evaluator = Evaluator(true_labels=test_true_labels, sentences=test_sentence_inputs, aspects=test_aspect_text_inputs)
epoch = 1
while epoch <= 50:
model = m.train_model(sentence_inputs=train_sentence_inputs,
position_inputs= train_positions,
aspect_input=train_aspects,
labels=train_aspect_labels,
model=model)
results = m.get_predict(sentence_inputs=test_sentence_inputs,
position_inputs=test_positions,
aspect_input=test_aspects,
model=model)
print("\n--epoch"+str(epoch)+"--")
F, acc = evaluator.get_macro_f1(predictions=results, epoch=epoch)
if epoch % 2 == 0:
print("current max f1 score"+str(evaluator.max_F1))
print("max f1 is gained in epoch"+str(evaluator.max_F1_epoch))
print("current max acc"+str(evaluator.max_acc))
print("max acc is gained in epoch"+str(evaluator.max_acc_epoch))
print("happy ending")
if acc > 0.8000:
model.save_weights(model_path+"_acc_"+str(acc*100)+"_F_"+str(F*100)+"_"+str(epoch))
elif F > 0.7100:
model.save_weights(model_path + "_acc_" + str(acc * 100) + "_F_" + str(F * 100) + "_" + str(epoch))
epoch += 1
