def test_unweighted_from_logits(self):
scce_obj = metrics.SparseCategoricalCrossentropy(from_logits=True)
y_true = np.asarray([1, 2])
logits = np.asarray([[1, 9, 0], [1, 8, 1]], dtype=np.float32)
result = scce_obj(y_true, logits)
assert np.allclose(K.eval(result), 3.5011, atol=1e-3)
def test_unweighted(self):
scce_obj = metrics.SparseCategoricalCrossentropy()
y_true = np.asarray([1, 2])
y_pred = np.asarray([[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
result = scce_obj(y_true, y_pred)
assert np.allclose(K.eval(result), 1.176, atol=1e-3)
def test_weighted_from_logits(self):
scce_obj = metrics.SparseCategoricalCrossentropy(from_logits=True)
y_true = np.asarray([1, 2])
logits = np.asarray([[1, 9, 0], [1, 8, 1]], dtype=np.float32)
sample_weight = [1.5, 2.]
result = scce_obj(y_true, logits, sample_weight=sample_weight)
assert np.allclose(K.eval(result), 4.0012, atol=1e-3)
def test_weighted(self):
scce_obj = metrics.SparseCategoricalCrossentropy()
y_true = np.asarray([1, 2])
y_pred = np.asarray([[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
sample_weight = [1.5, 2.]
result = scce_obj(y_true, y_pred, sample_weight=sample_weight)
assert np.allclose(K.eval(result), 1.338, atol=1e-3)
def test_config(self):
scce_obj = metrics.SparseCategoricalCrossentropy(
name='scce', dtype='int32')
assert scce_obj.name == 'scce'
assert scce_obj.dtype == 'int32'