def serialize(self, h5file):
h5file.create_group('encoder')
h5file['encoder'].attrs['name'] = self.encoder.name()
h5file['encoder'].attrs['board_width'] = self.encoder.board_width
h5file['encoder'].attrs['board_height'] = self.encoder.board_height
h5file.create_group('model')
kerasutil.save_model_to_hdf5_group(self.model, h5file['model'])
def serialize(self, h5file): # 9.9
h5file.create_group(
'encoder'
) # stores enough information to reconstruct the board encoder
h5file['encoder'].attrs['name'] = self._encoder.name()
h5file['encoder'].attrs['board_width'] = self._encoder.board_width
h5file['encoder'].attrs['board_height'] = self._encoder.board_height
h5file.create_group(
'model'
) # Uses build in Keras features to persist the model and its weights
kerasutil.save_model_to_hdf5_group(self._model, h5file['model'])
def serialize(self, h5file_dir):
if os.path.isfile(h5file_dir):
os.remove(h5file_dir)
with h5py.File(h5file_dir, 'a') as h5file:
#This saves encoding details and the model to a h5py file
h5file.create_group('encoder')
h5file['encoder'].attrs['name'] = self.encoder.name()
h5file['encoder'].attrs['board_width'] = self.encoder.board_width()
h5file['encoder'].attrs[
'board_height'] = self.encoder.board_height()
h5file.create_group('model')
kerasutil.save_model_to_hdf5_group(self.model, h5file['model'])
"""
def serialize(self, h5file):
h5file.create_group(
'encoder'
) # stores enough information to reconstruct the board encoder
h5file.create_group('meta')
h5file['encoder'].attrs['name'] = 'zeroencoder'
h5file['encoder'].attrs['board_size'] = self.encoder.board_size
h5file['meta'].attrs['num_rounds'] = self.num_rounds
h5file['meta'].attrs['c'] = self.c
h5file.create_group(
'model'
) # Uses built in Keras features to persist the model and its weights
kerasutil.save_model_to_hdf5_group(self.model, h5file['model'])
def save(self):
# Backup the original file in case something goes wrong while
# saving the new checkpoint.
backup = None
if os.path.exists(self.filename):
backup = self.filename + '.bak'
os.rename(self.filename, backup)
output = h5py.File(self.filename, 'w')
model_out = output.create_group('model')
kerasutil.save_model_to_hdf5_group(self.model, model_out)
metadata = output.create_group('metadata')
metadata.attrs['epochs_completed'] = self.epochs_completed
metadata.attrs['chunks_completed'] = self.chunks_completed
metadata.attrs['num_chunks'] = self.num_chunks
output.close()
# If we got here, we no longer need the backup.
if backup is not None:
os.unlink(backup)
