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'