)
sys.stdout.flush()
log.write(buffer)
# generate_samples(sess,
# generator,
# BATCH_SIZE,
# generated_num,
# eval_file)
# likelihood_data_loader.create_batches(eval_file)
# test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
# print 'pre-train epoch ', epoch, 'test_loss ', test_loss
# buffer = 'epoch:\t' + str(epoch) + '\tnllscore:\t' + str(test_loss) + '\n'
# log.write(buffer)
# todo: 2.##############pre-train discriminator##############
print 'Start pre-training discriminator...'
for _ in range(PRE_EPOCH_NUM_discriminator):
## 由于是对概率分布的采样，所以每次生成的fake data数据都是不同的
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3): ## 对每批fake_data进行训练discriminator
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
with tf.variable_scope("Train", reuse=None):
g_beta = ROLLOUT(generator, 0.8) ## 这是表示 g_beta
# todo: 3.############## Adversarial Training ##############
print '#########################################################################'
print 'Start Adversarial Training...'
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
# todo: Train the generator for one batch samples
for it in range(1):
samples = generator.generate(sess)
rewards = g_beta.get_reward(sess, samples, 16, discriminator)
feed = {generator.x: samples, generator.rewards: rewards}
_, g_loss = sess.run([generator.g_updates, generator.g_loss],
feed_dict=feed)
# Test
if total_batch % 10 == 0 or total_batch == TOTAL_BATCH - 1:
buffer = 'epoch:\t' + str(total_batch) + '\tg_loss:\t' + str(
g_loss)
print(buffer)
sys.stdout.flush()
log.write(buffer)
g_beta.update_params()
# todo: Train the discriminator
for _ in range(5):
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
if total_batch % 10 == 0 or total_batch == TOTAL_BATCH - 1:
out_file = out_negative_file + str(total_batch) + ".txt"
transform_file(negative_file, wordVocab, out_file)
generate_samples(sess, generator, BATCH_SIZE, need_generated_samples,
negative_file)
transform_file(negative_file, wordVocab, source_file + ".GEN")
def main():
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
vocab_size = 5000
dis_data_loader = Dis_dataloader(BATCH_SIZE)
generator = Generator(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN)
# with open('./save/target_params.pkl','rb') as f:
# print('readable:',f.readable())
# target_params=pickle.load(f)
target_params = pickle.load(open('save/target_params.pkl','rb'),encoding='ISO-8859-1')
target_lstm = TARGET_LSTM(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN, target_params) # The oracle model
discriminator = Discriminator(sequence_length=20, num_classes=2, vocab_size=vocab_size, embedding_size=dis_embedding_dim,
filter_sizes=dis_filter_sizes, num_filters=dis_num_filters, l2_reg_lambda=dis_l2_reg_lambda)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
# First, use the oracle model to provide the positive examples, which are sampled from the oracle data distribution
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, positive_file)
gen_data_loader.create_batches(positive_file)
log = open('save/experiment-log.txt', 'w')
# pre-train generator
print ('Start pre-training...')
log.write('pre-training...\n')
for epoch in range(PRE_EPOCH_NUM):
loss = pre_train_epoch(sess, generator, gen_data_loader)
if epoch % 5 == 0:
generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print ('pre-train epoch ', epoch, 'test_loss ', test_loss)
buffer = 'epoch:\t'+ str(epoch) + '\tnll:\t' + str(test_loss) + '\n'
log.write(buffer)
print ('Start pre-training discriminator...')
# Train 3 epoch on the generated data and do this for 50 times
for _ in range(50):
generate_samples(sess, generator, BATCH_SIZE, generated_num, negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
rollout = ROLLOUT(generator, 0.8)
print ('#########################################################################')
print ('Start Adversarial Training...')
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
# Train the generator for one step
for it in range(1):
samples = generator.generate(sess)
rewards = rollout.get_reward(sess, samples, 16, discriminator)
feed = {generator.x: samples, generator.rewards: rewards}
_ = sess.run(generator.g_updates, feed_dict=feed)
# Test
if total_batch % 5 == 0 or total_batch == TOTAL_BATCH - 1:
generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(test_loss) + '\n'
print('total_batch: ', total_batch, 'test_loss: ', test_loss)
log.write(buffer)
# Update roll-out parameters
rollout.update_params()
# Train the discriminator
for _ in range(5):
generate_samples(sess, generator, BATCH_SIZE, generated_num, negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
log.close()
print(test_loss)
def main():
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
gen_data_loader = Gen_Data_loader(BATCH_SIZE, SEQ_LENGTH)
val_data_loader = Gen_Data_loader(BATCH_SIZE, SEQ_LENGTH)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE,
SEQ_LENGTH) # For testing
vocab_size = 5000
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
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())
# First, use the oracle model to provide the positive examples, which are sampled from the oracle data distribution
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num,
positive_file)
gen_data_loader.create_batches(positive_file)
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, eval_file)
val_data_loader.create_batches(eval_file)
log = open('save/experiment-log.txt', 'w')
log_nll = open('save/experiment-log-nll.txt', 'w')
log_jsd = open('save/experiment-log-jsd.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 % 1 == 0:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
likelihood_data_loader.create_batches(negative_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print('pre-train epoch ', epoch, 'nll_oracle ', test_loss)
buffer = 'epoch:\t' + str(epoch) + '\tnll_oracle:\t' + str(
test_loss) + '\n'
log_nll.write(buffer)
if epoch % 1 == 0:
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(
'#########################################################################'
)
print('Start Cooperative Training...')
for iter_idx in range(TOTAL_BATCH):
# Train the generator for one step
for it in range(2):
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]
}
_ = sess.run(generator.g_updates, feed_dict=feed)
# Test
if iter_idx % 100 == 0 or iter_idx == TOTAL_BATCH - 1:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
likelihood_data_loader.create_batches(negative_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
buffer = 'batch:\t' + str(iter_idx) + '\tnll_oracle:\t' + str(
test_loss) + '\n'
print('batch: ', iter_idx, 'nll_oracle: ', test_loss)
log_nll.write(buffer)
if iter_idx % 100 == 0:
test_loss = target_loss(sess, generator, val_data_loader)
print('batch:\t', iter_idx, 'nll_test ', test_loss)
buffer = 'batch:\t' + str(iter_idx) + '\tnll_test:\t' + str(
test_loss) + '\n'
log_nll.write(buffer)
# Train the mediator
for _ in range(1):
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],
}
bnll = sess.run(mediator.likelihood_loss, feed)
bnll_.append(bnll)
# sess.run(mediator.dropout_on)
_ = sess.run(mediator.likelihood_updates, feed)
# sess.run(mediator.dropout_off)
if (iter_idx * 4) % gen_data_loader.num_batch == 0:
bnll = np.mean(bnll_)
gnll = sess.run(
mediator.likelihood_loss,
feed_dict={
mediator.x:
np.reshape(
[generator.generate(sess),
generator.generate(sess)],
[BATCH_SIZE * 2, SEQ_LENGTH])
})
print("mediator cooptrain iter#%d, balanced_nll %f, g_nll %f" %
(iter_idx, bnll, gnll))
log.write("%d\t%f\n" % (iter_idx, bnll))
if iter_idx % gen_data_loader.num_batch == 0:
jsd = jsd_calculate(sess, generator, target_lstm)
print('cooptrain epoch#', iter_idx // gen_data_loader.num_batch,
'jsd ', jsd)
log_jsd.write("%d\t%f\n" %
(iter_idx // gen_data_loader.num_batch, jsd))
log.close()
log_nll.close()
log_jsd.close()
def main():
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
dis_data_loader = Dis_dataloader(BATCH_SIZE)
with open('data/ihaiku.pickle', 'rb') as f:
haiku_list = pickle.load(f)
#usew2v---------------------------------------------------------------------------------------------
with open('data/index.pickle', 'rb') as f:
index = pickle.load(f)
vocab_size = len(index)
generator = Generator(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM,
SEQ_LENGTH, START_TOKEN)
discriminator = Discriminator(sequence_length=20,
num_classes=2,
vocab_size=vocab_size,
embedding_size=dis_embedding_dim,
filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters,
l2_reg_lambda=dis_l2_reg_lambda)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
log = open('save/experiment-log.txt', 'w')
# pre-train generator
print('Start pre-training...')
log.write('pre-training...\n')
for epoch in range(PRE_EPOCH_NUM):
select_haikus(haiku_list, generated_num, positive_file)
gen_data_loader.create_batches(positive_file)
loss = pre_train_epoch(sess, generator, gen_data_loader)
if epoch % 5 == 0:
print('pre-train epoch ', epoch)
buffer = 'epoch:\t' + str(epoch) + '\n'
log.write(buffer)
print('Start pre-training discriminator...')
# Train 3 epoch on the generated data and do this for 50 times
for _ in range(50):
select_haikus(haiku_list, generated_num, positive_file)
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
rollout = ROLLOUT(generator, 0.8)
print(
'#########################################################################'
)
print('Start Adversarial Training...')
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
# Train the generator for one step
for it in range(1):
kigos = select_kigos(kigo_list, BATCH_SIZE)
samples, rate = generator.generate_with_rate(sess, kigos)
rewards = rollout.get_reward(sess, samples, 16, discriminator,
rate)
feed = {generator.x: samples, generator.rewards: rewards}
_ = sess.run(generator.g_updates, feed_dict=feed)
# Update roll-out parameters
rollout.update_params()
# Train the discriminator
for _ in range(5):
select_haikus(haiku_list, generated_num, positive_file)
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
# Test
print(
'total_batch:',
total_batch,
)
if total_batch - 1 % 50 == 0:
output_file = 'result/result_{0:04d}_epoch.txt'.format(total_batch)
generate_samples_with_pred(sess, generator, discriminator,
BATCH_SIZE, generated_num, output_file)
buffer = 'epoch:\t' + str(total_batch) + '\n'
log.write(buffer)
log.close()
def main():
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
start_candidates = []
p_start_candidates = []
with open(START_TOKEN_CANDIDATES_PATH) as fin:
for l in fin:
token = l.strip().split(",")
start_candidates.append(token[0])
p_start_candidates.append(float(token[1]))
start_candidates = np.array(start_candidates, dtype=np.int32)
p_start_candidates = np.array(p_start_candidates, dtype=np.float32)
gen_data_loader = Gen_Data_loader(BATCH_SIZE, SEQ_LENGTH)
# likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
vocab_size = VOCAB_SIZE
dis_data_loader = Dis_dataloader(BATCH_SIZE, 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, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN, target_params) # The oracle model
discriminator = Discriminator(sequence_length=SEQ_LENGTH, num_classes=2, vocab_size=vocab_size, embedding_size=dis_embedding_dim,
filter_sizes=dis_filter_sizes, num_filters=dis_num_filters, l2_reg_lambda=dis_l2_reg_lambda)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
# First, use the oracle model to provide the positive examples, which are sampled from the oracle data distribution
#generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, positive_file)
gen_data_loader.create_batches(positive_file)
log = open('save/experiment-log.txt', 'w')
# pre-train generator
print('Start pre-training...')
log.write('pre-training...\n')
for epoch in range(PRE_EPOCH_NUM):
loss = pre_train_epoch(sess, generator, gen_data_loader)
#if epoch % 5 == 0:
# generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
# likelihood_data_loader.create_batches(eval_file)
# test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
# print('pre-train epoch ', epoch, 'test_loss ', test_loss)
# buffer = 'epoch:\t'+ str(epoch) + '\tnll:\t' + str(test_loss) + '\n'
# log.write(buffer)
buffer = 'epoch:%i\tnll:%f'%(epoch, loss)
print(buffer)
log.write(buffer+"\n")
print('Start pre-training discriminator...')
# Train 3 epoch on the generated data and do this for 50 times
for idx in range(50):
print(idx)
start_tokens = get_start_token(start_candidates, generated_num, p=p_start_candidates)
generate_samples(sess, generator, BATCH_SIZE, generated_num, negative_file+".pre%i"%idx, start_tokens)
dis_data_loader.load_train_data(positive_file, negative_file+".pre%i"%idx)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
rollout = ROLLOUT(generator, 0.8)
print('#########################################################################')
print('Start Adversarial Training...')
log.write('adversarial training...\n')
saver = tf.train.Saver()
for total_batch in range(TOTAL_BATCH):
print(total_batch)
# Train the generator for one step
for it in range(1):
start_token = get_start_token(start_candidates, BATCH_SIZE, p=p_start_candidates)
samples = generator.generate(sess, start_token)
rewards = rollout.get_reward(sess, samples, 16, discriminator, start_token)
feed = {generator.x: samples, generator.rewards: rewards, generator.start_token: start_token}
_ = sess.run(generator.g_updates, feed_dict=feed)
# Test
#if total_batch % 5 == 0 or total_batch == TOTAL_BATCH - 1:
# generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
# likelihood_data_loader.create_batches(eval_file)
# test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
# buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(test_loss) + '\n'
# print('total_batch: ', total_batch, 'test_loss: ', test_loss)
# log.write(buffer)
# Update roll-out parameters
rollout.update_params()
# Train the discriminator
for _ in range(5):
start_tokens = get_start_token(start_candidates, generated_num, p=p_start_candidates)
generate_samples(sess, generator, BATCH_SIZE, generated_num, negative_file+".gan%i"%total_batch, start_tokens)
dis_data_loader.load_train_data(positive_file, negative_file+".gan%i"%total_batch)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
if total_batch % 20 == 0 or total_batch == TOTAL_BATCH - 1:
saver.save(sess, "save/*model_%i.ckpt"%total_batch, global_step=total_batch)
log.close()
def main():
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
vocab_size = 5000
dis_data_loader = Dis_dataloader(BATCH_SIZE)
generator = Generator(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM,
SEQ_LENGTH, START_TOKEN)
target_params = cPickle.load(open('save/target_params.pkl'))
target_lstm = TARGET_LSTM(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM,
SEQ_LENGTH, START_TOKEN,
target_params) # The oracle model
discriminator = Discriminator(sequence_length=20,
num_classes=2,
vocab_size=vocab_size,
embedding_size=dis_embedding_dim,
filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters,
l2_reg_lambda=dis_l2_reg_lambda)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
# First, use the oracle model to provide the positive examples, which are sampled from the oracle data distribution
# generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, positive_file)
gen_data_loader.create_batches(positive_file)
log = open('save/experiment-log.txt', 'w')
# pre-train generator
print 'Start pre-training...'
log.write('pre-training...\n')
for epoch in xrange(PRE_EPOCH_NUM):
loss = pre_train_epoch(sess, generator, gen_data_loader)
if epoch % 5 == 0:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
eval_file)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print 'pre-train epoch ', epoch, 'test_loss ', test_loss
buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(
test_loss) + '\n'
log.write(buffer)
print 'Start pre-training discriminator...'
# Train 3 epoch on the generated data and do this for 50 times
for epoch in range(50):
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
if epoch % 5 == 0:
print 'pre-train discriminator epoch ', epoch
for _ in range(3):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
rollout = ROLLOUT(generator, 0.8)
print '#########################################################################'
print 'Start Adversarial Training...'
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
# Train the generator for one step
for it in range(1):
samples = generator.generate(sess)
rewards = rollout.get_reward(sess, samples, 16, discriminator)
feed = {generator.x: samples, generator.rewards: rewards}
_ = sess.run(generator.g_updates, feed_dict=feed)
# Test
if total_batch % 5 == 0 or total_batch == TOTAL_BATCH - 1:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
eval_file)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(
test_loss) + '\n'
print 'total_batch: ', total_batch, 'test_loss: ', test_loss
log.write(buffer)
# Update roll-out parameters
rollout.update_params()
# Train the discriminator
for _ in range(5):
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
log.close()
def main():
clock = Clock()
clock.start()
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
parser = argparse.ArgumentParser(description='conditional SeqGAN')
parser.add_argument('--conditional',
'-c',
type=int,
default=0,
help='If you make SeqGAN conditional, set `-c` 1.')
args = parser.parse_args()
cond = args.conditional
vocab = Vocab()
vocab.construct(parsed_haiku_file)
vocab.word2id(parsed_haiku_file, positive_file)
UNK = vocab.dic.token2id[u'<UNK>']
COMMA = vocab.dic.token2id[u',']
gen_data_loader = Gen_Data_loader(BATCH_SIZE, SEQ_LENGTH, COND_LENGTH, UNK)
# likelihood_data_loader = Gen_Data_loader(BATCH_SIZE, SEQ_LENGTH, COND_LENGTH, UNK) # For testing
vocab_size = len(vocab.dic.token2id)
with open(output_token2id, 'w') as f:
pickle.dump(vocab.dic.token2id, f)
dis_data_loader = Dis_dataloader(BATCH_SIZE, SEQ_LENGTH, UNK)
generator = Generator(vocab_size,
BATCH_SIZE,
EMB_DIM,
HIDDEN_DIM,
SEQ_LENGTH,
COND_LENGTH,
START_TOKEN,
is_cond=cond)
# target_params = cPickle.load(open('save/target_params.pkl'))
# target_lstm = TARGET_LSTM(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN, target_params) # The oracle model
discriminator = Discriminator(sequence_length=SEQ_LENGTH,
cond_length=COND_LENGTH,
num_classes=2,
vocab_size=vocab_size,
batch_size=BATCH_SIZE,
embedding_size=dis_embedding_dim,
filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters,
l2_reg_lambda=dis_l2_reg_lambda,
is_cond=cond)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
# First, use the oracle model to provide the positive examples, which are sampled from the oracle data distribution
# generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, positive_file)
gen_data_loader.create_batches(positive_file)
if cond:
vocab.word2id(parsed_kigo_file, positive_condition_file)
vocab.load_cond(positive_condition_file, COND_LENGTH, UNK)
gen_data_loader.create_cond_batches(positive_condition_file)
log = open('save/experiment-log.txt', 'w')
# pre-train generator
print 'Start pre-training...'
log.write('pre-training...\n')
for epoch in xrange(PRE_EPOCH_GEN_NUM):
loss = pre_train_epoch(sess, generator, gen_data_loader, cond=cond)
if epoch % 5 == 0:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
eval_file, cond, vocab)
# likelihood_data_loader.create_batches(eval_file)
# test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
# print 'pre-train epoch ', epoch, 'test_loss ', test_loss
# buffer = 'epoch:\t'+ str(epoch) + '\tnll:\t' + str(test_loss) + '\n'
# log.write(buffer)
clock.check_HMS()
print 'Start pre-training discriminator...'
# Train 3 epoch on the generated data and do this for 50 times
for _ in range(PRE_EPOCH_DIS_NUM):
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file, cond, vocab)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
clock.check_HMS()
rollout = ROLLOUT(generator, 0.8, SEQ_LENGTH)
print '#########################################################################'
print 'Start Adversarial Training...'
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
# Train the generator for one step
for it in range(1):
if cond:
cond_batch = vocab.choice_cond(BATCH_SIZE)
samples = generator.generate(sess, cond=cond_batch)
rewards = rollout.get_reward(sess,
samples,
16,
discriminator,
cond=cond_batch)
else:
samples = generator.generate(sess)
rewards = rollout.get_reward(sess, samples, 16, discriminator)
feed = {generator.x: samples, generator.rewards: rewards}
if cond:
feed[generator.cond] = cond_batch
_ = sess.run(generator.g_updates, feed_dict=feed)
# Test
if total_batch % 5 == 0 or total_batch == TOTAL_BATCH - 1:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
eval_file, cond, vocab)
# likelihood_data_loader.create_batches(eval_file)
# test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
# buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(test_loss) + '\n'
# print 'total_batch: ', total_batch, 'test_loss: ', test_loss
# log.write(buffer)
if total_batch % 20 == 0 or total_batch == TOTAL_BATCH - 1:
if cond:
vocab.id2word(
eval_file,
generated_haiku_with_kigo_file.format(total_batch))
else:
vocab.id2word(eval_file,
generated_haiku_file.format(total_batch))
# Update roll-out parameters
rollout.update_params()
# Train the discriminator
for _ in range(5):
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file, cond, vocab)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
clock.check_HMS()
saver = tf.train.Saver()
saver.save(sess, output_generator)
log.close()
def main():
# Create a parser to parse user input
def parse_arguments():
parser = argparse.ArgumentParser(description='Program for running several SeqGan applications.')
parser.add_argument('app', metavar='application', type=str, choices=['obama', 'haiku', 'synth'],
help='Enter either \'obama\' or \'haiku\'')
parser.add_argument('gen_n', type = int,
help='Number of generator pre-training steps')
parser.add_argument('disc_n', type = int,
help='Number of discriminator pre-training steps')
parser.add_argument('adv_n', type = int,
help='Number of adversarial pre-training steps')
parser.add_argument('-mn', metavar="model_name", type = str, default = "",
help = "Name for the checkpoint files. Will be stored at ./<app>/models/<model_name>")
parser.add_argument('-numeat', metavar="num_eat", type = int, default = 500,
help = "For synthetic data generation. Determines number of eaters in vocab.")
parser.add_argument('-numfeed', metavar="num_feed", type = int, default = 500,
help = "For synthetic data generation. Determines number of feeders in vocab.")
parser.add_argument('-numsent', metavar="num_sent", type = int, default = 10000,
help = "For synthetic data generation. Determines number of sentences generated.")
args = parser.parse_args()
synth_gen_params = ("NA", "NA", "NA")
if args.app == "synth":
synth_gen_params = (args.numsent, args.numfeed, args.numeat)
generate_random_sents("../data/synth/input.txt", args.numsent, args.numfeed, args.numeat)
task = load_task(args.app)
#Make the /models directory if its not there.
model_string = task.path +"/models/"
if not os.path.exists("./"+model_string):
os.mkdir("./"+model_string)
#make the checkpoint directory if its not there.
if args.mn == "":
model_string += str(args.gen_n)+ "_" + str(args.disc_n) + "_" + str(args.adv_n)
model_string += time.strftime("_on_%m_%d_%y", time.gmtime())
else:
model_string += args.mn
if not os.path.exists("./"+model_string):
os.mkdir("./"+model_string)
return args.gen_n, args.disc_n, args.adv_n, model_string, task, synth_gen_params
gen_n, disc_n, adv_n, MODEL_STRING, task, SYNTH_GEN_PARAMS = parse_arguments()
assert START_TOKEN == 0
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
# Initialize the data loaders
gen_data_loader = Gen_Data_loader(BATCH_SIZE, task.max_seq_length)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE, task.max_seq_length) # For validation
dis_data_loader = Dis_dataloader(BATCH_SIZE, task.max_seq_length)
# Initialize the Generator
generator = Generator(len(task.vocab), BATCH_SIZE, EMB_DIM, HIDDEN_DIM,
task.max_seq_length, START_TOKEN)
# Initialize the Discriminator
discriminator = Discriminator(sequence_length=task.max_seq_length,
num_classes=2,
vocab_size=len(task.vocab),
embedding_size=dis_embedding_dim,
filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters,
l2_reg_lambda=dis_l2_reg_lambda)
# Set session configurations.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
saver = tf.train.Saver()
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
# If restoring from a previous run ....
if len(os.listdir("./"+MODEL_STRING)) > 0:
saver.restore(sess, tf.train.latest_checkpoint(MODEL_STRING))
# Create batches from the positive file.
gen_data_loader.create_batches(task.train_file)
# Open log file for writing
log = open(task.log_file, 'w')
# Pre_train the generator with MLE.
pre_train_generator(sess, saver, MODEL_STRING, generator, gen_data_loader,
likelihood_data_loader, task, log, gen_n, BATCH_SIZE,
task.generated_num)
print('Start pre-training discriminator...')
# Do the discriminator pre-training steps
saver.restore(sess, tf.train.latest_checkpoint(MODEL_STRING))
train_discriminator(sess, generator, discriminator, dis_data_loader,
task, log, disc_n, BATCH_SIZE, task.generated_num,
dis_dropout_keep_prob)
print("Saving checkpoint ...")
saver.save(sess, MODEL_STRING+ "/model")
# Do the adversarial training steps
rollout = ROLLOUT(generator, 0.8)
train_adversarial(sess, saver, MODEL_STRING, generator, discriminator,
rollout, dis_data_loader, likelihood_data_loader,
task, log, adv_n)
#Use the best model to generate final sample
saver.restore(sess, tf.train.latest_checkpoint(MODEL_STRING))
generate_samples(sess, generator, BATCH_SIZE, task.generated_num,
task.eval_file)
#Writing results to CSV
with open(task.eval_file) as f:
generated = []
for line in f:
line = line.strip().split()
generated.append(line)
generated = task.vocab.decode(generated)
f.close()
with open(task.test_file) as f:
references = []
for line in f:
line = line.strip().split()
references.append(line)
references = task.vocab.decode(references)
f.close()
blue = corpus_bleu([references]*len(generated), generated)
print("Run with args {} {} {}: BLEUscore = {}\n".format(gen_n, disc_n, adv_n, blue))
prop = "NA"
if task.name == "synth":
total_correct = 0
for sentence in generated:
if is_valid_phrase(sentence):
total_correct +=1
prop = total_correct/len(generated)
if not os.path.exists("./results.csv"):
os.mknod("./results.csv")
with open("./results.csv", 'a') as csvfile:
fieldnames = ["name", "task_name", "num_gen", "num_disc", "num_adv",
"num_sents", "num_feeders", "num_eaters", "BLEU", "prop_valid"]
writer = csv.DictWriter(csvfile, fieldnames = fieldnames)
writer.writeheader()
writer.writerow({"name": MODEL_STRING, "task_name": task.name, "num_gen": gen_n,
"num_disc":disc_n, "num_adv": adv_n, "num_sents":SYNTH_GEN_PARAMS[0],
"num_feeders":SYNTH_GEN_PARAMS[1], "num_eaters":SYNTH_GEN_PARAMS[2],
"BLEU": blue, "prop_valid": prop})
f.close()
log.close()
def main():
# set random seed (may important to the result)
np.random.seed(SEED)
random.seed(SEED)
# data loader
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
dis_data_loader = Dis_dataloader(BATCH_SIZE)
D = Discriminator(SEQ_LENGTH, num_class, vocab_size, dis_emb_size,
dis_filter_sizes, dis_num_filters, 0.2)
G = Generator(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH,
START_TOKEN)
# avoid occupy all the memory of the GPU
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# sess
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
# change the train data to real poems to be done
gen_data_loader.create_batches(positive_file)
log = open('./experiment-log.txt', 'w')
# pre-train generator
print('Start pre-training...')
log.write('pre-training...\n')
for epoch in range(PRE_EPOCH_NUM):
start = time.time()
loss = pre_train_epoch(sess, G, gen_data_loader)
print("Epoch ", epoch, " loss: ", loss)
print("per epoch time consumed: ", time.time() - start)
# if epoch % 5 == 0:
# generate_samples(sess, G, BATCH_SIZE, generated_num, eval_file)
# likelihood_data_loader.create_batches(eval_file)
# test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
# print('pre-train epoch ', epoch, 'test_loss ', test_loss)
# buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(test_loss) + '\n'
# log.write(buffer)
print("Start pretraining the discriminator")
for _ in range(50):
generate_samples(sess, G, BATCH_SIZE, generated_num, negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
D.input_x: x_batch,
D.input_y: y_batch,
D.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(D.train_op, feed)
g_beta = G_beta(G, update_rate=0.8)
print(
'#########################################################################'
)
print('Start Adversarial Training...')
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
# train generator once
for it in range(1):
samples = G.generate(sess)
rewards = g_beta.get_reward(sess, samples, sample_time, D)
feed = {G.x: samples, G.rewards: rewards}
_ = sess.run(G.g_update, feed_dict=feed)
# Test
if total_batch % 5 == 0 or total_batch == TOTAL_BATCH - 1:
# generate_samples(sess, G, BATCH_SIZE, generated_num, eval_file)
# likelihood_data_loader.create_batches(eval_file)
# test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
buffer = 'epoch:\t' + str(total_batch) + '\treward:\t' + str(
rewards) + '\n'
print('total_batch: ', total_batch, 'reward: ', rewards)
log.write(buffer)
# update G_beta with weight decay
g_beta.update_params()
# train the discriminator
for it in range(10):
generate_samples(sess, G, BATCH_SIZE, generated_num, negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for batch in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
D.input_x: x_batch,
D.input_y: y_batch,
D.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(D.train_op, feed_dict=feed)
# finnal generation
print("Wrting final results to test file")
test_file = "./final2.txt"
generate_samples(sess, G, BATCH_SIZE, generated_num, test_file)
print("Finished")
def main():
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
gen_data_loader = Gen_Data_loader(BATCH_SIZE,FLAGS.length)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE,FLAGS.length) # For testing
vocab_size = 5000
file = open('save/target_params.pkl', 'rb')
target_params = cPickle.load(file)
dis_data_loader = Dis_dataloader(BATCH_SIZE,SEQ_LENGTH)
discriminator = Discriminator(SEQ_LENGTH,num_classes=2,vocab_size=vocab_size,dis_emb_dim=dis_embedding_dim,filter_sizes=dis_filter_sizes,num_filters=dis_num_filters,
batch_size=BATCH_SIZE,hidden_dim=HIDDEN_DIM,start_token=START_TOKEN,goal_out_size=GOAL_OUT_SIZE,step_size=4)
leakgan = LeakGAN(SEQ_LENGTH,num_classes=2,vocab_size=vocab_size,emb_dim=EMB_DIM,dis_emb_dim=dis_embedding_dim,filter_sizes=dis_filter_sizes,num_filters=dis_num_filters,
batch_size=BATCH_SIZE,hidden_dim=HIDDEN_DIM,start_token=START_TOKEN,goal_out_size=GOAL_OUT_SIZE,goal_size=GOAL_SIZE,step_size=4,D_model=discriminator,
learning_rate=LEARNING_RATE)
if SEQ_LENGTH == 40:
target_lstm = TARGET_LSTM(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN) # The oracle model
else:
target_lstm = TARGET_LSTM20(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN,target_params)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.5
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, positive_file, 0)
for a in range(1):
g = sess.run(leakgan.gen_x,feed_dict={leakgan.drop_out:0.8,leakgan.train:1})
print(g)
print("epoch:",a," ")
log = open('save/experiment-log.txt', 'w')
gen_data_loader.create_batches(positive_file)
saver_variables = tf.global_variables()
saver = tf.train.Saver(saver_variables)
model = tf.train.latest_checkpoint(model_path)
print(model)
if FLAGS.restore and model:
# model = tf.train.latest_checkpoint(model_path)
# if model and FLAGS.restore:
if model_path+'/' + FLAGS.model:
print(model_path+'/' + FLAGS.model)
saver.restore(sess, model_path+'/' + FLAGS.model)
else:
saver.restore(sess, model)
else:
if FLAGS.resD and model_path + '/' + FLAGS.model:
print(model_path + '/' + FLAGS.model)
saver.restore(sess, model_path + '/' + FLAGS.model)
print('Start pre-training...')
log.write('pre-training...\n')
for epoch in range(PRE_EPOCH_NUM):
loss = pre_train_epoch(sess, leakgan, gen_data_loader)
if epoch % 5 == 0:
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, eval_file, 0)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print('pre-train epoch ', epoch, 'test_loss ', test_loss)
buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(test_loss) + '\n'
log.write(buffer)
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, eval_file, 0)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print("Groud-Truth:", test_loss)
saver.save(sess, model_path + '/leakgan_pre')
else:
print('Start pre-training discriminator...')
# Train 3 epoch on the generated data and do this for 50 times
for i in range(10):
for _ in range(5):
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, negative_file,0)
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, positive_file,0)
# gen_data_loader.create_batches(positive_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.D_input_x: x_batch,
discriminator.D_input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
D_loss,_ = sess.run([discriminator.D_loss,discriminator.D_train_op], feed)
# # print 'D_loss ', D_loss
# buffer = str(D_loss) + '\n'
# log.write(buffer)
leakgan.update_feature_function(discriminator)
saver.save(sess, model_path + '/leakgan_preD')
# saver.save(sess, model_path + '/leakgan')
# pre-train generator
print('Start pre-training...')
log.write('pre-training...\n')
for epoch in range(PRE_EPOCH_NUM/10):
loss = pre_train_epoch(sess, leakgan, gen_data_loader)
if epoch % 5 == 0:
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, eval_file,0)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print('pre-train epoch ', epoch, 'test_loss ', test_loss)
buffer = 'epoch:\t'+ str(epoch) + '\tnll:\t' + str(test_loss) + '\n'
log.write(buffer)
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, eval_file, 0)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print("Groud-Truth:", test_loss)
saver.save(sess, model_path + '/leakgan_pre')
gencircle = 1
#
print('#########################################################################')
print('Start Adversarial Training...')
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
# Train the generator for one step
for it in range(1):
for gi in range(gencircle):
samples = leakgan.generate(sess,1.0,1)
rewards = get_reward(leakgan, discriminator,sess, samples, 4, dis_dropout_keep_prob)
feed = {leakgan.x: samples, leakgan.reward: rewards,leakgan.drop_out:1.0}
_,_,g_loss,w_loss = sess.run([leakgan.manager_updates,leakgan.worker_updates,leakgan.goal_loss,leakgan.worker_loss], feed_dict=feed)
print('total_batch: ', total_batch, " ",g_loss," ", w_loss)
# Test
if total_batch % 5 == 0 or total_batch == TOTAL_BATCH - 1:
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, eval_file,0)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(test_loss) + '\n'
print('total_batch: ', total_batch, 'test_loss: ', test_loss)
log.write(buffer)
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, eval_file, 0)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print("Groud-Truth:" ,test_loss)
# Train the discriminator
for _ in range(5):
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, negative_file,0)
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, positive_file,0)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.D_input_x: x_batch,
discriminator.D_input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
D_loss, _ = sess.run([discriminator.D_loss, discriminator.D_train_op], feed)
# print 'D_loss ', D_loss
leakgan.update_feature_function(discriminator)
log.close()
def main():
print 'start time : '
print datetime.now()
random.seed(SEED)
np.random.seed(SEED)
_, _, _, SEQ_LENGTH, vocab_size = cPickle.load(open(pickle_loc))
print 'SEQ_LENGTH' , SEQ_LENGTH, 'vocab_size', vocab_size
assert START_TOKEN == 0
gen_data_loader = Gen_Data_loader(BATCH_SIZE,SEQ_LENGTH)
dis_data_loader = Dis_dataloader(BATCH_SIZE,SEQ_LENGTH)
discriminator = Discriminator(SEQ_LENGTH,num_classes=2,vocab_size=vocab_size,dis_emb_dim=dis_embedding_dim,filter_sizes=dis_filter_sizes,num_filters=dis_num_filters,
batch_size=BATCH_SIZE,hidden_dim=HIDDEN_DIM,start_token=START_TOKEN,goal_out_size=GOAL_OUT_SIZE,step_size=4)
leakgan = LeakGAN(SEQ_LENGTH,num_classes=2,vocab_size=vocab_size,emb_dim=EMB_DIM,dis_emb_dim=dis_embedding_dim,filter_sizes=dis_filter_sizes,num_filters=dis_num_filters,
batch_size=BATCH_SIZE,hidden_dim=HIDDEN_DIM,start_token=START_TOKEN,goal_out_size=GOAL_OUT_SIZE,goal_size=GOAL_SIZE,step_size=4,D_model=discriminator)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 1
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
for a in range(1):
g = sess.run(leakgan.gen_x,feed_dict={leakgan.drop_out:0.8,leakgan.train:1})
print g
print "epoch:",a," "
log = open('save/experiment-log.txt', 'w')
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, negative_file, 0)
gen_data_loader.create_batches(positive_file)
saver_variables = tf.global_variables()
saver = tf.train.Saver(saver_variables, max_to_keep=maxModelSave)
if FLAGS.restore and FLAGS.model:
if model_path+'/' + FLAGS.model:
print model_path+'/' + FLAGS.model
saver.restore(sess, model_path+'/' + FLAGS.model)
else:
print 'input all arguments, \"restore\" and \"model\"'
exit()
else:
if FLAGS.resD and model_path + '/' + FLAGS.model:
print model_path + '/' + FLAGS.model
saver.restore(sess, model_path + '/' + FLAGS.model)
print 'Start pre-training...'
log.write('pre-training...\n')
for epoch in xrange(PRE_EPOCH_NUM):
loss = pre_train_epoch(sess, leakgan, gen_data_loader)
if epoch % 5 == 0:
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, negative_file)
buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(loss) + '\n'
log.write(buffer)
saver.save(sess, model_path + '/leakgan_pre')
else:
# pre-train generator
print 'Start pre-training...'
log.write('pre-training...\n')
for epoch in xrange(PRE_EPOCH_NUM):
loss = pre_train_epoch(sess, leakgan, gen_data_loader)
if epoch % 5 == 0:
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, negative_file,0)
print 'pre-train epoch ', epoch, 'test_loss ', loss
buffer = 'epoch:\t'+ str(epoch) + '\tnll:\t' + str(loss) + '\n'
log.write(buffer)
print 'Start pre-training discriminator...'
# Train 3 epoch on the generated data and do this for 80 times
for _ in range(PRE_EPOCH_NUM):
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, negative_file, 0)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.D_input_x: x_batch,
discriminator.D_input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
D_loss, _ = sess.run([discriminator.D_loss, discriminator.D_train_op], feed)
buffer = str(D_loss) + '\n'
log.write(buffer)
leakgan.update_feature_function(discriminator)
saver.save(sess, model_path + '/leakgan_pre')
gencircle = 1
print '#########################################################################'
print 'Start Adversarial Training...'
print 'start Adv time : '
print datetime.now()
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH // 10):
# Train the generator for one step
for iter1 in range(10):
for it in range(1):
for gi in range(gencircle):
samples = leakgan.generate(sess,1.0,1)
rewards = get_reward(leakgan, discriminator,sess, samples, 4, dis_dropout_keep_prob,(total_batch * 10 + iter1),gen_data_loader)
feed = {leakgan.x: samples, leakgan.reward: rewards,leakgan.drop_out:1.0}
_,_,g_loss,w_loss = sess.run([leakgan.manager_updates,leakgan.worker_updates,leakgan.goal_loss,leakgan.worker_loss], feed_dict=feed)
print 'total_batch: ', (total_batch * 10 + iter1), " ",g_loss," ", w_loss
# Test
testFileName = "./save/movie_" + str((total_batch * 10 + iter1)) + ".txt"
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, testFileName, 0)
convertor(testFileName, filedir='save/')
if iter1 == 1 or (total_batch * 10 + iter1) == TOTAL_BATCH - 1:
saver.save(sess, model_path + '/leakgan', global_step=(total_batch * 10 + iter1))
for _ in range(15):
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, negative_file, 0)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.D_input_x: x_batch,
discriminator.D_input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
D_loss, _ = sess.run([discriminator.D_loss, discriminator.D_train_op], feed)
buffer = str(D_loss) + '\n'
log.write(buffer)
leakgan.update_feature_function(discriminator)
for epoch in xrange(5):
loss = pre_train_epoch(sess, leakgan, gen_data_loader)
# Train the discriminator
for _ in range(5):
generate_samples(sess, leakgan, BATCH_SIZE, generated_num, negative_file, 0)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.D_input_x: x_batch,
discriminator.D_input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
D_loss, _ = sess.run([discriminator.D_loss, discriminator.D_train_op], feed)
buffer = str(D_loss) + '\n'
log.write(buffer)
leakgan.update_feature_function(discriminator)
log.close()
print 'end time : '
print datetime.now()
def main(unused_argv):
config_train = training_config()
config_gen = generator_config()
config_dis = discriminator_config()
np.random.seed(config_train.seed)
assert config_train.start_token == 0
#Build dataloader for generaotr, testing and discriminator
gen_data_loader = Gen_Data_loader(config_gen.gen_batch_size)
likelihood_data_loader = Gen_Data_loader(config_gen.gen_batch_size)
dis_data_loader = Dis_dataloader(config_dis.dis_batch_size)
#Build generator and its rollout
generator = Generator(config=config_gen)
# 生成 3个神经网络
generator.build()
# 快速展开网络，序列未生成完就预测后边的序列，用于计算reward
rollout_gen = rollout(config=config_gen)
#Build target LSTM
target_params = cPickle.load(open('save/target_params.pkl'))
target_lstm = TARGET_LSTM(config=config_gen,
params=target_params) # The oracle model
#Build discriminator
discriminator = Discriminator(config=config_dis)
discriminator.build_discriminator()
#Build optimizer op for pretraining
pretrained_optimizer = tf.train.AdamOptimizer(
config_train.gen_learning_rate)
# 取出 teller 的所有变量， teller在 generator和rollout网络中
var_pretrained = [
v for v in tf.trainable_variables() if 'teller' in v.name
] #Using name 'teller' here to prevent name collision of target LSTM
# zip函数将 2个迭代器 组成tuple
gradients, variables = zip(*pretrained_optimizer.compute_gradients(
generator.pretrained_loss, var_list=var_pretrained))
gradients, _ = tf.clip_by_global_norm(gradients, config_train.grad_clip)
gen_pre_upate = pretrained_optimizer.apply_gradients(
zip(gradients, variables))
#Initialize all variables
sess = tf.Session(config=config_hardware)
sess.run(tf.global_variables_initializer())
#Initalize data loader of generator utils.py文件中
# target_lstm 网络生成真实数据 写入config_train.positive_file 文件
generate_samples(sess, target_lstm, config_train.batch_size,
config_train.generated_num, config_train.positive_file)
gen_data_loader.create_batches(config_train.positive_file)
#Start pretraining
log = open('save/experiment-log.txt', 'w')
print 'Start pre-training generator...'
log.write('pre-training...\n')
for epoch in xrange(config_train.pretrained_epoch_num):
gen_data_loader.reset_pointer()
for it in xrange(gen_data_loader.num_batch):
#见第60行，加载target_lstm 神经网络的数据,用于预训练生成器====真实样本
batch = gen_data_loader.next_batch()
#真实数据训练 generator;有监督学习 batch 最后第一个是label
_, g_loss = sess.run([gen_pre_upate, generator.pretrained_loss], feed_dict={generator.input_seqs_pre:batch,\
generator.input_seqs_mask:np.ones_like(batch)})
if epoch % config_train.test_per_epoch == 0:
# generator 生成样本 与 真实数据的相似度
generate_samples(sess, generator, config_train.batch_size,
config_train.generated_num,
config_train.eval_file)
likelihood_data_loader.create_batches(config_train.eval_file)
#评估生成质量
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print 'pre-train epoch ', epoch, 'test_loss ', test_loss
buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(
test_loss) + '\n'
log.write(buffer)
print 'Start pre-training discriminator...'
for t in range(config_train.dis_update_time_pre):
print "Times: " + str(t)
# generator生成假数据+ target_lstm的真实数据;; 用于训练
generate_samples(sess, generator, config_train.batch_size,
config_train.generated_num,
config_train.negative_file)
# 混合真假数据
dis_data_loader.load_train_data(config_train.positive_file,
config_train.negative_file)
for _ in range(config_train.dis_update_epoch_pre):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x:
x_batch,
discriminator.input_y:
y_batch,
discriminator.dropout_keep_prob:
config_dis.dis_dropout_keep_prob
}
#交叉上最小; 主要是训练评分网络 用于给generator提供reward
_ = sess.run(discriminator.train_op, feed)
#Build optimizer op for adversarial training
train_adv_opt = tf.train.AdamOptimizer(config_train.gen_learning_rate)
gradients, variables = zip(*train_adv_opt.compute_gradients(
generator.gen_loss_adv, var_list=var_pretrained))
gradients, _ = tf.clip_by_global_norm(gradients, config_train.grad_clip)
train_adv_update = train_adv_opt.apply_gradients(zip(gradients, variables))
#Initialize global variables of optimizer for adversarial training
uninitialized_var = [
e for e in tf.global_variables() if e not in tf.trainable_variables()
]
init_vars_uninit_op = tf.variables_initializer(uninitialized_var)
sess.run(init_vars_uninit_op)
#Start adversarial training 开始对抗训练
for total_batch in xrange(config_train.total_batch):
for iter_gen in xrange(config_train.gen_update_time):
# 用generator进行抽样; LSTM 生成序列
samples = sess.run(generator.sample_word_list_reshape)
feed = {"pred_seq_rollout:0": samples}
reward_rollout = []
#calcuate the reward given in the specific stpe t by roll out
# 用rollout网络计算指定动作的回报
for iter_roll in xrange(config_train.rollout_num):
# 生成器采样的获得的单词传给 rollout ??有一个疑问？samples看代码是完整序列（与论文不符），为什么还要rollout
rollout_list = sess.run(rollout_gen.sample_rollout_step,
feed_dict=feed)
rollout_list_stack = np.vstack(
rollout_list
) #shape: #batch_size * #rollout_step, #sequence length
# 蒙特卡洛 展开成序列，贝尔曼方程计算 reward
reward_rollout_seq = sess.run(
discriminator.ypred_for_auc,
feed_dict={
discriminator.input_x: rollout_list_stack,
discriminator.dropout_keep_prob: 1.0
})
reward_last_tok = sess.run(discriminator.ypred_for_auc,
feed_dict={
discriminator.input_x: samples,
discriminator.dropout_keep_prob:
1.0
})
reward_allseq = np.concatenate(
(reward_rollout_seq, reward_last_tok), axis=0)[:, 1]
reward_tmp = []
for r in xrange(config_gen.gen_batch_size):
reward_tmp.append(reward_allseq[range(
r,
config_gen.gen_batch_size * config_gen.sequence_length,
config_gen.gen_batch_size)])
reward_rollout.append(np.array(reward_tmp))
#计算reward
rewards = np.sum(reward_rollout, axis=0) / config_train.rollout_num
# 用reward 指导 generator 更新梯度
_, gen_loss = sess.run([train_adv_update, generator.gen_loss_adv], feed_dict={generator.input_seqs_adv:samples,\
generator.rewards:rewards})
if total_batch % config_train.test_per_epoch == 0 or total_batch == config_train.total_batch - 1:
#对抗训练后 用generator再次生成样本与模拟器(target_lstm，真实数据)进行比对
generate_samples(sess, generator, config_train.batch_size,
config_train.generated_num,
config_train.eval_file)
likelihood_data_loader.create_batches(config_train.eval_file)
#util.py中定义
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(
test_loss) + '\n'
print 'total_batch: ', total_batch, 'test_loss: ', test_loss
log.write(buffer)
for _ in range(config_train.dis_update_time_adv):
generate_samples(sess, generator, config_train.batch_size,
config_train.generated_num,
config_train.negative_file)
dis_data_loader.load_train_data(config_train.positive_file,
config_train.negative_file)
for _ in range(config_train.dis_update_epoch_adv):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x:
x_batch,
discriminator.input_y:
y_batch,
discriminator.dropout_keep_prob:
config_dis.dis_dropout_keep_prob
}
#训练这个评分网络, score
_ = sess.run(discriminator.train_op, feed)
log.close()
def main():
random.seed(SEED)
np.random.seed(SEED)
assert START_TOKEN == 0
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
# vocab_size = 97
dis_data_loader = Dis_dataloader(BATCH_SIZE)
generator = Generator(vocab_size,
BATCH_SIZE,
EMB_DIM,
HIDDEN_DIM,
SEQ_LENGTH,
START_TOKEN,
learning_rate=0.01)
discriminator = Discriminator(sequence_length=SEQ_LENGTH,
num_classes=2,
vocab_size=vocab_size,
embedding_size=dis_embedding_dim,
filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters,
l2_reg_lambda=dis_l2_reg_lambda)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(max_to_keep=10)
# First, use the oracle model to provide the positive examples, which are sampled from the oracle data distribution
gen_data_loader.create_batches(positive_file)
# pre-train generator
print 'Start pre-training...'
early_stop_buffer = [10.] * 5
for e_cnt, epoch in enumerate(xrange(PRE_EPOCH_NUM)):
loss = pre_train_epoch(sess, generator, gen_data_loader)
if epoch % 2 == 0:
# generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
likelihood_data_loader.create_batches(eval_real_file)
test_loss = target_loss(sess, generator, likelihood_data_loader)
print 'pre-train epoch ', epoch, 'test_loss ', test_loss
early_stop_buffer = early_stop_buffer[1:]
early_stop_buffer.append(test_loss)
if all(early_stop_buffer[0] < np.asarray(early_stop_buffer[1:])):
break
print 'Start pre-training discriminator...'
# Train 3 epoch on the generated data and do this for 50 times
for e in range(50):
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
print 'Epoch {}'.format(e)
rollout = ROLLOUT_OLD(generator, 0.8)
print '#########################################################################'
print 'Start Adversarial Training...'
model_idx = 1
fname = 'model' + str(model_idx)
model_save_path = './Model/' + fname + '/'
while os.path.exists(model_save_path):
model_idx += 1
fname = 'model' + str(model_idx)
model_save_path = './Model/' + fname + '/'
os.makedirs(model_save_path)
os.makedirs(os.path.join('./log', fname))
early_stop_buffer = [10.] * 4
for total_batch in range(TOTAL_BATCH):
# Train the generator for one step
for it in range(1):
samples = generator.generate(sess)
rewards = rollout.get_reward(sess, samples, SAMP_NUM,
discriminator)
feed = {generator.x: samples, generator.rewards: rewards}
_ = sess.run(generator.g_updates, feed_dict=feed)
# Test
if total_batch % 3 == 0 or total_batch == TOTAL_BATCH - 1:
# generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
likelihood_data_loader.create_batches(eval_real_file)
test_loss = target_loss(sess, generator, likelihood_data_loader)
buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(
test_loss) + '\n'
print 'total_batch: ', total_batch, 'test_loss: ', test_loss
early_stop_buffer = early_stop_buffer[1:]
early_stop_buffer.append(test_loss)
if all(early_stop_buffer[0] < np.asarray(early_stop_buffer[1:])):
break
# Update roll-out parameters
rollout.update_params()
# Train the discriminator
for _ in range(1):
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(1):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
saver.save(sess,
os.path.join(model_save_path, fname),
global_step=total_batch,
write_meta_graph=False)
metagraph_filename = os.path.join(model_save_path, fname + '.meta')
if not os.path.exists(metagraph_filename):
saver.export_meta_graph(metagraph_filename)
def main():
random.seed(SEED)
np.random.seed(SEED)
# data loaders declaration
# loaders for generator, discriminator, and additional validation data loader
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
dis_data_loader = Dis_dataloader(BATCH_SIZE)
eval_data_loader = Gen_Data_loader(BATCH_SIZE)
# define generator and discriminator
# general structures are same with the original model
# learning rates for generator needs heavy tuning for general use
# l2 reg for D & G also affects performance
generator = Generator(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM,
SEQ_LENGTH, START_TOKEN, GENERATOR_LR, REWARD_GAMMA)
discriminator = Discriminator(sequence_length=SEQ_LENGTH,
num_classes=2,
vocab_size=vocab_size,
embedding_size=dis_embedding_dim,
filter_sizes=dis_filter_sizes,
num_filters=dis_num_filters,
l2_reg_lambda=dis_l2_reg_lambda)
# VRAM limitation for efficient deployment
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
sess = tf.Session(config=tf_config)
sess.run(tf.global_variables_initializer())
# define saver
saver = tf.train.Saver(tf.trainable_variables(), max_to_keep=1)
# generate real data from the true dataset
gen_data_loader.create_batches(positive_file)
# generate real validation data from true validation dataset
eval_data_loader.create_batches(valid_file)
time = str(datetime.datetime.now())[:-7]
log = open('save/experiment-log-conditional' + str(time) + '.txt', 'w')
log.write(str(config) + '\n')
log.write('D loss: original\n')
log.flush()
#summary_writer = tf.summary.FileWriter('save/tensorboard/', graph=tf.get_default_graph())
if config['pretrain'] == True:
# pre-train generator
print 'Start pre-training...'
log.write('pre-training...\n')
for epoch in xrange(PRE_GEN_EPOCH):
# calculate the loss by running an epoch
loss = pre_train_epoch_condtional(sess, generator, gen_data_loader)
# measure bleu score with the validation set
bleu_score = calculate_bleu(sess, generator, eval_data_loader)
# since the real data is the true data distribution, only evaluate the pretraining loss
# note the absence of the oracle model which is meaningless for general use
buffer = 'pre-train epoch: ' + str(
epoch) + ' pretrain_loss: ' + str(loss) + ' bleu: ' + str(
bleu_score)
print(buffer)
log.write(buffer + '\n')
log.flush()
# generate 5 test samples per epoch
# it automatically samples from the generator and postprocess to midi file
# midi files are saved to the pre-defined folder
if epoch == 0:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
POST.main(negative_file, 5,
str(-1) + '_vanilla_', 'midi_conditional')
elif epoch == PRE_GEN_EPOCH - 1:
generate_samples(sess, generator, BATCH_SIZE, generated_num,
negative_file)
POST.main(negative_file, 5,
str(-PRE_GEN_EPOCH) + '_vanilla_',
'midi_conditional')
print 'Start pre-training discriminator...'
# Train 3 epoch on the generated data and do this for 50 times
# this trick is also in spirit of the original work, but the epoch strategy needs tuning
for epochs in range(PRE_DIS_EPOCH):
generate_samples_conditional_v2(sess, gen_data_loader, generator,
BATCH_SIZE, generated_num,
negative_file)
D_loss = 0
for _ in range(3):
dis_data_loader.load_train_data(positive_file, negative_file)
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
D_loss += discriminator.loss.eval(feed, session=sess)
buffer = 'epoch: ' + str(epochs + 1) + ' D loss: ' + str(
D_loss / dis_data_loader.num_batch / 3)
print(buffer)
log.write(buffer + '\n')
log.flush()
# save the pre-trained checkpoint for future use
# if one wants adv. training only, comment out the pre-training section after the save
save_checkpoint(sess, saver, PRE_GEN_EPOCH, PRE_DIS_EPOCH)
# define rollout target object
# the second parameter specifies target update rate
# the higher rate makes rollout "conservative", with less update from the learned generator
# we found that higher update rate stabilized learning, constraining divergence of the generator
rollout = ROLLOUT(generator, ROLLOUT_UPDATE_RATE)
print '#########################################################################'
print 'Start Adversarial Training...'
log.write('adversarial training...\n')
if config['pretrain'] == False:
# load checkpoint of pre-trained model
load_checkpoint(sess, saver)
# 0.001 to 0.01
if config['x10adv_g'] == True:
generator.learning_rate *= 10
for total_batch in range(TOTAL_BATCH):
G_loss = 0
# Train the generator for one step
for it in range(epochs_generator):
samples = generator.generate(sess)
rewards = rollout.get_reward(sess, samples, config['rollout_num'],
discriminator)
feed = {generator.x: samples, generator.rewards: rewards}
_ = sess.run(generator.g_updates, feed_dict=feed)
G_loss += generator.g_loss.eval(feed, session=sess)
# Update roll-out parameters
rollout.update_params()
# Train the discriminator
D_loss = 0
for _ in range(epochs_discriminator):
generate_samples_conditional_v2(sess, gen_data_loader, generator,
BATCH_SIZE, generated_num,
negative_file)
for _ in range(config['epochs_discriminator_multiplier']):
dis_data_loader.load_train_data(positive_file, negative_file)
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
discriminator.input_y: y_batch,
discriminator.dropout_keep_prob: dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op, feed)
D_loss += discriminator.loss.eval(feed, session=sess)
# measure stability and performance evaluation with bleu score
bleu_score = calculate_bleu(sess, generator, eval_data_loader)
buffer = 'epoch: ' + str(total_batch + 1) + \
', G_adv_loss: %.12f' % (G_loss / epochs_generator) + \
', D loss: %.12f' % (D_loss / epochs_discriminator / config['epochs_discriminator_multiplier']) + \
', bleu score: %.12f' % bleu_score
print(buffer)
log.write(buffer + '\n')
log.flush()
if config['infinite_loop'] is True:
if bleu_score < config['loop_threshold']:
buffer = 'Mode collapse detected, restarting from pretrained model...'
print(buffer)
log.write(buffer + '\n')
log.flush()
load_checkpoint(sess, saver)
# generate random test samples and postprocess the sequence to midi file
#generate_samples(sess, generator, BATCH_SIZE, generated_num, negative_file)
# instead of the above, generate samples conditionally
# randomly sample a batch
# rng = np.random.randint(0, high=gen_data_loader.num_batch, size=1)
# random_batch = np.squeeze(gen_data_loader.sequence_batch[rng])
generate_samples_conditional_v2(sess, gen_data_loader, generator,
BATCH_SIZE, generated_num,
negative_file)
POST.main(negative_file, 5,
str(total_batch) + '_vanilla_', 'midi_conditional')
log.close()
def main():
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
gen_data_loader = Gen_Data_loader(BATCH_SIZE)
likelihood_data_loader = Gen_Data_loader(BATCH_SIZE) # For testing
vocab_size = 2000
dis_data_loader = Dis_dataloader(BATCH_SIZE)
generator = Generator(vocab_size, EMB_DIM, HIDDEN_DIM, 1, START_TOKEN,
SEQ_LENGTH).to(device)
target_lstm = Generator(vocab_size,
EMB_DIM,
HIDDEN_DIM,
1,
START_TOKEN,
SEQ_LENGTH,
oracle=True).to(device)
discriminator = Discriminator(vocab_size, dis_embedding_dim,
dis_filter_sizes, dis_num_filters,
dis_dropout).to(device)
generate_samples(target_lstm, BATCH_SIZE, generated_num, positive_file)
gen_data_loader.create_batches(positive_file)
pre_gen_opt = torch.optim.Adam(generator.parameters(), 1e-2)
adv_gen_opt = torch.optim.Adam(generator.parameters(), 1e-2)
dis_opt = torch.optim.Adam(discriminator.parameters(), 1e-4)
dis_criterion = nn.NLLLoss()
log = open('save/experiment-log.txt', 'w')
print('Start pre-training...')
log.write('pre-training...\n')
for epoch in range(PRE_EPOCH_NUM):
loss = pre_train_epoch(generator, pre_gen_opt, gen_data_loader)
if (epoch + 1) % 5 == 0:
generate_samples(generator, BATCH_SIZE, generated_num, eval_file)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(target_lstm, likelihood_data_loader)
print('pre-train epoch ', epoch + 1, '\tnll:\t', test_loss)
buffer = 'epoch:\t' + str(epoch +
1) + '\tnll:\t' + str(test_loss) + '\n'
log.write(buffer)
print('Start pre-training discriminator...')
# Train 3 epoch on the generated data and do this for 50 times
for e in range(50):
generate_samples(generator, BATCH_SIZE, generated_num, negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
d_total_loss = []
for _ in range(3):
dis_data_loader.reset_pointer()
total_loss = []
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
x_batch = x_batch.to(device)
y_batch = y_batch.to(device)
dis_output = discriminator(x_batch.detach())
d_loss = dis_criterion(dis_output, y_batch.detach())
dis_opt.zero_grad()
d_loss.backward()
dis_opt.step()
total_loss.append(d_loss.data.cpu().numpy())
d_total_loss.append(np.mean(total_loss))
if (e + 1) % 5 == 0:
buffer = 'Epoch [{}], discriminator loss [{:.4f}]\n'.format(
e + 1, np.mean(d_total_loss))
print(buffer)
log.write(buffer)
rollout = Rollout(generator, 0.8)
print(
'#########################################################################'
)
print('Start Adversarial Training...')
log.write('adversarial training...\n')
gan_loss = GANLoss()
for total_batch in range(TOTAL_BATCH):
# Train the generator for one step
discriminator.eval()
for it in range(1):
samples, _ = generator.sample(num_samples=BATCH_SIZE)
rewards = rollout.get_reward(samples, 16, discriminator)
prob = generator(samples.detach())
adv_loss = gan_loss(prob, samples.detach(), rewards.detach())
adv_gen_opt.zero_grad()
adv_loss.backward()
nn.utils.clip_grad_norm_(generator.parameters(), 5.0)
adv_gen_opt.step()
# Test
if (total_batch + 1) % 5 == 0:
generate_samples(generator, BATCH_SIZE, generated_num, eval_file)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(target_lstm, likelihood_data_loader)
self_bleu_score = self_bleu(generator)
buffer = 'epoch:\t' + str(total_batch + 1) + '\tnll:\t' + str(
test_loss) + '\tSelf Bleu:\t' + str(self_bleu_score) + '\n'
print(buffer)
log.write(buffer)
# Update roll-out parameters
rollout.update_params()
# Train the discriminator
discriminator.train()
for _ in range(5):
generate_samples(generator, BATCH_SIZE, generated_num,
negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
d_total_loss = []
for _ in range(3):
dis_data_loader.reset_pointer()
total_loss = []
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
x_batch = x_batch.to(device)
y_batch = y_batch.to(device)
dis_output = discriminator(x_batch.detach())
d_loss = dis_criterion(dis_output, y_batch.detach())
dis_opt.zero_grad()
d_loss.backward()
dis_opt.step()
total_loss.append(d_loss.data.cpu().numpy())
d_total_loss.append(np.mean(total_loss))
if (total_batch + 1) % 5 == 0:
buffer = 'Epoch [{}], discriminator loss [{:.4f}]\n'.format(
total_batch + 1, np.mean(d_total_loss))
print(buffer)
log.write(buffer)
log.close()
def main(unused_argv):
config_train = training_config()
config_gen = generator_config()
config_dis = discriminator_config()
np.random.seed(config_train.seed)
assert config_train.start_token == 0
gen_data_loader = Gen_Data_loader(config_gen.gen_batch_size)
likelihood_data_loader = Gen_Data_loader(config_gen.gen_batch_size)
dis_data_loader = Dis_dataloader(config_dis.dis_batch_size)
generator = Generator(config=config_gen)
generator.build()
rollout_gen = rollout(config=config_gen)
#Build target LSTM
target_params = pickle.load(open('save/target_params.pkl','rb'),encoding='iso-8859-1')
target_lstm = TARGET_LSTM(config=config_gen, params=target_params) # The oracle model
# Build discriminator
discriminator = Discriminator(config=config_dis)
discriminator.build_discriminator()
# Build optimizer op for pretraining
pretrained_optimizer = tf.train.AdamOptimizer(config_train.gen_learning_rate)
var_pretrained = [v for v in tf.trainable_variables() if 'teller' in v.name]
gradients, variables = zip(
*pretrained_optimizer.compute_gradients(generator.pretrained_loss, var_list=var_pretrained))
gradients, _ = tf.clip_by_global_norm(gradients, config_train.grad_clip)
gen_pre_update = pretrained_optimizer.apply_gradients(zip(gradients, variables))
sess = tf.Session()
sess.run(tf.global_variables_initializer())
generate_samples(sess,target_lstm,config_train.batch_size,config_train.generated_num,config_train.positive_file)
gen_data_loader.create_batches(config_train.positive_file)
log = open('save/experiment-log.txt','w')
print('Start pre-training generator....')
log.write('pre-training...\n')
for epoch in range(config_train.pretrained_epoch_num):
gen_data_loader.reset_pointer()
for it in range(gen_data_loader.num_batch):
batch = gen_data_loader.next_batch()
_,g_loss = sess.run([gen_pre_update,generator.pretrained_loss],feed_dict={generator.input_seqs_pre:batch,
generator.input_seqs_mask:np.ones_like(batch)})
if epoch % config_train.test_per_epoch == 0:
#进行测试，通过Generator产生一批序列，
generate_samples(sess,generator,config_train.batch_size,config_train.generated_num,config_train.eval_file)
# 创建这批序列的data-loader
likelihood_data_loader.create_batches(config_train.eval_file)
# 使用oracle 计算 交叉熵损失nll
test_loss = target_loss(sess,target_lstm,likelihood_data_loader)
# 打印并写入日志
print('pre-train ',epoch, ' test_loss ',test_loss)
buffer = 'epoch:\t' + str(epoch) + '\tnll:\t' + str(test_loss) + '\n'
log.write(buffer)
print('Start pre-training discriminator...')
for t in range(config_train.dis_update_time_pre):
print("Times: " + str(t))
generate_samples(sess,generator,config_train.batch_size,config_train.generated_num,config_train.negative_file)
dis_data_loader.load_train_data(config_train.positive_file,config_train.negative_file)
for _ in range(config_train.dis_update_time_pre):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch,y_batch = dis_data_loader.next_batch()
feed_dict = {
discriminator.input_x : x_batch,
discriminator.input_y : y_batch,
discriminator.dropout_keep_prob : config_dis.dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op,feed_dict)
# Build optimizer op for adversarial training
train_adv_opt = tf.train.AdamOptimizer(config_train.gen_learning_rate)
gradients, variables = zip(*train_adv_opt.compute_gradients(generator.gen_loss_adv, var_list=var_pretrained))
gradients, _ = tf.clip_by_global_norm(gradients, config_train.grad_clip)
train_adv_update = train_adv_opt.apply_gradients(zip(gradients, variables))
# Initialize global variables of optimizer for adversarial training
uninitialized_var = [e for e in tf.global_variables() if e not in tf.trainable_variables()]
init_vars_uninit_op = tf.variables_initializer(uninitialized_var)
sess.run(init_vars_uninit_op)
# Start adversarial training
for total_batch in range(config_train.total_batch):
for iter_gen in range(config_train.gen_update_time):
samples = sess.run(generator.sample_word_list_reshpae)
feed = {'pred_seq_rollout:0':samples}
reward_rollout = []
for iter_roll in range(config_train.rollout_num):
rollout_list = sess.run(rollout_gen.sample_rollout_step,feed_dict=feed)
# np.vstack 它是垂直（按照行顺序）的把数组给堆叠起来。
rollout_list_stack = np.vstack(rollout_list)
reward_rollout_seq = sess.run(discriminator.ypred_for_auc,feed_dict={
discriminator.input_x:rollout_list_stack,discriminator.dropout_keep_prob:1.0
})
reward_last_tok = sess.run(discriminator.ypred_for_auc,feed_dict={
discriminator.input_x:samples,discriminator.dropout_keep_prob:1.0
})
reward_allseq = np.concatenate((reward_rollout_seq,reward_last_tok),axis=0)[:,1]
reward_tmp = []
for r in range(config_gen.gen_batch_size):
reward_tmp.append(reward_allseq[range(r,config_gen.gen_batch_size * config_gen.sequence_length,config_gen.gen_batch_size)])
reward_rollout.append(np.array(reward_tmp))
rewards = np.sum(reward_rollout,axis = 0) / config_train.rollout_num
_,gen_loss = sess.run([train_adv_update,generator.gen_loss_adv],feed_dict={generator.input_seqs_adv:samples,
generator.rewards:rewards})
if total_batch % config_train.test_per_epoch == 0 or total_batch == config_train.total_batch - 1:
generate_samples(sess, generator, config_train.batch_size, config_train.generated_num, config_train.eval_file)
likelihood_data_loader.create_batches(config_train.eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(test_loss) + '\n'
print ('total_batch: ', total_batch, 'test_loss: ', test_loss)
log.write(buffer)
for _ in range(config_train.dis_update_time_adv):
generate_samples(sess,generator,config_train.batch_size,config_train.generated_num,config_train.negative_file)
dis_data_loader.load_train_data(config_train.positive_file,config_train.negative_file)
for _ in range(config_train.dis_update_time_adv):
dis_data_loader.reset_pointer()
for it in range(dis_data_loader.num_batch):
x_batch,y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x:x_batch,
discriminator.input_y:y_batch,
discriminator.dropout_keep_prob:config_dis.dis_dropout_keep_prob
}
_ = sess.run(discriminator.train_op,feed)
log.close()
