def save_test_data(y1, y2, y3, i):
sen_y1 = data_processor.getSentence(y1, vocab)[0]
sen_y2 = data_processor.getSentence(y2, vocab)[0]
sen_y3 = data_processor.getSentence(y3, vocab)
data_processor.saveData('\nQuestion ' + str(i + 1) + ':\n' + sen_y1)
data_processor.saveData('\nPositive Answer:\n' + sen_y2)
data_processor.saveData('\nNegative Answers:')
for j in range(4):
data_processor.saveData('\n' + str(j + 1) + ' ' + sen_y3[j])
return
def save_train_data(x1, x2, x3):
sen_x1 = data_processor.getSentence(x1, vocab)
sen_x2 = data_processor.getSentence(x2, vocab)
sen_x3 = data_processor.getSentence(x3, vocab)
for j in range(4):
data_processor.saveData('\nQuestion ' + str(j + 1) + ':\n' +
sen_x1[j])
data_processor.saveData('\nPositive Answer' + ':\n' + sen_x2[j])
data_processor.saveData('\nNegative Answer' + ':\n' + sen_x3[j])
return
def save_data_losses(x1, x2, x3, losses):
sen_x1 = data_processor.getSentence(x1, vocab)
sen_x2 = data_processor.getSentence(x2, vocab)
sen_x3 = data_processor.getSentence(x3, vocab)
# print (np.shape(losses),losses)
num = 0
for k in range(len(losses)):
# print ("losses", np.shape(losses), type(losses))
if (losses[k] != 0.0):
data_processor.saveData('\nQuestion_wrong ' + str(num + 1) +
':\n' + sen_x1[k])
data_processor.saveData('\nPositive Answer' + ':\n' +
sen_x2[k])
data_processor.saveData('\nNegative Answer' + ':\n' +
sen_x3[k])
num += 1
if (num == 4):
return
return
