class TrainInceptionV3GRU(Train):
def __init__(self,
config=CONFS,
existing_model_path='',
train_generator=None,
validation_generator=None,
language="english",
score_model=True):
super().__init__(config=config)
self.language = language
self._generator = Flickr8kGenerator(language=language)
self._train_generator = train_generator or self._generator.train_generator(
)
self._validation_generator = validation_generator or self._generator.validation_generator(
)
self._train_data_amount = len(self._generator.train_captions)
self._validation_data_amount = len(self._generator.validation_captions)
self._tokenizer = self._generator.cp.tokenizer
self.model = ImageCaptionModeler().get_model(
self._generator.cp.vocab_size,
tokenizer=self._generator.cp.tokenizer)
self._score_model = score_model
# load previous model if exist
if os.path.exists(existing_model_path):
self.model.load_weights(existing_model_path)
def run(self):
callback = Callback('InceptionV3GRU_' + self.model.layers[-2].name,
language=self.language)
self.model.fit_generator(generator=self._train_generator,
steps_per_epoch=self._train_data_amount //
self._batch_size,
epochs=5,
validation_data=self._validation_generator,
validation_steps=64,
callbacks=callback.callbacks)
cur_time = datetime.datetime.now()
model_path = 'models/InceptionV3GRU_model_' + str(
cur_time
) + '_' + self.language + "_" + self.model.layers[-2].name + '.h5'
model_weight_path = 'models/InceptionV3GRU_weight_' + str(cur_time) + '_' + self.language + "_" + \
self.model.layers[-2].name + '.h5'
self.model.save(model_path)
self.model.save_weights(model_weight_path)
self.save_tokenizer('models/tokenizer_' + self.language + '.pickle')
# Save model for serving
self._serving.save_model(self.model)
if self._score_model:
bs = BleuScore()
bs.get_model_score(weight_path=model_weight_path,
language=self.language)
class TrainInceptionV3GRU(Train):
def __init__(self,
config=CONFS,
existing_model_path='',
train_generator=None,
validation_generator=None):
super().__init__(config=config)
self._generator = Flickr8kGenerator()
self._train_generator = train_generator or self._generator.train_generator(
)
self._validation_generator = validation_generator or self._generator.validation_generator(
)
self._train_data_amount = len(self._generator.train_captions)
self._validation_data_amount = len(self._generator.validation_captions)
self._tokenizer = self._generator.cp.tokenizer
self.model = ImageCaptionModeler().get_model(
self._generator.cp.vocab_size)
# load previous model if exist
if os.path.exists(existing_model_path):
self.model.load_weights(existing_model_path)
def run(self):
callback = Callback('InceptionV3GRU')
self.model.fit_generator(generator=self._train_generator,
steps_per_epoch=self._train_data_amount //
self._batch_size,
epochs=10,
validation_data=self._validation_generator,
validation_steps=16,
callbacks=callback.callbacks)
cur_time = datetime.datetime.now()
self.model.save('models/InceptionV3GRU_model.h5' + str(cur_time))
self.model.save_weights('models/InceptionV3GRU_weight.h5' +
str(cur_time))
self.save_tokenizer('models/tokenizer.pickle' + str(cur_time))
# Save model for serving
self._serving.save_model(self.model)