def getFinalOutput(loaded_clf, reading):
output = model.predict([convert_input_to_bow(reading, allWords)])
#get the prediction with max probability.
#get the sentiment of user input
sent_out = loaded_clf.predict_proba(input_to_bow_sentiment(reading))
#Random response
return random.choice(output_depending_on_sentiment(sent_out, output))
def predict(data_set=None, data_per_batch=32, epoch=1, model_path='model.h5'):
Dataset = None
if data_set == None:
dataloaders = os.listdir('Dataset')
for dataloader in dataloaders:
loader_path = os.path.join('Dataset', dataloader)
if dataloader.endswith('.py') and os.path.isfile(
loader_path) and dataloader != '__init__.py':
try:
Dataset = importlib.import_module("Dataset." +
dataloader[:-3])
except Exception as ex:
print('failed to load Dataset from "%s".' % dataloader, ex)
else:
print('successfuly loaded Dataset from "%s"!' % dataloader)
break
if Dataset == None:
raise Exception('No vaild dataset found!')
else:
try:
Dataset = importlib.import_module("Dataset." + data_set)
except Exception as ex:
raise Exception('"%s" is not a vaild dataset!' % data_set)
data_loader = Dataset.DataLoader(1024, 1, 13)
# 加载网络模型
model = NN.model.create_pridict_model()
# 输出网络结构
model.summary()
# 加载之前训练的数据
model.load_weights(model_path)
# 验证集
validation_data = data_loader.get_validation_generator()
data = next(validation_data)[0]
r = model.predict(data['speech_data_input'])
r = K.ctc_decode(r, data['input_length'][0])
r1 = K.get_value(r[0][0])
r1 = r1[0]
tokens = NN.model.get_tokens()
print('predict: [', end='')
for i in r1:
print(tokens[i], end=', ')
print(']')
print('truth : [', end='')
for i in range(data['label_length'][0][0]):
print(tokens[int(data['speech_labels'][0][i])], end=', ')
print(']')
pass
def getFinalOutput(loaded_clf, reading, detected_language):
output = model.predict([convert_input_to_bow(reading, allWords)])
#get the prediction with max probability.
#get the sentiment of user input
sent_out = loaded_clf.predict_proba(input_to_bow_sentiment(reading))
#Random response
response = random.choice(output_depending_on_sentiment(sent_out, output))
#Translate the response
if (detected_language == 'en'):
return response
else:
return translate.translate_to_other_language(response,
detected_language)