引言
演化计算可以更好地调节一个机器学习系统中的参数表示，以文本生成任务中的一个模型CEGAN为例介绍一下演化计算在PyTorch中的应用，参考代码为masaikk/ceGan: Composite Evolutionary GAN for Natural Language Generation with Temper Control (github.com)。它是一个生成式对抗网络，在它的生成器里面使用到了演化计算的方式。以下的解释代码均出自于以上链接。
意图
在这篇文章中，使用了Gumble-Softmax对于传统的sotfmax进行改进，这里的意图是要得到更多的文本生成多样性。对于Gumble-Softmax来说，它有一个超参数，用于控制Gumble-Sofxmax温度。在这篇文章中，就是进行演化计算的对象。演化计算在这篇文章中的作用在于得到做迭代出来的全部子代中好的选择。在另一篇也做了文本生成的文章[1803.00657] Evolutionary Generative Adversarial Networks (arxiv.org)中也有类似的做法。
方法
在代码中，定义了具体的演化的方式，有lin、exp等方式，它们都对应着一个温度计算函数，它是根据当前的迭代次数n以及之前的温度以及计算方式adapt所计算当前的温度。具体计算的代码如下所示
def get_fixed_temperature(temper, i, N, adapt):
"""A function to set up different temperature control policies"""
if adapt == 'no':
temper_var_np = 1.0 # no increase, origin: temper
elif adapt == 'lin':
temper_var_np = 1 + i / (N - 1) * (temper - 1) # linear increase
elif adapt == 'exp':
temper_var_np = temper ** (i / N) # exponential increase
elif adapt == 'log':
temper_var_np = 1 + (temper - 1) / np.log(N) * np.log(i + 1) # logarithm increase
elif adapt == 'sigmoid':
temper_var_np = (temper - 1) * 1 / (1 + np.exp((N / 2 - i) * 20 / N)) + 1 # sigmoid increase
elif adapt == 'quad':
temper_var_np = (temper - 1) / (N - 1) ** 2 * i ** 2 + 1
elif adapt == 'sqrt':
temper_var_np = (temper - 1) / np.sqrt(N - 1) * np.sqrt(i) + 1
else:
raise Exception("Unknown adapt type!")
print('******* chosen evolution choice as {} and temp is {}'.format(adapt, temper_var_np))
print('####### i = {} and raw_temp is {} while N is {}'.format(i, temper, N))
log_info = '******* chosen evolution choice as {} and temp is {}\n'.format(adapt,
temper_var_np) + '####### i = {} and raw_temp is {} while N is {}\n'.format(
i, temper, N)
return temper_var_np, log_info
从备选的子代演化方式中随机选择出一个计算方法（模拟自然界中演化的规律），再通过上述公式在一轮中多次调用，可以得到这一轮中的所演化子代的参考。之后需要在这些子代中选择最好的子代中选择得到表现最好的子代作为本轮训练中使用到的最好的子代（即温度表示）。在这篇文章中，默认在每轮中得到三个子代，并且从这三个子代中选择出最好的子代。
@staticmethod
def get_evo_temp(cur_step):
"""
:param cur_step:
:return:
"""
"""randomly get different temperature according to current adversarial step"""
fn_mu_temp_type = cfg.fn_mu_temp.split()
mu_temp_type = cfg.mu_temp.split()
all_temp = list()
temp_info_to_log = ''
temp_n, temp_log_n = get_fixed_temperature(cfg.temperature, cur_step, cfg.ADV_train_epoch,
random.choice(fn_mu_temp_type))
# all_temp.append(get_fixed_temperature(1.0, 0, 0, 'no')) # temp=1.0
all_temp.append(temp_n) # current step
temp_info_to_log += temp_log_n
temp_n1, temp_log_n1 = get_fixed_temperature(cfg.temperature, cur_step + cfg.evo_temp_step, cfg.ADV_train_epoch,
random.choice(mu_temp_type))
all_temp.append(temp_n1)
temp_info_to_log += temp_log_n1
if cur_step > cfg.evo_temp_step:
temp_n2, temp_log_n2 = get_fixed_temperature(cfg.temperature, cur_step - cfg.evo_temp_step,
cfg.ADV_train_epoch,
random.choice(mu_temp_type))
all_temp.append(temp_n2)
temp_info_to_log += temp_log_n2
return torch.Tensor(all_temp), temp_info_to_log # three temp
从上面的代码中可以得到这一轮中三个子代中所得到的信息以及分别温度。
之后就需要在真实的数据里面中抓取数据进行子代的测试，需要注意的是，选择子代的过程中的梯度是参与计算图的，所以需要使用torch.no_grad()
def prepare_eval_real_data(self):
with torch.no_grad():
self.eval_real_samples = torch.cat(
[F.one_hot(self.train_data.random_batch()['target'], cfg.vocab_size).float()
for _ in range(cfg.eval_b_num)], dim=0)
if cfg.CUDA:
self.eval_real_samples = self.eval_real_samples.cuda()
if cfg.eval_type == 'rsgan' or cfg.eval_type == 'Ra':
self.eval_d_out_real_vec = self.vec_dis(self.eval_real_samples)
self.eval_d_out_real_sen = self.sen_dis(torch.argmax(self.eval_real_samples, dim=2))
具体在生成器中选择子代的过程，如下所示
def evolve_generator_with_temp(self, cur_adv_step, evo_g_step):
# evaluation real data
self.prepare_eval_real_data()
best_score = np.zeros(cfg.n_parent)
best_fit = []
best_child = []
best_child_opt = []
best_fake_samples = []
selected_mutation = []
count = 0
self.temp_log.log(logging.INFO, '{} Epoch {} train...'.format(get_format_time(), cur_adv_step))
# all children share the same real data output from Discriminator
with torch.no_grad():
real_samples = F.one_hot(self.train_data.random_batch()['target'], cfg.vocab_size).float()
if cfg.CUDA:
real_samples = real_samples.cuda()
self.d_out_real_vec = self.vec_dis(real_samples)
self.d_out_real_sen = self.sen_dis(torch.argmax(real_samples, dim=2))
for i, (parent, parent_opt) in enumerate(zip(self.parents, self.parent_adv_opts)):
for j, criterionG in enumerate(self.G_criterion):
all_temp, info_to_log = self.get_evo_temp(cur_adv_step) # get evo temp
self.temp_log.log(logging.INFO, info_to_log)
all_temp_with_score = []
temp_score = float('-inf')
temp_fit = None
temp_child = None
temp_child_opt = None
temp_fake_samples = None
best_child_index = 0
# Selection based on temperature, use eval_type=nll
for temp_index in range(len(all_temp)):
temp = all_temp[temp_index]
# Variation
self.load_gen(parent, parent_opt) # load state dict to self.gen
self.gen.temperature.data = temp # update Generator temperature
self.variation(evo_g_step, criterionG)
# Evaluation
self.prepare_eval_fake_data() # evaluation fake data
# print('epoch {} to evolution temp'.format(evo_g_step))
# _, _, t_score = self.evaluation('Ra') # for temp evolutionary
print('\n\nEpoch {}, criterionG {} to evolution loss...'.format(cur_adv_step, j))
# self.temp_log.log(logging.INFO,
# '\n{} Epoch {}, criterionG {} to evolution loss...'.format(get_format_time(), cur_adv_step, j))
loss_Fq, loss_Fd, loss_score = self.evaluation(cfg.eval_type) # for loss evolutionary
t_score = loss_score
all_temp_with_score.append([temp.item(), t_score])
if t_score > temp_score:
temp_score = loss_score
temp_fit = [loss_Fq, loss_Fd, loss_score]
temp_child = copy.deepcopy(self.gen.state_dict())
temp_child_opt = copy.deepcopy(self.gen_adv_opt.state_dict())
temp_fake_samples = copy.deepcopy(self.eval_fake_samples)
best_child_index = temp_index
# print(' This epoch temperature:' + str(temp.item()))
# self.temp_log.log(logging.INFO, ' This epoch temperature:' + str(temp.item()))
self.temp_log.log(logging.INFO,
' This epoch temperature: {} with score: {} with child_index as {}'.format(
all_temp_with_score[best_child_index][0],
all_temp_with_score[best_child_index][1], best_child_index))
print(' This epoch all temperature with score are {}'.format(str(all_temp_with_score)))
self.temp_log.log(logging.INFO,
'{} This epoch {} all temperature with score are {}'.format(get_format_time(),
cur_adv_step,
str(all_temp_with_score)))
# all_temp_with_score=[]
# Selection based on mu_type, use eval_type=cfg.eval_type
if count < cfg.n_parent:
best_score[count] = temp_score
best_fit.append(temp_fit)
best_child.append(temp_child)
best_child_opt.append(temp_child_opt)
best_fake_samples.append(temp_fake_samples)
selected_mutation.append(criterionG.loss_mode)
else: # larger than previous child, replace it
fit_com = temp_score - best_score
if max(fit_com) > 0:
id_replace = np.where(fit_com == max(fit_com))[0][0]
best_score[id_replace] = temp_score
best_fit[id_replace] = temp_fit
best_child[id_replace] = temp_child
best_child_opt[id_replace] = temp_child_opt
best_fake_samples[id_replace] = temp_fake_samples
selected_mutation[id_replace] = criterionG.loss_mode
count += 1
self.parents = copy.deepcopy(best_child)
self.parent_adv_opts = copy.deepcopy(best_child_opt)
self.best_fake_samples = torch.cat(best_fake_samples, dim=0)
return best_score, np.array(best_fit), selected_mutation
在这个函数中，对于每个子代，调用评测的函数以计算分数。
# Selection based on temperature, use eval_type=nll
for temp_index in range(len(all_temp)):
temp = all_temp[temp_index]
# Variation
self.load_gen(parent, parent_opt) # load state dict to self.gen
self.gen.temperature.data = temp # update Generator temperature
self.variation(evo_g_step, criterionG)
# Evaluation
self.prepare_eval_fake_data() # evaluation fake data
# print('epoch {} to evolution temp'.format(evo_g_step))
# _, _, t_score = self.evaluation('Ra') # for temp evolutionary
print('\n\nEpoch {}, criterionG {} to evolution loss...'.format(cur_adv_step, j))
# self.temp_log.log(logging.INFO,
# '\n{} Epoch {}, criterionG {} to evolution loss...'.format(get_format_time(), cur_adv_step, j))
loss_Fq, loss_Fd, loss_score = self.evaluation(cfg.eval_type) # for loss evolutionary
t_score = loss_score
all_temp_with_score.append([temp.item(), t_score])
if t_score > temp_score:
temp_score = loss_score
temp_fit = [loss_Fq, loss_Fd, loss_score]
temp_child = copy.deepcopy(self.gen.state_dict())
temp_child_opt = copy.deepcopy(self.gen_adv_opt.state_dict())
temp_fake_samples = copy.deepcopy(self.eval_fake_samples)
best_child_index = temp_index
# print(' This epoch temperature:' + str(temp.item()))
# self.temp_log.log(logging.INFO, ' This epoch temperature:' + str(temp.item()))
self.temp_log.log(logging.INFO,
' This epoch temperature: {} with score: {} with child_index as {}'.format(
all_temp_with_score[best_child_index][0],
all_temp_with_score[best_child_index][1], best_child_index))
print(' This epoch all temperature with score are {}'.format(str(all_temp_with_score)))
self.temp_log.log(logging.INFO,
'{} This epoch {} all temperature with score are {}'.format(get_format_time(),
cur_adv_step,
str(all_temp_with_score)))
得分表示loss_Fq, loss_Fd, loss_score = self.evaluation(cfg.eval_type) 。在本任务中，这个生成的评测函数的评价依据由多样性以及真实度两个部分表示，可以根据这两个评价指标计算出分数，以判断子代的好坏。
最终选择对比分数，选择出最好的子代，当作这一轮的父代（以下一轮生成新的子代）
if count < cfg.n_parent:
best_score[count] = temp_score
best_fit.append(temp_fit)
best_child.append(temp_child)
best_child_opt.append(temp_child_opt)
best_fake_samples.append(temp_fake_samples)
selected_mutation.append(criterionG.loss_mode)
else: # larger than previous child, replace it
fit_com = temp_score - best_score
if max(fit_com) > 0:
id_replace = np.where(fit_com == max(fit_com))[0][0]
best_score[id_replace] = temp_score
best_fit[id_replace] = temp_fit
best_child[id_replace] = temp_child
best_child_opt[id_replace] = temp_child_opt
best_fake_samples[id_replace] = temp_fake_samples
selected_mutation[id_replace] = criterionG.loss_mode
count += 1
self.parents = copy.deepcopy(best_child)
self.parent_adv_opts = copy.deepcopy(best_child_opt)
self.best_fake_samples = torch.cat(best_fake_samples, dim=0)
return best_score, np.array(best_fit), selected_mutation
结尾
总的来说，在文本生成任务中实验演化计算，是用来选择一个超参数的好坏的。类似生物界中的种族繁衍，它通过父代得到诺干个子代，再根据评价函数（后天筛选）选出最好的子代（分数最高）作为下一轮的父代并使用。毕竟，只有强者才能生存。
