scores = [] cui_scores = [] for train_idx, test_idx in skf.split(x, y): tv = TokenVectorizer(list(x[train_idx]), use_pos=[], stopword=True, stemming=True, as_numpy=True) train_vec = tv.vectorizer() in_seq_size = len(train_vec[0]) model = MLP(in_seq=in_seq_size, out_vec_size=len(collections.Counter(y)), dropout_rate=0.2, loss=MODEL_PARAMS['loss'], optimizer=MODEL_PARAMS['optimizer']) mlp = model.build_model() mlp.fit(train_vec, y_onehot[train_idx], batch_size=MODEL_PARAMS['batch_size'], epochs=MODEL_PARAMS['epochs'], verbose=1) test_vec = tv.vectorizer(x[test_idx]) score = mlp.evaluate(test_vec, y_onehot[test_idx]) scores.append(score[1]) print(f'SCORE: {score[1]}') mlp.save(f'../models/mlp_split{split_cnt}.h5', include_optimizer=False) # CUI訓練用データ作成部 --- rm_nv_tokens, _ = tv.rm_nv_tokenizer(y_id[train_idx]) rm_nv_vectors, rm_nv_labels = tv.rm_nv_vectorizer(y_id[train_idx]) multi_label_dict = tv.create_multi_label(rm_nv_tokens, rm_nv_labels)