A custom alternating update rule with keras - optimization

Question

I would like to use an alternating update rule with keras.
I.e. per-batch I would like to call a regular gradient-based step, and next call a custom step.
I thought about implementing it by either inheriting an optimizer or a callback (and use the on-batch calls). However, neither would do, because they both lack the batch-data and batch-labels (and I need both).
Any idea on how to implement a custom alternating update with keras?
If required, I don't mind directly calling tensorflow specific methods, as long as I can keep use the project wrapped with the keras framework (with model.fit, model.predict .. )

Answer 1

try create a custom callback
import keras.callbacks as callbacks
class JSONMetrics(callbacks.Callback):
_model = None
_each_epoch = None
_metrics = None
_epoch = None
_file_json = None
def __init__(self,model,each_epoch,logger=None):
self._file_json = "file_log.json"
self._model = model
self._each_epoch= each_epoch
self._epoch = 0
self._metrics = {'loss':[], 'acc':[]}
def on_epoch_begin(self, epoch, logs):
# print('Epoch {0} begin'.format(epoch))
try:
with open(self._file_json, 'r') as f:
self._metrics = json.load(f)
def on_epoch_end(self, epoch, logs):
self._logger.info('Nemesis: Epoch {0} end'.format(epoch))
self._metrics['loss'].append(logs.get('loss'))
self._metrics['acc'].append(logs.get('acc'))
with open(self._file_json, 'w') as f:
data = json.dump(self._metrics, f)
if self._epoch % self._each_epoch == 0:
file_name = 'weights%08d.h5' % self._epoch
#print('Saving weights at {0} file'.format(file_name))
self._model.save_weights(file_name)
self._epoch += 1
You can evoke the self.model to solve your problem and save the acc and loss for example.