def test_targets_dict_to_numpy_cnn_no_scalars(self):
"""Ensures correct output from targets_dict_to_numpy.
In this case, neural-net type is CNN with no scalar outputs.
"""
this_scalar_target_matrix = (
EXAMPLE_DICT[example_utils.SCALAR_TARGET_VALS_KEY] + 0.)
this_scalar_target_matrix = numpy.full(
(this_scalar_target_matrix.shape[0], 0), 0.)
this_example_dict = copy.deepcopy(EXAMPLE_DICT)
this_example_dict[example_utils.SCALAR_TARGET_VALS_KEY] = (
this_scalar_target_matrix)
these_matrices = neural_net.targets_dict_to_numpy(
example_dict=this_example_dict,
net_type_string=neural_net.CNN_TYPE_STRING)
self.assertTrue(
len(these_matrices) == len(CNN_TARGET_MATRICES_NO_SCALARS))
for i in range(len(these_matrices)):
self.assertTrue(
numpy.allclose(these_matrices[i],
CNN_TARGET_MATRICES_NO_SCALARS[i],
atol=TOLERANCE))
def test_targets_dict_to_numpy_u_net(self):
"""Ensures correct output from targets_dict_to_numpy.
In this case, neural-net type is U-net.
"""
these_matrices = neural_net.targets_dict_to_numpy(
example_dict=EXAMPLE_DICT,
net_type_string=neural_net.U_NET_TYPE_STRING)
self.assertTrue(len(these_matrices) == len(U_NET_TARGET_MATRICES))
for i in range(len(these_matrices)):
self.assertTrue(
numpy.allclose(these_matrices[i],
U_NET_TARGET_MATRICES[i],
atol=TOLERANCE))
def test_targets_dict_to_numpy_cnn_custom_loss(self):
"""Ensures correct output from targets_dict_to_numpy.
In this case, neural-net type is CNN with custom loss function.
"""
these_matrices = neural_net.targets_dict_to_numpy(
example_dict=EXAMPLE_DICT,
net_type_string=neural_net.CNN_TYPE_STRING,
is_loss_constrained_mse=True)
self.assertTrue(
len(these_matrices) == len(CNN_TARGET_MATRICES_CUSTOM_LOSS))
for i in range(len(these_matrices)):
self.assertTrue(
numpy.allclose(these_matrices[i],
CNN_TARGET_MATRICES_CUSTOM_LOSS[i],
atol=TOLERANCE))