def init_metric(self): nll = Nll(data_loader=self.oracle_data_loader, rnn=self.oracle, sess=self.sess) self.add_metric(nll) inll = Nll(data_loader=self.gen_data_loader, rnn=self.generator, sess=self.sess) inll.set_name('nll-test') self.add_metric(inll)
def init_real_metric(self): from utils.metrics.DocEmbSim import DocEmbSim docsim = DocEmbSim(oracle_file=self.oracle_file, generator_file=self.generator_file, num_vocabulary=self.vocab_size) self.add_metric(docsim) inll = Nll(data_loader=self.gen_data_loader, rnn=self.generator, sess=self.sess) inll.set_name('nll-test') self.add_metric(inll)
def init_metric(self): nll = Nll(data_loader=self.oracle_data_loader, rnn=self.oracle, sess=self.sess) self.add_metric(nll) inll = Nll(data_loader=self.gen_data_loader, rnn=self.generator, sess=self.sess) inll.set_name('nll-test') self.add_metric(inll) from utils.metrics.DocEmbSim import DocEmbSim docsim = DocEmbSim(oracle_file=self.oracle_file, generator_file=self.generator_file, num_vocabulary=self.vocab_size) self.add_metric(docsim) print("Metrics Applied: " + nll.get_name() + ", " + inll.get_name() + ", " + docsim.get_name())
def init_metric(self): # nll-oracle: 用oracle去评判generator产生的数据 nll = Nll(data_loader=self.oracle_data_loader, rnn=self.oracle, sess=self.sess) self.add_metric(nll) # nll-test: 用generator去评判真实数据 inll = Nll(data_loader=self.gen_data_loader, rnn=self.generator, sess=self.sess) inll.set_name('nll-test') self.add_metric(inll)
def init_real_metric(self): from utils.metrics.DocEmbSim import DocEmbSim docsim = DocEmbSim(oracle_file=self.oracle_file, generator_file=self.generator_file, num_vocabulary=self.vocab_size) self.add_metric(docsim) inll = Nll(data_loader=self.gen_data_loader, rnn=self.generator, sess=self.sess) inll.set_name('nll-test') self.add_metric(inll) bleu = Bleu(test_text=self.test_file, real_text='data/image_coco.txt', gram=2) self.add_metric(bleu) sbleu = SelfBleu(test_text=self.test_file, gram=2) self.add_metric(sbleu)
def init_real_metric(self): from utils.metrics.Nll import Nll from utils.metrics.PPL import PPL from utils.metrics.DocEmbSim import DocEmbSim from utils.others.Bleu import Bleu from utils.metrics.SelfBleu import SelfBleu if self.valid_ppl: valid_ppl = PPL(self.valid_data_loader, self.generator, self.sess) valid_ppl.set_name('valid_ppl') self.add_metric(valid_ppl) if self.nll_gen: nll_gen = Nll(self.gen_data_loader, self.generator, self.sess) nll_gen.set_name('nll_gen') self.add_metric(nll_gen) if self.doc_embsim: doc_embsim = DocEmbSim(self.oracle_file, self.generator_file, self.vocab_size) doc_embsim.set_name('doc_embsim') self.add_metric(doc_embsim) if self.bleu: FLAGS = tf.app.flags.FLAGS dataset = FLAGS.data if dataset == "image_coco": real_text = 'data/testdata/test_image_coco.txt' elif dataset == "emnlp_news": real_text = 'data/testdata/test_emnlp_news.txt' else: raise ValueError for i in range(3, 4): bleu = Bleu(test_text=self.text_file, real_text=real_text, gram=i) bleu.set_name(f"Bleu{i}") self.add_metric(bleu) if self.selfbleu: for i in range(2, 6): selfbleu = SelfBleu(test_text=self.text_file, gram=i) selfbleu.set_name(f"Selfbleu{i}") self.add_metric(selfbleu)
def init_metric(self): # docsim = DocEmbSim(oracle_file=self.truth_file, generator_file=self.generator_file, # num_vocabulary=self.vocab_size) # self.add_metric(docsim) inll = Nll(data_loader=self.gen_data_loader, rnn=self.generator, sess=self.sess) inll.set_name('nll-test') self.add_metric(inll) bleu1 = Bleu(test_text=self.test_file, real_text=self.trunc_train_file, gram=1) bleu1.set_name('BLEU-1') self.add_metric(bleu1) bleu2 = Bleu(test_text=self.test_file, real_text=self.trunc_train_file, gram=2) bleu2.set_name('BLEU-2') self.add_metric(bleu2)