def get_hyperparameter(name,
initializer=None,
shape=None,
dtype=None,
collections=None,
scalar=False):
"""
Creates an hyperparameter variable, which is a GLOBAL_VARIABLE
and HYPERPARAMETER. Mirrors the behavior of `tf.get_variable`.
:param name: name of this hyperparameter
:param initializer: initializer or initial value (can be also np.array or float)
:param shape: optional shape, may be not needed depending on initializer
:param dtype: optional type, may be not needed depending on initializer
:param collections: optional additional collection or list of collections, which will be added to
HYPERPARAMETER and GLOBAL_VARIABLES
:param scalar: default False, if True splits the hyperparameter in its scalar components, i.e. each component
will be a single scalar hyperparameter. In this case the method returns a tensor which of the
desired shape (use this option with `ForwardHG`)
:return: the newly created variable, or, if `scalar` is `True` a tensor composed by scalar variables.
"""
_coll = HYPERPARAMETERS_COLLECTIONS
if collections:
_coll += as_list(collections)
if not scalar:
return tf.get_variable(name,
shape,
dtype,
initializer,
trainable=False,
collections=_coll)
else:
with tf.variable_scope(name + '_components'):
_shape = shape or initializer.shape
if isinstance(_shape, tf.TensorShape):
_shape = _shape.as_list()
# _tmp_lst = reduce(lambda a, v: [a]*v, shape[::-1], None)
_tmp_lst = np.empty(_shape, object)
for k in range(np.multiply.reduce(_shape)):
indices = np.unravel_index(k, _shape)
# print(indices)
_ind_name = '_'.join([str(ind) for ind in indices])
_tmp_lst[indices] = tf.get_variable(
_ind_name, (),
dtype,
initializer
if callable(initializer) else initializer[indices],
trainable=False,
collections=_coll)
return tf.convert_to_tensor(_tmp_lst.tolist(), name=name)
def get_hyperparameter(name, initializer=None, shape=None, dtype=None, collections=None,
scalar=False):
"""
Creates an hyperparameter variable, which is a GLOBAL_VARIABLE
and HYPERPARAMETER. Mirrors the behavior of `tf.get_variable`.
:param name: name of this hyperparameter
:param initializer: initializer or initial value (can be np.array or float)
:param shape: optional shape, may be not needed depending on initializer
:param dtype: optional type, may be not needed depending on initializer
:param collections: optional additional collection or list of collections, which will be added to
HYPERPARAMETER and GLOBAL_VARIABLES
:param scalar: default False, if True splits the hyperparameter in its scalar components, i.e. each component
will be a single scalar hyperparameter. In this case the method returns a tensor which of the
desired shape (use this option with `ForwardHG`)
:return: the newly created variable, or, if `scalar` is `True` a tensor composed by scalar variables.
"""
_coll = HYPERPARAMETERS_COLLECTIONS
if collections:
_coll += as_list(collections)
if not scalar:
return tf.get_variable(name, shape, dtype, initializer, trainable=False,
collections=_coll)
else:
with tf.variable_scope(name + '_components'):
_shape = shape or initializer.shape
if isinstance(_shape, tf.TensorShape):
_shape = _shape.as_list()
# _tmp_lst = reduce(lambda a, v: [a]*v, shape[::-1], None)
_tmp_lst = np.empty(_shape, object)
for k in range(np.multiply.reduce(_shape)):
indices = np.unravel_index(k, _shape)
# print(indices)
_ind_name = '_'.join([str(ind) for ind in indices])
_tmp_lst[indices] = tf.get_variable(_ind_name, (), dtype,
initializer if callable(initializer) else initializer[indices],
trainable=False, collections=_coll)
return tf.convert_to_tensor(_tmp_lst.tolist(), name=name)