def run_finetuned20(config):
_set_config_files(config)
model = None
epochs_count = 0
while True:
train_loss, model_config, model = train_graph.do_train(config, model)
epochs_count +=1
eval_loss, eval_accuracy = eval_graph.do_eval(model_config, config)
pylog.info(epochs_count)
show.show_result_detail(eval_loss, eval_accuracy, train_loss, config)
if epochs_count > 20:
break
def run_directly(config):
_set_config_files(config)
train_loss, model_config = train_graph.direct_save(config)
eval_loss, eval_accuracy = eval_graph.do_eval(model_config, config)
show.show_result_detail(eval_loss, eval_accuracy, train_loss, config)
def run_finetuned(config):
_set_config_files(config)
model = None
train_loss, model_config, model = train_graph.do_train(config, model)
eval_loss, eval_accuracy = eval_graph.do_eval(model_config, config)
show.show_result_detail(eval_loss, eval_accuracy, train_loss, config)
def run_finetuned(config):
train_loss, model_config = train_graph.do_train(config)
eval_loss, eval_accuracy = eval_graph.do_eval(model_config, config)
show.show_result_detail(eval_loss, eval_accuracy, train_loss, config)