def main(args):
config = json.load(open(args.config, 'r'))
# save config into experiment directory
json.dump(config, open(os.path.join(args.experiment_dir, 'config.json'), 'w'))
logging.info("Config json: %s", config)
ball = Ball(config)
file_config = config['files']
base_data_dir = file_config['base-data-dir']
model_config = config['model']
train_config = config['training']
model_builder = ModelBuilder(model_config,
ball.get_word_vecs(),
ball.get_character_vecs(),
ball.get_feature_indxr(),
ball.get_ent_cbow_vecs())
logging.info("Model Summary:")
model_builder.build_f().summary()
logging.info("Building model...")
model = model_builder.build_trainable_model()
logging.info("Model Built!")
logging.info("Building data ball...")
data_converter = DataConverter(ball)
logging.info("Data ball built!")
trainer = Trainer(model,
ball,
data_converter,
neg_sample_k=train_config['neg_samples'],
batch_size=train_config['batch_size'],
neg_sample_from_cands=train_config['neg_sample_from_cands'])
# Optimization Loop
logging.info("--- Starting optimization loop ---")
for epoch in xrange(0, train_config['epochs']):
logging.info("Starting epoch %d", epoch + 1)
data_iterator = WikilinksIterator(base_data_dir + 'train')
for item in data_iterator.jsons():
trainer.train_on(item)
trainer.epoch_done()
logging.info("Finished training epoch %d", epoch + 1)
# temp weight save for evaluation
tmp_weights_path = os.path.join(args.experiment_dir, 'tmp-model.weights')
model.save_weights(tmp_weights_path)
logging.info("Evaluating epoch %d", epoch + 1)
test_model = model_builder.build_f(weights=tmp_weights_path)
evaluator = Evaluator(test_model, ball, data_converter)
data_iterator = WikilinksIterator(base_data_dir + 'dev')
for item in data_iterator.jsons():
evaluator.evaluate_on(item)
accuracy = evaluator.evaluate_model()
logging.info("Model accuracy for epoch %d is %.2f", epoch + 1, accuracy)
logging.info("Saving final model")
final_weights_path = os.path.join(args.experiment_dir, 'final-model.weights')
model.save_weights(final_weights_path)
test_model = model_builder.build_f(weights=final_weights_path)
data_iterator = WikilinksIterator(base_data_dir + 'dev')
final_evaluator = Evaluator(test_model, ball, data_converter)
logging.info("Starting final model dev evaluation")
for item in data_iterator.jsons():
final_evaluator.evaluate_on(item)
accuracy = final_evaluator.evaluate_model()
logging.info("Final model dev accuracy is %.2f", accuracy)
data_iterator = WikilinksIterator(base_data_dir + 'test')
final_evaluator = Evaluator(test_model, ball, data_converter)
logging.info("Starting final model test evaluation")
for item in data_iterator.jsons():
final_evaluator.evaluate_on(item)
accuracy = final_evaluator.evaluate_model()
logging.info("Final model test accuracy is %.2f", accuracy)
