class main(object):
def parse(self):
parser = argparse.ArgumentParser(description="chatbot")
parser.add_argument('--train', action='store_true', help='whether train')
parser.add_argument('--test', action='store_true', help='whether test')
parser.add_argument('--model_restore', action='store_true', help='whether restore the model')
try:
from argument import add_arguments
parser = add_arguments(parser)
except:
pass
args = parser.parse_args()
return args
def set_parameter(self, args):
print('setting parameters...')
if args.train:
self.batch_size = 200
self.mode = 'train'
elif args.test:
self.batch_size = 1
self.mode = 'test'
def set_training_data(self):
print('getting training data...')
self.max_len = 25
dataset = DataManager(max_len = self.max_len)
self.val_size,self.train_x,self.train_y,self.dictionary = dataset.getTrainData()
tf.reset_default_graph()
def set_testing_data(self):
print('setting testing data...')
self.max_len = 25
dataset = DataManager(max_len = self.max_len)
self.val_size, self.dictionary = dataset.getTestData()
tf.reset_default_graph()
def getting_model(self, args):
print('getting model...')
if args.train or args.test:
self.model = Model(batch_size = self.batch_size , val_size = self.val_size,
max_len = self.max_len ,args = args , dictionary = self.dictionary)#
self.model.compile()
if (args.train and args.model_restore) or args.test:
self.model.restore(mode = self.mode)
def train(self, args):
print('start traing...')
#start train
epoch = 0
min_loss = math.inf
while True:
epoch += 1
loss = self.model.fit(self.train_x, self.train_y, self.batch_size, epoch)
#store the Model
self.model.save()
def test(self, args):
print('start testing...')
while True:
ques = input('請說話...')
ans = self.model.predict(ques)
print(ans)
import sys
from utils import readjson
from models.SimpleRergression import Linear
from utils.DataLoader import DataLoader
from models.Model import Model
if __name__ == '__main__':
config = readjson(sys.argv[1])
linear = Linear(**config['linear'])
dataloader = DataLoader(**config['dataloader'])
modal = Model(linear, dataloader, **config['modal'])
modal.fit()
