def adv_train_generator(self, g_step, current_k=0):
"""
The gen is trained using policy gradients, using the reward from the discriminator.
Training is done for num_batches batches.
"""
rollout_func = rollout.ROLLOUT(self.gen, cfg.CUDA)
adv_mana_loss = 0
adv_work_loss = 0
for step in range(g_step):
with torch.no_grad():
gen_samples = self.gen.sample(
cfg.batch_size, cfg.batch_size, self.dis,
train=True) # !!! train=True, the only place
inp, target = GenDataIter.prepare(gen_samples, gpu=cfg.CUDA)
# ===Train===
rewards = rollout_func.get_reward_leakgan(
target, cfg.rollout_num, self.dis,
current_k).cpu() # reward with MC search
mana_loss, work_loss = self.gen.adversarial_loss(
target, rewards, self.dis)
# update parameters
self.optimize_multi(self.gen_opt, [mana_loss, work_loss])
adv_mana_loss += mana_loss.data.item()
adv_work_loss += work_loss.data.item()
# ===Test===
self.log.info(
'[ADV-GEN] adv_mana_loss = %.4f, adv_work_loss = %.4f, %s' %
(adv_mana_loss / g_step, adv_work_loss / g_step,
self.cal_metrics(fmt_str=True)))
def adv_train_generator(self, g_step):
"""
The gen is trained using policy gradients, using the reward from the discriminator.
Training is done for num_batches batches.
"""
rollout_func = rollout.ROLLOUT(self.gen, cfg.CUDA)
total_g_loss = 0
print('g_step in adv_train_generator->', g_step)
for step in range(g_step):
samples = self.gen.sample(cfg.batch_size, cfg.batch_size)
print('samples ->', samples.size())
inp, target = GenDataIter.prepare(samples, gpu=cfg.CUDA)
print('inp ->', inp.size())
print('target ->', target.size())
# ===Train===
rewards = rollout_func.get_reward(target, cfg.rollout_num, self.dis)
print('rewards ->', rewards.size(), rewards)
adv_loss = self.gen.batchPGLoss(inp, target, rewards)
self.optimize(self.gen_adv_opt, adv_loss)
total_g_loss += adv_loss.item()
# ===Test===
self.log.info('[ADV-GEN]: g_loss = %.4f, %s' % (total_g_loss, self.cal_metrics(fmt_str=True)))
def adv_train_generator(self, g_step):
"""
The gen is trained using policy gradients, using the reward from the discriminator.
Training is done for num_batches batches.
"""
for i in range(cfg.k_label):
rollout_func = rollout.ROLLOUT(self.gen_list[i], cfg.CUDA)
total_g_loss = 0
for step in range(g_step):
inp, target = GenDataIter.prepare(self.gen_list[i].sample(cfg.batch_size, cfg.batch_size), gpu=cfg.CUDA)
# ===Train===
rewards = rollout_func.get_reward(target, cfg.rollout_num, self.dis)
adv_loss = self.gen_list[i].batchPGLoss(inp, target, rewards)
self.optimize(self.gen_opt_list[i], adv_loss)
total_g_loss += adv_loss.item()
# ===Test===
self.log.info('[ADV-GEN]: %s', self.comb_metrics(fmt_str=True))
def adv_train_generator(self, g_step):
"""
The gen is trained using policy gradients, using the reward from the discriminator.
Training is done for num_batches batches.
"""
rollout_func = rollout.ROLLOUT(self.gen, cfg.CUDA)
total_g_loss = 0
for step in range(g_step):
inp, target = GenDataIter.prepare(
self.gen.sample(cfg.batch_size, cfg.batch_size),
gpu=cfg.CUDA) #把G sample出来的句子切割成错位的
# 这里有个问题，用这个数据集可以用来train Generator吗？
# ===Train===
rewards = rollout_func.get_reward(target, cfg.rollout_num,
self.dis)
adv_loss = self.gen.batchPGLoss(inp, target, rewards)
self.optimize(self.gen_adv_opt, adv_loss)
total_g_loss += adv_loss.item()
# ===Test===
self.log.info('[ADV-GEN]: g_loss = %.4f, %s' %
(total_g_loss, self.cal_metrics(fmt_str=True)))
