def train():
from data_trans import process
process()
from data_loader import train_data, SpoSearcher, dev_data, DataGenerator
init_keras_config()
train_model, subject_model, object_model = model()
EMAer = ExponentialMovingAverage(train_model)
EMAer.inject()
spoer = SpoSearcher(train_data)
train_D = DataGenerator(train_data)
evaluator = Evaluate(train_model,
EMAer=EMAer,
dev_data=dev_data,
spoer=spoer,
subject_model=subject_model,
object_model=object_model)
train_model.fit_generator(train_D.__iter__(),
steps_per_epoch=len(train_D),
epochs=120,
callbacks=[evaluator])
def train_on_train_test_split():
train_config = get_config()
bert_config = get_bert_config(train_config)
cased = train_config.BERT_DIR.split('/')[-1].startswith('cased')
tokenizer = FullTokenizer(bert_config.vocab, do_lower_case=cased)
with tf.device('/cpu:0'):
model = get_bert_base_model(bert_config)
text, label = load_data(os.path.join(train_config.DATA_DIR, 'train.csv'))
train_text, val_text, train_label, val_label = train_test_split(
text, label, test_size=0.055, random_state=59)
train_gen = DataGenerator(train_text,
train_label,
tokenizer,
batch_size=32)
val_text = tokenize_examples(val_text, tokenizer, max_len=512)
val_text = seq_padding(val_text)
logger = Logger(model=model,
val_text=val_text,
val_label=(val_label > 0.5).astype(np.float32))
# OPTIMIZER PARAMs
lr = 2e-5
weight_decay = 0.01
bsz = 32
decay_steps = 1 * len(train_gen)
warmup_steps = int(0.1 * decay_steps)
optimizer = AdamWarmup(
decay_steps=decay_steps,
warmup_steps=warmup_steps,
lr=lr,
weight_decay=weight_decay,
)
parallel_model = multi_gpu_model(model, gpus=4)
parallel_model.compile(loss='binary_crossentropy', optimizer=optimizer)
parallel_model.fit_generator(train_gen.__iter__(),
steps_per_epoch=len(train_gen),
epochs=1,
callbacks=[logger],
max_queue_size=100)
