def main():
# parser config
config_file = "./config.ini"
cp = ConfigParser()
cp.read(config_file)
# default config
output_fold = cp["TRAIN"].get("output_fold")
epochs = cp["TRAIN"].getint("epochs")
batch_size = cp["TRAIN"].getint("batch_size")
generator_workers = cp["TRAIN"].getint("generator_workers")
output_weights_name = cp["TRAIN"].get("output_weights_name")
sequence_length_max = cp["TRAIN"].getint("sequence_length_max")
output_model_name = cp["TRAIN"].get("output_model_name")
save_weights_only = cp["TRAIN"].getboolean("save_weights_only")
cyclicLR_mode = cp["TRAIN"].get("cyclicLR_mode")
base_lr = cp["TRAIN"].getfloat("base_lr")
max_lr = cp["TRAIN"].getfloat("max_lr")
today = datetime.date.today()
formatted_today = today.strftime('%y%m%d')
output_dir = os.path.join('experiments', formatted_today, output_fold)
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
output_dir_src = os.path.join(output_dir, 'src')
if not os.path.isdir(output_dir_src):
os.makedirs(output_dir_src)
print(f"backup config file to {output_dir_src}")
shutil.copy(config_file,
os.path.join(output_dir_src,
os.path.split(config_file)[1]))
train_file = os.path.basename(__file__)
shutil.copy(train_file, os.path.join(output_dir_src, train_file))
train_x, train_y = CoNLL2003Corpus.get_sequence_tagging_data('train')
validate_x, validate_y = CoNLL2003Corpus.get_sequence_tagging_data(
'validate')
test_x, test_y = CoNLL2003Corpus.get_sequence_tagging_data('test')
#'bert-large-cased'
embedding = BERTEmbedding('bert-large-cased', sequence_length_max)
# 还可以选择 `BLSTMModel` 和 `CNNLSTMModel`
model = BLSTMCRFModel(embedding)
# model.build_model(train_x, train_y)
# model.build_multi_gpu_model(gpus=2)
# print(model.summary())
if save_weights_only:
model_weights = os.path.join(output_dir, output_weights_name)
else:
model_weights = os.path.join(output_dir, output_model_name)
checkpoint = ModelCheckpoint(
model_weights,
save_weights_only=save_weights_only,
save_best_only=True,
verbose=1,
)
earlystop = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=20,
verbose=0,
mode='min')
csv_logger = CSVLogger(os.path.join(output_dir, 'training.csv'))
batch_size_cycliclr = ceil(len(train_x) / batch_size)
if cyclicLR_mode == 'exp_range':
gamma = 0.99994
else:
gamma = 1.
clr = CyclicLR(mode=cyclicLR_mode,
step_size=batch_size_cycliclr,
base_lr=base_lr,
max_lr=max_lr,
gamma=gamma)
save_min_loss = SaveMinLoss(filepath=output_dir)
tb = TensorBoard(log_dir=os.path.join(output_dir, "logs"),
batch_size=batch_size)
callbacks = [
checkpoint,
tb,
csv_logger,
# clr,
save_min_loss,
earlystop,
]
print("** start training **")
model.fit(train_x,
train_y,
x_validate=validate_x,
y_validate=validate_y,
epochs=epochs,
batch_size=batch_size,
labels_weight=True,
fit_kwargs={
'callbacks': callbacks,
'workers': generator_workers,
'use_multiprocessing': True,
'class_weight': 'auto',
})
model_path = os.path.join(output_dir, 'model')
model.save(model_path)
report_evaluate = model.evaluate(test_x, test_y, debug_info=True)
with open(os.path.join(output_dir, 'report_evaluate.log'), 'w') as f:
f.write(f"The evaluate report is : \n{str(report_evaluate)}")
'units': 64,
'activation': 'tanh'
}
}
model.fit(train_x,
train_y,
x_validate=validate_x,
y_validate=validate_y,
epochs=20,
batch_size=512,
labels_weight=True,
fit_kwargs={'callbacks': [early_stop, log]})
model.evaluate(test_x, test_y)
model.save(model_path)
"""
ep20
precision recall f1-score support
fictionalhuman 0.7541 0.7700 0.7620 661
tvshow 0.8809 0.9703 0.9234 404
place 0.8156 0.8581 0.8363 1402
thing 0.7811 0.7332 0.7564 5746
vocabulary 0.8681 0.7880 0.8261 15190
movie 0.8214 0.9014 0.8596 1674
organization 0.7618 0.7691 0.7654 524
human 0.8212 0.6310 0.7137 1805
entertainmentperson 0.8067 0.9194 0.8594 2247
tvplay 0.8573 0.9164 0.8859 1974