def test_h5dict_attrs(h5_path='test.h5'):
# test both HDF5 and dict implementations
paths = [h5_path, dict()]
for path in paths:
f = h5dict(path, mode='w')
# str
f['x'] = 'abcd'
# list<bytes>
f['y'] = [b'efg', b'hij', b'klmn']
# ndarray
array = np.random.random((3, 4, 5))
f['z'] = array
f.close()
f = h5dict(path, mode='r')
assert f['x'] == 'abcd'
assert f['y'] == [b'efg', b'hij', b'klmn']
assert_allclose(f['z'], array)
f.close()
def test_h5dict_attrs():
_, h5_path = tempfile.mkstemp('.h5')
# test both HDF5 and dict implementations
paths = [h5_path, dict()]
for path in paths:
f = h5dict(path, mode='w')
# str
f['x'] = 'abcd'
# list<bytes>
f['y'] = [b'efg', b'hij', b'klmn']
# ndarray
array = np.random.random((4, 5, 512))
f['z'] = array
f.close()
f = h5dict(path, mode='r')
assert f['x'] == 'abcd'
assert f['y'] == [b'efg', b'hij', b'klmn']
assert_allclose(f['z'], array)
f.close()
os.remove(h5_path)
def test_h5dict_attrs():
_, h5_path = tempfile.mkstemp('.h5')
# test both HDF5 and dict implementations
paths = [h5_path, dict()]
for path in paths:
f = h5dict(path, mode='w')
# str
f['x'] = 'abcd'
# list<bytes>
f['y'] = [b'efg', b'hij', b'klmn']
# ndarray
array = np.random.random((4, 5, 512))
f['z'] = array
f.close()
f = h5dict(path, mode='r')
assert f['x'] == 'abcd'
assert f['y'] == [b'efg', b'hij', b'klmn']
assert_allclose(f['z'], array)
f.close()
os.remove(h5_path)
def test_h5dict_groups():
_, h5_path = tempfile.mkstemp('.h5')
# test both HDF5 and dict implementations
paths = [h5_path, dict()]
for path in paths:
f = h5dict(path, mode='w')
group1 = f['group1']
group2 = group1['group2']
group2['x'] = 'abcd'
group3 = group2['group3']
group3['y'] = [b'efg', b'hij', b'klmn']
group4 = group3['group4']
array = np.random.random((4, 5, 512))
group4['z'] = array
f.close()
f = h5dict(path, mode='r')
assert 'group1' in f
group1 = f['group1']
assert 'group2' in group1
group2 = group1['group2']
assert group2['x'] == 'abcd'
assert 'group3' in group2
group3 = group2['group3']
assert group3['y'] == [b'efg', b'hij', b'klmn']
assert 'group4' in group3
group4 = group3['group4']
assert_allclose(group4['z'], array)
f.close()
os.remove(h5_path)
def test_h5dict_groups():
_, h5_path = tempfile.mkstemp('.h5')
# test both HDF5 and dict implementations
paths = [h5_path, dict()]
for path in paths:
f = h5dict(path, mode='w')
group1 = f['group1']
group2 = group1['group2']
group2['x'] = 'abcd'
group3 = group2['group3']
group3['y'] = [b'efg', b'hij', b'klmn']
group4 = group3['group4']
array = np.random.random((4, 5, 512))
group4['z'] = array
f.close()
f = h5dict(path, mode='r')
assert 'group1' in f
group1 = f['group1']
assert 'group2' in group1
group2 = group1['group2']
assert group2['x'] == 'abcd'
assert 'group3' in group2
group3 = group2['group3']
assert group3['y'] == [b'efg', b'hij', b'klmn']
assert 'group4' in group3
group4 = group3['group4']
assert_allclose(group4['z'], array)
f.close()
os.remove(h5_path)
def save_model(model, filepath, overwrite=True, include_optimizer=True):
"""Save a model to a HDF5 file.
Note: Please also see
[How can I install HDF5 or h5py to save my models in Keras?](
/getting-started/faq/
#how-can-i-install-HDF5-or-h5py-to-save-my-models-in-Keras)
in the FAQ for instructions on how to install `h5py`.
The saved model contains:
- the model's configuration (topology)
- the model's weights
- the model's optimizer's state (if any)
Thus the saved model can be reinstantiated in
the exact same state, without any of the code
used for model definition or training.
# Arguments
model: Keras model instance to be saved.
filepath: one of the following:
- string, path where to save the model, or
- h5py.File or h5py.Group object where to save the model
overwrite: Whether we should overwrite any existing
model at the target location, or instead
ask the user with a manual prompt.
include_optimizer: If True, save optimizer's state together.
# Raises
ImportError: if h5py is not available.
"""
if h5py is None:
raise ImportError('`save_model` requires h5py.')
if not isinstance(filepath, h5py.Group):
# If file exists and should not be overwritten.
if not overwrite and os.path.isfile(filepath):
proceed = ask_to_proceed_with_overwrite(filepath)
if not proceed:
return
opened_new_file = True
else:
opened_new_file = False
f = h5dict(filepath, mode='w')
try:
_serialize_model(model, f, include_optimizer)
finally:
if opened_new_file:
f.close()
def _load_model(filepath, model_class):
"""Load a model, its state and its hyperparameters from file.
Args:
filepath (TYPE): Path to model to load
"""
from keras.utils.io_utils import h5dict
from keras.engine.saving import load_weights_from_hdf5_group_by_name #, _deserialize_model
from json import loads
from keras.models import model_from_json
f = h5dict(filepath, mode='r')
parameters = loads(str(f['parameters']))
model = model_class(**parameters)
model._maybe_create_model()
model.model_.load_weights(filepath, by_name=True)
f.close()
return model
def load_model(filepath, custom_objects=None, compile=True):
"""Loads a model saved via `save_model`.
# Arguments
filepath: one of the following:
- string, path to the saved model, or
- h5py.File or h5py.Group object from which to load the model
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
compile: Boolean, whether to compile the model
after loading.
# Returns
A Keras model instance. If an optimizer was found
as part of the saved model, the model is already
compiled. Otherwise, the model is uncompiled and
a warning will be displayed. When `compile` is set
to False, the compilation is omitted without any
warning.
# Raises
ImportError: if h5py is not available.
ValueError: In case of an invalid savefile.
"""
if h5py is None:
raise ImportError('`load_model` requires h5py.')
model = None
opened_new_file = not isinstance(filepath, h5py.Group)
f = h5dict(filepath, 'r')
try:
model = _deserialize_model(f, custom_objects, compile)
finally:
if opened_new_file:
f.close()
return model
def unpickle_model(state):
f = h5dict(state, mode='r')
return _deserialize_model(f)
def pickle_model(model):
d = {}
f = h5dict(d)
_serialize_model(model, f)
return d
