def test_forward_propagation(self):
"""
check if the matrix multiplications in forward propagationin are
working properly.
"""
training_dataset = DataSet(os.path.join(combivep_settings.COMBIVEP_CENTRAL_TEST_DATASET_DIR,
'dummy_training_dataset'))
mlp = Mlp(training_dataset.n_features, seed=20)
out = mlp.forward_propagation(training_dataset)
self.assertEqual(round(out[0][0], 4), 0.5022, msg='forward propagation does not functional properly')
def test_fix_random_weight(self):
"""
check if the initial random values of weight matrixes are likely to be
random.
"""
training_dataset = DataSet(os.path.join(combivep_settings.COMBIVEP_CENTRAL_TEST_DATASET_DIR,
'dummy_training_dataset'))
mlp = Mlp(training_dataset.n_features, seed=20)
self.assertEqual(round(mlp.get_weights1()[0][1], 4), 0.0090, msg='MLP is not ready for test because the random value is not fix')
self.assertEqual(round(mlp.get_weights1()[0][0], 4), 0.0059, msg='MLP is not ready for test because the random value is not fix')
def __init__(self, training_dataset,
validation_dataset,
seed=combivep_settings.DEFAULT_SEED,
n_hidden_nodes=combivep_settings.DEFAULT_HIDDEN_NODES,
figure_dir=combivep_settings.DEFAULT_FIGURE_DIR):
Mlp.__init__(self, training_dataset.n_features,
seed=seed,
n_hidden_nodes=n_hidden_nodes)
self.__training_dataset = training_dataset
self.__validation_dataset = validation_dataset
self.__n_hidden_nodes = n_hidden_nodes
self.__figure_dir = figure_dir
def test_forward_propagation(self):
"""
check if the matrix multiplications in forward propagationin are
working properly.
"""
training_data = DataSet(os.path.join(cbv_const.CBV_SAMPLE_DATASET_DIR,
'dummy_training_dataset'))
mlp = Mlp(training_data.n_features, seed=20)
out = mlp.forward_propagation(training_data)
self.assertEqual(round(out[0][0], 4),
0.5022,
msg="forward propagation doesn't function properly")
def test_one_round_forward_backward_weight_update(self):
"""
to see if can correctly run one round of "forward", "backward" and
"weight update"
"""
training_dataset = DataSet(os.path.join(combivep_settings.COMBIVEP_CENTRAL_TEST_DATASET_DIR,
'dummy_training_dataset'))
mlp = Mlp(training_dataset.n_features, seed=20)
mlp.forward_propagation(training_dataset)
mlp.backward_propagation(training_dataset)
weights1, weights2 = mlp.weight_update(training_dataset)
self.assertEqual(round(weights1[0][0], 4), 0.0059, msg='one round of forward propagation, backward propagation and weight update, does not functional properly')
def test_one_round_forward_backward_weight_update(self):
"""
to see if can correctly run one round of "forward", "backward" and
"weight update"
"""
training_data = DataSet(os.path.join(cbv_const.CBV_SAMPLE_DATASET_DIR,
'dummy_training_dataset'))
mlp = Mlp(training_data.n_features, seed=20)
mlp.forward_propagation(training_data)
mlp.backward_propagation(training_data)
weights1, weights2 = mlp.weight_update(training_data)
self.assertEqual(round(weights1[0][0], 4),
0.0059,
msg='one round of forward propagation, backward propagation and weight update, does not function properly')
