tfRNN.py 文件源码

def label_test_file(self):
    outfile = open("pred_vld.txt","w")
    prep_alfa = lambda X: pad_sequences(sequences=self.indexer.texts_to_sequences(X),
                                        maxlen=self.SentMaxLen)
    vld = json.loads(open('validation.json', 'r').read())
    for prem, hypo, label in zip(vld[0], vld[1], vld[2]):
      prem_pad, hypo_pad = prep_alfa([prem]), prep_alfa([hypo])
      ans = np.reshape(self.model.predict(x=[prem_pad, hypo_pad], batch_size = 1), -1)  # PREDICTION
      if np.argmax(ans) != label:
        outfile.write(prem + "\n" + hypo + "\n")
        outfile.write("Truth: " + self.rLabels[label] + "\n")
        outfile.write('Contradiction \t{:.1f}%\n'.format(float(ans[0]) * 100) +
                      'Neutral \t\t{:.1f}%\n'.format(float(ans[1]) * 100) +
                      'Entailment \t{:.1f}%\n'.format(float(ans[2]) * 100))
        outfile.write("-"*15 + "\n")
    outfile.close()