if model_type == 'Transformer':
model = TransformerModel(config)
elif model_type == 'LSTM':
model = LSTMModel(config)
if cuda:
model = model.cuda()
optimizer = torch.optim.Adam(model.parameters(),
lr=config['train']['learning_rate'],
weight_decay=config['train']['weight_decay'])
criterion = torch.nn.MSELoss()
optimizer.zero_grad()
for it in range(n_iter):
model.train()
country = random.choice(train_countries)
inp, target = get_data_tensor(data, country, measure_mode, output_mode=output_mode, cuda=cuda)
out_nn, _ = get_net_output(inp, model_type, model, cuda)
temp_loss = criterion(out_nn, target)
loss += temp_loss
if (it + 1) % batch_size == 0:
loss.backward()
optimizer.step()
train_loss_seq.append(loss.item()/batch_size)
class Trainer(TrainerBase):
def __init__(self, args):
super(Trainer, self).__init__()
self.args = args
with open(
os.path.join(os.path.abspath(os.path.dirname(os.getcwd())),
args.config_path), "r") as fr:
self.config = json.load(fr)
self.train_data_obj = None
self.eval_data_obj = None
self.model = None
# save_path模型保存目录
self.save_path = os.path.join(
os.path.abspath(os.path.dirname(os.getcwd())),
self.config["ckpt_model_path"])
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
# self.builder = tf.saved_model.builder.SavedModelBuilder("../pb_model/weibo/bilstm/savedModel")
# 加载数据集
self.load_data()
self.train_inputs, self.train_labels, label_to_idx = self.train_data_obj.gen_data(
)
print("train data size: {}".format(len(self.train_labels)))
self.vocab_size = self.train_data_obj.vocab_size
print("vocab size: {}".format(self.vocab_size))
self.word_vectors = self.train_data_obj.word_vectors
self.label_list = [value for key, value in label_to_idx.items()]
self.eval_inputs, self.eval_labels = self.eval_data_obj.gen_data()
print("eval data size: {}".format(len(self.eval_labels)))
print("label numbers: ", len(self.label_list))
# 初始化模型对象
self.create_model()
def load_data(self):
"""
创建数据对象
:return:
"""
# 生成训练集对象并生成训练数据
self.train_data_obj = TrainData(self.config)
# 生成验证集对象和验证集数据
self.eval_data_obj = EvalData(self.config)
def create_model(self):
"""
根据config文件选择对应的模型，并初始化
:return:
"""
if self.config["model_name"] == "textcnn":
self.model = TextCnnModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "bilstm":
self.model = BiLstmModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "bilstm_atten":
self.model = BiLstmAttenModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "rcnn":
self.model = RcnnModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
elif self.config["model_name"] == "transformer":
self.model = TransformerModel(config=self.config,
vocab_size=self.vocab_size,
word_vectors=self.word_vectors)
def train(self):
"""
训练模型
:return:
"""
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9,
allow_growth=True)
sess_config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True,
gpu_options=gpu_options)
with tf.Session(config=sess_config) as sess:
# 初始化变量值
sess.run(tf.global_variables_initializer())
current_step = 0
eval_loss_lis = [0]
# 创建train和eval的summary路径和写入对象
train_summary_path = os.path.join(
os.path.abspath(os.path.dirname(os.getcwd())),
self.config["output_path"] + "/summary/train")
if not os.path.exists(train_summary_path):
os.makedirs(train_summary_path)
train_summary_writer = tf.summary.FileWriter(
train_summary_path, sess.graph)
eval_summary_path = os.path.join(
os.path.abspath(os.path.dirname(os.getcwd())),
self.config["output_path"] + "/summary/eval")
if not os.path.exists(eval_summary_path):
os.makedirs(eval_summary_path)
eval_summary_writer = tf.summary.FileWriter(
eval_summary_path, sess.graph)
for epoch in range(self.config["epochs"]):
print("----- Epoch {}/{} -----".format(epoch + 1,
self.config["epochs"]))
for batch in self.train_data_obj.next_batch(
self.train_inputs, self.train_labels,
self.config["batch_size"]):
summary, loss, predictions = self.model.train(
sess, batch, self.config["keep_prob"],
self.config['learning_rate'])
train_summary_writer.add_summary(summary)
current_step += 1
if self.config[
"num_classes"] == 1 and current_step % self.config[
"print_every"] == 0:
acc, auc, recall, prec, f_beta = get_binary_metrics(
pred_y=predictions, true_y=batch["y"])
print(
"train: step: {}, loss: {}, acc: {}, auc: {}, recall: {}, precision: {}, f_beta: {}"
.format(current_step, loss, acc, auc, recall, prec,
f_beta))
elif self.config[
"num_classes"] > 1 and current_step % self.config[
"print_every"] == 0:
acc, recall, prec, f_beta = get_multi_metrics(
pred_y=predictions,
true_y=batch["y"],
labels=self.label_list)
print(
"train: step: {}, loss: {}, acc: {}, recall: {}, precision: {}, f_beta: {}"
.format(current_step, loss, acc, recall, prec,
f_beta))
#每训练一个epoch输出验证集的评测结果
if self.eval_data_obj:
eval_losses = []
eval_accs = []
eval_aucs = []
eval_recalls = []
eval_precs = []
eval_f_betas = []
for eval_batch in self.eval_data_obj.next_batch(
self.eval_inputs, self.eval_labels,
self.config["batch_size"]):
eval_summary, eval_loss, eval_predictions = self.model.eval(
sess, eval_batch)
eval_summary_writer.add_summary(eval_summary)
eval_losses.append(eval_loss)
if self.config["num_classes"] == 1:
acc, auc, recall, prec, f_beta = get_binary_metrics(
pred_y=eval_predictions,
true_y=eval_batch["y"])
eval_accs.append(acc)
eval_aucs.append(auc)
eval_recalls.append(recall)
eval_precs.append(prec)
eval_f_betas.append(f_beta)
elif self.config["num_classes"] > 1:
acc, recall, prec, f_beta = get_multi_metrics(
pred_y=eval_predictions,
true_y=eval_batch["y"],
labels=self.label_list)
eval_accs.append(acc)
eval_recalls.append(recall)
eval_precs.append(prec)
eval_f_betas.append(f_beta)
eval_loss_lis.append(mean(eval_losses))
print("\n")
print(
"eval: loss: {}, acc: {}, auc: {}, recall: {}, precision: {}, f_beta: {}"
.format(mean(eval_losses), mean(eval_accs),
mean(eval_aucs), mean(eval_recalls),
mean(eval_precs), mean(eval_f_betas)))
print("\n")
if self.config["ckpt_model_path"] and eval_loss_lis[
-1] >= max(eval_loss_lis):
#self.model_save_path是模型保存具体的名字
self.model_save_path = os.path.join(
self.save_path, self.config["model_name"])
self.model.saver.save(sess,
self.model_save_path,
global_step=epoch + 1)
elif self.config["ckpt_model_path"] and eval_loss_lis[
-1] < max(eval_loss_lis):
if self.config['batch_size'] <= 256:
self.config['batch_size'] *= 2
if self.config['learning_rate'] <= 0.00001:
self.config['learning_rate'] *= 0.95
print(
"epoch: {} lr: {} self.batch_size: {}".format(
epoch, self.lr, self.batch_size))
self.save_path = tf.train.latest_checkpoint(
self.save_path)
print('最新加载的模型路径{}'.format(self.save_path))
else:
print('learn_rate 小于0.00001，训练结束')
