from __future__ import absolute_import
import tempfile
import os
import h5py
from keras.models import load_model, save_model
def save_model_to_hdf5_group(model, f):
# Use Keras save_model to save the full model (including optimizer
# state) to a file.
# Then we can embed the contents of that HDF5 file inside ours.
tempfd, tempfname = tempfile.mkstemp(prefix='tmp-betago')
try:
os.close(tempfd)
save_model(model, tempfname)
serialized_model = h5py.File(tempfname, 'r')
root_item = serialized_model.get('/')
serialized_model.copy(root_item, f, 'kerasmodel')
serialized_model.close()
finally:
os.unlink(tempfname)
def load_model_from_hdf5_group(f, custom_objects=None):
# Extract the model into a temporary file. Then we can use Keras
# load_model to read it.
tempfd, tempfname = tempfile.mkstemp(prefix='tmp-betago')
try:
os.close(tempfd)
serialized_model = h5py.File(tempfname, 'w')
root_item = f.get('kerasmodel')
for attr_name, attr_value in root_item.attrs.items():
serialized_model.attrs[attr_name] = attr_value
for k in root_item.keys():
f.copy(root_item.get(k), serialized_model, k)
serialized_model.close()
return load_model(tempfname)
finally:
os.unlink(tempfname)
