def _targets_numpy_to_dict(scalar_target_matrix, vector_target_matrix,
model_metadata_dict):
"""Converts either actual or predicted target values to dictionary.
This method is a wrapper for `neural_net.targets_numpy_to_dict`.
:param scalar_target_matrix: numpy array with scalar outputs (either
predicted or actual target values).
:param vector_target_matrix: Same but with vector outputs.
:param model_metadata_dict: Dictionary read by `neural_net.read_metafile`.
:return: example_dict: Equivalent dictionary, with keys listed in doc for
`example_io.read_file`.
"""
net_type_string = model_metadata_dict[neural_net.NET_TYPE_KEY]
generator_option_dict = copy.deepcopy(
model_metadata_dict[neural_net.TRAINING_OPTIONS_KEY])
vector_target_names = (
generator_option_dict[neural_net.VECTOR_TARGET_NAMES_KEY])
add_heating_rate = generator_option_dict[neural_net.OMIT_HEATING_RATE_KEY]
if net_type_string == neural_net.DENSE_NET_TYPE_STRING:
target_matrices = [scalar_target_matrix]
else:
if (add_heating_rate and vector_target_matrix.shape[-1]
== len(vector_target_names) - 1):
heating_rate_index = vector_target_names.index(
example_utils.SHORTWAVE_HEATING_RATE_NAME)
vector_target_matrix = numpy.insert(vector_target_matrix,
obj=heating_rate_index,
values=0.,
axis=-1)
target_matrices = [vector_target_matrix]
if scalar_target_matrix is not None:
target_matrices.append(scalar_target_matrix)
example_dict = {
example_utils.VECTOR_TARGET_NAMES_KEY:
generator_option_dict[neural_net.VECTOR_TARGET_NAMES_KEY],
example_utils.SCALAR_TARGET_NAMES_KEY:
generator_option_dict[neural_net.SCALAR_TARGET_NAMES_KEY],
example_utils.HEIGHTS_KEY:
generator_option_dict[neural_net.HEIGHTS_KEY]
}
new_example_dict = neural_net.targets_numpy_to_dict(
target_matrices=target_matrices,
example_dict=example_dict,
net_type_string=net_type_string)
for this_key in TARGET_VALUE_KEYS:
example_dict[this_key] = new_example_dict[this_key]
return example_dict
def test_targets_numpy_to_dict_cnn_no_scalars(self):
"""Ensures correct output from targets_numpy_to_dict.
In this case, neural-net type is CNN with no scalar outputs.
"""
this_example_dict = neural_net.targets_numpy_to_dict(
target_matrices=CNN_TARGET_MATRICES_NO_SCALARS,
example_dict=EXAMPLE_DICT,
net_type_string=neural_net.CNN_TYPE_STRING)
self.assertTrue(
numpy.allclose(
this_example_dict[example_utils.VECTOR_TARGET_VALS_KEY],
VECTOR_TARGET_MATRIX,
atol=TOLERANCE))
self.assertTrue(
this_example_dict[example_utils.SCALAR_TARGET_VALS_KEY].size == 0)
def test_targets_numpy_to_dict_u_net(self):
"""Ensures correct output from targets_numpy_to_dict.
In this case, neural-net type is U-net.
"""
this_example_dict = neural_net.targets_numpy_to_dict(
target_matrices=U_NET_TARGET_MATRICES,
example_dict=EXAMPLE_DICT,
net_type_string=neural_net.U_NET_TYPE_STRING)
self.assertTrue(
numpy.allclose(
this_example_dict[example_utils.VECTOR_TARGET_VALS_KEY],
VECTOR_TARGET_MATRIX,
atol=TOLERANCE))
self.assertTrue(
numpy.allclose(
this_example_dict[example_utils.SCALAR_TARGET_VALS_KEY],
SCALAR_TARGET_MATRIX,
atol=TOLERANCE))