def post(self):
data = request.get_json()
isbn = data['isbn']
name = data['name']
description = data['description']
price = data['price']
writer = data['writer']
handle = data['handle']
password = data['password']
if handle != utils.admin_handle or password != utils.admin_password:
return utils.return_response(message='authentication error')
model = BaseModel(isbn=isbn,
name=name,
description=description,
price=price,
writer=writer)
try:
db.session.add(model)
db.session.commit()
except SQLAlchemyError as e:
current_app.logger.error(e)
db.session.rollback()
return utils.return_response(message='Error in Database')
else:
return utils.return_response(message='Data inserted Ok')
def create_model(opt):
model = BaseModel(opt)
if opt.model == 'Base':
pass
elif opt.model == 'PATN':
model = PATNTransferModel(opt)
elif opt.model == 'CTPS':
model = CTPSModel(opt)
elif opt.model == 'CAN':
model = CANModel(opt)
elif opt.model == 'DCGAN':
model = DCGANModel(opt)
elif opt.model == "AdaIN":
model = AdaIN(opt)
else:
raise ValueError("Model [%s] not recognized." % opt.model)
print("=> model [{}] was created".format(model.name))
return model
def create_model(self, params=None):
"""
Create the input-output model.
"""
self.model = BaseModel(self.p)
#!/usr/bin/python3
from models import storage
from models.base import BaseModel
all_objs = storage.all()
print("-- Reloaded objects --")
for obj_id in all_objs.keys():
obj = all_objs[obj_id]
print(obj)
print("-- Create a new object --")
my_model = BaseModel()
my_model.name = "Holberton"
my_model.my_number = 89
my_model.save()
print(my_model)
parser.add_argument("--epochs", type=int, default=50, help="train epochs")
parser.add_argument("--batch_size", type=int, default=16, help="batch size")
parser.add_argument("--emb_drop_rate", type=float, default=0.2, help="dropout rate for embeddings")
parser.add_argument("--rnn_drop_rate", type=float, default=0.5, help="dropout rate for embeddings")
parser.add_argument("--max_to_keep", type=int, default=1, help="maximum trained model to be saved")
parser.add_argument("--no_imprv_tolerance", type=int, default=None, help="no improvement tolerance")
config = Configurations(parser.parse_args())
# os environment
os.environ['TF_CPP_MIN_LOG_LEVEL'] = "3"
os.environ["CUDA_VISIBLE_DEVICES"] = config.gpu_idx
# if dataset is not prepared, then build it
if not os.path.exists(config.save_path) or not os.listdir(config.save_path):
process_base(config)
print("load dataset...")
train_ratio = int(config.train_ratio) if float(config.train_ratio) > 1.0 else float(config.train_ratio)
dataset = Dataset(config.train_set, config.dev_set, config.test_set, batch_size=config.batch_size,
train_rate=train_ratio, shuffle=True)
print("build model and train...")
model = BaseModel(config)
if config.restore_model:
model.restore_last_session()
if config.train:
model.train(dataset)
model.restore_last_session()
model.evaluate_data(dataset.get_data_batches("test"), name="test")
model.close_session()