d = w2v.vector_size similar_val = 0.9999 non_similar_val = 0.0001 steps_per_epoch = 500 # 196961 muestras epochs = 200 validation_steps = 150 shann_obj = SHANN(max_len_doc_sents, max_len_doc_sent_words, max_len_summ_sents, max_len_summ_sent_words, d, path_models, name_models) shann_obj._set_model() train_file = "../../Corpora/CNNDM/dev.csv" dev_file = "../../Corpora/CNNDM/dev.csv" x_tr, y_tr = ut.load_csv_samples(train_file) x_dv, y_dv = ut.load_csv_samples(dev_file) generator_train = ut.generator_2(x_tr, y_tr, max_len_doc_sents, max_len_summ_sents, max_len_doc_sent_words, max_len_summ_sent_words, padding_val, pos_pairs, neg_pairs, w2v, d, similar_val=similar_val, non_similar_val=non_similar_val)
topk_sentences = 3 shann_obj = SHANN(max_len_doc_sents, max_len_doc_sent_words, max_len_summ_sents, max_len_summ_sent_words, d, path_models, name_models) shann_obj._set_model() shann_obj.load_weights(path_weights) decoder = Decoder(max_len_doc_sents, max_len_doc_sent_words, w2v, d, shann_obj.get_all_att_model(), topk_sentences=topk_sentences) test_file = "../../Corpora/CNNDM/test.csv" x_ts, y_ts = ut.load_csv_samples(test_file) """ # Word Level # print(len(x_ts)) summaries = decoder._word_decoder(x_ts[15]) print(summaries) print(y_ts[15]) exit() with open(output_file_words, "w", encoding="utf8") as fw: for i in range(len(summaries)): fw.write(summaries[i].strip() + "\t" + y_ts.iloc[i].strip() + "\n") """ # Sentence Level # print(len(x_ts))