def __call__(self):
logger_util.add_model_indicators(self.encoder)
for _ in self.loop:
self._train()
self._test()
self.__log_model_params()
def __call__(self):
logger_util.add_model_indicators(self.model)
logger.add_indicator(Queue("train_loss", 20, True))
logger.add_indicator(Histogram("test_loss", True))
logger.add_indicator(Histogram("accuracy", True))
for _ in self.loop:
self._train()
self._test()
self.__log_model_params()
def __call__(self):
# Training and testing
logger_util.add_model_indicators(self.model)
logger.add_indicator(Queue("train_loss", 20, True))
logger.add_indicator(Histogram("test_loss", True))
logger.add_indicator(Histogram("accuracy", True))
logger.add_indicator(IndexedScalar('test_sample_loss'))
logger.add_indicator(IndexedScalar('test_sample_pred'))
test_data = np.array([d[0].numpy() for d in self.test_loader.dataset])
logger.save_numpy("test_data", test_data)
for _ in self.loop:
self._train()
self._test()
self.__log_model_params()
def startup(self):
logger_util.add_model_indicators(self.model)
logger.add_indicator(Queue("train_loss", 20, True))
logger.add_indicator(Histogram("test_loss", True))
logger.add_indicator(Histogram("accuracy", True))