def _create_initializer(init_function_name, model_metadata_dict):
"""Creates initialization function.
:param init_function_name: See documentation at top of file.
:param model_metadata_dict: Dictionary returned by
`cnn.read_model_metadata`.
:return: init_function: Function (see below).
"""
backwards_opt.check_init_function(init_function_name)
training_option_dict = model_metadata_dict[cnn.TRAINING_OPTION_DICT_KEY]
used_minmax_norm = (
training_option_dict[trainval_io.NORMALIZATION_TYPE_KEY] ==
dl_utils.MINMAX_NORMALIZATION_TYPE_STRING)
if init_function_name == backwards_opt.CONSTANT_INIT_FUNCTION_NAME:
if used_minmax_norm:
return backwards_opt.create_constant_initializer(
(training_option_dict[trainval_io.MAX_NORMALIZED_VALUE_KEY] -
training_option_dict[trainval_io.MIN_NORMALIZED_VALUE_KEY]) /
2)
return backwards_opt.create_constant_initializer(0.)
if init_function_name == backwards_opt.UNIFORM_INIT_FUNCTION_NAME:
if used_minmax_norm:
return backwards_opt.create_uniform_random_initializer(
min_value=training_option_dict[
trainval_io.MIN_NORMALIZED_VALUE_KEY],
max_value=training_option_dict[
trainval_io.MAX_NORMALIZED_VALUE_KEY])
return backwards_opt.create_uniform_random_initializer(min_value=-3.,
max_value=3.)
if init_function_name == backwards_opt.GAUSSIAN_INIT_FUNCTION_NAME:
if used_minmax_norm:
return backwards_opt.create_gaussian_initializer(
mean=(
training_option_dict[trainval_io.MAX_NORMALIZED_VALUE_KEY]
-
training_option_dict[trainval_io.MIN_NORMALIZED_VALUE_KEY])
/ 2,
standard_deviation=(
training_option_dict[trainval_io.MAX_NORMALIZED_VALUE_KEY]
-
training_option_dict[trainval_io.MIN_NORMALIZED_VALUE_KEY])
/ 6)
return backwards_opt.create_gaussian_initializer(mean=0.,
standard_deviation=1.)
return backwards_opt.create_climo_initializer(
model_metadata_dict=model_metadata_dict)
def test_create_uniform_random_initializer_5d(self):
"""Ensures correct output from create_uniform_random_initializer.
In this case, the desired matrix is 5-D.
"""
this_init_function = backwards_opt.create_uniform_random_initializer(
min_value=MIN_UNIFORM_VALUE, max_value=MAX_UNIFORM_VALUE)
this_matrix = this_init_function(ARRAY_DIMENSIONS_5D)
self.assertTrue(
numpy.allclose(this_matrix, UNIFORM_MATRIX_5D, atol=TOLERANCE))
def _create_initializer(init_function_name):
"""Creates initializer function.
:param init_function_name: See documentation at top of file.
:return: init_function: Initializer function.
"""
if init_function_name == backwards_opt.CONSTANT_INIT_FUNCTION_NAME:
return backwards_opt.create_constant_initializer(0.)
if init_function_name == backwards_opt.UNIFORM_INIT_FUNCTION_NAME:
return backwards_opt.create_uniform_random_initializer(min_value=-1.,
max_value=1.)
return backwards_opt.create_gaussian_initializer(mean=0.,
standard_deviation=1.)