def fit(self, trn_data, dev_data, save_dir, batch_size, epochs, run_eagerly=False, logger=None, verbose=True,
**kwargs):
self._capture_config(locals())
self.transform = self.build_transform(**self.config)
if not save_dir:
save_dir = tempdir_human()
if not logger:
logger = init_logger(name='train', root_dir=save_dir, level=logging.INFO if verbose else logging.WARN)
logger.info('Hyperparameter:\n' + self.config.to_json())
num_examples = self.build_vocab(trn_data, logger)
# assert num_examples, 'You forgot to return the number of training examples in your build_vocab'
logger.info('Building...')
train_steps_per_epoch = math.ceil(num_examples / batch_size) if num_examples else None
self.config.train_steps = train_steps_per_epoch * epochs if num_examples else None
model, optimizer, loss, metrics = self.build(**merge_dict(self.config, logger=logger, training=True))
logger.info('Model built:\n' + summary_of_model(self.model))
self.save_config(save_dir)
self.save_vocabs(save_dir)
self.save_meta(save_dir)
trn_data = self.build_train_dataset(trn_data, batch_size, num_examples)
dev_data = self.build_valid_dataset(dev_data, batch_size)
callbacks = self.build_callbacks(save_dir, logger, **self.config)
# need to know #batches, otherwise progbar crashes
dev_steps = math.ceil(size_of_dataset(dev_data) / batch_size)
checkpoint = get_callback_by_class(callbacks, tf.keras.callbacks.ModelCheckpoint)
timer = Timer()
try:
history = self.train_loop(**merge_dict(self.config, trn_data=trn_data, dev_data=dev_data, epochs=epochs,
num_examples=num_examples,
train_steps_per_epoch=train_steps_per_epoch, dev_steps=dev_steps,
callbacks=callbacks, logger=logger, model=model, optimizer=optimizer,
loss=loss,
metrics=metrics, overwrite=True))
except KeyboardInterrupt:
print()
if not checkpoint or checkpoint.best in (np.Inf, -np.Inf):
self.save_weights(save_dir)
logger.info('Aborted with model saved')
else:
logger.info(f'Aborted with model saved with best {checkpoint.monitor} = {checkpoint.best:.4f}')
# noinspection PyTypeChecker
history: tf.keras.callbacks.History() = get_callback_by_class(callbacks, tf.keras.callbacks.History)
delta_time = timer.stop()
best_epoch_ago = 0
if history and hasattr(history, 'epoch'):
trained_epoch = len(history.epoch)
logger.info('Trained {} epochs in {}, each epoch takes {}'.
format(trained_epoch, delta_time, delta_time / trained_epoch if trained_epoch else delta_time))
io_util.save_json(history.history, io_util.path_join(save_dir, 'history.json'), cls=io_util.NumpyEncoder)
monitor_history: List = history.history.get(checkpoint.monitor, None)
if monitor_history:
best_epoch_ago = len(monitor_history) - monitor_history.index(checkpoint.best)
if checkpoint and monitor_history and checkpoint.best != monitor_history[-1]:
logger.info(f'Restored the best model saved with best '
f'{checkpoint.monitor} = {checkpoint.best:.4f} '
f'saved {best_epoch_ago} epochs ago')
self.load_weights(save_dir) # restore best model
return history
def save_meta(self, save_dir, filename='meta.json', **kwargs):
self.meta['create_time']: now_datetime()
self.meta.update(kwargs)
save_json(self.meta, os.path.join(save_dir, filename))
def save_json(self, path):
save_json(self, path)
def save(self, filepath):
save_json(self.meta, filepath)
def save_json(self, path):
save_json(self.to_dict(), path)
