def _local_fit(row):
"""
Fit locally a model with a combination of this estimator's param,
with overriding parameters provided by the input.
:param row: a list or tuple containing index and override_param_map. Index is an int
representing the index of parameter map and override_param_map is a dict
whose key is a string representing an MLllib Param Name. These are meant
to override the base estimator's params.
:return: tuple of index, override_param_map and serialized Keras HDF5 file bytes
"""
index, override_param_map = row
# Update params
params = baseParamsBc.value
params.update(override_param_map)
# Create Keras model
model = kmutil.bytes_to_model(modelBytesBc.value)
model.compile(optimizer=params['kerasOptimizer'], loss=params['kerasLoss'])
# Retrieve features and labels and fit Keras model
features = localFeaturesBc.value
labels = None if localLabelsBc is None else localLabelsBc.value
_fit_params = params['kerasFitParams']
model.fit(x=features, y=labels, **_fit_params)
return index, override_param_map, kmutil.model_to_bytes(model)
def _local_fit(row):
"""
Fit locally a model with a combination of this estimator's param,
with overriding parameters provided by the input.
:param row: a list or tuple containing index and override_param_map. Index is an int
representing the index of parameter map and override_param_map is a dict
whose key is a string representing an MLllib Param Name. These are meant
to override the base estimator's params.
:return: tuple of index, override_param_map and serialized Keras HDF5 file bytes
"""
index, override_param_map = row
# Update params
params = baseParamsBc.value
params.update(override_param_map)
# Create Keras model
model = kmutil.bytes_to_model(modelBytesBc.value)
model.compile(optimizer=params['kerasOptimizer'], loss=params['kerasLoss'])
# Retrieve features and labels and fit Keras model
features = localFeaturesBc.value
labels = None if localLabelsBc is None else localLabelsBc.value
_fit_params = params['kerasFitParams']
model.fit(x=features, y=labels, **_fit_params)
return index, override_param_map, kmutil.model_to_bytes(model)
def _local_fit(override_param_map):
"""
Fit locally a model with a combination of this estimator's param,
with overriding parameters provided by the input.
:param override_param_map: dict, key type is MLllib Param
They are meant to override the base estimator's params.
:return: serialized Keras HDF5 file bytes
"""
# Update params
params = baseParamDictBc.value
override_param_dict = dict([
(param.name, val) for param, val in override_param_map.items()
])
params.update(override_param_dict)
# Create Keras model
model = kmutil.bytes_to_model(modelBytesBc.value)
model.compile(optimizer=params['kerasOptimizer'],
loss=params['kerasLoss'])
# Retrieve features and labels and fit Keras model
features = localFeaturesBc.value
labels = None if localLabelsBc is None else localLabelsBc.value
_fit_params = params['kerasFitParams']
model.fit(x=features, y=labels, **_fit_params)
return kmutil.model_to_bytes(model)