def create_model(sess, config, name_scope, initializer=None):
"""
创建判别模型：如果已经有训练好的，读入；否则，初始化
:param sess:
:param config:
:param name_scope: 也就是config.name_model
:param initializer:
:return:
"""
print(just("Creating disc model"))
with tf.variable_scope(name_or_scope=name_scope,
initializer=initializer):
model = HierRNNModel(config=config, name_scope=name_scope)
disc_ckpt_dir = os.path.abspath(
os.path.join(config.train_dir, "checkpoints"))
ckpt = tf.train.get_checkpoint_state(disc_ckpt_dir)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print(
just("Reading Hier Disc model parameters from %s" %
ckpt.model_checkpoint_path))
model.saver.restore(sess, ckpt.model_checkpoint_path)
else:
print(just("Created Hier Disc model with fresh parameters."))
disc_global_variables = [
gv for gv in tf.global_variables() if name_scope in gv.name
]
sess.run(tf.variables_initializer(disc_global_variables))
return model
def get_dataset(gen_config):
"""
获取训练数据
:return: vocab, rev_vocab, dev_set, train_set
"""
train_path = os.path.join(gen_config.train_dir, "chitchat.train")
voc_file_path = [train_path + ".answer", train_path + ".query"]
vocab_path = os.path.join(gen_config.train_dir,
"vocab%d.all" % gen_config.vocab_size)
data_utils.create_vocabulary(vocab_path, voc_file_path,
gen_config.vocab_size)
vocab, rev_vocab = data_utils.initialize_vocabulary(
vocab_path) # {dog: 0, cat: 1} [dog, cat]
print(just("Preparing Chitchat gen_data in %s" % gen_config.train_dir))
train_query, train_answer, dev_query, dev_answer = data_utils.prepare_chitchat_data(
gen_config.train_dir, vocab, gen_config.vocab_size)
# Read disc_data into buckets and compute their sizes.
print(
just("Reading development and training gen_data (limit: %d)." %
gen_config.max_train_data_size))
dev_set = read_data(gen_config, dev_query, dev_answer)
train_set = read_data(gen_config, train_query, train_answer,
gen_config.max_train_data_size)
return vocab, rev_vocab, dev_set, train_set
def create_model(session,
gen_config,
forward_only,
name_scope,
initializer=None):
"""
创建生成模型：如果已经有训练好的，读入；否则，初始化
"""
print(
just("Creating Gen model: %d layers of %d units." %
(gen_config.num_layers, gen_config.emb_dim)))
with tf.variable_scope(name_or_scope=name_scope,
initializer=initializer):
model = GenModel(gen_config,
name_scope=name_scope,
forward_only=forward_only)
gen_ckpt_dir = os.path.abspath(
os.path.join(gen_config.train_dir, "checkpoints"))
ckpt = tf.train.get_checkpoint_state(gen_ckpt_dir)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print(
just("Reading Gen model parameters from %s" %
ckpt.model_checkpoint_path))
model.saver.restore(session, ckpt.model_checkpoint_path)
else:
print(just("Created Gen model with fresh parameters."))
gen_global_variables = [
gv for gv in tf.global_variables() if name_scope in gv.name
]
session.run(tf.variables_initializer(gen_global_variables))
return model
def _get_dataset(self):
print(just("Prepare_data"))
query_set, answer_set, gen_set = get_dataset(self.config_disc)
train_bucket_sizes = [
len(query_set[b]) for b in xrange(len(self.config_disc.buckets))
]
train_total_size = float(sum(train_bucket_sizes))
train_buckets_scale = [
sum(train_bucket_sizes[:i + 1]) / train_total_size
for i in xrange(len(train_bucket_sizes))
]
return query_set, answer_set, gen_set, train_buckets_scale
def _get_dataset(self, gen_config):
print(just("Prepare_data"))
vocab, rev_vocab, dev_set, train_set = get_dataset(gen_config)
train_bucket_sizes = [
len(train_set[b]) for b in xrange(len(gen_config.buckets))
]
train_total_size = float(sum(train_bucket_sizes))
train_buckets_scale = [
sum(train_bucket_sizes[:i + 1]) / train_total_size
for i in xrange(len(train_bucket_sizes))
] # 每个桶及前面的桶内数据占总数据的范围
return train_set, train_buckets_scale
def al_train():
gen_config = conf.gen_config
disc_config = conf.disc_config
adver_config = conf.adver_config
with tf.Session() as sess:
# ① 获取数据集
vocab, rev_vocab, dev_set, train_set = get_dataset(gen_config)
for set in train_set:
print("al train len: ", len(set))
train_bucket_sizes = [len(train_set[b]) for b in xrange(len(gen_config.buckets))]
train_total_size = float(sum(train_bucket_sizes))
train_buckets_scale = [sum(train_bucket_sizes[:i + 1]) / train_total_size
for i in xrange(len(train_bucket_sizes))]
# ② 创建模型
disc_model = HierRNNTrain.create_model(sess, disc_config, disc_config.name_model)
gen_model = GenTrain.create_model(sess, gen_config, forward_only=False, name_scope=gen_config.name_model)
# [Ignore]... log相关
current_step = 0
step_time, disc_loss, gen_loss, t_loss, batch_reward = 0.0, 0.0, 0.0, 0.0, 0.0
gen_loss_summary = tf.Summary()
disc_loss_summary = tf.Summary()
gen_writer = tf.summary.FileWriter(gen_config.tensorboard_dir, sess.graph)
disc_writer = tf.summary.FileWriter(disc_config.tensorboard_dir, sess.graph)
# ③ 开始对抗训练
while current_step <= adver_config.max_train_step:
start_time = time.time() # [Ignore]... log相关：开始时间
current_step += 1
bucket_id = get_random_bid(train_buckets_scale)
# =========================================== ③.① 训练判别器 =========================================== #
print(just("Update Discriminator: %d" % current_step))
# 1. 获取一个batch的真实数据
encoder_inputs, decoder_inputs, target_weights, source_inputs, source_outputs = get_batch(
gen_model, train_set, bucket_id, gen_config.batch_size)
# 2. 使用生成器生成负例数据，补充数据集 Sample (X,Y) and (X, ^Y) through ^Y ~ G(*|X)
train_query, train_answer, train_labels = disc_train_data(sess, gen_model, vocab, source_inputs,
source_outputs,
encoder_inputs, decoder_inputs, target_weights,
bucket_id, mc_search=False)
# [Ignore]... message
print(just("mc_search: False"))
if current_step % 200 == 0:
print("train_query: ", len(train_query))
print("train_answer: ", len(train_answer))
print("train_labels: ", len(train_labels))
for i in xrange(len(train_query)):
print("label: ", train_labels[i])
print("train_answer_sentence: ", train_answer[i])
print(" ".join([tf.compat.as_str(rev_vocab[output]) for output in train_answer[i]]))
# 转置为 time-major
train_query = np.transpose(train_query)
train_answer = np.transpose(train_answer)
# 3. 使用正反例训练判别器 Update D using (X, Y ) as positive examples and(X, ^Y) as negative examples
_, disc_step_loss = HierRNNTrain().step(sess, bucket_id, disc_model, train_query, train_answer, train_labels,
forward_only=False)
disc_loss += disc_step_loss / disc_config.steps_per_checkpoint
# =========================================== ③.② 训练生成器 =========================================== #
print(just("Update Generator: %d" % current_step))
# 1. 获取一批真实数据 Sample (X,Y) from real disc_data
update_gen_data = get_batch(gen_model, train_set, bucket_id, gen_config.batch_size)
encoder, decoder, weights, source_inputs, source_outputs = update_gen_data
# 2. 生成一批训练数据，包含自己生成的负例。生成负例时采用蒙特卡洛方法
# Sample (X,Y) and (X, ^Y) through ^Y ~ G(*|X) with Monte Carlo search
train_query, train_answer, train_labels = disc_train_data(sess, gen_model, vocab, source_inputs,
source_outputs,
encoder, decoder, weights, bucket_id,
mc_search=True)
# [Ignore]... message
print(just("mc_search: True"))
if current_step % 200 == 0:
for i in xrange(len(train_query)):
print("label: ", train_labels[i])
print(" ".join([tf.compat.as_str(rev_vocab[output]) for output in train_answer[i]]))
train_query = np.transpose(train_query)
train_answer = np.transpose(train_answer)
# 3. 计算生成器（基于蒙特卡洛方法）生成的数据的奖励值：最终奖励是这一批数据中所有负例的奖励的平均值
# Compute Reward r for (X, ^Y ) using D.---based on Monte Carlo search
reward, _ = HierRNNTrain().step(sess, bucket_id, disc_model, train_query, train_answer, train_labels,
forward_only=True)
batch_reward += reward / gen_config.steps_per_checkpoint
print("step_reward: ", reward)
# 4. 强化学习：使用奖励值r更新生成器 Update G on (X, ^Y ) using reward r
# TODO(Zhu) 如何使用奖励值reward实现强化学习？
gan_adjusted_loss, gen_step_loss, _ = GenTrain().step(gen_model, sess, encoder, decoder, weights, bucket_id,
forward_only=False, # forward_only=False 训练模型
reward=reward, up_reward=True) # up_reward：使用reward
gen_loss += gen_step_loss / gen_config.steps_per_checkpoint
print("gen_step_loss: ", gen_step_loss)
print("gen_step_adjusted_loss: ", gan_adjusted_loss)
# 5. Teacher-Forcing: Update G on (X, Y ) 这时候就不需要设置up_reward为true了
t_adjusted_loss, t_step_loss, a = GenTrain().step(gen_model, sess, encoder, decoder, weights, bucket_id,
forward_only=False) # forward_only=False 训练模型
t_loss += t_step_loss / gen_config.steps_per_checkpoint
print("t_step_loss: ", t_step_loss)
print("t_adjusted_loss", t_adjusted_loss)
# ================================ [Ignore]... log相关: 记录日志、保存变量 ================================ #
if current_step % gen_config.steps_per_checkpoint == 0:
step_time += (time.time() - start_time) / gen_config.steps_per_checkpoint
print("current_steps: %d, step time: %.4f, disc_loss: %.3f, gen_loss: %.3f, t_loss: %.3f, reward: %.3f"
% (current_step, step_time, disc_loss, gen_loss, t_loss, batch_reward))
disc_loss_value = disc_loss_summary.value.add()
disc_loss_value.tag = disc_config.name_loss
disc_loss_value.simple_value = float(disc_loss)
disc_writer.add_summary(disc_loss_summary, int(sess.run(disc_model.global_step)))
gen_global_steps = sess.run(gen_model.global_step)
gen_loss_value = gen_loss_summary.value.add()
gen_loss_value.tag = gen_config.name_loss
gen_loss_value.simple_value = float(gen_loss)
t_loss_value = gen_loss_summary.value.add()
t_loss_value.tag = gen_config.teacher_loss
t_loss_value.simple_value = float(t_loss)
batch_reward_value = gen_loss_summary.value.add()
batch_reward_value.tag = gen_config.reward_name
batch_reward_value.simple_value = float(batch_reward)
gen_writer.add_summary(gen_loss_summary, int(gen_global_steps))
if current_step % (gen_config.steps_per_checkpoint * 2) == 0:
print("current_steps: %d, save disc model" % current_step)
disc_ckpt_dir = os.path.abspath(os.path.join(disc_config.train_dir, "checkpoints"))
if not os.path.exists(disc_ckpt_dir):
os.makedirs(disc_ckpt_dir)
disc_model_path = os.path.join(disc_ckpt_dir, "disc.model")
disc_model.saver.save(sess, disc_model_path, global_step=disc_model.global_step)
print("current_steps: %d, save gen model" % current_step)
gen_ckpt_dir = os.path.abspath(os.path.join(gen_config.train_dir, "checkpoints"))
if not os.path.exists(gen_ckpt_dir):
os.makedirs(gen_ckpt_dir)
gen_model_path = os.path.join(gen_ckpt_dir, "gen.model")
gen_model.saver.save(sess, gen_model_path, global_step=gen_model.global_step)
step_time, disc_loss, gen_loss, t_loss, batch_reward = 0.0, 0.0, 0.0, 0.0, 0.0
sys.stdout.flush()
def pre_train(self):
"""
预训练判别器
:return:
"""
print(just("Begin training"))
with tf.Session() as session:
# ① 创建模型
model = self.create_model(session,
self.config_disc,
name_scope=self.config_disc.name_model)
# ② 获取数据集
self.query_set, \
self.answer_set, \
self.gen_set, \
self.train_buckets_scale = self._get_dataset()
# [Ignore]... log相关
step_time, loss = 0.0, 0.0
current_step = 0
step_loss_summary = tf.Summary()
disc_writer = tf.summary.FileWriter(
self.config_disc.tensorboard_dir, session.graph)
while current_step <= self.config_disc.max_pre_train_step:
start_time = time.time() # [Ignore]... log相关：开始时间
# ③ 获取一个batch的训练数据
bucket_id = self._get_random_bid()
train_query, train_answer, train_labels = self._get_batch(
bucket_id)
# ④ 获取处理后的输入数据
feed_dict = self._get_feed_dict(model, bucket_id, train_query,
train_answer, train_labels)
# ⑤ 选择训练OP，进行训练
fetches = [
model.b_train_op[bucket_id], model.b_logits[bucket_id],
model.b_loss[bucket_id], model.target
]
train_op, logits, step_loss, target = session.run(
fetches, feed_dict)
# ================================ [Ignore]... log相关: 记录日志、保存变量 ================================ #
# log相关：运行时间
step_time += (time.time() - start_time
) / self.config_disc.steps_per_checkpoint
loss += step_loss / self.config_disc.steps_per_checkpoint
current_step += 1
# 每运行 config_disc.steps_per_checkpoint 次记录一下
if current_step % self.config_disc.steps_per_checkpoint == 0:
# log相关
disc_loss_value = step_loss_summary.value.add()
disc_loss_value.tag = self.config_disc.name_loss
disc_loss_value.simple_value = float(loss)
disc_writer.add_summary(
step_loss_summary, int(session.run(model.global_step)))
# softmax operation
logits = np.transpose(softmax(np.transpose(logits)))
reward = 0.0
for logit, label in zip(logits,
train_labels): # ([1, 0], 1)
reward += logit[1] # only for true probility
reward = reward / len(train_labels)
print("reward: ", reward)
print("current_step: %d, step_loss: %.4f" %
(current_step, step_loss))
if current_step % (self.config_disc.steps_per_checkpoint *
3) == 0:
print("current_step: %d, save_model" % (current_step))
disc_ckpt_dir = os.path.abspath(
os.path.join(self.config_disc.train_dir,
"checkpoints"))
if not os.path.exists(disc_ckpt_dir):
os.makedirs(disc_ckpt_dir)
disc_model_path = os.path.join(disc_ckpt_dir,
"disc.model")
model.saver.save(session,
disc_model_path,
global_step=model.global_step)
step_time, loss = 0.0, 0.0
sys.stdout.flush()
def pre_train(self, gen_config):
"""
预训练生成器
:param gen_config:
:return:
"""
print(just("Begin training"))
with tf.Session() as sess:
# ① 创建模型
model = self.create_model(sess,
gen_config,
forward_only=False,
name_scope=gen_config.name_model)
# ② 获取数据集
self.train_set, self.train_buckets_scale = self._get_dataset(
gen_config)
# [Ignore]... log相关
step_time, loss = 0.0, 0.0
current_step = 0
gen_loss_summary = tf.Summary()
gen_writer = tf.summary.FileWriter(gen_config.tensorboard_dir,
sess.graph)
while current_step <= gen_config.max_pre_train_step:
start_time = time.time() # [Ignore]... log相关：开始时间
# ③ 获取一个batch的训练数据
bucket_id = self._get_random_bid()
encoder_inputs, decoder_inputs, target_weights, batch_source_encoder, batch_source_decoder = get_batch(
model, self.train_set, bucket_id, gen_config.batch_size)
# ④ 获取处理后的输入数据
# ⑤ 选择训练OP，进行训练
_, step_loss, _ = self.step(model,
sess,
encoder_inputs,
decoder_inputs,
target_weights,
bucket_id,
forward_only=False)
# ================================ [Ignore]... log相关: 记录日志、保存变量 ================================ #
# log相关：运行时间
step_time += (time.time() -
start_time) / gen_config.steps_per_checkpoint
loss += step_loss / gen_config.steps_per_checkpoint
current_step += 1
# 每运行 config_disc.steps_per_checkpoint 次记录一下
if current_step % gen_config.steps_per_checkpoint == 0:
# log相关
bucket_value = gen_loss_summary.value.add()
bucket_value.tag = gen_config.name_loss
bucket_value.simple_value = float(loss)
gen_writer.add_summary(gen_loss_summary,
int(model.global_step.eval()))
# Print statistics for the previous epoch.
perplexity = math.exp(loss) if loss < 300 else float('inf')
print(
"global step %d learning rate %.4f step-time %.2f perplexity "
"%.2f" %
(model.global_step.eval(), model.learning_rate.eval(),
step_time, perplexity))
# Decrease learning rate if no improvement was seen over last 3 times.
# if len(previous_losses) > 2 and loss > max(previous_losses[-3:]):
# sess.run(model.learning_rate_decay_op)
# previous_losses.append(loss)
# Save checkpoint and zero timer and loss.
if current_step % (gen_config.steps_per_checkpoint *
3) == 0:
print("current_step: %d, save model" % (current_step))
gen_ckpt_dir = os.path.abspath(
os.path.join(gen_config.train_dir, "checkpoints"))
if not os.path.exists(gen_ckpt_dir):
os.makedirs(gen_ckpt_dir)
checkpoint_path = os.path.join(gen_ckpt_dir,
"chitchat.model")
model.saver.save(sess,
checkpoint_path,
global_step=model.global_step)
step_time, loss = 0.0, 0.0
# Run evals on development set and print their perplexity.
# for bucket_id in xrange(len(gen_config.buckets)):
# encoder_inputs, decoder_inputs, target_weights = model.get_batch(
# dev_set, bucket_id)
# _, eval_loss, _ = model.step(sess, encoder_inputs, decoder_inputs,
# target_weights, bucket_id, True)
# eval_ppx = math.exp(eval_loss) if eval_loss < 300 else float('inf')
# print(" eval: bucket %d perplexity %.2f" % (bucket_id, eval_ppx))
sys.stdout.flush()
